diff --git a/CMake/CTestCustom.cmake.in b/CMake/CTestCustom.cmake.in
index a9d61cb9ff222df34bf5a2db728cd0e6402b42a7..1d3f118028b108d8669887eb8f51bba4297dd016 100644
--- a/CMake/CTestCustom.cmake.in
+++ b/CMake/CTestCustom.cmake.in
@@ -63,5 +63,7 @@ set(CTEST_CUSTOM_WARNING_EXCEPTION
# Ignore clang's summary warning, assuming prior text has matched some
# other warning expression:
"[0-9,]+ warnings? generated."
-
+ ".*include.opencv2.*warning.*"
+ ".*warning:.*deprecated.*"
+ ".*vcl_deprecated_header.*"
)
diff --git a/CMake/FindNumpy.cmake b/CMake/FindNumpy.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..764f57497c1dfbf43dda09118bc8e538fb513047
--- /dev/null
+++ b/CMake/FindNumpy.cmake
@@ -0,0 +1,28 @@
+# - Find Numpy
+# Find the native numpy includes
+#
+# NUMPY_FOUND - True if Numpy headers are found.
+# NUMPY_INCLUDE_DIR - where to find numpy/arrayobject.h, etc.
+
+EXEC_PROGRAM ("${PYTHON_EXECUTABLE}"
+ ARGS "-c 'import numpy; print numpy.get_include()'"
+ OUTPUT_VARIABLE NUMPY_INCLUDE_DIR
+ RETURN_VALUE NUMPY_NOT_FOUND)
+
+if( NUMPY_INCLUDE_DIR MATCHES "Traceback" )
+# Did not successfully include numpy
+set( NUMPY_INCLUDE_DIR )
+else()
+ set( NUMPY_INCLUDE_DIR ${NUMPY_INCLUDE_DIR} CACHE STRING "Numpy include path." )
+endif()
+
+include( FindPackageHandleStandardArgs )
+FIND_PACKAGE_HANDLE_STANDARD_ARGS( Numpy DEFAULT_MSG NUMPY_INCLUDE_DIR )
+
+mark_as_advanced( NUMPY_INCLUDE_DIR )
+
+if(NUMPY_FOUND)
+ set(NUMPY_INCLUDE_DIRS ${NUMPY_INCLUDE_DIR})
+else()
+ set(NUMPY_INCLUDE_DIRS)
+endif()
diff --git a/CMakeLists.txt b/CMakeLists.txt
index f1b0d6002bd3cb3fd1b8996baec47d89dfa730fd..c31d605bd28213d887e01e20bb954b5bdd8c0342 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -392,6 +392,17 @@ endif()
# Provide an option for generating documentation.
add_subdirectory(Utilities/Doxygen)
+#----------------------------------------------------------------------
+# Provide a target to generate the SuperBuild archive (only for Unix)
+if(UNIX)
+ add_custom_target(SuperBuild-archive
+ COMMAND ./SuperbuildDownloadList.sh
+ ${OTB_BINARY_DIR}/Download
+ ${OTB_BINARY_DIR}
+ WORKING_DIRECTORY ${OTB_SOURCE_DIR}/Utilities/Maintenance
+ )
+endif()
+
# TODO cleanup
# Create target to download data from the OTBData group. This must come after
# all tests have been added that reference the group, so we put it last.
diff --git a/Examples/BasicFilters/CMakeLists.txt b/Examples/BasicFilters/CMakeLists.txt
index c8fb34bd1304b95ad7c9f911bdf275115fd10b84..858e0fa5ae8d0fa920aa9e08e4c0aca335c57fe7 100644
--- a/Examples/BasicFilters/CMakeLists.txt
+++ b/Examples/BasicFilters/CMakeLists.txt
@@ -1,10 +1,16 @@
project(BasicFiltersExamples)
+#OTB_USE_MUPARSER is OFF
+if(OTBMathParser_LOADED)
add_executable(BandMathFilterExample BandMathFilterExample.cxx)
target_link_libraries(BandMathFilterExample ${OTB_LIBRARIES})
+endif()
+#OTB_USE_MUPARSERX is OFF
+if(OTBMathParserX_LOADED)
add_executable(BandMathXImageFilterExample BandMathXImageFilterExample.cxx)
target_link_libraries(BandMathXImageFilterExample ${OTB_LIBRARIES})
+endif()
add_executable(DEMToRainbowExample DEMToRainbowExample.cxx)
target_link_libraries(DEMToRainbowExample ${OTB_LIBRARIES})
@@ -21,8 +27,11 @@ target_link_libraries(IndexedToRGBExample ${OTB_LIBRARIES})
add_executable(LeeImageFilter LeeImageFilter.cxx)
target_link_libraries(LeeImageFilter ${OTB_LIBRARIES})
+#OTBMeanShift depends OTBMathParser. But OTB_USE_MUPARSER is OFF
+if(OTBMathParser_LOADED)
add_executable(MeanShiftSegmentationFilterExample MeanShiftSegmentationFilterExample.cxx)
target_link_libraries(MeanShiftSegmentationFilterExample ${OTB_LIBRARIES})
+endif()
add_executable(PrintableImageFilterExample PrintableImageFilterExample.cxx)
target_link_libraries(PrintableImageFilterExample ${OTB_LIBRARIES})
diff --git a/Examples/BasicFilters/test/CMakeLists.txt b/Examples/BasicFilters/test/CMakeLists.txt
index 15da042a010e826cedf26779452177d37a1b5f89..cc6dabe32daa2c486acaed79481b64b63d287baf 100644
--- a/Examples/BasicFilters/test/CMakeLists.txt
+++ b/Examples/BasicFilters/test/CMakeLists.txt
@@ -84,7 +84,7 @@ otb_add_test(NAME bfTeIndexedToRGBExampleTest COMMAND ${OTB_TEST_DRIVER}
${TEMP}/buildingExtractionRGB.png
${TEMP}/buildingExtractionIndexed_scaled.png
)
-
+if(OTBMathParser_LOADED)
otb_add_test(NAME bfTeMeanShiftSegmentationFilterExampleTest COMMAND ${OTB_TEST_DRIVER}
--compare-n-images ${NOTOL} 4
${BASELINE}/MSClusteredOutput.tif
@@ -107,7 +107,7 @@ otb_add_test(NAME bfTeMeanShiftSegmentationFilterExampleTest COMMAND ${OTB_TEST_
100
0.1
)
-
+endif()
# ------- HillShadingExampleTest ----------
otb_add_test(NAME bfTeHillShadingExampleTest COMMAND ${OTB_TEST_DRIVER}
diff --git a/Examples/CMakeLists.txt b/Examples/CMakeLists.txt
index 639761a6c6e94b30538e8febb535134ca33c6b1a..84b3542f4f25ff3f52fa79e88c29e1f1b99bb3ba 100644
--- a/Examples/CMakeLists.txt
+++ b/Examples/CMakeLists.txt
@@ -81,32 +81,12 @@ if(PROJECT_NAME STREQUAL "OTBExamples")
########################## end copied from OTB/CMakeLists.txt ##########################
- #must be updated for new modules!
- set(REQUIRED_MODULES "OTB6S;OTBITK;OTBAnomalyDetection;OTBBoost;OTBCommon;OTBGDAL;OTBOpenThreads;OTBOssim;OTBGeoTIFF;OTBOssimPlugins;OTBOSSIMAdapters;OTBObjectList;OTBMetadata;OTBImageBase;OTBBoostAdapters;OTBGdalAdapters;OTBTinyXML;OTBVectorDataBase;OTBIOGDAL;OTBExtendedFilename;OTBIOBSQ;OTBIOLUM;OTBIOMSTAR;OTBIOMW;OTBIOONERA;OTBIORAD;OTBCurl;OTBCurlAdapters;OTBIOTileMap;OTBStreaming;OTBImageIO;OTBInterpolation;OTBTransform;OTBImageManipulation;OTBTestKernel;OTBPath;OTBVectorDataManipulation;OTBProjection;OTBlibkml;OTBIOKML;OTBVectorDataIO;OTBApplicationEngine;OTBStatistics;OTBChangeDetection;OTBAppChangeDetection;OTBCommandLine;OTBColorMap;OTBMoments;OTBLabelMap;OTBConversion;OTBFuzzy;OTBMuParser;OTBMathParser;OTBSupervised;OTBDempsterShafer;OTBIOXML;OTBIndices;OTBMajorityVoting;OTBSOM;OTBAppClassification;OTBPointSet;OTBSiftFast;OTBDescriptors;OTBAppDescriptors;OTBDimensionalityReduction;OTBAppDimensionalityReduction;OTBSpatialObjects;OTBEdge;OTBAppEdge;OTBImageNoise;OTBAppFiltering;OTBConvolution;OTBPanSharpening;OTBAppFusion;OTBEndmembersExtraction;OTBWavelet;OTBUnmixing;OTBAppHyperspectral;OTBCarto;OTBAppImageUtils;OTBAppIndices;OTBKMZWriter;OTBAppKMZ;OTBAppMathParser;OTBMuParserX;OTBMathParserX;OTBAppMathParserX;OTBAppMoments;OTBAppMorphology;OTBOpticalCalibration;OTBAppOpticalCalibration;OTBAppProjection;OTBSARCalibration;OTBAppSARCalibration;OTBCCOBIA;OTBSmoothing;OTBMeanShift;OTBMetrics;OTBMorphologicalProfiles;OTBLabelling;OTBOGRProcessing;OTBWatersheds;OTBAppSegmentation;OTBDEM;OTBStereo;OTBDisparityMap;OTBAppStereo;OTBAppTest;OTBTextures;OTBAppTextures;OTBAppVectorDataTranslation;OTBAppVectorUtils;OTBCommandLineParser;OTBOpenThreadsAdapters;OTBCloudDetection;OTBComplexImage;OTBCorner;OTBDensity;OTBDisplacementField;OTBLandSatClassifier;OTBLearningBase;OTBMarkov;OTBMorphologicalPyramid;OTBObjectDetection;OTBPolarimetry;OTBRoadExtraction;OTBRCC8;OTBSWIGWrapper;OTBSeamCarving;OTBSimulation;OTBTimeSeries;OTBUrbanArea")
-
- find_package(OTB REQUIRED COMPONENTS ${REQUIRED_MODULES})
- include(${OTB_USE_FILE})
- message(STATUS "Found OTB: ${OTB_USE_FILE}")
-
-else()
- if(NOT OTB_BUILD_DEFAULT_MODULES)
- message(FATAL_ERROR "BUILD_EXAMPLES requires OTB_BUILD_DEFAULT_MODULES to be ON")
- endif()
- # We also need to prevent any OTB_USE_XXX option to be OFF (except mapnik and openjpeg)
- # For now we parse module activation list and test if option is ON. If not the cmake configuration stop.
- #TODO We should provide a better mechanism here wich will deactivate only examples required by deactivated options.
- foreach (_activationOption ${OTB_MODULE_ACTIVATION_OPTION_LIST})
- if(NOT ${_activationOption} AND NOT "x${_activationOption}" STREQUAL "xOTB_USE_MAPNIK" AND NOT "x${_activationOption}" STREQUAL "xOTB_USE_OPENJPEG")
- message(FATAL_ERROR "BUILD_EXAMPLES requires ${_activationOption} to be ON")
- endif()
- endforeach()
-
- find_package(OTB REQUIRED)
- include(${OTB_USE_FILE})
- message(STATUS "Found OTB: ${OTB_USE_FILE}")
-
endif()
+find_package(OTB REQUIRED)
+include(${OTB_USE_FILE})
+message(STATUS "Found OTB: ${OTB_USE_FILE}")
+
set(OTB_TEST_DRIVER otbTestDriver)
if(OTBApplicationEngine_LOADED)
diff --git a/Examples/Classification/CMakeLists.txt b/Examples/Classification/CMakeLists.txt
index 4aa4757783949ef289edc2dac459ff6805d25e64..02db6fd752225dc4bbf2508b347b7c7257dfd155 100644
--- a/Examples/Classification/CMakeLists.txt
+++ b/Examples/Classification/CMakeLists.txt
@@ -6,9 +6,11 @@ target_link_libraries(BayesianPluginClassifier ${OTB_LIBRARIES})
add_executable(ClassificationMapRegularizationExample ClassificationMapRegularizationExample.cxx)
target_link_libraries(ClassificationMapRegularizationExample ${OTB_LIBRARIES})
+#OTBDempsterShafer depends OTBMathParser. But OTB_USE_MUPARSER is OFF
+if(OTBMathParser_LOADED)
add_executable(DempsterShaferFusionOfClassificationMapsExample DempsterShaferFusionOfClassificationMapsExample.cxx)
target_link_libraries(DempsterShaferFusionOfClassificationMapsExample ${OTB_LIBRARIES})
-
+endif()
add_executable(ExpectationMaximizationMixtureModelEstimator ExpectationMaximizationMixtureModelEstimator.cxx)
target_link_libraries(ExpectationMaximizationMixtureModelEstimator ${OTB_LIBRARIES})
diff --git a/Examples/DisparityMap/CMakeLists.txt b/Examples/DisparityMap/CMakeLists.txt
index bd7b2c26c86cd6fb2fdbfa84dbcc28f8f200ecbc..f42b945930e97fd1cfa30117302d08759a1dc44e 100644
--- a/Examples/DisparityMap/CMakeLists.txt
+++ b/Examples/DisparityMap/CMakeLists.txt
@@ -9,8 +9,11 @@ target_link_libraries(NCCRegistrationFilterExample ${OTB_LIBRARIES})
add_executable(SimpleDisparityMapEstimationExample SimpleDisparityMapEstimationExample.cxx)
target_link_libraries(SimpleDisparityMapEstimationExample ${OTB_LIBRARIES})
+#StereoReconstructionExample depends OTBMathParser. But OTB_USE_MUPARSER is OFF
+if(OTBMathParser_LOADED)
add_executable(StereoReconstructionExample StereoReconstructionExample.cxx)
target_link_libraries(StereoReconstructionExample ${OTB_LIBRARIES})
+endif()
if(BUILD_TESTING)
add_subdirectory(test)
diff --git a/Examples/DisparityMap/test/CMakeLists.txt b/Examples/DisparityMap/test/CMakeLists.txt
index d90913ccfcaca5de71692741ba78d7765020168b..9c247434b91ffb8e9116882b05e8538380ae513d 100644
--- a/Examples/DisparityMap/test/CMakeLists.txt
+++ b/Examples/DisparityMap/test/CMakeLists.txt
@@ -17,6 +17,7 @@ otb_add_test(NAME dmTeSimpleDisparityMapEstimationExampleTest COMMAND ${OTB_TEST
100 10 15 10 10 250 0.9
)
+if(OTBMathParser_LOADED)
otb_add_test(NAME dmTeStereoReconstructionExampleTest COMMAND ${OTB_TEST_DRIVER}
--compare-image ${NOTOL}
${BASELINE}/elevationOutput.tif
@@ -28,3 +29,4 @@ otb_add_test(NAME dmTeStereoReconstructionExampleTest COMMAND ${OTB_TEST_DRIVER}
${TEMP}/elevationOutputPrintable.png
140
)
+endif()
\ No newline at end of file
diff --git a/Examples/FeatureExtraction/CMakeLists.txt b/Examples/FeatureExtraction/CMakeLists.txt
index ec46f9fd89a8065e4ac0d0b713aca977c89311eb..edce7bf6fac9ecc829033a2531cf3be9d5f4957c 100644
--- a/Examples/FeatureExtraction/CMakeLists.txt
+++ b/Examples/FeatureExtraction/CMakeLists.txt
@@ -1,8 +1,5 @@
project(FeatureExtractionExamples)
-add_executable(AlignmentsExample AlignmentsExample.cxx)
-target_link_libraries(AlignmentsExample ${OTB_LIBRARIES})
-
add_executable(AssymmetricFusionOfLineDetectorExample AssymmetricFusionOfLineDetectorExample.cxx)
target_link_libraries(AssymmetricFusionOfLineDetectorExample ${OTB_LIBRARIES})
@@ -21,12 +18,21 @@ target_link_libraries(CorrelationLineDetectorExample ${OTB_LIBRARIES})
add_executable(EdgeDensityExample EdgeDensityExample.cxx)
target_link_libraries(EdgeDensityExample ${OTB_LIBRARIES})
+#OTBRoadExtraction depends OTBMathParser. But OTB_USE_MUPARSER is OFF
+if(OTBMathParser_LOADED)
add_executable(ExtractRoadByStepsExample ExtractRoadByStepsExample.cxx)
target_link_libraries(ExtractRoadByStepsExample ${OTB_LIBRARIES})
add_executable(ExtractRoadExample ExtractRoadExample.cxx)
target_link_libraries(ExtractRoadExample ${OTB_LIBRARIES})
+add_executable(ParallelLineDetectionExample ParallelLineDetectionExample.cxx)
+target_link_libraries(ParallelLineDetectionExample ${OTB_LIBRARIES})
+
+add_executable(AlignmentsExample AlignmentsExample.cxx)
+target_link_libraries(AlignmentsExample ${OTB_LIBRARIES})
+endif()
+
add_executable(ExtractSegmentsByStepsExample ExtractSegmentsByStepsExample.cxx)
target_link_libraries(ExtractSegmentsByStepsExample ${OTB_LIBRARIES})
@@ -45,6 +51,9 @@ target_link_libraries(HuMomentsImageFunctionExample ${OTB_LIBRARIES})
if (Module_OTBVectorDataRendering AND OTB_USE_MAPNIK)
add_executable(LineSegmentDetectorExample LineSegmentDetectorExample.cxx)
target_link_libraries(LineSegmentDetectorExample ${OTB_LIBRARIES})
+
+ add_executable(RightAngleDetectionExample RightAngleDetectionExample.cxx)
+ target_link_libraries(RightAngleDetectionExample ${OTB_LIBRARIES})
endif()
add_executable(LocalHoughExample LocalHoughExample.cxx)
@@ -53,17 +62,9 @@ target_link_libraries(LocalHoughExample ${OTB_LIBRARIES})
add_executable(PanTexExample PanTexExample.cxx)
target_link_libraries(PanTexExample ${OTB_LIBRARIES})
-add_executable(ParallelLineDetectionExample ParallelLineDetectionExample.cxx)
-target_link_libraries(ParallelLineDetectionExample ${OTB_LIBRARIES})
-
add_executable(RatioLineDetectorExample RatioLineDetectorExample.cxx)
target_link_libraries(RatioLineDetectorExample ${OTB_LIBRARIES})
-if (Module_OTBVectorDataRendering AND OTB_USE_MAPNIK)
- add_executable(RightAngleDetectionExample RightAngleDetectionExample.cxx)
- target_link_libraries(RightAngleDetectionExample ${OTB_LIBRARIES})
-endif()
-
add_executable(SeamCarvingExample SeamCarvingExample.cxx)
target_link_libraries(SeamCarvingExample ${OTB_LIBRARIES})
diff --git a/Examples/FeatureExtraction/test/CMakeLists.txt b/Examples/FeatureExtraction/test/CMakeLists.txt
index 9e351acd2adf21b663afe76885b8baf7d868e91e..b748caca32cb41a2726011dc662e4836b98b86c2 100644
--- a/Examples/FeatureExtraction/test/CMakeLists.txt
+++ b/Examples/FeatureExtraction/test/CMakeLists.txt
@@ -107,7 +107,7 @@ otb_add_test(NAME feTeRatioLineDetectorExampleTest COMMAND ${OTB_TEST_DRIVER}
)
# ------- AlignmentsExampleTest----------
-
+if(OTBMathParser_LOADED)
otb_add_test(NAME feTeAlignmentsExampleTest COMMAND ${OTB_TEST_DRIVER}
--compare-n-images ${NOTOL} 1
${BASELINE}/QB_SuburbAlign.png
@@ -145,6 +145,8 @@ otb_add_test(NAME feTeExtractRoadExampleTest COMMAND ${OTB_TEST_DRIVER}
337 557 432 859 1.0 0.00005 1.0 0.39269 1.0 10.0 25.
)
+endif()
+
# ------- SeamCarvingExamplesTest----------
otb_add_test(NAME feTeSeamCarvingExampleTest COMMAND ${OTB_TEST_DRIVER}
diff --git a/Examples/IO/CMakeLists.txt b/Examples/IO/CMakeLists.txt
index a3d9b9063fee339286f5cc7b487d49560cf437d1..b4395d43a10ab34ded19381a315d54c89537d8a5 100644
--- a/Examples/IO/CMakeLists.txt
+++ b/Examples/IO/CMakeLists.txt
@@ -30,8 +30,10 @@ target_link_libraries(ImageReadWrite ${OTB_LIBRARIES})
add_executable(ImageSeriesIOExample ImageSeriesIOExample.cxx)
target_link_libraries(ImageSeriesIOExample ${OTB_LIBRARIES})
+if(OTBlibkml_LOADED)
add_executable(ImageToKmzAndMapFileProductExample ImageToKmzAndMapFileProductExample.cxx)
target_link_libraries(ImageToKmzAndMapFileProductExample ${OTB_LIBRARIES})
+endif()
add_executable(MetadataExample MetadataExample.cxx)
target_link_libraries(MetadataExample ${OTB_LIBRARIES})
diff --git a/Examples/IO/test/CMakeLists.txt b/Examples/IO/test/CMakeLists.txt
index d75692eee9a507dd0f5587cf7b209beabfef4565..720b16203b632a95e2d850822f5d722a875c9653 100644
--- a/Examples/IO/test/CMakeLists.txt
+++ b/Examples/IO/test/CMakeLists.txt
@@ -43,6 +43,7 @@ otb_add_test(NAME prTeDEMHandlerExampleTest COMMAND ${OTB_TEST_DRIVER}
0.001
)
+if(OTBlibkml_LOADED)
otb_add_test(NAME ioTeImageToKmzAndMapFileProductExample COMMAND ${OTB_TEST_DRIVER}
Execute $<TARGET_FILE:ImageToKmzAndMapFileProductExample>
${OTB_DATA_ROOT}/Input/Haiti1_sr10.jpeg
@@ -78,6 +79,7 @@ otb_add_test(NAME ioTeImageToKmzAndMapFileProductExample COMMAND ${OTB_TEST_DRIV
42.000000 232.000000 -72.426552 18.540876 0.000000
)
set_property(TEST ioTeImageToKmzAndMapFileProductExample PROPERTY RESOURCE_LOCK web_access)
+endif()
otb_add_test(NAME ioTeTileMapImageIOExampleTest COMMAND ${OTB_TEST_DRIVER}
--compare-image 10.0
diff --git a/Examples/Learning/CMakeLists.txt b/Examples/Learning/CMakeLists.txt
index 4963ecaed02cf4ac4a55240d66352d464e04d13b..cbf51262616e251ee86d0bd3c921ac35cd7b4d92 100644
--- a/Examples/Learning/CMakeLists.txt
+++ b/Examples/Learning/CMakeLists.txt
@@ -12,6 +12,7 @@ target_link_libraries(SOMClassifierExample ${OTB_LIBRARIES})
add_executable(SOMExample SOMExample.cxx)
target_link_libraries(SOMExample ${OTB_LIBRARIES})
+if(OTBLibSVM_LOADED)
add_executable(SVMImageClassificationExample SVMImageClassificationExample.cxx)
target_link_libraries(SVMImageClassificationExample ${OTB_LIBRARIES})
@@ -29,13 +30,15 @@ target_link_libraries(SVMPointSetExample ${OTB_LIBRARIES})
add_executable(SVMPointSetModelEstimatorExample SVMPointSetModelEstimatorExample.cxx)
target_link_libraries(SVMPointSetModelEstimatorExample ${OTB_LIBRARIES})
+endif()
+if(OTBOpenCV_LOADED)
add_executable(TrainMachineLearningModelFromImagesExample TrainMachineLearningModelFromImagesExample.cxx)
target_link_libraries(TrainMachineLearningModelFromImagesExample ${OTB_LIBRARIES})
add_executable(TrainMachineLearningModelFromSamplesExample TrainMachineLearningModelFromSamplesExample.cxx)
target_link_libraries(TrainMachineLearningModelFromSamplesExample ${OTB_LIBRARIES})
-
+endif()
if(BUILD_TESTING)
add_subdirectory(test)
diff --git a/Examples/Learning/test/CMakeLists.txt b/Examples/Learning/test/CMakeLists.txt
index d3918e252e6901babd9924b0dc092e302acc99fb..71e51049f8a57ffdca32b7606c1bcc034e7e467f 100644
--- a/Examples/Learning/test/CMakeLists.txt
+++ b/Examples/Learning/test/CMakeLists.txt
@@ -3,6 +3,7 @@ set(INPUTDATA ${OTB_DATA_ROOT}/Examples)
# ------- SVMImageClassificationExampleTest----------
+if(OTBLibSVM_LOADED)
otb_add_test(NAME leTeSVMImageClassificationExampleTest COMMAND ${OTB_TEST_DRIVER}
--compare-n-images ${NOTOL} 1
${BASELINE}/ROI_QB_MUL_1_SVN_CLASS.png
@@ -26,18 +27,6 @@ otb_add_test(NAME leTeSVMImageEstimatorClassificationMultiExampleTest COMMAND ${
${TEMP}/ROI_QB_MUL_1_SVN_CLASS_MULTI_Rescaled.png
)
-# ------- GenerateTrainingImageExampleTest----------
-
-otb_add_test(NAME leTeGenerateTrainingImageExampleTest COMMAND ${OTB_TEST_DRIVER}
- --compare-n-images ${NOTOL} 1
- ${BASELINE}/ROI_QB_MUL_4_training.png
- ${TEMP}/ROI_QB_MUL_4_training.png
- Execute $<TARGET_FILE:GenerateTrainingImageExample>
- ${INPUTDATA}/ROI_QB_MUL_4.tif
- ${INPUTDATA}/LearningROIs.txt
- ${TEMP}/ROI_QB_MUL_4_training.png
-)
-
# ------- SVMImageModelEstimatorExampleTest----------
otb_add_test(NAME leTeSVMImageModelEstimatorExampleTest COMMAND ${OTB_TEST_DRIVER}
@@ -51,6 +40,18 @@ otb_add_test(NAME leTeSVMImageModelEstimatorExampleTest COMMAND ${OTB_TEST_DRIVE
${TEMP}/svm_image_model.svn
)
+endif()
+# ------- GenerateTrainingImageExampleTest----------
+
+otb_add_test(NAME leTeGenerateTrainingImageExampleTest COMMAND ${OTB_TEST_DRIVER}
+ --compare-n-images ${NOTOL} 1
+ ${BASELINE}/ROI_QB_MUL_4_training.png
+ ${TEMP}/ROI_QB_MUL_4_training.png
+ Execute $<TARGET_FILE:GenerateTrainingImageExample>
+ ${INPUTDATA}/ROI_QB_MUL_4.tif
+ ${INPUTDATA}/LearningROIs.txt
+ ${TEMP}/ROI_QB_MUL_4_training.png
+)
# ------- SOMExampleTest----------
diff --git a/Examples/OBIA/CMakeLists.txt b/Examples/OBIA/CMakeLists.txt
index 202bd7ae29ce81a684e3b81211dd9ba8b3ff1cca..d2f7a79be47c7627549c03ac3f633fdaafb88fa9 100644
--- a/Examples/OBIA/CMakeLists.txt
+++ b/Examples/OBIA/CMakeLists.txt
@@ -12,8 +12,10 @@ target_link_libraries(KeepNObjects ${OTB_LIBRARIES})
add_executable(LabelMapToVectorData LabelMapToVectorData.cxx)
target_link_libraries(LabelMapToVectorData ${OTB_LIBRARIES})
+if(OTBMathParser_LOADED)
add_executable(RadiometricAttributesLabelMapFilterExample RadiometricAttributesLabelMapFilterExample.cxx)
target_link_libraries(RadiometricAttributesLabelMapFilterExample ${OTB_LIBRARIES})
+endif()
add_executable(ShapeAttributeComputation ShapeAttributeComputation.cxx)
target_link_libraries(ShapeAttributeComputation ${OTB_LIBRARIES})
diff --git a/Examples/OBIA/test/CMakeLists.txt b/Examples/OBIA/test/CMakeLists.txt
index 83a32c6339f4503a4fe36a72e982a6ea9bb559a5..f3c92b30ea3d96bd545bc5c333539db8274a8c5a 100644
--- a/Examples/OBIA/test/CMakeLists.txt
+++ b/Examples/OBIA/test/CMakeLists.txt
@@ -22,6 +22,7 @@ otb_add_test(NAME trTeOBIAShapeAttributeComputationTest COMMAND ${OTB_TEST_DRIVE
${TEMP}/OBIAShapeAttribute.txt
)
+if(OTBMathParser_LOADED)
otb_add_test(NAME trTeOBIARadiometricAttributesLabelMapFilterTest COMMAND ${OTB_TEST_DRIVER}
--compare-image ${NOTOL}
${BASELINE}/OBIARadiometricAttributes.png
@@ -38,6 +39,7 @@ otb_add_test(NAME trTeOBIARadiometricAttributesLabelMapFilterTest COMMAND ${OTB_
50
1.0
)
+endif()
otb_add_test(NAME trTeLabelMapToVectorData COMMAND ${OTB_TEST_DRIVER}
Execute $<TARGET_FILE:LabelMapToVectorData>
diff --git a/Examples/Patented/CMakeLists.txt b/Examples/Patented/CMakeLists.txt
index 07210c89bb2bc23db170f3b3d57925bfca0143c3..8648e956aa89809bf1eb6c47c21b1a3436fd0ddf 100644
--- a/Examples/Patented/CMakeLists.txt
+++ b/Examples/Patented/CMakeLists.txt
@@ -3,18 +3,18 @@ project(PatentedExamples)
add_executable(EstimateAffineTransformationExample EstimateAffineTransformationExample.cxx)
target_link_libraries(EstimateAffineTransformationExample ${OTB_LIBRARIES})
+if (OTBSiftFast_LOADED)
add_executable(SIFTDisparityMapEstimation SIFTDisparityMapEstimation.cxx)
target_link_libraries(SIFTDisparityMapEstimation ${OTB_LIBRARIES})
add_executable(SIFTExample SIFTExample.cxx)
target_link_libraries(SIFTExample ${OTB_LIBRARIES})
-if (Module_OTBSiftFast)
- add_executable(SIFTDensityExample SIFTDensityExample.cxx)
- target_link_libraries(SIFTDensityExample ${OTB_LIBRARIES})
+add_executable(SIFTDensityExample SIFTDensityExample.cxx)
+target_link_libraries(SIFTDensityExample ${OTB_LIBRARIES})
- add_executable(SIFTFastExample SIFTFastExample.cxx)
- target_link_libraries(SIFTFastExample ${OTB_LIBRARIES})
+add_executable(SIFTFastExample SIFTFastExample.cxx)
+target_link_libraries(SIFTFastExample ${OTB_LIBRARIES})
endif()
if(BUILD_TESTING)
diff --git a/Examples/Projections/test/CMakeLists.txt b/Examples/Projections/test/CMakeLists.txt
index 61c9985d0c46901bb172ebd19613d7871e78a3cd..7964d8655050eabe8f3b030ed237203666fa915a 100644
--- a/Examples/Projections/test/CMakeLists.txt
+++ b/Examples/Projections/test/CMakeLists.txt
@@ -68,7 +68,7 @@ otb_add_test(NAME prTeEstimateRPCSensorModelExampleTest COMMAND ${OTB_TEST_DRIVE
--compare-ascii ${EPSILON_4}
${BASELINE}/otbGCPsToRPCSensorModelImageFilterWithoutDEMOutput.txt
${TEMP}/otbGCPsToRPCSensorModelImageFilterWithoutDEMOutput.txt
- --ignore-lines-with 5 PipelineMTime ImportImageContaine Source: Image Time:
+ --ignore-lines-with 6 PipelineMTime ImportImageContaine Source: Image Time: Pointer:
Execute $<TARGET_FILE:EstimateRPCSensorModelExample>
LARGEINPUT{SPOT4/RIO_DE_JANEIRO/IMAG_01.DAT}
${TEMP}/otbGCPsToRPCSensorModelImageFilterWithoutDEMOutput.txt
@@ -88,6 +88,10 @@ otb_add_test(NAME prTeEstimateRPCSensorModelExampleTest COMMAND ${OTB_TEST_DRIVE
2803 1942 -22.958868 -42.808235 0.
980 2734 -22.99534 -43.309672 0.
713 2754 -23.007862 -43.365307 0.
+ 72 2955 -23.012153 -43.541331 0.
+ 2884 1898 -22.954691 -42.786188 0.
+ 2539 47 -22.616041 -42.777877 0.
+ 577 183 -22.551654 -43.271813 0.
)
otb_add_test(NAME prTePlaceNameToLonLatExampleTest COMMAND ${OTB_TEST_DRIVER}
diff --git a/Examples/Radiometry/CMakeLists.txt b/Examples/Radiometry/CMakeLists.txt
index 1c1e1bd3de3339396735c49983ca9509886d2c69..48c5d40d071238dd5dcd547fe80ac56f095adb08 100644
--- a/Examples/Radiometry/CMakeLists.txt
+++ b/Examples/Radiometry/CMakeLists.txt
@@ -3,8 +3,11 @@ project(RadiometryExamples)
add_executable(ARVIMultiChannelRAndBAndNIRVegetationIndexImageFilter ARVIMultiChannelRAndBAndNIRVegetationIndexImageFilter.cxx)
target_link_libraries(ARVIMultiChannelRAndBAndNIRVegetationIndexImageFilter ${OTB_LIBRARIES})
+#OTBOpticalCalibration -> OTB6S
+if(OTB6S_LOADED)
add_executable(AtmosphericCorrectionSequencement AtmosphericCorrectionSequencement.cxx)
target_link_libraries(AtmosphericCorrectionSequencement ${OTB_LIBRARIES})
+endif()
add_executable(AVIMultiChannelRAndGAndNIRVegetationIndexImageFilter AVIMultiChannelRAndGAndNIRVegetationIndexImageFilter.cxx)
target_link_libraries(AVIMultiChannelRAndGAndNIRVegetationIndexImageFilter ${OTB_LIBRARIES})
diff --git a/Examples/Radiometry/test/CMakeLists.txt b/Examples/Radiometry/test/CMakeLists.txt
index ff18563ad9aff969b7e84a237a1efd7377c6235e..b77c8c87e51835fe4d227fbed00b77c68f78075c 100644
--- a/Examples/Radiometry/test/CMakeLists.txt
+++ b/Examples/Radiometry/test/CMakeLists.txt
@@ -52,6 +52,7 @@ otb_add_test(NAME raTeNDVIRAndNIRVegetationIndexImageFilterTest COMMAND ${OTB_TE
${TEMP}/NDVIRAndNIRVegetationIndex4.tif
)
+if(OTBOpticalCalibration_LOADED)
otb_add_test(NAME raTeAtmosphericCorrectionSequencementTest COMMAND ${OTB_TEST_DRIVER}
--compare-image ${EPSILON_8}
${BASELINE}/Example_RomaniaAtmosphericCorrectionSequencement.tif
@@ -82,3 +83,4 @@ otb_add_test(NAME raTeAtmosphericCorrectionSequencementTest COMMAND ${OTB_TEST_D
2 # Radius;
0.020 # pixel spacing in kilometers
)
+endif()
\ No newline at end of file
diff --git a/Examples/Segmentation/CMakeLists.txt b/Examples/Segmentation/CMakeLists.txt
index 4fb4b89300757a0a24a675165264b9dacfbc83e0..c8cda537c20a206fbb46e6b670948e1edfb0313b 100644
--- a/Examples/Segmentation/CMakeLists.txt
+++ b/Examples/Segmentation/CMakeLists.txt
@@ -24,8 +24,10 @@ target_link_libraries(OtsuMultipleThresholdImageFilter ${OTB_LIBRARIES})
add_executable(OtsuThresholdImageFilter OtsuThresholdImageFilter.cxx)
target_link_libraries(OtsuThresholdImageFilter ${OTB_LIBRARIES})
-add_executable(StreamingMeanShiftSegmentation StreamingMeanShiftSegmentation.cxx)
-target_link_libraries(StreamingMeanShiftSegmentation ${OTB_LIBRARIES})
+if(OTBMathParser_LOADED)
+ add_executable(StreamingMeanShiftSegmentation StreamingMeanShiftSegmentation.cxx)
+ target_link_libraries(StreamingMeanShiftSegmentation ${OTB_LIBRARIES})
+endif()
add_executable(VectorConfidenceConnected VectorConfidenceConnected.cxx)
target_link_libraries(VectorConfidenceConnected ${OTB_LIBRARIES})
diff --git a/Examples/Segmentation/test/CMakeLists.txt b/Examples/Segmentation/test/CMakeLists.txt
index d43f19ecf89f64747f546278e5a09bb23e6806c2..37d36f1ca5f27611eb1e31dd3951dd2bc56ceb4b 100644
--- a/Examples/Segmentation/test/CMakeLists.txt
+++ b/Examples/Segmentation/test/CMakeLists.txt
@@ -272,7 +272,7 @@ otb_add_test(NAME seTeLabelizeNeighborhoodConnectedImageFilterTest COMMAND ${OTB
# ------- StreamingMeanShiftSegmentationTest ----------
-
+if(OTBMathParser_LOADED)
otb_add_test(NAME seTeStreamingMeanShiftSegmentationTest COMMAND ${OTB_TEST_DRIVER}
# --compare-ogr ${NOTOL}
# ${BASELINE_FILES}/seTeStreamingMeanShiftSegmentationTest.shp
@@ -291,3 +291,4 @@ otb_add_test(NAME seTeStreamingMeanShiftSegmentationTest COMMAND ${OTB_TEST_DRIV
1 #Simplify Flag
0.2 #Simplification tolerance
)
+endif()
\ No newline at end of file
diff --git a/Examples/Simulation/CMakeLists.txt b/Examples/Simulation/CMakeLists.txt
index ece736e87f61817c03a96ce358eebac6c368f355..0e0bc86b13a38327a5e71324c4972491e85c076e 100644
--- a/Examples/Simulation/CMakeLists.txt
+++ b/Examples/Simulation/CMakeLists.txt
@@ -1,14 +1,16 @@
project(SimulationExamples)
+#OTBSimulation -> OTBOpticalCalibration -> OTB6S
+if(OTB6S_LOADED)
add_executable(LAIAndPROSAILToSensorResponse LAIAndPROSAILToSensorResponse.cxx)
target_link_libraries(LAIAndPROSAILToSensorResponse ${OTB_LIBRARIES})
-add_executable(LAIFromNDVIImageTransform LAIFromNDVIImageTransform.cxx)
-target_link_libraries(LAIFromNDVIImageTransform ${OTB_LIBRARIES})
-
add_executable(ProsailModel ProsailModel.cxx)
target_link_libraries(ProsailModel ${OTB_LIBRARIES})
+endif()
+add_executable(LAIFromNDVIImageTransform LAIFromNDVIImageTransform.cxx)
+target_link_libraries(LAIFromNDVIImageTransform ${OTB_LIBRARIES})
if(BUILD_TESTING)
add_subdirectory(test)
diff --git a/Examples/Simulation/test/CMakeLists.txt b/Examples/Simulation/test/CMakeLists.txt
index eed146c98e2e69eb94b99f1bda805b50254f4821..9cb0323021507d2671a12e1af58e015a5d4a796d 100644
--- a/Examples/Simulation/test/CMakeLists.txt
+++ b/Examples/Simulation/test/CMakeLists.txt
@@ -3,6 +3,8 @@ set(INPUTDATA ${OTB_DATA_ROOT}/Examples)
# ------- ProsailModelExampleTest ----------
+#OTBSimulation -> OTBOpticalCalibration -> OTB6S
+if(OTB6S_LOADED)
otb_add_test(NAME siTvProsailModelExampleTest COMMAND ${OTB_TEST_DRIVER}
--compare-ascii ${EPSILON_3}
${BASELINE}/siTvProsailModelExampleTest.txt
@@ -25,20 +27,6 @@ otb_add_test(NAME siTvProsailModelExampleTest COMMAND ${OTB_TEST_DRIVER}
${TEMP}/siTvProsailModelExampleTest.txt
)
-
-otb_add_test(NAME siTvLAIFromNDVIImageTransfromExampleTest COMMAND ${OTB_TEST_DRIVER}
- --compare-image ${EPSILON_8}
- ${OTB_DATA_ROOT}/Baseline/OTB/Images/raRAndNIRVegetationIndex_LAIFromNDVIFormosat2_verySmallFSATSWcRcNir.tif
- ${TEMP}/siTvLAIFromNDVIImageTransfromExampleTest_verySmallFSATSW.tif
- Execute $<TARGET_FILE:LAIFromNDVIImageTransform>
- ${INPUTDATA}/verySmallFSATSW.tif
- ${TEMP}/siTvLAIFromNDVIImageTransfromExampleTest_verySmallFSATSW.tif
- ${TEMP}/verySmallFSATSW_visu.png
- ${TEMP}/siTvLAIFromNDVIImageTransfromExampleTest_verySmallFSATSW_visu.png
- 1 # red
- 4 # nir
-)
-
otb_add_test(NAME siTvLAIAndPROSAILToSensorResponseExampleTest COMMAND ${OTB_TEST_DRIVER}
--compare-image ${EPSILON_8}
${OTBAPP_BASELINE}/siTvLAIAndPROSAILToSensorResponseSmall.tif
@@ -54,3 +42,18 @@ Execute $<TARGET_FILE:LAIAndPROSAILToSensorResponse>
5
${OTB_DATA_ROOT}/Input/
)
+
+endif()
+
+otb_add_test(NAME siTvLAIFromNDVIImageTransfromExampleTest COMMAND ${OTB_TEST_DRIVER}
+ --compare-image ${EPSILON_8}
+ ${OTB_DATA_ROOT}/Baseline/OTB/Images/raRAndNIRVegetationIndex_LAIFromNDVIFormosat2_verySmallFSATSWcRcNir.tif
+ ${TEMP}/siTvLAIFromNDVIImageTransfromExampleTest_verySmallFSATSW.tif
+ Execute $<TARGET_FILE:LAIFromNDVIImageTransform>
+ ${INPUTDATA}/verySmallFSATSW.tif
+ ${TEMP}/siTvLAIFromNDVIImageTransfromExampleTest_verySmallFSATSW.tif
+ ${TEMP}/verySmallFSATSW_visu.png
+ ${TEMP}/siTvLAIFromNDVIImageTransfromExampleTest_verySmallFSATSW_visu.png
+ 1 # red
+ 4 # nir
+)
diff --git a/Modules/Adapters/BoostAdapters/include/otbStringUtils.h b/Modules/Adapters/BoostAdapters/include/otbStringUtils.h
new file mode 100644
index 0000000000000000000000000000000000000000..341b231eb2b73ce2895635aa609105b62234dea0
--- /dev/null
+++ b/Modules/Adapters/BoostAdapters/include/otbStringUtils.h
@@ -0,0 +1,130 @@
+/*=========================================================================
+
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+=========================================================================*/
+#ifndef otbStringUtils_h
+#define otbStringUtils_h
+
+#include <string>
+#include <vector>
+#include <limits>
+#include <stdexcept>
+#include <boost/lexical_cast.hpp>
+#include <boost/algorithm/string.hpp>
+#include <boost/algorithm/string/predicate.hpp>
+#include <boost/utility/enable_if.hpp>
+
+namespace otb
+{
+namespace Utils
+{
+template <typename Res, typename In >
+inline
+Res LexicalCast(In const& in, std::string const& kind) {
+ try
+ {
+ return boost::lexical_cast<Res>(in);
+ }
+ catch (boost::bad_lexical_cast &) {
+ std::ostringstream oss;
+ oss << "Cannot decode '" << in << "' as this is not a valid value for '" << kind << "'";
+ throw std::runtime_error(oss.str());
+ }
+}
+
+/**\ingroup Utils
+ * Converts a delimiter separated string into a collection of \c T's.
+ * \tparam Collection type. It shall define \c value_type and \c push_back()
+ * \param[out] ret output collection.
+ * \param[in] str input string
+ * \param[in] errmsg a msg complement used to build the error message to
+be shown if there is \c lexical_cast exception. See \c
+otb:Utils::LexicalCast().
+ * \param delims delimitter characters (space is default)
+ * \throw std::bad_alloc in case of exhausted memory
+ * \throw std::runtime_error in case an element from the input string
+cannot be converted into a valid \c T instance.
+ * \pre delims shall not be null (untested)
+ *
+ * \see \c otb::Utils::LexicalCast()
+ */
+template<typename T>
+void
+ConvertStringToVector(std::string const &str, T& ret, std::string const& errmsg, const char * delims=" ")
+{
+ typedef std::vector<boost::iterator_range<std::string::const_iterator> > ListType;
+
+ ListType splitted;
+
+ boost::split(splitted, str, boost::is_any_of(delims));
+
+ for(size_t i = 0; i < splitted.size(); i++)
+ {
+ typename T::value_type value = LexicalCast<typename T::value_type> (splitted[i], errmsg);
+ ret.push_back(value);
+ }
+}
+
+/**\ingroup Utils
+ * split a given std::string of into key value based on given delimitter
+ * string. default delimitter is '='. If the string does not have a delimitter
+ * the key is set to input string and value is set to defValue.
+ * \param[in] str input string
+ * \param key[out] An std::string reference where key will be stored
+ * \param value[out] a reference of \cT where value will be stored
+ * \param defValue[in] a default value if there is no delimitter fo
+ * \param[in] errmsg a msg complement used to build the error message to
+be shown if there is \c lexical_cast exception. See \cotb:Utils::LexicalCast().
+ * \param delims delimitter characters (space is default)
+ * \throw std::bad_alloc in case of exhausted memory
+ * \throw std::runtime_error in case an element from the input string
+cannot be converted into a valid \c T instance.
+ * \pre delims shall not be null (untested)
+ *
+ * \see \c otb::Utils::LexicalCast()
+ */
+template<typename T>
+void SplitStringToSingleKeyValue(const std::string& str,
+ std::string& key, T& value, const T& defValue,
+ std::string const& errmsg, const std::string delims="=")
+{
+
+ typedef boost::iterator_range<std::string::const_iterator> BoostRangeIteratorType;
+ typedef std::list< BoostRangeIteratorType > ListType;
+
+ ListType splitted;
+
+ boost::split( splitted, str, boost::is_any_of(delims), boost::token_compress_on );
+
+ typename ListType::iterator it = splitted.begin();
+ BoostRangeIteratorType kIt = boost::trim_copy((*it));
+ key.assign( kIt.begin(), kIt.end());
+ ++it;
+
+ if( it != splitted.end())
+ {
+ value = LexicalCast<T>(boost::trim_copy(*it), errmsg);
+ ++it;
+ }
+ else
+ {
+ value = defValue;
+ }
+}
+} // end namespace Utils
+} // end namespace otb
+
+#endif //otbStringUtils_h
diff --git a/Modules/Adapters/OSSIMAdapters/include/otbDEMHandler.h b/Modules/Adapters/OSSIMAdapters/include/otbDEMHandler.h
index 528144e916d3c06135cdb501bd37487718c5042d..7e51a0c79178a1e1e0489cd45eb0c797e6311343 100644
--- a/Modules/Adapters/OSSIMAdapters/include/otbDEMHandler.h
+++ b/Modules/Adapters/OSSIMAdapters/include/otbDEMHandler.h
@@ -107,9 +107,29 @@ public:
/** return true if the directoty contain DEM */
virtual bool IsValidDEMDirectory(const char* DEMDirectory);
- /** Open geoid file. */
- virtual void OpenGeoidFile(const char* geoidFile);
- virtual void OpenGeoidFile(const std::string& geoidFile);
+ /**
+ * \brief Open geoid file given its filename or throw an exception
+ * if geoid-file could not be loaded.
+ *
+ * \param geoidFile The filename of the geoid-file.
+ *
+ * \return <code>true</code> if geoid file has been changed or
+ * <code>false</code> if geod-file have been set before and could
+ * not be changed.
+ */
+ virtual bool OpenGeoidFile(const char* geoidFile);
+
+ /**
+ * \brief Open geoid file given its filename or throw an exception
+ * if geoid-file could not be loaded.
+ *
+ * \param geoidFile The filename of the geoid-file.
+ *
+ * \return <code>true</code> if geoid file has been changed or
+ * <code>false</code> if geod-file have been set before and could
+ * not be changed.
+ */
+ virtual bool OpenGeoidFile(const std::string& geoidFile);
/** Compute the height above MSL(Mean Sea Level) of a geographic point. */
virtual double GetHeightAboveMSL(double lon, double lat) const;
@@ -125,6 +145,11 @@ public:
/** Set the default height above ellipsoid in case no information is available*/
double GetDefaultHeightAboveEllipsoid() const;
+ /**
+ * \return The number of elevation databases in the
+ * <code>ossimElevManager</code>.
+ */
+ unsigned int GetDEMCount() const;
/** Get DEM directory */
std::string GetDEMDirectory(unsigned int idx = 0) const;
@@ -132,14 +157,18 @@ public:
/** Get Goid file */
std::string GetGeoidFile() const;
+ /**
+ * \brief Remove all the ossimElevationDatabases from the
+ * <code>ossimElevManager</code>.
+ */
+ void ClearDEMs();
+
protected:
DEMHandler();
virtual ~DEMHandler() {}
void PrintSelf(std::ostream& os, itk::Indent indent) const;
- ossimElevManager* m_ElevManager;
-
// Ossim does not allow to retrieve the geoid file path
// We therefore must keep it on our side
std::string m_GeoidFile;
diff --git a/Modules/Adapters/OSSIMAdapters/src/otbDEMHandler.cxx b/Modules/Adapters/OSSIMAdapters/src/otbDEMHandler.cxx
index 79b352417c22648288dc5a537bf99707a5f24a09..71b94952260fdcba5b6ed9f65a784976b1eaa3e1 100644
--- a/Modules/Adapters/OSSIMAdapters/src/otbDEMHandler.cxx
+++ b/Modules/Adapters/OSSIMAdapters/src/otbDEMHandler.cxx
@@ -18,6 +18,8 @@
#include "otbDEMHandler.h"
#include "otbMacro.h"
+#include <cassert>
+
#include "ossim/elevation/ossimElevManager.h"
#include "ossim/base/ossimGeoidManager.h"
#include "ossim/base/ossimFilename.h"
@@ -49,23 +51,25 @@ DEMHandler::Pointer DEMHandler::Instance()
DEMHandler
::DEMHandler() :
- m_ElevManager(ossimElevManager::instance()),
m_GeoidFile(""),
m_DefaultHeightAboveEllipsoid(0)
{
- m_ElevManager->setDefaultHeightAboveEllipsoid(m_DefaultHeightAboveEllipsoid);
+ assert( ossimElevManager::instance()!=NULL );
+
+ ossimElevManager::instance()->setDefaultHeightAboveEllipsoid(m_DefaultHeightAboveEllipsoid);
// Force geoid fallback
- m_ElevManager->setUseGeoidIfNullFlag(true);
+ ossimElevManager::instance()->setUseGeoidIfNullFlag(true);
}
void
DEMHandler
::OpenDEMDirectory(const char* DEMDirectory)
{
- ossimFilename ossimDEMDir;
- ossimDEMDir = ossimFilename(DEMDirectory);
+ assert( ossimElevManager::instance()!=NULL );
+
+ ossimFilename ossimDEMDir( DEMDirectory );
- if (!m_ElevManager->loadElevationPath(ossimDEMDir))
+ if (!ossimElevManager::instance()->loadElevationPath(ossimDEMDir))
{
// In ossim elevation database factory code, the
// ossimImageElevationDatabase is explicitly disabled by a #if 0
@@ -82,11 +86,22 @@ DEMHandler
else
{
otbMsgDevMacro(<< "DEM directory contains general elevation image files: " << ossimDEMDir);
- m_ElevManager->addDatabase(imageElevationDatabase.get());
+ ossimElevManager::instance()->addDatabase(imageElevationDatabase.get());
}
}
}
+
+void
+DEMHandler
+::ClearDEMs()
+{
+ assert( ossimElevManager::instance()!=NULL );
+
+ ossimElevManager::instance()->clear();
+}
+
+
void
DEMHandler
::OpenDEMDirectory(const std::string& DEMDirectory)
@@ -98,8 +113,10 @@ bool
DEMHandler
::IsValidDEMDirectory(const char* DEMDirectory)
{
+ assert( ossimElevManager::instance()!=NULL );
+
//Try to load elevation source
- bool result = m_ElevManager->loadElevationPath(DEMDirectory);
+ bool result = ossimElevManager::instance()->loadElevationPath(DEMDirectory);
if (!result)
{
@@ -111,7 +128,7 @@ DEMHandler
return result;
}
-void
+bool
DEMHandler
::OpenGeoidFile(const char* geoidFile)
{
@@ -131,22 +148,31 @@ DEMHandler
// The previous flag will be ignored if
// defaultHeightAboveEllipsoid is not NaN
- m_ElevManager->setDefaultHeightAboveEllipsoid(ossim::nan());
+ assert( ossimElevManager::instance()!=NULL );
+ ossimElevManager::instance()->setDefaultHeightAboveEllipsoid(ossim::nan());
+
+ return true;
}
else
{
otbMsgDevMacro(<< "Failure opening geoid");
geoidPtr.release();
+
+ itkExceptionMacro( << "Failed to open geoid file: '" << geoidFile << "'" );
+
+ return false;
}
}
+
+ return false;
}
-void
+bool
DEMHandler
::OpenGeoidFile(const std::string& geoidFile)
{
- OpenGeoidFile(geoidFile.c_str());
+ return OpenGeoidFile(geoidFile.c_str());
}
double
@@ -155,9 +181,14 @@ DEMHandler
{
double height;
ossimGpt ossimWorldPoint;
+
ossimWorldPoint.lon = lon;
ossimWorldPoint.lat = lat;
- height = m_ElevManager->getHeightAboveMSL(ossimWorldPoint);
+
+ assert( ossimElevManager::instance()!=NULL );
+
+ height = ossimElevManager::instance()->getHeightAboveMSL(ossimWorldPoint);
+
return height;
}
@@ -174,9 +205,13 @@ DEMHandler
{
double height;
ossimGpt ossimWorldPoint;
+
ossimWorldPoint.lon = lon;
ossimWorldPoint.lat = lat;
- height = m_ElevManager->getHeightAboveEllipsoid(ossimWorldPoint);
+
+ assert( ossimElevManager::instance()!=NULL );
+
+ height = ossimElevManager::instance()->getHeightAboveEllipsoid(ossimWorldPoint);
return height;
}
@@ -195,7 +230,10 @@ DEMHandler
// Ossim does not allow to retrieve the default height above
// ellipsoid We therefore must keep it on our side
m_DefaultHeightAboveEllipsoid = h;
- m_ElevManager->setDefaultHeightAboveEllipsoid(h);
+
+ assert( ossimElevManager::instance()!=NULL );
+
+ ossimElevManager::instance()->setDefaultHeightAboveEllipsoid(h);
}
double
@@ -207,13 +245,24 @@ DEMHandler
return m_DefaultHeightAboveEllipsoid;
}
+unsigned int
+DEMHandler
+::GetDEMCount() const
+{
+ assert( ossimElevManager::instance()!=NULL );
+
+ return ossimElevManager::instance()->getNumberOfElevationDatabases();
+}
+
std::string DEMHandler::GetDEMDirectory(unsigned int idx) const
{
std::string demDir = "";
- if(m_ElevManager->getNumberOfElevationDatabases() > 0)
+ if(ossimElevManager::instance()->getNumberOfElevationDatabases() > 0)
{
- demDir = m_ElevManager->getElevationDatabase(idx)->getConnectionString().string();
+ assert( ossimElevManager::instance()!=NULL );
+
+ demDir = ossimElevManager::instance()->getElevationDatabase(idx)->getConnectionString().string();
}
return demDir;
}
diff --git a/Modules/Adapters/OSSIMAdapters/src/otbImageKeywordlist.cxx b/Modules/Adapters/OSSIMAdapters/src/otbImageKeywordlist.cxx
index 0a0309e5f3dba0a46c02262d9a6529903f4fd798..98d873885e84be567549957fb78fa6985d72e2ee 100644
--- a/Modules/Adapters/OSSIMAdapters/src/otbImageKeywordlist.cxx
+++ b/Modules/Adapters/OSSIMAdapters/src/otbImageKeywordlist.cxx
@@ -139,41 +139,51 @@ bool
ImageKeywordlist::
convertToGDALRPC(GDALRPCInfo &rpc) const
{
- ossimKeywordlist geom_kwl;
-
- this->convertToOSSIMKeywordlist(geom_kwl);
-
- if( geom_kwl.hasKey("polynomial_format")) //RK
- {
- ossimRefPtr<ossimRpcModel> rpcModel = new ossimRpcModel;
- if (rpcModel->loadState(geom_kwl))
- {
- ossimRpcModel::rpcModelStruct ossimRpcStruct;
- rpcModel->getRpcParameters(ossimRpcStruct);
+ /* ossimRpcModel::loadState() actually expects certain keyword values to be
+ * present in the keywordlist. So We check a single keyword value
+ * (polynomial_format) first. Even though it is not enough to ensure a valid
+ * ossimRpcModel by checking for presence of one single key but atleast we
+ * are sure about not to create an ossimRpcModel.
+ *
+ * The current mechanism creates ossimRpcModel instance, calls loadState()
+ * and fails. The below check for 'polynomial_format' save us from creating
+ * an ossimRpcModel which will be invalid if the 'polynomial_format' is not
+ * present.
+ */
+ if( m_Keywordlist.find("polynomial_format") != m_Keywordlist.end() )
+ {
+ ossimKeywordlist geom_kwl;
+ this->convertToOSSIMKeywordlist(geom_kwl);
- if (ossimRpcStruct.type == 'B')
+ ossimRefPtr<ossimRpcModel> rpcModel = new ossimRpcModel;
+ if (rpcModel->loadState(geom_kwl))
{
- rpc.dfSAMP_OFF = ossimRpcStruct.sampOffset;
- rpc.dfLINE_OFF = ossimRpcStruct.lineOffset;
- rpc.dfSAMP_SCALE = ossimRpcStruct.sampScale;
- rpc.dfLINE_SCALE = ossimRpcStruct.lineScale;
- rpc.dfLAT_OFF = ossimRpcStruct.latOffset;
- rpc.dfLONG_OFF = ossimRpcStruct.lonOffset;
- rpc.dfHEIGHT_OFF = ossimRpcStruct.hgtOffset;
- rpc.dfLAT_SCALE = ossimRpcStruct.latScale;
- rpc.dfLONG_SCALE = ossimRpcStruct.lonScale;
- rpc.dfHEIGHT_SCALE = ossimRpcStruct.hgtScale;
-
- memcpy(rpc.adfLINE_NUM_COEFF, ossimRpcStruct.lineNumCoef, sizeof(double) * 20);
- memcpy(rpc.adfLINE_DEN_COEFF, ossimRpcStruct.lineDenCoef, sizeof(double) * 20);
- memcpy(rpc.adfSAMP_NUM_COEFF, ossimRpcStruct.sampNumCoef, sizeof(double) * 20);
- memcpy(rpc.adfSAMP_DEN_COEFF, ossimRpcStruct.sampDenCoef, sizeof(double) * 20);
-
- return true;
+ ossimRpcModel::rpcModelStruct ossimRpcStruct;
+ rpcModel->getRpcParameters(ossimRpcStruct);
+
+ if (ossimRpcStruct.type == 'B')
+ {
+ rpc.dfSAMP_OFF = ossimRpcStruct.sampOffset;
+ rpc.dfLINE_OFF = ossimRpcStruct.lineOffset;
+ rpc.dfSAMP_SCALE = ossimRpcStruct.sampScale;
+ rpc.dfLINE_SCALE = ossimRpcStruct.lineScale;
+ rpc.dfLAT_OFF = ossimRpcStruct.latOffset;
+ rpc.dfLONG_OFF = ossimRpcStruct.lonOffset;
+ rpc.dfHEIGHT_OFF = ossimRpcStruct.hgtOffset;
+ rpc.dfLAT_SCALE = ossimRpcStruct.latScale;
+ rpc.dfLONG_SCALE = ossimRpcStruct.lonScale;
+ rpc.dfHEIGHT_SCALE = ossimRpcStruct.hgtScale;
+
+ memcpy(rpc.adfLINE_NUM_COEFF, ossimRpcStruct.lineNumCoef, sizeof(double) * 20);
+ memcpy(rpc.adfLINE_DEN_COEFF, ossimRpcStruct.lineDenCoef, sizeof(double) * 20);
+ memcpy(rpc.adfSAMP_NUM_COEFF, ossimRpcStruct.sampNumCoef, sizeof(double) * 20);
+ memcpy(rpc.adfSAMP_DEN_COEFF, ossimRpcStruct.sampDenCoef, sizeof(double) * 20);
+
+ return true;
+ }
}
- }
- }
- return false;
+ }
+ return false;
}
void
diff --git a/Modules/Adapters/OSSIMAdapters/test/CMakeLists.txt b/Modules/Adapters/OSSIMAdapters/test/CMakeLists.txt
index d11608709f0552643a238bb01042453df869e9d2..42440cd9a65cea1d85f0099a6ba1760664f64e8e 100644
--- a/Modules/Adapters/OSSIMAdapters/test/CMakeLists.txt
+++ b/Modules/Adapters/OSSIMAdapters/test/CMakeLists.txt
@@ -441,6 +441,7 @@ otb_add_test(NAME uaTvDEMHandler_AboveEllipsoid_SRTM_BadGeoid COMMAND otbOSSIMAd
339.513
0.001
)
+set_property(TEST uaTvDEMHandler_AboveEllipsoid_SRTM_BadGeoid PROPERTY WILL_FAIL true)
otb_add_test(NAME uaTvDEMHandler_AboveMSL_SRTM_NoGeoid_NoData COMMAND otbOSSIMAdaptersTestDriver
otbDEMHandlerTest
diff --git a/Modules/Adapters/OSSIMAdapters/test/otbDEMHandlerTest.cxx b/Modules/Adapters/OSSIMAdapters/test/otbDEMHandlerTest.cxx
index 9987db6a5bcd5cd74ac0793cd8e5b0aaea5261f2..09ab37cf7c098c990a0668f176099eda0109ab94 100644
--- a/Modules/Adapters/OSSIMAdapters/test/otbDEMHandlerTest.cxx
+++ b/Modules/Adapters/OSSIMAdapters/test/otbDEMHandlerTest.cxx
@@ -59,7 +59,19 @@ int otbDEMHandlerTest(int argc, char * argv[])
if(geoid != "no")
{
- demHandler->OpenGeoidFile(geoid);
+ try
+ {
+ demHandler->OpenGeoidFile(geoid);
+ }
+ catch( const std::exception & exception )
+ {
+ std::cout
+ << "Exception thrown while opening geod-file '" << geoid << "':" << std::endl
+ << exception.what();
+
+ fail = true;
+ }
+
std::cout<<"GetGeoidFile() = "<<demHandler->GetGeoidFile()<<std::endl;
}
diff --git a/Modules/Applications/AppClassification/app/CMakeLists.txt b/Modules/Applications/AppClassification/app/CMakeLists.txt
index 7613f5a2612a7c62bfe8ecc648dab3f8b0b40436..692aa710eba22f1cf1274dcd08171c063764c9b1 100644
--- a/Modules/Applications/AppClassification/app/CMakeLists.txt
+++ b/Modules/Applications/AppClassification/app/CMakeLists.txt
@@ -65,16 +65,19 @@ otb_create_application(
SOURCES otbKMeansClassification.cxx
LINK_LIBRARIES ${${otb-module}_LIBRARIES})
-set(TrainImagesClassifier_SRC
- otbTrainSVM.cxx otbTrainBoost.cxx otbTrainDecisionTree.cxx
- otbTrainGradientBoostedTree.cxx otbTrainNeuralNetwork.cxx
- otbTrainNormalBayes.cxx otbTrainRandomForests.cxx otbTrainKNN.cxx
- otbTrainLibSVM.cxx
- otbTrainImagesClassifier.cxx)
-
otb_create_application(
NAME TrainImagesClassifier
- SOURCES ${TrainImagesClassifier_SRC}
+ SOURCES otbTrainImagesClassifier.cxx
+ LINK_LIBRARIES ${${otb-module}_LIBRARIES})
+
+otb_create_application(
+ NAME TrainRegression
+ SOURCES otbTrainRegression.cxx
+ LINK_LIBRARIES ${${otb-module}_LIBRARIES})
+
+otb_create_application(
+ NAME PredictRegression
+ SOURCES otbPredictRegression.cxx
LINK_LIBRARIES ${${otb-module}_LIBRARIES})
otb_create_application(
diff --git a/Modules/Applications/AppClassification/app/otbPredictRegression.cxx b/Modules/Applications/AppClassification/app/otbPredictRegression.cxx
new file mode 100644
index 0000000000000000000000000000000000000000..413d70f05d08d50efaef54a0bd38e0c58bd1ca7d
--- /dev/null
+++ b/Modules/Applications/AppClassification/app/otbPredictRegression.cxx
@@ -0,0 +1,275 @@
+/*=========================================================================
+
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+=========================================================================*/
+#include "otbWrapperApplication.h"
+#include "otbWrapperApplicationFactory.h"
+
+#include "itkUnaryFunctorImageFilter.h"
+#include "otbChangeLabelImageFilter.h"
+#include "otbStandardWriterWatcher.h"
+#include "otbStatisticsXMLFileReader.h"
+#include "otbShiftScaleVectorImageFilter.h"
+#include "otbImageClassificationFilter.h"
+#include "otbMultiToMonoChannelExtractROI.h"
+#include "otbImageToVectorImageCastFilter.h"
+#include "otbMachineLearningModelFactory.h"
+
+namespace otb
+{
+namespace Functor
+{
+/**
+ * simple affine function : y = ax+b
+ */
+template<class TInput, class TOutput>
+class AffineFunctor
+{
+public:
+ typedef double InternalType;
+
+ // constructor
+ AffineFunctor() : m_A(1.0),m_B(0.0) {}
+
+ // destructor
+ virtual ~AffineFunctor() {}
+
+ void SetA(InternalType a)
+ {
+ m_A = a;
+ }
+
+ void SetB(InternalType b)
+ {
+ m_B = b;
+ }
+
+ inline TOutput operator()(const TInput & x) const
+ {
+ return static_cast<TOutput>( static_cast<InternalType>(x)*m_A + m_B);
+ }
+private:
+ InternalType m_A;
+ InternalType m_B;
+};
+
+}
+
+namespace Wrapper
+{
+
+class PredictRegression : public Application
+{
+public:
+ /** Standard class typedefs. */
+ typedef PredictRegression Self;
+ typedef Application Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+
+ /** Standard macro */
+ itkNewMacro(Self);
+
+ itkTypeMacro(PredictRegression, otb::Application);
+
+ /** Filters typedef */
+ typedef UInt8ImageType MaskImageType;
+ typedef itk::VariableLengthVector<FloatVectorImageType::InternalPixelType> MeasurementType;
+ typedef otb::StatisticsXMLFileReader<MeasurementType> StatisticsReader;
+ typedef otb::ShiftScaleVectorImageFilter<FloatVectorImageType, FloatVectorImageType> RescalerType;
+ typedef itk::UnaryFunctorImageFilter<
+ FloatImageType,
+ FloatImageType,
+ otb::Functor::AffineFunctor<float,float> > OutputRescalerType;
+ typedef otb::ImageClassificationFilter<FloatVectorImageType, FloatImageType, MaskImageType> ClassificationFilterType;
+ typedef ClassificationFilterType::Pointer ClassificationFilterPointerType;
+ typedef ClassificationFilterType::ModelType ModelType;
+ typedef ModelType::Pointer ModelPointerType;
+ typedef ClassificationFilterType::ValueType ValueType;
+ typedef ClassificationFilterType::LabelType LabelType;
+ typedef otb::MachineLearningModelFactory<ValueType, LabelType> MachineLearningModelFactoryType;
+
+private:
+ void DoInit()
+ {
+ SetName("PredictRegression");
+ SetDescription("Performs a prediction of the input image according to a regression model file.");
+
+ // Documentation
+ SetDocName("Predict Regression");
+ SetDocLongDescription("This application predict output values from an input"
+ " image, based on a regression model file produced by"
+ " the TrainRegression application. Pixels of the "
+ "output image will contain the predicted values from"
+ "the regression model (single band). The input pixels"
+ " can be optionally centered and reduced according "
+ "to the statistics file produced by the "
+ "ComputeImagesStatistics application. An optional "
+ "input mask can be provided, in which case only "
+ "input image pixels whose corresponding mask value "
+ "is greater than 0 will be processed. The remaining"
+ " of pixels will be given the value 0 in the output"
+ " image.");
+
+ SetDocLimitations("The input image must contain the feature bands used for"
+ " the model training (without the predicted value). "
+ "If a statistics file was used during training by the "
+ "TrainRegression, it is mandatory to use the same "
+ "statistics file for prediction. If an input mask is "
+ "used, its size must match the input image size.");
+ SetDocAuthors("OTB-Team");
+ SetDocSeeAlso("TrainRegression, ComputeImagesStatistics");
+
+ AddDocTag(Tags::Learning);
+
+ AddParameter(ParameterType_InputImage, "in", "Input Image");
+ SetParameterDescription( "in", "The input image to predict.");
+
+ // TODO : use CSV input/output ?
+
+ AddParameter(ParameterType_InputImage, "mask", "Input Mask");
+ SetParameterDescription( "mask", "The mask allows to restrict "
+ "classification of the input image to the area where mask pixel values "
+ "are greater than 0.");
+ MandatoryOff("mask");
+
+ AddParameter(ParameterType_InputFilename, "model", "Model file");
+ SetParameterDescription("model", "A regression model file (produced by "
+ "TrainRegression application).");
+
+ AddParameter(ParameterType_InputFilename, "imstat", "Statistics file");
+ SetParameterDescription("imstat", "A XML file containing mean and standard"
+ " deviation to center and reduce samples before prediction "
+ "(produced by ComputeImagesStatistics application). If this file contains"
+ "one more band than the sample size, the last stat of last band will be"
+ "applied to expand the output predicted value");
+ MandatoryOff("imstat");
+
+ AddParameter(ParameterType_OutputImage, "out", "Output Image");
+ SetParameterDescription( "out", "Output image containing predicted values");
+
+ AddRAMParameter();
+
+ // Doc example parameter settings
+ SetDocExampleParameterValue("in", "QB_1_ortho.tif");
+ SetDocExampleParameterValue("imstat", "EstimateImageStatisticsQB1.xml");
+ SetDocExampleParameterValue("model", "clsvmModelQB1.svm");
+ SetDocExampleParameterValue("out", "clLabeledImageQB1.tif");
+ }
+
+ void DoUpdateParameters()
+ {
+ // Nothing to do here : all parameters are independent
+ }
+
+ void DoExecute()
+ {
+ // Load input image
+ FloatVectorImageType::Pointer inImage = GetParameterImage("in");
+ inImage->UpdateOutputInformation();
+ unsigned int nbFeatures = inImage->GetNumberOfComponentsPerPixel();
+
+ // Load svm model
+ otbAppLogINFO("Loading model");
+ m_Model = MachineLearningModelFactoryType::CreateMachineLearningModel(GetParameterString("model"),
+ MachineLearningModelFactoryType::ReadMode);
+
+ if (m_Model.IsNull())
+ {
+ otbAppLogFATAL(<< "Error when loading model " << GetParameterString("model") << " : unsupported model type");
+ }
+
+ m_Model->Load(GetParameterString("model"));
+ m_Model->SetRegressionMode(true);
+ otbAppLogINFO("Model loaded");
+
+ // Classify
+ m_ClassificationFilter = ClassificationFilterType::New();
+ m_ClassificationFilter->SetModel(m_Model);
+
+ FloatImageType::Pointer outputImage = m_ClassificationFilter->GetOutput();
+
+ // Normalize input image if asked
+ if(IsParameterEnabled("imstat") )
+ {
+ otbAppLogINFO("Input image normalization activated.");
+ // Normalize input image (optional)
+ StatisticsReader::Pointer statisticsReader = StatisticsReader::New();
+ MeasurementType meanMeasurementVector;
+ MeasurementType stddevMeasurementVector;
+ m_Rescaler = RescalerType::New();
+ // Load input image statistics
+ statisticsReader->SetFileName(GetParameterString("imstat"));
+ meanMeasurementVector = statisticsReader->GetStatisticVectorByName("mean");
+ stddevMeasurementVector = statisticsReader->GetStatisticVectorByName("stddev");
+ otbAppLogINFO( "mean used: " << meanMeasurementVector );
+ otbAppLogINFO( "standard deviation used: " << stddevMeasurementVector );
+ if (meanMeasurementVector.Size() == nbFeatures + 1)
+ {
+ double outMean = meanMeasurementVector[nbFeatures];
+ double outStdDev = stddevMeasurementVector[nbFeatures];
+ meanMeasurementVector.SetSize(nbFeatures,false);
+ stddevMeasurementVector.SetSize(nbFeatures,false);
+ m_OutRescaler = OutputRescalerType::New();
+ m_OutRescaler->SetInput(m_ClassificationFilter->GetOutput());
+ m_OutRescaler->GetFunctor().SetA(outStdDev);
+ m_OutRescaler->GetFunctor().SetB(outMean);
+ outputImage = m_OutRescaler->GetOutput();
+ }
+ else if (meanMeasurementVector.Size() != nbFeatures)
+ {
+ otbAppLogFATAL("Wrong number of components in statistics file : "<<meanMeasurementVector.Size());
+ }
+
+ // Rescale vector image
+ m_Rescaler->SetScale(stddevMeasurementVector);
+ m_Rescaler->SetShift(meanMeasurementVector);
+ m_Rescaler->SetInput(inImage);
+
+ m_ClassificationFilter->SetInput(m_Rescaler->GetOutput());
+ }
+ else
+ {
+ otbAppLogINFO("Input image normalization deactivated.");
+ m_ClassificationFilter->SetInput(inImage);
+ }
+
+
+ if(IsParameterEnabled("mask"))
+ {
+ otbAppLogINFO("Using input mask");
+ // Load mask image and cast into LabeledImageType
+ MaskImageType::Pointer inMask = GetParameterUInt8Image("mask");
+
+ m_ClassificationFilter->SetInputMask(inMask);
+ }
+
+ SetParameterOutputImage<FloatImageType>("out", outputImage);
+
+ }
+
+ ClassificationFilterType::Pointer m_ClassificationFilter;
+ ModelPointerType m_Model;
+ RescalerType::Pointer m_Rescaler;
+ OutputRescalerType::Pointer m_OutRescaler;
+
+};
+
+
+}
+}
+
+OTB_APPLICATION_EXPORT(otb::Wrapper::PredictRegression)
diff --git a/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx b/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx
index 1b9358f04832bed66ec463d1510fbb371ea2a3de..de1520dfca4855e523513f8a4542a95bff1cde84 100644
--- a/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx
+++ b/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx
@@ -14,15 +14,95 @@
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
+#include "otbLearningApplicationBase.h"
+#include "otbWrapperApplicationFactory.h"
-#include "otbTrainImagesClassifier.h"
+#include "otbListSampleGenerator.h"
+
+// Statistic XML Reader
+#include "otbStatisticsXMLFileReader.h"
+
+// Validation
+#include "otbConfusionMatrixCalculator.h"
+
+#include "itkTimeProbe.h"
+#include "otbStandardFilterWatcher.h"
+
+// Normalize the samples
+#include "otbShiftScaleSampleListFilter.h"
+
+// List sample concatenation
+#include "otbConcatenateSampleListFilter.h"
+
+// Balancing ListSample
+#include "otbListSampleToBalancedListSampleFilter.h"
+
+// VectorData projection filter
+
+// Extract a ROI of the vectordata
+#include "otbVectorDataIntoImageProjectionFilter.h"
+
+// Elevation handler
+#include "otbWrapperElevationParametersHandler.h"
namespace otb
{
namespace Wrapper
{
-void TrainImagesClassifier::DoInit()
+class TrainImagesClassifier: public LearningApplicationBase<float,int>
+{
+public:
+ /** Standard class typedefs. */
+ typedef TrainImagesClassifier Self;
+ typedef LearningApplicationBase<float,int> Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+
+ /** Standard macro */
+ itkNewMacro(Self)
+
+ itkTypeMacro(TrainImagesClassifier, otb::Wrapper::LearningApplicationBase)
+
+ typedef Superclass::SampleType SampleType;
+ typedef Superclass::ListSampleType ListSampleType;
+ typedef Superclass::TargetSampleType TargetSampleType;
+ typedef Superclass::TargetListSampleType TargetListSampleType;
+
+ typedef Superclass::SampleImageType SampleImageType;
+ typedef SampleImageType::PixelType PixelType;
+
+ // SampleList manipulation
+ typedef otb::ListSampleGenerator<SampleImageType, VectorDataType> ListSampleGeneratorType;
+
+ typedef otb::Statistics::ConcatenateSampleListFilter<ListSampleType> ConcatenateListSampleFilterType;
+ typedef otb::Statistics::ConcatenateSampleListFilter<TargetListSampleType> ConcatenateLabelListSampleFilterType;
+
+ // Statistic XML file Reader
+ typedef otb::StatisticsXMLFileReader<SampleType> StatisticsReader;
+
+ // Enhance List Sample typedef otb::Statistics::ListSampleToBalancedListSampleFilter<ListSampleType, LabelListSampleType> BalancingListSampleFilterType;
+ typedef otb::Statistics::ShiftScaleSampleListFilter<ListSampleType, ListSampleType> ShiftScaleFilterType;
+
+ // Estimate performance on validation sample
+ typedef otb::ConfusionMatrixCalculator<TargetListSampleType, TargetListSampleType> ConfusionMatrixCalculatorType;
+ typedef ConfusionMatrixCalculatorType::ConfusionMatrixType ConfusionMatrixType;
+ typedef ConfusionMatrixCalculatorType::MapOfIndicesType MapOfIndicesType;
+ typedef ConfusionMatrixCalculatorType::ClassLabelType ClassLabelType;
+
+ // VectorData projection filter
+ typedef otb::VectorDataProjectionFilter<VectorDataType, VectorDataType> VectorDataProjectionFilterType;
+
+ // Extract ROI
+ typedef otb::VectorDataIntoImageProjectionFilter<VectorDataType, SampleImageType> VectorDataReprojectionType;
+
+protected:
+ //using Superclass::AddParameter;
+ //friend void InitSVMParams(TrainImagesClassifier & app);
+
+private:
+
+void DoInit()
{
SetName("TrainImagesClassifier");
SetDescription(
@@ -46,8 +126,6 @@ void TrainImagesClassifier::DoInit()
SetDocAuthors("OTB-Team");
SetDocSeeAlso("OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html ");
- AddDocTag(Tags::Learning);
-
//Group IO
AddParameter(ParameterType_Group, "io", "Input and output data");
SetParameterDescription("io", "This group of parameters allows to set input and output data.");
@@ -101,24 +179,7 @@ void TrainImagesClassifier::DoInit()
SetParameterDescription("sample.vfn", "Name of the field used to discriminate class labels in the input vector data files.");
SetParameterString("sample.vfn", "Class");
- AddParameter(ParameterType_Choice, "classifier", "Classifier to use for the training");
- SetParameterDescription("classifier", "Choice of the classifier to use for the training.");
-
- //Group LibSVM
-#ifdef OTB_USE_LIBSVM
- InitLibSVMParams();
-#endif
-
-#ifdef OTB_USE_OPENCV
- InitSVMParams();
- InitBoostParams();
- InitDecisionTreeParams();
- InitGradientBoostedTreeParams();
- InitNeuralNetworkParams();
- InitNormalBayesParams();
- InitRandomForestsParams();
- InitKNNParams();
-#endif
+ Superclass::DoInit();
AddRANDParameter();
// Doc example parameter settings
@@ -136,15 +197,14 @@ void TrainImagesClassifier::DoInit()
SetDocExampleParameterValue("classifier.libsvm.opt", "false");
SetDocExampleParameterValue("io.out", "svmModelQB1.txt");
SetDocExampleParameterValue("io.confmatout", "svmConfusionMatrixQB1.csv");
-}
+}
-void TrainImagesClassifier::DoUpdateParameters()
+void DoUpdateParameters()
{
// Nothing to do here : all parameters are independent
}
-
-void TrainImagesClassifier::LogConfusionMatrix(ConfusionMatrixCalculatorType* confMatCalc)
+void LogConfusionMatrix(ConfusionMatrixCalculatorType* confMatCalc)
{
ConfusionMatrixCalculatorType::ConfusionMatrixType matrix = confMatCalc->GetConfusionMatrix();
@@ -215,24 +275,7 @@ void TrainImagesClassifier::LogConfusionMatrix(ConfusionMatrixCalculatorType* co
otbAppLogINFO("Confusion matrix (rows = reference labels, columns = produced labels):\n" << os.str());
}
-void TrainImagesClassifier::Classify(ListSampleType::Pointer validationListSample, LabelListSampleType::Pointer predictedList)
-{
- //Classification
- ModelPointerType model = MachineLearningModelFactoryType::CreateMachineLearningModel(GetParameterString("io.out"),
- MachineLearningModelFactoryType::ReadMode);
-
- if (model.IsNull())
- {
- otbAppLogFATAL(<< "Error when loading model " << GetParameterString("io.out"));
- }
-
- model->Load(GetParameterString("io.out"));
- model->SetInputListSample(validationListSample);
- model->SetTargetListSample(predictedList);
- model->PredictAll();
-}
-
-void TrainImagesClassifier::DoExecute()
+void DoExecute()
{
GetLogger()->Debug("Entering DoExecute\n");
//Create training and validation for list samples and label list samples
@@ -243,8 +286,8 @@ void TrainImagesClassifier::DoExecute()
ConcatenateLabelListSampleFilterType::New();
ConcatenateListSampleFilterType::Pointer concatenateValidationSamples = ConcatenateListSampleFilterType::New();
- MeasurementType meanMeasurementVector;
- MeasurementType stddevMeasurementVector;
+ SampleType meanMeasurementVector;
+ SampleType stddevMeasurementVector;
//--------------------------
// Load measurements from images
@@ -358,7 +401,7 @@ void TrainImagesClassifier::DoExecute()
}
ListSampleType::Pointer listSample;
- LabelListSampleType::Pointer labelListSample;
+ TargetListSampleType::Pointer labelListSample;
//--------------------------
// Balancing training sample (if needed)
// if (IsParameterEnabled("sample.b"))
@@ -384,9 +427,9 @@ void TrainImagesClassifier::DoExecute()
//--------------------------
// Split the data set into training/validation set
ListSampleType::Pointer trainingListSample = listSample;
- LabelListSampleType::Pointer trainingLabeledListSample = labelListSample;
+ TargetListSampleType::Pointer trainingLabeledListSample = labelListSample;
- LabelListSampleType::Pointer validationLabeledListSample = concatenateValidationLabels->GetOutput();
+ TargetListSampleType::Pointer validationLabeledListSample = concatenateValidationLabels->GetOutput();
otbAppLogINFO("Size of training set: " << trainingListSample->Size());
otbAppLogINFO("Size of validation set: " << validationListSample->Size());
otbAppLogINFO("Size of labeled training set: " << trainingLabeledListSample->Size());
@@ -395,88 +438,14 @@ void TrainImagesClassifier::DoExecute()
//--------------------------
// Estimate model
//--------------------------
- LabelListSampleType::Pointer predictedList = LabelListSampleType::New();
- const std::string classifierType = GetParameterString("classifier");
-
- if (classifierType == "libsvm")
- {
- #ifdef OTB_USE_LIBSVM
- TrainLibSVM(trainingListSample, trainingLabeledListSample);
- #else
- otbAppLogFATAL("Module LIBSVM is not installed. You should consider turning OTB_USE_LIBSVM on during cmake configuration.");
- #endif
- }
- else if (classifierType == "svm")
- {
- #ifdef OTB_USE_OPENCV
- TrainSVM(trainingListSample, trainingLabeledListSample);
- #else
- otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration.");
- #endif
- }
- else if (classifierType == "boost")
- {
- #ifdef OTB_USE_OPENCV
- TrainBoost(trainingListSample, trainingLabeledListSample);
- #else
- otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration.");
- #endif
- }
- else if (classifierType == "dt")
- {
- #ifdef OTB_USE_OPENCV
- TrainDecisionTree(trainingListSample, trainingLabeledListSample);
- #else
- otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration.");
- #endif
- }
- else if (classifierType == "gbt")
- {
- #ifdef OTB_USE_OPENCV
- TrainGradientBoostedTree(trainingListSample, trainingLabeledListSample);
- #else
- otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration.");
- #endif
- }
- else if (classifierType == "ann")
- {
- #ifdef OTB_USE_OPENCV
- TrainNeuralNetwork(trainingListSample, trainingLabeledListSample);
- #else
- otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration.");
- #endif
- }
- else if (classifierType == "bayes")
- {
- #ifdef OTB_USE_OPENCV
- TrainNormalBayes(trainingListSample, trainingLabeledListSample);
- #else
- otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration.");
- #endif
- }
- else if (classifierType == "rf")
- {
- #ifdef OTB_USE_OPENCV
- TrainRandomForests(trainingListSample, trainingLabeledListSample);
- #else
- otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration.");
- #endif
- }
- else if (classifierType == "knn")
- {
- #ifdef OTB_USE_OPENCV
- TrainKNN(trainingListSample, trainingLabeledListSample);
- #else
- otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration.");
- #endif
- }
-
+ this->Train(trainingListSample,trainingLabeledListSample,GetParameterString("io.out"));
//--------------------------
// Performances estimation
//--------------------------
+ TargetListSampleType::Pointer predictedList = TargetListSampleType::New();
ListSampleType::Pointer performanceListSample=ListSampleType::New();
- LabelListSampleType::Pointer performanceLabeledListSample=LabelListSampleType::New();
+ TargetListSampleType::Pointer performanceLabeledListSample=TargetListSampleType::New();
//Test the input validation set size
if(validationLabeledListSample->Size() != 0)
@@ -491,7 +460,7 @@ void TrainImagesClassifier::DoExecute()
performanceLabeledListSample = trainingLabeledListSample;
}
- Classify(performanceListSample, predictedList);
+ this->Classify(performanceListSample, predictedList, GetParameterString("io.out"));
ConfusionMatrixCalculatorType::Pointer confMatCalc = ConfusionMatrixCalculatorType::New();
@@ -605,11 +574,12 @@ void TrainImagesClassifier::DoExecute()
} // END if (this->HasValue("io.confmatout"))
// TODO: implement hyperplane distance classifier and performance validation (cf. object detection) ?
-
}
+ VectorDataReprojectionType::Pointer vdreproj;
+};
-}
-}
+} // end namespace Wrapper
+} // end namespace otb
OTB_APPLICATION_EXPORT(otb::Wrapper::TrainImagesClassifier)
diff --git a/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.h b/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.h
deleted file mode 100644
index 235613fd56eaa5a265dc66f1e60ad26c849e1a27..0000000000000000000000000000000000000000
--- a/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.h
+++ /dev/null
@@ -1,198 +0,0 @@
-/*=========================================================================
- Program: ORFEO Toolbox
- Language: C++
- Date: $Date$
- Version: $Revision$
-
-
- Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
- See OTBCopyright.txt for details.
-
-
- This software is distributed WITHOUT ANY WARRANTY; without even
- the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
- PURPOSE. See the above copyright notices for more information.
-
- =========================================================================*/
-#include "otbConfigure.h"
-
-#include "otbWrapperApplicationFactory.h"
-
-#include <iostream>
-
-//Image
-#include "otbVectorImage.h"
-#include "otbVectorData.h"
-#include "otbListSampleGenerator.h"
-
-// ListSample
-#include "itkVariableLengthVector.h"
-
-//Estimator
-#include "otbMachineLearningModelFactory.h"
-
-#ifdef OTB_USE_OPENCV
-# include "otbKNearestNeighborsMachineLearningModel.h"
-# include "otbRandomForestsMachineLearningModel.h"
-# include "otbSVMMachineLearningModel.h"
-# include "otbBoostMachineLearningModel.h"
-# include "otbDecisionTreeMachineLearningModel.h"
-# include "otbGradientBoostedTreeMachineLearningModel.h"
-# include "otbNormalBayesMachineLearningModel.h"
-# include "otbNeuralNetworkMachineLearningModel.h"
-#endif
-
-#ifdef OTB_USE_LIBSVM
-#include "otbLibSVMMachineLearningModel.h"
-#endif
-
-// Statistic XML Reader
-#include "otbStatisticsXMLFileReader.h"
-
-// Validation
-#include "otbConfusionMatrixCalculator.h"
-
-#include "itkTimeProbe.h"
-#include "otbStandardFilterWatcher.h"
-
-// Normalize the samples
-#include "otbShiftScaleSampleListFilter.h"
-
-// List sample concatenation
-#include "otbConcatenateSampleListFilter.h"
-
-// Balancing ListSample
-#include "otbListSampleToBalancedListSampleFilter.h"
-
-// VectorData projection filter
-
-// Extract a ROI of the vectordata
-#include "otbVectorDataIntoImageProjectionFilter.h"
-
-// Elevation handler
-#include "otbWrapperElevationParametersHandler.h"
-
-namespace otb
-{
-namespace Wrapper
-{
-
-class TrainImagesClassifier: public Application
-{
-public:
- /** Standard class typedefs. */
- typedef TrainImagesClassifier Self;
- typedef Application Superclass;
- typedef itk::SmartPointer<Self> Pointer;
- typedef itk::SmartPointer<const Self> ConstPointer;
-
- /** Standard macro */
- itkNewMacro(Self)
-
- itkTypeMacro(TrainImagesClassifier, otb::Application)
-
- typedef FloatVectorImageType::PixelType PixelType;
- typedef FloatVectorImageType::InternalPixelType InternalPixelType;
-
- // Training vectordata
- typedef itk::VariableLengthVector<InternalPixelType> MeasurementType;
-
- // SampleList manipulation
- typedef otb::ListSampleGenerator<FloatVectorImageType, VectorDataType> ListSampleGeneratorType;
-
- typedef ListSampleGeneratorType::ListSampleType ListSampleType;
- typedef ListSampleGeneratorType::LabelType LabelType;
- typedef ListSampleGeneratorType::ListLabelType LabelListSampleType;
- typedef otb::Statistics::ConcatenateSampleListFilter<ListSampleType> ConcatenateListSampleFilterType;
- typedef otb::Statistics::ConcatenateSampleListFilter<LabelListSampleType> ConcatenateLabelListSampleFilterType;
-
- // Statistic XML file Reader
- typedef otb::StatisticsXMLFileReader<MeasurementType> StatisticsReader;
-
- // Enhance List Sample typedef otb::Statistics::ListSampleToBalancedListSampleFilter<ListSampleType, LabelListSampleType> BalancingListSampleFilterType;
- typedef otb::Statistics::ShiftScaleSampleListFilter<ListSampleType, ListSampleType> ShiftScaleFilterType;
-
- // Machine Learning models
- typedef otb::MachineLearningModelFactory<InternalPixelType, ListSampleGeneratorType::ClassLabelType> MachineLearningModelFactoryType;
- typedef MachineLearningModelFactoryType::MachineLearningModelTypePointer ModelPointerType;
-
-#ifdef OTB_USE_OPENCV
- typedef otb::RandomForestsMachineLearningModel<InternalPixelType, ListSampleGeneratorType::ClassLabelType> RandomForestType;
- typedef otb::KNearestNeighborsMachineLearningModel<InternalPixelType, ListSampleGeneratorType::ClassLabelType> KNNType;
- typedef otb::SVMMachineLearningModel<InternalPixelType, ListSampleGeneratorType::ClassLabelType> SVMType;
- typedef otb::BoostMachineLearningModel<InternalPixelType, ListSampleGeneratorType::ClassLabelType> BoostType;
- typedef otb::DecisionTreeMachineLearningModel<InternalPixelType, ListSampleGeneratorType::ClassLabelType> DecisionTreeType;
- typedef otb::GradientBoostedTreeMachineLearningModel<InternalPixelType, ListSampleGeneratorType::ClassLabelType> GradientBoostedTreeType;
- typedef otb::NeuralNetworkMachineLearningModel<InternalPixelType, ListSampleGeneratorType::ClassLabelType> NeuralNetworkType;
- typedef otb::NormalBayesMachineLearningModel<InternalPixelType, ListSampleGeneratorType::ClassLabelType> NormalBayesType;
-#endif
-
-#ifdef OTB_USE_LIBSVM
- typedef otb::LibSVMMachineLearningModel<InternalPixelType, ListSampleGeneratorType::ClassLabelType> LibSVMType;
-#endif
-
- // Estimate performance on validation sample
- typedef otb::ConfusionMatrixCalculator<LabelListSampleType, LabelListSampleType> ConfusionMatrixCalculatorType;
- typedef ConfusionMatrixCalculatorType::ConfusionMatrixType ConfusionMatrixType;
- typedef ConfusionMatrixCalculatorType::MapOfIndicesType MapOfIndicesType;
- typedef ConfusionMatrixCalculatorType::ClassLabelType ClassLabelType;
-
-
- // VectorData projection filter
- typedef otb::VectorDataProjectionFilter<VectorDataType, VectorDataType> VectorDataProjectionFilterType;
-
- // Extract ROI
- typedef otb::VectorDataIntoImageProjectionFilter<VectorDataType, FloatVectorImageType> VectorDataReprojectionType;
-
-protected:
- using Superclass::AddParameter;
- friend void InitSVMParams(TrainImagesClassifier & app);
-
-private:
- void DoInit();
-
- void DoUpdateParameters();
-
- void LogConfusionMatrix(ConfusionMatrixCalculatorType* confMatCalc);
-
-#ifdef OTB_USE_LIBSVM
- void InitLibSVMParams();
-#endif
-
-#ifdef OTB_USE_OPENCV
- void InitBoostParams();
- void InitSVMParams();
- void InitDecisionTreeParams();
- void InitGradientBoostedTreeParams();
- void InitNeuralNetworkParams();
- void InitNormalBayesParams();
- void InitRandomForestsParams();
- void InitKNNParams();
-#endif
-
-#ifdef OTB_USE_LIBSVM
- void TrainLibSVM(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample);
-#endif
-
-#ifdef OTB_USE_OPENCV
- void TrainBoost(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample);
- void TrainSVM(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample);
- void TrainDecisionTree(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample);
- void TrainGradientBoostedTree(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample);
- void TrainNeuralNetwork(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample);
- void TrainNormalBayes(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample);
- void TrainRandomForests(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample);
- void TrainKNN(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample);
-#endif
-
- void Classify(ListSampleType::Pointer validationListSample, LabelListSampleType::Pointer predictedList);
-
- void DoExecute();
-
- VectorDataReprojectionType::Pointer vdreproj;
-};
-
-}
-}
-
-
diff --git a/Modules/Applications/AppClassification/app/otbTrainKNN.cxx b/Modules/Applications/AppClassification/app/otbTrainKNN.cxx
deleted file mode 100644
index bc31cd4fce6c1c0207570161bd6df033e8fbff45..0000000000000000000000000000000000000000
--- a/Modules/Applications/AppClassification/app/otbTrainKNN.cxx
+++ /dev/null
@@ -1,52 +0,0 @@
-/*=========================================================================
- Program: ORFEO Toolbox
- Language: C++
- Date: $Date$
- Version: $Revision$
-
-
- Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
- See OTBCopyright.txt for details.
-
-
- This software is distributed WITHOUT ANY WARRANTY; without even
- the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
- PURPOSE. See the above copyright notices for more information.
-
- =========================================================================*/
-
-#include "otbTrainImagesClassifier.h"
-
-
-namespace otb
-{
-namespace Wrapper
-{
-#ifdef OTB_USE_OPENCV
- void TrainImagesClassifier::InitKNNParams()
- {
- AddChoice("classifier.knn", "KNN classifier");
- SetParameterDescription("classifier.knn", "This group of parameters allows to set KNN classifier parameters. "
- "See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/k_nearest_neighbors.html}.");
-
- //K parameter
- AddParameter(ParameterType_Int, "classifier.knn.k", "Number of Neighbors");
- SetParameterInt("classifier.knn.k", 32);
- SetParameterDescription("classifier.knn.k","The number of neighbors to use.");
-
- }
-
-
- void TrainImagesClassifier::TrainKNN(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample)
- {
- KNNType::Pointer knnClassifier = KNNType::New();
- knnClassifier->SetInputListSample(trainingListSample);
- knnClassifier->SetTargetListSample(trainingLabeledListSample);
- knnClassifier->SetK(GetParameterInt("classifier.knn.k"));
-
- knnClassifier->Train();
- knnClassifier->Save(GetParameterString("io.out"));
- }
-#endif
-} //end namespace wrapper
-} //end namespace otb
diff --git a/Modules/Applications/AppClassification/app/otbTrainRegression.cxx b/Modules/Applications/AppClassification/app/otbTrainRegression.cxx
new file mode 100644
index 0000000000000000000000000000000000000000..82e865194dd0418e98d04c17550df9919f19ff76
--- /dev/null
+++ b/Modules/Applications/AppClassification/app/otbTrainRegression.cxx
@@ -0,0 +1,543 @@
+/*=========================================================================
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+ =========================================================================*/
+#include "otbLearningApplicationBase.h"
+#include "otbWrapperApplicationFactory.h"
+
+#include "otbListSampleGenerator.h"
+
+#include "otbImageToEnvelopeVectorDataFilter.h"
+#include "itkPreOrderTreeIterator.h"
+
+// Statistic XML Reader
+#include "otbStatisticsXMLFileReader.h"
+
+#include "itkTimeProbe.h"
+#include "otbStandardFilterWatcher.h"
+
+// Normalize the samples
+#include "otbShiftScaleSampleListFilter.h"
+
+// List sample concatenation
+#include "otbConcatenateSampleListFilter.h"
+
+// Balancing ListSample
+#include "otbListSampleToBalancedListSampleFilter.h"
+
+#include "itkMersenneTwisterRandomVariateGenerator.h"
+
+// Elevation handler
+#include "otbWrapperElevationParametersHandler.h"
+
+namespace otb
+{
+namespace Wrapper
+{
+
+class TrainRegression: public LearningApplicationBase<float,float>
+{
+public:
+ /** Standard class typedefs. */
+ typedef TrainRegression Self;
+ typedef LearningApplicationBase<float,float> Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+
+ /** Standard macro */
+ itkNewMacro(Self)
+
+ itkTypeMacro(TrainRegression, otb::Wrapper::LearningApplicationBase)
+
+ typedef Superclass::SampleType SampleType;
+ typedef Superclass::ListSampleType ListSampleType;
+ typedef Superclass::TargetSampleType TargetSampleType;
+ typedef Superclass::TargetListSampleType TargetListSampleType;
+
+ typedef Superclass::SampleImageType SampleImageType;
+ typedef SampleImageType::PixelType PixelType;
+
+ // SampleList manipulation
+ typedef otb::ListSampleGenerator<SampleImageType, VectorDataType> ListSampleGeneratorType;
+
+ typedef otb::Statistics::ConcatenateSampleListFilter<ListSampleType> ConcatenateListSampleFilterType;
+ typedef otb::Statistics::ConcatenateSampleListFilter<TargetListSampleType> ConcatenateLabelListSampleFilterType;
+
+ // Statistic XML file Reader
+ typedef otb::StatisticsXMLFileReader<SampleType> StatisticsReader;
+
+ // Enhance List Sample typedef otb::Statistics::ListSampleToBalancedListSampleFilter<ListSampleType, LabelListSampleType> BalancingListSampleFilterType;
+ typedef otb::Statistics::ShiftScaleSampleListFilter<ListSampleType, ListSampleType> ShiftScaleFilterType;
+
+ typedef otb::ImageToEnvelopeVectorDataFilter<SampleImageType,VectorDataType> EnvelopeFilterType;
+
+ typedef itk::PreOrderTreeIterator<VectorDataType::DataTreeType> TreeIteratorType;
+
+ typedef itk::Statistics::MersenneTwisterRandomVariateGenerator RandomGeneratorType;
+
+protected:
+ TrainRegression()
+ {
+ this->m_RegressionFlag = true;
+ }
+
+private:
+
+void DoInit()
+{
+ SetName("TrainRegression");
+ SetDescription(
+ "Train a classifier from multiple images to perform regression.");
+
+ // Documentation
+ SetDocName("Train a regression model");
+ SetDocLongDescription(
+ "This application trains a classifier from multiple input images or a csv "
+ "file, in order to perform regression. Predictors are composed of pixel "
+ "values in each band optionally centered and reduced using an XML "
+ "statistics file produced by the ComputeImagesStatistics application.\n "
+ "The output value for each predictor is assumed to be the last band "
+ "(or the last column for CSV files). Training and validation predictor "
+ "lists are built such that their size is inferior to maximum bounds given "
+ "by the user, and the proportion corresponds to the balance parameter. "
+ "Several classifier parameters can be set depending on the chosen "
+ "classifier. In the validation process, the mean square error is computed\n"
+ " This application is based on LibSVM and on OpenCV Machine Learning "
+ "classifiers, and is compatible with OpenCV 2.3.1 and later.");
+ SetDocLimitations("None");
+ SetDocAuthors("OTB-Team");
+ SetDocSeeAlso("OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html ");
+
+ //Group IO
+ AddParameter(ParameterType_Group, "io", "Input and output data");
+ SetParameterDescription("io", "This group of parameters allows to set input and output data.");
+ AddParameter(ParameterType_InputImageList, "io.il", "Input Image List");
+ SetParameterDescription("io.il", "A list of input images. First (n-1) bands should contain the predictor. The last band should contain the output value to predict.");
+ AddParameter(ParameterType_InputFilename, "io.csv", "Input CSV file");
+ SetParameterDescription("io.csv","Input CSV file containing the predictors, and the output values in last column. Only used when no input image is given");
+ MandatoryOff("io.csv");
+
+ AddParameter(ParameterType_InputFilename, "io.imstat", "Input XML image statistics file");
+ MandatoryOff("io.imstat");
+ SetParameterDescription("io.imstat",
+ "Input XML file containing the mean and the standard deviation of the input images.");
+ AddParameter(ParameterType_OutputFilename, "io.out", "Output regression model");
+ SetParameterDescription("io.out", "Output file containing the model estimated (.txt format).");
+
+ AddParameter(ParameterType_Float,"io.mse","Mean Square Error");
+ SetParameterDescription("io.mse","Mean square error computed with the validation predictors");
+ SetParameterRole("io.mse",Role_Output);
+ DisableParameter("io.mse");
+
+ //Group Sample list
+ AddParameter(ParameterType_Group, "sample", "Training and validation samples parameters");
+ SetParameterDescription("sample",
+ "This group of parameters allows to set training and validation sample lists parameters.");
+
+ AddParameter(ParameterType_Int, "sample.mt", "Maximum training predictors");
+ //MandatoryOff("mt");
+ SetDefaultParameterInt("sample.mt", 1000);
+ SetParameterDescription("sample.mt", "Maximum number of training predictors (default = 1000) (no limit = -1).");
+
+ AddParameter(ParameterType_Int, "sample.mv", "Maximum validation predictors");
+ // MandatoryOff("mv");
+ SetDefaultParameterInt("sample.mv", 1000);
+ SetParameterDescription("sample.mv", "Maximum number of validation predictors (default = 1000) (no limit = -1).");
+
+ AddParameter(ParameterType_Float, "sample.vtr", "Training and validation sample ratio");
+ SetParameterDescription("sample.vtr",
+ "Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5).");
+ SetParameterFloat("sample.vtr", 0.5);
+
+ Superclass::DoInit();
+
+ AddRANDParameter();
+
+ // Doc example parameter settings
+ SetDocExampleParameterValue("io.il", "training_dataset.tif");
+ SetDocExampleParameterValue("io.out", "regression_model.txt");
+ SetDocExampleParameterValue("io.imstat", "training_statistics.xml");
+ SetDocExampleParameterValue("classifier", "svm");
+}
+
+void DoUpdateParameters()
+{
+ if (HasValue("io.csv") && IsParameterEnabled("io.csv"))
+ {
+ MandatoryOff("io.il");
+ }
+ else
+ {
+ MandatoryOn("io.il");
+ }
+}
+
+void ParseCSVPredictors(std::string path, ListSampleType* outputList)
+{
+ std::ifstream ifs;
+ ifs.open(path.c_str());
+ unsigned int nbCols = 0;
+ char sep = '\t';
+ std::istringstream iss;
+ SampleType elem;
+ while(!ifs.eof())
+ {
+ std::string line;
+ std::getline(ifs,line);
+ // filter current line
+ while (!line.empty() && (line[0] == ' ' || line[0] == '\t'))
+ {
+ line.erase(line.begin());
+ }
+ while (!line.empty() && ( *(line.end()-1) == ' ' || *(line.end()-1) == '\t' || *(line.end()-1) == '\r'))
+ {
+ line.erase(line.end()-1);
+ }
+
+ // Avoid commented lines or too short ones
+ if (!line.empty() && line[0] != '#')
+ {
+ std::vector<itksys::String> words = itksys::SystemTools::SplitString(line.c_str(),sep);
+ if (nbCols == 0)
+ {
+ // detect separator and feature size
+ if (words.size() < 2)
+ {
+ sep = ' ';
+ words = itksys::SystemTools::SplitString(line.c_str(),sep);
+ }
+ if (words.size() < 2)
+ {
+ sep = ';';
+ words = itksys::SystemTools::SplitString(line.c_str(),sep);
+ }
+ if (words.size() < 2)
+ {
+ sep = ',';
+ words = itksys::SystemTools::SplitString(line.c_str(),sep);
+ }
+ if (words.size() < 2)
+ {
+ otbAppLogFATAL(<< "Can't parse CSV file : less than 2 columns or unknonw separator (knowns ones are tab, space, comma and semi-colon)");
+ }
+ nbCols = words.size();
+ elem.SetSize(nbCols,false);
+ outputList->SetMeasurementVectorSize(nbCols);
+ }
+ else if (words.size() != nbCols )
+ {
+ otbAppLogWARNING(<< "Skip CSV line, wrong number of columns : got "<<words.size() << ", expected "<<nbCols);
+ continue;
+ }
+ elem.Fill(0.0);
+ for (unsigned int i=0 ; i<nbCols ; ++i)
+ {
+ iss.str(words[i]);
+ iss >> elem[i];
+ }
+ outputList->PushBack(elem);
+ }
+ }
+ ifs.close();
+}
+
+void DoExecute()
+{
+ GetLogger()->Debug("Entering DoExecute\n");
+ //Create training and validation for list samples and label list samples
+ ConcatenateListSampleFilterType::Pointer concatenateTrainingSamples = ConcatenateListSampleFilterType::New();
+ ConcatenateListSampleFilterType::Pointer concatenateValidationSamples = ConcatenateListSampleFilterType::New();
+
+ SampleType meanMeasurementVector;
+ SampleType stddevMeasurementVector;
+
+ //--------------------------
+ // Load measurements from images
+ unsigned int nbBands = 0;
+ unsigned int nbFeatures = 0;
+ //Iterate over all input images
+
+ FloatVectorImageListType* imageList = GetParameterImageList("io.il");
+
+ //Iterate over all input images
+ for (unsigned int imgIndex = 0; imgIndex < imageList->Size(); ++imgIndex)
+ {
+ FloatVectorImageType::Pointer image = imageList->GetNthElement(imgIndex);
+ image->UpdateOutputInformation();
+
+ if (imgIndex == 0)
+ {
+ nbBands = image->GetNumberOfComponentsPerPixel();
+ nbFeatures = static_cast<unsigned int>(static_cast<int>(nbBands) - 1);
+ if (nbBands < 2)
+ {
+ otbAppLogFATAL(<< "Need at least two bands per image, got "<<nbBands);
+ }
+ else
+ {
+ if (nbBands != image->GetNumberOfComponentsPerPixel())
+ {
+ otbAppLogFATAL(<< "Image has a different number of components than "
+ "the first one, expected "<<nbBands<<", got "<< image->GetNumberOfComponentsPerPixel());
+ }
+ }
+ }
+
+ // Extract image envelope to feed in sampleGenerator
+ EnvelopeFilterType::Pointer envelopeFilter = EnvelopeFilterType::New();
+ envelopeFilter->SetInput(image);
+ envelopeFilter->SetSamplingRate(0);
+ if (!image->GetProjectionRef().empty())
+ {
+ envelopeFilter->SetOutputProjectionRef(image->GetProjectionRef());
+ }
+
+ // Setup the DEM Handler
+ // otb::Wrapper::ElevationParametersHandler::SetupDEMHandlerFromElevationParameters(this,"elev");
+
+ envelopeFilter->Update();
+
+ VectorDataType::Pointer envelope = envelopeFilter->GetOutput();
+
+ TreeIteratorType itVector(envelope->GetDataTree());
+ for (itVector.GoToBegin(); !itVector.IsAtEnd(); ++itVector)
+ {
+ if (itVector.Get()->IsPolygonFeature())
+ {
+ itVector.Get()->SetFieldAsInt(std::string("class"),1);
+ }
+ }
+
+
+ //Sample list generator
+ ListSampleGeneratorType::Pointer sampleGenerator = ListSampleGeneratorType::New();
+
+ sampleGenerator->SetInput(image);
+ sampleGenerator->SetInputVectorData(envelope);
+
+ sampleGenerator->SetClassKey("class");
+ sampleGenerator->SetMaxTrainingSize(GetParameterInt("sample.mt"));
+ sampleGenerator->SetMaxValidationSize(GetParameterInt("sample.mv"));
+ sampleGenerator->SetValidationTrainingProportion(GetParameterFloat("sample.vtr"));
+ sampleGenerator->SetBoundByMin(false);
+ sampleGenerator->SetPolygonEdgeInclusion(true);
+
+ sampleGenerator->Update();
+
+ //Concatenate training and validation samples from the image
+ concatenateTrainingSamples->AddInput(sampleGenerator->GetTrainingListSample());
+ concatenateValidationSamples->AddInput(sampleGenerator->GetValidationListSample());
+ }
+
+ // if no input image, try CSV
+ if (imageList->Size() == 0)
+ {
+ if (HasValue("io.csv") && IsParameterEnabled("io.csv"))
+ {
+ ListSampleType::Pointer csvListSample = ListSampleType::New();
+ this->ParseCSVPredictors(this->GetParameterString("io.csv"), csvListSample);
+ unsigned int totalCSVSize = csvListSample->Size();
+ if (totalCSVSize == 0)
+ {
+ otbAppLogFATAL("No input image and empty CSV file. Missing input data");
+ }
+ nbBands = csvListSample->GetMeasurementVectorSize();
+ nbFeatures = static_cast<unsigned int>(static_cast<int>(nbBands) - 1);
+ ListSampleType::Pointer csvTrainListSample = ListSampleType::New();
+ ListSampleType::Pointer csvValidListSample = ListSampleType::New();
+ csvTrainListSample->SetMeasurementVectorSize(nbBands);
+ csvValidListSample->SetMeasurementVectorSize(nbBands);
+ double ratio = this->GetParameterFloat("sample.vtr");
+ int trainSize = static_cast<int>(static_cast<double>(totalCSVSize)*(1.0-ratio));
+ int validSize = static_cast<int>(static_cast<double>(totalCSVSize)*(ratio));
+ if (trainSize > this->GetParameterInt("sample.mt"))
+ {
+ trainSize = this->GetParameterInt("sample.mt");
+ }
+ if (validSize > this->GetParameterInt("sample.mv"))
+ {
+ validSize = this->GetParameterInt("sample.mv");
+ }
+ double probaTrain = static_cast<double>(trainSize)/static_cast<double>(totalCSVSize);
+ double probaValid = static_cast<double>(validSize)/static_cast<double>(totalCSVSize);
+
+ RandomGeneratorType::Pointer randomGenerator = RandomGeneratorType::GetInstance();
+ for (unsigned int i=0; i<totalCSVSize; ++i)
+ {
+ double random = randomGenerator->GetUniformVariate(0.0, 1.0);
+ if (random < probaTrain)
+ {
+ csvTrainListSample->PushBack(csvListSample->GetMeasurementVector(i));
+ }
+ else if (random < probaTrain + probaValid)
+ {
+ csvValidListSample->PushBack(csvListSample->GetMeasurementVector(i));
+ }
+ }
+ concatenateTrainingSamples->AddInput(csvTrainListSample);
+ concatenateValidationSamples->AddInput(csvValidListSample);
+ }
+ }
+
+ // Update
+ concatenateTrainingSamples->Update();
+ concatenateValidationSamples->Update();
+
+ if (concatenateTrainingSamples->GetOutput()->Size() == 0)
+ {
+ otbAppLogFATAL("No training samples, cannot perform training.");
+ }
+
+ if (concatenateValidationSamples->GetOutput()->Size() == 0)
+ {
+ otbAppLogWARNING("No validation samples.");
+ }
+
+ if (IsParameterEnabled("io.imstat"))
+ {
+ StatisticsReader::Pointer statisticsReader = StatisticsReader::New();
+ statisticsReader->SetFileName(GetParameterString("io.imstat"));
+ meanMeasurementVector = statisticsReader->GetStatisticVectorByName("mean");
+ stddevMeasurementVector = statisticsReader->GetStatisticVectorByName("stddev");
+ // handle stat file without output normalization
+ if (meanMeasurementVector.Size() == nbFeatures)
+ {
+ meanMeasurementVector.SetSize(nbBands,false);
+ meanMeasurementVector[nbFeatures] = 0.0;
+ stddevMeasurementVector.SetSize(nbBands,false);
+ stddevMeasurementVector[nbFeatures] = 1.0;
+ }
+ }
+ else
+ {
+ meanMeasurementVector.SetSize(nbBands);
+ meanMeasurementVector.Fill(0.);
+ stddevMeasurementVector.SetSize(nbBands);
+ stddevMeasurementVector.Fill(1.);
+ }
+
+ // Shift scale the samples
+ ShiftScaleFilterType::Pointer trainingShiftScaleFilter = ShiftScaleFilterType::New();
+ trainingShiftScaleFilter->SetInput(concatenateTrainingSamples->GetOutput());
+ trainingShiftScaleFilter->SetShifts(meanMeasurementVector);
+ trainingShiftScaleFilter->SetScales(stddevMeasurementVector);
+ trainingShiftScaleFilter->Update();
+
+ ListSampleType::Pointer rawValidationListSample=ListSampleType::New();
+
+ //Test if the validation test is empty
+ if ( concatenateValidationSamples->GetOutput()->Size() != 0 )
+ {
+ ShiftScaleFilterType::Pointer validationShiftScaleFilter = ShiftScaleFilterType::New();
+ validationShiftScaleFilter->SetInput(concatenateValidationSamples->GetOutput());
+ validationShiftScaleFilter->SetShifts(meanMeasurementVector);
+ validationShiftScaleFilter->SetScales(stddevMeasurementVector);
+ validationShiftScaleFilter->Update();
+ rawValidationListSample = validationShiftScaleFilter->GetOutput();
+ }
+
+ // Split between predictors and output values
+ ListSampleType::Pointer rawlistSample = trainingShiftScaleFilter->GetOutput();
+ ListSampleType::Pointer listSample = ListSampleType::New();
+ listSample->SetMeasurementVectorSize(nbFeatures);
+ listSample->Resize(rawlistSample->Size());
+ TargetListSampleType::Pointer labelListSample = TargetListSampleType::New();
+ labelListSample->SetMeasurementVectorSize(1);
+ labelListSample->Resize(rawlistSample->Size());
+
+ ListSampleType::Pointer validationListSample = ListSampleType::New();
+ validationListSample->SetMeasurementVectorSize(nbFeatures);
+ validationListSample->Resize(rawValidationListSample->Size());
+ TargetListSampleType::Pointer validationLabeledListSample = TargetListSampleType::New();
+ validationLabeledListSample->SetMeasurementVectorSize(1);
+ validationLabeledListSample->Resize(rawValidationListSample->Size());
+
+ ListSampleType::MeasurementVectorType elem;
+ TargetListSampleType::MeasurementVectorType outElem;
+ for (ListSampleType::InstanceIdentifier i=0; i<rawlistSample->Size() ; ++i)
+ {
+ elem = rawlistSample->GetMeasurementVector(i);
+ outElem[0] = elem[nbFeatures];
+ labelListSample->SetMeasurementVector(i,outElem);
+ elem.SetSize(nbFeatures,false);
+ listSample->SetMeasurementVector(i,elem);
+ }
+ for (ListSampleType::InstanceIdentifier i=0; i<rawValidationListSample->Size() ; ++i)
+ {
+ elem = rawValidationListSample->GetMeasurementVector(i);
+ outElem[0] = elem[nbFeatures];
+ validationLabeledListSample->SetMeasurementVector(i,outElem);
+ elem.SetSize(nbFeatures,false);
+ validationListSample->SetMeasurementVector(i,elem);
+ }
+
+
+ otbAppLogINFO("Number of training samples: " << concatenateTrainingSamples->GetOutput()->Size());
+ //--------------------------
+ // Split the data set into training/validation set
+ ListSampleType::Pointer trainingListSample = listSample;
+ TargetListSampleType::Pointer trainingLabeledListSample = labelListSample;
+
+ otbAppLogINFO("Size of training set: " << trainingListSample->Size());
+ otbAppLogINFO("Size of validation set: " << validationListSample->Size());
+
+ //--------------------------
+ // Estimate model
+ //--------------------------
+ this->Train(trainingListSample,trainingLabeledListSample,GetParameterString("io.out"));
+
+ //--------------------------
+ // Performances estimation
+ //--------------------------
+ ListSampleType::Pointer performanceListSample;
+ TargetListSampleType::Pointer predictedList = TargetListSampleType::New();
+ predictedList->SetMeasurementVectorSize(1);
+ TargetListSampleType::Pointer performanceLabeledListSample;
+
+ //Test the input validation set size
+ if(validationLabeledListSample->Size() != 0)
+ {
+ performanceListSample = validationListSample;
+ performanceLabeledListSample = validationLabeledListSample;
+ }
+ else
+ {
+ otbAppLogWARNING("The validation set is empty. The performance estimation is done using the input training set in this case.");
+ performanceListSample = trainingListSample;
+ performanceLabeledListSample = trainingLabeledListSample;
+ }
+
+ this->Classify(performanceListSample, predictedList, GetParameterString("io.out"));
+
+ otbAppLogINFO("Training performances");
+ double mse=0.0;
+ TargetListSampleType::MeasurementVectorType predictedElem;
+ for (TargetListSampleType::InstanceIdentifier i=0; i<performanceListSample->Size() ; ++i)
+ {
+ outElem = performanceLabeledListSample->GetMeasurementVector(i);
+ predictedElem = predictedList->GetMeasurementVector(i);
+ mse += (outElem[0] - predictedElem[0]) * (outElem[0] - predictedElem[0]);
+ }
+ mse /= static_cast<double>(performanceListSample->Size());
+ otbAppLogINFO("Mean Square Error = "<<mse);
+ this->SetParameterFloat("io.mse",mse);
+}
+
+};
+
+} // end namespace Wrapper
+} // end namespace otb
+
+OTB_APPLICATION_EXPORT(otb::Wrapper::TrainRegression)
diff --git a/Modules/Applications/AppClassification/include/otbLearningApplicationBase.h b/Modules/Applications/AppClassification/include/otbLearningApplicationBase.h
new file mode 100644
index 0000000000000000000000000000000000000000..9af1259c73ff6356d515588f52887db569f63c43
--- /dev/null
+++ b/Modules/Applications/AppClassification/include/otbLearningApplicationBase.h
@@ -0,0 +1,222 @@
+/*=========================================================================
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+ =========================================================================*/
+#ifndef __otbLearningApplicationBase_h
+#define __otbLearningApplicationBase_h
+
+#include "otbConfigure.h"
+
+#include "otbWrapperApplication.h"
+
+#include <iostream>
+
+// ListSample
+#include "itkListSample.h"
+#include "itkVariableLengthVector.h"
+
+//Estimator
+#include "otbMachineLearningModelFactory.h"
+
+#ifdef OTB_USE_OPENCV
+# include "otbKNearestNeighborsMachineLearningModel.h"
+# include "otbRandomForestsMachineLearningModel.h"
+# include "otbSVMMachineLearningModel.h"
+# include "otbBoostMachineLearningModel.h"
+# include "otbDecisionTreeMachineLearningModel.h"
+# include "otbGradientBoostedTreeMachineLearningModel.h"
+# include "otbNormalBayesMachineLearningModel.h"
+# include "otbNeuralNetworkMachineLearningModel.h"
+#endif
+
+#ifdef OTB_USE_LIBSVM
+#include "otbLibSVMMachineLearningModel.h"
+#endif
+
+namespace otb
+{
+namespace Wrapper
+{
+
+/** \class LearningApplicationBase
+ * \brief LearningApplicationBase is the base class for application that
+ * use machine learning model.
+ *
+ * This base class offers a DoInit() method to initialize all the parameters
+ * related to machine learning models. They will all be in the choice parameter
+ * named "classifier". The class also offers generic Train() and Classify()
+ * methods. The classes derived from LearningApplicationBase only need these
+ * 3 methods to handle the machine learning model.
+ *
+ * There are multiple machine learning models in OTB, some imported from OpenCV,
+ * and one imported from LibSVM. They all have different parameters. The
+ * purpose of this class is to handle the creation of all parameters related to
+ * machine learning models (in DoInit() ), and to dispatch the calls to
+ * specific train functions in function Train().
+ *
+ * This class is templated over scalar types for input and output values.
+ * Typically, the input value type will be either float of double. The choice
+ * of an output value type depends on the learning mode. This base class
+ * supports both classification and regression modes. For classification
+ * (enabled by default), the output value type corresponds to a class
+ * identifier so integer types suit well. For regression, the output value
+ * should not be an integer type, but rather a floating point type. In addition,
+ * an application deriving this base class for regression should initialize
+ * the m_RegressionFlag to true in their constructor.
+ *
+ * \sa TrainImagesClassifier
+ * \sa TrainRegression
+ *
+ * \ingroup OTBAppClassification
+ */
+template <class TInputValue, class TOutputValue>
+class LearningApplicationBase: public Application
+{
+public:
+ /** Standard class typedefs. */
+ typedef LearningApplicationBase Self;
+ typedef Application Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+
+ /** Standard macro */
+ itkTypeMacro(LearningApplicationBase, otb::Application)
+
+ typedef TInputValue InputValueType;
+ typedef TOutputValue OutputValueType;
+
+ typedef otb::VectorImage<InputValueType> SampleImageType;
+ typedef typename SampleImageType::PixelType PixelType;
+
+ // Machine Learning models
+ typedef otb::MachineLearningModelFactory<
+ InputValueType, OutputValueType> ModelFactoryType;
+ typedef typename ModelFactoryType::MachineLearningModelTypePointer ModelPointerType;
+ typedef typename ModelFactoryType::MachineLearningModelType ModelType;
+
+ typedef typename ModelType::InputSampleType SampleType;
+ typedef typename ModelType::InputListSampleType ListSampleType;
+
+ typedef typename ModelType::TargetSampleType TargetSampleType;
+ typedef typename ModelType::TargetListSampleType TargetListSampleType;
+ typedef typename ModelType::TargetValueType TargetValueType;
+
+#ifdef OTB_USE_OPENCV
+ typedef otb::RandomForestsMachineLearningModel<InputValueType, OutputValueType> RandomForestType;
+ typedef otb::KNearestNeighborsMachineLearningModel<InputValueType, OutputValueType> KNNType;
+ typedef otb::SVMMachineLearningModel<InputValueType, OutputValueType> SVMType;
+ typedef otb::BoostMachineLearningModel<InputValueType, OutputValueType> BoostType;
+ typedef otb::DecisionTreeMachineLearningModel<InputValueType, OutputValueType> DecisionTreeType;
+ typedef otb::GradientBoostedTreeMachineLearningModel<InputValueType, OutputValueType> GradientBoostedTreeType;
+ typedef otb::NeuralNetworkMachineLearningModel<InputValueType, OutputValueType> NeuralNetworkType;
+ typedef otb::NormalBayesMachineLearningModel<InputValueType, OutputValueType> NormalBayesType;
+#endif
+
+#ifdef OTB_USE_LIBSVM
+ typedef otb::LibSVMMachineLearningModel<InputValueType, OutputValueType> LibSVMType;
+#endif
+
+protected:
+ LearningApplicationBase();
+
+ /** Generic method to train and save the machine learning model. This method
+ * uses specific train methods depending on the chosen model.*/
+ void Train(typename ListSampleType::Pointer trainingListSample,
+ typename TargetListSampleType::Pointer trainingLabeledListSample,
+ std::string modelPath);
+
+ /** Generic method to load a model file and use it to classify a sample list*/
+ void Classify(typename ListSampleType::Pointer validationListSample,
+ typename TargetListSampleType::Pointer predictedList,
+ std::string modelPath);
+
+ /** Init method that creates all the parameters for machine learning models */
+ void DoInit();
+
+ /** Flag to switch between classification and regression mode.
+ * False by default, child classes may change it in their constructor */
+ bool m_RegressionFlag;
+
+private:
+
+ /** Specific Init and Train methods for each machine learning model */
+ //@{
+#ifdef OTB_USE_LIBSVM
+ void InitLibSVMParams();
+
+ void TrainLibSVM(typename ListSampleType::Pointer trainingListSample,
+ typename TargetListSampleType::Pointer trainingLabeledListSample,
+ std::string modelPath);
+#endif
+
+#ifdef OTB_USE_OPENCV
+ void InitBoostParams();
+ void InitSVMParams();
+ void InitDecisionTreeParams();
+ void InitGradientBoostedTreeParams();
+ void InitNeuralNetworkParams();
+ void InitNormalBayesParams();
+ void InitRandomForestsParams();
+ void InitKNNParams();
+
+ void TrainBoost(typename ListSampleType::Pointer trainingListSample,
+ typename TargetListSampleType::Pointer trainingLabeledListSample,
+ std::string modelPath);
+ void TrainSVM(typename ListSampleType::Pointer trainingListSample,
+ typename TargetListSampleType::Pointer trainingLabeledListSample,
+ std::string modelPath);
+ void TrainDecisionTree(typename ListSampleType::Pointer trainingListSample,
+ typename TargetListSampleType::Pointer trainingLabeledListSample,
+ std::string modelPath);
+ void TrainGradientBoostedTree(typename ListSampleType::Pointer trainingListSample,
+ typename TargetListSampleType::Pointer trainingLabeledListSample,
+ std::string modelPath);
+ void TrainNeuralNetwork(typename ListSampleType::Pointer trainingListSample,
+ typename TargetListSampleType::Pointer trainingLabeledListSample,
+ std::string modelPath);
+ void TrainNormalBayes(typename ListSampleType::Pointer trainingListSample,
+ typename TargetListSampleType::Pointer trainingLabeledListSample,
+ std::string modelPath);
+ void TrainRandomForests(typename ListSampleType::Pointer trainingListSample,
+ typename TargetListSampleType::Pointer trainingLabeledListSample,
+ std::string modelPath);
+ void TrainKNN(typename ListSampleType::Pointer trainingListSample,
+ typename TargetListSampleType::Pointer trainingLabeledListSample,
+ std::string modelPath);
+#endif
+ //@}
+};
+
+}
+}
+
+#ifndef OTB_MANUAL_INSTANTIATION
+#include "otbLearningApplicationBase.txx"
+#ifdef OTB_USE_OPENCV
+#include "otbTrainBoost.txx"
+#include "otbTrainDecisionTree.txx"
+#include "otbTrainGradientBoostedTree.txx"
+#include "otbTrainKNN.txx"
+#include "otbTrainNeuralNetwork.txx"
+#include "otbTrainNormalBayes.txx"
+#include "otbTrainRandomForests.txx"
+#include "otbTrainSVM.txx"
+#endif
+#ifdef OTB_USE_LIBSVM
+#include "otbTrainLibSVM.txx"
+#endif
+#endif
+
+#endif
diff --git a/Modules/Applications/AppClassification/include/otbLearningApplicationBase.txx b/Modules/Applications/AppClassification/include/otbLearningApplicationBase.txx
new file mode 100644
index 0000000000000000000000000000000000000000..a545538656efbafd98fa520944b09a183ef87fcb
--- /dev/null
+++ b/Modules/Applications/AppClassification/include/otbLearningApplicationBase.txx
@@ -0,0 +1,177 @@
+/*=========================================================================
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+ =========================================================================*/
+#ifndef __otbLearningApplicationBase_txx
+#define __otbLearningApplicationBase_txx
+
+#include "otbLearningApplicationBase.h"
+
+namespace otb
+{
+namespace Wrapper
+{
+
+template <class TInputValue, class TOutputValue>
+LearningApplicationBase<TInputValue,TOutputValue>
+::LearningApplicationBase() : m_RegressionFlag(false)
+{
+}
+
+template <class TInputValue, class TOutputValue>
+void
+LearningApplicationBase<TInputValue,TOutputValue>
+::DoInit()
+{
+ AddDocTag(Tags::Learning);
+
+ // main choice parameter that will contain all machine learning options
+ AddParameter(ParameterType_Choice, "classifier", "Classifier to use for the training");
+ SetParameterDescription("classifier", "Choice of the classifier to use for the training.");
+
+ //Group LibSVM
+#ifdef OTB_USE_LIBSVM
+ InitLibSVMParams();
+#endif
+
+#ifdef OTB_USE_OPENCV
+ InitSVMParams();
+ if (!m_RegressionFlag)
+ {
+ InitBoostParams(); // Regression not supported
+ }
+ InitDecisionTreeParams();
+ InitGradientBoostedTreeParams();
+ InitNeuralNetworkParams();
+ if (!m_RegressionFlag)
+ {
+ InitNormalBayesParams(); // Regression not supported
+ }
+ InitRandomForestsParams();
+ InitKNNParams();
+#endif
+}
+
+template <class TInputValue, class TOutputValue>
+void
+LearningApplicationBase<TInputValue,TOutputValue>
+::Classify(typename ListSampleType::Pointer validationListSample,
+ typename TargetListSampleType::Pointer predictedList,
+ std::string modelPath)
+{
+ // load a machine learning model from file and predict the input sample list
+ ModelPointerType model = ModelFactoryType::CreateMachineLearningModel(modelPath,
+ ModelFactoryType::ReadMode);
+
+ if (model.IsNull())
+ {
+ otbAppLogFATAL(<< "Error when loading model " << modelPath);
+ }
+
+ model->Load(modelPath);
+ model->SetRegressionMode(this->m_RegressionFlag);
+ model->SetInputListSample(validationListSample);
+ model->SetTargetListSample(predictedList);
+ model->PredictAll();
+}
+
+template <class TInputValue, class TOutputValue>
+void
+LearningApplicationBase<TInputValue,TOutputValue>
+::Train(typename ListSampleType::Pointer trainingListSample,
+ typename TargetListSampleType::Pointer trainingLabeledListSample,
+ std::string modelPath)
+{
+ // get the name of the chosen machine learning model
+ const std::string modelName = GetParameterString("classifier");
+ // call specific train function
+ if (modelName == "libsvm")
+ {
+ #ifdef OTB_USE_LIBSVM
+ TrainLibSVM(trainingListSample, trainingLabeledListSample, modelPath);
+ #else
+ otbAppLogFATAL("Module LIBSVM is not installed. You should consider turning OTB_USE_LIBSVM on during cmake configuration.");
+ #endif
+ }
+ else if (modelName == "svm")
+ {
+ #ifdef OTB_USE_OPENCV
+ TrainSVM(trainingListSample, trainingLabeledListSample, modelPath);
+ #else
+ otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration.");
+ #endif
+ }
+ else if (modelName == "boost")
+ {
+ #ifdef OTB_USE_OPENCV
+ TrainBoost(trainingListSample, trainingLabeledListSample, modelPath);
+ #else
+ otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration.");
+ #endif
+ }
+ else if (modelName == "dt")
+ {
+ #ifdef OTB_USE_OPENCV
+ TrainDecisionTree(trainingListSample, trainingLabeledListSample, modelPath);
+ #else
+ otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration.");
+ #endif
+ }
+ else if (modelName == "gbt")
+ {
+ #ifdef OTB_USE_OPENCV
+ TrainGradientBoostedTree(trainingListSample, trainingLabeledListSample, modelPath);
+ #else
+ otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration.");
+ #endif
+ }
+ else if (modelName == "ann")
+ {
+ #ifdef OTB_USE_OPENCV
+ TrainNeuralNetwork(trainingListSample, trainingLabeledListSample, modelPath);
+ #else
+ otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration.");
+ #endif
+ }
+ else if (modelName == "bayes")
+ {
+ #ifdef OTB_USE_OPENCV
+ TrainNormalBayes(trainingListSample, trainingLabeledListSample, modelPath);
+ #else
+ otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration.");
+ #endif
+ }
+ else if (modelName == "rf")
+ {
+ #ifdef OTB_USE_OPENCV
+ TrainRandomForests(trainingListSample, trainingLabeledListSample, modelPath);
+ #else
+ otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration.");
+ #endif
+ }
+ else if (modelName == "knn")
+ {
+ #ifdef OTB_USE_OPENCV
+ TrainKNN(trainingListSample, trainingLabeledListSample, modelPath);
+ #else
+ otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration.");
+ #endif
+ }
+}
+
+}
+}
+
+#endif
diff --git a/Modules/Applications/AppClassification/app/otbTrainBoost.cxx b/Modules/Applications/AppClassification/include/otbTrainBoost.txx
similarity index 82%
rename from Modules/Applications/AppClassification/app/otbTrainBoost.cxx
rename to Modules/Applications/AppClassification/include/otbTrainBoost.txx
index c107f8016ea1bdecaf2ca9a68c8af259184715c2..23e666bfc1d877072b41312ce0478a8a41036500 100644
--- a/Modules/Applications/AppClassification/app/otbTrainBoost.cxx
+++ b/Modules/Applications/AppClassification/include/otbTrainBoost.txx
@@ -14,16 +14,19 @@
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
-
-#include "otbTrainImagesClassifier.h"
-
+#ifndef __otbTrainBoost_txx
+#define __otbTrainBoost_txx
+#include "otbLearningApplicationBase.h"
namespace otb
{
namespace Wrapper
{
-#ifdef OTB_USE_OPENCV
- void TrainImagesClassifier::InitBoostParams()
+
+ template <class TInputValue, class TOutputValue>
+ void
+ LearningApplicationBase<TInputValue,TOutputValue>
+ ::InitBoostParams()
{
AddChoice("classifier.boost", "Boost classifier");
SetParameterDescription("classifier.boost", "This group of parameters allows to set Boost classifier parameters. "
@@ -55,10 +58,15 @@ namespace Wrapper
SetParameterDescription("classifier.boost.m","Maximum depth of the tree.");
}
-
- void TrainImagesClassifier::TrainBoost(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample)
+ template <class TInputValue, class TOutputValue>
+ void
+ LearningApplicationBase<TInputValue,TOutputValue>
+ ::TrainBoost(typename ListSampleType::Pointer trainingListSample,
+ typename TargetListSampleType::Pointer trainingLabeledListSample,
+ std::string modelPath)
{
- BoostType::Pointer boostClassifier = BoostType::New();
+ typename BoostType::Pointer boostClassifier = BoostType::New();
+ boostClassifier->SetRegressionMode(this->m_RegressionFlag);
boostClassifier->SetInputListSample(trainingListSample);
boostClassifier->SetTargetListSample(trainingLabeledListSample);
boostClassifier->SetBoostType(GetParameterInt("classifier.boost.t"));
@@ -67,9 +75,10 @@ namespace Wrapper
boostClassifier->SetMaxDepth(GetParameterInt("classifier.boost.m"));
boostClassifier->Train();
- boostClassifier->Save(GetParameterString("io.out"));
+ boostClassifier->Save(modelPath);
}
-#endif
} //end namespace wrapper
} //end namespace otb
+
+#endif
diff --git a/Modules/Applications/AppClassification/app/otbTrainDecisionTree.cxx b/Modules/Applications/AppClassification/include/otbTrainDecisionTree.txx
similarity index 86%
rename from Modules/Applications/AppClassification/app/otbTrainDecisionTree.cxx
rename to Modules/Applications/AppClassification/include/otbTrainDecisionTree.txx
index 9f94a7fc6050bb13a2169242619e8232f32d47c3..04df9380caacca8975caf05f8bb0b6b8237d0ff5 100644
--- a/Modules/Applications/AppClassification/app/otbTrainDecisionTree.cxx
+++ b/Modules/Applications/AppClassification/include/otbTrainDecisionTree.txx
@@ -14,15 +14,19 @@
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
-
-#include "otbTrainImagesClassifier.h"
+#ifndef __otbTrainDecisionTree_txx
+#define __otbTrainDecisionTree_txx
+#include "otbLearningApplicationBase.h"
namespace otb
{
namespace Wrapper
{
-#ifdef OTB_USE_OPENCV
-void TrainImagesClassifier::InitDecisionTreeParams()
+
+template <class TInputValue, class TOutputValue>
+void
+LearningApplicationBase<TInputValue,TOutputValue>
+::InitDecisionTreeParams()
{
AddChoice("classifier.dt", "Decision Tree classifier");
SetParameterDescription("classifier.dt",
@@ -81,10 +85,15 @@ void TrainImagesClassifier::InitDecisionTreeParams()
}
-void TrainImagesClassifier::TrainDecisionTree(ListSampleType::Pointer trainingListSample,
- LabelListSampleType::Pointer trainingLabeledListSample)
+template <class TInputValue, class TOutputValue>
+void
+LearningApplicationBase<TInputValue,TOutputValue>
+::TrainDecisionTree(typename ListSampleType::Pointer trainingListSample,
+ typename TargetListSampleType::Pointer trainingLabeledListSample,
+ std::string modelPath)
{
- DecisionTreeType::Pointer classifier = DecisionTreeType::New();
+ typename DecisionTreeType::Pointer classifier = DecisionTreeType::New();
+ classifier->SetRegressionMode(this->m_RegressionFlag);
classifier->SetInputListSample(trainingListSample);
classifier->SetTargetListSample(trainingLabeledListSample);
classifier->SetMaxDepth(GetParameterInt("classifier.dt.max"));
@@ -101,8 +110,10 @@ void TrainImagesClassifier::TrainDecisionTree(ListSampleType::Pointer trainingLi
classifier->SetTruncatePrunedTree(false);
}
classifier->Train();
- classifier->Save(GetParameterString("io.out"));
+ classifier->Save(modelPath);
}
-#endif
+
} //end namespace wrapper
} //end namespace otb
+
+#endif
diff --git a/Modules/Applications/AppClassification/app/otbTrainGradientBoostedTree.cxx b/Modules/Applications/AppClassification/include/otbTrainGradientBoostedTree.txx
similarity index 63%
rename from Modules/Applications/AppClassification/app/otbTrainGradientBoostedTree.cxx
rename to Modules/Applications/AppClassification/include/otbTrainGradientBoostedTree.txx
index c703ad4c57a0d8efb8103dadc64a92701a16d162..b55612581cd1140185ae53eab3ae407c51536b6b 100644
--- a/Modules/Applications/AppClassification/app/otbTrainGradientBoostedTree.cxx
+++ b/Modules/Applications/AppClassification/include/otbTrainGradientBoostedTree.txx
@@ -14,23 +14,34 @@
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
-
-#include "otbTrainImagesClassifier.h"
+#ifndef __otbTrainGradientBoostedTree_txx
+#define __otbTrainGradientBoostedTree_txx
+#include "otbLearningApplicationBase.h"
namespace otb
{
namespace Wrapper
{
-#ifdef OTB_USE_OPENCV
-void TrainImagesClassifier::InitGradientBoostedTreeParams()
+
+template <class TInputValue, class TOutputValue>
+void
+LearningApplicationBase<TInputValue,TOutputValue>
+::InitGradientBoostedTreeParams()
{
AddChoice("classifier.gbt", "Gradient Boosted Tree classifier");
SetParameterDescription(
"classifier.gbt",
"This group of parameters allows to set Gradient Boosted Tree classifier parameters. "
"See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/gradient_boosted_trees.html}.");
- //LossFunctionType : not exposed, as only one type is used for Classification,
- // the other three are used for regression.
+
+ if (m_RegressionFlag)
+ {
+ AddParameter(ParameterType_Choice, "classifier.gbt.t", "Loss Function Type");
+ SetParameterDescription("classifier.gbt.t","Type of loss functionused for training.");
+ AddChoice("classifier.gbt.t.sqr","Squared Loss");
+ AddChoice("classifier.gbt.t.abs","Absolute Loss");
+ AddChoice("classifier.gbt.t.hub","Huber Loss");
+ }
//WeakCount
AddParameter(ParameterType_Int, "classifier.gbt.w", "Number of boosting algorithm iterations");
@@ -67,10 +78,15 @@ void TrainImagesClassifier::InitGradientBoostedTreeParams()
}
-void TrainImagesClassifier::TrainGradientBoostedTree(
- ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample)
+template <class TInputValue, class TOutputValue>
+void
+LearningApplicationBase<TInputValue,TOutputValue>
+::TrainGradientBoostedTree(typename ListSampleType::Pointer trainingListSample,
+ typename TargetListSampleType::Pointer trainingLabeledListSample,
+ std::string modelPath)
{
- GradientBoostedTreeType::Pointer classifier = GradientBoostedTreeType::New();
+ typename GradientBoostedTreeType::Pointer classifier = GradientBoostedTreeType::New();
+ classifier->SetRegressionMode(this->m_RegressionFlag);
classifier->SetInputListSample(trainingListSample);
classifier->SetTargetListSample(trainingLabeledListSample);
classifier->SetWeakCount(GetParameterInt("classifier.gbt.w"));
@@ -78,9 +94,34 @@ void TrainImagesClassifier::TrainGradientBoostedTree(
classifier->SetSubSamplePortion(GetParameterFloat("classifier.gbt.p"));
classifier->SetMaxDepth(GetParameterInt("classifier.gbt.max"));
+ if (m_RegressionFlag)
+ {
+ switch (GetParameterInt("classifier.gbt.t"))
+ {
+ case 0: // SQUARED_LOSS
+ classifier->SetLossFunctionType(CvGBTrees::SQUARED_LOSS);
+ break;
+ case 1: // ABSOLUTE_LOSS
+ classifier->SetLossFunctionType(CvGBTrees::ABSOLUTE_LOSS);
+ break;
+ case 2: // HUBER_LOSS
+ classifier->SetLossFunctionType(CvGBTrees::HUBER_LOSS);
+ break;
+ default:
+ classifier->SetLossFunctionType(CvGBTrees::SQUARED_LOSS);
+ break;
+ }
+ }
+ else
+ {
+ classifier->SetLossFunctionType(CvGBTrees::DEVIANCE_LOSS);
+ }
+
classifier->Train();
- classifier->Save(GetParameterString("io.out"));
+ classifier->Save(modelPath);
}
-#endif
+
} //end namespace wrapper
} //end namespace otb
+
+#endif
diff --git a/Modules/Applications/AppClassification/include/otbTrainKNN.txx b/Modules/Applications/AppClassification/include/otbTrainKNN.txx
new file mode 100644
index 0000000000000000000000000000000000000000..93656003a1b24e601a630393983934bb65b5e797
--- /dev/null
+++ b/Modules/Applications/AppClassification/include/otbTrainKNN.txx
@@ -0,0 +1,86 @@
+/*=========================================================================
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+ =========================================================================*/
+#ifndef __otbTrainKNN_txx
+#define __otbTrainKNN_txx
+#include "otbLearningApplicationBase.h"
+
+namespace otb
+{
+namespace Wrapper
+{
+
+ template <class TInputValue, class TOutputValue>
+ void
+ LearningApplicationBase<TInputValue,TOutputValue>
+ ::InitKNNParams()
+ {
+ AddChoice("classifier.knn", "KNN classifier");
+ SetParameterDescription("classifier.knn", "This group of parameters allows to set KNN classifier parameters. "
+ "See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/k_nearest_neighbors.html}.");
+
+ //K parameter
+ AddParameter(ParameterType_Int, "classifier.knn.k", "Number of Neighbors");
+ SetParameterInt("classifier.knn.k", 32);
+ SetParameterDescription("classifier.knn.k","The number of neighbors to use.");
+
+ if (this->m_RegressionFlag)
+ {
+ // Decision rule : mean / median
+ AddParameter(ParameterType_Choice, "classifier.knn.rule", "Decision rule");
+ SetParameterDescription("classifier.knn.rule", "Decision rule for regression output");
+
+ AddChoice("classifier.knn.rule.mean", "Mean of neighbors values");
+ SetParameterDescription("classifier.knn.rule.mean","Returns the mean of neighbors values");
+
+ AddChoice("classifier.knn.rule.median", "Median of neighbors values");
+ SetParameterDescription("classifier.knn.rule.median","Returns the median of neighbors values");
+ }
+ }
+
+ template <class TInputValue, class TOutputValue>
+ void
+ LearningApplicationBase<TInputValue,TOutputValue>
+ ::TrainKNN(typename ListSampleType::Pointer trainingListSample,
+ typename TargetListSampleType::Pointer trainingLabeledListSample,
+ std::string modelPath)
+ {
+ typename KNNType::Pointer knnClassifier = KNNType::New();
+ knnClassifier->SetRegressionMode(this->m_RegressionFlag);
+ knnClassifier->SetInputListSample(trainingListSample);
+ knnClassifier->SetTargetListSample(trainingLabeledListSample);
+ knnClassifier->SetK(GetParameterInt("classifier.knn.k"));
+ if (this->m_RegressionFlag)
+ {
+ std::string decision = this->GetParameterString("classifier.knn.rule");
+ if (decision == "mean")
+ {
+ knnClassifier->SetDecisionRule(KNNType::KNN_MEAN);
+ }
+ else if (decision == "median")
+ {
+ knnClassifier->SetDecisionRule(KNNType::KNN_MEDIAN);
+ }
+ }
+
+ knnClassifier->Train();
+ knnClassifier->Save(modelPath);
+ }
+
+} //end namespace wrapper
+} //end namespace otb
+
+#endif
diff --git a/Modules/Applications/AppClassification/app/otbTrainLibSVM.cxx b/Modules/Applications/AppClassification/include/otbTrainLibSVM.txx
similarity index 52%
rename from Modules/Applications/AppClassification/app/otbTrainLibSVM.cxx
rename to Modules/Applications/AppClassification/include/otbTrainLibSVM.txx
index e117337708d3effd267a462a1d24f8b2f6736e18..928245655fd30d983c970947feef81dbd0c8304f 100644
--- a/Modules/Applications/AppClassification/app/otbTrainLibSVM.cxx
+++ b/Modules/Applications/AppClassification/include/otbTrainLibSVM.txx
@@ -14,16 +14,19 @@
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
-
-#include "otbTrainImagesClassifier.h"
-
+#ifndef __otbTrainLibSVM_txx
+#define __otbTrainLibSVM_txx
+#include "otbLearningApplicationBase.h"
namespace otb
{
namespace Wrapper
{
-#ifdef OTB_USE_LIBSVM
- void TrainImagesClassifier::InitLibSVMParams()
+
+ template <class TInputValue, class TOutputValue>
+ void
+ LearningApplicationBase<TInputValue,TOutputValue>
+ ::InitLibSVMParams()
{
AddChoice("classifier.libsvm", "LibSVM classifier");
SetParameterDescription("classifier.libsvm", "This group of parameters allows to set SVM classifier parameters.");
@@ -34,6 +37,21 @@ namespace Wrapper
AddChoice("classifier.libsvm.k.sigmoid", "Sigmoid");
SetParameterString("classifier.libsvm.k", "linear");
SetParameterDescription("classifier.libsvm.k", "SVM Kernel Type.");
+ AddParameter(ParameterType_Choice, "classifier.libsvm.m", "SVM Model Type");
+ SetParameterDescription("classifier.libsvm.m", "Type of SVM formulation.");
+ if (this->m_RegressionFlag)
+ {
+ AddChoice("classifier.libsvm.m.epssvr", "Epsilon Support Vector Regression");
+ AddChoice("classifier.libsvm.m.nusvr", "Nu Support Vector Regression");
+ SetParameterString("classifier.libsvm.m", "epssvr");
+ }
+ else
+ {
+ AddChoice("classifier.libsvm.m.csvc", "C support vector classification");
+ AddChoice("classifier.libsvm.m.nusvc", "Nu support vector classification");
+ AddChoice("classifier.libsvm.m.oneclass", "Distribution estimation (One Class SVM)");
+ SetParameterString("classifier.libsvm.m", "csvc");
+ }
AddParameter(ParameterType_Float, "classifier.libsvm.c", "Cost parameter C");
SetParameterFloat("classifier.libsvm.c", 1.0);
SetParameterDescription(
@@ -45,12 +63,25 @@ namespace Wrapper
AddParameter(ParameterType_Empty, "classifier.libsvm.prob", "Probability estimation");
MandatoryOff("classifier.libsvm.prob");
SetParameterDescription("classifier.libsvm.prob", "Probability estimation flag.");
- }
+ if (this->m_RegressionFlag)
+ {
+ AddParameter(ParameterType_Float, "classifier.libsvm.eps", "Epsilon");
+ SetParameterFloat("classifier.libsvm.eps", 1e-3);
+ AddParameter(ParameterType_Float, "classifier.libsvm.nu", "Nu");
+ SetParameterFloat("classifier.libsvm.nu", 0.5);
+ }
+ }
- void TrainImagesClassifier::TrainLibSVM(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample)
+ template <class TInputValue, class TOutputValue>
+ void
+ LearningApplicationBase<TInputValue,TOutputValue>
+ ::TrainLibSVM(typename ListSampleType::Pointer trainingListSample,
+ typename TargetListSampleType::Pointer trainingLabeledListSample,
+ std::string modelPath)
{
- LibSVMType::Pointer libSVMClassifier = LibSVMType::New();
+ typename LibSVMType::Pointer libSVMClassifier = LibSVMType::New();
+ libSVMClassifier->SetRegressionMode(this->m_RegressionFlag);
libSVMClassifier->SetInputListSample(trainingListSample);
libSVMClassifier->SetTargetListSample(trainingLabeledListSample);
//SVM Option
@@ -83,9 +114,47 @@ namespace Wrapper
libSVMClassifier->SetKernelType(LINEAR);
break;
}
+ if (this->m_RegressionFlag)
+ {
+ switch (GetParameterInt("classifier.libsvm.m"))
+ {
+ case 0: // EPSILON_SVR
+ libSVMClassifier->SetSVMType(EPSILON_SVR);
+ break;
+ case 1: // NU_SVR
+ libSVMClassifier->SetSVMType(NU_SVR);
+ break;
+ default:
+ libSVMClassifier->SetSVMType(EPSILON_SVR);
+ break;
+ }
+ libSVMClassifier->SetEpsilon(GetParameterFloat("classifier.libsvm.eps"));
+ libSVMClassifier->SetNu(GetParameterFloat("classifier.libsvm.nu"));
+ }
+ else
+ {
+ switch (GetParameterInt("classifier.libsvm.m"))
+ {
+ case 0: // C_SVC
+ libSVMClassifier->SetSVMType(C_SVC);
+ break;
+ case 1: // NU_SVC
+ libSVMClassifier->SetSVMType(NU_SVC);
+ break;
+ case 2: // ONE_CLASS
+ libSVMClassifier->SetSVMType(ONE_CLASS);
+ break;
+ default:
+ libSVMClassifier->SetSVMType(C_SVC);
+ break;
+ }
+ }
+
libSVMClassifier->Train();
- libSVMClassifier->Save(GetParameterString("io.out"));
+ libSVMClassifier->Save(modelPath);
}
-#endif
+
} //end namespace wrapper
} //end namespace otb
+
+#endif
diff --git a/Modules/Applications/AppClassification/app/otbTrainNeuralNetwork.cxx b/Modules/Applications/AppClassification/include/otbTrainNeuralNetwork.txx
similarity index 87%
rename from Modules/Applications/AppClassification/app/otbTrainNeuralNetwork.cxx
rename to Modules/Applications/AppClassification/include/otbTrainNeuralNetwork.txx
index 29b3657ad2ef612a89a83f923cf370c3a9bbbc5c..40f120f69bcaa1df15454070d63e38feda46e960 100644
--- a/Modules/Applications/AppClassification/app/otbTrainNeuralNetwork.cxx
+++ b/Modules/Applications/AppClassification/include/otbTrainNeuralNetwork.txx
@@ -14,16 +14,20 @@
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
-
+#ifndef __otbTrainNeuralNetwork_txx
+#define __otbTrainNeuralNetwork_txx
#include <boost/lexical_cast.hpp>
-#include "otbTrainImagesClassifier.h"
+#include "otbLearningApplicationBase.h"
namespace otb
{
namespace Wrapper
{
-#ifdef OTB_USE_OPENCV
-void TrainImagesClassifier::InitNeuralNetworkParams()
+
+template <class TInputValue, class TOutputValue>
+void
+LearningApplicationBase<TInputValue,TOutputValue>
+::InitNeuralNetworkParams()
{
AddChoice("classifier.ann", "Artificial Neural Network classifier");
SetParameterDescription("classifier.ann",
@@ -119,10 +123,15 @@ void TrainImagesClassifier::InitNeuralNetworkParams()
}
-void TrainImagesClassifier::TrainNeuralNetwork(ListSampleType::Pointer trainingListSample,
- LabelListSampleType::Pointer trainingLabeledListSample)
+template <class TInputValue, class TOutputValue>
+void
+LearningApplicationBase<TInputValue,TOutputValue>
+::TrainNeuralNetwork(typename ListSampleType::Pointer trainingListSample,
+ typename TargetListSampleType::Pointer trainingLabeledListSample,
+ std::string modelPath)
{
- NeuralNetworkType::Pointer classifier = NeuralNetworkType::New();
+ typename NeuralNetworkType::Pointer classifier = NeuralNetworkType::New();
+ classifier->SetRegressionMode(this->m_RegressionFlag);
classifier->SetInputListSample(trainingListSample);
classifier->SetTargetListSample(trainingLabeledListSample);
@@ -153,18 +162,23 @@ void TrainImagesClassifier::TrainNeuralNetwork(ListSampleType::Pointer trainingL
unsigned int nbClasses = 0;
- LabelType currentLabel = 0, prevLabel = 0;
- for (unsigned int itLab = 0; itLab < trainingLabeledListSample->Size(); ++itLab)
+ if (this->m_RegressionFlag)
+ {
+ layerSizes.push_back(1);
+ }
+ else
{
- currentLabel = trainingLabeledListSample->GetMeasurementVector(itLab);
- if ((currentLabel != prevLabel) || (itLab == 0))
+ std::set<TargetValueType> labelSet;
+ TargetSampleType currentLabel;
+ for (unsigned int itLab = 0; itLab < trainingLabeledListSample->Size(); ++itLab)
{
- ++nbClasses;
+ currentLabel = trainingLabeledListSample->GetMeasurementVector(itLab);
+ labelSet.insert(currentLabel[0]);
}
- prevLabel = currentLabel;
+ nbClasses = labelSet.size();
+ layerSizes.push_back(nbClasses);
}
- layerSizes.push_back(nbClasses);
classifier->SetLayerSizes(layerSizes);
switch (GetParameterInt("classifier.ann.f"))
@@ -208,8 +222,10 @@ void TrainImagesClassifier::TrainNeuralNetwork(ListSampleType::Pointer trainingL
classifier->SetEpsilon(GetParameterFloat("classifier.ann.eps"));
classifier->SetMaxIter(GetParameterInt("classifier.ann.iter"));
classifier->Train();
- classifier->Save(GetParameterString("io.out"));
+ classifier->Save(modelPath);
}
-#endif
+
} //end namespace wrapper
} //end namespace otb
+
+#endif
diff --git a/Modules/Applications/AppClassification/app/otbTrainNormalBayes.cxx b/Modules/Applications/AppClassification/include/otbTrainNormalBayes.txx
similarity index 59%
rename from Modules/Applications/AppClassification/app/otbTrainNormalBayes.cxx
rename to Modules/Applications/AppClassification/include/otbTrainNormalBayes.txx
index d33e6eaf9cd79a99120a5176b096a94fb98b190c..d5f3cce8e3ef2ae3fbe02a2b21e99d0004df3944 100644
--- a/Modules/Applications/AppClassification/app/otbTrainNormalBayes.cxx
+++ b/Modules/Applications/AppClassification/include/otbTrainNormalBayes.txx
@@ -14,16 +14,19 @@
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
-
-#include "otbTrainImagesClassifier.h"
-
+#ifndef __otbTrainNormalBayes_txx
+#define __otbTrainNormalBayes_txx
+#include "otbLearningApplicationBase.h"
namespace otb
{
namespace Wrapper
{
-#ifdef OTB_USE_OPENCV
- void TrainImagesClassifier::InitNormalBayesParams()
+
+ template <class TInputValue, class TOutputValue>
+ void
+ LearningApplicationBase<TInputValue,TOutputValue>
+ ::InitNormalBayesParams()
{
AddChoice("classifier.bayes", "Normal Bayes classifier");
SetParameterDescription("classifier.bayes", "Use a Normal Bayes Classifier. "
@@ -31,15 +34,22 @@ namespace Wrapper
}
-
- void TrainImagesClassifier::TrainNormalBayes(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample)
+ template <class TInputValue, class TOutputValue>
+ void
+ LearningApplicationBase<TInputValue,TOutputValue>
+ ::TrainNormalBayes(typename ListSampleType::Pointer trainingListSample,
+ typename TargetListSampleType::Pointer trainingLabeledListSample,
+ std::string modelPath)
{
- NormalBayesType::Pointer classifier = NormalBayesType::New();
+ typename NormalBayesType::Pointer classifier = NormalBayesType::New();
+ classifier->SetRegressionMode(this->m_RegressionFlag);
classifier->SetInputListSample(trainingListSample);
classifier->SetTargetListSample(trainingLabeledListSample);
classifier->Train();
- classifier->Save(GetParameterString("io.out"));
+ classifier->Save(modelPath);
}
-#endif
+
} //end namespace wrapper
} //end namespace otb
+
+#endif
diff --git a/Modules/Applications/AppClassification/app/otbTrainRandomForests.cxx b/Modules/Applications/AppClassification/include/otbTrainRandomForests.txx
similarity index 87%
rename from Modules/Applications/AppClassification/app/otbTrainRandomForests.cxx
rename to Modules/Applications/AppClassification/include/otbTrainRandomForests.txx
index 1798b9d9b59b7ffa2323936084736b6b423a3cb5..a856243a300dfc35d559e683aef21268d8c25a12 100644
--- a/Modules/Applications/AppClassification/app/otbTrainRandomForests.cxx
+++ b/Modules/Applications/AppClassification/include/otbTrainRandomForests.txx
@@ -14,15 +14,19 @@
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
-
-#include "otbTrainImagesClassifier.h"
+#ifndef __otbTrainRandomForests_txx
+#define __otbTrainRandomForests_txx
+#include "otbLearningApplicationBase.h"
namespace otb
{
namespace Wrapper
{
-#ifdef OTB_USE_OPENCV
-void TrainImagesClassifier::InitRandomForestsParams()
+
+template <class TInputValue, class TOutputValue>
+void
+LearningApplicationBase<TInputValue,TOutputValue>
+::InitRandomForestsParams()
{
AddChoice("classifier.rf", "Random forests classifier");
SetParameterDescription("classifier.rf",
@@ -95,10 +99,15 @@ void TrainImagesClassifier::InitRandomForestsParams()
//TerminationCriteria not exposed
}
-void TrainImagesClassifier::TrainRandomForests(ListSampleType::Pointer trainingListSample,
- LabelListSampleType::Pointer trainingLabeledListSample)
+template <class TInputValue, class TOutputValue>
+void
+LearningApplicationBase<TInputValue,TOutputValue>
+::TrainRandomForests(typename ListSampleType::Pointer trainingListSample,
+ typename TargetListSampleType::Pointer trainingLabeledListSample,
+ std::string modelPath)
{
- RandomForestType::Pointer classifier = RandomForestType::New();
+ typename RandomForestType::Pointer classifier = RandomForestType::New();
+ classifier->SetRegressionMode(this->m_RegressionFlag);
classifier->SetInputListSample(trainingListSample);
classifier->SetTargetListSample(trainingLabeledListSample);
classifier->SetMaxDepth(GetParameterInt("classifier.rf.max"));
@@ -110,8 +119,10 @@ void TrainImagesClassifier::TrainRandomForests(ListSampleType::Pointer trainingL
classifier->SetForestAccuracy(GetParameterFloat("classifier.rf.acc"));
classifier->Train();
- classifier->Save(GetParameterString("io.out"));
+ classifier->Save(modelPath);
}
-#endif
+
} //end namespace wrapper
} //end namespace otb
+
+#endif
diff --git a/Modules/Applications/AppClassification/app/otbTrainSVM.cxx b/Modules/Applications/AppClassification/include/otbTrainSVM.txx
similarity index 57%
rename from Modules/Applications/AppClassification/app/otbTrainSVM.cxx
rename to Modules/Applications/AppClassification/include/otbTrainSVM.txx
index a41e103746f5f5d7968f26220791436232caac74..ac0f153ed4f78df8c5a7d838c3e32beed62ddd9f 100644
--- a/Modules/Applications/AppClassification/app/otbTrainSVM.cxx
+++ b/Modules/Applications/AppClassification/include/otbTrainSVM.txx
@@ -14,28 +14,38 @@
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
-
-#include "otbTrainImagesClassifier.h"
-
+#ifndef __otbTrainSVM_txx
+#define __otbTrainSVM_txx
+#include "otbLearningApplicationBase.h"
namespace otb
{
namespace Wrapper
{
-#ifdef OTB_USE_OPENCV
- void TrainImagesClassifier::InitSVMParams()
+
+ template <class TInputValue, class TOutputValue>
+ void
+ LearningApplicationBase<TInputValue,TOutputValue>
+ ::InitSVMParams()
{
AddChoice("classifier.svm", "SVM classifier (OpenCV)");
SetParameterDescription("classifier.svm", "This group of parameters allows to set SVM classifier parameters. "
"See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/support_vector_machines.html}.");
AddParameter(ParameterType_Choice, "classifier.svm.m", "SVM Model Type");
- AddChoice("classifier.svm.m.csvc", "C support vector classification");
- AddChoice("classifier.svm.m.nusvc", "Nu support vector classification");
- AddChoice("classifier.svm.m.oneclass", "Distribution estimation (One Class SVM)");
- //AddChoice("classifier.svm.m.epssvr", "Epsilon Support Vector Regression");
- //AddChoice("classifier.svm.m.nusvr", "Nu Support Vector Regression");
- SetParameterString("classifier.svm.m", "csvc");
SetParameterDescription("classifier.svm.m", "Type of SVM formulation.");
+ if (this->m_RegressionFlag)
+ {
+ AddChoice("classifier.svm.m.epssvr", "Epsilon Support Vector Regression");
+ AddChoice("classifier.svm.m.nusvr", "Nu Support Vector Regression");
+ SetParameterString("classifier.svm.m", "epssvr");
+ }
+ else
+ {
+ AddChoice("classifier.svm.m.csvc", "C support vector classification");
+ AddChoice("classifier.svm.m.nusvc", "Nu support vector classification");
+ AddChoice("classifier.svm.m.oneclass", "Distribution estimation (One Class SVM)");
+ SetParameterString("classifier.svm.m", "csvc");
+ }
AddParameter(ParameterType_Choice, "classifier.svm.k", "SVM Kernel Type");
AddChoice("classifier.svm.k.linear", "Linear");
AddChoice("classifier.svm.k.rbf", "Gaussian radial basis function");
@@ -52,9 +62,26 @@ namespace Wrapper
"Parameter nu of a SVM optimization problem (NU_SVC / ONE_CLASS)");
SetParameterFloat("classifier.svm.nu", 0.0);
SetParameterDescription("classifier.svm.nu", "Parameter nu of a SVM optimization problem.");
- //AddParameter(ParameterType_Float, "classifier.svm.p", "Parameter epsilon of a SVM optimization problem (EPS_SVR)");
- //SetParameterFloat("classifier.svm.p", 0.0);
- //SetParameterDescription("classifier.svm.p", "Parameter epsilon of a SVM optimization problem (EPS_SVR).");
+ if (this->m_RegressionFlag)
+ {
+ AddParameter(ParameterType_Float, "classifier.svm.p", "Parameter epsilon of a SVM optimization problem (EPS_SVR)");
+ SetParameterFloat("classifier.svm.p", 1.0);
+ SetParameterDescription("classifier.svm.p", "Parameter epsilon of a SVM optimization problem (EPS_SVR).");
+
+ AddParameter(ParameterType_Choice, "classifier.svm.term", "Termination criteria");
+ SetParameterDescription("classifier.svm.term","Termination criteria for iterative algorithm");
+ AddChoice("classifier.svm.term.iter", "Stops when maximum iteration is reached.");
+ AddChoice("classifier.svm.term.eps", "Stops when accuracy is lower than epsilon.");
+ AddChoice("classifier.svm.term.all", "Stops when either iteration or epsilon criteria is true");
+
+ AddParameter(ParameterType_Float, "classifier.svm.iter", "Maximum iteration");
+ SetParameterFloat("classifier.svm.iter", 1000);
+ SetParameterDescription("classifier.svm.iter", "Maximum number of iterations (corresponds to the termination criteria 'iter').");
+
+ AddParameter(ParameterType_Float, "classifier.svm.eps", "Epsilon accuracy threshold");
+ SetParameterFloat("classifier.svm.eps", FLT_EPSILON);
+ SetParameterDescription("classifier.svm.eps", "Epsilon accuracy (corresponds to the termination criteria 'eps').");
+ }
AddParameter(ParameterType_Float, "classifier.svm.coef0", "Parameter coef0 of a kernel function (POLY / SIGMOID)");
SetParameterFloat("classifier.svm.coef0", 0.0);
SetParameterDescription("classifier.svm.coef0", "Parameter coef0 of a kernel function (POLY / SIGMOID).");
@@ -76,9 +103,15 @@ namespace Wrapper
"because the samples are not identically processed within OpenCV.");
}
- void TrainImagesClassifier::TrainSVM(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample)
+ template <class TInputValue, class TOutputValue>
+ void
+ LearningApplicationBase<TInputValue,TOutputValue>
+ ::TrainSVM(typename ListSampleType::Pointer trainingListSample,
+ typename TargetListSampleType::Pointer trainingLabeledListSample,
+ std::string modelPath)
{
- SVMType::Pointer SVMClassifier = SVMType::New();
+ typename SVMType::Pointer SVMClassifier = SVMType::New();
+ SVMClassifier->SetRegressionMode(this->m_RegressionFlag);
SVMClassifier->SetInputListSample(trainingListSample);
SVMClassifier->SetTargetListSample(trainingLabeledListSample);
switch (GetParameterInt("classifier.svm.k"))
@@ -104,36 +137,69 @@ namespace Wrapper
std::cout << "CvSVM::LINEAR = " << CvSVM::LINEAR << std::endl;
break;
}
- switch (GetParameterInt("classifier.svm.m"))
+ if (this->m_RegressionFlag)
{
- case 0: // C_SVC
- SVMClassifier->SetSVMType(CvSVM::C_SVC);
- std::cout << "CvSVM::C_SVC = " << CvSVM::C_SVC << std::endl;
- break;
- case 1: // NU_SVC
- SVMClassifier->SetSVMType(CvSVM::NU_SVC);
- std::cout << "CvSVM::NU_SVC = " << CvSVM::NU_SVC << std::endl;
- break;
- case 2: // ONE_CLASS
- SVMClassifier->SetSVMType(CvSVM::ONE_CLASS);
- std::cout << "CvSVM::ONE_CLASS = " << CvSVM::ONE_CLASS << std::endl;
- break;
- /*case 3: // EPS_SVR
- SVMClassifier->SetSVMType(CvSVM::EPS_SVR);
- std::cout<<"CvSVM::EPS_SVR = "<<CvSVM::EPS_SVR<<std::endl;
- break;
- case 4: // NU_SVR
- SVMClassifier->SetSVMType(CvSVM::NU_SVR);
- std::cout<<"CvSVM::NU_SVR = "<<CvSVM::NU_SVR<<std::endl;
- break; */
- default: // DEFAULT = C_SVC
- SVMClassifier->SetSVMType(CvSVM::C_SVC);
- std::cout << "CvSVM::C_SVC = " << CvSVM::C_SVC << std::endl;
- break;
+ switch (GetParameterInt("classifier.svm.m"))
+ {
+ case 0: // EPS_SVR
+ SVMClassifier->SetSVMType(CvSVM::EPS_SVR);
+ std::cout<<"CvSVM::EPS_SVR = "<<CvSVM::EPS_SVR<<std::endl;
+ break;
+ case 1: // NU_SVR
+ SVMClassifier->SetSVMType(CvSVM::NU_SVR);
+ std::cout<<"CvSVM::NU_SVR = "<<CvSVM::NU_SVR<<std::endl;
+ break;
+ default: // DEFAULT = EPS_SVR
+ SVMClassifier->SetSVMType(CvSVM::EPS_SVR);
+ std::cout << "CvSVM::EPS_SVR = " << CvSVM::EPS_SVR << std::endl;
+ break;
+ }
+ }
+ else
+ {
+ switch (GetParameterInt("classifier.svm.m"))
+ {
+ case 0: // C_SVC
+ SVMClassifier->SetSVMType(CvSVM::C_SVC);
+ std::cout << "CvSVM::C_SVC = " << CvSVM::C_SVC << std::endl;
+ break;
+ case 1: // NU_SVC
+ SVMClassifier->SetSVMType(CvSVM::NU_SVC);
+ std::cout << "CvSVM::NU_SVC = " << CvSVM::NU_SVC << std::endl;
+ break;
+ case 2: // ONE_CLASS
+ SVMClassifier->SetSVMType(CvSVM::ONE_CLASS);
+ std::cout << "CvSVM::ONE_CLASS = " << CvSVM::ONE_CLASS << std::endl;
+ break;
+ default: // DEFAULT = C_SVC
+ SVMClassifier->SetSVMType(CvSVM::C_SVC);
+ std::cout << "CvSVM::C_SVC = " << CvSVM::C_SVC << std::endl;
+ break;
+ }
}
SVMClassifier->SetC(GetParameterFloat("classifier.svm.c"));
SVMClassifier->SetNu(GetParameterFloat("classifier.svm.nu"));
- //SVMClassifier->SetP(GetParameterFloat("classifier.svm.p"));
+ if (this->m_RegressionFlag)
+ {
+ SVMClassifier->SetP(GetParameterFloat("classifier.svm.p"));
+ switch (GetParameterInt("classifier.svm.term"))
+ {
+ case 0: // ITER
+ SVMClassifier->SetTermCriteriaType(CV_TERMCRIT_ITER);
+ break;
+ case 1: // EPS
+ SVMClassifier->SetTermCriteriaType(CV_TERMCRIT_EPS);
+ break;
+ case 2: // ITER+EPS
+ SVMClassifier->SetTermCriteriaType(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS);
+ break;
+ default:
+ SVMClassifier->SetTermCriteriaType(CV_TERMCRIT_ITER);
+ break;
+ }
+ SVMClassifier->SetMaxIter(GetParameterInt("classifier.svm.iter"));
+ SVMClassifier->SetEpsilon(GetParameterFloat("classifier.svm.eps"));
+ }
SVMClassifier->SetCoef0(GetParameterFloat("classifier.svm.coef0"));
SVMClassifier->SetGamma(GetParameterFloat("classifier.svm.gamma"));
SVMClassifier->SetDegree(GetParameterFloat("classifier.svm.degree"));
@@ -142,17 +208,21 @@ namespace Wrapper
SVMClassifier->SetParameterOptimization(true);
}
SVMClassifier->Train();
- SVMClassifier->Save(GetParameterString("io.out"));
+ SVMClassifier->Save(modelPath);
// Update the displayed parameters in the GUI after the training process, for further use of them
SetParameterFloat("classifier.svm.c", static_cast<float> (SVMClassifier->GetOutputC()));
SetParameterFloat("classifier.svm.nu", static_cast<float> (SVMClassifier->GetOutputNu()));
- //SetParameterFloat("classifier.svm.p", static_cast<float> (SVMClassifier->GetOutputP()));
+ if (this->m_RegressionFlag)
+ {
+ SetParameterFloat("classifier.svm.p", static_cast<float> (SVMClassifier->GetOutputP()));
+ }
SetParameterFloat("classifier.svm.coef0", static_cast<float> (SVMClassifier->GetOutputCoef0()));
SetParameterFloat("classifier.svm.gamma", static_cast<float> (SVMClassifier->GetOutputGamma()));
SetParameterFloat("classifier.svm.degree", static_cast<float> (SVMClassifier->GetOutputDegree()));
}
-#endif
} //end namespace wrapper
} //end namespace otb
+
+#endif
diff --git a/Modules/Applications/AppClassification/test/CMakeLists.txt b/Modules/Applications/AppClassification/test/CMakeLists.txt
index c4e275fe5a33fc8e629a13c9016a5584c8a4b0a3..63a5c0918f5df9138ab25e78968b8305743f2409 100644
--- a/Modules/Applications/AppClassification/test/CMakeLists.txt
+++ b/Modules/Applications/AppClassification/test/CMakeLists.txt
@@ -841,3 +841,36 @@ otb_test_application(NAME cdbTvVectorDataDSValidationWrongRoads_LI
${OTBAPP_BASELINE_FILES}/cdbTvVectorDataDSValidationOutpout_LI_wr.shp
${TEMP}/cdbTvVectorDataDSValidationOutpout_LI_wr.shp)
+if(OTB_USE_OPENCV)
+#----------- TrainRegression TESTS ----------------
+# y = 0.01*x^2 + 1.5*x - 300
+otb_test_application(NAME apTvClTrainRegressionTest_monovar
+ APP TrainRegression
+ OPTIONS -io.il ${INPUTDATA}/QB_Toulouse_Ortho_regression.tif
+ -io.imstat ${INPUTDATA}/QB_Toulouse_Ortho_regression.xml
+ -io.out ${TEMP}/apTvClTrainRegressionTest_monovar.rf
+ -sample.mt 20000
+ -sample.mv 20000
+ -sample.vtr 0.5
+ -rand 121212
+ -classifier rf
+ -classifier.rf.ra 0.0001
+ -classifier.rf.max 6
+ -classifier.rf.acc 0.0005
+ VALID --compare-ascii ${NOTOL}
+ ${OTBAPP_BASELINE_FILES}/apTvClTrainRegressionTest_monovar.rf
+ ${TEMP}/apTvClTrainRegressionTest_monovar.rf)
+
+#----------- PredictRegression TESTS ----------------
+otb_test_application(NAME apTvClPredictRegressionTest_monovar
+ APP PredictRegression
+ OPTIONS -in ${INPUTDATA}/QB_Toulouse_Ortho_PAN.tif
+ -model ${OTBAPP_BASELINE_FILES}/apTvClTrainRegressionTest_monovar.rf
+ -imstat ${INPUTDATA}/QB_Toulouse_Ortho_regression.xml
+ -out ${TEMP}/apTvClPredictRegressionTest_monovar.tif
+ VALID --compare-image 1
+ ${OTBAPP_BASELINE}/apTvClPredictRegressionTest_monovar.tif
+ ${TEMP}/apTvClPredictRegressionTest_monovar.tif)
+
+endif()
+
diff --git a/Modules/Applications/AppFiltering/test/CMakeLists.txt b/Modules/Applications/AppFiltering/test/CMakeLists.txt
index d497c9bf8e1b6d51f5668b4ce2ee4be134d91078..984c1830c277999af835fefc934ffcf6bbb2ef8d 100644
--- a/Modules/Applications/AppFiltering/test/CMakeLists.txt
+++ b/Modules/Applications/AppFiltering/test/CMakeLists.txt
@@ -51,8 +51,8 @@ otb_test_application(NAME apTvDespeckleFrost
-filter.frost.rad 5
-filter.frost.deramp 0.1
VALID --compare-image ${EPSILON_7}
- ${BASELINE}/bfFiltreLee_05_05_12.tif
- ${TEMP}/bfFiltreLee_05_05_12_app.tif)
+ ${BASELINE}/bfFiltreFrost_05_05_12_app.tif
+ ${TEMP}/bfFiltreFrost_05_05_12_app.tif)
otb_test_application(NAME apTvDespeckleGammaMAP
APP Despeckle
diff --git a/Modules/Applications/AppSARCalibration/app/otbSarRadiometricCalibration.cxx b/Modules/Applications/AppSARCalibration/app/otbSarRadiometricCalibration.cxx
index b3068c927ac1452f6b48af361950def7f55d29fb..3aa03c21a0f7bfc511697246794bf35695ae6300 100644
--- a/Modules/Applications/AppSARCalibration/app/otbSarRadiometricCalibration.cxx
+++ b/Modules/Applications/AppSARCalibration/app/otbSarRadiometricCalibration.cxx
@@ -24,7 +24,6 @@ namespace otb
{
namespace Wrapper
{
-
class SarRadiometricCalibration : public Application
{
public:
@@ -40,17 +39,17 @@ public:
itkTypeMacro(SarRadiometricCalibration, otb::Application);
typedef otb::SarRadiometricCalibrationToImageFilter<ComplexFloatImageType,
- ComplexFloatImageType> CalibrationFilterType;
+ FloatImageType> CalibrationFilterType;
private:
void DoInit()
{
SetName("SarRadiometricCalibration");
- SetDescription("Perform SAR calibration on input complex images");
+ SetDescription("Perform radiometric calibration of SAR images. Following sensors are supported: TerraSAR-X, Sentinel1 and Radarsat-2.Both Single Look Complex(SLC) and detected products are supported as input.\n");
// Documentation
SetDocName("SAR Radiometric calibration");
- SetDocLongDescription("This application performs SAR calibration on input complex images.");
+ SetDocLongDescription("The objective of SAR calibration is to provide imagery in which the pixel values can be directly related to the radar backscatter of the scene. This application allows to compute Sigma Naught (Radiometric Calibration) for TerraSAR-X, Sentinel1 L1 and Radarsat-2 sensors. Metadata are automatically retrieved from image products.The application supports complex and non-complex images (SLC or detected products).\n");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso(" ");
@@ -58,25 +57,39 @@ private:
AddDocTag(Tags::Calibration);
AddDocTag(Tags::SAR);
- AddParameter(ParameterType_ComplexInputImage, "in", "Input Complex Image");
+ AddParameter(ParameterType_ComplexInputImage, "in", "Input Image");
SetParameterDescription("in", "Input complex image");
- AddParameter(ParameterType_ComplexOutputImage, "out", "Output Image");
- SetParameterDescription("out", "Output calibrated complex image");
+ AddParameter(ParameterType_OutputImage, "out", "Output Image");
+ SetParameterDescription("out", "Output calibrated image. This image contains the backscatter (sigmaNought) of the input image.");
AddRAMParameter();
AddParameter(ParameterType_Empty, "noise", "Disable Noise");
- SetParameterDescription("noise", "Flag to disable noise");
+ SetParameterDescription("noise", "Flag to disable noise. For 5.2.0 release, the noise values are only read by TerraSARX product.");
MandatoryOff("noise");
+ AddParameter(ParameterType_Choice, "lut", "Lookup table sigma /gamma/ beta/ DN.");
+ SetParameterDescription("lut", "Lookup table values are not available with all SAR products. Products that provide lookup table with metadata are: Sentinel1, Radarsat2.");
+ AddChoice("lut.sigma", "Use sigma nought lookup");
+ SetParameterDescription("lut.sigma","Use Sigma nought lookup value from product metadata");
+ AddChoice("lut.gamma", "Use gamma nought lookup");
+ SetParameterDescription("lut.gamma","Use Gamma nought lookup value from product metadata");
+ AddChoice("lut.beta", "Use beta nought lookup");
+ SetParameterDescription("lut.beta","Use Beta nought lookup value from product metadata");
+ AddChoice("lut.dn", "Use DN value lookup");
+ SetParameterDescription("lut.dn","Use DN value lookup value from product metadata");
+ SetDefaultParameterInt("lut", 0);
+
// Doc example parameter settings
SetDocExampleParameterValue("in", "RSAT_imagery_HH.tif");
SetDocExampleParameterValue("out", "SarRadiometricCalibration.tif" );
}
void DoUpdateParameters()
- { }
+ {
+
+ }
void DoExecute()
{
@@ -92,11 +105,19 @@ private:
m_CalibrationFilter->SetEnableNoise(false);
}
+ short lut = 0;
+
+ lut = GetParameterInt("lut");
+
+ m_CalibrationFilter->SetLookupSelected(lut);
+
// Set the output image
- SetParameterComplexOutputImage("out", m_CalibrationFilter->GetOutput());
+ SetParameterOutputImage("out", m_CalibrationFilter->GetOutput());
+
}
CalibrationFilterType::Pointer m_CalibrationFilter;
+
};
}
}
diff --git a/Modules/Applications/AppSARCalibration/test/CMakeLists.txt b/Modules/Applications/AppSARCalibration/test/CMakeLists.txt
index 285dd10621352c2af1047c31a52db69076a4e299..02e9e48f3f4e073a84a970187f66ba67e308842a 100644
--- a/Modules/Applications/AppSARCalibration/test/CMakeLists.txt
+++ b/Modules/Applications/AppSARCalibration/test/CMakeLists.txt
@@ -1,2 +1,17 @@
otb_module_test()
#----------- SarRadiometricCalibration TESTS ----------------
+otb_test_application(NAME apTvRaSarRadiometricCalibration_SENTINEL1
+ APP SarRadiometricCalibration
+ OPTIONS -in ${INPUTDATA}/SENTINEL1_SLC_S6_1S_extract_300_300.tif?&geom=${INPUTDATA}/SENTINEL1_SLC_S6_1S_extract_300_300.geom
+ -out ${TEMP}/apTvRaSarRadiometricCalibration_SENTINEL1.tif
+ VALID --compare-image ${NOTOL}
+ ${BASELINE}/raTvSarRadiometricCalibration_SENTINEL1.tif
+ ${TEMP}/apTvRaSarRadiometricCalibration_SENTINEL1.tif )
+
+otb_test_application(NAME apTvRaSarRadiometricCalibration_RADARSAT2
+ APP SarRadiometricCalibration
+ OPTIONS -in ${INPUTDATA}/RADARSAT2_ALTONA_300_300_VV.tif?&geom=${INPUTDATA}/RADARSAT2_ALTONA_300_300_VV.geom
+ -out ${TEMP}/apTvRaSarRadiometricCalibration_RADARSAT2.tif
+ VALID --compare-image ${NOTOL}
+ ${BASELINE}/raTvSarRadiometricCalibration_RADARSAT2.tif
+ ${TEMP}/apTvRaSarRadiometricCalibration_RADARSAT2.tif )
diff --git a/Modules/Applications/AppSARDecompositions/CMakeLists.txt b/Modules/Applications/AppSARDecompositions/CMakeLists.txt
new file mode 100644
index 0000000000000000000000000000000000000000..aa7832f8f44840a95e5ac6bfdbcddee53ade5a7c
--- /dev/null
+++ b/Modules/Applications/AppSARDecompositions/CMakeLists.txt
@@ -0,0 +1,2 @@
+project(OTBAppSARDecompositions)
+otb_module_impl()
diff --git a/Modules/Applications/AppSARDecompositions/app/CMakeLists.txt b/Modules/Applications/AppSARDecompositions/app/CMakeLists.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d311a3c7e5755cf7de35c79e054dd6c0164c5316
--- /dev/null
+++ b/Modules/Applications/AppSARDecompositions/app/CMakeLists.txt
@@ -0,0 +1,12 @@
+set(OTBAppFiltering_LINK_LIBS
+ ${OTBPolarimetry_LIBRARIES}
+ ${OTBImageManipulation_LIBRARIES}
+ ${OTBApplicationEngine_LIBRARIES}
+ ${OTBImageBase_LIBRARIES}
+)
+
+otb_create_application(
+ NAME SARDecompositions
+ SOURCES otbSARDecompositions.cxx
+ LINK_LIBRARIES ${${otb-module}_LIBRARIES})
+
diff --git a/Modules/Applications/AppSARDecompositions/app/otbSARDecompositions.cxx b/Modules/Applications/AppSARDecompositions/app/otbSARDecompositions.cxx
new file mode 100644
index 0000000000000000000000000000000000000000..a159356f4a076b53fb2923877b050aab9ff11276
--- /dev/null
+++ b/Modules/Applications/AppSARDecompositions/app/otbSARDecompositions.cxx
@@ -0,0 +1,189 @@
+/*=========================================================================
+
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+ =========================================================================*/
+#include "otbWrapperApplication.h"
+#include "otbWrapperApplicationFactory.h"
+
+
+#include "otbReciprocalHAlphaImageFilter.h"
+#include "otbSinclairReciprocalImageFilter.h"
+#include "otbSinclairToReciprocalCoherencyMatrixFunctor.h"
+#include "otbPerBandVectorImageFilter.h"
+#include "itkMeanImageFilter.h"
+
+
+namespace otb
+{
+namespace Wrapper
+{
+
+class SARDecompositions : public Application
+{
+public:
+ /** Standard class typedefs. */
+ typedef SARDecompositions Self;
+ typedef Application Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+
+
+
+ typedef otb::Functor::SinclairToReciprocalCoherencyMatrixFunctor<ComplexDoubleImageType::PixelType,
+ ComplexDoubleImageType::PixelType,
+ ComplexDoubleImageType::PixelType,
+ ComplexDoubleVectorImageType::PixelType> FunctorType;
+
+
+ typedef SinclairReciprocalImageFilter<ComplexDoubleImageType,
+ ComplexDoubleImageType,
+ ComplexDoubleImageType,
+ ComplexDoubleVectorImageType,
+ FunctorType > SRFilterType;
+
+
+ typedef otb::ReciprocalHAlphaImageFilter<ComplexDoubleVectorImageType, DoubleVectorImageType> HAFilterType;
+
+
+ typedef itk::MeanImageFilter<ComplexDoubleImageType, ComplexDoubleImageType> MeanFilterType;
+ typedef otb::PerBandVectorImageFilter<ComplexDoubleVectorImageType, ComplexDoubleVectorImageType, MeanFilterType> PerBandMeanFilterType;
+ //FloatImageType
+
+ /** Standard macro */
+ itkNewMacro(Self);
+
+ itkTypeMacro(SARDecompositions, otb::Application);
+
+private:
+ void DoInit()
+ {
+ SetName("SARDecompositions");
+ SetDescription("From one-band complex images (each one related to an element of the Sinclair matrix), returns the selected decomposition.");
+
+ // Documentation
+ SetDocName("SARDecompositions");
+ SetDocLongDescription("From one-band complex images (HH, HV, VH, VV), returns the selected decomposition.\n \n"
+ "The H-alpha-A decomposition is currently the only one available; it is implemented for the monostatic case (transmitter and receiver are co-located).\n"
+ "User must provide three one-band complex images HH, HV or VH, and VV (monostatic case <=> HV = VH).\n"
+ "The H-alpha-A decomposition consists in averaging 3x3 complex coherency matrices (incoherent analysis); the user must provide the size of the averaging window, thanks to the parameter inco.kernelsize.\n "
+ "The applications returns a float vector image, made up of three channels : H (entropy), Alpha, A (Anisotropy)." );
+
+
+
+ SetDocLimitations("None");
+ SetDocAuthors("OTB-Team");
+ SetDocSeeAlso("SARPolarMatrixConvert, SARPolarSynth");
+
+ AddDocTag(Tags::SAR);
+
+ AddParameter(ParameterType_ComplexInputImage, "inhh", "Input Image");
+ SetParameterDescription("inhh", "Input image (HH)");
+
+ AddParameter(ParameterType_ComplexInputImage, "inhv", "Input Image");
+ SetParameterDescription("inhv", "Input image (HV)");
+ MandatoryOff("inhv");
+
+ AddParameter(ParameterType_ComplexInputImage, "invh", "Input Image");
+ SetParameterDescription("invh", "Input image (VH)");
+ MandatoryOff("invh");
+
+ AddParameter(ParameterType_ComplexInputImage, "invv", "Input Image");
+ SetParameterDescription("invv", "Input image (VV)");
+
+ AddParameter(ParameterType_OutputImage, "out", "Output Image");
+ SetParameterDescription("out", "Output image");
+
+ AddParameter(ParameterType_Choice, "decomp", "Decompositions");
+ AddChoice("decomp.haa","H-alpha-A decomposition");
+ SetParameterDescription("decomp.haa","H-alpha-A decomposition");
+
+ AddParameter(ParameterType_Group,"inco","Incoherent decompositions");
+ SetParameterDescription("inco","This group allows to set parameters related to the incoherent decompositions.");
+
+ AddParameter(ParameterType_Int, "inco.kernelsize", "Kernel size for spatial incoherent averaging.");
+ SetParameterDescription("inco.kernelsize", "Minute (0-59)");
+ SetMinimumParameterIntValue("inco.kernelsize", 1);
+ SetDefaultParameterInt("inco.kernelsize", 3);
+ MandatoryOff("inco.kernelsize");
+
+ AddRAMParameter();
+
+ // Default values
+ SetDefaultParameterInt("decomp", 0); // H-alpha-A
+
+ // Doc example parameter settings
+ SetDocExampleParameterValue("inhh", "HH.tif");
+ SetDocExampleParameterValue("invh", "VH.tif");
+ SetDocExampleParameterValue("invv", "VV.tif");
+ SetDocExampleParameterValue("decomp", "haa");
+ SetDocExampleParameterValue("out", "HaA.tif");
+ }
+
+ void DoUpdateParameters()
+ {
+ // Nothing to do here : all parameters are independent
+ }
+
+ void DoExecute()
+ {
+
+ bool inhv = HasUserValue("inhv");
+ bool invh = HasUserValue("invh");
+
+ if ( (!inhv) && (!invh) )
+ otbAppLogFATAL( << "Parameter inhv or invh not set. Please provide a HV or a VH complex image.");
+
+ switch (GetParameterInt("decomp"))
+ {
+ case 0: // H-alpha-A
+
+ m_SRFilter = SRFilterType::New();
+ m_HAFilter = HAFilterType::New();
+ m_MeanFilter = PerBandMeanFilterType::New();
+
+ if (inhv)
+ m_SRFilter->SetInputHV_VH(GetParameterComplexDoubleImage("inhv"));
+ else if (invh)
+ m_SRFilter->SetInputHV_VH(GetParameterComplexDoubleImage("invh"));
+
+ m_SRFilter->SetInputHH(GetParameterComplexDoubleImage("inhh"));
+ m_SRFilter->SetInputVV(GetParameterComplexDoubleImage("invv"));
+
+ MeanFilterType::InputSizeType radius;
+ radius.Fill( GetParameterInt("inco.kernelsize") );
+ m_MeanFilter->GetFilter()->SetRadius(radius);
+
+
+ m_MeanFilter->SetInput(m_SRFilter->GetOutput());
+ m_HAFilter->SetInput(m_MeanFilter->GetOutput());
+ SetParameterOutputImage("out", m_HAFilter->GetOutput() );
+
+ break;
+ }
+
+ }
+
+ //MCPSFilterType::Pointer m_MCPSFilter;
+ SRFilterType::Pointer m_SRFilter;
+ HAFilterType::Pointer m_HAFilter;
+ PerBandMeanFilterType::Pointer m_MeanFilter;
+
+};
+
+} //end namespace Wrapper
+} //end namespace otb
+
+OTB_APPLICATION_EXPORT(otb::Wrapper::SARDecompositions)
diff --git a/Modules/Applications/AppSARDecompositions/otb-module.cmake b/Modules/Applications/AppSARDecompositions/otb-module.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..71303c803d5d707c9918ec8b578e89a204cc53b6
--- /dev/null
+++ b/Modules/Applications/AppSARDecompositions/otb-module.cmake
@@ -0,0 +1,17 @@
+set(DOCUMENTATION "Basic filters application.")
+
+otb_module(OTBAppSARDecompositions
+ DEPENDS
+ OTBPolarimetry
+ OTBImageManipulation
+ OTBITK
+ OTBApplicationEngine
+ OTBImageBase
+
+ TEST_DEPENDS
+ OTBTestKernel
+ OTBCommandLine
+
+ DESCRIPTION
+ "${DOCUMENTATION}"
+)
diff --git a/Modules/Applications/AppSARDecompositions/test/CMakeLists.txt b/Modules/Applications/AppSARDecompositions/test/CMakeLists.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b99fa48fe8d6b2fea3afa6ab5a63b21228496094
--- /dev/null
+++ b/Modules/Applications/AppSARDecompositions/test/CMakeLists.txt
@@ -0,0 +1,14 @@
+otb_module_test()
+#----------- SARDecompositions TESTS ----------------
+
+otb_test_application(NAME apTvSARDecompositions
+ APP SARDecompositions
+ OPTIONS
+ -inhh ${INPUTDATA}/RSAT_imageryC_HH.tif
+ -inhv ${INPUTDATA}/RSAT_imageryC_HV.tif
+ -invv ${INPUTDATA}/RSAT_imageryC_VV.tif
+ -decomp haa
+ -out ${TEMP}/apTvReciprocalHAlpha.tif
+
+ )
+
diff --git a/Modules/Applications/AppSARPolarMatrixConvert/CMakeLists.txt b/Modules/Applications/AppSARPolarMatrixConvert/CMakeLists.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b20d3ebc6c81733b0ce1f5738dca23a6ff522139
--- /dev/null
+++ b/Modules/Applications/AppSARPolarMatrixConvert/CMakeLists.txt
@@ -0,0 +1,2 @@
+project(OTBAppSARPolarMatrixConvert)
+otb_module_impl()
diff --git a/Modules/Applications/AppSARPolarMatrixConvert/app/CMakeLists.txt b/Modules/Applications/AppSARPolarMatrixConvert/app/CMakeLists.txt
new file mode 100644
index 0000000000000000000000000000000000000000..81426caca5d49ecef97df614ebc0214cd79bd008
--- /dev/null
+++ b/Modules/Applications/AppSARPolarMatrixConvert/app/CMakeLists.txt
@@ -0,0 +1,12 @@
+set(OTBAppFiltering_LINK_LIBS
+ ${OTBPolarimetry_LIBRARIES}
+ ${OTBImageManipulation_LIBRARIES}
+ ${OTBApplicationEngine_LIBRARIES}
+ ${OTBImageBase_LIBRARIES}
+)
+
+otb_create_application(
+ NAME SARPolarMatrixConvert
+ SOURCES otbSARPolarMatrixConvert.cxx
+ LINK_LIBRARIES ${${otb-module}_LIBRARIES})
+
diff --git a/Modules/Applications/AppSARPolarMatrixConvert/app/otbSARPolarMatrixConvert.cxx b/Modules/Applications/AppSARPolarMatrixConvert/app/otbSARPolarMatrixConvert.cxx
new file mode 100644
index 0000000000000000000000000000000000000000..c670aabb0e2c2273daf5749368cd419142079b81
--- /dev/null
+++ b/Modules/Applications/AppSARPolarMatrixConvert/app/otbSARPolarMatrixConvert.cxx
@@ -0,0 +1,709 @@
+/*=========================================================================
+
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+ =========================================================================*/
+#include "otbWrapperApplication.h"
+#include "otbWrapperApplicationFactory.h"
+
+
+
+
+
+//monostatic case
+#include "otbSinclairReciprocalImageFilter.h"
+#include "otbSinclairToReciprocalCoherencyMatrixFunctor.h"
+#include "otbSinclairToReciprocalCovarianceMatrixFunctor.h"
+#include "otbSinclairToReciprocalCircularCovarianceMatrixFunctor.h"
+
+#include "otbReciprocalCoherencyToReciprocalMuellerImageFilter.h"
+#include "otbReciprocalCovarianceToCoherencyDegreeImageFilter.h"
+#include "otbReciprocalCovarianceToReciprocalCoherencyImageFilter.h"
+#include "otbReciprocalLinearCovarianceToReciprocalCircularCovarianceImageFilter.h"
+
+
+//bistatic case
+#include "otbSinclairImageFilter.h"
+#include "otbSinclairToCoherencyMatrixFunctor.h"
+#include "otbSinclairToCovarianceMatrixFunctor.h"
+#include "otbSinclairToCircularCovarianceMatrixFunctor.h"
+#include "otbSinclairToMuellerMatrixFunctor.h"
+
+#include "otbMuellerToReciprocalCovarianceImageFilter.h"
+#include "otbMuellerToPolarisationDegreeAndPowerImageFilter.h"
+
+
+
+
+
+
+namespace otb
+{
+namespace Wrapper
+{
+
+class SARPolarMatrixConvert : public Application
+{
+public:
+ /** Standard class typedefs. */
+ typedef SARPolarMatrixConvert Self;
+ typedef Application Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+
+
+ //Monostatic case
+ typedef otb::Functor::SinclairToReciprocalCoherencyMatrixFunctor<ComplexDoubleImageType::PixelType,
+ ComplexDoubleImageType::PixelType,
+ ComplexDoubleImageType::PixelType,
+ ComplexDoubleVectorImageType::PixelType> RCoherencyFunctorType;
+
+ typedef otb::Functor::SinclairToReciprocalCovarianceMatrixFunctor<ComplexDoubleImageType::PixelType,
+ ComplexDoubleImageType::PixelType,
+ ComplexDoubleImageType::PixelType,
+ ComplexDoubleVectorImageType::PixelType> RCovarianceFunctorType;
+
+ typedef otb::Functor::SinclairToReciprocalCircularCovarianceMatrixFunctor<ComplexDoubleImageType::PixelType,
+ ComplexDoubleImageType::PixelType,
+ ComplexDoubleImageType::PixelType,
+ ComplexDoubleVectorImageType::PixelType> RCircularCovarianceFunctorType;
+
+
+ typedef SinclairReciprocalImageFilter<ComplexDoubleImageType,
+ ComplexDoubleImageType,
+ ComplexDoubleImageType,
+ ComplexDoubleVectorImageType,
+ RCoherencyFunctorType > RCohSRFilterType;
+
+
+ typedef SinclairReciprocalImageFilter<ComplexDoubleImageType,
+ ComplexDoubleImageType,
+ ComplexDoubleImageType,
+ ComplexDoubleVectorImageType,
+ RCovarianceFunctorType > RCovSRFilterType;
+
+ typedef SinclairReciprocalImageFilter<ComplexDoubleImageType,
+ ComplexDoubleImageType,
+ ComplexDoubleImageType,
+ ComplexDoubleVectorImageType,
+ RCircularCovarianceFunctorType > RCCSRFilterType;
+
+
+ typedef otb::ReciprocalCoherencyToReciprocalMuellerImageFilter<ComplexDoubleVectorImageType, DoubleVectorImageType> RCRMFilterType;
+
+ typedef otb::ReciprocalCovarianceToCoherencyDegreeImageFilter<ComplexDoubleVectorImageType, ComplexDoubleVectorImageType> RCCDFilterType;
+
+ typedef otb::ReciprocalCovarianceToReciprocalCoherencyImageFilter<ComplexDoubleVectorImageType, ComplexDoubleVectorImageType> RCRCFilterType;
+
+ typedef otb::ReciprocalLinearCovarianceToReciprocalCircularCovarianceImageFilter<ComplexDoubleVectorImageType, ComplexDoubleVectorImageType> RLCRCCFilterType;
+
+
+ //Bistatic case
+ typedef otb::Functor::SinclairToCoherencyMatrixFunctor<ComplexDoubleImageType::PixelType,
+ ComplexDoubleImageType::PixelType,
+ ComplexDoubleImageType::PixelType,
+ ComplexDoubleImageType::PixelType,
+ ComplexDoubleVectorImageType::PixelType> CoherencyFunctorType;
+
+
+ typedef otb::Functor::SinclairToCovarianceMatrixFunctor<ComplexDoubleImageType::PixelType,
+ ComplexDoubleImageType::PixelType,
+ ComplexDoubleImageType::PixelType,
+ ComplexDoubleImageType::PixelType,
+ ComplexDoubleVectorImageType::PixelType> CovarianceFunctorType;
+
+
+ typedef otb::Functor::SinclairToCircularCovarianceMatrixFunctor<ComplexDoubleImageType::PixelType,
+ ComplexDoubleImageType::PixelType,
+ ComplexDoubleImageType::PixelType,
+ ComplexDoubleImageType::PixelType,
+ ComplexDoubleVectorImageType::PixelType> CircularCovarianceFunctorType;
+
+
+ typedef otb::Functor::SinclairToMuellerMatrixFunctor<ComplexDoubleImageType::PixelType,
+ ComplexDoubleImageType::PixelType,
+ ComplexDoubleImageType::PixelType,
+ ComplexDoubleImageType::PixelType,
+ DoubleVectorImageType::PixelType> MuellerFunctorType;
+
+
+ typedef SinclairImageFilter<ComplexDoubleImageType,
+ ComplexDoubleImageType,
+ ComplexDoubleImageType,
+ ComplexDoubleImageType,
+ ComplexDoubleVectorImageType,
+ CoherencyFunctorType > CohSRFilterType;
+
+
+ typedef SinclairImageFilter<ComplexDoubleImageType,
+ ComplexDoubleImageType,
+ ComplexDoubleImageType,
+ ComplexDoubleImageType,
+ ComplexDoubleVectorImageType,
+ CovarianceFunctorType > CovSRFilterType;
+
+ typedef SinclairImageFilter<ComplexDoubleImageType,
+ ComplexDoubleImageType,
+ ComplexDoubleImageType,
+ ComplexDoubleImageType,
+ ComplexDoubleVectorImageType,
+ CircularCovarianceFunctorType > CCSRFilterType;
+
+ typedef SinclairImageFilter<ComplexDoubleImageType,
+ ComplexDoubleImageType,
+ ComplexDoubleImageType,
+ ComplexDoubleImageType,
+ DoubleVectorImageType,
+ MuellerFunctorType > MSRFilterType;
+
+
+ typedef otb::MuellerToReciprocalCovarianceImageFilter<DoubleVectorImageType, ComplexDoubleVectorImageType> MRCFilterType;
+
+ typedef otb::MuellerToPolarisationDegreeAndPowerImageFilter<DoubleVectorImageType, DoubleVectorImageType> MPDPFilterType;
+
+
+
+ /** Standard macro */
+ itkNewMacro(Self);
+
+ itkTypeMacro(SARPolarMatrixConvert, otb::Application);
+
+private:
+ void DoInit()
+ {
+ SetName("SARPolarMatrixConvert");
+ SetDescription("This applications allows converting classical polarimetric matrices to each other.");
+
+ // Documentation
+ SetDocName("SARPolarMatrixConvert");
+ SetDocLongDescription(
+
+ "This application allows converting classical polarimetric matrices to each other.\n"
+ "For instance, it is possible to get the coherency matrix from the Sinclar one, or the Mueller matrix from the coherency one.\n"
+ "The filters used in this application never handle matrices, but images where each band is related to their elements.\n"
+ "As most of the time SAR polarimetry handles symetric/hermitian matrices, only the relevant elements are stored, so that the images representing them have a minimal number of bands.\n"
+ "For instance, the coherency matrix size is 3x3 in the monostatic case, and 4x4 in the bistatic case : it will thus be stored in a 6-band or a 10-band complex image (the diagonal and the upper elements of the matrix).\n"
+ "\n"
+ "The Sinclair matrix is a special case : it is always represented as 3 or 4 one-band complex images (for mono- or bistatic case).\n"
+ "The available conversions are listed below:\n"
+
+ "\n--- Monostatic case ---\n"
+
+ "1 msinclairtocoherency --> Sinclair matrix to coherency matrix (input : 3 x 1 complex channel (HH, HV or VH, VV) | output : 6 complex channels)\n"
+ "2 msinclairtocovariance --> Sinclair matrix to covariance matrix (input : 3 x 1 complex channel (HH, HV or VH, VV) | output : 6 complex channels)\n"
+ "3 msinclairtocircovariance --> Sinclair matrix to circular covariance matrix (input : 3 x 1 complex channel (HH, HV or VH, VV) | output : 6 complex channels)\n"
+ "4 mcoherencytomueller --> Coherency matrix to Mueller matrix (input : 6 complex channels | 16 real channels)\n"
+ "5 mcovariancetocoherencydegree --> Covariance matrix to coherency degree (input : 6 complex channels | 3 complex channels)\n"
+ "6 mcovariancetocoherency --> Covariance matrix to coherency matrix (input : 6 complex channels | 6 complex channels)\n"
+ "7 mlinearcovariancetocircularcovariance --> Covariance matrix to circular covariance matrix (input : 6 complex channels | output : 6 complex channels)\n"
+
+ "\n--- Bistatic case ---\n"
+
+ "8 bsinclairtocoherency --> Sinclair matrix to coherency matrix (input : 4 x 1 complex channel (HH, HV, VH, VV) | 10 complex channels)\n"
+ "9 bsinclairtocovariance --> Sinclair matrix to covariance matrix (input : 4 x 1 complex channel (HH, HV, VH, VV) | output : 10 complex channels)\n"
+ "10 bsinclairtocircovariance --> Sinclair matrix to circular covariance matrix (input : 4 x 1 complex channel (HH, HV, VH, VV) | output : 10 complex channels)\n"
+
+ "\n--- Both cases ---\n"
+
+ "11 sinclairtomueller --> Sinclair matrix to Mueller matrix (input : 4 x 1 complex channel (HH, HV, VH, VV) | output : 16 real channels)\n"
+ "12 muellertomcovariance --> Mueller matrix to covariance matrix (input : 16 real channels | output : 6 complex channels)\n"
+ "13 muellertopoldegandpower --> Mueller matrix to polarization degree and power (input : 16 real channels | output : 4 real channels)\n"
+
+ );
+
+ SetDocLimitations("None");
+ SetDocAuthors("OTB-Team");
+ SetDocSeeAlso("SARPolarSynth, SARDecompositions");
+
+ AddDocTag(Tags::SAR);
+
+ AddParameter(ParameterType_ComplexInputImage, "inc", "Input : multi-band complex image");
+ SetParameterDescription("inc", "Input : multi-band complex image");
+ MandatoryOff("inc");
+
+ AddParameter(ParameterType_InputImage, "inf", "Input : multi-band real image");
+ SetParameterDescription("inf", "Input : multi-band real image");
+ MandatoryOff("inf");
+
+
+ AddParameter(ParameterType_ComplexInputImage, "inhh", "Input : one-band complex image (HH)");
+ SetParameterDescription("inhh", "Input : one-band complex image (HH)");
+ MandatoryOff("inhh");
+
+ AddParameter(ParameterType_ComplexInputImage, "inhv", "Input : one-band complex image (HV)");
+ SetParameterDescription("inhv", "Input : one-band complex image (HV)");
+ MandatoryOff("inhv");
+
+ AddParameter(ParameterType_ComplexInputImage, "invh", "Input : one-band complex image (VH)");
+ SetParameterDescription("invh", "Input : one-band complex image (VH)");
+ MandatoryOff("invh");
+
+ AddParameter(ParameterType_ComplexInputImage, "invv", "Input : one-band complex image (VV)");
+ SetParameterDescription("invv", "Input : one-band complex image (VV)");
+ MandatoryOff("invv");
+
+ AddParameter(ParameterType_ComplexOutputImage, "outc", "Output Complex Image");
+ SetParameterDescription("outc", "Output Complex image.");
+ MandatoryOff("outc");
+
+ AddParameter(ParameterType_OutputImage, "outf", "Output Real Image");
+ SetParameterDescription("outf", "Output Real image.");
+ MandatoryOff("outf");
+
+
+
+ AddParameter(ParameterType_Choice, "conv", "Convertion");
+
+ //Monostatic case
+
+ // #1
+ // SinclairToReciprocalCoherency
+ AddChoice("conv.msinclairtocoherency","1 Monostatic : Sinclair matrix to coherency matrix (complex output)");
+ SetParameterDescription("conv.msinclairtocoherency","1 Monostatic :Sinclair matrix to coherency matrix (complex output)");
+
+ // #2
+ // SinclairToReciprocalCovariance
+ AddChoice("conv.msinclairtocovariance","2 Monostatic : Sinclair matrix to covariance matrix (complex output)");
+ SetParameterDescription("conv.msinclairtocovariance","2 Monostatic : Sinclair matrix to covariance matrix (complex output)");
+
+ // #3
+ // SinclairToReciprocalCircularCovariance
+ AddChoice("conv.msinclairtocircovariance","3 Monostatic : Sinclair matrix to circular covariance matrix (complex output)");
+ SetParameterDescription("conv.msinclairtocircovariance","3 Monostatic : Sinclair matrix to circular covariance matrix (complex output)");
+
+ // #4
+ // ReciprocalCoherencyToReciprocalMuellerImageFilter
+ AddChoice("conv.mcoherencytomueller","4 Monostatic : Coherency matrix to Mueller matrix");
+ SetParameterDescription("conv.mcoherencytomueller","4 Monostatic : Coherency matrix to Mueller matrix");
+
+ // #5
+ // ReciprocalCovarianceToCoherencyDegreeImageFilter
+ AddChoice("conv.mcovariancetocoherencydegree","5 Monostatic : Covariance matrix to coherency degree ");
+ SetParameterDescription("conv.mcovariancetocoherencydegree","5 Monostatic : Covariance matrix to coherency degree ");
+
+ // #6
+ // ReciprocalCovarianceToReciprocalCoherencyImageFilter
+ AddChoice("conv.mcovariancetocoherency","6 Monostatic : Covariance matrix to coherency matrix (complex output)");
+ SetParameterDescription("conv.mcovariancetocoherency","6 Monostatic : Covariance matrix to coherency matrix (complex output)");
+
+ // #7
+ // ReciprocalLinearCovarianceToReciprocalCircularCovarianceImageFilter
+ AddChoice("conv.mlinearcovariancetocircularcovariance","7 Monostatic : Covariance matrix to circular covariance matrix (complex output)");
+ SetParameterDescription("conv.mlinearcovariancetocircularcovariance","7 Monostatic : Covariance matrix to circular covariance matrix (complex output)");
+
+ // #8
+ // MuellerToReciprocalCovarianceImageFilter
+ AddChoice("conv.muellertomcovariance","8 Bi/mono : Mueller matrix to monostatic covariance matrix");
+ SetParameterDescription("conv.muellertomcovariance","8 Bi/mono : Mueller matrix to monostatic covariance matrix");
+
+ //Bistatic case
+
+ // #9
+ // SinclairToCoherency
+ AddChoice("conv.bsinclairtocoherency","9 Bistatic : Sinclair matrix to coherency matrix (complex output)");
+ SetParameterDescription("conv.bsinclairtocoherency","9 Bistatic : Sinclair matrix to coherency matrix (complex output)");
+
+ // #10
+ // SinclairToCovariance
+ AddChoice("conv.bsinclairtocovariance","10 Bistatic : Sinclair matrix to covariance matrix (complex output)");
+ SetParameterDescription("conv.bsinclairtocovariance","10 Bistatic : Sinclair matrix to covariance matrix (complex output)");
+
+ // #11
+ // SinclairToCircularCovariance
+ AddChoice("conv.bsinclairtocircovariance","11 Bistatic : Sinclair matrix to circular covariance matrix (complex output)");
+ SetParameterDescription("conv.bsinclairtocircovariance","11 Bistatic : Sinclair matrix to circular covariance matrix (complex output)");
+
+ //Both case
+
+ // #12
+ // SinclairToMueller
+ AddChoice("conv.sinclairtomueller","12 Bi/mono : Sinclair matrix to Mueller matrix");
+ SetParameterDescription("conv.sinclairtomueller","12 Bi/mono : Sinclair matrix to Mueller matrix");
+
+
+ // #13
+ // MuellerToPolarisationDegreeAndPowerImageFilter
+ AddChoice("conv.muellertopoldegandpower","13 Bi/mono : Mueller matrix to polarisation degree and power");
+ SetParameterDescription("conv.muellertopoldegandpower","13 Bi/mono : Mueller matrix to polarisation degree and power");
+
+ AddRAMParameter();
+
+ // Default values
+ SetDefaultParameterInt("conv", 0); // SinclairToReciprocalCoherency
+
+ // Doc example parameter settings
+ SetDocExampleParameterValue("inhh", "HH.tif");
+ SetDocExampleParameterValue("invh", "VH.tif");
+ SetDocExampleParameterValue("invv", "VV.tif");
+ SetDocExampleParameterValue("conv", "msinclairtocoherency");
+ SetDocExampleParameterValue("outc", "mcoherency.tif");
+ }
+
+ void DoUpdateParameters()
+ {
+
+ int convType = GetParameterInt("conv");
+
+ if ( (convType>=0) && (convType<=2)) //msinclairtocoherency msinclairtocovariance msinclairtocircovariance
+ {
+ GetParameterByKey("inc")->SetActive(false);
+ GetParameterByKey("inf")->SetActive(false);
+ GetParameterByKey("inhh")->SetActive(true);
+ GetParameterByKey("inhv")->SetActive(true);
+ GetParameterByKey("invh")->SetActive(true);
+ GetParameterByKey("invv")->SetActive(true);
+ GetParameterByKey("outc")->SetActive(true);
+ GetParameterByKey("outf")->SetActive(false);
+ }
+ else if ( (convType>=3) && (convType<=6)) // mcoherencytomueller mcovariancetocoherencydegree mcovariancetocoherency mlinearcovariancetocircularcovariance
+ {
+ GetParameterByKey("inc")->SetActive(true);
+ GetParameterByKey("inf")->SetActive(false);
+ GetParameterByKey("inhh")->SetActive(false);
+ GetParameterByKey("inhv")->SetActive(false);
+ GetParameterByKey("invh")->SetActive(false);
+ GetParameterByKey("invv")->SetActive(false);
+
+ if (convType == 3)
+ {
+ GetParameterByKey("outc")->SetActive(false);
+ GetParameterByKey("outf")->SetActive(true);
+ }
+ else
+ {
+ GetParameterByKey("outc")->SetActive(true);
+ GetParameterByKey("outf")->SetActive(false);
+ }
+ }
+ else if ( convType==7) // muellertomcovariance
+ {
+ GetParameterByKey("inc")->SetActive(false);
+ GetParameterByKey("inf")->SetActive(true);
+ GetParameterByKey("inhh")->SetActive(false);
+ GetParameterByKey("inhv")->SetActive(false);
+ GetParameterByKey("invh")->SetActive(false);
+ GetParameterByKey("invv")->SetActive(false);
+ GetParameterByKey("outc")->SetActive(true);
+ GetParameterByKey("outf")->SetActive(false);
+ }
+ else if ( (convType>=8) && (convType<=11)) // bsinclairtocoherency bsinclairtocovariance bsinclairtocircovariance sinclairtomueller
+ {
+ GetParameterByKey("inc")->SetActive(false);
+ GetParameterByKey("inf")->SetActive(false);
+ GetParameterByKey("inhh")->SetActive(true);
+ GetParameterByKey("inhv")->SetActive(true);
+ GetParameterByKey("invh")->SetActive(true);
+ GetParameterByKey("invv")->SetActive(true);
+
+ if (convType == 11)
+ {
+ GetParameterByKey("outc")->SetActive(false);
+ GetParameterByKey("outf")->SetActive(true);
+ }
+ else
+ {
+ GetParameterByKey("outc")->SetActive(true);
+ GetParameterByKey("outf")->SetActive(false);
+ }
+
+ }
+ else if ( convType==12 ) // muellertopoldegandpower
+ {
+ GetParameterByKey("inc")->SetActive(false);
+ GetParameterByKey("inf")->SetActive(true);
+ GetParameterByKey("inhh")->SetActive(false);
+ GetParameterByKey("inhv")->SetActive(false);
+ GetParameterByKey("invh")->SetActive(false);
+ GetParameterByKey("invv")->SetActive(false);
+ GetParameterByKey("outc")->SetActive(false);
+ GetParameterByKey("outf")->SetActive(true);
+ }
+
+
+
+ }
+
+ void DoExecute()
+ {
+
+ //****************************************
+ //* Check inputs and outputs consistency *
+ //****************************************
+
+ bool inc = HasUserValue("inc");
+ bool inf = HasUserValue("inf");
+ bool inhh = HasUserValue("inhh");
+ bool inhv = HasUserValue("inhv");
+ bool invh = HasUserValue("invh");
+ bool invv = HasUserValue("invv");
+ bool outc = HasUserValue("outc");
+ bool outf = HasUserValue("outf");
+
+ int convType = GetParameterInt("conv");
+
+
+ if ( (!outc) && (!outf) )
+ otbAppLogFATAL( << "No output image provided; please, set the parameter 'outc' or 'outf'.");
+
+
+ if ( (convType>=0) && (convType<=2)) //msinclairtocoherency msinclairtocovariance msinclairtocircovariance
+ {
+ if ( (!inhv) && (!invh) )
+ otbAppLogFATAL( << "Parameter 'inhv' or 'invh' not set.");
+ if ( !inhh )
+ otbAppLogFATAL( << "Parameter 'inhh' not set.");
+ if ( !invv )
+ otbAppLogFATAL( << "Parameter 'invv' not set.");
+
+ }
+
+ else if ( (convType>=3) && (convType<=6)) // mcoherencytomueller mcovariancetocoherencydegree mcovariancetocoherency mlinearcovariancetocircularcovariance
+ {
+ if ( !inc )
+ otbAppLogFATAL( << "Parameter 'inc' not set.");
+ }
+ else if ( (convType>=8) && (convType<=11)) // bsinclairtocoherency bsinclairtocovariance bsinclairtocircovariance sinclairtomueller
+ {
+ if ( (!inhh) || (!inhv) || (!invh) || (!invv) )
+ otbAppLogFATAL( << "Please, ensure that HH, HV, VH and VV complex images have been provided (paramaters inhh, inhv, invh, invv).");
+ }
+ else if ( (convType==7) || (convType==12) ) // muellertomcovariance muellertopoldegandpower
+ {
+ if ( !inf )
+ otbAppLogFATAL( << "Parameter 'inf' not set.");
+ }
+
+
+ switch (GetParameterInt("conv"))
+ {
+
+ //***************************************
+ //* MONOSTATIC *
+ //***************************************
+
+ case 0: // SinclairToReciprocalCoherency
+ m_RCohSRFilter = RCohSRFilterType::New();
+
+ if (inhv)
+ m_RCohSRFilter->SetInputHV_VH(GetParameterComplexDoubleImage("inhv"));
+ else if (invh)
+ m_RCohSRFilter->SetInputHV_VH(GetParameterComplexDoubleImage("invh"));
+
+ m_RCohSRFilter->SetInputHH(GetParameterComplexDoubleImage("inhh"));
+ m_RCohSRFilter->SetInputVV(GetParameterComplexDoubleImage("invv"));
+
+ SetParameterComplexOutputImage("outc", m_RCohSRFilter->GetOutput() ); // input : 3 x 1 complex channel | output : 6 complex channels
+
+ break;
+
+
+ case 1: // SinclairToReciprocalCovariance
+
+ m_RCovSRFilter = RCovSRFilterType::New();
+
+ if (inhv)
+ m_RCovSRFilter->SetInputHV_VH(GetParameterComplexDoubleImage("inhv"));
+ else if (invh)
+ m_RCovSRFilter->SetInputHV_VH(GetParameterComplexDoubleImage("invh"));
+
+ m_RCovSRFilter->SetInputHH(GetParameterComplexDoubleImage("inhh"));
+ m_RCovSRFilter->SetInputVV(GetParameterComplexDoubleImage("invv"));
+
+ SetParameterComplexOutputImage("outc", m_RCovSRFilter->GetOutput() ); // input : 3 x 1 complex channel | output : 6 complex channels
+
+ break;
+
+
+
+ case 2: // SinclairToReciprocalCircularCovariance
+
+ m_RCCSRFilter = RCCSRFilterType::New();
+
+ if (inhv)
+ m_RCCSRFilter->SetInputHV_VH(GetParameterComplexDoubleImage("inhv"));
+ else if (invh)
+ m_RCCSRFilter->SetInputHV_VH(GetParameterComplexDoubleImage("invh"));
+
+ m_RCCSRFilter->SetInputHH(GetParameterComplexDoubleImage("inhh"));
+ m_RCCSRFilter->SetInputVV(GetParameterComplexDoubleImage("invv"));
+
+ SetParameterComplexOutputImage("outc", m_RCCSRFilter->GetOutput() ); // input : 3 x 1 complex channel | output : 6 complex channels
+
+ break;
+
+
+ case 3: // ReciprocalCoherencyToReciprocalMuellerImageFilter
+
+ m_RCRMFilter = RCRMFilterType::New();
+ m_RCRMFilter->SetInput(GetParameterComplexDoubleVectorImage("inc"));
+
+ SetParameterOutputImage("outf", m_RCRMFilter->GetOutput() ); // input : 6 complex channels | 16 real channels
+
+ break;
+
+
+
+ case 4: // ReciprocalCovarianceToCoherencyDegreeImageFilter
+
+ m_RCCDFilter = RCCDFilterType::New();
+ m_RCCDFilter->SetInput(GetParameterComplexDoubleVectorImage("inc"));
+
+ SetParameterComplexOutputImage("outc", m_RCCDFilter->GetOutput() ); // input : 6 complex channels | 3 complex channels
+
+ break;
+
+
+ case 5: // ReciprocalCovarianceToReciprocalCoherencyImageFilter
+
+ m_RCRCFilter = RCRCFilterType::New();
+ m_RCRCFilter->SetInput(GetParameterComplexDoubleVectorImage("inc"));
+
+ SetParameterComplexOutputImage("outc", m_RCRCFilter->GetOutput() ); // input : 6 complex channels | 6 complex channels
+
+ break;
+
+
+
+ case 6: // ReciprocalLinearCovarianceToReciprocalCircularCovarianceImageFilter
+
+ m_RLCRCCFilter = RLCRCCFilterType::New();
+ m_RLCRCCFilter->SetInput(GetParameterComplexDoubleVectorImage("inc"));
+
+ SetParameterComplexOutputImage("outc", m_RLCRCCFilter->GetOutput() ); // input : 6 complex channels | output : 6 complex channels
+
+ break;
+
+
+ case 7: // MuellerToReciprocalCovarianceImageFilter
+
+ m_MRCFilter = MRCFilterType::New();
+
+ m_MRCFilter->SetInput(GetParameterDoubleVectorImage("inf"));
+
+ SetParameterComplexOutputImage("outc", m_MRCFilter->GetOutput() ); // input : 16 real channels | output : 6 complex channels
+
+ break;
+
+
+ //***************************************
+ //* BISTATIC *
+ //***************************************
+
+ case 8: // SinclairToCoherency
+
+ m_CohSRFilter = CohSRFilterType::New();
+
+ m_CohSRFilter->SetInputHH(GetParameterComplexDoubleImage("inhh"));
+ m_CohSRFilter->SetInputHV(GetParameterComplexDoubleImage("inhv"));
+ m_CohSRFilter->SetInputVH(GetParameterComplexDoubleImage("invh"));
+ m_CohSRFilter->SetInputVV(GetParameterComplexDoubleImage("invv"));
+
+ SetParameterComplexOutputImage("outc", m_CohSRFilter->GetOutput() ); // input : 4 x 1 complex channel | 10 complex channels
+
+ break;
+
+
+
+ case 9: // SinclairToCovariance
+
+ m_CovSRFilter = CovSRFilterType::New();
+
+ m_CovSRFilter->SetInputHH(GetParameterComplexDoubleImage("inhh"));
+ m_CovSRFilter->SetInputHV(GetParameterComplexDoubleImage("inhv"));
+ m_CovSRFilter->SetInputVH(GetParameterComplexDoubleImage("invh"));
+ m_CovSRFilter->SetInputVV(GetParameterComplexDoubleImage("invv"));
+
+ SetParameterComplexOutputImage("outc", m_CovSRFilter->GetOutput() ); // input : 4 x 1 complex channel | output : 10 complex channels
+
+ break;
+
+
+ case 10: // SinclairToCircularCovariance
+
+ m_CCSRFilter = CCSRFilterType::New();
+
+ m_CCSRFilter->SetInputHH(GetParameterComplexDoubleImage("inhh"));
+ m_CCSRFilter->SetInputHV(GetParameterComplexDoubleImage("inhv"));
+ m_CCSRFilter->SetInputVH(GetParameterComplexDoubleImage("invh"));
+ m_CCSRFilter->SetInputVV(GetParameterComplexDoubleImage("invv"));
+
+ SetParameterComplexOutputImage("outc", m_CCSRFilter->GetOutput() ); // input : 4 x 1 complex channel | output : 10 complex channels
+
+ break;
+
+
+ //***************************************
+ //* BOTH CASES *
+ //***************************************
+
+
+ case 11: // SinclairToMueller
+ std::cout << "taloula 1" << std::endl;
+ m_MSRFilter = MSRFilterType::New();
+
+ m_MSRFilter->SetInputHH(GetParameterComplexDoubleImage("inhh"));
+ m_MSRFilter->SetInputHV(GetParameterComplexDoubleImage("inhv"));
+ m_MSRFilter->SetInputVH(GetParameterComplexDoubleImage("invh"));
+ m_MSRFilter->SetInputVV(GetParameterComplexDoubleImage("invv"));
+
+ SetParameterOutputImage("outf", m_MSRFilter->GetOutput() ); // input : 4 x 1 complex channel | output : 16 real channels
+
+ break;
+
+
+ case 12: // MuellerToPolarisationDegreeAndPowerImageFilter
+ m_MPDPFilter = MPDPFilterType::New();
+
+ m_MPDPFilter->SetInput(GetParameterDoubleVectorImage("inf"));
+
+ SetParameterOutputImage("outf", m_MPDPFilter->GetOutput() ); // input : 16 real channels | output : 4 real channels
+
+ break;
+
+ }
+
+
+
+ }
+
+ //Monostatic
+ RCohSRFilterType::Pointer m_RCohSRFilter;
+ RCovSRFilterType::Pointer m_RCovSRFilter;
+ RCCSRFilterType::Pointer m_RCCSRFilter;
+ RCRMFilterType::Pointer m_RCRMFilter;
+ RCCDFilterType::Pointer m_RCCDFilter;
+ RCRCFilterType::Pointer m_RCRCFilter;
+ RLCRCCFilterType::Pointer m_RLCRCCFilter;
+
+ //Bistatic
+ CohSRFilterType::Pointer m_CohSRFilter;
+ CovSRFilterType::Pointer m_CovSRFilter;
+ CCSRFilterType::Pointer m_CCSRFilter;
+ MSRFilterType::Pointer m_MSRFilter;
+
+ //Both cases
+ MRCFilterType::Pointer m_MRCFilter;
+ MPDPFilterType::Pointer m_MPDPFilter;
+
+
+
+
+
+};
+
+} //end namespace Wrapper
+} //end namespace otb
+
+OTB_APPLICATION_EXPORT(otb::Wrapper::SARPolarMatrixConvert)
diff --git a/Modules/Applications/AppSARPolarMatrixConvert/otb-module.cmake b/Modules/Applications/AppSARPolarMatrixConvert/otb-module.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..759161f8b45b6a0e22916b9838c78f29c36b75ba
--- /dev/null
+++ b/Modules/Applications/AppSARPolarMatrixConvert/otb-module.cmake
@@ -0,0 +1,17 @@
+set(DOCUMENTATION "Basic filters application.")
+
+otb_module(OTBAppSARPolarMatrixConvert
+ DEPENDS
+ OTBPolarimetry
+ OTBImageManipulation
+ OTBITK
+ OTBApplicationEngine
+ OTBImageBase
+
+ TEST_DEPENDS
+ OTBTestKernel
+ OTBCommandLine
+
+ DESCRIPTION
+ "${DOCUMENTATION}"
+)
diff --git a/Modules/Applications/AppSARPolarMatrixConvert/test/CMakeLists.txt b/Modules/Applications/AppSARPolarMatrixConvert/test/CMakeLists.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a7c518f53999ce2c19ca2c74db8ce4eea95d486b
--- /dev/null
+++ b/Modules/Applications/AppSARPolarMatrixConvert/test/CMakeLists.txt
@@ -0,0 +1,157 @@
+otb_module_test()
+#----------- SARPolarMatrixConvert TESTS ----------------
+
+#1
+otb_test_application(NAME apTvSARPolarMatrixConvertRecCoherency
+ APP SARPolarMatrixConvert
+ OPTIONS
+ -inhh ${INPUTDATA}/RSAT_imageryC_HH.tif
+ -inhv ${INPUTDATA}/RSAT_imageryC_HV.tif
+ -invv ${INPUTDATA}/RSAT_imageryC_VV.tif
+ -conv msinclairtocoherency
+ -outc ${TEMP}/apTvSARPolarMatrixConvertRecCoherency.tif
+
+ )
+
+#2
+otb_test_application(NAME apTvSARPolarMatrixConvertRecCovariance
+ APP SARPolarMatrixConvert
+ OPTIONS
+ -inhh ${INPUTDATA}/RSAT_imageryC_HH.tif
+ -inhv ${INPUTDATA}/RSAT_imageryC_HV.tif
+ -invv ${INPUTDATA}/RSAT_imageryC_VV.tif
+ -conv msinclairtocovariance
+ -outc ${TEMP}/apTvSARPolarMatrixConvertRecCovariance.tif
+
+ )
+
+#3
+otb_test_application(NAME apTvSARPolarMatrixConvertRecCirCovariance
+ APP SARPolarMatrixConvert
+ OPTIONS
+ -inhh ${INPUTDATA}/RSAT_imageryC_HH.tif
+ -inhv ${INPUTDATA}/RSAT_imageryC_HV.tif
+ -invv ${INPUTDATA}/RSAT_imageryC_VV.tif
+ -conv msinclairtocircovariance
+ -outc ${TEMP}/apTvSARPolarMatrixConvertRecCirCovariance.tif
+
+ )
+
+#4
+otb_test_application(NAME apTvSARPolarMatrixConvertRecCohToMueller
+ APP SARPolarMatrixConvert
+ OPTIONS
+ -inc ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCovariance.tif
+ -conv mcoherencytomueller
+ -outf ${TEMP}/apTvSARPolarMatrixConvertRecCohToMueller.tif
+
+ )
+
+
+#5
+otb_test_application(NAME apTvSARPolarMatrixConvertRecCovToCohDeg
+ APP SARPolarMatrixConvert
+ OPTIONS
+ -inc ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCovariance.tif
+ -conv mcovariancetocoherencydegree
+ -outc ${TEMP}/apTvSARPolarMatrixConvertRecCovToCohDeg.tif
+
+ )
+
+
+#6
+otb_test_application(NAME apTvSARPolarMatrixConvertRecCovToRecCoh
+ APP SARPolarMatrixConvert
+ OPTIONS
+ -inc ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCovariance.tif
+ -conv mcovariancetocoherency
+ -outc ${TEMP}/apTvSARPolarMatrixConvertRecCovToRecCoh.tif
+
+ )
+
+#7
+otb_test_application(NAME apTvSARPolarMatrixConvertRecLinCovToRecCirCov
+ APP SARPolarMatrixConvert
+ OPTIONS
+ -inc ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCovariance.tif
+ -conv mlinearcovariancetocircularcovariance
+ -outc ${TEMP}/apTvSARPolarMatrixConvertRecCovToRecCoh.tif
+
+ )
+
+
+#8
+otb_test_application(NAME apTvSARPolarMatrixConvertMuellerToRecCov
+ APP SARPolarMatrixConvert
+ OPTIONS
+ -inf ${BASELINE}/saTvSinclairImageFilter_SinclairToMueller.tif
+ -conv muellertomcovariance
+ -outc ${TEMP}/apTvSARPolarMatrixConvertMuellerToRecCov.tif
+
+ )
+
+
+#9
+otb_test_application(NAME apTvSARPolarMatrixConvertBiSincToCoherency
+ APP SARPolarMatrixConvert
+ OPTIONS
+ -inhh ${INPUTDATA}/RSAT_imageryC_HH.tif
+ -inhv ${INPUTDATA}/RSAT_imageryC_HV.tif
+ -invh ${INPUTDATA}/RSAT_imageryC_VH.tif
+ -invv ${INPUTDATA}/RSAT_imageryC_VV.tif
+ -conv bsinclairtocoherency
+ -outc ${TEMP}/apTvSARPolarMatrixConvertBiSincToCoherency.tif
+
+ )
+
+
+#10
+otb_test_application(NAME apTvSARPolarMatrixConvertBiSincToCovariance
+ APP SARPolarMatrixConvert
+ OPTIONS
+ -inhh ${INPUTDATA}/RSAT_imageryC_HH.tif
+ -inhv ${INPUTDATA}/RSAT_imageryC_HV.tif
+ -invh ${INPUTDATA}/RSAT_imageryC_VH.tif
+ -invv ${INPUTDATA}/RSAT_imageryC_VV.tif
+ -conv bsinclairtocovariance
+ -outc ${TEMP}/apTvSARPolarMatrixConvertBiSincToCovariance.tif
+
+ )
+
+#11
+otb_test_application(NAME apTvSARPolarMatrixConvertBiSincToCirCovariance
+ APP SARPolarMatrixConvert
+ OPTIONS
+ -inhh ${INPUTDATA}/RSAT_imageryC_HH.tif
+ -inhv ${INPUTDATA}/RSAT_imageryC_HV.tif
+ -invh ${INPUTDATA}/RSAT_imageryC_VH.tif
+ -invv ${INPUTDATA}/RSAT_imageryC_VV.tif
+ -conv bsinclairtocircovariance
+ -outc ${TEMP}/apTvSARPolarMatrixConvertBiSincToCirCovariance.tif
+
+ )
+
+
+#12
+otb_test_application(NAME apTvSARPolarMatrixConvertSincToMueller
+ APP SARPolarMatrixConvert
+ OPTIONS
+ -inhh ${INPUTDATA}/RSAT_imageryC_HH.tif
+ -inhv ${INPUTDATA}/RSAT_imageryC_HV.tif
+ -invh ${INPUTDATA}/RSAT_imageryC_HV.tif #monostatic
+ -invv ${INPUTDATA}/RSAT_imageryC_VV.tif
+ -conv sinclairtomueller
+ -outf ${TEMP}/apTvSARPolarMatrixConvertSincToMueller.tif
+
+ )
+
+
+#13
+otb_test_application(NAME apTvSARPolarMatrixConvertMuellerToPolDeGPow
+ APP SARPolarMatrixConvert
+ OPTIONS
+ -inf ${BASELINE}/saTvSinclairImageFilter_SinclairToMueller.tif
+ -conv muellertopoldegandpower
+ -outf ${TEMP}/apTvSARPolarMatrixConvertMuellerToPolDeGPow.tif
+
+ )
diff --git a/Modules/Applications/AppSARPolarSynth/CMakeLists.txt b/Modules/Applications/AppSARPolarSynth/CMakeLists.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f6c2324b5b9053c0a5f0f3818560e586c30ba62c
--- /dev/null
+++ b/Modules/Applications/AppSARPolarSynth/CMakeLists.txt
@@ -0,0 +1,2 @@
+project(OTBAppSARPolarSynth)
+otb_module_impl()
diff --git a/Modules/Applications/AppSARPolarSynth/app/CMakeLists.txt b/Modules/Applications/AppSARPolarSynth/app/CMakeLists.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ea87ee870ad0182cd2338fc42e915c81279f6495
--- /dev/null
+++ b/Modules/Applications/AppSARPolarSynth/app/CMakeLists.txt
@@ -0,0 +1,12 @@
+set(OTBAppFiltering_LINK_LIBS
+ ${OTBPolarimetry_LIBRARIES}
+ ${OTBImageManipulation_LIBRARIES}
+ ${OTBApplicationEngine_LIBRARIES}
+ ${OTBImageBase_LIBRARIES}
+)
+
+otb_create_application(
+ NAME SARPolarSynth
+ SOURCES otbSARPolarSynth.cxx
+ LINK_LIBRARIES ${${otb-module}_LIBRARIES})
+
diff --git a/Modules/Applications/AppSARPolarSynth/app/otbSARPolarSynth.cxx b/Modules/Applications/AppSARPolarSynth/app/otbSARPolarSynth.cxx
new file mode 100644
index 0000000000000000000000000000000000000000..4963306b34a459f87283d7bf17cc98b0ad4060d9
--- /dev/null
+++ b/Modules/Applications/AppSARPolarSynth/app/otbSARPolarSynth.cxx
@@ -0,0 +1,185 @@
+/*=========================================================================
+
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+ =========================================================================*/
+#include "otbWrapperApplication.h"
+#include "otbWrapperApplicationFactory.h"
+
+#include "otbMultiChannelsPolarimetricSynthesisFilter.h"
+
+namespace otb
+{
+namespace Wrapper
+{
+
+class SARPolarSynth : public Application
+{
+public:
+ /** Standard class typedefs. */
+ typedef SARPolarSynth Self;
+ typedef Application Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+
+ typedef MultiChannelsPolarimetricSynthesisFilter<ComplexDoubleVectorImageType, FloatImageType> MCPSFilterType;
+ /** Standard macro */
+ itkNewMacro(Self);
+
+ itkTypeMacro(SARPolarSynth, otb::Application);
+
+private:
+ void DoInit()
+ {
+ SetName("SARPolarSynth");
+ SetDescription("Gives, for each pixel, the power that would have been received by a SAR system with a basis different from the classical (H,V) one (polarimetric synthetis).");
+
+ // Documentation
+ SetDocName("SARPolarSynth");
+ SetDocLongDescription("This application gives, for each pixel, the power that would have been received by a SAR system with a basis different from the classical (H,V) one (polarimetric synthetis).\n"
+ "The new basis A and B are indicated through two Jones vectors, defined by the user thanks to orientation (psi) and ellipticity (khi) parameters.\n"
+ "These parameters are namely psii, khii, psir and khir. The suffixes (i) and (r) refer to the transmiting antenna and the receiving antenna respectively.\n"
+ "Orientations and ellipticities are given in degrees, and are between -90°/90° and -45°/45° respectively.\n "
+ "\n"
+ "Four polarization architectures can be processed : \n"
+ "1) HH_HV_VH_VV : full polarization, general bistatic case.\n"
+ "2) HH_HV_VV or HH_VH_VV : full polarization, monostatic case (transmitter and receiver are co-located).\n"
+ "3) HH_HV : dual polarization.\n"
+ "4) VH_VV : dual polarization.\n"
+ "The application takes a complex vector image as input, where each band correspond to a particular emission/reception polarization scheme.\n"
+ "User must comply with the band order given above, since the bands are used to build the Sinclair matrix.\n"
+ "\n"
+ "In order to determine the architecture, the application first relies on the number of bands of the input image.\n"
+ "1) Architecture HH_HV_VH_VV is the only one with four bands, there is no possible confusion.\n"
+ "2) Concerning HH_HV_VV and HH_VH_VV architectures, both correspond to a three channels image. But they are processed in the same way, as the Sinclair matrix is symetric in the monostatic case.\n"
+ "3) Finally, the two last architectures (dual polarizations), can't be distinguished only by the number of bands of the input image.\n"
+ " User must then use the parameters emissionh and emissionv to indicate the architecture of the system : emissionh=1 and emissionv=0 --> HH_HV, emissionh=0 and emissionv=1 --> VH_VV.\n"
+ "Note : if the architecture is HH_HV, khii and psii are automatically set to 0°/0°; if the architecture is VH_VV, khii and psii are automatically set to 0°/90°.\n"
+ "\n"
+ "It is also possible to force the calculation to co-polar or cross-polar modes.\n"
+ "In the co-polar case, values for psir and khir will be ignored and forced to psii and khii; same as the cross-polar mode, where khir and psir will be forced to psii+90° and -khii.\n"
+ "\n"
+ "Finally, the result of the polarimetric synthetis is expressed in the power domain, through a one-band scalar image.\n"
+ "Note: this application doesn't take into account the terms which do not depend on the polarization of the antennas. \n"
+ "The parameter gain can be used for this purpose.\n"
+ "\n"
+ "The final formula is thus : P = | B^T . [S] . A |², where A ans B are two Jones vectors and S is a Sinclair matrix.");
+
+ SetDocLimitations("None");
+ SetDocAuthors("OTB-Team");
+ SetDocSeeAlso("SARDecompositions, SARPolarMatrixConvert");
+
+ AddDocTag(Tags::SAR);
+
+ AddParameter(ParameterType_ComplexInputImage, "in", "Input Image");
+ SetParameterDescription("in", "Input image.");
+ AddParameter(ParameterType_OutputImage, "out", "Output Image");
+ SetParameterDescription("out", "Output image.");
+
+ AddParameter(ParameterType_Float,"psii","psii");
+ SetParameterDescription("psii","Orientation (transmitting antenna)");
+ SetMinimumParameterFloatValue("psii",-90.0);
+ SetMaximumParameterFloatValue("psii",90.0);
+
+ AddParameter(ParameterType_Float,"khii","khii");
+ SetParameterDescription("khii","Ellipticity (transmitting antenna)");
+ SetMinimumParameterFloatValue("khii",-45.0);
+ SetMaximumParameterFloatValue("khii",45.0);
+
+ AddParameter(ParameterType_Float,"psir","psir");
+ SetParameterDescription("psir","Orientation (receiving antenna)");
+ SetMinimumParameterFloatValue("psir",-90.0);
+ SetMaximumParameterFloatValue("psir",90.0);
+
+ AddParameter(ParameterType_Float,"khir","khir");
+ SetParameterDescription("khir","Ellipticity (receiving antenna)");
+ SetMinimumParameterFloatValue("khir",-45.0);
+ SetMaximumParameterFloatValue("khir",45.0);
+
+ AddParameter(ParameterType_Int,"emissionh","Emission H");
+ SetParameterDescription("emissionh","This parameter is useful in determining the polarization architecture (dual polarization case).");
+ SetMinimumParameterIntValue("emissionh",0);
+ SetMaximumParameterIntValue("emissionh",1);
+ MandatoryOff("emissionh");
+
+ AddParameter(ParameterType_Int,"emissionv","Emission V");
+ SetParameterDescription("emissionv","This parameter is useful in determining the polarization architecture (dual polarization case).");
+ SetMinimumParameterIntValue("emissionv",0);
+ SetMaximumParameterIntValue("emissionv",1);
+ MandatoryOff("emissionv");
+
+ AddParameter(ParameterType_Choice, "mode", "Forced mode");
+ AddChoice("mode.none","None");
+ SetParameterDescription("mode.none","None");
+ AddChoice("mode.co","Copolarization");
+ SetParameterDescription("mode.none","Copolarization");
+ AddChoice("mode.cross","Crosspolarization");
+ SetParameterDescription("mode.cross","Crosspolarization");
+
+
+ AddRAMParameter();
+
+ // Default values
+ SetDefaultParameterFloat("psii", 0.);
+ SetDefaultParameterFloat("khii", 0.);
+ SetDefaultParameterFloat("psir", 0.);
+ SetDefaultParameterFloat("khir", 0.);
+ SetDefaultParameterInt("emissionh", 0);
+ SetDefaultParameterInt("emissionv", 0);
+ SetDefaultParameterFloat("mode", 0);
+
+ // Doc example parameter settings
+ SetDocExampleParameterValue("in", "sar.tif");
+ SetDocExampleParameterValue("psii","15.");
+ SetDocExampleParameterValue("khii", "5.");
+ SetDocExampleParameterValue("psir","-25.");
+ SetDocExampleParameterValue("khir", "10.");
+ SetDocExampleParameterValue("out", "newbasis.tif");
+ }
+
+ void DoUpdateParameters()
+ {
+ // Nothing to do here : all parameters are independent
+ }
+
+ void DoExecute()
+ {
+
+ m_MCPSFilter = MCPSFilterType::New();
+ m_MCPSFilter->SetPsiI(GetParameterFloat("psii"));
+ m_MCPSFilter->SetKhiI(GetParameterFloat("khii"));
+ m_MCPSFilter->SetPsiR(GetParameterFloat("psir"));
+ m_MCPSFilter->SetKhiR(GetParameterFloat("khir"));
+ m_MCPSFilter->SetEmissionH(GetParameterInt("emissionh"));
+ m_MCPSFilter->SetEmissionV(GetParameterInt("emissionv"));
+ m_MCPSFilter->SetMode(GetParameterInt("mode"));
+
+ ComplexDoubleVectorImageType* inVImage = GetParameterComplexDoubleVectorImage("in");
+ inVImage->UpdateOutputInformation();
+ int nbBands = inVImage->GetNumberOfComponentsPerPixel();
+ otbAppLogINFO( << "nbBands = " << nbBands);
+
+ m_MCPSFilter->SetInput(inVImage);
+
+ SetParameterOutputImage("out", m_MCPSFilter->GetOutput());
+ }
+ //std::vector<itk::ProcessObject::Pointer> m_Ref;
+ MCPSFilterType::Pointer m_MCPSFilter;
+};
+
+} //end namespace Wrapper
+} //end namespace otb
+
+OTB_APPLICATION_EXPORT(otb::Wrapper::SARPolarSynth)
diff --git a/Modules/Applications/AppSARPolarSynth/otb-module.cmake b/Modules/Applications/AppSARPolarSynth/otb-module.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..16d19e0a98d9dde0b29258a43063e6d2696e563e
--- /dev/null
+++ b/Modules/Applications/AppSARPolarSynth/otb-module.cmake
@@ -0,0 +1,17 @@
+set(DOCUMENTATION "Basic filters application.")
+
+otb_module(OTBAppSARPolarSynth
+ DEPENDS
+ OTBPolarimetry
+ OTBImageManipulation
+ OTBITK
+ OTBApplicationEngine
+ OTBImageBase
+
+ TEST_DEPENDS
+ OTBTestKernel
+ OTBCommandLine
+
+ DESCRIPTION
+ "${DOCUMENTATION}"
+)
diff --git a/Modules/Applications/AppSARPolarSynth/test/CMakeLists.txt b/Modules/Applications/AppSARPolarSynth/test/CMakeLists.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1adeb2028634a4d0cb63774dd91a7d07cfb44110
--- /dev/null
+++ b/Modules/Applications/AppSARPolarSynth/test/CMakeLists.txt
@@ -0,0 +1,13 @@
+otb_module_test()
+#----------- PolarSynth TESTS ----------------
+
+otb_test_application(NAME apTvSARPolarSynth
+ APP SARPolarSynth
+ OPTIONS -in ${INPUTDATA}/RSAT2_AltonaExtract_1000_1000_100_100.hdr
+ -out ${TEMP}/resApMultiPolarimetricSynthesis1.tif
+ -psii 10.0
+ -khii 0.0
+ -psir 0.0
+ -khir 0.0
+ )
+
diff --git a/Modules/Core/Common/include/otbImageRegionAdaptativeSplitter.txx b/Modules/Core/Common/include/otbImageRegionAdaptativeSplitter.txx
index 9595466d47d0a1bb66768e6d241a8c26f8341f88..ef5b28192b5d1f658327d89fa9b8a0b0ec403610 100644
--- a/Modules/Core/Common/include/otbImageRegionAdaptativeSplitter.txx
+++ b/Modules/Core/Common/include/otbImageRegionAdaptativeSplitter.txx
@@ -112,8 +112,8 @@ ImageRegionAdaptativeSplitter<VImageDimension>
// First, we need to get which tiles are covered by ROI
firstTileCovered[0] = m_ImageRegion.GetIndex()[0] / m_TileHint[0];
firstTileCovered[1] = m_ImageRegion.GetIndex()[1] / m_TileHint[1];
- tilesPerDim[0] = (m_ImageRegion.GetIndex()[0] + m_ImageRegion.GetSize()[0] -1 + m_TileHint[0] -1) / m_TileHint[0] - firstTileCovered[0];
- tilesPerDim[1] = (m_ImageRegion.GetIndex()[1] + m_ImageRegion.GetSize()[1] -1 + m_TileHint[1] -1) / m_TileHint[1] - firstTileCovered[1];
+ tilesPerDim[0] = (m_ImageRegion.GetIndex()[0] + m_ImageRegion.GetSize()[0] + m_TileHint[0] -1) / m_TileHint[0] - firstTileCovered[0];
+ tilesPerDim[1] = (m_ImageRegion.GetIndex()[1] + m_ImageRegion.GetSize()[1] + m_TileHint[1] -1) / m_TileHint[1] - firstTileCovered[1];
unsigned int totalTiles = tilesPerDim[0] * tilesPerDim[1];
@@ -186,7 +186,9 @@ ImageRegionAdaptativeSplitter<VImageDimension>
unsigned int i = 1;
- while(totalTiles * (divideTiles[0] * divideTiles[1]) < m_RequestedNumberOfSplits)
+ // Exit condition if divideTiles=m_TileHint (i.e. no more subdivision available)
+ while(totalTiles * (divideTiles[0] * divideTiles[1]) < m_RequestedNumberOfSplits
+ && (divideTiles[0] < m_TileHint[0] || divideTiles[1] < m_TileHint[1]))
{
if(divideTiles[i] < m_TileHint[i])
{
@@ -200,6 +202,8 @@ ImageRegionAdaptativeSplitter<VImageDimension>
splitSize[0] = (m_TileHint[0] + divideTiles[0] - 1)/ divideTiles[0];
splitSize[1] = (m_TileHint[1] + divideTiles[1] - 1)/ divideTiles[1];
+ RegionType tileHintRegion;
+ tileHintRegion.SetSize(m_TileHint);
// Fill the tiling scheme
for(unsigned int tiley = 0; tiley < tilesPerDim[1]; ++tiley)
{
@@ -219,13 +223,21 @@ ImageRegionAdaptativeSplitter<VImageDimension>
newSplit.SetIndex(newSplitIndex);
newSplit.SetSize(splitSize);
+ tileHintRegion.SetIndex(0, tilex * m_TileHint[0]);
+ tileHintRegion.SetIndex(1, tiley * m_TileHint[1]);
+
bool cropped = newSplit.Crop(m_ImageRegion);
// If newSplit could not be cropped, it means that it is
// outside m_ImageRegion. In this case we ignore it.
if(cropped)
{
- m_StreamVector.push_back(newSplit);
+ // check that the split stays inside its tile
+ cropped = newSplit.Crop(tileHintRegion);
+ if (cropped)
+ {
+ m_StreamVector.push_back(newSplit);
+ }
}
}
}
diff --git a/Modules/Core/Common/test/CMakeLists.txt b/Modules/Core/Common/test/CMakeLists.txt
index 40f89e72aeb1144f9fc5c7166c32aaef6d62870b..c40b6a289fdce16ec39937f9db531dbe9d61a38c 100644
--- a/Modules/Core/Common/test/CMakeLists.txt
+++ b/Modules/Core/Common/test/CMakeLists.txt
@@ -96,6 +96,18 @@ otb_add_test(NAME coTvImageRegionAdaptativeSplitterShiftedROISmallStream COMMAND
${TEMP}/coTvImageRegionAdaptativeSplitterShiftedROISmallStreamOutput.txt
)
+otb_add_test(NAME coTvImageRegionAdaptativeSplitterMissingLine COMMAND otbCommonTestDriver
+ otbImageRegionAdaptativeSplitter
+ 0 0 1000 1000 1000 1 37
+ ${TEMP}/coTvImageRegionAdaptativeSplitterMissingLine.txt
+ )
+
+otb_add_test(NAME coTvImageRegionAdaptativeSplitterDivideBlock COMMAND otbCommonTestDriver
+ otbImageRegionAdaptativeSplitter
+ 0 0 10 10 5 5 36
+ ${TEMP}/coTvImageRegionAdaptativeSplitterDivideBlock.txt
+ )
+
otb_add_test(NAME coTuRGBAPixelConverter COMMAND otbCommonTestDriver
otbRGBAPixelConverterNew
)
diff --git a/Modules/Core/Common/test/otbImageRegionAdaptativeSplitter.cxx b/Modules/Core/Common/test/otbImageRegionAdaptativeSplitter.cxx
index 638993505239432d2926178eff637ca2b94c6720..66cc0ae45d7159fe3f84b945cdbaf2edf2a903bb 100644
--- a/Modules/Core/Common/test/otbImageRegionAdaptativeSplitter.cxx
+++ b/Modules/Core/Common/test/otbImageRegionAdaptativeSplitter.cxx
@@ -59,16 +59,63 @@ int otbImageRegionAdaptativeSplitter(int itkNotUsed(argc), char * argv[])
unsigned int nbSplits = splitter->GetNumberOfSplits(region, requestedNbSplits);
+ std::vector<RegionType> splits;
outfile<<splitter<<std::endl;
outfile<<"Split map: "<<std::endl;
for(unsigned int i = 0; i < nbSplits; ++i)
{
- outfile<<"Split "<<i<<": "<<splitter->GetSplit(i, requestedNbSplits, region);
+ RegionType tmpRegion = splitter->GetSplit(i, requestedNbSplits, region);
+ splits.push_back(tmpRegion);
+ outfile<<"Split "<<i<<": "<<tmpRegion;
}
outfile.close();
+ // Basic consistency check on split map
+ if (requestedNbSplits != nbSplits)
+ {
+ std::cout << "Wrong number of splits : got "<<nbSplits<<" , expected "<<requestedNbSplits<< std::endl;
+ }
+ IndexType tmpIndex;
+ for (unsigned int i=regionIndex[0] ; i<(regionIndex[0]+regionSize[0]) ; ++i)
+ {
+ for (unsigned int j=regionIndex[1] ; j<(regionIndex[1]+regionSize[1]) ; ++j)
+ {
+ tmpIndex[0] = i;
+ tmpIndex[1] = j;
+ unsigned int count = 0;
+ for (unsigned int k=0 ; k<nbSplits ; ++k )
+ {
+ if (splits[k].IsInside(tmpIndex))
+ {
+ count++;
+ }
+ }
+ if (count == 0)
+ {
+ std::cout << "Index ["<<i<<","<<j<<"] is missing in split map" << std::endl;
+ return EXIT_FAILURE;
+ }
+ if (count > 1)
+ {
+ std::cout << "Index ["<<i<<","<<j<<"] occurs more than once in the split map" << std::endl;
+ return EXIT_FAILURE;
+ }
+ }
+ }
+
+ unsigned int pixelInSplit = 0;
+ for (unsigned int k=0 ; k<nbSplits ; ++k )
+ {
+ pixelInSplit += splits[k].GetSize(0) * splits[k].GetSize(1);
+ }
+ if (pixelInSplit != regionSize[0]*regionSize[1])
+ {
+ std::cout << "Wrong number of pixels in split : got "<<pixelInSplit << " , expected "<< regionSize[0]*regionSize[1] << std::endl;
+ return EXIT_FAILURE;
+ }
+
return EXIT_SUCCESS;
}
diff --git a/Modules/Core/Metadata/include/otbImageMetadataInterfaceBase.h b/Modules/Core/Metadata/include/otbImageMetadataInterfaceBase.h
index 8340cedac73674309f124cc4c8b24499dc3c34b3..2a685790333b2959489715b8cf4450ceee3ba58c 100644
--- a/Modules/Core/Metadata/include/otbImageMetadataInterfaceBase.h
+++ b/Modules/Core/Metadata/include/otbImageMetadataInterfaceBase.h
@@ -53,6 +53,8 @@ public:
typedef MetaDataKey::VariableLengthVectorType VariableLengthVectorType;
typedef ImageKeywordlist ImageKeywordlistType;
typedef unsigned int UnsignedIntType;
+ typedef std::vector<std::string> StringVectorType;
+ typedef std::vector<unsigned int> UIntVectorType;
/** Set the image used to get the metadata */
@@ -80,7 +82,7 @@ public:
std::string GetGCPProjection() const;
// otbMetadataGetMacro(GCPProjection, std::string);
- unsigned int GetGCPCount() const;
+ UnsignedIntType GetGCPCount() const;
// otbMetadataGetMacro(GCPCount, unsigned int);
OTB_GCP& GetGCPs(unsigned int GCPnum);
@@ -107,7 +109,7 @@ public:
double GetGCPZ(unsigned int GCPnum) const;
// otbMetadataGetGCPnumMacro(GCPZ, double, GCPnum, unsigned int);
- /**
+ /**
* Get The no data flags if existing
* return False otherwise
*/
@@ -141,11 +143,11 @@ public:
//otbMetadataGetMacro(SensorID, std::string);
/** Get the number of bands from the ossim metadata */
- unsigned int GetNumberOfBands() const;
+ UnsignedIntType GetNumberOfBands() const;
//otbMetadataGetMacro(NumberOfBands, unsigned int);
/** Get the band name from the ossim metadata */
- std::vector<std::string> GetBandName() const;
+ StringVectorType GetBandName() const;
//otbMetadataGetMacro(BandName, std::vector<std::string>);
/** Get the x pixel spacing*/
@@ -156,7 +158,6 @@ public:
double GetYPixelSpacing() const;
//otbMetadataGetMacro(YPixelSpacing, double);
-
/** Get the imaging acquisition day from the ossim metadata */
virtual int GetDay() const = 0;
// otbMetadataGetMacro(Day, int);
@@ -190,11 +191,11 @@ public:
//otbMetadataGetMacro(ProductionYear, int);
/** Convert the band names provided by ossim to the official band names */
- virtual std::vector<std::string> GetEnhancedBandNames () const = 0;
+ virtual StringVectorType GetEnhancedBandNames () const = 0;
/** Get the 3 spectral band numbers corresponding to the default display for visualization,
* in the order R, G, B */
- virtual std::vector<unsigned int> GetDefaultDisplay() const = 0;
+ virtual UIntVectorType GetDefaultDisplay() const = 0;
virtual bool CanRead() const = 0;
diff --git a/Modules/Core/Metadata/include/otbRadarsat2ImageMetadataInterface.h b/Modules/Core/Metadata/include/otbRadarsat2ImageMetadataInterface.h
new file mode 100644
index 0000000000000000000000000000000000000000..46439ca65227e1671ad6cfb565d2a0c6786da27b
--- /dev/null
+++ b/Modules/Core/Metadata/include/otbRadarsat2ImageMetadataInterface.h
@@ -0,0 +1,203 @@
+/*=========================================================================
+
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+=========================================================================*/
+#ifndef __otbRadarsat2ImageMetadataInterface_h
+#define __otbRadarsat2ImageMetadataInterface_h
+
+#include "otbSarImageMetadataInterface.h"
+
+namespace otb
+{
+/** \class Radarsat2ImageMetadataInterface
+ *
+ * \brief Creation of an "otb" Radarsat2ImageMetadataInterface that gets metadata.
+ *
+ *
+ * \ingroup OTBMetadata
+ */
+
+class ITK_ABI_EXPORT Radarsat2ImageMetadataInterface : public SarImageMetadataInterface
+{
+public:
+
+ typedef Radarsat2ImageMetadataInterface Self;
+ typedef SarImageMetadataInterface Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+
+ /** Method for creation through the object factory. */
+ itkNewMacro(Self);
+
+ /** Run-time type information (and related methods). */
+ itkTypeMacro(Radarsat2ImageMetadataInterface, SarImageMetadataInterface);
+
+ typedef Superclass::ImageType ImageType;
+ typedef Superclass::MetaDataDictionaryType MetaDataDictionaryType;
+ typedef Superclass::VectorType VectorType;
+ typedef Superclass::VariableLengthVectorType VariableLengthVectorType;
+ typedef Superclass::ImageKeywordlistType ImageKeywordlistType;
+ typedef Superclass::LookupDataPointerType LookupDataPointerType;
+// typedef Radarsat2CalibrationLookupData::Pointer LookupDataPointerType;
+
+ /*ImageMetadataInterfaceBase pure virtuals */
+ /** Get the imaging production day from the ossim metadata : DATASET_PRODUCTION_DATE metadata variable */
+ int GetProductionDay() const;
+
+ /** Get the imaging production month from the ossim metadata : DATASET_PRODUCTION_DATE metadata variable */
+ int GetProductionMonth() const;
+
+ /** Get the imaging production year from the ossim metadata : DATASET_PRODUCTION_DATE metadata variable */
+ int GetProductionYear() const;
+
+ /** check sensor ID */
+ bool CanRead() const;
+
+ int GetDay() const;
+
+ int GetMonth() const;
+
+ int GetYear() const;
+
+ int GetHour() const;
+
+ int GetMinute() const;
+
+ UIntVectorType GetDefaultDisplay() const;
+
+ /*SarImageMetadataInterface pure virutals rituals */
+ double GetPRF() const;
+
+ double GetRSF() const;
+
+ double GetRadarFrequency() const;
+
+ double GetCenterIncidenceAngle() const;
+
+ /*get lookup data for calulating backscatter */
+ void CreateCalibrationLookupData(const short type);
+
+
+protected:
+ /* class constructor */
+ Radarsat2ImageMetadataInterface();
+
+ /* class desctructor */
+ virtual ~Radarsat2ImageMetadataInterface() {}
+
+private:
+ Radarsat2ImageMetadataInterface(const Self &); //purposely not implemented
+ void operator =(const Self&); //purposely not implemented
+
+/* Helper function to parse date and time into a std::vector<std::string>
+ * using boost::split() expect date time in yyyy-mm-ddThh:mm:ss.ms
+ * the date-time string is to be found in keywordlist with key 'key'
+ * fills argument dateFields of type std::vector<std::string> which is mutable!
+ * TODO: move this method into base class
+ */
+ void ParseDateTime(const char* key, std::vector<int>& dateFields) const;
+
+ mutable std::vector<int> m_ProductionDateFields;
+ mutable std::vector<int> m_AcquisitionDateFields;
+
+};
+
+
+class Radarsat2CalibrationLookupData : public SarCalibrationLookupData
+{
+
+public:
+
+ /** Standard typedefs */
+ typedef Radarsat2CalibrationLookupData Self;
+ typedef SarCalibrationLookupData Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+
+ /** Creation through the object factory */
+ itkNewMacro(Self);
+
+ /** RTTI */
+ itkTypeMacro(Radarsat2CalibrationLookupData, SarCalibrationLookupData);
+
+ typedef itk::IndexValueType IndexValueType;
+
+ typedef std::vector<float> GainListType;
+
+
+ Radarsat2CalibrationLookupData()
+ : m_Offset(0)
+ {
+
+ }
+
+ virtual ~Radarsat2CalibrationLookupData()
+ {
+
+ }
+
+ void InitParameters(short type, int offset, GainListType gains)
+ {
+ this->SetType(type);
+ m_Offset = offset;
+ m_Gains = gains;
+ }
+
+ double GetValue(const IndexValueType x, const IndexValueType itkNotUsed(y))
+ {
+ double lutVal = 1.0;
+
+ const size_t pos = x + m_Offset;
+ if(pos < m_Gains.size())
+ {
+ lutVal = m_Gains[pos];
+ }
+ else
+ {
+ //itkExceptionMacro( << "error: (pos < list.size() )" << pos << " < " << list.size())
+ }
+ return lutVal;
+ }
+
+ void PrintSelf(std::ostream & os, itk::Indent indent) const
+ {
+ os << indent << " offset:'" << m_Offset << "'" << std::endl;
+ os << " referenceNoiseLevel.gain: " << std::endl;
+ std::vector<float>::const_iterator it = m_Gains.begin();
+ while (it != m_Gains.end())
+ {
+ os << (*it) << " ";
+ ++it;
+ }
+ os << std::endl;
+
+ Superclass::PrintSelf(os, indent);
+ }
+
+private:
+
+ Radarsat2CalibrationLookupData(const Self&); //purposely not implemented
+ void operator =(const Self&); //purposely not implemented
+
+ GainListType m_Gains;
+ int m_Offset;
+
+
+};
+
+} // end namespace otb
+
+#endif
diff --git a/Modules/Core/Metadata/include/otbRadarsat2ImageMetadataInterfaceFactory.h b/Modules/Core/Metadata/include/otbRadarsat2ImageMetadataInterfaceFactory.h
new file mode 100644
index 0000000000000000000000000000000000000000..2aeb8a8f583be04f5b8fcb0f022015daea6d283f
--- /dev/null
+++ b/Modules/Core/Metadata/include/otbRadarsat2ImageMetadataInterfaceFactory.h
@@ -0,0 +1,68 @@
+/*=========================================================================
+
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+=========================================================================*/
+#ifndef __otbRadarsat2ImageMetadataInterfaceFactory_h
+#define __otbRadarsat2ImageMetadataInterfaceFactory_h
+
+#include "itkObjectFactoryBase.h"
+
+namespace otb
+{
+/** \class Radarsat2ImageMetadataInterfaceFactory
+ * \brief Creating an instance of a ImageMetadataInterface object using object factory.
+ *
+ * \ingroup OTBMetadata
+ */
+class ITK_EXPORT Radarsat2ImageMetadataInterfaceFactory : public itk::ObjectFactoryBase
+{
+public:
+ /** Standard class typedefs. */
+ typedef Radarsat2ImageMetadataInterfaceFactory Self;
+ typedef itk::ObjectFactoryBase Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+
+ /** Class methods used to interface with the registered factories. */
+ virtual const char* GetITKSourceVersion(void) const;
+ virtual const char* GetDescription(void) const;
+
+ /** Method for class instantiation. */
+ itkFactorylessNewMacro(Self);
+
+ /** Run-time type information (and related methods). */
+ itkTypeMacro(Radarsat2ImageMetadataInterfaceFactory, itk::ObjectFactoryBase);
+
+ /** Register one factory of this type */
+ static void RegisterOneFactory(void)
+ {
+ Pointer factory = Radarsat2ImageMetadataInterfaceFactory::New();
+ itk::ObjectFactoryBase::RegisterFactory(factory);
+ }
+
+protected:
+ Radarsat2ImageMetadataInterfaceFactory();
+ virtual ~Radarsat2ImageMetadataInterfaceFactory();
+
+private:
+ Radarsat2ImageMetadataInterfaceFactory(const Self &); //purposely not implemented
+ void operator =(const Self&); //purposely not implemented
+
+};
+
+} // end namespace otb
+
+#endif
diff --git a/Modules/Core/Metadata/include/otbSarCalibrationLookupData.h b/Modules/Core/Metadata/include/otbSarCalibrationLookupData.h
new file mode 100644
index 0000000000000000000000000000000000000000..ef95f2b687f54ac5b3f4e9d9239d64a96a7c5fc6
--- /dev/null
+++ b/Modules/Core/Metadata/include/otbSarCalibrationLookupData.h
@@ -0,0 +1,72 @@
+#ifndef SarCalibrationLookupData_H
+#define SarCalibrationLookupData_H 1
+#include <string>
+#include <itkLightObject.h>
+#include <itkNumericTraits.h>
+#include <itkObjectFactory.h>
+
+namespace otb {
+
+class ITK_EXPORT SarCalibrationLookupData : public itk::LightObject {
+
+ public:
+
+ /** Standard typedefs */
+ typedef SarCalibrationLookupData Self;
+ typedef itk::LightObject Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+
+ /** Creation through the object factory */
+ itkNewMacro(Self);
+
+ /** RTTI */
+ itkTypeMacro(SarCalibrationLookupData, itk::LightObject);
+
+ typedef itk::IndexValueType IndexValueType;
+
+ enum {
+ SIGMA = 0,
+ BETA,
+ GAMMA,
+ DN
+ };
+
+ SarCalibrationLookupData()
+ :m_Type(0)
+ {
+
+ }
+
+ virtual ~SarCalibrationLookupData()
+ {
+ }
+
+ virtual double GetValue(const IndexValueType itkNotUsed(x), const IndexValueType itkNotUsed(y))
+ {
+ return 1.0;
+ }
+
+ void SetType(short t)
+ {
+ m_Type = t;
+ }
+
+ itkGetMacro(Type, short);
+
+ void PrintSelf(std::ostream & os, itk::Indent indent) const
+ {
+ os << indent << " lookup table type:'" << m_Type << "'" << std::endl;
+ Superclass::PrintSelf(os, indent);
+ }
+
+private:
+
+ SarCalibrationLookupData(const Self&); //purposely not implemented
+ void operator =(const Self&); //purposely not implemented
+ short m_Type;
+
+};
+
+}
+#endif
diff --git a/Modules/Core/Metadata/include/otbSarDefaultImageMetadataInterface.h b/Modules/Core/Metadata/include/otbSarDefaultImageMetadataInterface.h
index f78fa3e87cabf9cd888cad5900c3636c2120795d..dd6529ab2454abc3274b3e5d7645cec4b06d4106 100644
--- a/Modules/Core/Metadata/include/otbSarDefaultImageMetadataInterface.h
+++ b/Modules/Core/Metadata/include/otbSarDefaultImageMetadataInterface.h
@@ -58,8 +58,7 @@ public:
typedef double RealType;
typedef PointSetType::PointType PointType;
-
- RealType GetRadiometricCalibrationScale() const
+ RealType GetRadiometricCalibrationScale() const
{
itkExceptionMacro("GetRadiometricCalibrationScale() not implemented in SarDefaultImageMetadataInterface, no captor type found");
}
@@ -194,9 +193,9 @@ public:
/** Get the 3 spectral band numbers corresponding to the default display for visualization,
* in the order R, G, B */
- std::vector<unsigned int> GetDefaultDisplay() const
+ UIntVectorType GetDefaultDisplay() const
{
- std::vector<unsigned int> rgb(3);
+ UIntVectorType rgb(3);
rgb[0] = 0;
rgb[1] = 0;
rgb[2] = 0;
diff --git a/Modules/Core/Metadata/include/otbSarImageMetadataInterface.h b/Modules/Core/Metadata/include/otbSarImageMetadataInterface.h
index 73f055cbdcab660835dd526a3c806f8d318e4703..7ce844f4207ca378e11c7e8d49722bcd6e953bfd 100644
--- a/Modules/Core/Metadata/include/otbSarImageMetadataInterface.h
+++ b/Modules/Core/Metadata/include/otbSarImageMetadataInterface.h
@@ -19,9 +19,11 @@
#define __otbSarImageMetadataInterface_h
#include <string>
-
+#include <vector>
#include "otbImageMetadataInterfaceBase.h"
#include "itkPointSet.h"
+#include "otbSarCalibrationLookupData.h"
+#include "otbStringUtils.h"
namespace otb
{
@@ -50,11 +52,25 @@ public:
typedef Superclass::VectorType VectorType;
typedef Superclass::VariableLengthVectorType VariableLengthVectorType;
typedef Superclass::ImageKeywordlistType ImageKeywordlistType;
+ typedef Superclass::UIntVectorType UIntVectorType;
+ typedef Superclass::StringVectorType StringVectorType;
typedef itk::PointSet<double, 2> PointSetType;
typedef PointSetType::Pointer PointSetPointer;
typedef double RealType;
typedef PointSetType::PointType PointType;
+ typedef SarCalibrationLookupData LookupDataType;
+ typedef LookupDataType::Pointer LookupDataPointerType;
+
+ virtual void CreateCalibrationLookupData(const short t);
+ const LookupDataPointerType GetCalibrationLookupData(const short type);
+
+ bool HasCalibrationLookupDataFlag() const;
+
+ void SetCalibrationLookupData(LookupDataType* lut)
+ {
+ m_SarLut = lut;
+ }
virtual RealType GetRadiometricCalibrationScale() const;
@@ -75,10 +91,18 @@ public:
virtual double GetRadarFrequency () const = 0;
virtual double GetCenterIncidenceAngle() const = 0;
+
+
+ virtual double GetRescalingFactor() const;
+
+ virtual const std::string GetProductType() const;
+
+ virtual const std::string GetAcquisitionMode() const;
+
/** Get the enhanced band names (No enhanced band name support for SAR) */
- std::vector<std::string> GetEnhancedBandNames() const
+ StringVectorType GetEnhancedBandNames() const
{
- std::vector<std::string> nothing;
+ StringVectorType nothing;
return nothing;
}
@@ -91,9 +115,13 @@ protected:
void PrintSelf(std::ostream& os, itk::Indent indent) const;
+ LookupDataPointerType m_SarLut;
+
+
private:
SarImageMetadataInterface(const Self &); //purposely not implemented
void operator =(const Self&); //purposely not implemented
+
};
} // end namespace otb
diff --git a/Modules/Core/Metadata/include/otbSentinel1ImageMetadataInterface.h b/Modules/Core/Metadata/include/otbSentinel1ImageMetadataInterface.h
new file mode 100644
index 0000000000000000000000000000000000000000..c67ead32a4656b69c84f35dfabb0072dfd9d8e1a
--- /dev/null
+++ b/Modules/Core/Metadata/include/otbSentinel1ImageMetadataInterface.h
@@ -0,0 +1,236 @@
+/*=========================================================================
+
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+=========================================================================*/
+#ifndef __otbSentinel1ImageMetadataInterface_h
+#define __otbSentinel1ImageMetadataInterface_h
+
+#include "otbSarImageMetadataInterface.h"
+
+
+namespace otb
+{
+/** \class Sentinel1ImageMetadataInterface
+ *
+ * \brief Creation of an "otb" Sentinel1ImageMetadataInterface that gets metadata.
+ *
+ *
+ * \ingroup OTBMetadata
+ */
+
+class ITK_ABI_EXPORT Sentinel1ImageMetadataInterface : public SarImageMetadataInterface
+{
+public:
+
+ typedef Sentinel1ImageMetadataInterface Self;
+ typedef SarImageMetadataInterface Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+
+ /** Method for creation through the object factory. */
+ itkNewMacro(Self);
+
+
+ /** Run-time type information (and related methods). */
+ itkTypeMacro(Sentinel1ImageMetadataInterface, SarImageMetadataInterface);
+
+ typedef Superclass::ImageType ImageType;
+ typedef Superclass::MetaDataDictionaryType MetaDataDictionaryType;
+ typedef Superclass::VectorType VectorType;
+ typedef Superclass::VariableLengthVectorType VariableLengthVectorType;
+ typedef Superclass::ImageKeywordlistType ImageKeywordlistType;
+ typedef Superclass::RealType RealType;
+ typedef Superclass::LookupDataPointerType LookupDataPointerType;
+
+ /** Get the imaging production day from the ossim metadata : DATASET_PRODUCTION_DATE metadata variable */
+ int GetProductionDay() const;
+
+ /** Get the imaging production month from the ossim metadata : DATASET_PRODUCTION_DATE metadata variable */
+ int GetProductionMonth() const;
+
+ /** Get the imaging production year from the ossim metadata : DATASET_PRODUCTION_DATE metadata variable */
+ int GetProductionYear() const;
+
+ /** check sensor ID */
+ bool CanRead() const;
+
+ int GetDay() const;
+
+ int GetMonth() const;
+
+ int GetYear() const;
+
+ int GetHour() const;
+
+ int GetMinute() const;
+
+ UIntVectorType GetDefaultDisplay() const;
+
+ /*SarImageMetadataInterface pure virutals rituals */
+ double GetPRF() const;
+
+ double GetRSF() const;
+
+ double GetRadarFrequency() const;
+
+ double GetCenterIncidenceAngle() const;
+
+ /*get lookup data for calulating backscatter */
+ void CreateCalibrationLookupData(const short type);
+
+protected:
+
+ /* class ctor */
+ Sentinel1ImageMetadataInterface();
+
+ /* class dtor */
+ virtual ~Sentinel1ImageMetadataInterface() {}
+
+private:
+
+ Sentinel1ImageMetadataInterface(const Self &); //purposely not implemented
+ void operator =(const Self&); //purposely not implemented
+
+/* Helper function to parse date and time into a std::vector<std::string>
+ * using boost::split() expect date time in yyyy-mm-ddThh:mm:ss.ms
+ * the date-time string is to be found in keywordlist with key 'key'
+ * fills argument dateFields of type std::vector<std::string> which is mutable!
+ * TODO: move this method into base class
+ */
+ void ParseDateTime(const char* key, std::vector<int>& dateFields) const;
+
+ mutable std::vector<int> m_ProductionDateFields;
+ mutable std::vector<int> m_AcquisitionDateFields;
+};
+
+
+
+struct Sentinel1CalibrationStruct {
+
+public:
+ double timeMJD;
+ int line;
+ std::vector<int> pixels;
+ std::vector<float> vect;
+};
+
+class Sentinel1CalibrationLookupData : public SarCalibrationLookupData
+{
+
+public:
+
+
+ /** Standard typedefs */
+ typedef Sentinel1CalibrationLookupData Self;
+ typedef SarCalibrationLookupData Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+
+ /** Creation through the object factory */
+ itkNewMacro(Self);
+
+ /** RTTI */
+ itkTypeMacro(Sentinel1CalibrationLookupData, SarCalibrationLookupData);
+
+ typedef itk::IndexValueType IndexValueType;
+
+ Sentinel1CalibrationLookupData()
+ : firstLineTime(0.)
+ , lastLineTime(0.)
+ , numOfLines(0)
+ , count(0)
+ , lineTimeInterval(0.)
+ {
+
+ }
+
+ virtual ~Sentinel1CalibrationLookupData()
+ {
+
+ }
+
+ void InitParameters(short type, double ft, double lt,
+ int lines, int c,
+ std::vector<Sentinel1CalibrationStruct> vlist)
+ {
+ firstLineTime = ft;
+ lastLineTime = lt;
+ numOfLines = lines;
+ count = c;
+ calibrationVectorList = vlist;
+ this->SetType(type);
+ lineTimeInterval = (lt - ft) / ((lines - 1) * 1.0);
+ }
+
+ virtual double GetValue(const IndexValueType x, const IndexValueType y)
+ {
+ const int calVecIdx = GetVectorIndex(y);
+ const Sentinel1CalibrationStruct vec0 = calibrationVectorList[calVecIdx];
+ const Sentinel1CalibrationStruct vec1 = calibrationVectorList[calVecIdx + 1];
+ const double azTime = firstLineTime + y * lineTimeInterval;
+ const double muY = (azTime - vec0.timeMJD) /(vec1.timeMJD - vec0.timeMJD);
+ const int pixelIdx = GetPixelIndex(x, calibrationVectorList[calVecIdx]);
+ const double muX = (static_cast<float>(x) - static_cast<float>(vec0.pixels[pixelIdx])) / (static_cast<float>(vec0.pixels[pixelIdx + 1] - vec0.pixels[pixelIdx]));
+ const double lutVal = (1 - muY) * ((1 - muX) * vec0.vect[pixelIdx] + muX * vec0.vect[pixelIdx + 1]) +
+ muY * ((1 - muX) * vec1.vect[pixelIdx] + muX * vec1.vect[pixelIdx + 1]);
+ return lutVal;
+ }
+
+ int GetVectorIndex(int y)
+ {
+ for (int i = 1; i < count; i++)
+ {
+ if (y < calibrationVectorList[i].line)
+ {
+ return i - 1;
+ }
+ }
+ return -1;
+
+ }
+
+ int GetPixelIndex(int x, const Sentinel1CalibrationStruct& calVec)
+ {
+ const int size = calVec.pixels.size();
+ for (int i = 0; i < size; i++)
+ {
+ if (x < calVec.pixels[i])
+ {
+ return i - 1;
+ }
+ }
+ return size - 2;
+ }
+
+private:
+
+ Sentinel1CalibrationLookupData(const Self&); //purposely not implemented
+
+ void operator =(const Self&); //purposely not implemented
+
+ double firstLineTime;
+ double lastLineTime;
+ int numOfLines;
+ int count;
+ std::vector<Sentinel1CalibrationStruct> calibrationVectorList;
+ double lineTimeInterval;
+
+};
+
+
+} // end namespace otb
+
+#endif
diff --git a/Modules/Core/Metadata/include/otbSentinel1ImageMetadataInterfaceFactory.h b/Modules/Core/Metadata/include/otbSentinel1ImageMetadataInterfaceFactory.h
new file mode 100644
index 0000000000000000000000000000000000000000..76964bb4fc2beca05728862e17f16719e0087041
--- /dev/null
+++ b/Modules/Core/Metadata/include/otbSentinel1ImageMetadataInterfaceFactory.h
@@ -0,0 +1,69 @@
+/*=========================================================================
+
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+=========================================================================*/
+#ifndef __otbSentinel1ImageMetadataInterfaceFactory_h
+#define __otbSentinel1ImageMetadataInterfaceFactory_h
+
+
+#include "itkObjectFactoryBase.h"
+
+namespace otb
+{
+/** \class Sentinel1ImageMetadataInterfaceFactory
+ * \brief Creating an instance of a ImageMetadataInterface object using object factory.
+ *
+ * \ingroup OTBMetadata
+ */
+class ITK_EXPORT Sentinel1ImageMetadataInterfaceFactory : public itk::ObjectFactoryBase
+{
+public:
+ /** Standard class typedefs. */
+ typedef Sentinel1ImageMetadataInterfaceFactory Self;
+ typedef itk::ObjectFactoryBase Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+
+ /** Class methods used to interface with the registered factories. */
+ virtual const char* GetITKSourceVersion(void) const;
+ virtual const char* GetDescription(void) const;
+
+ /** Method for class instantiation. */
+ itkFactorylessNewMacro(Self);
+
+ /** Run-time type information (and related methods). */
+ itkTypeMacro(Sentinel1ImageMetadataInterfaceFactory, itk::ObjectFactoryBase);
+
+ /** Register one factory of this type */
+ static void RegisterOneFactory(void)
+ {
+ Pointer factory = Sentinel1ImageMetadataInterfaceFactory::New();
+ itk::ObjectFactoryBase::RegisterFactory(factory);
+ }
+
+protected:
+ Sentinel1ImageMetadataInterfaceFactory();
+ virtual ~Sentinel1ImageMetadataInterfaceFactory();
+
+private:
+ Sentinel1ImageMetadataInterfaceFactory(const Self &); //purposely not implemented
+ void operator =(const Self&); //purposely not implemented
+
+};
+
+} // end namespace otb
+
+#endif
diff --git a/Modules/Core/Metadata/otb-module.cmake b/Modules/Core/Metadata/otb-module.cmake
index 240e6a3ee1cec76586a97c0078a34861dfb410ee..6e0e51fa4d48d4a1238a7a8de978f6f1a0a4ddc8 100644
--- a/Modules/Core/Metadata/otb-module.cmake
+++ b/Modules/Core/Metadata/otb-module.cmake
@@ -5,7 +5,7 @@ are supported. for instance: Pleiades, SPOT6, TerraSar, and so on).")
otb_module(OTBMetadata
DEPENDS
OTBObjectList
- OTBBoost
+ OTBBoostAdapters
OTBITK
OTBOSSIMAdapters
OTBCommon
diff --git a/Modules/Core/Metadata/src/CMakeLists.txt b/Modules/Core/Metadata/src/CMakeLists.txt
index 458188d5246599d101d8cfa68384704f8e7929ae..a81509ee9cba75db47fa7fb168357b91525385f2 100644
--- a/Modules/Core/Metadata/src/CMakeLists.txt
+++ b/Modules/Core/Metadata/src/CMakeLists.txt
@@ -1,34 +1,52 @@
set(OTBMetadata_SRC
+ otbWorldView2ImageMetadataInterfaceFactory.cxx
otbWorldView2ImageMetadataInterface.cxx
+
+ otbQuickBirdImageMetadataInterfaceFactory.cxx
otbQuickBirdImageMetadataInterface.cxx
- otbWorldView2ImageMetadataInterfaceFactory.cxx
- otbTerraSarImageMetadataInterface.cxx
- otbImageMetadataInterfaceBase.cxx
+
otbImageMetadataInterfaceFactory.cxx
- otbFormosatImageMetadataInterface.cxx
+ otbImageMetadataInterfaceBase.cxx
+
otbSpotImageMetadataInterfaceFactory.cxx
+ otbSpotImageMetadataInterface.cxx
+
otbDefaultImageMetadataInterfaceFactory.cxx
otbSarDefaultImageMetadataInterfaceFactory.cxx
otbOpticalDefaultImageMetadataInterfaceFactory.cxx
- otbSpotImageMetadataInterface.cxx
+
otbFormosatImageMetadataInterfaceFactory.cxx
+ otbFormosatImageMetadataInterface.cxx
+
otbOpticalImageMetadataInterfaceFactory.cxx
+ otbOpticalImageMetadataInterface.cxx
+
otbSpot6ImageMetadataInterfaceFactory.cxx
- otbSarImageMetadataInterfaceFactory.cxx
otbSpot6ImageMetadataInterface.cxx
+
+ otbIkonosImageMetadataInterfaceFactory.cxx
otbIkonosImageMetadataInterface.cxx
+
+ otbPleiadesImageMetadataInterfaceFactory.cxx
otbPleiadesImageMetadataInterface.cxx
- otbQuickBirdImageMetadataInterfaceFactory.cxx
- otbOpticalImageMetadataInterface.cxx
- otbIkonosImageMetadataInterfaceFactory.cxx
+
+ otbSarImageMetadataInterfaceFactory.cxx
otbSarImageMetadataInterface.cxx
- otbPleiadesImageMetadataInterfaceFactory.cxx
+
otbTerraSarImageMetadataInterfaceFactory.cxx
+ otbTerraSarImageMetadataInterface.cxx
+
+ otbSentinel1ImageMetadataInterfaceFactory.cxx
+ otbSentinel1ImageMetadataInterface.cxx
+
+ otbRadarsat2ImageMetadataInterfaceFactory.cxx
+ otbRadarsat2ImageMetadataInterface.cxx
+
otbNoDataHelper.cxx
)
add_library(OTBMetadata ${OTBMetadata_SRC})
-target_link_libraries(OTBMetadata
+target_link_libraries(OTBMetadata
${OTBBoost_LIBRARIES}
${OTBCommon_LIBRARIES}
${OTBOSSIMAdapters_LIBRARIES}
diff --git a/Modules/Core/Metadata/src/otbImageMetadataInterfaceFactory.cxx b/Modules/Core/Metadata/src/otbImageMetadataInterfaceFactory.cxx
index 248a69f7f936040f9c7ebf8f6529b9cc9743dc13..31ff54ac85a44fed7046c77541c120fc79204146 100644
--- a/Modules/Core/Metadata/src/otbImageMetadataInterfaceFactory.cxx
+++ b/Modules/Core/Metadata/src/otbImageMetadataInterfaceFactory.cxx
@@ -32,6 +32,8 @@
// SAR Sensors
#include "otbTerraSarImageMetadataInterfaceFactory.h"
+#include "otbSentinel1ImageMetadataInterfaceFactory.h"
+#include "otbRadarsat2ImageMetadataInterfaceFactory.h"
#include "itkMutexLock.h"
#include "itkMutexLockHolder.h"
@@ -109,7 +111,9 @@ ImageMetadataInterfaceFactory
itk::ObjectFactoryBase::RegisterFactory(FormosatImageMetadataInterfaceFactory::New());
itk::ObjectFactoryBase::RegisterFactory(QuickBirdImageMetadataInterfaceFactory::New());
itk::ObjectFactoryBase::RegisterFactory(WorldView2ImageMetadataInterfaceFactory::New());
- itk::ObjectFactoryBase::RegisterFactory( TerraSarImageMetadataInterfaceFactory::New() );
+ itk::ObjectFactoryBase::RegisterFactory(TerraSarImageMetadataInterfaceFactory::New());
+ itk::ObjectFactoryBase::RegisterFactory(Sentinel1ImageMetadataInterfaceFactory::New());
+ itk::ObjectFactoryBase::RegisterFactory(Radarsat2ImageMetadataInterfaceFactory::New());
firstTime = false;
}
}
diff --git a/Modules/Core/Metadata/src/otbRadarsat2ImageMetadataInterface.cxx b/Modules/Core/Metadata/src/otbRadarsat2ImageMetadataInterface.cxx
new file mode 100644
index 0000000000000000000000000000000000000000..748c45d65419a9b9dea843f12fed6d93ba3e4f00
--- /dev/null
+++ b/Modules/Core/Metadata/src/otbRadarsat2ImageMetadataInterface.cxx
@@ -0,0 +1,260 @@
+/*=========================================================================
+
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+=========================================================================*/
+
+#include "otbSarImageMetadataInterface.h"
+#include "otbRadarsat2ImageMetadataInterface.h"
+
+#include "otbMacro.h"
+#include "itkMetaDataObject.h"
+#include "otbImageKeywordlist.h"
+
+//useful constants
+#include <otbMath.h>
+
+namespace otb
+{
+
+Radarsat2ImageMetadataInterface
+::Radarsat2ImageMetadataInterface()
+{
+
+}
+
+bool
+Radarsat2ImageMetadataInterface::CanRead() const
+{
+ std::string sensorID = GetSensorID();
+
+ if (sensorID.find("RADARSAT-2") != std::string::npos)
+ {
+ return true;
+ }
+ else
+ return false;
+}
+
+
+void
+Radarsat2ImageMetadataInterface::
+CreateCalibrationLookupData(const short type)
+ {
+ std::string lut = "SigmaNought";
+
+ switch (type)
+ {
+ case SarCalibrationLookupData::BETA:
+ {
+ lut = "BetaNought";
+ }
+ break;
+
+ case SarCalibrationLookupData::GAMMA:
+ {
+ lut = "GammaNought";
+ }
+ break;
+
+ case SarCalibrationLookupData::DN:
+ {
+ lut = "DN";
+ }
+ break;
+
+ case SarCalibrationLookupData::SIGMA:
+ default:
+ {
+ lut = "SigmaNought";
+ }
+ break;
+ }
+
+ const ImageKeywordlistType imageKeywordlist = this->GetImageKeywordlist();
+ const std::string key = "referenceNoiseLevel[" + lut + "].gain";
+
+ Radarsat2CalibrationLookupData::GainListType glist;
+ int offset = 0;
+
+ Utils::ConvertStringToVector(imageKeywordlist.GetMetadataByKey("referenceNoiseLevel[" + lut + "].gain"), glist, "referenceNoiseLevel[" + lut + "].gain");
+
+ Utils::LexicalCast<int>(imageKeywordlist.GetMetadataByKey("referenceNoiseLevel[" + lut + "].offset"), "referenceNoiseLevel[" + lut + "].offset");
+
+ Radarsat2CalibrationLookupData::Pointer sarLut;
+ sarLut = Radarsat2CalibrationLookupData::New();
+ sarLut->InitParameters(type, offset, glist);
+ this->SetCalibrationLookupData(sarLut);
+
+}
+
+void
+Radarsat2ImageMetadataInterface
+::ParseDateTime(const char* key, std::vector<int>& dateFields) const
+{
+ if(dateFields.size() < 1 )
+ {
+ //parse from keyword list
+ if (!this->CanRead())
+ {
+ itkExceptionMacro(<< "Invalid Metadata, not a valid product");
+ }
+
+ const ImageKeywordlistType imageKeywordlist = this->GetImageKeywordlist();
+ if (!imageKeywordlist.HasKey(key))
+ {
+ itkExceptionMacro( << "no key named '" << key << "'");
+ }
+
+ std::string date_time_str = imageKeywordlist.GetMetadataByKey(key);
+ date_time_str.resize(date_time_str.size() - 1);
+ Utils::ConvertStringToVector(date_time_str, dateFields, key, "-T:.");
+ }
+}
+
+int
+Radarsat2ImageMetadataInterface::GetYear() const
+{
+ int value = 0;
+ ParseDateTime("support_data.image_date", m_AcquisitionDateFields);
+ if(m_AcquisitionDateFields.size() > 0 )
+ {
+ value = Utils::LexicalCast<int>( m_AcquisitionDateFields[0], "support_data.image_date(year)" );
+ }
+ return value;
+}
+
+int
+Radarsat2ImageMetadataInterface::GetMonth() const
+{
+ int value = 0;
+ ParseDateTime("support_data.image_date", m_AcquisitionDateFields);
+ if(m_AcquisitionDateFields.size() > 1 )
+ {
+ value = Utils::LexicalCast<int>( m_AcquisitionDateFields[1], "support_data.image_date(month)" );
+ }
+ return value;
+}
+
+int
+Radarsat2ImageMetadataInterface::GetDay() const
+{
+ int value = 0;
+ ParseDateTime("support_data.image_date", m_AcquisitionDateFields);
+ if(m_AcquisitionDateFields.size() > 2 )
+ {
+ value = Utils::LexicalCast<int>( m_AcquisitionDateFields[2], "support_data.image_date(day)" );
+ }
+ return value;
+}
+
+int
+Radarsat2ImageMetadataInterface::GetHour() const
+{
+ int value = 0;
+ ParseDateTime("support_data.image_date", m_AcquisitionDateFields);
+ if(m_AcquisitionDateFields.size() > 3 )
+ {
+ value = Utils::LexicalCast<int>( m_AcquisitionDateFields[3], "support_data.image_date(hour)" );
+ }
+ return value;
+}
+
+int
+Radarsat2ImageMetadataInterface::GetMinute() const
+{
+ int value = 0;
+ ParseDateTime("support_data.image_date", m_AcquisitionDateFields);
+ if(m_AcquisitionDateFields.size() > 4 )
+ {
+ value = Utils::LexicalCast<int>( m_AcquisitionDateFields[4], "support_data.image_date(minute)" );
+ }
+ return value;
+}
+
+int
+Radarsat2ImageMetadataInterface::GetProductionYear() const
+{
+ int value = 0;
+ ParseDateTime("support_data.date", m_ProductionDateFields);
+ if(m_ProductionDateFields.size() > 0 )
+ {
+ value = Utils::LexicalCast<int>( m_ProductionDateFields[0], "support_data.image_date(year)" );
+ }
+ return value;
+
+}
+
+int
+Radarsat2ImageMetadataInterface::GetProductionMonth() const
+{
+ int value = 0;
+ ParseDateTime("support_data.date", m_ProductionDateFields);
+ if(m_ProductionDateFields.size() > 1 )
+ {
+ value = Utils::LexicalCast<int>( m_ProductionDateFields[1], "support_data.image_date(production month)" );
+ }
+ return value;
+}
+
+int
+Radarsat2ImageMetadataInterface::GetProductionDay() const
+{
+ int value = 0;
+ ParseDateTime("support_data.date", m_ProductionDateFields);
+ if(m_ProductionDateFields.size() > 2 )
+ {
+ value = Utils::LexicalCast<int>( m_ProductionDateFields[2], "support_data.image_date(production day)" );
+ }
+ return value;
+}
+
+
+double
+Radarsat2ImageMetadataInterface::GetPRF() const
+{
+ return 0;
+}
+
+double
+Radarsat2ImageMetadataInterface::GetRSF() const
+{
+ return 0;
+}
+
+double
+Radarsat2ImageMetadataInterface::GetRadarFrequency() const
+{
+ return 0;
+}
+
+double
+Radarsat2ImageMetadataInterface::GetCenterIncidenceAngle() const
+{
+ return 0;
+}
+
+Radarsat2ImageMetadataInterface::UIntVectorType
+Radarsat2ImageMetadataInterface::
+GetDefaultDisplay() const
+{
+ UIntVectorType rgb(3);
+ rgb[0] = 0;
+ rgb[1] = 0;
+ rgb[2] = 0;
+ return rgb;
+}
+
+} // end namespace otb
diff --git a/Modules/Core/Metadata/src/otbRadarsat2ImageMetadataInterfaceFactory.cxx b/Modules/Core/Metadata/src/otbRadarsat2ImageMetadataInterfaceFactory.cxx
new file mode 100644
index 0000000000000000000000000000000000000000..e9f656cfc1ef64bdb6225559b6de5091d97c9f60
--- /dev/null
+++ b/Modules/Core/Metadata/src/otbRadarsat2ImageMetadataInterfaceFactory.cxx
@@ -0,0 +1,58 @@
+/*=========================================================================
+
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+=========================================================================*/
+
+
+#include <typeinfo>
+#include <cassert>
+
+#include "otbRadarsat2ImageMetadataInterfaceFactory.h"
+#include "otbRadarsat2ImageMetadataInterface.h"
+
+#include "itkCreateObjectFunction.h"
+#include "itkVersion.h"
+
+namespace otb
+{
+Radarsat2ImageMetadataInterfaceFactory
+::Radarsat2ImageMetadataInterfaceFactory()
+{
+ this->RegisterOverride("SarImageMetadataInterface",
+ "otbRadarsat2ImageMetadataInterface",
+ "Radarsat2 Metadata Interface",
+ 1,
+ itk::CreateObjectFunction<Radarsat2ImageMetadataInterface>::New());
+}
+
+Radarsat2ImageMetadataInterfaceFactory
+::~Radarsat2ImageMetadataInterfaceFactory()
+{
+}
+
+const char*
+Radarsat2ImageMetadataInterfaceFactory::GetITKSourceVersion(void) const
+{
+ return ITK_SOURCE_VERSION;
+}
+
+const char*
+Radarsat2ImageMetadataInterfaceFactory::GetDescription() const
+{
+ return "Radarsat2 Metadata Interface Factory, handle Radarsat2 metadata in OTB";
+}
+
+} // end namespace otb
diff --git a/Modules/Core/Metadata/src/otbSarImageMetadataInterface.cxx b/Modules/Core/Metadata/src/otbSarImageMetadataInterface.cxx
index d1a46a96c6deb37dcdd80531bba5ba1ce72c5332..4dc45688d6f3e86a3637a86ae5735456627062f8 100644
--- a/Modules/Core/Metadata/src/otbSarImageMetadataInterface.cxx
+++ b/Modules/Core/Metadata/src/otbSarImageMetadataInterface.cxx
@@ -28,8 +28,90 @@ namespace otb
SarImageMetadataInterface
::SarImageMetadataInterface()
{
+
}
+const std::string
+SarImageMetadataInterface
+::GetProductType() const
+{
+ const MetaDataDictionaryType& dict = this->GetMetaDataDictionary();
+ if (!this->CanRead())
+ {
+ itkExceptionMacro(<< "Invalid Metadata");
+ }
+
+ ImageKeywordlistType imageKeywordlist;
+
+ if (dict.HasKey(MetaDataKey::OSSIMKeywordlistKey))
+ {
+ itk::ExposeMetaData<ImageKeywordlistType>(dict, MetaDataKey::OSSIMKeywordlistKey, imageKeywordlist);
+ }
+
+ if (imageKeywordlist.HasKey("support_data.product_type"))
+ {
+ const std::string product_type = imageKeywordlist.GetMetadataByKey("support_data.product_type");
+ return product_type;
+ }
+ return "";
+}
+
+const std::string
+SarImageMetadataInterface
+::GetAcquisitionMode() const
+{
+ const MetaDataDictionaryType& dict = this->GetMetaDataDictionary();
+ if (!this->CanRead())
+ {
+ itkExceptionMacro(<< "Invalid Metadata");
+ }
+
+ ImageKeywordlistType imageKeywordlist;
+
+ if (dict.HasKey(MetaDataKey::OSSIMKeywordlistKey))
+ {
+ itk::ExposeMetaData<ImageKeywordlistType>(dict, MetaDataKey::OSSIMKeywordlistKey, imageKeywordlist);
+ }
+
+ if (imageKeywordlist.HasKey("support_data.acquisition_mode"))
+ {
+ const std::string acquisition_mode = imageKeywordlist.GetMetadataByKey("support_data.acquisition_mode");
+ return acquisition_mode;
+ }
+ return "";
+}
+
+void
+SarImageMetadataInterface
+::CreateCalibrationLookupData(const short itkNotUsed(t))
+{
+
+}
+
+const SarImageMetadataInterface::LookupDataPointerType
+SarImageMetadataInterface
+::GetCalibrationLookupData(const short type)
+{
+ if (HasCalibrationLookupDataFlag())
+ {
+ CreateCalibrationLookupData(type);
+ }
+
+ return m_SarLut;
+}
+
+bool
+SarImageMetadataInterface
+::HasCalibrationLookupDataFlag() const
+{
+ const ImageKeywordlist imageKeywordlist = this->GetImageKeywordlist();
+ /* checking if the key exist is more than enough */
+ if (imageKeywordlist.HasKey("support_data.calibration_lookup_flag"))
+ {
+ return true;
+ }
+ return false;
+}
SarImageMetadataInterface::RealType
SarImageMetadataInterface
@@ -63,7 +145,6 @@ SarImageMetadataInterface
return SarImageMetadataInterface::GetConstantValuePointSet(0.0);
}
-
SarImageMetadataInterface::PointSetPointer
SarImageMetadataInterface
::GetRadiometricCalibrationAntennaPatternNewGain() const
@@ -106,6 +187,12 @@ SarImageMetadataInterface
return polynomialDegree;
}
+double
+SarImageMetadataInterface
+::GetRescalingFactor() const
+{
+ return 1.0;
+}
SarImageMetadataInterface::IndexType
SarImageMetadataInterface
::GetRadiometricCalibrationNoisePolynomialDegree() const
diff --git a/Modules/Core/Metadata/src/otbSarImageMetadataInterfaceFactory.cxx b/Modules/Core/Metadata/src/otbSarImageMetadataInterfaceFactory.cxx
index 8075a2303c4dd83a1aa58e95ef490cb852bb7fe3..f0c7bcff04e050b29babcf8d89c88a5493cec70e 100644
--- a/Modules/Core/Metadata/src/otbSarImageMetadataInterfaceFactory.cxx
+++ b/Modules/Core/Metadata/src/otbSarImageMetadataInterfaceFactory.cxx
@@ -22,6 +22,8 @@
// SAR Sensors
#include "otbTerraSarImageMetadataInterfaceFactory.h"
+#include "otbSentinel1ImageMetadataInterfaceFactory.h"
+#include "otbRadarsat2ImageMetadataInterfaceFactory.h"
#include "itkMutexLock.h"
#include "itkMutexLockHolder.h"
@@ -78,7 +80,9 @@ SarImageMetadataInterfaceFactory
itk::MutexLockHolder<itk::SimpleMutexLock> mutexHolder(mutex);
if (firstTime)
{
- itk::ObjectFactoryBase::RegisterFactory( TerraSarImageMetadataInterfaceFactory::New() );
+ itk::ObjectFactoryBase::RegisterFactory(TerraSarImageMetadataInterfaceFactory::New());
+ itk::ObjectFactoryBase::RegisterFactory(Sentinel1ImageMetadataInterfaceFactory::New());
+ itk::ObjectFactoryBase::RegisterFactory(Radarsat2ImageMetadataInterfaceFactory::New());
firstTime = false;
}
}
diff --git a/Modules/Core/Metadata/src/otbSentinel1ImageMetadataInterface.cxx b/Modules/Core/Metadata/src/otbSentinel1ImageMetadataInterface.cxx
new file mode 100644
index 0000000000000000000000000000000000000000..daac6d7c363cabf02cf7036876653768b23144b3
--- /dev/null
+++ b/Modules/Core/Metadata/src/otbSentinel1ImageMetadataInterface.cxx
@@ -0,0 +1,339 @@
+/*=========================================================================
+
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+=========================================================================*/
+
+#include "otbSarImageMetadataInterface.h"
+#include "otbSentinel1ImageMetadataInterface.h"
+
+#include "otbMacro.h"
+#include "itkMetaDataObject.h"
+#include "otbImageKeywordlist.h"
+
+//useful constants
+#include <otbMath.h>
+
+namespace otb
+{
+
+Sentinel1ImageMetadataInterface
+::Sentinel1ImageMetadataInterface()
+{
+
+}
+
+bool
+Sentinel1ImageMetadataInterface::CanRead() const
+{
+ std::string sensorID = GetSensorID();
+
+ if (sensorID.find("SENTINEL-1") != std::string::npos)
+ {
+ return true;
+ }
+ else
+ return false;
+}
+
+void
+Sentinel1ImageMetadataInterface
+::CreateCalibrationLookupData(const short type)
+ {
+ bool sigmaLut = false;
+ bool betaLut = false;
+ bool gammaLut = false;
+ bool dnLut = false;
+
+ switch (type)
+ {
+ case SarCalibrationLookupData::BETA:
+ {
+ betaLut = true;
+ }
+ break;
+
+ case SarCalibrationLookupData::GAMMA:
+ {
+ gammaLut = true;
+ }
+ break;
+
+ case SarCalibrationLookupData::DN:
+ {
+ dnLut = true;
+ }
+ break;
+
+ case SarCalibrationLookupData::SIGMA:
+ default:
+ sigmaLut = true;
+ break;
+ }
+
+ const ImageKeywordlistType imageKeywordlist = this->GetImageKeywordlist();
+
+ const double firstLineTime = Utils::LexicalCast<double>(imageKeywordlist.GetMetadataByKey("calibration.startTime"), "calibration.startTime(double)");
+
+ const double lastLineTime = Utils::LexicalCast<double>(imageKeywordlist.GetMetadataByKey("calibration.stopTime"), "calibration.stopTime(double)");
+
+ const std::string bandPrefix = "Band[0]."; //make && use GetBandPrefix(subSwath, polarisation)
+
+ const int numOfLines = Utils::LexicalCast<int>(imageKeywordlist.GetMetadataByKey(bandPrefix + "number_lines"), bandPrefix + "number_lines(int)");
+
+ const int count = Utils::LexicalCast<int>(imageKeywordlist.GetMetadataByKey("calibration.count"), "calibration.count");
+
+ std::vector<Sentinel1CalibrationStruct> calibrationVectorList(count);
+
+ for(int i = 0; i < count; i++)
+ {
+ Sentinel1CalibrationStruct calibrationVector;
+
+ std::stringstream prefix;
+ prefix << "calibration.calibrationVector[" << i << "].";
+
+ calibrationVector.line = Utils::LexicalCast<int>(imageKeywordlist.GetMetadataByKey(prefix.str() + "line"), prefix.str() + "line");
+
+ calibrationVector.timeMJD = Utils::LexicalCast<double>(imageKeywordlist.GetMetadataByKey(prefix.str() + "azimuthTime"), prefix.str() + "azimuthTime");
+
+ Utils::ConvertStringToVector(imageKeywordlist.GetMetadataByKey(prefix.str() + "pixel"), calibrationVector.pixels, prefix.str() + "pixel");
+
+ if (sigmaLut) {
+ Utils::ConvertStringToVector(imageKeywordlist.GetMetadataByKey(prefix.str() + "sigmaNought"), calibrationVector.vect, prefix.str() + "sigmaNought");
+ }
+
+ if (betaLut) {
+ Utils::ConvertStringToVector(imageKeywordlist.GetMetadataByKey(prefix.str() + "betaNought"), calibrationVector.vect, prefix.str() + "betaNought");
+ }
+
+ if (gammaLut) {
+ Utils::ConvertStringToVector(imageKeywordlist.GetMetadataByKey(prefix.str() + "gamma"), calibrationVector.vect, prefix.str() + "gamma");
+ }
+
+ if (dnLut) {
+ Utils::ConvertStringToVector(imageKeywordlist.GetMetadataByKey(prefix.str() + "dn"), calibrationVector.vect, prefix.str() + "dn");
+ }
+
+ calibrationVectorList[i] = calibrationVector;
+
+ }
+
+ Sentinel1CalibrationLookupData::Pointer sarLut;
+ sarLut = Sentinel1CalibrationLookupData::New();
+ sarLut->InitParameters(type, firstLineTime, lastLineTime, numOfLines, count, calibrationVectorList);
+ this->SetCalibrationLookupData(sarLut);
+
+
+ }
+
+void
+Sentinel1ImageMetadataInterface
+::ParseDateTime(const char* key, std::vector<int>& dateFields) const
+{
+ if(dateFields.size() < 1 )
+ {
+ //parse from keyword list
+ if (!this->CanRead())
+ {
+ itkExceptionMacro(<< "Invalid Metadata, not a valid product");
+ }
+
+ const ImageKeywordlistType imageKeywordlist = this->GetImageKeywordlist();
+ if (!imageKeywordlist.HasKey(key))
+ {
+ itkExceptionMacro( << "no key named " << key );
+ }
+
+ const std::string date_time_str = imageKeywordlist.GetMetadataByKey(key);
+ Utils::ConvertStringToVector(date_time_str, dateFields, key, "T:-.");
+ }
+
+}
+
+int
+Sentinel1ImageMetadataInterface::GetYear() const
+{
+ int value = 0;
+ ParseDateTime("support_data.image_date", m_AcquisitionDateFields);
+ if(m_AcquisitionDateFields.size() > 0 )
+ {
+ value = Utils::LexicalCast<int>( m_AcquisitionDateFields[0], "support_data.image_date:year(int)" );
+ }
+ else
+ {
+ itkExceptionMacro( << "Invalid year" );
+ }
+ return value;
+}
+
+int
+Sentinel1ImageMetadataInterface::GetMonth() const
+{
+ int value = 0;
+ ParseDateTime("support_data.image_date", m_AcquisitionDateFields);
+ if(m_AcquisitionDateFields.size() > 1 )
+ {
+ value = Utils::LexicalCast<int>( m_AcquisitionDateFields[1], "support_data.image_date:month(int)" );
+ }
+ else
+ {
+ itkExceptionMacro( << "Invalid month" );
+ }
+ return value;
+}
+
+int
+Sentinel1ImageMetadataInterface::GetDay() const
+{
+ int value = 0;
+ ParseDateTime("support_data.image_date", m_AcquisitionDateFields);
+ if(m_AcquisitionDateFields.size() > 2 )
+ {
+ value = Utils::LexicalCast<int>( m_AcquisitionDateFields[2], "support_data.image_date:day(int)");
+ }
+ else
+ {
+ itkExceptionMacro( << "Invalid day" );
+ }
+ return value;
+}
+
+int
+Sentinel1ImageMetadataInterface::GetHour() const
+{
+ int value = 0;
+ ParseDateTime("support_data.image_date", m_AcquisitionDateFields);
+ if(m_AcquisitionDateFields.size() > 3 )
+ {
+ value = Utils::LexicalCast<int>( m_AcquisitionDateFields[3], "support_data.image_date:hour(int)");
+ }
+ else
+ {
+ itkExceptionMacro( << "Invalid hour" );
+ }
+ return value;
+}
+
+int
+Sentinel1ImageMetadataInterface::GetMinute() const
+{
+ int value = 0;
+ ParseDateTime("support_data.image_date", m_AcquisitionDateFields);
+ if(m_AcquisitionDateFields.size() > 4 )
+ {
+ value = Utils::LexicalCast<int>( m_AcquisitionDateFields[4], "support_data.image_date:minute(int)");
+ }
+ else
+ {
+ itkExceptionMacro( << "Invalid minute" );
+ }
+ return value;
+}
+
+int
+Sentinel1ImageMetadataInterface::GetProductionYear() const
+{
+ int value = 0;
+ ParseDateTime("support_data.date", m_ProductionDateFields);
+ if(m_ProductionDateFields.size() > 0 )
+ {
+ value = Utils::LexicalCast<int>( m_ProductionDateFields[0], "support_data.date:year(int)" );
+ }
+ else
+ {
+ itkExceptionMacro( << "Invalid production year" );
+ }
+ return value;
+
+}
+
+int
+Sentinel1ImageMetadataInterface::GetProductionMonth() const
+{
+ int value = 0;
+ ParseDateTime("support_data.date", m_ProductionDateFields);
+ if(m_ProductionDateFields.size() > 1 )
+ {
+ value = Utils::LexicalCast<int>( m_ProductionDateFields[1], "support_data.date:month(int)" );
+ }
+ else
+ {
+ itkExceptionMacro( << "Invalid production month" );
+ }
+ return value;
+}
+
+int
+Sentinel1ImageMetadataInterface::GetProductionDay() const
+{
+ int value = 0;
+ ParseDateTime("support_data.date", m_ProductionDateFields);
+ if(m_ProductionDateFields.size() > 2 )
+ {
+ value = Utils::LexicalCast<int>( m_ProductionDateFields[2], "support_data.date:day(int)" );
+ }
+ else
+ {
+ itkExceptionMacro( << "Invalid production day" );
+ }
+ return value;
+}
+
+double
+Sentinel1ImageMetadataInterface::GetPRF() const
+{
+ double value = 0;
+ const ImageKeywordlistType imageKeywordlist = this->GetImageKeywordlist();
+ if (!imageKeywordlist.HasKey("support_data.pulse_repetition_frequency"))
+ {
+ return value;
+ }
+
+ value = Utils::LexicalCast<double>( imageKeywordlist.GetMetadataByKey("support_data.pulse_repetition_frequency"),
+ "support_data.pulse_repetition_frequency(double)" );
+
+ return value;
+}
+
+
+Sentinel1ImageMetadataInterface::UIntVectorType
+Sentinel1ImageMetadataInterface::GetDefaultDisplay() const
+{
+ UIntVectorType rgb(3);
+ rgb[0] = 0;
+ rgb[1] = 0;
+ rgb[2] = 0;
+ return rgb;
+}
+
+double
+Sentinel1ImageMetadataInterface::GetRSF() const
+{
+ return 0;
+}
+
+double
+Sentinel1ImageMetadataInterface::GetRadarFrequency() const
+{
+ return 0;
+}
+
+double
+Sentinel1ImageMetadataInterface::GetCenterIncidenceAngle() const
+{
+ return 0;
+}
+
+} // end namespace otb
diff --git a/Modules/Core/Metadata/src/otbSentinel1ImageMetadataInterfaceFactory.cxx b/Modules/Core/Metadata/src/otbSentinel1ImageMetadataInterfaceFactory.cxx
new file mode 100644
index 0000000000000000000000000000000000000000..424f825c20ff9e735ab582d44b1519e430374f73
--- /dev/null
+++ b/Modules/Core/Metadata/src/otbSentinel1ImageMetadataInterfaceFactory.cxx
@@ -0,0 +1,58 @@
+/*=========================================================================
+
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+=========================================================================*/
+
+
+#include <typeinfo>
+#include <cassert>
+
+#include "otbSentinel1ImageMetadataInterfaceFactory.h"
+#include "otbSentinel1ImageMetadataInterface.h"
+
+#include "itkCreateObjectFunction.h"
+#include "itkVersion.h"
+
+namespace otb
+{
+Sentinel1ImageMetadataInterfaceFactory
+::Sentinel1ImageMetadataInterfaceFactory()
+{
+ this->RegisterOverride("SarImageMetadataInterface",
+ "otbSentinel1ImageMetadataInterface",
+ "Sentinel1 Metadata Interface",
+ 1,
+ itk::CreateObjectFunction<Sentinel1ImageMetadataInterface>::New());
+}
+
+Sentinel1ImageMetadataInterfaceFactory
+::~Sentinel1ImageMetadataInterfaceFactory()
+{
+}
+
+const char*
+Sentinel1ImageMetadataInterfaceFactory::GetITKSourceVersion(void) const
+{
+ return ITK_SOURCE_VERSION;
+}
+
+const char*
+Sentinel1ImageMetadataInterfaceFactory::GetDescription() const
+{
+ return "Sentinel1 Metadata Interface Factory, handle Sentinel1 metadata in OTB";
+}
+
+} // end namespace otb
diff --git a/Modules/Core/Metadata/test/CMakeLists.txt b/Modules/Core/Metadata/test/CMakeLists.txt
index b48fbffaef79197293daba9fec698e7fe0581bfb..23e6ef48e261edb6a9d8a5e4cd89acb047b646da 100644
--- a/Modules/Core/Metadata/test/CMakeLists.txt
+++ b/Modules/Core/Metadata/test/CMakeLists.txt
@@ -19,10 +19,13 @@ otbSpotImageMetadataInterfaceNew.cxx
otbQuickBirdImageMetadataInterfaceNew.cxx
otbIkonosImageMetadataInterfaceNew.cxx
otbTerraSarImageMetadataInterfaceNew.cxx
+otbSentinel1ImageMetadataInterfaceNew.cxx
otbWorldView2ImageMetadataInterfaceNew.cxx
otbDefaultImageMetadataInterface.cxx
otbImageMetadataInterfaceTest2.cxx
otbNoDataHelperTest.cxx
+otbSarCalibrationLookupDataTest.cxx
+otbRadarsat2ImageMetadataInterfaceNew.cxx
)
add_executable(otbMetadataTestDriver ${OTBMetadataTests})
@@ -74,6 +77,28 @@ otb_add_test(NAME ioTvSarImageMetadataInterfaceTest_TSX1PANGKALANBUUNUsingHHCosF
${TEMP}/ioTvSarImageMetadataInterface_TSX1PANGKALANBUUNUsingHHCosFile.txt
)
+otb_add_test(NAME ioTvImageMetadataInterfaceBaseTest_SENTINEL1 COMMAND otbMetadataTestDriver
+ --compare-ascii ${EPSILON_9} ${BASELINE_FILES}/ioTvImageMetadataInterfaceBase_SENTINEL1.txt
+ ${TEMP}/ioTvImageMetadataInterfaceBase_SENTINEL1.txt
+ otbImageMetadataInterfaceBaseTest
+ LARGEINPUT{SENTINEL1/S1A_S6_SLC__1SSV_20150619T195043/measurement/s1a-s6-slc-vv-20150619t195043-20150619t195101-006447-00887d-001.tiff}
+ ${TEMP}/ioTvImageMetadataInterfaceBase_SENTINEL1.txt
+ )
+
+otb_add_test(NAME ioTvImageMetadataInterfaceBaseTest_RADARSAT2 COMMAND otbMetadataTestDriver
+ --compare-ascii ${EPSILON_9} ${BASELINE_FILES}/ioTvImageMetadataInterfaceBase_RADARSAT2.txt
+ ${TEMP}/ioTvImageMetadataInterfaceBase_RADARSAT2.txt
+ otbImageMetadataInterfaceBaseTest
+ LARGEINPUT{RADARSAT2/ALTONA/Fine_Quad-Pol_Dataset/PK6621_DK406_FQ9_20080405_124900_HH_VV_HV_VH_SLC_Altona/imagery_HV.tif}
+ ${TEMP}/ioTvImageMetadataInterfaceBase_RADARSAT2.txt
+ )
+
+otb_add_test(NAME ioTuSentinel1ImageMetadataInterfaceNew COMMAND otbMetadataTestDriver
+ otbSentinel1ImageMetadataInterfaceNew )
+
+otb_add_test(NAME ioTuRadarsat2ImageMetadataInterfaceNew COMMAND otbMetadataTestDriver
+ otbRadarsat2ImageMetadataInterfaceNew )
+
otb_add_test(NAME ioTuDefaultImageMetadataInterfaceFactoryNew COMMAND otbMetadataTestDriver
otbDefaultImageMetadataInterfaceFactoryNew )
@@ -353,6 +378,21 @@ foreach( current_file ${GenericTestSPOT6_DATA_INPUTS} )
)
endforeach()
-
otb_add_test(NAME coreMetaDataNoDataHelperTest COMMAND otbMetadataTestDriver
otbNoDataHelperTest)
+
+otb_add_test(NAME ioTvSarCalibrationLookupDataTest_SENTINEL1 COMMAND otbMetadataTestDriver
+ --compare-ascii ${NOTOL} ${BASELINE_FILES}/ioTvSarCalibrationLookupDataTest_SENTINEL1.txt
+ ${TEMP}/ioTvSarCalibrationLookupDataTest_SENTINEL1.txt
+ otbSarCalibrationLookupDataTest
+ ${INPUTDATA}/SENTINEL1_SLC_S6_1S_extract_300_300.tif?&geom=${INPUTDATA}/SENTINEL1_SLC_S6_1S_extract_300_300.geom
+ ${TEMP}/ioTvSarCalibrationLookupDataTest_SENTINEL1.txt
+ )
+
+otb_add_test(NAME ioTvSarCalibrationLookupDataTest_RADARSAT2 COMMAND otbMetadataTestDriver
+ --compare-ascii ${NOTOL} ${BASELINE_FILES}/ioTvSarCalibrationLookupDataTest_RADARSAT2.txt
+ ${TEMP}/ioTvSarCalibrationLookupDataTest_RADARSAT2.txt
+ otbSarCalibrationLookupDataTest
+ ${INPUTDATA}/RADARSAT2_ALTONA_300_300_VV.tif?&geom=${INPUTDATA}/RADARSAT2_ALTONA_300_300_VV.geom
+ ${TEMP}/ioTvSarCalibrationLookupDataTest_RADARSAT2.txt
+ )
\ No newline at end of file
diff --git a/Modules/Core/Metadata/test/otbMetadataTestDriver.cxx b/Modules/Core/Metadata/test/otbMetadataTestDriver.cxx
index cd06f2f46e9f46fb412b1f30a278374070ac7405..ea205e1d08d67b76de16468be0578a459cb549ec 100644
--- a/Modules/Core/Metadata/test/otbMetadataTestDriver.cxx
+++ b/Modules/Core/Metadata/test/otbMetadataTestDriver.cxx
@@ -18,8 +18,11 @@ void RegisterTests()
REGISTER_TEST(otbQuickBirdImageMetadataInterfaceNew);
REGISTER_TEST(otbIkonosImageMetadataInterfaceNew);
REGISTER_TEST(otbTerraSarImageMetadataInterfaceNew);
+ REGISTER_TEST(otbSentinel1ImageMetadataInterfaceNew);
REGISTER_TEST(otbWorldView2ImageMetadataInterfaceNew);
REGISTER_TEST(otbDefaultImageMetadataInterface);
REGISTER_TEST(otbImageMetadataInterfaceTest2);
REGISTER_TEST(otbNoDataHelperTest);
+ REGISTER_TEST(otbSarCalibrationLookupDataTest);
+ REGISTER_TEST(otbRadarsat2ImageMetadataInterfaceNew);
}
diff --git a/Modules/Core/Metadata/test/otbRadarsat2ImageMetadataInterfaceNew.cxx b/Modules/Core/Metadata/test/otbRadarsat2ImageMetadataInterfaceNew.cxx
new file mode 100644
index 0000000000000000000000000000000000000000..191ac66e7a3628638cba3443631d207d263f6178
--- /dev/null
+++ b/Modules/Core/Metadata/test/otbRadarsat2ImageMetadataInterfaceNew.cxx
@@ -0,0 +1,32 @@
+/*=========================================================================
+
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+=========================================================================*/
+
+
+
+#include "itkMacro.h"
+#include <iostream>
+#include "otbRadarsat2ImageMetadataInterface.h"
+
+int otbRadarsat2ImageMetadataInterfaceNew(int itkNotUsed(argc), char * itkNotUsed(argv) [])
+{
+ otb::Radarsat2ImageMetadataInterface::Pointer object = otb::Radarsat2ImageMetadataInterface::New();
+
+ std::cout << object << std::endl;
+
+ return EXIT_SUCCESS;
+}
diff --git a/Modules/Core/Metadata/test/otbSarCalibrationLookupDataTest.cxx b/Modules/Core/Metadata/test/otbSarCalibrationLookupDataTest.cxx
new file mode 100644
index 0000000000000000000000000000000000000000..5d323c95d9f81644e7256057177b01b4e3e25875
--- /dev/null
+++ b/Modules/Core/Metadata/test/otbSarCalibrationLookupDataTest.cxx
@@ -0,0 +1,75 @@
+/*=========================================================================
+
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+=========================================================================*/
+
+
+#include <iostream>
+#include "itkMacro.h"
+#include "otbImage.h"
+#include "otbImageFileReader.h"
+#include "otbSarImageMetadataInterfaceFactory.h"
+
+int otbSarCalibrationLookupDataTest(int argc, char* argv[])
+{
+ typedef double RealType;
+ typedef otb::SarImageMetadataInterface ImageMetadataInterfaceType;
+ typedef otb::SarCalibrationLookupData LookupDataType;
+ typedef otb::Image<double, 2> InputImageType;
+ typedef otb::ImageFileReader<InputImageType> ImageReaderType;
+
+ if (argc < 3 )
+ {
+ std::cerr << "Usage: otbSarCalibationLookupDataTest /path/to/input/file /path/to/output/file !"<< std::endl;
+ return EXIT_FAILURE;
+ }
+ ImageReaderType::Pointer reader = ImageReaderType::New();
+ reader->SetFileName(argv[1]);
+ reader->UpdateOutputInformation();
+
+ const char * outFileName = argv[2];
+ std::ofstream outfile;
+ outfile.open(outFileName);
+
+ ImageMetadataInterfaceType::Pointer imageMetadataInterface =
+ otb::SarImageMetadataInterfaceFactory::CreateIMI( reader->GetOutput()->GetMetaDataDictionary() );
+
+ if (!imageMetadataInterface.IsNotNull())
+ {
+ std::cerr << "cannot create a otb::SarImageMetadataInterface for input image." << std::endl;
+ return EXIT_FAILURE;
+ }
+
+ const std::string sensorId = imageMetadataInterface->GetSensorID();
+
+ outfile << sensorId << std::endl;
+
+ LookupDataType::Pointer lookupDataObj = imageMetadataInterface->GetCalibrationLookupData(0);
+
+ if (!lookupDataObj.IsNotNull())
+ {
+ std::cerr << "lookupDataObj is Null"<< std::endl;
+ return EXIT_FAILURE;
+ }
+
+ RealType lutVal = static_cast<RealType>(lookupDataObj->GetValue(10,19));
+
+ outfile << imageMetadataInterface->HasCalibrationLookupDataFlag() << std::endl;
+ outfile << lutVal << std::endl;
+
+ return EXIT_SUCCESS;
+
+}
diff --git a/Modules/Core/Metadata/test/otbSentinel1ImageMetadataInterfaceNew.cxx b/Modules/Core/Metadata/test/otbSentinel1ImageMetadataInterfaceNew.cxx
new file mode 100644
index 0000000000000000000000000000000000000000..c0a805ab1832a490c49fbbf2c1732c239efdbe8b
--- /dev/null
+++ b/Modules/Core/Metadata/test/otbSentinel1ImageMetadataInterfaceNew.cxx
@@ -0,0 +1,32 @@
+/*=========================================================================
+
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+=========================================================================*/
+
+
+
+#include "itkMacro.h"
+#include <iostream>
+#include "otbSentinel1ImageMetadataInterface.h"
+
+int otbSentinel1ImageMetadataInterfaceNew(int itkNotUsed(argc), char * itkNotUsed(argv) [])
+{
+ otb::Sentinel1ImageMetadataInterface::Pointer object = otb::Sentinel1ImageMetadataInterface::New();
+
+ std::cout << object << std::endl;
+
+ return EXIT_SUCCESS;
+}
diff --git a/Modules/Filtering/ImageManipulation/test/otbChangeNoDataValueFilter.cxx b/Modules/Filtering/ImageManipulation/test/otbChangeNoDataValueFilter.cxx
index 4bd558f4a9589795edaac72e023f28d1e67f737a..db371582435603f927fdd60004275ee41ed9e28e 100644
--- a/Modules/Filtering/ImageManipulation/test/otbChangeNoDataValueFilter.cxx
+++ b/Modules/Filtering/ImageManipulation/test/otbChangeNoDataValueFilter.cxx
@@ -70,12 +70,12 @@ int otbChangeNoDataValueFilter(int itkNotUsed(argc),char * itkNotUsed(argv) [])
for(it.GoToBegin();!it.IsAtEnd();++it,++count)
{
- if (count%2 == 0 and it.Get()!=-20.)
+ if (count%2 == 0 && it.Get()!=-20.)
{
std::cerr<<"Pixel should have new no-data value"<<std::endl;
failed = true;
}
- else if(count%2 == 1 and it.Get()!=10.)
+ else if(count%2 == 1 && it.Get()!=10.)
{
std::cerr<<"Pixel value should be unchanged"<<std::endl;
failed = true;
diff --git a/Modules/Filtering/ImageManipulation/test/otbImageToNoDataMaskFilter.cxx b/Modules/Filtering/ImageManipulation/test/otbImageToNoDataMaskFilter.cxx
index 109a2ec7d44c4ad2ff420337613260d64ba66b8a..d5c2b4283635847be4947e15791e785ec1924d9d 100644
--- a/Modules/Filtering/ImageManipulation/test/otbImageToNoDataMaskFilter.cxx
+++ b/Modules/Filtering/ImageManipulation/test/otbImageToNoDataMaskFilter.cxx
@@ -69,12 +69,12 @@ int otbImageToNoDataMaskFilter(int itkNotUsed(argc),char * itkNotUsed(argv) [])
for(it.GoToBegin();!it.IsAtEnd();++it,++count)
{
- if (count%2 == 0 and it.Get()!=0)
+ if (count%2 == 0 && it.Get()!=0)
{
std::cerr<<"Pixel should be masked"<<std::endl;
failed = true;
}
- else if(count%2 == 1 and it.Get()!=255)
+ else if(count%2 == 1 && it.Get()!=255)
{
std::cerr<<"Pixel should not be masked"<<std::endl;
failed = true;
diff --git a/Modules/Filtering/Polarimetry/include/otbReciprocalHAlphaImageFilter.h b/Modules/Filtering/Polarimetry/include/otbReciprocalHAlphaImageFilter.h
index f1747c595c0ad69b2f819cec9376e9b79634e1e3..0967d1747196fa2632a926d3eedfe26692a92927 100644
--- a/Modules/Filtering/Polarimetry/include/otbReciprocalHAlphaImageFilter.h
+++ b/Modules/Filtering/Polarimetry/include/otbReciprocalHAlphaImageFilter.h
@@ -78,13 +78,13 @@ public:
VNLMatrixType vnlMat(3, 3, 0.);
vnlMat[0][0] = ComplexType(T0, 0.);
- vnlMat[0][1] = std::conj(ComplexType(Coherency[1]));
- vnlMat[0][2] = std::conj(ComplexType(Coherency[2]));
- vnlMat[1][0] = ComplexType(Coherency[1]);
+ vnlMat[0][1] = ComplexType(Coherency[1]);
+ vnlMat[0][2] = ComplexType(Coherency[2]);
+ vnlMat[1][0] = std::conj(ComplexType(Coherency[1]));
vnlMat[1][1] = ComplexType(T1, 0.);
- vnlMat[1][2] = std::conj(ComplexType(Coherency[4]));
- vnlMat[2][0] = ComplexType(Coherency[2]);
- vnlMat[2][1] = ComplexType(Coherency[4]);
+ vnlMat[1][2] = ComplexType(Coherency[4]);
+ vnlMat[2][0] = std::conj(ComplexType(Coherency[2]));
+ vnlMat[2][1] = std::conj(ComplexType(Coherency[4]));
vnlMat[2][2] = ComplexType(T2, 0.);
// Only compute the left symetry to respect the previous Hermitian Analisys code
@@ -95,6 +95,7 @@ public:
// Entropy estimation
double totalEigenValues(0.0);
double p[3];
+ double plog[3];
double entropy;
double alpha;
double anisotropy;
@@ -120,46 +121,33 @@ public:
}
}
- totalEigenValues = sortedRealEigenValues[0] + sortedRealEigenValues[1] + sortedRealEigenValues[2];
- if (totalEigenValues <m_Epsilon)
+ totalEigenValues = 0.0;
+ for (unsigned int k = 0; k < 3; ++k)
{
- totalEigenValues = m_Epsilon;
+ sortedRealEigenValues[k] = std::max(sortedRealEigenValues[k], 0.);
+ totalEigenValues += sortedRealEigenValues[k];
}
+
for (unsigned int k = 0; k < 3; ++k)
{
- p[k] = std::max(sortedRealEigenValues[k], 0.) / totalEigenValues;
- }
+ p[k] = sortedRealEigenValues[k] / totalEigenValues;
+
+ if (p[k]<m_Epsilon) //n=log(n)-->0 when n-->0
+ plog[k]=0.0;
+ else
+ plog[k]=-p[k]*log(p[k])/log(3.0);
- if ( (p[0] < m_Epsilon) || (p[1] < m_Epsilon) || (p[2] < m_Epsilon) )
- {
- entropy =0.0;
- }
- else
- {
- entropy = p[0]*log(p[0]) + p[1]*log(p[1]) + p[2]*log(p[2]);
- entropy /= -log(3.);
}
+ entropy = 0.0;
+ for (unsigned int k = 0; k < 3; ++k)
+ entropy += plog[k];
+
// alpha estimation
double val0, val1, val2;
double a0, a1, a2;
- for(unsigned int k = 0; k < 3; ++k)
- {
- p[k] = sortedRealEigenValues[k] / totalEigenValues;
-
- if (p[k] < 0.)
- {
- p[k] = 0.;
- }
-
- if (p[k] > 1.)
- {
- p[k] = 1.;
- }
- }
-
val0 = std::abs(sortedGreaterEigenVector[0]);
a0=acos(vcl_abs(val0)) * CONST_180_PI;
@@ -171,14 +159,10 @@ public:
alpha=p[0]*a0 + p[1]*a1 + p[2]*a2;
- if (alpha>90)
- {
- alpha=0.;
- }
-
// Anisotropy estimation
anisotropy=(sortedRealEigenValues[1] - sortedRealEigenValues[2])/(sortedRealEigenValues[1] + sortedRealEigenValues[2] + m_Epsilon);
+
result[0] = static_cast<OutputValueType>(entropy);
result[1] = static_cast<OutputValueType>(alpha);
result[2] = static_cast<OutputValueType>(anisotropy);
diff --git a/Modules/Filtering/Polarimetry/include/otbSinclairToReciprocalCoherencyMatrixFunctor.h b/Modules/Filtering/Polarimetry/include/otbSinclairToReciprocalCoherencyMatrixFunctor.h
index e1d6743c6f9a4f5e960f1897c457f371f50a305e..7fb9ddc327a721737781b24eb065de7bc07dcf63 100644
--- a/Modules/Filtering/Polarimetry/include/otbSinclairToReciprocalCoherencyMatrixFunctor.h
+++ b/Modules/Filtering/Polarimetry/include/otbSinclairToReciprocalCoherencyMatrixFunctor.h
@@ -18,8 +18,10 @@
#ifndef __otbSinclairToReciprocalCoherencyMatrixFunctor_h
#define __otbSinclairToReciprocalCoherencyMatrixFunctor_h
-#include "vcl_complex.h"
#include "itkMacro.h"
+#include "vcl_complex.h"
+#include "otbMath.h"
+#include "vnl/vnl_matrix.h"
namespace otb
{
@@ -62,6 +64,7 @@ class SinclairToReciprocalCoherencyMatrixFunctor
public:
/** Some typedefs. */
typedef typename std::complex <double> ComplexType;
+ typedef vnl_matrix<ComplexType> VNLMatrixType;
typedef typename TOutput::ValueType OutputValueType;
itkStaticConstMacro(NumberOfComponentsPerPixel, unsigned int, 6);
@@ -75,19 +78,23 @@ public:
const ComplexType S_hh = static_cast<ComplexType>(Shh);
const ComplexType S_hv = static_cast<ComplexType>(Shv);
const ComplexType S_vv = static_cast<ComplexType>(Svv);
-
- const ComplexType HHPlusVV = S_hh + S_vv;
- const ComplexType HHMinusVV = S_hh - S_vv;
- const ComplexType twoHV = ComplexType( 2.0 ) * S_hv;
-
- result[0] = static_cast<OutputValueType>( std::norm(HHPlusVV) );
- result[1] = static_cast<OutputValueType>( HHPlusVV * vcl_conj(HHMinusVV) );
- result[2] = static_cast<OutputValueType>( HHPlusVV * vcl_conj(twoHV) );
- result[3] = static_cast<OutputValueType>( std::norm(HHMinusVV) );
- result[4] = static_cast<OutputValueType>( HHMinusVV *vcl_conj(twoHV) );
- result[5] = static_cast<OutputValueType>( std::norm(twoHV) );
-
- result /= 2.0;
+
+
+ VNLMatrixType f3p(3, 1, 0.);
+ f3p[0][0]= (S_hh + S_vv) / ComplexType( std::sqrt(2.0) , 0.0);
+ f3p[1][0]= (S_hh - S_vv) / ComplexType( std::sqrt(2.0) , 0.0);
+ f3p[2][0]= ComplexType( std::sqrt(2.0) , 0.0) * S_hv;
+
+
+ VNLMatrixType res = f3p*f3p.conjugate_transpose();
+
+ result[0] = static_cast<OutputValueType>( res[0][0] );
+ result[1] = static_cast<OutputValueType>( res[0][1] );
+ result[2] = static_cast<OutputValueType>( res[0][2] );
+ result[3] = static_cast<OutputValueType>( res[1][1] );
+ result[4] = static_cast<OutputValueType>( res[1][2] );
+ result[5] = static_cast<OutputValueType>( res[2][2] );
+
return (result);
}
diff --git a/Modules/Filtering/Polarimetry/include/otbSinclairToReciprocalCovarianceMatrixFunctor.h b/Modules/Filtering/Polarimetry/include/otbSinclairToReciprocalCovarianceMatrixFunctor.h
index 8ad714abef0d6c103c84e882489cf7d3fa634226..632033676ae0d5b2895f723074d9459a736969ba 100644
--- a/Modules/Filtering/Polarimetry/include/otbSinclairToReciprocalCovarianceMatrixFunctor.h
+++ b/Modules/Filtering/Polarimetry/include/otbSinclairToReciprocalCovarianceMatrixFunctor.h
@@ -19,6 +19,8 @@
#define __otbSinclairToReciprocalCovarianceMatrixFunctor_h
#include "vcl_complex.h"
+#include "otbMath.h"
+#include "vnl/vnl_matrix.h"
namespace otb
{
@@ -61,6 +63,7 @@ class SinclairToReciprocalCovarianceMatrixFunctor
public:
/** Some typedefs. */
typedef typename std::complex <double> ComplexType;
+ typedef vnl_matrix<ComplexType> VNLMatrixType;
typedef typename TOutput::ValueType OutputValueType;
inline TOutput operator ()(const TInput1& Shh, const TInput2& Shv, const TInput3& Svv)
{
@@ -72,12 +75,19 @@ public:
const ComplexType S_hv = static_cast<ComplexType>(Shv);
const ComplexType S_vv = static_cast<ComplexType>(Svv);
- result[0] = static_cast<OutputValueType>( std::norm( S_hh ) );
- result[1] = static_cast<OutputValueType>( vcl_sqrt(2.0)*S_hh*vcl_conj(S_hv) );
- result[2] = static_cast<OutputValueType>( S_hh*vcl_conj(S_vv) );
- result[3] = static_cast<OutputValueType>( 2.0*std::norm( S_hv ) );
- result[4] = static_cast<OutputValueType>( vcl_sqrt(2.0)*S_hv*vcl_conj(S_vv) );
- result[5] = static_cast<OutputValueType>( std::norm( S_vv ) );
+ VNLMatrixType f3l(3, 1, 0.);
+ f3l[0][0]=S_hh;
+ f3l[1][0]=ComplexType(std::sqrt(2.0),0.0)*S_hv;
+ f3l[2][0]=S_vv;
+
+ VNLMatrixType res = f3l*f3l.conjugate_transpose();
+
+ result[0] = static_cast<OutputValueType>( res[0][0] );
+ result[1] = static_cast<OutputValueType>( res[0][1] );
+ result[2] = static_cast<OutputValueType>( res[0][2] );
+ result[3] = static_cast<OutputValueType>( res[1][1] );
+ result[4] = static_cast<OutputValueType>( res[1][2] );
+ result[5] = static_cast<OutputValueType>( res[2][2] );
return (result);
}
diff --git a/Modules/Filtering/Polarimetry/test/CMakeLists.txt b/Modules/Filtering/Polarimetry/test/CMakeLists.txt
index c3825403977252f806d4e27ec4fa085eefcdbb5e..f69bd683b63d17200a0d8d0a0b79b09b7f46de9c 100644
--- a/Modules/Filtering/Polarimetry/test/CMakeLists.txt
+++ b/Modules/Filtering/Polarimetry/test/CMakeLists.txt
@@ -40,7 +40,7 @@ otb_module_target_label(otbPolarimetryTestDriver)
# Tests Declaration
otb_add_test(NAME saTvMuellerToPolarisationDegreeAndPowerImageFilter COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvMuellerToPolarisationDegreeAndPowerImageFilter.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvMuellerToPolarisationDegreeAndPowerImageFilter.tif
${TEMP}/saTvMuellerToPolarisationDegreeAndPowerImageFilter.tif
otbMuellerToPolarisationDegreeAndPowerImageFilter
${BASELINE}/saTvSinclairImageFilter_SinclairToMueller.tif
@@ -48,7 +48,7 @@ otb_add_test(NAME saTvMuellerToPolarisationDegreeAndPowerImageFilter COMMAND otb
)
otb_add_test(NAME saTvVectorMultiChannelsPolarimetricSynthesisFilter COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvMultiPolarimetricSynthesis1.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvMultiPolarimetricSynthesis1.tif
${TEMP}/resMultiPolarimetricSynthesis1.tif
otbVectorMultiChannelsPolarimetricSynthesisFilter
${INPUTDATA}/RSAT2_AltonaExtract_1000_1000_100_100.hdr
@@ -60,10 +60,13 @@ otb_add_test(NAME saTvVectorMultiChannelsPolarimetricSynthesisFilter COMMAND otb
)
otb_add_test(NAME saTvReciprocalHAlphaImageFilter COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvReciprocalHAlphaImageFilter.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvReciprocalHAlphaImageFilter.tif
${TEMP}/saTvReciprocalHAlphaImageFilter.tif
otbReciprocalHAlphaImageFilter
- ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCoherency.tif
+ ${INPUTDATA}/RSAT_imagery_HH.tif
+ ${INPUTDATA}/RSAT_imagery_HV.tif
+ ${INPUTDATA}/RSAT_imagery_VV.tif
+ 5
${TEMP}/saTvReciprocalHAlphaImageFilter.tif
)
@@ -72,7 +75,7 @@ otb_add_test(NAME saTuReciprocalLinearCovarianceToReciprocalCircularCovarianceIm
)
otb_add_test(NAME saTvReciprocalCovarianceToReciprocalCoherencyImageFilter COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCoherency.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCoherency.tif
${TEMP}/saTvMLCToCoherencyImageFilter.tif
otbReciprocalCovarianceToReciprocalCoherencyImageFilter
${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCovariance.tif
@@ -91,7 +94,7 @@ otb_add_test(NAME saTuPolarimetricSynthesisFunctor COMMAND otbPolarimetryTestDri
otbPolarimetricSynthesisFunctor )
otb_add_test(NAME saTvMultiChannelsPolarimetricSynthesisFilter COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvMultiPolarimetricSynthesis2.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvMultiPolarimetricSynthesis2.tif
${TEMP}/resMultiPolarimetricSynthesis2.tif
otbMultiChannelsPolarimetricSynthesisFilter
${INPUTDATA}/RSAT_imagery_HH.tif
@@ -105,13 +108,13 @@ otb_add_test(NAME saTvMultiChannelsPolarimetricSynthesisFilter COMMAND otbPolari
)
otb_add_test(NAME saTvMultiChannelsPolarimetricSynthesisFilter_C COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvMultiPolarimetricSynthesis2.tif
- ${TEMP}/resMultiPolarimetricSynthesis2.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvMultiPolarimetricSynthesis2.tif
+ ${TEMP}/resMultiPolarimetricSynthesis2_C.tif
otbMultiChannelsPolarimetricSynthesisFilter
${INPUTDATA}/RSAT_imageryC_HH.tif
${INPUTDATA}/RSAT_imageryC_HV.tif
${INPUTDATA}/RSAT_imageryC_VV.tif
- ${TEMP}/resMultiPolarimetricSynthesis2.tif
+ ${TEMP}/resMultiPolarimetricSynthesis2_C.tif
32.0 # PsiI
22.0 # KhiI
58.0 # PsiR
@@ -119,7 +122,7 @@ otb_add_test(NAME saTvMultiChannelsPolarimetricSynthesisFilter_C COMMAND otbPola
)
otb_add_test(NAME saTvSinclairImageFilter_SinclairToMueller COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvSinclairImageFilter_SinclairToMueller.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvSinclairImageFilter_SinclairToMueller.tif
${TEMP}/saTvSinclairImageFilter_SinclairToMueller.tif
otbSinclairImageFilter
SinclairToMuellerMatrix
@@ -130,18 +133,18 @@ otb_add_test(NAME saTvSinclairImageFilter_SinclairToMueller COMMAND otbPolarimet
)
otb_add_test(NAME saTvSinclairImageFilter_SinclairToMueller_C COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvSinclairImageFilter_SinclairToMueller.tif
- ${TEMP}/saTvSinclairImageFilter_SinclairToMueller.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvSinclairImageFilter_SinclairToMueller.tif
+ ${TEMP}/saTvSinclairImageFilter_SinclairToMueller_C.tif
otbSinclairImageFilter
SinclairToMuellerMatrix
${INPUTDATA}/RSAT_imageryC_HH.tif
${INPUTDATA}/RSAT_imageryC_HV.tif
${INPUTDATA}/RSAT_imageryC_VV.tif
- ${TEMP}/saTvSinclairImageFilter_SinclairToMueller.tif
+ ${TEMP}/saTvSinclairImageFilter_SinclairToMueller_C.tif
)
otb_add_test(NAME saTvSinclairImageFilter_SinclairToCovariance COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvSinclairImageFilter_SinclairToCovariance.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvSinclairImageFilter_SinclairToCovariance.tif
${TEMP}/saTvSinclairImageFilter_SinclairToCovariance.tif
otbSinclairImageFilter
SinclairToCovarianceMatrix
@@ -152,14 +155,14 @@ otb_add_test(NAME saTvSinclairImageFilter_SinclairToCovariance COMMAND otbPolari
)
otb_add_test(NAME saTvSinclairImageFilter_SinclairToCovariance_C COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvSinclairImageFilter_SinclairToCovariance.tif
- ${TEMP}/saTvSinclairImageFilter_SinclairToCovariance.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvSinclairImageFilter_SinclairToCovariance.tif
+ ${TEMP}/saTvSinclairImageFilter_SinclairToCovariance_C.tif
otbSinclairImageFilter
SinclairToCovarianceMatrix
${INPUTDATA}/RSAT_imageryC_HH.tif
${INPUTDATA}/RSAT_imageryC_HV.tif
${INPUTDATA}/RSAT_imageryC_VV.tif
- ${TEMP}/saTvSinclairImageFilter_SinclairToCovariance.tif
+ ${TEMP}/saTvSinclairImageFilter_SinclairToCovariance_C.tif
)
otb_add_test(NAME saTuSinclairImageFilterNew COMMAND otbPolarimetryTestDriver
@@ -167,7 +170,7 @@ otb_add_test(NAME saTuSinclairImageFilterNew COMMAND otbPolarimetryTestDriver
)
otb_add_test(NAME saTvSinclairImageFilter_SinclairToCircularCovarianceMatrix COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvSinclairImageFilter_SinclairToCircularCovarianceMatrix.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvSinclairImageFilter_SinclairToCircularCovarianceMatrix.tif
${TEMP}/saTvSinclairImageFilter_SinclairToCircularCovarianceMatrix.tif
otbSinclairImageFilter
SinclairToCircularCovarianceMatrix
@@ -178,18 +181,18 @@ otb_add_test(NAME saTvSinclairImageFilter_SinclairToCircularCovarianceMatrix COM
)
otb_add_test(NAME saTvSinclairImageFilter_SinclairToCircularCovarianceMatrix_C COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvSinclairImageFilter_SinclairToCircularCovarianceMatrix.tif
- ${TEMP}/saTvSinclairImageFilter_SinclairToCircularCovarianceMatrix.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvSinclairImageFilter_SinclairToCircularCovarianceMatrix.tif
+ ${TEMP}/saTvSinclairImageFilter_SinclairToCircularCovarianceMatrix_C.tif
otbSinclairImageFilter
SinclairToCircularCovarianceMatrix
${INPUTDATA}/RSAT_imageryC_HH.tif
${INPUTDATA}/RSAT_imageryC_HV.tif
${INPUTDATA}/RSAT_imageryC_VV.tif
- ${TEMP}/saTvSinclairImageFilter_SinclairToCircularCovarianceMatrix.tif
+ ${TEMP}/saTvSinclairImageFilter_SinclairToCircularCovarianceMatrix_C.tif
)
otb_add_test(NAME saTvSinclairImageFilter_SinclairToCoherency COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvSinclairImageFilter_SinclairToCoherency.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvSinclairImageFilter_SinclairToCoherency.tif
${TEMP}/saTvSinclairImageFilter_SinclairToCoherency.tif
otbSinclairImageFilter
SinclairToCoherencyMatrix
@@ -200,14 +203,14 @@ otb_add_test(NAME saTvSinclairImageFilter_SinclairToCoherency COMMAND otbPolarim
)
otb_add_test(NAME saTvSinclairImageFilter_SinclairToCoherency_C COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvSinclairImageFilter_SinclairToCoherency.tif
- ${TEMP}/saTvSinclairImageFilter_SinclairToCoherency.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvSinclairImageFilter_SinclairToCoherency.tif
+ ${TEMP}/saTvSinclairImageFilter_SinclairToCoherency_C.tif
otbSinclairImageFilter
SinclairToCoherencyMatrix
${INPUTDATA}/RSAT_imageryC_HH.tif
${INPUTDATA}/RSAT_imageryC_HV.tif
${INPUTDATA}/RSAT_imageryC_VV.tif
- ${TEMP}/saTvSinclairImageFilter_SinclairToCoherency.tif
+ ${TEMP}/saTvSinclairImageFilter_SinclairToCoherency_C.tif
)
otb_add_test(NAME saTuReciprocalCovarianceToReciprocalCoherencyImageFilterNew COMMAND otbPolarimetryTestDriver
@@ -223,7 +226,7 @@ otb_add_test(NAME saTuSinclairReciprocalImageFilterNew COMMAND otbPolarimetryTes
)
otb_add_test(NAME saTvSinclairReciprocalImageFilter_SinclairToReciprocalCovariance COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCovariance.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCovariance.tif
${TEMP}/saTvSinclairImageFilter_SinclairToReciprocalCovariance.tif
otbSinclairReciprocalImageFilter
SinclairToReciprocalCovarianceMatrix
@@ -234,18 +237,18 @@ otb_add_test(NAME saTvSinclairReciprocalImageFilter_SinclairToReciprocalCovarian
)
otb_add_test(NAME saTvSinclairReciprocalImageFilter_SinclairToReciprocalCovariance_C COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCovariance.tif
- ${TEMP}/saTvSinclairImageFilter_SinclairToReciprocalCovariance.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCovariance.tif
+ ${TEMP}/saTvSinclairImageFilter_SinclairToReciprocalCovariance_C.tif
otbSinclairReciprocalImageFilter
SinclairToReciprocalCovarianceMatrix
${INPUTDATA}/RSAT_imageryC_HH.tif
${INPUTDATA}/RSAT_imageryC_HV.tif
${INPUTDATA}/RSAT_imageryC_VV.tif
- ${TEMP}/saTvSinclairImageFilter_SinclairToReciprocalCovariance.tif
+ ${TEMP}/saTvSinclairImageFilter_SinclairToReciprocalCovariance_C.tif
)
otb_add_test(NAME saTvSinclairReciprocalImageFilter_SinclairToReciprocalCircularCovarianceMatrix COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCircularCovarianceMatrix.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCircularCovarianceMatrix.tif
${TEMP}/saTvSinclairImageFilter_SinclairToReciprocalCircularCovarianceMatrix.tif
otbSinclairReciprocalImageFilter
SinclairToReciprocalCircularCovarianceMatrix
@@ -257,18 +260,18 @@ otb_add_test(NAME saTvSinclairReciprocalImageFilter_SinclairToReciprocalCircular
otb_add_test(NAME saTvSinclairReciprocalImageFilter_SinclairToReciprocalCircularCovarianceMatrix_C COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCircularCovarianceMatrix.tif
- ${TEMP}/saTvSinclairImageFilter_SinclairToReciprocalCircularCovarianceMatrix.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCircularCovarianceMatrix.tif
+ ${TEMP}/saTvSinclairImageFilter_SinclairToReciprocalCircularCovarianceMatrix_C.tif
otbSinclairReciprocalImageFilter
SinclairToReciprocalCircularCovarianceMatrix
${INPUTDATA}/RSAT_imageryC_HH.tif
${INPUTDATA}/RSAT_imageryC_HV.tif
${INPUTDATA}/RSAT_imageryC_VV.tif
- ${TEMP}/saTvSinclairImageFilter_SinclairToReciprocalCircularCovarianceMatrix.tif
+ ${TEMP}/saTvSinclairImageFilter_SinclairToReciprocalCircularCovarianceMatrix_C.tif
)
otb_add_test(NAME saTvSinclairReciprocalImageFilter_SinclairToReciprocalCoherency COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCoherency.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCoherency.tif
${TEMP}/saTvSinclairImageFilter_SinclairToReciprocalCoherency.tif
otbSinclairReciprocalImageFilter
SinclairToReciprocalCoherencyMatrix
@@ -279,19 +282,19 @@ otb_add_test(NAME saTvSinclairReciprocalImageFilter_SinclairToReciprocalCoherenc
)
otb_add_test(NAME saTvSinclairReciprocalImageFilter_SinclairToReciprocalCoherency_C COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCoherency.tif
- ${TEMP}/saTvSinclairImageFilter_SinclairToReciprocalCoherency.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCoherency.tif
+ ${TEMP}/saTvSinclairImageFilter_SinclairToReciprocalCoherency_C.tif
otbSinclairReciprocalImageFilter
SinclairToReciprocalCoherencyMatrix
${INPUTDATA}/RSAT_imageryC_HH.tif
${INPUTDATA}/RSAT_imageryC_HV.tif
${INPUTDATA}/RSAT_imageryC_VV.tif
- ${TEMP}/saTvSinclairImageFilter_SinclairToReciprocalCoherency.tif
+ ${TEMP}/saTvSinclairImageFilter_SinclairToReciprocalCoherency_C.tif
)
otb_add_test(NAME saTvReciprocalLinearCovarianceToReciprocalCircularCovarianceImageFilter COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCircularCovarianceMatrix.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCircularCovarianceMatrix.tif
${TEMP}/saTvMLCToCircularCoherencyDegreeImageFilter.tif
otbReciprocalLinearCovarianceToReciprocalCircularCovarianceImageFilter
${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCovariance.tif
@@ -299,7 +302,7 @@ otb_add_test(NAME saTvReciprocalLinearCovarianceToReciprocalCircularCovarianceIm
)
otb_add_test(NAME saTvReciprocalCoherencyToReciprocalMuellerImageFilter COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvSinclairImageFilter_SinclairToMueller.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvSinclairImageFilter_SinclairToMueller.tif
${TEMP}/saTvReciprocalCoherencyToMuellerImageFilter.tif
otbReciprocalCoherencyToReciprocalMuellerImageFilter
${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCoherency.tif
@@ -315,7 +318,7 @@ otb_add_test(NAME saTuReciprocalCovarianceToCoherencyDegreeImageFilterNew COMMAN
)
otb_add_test(NAME saTvMuellerToReciprocalCovarianceImageFilter COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCovariance.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCovariance.tif
${TEMP}/saTvMuellerToMLCImageFilter.tif
otbMuellerToReciprocalCovarianceImageFilter
${BASELINE}/saTvSinclairImageFilter_SinclairToMueller.tif
@@ -363,7 +366,7 @@ otb_add_test(NAME saTuSinclairToCovarianceMatrixFunctor COMMAND otbPolarimetryTe
)
otb_add_test(NAME saTvReciprocalCovarianceToCoherencyDegreeImageFilter COMMAND otbPolarimetryTestDriver
- --compare-image ${EPSILON_12} ${BASELINE}/saTvMLCToCoherencyDegreeImageFilter.tif
+ --compare-image ${EPSILON_7} ${BASELINE}/saTvMLCToCoherencyDegreeImageFilter.tif
${TEMP}/saTvMLCToCoherencyDegreeImageFilter.tif
otbReciprocalCovarianceToCoherencyDegreeImageFilter
${BASELINE}/saTvSinclairImageFilter_SinclairToReciprocalCovariance.tif
diff --git a/Modules/Filtering/Polarimetry/test/otbReciprocalHAlphaImageFilter.cxx b/Modules/Filtering/Polarimetry/test/otbReciprocalHAlphaImageFilter.cxx
index 702a2dab4d74aab3d005d39f3f60996cf48e20d5..8855b9d3c022c750079f67820a67df0bbfb5002e 100644
--- a/Modules/Filtering/Polarimetry/test/otbReciprocalHAlphaImageFilter.cxx
+++ b/Modules/Filtering/Polarimetry/test/otbReciprocalHAlphaImageFilter.cxx
@@ -24,35 +24,80 @@
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
#include "otbReciprocalHAlphaImageFilter.h"
+#include "itkMeanImageFilter.h"
+#include "otbPerBandVectorImageFilter.h"
+#include "otbSinclairReciprocalImageFilter.h"
+#include "otbSinclairToReciprocalCoherencyMatrixFunctor.h"
+
int otbReciprocalHAlphaImageFilter(int itkNotUsed(argc), char * argv[])
{
- const char * inputFilename = argv[1];
- const char * outputFilename = argv[2];
-
- typedef double PixelType;
- typedef std::complex<PixelType> InputPixelType;
- const unsigned int Dimension = 2;
+ const char * inputFilenameHH = argv[1];
+ const char * inputFilenameHV = argv[2];
+ const char * inputFilenameVV = argv[3];
+ int size = atoi(argv[4]);
+ const char * outputFilename = argv[5];
- typedef otb::VectorImage<InputPixelType, Dimension> ImageType;
- typedef otb::VectorImage<PixelType, Dimension> RealImageType;
-
- typedef otb::ReciprocalHAlphaImageFilter<ImageType, RealImageType> FilterType;
+ typedef std::complex<double> ComplexPixelType;
+ const unsigned int Dimension = 2;
- typedef otb::ImageFileReader<ImageType> ReaderType;
- typedef otb::ImageFileWriter<RealImageType> WriterType;
- ReaderType::Pointer reader = ReaderType::New();
+ typedef otb::Image<ComplexPixelType, Dimension> ComplexImageType;
+ typedef otb::VectorImage<ComplexPixelType, Dimension> ComplexVectorImageType;
+ typedef otb::VectorImage<double, Dimension> RealVectorImageType;
+
+
+
+ typedef otb::ImageFileReader<ComplexImageType> ReaderType;
+ typedef otb::ImageFileWriter<RealVectorImageType> WriterType;
+
+
+ typedef otb::SinclairReciprocalImageFilter<ComplexImageType, ComplexImageType, ComplexImageType, ComplexVectorImageType,
+ otb::Functor::SinclairToReciprocalCovarianceMatrixFunctor<ComplexImageType::PixelType,
+ ComplexImageType::PixelType,
+ ComplexImageType::PixelType,
+ ComplexVectorImageType::PixelType> > SinclaireToC3FilterType;
+
+
+ typedef itk::MeanImageFilter<ComplexImageType, ComplexImageType> MeanFilterType;
+ typedef otb::PerBandVectorImageFilter<ComplexVectorImageType, ComplexVectorImageType, MeanFilterType> PerBandMeanFilterType;
+
+
+ typedef otb::ReciprocalHAlphaImageFilter<ComplexVectorImageType, RealVectorImageType> HAlphaFilterType;
+
+
+
+ ReaderType::Pointer readerHH = ReaderType::New();
+ ReaderType::Pointer readerHV = ReaderType::New();
+ ReaderType::Pointer readerVV = ReaderType::New();
+
WriterType::Pointer writer = WriterType::New();
- reader->SetFileName(inputFilename);
-
- FilterType::Pointer filter = FilterType::New();
- filter->SetInput(reader->GetOutput());
+ SinclaireToC3FilterType::Pointer sinclairtoc3 = SinclaireToC3FilterType::New();
+ PerBandMeanFilterType::Pointer perBand = PerBandMeanFilterType::New();
+ HAlphaFilterType::Pointer haafilter = HAlphaFilterType::New();
+
+
+ MeanFilterType::InputSizeType radius;
+ radius.Fill( size );
+ perBand->GetFilter()->SetRadius(radius);
+
+
+ readerHH->SetFileName(inputFilenameHH);
+ readerHV->SetFileName(inputFilenameHV);
+ readerVV->SetFileName(inputFilenameVV);
+
+ sinclairtoc3->SetInputHH(readerHH->GetOutput());
+ sinclairtoc3->SetInputHV_VH(readerHV->GetOutput());
+ sinclairtoc3->SetInputVV(readerVV->GetOutput());
+
+ perBand->SetInput(sinclairtoc3->GetOutput());
+
+ haafilter->SetInput(perBand->GetOutput());
writer->SetFileName(outputFilename);
- writer->SetInput(filter->GetOutput());
+ writer->SetInput(haafilter->GetOutput());
writer->Update();
return EXIT_SUCCESS;
diff --git a/Modules/IO/ExtendedFilename/src/otbExtendedFilenameHelper.cxx b/Modules/IO/ExtendedFilename/src/otbExtendedFilenameHelper.cxx
index eb08b5e874318b3825359a16350774c72a7635a6..e7d77129417b8d6ea4015f210a6eeb314e463cae 100644
--- a/Modules/IO/ExtendedFilename/src/otbExtendedFilenameHelper.cxx
+++ b/Modules/IO/ExtendedFilename/src/otbExtendedFilenameHelper.cxx
@@ -30,6 +30,7 @@ ExtendedFilenameHelper
itkGenericExceptionMacro( << "Filename is NULL" );
}
this->m_ExtendedFileName = extFname;
+ this->m_OptionMap.clear();
std::vector<std::string> tmp1;
std::vector<std::string> tmp2;
if (!m_ExtendedFileName.empty())
diff --git a/Modules/IO/IOGDAL/src/otbGDALImageIO.cxx b/Modules/IO/IOGDAL/src/otbGDALImageIO.cxx
index 1085a23c6f8b86a0b6b7b4e76925f064e45332d1..37c14462dcafe9c7e086da4a10a00f8b4c3019c4 100644
--- a/Modules/IO/IOGDAL/src/otbGDALImageIO.cxx
+++ b/Modules/IO/IOGDAL/src/otbGDALImageIO.cxx
@@ -1679,6 +1679,28 @@ void GDALImageIO::InternalWriteImageInformation(const void* buffer)
{
dataset->SetProjection(projectionRef.c_str());
}
+ else
+ {
+ /* -------------------------------------------------------------------- */
+ /* Set the RPC coeffs if no projection available (since GDAL 1.10.0) */
+ /* -------------------------------------------------------------------- */
+#if GDAL_VERSION_NUM >= 1100000
+ ImageKeywordlist otb_kwl;
+ itk::ExposeMetaData<ImageKeywordlist>(dict,
+ MetaDataKey::OSSIMKeywordlistKey,
+ otb_kwl);
+ if( otb_kwl.GetSize() != 0 )
+ {
+ GDALRPCInfo gdalRpcStruct;
+ if ( otb_kwl.convertToGDALRPC(gdalRpcStruct) )
+ {
+ char **rpcMetadata = RPCInfoToMD(&gdalRpcStruct);
+ dataset->SetMetadata(rpcMetadata, "RPC");
+ CSLDestroy( rpcMetadata );
+ }
+ }
+#endif
+ }
/* -------------------------------------------------------------------- */
/* Set the six coefficients of affine geoTransform */
@@ -1719,23 +1741,6 @@ void GDALImageIO::InternalWriteImageInformation(const void* buffer)
}
}
-#if GDAL_VERSION_NUM >= 1100000
- // Report any RPC coefficients (feature available since GDAL 1.10.0)
- ImageKeywordlist otb_kwl;
- itk::ExposeMetaData<ImageKeywordlist>(dict,
- MetaDataKey::OSSIMKeywordlistKey,
- otb_kwl);
- if( otb_kwl.GetSize() != 0 )
- {
- GDALRPCInfo gdalRpcStruct;
- if ( otb_kwl.convertToGDALRPC(gdalRpcStruct) )
- {
- char **rpcMetadata = RPCInfoToMD(&gdalRpcStruct);
- dataset->SetMetadata(rpcMetadata, "RPC");
- CSLDestroy( rpcMetadata );
- }
- }
-#endif
// END
diff --git a/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.h b/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.h
index 185d10ba44bf26db0a9444c5eb61025ef4f2899a..a812eca2b8f36708b19e186d7221a0a23cc07f4f 100644
--- a/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.h
@@ -92,6 +92,11 @@ public:
itkGetMacro(MaxDepth, int);
itkSetMacro(MaxDepth, int);
+ /** Train the machine learning model */
+ virtual void Train();
+ /** Predict values using the model */
+ virtual TargetSampleType Predict(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
+
/** Save the model to file */
virtual void Save(const std::string & filename, const std::string & name="");
@@ -117,11 +122,6 @@ protected:
/** PrintSelf method */
void PrintSelf(std::ostream& os, itk::Indent indent) const;
- /** Train the machine learning model */
- virtual void TrainClassification();
- /** Predict values using the model */
- virtual TargetSampleType PredictClassification(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
-
private:
BoostMachineLearningModel(const Self &); //purposely not implemented
void operator =(const Self&); //purposely not implemented
diff --git a/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.txx
index 493107f9ff2b8da6c652fc67bc31376664eed1cc..15a9d5c293b8fa61a87b2f714aef15d0379a0686 100644
--- a/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.txx
@@ -52,7 +52,7 @@ BoostMachineLearningModel<TInputValue,TOutputValue>
template <class TInputValue, class TOutputValue>
void
BoostMachineLearningModel<TInputValue,TOutputValue>
-::TrainClassification()
+::Train()
{
//convert listsample to opencv matrix
cv::Mat samples;
@@ -77,7 +77,7 @@ template <class TInputValue, class TOutputValue>
typename BoostMachineLearningModel<TInputValue,TOutputValue>
::TargetSampleType
BoostMachineLearningModel<TInputValue,TOutputValue>
-::PredictClassification(const InputSampleType & input, ConfidenceValueType *quality) const
+::Predict(const InputSampleType & input, ConfidenceValueType *quality) const
{
//convert listsample to Mat
cv::Mat sample;
diff --git a/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.h b/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.h
index 6415fa940e273e5d9074a130ba95a8d8105a4978..651a48f6ee319e2c4171f15741df7e9615205c2d 100644
--- a/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.h
@@ -147,11 +147,10 @@ public:
return m_Priors;
}
- /** Setters/Getters to IsRegression flag
- * Default is False
- */
- itkGetMacro(IsRegression, bool);
- itkSetMacro(IsRegression, bool);
+ /** Train the machine learning model */
+ virtual void Train();
+ /** Predict values using the model */
+ virtual TargetSampleType Predict(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
/** Save the model to file */
virtual void Save(const std::string & filename, const std::string & name="");
@@ -178,11 +177,6 @@ protected:
/** PrintSelf method */
void PrintSelf(std::ostream& os, itk::Indent indent) const;
- /** Train the machine learning model */
- virtual void TrainClassification();
- /** Predict values using the model */
- virtual TargetSampleType PredictClassification(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
-
private:
DecisionTreeMachineLearningModel(const Self &); //purposely not implemented
void operator =(const Self&); //purposely not implemented
@@ -196,7 +190,6 @@ private:
int m_MaxCategories;
int m_CVFolds;
bool m_Use1seRule;
- bool m_IsRegression;
bool m_TruncatePrunedTree;
std::vector<float> m_Priors;
diff --git a/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.txx
index ada456e94c35ff1467e97b8301a5e4a315fa35f6..283fb039c35fd04aea4cc2a63c580ee665014ab4 100644
--- a/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.txx
@@ -38,9 +38,9 @@ DecisionTreeMachineLearningModel<TInputValue,TOutputValue>
m_MaxCategories(10),
m_CVFolds(10),
m_Use1seRule(true),
- m_IsRegression(false),
m_TruncatePrunedTree(true)
{
+ this->m_IsRegressionSupported = true;
}
@@ -55,7 +55,7 @@ DecisionTreeMachineLearningModel<TInputValue,TOutputValue>
template <class TInputValue, class TOutputValue>
void
DecisionTreeMachineLearningModel<TInputValue,TOutputValue>
-::TrainClassification()
+::Train()
{
//convert listsample to opencv matrix
cv::Mat samples;
@@ -73,7 +73,7 @@ DecisionTreeMachineLearningModel<TInputValue,TOutputValue>
cv::Mat var_type = cv::Mat(this->GetInputListSample()->GetMeasurementVectorSize() + 1, 1, CV_8U );
var_type.setTo(cv::Scalar(CV_VAR_NUMERICAL) ); // all inputs are numerical
- if (!m_IsRegression) //Classification
+ if (!this->m_RegressionMode) //Classification
var_type.at<uchar>(this->GetInputListSample()->GetMeasurementVectorSize(), 0) = CV_VAR_CATEGORICAL;
m_DTreeModel->train(samples,CV_ROW_SAMPLE,labels,cv::Mat(),cv::Mat(),var_type,cv::Mat(),params);
@@ -83,7 +83,7 @@ template <class TInputValue, class TOutputValue>
typename DecisionTreeMachineLearningModel<TInputValue,TOutputValue>
::TargetSampleType
DecisionTreeMachineLearningModel<TInputValue,TOutputValue>
-::PredictClassification(const InputSampleType & input, ConfidenceValueType *quality) const
+::Predict(const InputSampleType & input, ConfidenceValueType *quality) const
{
//convert listsample to Mat
cv::Mat sample;
diff --git a/Modules/Learning/Supervised/include/otbGradientBoostedTreeMachineLearningModel.h b/Modules/Learning/Supervised/include/otbGradientBoostedTreeMachineLearningModel.h
index 3126f44c0ff7073def41f025df615e74013b590b..f10a89c68066f8ee6bbacc252b4088fa057157be 100644
--- a/Modules/Learning/Supervised/include/otbGradientBoostedTreeMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbGradientBoostedTreeMachineLearningModel.h
@@ -100,6 +100,11 @@ public:
itkGetMacro(UseSurrogates, bool);
itkSetMacro(UseSurrogates, bool);
+ /** Train the machine learning model */
+ virtual void Train();
+ /** Predict values using the model */
+ virtual TargetSampleType Predict(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
+
/** Save the model to file */
virtual void Save(const std::string & filename, const std::string & name="");
@@ -125,11 +130,6 @@ protected:
/** PrintSelf method */
void PrintSelf(std::ostream& os, itk::Indent indent) const;
- /** Train the machine learning model */
- virtual void TrainClassification();
- /** Predict values using the model */
- virtual TargetSampleType PredictClassification(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
-
private:
GradientBoostedTreeMachineLearningModel(const Self &); //purposely not implemented
void operator =(const Self&); //purposely not implemented
@@ -142,7 +142,6 @@ private:
double m_SubSamplePortion;
int m_MaxDepth;
bool m_UseSurrogates;
- bool m_IsRegression;
};
diff --git a/Modules/Learning/Supervised/include/otbGradientBoostedTreeMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbGradientBoostedTreeMachineLearningModel.txx
index 595f1bb060b8ffbcb533352915f41b69c2bd1458..d08abec0290776a5d6a7ddfd3df27e6b5216d620 100644
--- a/Modules/Learning/Supervised/include/otbGradientBoostedTreeMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbGradientBoostedTreeMachineLearningModel.txx
@@ -36,9 +36,9 @@ GradientBoostedTreeMachineLearningModel<TInputValue,TOutputValue>
m_Shrinkage(0.01),
m_SubSamplePortion(0.8),
m_MaxDepth(3),
- m_UseSurrogates(false),
- m_IsRegression(false)
+ m_UseSurrogates(false)
{
+ this->m_IsRegressionSupported = true;
}
@@ -53,7 +53,7 @@ GradientBoostedTreeMachineLearningModel<TInputValue,TOutputValue>
template <class TInputValue, class TOutputValue>
void
GradientBoostedTreeMachineLearningModel<TInputValue,TOutputValue>
-::TrainClassification()
+::Train()
{
//convert listsample to opencv matrix
cv::Mat samples;
@@ -62,7 +62,6 @@ GradientBoostedTreeMachineLearningModel<TInputValue,TOutputValue>
cv::Mat labels;
otb::ListSampleToMat<TargetListSampleType>(this->GetTargetListSample(),labels);
-
CvGBTreesParams params = CvGBTreesParams(m_LossFunctionType, m_WeakCount, m_Shrinkage, m_SubSamplePortion,
m_MaxDepth, m_UseSurrogates);
@@ -70,7 +69,7 @@ GradientBoostedTreeMachineLearningModel<TInputValue,TOutputValue>
cv::Mat var_type = cv::Mat(this->GetInputListSample()->GetMeasurementVectorSize() + 1, 1, CV_8U );
var_type.setTo(cv::Scalar(CV_VAR_NUMERICAL) ); // all inputs are numerical
- if (!m_IsRegression) //Classification
+ if (!this->m_RegressionMode) //Classification
var_type.at<uchar>(this->GetInputListSample()->GetMeasurementVectorSize(), 0) = CV_VAR_CATEGORICAL;
m_GBTreeModel->train(samples,CV_ROW_SAMPLE,labels,cv::Mat(),cv::Mat(),var_type,cv::Mat(),params, false);
@@ -80,7 +79,7 @@ template <class TInputValue, class TOutputValue>
typename GradientBoostedTreeMachineLearningModel<TInputValue,TOutputValue>
::TargetSampleType
GradientBoostedTreeMachineLearningModel<TInputValue,TOutputValue>
-::PredictClassification(const InputSampleType & input, ConfidenceValueType *quality) const
+::Predict(const InputSampleType & input, ConfidenceValueType *quality) const
{
//convert listsample to Mat
cv::Mat sample;
diff --git a/Modules/Learning/Supervised/include/otbImageClassificationFilter.txx b/Modules/Learning/Supervised/include/otbImageClassificationFilter.txx
index 88d2ad012441455cc2c2d221e79e175731b3e31f..b0a5d5e3e842ec986dfc2400736b84e2a405bb53 100644
--- a/Modules/Learning/Supervised/include/otbImageClassificationFilter.txx
+++ b/Modules/Learning/Supervised/include/otbImageClassificationFilter.txx
@@ -118,7 +118,7 @@ ImageClassificationFilter<TInputImage, TOutputImage, TMaskImage>
}
// setup iterator for confidence map
- bool computeConfidenceMap(m_UseConfidenceMap && m_Model->HasConfidenceIndex());
+ bool computeConfidenceMap(m_UseConfidenceMap && m_Model->HasConfidenceIndex() && !m_Model->GetRegressionMode());
ConfidenceMapIteratorType confidenceIt;
if (computeConfidenceMap)
{
diff --git a/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.h b/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.h
index b8524b7fb677aff35b706a54b87b6a917251754b..3f29a10af3ed6b46d700684eddeab80a5076c17a 100644
--- a/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.h
@@ -58,12 +58,23 @@ public:
itkGetMacro(K, int);
itkSetMacro(K, int);
- /** Setters/Getters to IsRegression flag
- * Default is False
- * \see http://docs.opencv.org/modules/ml/doc/k_nearest_neighbors.html
+ /** Decision rule once the KNN are found :
+ * [for classification]
+ * - KNN_VOTING : output value with maximum occurences (for classification)
+ * [for regression]
+ * - KNN_MEAN : output mean value of neighbors
+ * - KNN_MEDIAN : output median value of neighbors
*/
- itkGetMacro(IsRegression, bool);
- itkSetMacro(IsRegression, bool);
+ enum {KNN_VOTING, KNN_MEAN, KNN_MEDIAN};
+
+ /** Setters/Getters to the decision rule */
+ itkGetMacro(DecisionRule, int);
+ itkSetMacro(DecisionRule, int);
+
+ /** Train the machine learning model */
+ virtual void Train();
+ /** Predict values using the model */
+ virtual TargetSampleType Predict(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
/** Save the model to file */
virtual void Save(const std::string & filename, const std::string & name="");
@@ -90,18 +101,14 @@ protected:
/** PrintSelf method */
void PrintSelf(std::ostream& os, itk::Indent indent) const;
- /** Train the machine learning model */
- virtual void TrainClassification();
- /** Predict values using the model */
- virtual TargetSampleType PredictClassification(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
-
private:
KNearestNeighborsMachineLearningModel(const Self &); //purposely not implemented
void operator =(const Self&); //purposely not implemented
CvKNearest * m_KNearestModel;
int m_K;
- bool m_IsRegression;
+
+ int m_DecisionRule;
};
} // end namespace otb
diff --git a/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.txx
index e890b9137ac284848eda3c10885bfd736a4c28ac..fd33b7c9061fc3ce66c70cf9ea5c4c7780c42dc8 100644
--- a/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.txx
@@ -23,6 +23,7 @@
#include "otbOpenCVUtils.h"
#include <fstream>
+#include <set>
#include "itkMacro.h"
namespace otb
@@ -33,9 +34,10 @@ KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
::KNearestNeighborsMachineLearningModel() :
m_KNearestModel (new CvKNearest),
m_K(32),
- m_IsRegression(false)
+ m_DecisionRule(KNN_VOTING)
{
this->m_ConfidenceIndex = true;
+ this->m_IsRegressionSupported = true;
}
@@ -50,7 +52,7 @@ KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
template <class TInputValue, class TTargetValue>
void
KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
-::TrainClassification()
+::Train()
{
//convert listsample to opencv matrix
cv::Mat samples;
@@ -59,26 +61,44 @@ KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
cv::Mat labels;
otb::ListSampleToMat<TargetListSampleType>(this->GetTargetListSample(), labels);
+ // update decision rule if needed
+ if (this->m_RegressionMode)
+ {
+ if (this->m_DecisionRule == KNN_VOTING)
+ {
+ this->SetDecisionRule(KNN_MEAN);
+ }
+ }
+ else
+ {
+ if (this->m_DecisionRule != KNN_VOTING)
+ {
+ this->SetDecisionRule(KNN_VOTING);
+ }
+ }
+
//train the KNN model
- m_KNearestModel->train(samples, labels, cv::Mat(), m_IsRegression, m_K, false);
+ m_KNearestModel->train(samples, labels, cv::Mat(), this->m_RegressionMode, m_K, false);
}
template <class TInputValue, class TTargetValue>
typename KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
::TargetSampleType
KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
-::PredictClassification(const InputSampleType & input, ConfidenceValueType *quality) const
+::Predict(const InputSampleType & input, ConfidenceValueType *quality) const
{
//convert listsample to Mat
cv::Mat sample;
otb::SampleToMat<InputSampleType>(input, sample);
float result;
+ cv::Mat nearest(1,m_K,CV_32FC1);
+ result = m_KNearestModel->find_nearest(sample, m_K,0,0,&nearest,0);
+ // compute quality if asked (only happens in classification mode)
if (quality != NULL)
{
- cv::Mat nearest(1,m_K,CV_32FC1);
- result = m_KNearestModel->find_nearest(sample, m_K,0,0,&nearest,0);
+ assert(!this->m_RegressionMode);
unsigned int accuracy = 0;
for (int k=0 ; k < m_K ; ++k)
{
@@ -89,9 +109,22 @@ KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
}
(*quality) = static_cast<ConfidenceValueType>(accuracy);
}
- else
+
+ // Decision rule :
+ // VOTING is OpenCV default behaviour for classification
+ // MEAN is OpenCV default behaviour for regression
+ // MEDIAN : only case that must be handled here
+ if (this->m_DecisionRule == KNN_MEDIAN)
{
- result = m_KNearestModel->find_nearest(sample, m_K);
+ std::multiset<float> values;
+ for (int k=0 ; k < m_K ; ++k)
+ {
+ values.insert(nearest.at<float>(0,k));
+ }
+ std::multiset<float>::iterator median = values.begin();
+ int pos = (m_K >> 1);
+ for (int k=0 ; k < pos ; ++k , ++median) {}
+ result = *median;
}
TargetSampleType target;
@@ -106,12 +139,17 @@ KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
::Save(const std::string & filename, const std::string & itkNotUsed(name))
{
//there is no m_KNearestModel->save(filename.c_str(), name.c_str()).
- //We need to save the K parameter and IsRegression flag used and the samples.
+ //We need to save the K parameter, IsRegression flag, DecisionRule and the samples.
std::ofstream ofs(filename.c_str());
//Save K parameter and IsRegression flag.
ofs << "K=" << m_K << "\n";
- ofs << "IsRegression=" << m_IsRegression << "\n";
+ ofs << "IsRegression=" << this->m_RegressionMode << "\n";
+ // Save the DecisionRule if regression
+ if (this->m_RegressionMode)
+ {
+ ofs << "DecisionRule=" << m_DecisionRule << "\n";
+ }
//Save the samples. First column is the Label and other columns are the sample data.
typename InputListSampleType::ConstIterator sampleIt = this->GetInputListSample()->Begin();
@@ -154,8 +192,15 @@ KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
std::getline(ifs, line);
pos = line.find_first_of("=", 0);
nextpos = line.find_first_of(" \n\r", pos+1);
- this->SetIsRegression(boost::lexical_cast<bool>(line.substr(pos+1, nextpos-pos-1)));
-
+ this->SetRegressionMode(boost::lexical_cast<bool>(line.substr(pos+1, nextpos-pos-1)));
+ //third line is the DecisionRule parameter (only for regression)
+ if (this->m_RegressionMode)
+ {
+ std::getline(ifs, line);
+ pos = line.find_first_of("=", 0);
+ nextpos = line.find_first_of(" \n\r", pos+1);
+ this->SetDecisionRule(boost::lexical_cast<int>(line.substr(pos+1, nextpos-pos-1)));
+ }
//Clear previous listSample (if any)
typename InputListSampleType::Pointer samples = InputListSampleType::New();
typename TargetListSampleType::Pointer labels = TargetListSampleType::New();
diff --git a/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.h b/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.h
index 18eba7fbb28d336967ac669d05cda51bc5ebdc4c..622923753e37d1216de669a9724c72b979015f83 100644
--- a/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.h
@@ -59,6 +59,11 @@ public:
itkNewMacro(Self);
itkTypeMacro(SVMMachineLearningModel, MachineLearningModel);
+ /** Train the machine learning model */
+ virtual void Train();
+ /** Predict values using the model */
+ virtual TargetSampleType Predict(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
+
/** Save the model to file */
virtual void Save(const std::string &filename, const std::string & name="");
@@ -75,23 +80,40 @@ public:
//@}
//Setters/Getters to SVM model
- // itkGetMacro(SVMType, int);
- // itkSetMacro(SVMType, int);
+ otbGetObjectMemberMacro(SVMestimator, SVMType, int);
+ otbSetObjectMemberMacro(SVMestimator, SVMType, int);
+
+ otbGetObjectMemberMacro(SVMestimator, KernelType, int);
+ otbSetObjectMemberMacro(SVMestimator, KernelType, int);
+
+ otbGetObjectMemberMacro(SVMestimator, C, double);
+ otbSetObjectMemberMacro(SVMestimator, C, double);
- itkGetMacro(KernelType, int);
- itkSetMacro(KernelType, int);
+ // TODO : we should harmonize this parameter name : ParameterOptimization -> ParametersOptimization
+ bool GetParameterOptimization()
+ {
+ return this->m_SVMestimator->GetParametersOptimization();
+ }
+ void SetParameterOptimization(bool value)
+ {
+ this->m_SVMestimator->SetParametersOptimization(value);
+ this->Modified();
+ }
- itkGetMacro(C, float);
- itkSetMacro(C, float);
+ otbGetObjectMemberMacro(SVMestimator, DoProbabilityEstimates, bool);
+ void SetDoProbabilityEstimates(bool value)
+ {
+ this->m_SVMestimator->DoProbabilityEstimates(value);
+ }
- itkGetMacro(ParameterOptimization, bool);
- itkSetMacro(ParameterOptimization, bool);
-
- itkGetMacro(DoProbabilityEstimates, bool);
- itkSetMacro(DoProbabilityEstimates, bool);
+ otbGetObjectMemberMacro(SVMestimator, Epsilon, double);
+ otbSetObjectMemberMacro(SVMestimator, Epsilon, double);
- // itkGetMacro(Epsilon, int);
- // itkSetMacro(Epsilon, int);
+ otbGetObjectMemberMacro(SVMestimator, P, double);
+ otbSetObjectMemberMacro(SVMestimator, P, double);
+
+ otbGetObjectMemberMacro(SVMestimator, Nu, double);
+ otbSetObjectMemberMacro(SVMestimator, Nu, double);
protected:
/** Constructor */
@@ -103,19 +125,10 @@ protected:
/** PrintSelf method */
void PrintSelf(std::ostream& os, itk::Indent indent) const;
- /** Train the machine learning model */
- virtual void TrainClassification();
- /** Predict values using the model */
- virtual TargetSampleType PredictClassification(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
-
private:
LibSVMMachineLearningModel(const Self &); //purposely not implemented
void operator =(const Self&); //purposely not implemented
- int m_KernelType;
- float m_C;
- bool m_ParameterOptimization;
- bool m_DoProbabilityEstimates;
typename SVMEstimatorType::Pointer m_SVMestimator;
};
} // end namespace otb
diff --git a/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.txx
index 24e91e54a25406d100a8cae8f4b09741f124e80f..e95e97543d3a67537c848c8933165d5907370400 100644
--- a/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.txx
@@ -20,10 +20,6 @@
#include <fstream>
#include "otbLibSVMMachineLearningModel.h"
-//#include "otbOpenCVUtils.h"
-
-// SVM estimator
-//#include "otbSVMSampleListModelEstimator.h"
namespace otb
{
@@ -32,15 +28,14 @@ template <class TInputValue, class TOutputValue>
LibSVMMachineLearningModel<TInputValue,TOutputValue>
::LibSVMMachineLearningModel()
{
- // m_SVMModel = new CvSVM;
- // m_SVMType = CvSVM::C_SVC;
- m_KernelType = LINEAR;
- // m_TermCriteriaType = CV_TERMCRIT_ITER;
- m_C = 1.0;
- // m_Epsilon = 1e-6;
- m_ParameterOptimization = false;
- m_DoProbabilityEstimates = false;
m_SVMestimator = SVMEstimatorType::New();
+ m_SVMestimator->SetSVMType(C_SVC);
+ m_SVMestimator->SetC(1.0);
+ m_SVMestimator->SetKernelType(LINEAR);
+ m_SVMestimator->SetParametersOptimization(false);
+ m_SVMestimator->DoProbabilityEstimates(false);
+ //m_SVMestimator->SetEpsilon(1e-6);
+ this->m_IsRegressionSupported = true;
}
@@ -55,7 +50,7 @@ LibSVMMachineLearningModel<TInputValue,TOutputValue>
template <class TInputValue, class TOutputValue>
void
LibSVMMachineLearningModel<TInputValue,TOutputValue>
-::TrainClassification()
+::Train()
{
// Set up SVM's parameters
// CvSVMParams params;
@@ -64,25 +59,19 @@ LibSVMMachineLearningModel<TInputValue,TOutputValue>
// params.term_crit = cvTermCriteria(m_TermCriteriaType, m_MaxIter, m_Epsilon);
// // Train the SVM
-
- m_SVMestimator->SetC(m_C);
- m_SVMestimator->SetKernelType(m_KernelType);
- m_SVMestimator->SetParametersOptimization(m_ParameterOptimization);
- m_SVMestimator->DoProbabilityEstimates(m_DoProbabilityEstimates);
-
m_SVMestimator->SetInputSampleList(this->GetInputListSample());
m_SVMestimator->SetTrainingSampleList(this->GetTargetListSample());
m_SVMestimator->Update();
- this->m_ConfidenceIndex = m_DoProbabilityEstimates;
+ this->m_ConfidenceIndex = this->GetDoProbabilityEstimates();
}
template <class TInputValue, class TOutputValue>
typename LibSVMMachineLearningModel<TInputValue,TOutputValue>
::TargetSampleType
LibSVMMachineLearningModel<TInputValue,TOutputValue>
-::PredictClassification(const InputSampleType & input, ConfidenceValueType *quality) const
+::Predict(const InputSampleType & input, ConfidenceValueType *quality) const
{
TargetSampleType target;
diff --git a/Modules/Learning/Supervised/include/otbMachineLearningModel.h b/Modules/Learning/Supervised/include/otbMachineLearningModel.h
index ba0ca60d224fa138fe5a9e8e702993555394559b..b18e39e15c89fabff9ff2c640122740e710972f5 100644
--- a/Modules/Learning/Supervised/include/otbMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbMachineLearningModel.h
@@ -97,10 +97,10 @@ public:
//@}
/** Train the machine learning model */
- void Train();
+ virtual void Train() =0;
/** Predict values using the model */
- TargetSampleType Predict(const InputSampleType& input, ConfidenceValueType *quality = NULL) const;
+ virtual TargetSampleType Predict(const InputSampleType& input, ConfidenceValueType *quality = NULL) const = 0;
/** Classify all samples in InputListSample and fill TargetListSample with the associated label */
void PredictAll();
@@ -140,6 +140,12 @@ public:
itkGetObjectMacro(TargetListSample,TargetListSampleType);
//@}
+ /**\name Use model in regression mode */
+ //@{
+ itkGetMacro(RegressionMode,bool);
+ void SetRegressionMode(bool flag);
+ //@}
+
protected:
/** Constructor */
MachineLearningModel();
@@ -156,22 +162,14 @@ protected:
/** Target list sample */
typename TargetListSampleType::Pointer m_TargetListSample;
- /** Train the machine learning model */
- virtual void TrainRegression()
- {
- itkGenericExceptionMacro(<< "Regression mode not implemented.");
- }
- virtual void TrainClassification() = 0;
- /** Predict values using the model */
- virtual TargetSampleType PredictRegression(const InputSampleType& itkNotUsed(input)) const
- {
- itkGenericExceptionMacro(<< "Regression mode not implemented.");
- }
-
- virtual TargetSampleType PredictClassification(const InputSampleType& input, ConfidenceValueType *quality = NULL) const = 0;
-
+ /** flag to choose between classification and regression modes */
bool m_RegressionMode;
+ /** flag that indicates if the model supports regression, child
+ * classes should modify it in their constructor if they support
+ * regression mode */
+ bool m_IsRegressionSupported;
+
/** flag that tells if the model support confidence index output */
bool m_ConfidenceIndex;
private:
diff --git a/Modules/Learning/Supervised/include/otbMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbMachineLearningModel.txx
index b4b6127c11c252c3d5a73d0a50716d48da6e7a49..8cc82055fe1a7a08594a01f3d9aeba6dfc12d927 100644
--- a/Modules/Learning/Supervised/include/otbMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbMachineLearningModel.txx
@@ -25,7 +25,10 @@ namespace otb
template <class TInputValue, class TOutputValue, class TConfidenceValue>
MachineLearningModel<TInputValue,TOutputValue,TConfidenceValue>
-::MachineLearningModel() : m_RegressionMode(false),m_ConfidenceIndex(false)
+::MachineLearningModel() :
+ m_RegressionMode(false),
+ m_IsRegressionSupported(false),
+ m_ConfidenceIndex(false)
{}
@@ -37,23 +40,17 @@ MachineLearningModel<TInputValue,TOutputValue,TConfidenceValue>
template <class TInputValue, class TOutputValue, class TConfidenceValue>
void
MachineLearningModel<TInputValue,TOutputValue,TConfidenceValue>
-::Train()
+::SetRegressionMode(bool flag)
{
- if(m_RegressionMode)
- return this->TrainRegression();
- else
- return this->TrainClassification();
-}
-
-template <class TInputValue, class TOutputValue, class TConfidenceValue>
-typename MachineLearningModel<TInputValue,TOutputValue,TConfidenceValue>::TargetSampleType
-MachineLearningModel<TInputValue,TOutputValue,TConfidenceValue>
-::Predict(const InputSampleType& input, ConfidenceValueType *quality) const
-{
- if(m_RegressionMode)
- return this->PredictRegression(input);
- else
- return this->PredictClassification(input,quality);
+ if (flag && !m_IsRegressionSupported)
+ {
+ itkGenericExceptionMacro(<< "Regression mode not implemented.");
+ }
+ if (m_RegressionMode != flag)
+ {
+ m_RegressionMode = flag;
+ this->Modified();
+ }
}
template <class TInputValue, class TOutputValue, class TConfidenceValue>
diff --git a/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.h b/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.h
index 2eab72042d9ff5b3228df2d628ee0618127b01f4..fa1abade18cce80abb493406fabbcebbe97c9ddc 100644
--- a/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.h
@@ -151,6 +151,11 @@ public:
itkGetMacro(Epsilon, double);
itkSetMacro(Epsilon, double);
+ /** Train the machine learning model */
+ virtual void Train();
+ /** Predict values using the model */
+ virtual TargetSampleType Predict(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
+
/** Save the model to file */
virtual void Save(const std::string & filename, const std::string & name="");
@@ -178,15 +183,14 @@ protected:
/** PrintSelf method */
void PrintSelf(std::ostream& os, itk::Indent indent) const;
- /** Train the machine learning model */
- virtual void TrainClassification();
- /** Predict values using the model */
- virtual TargetSampleType PredictClassification(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
-
private:
NeuralNetworkMachineLearningModel(const Self &); //purposely not implemented
void operator =(const Self&); //purposely not implemented
+ void CreateNetwork();
+ CvANN_MLP_TrainParams SetNetworkParameters();
+ void SetupNetworkAndTrain(cv::Mat& labels);
+
CvANN_MLP * m_ANNModel;
int m_TrainMethod;
int m_ActivateFunction;
diff --git a/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.txx
index 01c31811d7c793630dfcfe5f0026cc6b7be73356..b76e6dd70d3f57741ac83ef9ce448362d9e942fc 100644
--- a/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.txx
@@ -43,6 +43,7 @@ NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::NeuralNetworkMachi
m_CvMatOfLabels(0)
{
this->m_ConfidenceIndex = true;
+ this->m_IsRegressionSupported = true;
}
template<class TInputValue, class TOutputValue>
@@ -52,7 +53,7 @@ NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::~NeuralNetworkMach
cvReleaseMat(&m_CvMatOfLabels);
}
-/** Train the machine learning model */
+/** Sets the topology of the NN */
template<class TInputValue, class TOutputValue>
void NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::SetLayerSizes(const std::vector<unsigned int> layers)
{
@@ -72,10 +73,14 @@ template<class TInputValue, class TOutputValue>
void NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::LabelsToMat(const TargetListSampleType * labels,
cv::Mat & output)
{
- unsigned int nbSamples = labels->Size();
+ unsigned int nbSamples = 0;
+ if (labels != NULL)
+ {
+ nbSamples = labels->Size();
+ }
// Check for valid listSample
- if (labels != NULL && nbSamples > 0)
+ if (nbSamples > 0)
{
// Build an iterator
typename TargetListSampleType::ConstIterator labelSampleIt = labels->Begin();
@@ -130,9 +135,8 @@ void NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::LabelsToMat(c
}
}
-/** Train the machine learning model */
template<class TInputValue, class TOutputValue>
-void NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::TrainClassification()
+void NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::CreateNetwork()
{
//Create the neural network
const unsigned int nbLayers = m_LayerSizes.size();
@@ -147,14 +151,11 @@ void NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::TrainClassifi
}
m_ANNModel->create(layers, m_ActivateFunction, m_Alpha, m_Beta);
+}
- //convert listsample to opencv matrix
- cv::Mat samples;
- otb::ListSampleToMat<InputListSampleType>(this->GetInputListSample(), samples);
-
- cv::Mat matOutputANN;
- LabelsToMat(this->GetTargetListSample(), matOutputANN);
-
+template<class TInputValue, class TOutputValue>
+CvANN_MLP_TrainParams NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::SetNetworkParameters()
+{
CvANN_MLP_TrainParams params;
params.train_method = m_TrainMethod;
params.bp_dw_scale = m_BackPropDWScale;
@@ -163,14 +164,43 @@ void NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::TrainClassifi
params.rp_dw_min = m_RegPropDWMin;
CvTermCriteria term_crit = cvTermCriteria(m_TermCriteriaType, m_MaxIter, m_Epsilon);
params.term_crit = term_crit;
+ return params;
+}
+template<class TInputValue, class TOutputValue>
+void NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::SetupNetworkAndTrain(cv::Mat& labels)
+{
+ //convert listsample to opencv matrix
+ cv::Mat samples;
+ otb::ListSampleToMat<InputListSampleType>(this->GetInputListSample(), samples);
+ this->CreateNetwork();
+ CvANN_MLP_TrainParams params = this->SetNetworkParameters();
//train the Neural network model
- m_ANNModel->train(samples, matOutputANN, cv::Mat(), cv::Mat(), params);
+ m_ANNModel->train(samples, labels, cv::Mat(), cv::Mat(), params);
+}
+
+/** Train the machine learning model for classification*/
+template<class TInputValue, class TOutputValue>
+void NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::Train()
+{
+ //Transform the targets into a matrix of labels
+ cv::Mat matOutputANN;
+ if (this->m_RegressionMode)
+ {
+ // MODE REGRESSION
+ otb::ListSampleToMat<TargetListSampleType>(this->GetTargetListSample(), matOutputANN);
+ }
+ else
+ {
+ // MODE CLASSIFICATION : store the map between internal labels and output labels
+ LabelsToMat(this->GetTargetListSample(), matOutputANN);
+ }
+ this->SetupNetworkAndTrain(matOutputANN);
}
template<class TInputValue, class TOutputValue>
typename NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::TargetSampleType NeuralNetworkMachineLearningModel<
- TInputValue, TOutputValue>::PredictClassification(const InputSampleType & input, ConfidenceValueType *quality) const
+ TInputValue, TOutputValue>::Predict(const InputSampleType & input, ConfidenceValueType *quality) const
{
//convert listsample to Mat
cv::Mat sample;
@@ -183,6 +213,15 @@ typename NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::TargetSam
TargetSampleType target;
float currentResponse = 0;
float maxResponse = response.at<float> (0, 0);
+
+ if (this->m_RegressionMode)
+ {
+ // MODE REGRESSION : only output first response
+ target[0] = maxResponse;
+ return target;
+ }
+
+ // MODE CLASSIFICATION : find the highest response
float secondResponse = -1e10;
target[0] = m_CvMatOfLabels->data.i[0];
diff --git a/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.h b/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.h
index 31b5373ce16510512aa90065e151408a764898ed..8ecfcd2b51d607169b30dcc86dd59d2096d728f4 100644
--- a/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.h
@@ -52,6 +52,11 @@ public:
itkNewMacro(Self);
itkTypeMacro(NormalBayesMachineLearningModel, MachineLearningModel);
+ /** Train the machine learning model */
+ virtual void Train();
+ /** Predict values using the model */
+ virtual TargetSampleType Predict(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
+
/** Save the model to file */
virtual void Save(const std::string & filename, const std::string & name="");
@@ -77,11 +82,6 @@ protected:
/** PrintSelf method */
void PrintSelf(std::ostream& os, itk::Indent indent) const;
- /** Train the machine learning model */
- virtual void TrainClassification();
- /** Predict values using the model */
- virtual TargetSampleType PredictClassification(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
-
private:
NormalBayesMachineLearningModel(const Self &); //purposely not implemented
void operator =(const Self&); //purposely not implemented
diff --git a/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.txx
index bfc163553ddb58eb7e3e9f2150fc9cb63dbfaca2..7538f02c8818fcd07851f26044724992d4747af5 100644
--- a/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.txx
@@ -45,7 +45,7 @@ NormalBayesMachineLearningModel<TInputValue,TOutputValue>
template <class TInputValue, class TOutputValue>
void
NormalBayesMachineLearningModel<TInputValue,TOutputValue>
-::TrainClassification()
+::Train()
{
//convert listsample to opencv matrix
cv::Mat samples;
@@ -61,7 +61,7 @@ template <class TInputValue, class TOutputValue>
typename NormalBayesMachineLearningModel<TInputValue,TOutputValue>
::TargetSampleType
NormalBayesMachineLearningModel<TInputValue,TOutputValue>
-::PredictClassification(const InputSampleType & input, ConfidenceValueType *quality) const
+::Predict(const InputSampleType & input, ConfidenceValueType *quality) const
{
//convert listsample to Mat
cv::Mat sample;
diff --git a/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h b/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h
index 20aff52a7e200f47f9127005addc442d25fd5799..ec62b761358634daa8e268c1488770bb801d3543 100644
--- a/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h
@@ -59,6 +59,11 @@ public:
itkNewMacro(Self);
itkTypeMacro(RandomForestsMachineLearningModel, MachineLearningModel);
+ /** Train the machine learning model */
+ virtual void Train();
+ /** Predict values using the model */
+ virtual TargetSampleType Predict(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
+
/** Save the model to file */
virtual void Save(const std::string & filename, const std::string & name="");
@@ -115,9 +120,6 @@ public:
itkGetMacro(TerminationCriteria, int);
itkSetMacro(TerminationCriteria, int);
- itkGetMacro(RegressionMode, bool);
- itkSetMacro(RegressionMode, bool);
-
/** Returns a matrix containing variable importance */
VariableImportanceMatrixType GetVariableImportance();
@@ -139,11 +141,6 @@ protected:
/* /\** Target list sample *\/ */
/* typename TargetListSampleType::Pointer m_TargetListSample; */
- /** Train the machine learning model */
- virtual void TrainClassification();
- /** Predict values using the model */
- virtual TargetSampleType PredictClassification(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
-
private:
RandomForestsMachineLearningModel(const Self &); //purposely not implemented
void operator =(const Self&); //purposely not implemented
@@ -208,8 +205,6 @@ private:
float m_ForestAccuracy;
/** The type of the termination criteria */
int m_TerminationCriteria;
- /** Perform regression instead of classification */
- bool m_RegressionMode;
};
} // end namespace otb
diff --git a/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.txx
index 0797c2fabf18077612470b70604d8a3492a64446..78642f1212ac9d75dc75d3bd9e9482d73bc948dc 100644
--- a/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.txx
@@ -32,17 +32,17 @@ RandomForestsMachineLearningModel<TInputValue,TOutputValue>
m_RFModel (new CvRTrees),
m_MaxDepth(5),
m_MinSampleCount(10),
- m_RegressionAccuracy(0),
+ m_RegressionAccuracy(0.01),
m_ComputeSurrogateSplit(false),
m_MaxNumberOfCategories(10),
m_CalculateVariableImportance(false),
m_MaxNumberOfVariables(0),
m_MaxNumberOfTrees(100),
m_ForestAccuracy(0.01),
- m_TerminationCriteria(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS),
- m_RegressionMode(false)
+ m_TerminationCriteria(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS)
{
this->m_ConfidenceIndex = true;
+ this->m_IsRegressionSupported = true;
}
@@ -65,7 +65,7 @@ RandomForestsMachineLearningModel<TInputValue,TOutputValue>
template <class TInputValue, class TOutputValue>
void
RandomForestsMachineLearningModel<TInputValue,TOutputValue>
-::TrainClassification()
+::Train()
{
//convert listsample to opencv matrix
cv::Mat samples;
@@ -96,7 +96,7 @@ RandomForestsMachineLearningModel<TInputValue,TOutputValue>
cv::Mat var_type = cv::Mat(this->GetInputListSample()->GetMeasurementVectorSize() + 1, 1, CV_8U );
var_type.setTo(cv::Scalar(CV_VAR_NUMERICAL) ); // all inputs are numerical
- if(m_RegressionMode)
+ if(this->m_RegressionMode)
var_type.at<uchar>(this->GetInputListSample()->GetMeasurementVectorSize(), 0) = CV_VAR_NUMERICAL;
else
var_type.at<uchar>(this->GetInputListSample()->GetMeasurementVectorSize(), 0) = CV_VAR_CATEGORICAL;
@@ -110,7 +110,7 @@ template <class TInputValue, class TOutputValue>
typename RandomForestsMachineLearningModel<TInputValue,TOutputValue>
::TargetSampleType
RandomForestsMachineLearningModel<TInputValue,TOutputValue>
-::PredictClassification(const InputSampleType & value, ConfidenceValueType *quality) const
+::Predict(const InputSampleType & value, ConfidenceValueType *quality) const
{
//convert listsample to Mat
cv::Mat sample;
diff --git a/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.h b/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.h
index 406bdd10e6b04a808fbd2be168e0f7504aa79578..569ab321427925e1e662d941d2be1a398fda54cc 100644
--- a/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.h
@@ -51,6 +51,11 @@ public:
itkNewMacro(Self);
itkTypeMacro(SVMMachineLearningModel, MachineLearningModel);
+ /** Train the machine learning model */
+ virtual void Train();
+ /** Predict values using the model */
+ virtual TargetSampleType Predict(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
+
/** Save the model to file */
virtual void Save(const std::string & filename, const std::string & name="");
@@ -126,11 +131,6 @@ protected:
/** PrintSelf method */
void PrintSelf(std::ostream& os, itk::Indent indent) const;
- /** Train the machine learning model */
- virtual void TrainClassification();
- /** Predict values using the model */
- virtual TargetSampleType PredictClassification(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
-
private:
SVMMachineLearningModel(const Self &); //purposely not implemented
void operator =(const Self&); //purposely not implemented
diff --git a/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.txx
index 064db060d1670146d4b618d94fa529017475758e..9d0b7d685948ebf67b070c6a16fcc9e6f760ea34 100644
--- a/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.txx
@@ -50,6 +50,7 @@ SVMMachineLearningModel<TInputValue,TOutputValue>
m_OutputP(0)
{
this->m_ConfidenceIndex = true;
+ this->m_IsRegressionSupported = true;
}
@@ -64,8 +65,16 @@ SVMMachineLearningModel<TInputValue,TOutputValue>
template <class TInputValue, class TOutputValue>
void
SVMMachineLearningModel<TInputValue,TOutputValue>
-::TrainClassification()
+::Train()
{
+ // Check that the SVM type is compatible with the chosen mode (classif/regression)
+ if ( bool(m_SVMType == CvSVM::NU_SVR || m_SVMType == CvSVM::EPS_SVR) != this->m_RegressionMode)
+ {
+ itkGenericExceptionMacro("SVM type incompatible with chosen mode (classification or regression."
+ "SVM types for classification are C_SVC, NU_SVC, ONE_CLASS. "
+ "SVM types for regression are NU_SVR, EPS_SVR");
+ }
+
//convert listsample to opencv matrix
cv::Mat samples;
otb::ListSampleToMat<InputListSampleType>(this->GetInputListSample(), samples);
@@ -109,7 +118,7 @@ template <class TInputValue, class TOutputValue>
typename SVMMachineLearningModel<TInputValue,TOutputValue>
::TargetSampleType
SVMMachineLearningModel<TInputValue,TOutputValue>
-::PredictClassification(const InputSampleType & input, ConfidenceValueType *quality) const
+::Predict(const InputSampleType & input, ConfidenceValueType *quality) const
{
//convert listsample to Mat
cv::Mat sample;
@@ -135,10 +144,10 @@ void
SVMMachineLearningModel<TInputValue,TOutputValue>
::Save(const std::string & filename, const std::string & name)
{
- if (name == "")
- m_SVMModel->save(filename.c_str(), 0);
- else
- m_SVMModel->save(filename.c_str(), name.c_str());
+ if (name == "")
+ m_SVMModel->save(filename.c_str(), 0);
+ else
+ m_SVMModel->save(filename.c_str(), name.c_str());
}
template <class TInputValue, class TOutputValue>
@@ -147,9 +156,9 @@ SVMMachineLearningModel<TInputValue,TOutputValue>
::Load(const std::string & filename, const std::string & name)
{
if (name == "")
- m_SVMModel->load(filename.c_str(), 0);
+ m_SVMModel->load(filename.c_str(), 0);
else
- m_SVMModel->load(filename.c_str(), name.c_str());
+ m_SVMModel->load(filename.c_str(), name.c_str());
}
template <class TInputValue, class TOutputValue>
@@ -157,29 +166,29 @@ bool
SVMMachineLearningModel<TInputValue,TOutputValue>
::CanReadFile(const std::string & file)
{
- std::ifstream ifs;
- ifs.open(file.c_str());
-
- if(!ifs)
- {
- std::cerr<<"Could not read file "<<file<<std::endl;
- return false;
- }
-
- while (!ifs.eof())
- {
- std::string line;
- std::getline(ifs, line);
-
- //if (line.find(m_SVMModel->getName()) != std::string::npos)
- if (line.find(CV_TYPE_NAME_ML_SVM) != std::string::npos)
+ std::ifstream ifs;
+ ifs.open(file.c_str());
+
+ if(!ifs)
+ {
+ std::cerr<<"Could not read file "<<file<<std::endl;
+ return false;
+ }
+
+ while (!ifs.eof())
+ {
+ std::string line;
+ std::getline(ifs, line);
+
+ //if (line.find(m_SVMModel->getName()) != std::string::npos)
+ if (line.find(CV_TYPE_NAME_ML_SVM) != std::string::npos)
{
- //std::cout<<"Reading a "<<CV_TYPE_NAME_ML_SVM<<" model"<<std::endl;
- return true;
+ //std::cout<<"Reading a "<<CV_TYPE_NAME_ML_SVM<<" model"<<std::endl;
+ return true;
}
- }
- ifs.close();
- return false;
+ }
+ ifs.close();
+ return false;
}
template <class TInputValue, class TOutputValue>
diff --git a/Modules/Learning/Supervised/test/CMakeLists.txt b/Modules/Learning/Supervised/test/CMakeLists.txt
index 7ffe8757b490c64081ffb6fe424a582d382d4df5..abca168f4f7794ea1a9133370c65555593b9bf19 100644
--- a/Modules/Learning/Supervised/test/CMakeLists.txt
+++ b/Modules/Learning/Supervised/test/CMakeLists.txt
@@ -7,6 +7,7 @@ otbConfusionMatrixMeasurementsTest.cxx
otbMachineLearningModelCanRead.cxx
otbTrainMachineLearningModel.cxx
otbImageClassificationFilter.cxx
+otbMachineLearningRegressionTests.cxx
)
add_executable(otbSupervisedTestDriver ${OTBSupervisedTests})
diff --git a/Modules/Learning/Supervised/test/otbMachineLearningRegressionTests.cxx b/Modules/Learning/Supervised/test/otbMachineLearningRegressionTests.cxx
new file mode 100644
index 0000000000000000000000000000000000000000..f54a9a96adc5a60f18f2a2411ff0e078ad5aba6d
--- /dev/null
+++ b/Modules/Learning/Supervised/test/otbMachineLearningRegressionTests.cxx
@@ -0,0 +1,669 @@
+/*=========================================================================
+
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+ =========================================================================*/
+
+#include "otbConfigure.h"
+
+#include "itkMersenneTwisterRandomVariateGenerator.h"
+
+#ifdef OTB_USE_OPENCV
+#include "otbNeuralNetworkMachineLearningModel.h"
+#include "otbSVMMachineLearningModel.h"
+#include "otbBoostMachineLearningModel.h"
+#include "otbDecisionTreeMachineLearningModel.h"
+#include "otbGradientBoostedTreeMachineLearningModel.h"
+#include "otbKNearestNeighborsMachineLearningModel.h"
+#include "otbRandomForestsMachineLearningModel.h"
+#endif
+
+#ifdef OTB_USE_LIBSVM
+#include "otbLibSVMMachineLearningModel.h"
+#endif
+
+typedef float PrecisionType;
+typedef otb::MachineLearningModel<PrecisionType,PrecisionType> MachineLearningModelRegressionType;
+typedef MachineLearningModelRegressionType::InputValueType InputValueRegressionType;
+typedef MachineLearningModelRegressionType::InputSampleType InputSampleRegressionType;
+typedef MachineLearningModelRegressionType::InputListSampleType InputListSampleRegressionType;
+typedef MachineLearningModelRegressionType::TargetValueType TargetValueRegressionType;
+typedef MachineLearningModelRegressionType::TargetSampleType TargetSampleRegressionType;
+typedef MachineLearningModelRegressionType::TargetListSampleType TargetListSampleRegressionType;
+typedef itk::Statistics::MersenneTwisterRandomVariateGenerator RandomGeneratorType;
+
+const double epsilon = 0.1;
+
+typedef struct RegressionTestParamStruct
+{
+ double vMin;
+ double vMax;
+ size_t count;
+ double eps;
+} RegressionTestParam;
+
+template <typename TPrecision>
+struct LinearFunctionSampleGenerator
+{
+ typedef TPrecision PrecisionType;
+ LinearFunctionSampleGenerator(TPrecision a, TPrecision b)
+ : m_a(a), m_b(b), m_NbInputVars(1), m_NbOutputVars(1) {
+ m_isl = InputListSampleRegressionType::New();
+ m_tsl = TargetListSampleRegressionType::New();
+ };
+ void GenerateSamples(TPrecision sMin, TPrecision sMax, size_t nbSamples)
+ {
+ m_isl->Clear();
+ m_tsl->Clear();
+ m_isl->SetMeasurementVectorSize(m_NbInputVars);
+ m_tsl->SetMeasurementVectorSize(m_NbOutputVars);
+
+ RandomGeneratorType::Pointer randomGenerator = RandomGeneratorType::GetInstance();
+ InputSampleRegressionType inputSample;
+ inputSample.SetSize(m_NbInputVars);
+ TargetSampleRegressionType outputSample;
+
+ TPrecision sampleStep = (sMax-sMin)/nbSamples;
+ for(size_t i=0; i<nbSamples; ++i)
+ {
+ TPrecision x = randomGenerator->GetUniformVariate(0.0, 1.0) * static_cast<TPrecision>(nbSamples);
+ TPrecision inputValue = sMin+ x*sampleStep;
+ inputSample[0] = inputValue;
+ outputSample[0] = m_a*inputValue+m_b;
+ m_isl->PushBack(inputSample);
+ m_tsl->PushBack(outputSample);
+ }
+ }
+
+ TPrecision m_a;
+ TPrecision m_b;
+ const size_t m_NbInputVars;
+ const size_t m_NbOutputVars;
+ InputListSampleRegressionType::Pointer m_isl;
+ TargetListSampleRegressionType::Pointer m_tsl;
+};
+
+template <typename TPrecision>
+struct BilinearFunctionSampleGenerator
+{
+ typedef TPrecision PrecisionType;
+ BilinearFunctionSampleGenerator(TPrecision a, TPrecision b, TPrecision c)
+ : m_a(a), m_b(b), m_c(c), m_NbInputVars(2), m_NbOutputVars(1) {
+ m_isl = InputListSampleRegressionType::New();
+ m_tsl = TargetListSampleRegressionType::New();
+ };
+ void GenerateSamples(TPrecision sMin, TPrecision sMax, size_t nbSamples)
+ {
+ m_isl->Clear();
+ m_tsl->Clear();
+ m_isl->SetMeasurementVectorSize(m_NbInputVars);
+ m_tsl->SetMeasurementVectorSize(m_NbOutputVars);
+
+ RandomGeneratorType::Pointer randomGenerator = RandomGeneratorType::GetInstance();
+ InputSampleRegressionType inputSample;
+ inputSample.SetSize(m_NbInputVars);
+ TargetSampleRegressionType outputSample;
+
+ TPrecision sampleStep = (sMax-sMin)/nbSamples;
+ for(size_t i=0; i<nbSamples; ++i)
+ {
+ TPrecision x = randomGenerator->GetUniformVariate(0.0, 1.0) * static_cast<TPrecision>(nbSamples);
+ TPrecision inputValue1 = sMin+ x*sampleStep;
+ x = randomGenerator->GetUniformVariate(0.0, 1.0) * static_cast<TPrecision>(nbSamples);
+ TPrecision inputValue2 = sMin+ x*sampleStep;
+ inputSample[0] = inputValue1;
+ inputSample[1] = inputValue2;
+ outputSample[0] = m_a*inputValue1+m_b*inputValue2+m_c;
+ m_isl->PushBack(inputSample);
+ m_tsl->PushBack(outputSample);
+ }
+ }
+
+ TPrecision m_a;
+ TPrecision m_b;
+ TPrecision m_c;
+ const size_t m_NbInputVars;
+ const size_t m_NbOutputVars;
+ InputListSampleRegressionType::Pointer m_isl;
+ TargetListSampleRegressionType::Pointer m_tsl;
+};
+
+template <typename TPrecision>
+struct PolynomialFunctionSampleGenerator
+{
+ typedef TPrecision PrecisionType;
+ PolynomialFunctionSampleGenerator(std::vector<TPrecision> c)
+ : m_c(c), m_NbInputVars(1), m_NbOutputVars(1) {
+ m_isl = InputListSampleRegressionType::New();
+ m_tsl = TargetListSampleRegressionType::New();
+ };
+ void GenerateSamples(TPrecision sMin, TPrecision sMax, size_t nbSamples)
+ {
+ m_isl->Clear();
+ m_tsl->Clear();
+ m_isl->SetMeasurementVectorSize(m_NbInputVars);
+ m_tsl->SetMeasurementVectorSize(m_NbOutputVars);
+
+ RandomGeneratorType::Pointer randomGenerator = RandomGeneratorType::GetInstance();
+ InputSampleRegressionType inputSample;
+ inputSample.SetSize(m_NbInputVars);
+ TargetSampleRegressionType outputSample;
+
+ TPrecision sampleStep = (sMax-sMin)/nbSamples;
+ for(size_t i=0; i<nbSamples; ++i)
+ {
+ TPrecision x = randomGenerator->GetUniformVariate(0.0, 1.0) * static_cast<TPrecision>(nbSamples);
+ TPrecision inputValue = sMin+ x*sampleStep;
+ inputSample[0] = inputValue;
+ TPrecision y = 0.0;
+ for (unsigned int j=0; j<m_c.size() ; ++j)
+ {
+ y += m_c[j] * pow(static_cast<double>(inputValue), static_cast<double>(j));
+ }
+ outputSample[0] = y;
+ m_isl->PushBack(inputSample);
+ m_tsl->PushBack(outputSample);
+ }
+ }
+
+ std::vector<TPrecision> m_c;
+ const size_t m_NbInputVars;
+ const size_t m_NbOutputVars;
+ InputListSampleRegressionType::Pointer m_isl;
+ TargetListSampleRegressionType::Pointer m_tsl;
+};
+
+template <typename SampleGeneratorType, typename RegressionType>
+int testRegression(SampleGeneratorType& sg, RegressionType& rgrsn, RegressionTestParam param)
+{
+ std::cout << "Generating training samples" << std::endl;
+ sg.GenerateSamples(param.vMin, param.vMax, param.count);
+
+ rgrsn->SetInputListSample(sg.m_isl);
+ rgrsn->SetTargetListSample(sg.m_tsl);
+ std::cout << "Training" << std::endl;
+ rgrsn->Train();
+
+ std::cout << "Generate validation samples"<<std::endl;
+ sg.GenerateSamples(param.vMin, param.vMax, param.count);
+
+ std::cout << "Validation" << std::endl;
+ //Check the prediction accuracy
+ typename InputListSampleRegressionType::Iterator sampleIt = sg.m_isl->Begin();
+ typename TargetListSampleRegressionType::Iterator resultIt = sg.m_tsl->Begin();
+ typename InputListSampleRegressionType::Iterator sampleLast = sg.m_isl->End();
+ typename TargetListSampleRegressionType::Iterator resultLast = sg.m_tsl->End();
+ typename SampleGeneratorType::PrecisionType rmse = 0.0;
+ while(sampleIt != sampleLast && resultIt != resultLast)
+ {
+ //typename SampleGeneratorType::PrecisionType invalue = sampleIt.GetMeasurementVector()[0];
+ typename SampleGeneratorType::PrecisionType prediction = rgrsn->Predict(sampleIt.GetMeasurementVector())[0];
+ typename SampleGeneratorType::PrecisionType expected = resultIt.GetMeasurementVector()[0];
+ rmse += std::pow(prediction - expected, static_cast<PrecisionType>(2.0));
+ ++sampleIt;
+ ++resultIt;
+ }
+
+ rmse = sqrt( rmse / static_cast<double>(param.count) );
+ std::cout << "RMSE = "<< rmse << std::endl;
+ if(rmse > param.eps)
+ {
+ std::cout << "Failed : RMSE above expected precision !" << std::endl;
+ return EXIT_FAILURE;
+ }
+
+ return EXIT_SUCCESS;
+}
+
+#ifdef OTB_USE_LIBSVM
+MachineLearningModelRegressionType::Pointer getLibSVMRegressionModel()
+{
+ typedef otb::LibSVMMachineLearningModel<InputValueRegressionType,
+ TargetValueRegressionType>
+ libsvmType;
+ libsvmType::Pointer regression = libsvmType::New();
+ regression->SetRegressionMode(true);
+ regression->SetSVMType(EPSILON_SVR);
+ regression->SetKernelType(RBF);
+ regression->SetEpsilon(1e-5);
+ regression->SetParameterOptimization(true);
+ return regression.GetPointer();
+}
+
+int otbLibSVMRegressionTests(int itkNotUsed(argc),
+ char * itkNotUsed(argv) [])
+{
+ int status = EXIT_SUCCESS;
+ int ret;
+ MachineLearningModelRegressionType::Pointer regression;
+
+ RegressionTestParam param;
+ param.vMin = -0.5;
+ param.vMax = 0.5;
+ param.count = 200;
+ param.eps = 0.1;
+
+ std::cout << "Testing regression on a linear monovariate function" << std::endl;
+ LinearFunctionSampleGenerator<PrecisionType> lfsg(2.0, 1.0);
+ regression = getLibSVMRegressionModel();
+ ret = testRegression(lfsg,regression,param);
+ if (ret == EXIT_FAILURE)
+ {
+ status = EXIT_FAILURE;
+ }
+ std::cout << "Testing regression on a bilinear function" << std::endl;
+ BilinearFunctionSampleGenerator<PrecisionType> bfsg(2.0,-1.0,1.0);
+ regression = getLibSVMRegressionModel();
+ ret = testRegression(bfsg,regression,param);
+ if (ret == EXIT_FAILURE)
+ {
+ status = EXIT_FAILURE;
+ }
+ std::cout << "Testing regression on a polynomial function" << std::endl;
+ std::vector<PrecisionType> coeffs;
+ coeffs.push_back(0.0);
+ coeffs.push_back(-1.0);
+ coeffs.push_back(0.0);
+ coeffs.push_back(4.0);
+ PolynomialFunctionSampleGenerator<PrecisionType> pfsg(coeffs);
+ regression = getLibSVMRegressionModel();
+ ret = testRegression(pfsg,regression,param);
+ if (ret == EXIT_FAILURE)
+ {
+ status = EXIT_FAILURE;
+ }
+ return status;
+}
+
+#endif
+
+#ifdef OTB_USE_OPENCV
+MachineLearningModelRegressionType::Pointer getNeuralNetworkRegressionModel(unsigned int nbInputVar)
+{
+ typedef otb::NeuralNetworkMachineLearningModel<InputValueRegressionType,
+ TargetValueRegressionType>
+ NeuralNetworkType;
+ NeuralNetworkType::Pointer regression = NeuralNetworkType::New();
+
+ regression->SetRegressionMode(1);
+ regression->SetTrainMethod(CvANN_MLP_TrainParams::BACKPROP);
+ std::vector<unsigned int> layerSizes;
+ layerSizes.push_back(nbInputVar);
+ layerSizes.push_back(5);
+ layerSizes.push_back(1);
+ regression->SetLayerSizes(layerSizes);
+ regression->SetActivateFunction(CvANN_MLP::SIGMOID_SYM);
+ regression->SetAlpha(1.0);
+ regression->SetBeta(1.0);
+ regression->SetBackPropDWScale(0.1);
+ regression->SetBackPropMomentScale(0.1);
+ regression->SetRegPropDW0(0.1);
+ regression->SetRegPropDWMin(1e-7);
+ regression->SetTermCriteriaType(CV_TERMCRIT_EPS);
+ regression->SetEpsilon(1e-5);
+ regression->SetMaxIter(1e4);
+ return regression.GetPointer();
+}
+
+int otbNeuralNetworkRegressionTests(int itkNotUsed(argc),
+ char * itkNotUsed(argv) [])
+{
+ int status = EXIT_SUCCESS;
+ int ret;
+ MachineLearningModelRegressionType::Pointer regression;
+
+ RegressionTestParam param;
+ param.vMin = -0.5;
+ param.vMax = 0.5;
+ param.count = 20000;
+ param.eps = 0.1;
+
+ std::cout << "Testing regression on a linear monovariate function" << std::endl;
+ LinearFunctionSampleGenerator<PrecisionType> lfsg(2.0, 1.0);
+ regression = getNeuralNetworkRegressionModel(1);
+ ret = testRegression(lfsg,regression,param);
+ if (ret == EXIT_FAILURE)
+ {
+ status = EXIT_FAILURE;
+ }
+ std::cout << "Testing regression on a bilinear function" << std::endl;
+ BilinearFunctionSampleGenerator<PrecisionType> bfsg(2.0,-1.0,1.0);
+ regression = getNeuralNetworkRegressionModel(2);
+ ret = testRegression(bfsg,regression,param);
+ if (ret == EXIT_FAILURE)
+ {
+ status = EXIT_FAILURE;
+ }
+ std::cout << "Testing regression on a polynomial function" << std::endl;
+ std::vector<PrecisionType> coeffs;
+ coeffs.push_back(0.0);
+ coeffs.push_back(-1.0);
+ coeffs.push_back(0.0);
+ coeffs.push_back(4.0);
+ PolynomialFunctionSampleGenerator<PrecisionType> pfsg(coeffs);
+ regression = getNeuralNetworkRegressionModel(1);
+ ret = testRegression(pfsg,regression,param);
+ if (ret == EXIT_FAILURE)
+ {
+ status = EXIT_FAILURE;
+ }
+ return status;
+}
+
+
+MachineLearningModelRegressionType::Pointer getSVMRegressionModel()
+{
+ typedef otb::SVMMachineLearningModel<InputValueRegressionType,
+ TargetValueRegressionType>
+ SVMType;
+ SVMType::Pointer regression = SVMType::New();
+
+ regression->SetRegressionMode(1);
+ regression->SetSVMType(CvSVM::NU_SVR);
+ regression->SetNu(0.5);
+ regression->SetKernelType(CvSVM::RBF);
+ regression->SetTermCriteriaType(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS);
+ regression->SetMaxIter(100000);
+ regression->SetEpsilon(1e-5);
+ regression->SetParameterOptimization(true);
+ return regression.GetPointer();
+}
+
+int otbSVMRegressionTests(int itkNotUsed(argc),
+ char * itkNotUsed(argv) [])
+{
+ int status = EXIT_SUCCESS;
+ int ret;
+ MachineLearningModelRegressionType::Pointer regression;
+
+ RegressionTestParam param;
+ param.vMin = -0.5;
+ param.vMax = 0.5;
+ param.count = 200;
+ param.eps = 0.1;
+
+ std::cout << "Testing regression on a linear monovariate function" << std::endl;
+ LinearFunctionSampleGenerator<PrecisionType> lfsg(2.0, 1.0);
+ regression = getSVMRegressionModel();
+ ret = testRegression(lfsg,regression,param);
+ if (ret == EXIT_FAILURE)
+ {
+ status = EXIT_FAILURE;
+ }
+ std::cout << "Testing regression on a bilinear function" << std::endl;
+ BilinearFunctionSampleGenerator<PrecisionType> bfsg(2.0,-1.0,1.0);
+ regression = getSVMRegressionModel();
+ ret = testRegression(bfsg,regression,param);
+ if (ret == EXIT_FAILURE)
+ {
+ status = EXIT_FAILURE;
+ }
+ std::cout << "Testing regression on a polynomial function" << std::endl;
+ std::vector<PrecisionType> coeffs;
+ coeffs.push_back(0.0);
+ coeffs.push_back(-1.0);
+ coeffs.push_back(0.0);
+ coeffs.push_back(4.0);
+ PolynomialFunctionSampleGenerator<PrecisionType> pfsg(coeffs);
+ regression = getSVMRegressionModel();
+ ret = testRegression(pfsg,regression,param);
+ if (ret == EXIT_FAILURE)
+ {
+ status = EXIT_FAILURE;
+ }
+ return status;
+}
+
+
+MachineLearningModelRegressionType::Pointer getDecisionTreeRegressionModel()
+{
+ typedef otb::DecisionTreeMachineLearningModel<InputValueRegressionType,
+ TargetValueRegressionType>
+ DTreeType;
+ DTreeType::Pointer regression = DTreeType::New();
+ regression->SetRegressionMode(true);
+ regression->SetRegressionAccuracy(0.005);
+ return regression.GetPointer();
+}
+
+int otbDecisionTreeRegressionTests(int itkNotUsed(argc),
+ char * itkNotUsed(argv) [])
+{
+ int status = EXIT_SUCCESS;
+ int ret;
+ MachineLearningModelRegressionType::Pointer regression;
+
+ RegressionTestParam param;
+ param.vMin = -0.5;
+ param.vMax = 0.5;
+ param.count = 200;
+ param.eps = 0.1;
+
+ std::cout << "Testing regression on a linear monovariate function" << std::endl;
+ LinearFunctionSampleGenerator<PrecisionType> lfsg(2.0, 1.0);
+ regression = getDecisionTreeRegressionModel();
+ ret = testRegression(lfsg,regression,param);
+ if (ret == EXIT_FAILURE)
+ {
+ status = EXIT_FAILURE;
+ }
+ std::cout << "Testing regression on a bilinear function" << std::endl;
+ BilinearFunctionSampleGenerator<PrecisionType> bfsg(2.0,-1.0,1.0);
+ regression = getDecisionTreeRegressionModel();
+ // increase the number of training samples for bilinear function
+ param.count = 1000;
+ ret = testRegression(bfsg,regression,param);
+ if (ret == EXIT_FAILURE)
+ {
+ status = EXIT_FAILURE;
+ }
+ std::cout << "Testing regression on a polynomial function" << std::endl;
+ std::vector<PrecisionType> coeffs;
+ coeffs.push_back(0.0);
+ coeffs.push_back(-1.0);
+ coeffs.push_back(0.0);
+ coeffs.push_back(4.0);
+ PolynomialFunctionSampleGenerator<PrecisionType> pfsg(coeffs);
+ param.count = 200;
+ regression = getDecisionTreeRegressionModel();
+ ret = testRegression(pfsg,regression,param);
+ if (ret == EXIT_FAILURE)
+ {
+ status = EXIT_FAILURE;
+ }
+ return status;
+}
+
+
+MachineLearningModelRegressionType::Pointer getGradientBoostedTreeRegressionModel()
+{
+ typedef otb::GradientBoostedTreeMachineLearningModel<InputValueRegressionType,
+ TargetValueRegressionType>
+ GBTreeType;
+ GBTreeType::Pointer regression = GBTreeType::New();
+ regression->SetRegressionMode(true);
+ regression->SetShrinkage(0.1);
+ regression->SetSubSamplePortion(0.8);
+ regression->SetLossFunctionType(CvGBTrees::SQUARED_LOSS);
+ return regression.GetPointer();
+}
+
+int otbGradientBoostedTreeRegressionTests(int itkNotUsed(argc),
+ char * itkNotUsed(argv) [])
+{
+ int status = EXIT_SUCCESS;
+ int ret;
+ MachineLearningModelRegressionType::Pointer regression;
+
+ RegressionTestParam param;
+ param.vMin = -0.5;
+ param.vMax = 0.5;
+ param.count = 200;
+ param.eps = 0.1;
+
+ std::cout << "Testing regression on a linear monovariate function" << std::endl;
+ LinearFunctionSampleGenerator<PrecisionType> lfsg(2.0, 1.0);
+ regression = getGradientBoostedTreeRegressionModel();
+ ret = testRegression(lfsg,regression,param);
+ if (ret == EXIT_FAILURE)
+ {
+ status = EXIT_FAILURE;
+ }
+ std::cout << "Testing regression on a bilinear function" << std::endl;
+ BilinearFunctionSampleGenerator<PrecisionType> bfsg(2.0,-1.0,1.0);
+ // increase number of training samples for bilinear function
+ param.count = 1000;
+ regression = getGradientBoostedTreeRegressionModel();
+ ret = testRegression(bfsg,regression,param);
+ if (ret == EXIT_FAILURE)
+ {
+ status = EXIT_FAILURE;
+ }
+ std::cout << "Testing regression on a polynomial function" << std::endl;
+ std::vector<PrecisionType> coeffs;
+ coeffs.push_back(0.0);
+ coeffs.push_back(-1.0);
+ coeffs.push_back(0.0);
+ coeffs.push_back(4.0);
+ PolynomialFunctionSampleGenerator<PrecisionType> pfsg(coeffs);
+ param.count = 200;
+ regression = getGradientBoostedTreeRegressionModel();
+ ret = testRegression(pfsg,regression,param);
+ if (ret == EXIT_FAILURE)
+ {
+ status = EXIT_FAILURE;
+ }
+ return status;
+}
+
+
+MachineLearningModelRegressionType::Pointer getKNearestNeighborsRegressionModel()
+{
+ typedef otb::KNearestNeighborsMachineLearningModel<InputValueRegressionType,
+ TargetValueRegressionType>
+ KNNType;
+ KNNType::Pointer regression = KNNType::New();
+ regression->SetRegressionMode(true);
+ regression->SetK(5);
+ regression->SetDecisionRule(KNNType::KNN_MEDIAN);
+ return regression.GetPointer();
+}
+
+int otbKNearestNeighborsRegressionTests(int itkNotUsed(argc),
+ char * itkNotUsed(argv) [])
+{
+ int status = EXIT_SUCCESS;
+ int ret;
+ MachineLearningModelRegressionType::Pointer regression;
+
+ RegressionTestParam param;
+ param.vMin = -0.5;
+ param.vMax = 0.5;
+ param.count = 200;
+ param.eps = 0.1;
+
+ std::cout << "Testing regression on a linear monovariate function" << std::endl;
+ LinearFunctionSampleGenerator<PrecisionType> lfsg(2.0, 1.0);
+ regression = getKNearestNeighborsRegressionModel();
+ ret = testRegression(lfsg,regression,param);
+ if (ret == EXIT_FAILURE)
+ {
+ status = EXIT_FAILURE;
+ }
+ std::cout << "Testing regression on a bilinear function" << std::endl;
+ BilinearFunctionSampleGenerator<PrecisionType> bfsg(2.0,-1.0,1.0);
+ regression = getKNearestNeighborsRegressionModel();
+ ret = testRegression(bfsg,regression,param);
+ if (ret == EXIT_FAILURE)
+ {
+ status = EXIT_FAILURE;
+ }
+ std::cout << "Testing regression on a polynomial function" << std::endl;
+ std::vector<PrecisionType> coeffs;
+ coeffs.push_back(0.0);
+ coeffs.push_back(-1.0);
+ coeffs.push_back(0.0);
+ coeffs.push_back(4.0);
+ PolynomialFunctionSampleGenerator<PrecisionType> pfsg(coeffs);
+ regression = getKNearestNeighborsRegressionModel();
+ ret = testRegression(pfsg,regression,param);
+ if (ret == EXIT_FAILURE)
+ {
+ status = EXIT_FAILURE;
+ }
+ return status;
+}
+
+
+MachineLearningModelRegressionType::Pointer getRandomForestsRegressionModel()
+{
+ typedef otb::RandomForestsMachineLearningModel<InputValueRegressionType,
+ TargetValueRegressionType>
+ RFType;
+ RFType::Pointer regression = RFType::New();
+ regression->SetRegressionMode(true);
+ regression->SetRegressionAccuracy(0.005);
+ return regression.GetPointer();
+}
+
+
+int otbRandomForestsRegressionTests(int itkNotUsed(argc),
+ char * itkNotUsed(argv) [])
+{
+ int status = EXIT_SUCCESS;
+ int ret;
+ MachineLearningModelRegressionType::Pointer regression;
+
+ RegressionTestParam param;
+ param.vMin = -0.5;
+ param.vMax = 0.5;
+ param.count = 200;
+ param.eps = 0.1;
+
+ std::cout << "Testing regression on a linear monovariate function" << std::endl;
+ LinearFunctionSampleGenerator<PrecisionType> lfsg(2.0, 1.0);
+ regression = getRandomForestsRegressionModel();
+ ret = testRegression(lfsg,regression,param);
+ if (ret == EXIT_FAILURE)
+ {
+ status = EXIT_FAILURE;
+ }
+ std::cout << "Testing regression on a bilinear function" << std::endl;
+ BilinearFunctionSampleGenerator<PrecisionType> bfsg(2.0,-1.0,1.0);
+ // increase number of training samples for bilinear function
+ param.count = 1000;
+ regression = getRandomForestsRegressionModel();
+ ret = testRegression(bfsg,regression,param);
+ if (ret == EXIT_FAILURE)
+ {
+ status = EXIT_FAILURE;
+ }
+ std::cout << "Testing regression on a polynomial function" << std::endl;
+ std::vector<PrecisionType> coeffs;
+ coeffs.push_back(0.0);
+ coeffs.push_back(-1.0);
+ coeffs.push_back(0.0);
+ coeffs.push_back(4.0);
+ PolynomialFunctionSampleGenerator<PrecisionType> pfsg(coeffs);
+ param.count = 200;
+ regression = getRandomForestsRegressionModel();
+ ret = testRegression(pfsg,regression,param);
+ if (ret == EXIT_FAILURE)
+ {
+ status = EXIT_FAILURE;
+ }
+ return status;
+}
+#endif
diff --git a/Modules/Learning/Supervised/test/otbSupervisedTestDriver.cxx b/Modules/Learning/Supervised/test/otbSupervisedTestDriver.cxx
index e498bd082ed53ec2862ea5b99913b6b7de897b38..2bf373c87f9f0579df333951b83ae818739d25c2 100644
--- a/Modules/Learning/Supervised/test/otbSupervisedTestDriver.cxx
+++ b/Modules/Learning/Supervised/test/otbSupervisedTestDriver.cxx
@@ -28,6 +28,7 @@ void RegisterTests()
#ifdef OTB_USE_LIBSVM
REGISTER_TEST(otbLibSVMMachineLearningModelNew);
REGISTER_TEST(otbLibSVMMachineLearningModel);
+ REGISTER_TEST(otbLibSVMRegressionTests);
#endif
#ifdef OTB_USE_OPENCV
@@ -48,8 +49,14 @@ void RegisterTests()
REGISTER_TEST(otbDecisionTreeMachineLearningModel);
REGISTER_TEST(otbGradientBoostedTreeMachineLearningModelNew);
REGISTER_TEST(otbGradientBoostedTreeMachineLearningModel);
- #endif
+ REGISTER_TEST(otbNeuralNetworkRegressionTests);
+ REGISTER_TEST(otbSVMRegressionTests);
+ REGISTER_TEST(otbDecisionTreeRegressionTests);
+ REGISTER_TEST(otbGradientBoostedTreeRegressionTests);
+ REGISTER_TEST(otbKNearestNeighborsRegressionTests);
+ REGISTER_TEST(otbRandomForestsRegressionTests);
+#endif
- REGISTER_TEST(otbImageClassificationFilterNew);
+ REGISTER_TEST(otbImageClassificationFilterNew);
REGISTER_TEST(otbImageClassificationFilter);
}
diff --git a/Modules/Learning/Supervised/test/otbTrainMachineLearningModel.cxx b/Modules/Learning/Supervised/test/otbTrainMachineLearningModel.cxx
index 39a2a2f7628c98aca338093af70d1d75d35badee..64d69a120a2dd2cd8e3418ef248ff8c12324f6ed 100644
--- a/Modules/Learning/Supervised/test/otbTrainMachineLearningModel.cxx
+++ b/Modules/Learning/Supervised/test/otbTrainMachineLearningModel.cxx
@@ -381,20 +381,12 @@ int otbSVMMachineLearningRegressionModel(int argc, char * argv[])
SVMType::Pointer classifier = SVMType::New();
//Init SVM type in regression mode
- //Available mode for regression in openCV are eps_svr and nu_svr
+ classifier->SetRegressionMode(1);
classifier->SetSVMType(CvSVM::EPS_SVR);
- //classifier->SetSVMType(CvSVM::NU_SVR);
-
- //P should be >0. Increasing value give better result. Need to investigate why.
classifier->SetP(10);
-
- //IN case you're using nu_svr you should set nu to a positive value between 0
- //and 1.
- //classifier->SetNu(0.9);
-
- //Use RBF kernel.Don't know what is recommended in case of svm regression
classifier->SetKernelType(CvSVM::RBF);
+
classifier->SetInputListSample(samples);
classifier->SetTargetListSample(labels);
classifier->Train();
@@ -432,6 +424,7 @@ int otbSVMMachineLearningRegressionModel(int argc, char * argv[])
}
else
{
+ std::cout << age << "\t" << predicted->GetMeasurementVector(0)[0] << "\n";
return EXIT_FAILURE;
}
}
@@ -447,9 +440,9 @@ int otbKNearestNeighborsMachineLearningModel(int argc, char * argv[])
{
if (argc != 3 )
{
- std::cout<<"Wrong number of arguments "<<std::endl;
- std::cout<<"Usage : sample file, output file"<<std::endl;
- return EXIT_FAILURE;
+ std::cout<<"Wrong number of arguments "<<std::endl;
+ std::cout<<"Usage : sample file, output file"<<std::endl;
+ return EXIT_FAILURE;
}
typedef otb::KNearestNeighborsMachineLearningModel<InputValueType,TargetValueType> KNearestNeighborsType;
@@ -528,11 +521,11 @@ int otbRandomForestsMachineLearningModelNew(int itkNotUsed(argc), char * itkNotU
int otbRandomForestsMachineLearningModel(int argc, char * argv[])
{
if (argc != 3 )
- {
+ {
std::cout<<"Wrong number of arguments "<<std::endl;
std::cout<<"Usage : sample file, output file "<<std::endl;
return EXIT_FAILURE;
- }
+ }
typedef otb::RandomForestsMachineLearningModel<InputValueType,TargetValueType> RandomForestType;
InputListSampleType::Pointer samples = InputListSampleType::New();
@@ -620,9 +613,9 @@ int otbBoostMachineLearningModel(int argc, char * argv[])
{
if (argc != 3 )
{
- std::cout<<"Wrong number of arguments "<<std::endl;
- std::cout<<"Usage : sample file, output file "<<std::endl;
- return EXIT_FAILURE;
+ std::cout<<"Wrong number of arguments "<<std::endl;
+ std::cout<<"Usage : sample file, output file "<<std::endl;
+ return EXIT_FAILURE;
}
typedef otb::BoostMachineLearningModel<InputValueType, TargetValueType> BoostType;
diff --git a/Modules/Learning/Supervised/test/tests-libsvm.cmake b/Modules/Learning/Supervised/test/tests-libsvm.cmake
index 04764df301cb2b2fa7f1951d023495ddf93b5b4b..30dc7eec130d6baa87a8fd37296ce7501548dc7c 100644
--- a/Modules/Learning/Supervised/test/tests-libsvm.cmake
+++ b/Modules/Learning/Supervised/test/tests-libsvm.cmake
@@ -22,3 +22,7 @@ otb_add_test(NAME leTuLibSVMMachineLearningModelCanRead COMMAND otbSupervisedTes
${TEMP}/libsvm_model.txt
)
set_property(TEST leTuLibSVMMachineLearningModelCanRead PROPERTY DEPENDS leTvLibSVMMachineLearningModel)
+
+otb_add_test(NAME leTvLibSVMMachineLearningModelReg COMMAND otbSupervisedTestDriver
+ otbLibSVMRegressionTests
+ )
diff --git a/Modules/Learning/Supervised/test/tests-opencv.cmake b/Modules/Learning/Supervised/test/tests-opencv.cmake
index 55855a4aa912a3b3497bb9b8d9f2bc69d6dccea4..3be269fe8d8e7f17f85f9e4d11c944feb2dc3e02 100644
--- a/Modules/Learning/Supervised/test/tests-opencv.cmake
+++ b/Modules/Learning/Supervised/test/tests-opencv.cmake
@@ -10,6 +10,32 @@ otb_add_test(NAME leTvANNMachineLearningModel COMMAND otbSupervisedTestDriver
${TEMP}/ann_model.txt
)
+# ------------------ Regression tests --------------------
+otb_add_test(NAME leTvANNMachineLearningModelReg COMMAND otbSupervisedTestDriver
+ otbNeuralNetworkRegressionTests
+ )
+
+otb_add_test(NAME leTvSVMMachineLearningModelReg COMMAND otbSupervisedTestDriver
+ otbSVMRegressionTests
+ )
+
+otb_add_test(NAME leTvDecisionTreeMachineLearningModelReg COMMAND otbSupervisedTestDriver
+ otbDecisionTreeRegressionTests
+ )
+
+otb_add_test(NAME leTvGradientBoostedTreeMachineLearningModelReg COMMAND otbSupervisedTestDriver
+ otbGradientBoostedTreeRegressionTests
+ )
+
+otb_add_test(NAME leTvKNearestNeighborsMachineLearningModelReg COMMAND otbSupervisedTestDriver
+ otbKNearestNeighborsRegressionTests
+ )
+
+otb_add_test(NAME leTvRandomForestsMachineLearningModelReg COMMAND otbSupervisedTestDriver
+ otbRandomForestsRegressionTests
+ )
+# --------------------------------------------------------------
+
otb_add_test(NAME leTuSVMMachineLearningModelNew COMMAND otbSupervisedTestDriver
otbSVMMachineLearningModelNew)
diff --git a/Modules/Radiometry/SARCalibration/include/otbSarRadiometricCalibrationFunction.h b/Modules/Radiometry/SARCalibration/include/otbSarRadiometricCalibrationFunction.h
index 541afff72d4dc35ffc3ae3055abda78f4ef74846..1331f740d5e4991b9d77d540019f003922baf2a6 100644
--- a/Modules/Radiometry/SARCalibration/include/otbSarRadiometricCalibrationFunction.h
+++ b/Modules/Radiometry/SARCalibration/include/otbSarRadiometricCalibrationFunction.h
@@ -18,13 +18,11 @@
#ifndef __otbSarRadiometricCalibrationFunction_h
#define __otbSarRadiometricCalibrationFunction_h
-#include "otbSarRadiometricCalibrationFunctor.h"
#include "otbSarParametricMapFunction.h"
-
-
+#include "otbSarCalibrationLookupData.h"
+#include "otbMath.h"
namespace otb
{
-
/**
* \class SarRadiometricCalibrationFunction
* \brief Calculate the backscatter for the given pixel
@@ -71,33 +69,31 @@ public:
itkStaticConstMacro(ImageDimension, unsigned int, InputImageType::ImageDimension);
-
/** Datatype used for the evaluation */
typedef double RealType;
- typedef otb::Functor::SarRadiometricCalibrationFunctor<RealType, RealType> FunctorType;
- typedef typename FunctorType::RealType FunctorRealType;
+// typedef otb::Functor::SarRadiometricCalibrationFunctor<RealType, RealType> FunctorType;
+// typedef typename FunctorType::RealType FunctorRealType;
typedef otb::SarParametricMapFunction<InputImageType> ParametricFunctionType;
typedef typename ParametricFunctionType::Pointer ParametricFunctionPointer;
typedef typename ParametricFunctionType::ConstPointer ParametricFunctionConstPointer;
- /** Evaluate the function at non-integer positions */
- virtual OutputType Evaluate(const PointType& point) const;
-
/** Evalulate the function at specified index */
- virtual OutputType EvaluateAtIndex(const IndexType& index) const
+ virtual OutputType EvaluateAtIndex(const IndexType& index) const;
+
+ /** Evaluate the function at non-integer positions */
+ virtual OutputType Evaluate(const PointType& point) const
{
- PointType point;
- this->GetInputImage()->TransformIndexToPhysicalPoint( index, point);
- return this->Evaluate(point);
+ IndexType index;
+ this->ConvertPointToNearestIndex(point, index);
+ return this->EvaluateAtIndex(index);
}
- virtual OutputType EvaluateAtContinuousIndex(
- const ContinuousIndexType& cindex) const
+ virtual OutputType EvaluateAtContinuousIndex(const ContinuousIndexType& cindex) const
{
- PointType point;
- this->GetInputImage()->TransformContinuousIndexToPhysicalPoint( cindex, point);
- return this->Evaluate(point);
+ IndexType index;
+ this->ConvertContinuousIndexToNearestIndex(cindex, index);
+ return this->EvaluateAtIndex(index);
}
/** Set the input image.
@@ -108,10 +104,10 @@ public:
/** Get/Set the Scale value */
- itkSetMacro(Scale, FunctorRealType);
- itkGetMacro(Scale, FunctorRealType);
+ itkSetMacro(Scale, RealType);
+ itkGetMacro(Scale, RealType);
- /** Get/Set the Offset value */
+ /** Get/Set the Noise value */
itkSetObjectMacro(Noise, ParametricFunctionType);
itkGetConstObjectMacro(Noise, ParametricFunctionType);
itkGetObjectMacro(Noise, ParametricFunctionType);
@@ -140,23 +136,66 @@ public:
itkGetConstObjectMacro(RangeSpreadLoss, ParametricFunctionType);
itkGetObjectMacro(RangeSpreadLoss, ParametricFunctionType);
+ /** Set the RescalingFactor value */
+ itkSetMacro(RescalingFactor, RealType);
+
+ /** Get/Set flag to indicate if these are used */
+ itkSetMacro(ApplyAntennaPatternGain, bool);
+ itkGetMacro(ApplyAntennaPatternGain, bool);
+
+ itkSetMacro(ApplyIncidenceAngleCorrection, bool);
+ itkGetMacro(ApplyIncidenceAngleCorrection, bool);
+
+ itkSetMacro(ApplyRangeSpreadLossCorrection, bool);
+ itkGetMacro(ApplyRangeSpreadLossCorrection, bool);
+
+ itkSetMacro(ApplyLookupDataCorrection, bool);
+ itkGetMacro(ApplyLookupDataCorrection, bool);
+
+ itkSetMacro(ApplyRescalingFactor, bool);
+ itkGetMacro(ApplyRescalingFactor, bool);
+
+ typedef SarCalibrationLookupData::Pointer LookupDataPointer;
+
+ /** Set SetCalibrationLookupData instance */
+ void SetCalibrationLookupData(LookupDataPointer lut)
+ {
+ m_Lut = lut;
+ }
protected:
+
+ /** ctor */
SarRadiometricCalibrationFunction();
+
+ /** default, empty, virtual dtor */
virtual ~SarRadiometricCalibrationFunction(){}
+
+ /** print method */
void PrintSelf(std::ostream& os, itk::Indent indent) const;
+ /** Flags to indiciate if these values needs to be applied in calibration*/
+
private:
SarRadiometricCalibrationFunction(const Self &); //purposely not implemented
void operator =(const Self&); //purposely not implemented
- FunctorRealType m_Scale;
- ParametricFunctionPointer m_Noise;
+ RealType m_Scale;
bool m_EnableNoise;
+ RealType m_RescalingFactor;
+ bool m_ApplyAntennaPatternGain;
+ bool m_ApplyIncidenceAngleCorrection;
+ bool m_ApplyRangeSpreadLossCorrection;
+ bool m_ApplyLookupDataCorrection;
+ bool m_ApplyRescalingFactor;
+ ParametricFunctionPointer m_Noise;
ParametricFunctionPointer m_AntennaPatternNewGain;
ParametricFunctionPointer m_AntennaPatternOldGain;
ParametricFunctionPointer m_IncidenceAngle;
ParametricFunctionPointer m_RangeSpreadLoss;
+ LookupDataPointer m_Lut;
+
+
};
} // end namespace otb
diff --git a/Modules/Radiometry/SARCalibration/include/otbSarRadiometricCalibrationFunction.txx b/Modules/Radiometry/SARCalibration/include/otbSarRadiometricCalibrationFunction.txx
index b04ee4c8fc450006826cf3db1f5ad80777ca518b..4e22a24c59b7c33f252a95064f96446eae88a47a 100644
--- a/Modules/Radiometry/SARCalibration/include/otbSarRadiometricCalibrationFunction.txx
+++ b/Modules/Radiometry/SARCalibration/include/otbSarRadiometricCalibrationFunction.txx
@@ -24,27 +24,38 @@
namespace otb
{
-
/**
* Constructor
*/
template <class TInputImage, class TCoordRep>
SarRadiometricCalibrationFunction<TInputImage, TCoordRep>
-::SarRadiometricCalibrationFunction():
- m_Scale(1.0)
+::SarRadiometricCalibrationFunction()
+: m_Scale(1.0)
+, m_EnableNoise(false)
+, m_RescalingFactor(1.0)
+, m_ApplyAntennaPatternGain(true)
+, m_ApplyIncidenceAngleCorrection(true)
+, m_ApplyRangeSpreadLossCorrection(true)
+, m_ApplyLookupDataCorrection(false)
+, m_ApplyRescalingFactor(false)
+
{
+ /* intialize parametric functions */
m_Noise = ParametricFunctionType::New();
m_AntennaPatternNewGain = ParametricFunctionType::New();
m_AntennaPatternOldGain = ParametricFunctionType::New();
m_IncidenceAngle = ParametricFunctionType::New();
m_RangeSpreadLoss = ParametricFunctionType::New();
+ /* intialize default values in paramerticFunction instances */
m_Noise->SetConstantValue(0.0);
- m_EnableNoise = true;
m_AntennaPatternNewGain->SetConstantValue(1.0);
m_AntennaPatternOldGain->SetConstantValue(1.0);
m_IncidenceAngle->SetConstantValue(CONST_PI_2);
m_RangeSpreadLoss->SetConstantValue(1.0);
+
+// m_Lut = 0; //new LookupTableBase();
+
}
/**
@@ -65,7 +76,7 @@ SarRadiometricCalibrationFunction<TInputImage, TCoordRep>
}
/**
- *
+ * Print
*/
template <class TInputImage, class TCoordRep>
void
@@ -75,23 +86,16 @@ SarRadiometricCalibrationFunction<TInputImage, TCoordRep>
this->Superclass::PrintSelf(os, indent);
}
-/**
- *
- */
+/* Function: EvaluateAtIndex. This computes the required values for each pixel
+* whose index is given in indexType argument. To convert index to point it uses
+* InputImage::TransformIndexToPhysicalPoint(). IncidenceAngle and similar are
+* computed based on this calculated point in SarParametricFunction */
template <class TInputImage, class TCoordRep>
typename SarRadiometricCalibrationFunction<TInputImage, TCoordRep>
::OutputType
SarRadiometricCalibrationFunction<TInputImage, TCoordRep>
-::Evaluate(const PointType& point) const
+::EvaluateAtIndex(const IndexType& index) const
{
- IndexType index;
- this->GetInputImage()->TransformPhysicalPointToIndex(point, index);
-
- if (!this->GetInputImage())
- {
- itkDebugMacro( <<"ERROR with GetInputImage()");
- return (itk::NumericTraits<OutputType>::max());
- }
if (!this->IsInsideBuffer(index))
{
@@ -99,21 +103,69 @@ SarRadiometricCalibrationFunction<TInputImage, TCoordRep>
return (itk::NumericTraits<OutputType>::max());
}
- FunctorType functor;
+ /* convert index to point */
+ PointType point;
+ if (m_ApplyAntennaPatternGain || m_ApplyIncidenceAngleCorrection || m_ApplyRangeSpreadLossCorrection)
+ this->GetInputImage()->TransformIndexToPhysicalPoint( index, point);
+
+ /** digitalNumber:
+ * For complex pixel type, vcl_abs() returns the modulus. which is
+ * sqrt((I*I) + (Q*Q)). Where I and Q are real and imaginary part of the
+ * complex pixel. So to to get (I*I) + (Q*Q) in our calculation, the output
+ * of vcl_abs() is squared. See below (digitalNumber * digitalNumber) where
+ * digitalNumber is the output of vcl_abs() which is sqrt((I*I) + (Q*Q)). For
+ * non-complex pixel types, vcl_abs() simply returns absolute value.
+ */
+
+ RealType digitalNumber = static_cast<RealType>(vcl_abs(this->GetInputImage()->GetPixel(index)));
+ RealType sigma = m_Scale * digitalNumber * digitalNumber;
+
+ /** substract noise if enabled. */
if (m_EnableNoise)
{
- functor.SetNoise(static_cast<FunctorRealType>(m_Noise->Evaluate(point)));
+ sigma -= static_cast<RealType>(m_Noise->Evaluate(point));
+ }
+
+ /** Apply incidence angle correction if needed */
+ if (m_ApplyIncidenceAngleCorrection)
+ {
+ sigma *= vcl_sin(static_cast<RealType>(m_IncidenceAngle->Evaluate(point)));
}
- functor.SetScale(m_Scale);
- functor.SetAntennaPatternNewGain(static_cast<FunctorRealType>(m_AntennaPatternNewGain->Evaluate(point)));
- functor.SetAntennaPatternOldGain(static_cast<FunctorRealType>(m_AntennaPatternOldGain->Evaluate(point)));
- functor.SetIncidenceAngle(static_cast<FunctorRealType>(m_IncidenceAngle->Evaluate(point)));
- functor.SetRangeSpreadLoss(static_cast<FunctorRealType>(m_RangeSpreadLoss->Evaluate(point)));
- const RealType value = static_cast<RealType>(vcl_abs(this->GetInputImage()->GetPixel(index)));
- RealType result = functor(value);
+ /** Apply old and new antenna pattern gain. */
+ if (m_ApplyAntennaPatternGain)
+ {
+ sigma *= static_cast<RealType>(m_AntennaPatternNewGain->Evaluate(point));
+ sigma /= static_cast<RealType>(m_AntennaPatternOldGain->Evaluate(point));
+ }
+
+ /** Apply range spread loss if needed. */
+ if (m_ApplyRangeSpreadLossCorrection)
+ {
+ sigma *= static_cast<RealType>(m_RangeSpreadLoss->Evaluate(point));
+ }
+
+ /** Lookup value has effect on for some sensors which does not required the
+ * above values (incidence angle, rangespreadloss etc.. */
+ if (m_ApplyLookupDataCorrection)
+ {
+ RealType lutVal = static_cast<RealType>(m_Lut->GetValue(index[0], index[1]));
+ sigma /= lutVal * lutVal;
+ }
+
+ /** rescaling factor has effect only with CosmoSkymed Products */
+ if (m_ApplyRescalingFactor)
+ {
+ sigma /= m_RescalingFactor;
+ }
+
+
+ if(sigma < 0.0)
+ {
+ sigma = 0.0;
+ }
- return static_cast<OutputType>(result);
+ return static_cast<OutputType>(sigma);
}
} // end namespace otb
diff --git a/Modules/Radiometry/SARCalibration/include/otbSarRadiometricCalibrationFunctor.h b/Modules/Radiometry/SARCalibration/include/otbSarRadiometricCalibrationFunctor.h
deleted file mode 100644
index db02de332b500272b71d92b3844816cf81d6f628..0000000000000000000000000000000000000000
--- a/Modules/Radiometry/SARCalibration/include/otbSarRadiometricCalibrationFunctor.h
+++ /dev/null
@@ -1,161 +0,0 @@
-/*=========================================================================
-
- Program: ORFEO Toolbox
- Language: C++
- Date: $Date$
- Version: $Revision$
-
-
- Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
- See OTBCopyright.txt for details.
-
-
- This software is distributed WITHOUT ANY WARRANTY; without even
- the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
- PURPOSE. See the above copyright notices for more information.
-
-=========================================================================*/
-
-#ifndef __otbSarRadiometricCalibrationFunctor_h
-#define __otbSarRadiometricCalibrationFunctor_h
-
-#include "otbMath.h"
-#include "itkNumericTraits.h"
-
-namespace otb
-{
-
-namespace Functor
-{
-/**
- * \class SarRadiometricCalibrationFunctor
- * \brief Compute the backscatter value.
- * \f$ \sigma^{0} = (scale * DN^{2} + offset) * sin( \theta_{inc}) * OldGain / NewGain * RangeSpreadLoss \f$
- *
- *
- * \ingroup OTBSARCalibration
- */
-template<class TInput, class TOutput>
-class ITK_EXPORT SarRadiometricCalibrationFunctor
-{
-public:
- typedef TInput InputType;
- typedef TOutput OutputType;
- typedef typename itk::NumericTraits<InputType>::AbsType RealType;
-
- SarRadiometricCalibrationFunctor()
- {
- m_Noise = 0.0;
- m_Scale = 1.0;
- m_IncidenceAngle = CONST_PI_2;
- m_AntennaPatternOldGain = 1.0;
- m_AntennaPatternNewGain = 1.0;
- m_RangeSpreadLoss = 1.0;
- };
-
- ~SarRadiometricCalibrationFunctor(){};
-
- inline TOutput operator ()(const TInput& value) const
- {
- RealType digitalNumber = static_cast<RealType> (vcl_abs(value));
- RealType sigma;
-
- sigma = m_Scale * (digitalNumber * digitalNumber - m_Noise);
- sigma *= vcl_sin(m_IncidenceAngle);
- sigma *= m_AntennaPatternOldGain;
- sigma /= m_AntennaPatternNewGain;
- sigma *= m_RangeSpreadLoss;
-
- if(sigma < 0.0)
- {
- sigma = 0.0;
- }
-
- return static_cast<OutputType>(sigma);
- }
-
- /** Set offset method */
- void SetNoise(RealType value)
- {
- m_Noise = value;
- }
-
- /** Get offset method */
- RealType GetNoise() const
- {
- return m_Noise;
- }
-
- /** Set scale method */
- void SetScale(RealType value)
- {
- m_Scale = value;
- }
-
- /** Get scale method */
- RealType GetScale() const
- {
- return m_Scale;
- }
-
- /** Set antennaPatternNewGain method */
- void SetAntennaPatternNewGain(RealType value)
- {
- m_AntennaPatternNewGain = value;
- }
-
- /** Get antennaPatternNewGain method */
- RealType GetAntennaPatternNewGain() const
- {
- return m_AntennaPatternNewGain;
- }
-
- /** Set antennaPatternOldGain method */
- void SetAntennaPatternOldGain(RealType value)
- {
- m_AntennaPatternOldGain = value;
- }
-
- /** Get antennaPatternOldGain method */
- RealType GetAntennaPatternOldGain() const
- {
- return m_AntennaPatternOldGain;
- }
-
- /** Set incidenceAngle method */
- void SetIncidenceAngle(RealType value)
- {
- m_IncidenceAngle = value;
- }
-
- /** Get incidenceAngle method */
- RealType GetIncidenceAngle() const
- {
- return m_IncidenceAngle;
- }
-
- /** Set rangeSpreadLoss method */
- void SetRangeSpreadLoss(RealType value)
- {
- m_RangeSpreadLoss = value;
- }
-
- /** Get scale method */
- RealType GetRangeSpreadLoss() const
- {
- return m_RangeSpreadLoss;
- }
-
-private:
- RealType m_Noise;
- RealType m_Scale;
- RealType m_AntennaPatternNewGain;
- RealType m_AntennaPatternOldGain;
- RealType m_IncidenceAngle;
- RealType m_RangeSpreadLoss;
-};
-}
-
-}
-
-#endif
diff --git a/Modules/Radiometry/SARCalibration/include/otbSarRadiometricCalibrationToImageFilter.h b/Modules/Radiometry/SARCalibration/include/otbSarRadiometricCalibrationToImageFilter.h
index 1212e9ae682de9b3f46e885d24efee34ebef12ba..523cdbccb1a8a4d3c37938df7435d87e696c7820 100644
--- a/Modules/Radiometry/SARCalibration/include/otbSarRadiometricCalibrationToImageFilter.h
+++ b/Modules/Radiometry/SARCalibration/include/otbSarRadiometricCalibrationToImageFilter.h
@@ -28,9 +28,29 @@ namespace otb
{
/** \class SarRadiometricCalibrationToImageFilter
- * \brief Evaluates the SarRadiometricCalibrationFunction onto a source image
+ * \brief Evaluates the SarRadiometricCalibrationFunction onto a source image
+ * The BeforeThreadedGenerateData create a SarImageMetadataInterface based on
+ * input metadata dictionary. The nature of product(TerrSARX, Sentinel1, etc..)
+ * are thus detected automatically from this. The filter then reads necessary
+ * parameters required to perform SarCalibration in a generic way.
*
- * The function has to inherit from itkImageFunction
+ * BeforeThreadedGenerateData() instanciate a SarRadiometricCalibrationFunction
+ * and pass the values taken from SarImageMetadataInterface instance to it. This
+ * is where the actual computation of sigma (backscatter) occurs.
+ *
+ * Noise, Antenna pattern gain (old && new), range spread loss, incidence angle
+ * data members used in this class are all instances of SarPrametricFunction
+ * class. Each have a Evaluate() method and a special
+ * EvaluateParametricCoefficient() which computes the actual value.
+ *
+ * The technical details and more discussion of SarCalibration can be found in jira
+ * story #863.
+ *
+ * \see \c otb::SarParametricFunction
+ * \see \c otb::SarCalibrationLookupBase
+ * References (Retreived on 08-Sept-2015)
+ * Sentinel1 - https://sentinel.esa.int/web/sentinel/sentinel-1-sar-wiki/-/wiki/Sentinel%20One/Application+of+Radiometric+Calibration+LUT
+ * Radarsat2 - http://gs.mdacorporation.com/products/sensor/radarsat2/RS2_Product_Description.pdf
*
* \ingroup ImageFilters
*
@@ -76,20 +96,37 @@ public:
typedef typename FunctionType::ParametricFunctionConstPointer ParametricFunctionConstPointer;
typedef typename FunctionType::ParametricFunctionType ParametricFunctionType;
+
+ /** Enable/disable the noise flag in SarRadiometricCalibrationFunction */
void SetEnableNoise(bool inArg)
{
this->GetFunction()->SetEnableNoise(inArg);
}
+
+ itkSetMacro(LookupSelected, short);
+ itkGetConstMacro(LookupSelected, short);
+
protected:
+ /** Default ctor */
SarRadiometricCalibrationToImageFilter();
+
+ /** Empty, default virtual dtor */
virtual ~SarRadiometricCalibrationToImageFilter() {}
+ /** Generate output information */
+ virtual void GenerateOutputInformation();
+
/** Update the function list and input parameters*/
virtual void BeforeThreadedGenerateData();
+
private:
+
SarRadiometricCalibrationToImageFilter(const Self &); //purposely not implemented
void operator =(const Self&); //purposely not implemented
+
+ short m_LookupSelected;
+
};
} // end namespace otb
diff --git a/Modules/Radiometry/SARCalibration/include/otbSarRadiometricCalibrationToImageFilter.txx b/Modules/Radiometry/SARCalibration/include/otbSarRadiometricCalibrationToImageFilter.txx
index 15b09c7fd73a1381545b48a64bcd16593fd24b38..ad0f5b47e035d1218ea856d7788567f40c67f9f9 100644
--- a/Modules/Radiometry/SARCalibration/include/otbSarRadiometricCalibrationToImageFilter.txx
+++ b/Modules/Radiometry/SARCalibration/include/otbSarRadiometricCalibrationToImageFilter.txx
@@ -22,8 +22,8 @@
#define __otbSarRadiometricCalibrationToImageFilter_txx
#include "otbSarRadiometricCalibrationToImageFilter.h"
-
#include "otbSarImageMetadataInterfaceFactory.h"
+#include "otbSarCalibrationLookupData.h"
namespace otb
{
@@ -34,7 +34,33 @@ namespace otb
template<class TInputImage, class TOutputImage>
SarRadiometricCalibrationToImageFilter<TInputImage, TOutputImage>
::SarRadiometricCalibrationToImageFilter()
+: m_LookupSelected(0)
{
+
+}
+
+template<class TInputImage, class TOutputImage>
+void
+SarRadiometricCalibrationToImageFilter<TInputImage, TOutputImage>
+::GenerateOutputInformation( )
+{
+ Superclass::GenerateOutputInformation();
+
+ // Retrieving input/output pointers
+ InputImagePointer inputPtr = this->GetInput();
+
+ if (inputPtr.IsNull())
+ {
+ itkExceptionMacro(<< "At least one input is missing."
+ << " Input is missing :" << inputPtr.GetPointer() )
+ }
+
+ OutputImagePointer outputPtr = this->GetOutput();
+ if (outputPtr.IsNull())
+ {
+ itkExceptionMacro(<< "At least one output is missing."
+ << " Output is missing :" << outputPtr.GetPointer() )
+ }
}
template<class TInputImage, class TOutputImage>
@@ -45,60 +71,104 @@ SarRadiometricCalibrationToImageFilter<TInputImage, TOutputImage>
// will SetInputImage on the function
Superclass::BeforeThreadedGenerateData();
+ /** cretate a SarImageMetadataInterface instance from
+ * GetMetaDataDictionary(). This will return the appropriate IMI depending on
+ * the Sensor information & co available in GetMetaDataDictionary() */
SarImageMetadataInterface::Pointer imageMetadataInterface = SarImageMetadataInterfaceFactory::CreateIMI(
this->GetInput()->GetMetaDataDictionary());
+ /** Get the SarRadiometricCalibrationFunction function instance. */
FunctionPointer function = this->GetFunction();
- function->SetScale(imageMetadataInterface->GetRadiometricCalibrationScale());
+ /** check if there is a calibration lookupdata is available with the
+ * product. eg. Sentinel1. This means
+ * A. The computation of the backscatter is based on this lookup value which
+ * depends on the given product.*
+ * B. The other value such as antenna pattern gain, rangespread loss, incidence
+ * angle has no effect in calibration */
+
+ bool apply = imageMetadataInterface->HasCalibrationLookupDataFlag();
+ /* Below lines will toggle the necessary flags which can help skip some
+ * computation. For example, if there is lookup value and ofcourse antenna
+ * pattern gain is not required. Even if we try to compute the value with
+ * SarParametricFuntion we get 1. This is the safe side. But as we are so sure
+ * we skip all those calls to EvaluateParametricCoefficient and also the
+ * Evalute(). For the function the value is 1 by default.
+ */
+ function->SetApplyAntennaPatternGain(!apply);
+ function->SetApplyIncidenceAngleCorrection(!apply);
+ function->SetApplyRangeSpreadLossCorrection(!apply);
+ function->SetApplyRescalingFactor(!apply);
+ function->SetApplyLookupDataCorrection(apply);
- ParametricFunctionPointer noise;
- ParametricFunctionPointer antennaPatternNewGain;
- ParametricFunctionPointer antennaPatternOldGain;
- ParametricFunctionPointer incidenceAngle;
- ParametricFunctionPointer rangeSpreadLoss;
-
- noise = function->GetNoise();
- noise->SetPointSet(imageMetadataInterface->GetRadiometricCalibrationNoise());
- noise->SetPolynomalSize(imageMetadataInterface->GetRadiometricCalibrationNoisePolynomialDegree());
- noise->EvaluateParametricCoefficient();
-
- antennaPatternNewGain = function->GetAntennaPatternNewGain();
- antennaPatternNewGain->SetPointSet(imageMetadataInterface->GetRadiometricCalibrationAntennaPatternNewGain());
- antennaPatternNewGain->SetPolynomalSize(imageMetadataInterface->GetRadiometricCalibrationAntennaPatternNewGainPolynomialDegree());
- antennaPatternNewGain->EvaluateParametricCoefficient();
-
- antennaPatternOldGain = function->GetAntennaPatternOldGain();
- antennaPatternOldGain->SetPointSet(imageMetadataInterface->GetRadiometricCalibrationAntennaPatternOldGain());
- antennaPatternOldGain->SetPolynomalSize(imageMetadataInterface->GetRadiometricCalibrationAntennaPatternOldGainPolynomialDegree());
- antennaPatternOldGain->EvaluateParametricCoefficient();
-
- incidenceAngle = function->GetIncidenceAngle();
- incidenceAngle->SetPointSet(imageMetadataInterface->GetRadiometricCalibrationIncidenceAngle());
-
- typename ParametricFunctionType::PointType point;
- point.Fill(0);
- typename ParametricFunctionType::PointSetType::PixelType pointValue;
- pointValue = itk::NumericTraits<typename ParametricFunctionType::PointSetType::PixelType>::Zero;
- unsigned int nbRecords = imageMetadataInterface->GetRadiometricCalibrationIncidenceAngle()->GetNumberOfPoints();
-
- // Fill the linear system
- for (unsigned int i = 0; i < nbRecords; ++i)
- {
- imageMetadataInterface->GetRadiometricCalibrationIncidenceAngle()->GetPoint(i, &point);
- imageMetadataInterface->GetRadiometricCalibrationIncidenceAngle()->GetPointData(i, &pointValue);
- }
- incidenceAngle->SetPolynomalSize(imageMetadataInterface->GetRadiometricCalibrationIncidenceAnglePolynomialDegree());
-
- incidenceAngle->EvaluateParametricCoefficient();
- rangeSpreadLoss = function->GetRangeSpreadLoss();
- rangeSpreadLoss->SetPointSet(imageMetadataInterface->GetRadiometricCalibrationRangeSpreadLoss());
- rangeSpreadLoss->SetPolynomalSize(imageMetadataInterface->GetRadiometricCalibrationRangeSpreadLossPolynomialDegree());
- rangeSpreadLoss->EvaluateParametricCoefficient();
+ function->SetScale(imageMetadataInterface->GetRadiometricCalibrationScale());
+ /* Compute noise if enabled */
+ if( function->GetEnableNoise())
+ {
+ ParametricFunctionPointer noise;
+ noise = function->GetNoise();
+ noise->SetPointSet(imageMetadataInterface->GetRadiometricCalibrationNoise());
+ noise->SetPolynomalSize(imageMetadataInterface->GetRadiometricCalibrationNoisePolynomialDegree());
+ noise->EvaluateParametricCoefficient();
+ }
+
+ /* Compute old and new antenna pattern gain */
+ if(function->GetApplyAntennaPatternGain())
+ {
+ ParametricFunctionPointer antennaPatternNewGain;
+ antennaPatternNewGain = function->GetAntennaPatternNewGain();
+ antennaPatternNewGain->SetPointSet(imageMetadataInterface->GetRadiometricCalibrationAntennaPatternNewGain());
+ antennaPatternNewGain->SetPolynomalSize(imageMetadataInterface->GetRadiometricCalibrationAntennaPatternNewGainPolynomialDegree());
+ antennaPatternNewGain->EvaluateParametricCoefficient();
+
+ ParametricFunctionPointer antennaPatternOldGain;
+ antennaPatternOldGain = function->GetAntennaPatternOldGain();
+ antennaPatternOldGain->SetPointSet(imageMetadataInterface->GetRadiometricCalibrationAntennaPatternOldGain());
+ antennaPatternOldGain->SetPolynomalSize(imageMetadataInterface->GetRadiometricCalibrationAntennaPatternOldGainPolynomialDegree());
+ antennaPatternOldGain->EvaluateParametricCoefficient();
+ }
+
+ /* Compute incidence angle */
+ if (function->GetApplyIncidenceAngleCorrection())
+ {
+ ParametricFunctionPointer incidenceAngle;
+ incidenceAngle = function->GetIncidenceAngle();
+ incidenceAngle->SetPointSet(imageMetadataInterface->GetRadiometricCalibrationIncidenceAngle());
+ incidenceAngle->SetPolynomalSize(imageMetadataInterface->GetRadiometricCalibrationIncidenceAnglePolynomialDegree());
+ incidenceAngle->EvaluateParametricCoefficient();
+ }
+
+ /* Compute Range spread Loss */
+ if (function->GetApplyRangeSpreadLossCorrection())
+ {
+ ParametricFunctionPointer rangeSpreadLoss;
+ rangeSpreadLoss = function->GetRangeSpreadLoss();
+ rangeSpreadLoss->SetPointSet(imageMetadataInterface->GetRadiometricCalibrationRangeSpreadLoss());
+ rangeSpreadLoss->SetPolynomalSize(imageMetadataInterface->GetRadiometricCalibrationRangeSpreadLossPolynomialDegree());
+ rangeSpreadLoss->EvaluateParametricCoefficient();
+ }
+
+ /** Get the lookupdata instance. unlike the all the above this is not a
+* parametricFunction instance. But rather an internal class in IMI called
+* SarCalibrationLookupData.
+*
+*NOTE: As the computation of lookup data for sensors is not universal. One must
+*provide a sub-class.
+See Also: otbSentinel1ImageMetadataInterface, otbTerraSarImageMetadataInterface,
+*otbRadarsat2ImageMetadataInterface */
+ if (function->GetApplyLookupDataCorrection())
+ {
+ function->SetCalibrationLookupData(imageMetadataInterface->GetCalibrationLookupData(this->GetLookupSelected()));
+ }
+
+ /** This was introduced for cosmoskymed which required a rescaling factor */
+ if (function->GetApplyRescalingFactor())
+ {
+ function->SetRescalingFactor(imageMetadataInterface->GetRescalingFactor());
+ }
}
-
} // end namespace otb
#endif
diff --git a/Modules/Radiometry/SARCalibration/test/CMakeLists.txt b/Modules/Radiometry/SARCalibration/test/CMakeLists.txt
index 0e3ddd573154ea1c3c393d1206cee83df23c20fc..09dde97aa2a6309a7c4cf555618952eeb1df4f07 100644
--- a/Modules/Radiometry/SARCalibration/test/CMakeLists.txt
+++ b/Modules/Radiometry/SARCalibration/test/CMakeLists.txt
@@ -18,8 +18,6 @@ otbSarRadiometricCalibrationToImageFilterCompareTest.cxx
otbSarBrightnessFunctor.cxx
otbSarBrightnessFunctionWithoutNoise.cxx
otbSarRadiometricCalibrationFunction.cxx
-otbSarRadiometricCalibrationFunctor.cxx
-otbSarRadiometricCalibrationFunctorWithoutNoise.cxx
otbSarRadiometricCalibrationFunctionWithoutNoise.cxx
otbTerraSarBrightnessImageComplexFilterTest.cxx
otbSarRadiometricCalibrationToImageFilterWithComplexPixelTest.cxx
@@ -127,6 +125,28 @@ otb_add_test(NAME raTvSarRadiometricCalibrationToImageWithComplexPixelFilterWith
1000 1000 250 250 # Extract
)
+#Sentinel-1 L1, SLC
+otb_add_test(NAME raTvSarRadiometricCalibrationToImageWithComplexPixelFilterWithoutNoise_SENTINEL1 COMMAND otbSARCalibrationTestDriver
+ --compare-image ${EPSILON_12}
+ ${BASELINE}/raTvSarRadiometricCalibrationToImageWithComplexPixelFilterWithoutNoise_SENTINEL1_VV.tif
+ ${TEMP}/raTvSarRadiometricCalibrationToImageWithComplexPixelFilterWithoutNoise_SENTINEL1_VV.tif
+ otbSarRadiometricCalibrationToImageFilterWithComplexPixelTestWithoutNoise
+ LARGEINPUT{SENTINEL1/S1A_S6_SLC__1SSV_20150619T195043/measurement/s1a-s6-slc-vv-20150619t195043-20150619t195101-006447-00887d-001.tiff}
+ ${TEMP}/raTvSarRadiometricCalibrationToImageWithComplexPixelFilterWithoutNoise_SENTINEL1_VV.tif
+ 1100 1900 450 450 # Extract
+ )
+
+#Radarsat2
+otb_add_test(NAME raTvSarRadiometricCalibrationToImageWithComplexPixelFilterWithoutNoise_RADARSAT2 COMMAND otbSARCalibrationTestDriver
+ --compare-image ${EPSILON_12}
+ ${BASELINE}/raTvSarRadiometricCalibrationToImageWithComplexPixelFilterWithoutNoise_RADARSAT2_HV.tif
+ ${TEMP}/raTvSarRadiometricCalibrationToImageWithComplexPixelFilterWithoutNoise_RADARSAT2_HV.tif
+ otbSarRadiometricCalibrationToImageFilterWithComplexPixelTestWithoutNoise
+ LARGEINPUT{RADARSAT2/ALTONA/Fine_Quad-Pol_Dataset/PK6621_DK406_FQ9_20080405_124900_HH_VV_HV_VH_SLC_Altona/imagery_HV.tif}
+ ${TEMP}/raTvSarRadiometricCalibrationToImageWithComplexPixelFilterWithoutNoise_RADARSAT2_HV.tif
+ 11 11 650 750 # Extract
+ )
+
otb_add_test(NAME raTuSarBrightnessFunctorWithoutNoise COMMAND otbSARCalibrationTestDriver
otbSarBrightnessFunctorWithoutNoise
)
@@ -190,13 +210,6 @@ otb_add_test(NAME raTvSarRadiometricCalibrationFunction COMMAND otbSARCalibratio
${TEMP}/raTvSarRadiometricCalibrationFunctionOutputAsciiWithNoise.txt
)
-otb_add_test(NAME raTuSarRadiometricCalibrationFunctor COMMAND otbSARCalibrationTestDriver
- otbSarRadiometricCalibrationFunctor
- )
-
-otb_add_test(NAME raTuSarRadiometricCalibrationFunctorWithoutNoise COMMAND otbSARCalibrationTestDriver
- otbSarRadiometricCalibrationFunctorWithoutNoise
- )
otb_add_test(NAME raTvSarRadiometricCalibrationFunctionWithoutNoise COMMAND otbSARCalibrationTestDriver
--compare-ascii ${NOTOL}
@@ -259,4 +272,3 @@ otb_add_test(NAME raTvSarBrightnessToImageFilter COMMAND otbSARCalibrationTestD
${TEMP}/raTvSarBrightnessToImageFilter_TSX_PANGKALANBUUN_HH.tif
1000 1000 250 250 # Extract
)
-
diff --git a/Modules/Radiometry/SARCalibration/test/otbSARCalibrationTestDriver.cxx b/Modules/Radiometry/SARCalibration/test/otbSARCalibrationTestDriver.cxx
index 19236b9438f890df8aefde837c12ad00692c636c..873a912bbe6538f0583e348a02184b4bcca4e6f9 100644
--- a/Modules/Radiometry/SARCalibration/test/otbSARCalibrationTestDriver.cxx
+++ b/Modules/Radiometry/SARCalibration/test/otbSARCalibrationTestDriver.cxx
@@ -17,8 +17,6 @@ void RegisterTests()
REGISTER_TEST(otbSarBrightnessFunctor);
REGISTER_TEST(otbSarBrightnessFunctionWithoutNoise);
REGISTER_TEST(otbSarRadiometricCalibrationFunction);
- REGISTER_TEST(otbSarRadiometricCalibrationFunctor);
- REGISTER_TEST(otbSarRadiometricCalibrationFunctorWithoutNoise);
REGISTER_TEST(otbSarRadiometricCalibrationFunctionWithoutNoise);
REGISTER_TEST(otbTerraSarBrightnessImageComplexFilterTest);
REGISTER_TEST(otbSarRadiometricCalibrationToImageFilterWithComplexPixelTest);
diff --git a/Modules/ThirdParty/OssimPlugins/src/ossim/ossimPluginProjectionFactory.cpp b/Modules/ThirdParty/OssimPlugins/src/ossim/ossimPluginProjectionFactory.cpp
index 0da1b33a18dd02fcbc479cfdea9fca1da468288e..dd59bd2d8d9d7db8cad0d02cded1f5a12bab636f 100644
--- a/Modules/ThirdParty/OssimPlugins/src/ossim/ossimPluginProjectionFactory.cpp
+++ b/Modules/ThirdParty/OssimPlugins/src/ossim/ossimPluginProjectionFactory.cpp
@@ -30,7 +30,7 @@
#include <ossim/base/ossimNotifyContext.h>
#include "ossimTileMapModel.h"
#include "ossimSpot6Model.h"
-
+#include "ossimSentinel1Model.h"
//***
// Define Trace flags for use within this file:
//***
@@ -45,15 +45,16 @@ static ossimTrace traceDebug = ossimTrace("ossimPluginProjectionFactory:debug");
namespace ossimplugins
{
+ bool ossimPluginProjectionFactory::initalized_;
-ossimPluginProjectionFactory* ossimPluginProjectionFactory::instance()
-{
- static ossimPluginProjectionFactory* factoryInstance =
- new ossimPluginProjectionFactory();
+ ossimPluginProjectionFactory ossimPluginProjectionFactory::factoryInstance;
+
+ ossimPluginProjectionFactory* ossimPluginProjectionFactory::instance()
+ {
+ return initalized_ ? &factoryInstance : 0;
+
+ }
- return factoryInstance;
-}
-
ossimProjection* ossimPluginProjectionFactory::createProjection(
const ossimFilename& filename, ossim_uint32 /*entryIdx*/)const
{
@@ -61,14 +62,35 @@ ossimProjection* ossimPluginProjectionFactory::createProjection(
ossimRefPtr<ossimProjection> projection = 0;
//traceDebug.setTraceFlag(true);
- if(traceDebug())
+ // Sentinel1
+ if ( !projection )
{
- ossimNotify(ossimNotifyLevel_DEBUG)
- << MODULE << " DEBUG: testing ossimRadarSat2Model" << std::endl;
+ if(traceDebug())
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG)
+ << MODULE << " DEBUG: testing ossimSentinel1Model" << std::endl;
+ }
+
+ ossimRefPtr<ossimSentinel1Model> model = new ossimSentinel1Model();
+ if ( model->open(filename) )
+ {
+ projection = model.get();
+ }
+ else
+ {
+ model = 0;
+ }
}
if ( !projection )
{
+
+ if(traceDebug())
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG)
+ << MODULE << " DEBUG: testing ossimRadarSat2Model" << std::endl;
+ }
+
ossimRefPtr<ossimRadarSat2Model> model = new ossimRadarSat2Model();
if ( model->open(filename) )
{
@@ -84,16 +106,16 @@ ossimProjection* ossimPluginProjectionFactory::createProjection(
model = 0;
}
}
-
- if(traceDebug())
- {
- ossimNotify(ossimNotifyLevel_DEBUG)
- << MODULE << " DEBUG: testing ossimPleiadesModel" << std::endl;
- }
// Pleiades
if ( !projection )
{
+ if(traceDebug())
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG)
+ << MODULE << " DEBUG: testing ossimPleiadesModel" << std::endl;
+ }
+
ossimRefPtr<ossimPleiadesModel> model = new ossimPleiadesModel();
if ( model->open(filename) )
{
@@ -105,14 +127,14 @@ ossimProjection* ossimPluginProjectionFactory::createProjection(
}
}
- if(traceDebug())
- {
- ossimNotify(ossimNotifyLevel_DEBUG)
- << MODULE << " DEBUG: testing ossimTerraSarModel" << std::endl;
- }
-
if ( !projection )
{
+ if(traceDebug())
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG)
+ << MODULE << " DEBUG: testing ossimTerraSarModel" << std::endl;
+ }
+
ossimRefPtr<ossimTerraSarModel> model = new ossimTerraSarModel();
if ( model->open(filename) )
@@ -130,14 +152,15 @@ ossimProjection* ossimPluginProjectionFactory::createProjection(
}
}
- if(traceDebug())
- {
- ossimNotify(ossimNotifyLevel_DEBUG)
- << MODULE << " DEBUG: testing ossimErsSarModel" << std::endl;
- }
+ // ErsSar
if ( !projection )
{
+ if(traceDebug())
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG)
+ << MODULE << " DEBUG: testing ossimErsSarModel" << std::endl;
+ }
ossimRefPtr<ossimErsSarModel> model = new ossimErsSarModel();
if ( model->open(filename) )
{
@@ -149,14 +172,14 @@ ossimProjection* ossimPluginProjectionFactory::createProjection(
}
}
- if(traceDebug())
- {
- ossimNotify(ossimNotifyLevel_DEBUG)
- << MODULE << " DEBUG: testing ossimEnvisatSarModel" << std::endl;
- }
-
if (!projection)
{
+ if(traceDebug())
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG)
+ << MODULE << " DEBUG: testing ossimEnvisatSarModel" << std::endl;
+ }
+
ossimRefPtr<ossimEnvisatAsarModel> model = new ossimEnvisatAsarModel();
if (model->open(filename))
{
@@ -168,14 +191,14 @@ ossimProjection* ossimPluginProjectionFactory::createProjection(
}
}
- if(traceDebug())
+ if (!projection)
{
- ossimNotify(ossimNotifyLevel_DEBUG)
+ if(traceDebug())
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG)
<< MODULE << " DEBUG: testing ossimRadarSatModel" << std::endl;
- }
+ }
- if (!projection)
- {
ossimRefPtr<ossimRadarSatModel> model = new ossimRadarSatModel();
if (model->open(filename))
{
@@ -187,14 +210,14 @@ ossimProjection* ossimPluginProjectionFactory::createProjection(
}
}
- if(traceDebug())
- {
- ossimNotify(ossimNotifyLevel_DEBUG)
- << MODULE << " DEBUG: testing ossimAlosPalsarModel" << std::endl;
- }
-
if (!projection)
{
+ if(traceDebug())
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG)
+ << MODULE << " DEBUG: testing ossimAlosPalsarModel" << std::endl;
+ }
+
ossimRefPtr<ossimAlosPalsarModel> model = new ossimAlosPalsarModel();
if (model->open(filename))
{
@@ -206,47 +229,52 @@ ossimProjection* ossimPluginProjectionFactory::createProjection(
}
}
- if(traceDebug())
- {
- ossimNotify(ossimNotifyLevel_DEBUG)
- << MODULE << " DEBUG: testing ossimFormosatModel" << std::endl;
- }
-
- ossimFilename formosatTest = filename;
- formosatTest = formosatTest.setExtension("geom");
- if(!formosatTest.exists())
+ if (!projection)
{
- formosatTest = filename.path();
- formosatTest = formosatTest.dirCat(ossimFilename("METADATA.DIM"));
- if (formosatTest.exists() == false)
+
+ if(traceDebug())
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG)
+ << MODULE << " DEBUG: testing ossimFormosatModel" << std::endl;
+ }
+
+ ossimFilename formosatTest = filename;
+ formosatTest = formosatTest.setExtension("geom");
+ if(!formosatTest.exists())
{
formosatTest = filename.path();
- formosatTest = formosatTest.dirCat(ossimFilename("metadata.dim"));
+ formosatTest = formosatTest.dirCat(ossimFilename("METADATA.DIM"));
+ if (formosatTest.exists() == false)
+ {
+ formosatTest = filename.path();
+ formosatTest = formosatTest.dirCat(ossimFilename("metadata.dim"));
+ }
}
- }
- if(formosatTest.exists())
- {
- ossimRefPtr<ossimFormosatDimapSupportData> meta =
- new ossimFormosatDimapSupportData;
- if(meta->loadXmlFile(formosatTest))
+ if(formosatTest.exists())
{
- ossimRefPtr<ossimFormosatModel> model = new ossimFormosatModel(meta.get());
- if(!model->getErrorStatus())
+ ossimRefPtr<ossimFormosatDimapSupportData> meta =
+ new ossimFormosatDimapSupportData;
+ if(meta->loadXmlFile(formosatTest))
{
- projection = model.get();
+ ossimRefPtr<ossimFormosatModel> model = new ossimFormosatModel(meta.get());
+ if(!model->getErrorStatus())
+ {
+ projection = model.get();
+ }
+ model = 0;
}
- model = 0;
}
}
- if(traceDebug())
- {
- ossimNotify(ossimNotifyLevel_DEBUG)
- << MODULE << " DEBUG: testing ossimTileMapModel" << std::endl;
- }
if (!projection)
{
+ if(traceDebug())
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG)
+ << MODULE << " DEBUG: testing ossimTileMapModel" << std::endl;
+ }
+
ossimRefPtr<ossimTileMapModel> model = new ossimTileMapModel();
if (model->open(filename))
{
@@ -258,19 +286,20 @@ ossimProjection* ossimPluginProjectionFactory::createProjection(
}
}
- if(traceDebug())
- {
- ossimNotify(ossimNotifyLevel_DEBUG)
- << MODULE << " DEBUG: testing ossimSpot6Model" << std::endl;
- }
// Spot6
if ( !projection )
{
+ if(traceDebug())
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG)
+ << MODULE << " DEBUG: testing ossimSpot6Model" << std::endl;
+ }
+
ossimRefPtr<ossimSpot6Model> model = new ossimSpot6Model();
if ( model->open(filename) )
{
- projection = model.get();
+ projection = model.get();
}
else
{
@@ -278,6 +307,7 @@ ossimProjection* ossimPluginProjectionFactory::createProjection(
}
}
+
//***
// ADD_MODEL: (Please leave this comment for the next programmer)
//***
@@ -315,10 +345,10 @@ ossimProjection* ossimPluginProjectionFactory::createProjection(
<< MODULE << " DEBUG: Entering ...." << std::endl;
}
- // else if (name == STATIC_TYPE_NAME(ossimCosmoSkymedModel))
- // {
- // return new ossimCosmoSkymedModel;
- // }
+ // else if (name == STATIC_TYPE_NAME(ossimCosmoSkymedModel))
+ // {
+ // return new ossimCosmoSkymedModel;
+ // }
if (name == STATIC_TYPE_NAME(ossimRadarSat2Model))
{
return new ossimRadarSat2Model();
@@ -359,6 +389,11 @@ ossimProjection* ossimPluginProjectionFactory::createProjection(
{
return new ossimSpot6Model;
}
+ else if (name == STATIC_TYPE_NAME(ossimSentinel1Model))
+ {
+ return new ossimSentinel1Model;
+ }
+
//***
// ADD_MODEL: (Please leave this comment for the next programmer)
@@ -474,6 +509,15 @@ ossimProjection* ossimPluginProjectionFactory::createProjection(
}
}
+ else if (type == "ossimSentinel1Model")
+ {
+ result = new ossimSentinel1Model();
+ if ( !result->loadState(kwl, prefix) )
+ {
+ result = 0;
+ }
+ }
+
//***
// ADD_MODEL: (Please leave this comment for the next programmer)
//***
@@ -493,7 +537,7 @@ ossimProjection* ossimPluginProjectionFactory::createProjection(
ossimNotify(ossimNotifyLevel_DEBUG)
<< MODULE << " DEBUG: End ...." << std::endl;
}
-
+
return result.release();
}
@@ -515,13 +559,14 @@ void ossimPluginProjectionFactory::getTypeNameList(std::vector<ossimString>& typ
typeList.push_back(STATIC_TYPE_NAME(ossimRadarSatModel));
typeList.push_back(STATIC_TYPE_NAME(ossimRadarSat2Model));
typeList.push_back(STATIC_TYPE_NAME(ossimTerraSarModel));
- // result.push_back(STATIC_TYPE_NAME(ossimCosmoSkymedModel));
+// typeList.push_back(STATIC_TYPE_NAME(ossimCosmoSkymedModel));
typeList.push_back(STATIC_TYPE_NAME(ossimEnvisatAsarModel));
typeList.push_back(STATIC_TYPE_NAME(ossimErsSarModel));
typeList.push_back(STATIC_TYPE_NAME(ossimAlosPalsarModel));
typeList.push_back(STATIC_TYPE_NAME(ossimFormosatModel));
typeList.push_back(STATIC_TYPE_NAME(ossimTileMapModel));
typeList.push_back(STATIC_TYPE_NAME(ossimPleiadesModel));
+ typeList.push_back(STATIC_TYPE_NAME(ossimSentinel1Model));
typeList.push_back(STATIC_TYPE_NAME(ossimSpot6Model));
//***
@@ -534,9 +579,9 @@ bool ossimPluginProjectionFactory::isTileMap(const ossimFilename& filename)const
{
ossimFilename temp(filename);
temp.downcase();
-
+
ossimString os = temp.beforePos(4);
-
+
if(temp.ext()=="otb")
{
return true;
diff --git a/Modules/ThirdParty/OssimPlugins/src/ossim/ossimPluginProjectionFactory.h b/Modules/ThirdParty/OssimPlugins/src/ossim/ossimPluginProjectionFactory.h
index 5205d76900b981971e903c497f4a0ef70ecd7c73..f245079a9735c077e6231e51d507fa550269172f 100644
--- a/Modules/ThirdParty/OssimPlugins/src/ossim/ossimPluginProjectionFactory.h
+++ b/Modules/ThirdParty/OssimPlugins/src/ossim/ossimPluginProjectionFactory.h
@@ -52,8 +52,29 @@ public:
virtual void getTypeNameList(std::vector<ossimString>& typeList)const;
protected:
- ossimPluginProjectionFactory(){}
+
bool isTileMap(const ossimFilename& filename) const;
+
+private:
+ ossimPluginProjectionFactory()
+ {
+ initalized_ = true;
+ }
+
+ ~ossimPluginProjectionFactory()
+ {
+ initalized_ = false;
+ }
+
+ static bool initalized_;
+
+ static ossimPluginProjectionFactory factoryInstance;
+
+ ossimPluginProjectionFactory(ossimPluginProjectionFactory const&); // Don't
+ // Implement
+
+ void operator=(ossimPluginProjectionFactory const&); // Don't implement
+
};
}
diff --git a/Modules/ThirdParty/OssimPlugins/src/ossim/ossimRadarSat2Model.cpp b/Modules/ThirdParty/OssimPlugins/src/ossim/ossimRadarSat2Model.cpp
index c7e78de798ce0b89b77360089f69facb77572dbf..56719938ac1d516ddc7a71a7d42c73c8944072b8 100644
--- a/Modules/ThirdParty/OssimPlugins/src/ossim/ossimRadarSat2Model.cpp
+++ b/Modules/ThirdParty/OssimPlugins/src/ossim/ossimRadarSat2Model.cpp
@@ -47,21 +47,24 @@ RTTI_DEF1(ossimRadarSat2Model, "ossimRadarSat2Model", ossimGeometricSarSensorMod
ossimRadarSat2Model::ossimRadarSat2Model()
- :
- ossimGeometricSarSensorModel(),
- _n_srgr(0),
- _srgr_update(),
- _SrGr_R0()
+ : ossimGeometricSarSensorModel()
+ , _n_srgr(0)
+ , _srgr_update()
+ , _SrGr_R0()
+ , theAcquisitionDateUTCString("")
+ , theProductionDateUTCString("")
{
}
ossimRadarSat2Model::ossimRadarSat2Model(const ossimRadarSat2Model& rhs)
- :
- ossimGeometricSarSensorModel(rhs),
- _n_srgr(rhs._n_srgr),
- _srgr_update(rhs._srgr_update),
- _SrGr_R0(rhs._SrGr_R0)
+ : ossimGeometricSarSensorModel(rhs)
+ , _n_srgr(rhs._n_srgr)
+ , _srgr_update(rhs._srgr_update)
+ , _SrGr_R0(rhs._SrGr_R0)
+ , theProductionDateUTCString(rhs.theProductionDateUTCString)
+ , theAcquisitionDateUTCString(rhs.theAcquisitionDateUTCString)
{
+
}
ossimRadarSat2Model::~ossimRadarSat2Model()
@@ -81,15 +84,15 @@ ossimObject* ossimRadarSat2Model::dup() const
double ossimRadarSat2Model::getSlantRangeFromGeoreferenced(double col) const
{
if (_n_srgr==0) return(-1) ;
-
+
double relativeGroundRange, slantRange = 0.0 ;
-
+
// in the case of Georeferenced images, _refPoint->get_distance()
// contains the ground range
relativeGroundRange = _refPoint->get_distance() + _sensor->get_col_direction() * (col-_refPoint->get_pix_col())* theGSD.x;
//relativeGroundRange = 1 + _sensor->get_col_direction() * (col-_refPoint->get_pix_col())* theGSD.x;
//relativeGroundRange = (8.78400000e+03)*theGSD.x;
-
+
if ( traceDebug() )
{
ossimNotify(ossimNotifyLevel_DEBUG)
@@ -100,15 +103,15 @@ double ossimRadarSat2Model::getSlantRangeFromGeoreferenced(double col) const
<< "\n_refPoint->get_pix_col() : " << _refPoint->get_pix_col()
<< "\n relativeGroundRange : " << relativeGroundRange << endl;
}
-
+
int numSet = FindSRGRSetNumber((_refPoint->get_ephemeris())->get_date()) ;
/**
* @todo : could be improved (date choice)
*/
-
+
for (int i=0 ; i < static_cast<int>(_SrGr_coeffs[numSet].size()); i++)
{
-
+
slantRange += _SrGr_coeffs[numSet][i]*pow(relativeGroundRange,i) ;
}
@@ -160,6 +163,8 @@ bool ossimRadarSat2Model::open(const ossimFilename& file)
if (result)
{
+ _productXmlFile = xmlFile;
+
if (traceDebug())
{
ossimNotify(ossimNotifyLevel_DEBUG)
@@ -204,13 +209,27 @@ bool ossimRadarSat2Model::open(const ossimFilename& file)
result = rsDoc.getSatellite(xdoc, theSensorID);
}
+ if (result)
+ {
// Set the base class gsd:
result = rsDoc.initGsd(xdoc, theGSD);
+ }
+
if (result)
{
theMeanGSD = (theGSD.x + theGSD.y)/2.0;
}
+ if (result)
+ {
+ result = rsDoc.getAcquistionDate(xdoc, theAcquisitionDateUTCString);
+ }
+
+ if (result)
+ {
+ result = rsDoc.getProductionDate(xdoc, theProductionDateUTCString);
+ }
+
if (result)
{
result = initSRGR(xdoc, rsDoc);
@@ -226,7 +245,7 @@ bool ossimRadarSat2Model::open(const ossimFilename& file)
if (result)
{
result = initRefPoint(xdoc, rsDoc);
-
+
if (result)
{
result = InitRefNoiseLevel(xdoc);
@@ -246,7 +265,6 @@ bool ossimRadarSat2Model::open(const ossimFilename& file)
if (result)
{
- _productXmlFile = xmlFile;
ossimSupportFilesList::instance()->add(_productXmlFile);
}
else
@@ -273,7 +291,7 @@ bool ossimRadarSat2Model::open(const ossimFilename& file)
<< "ul, ur, lr, ll " << ul << ", " << ur
<< ", " << lr << " , " << ll << endl;
}
-
+
setGroundRect(ul, ur, lr, ll); // ossimSensorModel method.
// OSSIM preferences specifies whether a coarse grid needs to be generated:
@@ -336,7 +354,7 @@ std::ostream& ossimRadarSat2Model::print(std::ostream& out) const
}
ossimGeometricSarSensorModel::print(out);
-
+
// Reset flags.
out.setf(f);
@@ -523,7 +541,7 @@ bool ossimRadarSat2Model::InitRefPoint(const ossimKeywordlist &kwl,
date->set_decimal(time - floor(time)) ;
}
delete date;//FIXME to confirm
-
+
if(_platformPosition != 0)
{
Ephemeris * ephemeris = _platformPosition->Interpolate((JSDDateTime)*date);
@@ -928,7 +946,7 @@ bool ossimRadarSat2Model::initRefPoint(const ossimXmlDocument* xdoc,
double distance = 1;
- // Only set distance to
+ // Only set distance to
if (!_isProductGeoreferenced)
{
if ( !rsDoc.getSlantRangeNearEdge(xdoc, s) )
@@ -936,9 +954,9 @@ bool ossimRadarSat2Model::initRefPoint(const ossimXmlDocument* xdoc,
if (traceDebug())
{
ossimNotify(ossimNotifyLevel_DEBUG)
-
+
<< MODULE << "getSlantRangeNearEdge error! exiting\n";
- }
+ }
return false;
}
distance = s.toDouble();
@@ -981,7 +999,8 @@ bool ossimRadarSat2Model::initRefPoint(const ossimXmlDocument* xdoc,
bool ossimRadarSat2Model::InitLut( const ossimXmlDocument* xmlDocument,
RadarSat2NoiseLevel& noise)
{
- static const char MODULE[] = "ossimRadarSat2Model::initLut";
+ static const char MODULE[] = "ossimRadarSat2Model::InitLut";
+
if (traceDebug())
{
ossimNotify(ossimNotifyLevel_DEBUG)<< MODULE << " entered...\n";
@@ -992,8 +1011,8 @@ bool ossimRadarSat2Model::InitLut( const ossimXmlDocument* xmlDocument,
std::vector<ossimRefPtr<ossimXmlNode> > xml_nodes;
std::vector<ossimRefPtr<ossimXmlNode> >::iterator node;
ossimFilename lutXmlFile;
-
- incidenceAngleCorrectionName = noise.get_incidenceAngleCorrectionName();
+
+ incidenceAngleCorrectionName = noise.get_incidenceAngleCorrectionName();
xpath = "/product/imageAttributes/lookupTable";
xml_nodes.clear();
@@ -1009,8 +1028,8 @@ bool ossimRadarSat2Model::InitLut( const ossimXmlDocument* xmlDocument,
<< std::endl;
}
return false;
- }
-
+ }
+
node = xml_nodes.begin();
while (node != xml_nodes.end())
{
@@ -1024,56 +1043,67 @@ bool ossimRadarSat2Model::InitLut( const ossimXmlDocument* xmlDocument,
//---
// Instantiate the XML parser:
//---
- ossimXmlDocument* xmlLutDocument = new ossimXmlDocument();
- if ( xmlLutDocument->openFile(lutXmlFile) )
+ ossimRefPtr<ossimXmlDocument> xmlLutDocument;
+ xmlLutDocument = new ossimXmlDocument();
+ if ( xmlLutDocument.get()->openFile(lutXmlFile) )
{
- std::vector<ossimRefPtr<ossimXmlNode> > xml_lutNodes;
- ossimString s;
-
- xpath = "/lut/offset";
- xml_lutNodes.clear();
- xmlLutDocument->findNodes(xpath, xml_lutNodes);
- if(xml_lutNodes.size() == 0)
- {
- setErrorStatus();
- if(traceDebug())
- {
- ossimNotify(ossimNotifyLevel_DEBUG)
+ const ossimRefPtr<ossimXmlNode> lutRoot = xmlLutDocument.get()->getRoot(); //->findFirstNode("lut");
+
+ if(! lutRoot.get())
+ {
+ setErrorStatus();
+ if(traceDebug())
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG)
+ << MODULE << " DEBUG:"
+ << "\nCould not find: lut" << std::endl;
+ }
+ return false;
+ }
+
+ ossimString offsetVal = lutRoot->getChildTextValue("offset");
+ if( !offsetVal.empty())
+ {
+ noise.set_offset(offsetVal.toFloat64());
+ }
+ else
+ {
+ setErrorStatus();
+ if(traceDebug())
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG)
<< MODULE << " DEBUG:"
- << "\nCould not find: " << xpath
- << std::endl;
- }
- return false;
- }
- ossim_float64 offset = xml_lutNodes[0]->getText().toFloat64();
- noise.set_offset(offset);
-
- xpath = "/lut/gains";
- xml_lutNodes.clear();
- xmlLutDocument->findNodes(xpath, xml_lutNodes);
- if(xml_lutNodes.size() == 0)
- {
- setErrorStatus();
- if(traceDebug())
- {
- ossimNotify(ossimNotifyLevel_DEBUG)
+ << "\nCould not find: offset" << std::endl;
+ }
+ return false;
+ }
+
+ ossimString gainVal = lutRoot->getChildTextValue("gains");
+ if( !gainVal.empty())
+ {
+ noise.set_gain(gainVal);
+ }
+ else
+ {
+ setErrorStatus();
+ if(traceDebug())
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG)
<< MODULE << " DEBUG:"
- << "\nCould not find: " << xpath
- << std::endl;
- }
- return false;
- }
- noise.set_gain(xml_lutNodes[0]->getText());
- }
+ << "\nCould not find: gains" << std::endl;
+ }
+ return false;
+ }
+ }
}
}
- ++node;
+ ++node;
}
if (traceDebug())
{
ossimNotify(ossimNotifyLevel_DEBUG)<< MODULE << " leaving...\n";
- }
+ }
return true;
}
@@ -1095,8 +1125,8 @@ bool ossimRadarSat2Model::InitRefNoiseLevel(
}
_noiseLevel.clear();
-
-
+
+
xpath = "/product/sourceAttributes/radarParameters/referenceNoiseLevel";
xml_nodes.clear();
xmlDocument->findNodes(xpath, xml_nodes);
@@ -1111,14 +1141,14 @@ bool ossimRadarSat2Model::InitRefNoiseLevel(
<< std::endl;
}
return false;
- }
-
+ }
+
node = xml_nodes.begin();
while (node != xml_nodes.end())
{
-
- ev.set_incidenceAngleCorrectionName( (*node)->getAttributeValue("incidenceAngleCorrection") );
-
+
+ ev.set_incidenceAngleCorrectionName( (*node)->getAttributeValue("incidenceAngleCorrection") );
+
sub_nodes.clear();
xpath = "pixelFirstNoiseValue";
(*node)->findChildNodes(xpath, sub_nodes);
@@ -1185,10 +1215,10 @@ bool ossimRadarSat2Model::InitRefNoiseLevel(
}
return false;
}
- ev.set_units( sub_nodes[0]->getAttributeValue("units") );
+ ev.set_units( sub_nodes[0]->getAttributeValue("units") );
- std::vector<ossimString> s2;
+ std::vector<ossimString> s2;
std::vector<ossim_float64> noiseLevelValues;
s2.clear();
noiseLevelValues.clear();
@@ -1197,7 +1227,7 @@ bool ossimRadarSat2Model::InitRefNoiseLevel(
{
noiseLevelValues.push_back( s2[i].toFloat64() );
}
- ev.set_noiseLevelValues( noiseLevelValues );
+ ev.set_noiseLevelValues( noiseLevelValues );
InitLut(xmlDocument, ev);
@@ -1206,11 +1236,11 @@ bool ossimRadarSat2Model::InitRefNoiseLevel(
++node;
}
-
+
if (traceDebug())
{
ossimNotify(ossimNotifyLevel_DEBUG)<< MODULE << " leaving...\n";
- }
+ }
return true;
}
@@ -1234,6 +1264,33 @@ bool ossimRadarSat2Model::saveState(ossimKeywordlist& kwl,
kwl.add(prefix, PRODUCT_XML_FILE_KW, _productXmlFile.c_str());
kwl.add(prefix, NUMBER_SRGR_COEFFICIENTS_KW, _n_srgr);
+ kwl.add("support_data.",
+ "calibration_lookup_flag",
+ "true",
+ true);
+
+
+ if(! theProductionDateUTCString.empty())
+ kwl.add("support_data.",
+ ossimKeywordNames::DATE_KW,
+ theProductionDateUTCString.c_str(),
+ true);
+
+ if(! theAcquisitionDateUTCString.empty())
+ kwl.add("support_data.",
+ ossimKeywordNames::IMAGE_DATE_KW,
+ theAcquisitionDateUTCString.c_str(),
+ true);
+
+ //RK ...fix this part as part of refractoring
+ //if(theSLC)// numBands*=2; // real and imaginary
+
+
+ kwl.add("support_data.",
+ ossimKeywordNames::NUMBER_BANDS_KW,
+ 2,
+ true);
+
// Make sure all the arrays are equal in size.
const ossim_uint32 COUNT = static_cast<ossim_uint32>(_n_srgr);
@@ -1282,10 +1339,10 @@ bool ossimRadarSat2Model::saveState(ossimKeywordlist& kwl,
if (result)
{
for(ossim_uint32 i = 0; i < _noiseLevel.size(); ++i)
- {
+ {
_noiseLevel[i].saveState(kwl, prefix);
}
-
+
}
//---
@@ -1485,9 +1542,9 @@ bool ossimRadarSat2Model::loadState (const ossimKeywordlist &kwl,
if(result)
{
for(ossim_uint32 i = 0; i < _noiseLevel.size(); ++i)
- {
+ {
_noiseLevel[i].loadState(kwl, prefix);
- }
+ }
}
if (traceDebug())
diff --git a/Modules/ThirdParty/OssimPlugins/src/ossim/ossimRadarSat2Model.h b/Modules/ThirdParty/OssimPlugins/src/ossim/ossimRadarSat2Model.h
index 2269a0266027a81dd1d39aae9c33b85b67a02a14..606e41af9bc8e6b4791e22fd288b9935a4015149 100644
--- a/Modules/ThirdParty/OssimPlugins/src/ossim/ossimRadarSat2Model.h
+++ b/Modules/ThirdParty/OssimPlugins/src/ossim/ossimRadarSat2Model.h
@@ -169,6 +169,11 @@ private:
*/
int _n_srgr;
+ //RK remove this part soon and use kwl directly.
+ ossimString theAcquisitionDateUTCString;
+
+ ossimString theProductionDateUTCString;
+
/**
* @brief Slant Range FOR EACH Ground Range coefficient sets update times
*/
diff --git a/Modules/ThirdParty/OssimPlugins/src/ossim/ossimRadarSat2ProductDoc.cpp b/Modules/ThirdParty/OssimPlugins/src/ossim/ossimRadarSat2ProductDoc.cpp
index 65a03bea50565382b7fb2fb3e82ff5616e0934dc..569f21e79fd9b945a5c54f7aa616d12db2976ab4 100644
--- a/Modules/ThirdParty/OssimPlugins/src/ossim/ossimRadarSat2ProductDoc.cpp
+++ b/Modules/ThirdParty/OssimPlugins/src/ossim/ossimRadarSat2ProductDoc.cpp
@@ -465,7 +465,7 @@ bool ossimRadarSat2ProductDoc::initGsd(const ossimXmlDocument* xdoc,
ossimString s;
if ( getSampledPixelSpacing(xdoc, s) )
{
- gsd.x = s.toFloat64();
+ gsd.x = ossimString::toFloat64(s);
}
else
{
@@ -473,7 +473,7 @@ bool ossimRadarSat2ProductDoc::initGsd(const ossimXmlDocument* xdoc,
}
if ( getSampledLineSpacing(xdoc, s) )
{
- gsd.y = s.toFloat64(s);
+ gsd.y = ossimString::toFloat64(s);
}
else
{
@@ -496,6 +496,20 @@ bool ossimRadarSat2ProductDoc::initGsd(const ossimXmlDocument* xdoc,
return result;
}
+
+
+bool ossimRadarSat2ProductDoc::getAcquistionDate(const ossimXmlDocument* xdoc, ossimString& adate)
+{
+ ossimString path = "/product/sourceAttributes/rawDataStartTime";
+ return ossim::getPath(path, xdoc, adate);
+}
+
+bool ossimRadarSat2ProductDoc::getProductionDate(const ossimXmlDocument* xdoc, ossimString& pdate)
+{
+ ossimString path = "/product/imageGenerationParameters/generalProcessingInformation/processingTime";
+ return ossim::getPath(path, xdoc, pdate);
+}
+
bool ossimRadarSat2ProductDoc::initTiePoints(const ossimXmlDocument* xdoc,
std::list<ossimGpt>& gcp,
std::list<ossimDpt>& icp) const
@@ -889,15 +903,15 @@ bool ossimRadarSat2ProductDoc::getImageFile(const ossimXmlDocument* xdoc,
{
bool result = false;
ossimString fileName;
-
+
ossimString path = "/product/imageAttributes/fullResolutionImageData";
-
+
if ( ossim::getPath(path, xdoc, fileName) )
{
result = true;
s = fileName;
}
-
+
return result;
}
diff --git a/Modules/ThirdParty/OssimPlugins/src/ossim/ossimRadarSat2ProductDoc.h b/Modules/ThirdParty/OssimPlugins/src/ossim/ossimRadarSat2ProductDoc.h
index 56bf0a7ab75915bd15c3281189bb72eeac80a10f..ae376b08f88bcab27af8d9c9b303b64bf12e5bed 100644
--- a/Modules/ThirdParty/OssimPlugins/src/ossim/ossimRadarSat2ProductDoc.h
+++ b/Modules/ThirdParty/OssimPlugins/src/ossim/ossimRadarSat2ProductDoc.h
@@ -51,7 +51,7 @@ typedef struct
vector<double> lineDenominatorCoefficients;
vector<double> pixelNumeratorCoefficients;
vector<double> pixelDenominatorCoefficients;
-
+
}RPCModel;
namespace ossimplugins
@@ -59,17 +59,17 @@ namespace ossimplugins
class PlatformPosition;
class RefPoint;
class SensorParams;
-
+
/** @brief Class to encapsulate parsing RadarSat2 product.xml file. */
class OSSIM_PLUGINS_DLL ossimRadarSat2ProductDoc
{
public:
/** @brief default constructor */
ossimRadarSat2ProductDoc();
-
+
/** @brief destructor */
~ossimRadarSat2ProductDoc();
-
+
/**
* @brief Checks for node /product/sourceAttributes/satellite containing
* RADARSAT-2.
@@ -77,9 +77,9 @@ public:
* @return true if present, false if not.
*/
bool isRadarSat2(const ossimXmlDocument* xdoc) const;
-
+
RPCModel getRpcData(const ossimXmlDocument* xdoc) const;
-
+
/**
* @brief Method to initialize PlatformPosition object from
* RadarSat "product.xml" file.
@@ -89,7 +89,7 @@ public:
*/
bool initPlatformPosition(const ossimXmlDocument* xdoc,
PlatformPosition* pos) const;
-
+
/**
* @brief Method to initialize SensorParams object from
* RadarSat "product.xml" file.
@@ -99,7 +99,7 @@ public:
*/
bool initSensorParams(const ossimXmlDocument* xdoc,
SensorParams* sp) const;
-
+
/**
* @brief Method to initialize image size from
* RadarSat "product.xml" file.
@@ -109,7 +109,7 @@ public:
*/
bool initImageSize(const ossimXmlDocument* xdoc,
ossimIpt& imageSize) const;
-
+
/**
* @brief Method to initialize gsd from
* RadarSat "product.xml" file.
@@ -119,7 +119,11 @@ public:
*/
bool initGsd(const ossimXmlDocument* xdoc,
ossimDpt& gsd) const;
-
+
+ bool getAcquistionDate(const ossimXmlDocument* xdoc, ossimString& adate);
+
+ bool getProductionDate(const ossimXmlDocument* xdoc, ossimString& pdate);
+
/**
* @brief Method to initialize image tie points from
* RadarSat "product.xml" file.
@@ -132,7 +136,7 @@ public:
std::list<ossimDpt>& icp) const;
bool getSatellite(const ossimXmlDocument* xdoc,
ossimString& s) const;
-
+
bool getSensor(const ossimXmlDocument* xdoc,
ossimString& s) const;
@@ -222,7 +226,7 @@ public:
bool getIncidenceAngleFarRange(const ossimXmlDocument* xdoc,
ossimString& s) const;
-
+
bool getSatelliteHeight(const ossimXmlDocument* xdoc,
ossimString& s) const;
};
diff --git a/Modules/ThirdParty/OssimPlugins/src/ossim/ossimSentinel1Model.cpp b/Modules/ThirdParty/OssimPlugins/src/ossim/ossimSentinel1Model.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..f0bd0d5b477546e035d4ee3dac1e748b9ca096af
--- /dev/null
+++ b/Modules/ThirdParty/OssimPlugins/src/ossim/ossimSentinel1Model.cpp
@@ -0,0 +1,546 @@
+#include "ossimSentinel1Model.h"
+
+namespace ossimplugins
+{
+
+// Define Trace flags for use within this file:
+ static ossimTrace traceExec ("ossimSentinel1Model:exec");
+ static ossimTrace traceDebug ("ossimSentinel1Model:debug");
+
+ RTTI_DEF1(ossimSentinel1Model, "ossimSentinel1Model", ossimSarModel);
+
+//*************************************************************************************************
+// Constructor
+//*************************************************************************************************
+ ossimSentinel1Model::ossimSentinel1Model()
+ : ossimSarModel()
+ , theOCN(false)
+ , theSLC(false)
+ {
+ theManifestDoc = new ossimXmlDocument();
+ theProduct = new ossimSentinel1ProductDoc();
+ this->clearFields();
+
+ }
+
+ void ossimSentinel1Model::clearFields()
+ {
+ theOCN = false;
+ theSLC = false;
+ theManifestKwl.clear();
+ theManifestFile = ossimFilename::NIL;
+ theProductXmlFile = ossimFilename::NIL;
+ theProduct->clearFields();
+
+ }
+
+
+//*************************************************************************************************
+// Constructor
+//*************************************************************************************************
+ ossimSentinel1Model::ossimSentinel1Model(const ossimSentinel1Model& rhs)
+ :ossimSarModel(rhs)
+ , theOCN(rhs.theOCN)
+ , theSLC(rhs.theSLC)
+ {
+
+ }
+
+//*************************************************************************************************
+// Destructor
+//*************************************************************************************************
+ ossimSentinel1Model::~ossimSentinel1Model()
+ {
+ theProduct = 0;
+ if (traceExec()) ossimNotify(ossimNotifyLevel_DEBUG) << "DEBUG DESTRUCTOR: ~ossimSentinel1Model(): entering..." << std::endl;
+ }
+
+//*************************************************************************************************
+// Infamous DUP
+//*************************************************************************************************
+ ossimObject* ossimSentinel1Model::dup() const
+ {
+ return new ossimSentinel1Model(*this);
+ }
+
+//*************************************************************************************************
+// Print
+//*************************************************************************************************
+ std::ostream& ossimSentinel1Model::print(std::ostream& out) const
+ {
+ // Capture stream flags since we are going to mess with them.
+ std::ios_base::fmtflags f = out.flags();
+
+ out << "\nDump of ossimSentinel1Model at address " << (hex) << this
+ << (dec)
+ << "\n------------------------------------------------"
+ << "\n theImageID = " << theImageID
+ << "\n theImageSize = " << theImageSize
+
+ << "\n------------------------------------------------"
+ << "\n " << endl;
+
+ // Set the flags back.
+ out.flags(f);
+ return ossimSarModel::print(out);
+
+ }
+
+//*************************************************************************************************
+// Save State
+//*************************************************************************************************
+ bool ossimSentinel1Model::saveState(ossimKeywordlist& kwl,
+ const char* prefix) const
+ {
+
+ kwl.add(prefix,
+ ossimKeywordNames::TYPE_KW,
+ "ossimSentinel1Model",
+ true);
+
+ kwl.add("support_data.",
+ "calibration_lookup_flag",
+ "true",
+ true);
+
+ kwl.addList(theManifestKwl, true);
+
+ if(theProduct.get())
+ {
+ kwl.addList(theProduct->getProductKwl(), true);
+ // theProduct->saveState(kwl, prefix);
+ }
+ ossimSarModel::saveState(kwl, prefix);
+ return true;
+ }
+
+
+//*************************************************************************************************
+// Load State
+//*************************************************************************************************
+ bool ossimSentinel1Model::loadState(const ossimKeywordlist& kwl,
+ const char* prefix)
+ {
+ //theManifestKwl.addList(kwl, true);
+
+ ossimSarModel::loadState(kwl, prefix);
+ return true;
+ }
+
+ bool ossimSentinel1Model::findSafeManifest(const ossimFilename& file, ossimFilename& manifestFile)
+ {
+ manifestFile = ossimFilename(file.path().path() + "/manifest.safe");
+
+ if(!manifestFile.exists())
+ {
+ if (traceDebug())
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG) << "manifest.safe " << manifestFile << " doesn't exist ...\n";
+ }
+ return false;
+ }
+ return true;
+ }
+
+ bool ossimSentinel1Model::open(const ossimFilename& file)
+ {
+
+ static const char MODULE[] = "ossimplugins::ossimSentinel1Model::open";
+ //traceDebug.setTraceFlag(true);
+
+ if (traceDebug())
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG) << MODULE << " entered...\n";
+ }
+
+ bool result = false;
+
+ if ( !file.exists() || (file.ext().downcase() != "tiff") )
+ {
+ return false;
+ }
+ else
+ {
+ theGSD.makeNan();
+
+ ossimFilename safeFile;
+ bool foundManifestFile = findSafeManifest(file, safeFile);
+ while (foundManifestFile)
+ {
+ if(theManifestDoc.get())
+ {
+ if( !theManifestDoc->openFile(safeFile))
+ {
+ std::cerr << MODULE << "error at line:" << __LINE__ << std::endl;
+ break;
+ }
+ }
+ else
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG) << MODULE << " theManifestDoc..\n";
+ break;
+ }
+
+ if ( !this->isSentinel1(safeFile))
+ {
+ std::cerr << MODULE << "error at line:" << __LINE__ << std::endl;
+ break;
+ }
+
+ ossimString productFile;
+ if ( !this->getAnnotationFileLocation(safeFile, "^product"))
+ {
+ std::cerr << MODULE << "error at line:" << __LINE__ << std::endl;
+ break;
+ }
+
+ // Set the image ID to the scene ID.
+ if ( !this->getImageId( theImageID ) )
+ {
+ std::cerr << MODULE << "error at line:" << __LINE__ << std::endl;
+ break;
+ }
+
+ if ( !this->standAloneProductInformation( ) )
+ {
+ std::cerr << MODULE << "error at line:" << __LINE__ << std::endl;
+ break;
+ }
+
+ // Set the sensor ID to the mission ID.
+ if ( !this->initSensorID( theSensorID ) )
+ {
+ std::cerr << MODULE << "error at line:" << __LINE__ << std::endl;
+ break;
+ }
+
+ if ( !this->readProduct( safeFile ) )
+ {
+ std::cerr << MODULE << "error at line:" << __LINE__ << std::endl;
+ break;
+ }
+
+ if ( !this->initImageSize( theImageSize ) )
+ {
+ std::cerr << MODULE << "error at line:" << __LINE__ << std::endl;
+ break;
+ }
+
+ theImageClipRect = ossimDrect( 0, 0, theImageSize.x-1, theImageSize.y-1 );
+ theSubImageOffset.x = 0.0;
+ theSubImageOffset.y = 0.0;
+
+ if ( !this->initGsd( theGSD ) )
+ {
+ std::cerr << MODULE << "error at line:" << __LINE__ << std::endl;
+ break;
+ }
+
+ theMeanGSD = (theGSD.x + theGSD.y)/2.0;
+
+ if ( !this->initSRGR( ) )
+ {
+ std::cerr << MODULE << "error at line:" << __LINE__ << std::endl;
+ break;
+ }
+
+ // if ( !theSafeManifest->initPlatformPosition( ) )
+ // {
+ // break;
+ // }
+ // else
+ // {
+ // std::cerr << MODULE << "error at line:" << __LINE__ << std::endl;
+ // }
+
+
+ return true;
+ }
+
+// // If we broke out of the while, something happened...
+ return false;
+ }
+
+ }
+
+
+ bool ossimSentinel1Model::getImageId( ossimString& s) const
+ {
+ ossimString xpath;
+ xpath = "/xfdu:XFDU/metadataSection/metadataObject/metadataWrap/xmlData/s1sarl1:standAloneProductInformation/s1sarl1:missionDataTakeID";
+ return ossim::getPath(xpath, theManifestDoc.get(), s);
+ }
+
+ bool ossimSentinel1Model::initSensorID(ossimString& s)
+ {
+ const ossimRefPtr<ossimXmlNode> safePlatform = theManifestDoc->getRoot()->findFirstNode("metadataSection/metadataObject/metadataWrap/xmlData/safe:platform");
+ ossimString familyName, instrumentId;
+ bool ret1 = safePlatform->getChildTextValue(familyName, "safe:familyName");
+ bool ret2 = safePlatform->getChildTextValue(instrumentId, "safe:number");
+
+ theManifestKwl.add("support_data.",
+ "instrument",
+ "S1" + instrumentId,
+ true);
+
+ s = familyName + instrumentId;
+ return (ret1 && ret2);
+ }
+
+ bool ossimSentinel1Model::getAnnotationFileLocation(const ossimFilename &manifestFile, const char* pattern)
+ {
+
+ static const char MODULE[] = "ossimSentinel1SafeManifest::getAnnotationFileLocation";
+ //traceDebug.setTraceFlag(true);
+ const ossimString prefix = "support_data.";
+ const ossimString xpath = "/xfdu:XFDU/dataObjectSection/dataObject";
+
+ vector<ossimRefPtr<ossimXmlNode> > xml_nodes;
+
+ theManifestDoc->findNodes(xpath, xml_nodes);
+
+ ossimRefPtr<ossimXmlNode> dataObjectNode = xml_nodes[0];
+ ossimString dataObjectId;
+ dataObjectNode->getAttributeValue(dataObjectId , "ID");
+
+ if ( dataObjectId.match(pattern) )
+ {
+ ossim::getPath(xpath + "/byteStream/fileLocation", theManifestDoc.get(), theProductXmlFile);
+ xml_nodes.clear();
+ theManifestDoc->findNodes(xpath + "/byteStream/fileLocation", xml_nodes);
+ if(xml_nodes.size() < 1 )
+ {
+ std::cerr << "error :" << __LINE__ << std::endl;
+ return false;
+ }
+ xml_nodes[0]->getAttributeValue(theProductXmlFile , "href");
+ theProductXmlFile.setPath(manifestFile.path() + "/annotation");
+
+ }
+ return true;
+ }
+
+
+ bool ossimSentinel1Model::standAloneProductInformation()
+ {
+ static const char MODULE[] = "ossimSentinel1ProductDoc::parseSafe";
+
+ const ossimString prefix = "support_data.";
+
+ const ossimRefPtr<ossimXmlNode> safeProcessing = theManifestDoc->getRoot()->findFirstNode("metadataSection/metadataObject/metadataWrap/xmlData/safe:processing");
+ const ossimRefPtr<ossimXmlNode> facility = safeProcessing->findFirstNode("safe:facility");
+ const ossimRefPtr<ossimXmlNode> software = facility->findFirstNode("safe:software");
+ const ossimString org = facility->getAttributeValue("organisation");
+ const ossimString name = software->getAttributeValue("name");
+ const ossimString version = software->getAttributeValue("version");
+
+ theManifestKwl.add(prefix,
+ "Processing_system_identifier",
+ org + " " + name + " " + version,
+ true);
+
+ theManifestKwl.add(prefix,
+ ossimKeywordNames::DATE_KW,
+ safeProcessing->getAttributeValue("start"),
+ true);
+
+ const ossimRefPtr<ossimXmlNode> acquisitionPeriod = theManifestDoc->getRoot()->findFirstNode("metadataSection/metadataObject/metadataWrap/xmlData/safe:acquisitionPeriod");
+ ossimString acqStartTime = acquisitionPeriod->getChildTextValue("safe:startTime");
+
+ theManifestKwl.add(prefix,
+ "first_line_time",
+ acqStartTime,
+ true);
+
+ theManifestKwl.add(prefix,
+ "last_line_time",
+ acquisitionPeriod->getChildTextValue("safe:stopTime"),
+ true);
+ //RK
+ theManifestKwl.add(prefix,
+ ossimKeywordNames::IMAGE_DATE_KW,
+ acqStartTime,
+ true);
+
+ const ossimRefPtr<ossimXmlNode> instrumentNode =
+ theManifestDoc->getRoot()->findFirstNode("metadataSection/metadataObject/metadataWrap/xmlData/safe:platform/safe:instrument");
+ ossimString swath = instrumentNode->getChildTextValue("s1sarl1:swath");
+ ossimString acquisition_mode = instrumentNode->getChildTextValue("s1sarl1:mode");
+
+ if( acquisition_mode.empty())
+ {
+ ossimRefPtr<ossimXmlNode> instrumentModeNode = instrumentNode->findFirstNode("safe:extension/s1sarl1:instrumentMode");
+ if(instrumentModeNode.get())
+ {
+ acquisition_mode = instrumentModeNode->getChildTextValue("s1sarl1:mode");
+ swath = instrumentModeNode->getChildTextValue("s1sarl1:swath");
+ }
+ }
+
+ theManifestKwl.add(prefix,
+ "acquisition_mode",
+ acquisition_mode,
+ true);
+
+ theManifestKwl.add(prefix,
+ "swath",
+ swath,
+ true);
+
+ if (acquisition_mode == "IW" || acquisition_mode == "EW")
+ theProduct->setTOPSAR(true);
+
+ const ossimRefPtr<ossimXmlNode> orbitReference =
+ theManifestDoc->getRoot()->findFirstNode("metadataSection/metadataObject/metadataWrap/xmlData/safe:orbitReference");
+
+ std::vector<ossimRefPtr<ossimXmlNode> > orbitNumberNodes;
+ const ossimString orbitReference_xpath = "/xfdu:XFDU/metadataSection/metadataObject/metadataWrap/xmlData/safe:orbitReference";
+ theManifestDoc->findNodes(orbitReference_xpath + "/safe:orbitNumber", orbitNumberNodes);
+
+ std::vector<ossimRefPtr<ossimXmlNode> >::const_iterator it = orbitNumberNodes.begin();
+ while( it != orbitNumberNodes.end())
+ {
+ const ossimRefPtr<ossimXmlAttribute> attribute = (*it)->findAttribute("type");
+ if( attribute.get() )
+ {
+ if( attribute->getValue() == "start" )
+ {
+ theManifestKwl.add(prefix, "abs_orbit", (*it)->getText(), true);
+ break;
+ }
+ }
+ ++it;
+ }
+
+ orbitNumberNodes.clear();
+ theManifestDoc->findNodes(orbitReference_xpath + "/safe:relativeOrbitNumber", orbitNumberNodes);
+
+ std::vector<ossimRefPtr<ossimXmlNode> >::const_iterator it2 = orbitNumberNodes.begin();
+ while( it2 != orbitNumberNodes.end())
+ {
+ const ossimRefPtr<ossimXmlAttribute> attribute = (*it2)->findAttribute("start");
+ if( attribute.get() )
+ {
+ if( attribute->getValue() == "start" )
+ {
+ theManifestKwl.add(prefix, "rel_orbit", (*it2)->getText(), true);
+ break;
+ }
+ }
+ ++it2;
+ }
+
+ theManifestKwl.add(prefix, "orbit_cycle", orbitReference->getChildTextValue("safe:cycleNumber"), true);
+
+ ossimString orbit_pass = "";
+ orbit_pass = orbitReference->getChildTextValue("s1:pass");
+
+ if( orbit_pass.empty() )
+ {
+ orbit_pass = orbitReference->getChildTextValue("safe:extension/s1:orbitProperties/s1:pass");
+ }
+
+ theManifestKwl.add(prefix,
+ "orbit_pass",
+ orbit_pass,
+ true);
+
+ ossimString productType = "unknown";
+ const ossimRefPtr<ossimXmlNode> standAloneProductInformation =
+ theManifestDoc->getRoot()->findFirstNode("metadataSection/metadataObject/metadataWrap/xmlData/s1sarl1:standAloneProductInformation");
+
+ if (theOCN)
+ {
+ productType = "OCN";
+ }
+ else
+ {
+ if (standAloneProductInformation.get())
+ productType = standAloneProductInformation->getChildTextValue("s1sarl1:productType");
+ }
+
+ theManifestKwl.add(prefix,
+ "product_type",
+ productType,
+ true);
+
+ if( productType.contains("SLC" ) )
+ {
+ //ossimKeywordNames::PIXEL_TYPE_KW; RK
+ theManifestKwl.add("sample_type", "COMPLEX", true);
+ theProduct->setSLC(true);
+
+ }
+ else
+ {
+ theManifestKwl.add("sample_type", "DETECTED", true);
+ theManifestKwl.add(prefix, "srgr_flag", "true", true);
+ }
+
+ return true;
+ }
+
+ bool ossimSentinel1Model::isSentinel1(const ossimFilename &manifestFile )
+ {
+ theOCN = isLevel2(manifestFile);
+ if( isLevel1(manifestFile) || theOCN || isLevel0(manifestFile))
+ return true;
+ else
+ return false;
+ }
+
+ bool ossimSentinel1Model::isLevel1(const ossimFilename& file)
+ {
+ bool productXmlCheck = checkDirectory(file, "annotation", ".xml");
+
+ return productXmlCheck && checkDirectory(file, "measurement", ".tiff");
+ }
+
+ bool ossimSentinel1Model::isLevel2(const ossimFilename& file)
+ {
+
+ return checkDirectory(file, "measurement",".nc");
+ }
+
+ bool ossimSentinel1Model::isLevel0(const ossimFilename& file)
+ {
+ return checkDirectory(file, "measurement",".dat");
+ }
+
+ bool ossimSentinel1Model::checkDirectory(const ossimFilename& file, const char* d, const char* ext)
+ {
+ //check dir is valid first
+ ossimDirectory dir = ossimDirectory(file.path() + "/" + d + "/");
+ std::vector<ossimFilename> result;
+ dir.findAllFilesThatMatch(result, ext);
+ if ( result.size() < 1 )
+ {
+ if (traceExec())
+ {
+ ossimNotify(ossimNotifyLevel_FATAL)
+ << " DEBUG:" << " checkDirectory failed for: " << file.path() << "/" << d << " with ext ="<< ext << std::endl;
+ }
+ return false;
+ }
+
+ return true;
+ }
+
+ bool ossimSentinel1Model::readProduct(const ossimFilename &manifestFile )
+ {
+ theProduct->setMetadataDirectory( manifestFile.path() );
+ bool ret = theProduct->readProductMetadata( );
+ if ( ret )
+ {
+ theProduct->readCalibrationMetadata();
+ theProduct->readNoiseMetadata();
+ }
+ else
+ {
+ ossimNotify(ossimNotifyLevel_FATAL) << " theProduct->readProductMetadata() failed" << std::endl;
+ }
+
+ return true;
+ }
+
+} //end namespace
diff --git a/Modules/ThirdParty/OssimPlugins/src/ossim/ossimSentinel1Model.h b/Modules/ThirdParty/OssimPlugins/src/ossim/ossimSentinel1Model.h
new file mode 100644
index 0000000000000000000000000000000000000000..65b1fdc77e0d95a76fc96fd3bf2d78e80f11e610
--- /dev/null
+++ b/Modules/ThirdParty/OssimPlugins/src/ossim/ossimSentinel1Model.h
@@ -0,0 +1,160 @@
+//----------------------------------------------------------------------------
+//
+// "Copyright Centre National d'Etudes Spatiales"
+//
+// License: LGPL-2
+//
+// See LICENSE.txt file in the top level directory for more details.
+//
+//----------------------------------------------------------------------------
+// $Id$
+
+#ifndef ossimSentinel1Model_HEADER
+#define ossimSentinel1Model_HEADER
+
+#include <ossim/base/ossimCommon.h>
+#include <ossim/base/ossimFilename.h>
+#include <ossim/base/ossimDirectory.h>
+#include <ossim/base/ossimKeywordNames.h>
+#include <ossim/base/ossimNotify.h>
+#include <ossim/base/ossimRefPtr.h>
+#include <ossim/base/ossimString.h>
+#include <ossim/base/ossimTrace.h>
+#include <ossim/base/ossimXmlDocument.h>
+#include <ossim/base/ossimXmlNode.h>
+
+//#include <ossim/projection/ossimSensorModel.h>
+//#include <ossim/projection/ossimCoarseGridModel.h>
+#include <ossim/projection/ossimSarModel.h>
+
+#include <ossim/support_data/ossimSupportFilesList.h>
+
+#include "ossimPluginConstants.h"
+#include "ossimPluginCommon.h"
+#include <iostream>
+#include <cmath>
+#include <cstdio>
+
+//#include "ossimGeometricSarSensorModel.h"
+
+#include "ossimSentinel1ProductDoc.h"
+
+//ossimGeometricSarSensorModel
+
+namespace ossimplugins
+{
+
+ class OSSIM_PLUGINS_DLL ossimSentinel1Model : public ossimSarModel
+ {
+ public:
+ /*!
+ * CONSTRUCTORS:
+ */
+ /** @brief default constructor */
+ ossimSentinel1Model();
+
+ /** @brief copy constructor */
+ ossimSentinel1Model(const ossimSentinel1Model& rhs);
+
+ /** @brief Destructor */
+ virtual ~ossimSentinel1Model();
+
+
+ bool open(const ossimFilename& file);
+
+ /*!
+ * Returns pointer to a new instance, copy of this.
+ * Not implemented yet! Returns NULL...
+ */
+ virtual ossimObject* dup() const;
+
+ /*!
+ * Extends base-class implementation. Dumps contents of object to ostream.
+ */
+ virtual std::ostream& print(std::ostream& out) const;
+
+ /*!
+ * Fulfills ossimObject base-class pure virtuals. Loads and saves geometry
+ * KWL files. Returns true if successful.
+ */
+ virtual bool saveState(ossimKeywordlist& kwl,
+ const char* prefix=NULL) const;
+
+ virtual bool loadState(const ossimKeywordlist& kwl,
+ const char* prefix=NULL);
+
+ bool checkDirectory(const ossimFilename& file, const char* d, const char *ext);
+
+ bool isLevel0(const ossimFilename& file);
+
+ bool isLevel2(const ossimFilename& file);
+
+ bool isLevel1(const ossimFilename& file);
+
+ bool isSentinel1(const ossimFilename &manifestFile);
+
+ bool readProduct(const ossimFilename &manifestFile);
+
+ bool getImageId(ossimString& s) const;
+
+ bool initSensorID(ossimString& s);
+
+ bool standAloneProductInformation();
+
+ bool getAnnotationFileLocation(const ossimFilename &manifestFile, const char* pattern);
+
+
+ bool initImageSize(ossimIpt& imageSize) const
+ {
+ // theImageClipRect = ossimDrect(0, 0, theImageSize.x-1, theImageSize.y-1);
+ // theSubImageOffset.x = 0.0;
+ // theSubImageOffset.y = 0.0;
+
+ return theProduct->initImageSize(imageSize );
+ }
+
+ bool initGsd(ossimDpt& gsd) const
+ {
+ return theProduct->initGsd( gsd );
+ }
+
+ const ossimKeywordlist getManifestKwl() const
+ {
+ return theManifestKwl;
+ }
+
+ bool isSLC() { return theSLC; }
+ bool isOCN() { return theOCN; }
+
+ bool initSRGR()
+ {
+ // theProduct->initSRGR( );
+ return true;
+ }
+
+ bool findSafeManifest(const ossimFilename& file, ossimFilename& safeFile);
+
+ void clearFields();
+
+
+ protected:
+
+ TYPE_DATA
+
+ private:
+
+ ossimFilename theProductXmlFile;
+ ossimFilename theManifestFile;
+ ossimKeywordlist theManifestKwl;
+ ossimRefPtr<ossimXmlDocument> theManifestDoc;
+ ossimRefPtr<ossimSentinel1ProductDoc> theProduct;
+ bool theOCN;
+ bool theSLC;
+
+
+ }; //end class ossimSentinel1Model
+
+} //end namespace ossimplugins
+
+
+#endif /* #ifndef ossimSentinel1Model_HEADER */
diff --git a/Modules/ThirdParty/OssimPlugins/src/ossim/ossimSentinel1ProductDoc.cpp b/Modules/ThirdParty/OssimPlugins/src/ossim/ossimSentinel1ProductDoc.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..64840e227889d5246096f139694086c94bfc5573
--- /dev/null
+++ b/Modules/ThirdParty/OssimPlugins/src/ossim/ossimSentinel1ProductDoc.cpp
@@ -0,0 +1,722 @@
+#include "ossimSentinel1ProductDoc.h"
+#include <ossim/base/ossimDirectory.h>
+#include <ossim/base/ossimDate.h>
+
+// Define Trace flags for use within this file:
+static ossimTrace traceExec ("ossimSentinel1SupportData:exec");
+static ossimTrace traceDebug ("ossimSentinel1SupportData:debug");
+
+namespace ossimplugins
+{
+
+ static const char LOAD_FROM_PRODUCT_FILE_KW[] = "load_from_product_file_flag";
+ static const char PRODUCT_XML_FILE_KW[] = "product_xml_filename";
+ static const char SUPPORT_DATA_PREFIX[] = "support_data.";
+
+ ossimSentinel1ProductDoc::ossimSentinel1ProductDoc ()
+ : ossimErrorStatusInterface ()
+ , theRangeSpacingTotal (0.0)
+ , theAzimuthSpacingTotal (0.0)
+ , theSLC (false)
+ , theTOPSAR (false)
+ {
+
+ }
+
+ ossimSentinel1ProductDoc::~ossimSentinel1ProductDoc ()
+ {
+
+ }
+
+ bool ossimSentinel1ProductDoc::openMetadataFile(ossimRefPtr<ossimXmlDocument>& doc, const ossimString file) const
+ {
+ if ( !doc->openFile( file ) )
+ {
+ std::cerr << "ossimSentinel1ProductDoc::openMetadataFile() failed." << std::endl;
+ return false;
+ }
+
+ return true;
+ }
+
+
+ double
+ ossimSentinel1ProductDoc
+ ::getModifiedJulianDate(const ossimString utcTimeString)
+ {
+
+ std::vector<ossimString> date_time;
+ std::vector<ossimString> output;
+
+ utcTimeString.split(date_time, "T");
+
+ date_time[0].split(output, "-");
+
+ if (output.size () != 3)
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG) << "invalid date\n";
+ }
+
+ char* stop;
+ int year = std::strtol( output[0].c_str(), &stop, 10);
+ if ( *stop != 0 )
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG) << "invalid year\n";
+ }
+
+ stop = 0;
+ int month = std::strtol( output[1].c_str(), &stop, 10);
+ if ( *stop != 0 )
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG) << "invalid month\n";
+ }
+ stop = 0;
+ int day = std::strtol( output[2].c_str(), &stop, 10);
+ if ( *stop != 0 )
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG) << "invalid day\n";
+ }
+ /* Parse time */
+ output.clear();
+ date_time[1].split(output, ":");
+ if (output.size () != 3)
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG) << "invalid time\n";
+ }
+
+ stop = 0;
+ int hours = std::strtol( output[0].c_str(), &stop, 10);
+ if ( *stop != 0 )
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG) << "invalid hours\n";
+ }
+
+ stop = 0;
+ int minutes = std::strtol( output[1].c_str(), &stop, 10);
+ if ( *stop != 0 )
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG) << "invalid minutes\n";
+ }
+
+ stop = 0;
+ int seconds = std::strtod( output[2].c_str(), &stop);
+ if ( *stop != 0 )
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG) << "invalid seconds in float\n";
+ }
+
+/* Below method could replace all above split and convert. But there is a lot of
+ checks done in ossimLocalTm::setIso8601( ) which slows us down. And I dont
+ want that. */
+/*
+ ossimLocalTm otm;
+ if ( !otm.setIso8601(utcString) )
+ {
+ }
+ ossimDate ossimdate(otm);
+*/
+
+ ossimDate ossimdate(month, day, year);
+ ossimdate.setHour(hours);
+ ossimdate.setMin(minutes);
+ ossimdate.setFloatSec(seconds);
+ return ossimdate.getModifiedJulian();
+
+ }
+
+ bool ossimSentinel1ProductDoc::readProductMetadata()
+ {
+ bool commonMetadataRetrieved = false;
+ double heightSum = 0.0;
+ int numBands = 0;
+
+ ossimDirectory annotationDir( theManifestDirectory.dirCat( "annotation") );
+ std::vector<ossimFilename> files;
+ annotationDir.findAllFilesThatMatch(files, ".xml");
+ std::vector<ossimFilename>::const_iterator it = files.begin();
+
+ /* avoid zero value for numBands. This will result in division by zero below */
+ if( files.size() < 1 ) numBands = 1;
+
+ for (int count=0; it != files.end(); ++it, ++count)
+ {
+ ossimRefPtr<ossimXmlDocument> theProductXmlDocument = new ossimXmlDocument();
+ openMetadataFile(theProductXmlDocument, (*it) );
+
+ const ossimRefPtr<ossimXmlNode> adsHeader = theProductXmlDocument->getRoot()->findFirstNode("adsHeader");
+
+ const ossimString swath = adsHeader->getChildTextValue("swath");
+ const ossimString polarisation = adsHeader->getChildTextValue("polarisation");
+
+ std::stringstream _tempstrm;
+ _tempstrm << "Band[" << count << "].";
+ const ossimString bandPrefix = _tempstrm.str();
+
+ theProductKwl.add(bandPrefix,
+ "swath",
+ swath,
+ true);
+
+ theProductKwl.add(bandPrefix,
+ "polarisation",
+ polarisation,
+ true);
+
+ theProductKwl.add(bandPrefix,
+ "annotation",
+ (*it).file(),
+ true);
+
+ theProductKwl.add(bandPrefix,
+ "first_line_time",
+ adsHeader->getChildTextValue("startTime"),
+ true);
+
+ theProductKwl.add(bandPrefix,
+ "last_line_time",
+ adsHeader->getChildTextValue("stopTime"),
+ true);
+ //RK maybe use this->getManifestPrefix()
+ theProductKwl.add(SUPPORT_DATA_PREFIX,
+ "mds1_tx_rx_polar",
+ polarisation,
+ true);
+
+ const ossimRefPtr<ossimXmlNode> imageInformation = theProductXmlDocument->getRoot()->findFirstNode("imageAnnotation/imageInformation");
+
+ const ossimRefPtr<ossimXmlNode> productInformation = theProductXmlDocument->getRoot()->findFirstNode("generalAnnotation/productInformation");
+
+ theProductKwl.add(SUPPORT_DATA_PREFIX,
+ "data_take_id",
+ adsHeader->getChildTextValue("missionDataTakeId"),
+ true);
+
+ theProductKwl.add(SUPPORT_DATA_PREFIX,
+ "slice_num",
+ imageInformation->getChildTextValue("sliceNumber"),
+ true);
+
+ theProductKwl.add(SUPPORT_DATA_PREFIX,
+ "line_time_interval",
+ imageInformation->getChildTextValue("azimuthTimeInterval"),
+ true);
+
+ theRangeSpacingTotal += imageInformation->getChildTextValue("rangePixelSpacing").toFloat64();
+
+ theAzimuthSpacingTotal += imageInformation->getChildTextValue("azimuthPixelSpacing").toFloat64();
+
+ theProductKwl.add(bandPrefix,
+ ossimKeywordNames::NUMBER_SAMPLES_KW,
+ imageInformation->getChildTextValue("numberOfSamples"),
+ true);
+
+ theProductKwl.add(bandPrefix,
+ ossimKeywordNames::NUMBER_LINES_KW,
+ imageInformation->getChildTextValue("numberOfLines"),
+ true);
+
+ theProductKwl.add(bandPrefix,
+ "sample_type", //ossimKeywordNames::PIXEL_TYPE_KW,
+ imageInformation->getChildTextValue("pixelValue").upcase(),
+ true);
+
+ heightSum += getBandTerrainHeight(theProductXmlDocument);
+
+
+ if (!commonMetadataRetrieved)
+ {
+ // these should be the same for all swaths
+ //RK div by oneMillion taken from S1tlbx
+ theProductKwl.add(SUPPORT_DATA_PREFIX,
+ "range_sampling_rate",
+ productInformation->getChildTextValue("rangeSamplingRate"),
+ true);
+
+ theProductKwl.add(SUPPORT_DATA_PREFIX,
+ "radar_frequency",
+ productInformation->getChildTextValue("radarFrequency"),
+ true);
+
+ theProductKwl.add(SUPPORT_DATA_PREFIX,
+ "line_time_interval",
+ imageInformation->getChildTextValue("azimuthTimeInterval"),
+ true);
+
+ theProductKwl.add(SUPPORT_DATA_PREFIX,
+ "slant_range_to_first_pixel",
+ imageInformation->getChildTextValue("slantRangeTime"),
+ true);
+
+ const ossimRefPtr<ossimXmlNode> downlinkInformation =
+ theProductXmlDocument->getRoot()->findFirstNode("generalAnnotation/downlinkInformationList/downlinkInformation");
+
+ theProductKwl.add(SUPPORT_DATA_PREFIX,
+ "pulse_repetition_frequency",
+ downlinkInformation->getChildTextValue("prf"),
+ true);
+
+ const ossimRefPtr<ossimXmlNode> swathProcParams =
+ theProductXmlDocument->getRoot()->findFirstNode("imageAnnotation/processingInformation/swathProcParamsList/swathProcParams");
+ const ossimRefPtr<ossimXmlNode> rangeProcessingNode = swathProcParams->findFirstNode("rangeProcessing");
+ const ossimRefPtr<ossimXmlNode> azimuthProcessingNode = swathProcParams->findFirstNode("azimuthProcessing");
+
+ theProductKwl.add(SUPPORT_DATA_PREFIX,
+ "azimuth_bandwidth",
+ azimuthProcessingNode->getChildTextValue("processingBandwidth"),
+ true);
+
+ theProductKwl.add(SUPPORT_DATA_PREFIX,
+ "range_bandwidth",
+ rangeProcessingNode->getChildTextValue("processingBandwidth"),
+ true);
+
+ theProductKwl.add(SUPPORT_DATA_PREFIX,
+ "range_looks",
+ rangeProcessingNode->getChildTextValue("numberOfLooks"),
+ true);
+
+ theProductKwl.add(SUPPORT_DATA_PREFIX,
+ "azimuth_looks",
+ azimuthProcessingNode->getChildTextValue("numberOfLooks"),
+ true);
+
+ if(!theTOPSAR || !theSLC)
+ {
+ theProductKwl.add(SUPPORT_DATA_PREFIX,
+ ossimKeywordNames::NUMBER_SAMPLES_KW,
+ imageInformation->getChildTextValue("numberOfSamples"),
+ true);
+
+ theProductKwl.add(SUPPORT_DATA_PREFIX,
+ ossimKeywordNames::NUMBER_LINES_KW,
+ imageInformation->getChildTextValue("numberOfLines"),
+ true);
+ }
+
+ const ossimRefPtr<ossimXmlNode> orbitList = theProductXmlDocument->getRoot()->findFirstNode("generalAnnotation/orbitList");
+ addOrbitStateVectors(orbitList);
+
+ const ossimRefPtr<ossimXmlNode> coordinateConversionList = theProductXmlDocument->getRoot()->findFirstNode("coordinateConversion/coordinateConversionList");
+ addSRGRCoefficients(SUPPORT_DATA_PREFIX, coordinateConversionList);
+
+ const ossimRefPtr<ossimXmlNode> dcEstimateList = theProductXmlDocument->getRoot()->findFirstNode("dopplerCentroid/dcEstimateList");
+ addDopplerCentroidCoefficients(dcEstimateList);
+
+ commonMetadataRetrieved = true;
+ }
+
+ ++numBands;
+ }
+
+ if(theSLC)
+ {
+ numBands = numBands * 2; // real and imaginary
+ }
+
+
+ theProductKwl.add(SUPPORT_DATA_PREFIX,
+ "range_spacing",
+ theRangeSpacingTotal / (double)numBands,
+ true);
+
+ theProductKwl.add(SUPPORT_DATA_PREFIX,
+ "azimuth_spacing",
+ theAzimuthSpacingTotal / (double)numBands,
+ true);
+
+ theProductKwl.add(SUPPORT_DATA_PREFIX,
+ "avg_scene_height",
+ heightSum / (double)files.size(),
+ true);
+
+ theProductKwl.add(SUPPORT_DATA_PREFIX,
+ ossimKeywordNames::NUMBER_BANDS_KW,
+ numBands,
+ true);
+ return true;
+ }
+
+ void ossimSentinel1ProductDoc::addDopplerCentroidCoefficients(const ossimRefPtr<ossimXmlNode>& dcEstimateList)
+ {
+ if (!dcEstimateList.get())
+ return;
+
+ ossimString count_str;
+ dcEstimateList->getAttributeValue(count_str, "count");
+ const int count = count_str.toInt();
+ if( count < 1)
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG) << "No doppler centroid coefficients available in metadata!!\n";
+ return;
+ }
+ else
+ {
+ ossimXmlNode::ChildListType dcEstimates;
+ dcEstimateList->findChildNodes("dcEstimate", dcEstimates);
+
+ ossimXmlNode::ChildListType::const_iterator it = dcEstimates.begin();
+
+ int index = 1;
+
+ while( it != dcEstimates.end())
+ {
+ //Doppler_Centroid_Coefficients.dop_coef_list;
+ const ossimString prefix = "dopplerCentroid.dop_coef_list" + ossimString::toString(index) + ".";
+
+ ++index;
+
+ const ossimRefPtr<ossimXmlNode> dcEstimate = (*it);
+ theProductKwl.add(prefix,
+ "dop_coef_time",
+ dcEstimate->getChildTextValue("azimuthTime"),
+ true);
+ //RK
+ const double ref_time = dcEstimate->getChildTextValue("t0").toFloat64() * 1e9; // s to ns
+
+ theProductKwl.add(prefix,
+ "slant_range_time",
+ ref_time,
+ true);
+
+ const ossimString ns = dcEstimate->getChildTextValue("ns");
+
+ if( !ns.empty() )
+ theProductKwl.add(prefix,
+ "slant_range_time",
+ ns,
+ true);
+
+ const ossimString coeffStr = dcEstimate->getChildTextValue("geometryDcPolynomial");
+
+ if (!coeffStr.empty())
+ {
+ const ossimString separatorList = " ";
+ std::vector<ossimString> result;
+
+ coeffStr.split(result, separatorList, true);
+
+ std::vector<ossimString>::const_iterator coeff = result.begin();
+
+ int count = 1;
+
+ while (coeff != result.end())
+ {
+ const ossimString coeff_prefix = prefix + ossimString::toString(count) + ".";
+
+ theProductKwl.add(coeff_prefix,
+ "dop_coef",
+ (*coeff),
+ true);
+
+ ++coeff;
+
+ ++count;
+ }
+
+ } //if (!coeffStr.empty())
+
+ ++it;
+
+ } // while( it != dcEstimates.end())
+
+ } // else count < 1
+ }
+
+
+ double ossimSentinel1ProductDoc::getBandTerrainHeight( const ossimRefPtr<ossimXmlDocument>& theProductXmlDocument )
+ {
+ double heightSum = 0.0;
+ vector< ossimRefPtr<ossimXmlNode> > heightList;
+ theProductXmlDocument->findNodes("/product/generalAnnotation/terrainHeightList/terrainHeight", heightList);
+ vector<ossimRefPtr<ossimXmlNode> >::const_iterator it = heightList.begin();
+ while(it != heightList.end())
+ {
+ heightSum += (*it)->getChildTextValue("value").toFloat64();
+ ++it;
+ }
+ return heightSum / heightList.size();
+ }
+
+ bool ossimSentinel1ProductDoc::initImageSize(ossimIpt& imageSize) const
+ {
+ const ossimString samples_str = theProductKwl.find(SUPPORT_DATA_PREFIX, ossimKeywordNames::NUMBER_SAMPLES_KW);
+ const ossimString lines_str = theProductKwl.find(SUPPORT_DATA_PREFIX, ossimKeywordNames::NUMBER_LINES_KW);
+
+ imageSize.samp = samples_str.toInt();
+ imageSize.line = lines_str.toInt();
+
+ return true;
+ }
+
+ bool ossimSentinel1ProductDoc::initGsd( ossimDpt& gsd) const
+ {
+ gsd.x = theRangeSpacingTotal;
+ gsd.y = theAzimuthSpacingTotal;
+ return true;
+ }
+
+ void ossimSentinel1ProductDoc::addOrbitStateVectors(const ossimRefPtr<ossimXmlNode>& orbitList)
+ {
+
+ if (!orbitList.get())
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG) << "No orbitVectorList info available in metadata!!\n";
+ return;
+ }
+
+ ossimXmlNode::ChildListType stateVectorList;
+ orbitList->findChildNodes("orbit", stateVectorList);
+
+ if(stateVectorList.size() < 1)
+ {
+ ossimNotify(ossimNotifyLevel_DEBUG) << "No orbitVectorList info available in metadata!!\n";
+ return;
+ }
+
+ for (int i = 0; i < stateVectorList.size(); ++i)
+ {
+ //orbit_state_vectors
+ const ossimString orbit_prefix = "orbitList.orbit[" + ossimString::toString(i) + "].";
+
+ theProductKwl.add(orbit_prefix,
+ "time",
+ stateVectorList[i]->getChildTextValue("time"),
+ true);
+
+ theProductKwl.add(orbit_prefix,
+ "x_pos",
+ stateVectorList[i]->getChildTextValue("position/x").toFloat64(),
+ true);
+
+ theProductKwl.add(orbit_prefix,
+ "y_pos",
+ stateVectorList[i]->getChildTextValue("position/y").toFloat64(),
+ true);
+
+ theProductKwl.add(orbit_prefix,
+ "z_pos",
+ stateVectorList[i]->getChildTextValue("position/z").toFloat64(),
+ true);
+
+ theProductKwl.add(orbit_prefix,
+ "x_vel",
+ stateVectorList[i]->getChildTextValue("velocity/x").toFloat64(),
+ true);
+
+ theProductKwl.add(orbit_prefix,
+ "y_vel",
+ stateVectorList[i]->getChildTextValue("velocity/y").toFloat64(),
+ true);
+
+ theProductKwl.add(orbit_prefix,
+ "z_vel",
+ stateVectorList[i]->getChildTextValue("velocity/z").toFloat64(),
+ true);
+ }
+ }
+
+ void ossimSentinel1ProductDoc::addSRGRCoefficients(const char* prefix, const ossimRefPtr<ossimXmlNode>& coordinateConversionList)
+ {
+ if (!coordinateConversionList.get())
+ return;
+
+ ossimString count_str;
+ coordinateConversionList->getAttributeValue(count_str, "count");
+ const int count = count_str.toInt();
+ if( count < 1)
+ {
+ if(traceDebug())
+ ossimNotify(ossimNotifyLevel_DEBUG) << "No coordinate conversion info available in metadata!!\n";
+ return;
+ }
+ else
+ {
+ ossimNotify(ossimNotifyLevel_FATAL) << "shouldn't reach here!!\n";
+ return;
+ }
+
+ }
+
+ void ossimSentinel1ProductDoc::readCalibrationMetadata()
+ {
+ ossimDirectory calibrationDir( theManifestDirectory.dirCat( "annotation/calibration") );
+ std::vector<ossimFilename> files;
+ calibrationDir.findAllFilesThatMatch(files, "calibration*");
+ std::vector<ossimFilename>::const_iterator it = files.begin();
+
+ std::stringstream strm;
+ for (; it != files.end(); ++it)
+ {
+ ossimRefPtr<ossimXmlDocument> calibrationDoc = new ossimXmlDocument();
+ openMetadataFile(calibrationDoc, (*it) );
+
+ ossimRefPtr<ossimXmlNode> adsHeader = calibrationDoc->getRoot()->findFirstNode("adsHeader");
+ ossimRefPtr<ossimXmlNode> calibrationInformation = calibrationDoc->getRoot()->findFirstNode("calibrationInformation");
+ ossimRefPtr<ossimXmlNode> calibrationVectorList = calibrationDoc->getRoot()->findFirstNode("calibrationVectorList");
+
+ const ossimString calibrationPrefix = "calibration.";
+
+ theProductKwl.add(calibrationPrefix,
+ "swath",
+ adsHeader->getChildTextValue("swath"),
+ true);
+
+ theProductKwl.add(calibrationPrefix,
+ "polarisation",
+ adsHeader->getChildTextValue("polarisation"),
+ true);
+
+ theProductKwl.add(calibrationPrefix,
+ "startTime",
+ getModifiedJulianDate(adsHeader->getChildTextValue("startTime")),
+ true);
+
+ theProductKwl.add(calibrationPrefix,
+ "stopTime",
+ getModifiedJulianDate(adsHeader->getChildTextValue("stopTime")),
+ true);
+
+ theProductKwl.add(calibrationPrefix,
+ "absoluteCalibrationConstant",
+ calibrationInformation->getChildTextValue("absoluteCalibrationConstant"),
+ true);
+
+ theProductKwl.add(calibrationPrefix,
+ "count",
+ calibrationVectorList->getAttributeValue("count"),
+ true);
+
+ const ossimRefPtr<ossimXmlNode> calibrationRoot = calibrationDoc->getRoot();
+ std::vector< ossimRefPtr<ossimXmlNode> > calibrationVectors;
+ calibrationRoot->findChildNodes("calibrationVectorList/calibrationVector", calibrationVectors);
+ for( unsigned int idx = 0; idx < calibrationVectors.size(); ++idx)
+ {
+
+ const ossimString calibrationVectorPrefix = calibrationPrefix + "calibrationVector[" + ossimString::toString(idx) + "].";
+
+ const ossimRefPtr<ossimXmlNode> node = calibrationVectors[idx]->findFirstNode("pixel");
+
+ theProductKwl.add(calibrationVectorPrefix,
+ "pixel_count",
+ node->getAttributeValue("count"),
+ false);
+
+ theProductKwl.add(calibrationVectorPrefix,
+ "azimuthTime",
+ getModifiedJulianDate(calibrationVectors[idx]->getChildTextValue("azimuthTime")),
+ true);
+
+ theProductKwl.add(calibrationVectorPrefix,
+ "line",
+ calibrationVectors[idx]->getChildTextValue("line"),
+ true);
+
+ theProductKwl.add(calibrationVectorPrefix,
+ "pixel",
+ calibrationVectors[idx]->getChildTextValue("pixel"),
+ true);
+
+ theProductKwl.add(calibrationVectorPrefix,
+ "sigmaNought",
+ calibrationVectors[idx]->getChildTextValue("sigmaNought"),
+ true);
+
+ theProductKwl.add(calibrationVectorPrefix,
+ "betaNought",
+ calibrationVectors[idx]->getChildTextValue("betaNought"),
+ true);
+
+ theProductKwl.add(calibrationVectorPrefix,
+ "gamma",
+ calibrationVectors[idx]->getChildTextValue("gamma"),
+ true);
+
+ theProductKwl.add(calibrationVectorPrefix,
+ "dn",
+ calibrationVectors[idx]->getChildTextValue("dn"),
+ true);
+
+ // calibrationVectors[idx]->toKwl(theProductKwl, "calibrationVectorList_" + ossimString::toString(idx+1) + ".");
+ }
+ }
+ }
+
+ void ossimSentinel1ProductDoc::readNoiseMetadata()
+ {
+ ossimDirectory calibrationDir( theManifestDirectory.dirCat( "annotation/calibration") );
+ std::vector<ossimFilename> files;
+ calibrationDir.findAllFilesThatMatch(files, "noise*");
+ std::vector<ossimFilename>::const_iterator it = files.begin();
+ const ossimString noisePrefix = "noise.";
+
+ for (; it != files.end(); ++it)
+ {
+ ossimRefPtr<ossimXmlDocument> noiseDoc = new ossimXmlDocument();
+ openMetadataFile(noiseDoc, (*it) );
+
+ const ossimRefPtr<ossimXmlNode> noiseRoot = noiseDoc->getRoot();
+ std::vector< ossimRefPtr<ossimXmlNode> > noiseVectors;
+ noiseRoot->findChildNodes("noiseVectorList/noiseVector", noiseVectors);
+ for( unsigned int idx = 0; idx < noiseVectors.size(); ++idx)
+ {
+ const ossimString noiseVectorPrefix = noisePrefix + "noiseVector[" + ossimString::toString(idx) + "].";
+
+ const ossimRefPtr<ossimXmlNode> node = noiseVectors[idx]->findFirstNode("pixel");
+
+ theProductKwl.add(noiseVectorPrefix,
+ "pixel_count",
+ node->getAttributeValue("count"),
+ false);
+
+ theProductKwl.add(noiseVectorPrefix,
+ "azimuthTime",
+ getModifiedJulianDate(noiseVectors[idx]->getChildTextValue("azimuthTime")),
+ true);
+
+ theProductKwl.add(noiseVectorPrefix,
+ "line",
+ noiseVectors[idx]->getChildTextValue("line"),
+ true);
+
+ theProductKwl.add(noiseVectorPrefix,
+ "pixel",
+ noiseVectors[idx]->getChildTextValue("pixel"),
+ true);
+
+ theProductKwl.add(noiseVectorPrefix,
+ "noiseLut",
+ noiseVectors[idx]->getChildTextValue("noiseLut"),
+ true);
+
+ //noiseVectorList[idx]->toKwl(theProductKwl, "noiseVectorList_" + ossimString::toString(idx+1) + ".");
+ }
+ }
+ }
+
+
+ void ossimSentinel1ProductDoc::clearFields( )
+ {
+ theProductKwl.clear();
+ }
+ //RK
+ // bool ossimSentinel1ProductDoc::saveState(ossimKeywordlist& kwl,
+ // const char* prefix)const
+ // {
+ // kwl.addList(theProductKwl);
+
+ // kwl.add(prefix,
+ // "sample_type",
+ // theSampleType,
+ // true);
+
+ // return true;
+ // }
+
+ // bool ossimSentinel1ProductDoc::loadState(const ossimKeywordlist& kwl,
+ // const char* prefix)
+ // {
+
+ // return true;
+
+ // }
+}
diff --git a/Modules/ThirdParty/OssimPlugins/src/ossim/ossimSentinel1ProductDoc.h b/Modules/ThirdParty/OssimPlugins/src/ossim/ossimSentinel1ProductDoc.h
new file mode 100644
index 0000000000000000000000000000000000000000..50b0f5107b7141c6a9cde719ffab4551d8d0b524
--- /dev/null
+++ b/Modules/ThirdParty/OssimPlugins/src/ossim/ossimSentinel1ProductDoc.h
@@ -0,0 +1,112 @@
+//----------------------------------------------------------------------------
+//
+// "Copyright Centre National d'Etudes Spatiales"
+//
+// License: LGPL-2
+//
+// See LICENSE.txt file in the top level directory for more details.
+//
+//----------------------------------------------------------------------------
+// $Id$
+
+#ifndef ossimSentinel1ProductDoc_HEADER
+#define ossimSentinel1ProductDoc_HEADER
+
+#include <ossim/base/ossimConstants.h>
+#include <ossim/base/ossimIpt.h>
+#include <ossim/base/ossimObject.h>
+#include <ossim/base/ossimString.h>
+#include <ossim/base/ossimErrorStatusInterface.h>
+#include <ossim/base/ossimFilename.h>
+#include <ossim/base/ossimDpt.h>
+#include <ossim/base/ossimDrect.h>
+#include <ossim/base/ossimGpt.h>
+#include <ossim/base/ossimXmlDocument.h>
+#include <ossim/base/ossimXmlNode.h>
+#include <ossim/base/ossimKeywordlist.h>
+#include <ossim/base/ossimNotify.h>
+#include <ossim/base/ossimNotifyContext.h>
+#include <ossim/base/ossimKeywordNames.h>
+#include <ossim/base/ossimTrace.h>
+#include <ossim/base/ossimCommon.h>
+#include "ossimPluginCommon.h"
+#include "ossimPluginConstants.h"
+
+#include <cstdio>
+#include <vector>
+#include <sstream>
+#include <iostream>
+#include <iterator>
+
+namespace ossimplugins
+{
+
+ class OSSIM_PLUGINS_DLL ossimSentinel1ProductDoc : public ossimObject,
+ public ossimErrorStatusInterface
+ {
+ public:
+
+ ossimSentinel1ProductDoc();
+
+ virtual ~ossimSentinel1ProductDoc();
+
+ void clearFields();
+
+ bool openMetadataFile(ossimRefPtr<ossimXmlDocument>& doc, const ossimString file) const;
+
+ bool initImageSize(ossimIpt& imageSize) const;
+
+ bool initGsd( ossimDpt& gsd) const;
+
+ void addSRGRCoefficients(const char* prefix, const ossimRefPtr<ossimXmlNode>& coordinateConversion);
+
+ void addDopplerCentroidCoefficients(const ossimRefPtr<ossimXmlNode>& dcEstimateList);
+
+ void addOrbitStateVectors(const ossimRefPtr<ossimXmlNode>& dcEstimateList);
+
+ void readCalibrationMetadata();
+
+ void readNoiseMetadata();
+
+ double getModifiedJulianDate(const ossimString utc_time);
+
+ bool readProductMetadata( );
+
+ double getBandTerrainHeight( const ossimRefPtr<ossimXmlDocument>& theProductXmlDocument );
+
+ inline const ossimKeywordlist getProductKwl() const
+ {
+ return theProductKwl;
+ }
+
+ inline void setMetadataDirectory(const ossimFilename d)
+ {
+ theManifestDirectory = d;
+ }
+
+
+ inline void setSLC(bool b)
+ {
+ theSLC = true;
+ }
+
+ inline void setTOPSAR(bool b)
+ {
+ theTOPSAR = true;
+ }
+
+ private:
+ ossimFilename theManifestDirectory;
+ double theRangeSpacingTotal;
+ double theAzimuthSpacingTotal;
+ bool theTOPSAR;
+ bool theSLC;
+ ossimFilename theProductXmlFile;
+ ossimString theSampleType;
+ ossimKeywordlist theProductKwl;
+
+ };
+
+}
+
+#endif /* #ifndef ossimSentinel1ProductDoc_HEADER */
diff --git a/Modules/Wrappers/SWIG/otb-module-init.cmake b/Modules/Wrappers/SWIG/otb-module-init.cmake
index c5ae8a9992535524c193725380679c16888a5b25..819a60d7718fd005fe537b1fede1ad2e1ea6eff8 100644
--- a/Modules/Wrappers/SWIG/otb-module-init.cmake
+++ b/Modules/Wrappers/SWIG/otb-module-init.cmake
@@ -25,6 +25,7 @@ if ( OTB_WRAP_PYTHON )
check_PIC_flag ( Python )
find_package ( PythonLibs REQUIRED )
find_package ( PythonInterp REQUIRED )
+ find_package ( Numpy )
endif()
#
diff --git a/Modules/Wrappers/SWIG/src/CMakeLists.txt b/Modules/Wrappers/SWIG/src/CMakeLists.txt
index 786f3a68657cfb5987b13dd2ea1f93bb0521584e..79150ccc03c2ce3fd8cd6362159e6d215312d8d3 100644
--- a/Modules/Wrappers/SWIG/src/CMakeLists.txt
+++ b/Modules/Wrappers/SWIG/src/CMakeLists.txt
@@ -24,6 +24,10 @@ if ( OTB_WRAP_PYTHON )
# Run swig
set(CMAKE_SWIG_FLAGS ${CMAKE_SWIG_GLOBAL_FLAGS})
+ if(NUMPY_FOUND)
+ include_directories(${NUMPY_INCLUDE_DIRS})
+ list(APPEND CMAKE_SWIG_FLAGS "-DOTB_SWIGNUMPY=1")
+ endif()
set(CMAKE_SWIG_OUTDIR ${CMAKE_CURRENT_BINARY_DIR})
set(SWIG_MODULE_otbApplication_EXTRA_DEPS
${CMAKE_CURRENT_SOURCE_DIR}/Python.i
@@ -42,7 +46,7 @@ if ( OTB_WRAP_PYTHON )
COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_SOURCE_DIR}/CMake/PythonCompile.py otbApplication.py
DEPENDS _otbApplication
)
-
+
otb_module_target_label( _otbApplication )
install( TARGETS _otbApplication
@@ -80,7 +84,7 @@ if ( OTB_WRAP_JAVA )
# Add target for org.otb.Application.jar
add_custom_target(org_otb_Application_jar ALL DEPENDS org.otb.application.jar)
-
+
otb_module_target_label( org_otb_Application_jar )
# Add custom command and target to compile the generated files and put them in a jar file
@@ -106,4 +110,3 @@ if ( OTB_WRAP_JAVA )
COMPONENT RuntimeLibraries )
endif()
-
diff --git a/Modules/Wrappers/SWIG/src/numpy.i b/Modules/Wrappers/SWIG/src/numpy.i
new file mode 100644
index 0000000000000000000000000000000000000000..b9a7ce7f40b3f83bf3e2b82d43d6a477f347a372
--- /dev/null
+++ b/Modules/Wrappers/SWIG/src/numpy.i
@@ -0,0 +1,3117 @@
+/* -*- C -*- (not really, but good for syntax highlighting) */
+
+/*
+ * Copyright (c) 2005-2015, NumPy Developers.
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are
+ * met:
+ *
+ * * Redistributions of source code must retain the above copyright
+ * notice, this list of conditions and the following disclaimer.
+ *
+ * * Redistributions in binary form must reproduce the above
+ * copyright notice, this list of conditions and the following
+ * disclaimer in the documentation and/or other materials provided
+ * with the distribution.
+ *
+ * * Neither the name of the NumPy Developers nor the names of any
+ * contributors may be used to endorse or promote products derived
+ * from this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+ * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ */
+
+#ifdef SWIGPYTHON
+
+%{
+#ifndef SWIG_FILE_WITH_INIT
+#define NO_IMPORT_ARRAY
+#endif
+#include "stdio.h"
+#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
+#include <numpy/arrayobject.h>
+%}
+
+/**********************************************************************/
+
+%fragment("NumPy_Backward_Compatibility", "header")
+{
+%#if NPY_API_VERSION < 0x00000007
+%#define NPY_ARRAY_DEFAULT NPY_DEFAULT
+%#define NPY_ARRAY_FARRAY NPY_FARRAY
+%#define NPY_FORTRANORDER NPY_FORTRAN
+%#endif
+}
+
+/**********************************************************************/
+
+/* The following code originally appeared in
+ * enthought/kiva/agg/src/numeric.i written by Eric Jones. It was
+ * translated from C++ to C by John Hunter. Bill Spotz has modified
+ * it to fix some minor bugs, upgrade from Numeric to numpy (all
+ * versions), add some comments and functionality, and convert from
+ * direct code insertion to SWIG fragments.
+ */
+
+%fragment("NumPy_Macros", "header")
+{
+/* Macros to extract array attributes.
+ */
+%#if NPY_API_VERSION < 0x00000007
+%#define is_array(a) ((a) && PyArray_Check((PyArrayObject*)a))
+%#define array_type(a) (int)(PyArray_TYPE((PyArrayObject*)a))
+%#define array_numdims(a) (((PyArrayObject*)a)->nd)
+%#define array_dimensions(a) (((PyArrayObject*)a)->dimensions)
+%#define array_size(a,i) (((PyArrayObject*)a)->dimensions[i])
+%#define array_strides(a) (((PyArrayObject*)a)->strides)
+%#define array_stride(a,i) (((PyArrayObject*)a)->strides[i])
+%#define array_data(a) (((PyArrayObject*)a)->data)
+%#define array_descr(a) (((PyArrayObject*)a)->descr)
+%#define array_flags(a) (((PyArrayObject*)a)->flags)
+%#define array_enableflags(a,f) (((PyArrayObject*)a)->flags) = f
+%#else
+%#define is_array(a) ((a) && PyArray_Check(a))
+%#define array_type(a) PyArray_TYPE((PyArrayObject*)a)
+%#define array_numdims(a) PyArray_NDIM((PyArrayObject*)a)
+%#define array_dimensions(a) PyArray_DIMS((PyArrayObject*)a)
+%#define array_strides(a) PyArray_STRIDES((PyArrayObject*)a)
+%#define array_stride(a,i) PyArray_STRIDE((PyArrayObject*)a,i)
+%#define array_size(a,i) PyArray_DIM((PyArrayObject*)a,i)
+%#define array_data(a) PyArray_DATA((PyArrayObject*)a)
+%#define array_descr(a) PyArray_DESCR((PyArrayObject*)a)
+%#define array_flags(a) PyArray_FLAGS((PyArrayObject*)a)
+%#define array_enableflags(a,f) PyArray_ENABLEFLAGS((PyArrayObject*)a,f)
+%#endif
+%#define array_is_contiguous(a) (PyArray_ISCONTIGUOUS((PyArrayObject*)a))
+%#define array_is_native(a) (PyArray_ISNOTSWAPPED((PyArrayObject*)a))
+%#define array_is_fortran(a) (PyArray_ISFORTRAN((PyArrayObject*)a))
+}
+
+/**********************************************************************/
+
+%fragment("NumPy_Utilities",
+ "header")
+{
+ /* Given a PyObject, return a string describing its type.
+ */
+ const char* pytype_string(PyObject* py_obj)
+ {
+ if (py_obj == NULL ) return "C NULL value";
+ if (py_obj == Py_None ) return "Python None" ;
+ if (PyCallable_Check(py_obj)) return "callable" ;
+ if (PyString_Check( py_obj)) return "string" ;
+ if (PyInt_Check( py_obj)) return "int" ;
+ if (PyFloat_Check( py_obj)) return "float" ;
+ if (PyDict_Check( py_obj)) return "dict" ;
+ if (PyList_Check( py_obj)) return "list" ;
+ if (PyTuple_Check( py_obj)) return "tuple" ;
+%#if PY_MAJOR_VERSION < 3
+ if (PyFile_Check( py_obj)) return "file" ;
+ if (PyModule_Check( py_obj)) return "module" ;
+ if (PyInstance_Check(py_obj)) return "instance" ;
+%#endif
+
+ return "unkown type";
+ }
+
+ /* Given a NumPy typecode, return a string describing the type.
+ */
+ const char* typecode_string(int typecode)
+ {
+ static const char* type_names[25] = {"bool",
+ "byte",
+ "unsigned byte",
+ "short",
+ "unsigned short",
+ "int",
+ "unsigned int",
+ "long",
+ "unsigned long",
+ "long long",
+ "unsigned long long",
+ "float",
+ "double",
+ "long double",
+ "complex float",
+ "complex double",
+ "complex long double",
+ "object",
+ "string",
+ "unicode",
+ "void",
+ "ntypes",
+ "notype",
+ "char",
+ "unknown"};
+ return typecode < 24 ? type_names[typecode] : type_names[24];
+ }
+
+ /* Make sure input has correct numpy type. This now just calls
+ PyArray_EquivTypenums().
+ */
+ int type_match(int actual_type,
+ int desired_type)
+ {
+ return PyArray_EquivTypenums(actual_type, desired_type);
+ }
+
+%#ifdef SWIGPY_USE_CAPSULE
+ void free_cap(PyObject * cap)
+ {
+ void* array = (void*) PyCapsule_GetPointer(cap,SWIGPY_CAPSULE_NAME);
+ if (array != NULL) free(array);
+ }
+%#endif
+
+
+}
+
+/**********************************************************************/
+
+%fragment("NumPy_Object_to_Array",
+ "header",
+ fragment="NumPy_Backward_Compatibility",
+ fragment="NumPy_Macros",
+ fragment="NumPy_Utilities")
+{
+ /* Given a PyObject pointer, cast it to a PyArrayObject pointer if
+ * legal. If not, set the python error string appropriately and
+ * return NULL.
+ */
+ PyArrayObject* obj_to_array_no_conversion(PyObject* input,
+ int typecode)
+ {
+ PyArrayObject* ary = NULL;
+ if (is_array(input) && (typecode == NPY_NOTYPE ||
+ PyArray_EquivTypenums(array_type(input), typecode)))
+ {
+ ary = (PyArrayObject*) input;
+ }
+ else if is_array(input)
+ {
+ const char* desired_type = typecode_string(typecode);
+ const char* actual_type = typecode_string(array_type(input));
+ PyErr_Format(PyExc_TypeError,
+ "Array of type '%s' required. Array of type '%s' given",
+ desired_type, actual_type);
+ ary = NULL;
+ }
+ else
+ {
+ const char* desired_type = typecode_string(typecode);
+ const char* actual_type = pytype_string(input);
+ PyErr_Format(PyExc_TypeError,
+ "Array of type '%s' required. A '%s' was given",
+ desired_type,
+ actual_type);
+ ary = NULL;
+ }
+ return ary;
+ }
+
+ /* Convert the given PyObject to a NumPy array with the given
+ * typecode. On success, return a valid PyArrayObject* with the
+ * correct type. On failure, the python error string will be set and
+ * the routine returns NULL.
+ */
+ PyArrayObject* obj_to_array_allow_conversion(PyObject* input,
+ int typecode,
+ int* is_new_object)
+ {
+ PyArrayObject* ary = NULL;
+ PyObject* py_obj;
+ if (is_array(input) && (typecode == NPY_NOTYPE ||
+ PyArray_EquivTypenums(array_type(input),typecode)))
+ {
+ ary = (PyArrayObject*) input;
+ *is_new_object = 0;
+ }
+ else
+ {
+ py_obj = PyArray_FROMANY(input, typecode, 0, 0, NPY_ARRAY_DEFAULT);
+ /* If NULL, PyArray_FromObject will have set python error value.*/
+ ary = (PyArrayObject*) py_obj;
+ *is_new_object = 1;
+ }
+ return ary;
+ }
+
+ /* Given a PyArrayObject, check to see if it is contiguous. If so,
+ * return the input pointer and flag it as not a new object. If it is
+ * not contiguous, create a new PyArrayObject using the original data,
+ * flag it as a new object and return the pointer.
+ */
+ PyArrayObject* make_contiguous(PyArrayObject* ary,
+ int* is_new_object,
+ int min_dims,
+ int max_dims)
+ {
+ PyArrayObject* result;
+ if (array_is_contiguous(ary))
+ {
+ result = ary;
+ *is_new_object = 0;
+ }
+ else
+ {
+ result = (PyArrayObject*) PyArray_ContiguousFromObject((PyObject*)ary,
+ array_type(ary),
+ min_dims,
+ max_dims);
+ *is_new_object = 1;
+ }
+ return result;
+ }
+
+ /* Given a PyArrayObject, check to see if it is Fortran-contiguous.
+ * If so, return the input pointer, but do not flag it as not a new
+ * object. If it is not Fortran-contiguous, create a new
+ * PyArrayObject using the original data, flag it as a new object
+ * and return the pointer.
+ */
+ PyArrayObject* make_fortran(PyArrayObject* ary,
+ int* is_new_object)
+ {
+ PyArrayObject* result;
+ if (array_is_fortran(ary))
+ {
+ result = ary;
+ *is_new_object = 0;
+ }
+ else
+ {
+ Py_INCREF(array_descr(ary));
+ result = (PyArrayObject*) PyArray_FromArray(ary,
+ array_descr(ary),
+ NPY_FORTRANORDER);
+ *is_new_object = 1;
+ }
+ return result;
+ }
+
+ /* Convert a given PyObject to a contiguous PyArrayObject of the
+ * specified type. If the input object is not a contiguous
+ * PyArrayObject, a new one will be created and the new object flag
+ * will be set.
+ */
+ PyArrayObject* obj_to_array_contiguous_allow_conversion(PyObject* input,
+ int typecode,
+ int* is_new_object)
+ {
+ int is_new1 = 0;
+ int is_new2 = 0;
+ PyArrayObject* ary2;
+ PyArrayObject* ary1 = obj_to_array_allow_conversion(input,
+ typecode,
+ &is_new1);
+ if (ary1)
+ {
+ ary2 = make_contiguous(ary1, &is_new2, 0, 0);
+ if ( is_new1 && is_new2)
+ {
+ Py_DECREF(ary1);
+ }
+ ary1 = ary2;
+ }
+ *is_new_object = is_new1 || is_new2;
+ return ary1;
+ }
+
+ /* Convert a given PyObject to a Fortran-ordered PyArrayObject of the
+ * specified type. If the input object is not a Fortran-ordered
+ * PyArrayObject, a new one will be created and the new object flag
+ * will be set.
+ */
+ PyArrayObject* obj_to_array_fortran_allow_conversion(PyObject* input,
+ int typecode,
+ int* is_new_object)
+ {
+ int is_new1 = 0;
+ int is_new2 = 0;
+ PyArrayObject* ary2;
+ PyArrayObject* ary1 = obj_to_array_allow_conversion(input,
+ typecode,
+ &is_new1);
+ if (ary1)
+ {
+ ary2 = make_fortran(ary1, &is_new2);
+ if (is_new1 && is_new2)
+ {
+ Py_DECREF(ary1);
+ }
+ ary1 = ary2;
+ }
+ *is_new_object = is_new1 || is_new2;
+ return ary1;
+ }
+} /* end fragment */
+
+/**********************************************************************/
+
+%fragment("NumPy_Array_Requirements",
+ "header",
+ fragment="NumPy_Backward_Compatibility",
+ fragment="NumPy_Macros")
+{
+ /* Test whether a python object is contiguous. If array is
+ * contiguous, return 1. Otherwise, set the python error string and
+ * return 0.
+ */
+ int require_contiguous(PyArrayObject* ary)
+ {
+ int contiguous = 1;
+ if (!array_is_contiguous(ary))
+ {
+ PyErr_SetString(PyExc_TypeError,
+ "Array must be contiguous. A non-contiguous array was given");
+ contiguous = 0;
+ }
+ return contiguous;
+ }
+
+ /* Require that a numpy array is not byte-swapped. If the array is
+ * not byte-swapped, return 1. Otherwise, set the python error string
+ * and return 0.
+ */
+ int require_native(PyArrayObject* ary)
+ {
+ int native = 1;
+ if (!array_is_native(ary))
+ {
+ PyErr_SetString(PyExc_TypeError,
+ "Array must have native byteorder. "
+ "A byte-swapped array was given");
+ native = 0;
+ }
+ return native;
+ }
+
+ /* Require the given PyArrayObject to have a specified number of
+ * dimensions. If the array has the specified number of dimensions,
+ * return 1. Otherwise, set the python error string and return 0.
+ */
+ int require_dimensions(PyArrayObject* ary,
+ int exact_dimensions)
+ {
+ int success = 1;
+ if (array_numdims(ary) != exact_dimensions)
+ {
+ PyErr_Format(PyExc_TypeError,
+ "Array must have %d dimensions. Given array has %d dimensions",
+ exact_dimensions,
+ array_numdims(ary));
+ success = 0;
+ }
+ return success;
+ }
+
+ /* Require the given PyArrayObject to have one of a list of specified
+ * number of dimensions. If the array has one of the specified number
+ * of dimensions, return 1. Otherwise, set the python error string
+ * and return 0.
+ */
+ int require_dimensions_n(PyArrayObject* ary,
+ int* exact_dimensions,
+ int n)
+ {
+ int success = 0;
+ int i;
+ char dims_str[255] = "";
+ char s[255];
+ for (i = 0; i < n && !success; i++)
+ {
+ if (array_numdims(ary) == exact_dimensions[i])
+ {
+ success = 1;
+ }
+ }
+ if (!success)
+ {
+ for (i = 0; i < n-1; i++)
+ {
+ sprintf(s, "%d, ", exact_dimensions[i]);
+ strcat(dims_str,s);
+ }
+ sprintf(s, " or %d", exact_dimensions[n-1]);
+ strcat(dims_str,s);
+ PyErr_Format(PyExc_TypeError,
+ "Array must have %s dimensions. Given array has %d dimensions",
+ dims_str,
+ array_numdims(ary));
+ }
+ return success;
+ }
+
+ /* Require the given PyArrayObject to have a specified shape. If the
+ * array has the specified shape, return 1. Otherwise, set the python
+ * error string and return 0.
+ */
+ int require_size(PyArrayObject* ary,
+ npy_intp* size,
+ int n)
+ {
+ int i;
+ int success = 1;
+ int len;
+ char desired_dims[255] = "[";
+ char s[255];
+ char actual_dims[255] = "[";
+ for(i=0; i < n;i++)
+ {
+ if (size[i] != -1 && size[i] != array_size(ary,i))
+ {
+ success = 0;
+ }
+ }
+ if (!success)
+ {
+ for (i = 0; i < n; i++)
+ {
+ if (size[i] == -1)
+ {
+ sprintf(s, "*,");
+ }
+ else
+ {
+ sprintf(s, "%ld,", (long int)size[i]);
+ }
+ strcat(desired_dims,s);
+ }
+ len = strlen(desired_dims);
+ desired_dims[len-1] = ']';
+ for (i = 0; i < n; i++)
+ {
+ sprintf(s, "%ld,", (long int)array_size(ary,i));
+ strcat(actual_dims,s);
+ }
+ len = strlen(actual_dims);
+ actual_dims[len-1] = ']';
+ PyErr_Format(PyExc_TypeError,
+ "Array must have shape of %s. Given array has shape of %s",
+ desired_dims,
+ actual_dims);
+ }
+ return success;
+ }
+
+ /* Require the given PyArrayObject to to be Fortran ordered. If the
+ * the PyArrayObject is already Fortran ordered, do nothing. Else,
+ * set the Fortran ordering flag and recompute the strides.
+ */
+ int require_fortran(PyArrayObject* ary)
+ {
+ int success = 1;
+ int nd = array_numdims(ary);
+ int i;
+ npy_intp * strides = array_strides(ary);
+ if (array_is_fortran(ary)) return success;
+ /* Set the Fortran ordered flag */
+ array_enableflags(ary,NPY_ARRAY_FARRAY);
+ /* Recompute the strides */
+ strides[0] = strides[nd-1];
+ for (i=1; i < nd; ++i)
+ strides[i] = strides[i-1] * array_size(ary,i-1);
+ return success;
+ }
+}
+
+/* Combine all NumPy fragments into one for convenience */
+%fragment("NumPy_Fragments",
+ "header",
+ fragment="NumPy_Backward_Compatibility",
+ fragment="NumPy_Macros",
+ fragment="NumPy_Utilities",
+ fragment="NumPy_Object_to_Array",
+ fragment="NumPy_Array_Requirements")
+{
+}
+
+/* End John Hunter translation (with modifications by Bill Spotz)
+ */
+
+/* %numpy_typemaps() macro
+ *
+ * This macro defines a family of 74 typemaps that allow C arguments
+ * of the form
+ *
+ * 1. (DATA_TYPE IN_ARRAY1[ANY])
+ * 2. (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
+ * 3. (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
+ *
+ * 4. (DATA_TYPE IN_ARRAY2[ANY][ANY])
+ * 5. (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+ * 6. (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
+ * 7. (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+ * 8. (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2)
+ *
+ * 9. (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY])
+ * 10. (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+ * 11. (DATA_TYPE** IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+ * 12. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3)
+ * 13. (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+ * 14. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3)
+ *
+ * 15. (DATA_TYPE IN_ARRAY4[ANY][ANY][ANY][ANY])
+ * 16. (DATA_TYPE* IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
+ * 17. (DATA_TYPE** IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
+ * 18. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, , DIM_TYPE DIM4, DATA_TYPE* IN_ARRAY4)
+ * 19. (DATA_TYPE* IN_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
+ * 20. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_FARRAY4)
+ *
+ * 21. (DATA_TYPE INPLACE_ARRAY1[ANY])
+ * 22. (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1)
+ * 23. (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
+ *
+ * 24. (DATA_TYPE INPLACE_ARRAY2[ANY][ANY])
+ * 25. (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+ * 26. (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
+ * 27. (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+ * 28. (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2)
+ *
+ * 29. (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY])
+ * 30. (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+ * 31. (DATA_TYPE** INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+ * 32. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3)
+ * 33. (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+ * 34. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3)
+ *
+ * 35. (DATA_TYPE INPLACE_ARRAY4[ANY][ANY][ANY][ANY])
+ * 36. (DATA_TYPE* INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
+ * 37. (DATA_TYPE** INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
+ * 38. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_ARRAY4)
+ * 39. (DATA_TYPE* INPLACE_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
+ * 40. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_FARRAY4)
+ *
+ * 41. (DATA_TYPE ARGOUT_ARRAY1[ANY])
+ * 42. (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1)
+ * 43. (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1)
+ *
+ * 44. (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])
+ *
+ * 45. (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY])
+ *
+ * 46. (DATA_TYPE ARGOUT_ARRAY4[ANY][ANY][ANY][ANY])
+ *
+ * 47. (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1)
+ * 48. (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1)
+ *
+ * 49. (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
+ * 50. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2)
+ * 51. (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
+ * 52. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2)
+ *
+ * 53. (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3)
+ * 54. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3)
+ * 55. (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3)
+ * 56. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3)
+ *
+ * 57. (DATA_TYPE** ARGOUTVIEW_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4)
+ * 58. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEW_ARRAY4)
+ * 59. (DATA_TYPE** ARGOUTVIEW_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4)
+ * 60. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEW_FARRAY4)
+ *
+ * 61. (DATA_TYPE** ARGOUTVIEWM_ARRAY1, DIM_TYPE* DIM1)
+ * 62. (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEWM_ARRAY1)
+ *
+ * 63. (DATA_TYPE** ARGOUTVIEWM_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
+ * 64. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_ARRAY2)
+ * 65. (DATA_TYPE** ARGOUTVIEWM_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
+ * 66. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_FARRAY2)
+ *
+ * 67. (DATA_TYPE** ARGOUTVIEWM_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3)
+ * 68. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEWM_ARRAY3)
+ * 69. (DATA_TYPE** ARGOUTVIEWM_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3)
+ * 70. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEWM_FARRAY3)
+ *
+ * 71. (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4)
+ * 72. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_ARRAY4)
+ * 73. (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4)
+ * 74. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_FARRAY4)
+ *
+ * where "DATA_TYPE" is any type supported by the NumPy module, and
+ * "DIM_TYPE" is any int-like type suitable for specifying dimensions.
+ * The difference between "ARRAY" typemaps and "FARRAY" typemaps is
+ * that the "FARRAY" typemaps expect Fortran ordering of
+ * multidimensional arrays. In python, the dimensions will not need
+ * to be specified (except for the "DATA_TYPE* ARGOUT_ARRAY1"
+ * typemaps). The IN_ARRAYs can be a numpy array or any sequence that
+ * can be converted to a numpy array of the specified type. The
+ * INPLACE_ARRAYs must be numpy arrays of the appropriate type. The
+ * ARGOUT_ARRAYs will be returned as new numpy arrays of the
+ * appropriate type.
+ *
+ * These typemaps can be applied to existing functions using the
+ * %apply directive. For example:
+ *
+ * %apply (double* IN_ARRAY1, int DIM1) {(double* series, int length)};
+ * double prod(double* series, int length);
+ *
+ * %apply (int DIM1, int DIM2, double* INPLACE_ARRAY2)
+ * {(int rows, int cols, double* matrix )};
+ * void floor(int rows, int cols, double* matrix, double f);
+ *
+ * %apply (double IN_ARRAY3[ANY][ANY][ANY])
+ * {(double tensor[2][2][2] )};
+ * %apply (double ARGOUT_ARRAY3[ANY][ANY][ANY])
+ * {(double low[2][2][2] )};
+ * %apply (double ARGOUT_ARRAY3[ANY][ANY][ANY])
+ * {(double upp[2][2][2] )};
+ * void luSplit(double tensor[2][2][2],
+ * double low[2][2][2],
+ * double upp[2][2][2] );
+ *
+ * or directly with
+ *
+ * double prod(double* IN_ARRAY1, int DIM1);
+ *
+ * void floor(int DIM1, int DIM2, double* INPLACE_ARRAY2, double f);
+ *
+ * void luSplit(double IN_ARRAY3[ANY][ANY][ANY],
+ * double ARGOUT_ARRAY3[ANY][ANY][ANY],
+ * double ARGOUT_ARRAY3[ANY][ANY][ANY]);
+ */
+
+%define %numpy_typemaps(DATA_TYPE, DATA_TYPECODE, DIM_TYPE)
+
+/************************/
+/* Input Array Typemaps */
+/************************/
+
+/* Typemap suite for (DATA_TYPE IN_ARRAY1[ANY])
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE IN_ARRAY1[ANY])
+{
+ $1 = is_array($input) || PySequence_Check($input);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE IN_ARRAY1[ANY])
+ (PyArrayObject* array=NULL, int is_new_object=0)
+{
+ npy_intp size[1] = { $1_dim0 };
+ array = obj_to_array_contiguous_allow_conversion($input,
+ DATA_TYPECODE,
+ &is_new_object);
+ if (!array || !require_dimensions(array, 1) ||
+ !require_size(array, size, 1)) SWIG_fail;
+ $1 = ($1_ltype) array_data(array);
+}
+%typemap(freearg)
+ (DATA_TYPE IN_ARRAY1[ANY])
+{
+ if (is_new_object$argnum && array$argnum)
+ { Py_DECREF(array$argnum); }
+}
+
+/* Typemap suite for (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
+{
+ $1 = is_array($input) || PySequence_Check($input);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
+ (PyArrayObject* array=NULL, int is_new_object=0)
+{
+ npy_intp size[1] = { -1 };
+ array = obj_to_array_contiguous_allow_conversion($input,
+ DATA_TYPECODE,
+ &is_new_object);
+ if (!array || !require_dimensions(array, 1) ||
+ !require_size(array, size, 1)) SWIG_fail;
+ $1 = (DATA_TYPE*) array_data(array);
+ $2 = (DIM_TYPE) array_size(array,0);
+}
+%typemap(freearg)
+ (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
+{
+ if (is_new_object$argnum && array$argnum)
+ { Py_DECREF(array$argnum); }
+}
+
+/* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
+{
+ $1 = is_array($input) || PySequence_Check($input);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
+ (PyArrayObject* array=NULL, int is_new_object=0)
+{
+ npy_intp size[1] = {-1};
+ array = obj_to_array_contiguous_allow_conversion($input,
+ DATA_TYPECODE,
+ &is_new_object);
+ if (!array || !require_dimensions(array, 1) ||
+ !require_size(array, size, 1)) SWIG_fail;
+ $1 = (DIM_TYPE) array_size(array,0);
+ $2 = (DATA_TYPE*) array_data(array);
+}
+%typemap(freearg)
+ (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
+{
+ if (is_new_object$argnum && array$argnum)
+ { Py_DECREF(array$argnum); }
+}
+
+/* Typemap suite for (DATA_TYPE IN_ARRAY2[ANY][ANY])
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE IN_ARRAY2[ANY][ANY])
+{
+ $1 = is_array($input) || PySequence_Check($input);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE IN_ARRAY2[ANY][ANY])
+ (PyArrayObject* array=NULL, int is_new_object=0)
+{
+ npy_intp size[2] = { $1_dim0, $1_dim1 };
+ array = obj_to_array_contiguous_allow_conversion($input,
+ DATA_TYPECODE,
+ &is_new_object);
+ if (!array || !require_dimensions(array, 2) ||
+ !require_size(array, size, 2)) SWIG_fail;
+ $1 = ($1_ltype) array_data(array);
+}
+%typemap(freearg)
+ (DATA_TYPE IN_ARRAY2[ANY][ANY])
+{
+ if (is_new_object$argnum && array$argnum)
+ { Py_DECREF(array$argnum); }
+}
+
+/* Typemap suite for (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+{
+ $1 = is_array($input) || PySequence_Check($input);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+ (PyArrayObject* array=NULL, int is_new_object=0)
+{
+ npy_intp size[2] = { -1, -1 };
+ array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE,
+ &is_new_object);
+ if (!array || !require_dimensions(array, 2) ||
+ !require_size(array, size, 2)) SWIG_fail;
+ $1 = (DATA_TYPE*) array_data(array);
+ $2 = (DIM_TYPE) array_size(array,0);
+ $3 = (DIM_TYPE) array_size(array,1);
+}
+%typemap(freearg)
+ (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+{
+ if (is_new_object$argnum && array$argnum)
+ { Py_DECREF(array$argnum); }
+}
+
+/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
+{
+ $1 = is_array($input) || PySequence_Check($input);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
+ (PyArrayObject* array=NULL, int is_new_object=0)
+{
+ npy_intp size[2] = { -1, -1 };
+ array = obj_to_array_contiguous_allow_conversion($input,
+ DATA_TYPECODE,
+ &is_new_object);
+ if (!array || !require_dimensions(array, 2) ||
+ !require_size(array, size, 2)) SWIG_fail;
+ $1 = (DIM_TYPE) array_size(array,0);
+ $2 = (DIM_TYPE) array_size(array,1);
+ $3 = (DATA_TYPE*) array_data(array);
+}
+%typemap(freearg)
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
+{
+ if (is_new_object$argnum && array$argnum)
+ { Py_DECREF(array$argnum); }
+}
+
+/* Typemap suite for (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+{
+ $1 = is_array($input) || PySequence_Check($input);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+ (PyArrayObject* array=NULL, int is_new_object=0)
+{
+ npy_intp size[2] = { -1, -1 };
+ array = obj_to_array_fortran_allow_conversion($input,
+ DATA_TYPECODE,
+ &is_new_object);
+ if (!array || !require_dimensions(array, 2) ||
+ !require_size(array, size, 2) || !require_fortran(array)) SWIG_fail;
+ $1 = (DATA_TYPE*) array_data(array);
+ $2 = (DIM_TYPE) array_size(array,0);
+ $3 = (DIM_TYPE) array_size(array,1);
+}
+%typemap(freearg)
+ (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+{
+ if (is_new_object$argnum && array$argnum)
+ { Py_DECREF(array$argnum); }
+}
+
+/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2)
+{
+ $1 = is_array($input) || PySequence_Check($input);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2)
+ (PyArrayObject* array=NULL, int is_new_object=0)
+{
+ npy_intp size[2] = { -1, -1 };
+ array = obj_to_array_fortran_allow_conversion($input,
+ DATA_TYPECODE,
+ &is_new_object);
+ if (!array || !require_dimensions(array, 2) ||
+ !require_size(array, size, 2) || !require_fortran(array)) SWIG_fail;
+ $1 = (DIM_TYPE) array_size(array,0);
+ $2 = (DIM_TYPE) array_size(array,1);
+ $3 = (DATA_TYPE*) array_data(array);
+}
+%typemap(freearg)
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2)
+{
+ if (is_new_object$argnum && array$argnum)
+ { Py_DECREF(array$argnum); }
+}
+
+/* Typemap suite for (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY])
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY])
+{
+ $1 = is_array($input) || PySequence_Check($input);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY])
+ (PyArrayObject* array=NULL, int is_new_object=0)
+{
+ npy_intp size[3] = { $1_dim0, $1_dim1, $1_dim2 };
+ array = obj_to_array_contiguous_allow_conversion($input,
+ DATA_TYPECODE,
+ &is_new_object);
+ if (!array || !require_dimensions(array, 3) ||
+ !require_size(array, size, 3)) SWIG_fail;
+ $1 = ($1_ltype) array_data(array);
+}
+%typemap(freearg)
+ (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY])
+{
+ if (is_new_object$argnum && array$argnum)
+ { Py_DECREF(array$argnum); }
+}
+
+/* Typemap suite for (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2,
+ * DIM_TYPE DIM3)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+{
+ $1 = is_array($input) || PySequence_Check($input);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+ (PyArrayObject* array=NULL, int is_new_object=0)
+{
+ npy_intp size[3] = { -1, -1, -1 };
+ array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE,
+ &is_new_object);
+ if (!array || !require_dimensions(array, 3) ||
+ !require_size(array, size, 3)) SWIG_fail;
+ $1 = (DATA_TYPE*) array_data(array);
+ $2 = (DIM_TYPE) array_size(array,0);
+ $3 = (DIM_TYPE) array_size(array,1);
+ $4 = (DIM_TYPE) array_size(array,2);
+}
+%typemap(freearg)
+ (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+{
+ if (is_new_object$argnum && array$argnum)
+ { Py_DECREF(array$argnum); }
+}
+
+/* Typemap suite for (DATA_TYPE** IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2,
+ * DIM_TYPE DIM3)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE** IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+{
+ /* for now, only concerned with lists */
+ $1 = PySequence_Check($input);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE** IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+ (DATA_TYPE** array=NULL, PyArrayObject** object_array=NULL, int* is_new_object_array=NULL)
+{
+ npy_intp size[2] = { -1, -1 };
+ PyArrayObject* temp_array;
+ Py_ssize_t i;
+ int is_new_object;
+
+ /* length of the list */
+ $2 = PyList_Size($input);
+
+ /* the arrays */
+ array = (DATA_TYPE **)malloc($2*sizeof(DATA_TYPE *));
+ object_array = (PyArrayObject **)calloc($2,sizeof(PyArrayObject *));
+ is_new_object_array = (int *)calloc($2,sizeof(int));
+
+ if (array == NULL || object_array == NULL || is_new_object_array == NULL)
+ {
+ SWIG_fail;
+ }
+
+ for (i=0; i<$2; i++)
+ {
+ temp_array = obj_to_array_contiguous_allow_conversion(PySequence_GetItem($input,i), DATA_TYPECODE, &is_new_object);
+
+ /* the new array must be stored so that it can be destroyed in freearg */
+ object_array[i] = temp_array;
+ is_new_object_array[i] = is_new_object;
+
+ if (!temp_array || !require_dimensions(temp_array, 2)) SWIG_fail;
+
+ /* store the size of the first array in the list, then use that for comparison. */
+ if (i == 0)
+ {
+ size[0] = array_size(temp_array,0);
+ size[1] = array_size(temp_array,1);
+ }
+
+ if (!require_size(temp_array, size, 2)) SWIG_fail;
+
+ array[i] = (DATA_TYPE*) array_data(temp_array);
+ }
+
+ $1 = (DATA_TYPE**) array;
+ $3 = (DIM_TYPE) size[0];
+ $4 = (DIM_TYPE) size[1];
+}
+%typemap(freearg)
+ (DATA_TYPE** IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+{
+ Py_ssize_t i;
+
+ if (array$argnum!=NULL) free(array$argnum);
+
+ /*freeing the individual arrays if needed */
+ if (object_array$argnum!=NULL)
+ {
+ if (is_new_object_array$argnum!=NULL)
+ {
+ for (i=0; i<$2; i++)
+ {
+ if (object_array$argnum[i] != NULL && is_new_object_array$argnum[i])
+ { Py_DECREF(object_array$argnum[i]); }
+ }
+ free(is_new_object_array$argnum);
+ }
+ free(object_array$argnum);
+ }
+}
+
+/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3,
+ * DATA_TYPE* IN_ARRAY3)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3)
+{
+ $1 = is_array($input) || PySequence_Check($input);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3)
+ (PyArrayObject* array=NULL, int is_new_object=0)
+{
+ npy_intp size[3] = { -1, -1, -1 };
+ array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE,
+ &is_new_object);
+ if (!array || !require_dimensions(array, 3) ||
+ !require_size(array, size, 3)) SWIG_fail;
+ $1 = (DIM_TYPE) array_size(array,0);
+ $2 = (DIM_TYPE) array_size(array,1);
+ $3 = (DIM_TYPE) array_size(array,2);
+ $4 = (DATA_TYPE*) array_data(array);
+}
+%typemap(freearg)
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3)
+{
+ if (is_new_object$argnum && array$argnum)
+ { Py_DECREF(array$argnum); }
+}
+
+/* Typemap suite for (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2,
+ * DIM_TYPE DIM3)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+{
+ $1 = is_array($input) || PySequence_Check($input);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+ (PyArrayObject* array=NULL, int is_new_object=0)
+{
+ npy_intp size[3] = { -1, -1, -1 };
+ array = obj_to_array_fortran_allow_conversion($input, DATA_TYPECODE,
+ &is_new_object);
+ if (!array || !require_dimensions(array, 3) ||
+ !require_size(array, size, 3) | !require_fortran(array)) SWIG_fail;
+ $1 = (DATA_TYPE*) array_data(array);
+ $2 = (DIM_TYPE) array_size(array,0);
+ $3 = (DIM_TYPE) array_size(array,1);
+ $4 = (DIM_TYPE) array_size(array,2);
+}
+%typemap(freearg)
+ (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+{
+ if (is_new_object$argnum && array$argnum)
+ { Py_DECREF(array$argnum); }
+}
+
+/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3,
+ * DATA_TYPE* IN_FARRAY3)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3)
+{
+ $1 = is_array($input) || PySequence_Check($input);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3)
+ (PyArrayObject* array=NULL, int is_new_object=0)
+{
+ npy_intp size[3] = { -1, -1, -1 };
+ array = obj_to_array_fortran_allow_conversion($input,
+ DATA_TYPECODE,
+ &is_new_object);
+ if (!array || !require_dimensions(array, 3) ||
+ !require_size(array, size, 3) || !require_fortran(array)) SWIG_fail;
+ $1 = (DIM_TYPE) array_size(array,0);
+ $2 = (DIM_TYPE) array_size(array,1);
+ $3 = (DIM_TYPE) array_size(array,2);
+ $4 = (DATA_TYPE*) array_data(array);
+}
+%typemap(freearg)
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3)
+{
+ if (is_new_object$argnum && array$argnum)
+ { Py_DECREF(array$argnum); }
+}
+
+/* Typemap suite for (DATA_TYPE IN_ARRAY4[ANY][ANY][ANY][ANY])
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE IN_ARRAY4[ANY][ANY][ANY][ANY])
+{
+ $1 = is_array($input) || PySequence_Check($input);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE IN_ARRAY4[ANY][ANY][ANY][ANY])
+ (PyArrayObject* array=NULL, int is_new_object=0)
+{
+ npy_intp size[4] = { $1_dim0, $1_dim1, $1_dim2 , $1_dim3};
+ array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE,
+ &is_new_object);
+ if (!array || !require_dimensions(array, 4) ||
+ !require_size(array, size, 4)) SWIG_fail;
+ $1 = ($1_ltype) array_data(array);
+}
+%typemap(freearg)
+ (DATA_TYPE IN_ARRAY4[ANY][ANY][ANY][ANY])
+{
+ if (is_new_object$argnum && array$argnum)
+ { Py_DECREF(array$argnum); }
+}
+
+/* Typemap suite for (DATA_TYPE* IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2,
+ * DIM_TYPE DIM3, DIM_TYPE DIM4)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE* IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
+{
+ $1 = is_array($input) || PySequence_Check($input);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE* IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
+ (PyArrayObject* array=NULL, int is_new_object=0)
+{
+ npy_intp size[4] = { -1, -1, -1, -1 };
+ array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE,
+ &is_new_object);
+ if (!array || !require_dimensions(array, 4) ||
+ !require_size(array, size, 4)) SWIG_fail;
+ $1 = (DATA_TYPE*) array_data(array);
+ $2 = (DIM_TYPE) array_size(array,0);
+ $3 = (DIM_TYPE) array_size(array,1);
+ $4 = (DIM_TYPE) array_size(array,2);
+ $5 = (DIM_TYPE) array_size(array,3);
+}
+%typemap(freearg)
+ (DATA_TYPE* IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
+{
+ if (is_new_object$argnum && array$argnum)
+ { Py_DECREF(array$argnum); }
+}
+
+/* Typemap suite for (DATA_TYPE** IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2,
+ * DIM_TYPE DIM3, DIM_TYPE DIM4)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE** IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
+{
+ /* for now, only concerned with lists */
+ $1 = PySequence_Check($input);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE** IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
+ (DATA_TYPE** array=NULL, PyArrayObject** object_array=NULL, int* is_new_object_array=NULL)
+{
+ npy_intp size[3] = { -1, -1, -1 };
+ PyArrayObject* temp_array;
+ Py_ssize_t i;
+ int is_new_object;
+
+ /* length of the list */
+ $2 = PyList_Size($input);
+
+ /* the arrays */
+ array = (DATA_TYPE **)malloc($2*sizeof(DATA_TYPE *));
+ object_array = (PyArrayObject **)calloc($2,sizeof(PyArrayObject *));
+ is_new_object_array = (int *)calloc($2,sizeof(int));
+
+ if (array == NULL || object_array == NULL || is_new_object_array == NULL)
+ {
+ SWIG_fail;
+ }
+
+ for (i=0; i<$2; i++)
+ {
+ temp_array = obj_to_array_contiguous_allow_conversion(PySequence_GetItem($input,i), DATA_TYPECODE, &is_new_object);
+
+ /* the new array must be stored so that it can be destroyed in freearg */
+ object_array[i] = temp_array;
+ is_new_object_array[i] = is_new_object;
+
+ if (!temp_array || !require_dimensions(temp_array, 3)) SWIG_fail;
+
+ /* store the size of the first array in the list, then use that for comparison. */
+ if (i == 0)
+ {
+ size[0] = array_size(temp_array,0);
+ size[1] = array_size(temp_array,1);
+ size[2] = array_size(temp_array,2);
+ }
+
+ if (!require_size(temp_array, size, 3)) SWIG_fail;
+
+ array[i] = (DATA_TYPE*) array_data(temp_array);
+ }
+
+ $1 = (DATA_TYPE**) array;
+ $3 = (DIM_TYPE) size[0];
+ $4 = (DIM_TYPE) size[1];
+ $5 = (DIM_TYPE) size[2];
+}
+%typemap(freearg)
+ (DATA_TYPE** IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
+{
+ Py_ssize_t i;
+
+ if (array$argnum!=NULL) free(array$argnum);
+
+ /*freeing the individual arrays if needed */
+ if (object_array$argnum!=NULL)
+ {
+ if (is_new_object_array$argnum!=NULL)
+ {
+ for (i=0; i<$2; i++)
+ {
+ if (object_array$argnum[i] != NULL && is_new_object_array$argnum[i])
+ { Py_DECREF(object_array$argnum[i]); }
+ }
+ free(is_new_object_array$argnum);
+ }
+ free(object_array$argnum);
+ }
+}
+
+/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4,
+ * DATA_TYPE* IN_ARRAY4)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_ARRAY4)
+{
+ $1 = is_array($input) || PySequence_Check($input);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_ARRAY4)
+ (PyArrayObject* array=NULL, int is_new_object=0)
+{
+ npy_intp size[4] = { -1, -1, -1 , -1};
+ array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE,
+ &is_new_object);
+ if (!array || !require_dimensions(array, 4) ||
+ !require_size(array, size, 4)) SWIG_fail;
+ $1 = (DIM_TYPE) array_size(array,0);
+ $2 = (DIM_TYPE) array_size(array,1);
+ $3 = (DIM_TYPE) array_size(array,2);
+ $4 = (DIM_TYPE) array_size(array,3);
+ $5 = (DATA_TYPE*) array_data(array);
+}
+%typemap(freearg)
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_ARRAY4)
+{
+ if (is_new_object$argnum && array$argnum)
+ { Py_DECREF(array$argnum); }
+}
+
+/* Typemap suite for (DATA_TYPE* IN_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2,
+ * DIM_TYPE DIM3, DIM_TYPE DIM4)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE* IN_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
+{
+ $1 = is_array($input) || PySequence_Check($input);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE* IN_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
+ (PyArrayObject* array=NULL, int is_new_object=0)
+{
+ npy_intp size[4] = { -1, -1, -1, -1 };
+ array = obj_to_array_fortran_allow_conversion($input, DATA_TYPECODE,
+ &is_new_object);
+ if (!array || !require_dimensions(array, 4) ||
+ !require_size(array, size, 4) | !require_fortran(array)) SWIG_fail;
+ $1 = (DATA_TYPE*) array_data(array);
+ $2 = (DIM_TYPE) array_size(array,0);
+ $3 = (DIM_TYPE) array_size(array,1);
+ $4 = (DIM_TYPE) array_size(array,2);
+ $5 = (DIM_TYPE) array_size(array,3);
+}
+%typemap(freearg)
+ (DATA_TYPE* IN_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
+{
+ if (is_new_object$argnum && array$argnum)
+ { Py_DECREF(array$argnum); }
+}
+
+/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4,
+ * DATA_TYPE* IN_FARRAY4)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_FARRAY4)
+{
+ $1 = is_array($input) || PySequence_Check($input);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_FARRAY4)
+ (PyArrayObject* array=NULL, int is_new_object=0)
+{
+ npy_intp size[4] = { -1, -1, -1 , -1 };
+ array = obj_to_array_fortran_allow_conversion($input, DATA_TYPECODE,
+ &is_new_object);
+ if (!array || !require_dimensions(array, 4) ||
+ !require_size(array, size, 4) || !require_fortran(array)) SWIG_fail;
+ $1 = (DIM_TYPE) array_size(array,0);
+ $2 = (DIM_TYPE) array_size(array,1);
+ $3 = (DIM_TYPE) array_size(array,2);
+ $4 = (DIM_TYPE) array_size(array,3);
+ $5 = (DATA_TYPE*) array_data(array);
+}
+%typemap(freearg)
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_FARRAY4)
+{
+ if (is_new_object$argnum && array$argnum)
+ { Py_DECREF(array$argnum); }
+}
+
+/***************************/
+/* In-Place Array Typemaps */
+/***************************/
+
+/* Typemap suite for (DATA_TYPE INPLACE_ARRAY1[ANY])
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE INPLACE_ARRAY1[ANY])
+{
+ $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+ DATA_TYPECODE);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE INPLACE_ARRAY1[ANY])
+ (PyArrayObject* array=NULL)
+{
+ npy_intp size[1] = { $1_dim0 };
+ array = obj_to_array_no_conversion($input, DATA_TYPECODE);
+ if (!array || !require_dimensions(array,1) || !require_size(array, size, 1) ||
+ !require_contiguous(array) || !require_native(array)) SWIG_fail;
+ $1 = ($1_ltype) array_data(array);
+}
+
+/* Typemap suite for (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1)
+{
+ $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+ DATA_TYPECODE);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1)
+ (PyArrayObject* array=NULL, int i=1)
+{
+ array = obj_to_array_no_conversion($input, DATA_TYPECODE);
+ if (!array || !require_dimensions(array,1) || !require_contiguous(array)
+ || !require_native(array)) SWIG_fail;
+ $1 = (DATA_TYPE*) array_data(array);
+ $2 = 1;
+ for (i=0; i < array_numdims(array); ++i) $2 *= array_size(array,i);
+}
+
+/* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
+{
+ $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+ DATA_TYPECODE);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
+ (PyArrayObject* array=NULL, int i=0)
+{
+ array = obj_to_array_no_conversion($input, DATA_TYPECODE);
+ if (!array || !require_dimensions(array,1) || !require_contiguous(array)
+ || !require_native(array)) SWIG_fail;
+ $1 = 1;
+ for (i=0; i < array_numdims(array); ++i) $1 *= array_size(array,i);
+ $2 = (DATA_TYPE*) array_data(array);
+}
+
+/* Typemap suite for (DATA_TYPE INPLACE_ARRAY2[ANY][ANY])
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE INPLACE_ARRAY2[ANY][ANY])
+{
+ $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+ DATA_TYPECODE);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE INPLACE_ARRAY2[ANY][ANY])
+ (PyArrayObject* array=NULL)
+{
+ npy_intp size[2] = { $1_dim0, $1_dim1 };
+ array = obj_to_array_no_conversion($input, DATA_TYPECODE);
+ if (!array || !require_dimensions(array,2) || !require_size(array, size, 2) ||
+ !require_contiguous(array) || !require_native(array)) SWIG_fail;
+ $1 = ($1_ltype) array_data(array);
+}
+
+/* Typemap suite for (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+{
+ $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+ DATA_TYPECODE);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+ (PyArrayObject* array=NULL)
+{
+ array = obj_to_array_no_conversion($input, DATA_TYPECODE);
+ if (!array || !require_dimensions(array,2) || !require_contiguous(array)
+ || !require_native(array)) SWIG_fail;
+ $1 = (DATA_TYPE*) array_data(array);
+ $2 = (DIM_TYPE) array_size(array,0);
+ $3 = (DIM_TYPE) array_size(array,1);
+}
+
+/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
+{
+ $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+ DATA_TYPECODE);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
+ (PyArrayObject* array=NULL)
+{
+ array = obj_to_array_no_conversion($input, DATA_TYPECODE);
+ if (!array || !require_dimensions(array,2) || !require_contiguous(array) ||
+ !require_native(array)) SWIG_fail;
+ $1 = (DIM_TYPE) array_size(array,0);
+ $2 = (DIM_TYPE) array_size(array,1);
+ $3 = (DATA_TYPE*) array_data(array);
+}
+
+/* Typemap suite for (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+{
+ $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+ DATA_TYPECODE);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+ (PyArrayObject* array=NULL)
+{
+ array = obj_to_array_no_conversion($input, DATA_TYPECODE);
+ if (!array || !require_dimensions(array,2) || !require_contiguous(array)
+ || !require_native(array) || !require_fortran(array)) SWIG_fail;
+ $1 = (DATA_TYPE*) array_data(array);
+ $2 = (DIM_TYPE) array_size(array,0);
+ $3 = (DIM_TYPE) array_size(array,1);
+}
+
+/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2)
+{
+ $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+ DATA_TYPECODE);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2)
+ (PyArrayObject* array=NULL)
+{
+ array = obj_to_array_no_conversion($input, DATA_TYPECODE);
+ if (!array || !require_dimensions(array,2) || !require_contiguous(array) ||
+ !require_native(array) || !require_fortran(array)) SWIG_fail;
+ $1 = (DIM_TYPE) array_size(array,0);
+ $2 = (DIM_TYPE) array_size(array,1);
+ $3 = (DATA_TYPE*) array_data(array);
+}
+
+/* Typemap suite for (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY])
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY])
+{
+ $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+ DATA_TYPECODE);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY])
+ (PyArrayObject* array=NULL)
+{
+ npy_intp size[3] = { $1_dim0, $1_dim1, $1_dim2 };
+ array = obj_to_array_no_conversion($input, DATA_TYPECODE);
+ if (!array || !require_dimensions(array,3) || !require_size(array, size, 3) ||
+ !require_contiguous(array) || !require_native(array)) SWIG_fail;
+ $1 = ($1_ltype) array_data(array);
+}
+
+/* Typemap suite for (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2,
+ * DIM_TYPE DIM3)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+{
+ $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+ DATA_TYPECODE);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+ (PyArrayObject* array=NULL)
+{
+ array = obj_to_array_no_conversion($input, DATA_TYPECODE);
+ if (!array || !require_dimensions(array,3) || !require_contiguous(array) ||
+ !require_native(array)) SWIG_fail;
+ $1 = (DATA_TYPE*) array_data(array);
+ $2 = (DIM_TYPE) array_size(array,0);
+ $3 = (DIM_TYPE) array_size(array,1);
+ $4 = (DIM_TYPE) array_size(array,2);
+}
+
+/* Typemap suite for (DATA_TYPE** INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2,
+ * DIM_TYPE DIM3)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE** INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+{
+ $1 = PySequence_Check($input);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE** INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+ (DATA_TYPE** array=NULL, PyArrayObject** object_array=NULL)
+{
+ npy_intp size[2] = { -1, -1 };
+ PyArrayObject* temp_array;
+ Py_ssize_t i;
+
+ /* length of the list */
+ $2 = PyList_Size($input);
+
+ /* the arrays */
+ array = (DATA_TYPE **)malloc($2*sizeof(DATA_TYPE *));
+ object_array = (PyArrayObject **)calloc($2,sizeof(PyArrayObject *));
+
+ if (array == NULL || object_array == NULL)
+ {
+ SWIG_fail;
+ }
+
+ for (i=0; i<$2; i++)
+ {
+ temp_array = obj_to_array_no_conversion(PySequence_GetItem($input,i), DATA_TYPECODE);
+
+ /* the new array must be stored so that it can be destroyed in freearg */
+ object_array[i] = temp_array;
+
+ if ( !temp_array || !require_dimensions(temp_array, 2) ||
+ !require_contiguous(temp_array) ||
+ !require_native(temp_array) ||
+ !PyArray_EquivTypenums(array_type(temp_array), DATA_TYPECODE)
+ ) SWIG_fail;
+
+ /* store the size of the first array in the list, then use that for comparison. */
+ if (i == 0)
+ {
+ size[0] = array_size(temp_array,0);
+ size[1] = array_size(temp_array,1);
+ }
+
+ if (!require_size(temp_array, size, 2)) SWIG_fail;
+
+ array[i] = (DATA_TYPE*) array_data(temp_array);
+ }
+
+ $1 = (DATA_TYPE**) array;
+ $3 = (DIM_TYPE) size[0];
+ $4 = (DIM_TYPE) size[1];
+}
+%typemap(freearg)
+ (DATA_TYPE** INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+{
+ if (array$argnum!=NULL) free(array$argnum);
+ if (object_array$argnum!=NULL) free(object_array$argnum);
+}
+
+/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3,
+ * DATA_TYPE* INPLACE_ARRAY3)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3)
+{
+ $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+ DATA_TYPECODE);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3)
+ (PyArrayObject* array=NULL)
+{
+ array = obj_to_array_no_conversion($input, DATA_TYPECODE);
+ if (!array || !require_dimensions(array,3) || !require_contiguous(array)
+ || !require_native(array)) SWIG_fail;
+ $1 = (DIM_TYPE) array_size(array,0);
+ $2 = (DIM_TYPE) array_size(array,1);
+ $3 = (DIM_TYPE) array_size(array,2);
+ $4 = (DATA_TYPE*) array_data(array);
+}
+
+/* Typemap suite for (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2,
+ * DIM_TYPE DIM3)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+{
+ $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+ DATA_TYPECODE);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+ (PyArrayObject* array=NULL)
+{
+ array = obj_to_array_no_conversion($input, DATA_TYPECODE);
+ if (!array || !require_dimensions(array,3) || !require_contiguous(array) ||
+ !require_native(array) || !require_fortran(array)) SWIG_fail;
+ $1 = (DATA_TYPE*) array_data(array);
+ $2 = (DIM_TYPE) array_size(array,0);
+ $3 = (DIM_TYPE) array_size(array,1);
+ $4 = (DIM_TYPE) array_size(array,2);
+}
+
+/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3,
+ * DATA_TYPE* INPLACE_FARRAY3)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3)
+{
+ $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+ DATA_TYPECODE);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3)
+ (PyArrayObject* array=NULL)
+{
+ array = obj_to_array_no_conversion($input, DATA_TYPECODE);
+ if (!array || !require_dimensions(array,3) || !require_contiguous(array)
+ || !require_native(array) || !require_fortran(array)) SWIG_fail;
+ $1 = (DIM_TYPE) array_size(array,0);
+ $2 = (DIM_TYPE) array_size(array,1);
+ $3 = (DIM_TYPE) array_size(array,2);
+ $4 = (DATA_TYPE*) array_data(array);
+}
+
+/* Typemap suite for (DATA_TYPE INPLACE_ARRAY4[ANY][ANY][ANY][ANY])
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE INPLACE_ARRAY4[ANY][ANY][ANY][ANY])
+{
+ $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+ DATA_TYPECODE);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE INPLACE_ARRAY4[ANY][ANY][ANY][ANY])
+ (PyArrayObject* array=NULL)
+{
+ npy_intp size[4] = { $1_dim0, $1_dim1, $1_dim2 , $1_dim3 };
+ array = obj_to_array_no_conversion($input, DATA_TYPECODE);
+ if (!array || !require_dimensions(array,4) || !require_size(array, size, 4) ||
+ !require_contiguous(array) || !require_native(array)) SWIG_fail;
+ $1 = ($1_ltype) array_data(array);
+}
+
+/* Typemap suite for (DATA_TYPE* INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2,
+ * DIM_TYPE DIM3, DIM_TYPE DIM4)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE* INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
+{
+ $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+ DATA_TYPECODE);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE* INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
+ (PyArrayObject* array=NULL)
+{
+ array = obj_to_array_no_conversion($input, DATA_TYPECODE);
+ if (!array || !require_dimensions(array,4) || !require_contiguous(array) ||
+ !require_native(array)) SWIG_fail;
+ $1 = (DATA_TYPE*) array_data(array);
+ $2 = (DIM_TYPE) array_size(array,0);
+ $3 = (DIM_TYPE) array_size(array,1);
+ $4 = (DIM_TYPE) array_size(array,2);
+ $5 = (DIM_TYPE) array_size(array,3);
+}
+
+/* Typemap suite for (DATA_TYPE** INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2,
+ * DIM_TYPE DIM3, DIM_TYPE DIM4)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE** INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
+{
+ $1 = PySequence_Check($input);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE** INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
+ (DATA_TYPE** array=NULL, PyArrayObject** object_array=NULL)
+{
+ npy_intp size[3] = { -1, -1, -1 };
+ PyArrayObject* temp_array;
+ Py_ssize_t i;
+
+ /* length of the list */
+ $2 = PyList_Size($input);
+
+ /* the arrays */
+ array = (DATA_TYPE **)malloc($2*sizeof(DATA_TYPE *));
+ object_array = (PyArrayObject **)calloc($2,sizeof(PyArrayObject *));
+
+ if (array == NULL || object_array == NULL)
+ {
+ SWIG_fail;
+ }
+
+ for (i=0; i<$2; i++)
+ {
+ temp_array = obj_to_array_no_conversion(PySequence_GetItem($input,i), DATA_TYPECODE);
+
+ /* the new array must be stored so that it can be destroyed in freearg */
+ object_array[i] = temp_array;
+
+ if ( !temp_array || !require_dimensions(temp_array, 3) ||
+ !require_contiguous(temp_array) ||
+ !require_native(temp_array) ||
+ !PyArray_EquivTypenums(array_type(temp_array), DATA_TYPECODE)
+ ) SWIG_fail;
+
+ /* store the size of the first array in the list, then use that for comparison. */
+ if (i == 0)
+ {
+ size[0] = array_size(temp_array,0);
+ size[1] = array_size(temp_array,1);
+ size[2] = array_size(temp_array,2);
+ }
+
+ if (!require_size(temp_array, size, 3)) SWIG_fail;
+
+ array[i] = (DATA_TYPE*) array_data(temp_array);
+ }
+
+ $1 = (DATA_TYPE**) array;
+ $3 = (DIM_TYPE) size[0];
+ $4 = (DIM_TYPE) size[1];
+ $5 = (DIM_TYPE) size[2];
+}
+%typemap(freearg)
+ (DATA_TYPE** INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
+{
+ if (array$argnum!=NULL) free(array$argnum);
+ if (object_array$argnum!=NULL) free(object_array$argnum);
+}
+
+/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4,
+ * DATA_TYPE* INPLACE_ARRAY4)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_ARRAY4)
+{
+ $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+ DATA_TYPECODE);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_ARRAY4)
+ (PyArrayObject* array=NULL)
+{
+ array = obj_to_array_no_conversion($input, DATA_TYPECODE);
+ if (!array || !require_dimensions(array,4) || !require_contiguous(array)
+ || !require_native(array)) SWIG_fail;
+ $1 = (DIM_TYPE) array_size(array,0);
+ $2 = (DIM_TYPE) array_size(array,1);
+ $3 = (DIM_TYPE) array_size(array,2);
+ $4 = (DIM_TYPE) array_size(array,3);
+ $5 = (DATA_TYPE*) array_data(array);
+}
+
+/* Typemap suite for (DATA_TYPE* INPLACE_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2,
+ * DIM_TYPE DIM3, DIM_TYPE DIM4)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DATA_TYPE* INPLACE_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
+{
+ $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+ DATA_TYPECODE);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE* INPLACE_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
+ (PyArrayObject* array=NULL)
+{
+ array = obj_to_array_no_conversion($input, DATA_TYPECODE);
+ if (!array || !require_dimensions(array,4) || !require_contiguous(array) ||
+ !require_native(array) || !require_fortran(array)) SWIG_fail;
+ $1 = (DATA_TYPE*) array_data(array);
+ $2 = (DIM_TYPE) array_size(array,0);
+ $3 = (DIM_TYPE) array_size(array,1);
+ $4 = (DIM_TYPE) array_size(array,2);
+ $5 = (DIM_TYPE) array_size(array,3);
+}
+
+/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3,
+ * DATA_TYPE* INPLACE_FARRAY4)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+ fragment="NumPy_Macros")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_FARRAY4)
+{
+ $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+ DATA_TYPECODE);
+}
+%typemap(in,
+ fragment="NumPy_Fragments")
+ (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_FARRAY4)
+ (PyArrayObject* array=NULL)
+{
+ array = obj_to_array_no_conversion($input, DATA_TYPECODE);
+ if (!array || !require_dimensions(array,4) || !require_contiguous(array)
+ || !require_native(array) || !require_fortran(array)) SWIG_fail;
+ $1 = (DIM_TYPE) array_size(array,0);
+ $2 = (DIM_TYPE) array_size(array,1);
+ $3 = (DIM_TYPE) array_size(array,2);
+ $4 = (DIM_TYPE) array_size(array,3);
+ $5 = (DATA_TYPE*) array_data(array);
+}
+
+/*************************/
+/* Argout Array Typemaps */
+/*************************/
+
+/* Typemap suite for (DATA_TYPE ARGOUT_ARRAY1[ANY])
+ */
+%typemap(in,numinputs=0,
+ fragment="NumPy_Backward_Compatibility,NumPy_Macros")
+ (DATA_TYPE ARGOUT_ARRAY1[ANY])
+ (PyObject* array = NULL)
+{
+ npy_intp dims[1] = { $1_dim0 };
+ array = PyArray_SimpleNew(1, dims, DATA_TYPECODE);
+ if (!array) SWIG_fail;
+ $1 = ($1_ltype) array_data(array);
+}
+%typemap(argout)
+ (DATA_TYPE ARGOUT_ARRAY1[ANY])
+{
+ $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum);
+}
+
+/* Typemap suite for (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1)
+ */
+%typemap(in,numinputs=1,
+ fragment="NumPy_Fragments")
+ (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1)
+ (PyObject* array = NULL)
+{
+ npy_intp dims[1];
+ if (!PyInt_Check($input))
+ {
+ const char* typestring = pytype_string($input);
+ PyErr_Format(PyExc_TypeError,
+ "Int dimension expected. '%s' given.",
+ typestring);
+ SWIG_fail;
+ }
+ $2 = (DIM_TYPE) PyInt_AsLong($input);
+ dims[0] = (npy_intp) $2;
+ array = PyArray_SimpleNew(1, dims, DATA_TYPECODE);
+ if (!array) SWIG_fail;
+ $1 = (DATA_TYPE*) array_data(array);
+}
+%typemap(argout)
+ (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1)
+{
+ $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum);
+}
+
+/* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1)
+ */
+%typemap(in,numinputs=1,
+ fragment="NumPy_Fragments")
+ (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1)
+ (PyObject* array = NULL)
+{
+ npy_intp dims[1];
+ if (!PyInt_Check($input))
+ {
+ const char* typestring = pytype_string($input);
+ PyErr_Format(PyExc_TypeError,
+ "Int dimension expected. '%s' given.",
+ typestring);
+ SWIG_fail;
+ }
+ $1 = (DIM_TYPE) PyInt_AsLong($input);
+ dims[0] = (npy_intp) $1;
+ array = PyArray_SimpleNew(1, dims, DATA_TYPECODE);
+ if (!array) SWIG_fail;
+ $2 = (DATA_TYPE*) array_data(array);
+}
+%typemap(argout)
+ (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1)
+{
+ $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum);
+}
+
+/* Typemap suite for (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])
+ */
+%typemap(in,numinputs=0,
+ fragment="NumPy_Backward_Compatibility,NumPy_Macros")
+ (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])
+ (PyObject* array = NULL)
+{
+ npy_intp dims[2] = { $1_dim0, $1_dim1 };
+ array = PyArray_SimpleNew(2, dims, DATA_TYPECODE);
+ if (!array) SWIG_fail;
+ $1 = ($1_ltype) array_data(array);
+}
+%typemap(argout)
+ (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])
+{
+ $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum);
+}
+
+/* Typemap suite for (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY])
+ */
+%typemap(in,numinputs=0,
+ fragment="NumPy_Backward_Compatibility,NumPy_Macros")
+ (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY])
+ (PyObject* array = NULL)
+{
+ npy_intp dims[3] = { $1_dim0, $1_dim1, $1_dim2 };
+ array = PyArray_SimpleNew(3, dims, DATA_TYPECODE);
+ if (!array) SWIG_fail;
+ $1 = ($1_ltype) array_data(array);
+}
+%typemap(argout)
+ (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY])
+{
+ $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum);
+}
+
+/* Typemap suite for (DATA_TYPE ARGOUT_ARRAY4[ANY][ANY][ANY][ANY])
+ */
+%typemap(in,numinputs=0,
+ fragment="NumPy_Backward_Compatibility,NumPy_Macros")
+ (DATA_TYPE ARGOUT_ARRAY4[ANY][ANY][ANY][ANY])
+ (PyObject* array = NULL)
+{
+ npy_intp dims[4] = { $1_dim0, $1_dim1, $1_dim2, $1_dim3 };
+ array = PyArray_SimpleNew(4, dims, DATA_TYPECODE);
+ if (!array) SWIG_fail;
+ $1 = ($1_ltype) array_data(array);
+}
+%typemap(argout)
+ (DATA_TYPE ARGOUT_ARRAY4[ANY][ANY][ANY][ANY])
+{
+ $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum);
+}
+
+/*****************************/
+/* Argoutview Array Typemaps */
+/*****************************/
+
+/* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1)
+ */
+%typemap(in,numinputs=0)
+ (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1 )
+ (DATA_TYPE* data_temp = NULL , DIM_TYPE dim_temp)
+{
+ $1 = &data_temp;
+ $2 = &dim_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility")
+ (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1)
+{
+ npy_intp dims[1] = { *$2 };
+ PyObject* obj = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$1));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array) SWIG_fail;
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1)
+ */
+%typemap(in,numinputs=0)
+ (DIM_TYPE* DIM1 , DATA_TYPE** ARGOUTVIEW_ARRAY1)
+ (DIM_TYPE dim_temp, DATA_TYPE* data_temp = NULL )
+{
+ $1 = &dim_temp;
+ $2 = &data_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility")
+ (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1)
+{
+ npy_intp dims[1] = { *$1 };
+ PyObject* obj = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$2));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array) SWIG_fail;
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
+ */
+%typemap(in,numinputs=0)
+ (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 )
+ (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp)
+{
+ $1 = &data_temp;
+ $2 = &dim1_temp;
+ $3 = &dim2_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility")
+ (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
+{
+ npy_intp dims[2] = { *$2, *$3 };
+ PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array) SWIG_fail;
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2)
+ */
+%typemap(in,numinputs=0)
+ (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEW_ARRAY2)
+ (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp = NULL )
+{
+ $1 = &dim1_temp;
+ $2 = &dim2_temp;
+ $3 = &data_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility")
+ (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2)
+{
+ npy_intp dims[2] = { *$1, *$2 };
+ PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array) SWIG_fail;
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
+ */
+%typemap(in,numinputs=0)
+ (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 )
+ (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp)
+{
+ $1 = &data_temp;
+ $2 = &dim1_temp;
+ $3 = &dim2_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements")
+ (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
+{
+ npy_intp dims[2] = { *$2, *$3 };
+ PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array || !require_fortran(array)) SWIG_fail;
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2)
+ */
+%typemap(in,numinputs=0)
+ (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEW_FARRAY2)
+ (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp = NULL )
+{
+ $1 = &dim1_temp;
+ $2 = &dim2_temp;
+ $3 = &data_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements")
+ (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2)
+{
+ npy_intp dims[2] = { *$1, *$2 };
+ PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array || !require_fortran(array)) SWIG_fail;
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2,
+ DIM_TYPE* DIM3)
+ */
+%typemap(in,numinputs=0)
+ (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 )
+ (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp)
+{
+ $1 = &data_temp;
+ $2 = &dim1_temp;
+ $3 = &dim2_temp;
+ $4 = &dim3_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility")
+ (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3)
+{
+ npy_intp dims[3] = { *$2, *$3, *$4 };
+ PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array) SWIG_fail;
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3,
+ DATA_TYPE** ARGOUTVIEW_ARRAY3)
+ */
+%typemap(in,numinputs=0)
+ (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3)
+ (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp = NULL)
+{
+ $1 = &dim1_temp;
+ $2 = &dim2_temp;
+ $3 = &dim3_temp;
+ $4 = &data_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility")
+ (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3)
+{
+ npy_intp dims[3] = { *$1, *$2, *$3 };
+ PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$4));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array) SWIG_fail;
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2,
+ DIM_TYPE* DIM3)
+ */
+%typemap(in,numinputs=0)
+ (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 )
+ (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp)
+{
+ $1 = &data_temp;
+ $2 = &dim1_temp;
+ $3 = &dim2_temp;
+ $4 = &dim3_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements")
+ (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3)
+{
+ npy_intp dims[3] = { *$2, *$3, *$4 };
+ PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array || !require_fortran(array)) SWIG_fail;
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3,
+ DATA_TYPE** ARGOUTVIEW_FARRAY3)
+ */
+%typemap(in,numinputs=0)
+ (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DATA_TYPE** ARGOUTVIEW_FARRAY3)
+ (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp = NULL )
+{
+ $1 = &dim1_temp;
+ $2 = &dim2_temp;
+ $3 = &dim3_temp;
+ $4 = &data_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements")
+ (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3)
+{
+ npy_intp dims[3] = { *$1, *$2, *$3 };
+ PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$4));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array || !require_fortran(array)) SWIG_fail;
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2,
+ DIM_TYPE* DIM3, DIM_TYPE* DIM4)
+ */
+%typemap(in,numinputs=0)
+ (DATA_TYPE** ARGOUTVIEW_ARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 )
+ (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp)
+{
+ $1 = &data_temp;
+ $2 = &dim1_temp;
+ $3 = &dim2_temp;
+ $4 = &dim3_temp;
+ $5 = &dim4_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility")
+ (DATA_TYPE** ARGOUTVIEW_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4)
+{
+ npy_intp dims[4] = { *$2, *$3, *$4 , *$5 };
+ PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array) SWIG_fail;
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4,
+ DATA_TYPE** ARGOUTVIEW_ARRAY4)
+ */
+%typemap(in,numinputs=0)
+ (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEW_ARRAY4)
+ (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL )
+{
+ $1 = &dim1_temp;
+ $2 = &dim2_temp;
+ $3 = &dim3_temp;
+ $4 = &dim4_temp;
+ $5 = &data_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility")
+ (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEW_ARRAY4)
+{
+ npy_intp dims[4] = { *$1, *$2, *$3 , *$4 };
+ PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array) SWIG_fail;
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DATA_TYPE** ARGOUTVIEW_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2,
+ DIM_TYPE* DIM3, DIM_TYPE* DIM4)
+ */
+%typemap(in,numinputs=0)
+ (DATA_TYPE** ARGOUTVIEW_FARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 )
+ (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp)
+{
+ $1 = &data_temp;
+ $2 = &dim1_temp;
+ $3 = &dim2_temp;
+ $4 = &dim3_temp;
+ $5 = &dim4_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements")
+ (DATA_TYPE** ARGOUTVIEW_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4)
+{
+ npy_intp dims[4] = { *$2, *$3, *$4 , *$5 };
+ PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array || !require_fortran(array)) SWIG_fail;
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4,
+ DATA_TYPE** ARGOUTVIEW_FARRAY4)
+ */
+%typemap(in,numinputs=0)
+ (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEW_FARRAY4)
+ (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL )
+{
+ $1 = &dim1_temp;
+ $2 = &dim2_temp;
+ $3 = &dim3_temp;
+ $4 = &dim4_temp;
+ $5 = &data_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements")
+ (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEW_FARRAY4)
+{
+ npy_intp dims[4] = { *$1, *$2, *$3 , *$4 };
+ PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array || !require_fortran(array)) SWIG_fail;
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/*************************************/
+/* Managed Argoutview Array Typemaps */
+/*************************************/
+
+/* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_ARRAY1, DIM_TYPE* DIM1)
+ */
+%typemap(in,numinputs=0)
+ (DATA_TYPE** ARGOUTVIEWM_ARRAY1, DIM_TYPE* DIM1 )
+ (DATA_TYPE* data_temp = NULL , DIM_TYPE dim_temp)
+{
+ $1 = &data_temp;
+ $2 = &dim_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Utilities")
+ (DATA_TYPE** ARGOUTVIEWM_ARRAY1, DIM_TYPE* DIM1)
+{
+ npy_intp dims[1] = { *$2 };
+ PyObject* obj = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$1));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array) SWIG_fail;
+
+%#ifdef SWIGPY_USE_CAPSULE
+ PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
+%#else
+ PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
+%#endif
+
+%#if NPY_API_VERSION < 0x00000007
+ PyArray_BASE(array) = cap;
+%#else
+ PyArray_SetBaseObject(array,cap);
+%#endif
+
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEWM_ARRAY1)
+ */
+%typemap(in,numinputs=0)
+ (DIM_TYPE* DIM1 , DATA_TYPE** ARGOUTVIEWM_ARRAY1)
+ (DIM_TYPE dim_temp, DATA_TYPE* data_temp = NULL )
+{
+ $1 = &dim_temp;
+ $2 = &data_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Utilities")
+ (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEWM_ARRAY1)
+{
+ npy_intp dims[1] = { *$1 };
+ PyObject* obj = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$2));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array) SWIG_fail;
+
+%#ifdef SWIGPY_USE_CAPSULE
+ PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
+%#else
+ PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
+%#endif
+
+%#if NPY_API_VERSION < 0x00000007
+ PyArray_BASE(array) = cap;
+%#else
+ PyArray_SetBaseObject(array,cap);
+%#endif
+
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
+ */
+%typemap(in,numinputs=0)
+ (DATA_TYPE** ARGOUTVIEWM_ARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 )
+ (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp)
+{
+ $1 = &data_temp;
+ $2 = &dim1_temp;
+ $3 = &dim2_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Utilities")
+ (DATA_TYPE** ARGOUTVIEWM_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
+{
+ npy_intp dims[2] = { *$2, *$3 };
+ PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array) SWIG_fail;
+
+%#ifdef SWIGPY_USE_CAPSULE
+ PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
+%#else
+ PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
+%#endif
+
+%#if NPY_API_VERSION < 0x00000007
+ PyArray_BASE(array) = cap;
+%#else
+ PyArray_SetBaseObject(array,cap);
+%#endif
+
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_ARRAY2)
+ */
+%typemap(in,numinputs=0)
+ (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEWM_ARRAY2)
+ (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp = NULL )
+{
+ $1 = &dim1_temp;
+ $2 = &dim2_temp;
+ $3 = &data_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Utilities")
+ (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_ARRAY2)
+{
+ npy_intp dims[2] = { *$1, *$2 };
+ PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array) SWIG_fail;
+
+%#ifdef SWIGPY_USE_CAPSULE
+ PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
+%#else
+ PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
+%#endif
+
+%#if NPY_API_VERSION < 0x00000007
+ PyArray_BASE(array) = cap;
+%#else
+ PyArray_SetBaseObject(array,cap);
+%#endif
+
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
+ */
+%typemap(in,numinputs=0)
+ (DATA_TYPE** ARGOUTVIEWM_FARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 )
+ (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp)
+{
+ $1 = &data_temp;
+ $2 = &dim1_temp;
+ $3 = &dim2_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements,NumPy_Utilities")
+ (DATA_TYPE** ARGOUTVIEWM_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
+{
+ npy_intp dims[2] = { *$2, *$3 };
+ PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array || !require_fortran(array)) SWIG_fail;
+
+%#ifdef SWIGPY_USE_CAPSULE
+ PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
+%#else
+ PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
+%#endif
+
+%#if NPY_API_VERSION < 0x00000007
+ PyArray_BASE(array) = cap;
+%#else
+ PyArray_SetBaseObject(array,cap);
+%#endif
+
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_FARRAY2)
+ */
+%typemap(in,numinputs=0)
+ (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEWM_FARRAY2)
+ (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp = NULL )
+{
+ $1 = &dim1_temp;
+ $2 = &dim2_temp;
+ $3 = &data_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements,NumPy_Utilities")
+ (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_FARRAY2)
+{
+ npy_intp dims[2] = { *$1, *$2 };
+ PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array || !require_fortran(array)) SWIG_fail;
+
+%#ifdef SWIGPY_USE_CAPSULE
+ PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
+%#else
+ PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
+%#endif
+
+%#if NPY_API_VERSION < 0x00000007
+ PyArray_BASE(array) = cap;
+%#else
+ PyArray_SetBaseObject(array,cap);
+%#endif
+
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2,
+ DIM_TYPE* DIM3)
+ */
+%typemap(in,numinputs=0)
+ (DATA_TYPE** ARGOUTVIEWM_ARRAY3, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 )
+ (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp)
+{
+ $1 = &data_temp;
+ $2 = &dim1_temp;
+ $3 = &dim2_temp;
+ $4 = &dim3_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Utilities")
+ (DATA_TYPE** ARGOUTVIEWM_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3)
+{
+ npy_intp dims[3] = { *$2, *$3, *$4 };
+ PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array) SWIG_fail;
+
+%#ifdef SWIGPY_USE_CAPSULE
+ PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
+%#else
+ PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
+%#endif
+
+%#if NPY_API_VERSION < 0x00000007
+ PyArray_BASE(array) = cap;
+%#else
+ PyArray_SetBaseObject(array,cap);
+%#endif
+
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3,
+ DATA_TYPE** ARGOUTVIEWM_ARRAY3)
+ */
+%typemap(in,numinputs=0)
+ (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DATA_TYPE** ARGOUTVIEWM_ARRAY3)
+ (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp = NULL )
+{
+ $1 = &dim1_temp;
+ $2 = &dim2_temp;
+ $3 = &dim3_temp;
+ $4 = &data_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Utilities")
+ (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEWM_ARRAY3)
+{
+ npy_intp dims[3] = { *$1, *$2, *$3 };
+ PyObject* obj= PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$4));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array) SWIG_fail;
+
+%#ifdef SWIGPY_USE_CAPSULE
+ PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
+%#else
+ PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
+%#endif
+
+%#if NPY_API_VERSION < 0x00000007
+ PyArray_BASE(array) = cap;
+%#else
+ PyArray_SetBaseObject(array,cap);
+%#endif
+
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2,
+ DIM_TYPE* DIM3)
+ */
+%typemap(in,numinputs=0)
+ (DATA_TYPE** ARGOUTVIEWM_FARRAY3, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 )
+ (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp)
+{
+ $1 = &data_temp;
+ $2 = &dim1_temp;
+ $3 = &dim2_temp;
+ $4 = &dim3_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements,NumPy_Utilities")
+ (DATA_TYPE** ARGOUTVIEWM_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3)
+{
+ npy_intp dims[3] = { *$2, *$3, *$4 };
+ PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array || !require_fortran(array)) SWIG_fail;
+
+%#ifdef SWIGPY_USE_CAPSULE
+ PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
+%#else
+ PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
+%#endif
+
+%#if NPY_API_VERSION < 0x00000007
+ PyArray_BASE(array) = cap;
+%#else
+ PyArray_SetBaseObject(array,cap);
+%#endif
+
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3,
+ DATA_TYPE** ARGOUTVIEWM_FARRAY3)
+ */
+%typemap(in,numinputs=0)
+ (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DATA_TYPE** ARGOUTVIEWM_FARRAY3)
+ (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp = NULL )
+{
+ $1 = &dim1_temp;
+ $2 = &dim2_temp;
+ $3 = &dim3_temp;
+ $4 = &data_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements,NumPy_Utilities")
+ (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEWM_FARRAY3)
+{
+ npy_intp dims[3] = { *$1, *$2, *$3 };
+ PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$4));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array || !require_fortran(array)) SWIG_fail;
+
+%#ifdef SWIGPY_USE_CAPSULE
+ PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
+%#else
+ PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
+%#endif
+
+%#if NPY_API_VERSION < 0x00000007
+ PyArray_BASE(array) = cap;
+%#else
+ PyArray_SetBaseObject(array,cap);
+%#endif
+
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2,
+ DIM_TYPE* DIM3, DIM_TYPE* DIM4)
+ */
+%typemap(in,numinputs=0)
+ (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 )
+ (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp)
+{
+ $1 = &data_temp;
+ $2 = &dim1_temp;
+ $3 = &dim2_temp;
+ $4 = &dim3_temp;
+ $5 = &dim4_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Utilities")
+ (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4)
+{
+ npy_intp dims[4] = { *$2, *$3, *$4 , *$5 };
+ PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array) SWIG_fail;
+
+%#ifdef SWIGPY_USE_CAPSULE
+ PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
+%#else
+ PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
+%#endif
+
+%#if NPY_API_VERSION < 0x00000007
+ PyArray_BASE(array) = cap;
+%#else
+ PyArray_SetBaseObject(array,cap);
+%#endif
+
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4,
+ DATA_TYPE** ARGOUTVIEWM_ARRAY4)
+ */
+%typemap(in,numinputs=0)
+ (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEWM_ARRAY4)
+ (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL )
+{
+ $1 = &dim1_temp;
+ $2 = &dim2_temp;
+ $3 = &dim3_temp;
+ $4 = &dim4_temp;
+ $5 = &data_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Utilities")
+ (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_ARRAY4)
+{
+ npy_intp dims[4] = { *$1, *$2, *$3 , *$4 };
+ PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array) SWIG_fail;
+
+%#ifdef SWIGPY_USE_CAPSULE
+ PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
+%#else
+ PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
+%#endif
+
+%#if NPY_API_VERSION < 0x00000007
+ PyArray_BASE(array) = cap;
+%#else
+ PyArray_SetBaseObject(array,cap);
+%#endif
+
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2,
+ DIM_TYPE* DIM3, DIM_TYPE* DIM4)
+ */
+%typemap(in,numinputs=0)
+ (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 )
+ (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp)
+{
+ $1 = &data_temp;
+ $2 = &dim1_temp;
+ $3 = &dim2_temp;
+ $4 = &dim3_temp;
+ $5 = &dim4_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements,NumPy_Utilities")
+ (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3)
+{
+ npy_intp dims[4] = { *$2, *$3, *$4 , *$5 };
+ PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array || !require_fortran(array)) SWIG_fail;
+
+%#ifdef SWIGPY_USE_CAPSULE
+ PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
+%#else
+ PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
+%#endif
+
+%#if NPY_API_VERSION < 0x00000007
+ PyArray_BASE(array) = cap;
+%#else
+ PyArray_SetBaseObject(array,cap);
+%#endif
+
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4,
+ DATA_TYPE** ARGOUTVIEWM_FARRAY4)
+ */
+%typemap(in,numinputs=0)
+ (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEWM_FARRAY4)
+ (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL )
+{
+ $1 = &dim1_temp;
+ $2 = &dim2_temp;
+ $3 = &dim3_temp;
+ $4 = &dim4_temp;
+ $5 = &data_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements,NumPy_Utilities")
+ (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_FARRAY4)
+{
+ npy_intp dims[4] = { *$1, *$2, *$3 , *$4 };
+ PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array || !require_fortran(array)) SWIG_fail;
+
+%#ifdef SWIGPY_USE_CAPSULE
+ PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
+%#else
+ PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
+%#endif
+
+%#if NPY_API_VERSION < 0x00000007
+ PyArray_BASE(array) = cap;
+%#else
+ PyArray_SetBaseObject(array,cap);
+%#endif
+
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2,
+ DIM_TYPE* DIM3, DIM_TYPE* DIM4)
+ */
+%typemap(in,numinputs=0)
+ (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 )
+ (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp)
+{
+ $1 = &data_temp;
+ $2 = &dim1_temp;
+ $3 = &dim2_temp;
+ $4 = &dim3_temp;
+ $5 = &dim4_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Utilities")
+ (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4)
+{
+ npy_intp dims[4] = { *$2, *$3, *$4 , *$5 };
+ PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array) SWIG_fail;
+
+%#ifdef SWIGPY_USE_CAPSULE
+ PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
+%#else
+ PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
+%#endif
+
+%#if NPY_API_VERSION < 0x00000007
+ PyArray_BASE(array) = cap;
+%#else
+ PyArray_SetBaseObject(array,cap);
+%#endif
+
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4,
+ DATA_TYPE** ARGOUTVIEWM_ARRAY4)
+ */
+%typemap(in,numinputs=0)
+ (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEWM_ARRAY4)
+ (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL )
+{
+ $1 = &dim1_temp;
+ $2 = &dim2_temp;
+ $3 = &dim3_temp;
+ $4 = &dim4_temp;
+ $5 = &data_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Utilities")
+ (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_ARRAY4)
+{
+ npy_intp dims[4] = { *$1, *$2, *$3 , *$4 };
+ PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array) SWIG_fail;
+
+%#ifdef SWIGPY_USE_CAPSULE
+ PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
+%#else
+ PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
+%#endif
+
+%#if NPY_API_VERSION < 0x00000007
+ PyArray_BASE(array) = cap;
+%#else
+ PyArray_SetBaseObject(array,cap);
+%#endif
+
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2,
+ DIM_TYPE* DIM3, DIM_TYPE* DIM4)
+ */
+%typemap(in,numinputs=0)
+ (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 )
+ (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp)
+{
+ $1 = &data_temp;
+ $2 = &dim1_temp;
+ $3 = &dim2_temp;
+ $4 = &dim3_temp;
+ $5 = &dim4_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements,NumPy_Utilities")
+ (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4)
+{
+ npy_intp dims[4] = { *$2, *$3, *$4 , *$5 };
+ PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array || !require_fortran(array)) SWIG_fail;
+
+%#ifdef SWIGPY_USE_CAPSULE
+ PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
+%#else
+ PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
+%#endif
+
+%#if NPY_API_VERSION < 0x00000007
+ PyArray_BASE(array) = cap;
+%#else
+ PyArray_SetBaseObject(array,cap);
+%#endif
+
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4,
+ DATA_TYPE** ARGOUTVIEWM_FARRAY4)
+ */
+%typemap(in,numinputs=0)
+ (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEWM_FARRAY4)
+ (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL )
+{
+ $1 = &dim1_temp;
+ $2 = &dim2_temp;
+ $3 = &dim3_temp;
+ $4 = &dim4_temp;
+ $5 = &data_temp;
+}
+%typemap(argout,
+ fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements,NumPy_Utilities")
+ (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_FARRAY4)
+{
+ npy_intp dims[4] = { *$1, *$2, *$3 , *$4 };
+ PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5));
+ PyArrayObject* array = (PyArrayObject*) obj;
+
+ if (!array || !require_fortran(array)) SWIG_fail;
+
+%#ifdef SWIGPY_USE_CAPSULE
+ PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
+%#else
+ PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
+%#endif
+
+%#if NPY_API_VERSION < 0x00000007
+ PyArray_BASE(array) = cap;
+%#else
+ PyArray_SetBaseObject(array,cap);
+%#endif
+
+ $result = SWIG_Python_AppendOutput($result,obj);
+}
+
+%enddef /* %numpy_typemaps() macro */
+/* *************************************************************** */
+
+/* Concrete instances of the %numpy_typemaps() macro: Each invocation
+ * below applies all of the typemaps above to the specified data type.
+ */
+%numpy_typemaps(signed char , NPY_BYTE , int)
+%numpy_typemaps(unsigned char , NPY_UBYTE , int)
+%numpy_typemaps(short , NPY_SHORT , int)
+%numpy_typemaps(unsigned short , NPY_USHORT , int)
+%numpy_typemaps(int , NPY_INT , int)
+%numpy_typemaps(unsigned int , NPY_UINT , int)
+%numpy_typemaps(long , NPY_LONG , int)
+%numpy_typemaps(unsigned long , NPY_ULONG , int)
+%numpy_typemaps(long long , NPY_LONGLONG , int)
+%numpy_typemaps(unsigned long long, NPY_ULONGLONG, int)
+%numpy_typemaps(float , NPY_FLOAT , int)
+%numpy_typemaps(double , NPY_DOUBLE , int)
+
+/* ***************************************************************
+ * The follow macro expansion does not work, because C++ bool is 4
+ * bytes and NPY_BOOL is 1 byte
+ *
+ * %numpy_typemaps(bool, NPY_BOOL, int)
+ */
+
+/* ***************************************************************
+ * On my Mac, I get the following warning for this macro expansion:
+ * 'swig/python detected a memory leak of type 'long double *', no destructor found.'
+ *
+ * %numpy_typemaps(long double, NPY_LONGDOUBLE, int)
+ */
+
+#ifdef __cplusplus
+
+%include <std_complex.i>
+
+%numpy_typemaps(std::complex<float>, NPY_CFLOAT , int)
+%numpy_typemaps(std::complex<double>, NPY_CDOUBLE, int)
+
+#endif
+
+#endif /* SWIGPYTHON */
diff --git a/Modules/Wrappers/SWIG/src/otbApplication.i b/Modules/Wrappers/SWIG/src/otbApplication.i
index 39768eddbad6047e0251be34e6e97245af411f0d..3a8247b761fc0dadf99bc6aa1e372ec5f005e93b 100644
--- a/Modules/Wrappers/SWIG/src/otbApplication.i
+++ b/Modules/Wrappers/SWIG/src/otbApplication.i
@@ -6,38 +6,74 @@
Version: $Revision$
- Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
- See OTBCopyright.txt for details.
+ Copyright:
+ Centre National d'Etudes Spatiales,
+ CS Systemes d'information.
+ See OTBCopyright.txt, CSCopyright.txt for details.
+ All rights reserved.
- This software is distributed WITHOUT ANY WARRANTY; without even
- the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
- PURPOSE. See the above copyright notices for more information.
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
=========================================================================*/
%module otbApplication
-
%{
#include "itkBase.includes"
#include "otbWrapperSWIGIncludes.h"
- %}
+#include <string> // std::string
+#include <locale>
+#define SWIG_FILE_WITH_INIT
+%}
// Langage specific extension
%include "Python.i"
%include "Java.i"
%include "Ruby.i"
%include "Lua.i"
-
%include "itkMacro.i"
%include "itkBase.i"
+#if OTB_SWIGNUMPY
+%include "numpy.i"
+
+%init
+%{
+import_array();
+%}
+
+/*leave the mess to SWIG and let us not worry.*/
+%apply (signed char* INPLACE_ARRAY3, int DIM1, int DIM2, int DIM3) {(signed char* buffer, int dim1, int dim2, int dim3)};
+%apply (signed short* INPLACE_ARRAY3, int DIM1, int DIM2, int DIM3) {(signed short* buffer, int dim1, int dim2, int dim3)};
+%apply (signed int* INPLACE_ARRAY3, int DIM1, int DIM2, int DIM3) {(signed int* buffer, int dim1, int dim2, int dim3)};
+%apply (signed long* INPLACE_ARRAY3, int DIM1, int DIM2, int DIM3) {(signed long* buffer, int dim1, int dim2, int dim3)};
+%apply (float* INPLACE_ARRAY3, int DIM1, int DIM2, int DIM3) {(float* buffer, int dim1, int dim2, int dim3)};
+%apply (unsigned char* INPLACE_ARRAY3, int DIM1, int DIM2, int DIM3) {(unsigned char* buffer, int dim1, int dim2, int dim3)};
+%apply (unsigned short* INPLACE_ARRAY3, int DIM1, int DIM2, int DIM3) {(unsigned short* buffer, int dim1, int dim2, int dim3)};
+%apply (unsigned int* INPLACE_ARRAY3, int DIM1, int DIM2, int DIM3) {(unsigned int* buffer, int dim1, int dim2, int dim3)};
+%apply (unsigned long* INPLACE_ARRAY3, int DIM1, int DIM2, int DIM3) {(unsigned long* buffer, int dim1, int dim2, int dim3)};
+%apply (double* INPLACE_ARRAY3, int DIM1, int DIM2, int DIM3) {(double* buffer, int dim1, int dim2, int dim3)};
+
+%apply (signed char** ARGOUTVIEW_ARRAY3, int *DIM1, int *DIM2, int *DIM3) {(signed char** buffer, int *dim1, int *dim2, int *dim3)};
+%apply (signed short** ARGOUTVIEW_ARRAY3, int *DIM1, int *DIM2, int *DIM3) {(signed short** buffer, int *dim1, int *dim2, int *dim3)};
+%apply (signed int** ARGOUTVIEW_ARRAY3, int *DIM1, int *DIM2, int *DIM3) {(signed int** buffer, int *dim1, int *dim2, int *dim3)};
+%apply (signed long** ARGOUTVIEW_ARRAY3, int *DIM1, int *DIM2, int *DIM3) {(signed long** buffer, int *dim1, int *dim2, int *dim3)};
+%apply (float** ARGOUTVIEW_ARRAY3, int *DIM1, int *DIM2, int *DIM3) {(float** buffer, int *dim1, int *dim2, int *dim3)};
+%apply (unsigned char** ARGOUTVIEW_ARRAY3, int *DIM1, int *DIM2, int *DIM3) {(unsigned char** buffer, int *dim1, int *dim2, int *dim3)};
+%apply (unsigned short** ARGOUTVIEW_ARRAY3, int *DIM1, int *DIM2, int *DIM3) {(unsigned short** buffer, int *dim1, int *dim2, int *dim3)};
+%apply (unsigned int** ARGOUTVIEW_ARRAY3, int *DIM1, int *DIM2, int *DIM3) {(unsigned int** buffer, int *dim1, int *dim2, int *dim3)};
+%apply (unsigned long** ARGOUTVIEW_ARRAY3, int *DIM1, int *DIM2, int *DIM3) {(unsigned long** buffer, int *dim1, int *dim2, int *dim3)};
+%apply (double** ARGOUTVIEW_ARRAY3, int *DIM1, int *DIM2, int *DIM3) {(double** buffer, int *dim1, int *dim2, int *dim3)};
+
+#endif /* OTB_SWIGNUMPY */
+
namespace otb
{
namespace Wrapper
{
-
enum DefaultValueMode
{
DefaultValueMode_UNKNOWN,
@@ -90,19 +126,20 @@ namespace Wrapper
ImagePixelType_double,
} ImagePixelType;
- typedef enum
- {
- Role_Input,
- Role_Output
- } Role;
+ typedef enum
+ {
+ Role_Input,
+ Role_Output
+ } Role;
- typedef enum
- {
- ComplexImagePixelType_float,
- ComplexImagePixelType_double,
- } ComplexImagePixelType;
+ typedef enum
+ {
+ ComplexImagePixelType_float,
+ ComplexImagePixelType_double,
+ } ComplexImagePixelType;
}
+
}
class Application: public itkObject
@@ -120,11 +157,13 @@ public:
int ExecuteAndWriteOutput();
std::vector<std::string> GetParametersKeys(bool recursive = true);
+ Parameter* Application::GetParameterByKey(std::string name);
std::string GetParameterName(std::string);
std::string GetParameterDescription(std::string);
void SetParameterDescription(std::string paramKey, std::string dec);
void SetParameterUserValue(std::string paramKey, bool value);
+
void EnableParameter(std::string paramKey);
void DisableParameter(std::string paramKey);
bool IsParameterEnabled(std::string paramKey) const;
@@ -153,7 +192,6 @@ public:
void SetParameterStringList(std::string parameter, std::vector<std::string> values, bool hasUserValueFlag);
void SetParameterEmpty(std::string parameter, bool value, bool hasUserValueFlag);
-
void SetParameterOutputImagePixelType(std::string parameter, otb::Wrapper::ImagePixelType pixelType);
void SetParameterComplexOutputImagePixelType(std::string parameter, otb::Wrapper::ComplexImagePixelType cpixelType);
@@ -184,6 +222,7 @@ public:
void AddDocTag( const std::string & tag );
std::vector<std::string> GetDocTags();
+ otb::Wrapper::ParameterGroup* GetParameterList();
unsigned int GetNumberOfExamples();
std::string GetExampleComment(unsigned int id);
@@ -199,6 +238,161 @@ public:
std::string GetHtmlExample();
std::vector< std::pair<std::string, std::string> > GetOutputParametersSumUp();
+#if OTB_SWIGNUMPY
+ %extend {
+
+#define SetFromNumpyArrayMacro(prefix, PixelDataType, ImageClass) \
+ void Set##ImageClass##From##prefix##NumpyArray_(std::string pkey, ##PixelDataType##* buffer, int dim1, int dim2, int dim3) \
+ { \
+ otb::Wrapper::Parameter *parameter = $self->GetParameterList()->GetParameterByKey(pkey); \
+ InputImageParameter* inputImageParam = dynamic_cast<InputImageParameter*>(parameter); \
+ typedef otb::##ImageClass##<##PixelDataType##> ImageType; \
+ typename ImageType::Pointer output = ImageType::New(); \
+ typedef typename ImageType::SizeType SizeType; \
+ typedef typename ImageType::IndexType IndexType; \
+ typedef typename ImageType::RegionType RegionType; \
+ typedef typename ImageType::PointType PointType; \
+ typedef typename ImageType::SpacingType SpacingType; \
+ typedef typename ImageType::DirectionType DirectionType; \
+ IndexType start; \
+ DirectionType direction; \
+ start.Fill( 0 ); \
+ SizeType size; \
+ size[0] = dim2; size[1] = dim1; \
+ SetVectorLengthMacro \
+ output->Allocate(); \
+ unsigned int numberOfPixels = dim1 * dim2 * dim3; \
+ RegionType region; \
+ region.SetIndex( start ); \
+ region.SetSize( size ); \
+ PointType origin; \
+ origin.Fill( 0.0 ); \
+ SpacingType spacing; \
+ spacing.Fill( 1.0 ); \
+ direction.SetIdentity(); \
+ output->SetOrigin( origin ); \
+ output->SetSpacing( spacing ); \
+ output->SetDirection(direction); \
+ output->SetLargestPossibleRegion(region); \
+ output->SetRequestedRegion(output->GetLargestPossibleRegion()); \
+ output->SetBufferedRegion(output->GetLargestPossibleRegion()); \
+ output->GetPixelContainer()->SetImportPointer(buffer, numberOfPixels, false); \
+ inputImageParam->SetImage<ImageType>(output); \
+ }
+
+#define SetVectorLengthMacro output->SetVectorLength(dim3);
+ SetFromNumpyArrayMacro(Float, float, VectorImage)
+ SetFromNumpyArrayMacro(Int8, signed char, VectorImage)
+ SetFromNumpyArrayMacro(Int16, signed short, VectorImage)
+ SetFromNumpyArrayMacro(Int32, signed int, VectorImage)
+ SetFromNumpyArrayMacro(Int64, signed long, VectorImage)
+ SetFromNumpyArrayMacro(UInt8, unsigned char, VectorImage)
+ SetFromNumpyArrayMacro(UInt16, unsigned short, VectorImage)
+ SetFromNumpyArrayMacro(UInt32, unsigned int, VectorImage)
+ SetFromNumpyArrayMacro(UInt64, unsigned long, VectorImage)
+ SetFromNumpyArrayMacro(Double, double, VectorImage)
+#undef SetVectorLengthMacro
+
+#define SetVectorLengthMacro dim3=1;
+ SetFromNumpyArrayMacro(Float, float, Image)
+ SetFromNumpyArrayMacro(Int8, signed char, Image)
+ SetFromNumpyArrayMacro(Int16, signed short, Image)
+ SetFromNumpyArrayMacro(Int32, signed int, Image)
+ SetFromNumpyArrayMacro(Int64, signed long, Image)
+ SetFromNumpyArrayMacro(UInt8, unsigned char, Image)
+ SetFromNumpyArrayMacro(UInt16, unsigned short, Image)
+ SetFromNumpyArrayMacro(UInt32, unsigned int, Image)
+ SetFromNumpyArrayMacro(UInt64, unsigned long, Image)
+ SetFromNumpyArrayMacro(Double, double, Image)
+#undef SetVectorLengthMacro
+#undef SetFromNumpyArrayMacro
+
+#define GetVectorImageAsNumpyArrayMacro(prefix, PixelType) \
+ void GetVectorImageAs##prefix##NumpyArray_(std::string pkey, ##PixelType##** buffer, int *dim1, int *dim2, int *dim3) \
+ { \
+ otb::Wrapper::Parameter *parameter = $self->GetParameterList()->GetParameterByKey(pkey); \
+ OutputImageParameter* outputImageParam = dynamic_cast<OutputImageParameter*>(parameter); \
+ typedef itk::ImageBase<2> ImageBaseType; \
+ typedef typename ImageBaseType::RegionType RegionType; \
+ ImageBaseType::Pointer imageBase; \
+ imageBase = outputImageParam->GetValue(); \
+ imageBase->Update(); \
+ typedef typename ImageBaseType::SizeType SizeType; \
+ typedef typename ImageBaseType::IndexType IndexType; \
+ typedef typename ImageBaseType::PointType PointType; \
+ typedef typename ImageBaseType::SpacingType SpacingType; \
+ RegionType region = imageBase->GetBufferedRegion(); \
+ SizeType size = region.GetSize(); \
+ *dim1 = size[1]; \
+ *dim2 = size[0]; \
+ typedef otb::VectorImage<signed char> Int8ImageType; \
+ typedef otb::VectorImage<signed short> Int16ImageType; \
+ typedef otb::VectorImage<signed int> Int32ImageType; \
+ typedef otb::VectorImage<unsigned char> UInt8ImageType; \
+ typedef otb::VectorImage<unsigned short> UInt16ImageType; \
+ typedef otb::VectorImage<unsigned int> UInt32ImageType; \
+ typedef otb::VectorImage<float> FloatImageType; \
+ typedef otb::VectorImage<double> DoubleImageType; \
+ if (dynamic_cast<UInt8ImageType*>(imageBase.GetPointer())) \
+ { \
+ UInt8ImageType* output = dynamic_cast<UInt8ImageType*>(imageBase.GetPointer()); \
+ *buffer = reinterpret_cast<##PixelType##*>(output->GetBufferPointer()); \
+ *dim3 = output->GetNumberOfComponentsPerPixel(); \
+ } \
+ else if (dynamic_cast<Int16ImageType*>(imageBase.GetPointer())) \
+ { \
+ Int16ImageType* output = dynamic_cast<Int16ImageType*>(imageBase.GetPointer()); \
+ *buffer = reinterpret_cast<##PixelType##*>(output->GetBufferPointer()); \
+ *dim3 = output->GetNumberOfComponentsPerPixel(); \
+ } \
+ else if (dynamic_cast<UInt16ImageType*>(imageBase.GetPointer())) \
+ { \
+ UInt16ImageType* output = dynamic_cast<UInt16ImageType*>(imageBase.GetPointer()); \
+ *buffer = reinterpret_cast<##PixelType##*>(output->GetBufferPointer()); \
+ *dim3 = output->GetNumberOfComponentsPerPixel(); \
+ } \
+ else if (dynamic_cast<Int32ImageType*>(imageBase.GetPointer())) \
+ { \
+ Int32ImageType* output = dynamic_cast<Int32ImageType*>(imageBase.GetPointer()); \
+ *buffer = reinterpret_cast<##PixelType##*>(output->GetBufferPointer()); \
+ *dim3 = output->GetNumberOfComponentsPerPixel(); \
+ } \
+ else if (dynamic_cast<UInt32ImageType*>(imageBase.GetPointer())) \
+ { \
+ UInt32ImageType* output = dynamic_cast<UInt32ImageType*>(imageBase.GetPointer()); \
+ *buffer = reinterpret_cast<##PixelType##*>(output->GetBufferPointer()); \
+ *dim3 = output->GetNumberOfComponentsPerPixel(); \
+ } \
+ else if (dynamic_cast<FloatImageType*>(imageBase.GetPointer())) \
+ { \
+ FloatImageType* output = dynamic_cast<FloatImageType*>(imageBase.GetPointer()); \
+ *buffer = reinterpret_cast<##PixelType##*>(output->GetBufferPointer()); \
+ *dim3 = output->GetNumberOfComponentsPerPixel(); \
+ } \
+ else if (dynamic_cast<DoubleImageType*>(imageBase.GetPointer())) \
+ { \
+ DoubleImageType* output = dynamic_cast<DoubleImageType*>(imageBase.GetPointer()); \
+ *buffer = reinterpret_cast<##PixelType##*>(output->GetBufferPointer()); \
+ *dim3 = output->GetNumberOfComponentsPerPixel(); \
+ } \
+ else \
+ { \
+ std::cerr << "unknown image type. cannot make a numpy array" << std::endl; \
+ } \
+ }
+
+ GetVectorImageAsNumpyArrayMacro(Float, float)
+ GetVectorImageAsNumpyArrayMacro(Int16, signed short)
+ GetVectorImageAsNumpyArrayMacro(Int32, signed int)
+ GetVectorImageAsNumpyArrayMacro(UInt8, unsigned char)
+ GetVectorImageAsNumpyArrayMacro(UInt16, unsigned short)
+ GetVectorImageAsNumpyArrayMacro(UInt32, unsigned int)
+ GetVectorImageAsNumpyArrayMacro(Double, double)
+#undef GetVectorImageAsNumpyArrayMacro
+
+} /* end of %extend */
+#endif /* OTB_SWIGNUMPY */
+
protected:
Application();
#if SWIGJAVA
@@ -208,57 +402,351 @@ private:
Application(const Application &);
void operator =(const Application&);
};
+
DECLARE_REF_COUNT_CLASS( Application )
+
+ /* Int8 Int16 Int32 Int64 */
+ /* UInt8 UInt16 UInt32 UInt64 */
+ /* Float32 Double64 */
+ /* Complex32 Complex64 */
+
+ /* typedef signed char Int8; */
+ /* typedef signed short Int16; */
+ /* typedef signed int Int32; */
+ /* typedef signed long Int64; */
+ /* typedef unsigned char UInt8; */
+ /* typedef unsigned short UInt16; */
+ /* typedef unsigned int UInt32; */
+ /* typedef unsigned long UInt64; */
+ /* typedef float Float32; */
+ /* typedef double Float64; */
+
#if SWIGPYTHON
+%pythoncode {
+import sys
+
+class ApplicationProxy(object):
+ def __init__(self, application, groupkey, value = None):
+ self.__dict__["application"] = application
+ self.__dict__["groupkey"] = groupkey
+ if value is not None:
+ self.__dict__["application"].SetParameterString(groupkey, value)
+
+ def __str__(self):
+ return self.__dict__["application"].GetParameterAsString(self.groupkey)
+
+ def __eq__(self, other):
+ if not type(other) == type(self):
+ return (self.__str__() == other)
+ else:
+ return (isinstance(other, self.__class__) and self.__dict__ == other.__dict__)
+
+ def __ne__(self, other):
+ return not self.__eq__(other)
+
+ def __getattr__(self,attr):
+ return self.__dict__["application"].GetParameterValue( self.groupkey + "." + attr.lower() )
+
+ def __setattr__(self,attr,value):
+ if attr not in self.__dict__:
+ return self.__dict__["application"].SetParameterValue( self.groupkey + "." + attr.lower(), value )
+ else:
+ return dict.__setattr__(self, attr, value)
+
+
+}
+#endif
+
+#if SWIGPYTHON
+/*Maybe TODO: change use a dict to GetParameterTypeAsString */
+
%extend Application {
%pythoncode {
- def GetParameterValue(self, paramKey):
- paramType = self.GetParameterType(paramKey)
- if paramType in [ParameterType_InputProcessXML, ParameterType_Choice,
- ParameterType_String, ParameterType_InputFilename,
- ParameterType_OutputImage, ParameterType_OutputVectorData,
- ParameterType_OutputProcessXML, ParameterType_OutputFilename,
- ParameterType_Directory, ParameterType_InputImage,
- ParameterType_ComplexInputImage, ParameterType_InputVectorData]:
- return self.GetParameterString(paramKey)
-
- elif paramType in [ ParameterType_InputImageList, ParameterType_InputVectorDataList,
- ParameterType_InputFilenameList, ParameterType_StringList,
- ParameterType_ListView]:
- return self.GetParameterStringList(paramKey)
-
- elif paramType in [ParameterType_Int, ParameterType_Radius, ParameterType_RAM]:
- return self.GetParameterInt(paramKey)
-
- elif paramType in [ParameterType_Float]:
- return self.GetParameterFloat(paramKey)
-
- elif paramType in [ParameterType_Empty]:
- return self.IsParameterEnabled(paramKey)
-
- else:
- print "Unsupported parameter type for '" + paramKey + "'"
- return None
+
+ def __str__(self):
+ s = self.GetDocName()
+
+ def GetParameterTypeAsString(self, parameter_type):
+ return {
+ ParameterType_InputProcessXML : 'ParameterType_InputProcessXML',
+ ParameterType_String : 'ParameterType_String',
+ ParameterType_InputFilename : 'ParameterType_InputFilename',
+ ParameterType_OutputImage : 'ParameterType_OutputImage',
+ ParameterType_OutputVectorData : 'ParameterType_OutputVectorData',
+ ParameterType_OutputProcessXML : 'ParameterType_OutputProcessXML',
+ ParameterType_OutputFilename : 'ParameterType_OutputFilename',
+ ParameterType_Directory : 'ParameterType_Directory',
+ ParameterType_InputImage : 'ParameterType_InputImage',
+ ParameterType_ComplexInputImage : 'ParameterType_ComplexInputImage',
+ ParameterType_InputVectorData : 'ParameterType_InputVectorData',
+ ParameterType_InputImageList : 'ParameterType_InputImageList',
+ ParameterType_InputVectorDataList : 'ParameterType_InputImageList',
+ ParameterType_InputFilenameList : 'ParameterType_InputFilenameList',
+ ParameterType_StringList : 'ParameterType_StringList',
+ ParameterType_ListView : 'ParameterType_ListView',
+ ParameterType_Int : 'ParameterType_Int',
+ ParameterType_Radius : 'ParameterType_Radius',
+ ParameterType_RAM : 'ParameterType_RAM',
+ ParameterType_Float : 'ParameterType_Float',
+ ParameterType_Empty : 'ParameterType_Empty',
+ ParameterType_Choice : 'ParameterType_Choice',
+ ParameterType_Group : 'ParameterType_Group',
+ }.get(parameter_type, 'ParameterType_UNKNOWN')
+
+
+ def __str__(self):
+ s = self.GetDocName()
+ s += '\n'
+ s += self.GetDocLongDescription()
+ return s
+
+ def SetParameterValue(self, paramKey, value):
+ paramType = self.GetParameterType(paramKey)
+ if paramType in [ParameterType_InputProcessXML, ParameterType_RAM,
+ ParameterType_String, ParameterType_InputFilename,
+ ParameterType_OutputImage, ParameterType_OutputVectorData,
+ ParameterType_OutputProcessXML, ParameterType_OutputFilename,
+ ParameterType_Directory, ParameterType_InputImage,
+ ParameterType_ComplexInputImage, ParameterType_InputVectorData]:
+ return self.SetParameterString(paramKey, value)
+ elif paramType in [ParameterType_InputImageList, ParameterType_InputVectorDataList,
+ ParameterType_InputFilenameList, ParameterType_StringList,
+ ParameterType_ListView]:
+ return self.setParameterStringList(paramKey, value)
+ elif paramType in [ParameterType_Int, ParameterType_Radius]:
+ return self.SetParameterInt(paramKey, value)
+ elif paramType in [ParameterType_Float]:
+ return self.SetParameterFloat(paramKey, value)
+ elif paramType in [ParameterType_Empty]:
+ return self.EnableParameter(paramKey)
+ elif paramType in [ParameterType_Group]:
+ return ApplicationProxy(self, paramKey)
+ elif paramType in [ParameterType_Choice]:
+ return ApplicationProxy(self, paramKey, value)
+ else:
+ print "Unsupported parameter type '%s' with key '%s'" %(self.GetParameterTypeAsString(paramType) ,paramKey)
+ return
+
+ def GetParameterValue(self, paramKey):
+ paramType = self.GetParameterType(paramKey)
+ if paramType in [ParameterType_InputProcessXML,
+ ParameterType_String, ParameterType_InputFilename,
+ ParameterType_OutputImage, ParameterType_OutputVectorData,
+ ParameterType_OutputProcessXML, ParameterType_OutputFilename,
+ ParameterType_Directory, ParameterType_InputImage,
+ ParameterType_ComplexInputImage, ParameterType_InputVectorData]:
+ return self.GetParameterString(paramKey)
+ elif paramType in [ParameterType_InputImageList, ParameterType_InputVectorDataList,
+ ParameterType_InputFilenameList, ParameterType_StringList,
+ ParameterType_ListView]:
+ return self.GetParameterStringList(paramKey)
+ elif paramType in [ParameterType_Int, ParameterType_Radius, ParameterType_RAM]:
+ return self.GetParameterInt(paramKey)
+ elif paramType in [ParameterType_Float]:
+ return self.GetParameterFloat(paramKey)
+ elif paramType in [ParameterType_Empty]:
+ return self.IsParameterEnabled(paramKey)
+ elif paramType in [ParameterType_Group, ParameterType_Choice]:
+ return ApplicationProxy(self, paramKey)
+ else:
+ print "Unsupported parameter type '%s' with key '%s'" %(self.GetParameterTypeAsString(paramType) ,paramKey)
+ return None
+
+ def __getattr__(self,attr):
+ """
+ __get_attribute__ is called whenever an instance request an attribute.
+ eg: App.SetParameterString(), App.GetName() ..
+ __getattr__ is only called if the attribute is not found by __get_attribute__ call
+ So we keep hide the GetParameter** calls within this method so that it seems like
+ an obivous call for users. App.IN , App.OUT , where 'in' and 'out' are
+ parameters in the 'otb application' with instance App
+ """
+ if attr is not None:
+ key_list = [k.upper() for k in self.GetParametersKeys(True)]
+ if attr in key_list:
+ return self.GetParameterValue(attr.lower())
+ else:
+ raise AttributeError
+
+ def __setattr__(self, attr, value):
+ """
+ __setattr__ is called if the attribute requested is not found in the attribute list.
+ So these attributes are supposed to be 'key' of parameters used. Here we
+ keep hide the SetParameter** calls within this method so that it seems like
+ an obivous call for users. App.IN='my-input-file-name' , App.OUT='my-output-file-name'w
+ here 'in' and 'out' are parameters in the 'otb application' with instance App
+ Ofcourse, we dont blindly accept any attributes as python, we check them against
+ list of existing parameters for application with 'self.GetParametersKeys(True)'
+ """
+ if attr is not None:
+ key_list = [k.upper() for k in self.GetParametersKeys(True)]
+ if attr in key_list:
+ self.SetParameterValue(attr.lower(), value)
+ else:
+ raise AttributeError
}
}
#endif
+#if OTB_SWIGNUMPY
+%extend Application {
+ %pythoncode {
+
+ def SetImageFromNumpyArray(self, paramKey, npArray):
+ """
+ This method takes a numpy array and set ImageIOBase of
+ InputImageParameter by creating an otbImage with
+ same pixel type as numpyarray.dtype
+ """
+ if len(npArray.shape) == 3:
+ raise ValueError( "(len(npArray.shape) == 3)\n"
+ "Input array given is of 3 dimension.\n"
+ "SetImageFromNumpyArray create ImageIO from otbImage and thus demands a 2d array.\n"
+ "you can either provide an 2d numpy array or use SetVectorImageFromNumpyArray depending on your application.\n")
+
+ dt = npArray.dtype.name
+ if dt == 'int8':
+ self.SetImageFromInt8NumpyArray_(paramKey, npArray)
+ elif dt == 'int16':
+ self.SetImageFromInt16NumpyArray_(paramKey, npArray)
+ elif dt == 'int32':
+ self.SetImageFromInt32NumpyArray_(paramKey, npArray)
+ elif dt == 'uint8':
+ self.SetImageFromUInt8NumpyArray_(paramKey, npArray)
+ elif dt == 'uint16':
+ self.SetImageFromUInt16NumpyArray_(paramKey, npArray)
+ elif dt == 'uint32':
+ self.SetImageFromUInt32NumpyArray_(paramKey, npArray)
+ elif dt == 'float':
+ self.SetImageFromFloatNumpyArray_(paramKey, npArray)
+ elif dt == 'double':
+ self.SetImageFromDoubleNumpyArray_(paramKey, npArray)
+ else:
+ self.SetImageFromFloatNumpyArray_(paramKey, npArray)
+ return
+
+ def SetVectorImageFromNumpyArray(self, paramKey, npArray):
+ """
+ This method takes a numpy array and set ImageIOBase of
+ InputImageParameter by creating an otbVectorImage with
+ same pixel type as numpyarray.dtype.
+ NOTE: Input (npArray) must be an ndarray with 3 dimension,
+ len(npArray.shape) must be > 2
+ """
+ if len(npArray.shape) < 3:
+ raise ValueError( "(len(npArray.shape) < 3)\n"
+ "Input array given is not of 3 dimension.\n"
+ "SetVectorImageFromNumpyArray create ImageIO from otbVectorImage and thus demands an array of shape 3.\n"
+ "you can either provide an 3d numpy array or use SetImageFromNumpyArray depending on your application.\n")
+
+ dt = npArray.dtype.name
+ if dt == 'int8':
+ self.SetVectorImageFromInt8NumpyArray_(paramKey, npArray)
+ elif dt == 'int16':
+ self.SetVectorImageFromInt16NumpyArray_(paramKey, npArray)
+ elif dt == 'int32':
+ self.SetVectorImageFromInt32NumpyArray_(paramKey, npArray)
+ elif dt == 'uint8':
+ self.SetVectorImageFromUInt8NumpyArray_(paramKey, npArray)
+ elif dt == 'uint16':
+ self.SetVectorImageFromUInt16NumpyArray_(paramKey, npArray)
+ elif dt == 'uint32':
+ self.SetVectorImageFromUInt32NumpyArray_(paramKey, npArray)
+ elif dt == 'float':
+ self.SetVectorImageFromFloatNumpyArray_(paramKey, npArray)
+ elif dt == 'double':
+ self.SetVectorImageFromDoubleNumpyArray_(paramKey, npArray)
+ else:
+ self.SetVectorImageFromFloatNumpyArray_(paramKey, npArray)
+ return
+
+ def GetVectorImageAsNumpyArray(self, paramKey, dt='float'):
+ """
+ If datatype is unknown this method assumes to numpy.float32
+ Valid datatypes are:
+ int8, int16, int32, uint8, uint16, uint32, float, double.
+ NOTE: This method always return an numpy array with dimension 3
+ """
+ if dt == 'int8':
+ return self.GetVectorImageAsInt8NumpyArray_(paramKey)
+ elif dt == 'int16':
+ return self.GetVectorImageAsInt16NumpyArray_(paramKey)
+ elif dt == 'int32':
+ return self.GetVectorImageAsInt32NumpyArray_(paramKey)
+ elif dt == 'uint8':
+ return self.GetVectorImageAsUInt8NumpyArray_(paramKey)
+ elif dt == 'uint16':
+ return self.GetVectorImageAsUInt16NumpyArray_(paramKey)
+ elif dt == 'uint32':
+ return self.GetVectorImageAsUInt32NumpyArray_(paramKey)
+ elif dt == 'float':
+ return self.GetVectorImageAsFloatNumpyArray_(paramKey)
+ elif dt == 'double':
+ return self.GetVectorImageAsDoubleNumpyArray_(paramKey)
+ else:
+ print "Unknown datatype '" + dt + "'. Using float instead. Available types are:"
+ print "int8, int16, int32, uint8, uint16, uint32, float, double"
+ return self.GetVectorImageAsFloatNumpyArray_(paramKey)
+
+ def GetImageAsNumpyArray(self, paramKey, dt='float'):
+ """
+ If datatype is unknown this method assumes to numpy.float32
+ Valid datatypes are:
+ int8, int16, int32, uint8, uint16, uint32, float, double.
+ NOTE: This method always return an numpy array with dimension 3
+ """
+ if dt == 'int8':
+ numpy_vector_image = self.GetVectorImageAsInt8NumpyArray_(paramKey)
+ elif dt == 'int16':
+ numpy_vector_image = self.GetVectorImageAsInt16NumpyArray_(paramKey)
+ elif dt == 'int32':
+ numpy_vector_image = self.GetVectorImageAsInt32NumpyArray_(paramKey)
+ elif dt == 'uint8':
+ numpy_vector_image = self.GetVectorImageAsUInt8NumpyArray_(paramKey)
+ elif dt == 'uint16':
+ numpy_vector_image = self.GetVectorImageAsUInt16NumpyArray_(paramKey)
+ elif dt == 'uint32':
+ numpy_vector_image = self.GetVectorImageAsUInt32NumpyArray_(paramKey)
+ elif dt == 'float':
+ numpy_vector_image = self.GetVectorImageAsFloatNumpyArray_(paramKey)
+ elif dt == 'double':
+ numpy_vector_image = self.GetVectorImageAsDoubleNumpyArray_(paramKey)
+
+ else:
+ print "Unknown datatype '" + dt + "'. Using float instead. Available types are:"
+ print "int8, int16, int32, uint8, uint16, uint32, float, double"
+ numpy_vector_image = self.GetVectorImageAsFloatNumpyArray_(paramKey)
+
+ if len(numpy_vector_image.shape) > 2:
+ raise ValueError("len(numpy_vector_image.shape) > 2\n"
+ "Output image from application is of 3 dimension (len(nparray.shape) > 2). \n"
+ "GetImageFromNumpyArray returns an numpy array of dimension 2 that will result is loss of data.\n"
+ "In this case you must use GetVectorImageFromNumpyArray which is capable of return a 3 dimension image.\n")
+
+ numpy_vector_image = numpy_vector_image[:,:,1]
+ return numpy_vector_image
+
+ }
+}
+
+#endif /* OTB_SWIGNUMPY */
+
+
class Registry : public itkObject
{
public:
+
static std::vector<std::string> GetAvailableApplications();
static Application_Pointer CreateApplication(const std::string& name);
static void AddApplicationPath(std::string newpath);
static void SetApplicationPath(std::string newpath);
-
protected:
Registry();
virtual ~Registry();
};
-
class AddProcessToWatchEvent : public itkEventObject
{
public:
diff --git a/Modules/Wrappers/SWIG/src/otbWrapperSWIGIncludes.h b/Modules/Wrappers/SWIG/src/otbWrapperSWIGIncludes.h
index 3bf30746dcf81c1f9b8633c7152a4f0fdccd6f6c..80459d30c634d15b49e5f5fe345e9f2e27991c94 100644
--- a/Modules/Wrappers/SWIG/src/otbWrapperSWIGIncludes.h
+++ b/Modules/Wrappers/SWIG/src/otbWrapperSWIGIncludes.h
@@ -6,13 +6,16 @@
Version: $Revision$
- Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
- See OTBCopyright.txt for details.
+ Copyright:
+ Centre National d'Etudes Spatiales,
+ CS Systemes d'information.
+ See OTBCopyright.txt, CSCopyright.txt for details.
+ All rights reserved.
- This software is distributed WITHOUT ANY WARRANTY; without even
- the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
- PURPOSE. See the above copyright notices for more information.
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#ifndef __otbWrapperSWIGIncludes_h
@@ -27,5 +30,7 @@ typedef otb::Wrapper::Application::Pointer Application_Pointer;
typedef otb::Wrapper::ApplicationRegistry Registry;
typedef otb::Wrapper::AddProcessToWatchEvent AddProcessToWatchEvent;
typedef otb::Wrapper::DocExampleStructure DocExampleStructure;
-
+typedef otb::Wrapper::Parameter Parameter;
+typedef otb::Wrapper::OutputImageParameter OutputImageParameter;
+typedef otb::Wrapper::InputImageParameter InputImageParameter;
#endif
diff --git a/Modules/Wrappers/SWIG/test/python/CMakeLists.txt b/Modules/Wrappers/SWIG/test/python/CMakeLists.txt
index e51c82f85da0a7b73c9dc8c0ad552ca388706733..2eb46785fb95436f872bfec5c724d7070f1ce62b 100644
--- a/Modules/Wrappers/SWIG/test/python/CMakeLists.txt
+++ b/Modules/Wrappers/SWIG/test/python/CMakeLists.txt
@@ -72,3 +72,14 @@ add_test( NAME pyTvBandMathInXML
${TEMP}/pyTvBandMathInXML.tif
)
+add_test( NAME pyTvNumpyIO
+ COMMAND ${TEST_DRIVER} Execute
+ ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/PythonNumpyTest.py
+ ${OTB_DATA_ROOT}/Examples/ROI_QB_MUL_1_SVN_CLASS_MULTI.png
+ ${TEMP}/pyTvNumpyIO_SmoothingOut.png )
+
+add_test( NAME pyTvNewStyleParameters
+ COMMAND ${TEST_DRIVER} Execute
+ ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/PythonNewStyleParametersTest.py
+ ${OTB_DATA_ROOT}/Input/poupees.tif
+ ${TEMP}/pyTvNewStyleParametersTest.tif )
diff --git a/Modules/Wrappers/SWIG/test/python/PythonNewStyleParametersTest.py b/Modules/Wrappers/SWIG/test/python/PythonNewStyleParametersTest.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f15b51ec972a06578b4ac2b91f3f52955bcb972
--- /dev/null
+++ b/Modules/Wrappers/SWIG/test/python/PythonNewStyleParametersTest.py
@@ -0,0 +1,130 @@
+# -*- coding: utf-8 -*-
+# Authors: Rashad Kanavath <rashad.kanavath@c-s.fr>
+# Julien Malik <julien.malik@c-s.fr>
+#
+# Copyright: (c) CS Systemes d'information. All rights reserved
+#
+# Example on the use of otb "pythonization"
+#
+from sys import argv
+import otbApplication as otb
+
+def cm_assert(a,b):
+ print "debug print before assert check: '%s'== '%s'" %(a, b)
+ assert a == b
+
+app = otb.Registry.CreateApplication('OrthoRectification')
+
+#test GetParameterTypeAsString() method in python.
+print app.GetParameterTypeAsString(otb.ParameterType_InputImage)
+print app.GetParameterTypeAsString(otb.ParameterType_String)
+print app.GetParameterTypeAsString(otb.ParameterType_Empty)
+
+# one test for each parameter type (string, float, int, ...)
+
+# # parameter group io.in
+# 1 - input image parameter set
+app.IO.IN = argv[1]
+cm_assert(app.GetParameterString('io.in'), argv[1])
+
+# 2 - input image parameter get
+cm_assert(app.IO.IN, argv[1])
+
+# # parameter group io.in
+# 3 - output image parameter set
+app.IO.OUT = argv[2]
+cm_assert(app.GetParameterString('io.out'), argv[2])
+
+# 4 - output image parameter get
+cm_assert(app.IO.OUT, argv[2])
+
+# 5 - choice with sub parameters set
+app.MAP = 'lambert2'
+cm_assert(app.GetParameterString('map'), 'lambert2')
+
+# 5.1 - choice with sub parameters get
+app.SetParameterString('map', 'wgs')
+cm_assert('wgs', app.MAP)
+
+# 5.2 - choice with sub parameters set
+app.MAP = 'lambert93'
+cm_assert('lambert93', app.GetParameterString('map'))
+
+# 5.3 - choice with sub parameters set
+app.SetParameterString('map', 'epsg')
+cm_assert(app.MAP, 'epsg')
+
+# 6 - int type 2nd level sub parameters of choice parameter set
+app.MAP.EPSG.CODE = 32768
+cm_assert(32768, app.GetParameterInt('map.epsg.code'))
+
+# 7 - another choice with sub parameters set
+app.MAP = 'utm'
+cm_assert('utm', app.GetParameterString('map'))
+
+# 8 - int type sub parameters of choice parameter set
+app.MAP.UTM.ZONE = 47
+cm_assert(47,app.GetParameterInt('map.utm.zone'))
+
+# 9 - int type sub parameters of choice parameter get
+app.SetParameterInt('map.utm.zone', 31)
+cm_assert(app.MAP.UTM.ZONE, 31)
+
+# 10 - bool type sub parameters of choice parameter get
+app.DisableParameter('map.utm.northhem')
+cm_assert(app.MAP.UTM.NORTHHEM, False)
+
+# 11 - bool type sub parameters of choice parameter set
+app.MAP.UTM.NORTHHEM = True
+cm_assert(True, app.IsParameterEnabled('map.utm.northhem') )
+
+#12 - simple choice parameter set
+app.OUTPUTS.MODE = 'auto'
+cm_assert('auto', app.GetParameterString('outputs.mode'))
+
+#13 - simple choice parameter get
+app.SetParameterString('outputs.mode', 'orthofit')
+cm_assert(app.OUTPUTS.MODE, 'orthofit')
+
+#14 - inputxml parameter set
+app.INXML = 'input.xml'
+cm_assert(app.GetParameterString('inxml'), 'input.xml')
+
+#15 - outputxml parameter get
+app.SetParameterString('outxml', 'output.xml')
+cm_assert("output.xml", app.OUTXML)
+
+#16 - parameter float get
+app.SetParameterFloat('elev.default', 5.0)
+cm_assert(5.0, app.ELEV.DEFAULT)
+
+#17 -parameter float set
+app.ELEV.DEFAULT = -2.5
+cm_assert(app.GetParameterFloat('elev.default'), -2.5)
+
+#18 - parameter ram get
+app.SetParameterString('opt.ram', '256')
+cm_assert(256, app.OPT.RAM)
+
+#19 - parameter ram set
+app.OPT.RAM = '512'
+cm_assert(app.GetParameterInt('opt.ram'), 512)
+
+#20 - parameter bool set
+app.OUTPUTS.ISOTROPIC = True
+cm_assert(app.IsParameterEnabled('outputs.isotropic'), True)
+
+#21 - parameter bool get
+app.DisableParameter('outputs.isotropic')
+cm_assert(False, app.OUTPUTS.ISOTROPIC)
+
+#Do not execute. we need LARGEINPUT. so we tried a small application
+#app.Execute()
+
+app = None
+
+app = otb.Registry.CreateApplication('Smoothing')
+app.IN = argv[1]
+app.TYPE='anidif'
+app.OUT = argv[2]
+app.ExecuteAndWriteOutput()
diff --git a/Modules/Wrappers/SWIG/test/python/PythonNumpyTest.py b/Modules/Wrappers/SWIG/test/python/PythonNumpyTest.py
new file mode 100644
index 0000000000000000000000000000000000000000..7aee4e2a5d11fd8bfb2d2c2c0d99d30190a6d0cb
--- /dev/null
+++ b/Modules/Wrappers/SWIG/test/python/PythonNumpyTest.py
@@ -0,0 +1,64 @@
+# -*- coding: utf-8 -*-
+# Author: Rashad Kanavath <rashad.kanavath@c-s.fr>
+#
+# Copyright: (c) CS Systemes d'information. All rights reserved
+#
+# Example on testing numpy
+#
+
+import sys
+import os
+#from scipy import misc
+
+import numpy as np
+import otbApplication
+
+#from PIL import Image as PILImage
+
+inFile = sys.argv[1]
+outFile = sys.argv[2]
+# pilimage = PILImage.open(inFile)
+# npimage = np.asarray(pilimage)
+# print npimage.dtype
+
+ExtractROI = otbApplication.Registry.CreateApplication("ExtractROI")
+ExtractROI.SetParameterString("in", inFile)
+ExtractROI.SetParameterInt("startx", 10)
+ExtractROI.SetParameterInt("starty", 10)
+ExtractROI.SetParameterInt("sizex", 250)
+ExtractROI.SetParameterInt("sizey", 250)
+
+#Bug or Design ?.
+#Anyway below two is needed only for ExtractROI application
+ExtractROI.SetParameterUserValue("sizex", True)
+ExtractROI.SetParameterUserValue("sizey", True)
+ExtractROI.Execute()
+
+ExtractROIOut = ExtractROI.GetVectorImageAsNumpyArray("out", 'float')
+
+#write RGB image to file via python
+#misc.imsave('ExtractROIOut.jpg', ExtractROIOut)
+
+Rescale = otbApplication.Registry.CreateApplication("Rescale")
+#take numpy array from ExtractROI and feed into Rescale
+Rescale.SetVectorImageFromNumpyArray("in", ExtractROIOut)
+Rescale.SetParameterFloat("outmin", 50, True)
+Rescale.SetParameterFloat("outmax", 100, True)
+Rescale.Execute()
+
+RescaleOut = Rescale.GetVectorImageAsNumpyArray("out", 'float')
+#misc.imsave('RescaleOut.jpg', RescaleOut)
+
+Convert = otbApplication.Registry.CreateApplication("Convert")
+#take numpy output from Rescale application and feed into Convert
+Convert.SetVectorImageFromNumpyArray("in", RescaleOut)
+Convert.SetParameterString("out", "ConvertOut.png")
+Convert.ExecuteAndWriteOutput()
+ConvertOut = Convert.GetVectorImageAsNumpyArray("out", 'float')
+
+Smoothing = otbApplication.Registry.CreateApplication("Smoothing")
+#take numpy output from Convert application and feed into Smoothing
+Smoothing.SetVectorImageFromNumpyArray("in", ConvertOut)
+Smoothing.SetParameterString("type", 'anidif')
+Smoothing.SetParameterString("out", outFile)
+Smoothing.ExecuteAndWriteOutput()
diff --git a/SuperBuild/CMake/External_gdal.cmake b/SuperBuild/CMake/External_gdal.cmake
index a76075af1adfc2996686a606df020c750f99f151..61ce0d3b757c83b8f1755191f190832a650110ec 100644
--- a/SuperBuild/CMake/External_gdal.cmake
+++ b/SuperBuild/CMake/External_gdal.cmake
@@ -51,9 +51,9 @@ else()
INSTALL_DIR ${SB_INSTALL_PREFIX}
DOWNLOAD_DIR ${DOWNLOAD_LOCATION}
DEPENDS ${${proj}_DEPENDENCIES}
- UPDATE_COMMAND ${CMAKE_COMMAND} -E copy_directory ${GDAL_SB_SRC} ${GDAL_SB_BUILD_DIR}
- PATCH_COMMAND ${CMAKE_COMMAND} -E touch ${GDAL_SB_SRC}/config.rpath
- CONFIGURE_COMMAND
+ UPDATE_COMMAND ${CMAKE_COMMAND} -E copy_directory ${GDAL_SB_SRC} ${GDAL_SB_BUILD_DIR}
+ PATCH_COMMAND ${CMAKE_COMMAND} -E touch ${GDAL_SB_SRC}/config.rpath COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_SOURCE_DIR}/patches/GDAL/GNUmakefile ${GDAL_SB_SRC}/swig/python/GNUmakefile
+ CONFIGURE_COMMAND
# use 'env' because CTest launcher doesn't perform shell interpretation
${SB_ENV_CONFIGURE_CMD}
${GDAL_SB_BUILD_DIR}/configure
@@ -86,7 +86,7 @@ else()
INSTALL_DIR ${SB_INSTALL_PREFIX}
DOWNLOAD_DIR ${DOWNLOAD_LOCATION}
DEPENDS ${${proj}_DEPENDENCIES}
- PATCH_COMMAND ${CMAKE_COMMAND} -E copy_directory ${GDAL_SB_SRC} ${GDAL_SB_BUILD_DIR}
+ PATCH_COMMAND ${CMAKE_COMMAND} -E copy_directory ${GDAL_SB_SRC} ${GDAL_SB_BUILD_DIR} COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_SOURCE_DIR}/patches/GDAL/GNUmakefile ${GDAL_SB_SRC}/swig/python/GNUmakefile
CONFIGURE_COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_SOURCE_DIR}/patches/${proj}/ogrsqlitevirtualogr.cpp
${GDAL_SB_BUILD_DIR}/ogr/ogrsf_frmts/sqlite/ogrsqlitevirtualogr.cpp
BUILD_COMMAND nmake /f ${GDAL_SB_BUILD_DIR}/makefile.vc MSVC_VER=${MSVC_VERSION} EXT_NMAKE_OPT=${CMAKE_BINARY_DIR}/nmake_gdal_extra.opt
diff --git a/SuperBuild/CMake/External_ossim.cmake b/SuperBuild/CMake/External_ossim.cmake
index 20948d3bf68e676ee3fbad5728bb38b6355f116e..7303c9f9c0659c00f784c05526cb4530d180fd99 100644
--- a/SuperBuild/CMake/External_ossim.cmake
+++ b/SuperBuild/CMake/External_ossim.cmake
@@ -17,14 +17,14 @@ if(USE_SYSTEM_OSSIM)
else()
SETUP_SUPERBUILD(PROJECT ${proj})
message(STATUS " Using OSSIM SuperBuild version")
-
+
# declare dependencies
ADDTO_DEPENDENCIES_IF_NOT_SYSTEM(${proj} TIFF GEOTIFF GEOS JPEG OPENTHREADS FREETYPE)
INCLUDE_SUPERBUILD_DEPENDENCIES(${${proj}_DEPENDENCIES})
# set proj back to its original value
set(proj OSSIM)
-
+
ADD_SUPERBUILD_CMAKE_VAR(TIFF_INCLUDE_DIR)
ADD_SUPERBUILD_CMAKE_VAR(TIFF_LIBRARY)
ADD_SUPERBUILD_CMAKE_VAR(GEOTIFF_INCLUDE_DIR)
@@ -39,93 +39,45 @@ else()
ADD_SUPERBUILD_CMAKE_VAR(FREETYPE_LIBRARY)
set(OSSIM_CXX_FLAGS -D__STDC_CONSTANT_MACROS)
-
+
if(MSVC)
set(OSSIM_CXX_FLAGS /EHsc)
endif()
-
- if(0)
- # SVN version
- ExternalProject_Add(ossim_cmakemodules
- PREFIX ${proj}/_cmakemodules
- SVN_REPOSITORY "http://svn.osgeo.org/ossim/trunk/ossim_package_support/cmake/CMakeModules"
- SVN_REVISION -r 23087
- CONFIGURE_COMMAND ""
- BUILD_COMMAND ""
- INSTALL_COMMAND ""
- PATCH_COMMAND ${CMAKE_COMMAND} -E copy
- ${CMAKE_SOURCE_DIR}/patches/${proj}/OssimUtilities.cmake
- ${CMAKE_BINARY_DIR}/${proj}/_cmakemodules/src/ossim_cmakemodules)
-
- list(APPEND ${proj}_DEPENDENCIES ossim_cmakemodules)
-
- ExternalProject_Add(${proj}
- PREFIX ${proj}
- SVN_REPOSITORY "http://svn.osgeo.org/ossim/trunk/ossim/"
- SVN_REVISION -r 23092
- BINARY_DIR ${OSSIM_SB_BUILD_DIR}
- INSTALL_DIR ${SB_INSTALL_PREFIX}
- DOWNLOAD_DIR ${DOWNLOAD_LOCATION}
- CMAKE_CACHE_ARGS
- -DOSSIM_VERSION:STRING=1.8.18-1
- -DCMAKE_INSTALL_PREFIX:STRING=${SB_INSTALL_PREFIX}
- -DCMAKE_BUILD_TYPE:STRING=Release
- -DCMAKE_CXX_FLAGS:STRING=${OSSIM_CXX_FLAGS}
- -DBUILD_OSSIM_MPI_SUPPORT:BOOL=OFF
- -DBUILD_OSSIM_APPS:BOOL=OFF
- -DBUILD_OSSIM_FRAMEWORKS:BOOL=OFF
- -DINSTALL_ARCHIVE_DIR:STRING=lib
- -DINSTALL_LIBRARY_DIR:STRING=lib
- -DCMAKE_PREFIX_PATH:STRING=${SB_INSTALL_PREFIX};${CMAKE_PREFIX_PATH}
- ${OSSIM_SB_CONFIG}
- DEPENDS ${${proj}_DEPENDENCIES}
- CMAKE_COMMAND ${SB_CMAKE_COMMAND}
- )
-
- ExternalProject_Add_Step(${proj} copy_CMakeModules
- COMMAND ${CMAKE_COMMAND} -E copy_directory
- ${CMAKE_BINARY_DIR}/${proj}/_cmakemodules/src/ossim_cmakemodules ${OSSIM_SB_SRC}/CMakeModules
- DEPENDEES patch update
- DEPENDERS configure
- )
- else()
- # archive version
- ExternalProject_Add(${proj}
- PREFIX ${proj}
- URL "https://www.orfeo-toolbox.org/packages/ossim-minimal-r23092.tar.gz"
- URL_MD5 1bb7247fa490eb4a6e57b3c2e129d587
- BINARY_DIR ${OSSIM_SB_BUILD_DIR}
- INSTALL_DIR ${SB_INSTALL_PREFIX}
- DOWNLOAD_DIR ${DOWNLOAD_LOCATION}
- CMAKE_CACHE_ARGS
- -DOSSIM_VERSION:STRING=1.8.18-1
- -DProject_WC_REVISION:STRING=23092
- -DCMAKE_INSTALL_PREFIX:STRING=${SB_INSTALL_PREFIX}
- -DCMAKE_BUILD_TYPE:STRING=Release
- -DCMAKE_CXX_FLAGS:STRING=${OSSIM_CXX_FLAGS}
- -DBUILD_OSSIM_MPI_SUPPORT:BOOL=OFF
- -DBUILD_OSSIM_FREETYPE_SUPPORT:BOOL=ON
- -DBUILD_OSSIM_APPS:BOOL=OFF
- -DBUILD_OSSIM_FRAMEWORKS:BOOL=OFF
- -DINSTALL_ARCHIVE_DIR:STRING=lib
- -DINSTALL_LIBRARY_DIR:STRING=lib
- -DCMAKE_PREFIX_PATH:STRING=${SB_INSTALL_PREFIX};${CMAKE_PREFIX_PATH}
- ${OSSIM_SB_CONFIG}
- PATCH_COMMAND ${CMAKE_COMMAND} -E copy
- ${CMAKE_SOURCE_DIR}/patches/${proj}/OssimUtilities.cmake
- ${OSSIM_SB_SRC}/CMakeModules
- DEPENDS ${${proj}_DEPENDENCIES}
- CMAKE_COMMAND ${SB_CMAKE_COMMAND}
- )
- endif()
-
+
+ # archive version
+ ExternalProject_Add(${proj}
+ PREFIX ${proj}
+ URL "https://www.orfeo-toolbox.org/packages/ossim-minimal-r23537.tar.gz"
+ URL_MD5 f77d574ab2817bcc36633f77824facb5
+ BINARY_DIR ${OSSIM_SB_BUILD_DIR}
+ INSTALL_DIR ${SB_INSTALL_PREFIX}
+ DOWNLOAD_DIR ${DOWNLOAD_LOCATION}
+ CMAKE_CACHE_ARGS
+ -DProject_WC_REVISION:STRING=23537
+ -DCMAKE_INSTALL_PREFIX:STRING=${SB_INSTALL_PREFIX}
+ -DCMAKE_BUILD_TYPE:STRING=Release
+ -DCMAKE_CXX_FLAGS:STRING=${OSSIM_CXX_FLAGS}
+ -DBUILD_OSSIM_MPI_SUPPORT:BOOL=OFF
+ -DBUILD_OSSIM_FREETYPE_SUPPORT:BOOL=ON
+ -DBUILD_OSSIM_APPS:BOOL=OFF
+ -DBUILD_OSSIM_TEST_APPS:BOOL=OFF
+ -DBUILD_OSSIM_FRAMEWORKS:BOOL=OFF
+ -DBUILD_WMS:BOOL=OFF
+ -DINSTALL_ARCHIVE_DIR:STRING=lib
+ -DINSTALL_LIBRARY_DIR:STRING=lib
+ -DCMAKE_PREFIX_PATH:STRING=${SB_INSTALL_PREFIX};${CMAKE_PREFIX_PATH}
+ ${OSSIM_SB_CONFIG}
+ DEPENDS ${${proj}_DEPENDENCIES}
+ CMAKE_COMMAND ${SB_CMAKE_COMMAND}
+ )
+
set(_SB_${proj}_INCLUDE_DIR ${SB_INSTALL_PREFIX}/include)
if(WIN32)
set(_SB_${proj}_LIBRARY ${SB_INSTALL_PREFIX}/lib/ossim.lib)
elseif(UNIX)
set(_SB_${proj}_LIBRARY ${SB_INSTALL_PREFIX}/lib/libossim${CMAKE_SHARED_LIBRARY_SUFFIX})
endif()
-
+
endif()
endif()
diff --git a/SuperBuild/patches/GDAL/GNUmakefile b/SuperBuild/patches/GDAL/GNUmakefile
new file mode 100644
index 0000000000000000000000000000000000000000..71f029d8726dbf9cda8644471ceee789f1e279cf
--- /dev/null
+++ b/SuperBuild/patches/GDAL/GNUmakefile
@@ -0,0 +1,95 @@
+
+
+include ../../GDALmake.opt
+
+ifndef PYTHON
+ PYTHON=python
+endif
+
+all: build
+
+BINDING = python
+include ../SWIGmake.base
+
+PACKAGE_DIR=osgeo
+SWIGOUTPUTDIR=extensions/
+
+SCRIPTS = `ls ./scripts`
+PY_COMMANDS = epsg_tr.py gdalchksum.py gdal2xyz.py gcps2wld.py \
+ gdalimport.py gdal_merge.py pct2rgb.py rgb2pct.py \
+ gcps2vec.py
+PY_MODULES = ${PACKAGE_DIR}/gdal.py ${PACKAGE_DIR}/ogr.py ${PACKAGE_DIR}/osr.py ${PACKAGE_DIR}/gdalconst.py ${PACKAGE_DIR}/gdal_array.py
+
+clean:
+ -rm -f ${PACKAGE_DIR}/*.pyc
+ -rm -rf build
+ -rm -f *.pyc
+ -rm -rf *.egg-info
+ -rm -f *.so ./osgeo/*.so
+ -rm -rf dist
+
+SWIGARGS += -outdir "${PACKAGE_DIR}"
+
+
+veryclean: clean
+ -rm -f ${WRAPPERS} ${PY_MODULES}
+ -rm -f ${SWIGOUTPUTDIR}/gdal_wrap.cpp
+ -rm -f ${SWIGOUTPUTDIR}/gdalconst_wrap.c
+ -rm -f ${SWIGOUTPUTDIR}/ogr_wrap.cpp
+ -rm -f ${SWIGOUTPUTDIR}/osr_wrap.cpp
+ -rm -f ${SWIGOUTPUTDIR}/gdal_array_wrap.cpp
+
+gdal_wrap.cpp: ../include/python/gdal_python.i
+
+ogr_wrap.cpp: ../include/python/ogr_python.i
+
+osr_wrap.cpp: ../include/python/osr_python.i
+
+gdal_array_wrap.cpp: ../include/gdal_array.i ../include/python/typemaps_python.i
+ $(SWIG) $(SWIGARGS) $(SWIGDEFINES) -I$(GDAL_ROOT) -c++ -$(BINDING) -o $(SWIGOUTPUTDIR)$@ gdal_array.i
+
+# A few hacks (cat, mv) : the first one for SWIG < 1.3.36 and the second one for SWIG <= 1.3.39 python 3.X on 64bit platforms
+# The python3.2.patch is from https://sourceforge.net/tracker/?func=detail&aid=3057804&group_id=1645&atid=101645
+# and is no longer necessary with swig 2.0.4
+generate: ${WRAPPERS} gdal_array_wrap.cpp
+ for i in gdal_wrap.cpp gdalconst_wrap.c ogr_wrap.cpp osr_wrap.cpp gdal_array_wrap.cpp; do sed "s/PyErr_Format(PyExc_RuntimeError, mesg)/PyErr_SetString(PyExc_RuntimeError, mesg)/" ${SWIGOUTPUTDIR}/$$i | sed "s/int len;/Py_ssize_t len;/" > ${SWIGOUTPUTDIR}/$$i.tmp; mv -f ${SWIGOUTPUTDIR}/$$i.tmp ${SWIGOUTPUTDIR}/$$i; done
+ -grep "1\.3\.40" extensions/gdal_wrap.cpp >/dev/null && patch -p0 < python3.2.patch
+ -grep "1\.3\.40" extensions/gdal_wrap.cpp >/dev/null && cat python3.2.patch | sed "s/gdal_wrap/ogr_wrap/" | patch -p0
+ -grep "1\.3\.40" extensions/gdal_wrap.cpp >/dev/null && cat python3.2.patch | sed "s/gdal_wrap/osr_wrap/" | patch -p0
+ -grep "1\.3\.40" extensions/gdal_wrap.cpp >/dev/null && cat python3.2.patch | sed "s/gdal_wrap\.cpp/gdalconst_wrap\.c/" | patch -p0
+ -grep "1\.3\.40" extensions/gdal_wrap.cpp >/dev/null && cat python3.2.patch | sed "s/gdal_wrap/gdal_array_wrap/" | patch -p0
+
+build:
+ $(PYTHON) setup.py build
+
+egg:
+ $(PYTHON) setup.py bdist_egg
+
+install:
+
+ifeq ($(PY_HAVE_SETUPTOOLS),1)
+ $(PYTHON) setup.py install --prefix=$(DESTDIR)$(prefix) --install-purelib=$(prefix)/lib --install-scripts=$(prefix)/bin --install-platlib=$(prefix)/lib
+else
+ $(PYTHON) setup.py install --prefix=$(DESTDIR)$(prefix)
+endif
+
+ for f in $(SCRIPTS) ; do $(INSTALL) ./scripts/$$f $(DESTDIR)$(INST_BIN) ; done
+
+docs:
+ $(PYTHON) ../include/python/docs/doxy2swig.py ../../ogr/xml/ogrlayer_8cpp.xml ../include/python/docs/ogr_layer_docs.i OGRLayerShadow OGR_L_
+
+ $(PYTHON) ../include/python/docs/doxy2swig.py ../../ogr/xml/ogrgeometry_8cpp.xml ../include/python/docs/ogr_geometry_docs.i OGRGeometryShadow OGR_G_
+
+ $(PYTHON) ../include/python/docs/doxy2swig.py ../../ogr/xml/ogrdatasource_8cpp.xml ../include/python/docs/ogr_datasource_docs.i OGRDataSourceShadow OGR_DS_
+
+
+ $(PYTHON) ../include/python/docs/doxy2swig.py ../../ogr/xml/ogrsfdriver_8cpp.xml ../include/python/docs/ogr_driver_docs.i OGRDriverShadow OGR_Dr_
+
+ $(PYTHON) ../include/python/docs/doxy2swig.py ../../ogr/xml/ogrfeature_8cpp.xml ../include/python/docs/ogr_feature_docs.i OGRFeatureShadow OGR_F_
+
+ $(PYTHON) ../include/python/docs/doxy2swig.py ../../ogr/xml/ogrfeaturedefn_8cpp.xml ../include/python/docs/ogr_featuredef_docs.i OGRFeatureDefnShadow OGR_FD_
+
+ $(PYTHON) ../include/python/docs/doxy2swig.py ../../ogr/xml/ogrfielddefn_8cpp.xml ../include/python/docs/ogr_fielddef_docs.i OGRFieldDefnShadow OGR_Fld_
+
+epydoc: generate
+ epydoc --config epydoc.conf
diff --git a/Utilities/Maintenance/SuperbuildDownloadList.sh b/Utilities/Maintenance/SuperbuildDownloadList.sh
index e0cc7033b1933fc5d8943747184a249d51d01f5c..2cb32210b12d8d28a5c985047956271ad4e970e2 100755
--- a/Utilities/Maintenance/SuperbuildDownloadList.sh
+++ b/Utilities/Maintenance/SuperbuildDownloadList.sh
@@ -1,2 +1,62 @@
#/bin/bash
-grep -h -E "^[^#]*\"https?://.*(\.tar\.gz|\.tar\.bz2|\.tgz|\.tar\.xz|\.zip|export=download).*\"" ../../SuperBuild/CMake/*.cmake | grep -o -E "https?://[^\"]*" | sed "s/\/download$//" | sort | uniq
+
+if [ $# -eq 2 ]; then
+DOWNLOAD_DIR=$(readlink -f $1)
+OUTPUT_DIR=$(readlink -f $2)
+else
+echo 'Usage: '$0' <download_directory> <archive_output_directory>'
+exit 1
+fi
+
+SB_CMAKE_DIR_REL=$(dirname $0)/../../SuperBuild/CMake
+SB_CMAKE_DIR=$(readlink -f ${SB_CMAKE_DIR_REL})
+
+DOWNLOAD_LIST=$(grep -h -E "^[^#]*\"https?://.*(\.tar\.gz|\.tar\.bz2|\.tgz|\.tar\.xz|\.zip|export=download).*\"" ${SB_CMAKE_DIR}/*.cmake | grep -o -E "https?://[^\"]*" | sed "s/\/\(download\)\?$//" | sort | uniq)
+
+DOWNLOAD_NAMES=
+
+mkdir -p ${DOWNLOAD_DIR}
+cd ${DOWNLOAD_DIR}
+
+echo "Start downloads..."
+
+for url in ${DOWNLOAD_LIST}; do
+ # clean log file
+ echo "" >download.log
+
+ file_name=$(echo "${url}" | grep -o -E "[^\/]+$")
+ docs_google=$(echo "${file_name}" | grep -F "export=download")
+ if [ "${docs_google}" = "" ]; then
+ echo "Download ${file_name}..."
+ wget -N ${url} -o download.log
+ else
+ # look into the original file for a valid download name
+ file_name=$(grep -h -A 3 -B 3 -F "${url}" ${SB_CMAKE_DIR}/*.cmake | grep -E "^[^#]*DOWNLOAD_NAME" | grep -o -E "[^ ]*\.(tar\.gz|tar\.bz2|tar\.xz|zip)" | uniq)
+ if [ -z "$file_name" ]; then
+ echo "Can't find correct filename for url ${url} : skip download"
+ else
+ echo "Download ${file_name}..."
+ wget -O ${file_name} ${url} -o download.log
+ fi
+ fi
+
+ download_errors=$(grep "ERROR" download.log)
+ if [ "$download_errors" != "" ]; then
+ echo "=== Error downloading ${file_name} ==="
+ cat download.log
+ else
+ if [ "$file_name" != "" ]; then
+ DOWNLOAD_NAMES="${DOWNLOAD_NAMES} ${file_name}"
+ fi
+ fi
+done
+
+rm download.log
+
+echo "Create archive..."
+
+cd ${OUTPUT_DIR}
+tar -cjf SuperBuild-archives.tar.bz2 -C ${DOWNLOAD_DIR} ${DOWNLOAD_NAMES}
+
+echo "Compute md5sum..."
+md5sum SuperBuild-archives.tar.bz2 >SuperBuild-archives.md5