diff --git a/CMake/OTBModuleMacros.cmake b/CMake/OTBModuleMacros.cmake
index 0938a0f2fb9ed50fe44edf7017d9a4ac325e41b7..afb9a29f5c45b2509d0cd0bfe350df5a59ea846f 100644
--- a/CMake/OTBModuleMacros.cmake
+++ b/CMake/OTBModuleMacros.cmake
@@ -277,6 +277,12 @@ macro(otb_module_test)
foreach(dep IN LISTS OTB_MODULE_${otb-module-test}_DEPENDS)
list(APPEND ${otb-module-test}_LIBRARIES "${${dep}_LIBRARIES}")
endforeach()
+ # make sure the test can link with optional libs
+ foreach(dep IN LISTS OTB_MODULE_${otb-module}_OPTIONAL_DEPENDS)
+ if (${dep}_ENABLED)
+ list(APPEND ${otb-module-test}_LIBRARIES "${${dep}_LIBRARIES}")
+ endif()
+ endforeach()
endmacro()
macro(otb_module_warnings_disable)
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 985c8e349de0fdee5191f10d5e6b56167e6c6ae7..408e015d961232587c6809b168192fe0c11ce867 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -58,6 +58,13 @@ then send a merge request.
Note that we also accept PRs on our [GitHub mirror](https://github.com/orfeotoolbox/OTB)
which we will manually merge.
+Feature branches are tested on multiple platforms on the OTB test infrastructure (a.k.a the [Dashboard](https://dash.orfeo-toolbox.org/)). They appear in the FeatureBranches section.
+
+Caveat: even if the Dashboard build on develop branch is broken, it is not
+allowed to push fixes directly on develop. The developer trying to fix the
+build should create a merge request and submit it for review. Direct push to
+develop without review must be avoided.
+
### Commit message
On your feature branch, write a good [commit message](https://xkcd.com/1296/):
@@ -93,7 +100,11 @@ OTB team.
* Merge requests **must receive at least 2 positives votes from core developers** (members of Main Repositories group in Gitlab with at least "Developer" level; this includes PSC members) before being merged
* The merger is responsible for checking that the branch is up-to-date with develop
* Merge requests can be merged by anyone (not just PSC or RM) with push access to develop
-* Merge requests can be merged once the dashboard is proven green for this branch
+* Merge requests can be merged once the dashboard is proven green for this branch.
+ This condition is mandatory unless reviewers and authors explicitely agree that
+ it can be skipped (for instance in case of documentation merges or compilation
+ fixes on develop). Branches of that sort can be identified with the ~patch label,
+ which tells the reviewer that the author would like to merge without dashboard testing.
Branches can be registered for dashboard testing by adding one line in `Config/feature_branches.txt` in [otb-devutils repository](https://gitlab.orfeo-toolbox.org/orfeotoolbox/otb-devutils.git).
@@ -162,6 +173,7 @@ Regarding labels, we use the following set:
correspond to a Request for Comments that has turned into a development action
* ~bug: Bug, crash or unexpected behavior, reported by a user or a developer
* ~feature: Feature request expressed by an OTB user/developer
+* ~patch: A small patch fixing build warnings, compilation errors, typos in logs or documentation
* ~"To Do": action is planned
* ~Doing: work in progress
* ~api ~app ~documentation ~monteverdi ~packaging ~qgis: optional context information
diff --git a/Documentation/Cookbook/rst/conf.py.in b/Documentation/Cookbook/rst/conf.py.in
index 30ce0dc728056846e6a2bdf192840a2beb1b0df6..1e4442b07f398766735fa3898e75692c5b9d52d4 100644
--- a/Documentation/Cookbook/rst/conf.py.in
+++ b/Documentation/Cookbook/rst/conf.py.in
@@ -211,7 +211,7 @@ latex_elements = {
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
- ('index_TOC', 'CookBook-@OTB_VERSION_MAJOR@.@OTB_VERSION_MINOR@.tex', u'OTB CookBook Documentation',
+ ('index_TOC', 'CookBook-@OTB_VERSION_MAJOR@.@OTB_VERSION_MINOR@.@OTB_VERSION_PATCH@.tex', u'OTB CookBook Documentation',
u'OTB Team', 'manual'),
]
diff --git a/Modules/Applications/AppClassification/app/otbKMeansClassification.cxx b/Modules/Applications/AppClassification/app/otbKMeansClassification.cxx
index 52e0d6b426d7defb6343113029c17abe6dcacd15..ee04d9cd0fb2ff3035ca2208a8cd64c735cd93d8 100644
--- a/Modules/Applications/AppClassification/app/otbKMeansClassification.cxx
+++ b/Modules/Applications/AppClassification/app/otbKMeansClassification.cxx
@@ -291,12 +291,15 @@ protected:
itkExceptionMacro(<< "File : " << modelFileName << " couldn't be opened");
}
- // get the end line with the centroids
+ // get the line with the centroids (starts with "2 ")
std::string line, centroidLine;
while(std::getline(infile,line))
{
- if (!line.empty())
+ if (line.size() > 2 && line[0] == '2' && line[1] == ' ')
+ {
centroidLine = line;
+ break;
+ }
}
std::vector<std::string> centroidElm;
diff --git a/Modules/Applications/AppClassification/app/otbSampleAugmentation.cxx b/Modules/Applications/AppClassification/app/otbSampleAugmentation.cxx
index 6bb7382856e619cce13f501acea90530cfd18f37..093e690739de5eba191f11ac0dba3c48e93ae64d 100644
--- a/Modules/Applications/AppClassification/app/otbSampleAugmentation.cxx
+++ b/Modules/Applications/AppClassification/app/otbSampleAugmentation.cxx
@@ -51,7 +51,7 @@ public:
private:
SampleAugmentation() {}
- void DoInit()
+ void DoInit() override
{
SetName("SampleAugmentation");
SetDescription("Generates synthetic samples from a sample data file.");
@@ -145,7 +145,7 @@ private:
SetOfficialDocLink();
}
- void DoUpdateParameters()
+ void DoUpdateParameters() override
{
if ( HasValue("in") )
{
@@ -182,7 +182,7 @@ private:
}
}
- void DoExecute()
+ void DoExecute() override
{
ogr::DataSource::Pointer vectors;
ogr::DataSource::Pointer output;
diff --git a/Modules/Applications/AppDimensionalityReduction/app/otbImageDimensionalityReduction.cxx b/Modules/Applications/AppDimensionalityReduction/app/otbImageDimensionalityReduction.cxx
index 1df2c463fc26c531095154515039a9245d0ba5c1..d9dd8e8816fec2cce2502a81b624a59ce33817ab 100644
--- a/Modules/Applications/AppDimensionalityReduction/app/otbImageDimensionalityReduction.cxx
+++ b/Modules/Applications/AppDimensionalityReduction/app/otbImageDimensionalityReduction.cxx
@@ -124,12 +124,12 @@ protected:
private:
void DoInit() override
{
- SetName("DimensionalityReduction");
+ SetName("ImageDimensionalityReduction");
SetDescription("Performs dimensionality reduction of the input image "
"according to a dimensionality reduction model file.");
// Documentation
- SetDocName("DimensionalityReduction");
+ SetDocName("Image Dimensionality Reduction");
SetDocLongDescription("This application reduces the dimension of an input"
" image, based on a machine learning model file produced by"
" the TrainDimensionalityReduction application. Pixels of the "
diff --git a/Modules/Applications/AppImageUtils/app/otbExtractROI.cxx b/Modules/Applications/AppImageUtils/app/otbExtractROI.cxx
index 66f2886edc0c2b5d47acca8bef211319d023726e..adcb2f3f43e3596bd5ce3030bbf94940df8033ed 100644
--- a/Modules/Applications/AppImageUtils/app/otbExtractROI.cxx
+++ b/Modules/Applications/AppImageUtils/app/otbExtractROI.cxx
@@ -255,31 +255,39 @@ private:
inImage->UpdateOutputInformation();
ImageType::RegionType largestRegion = inImage->GetLargestPossibleRegion();
- bool userExtent = !HasUserValue( "mode.extent.ulx" ) \
- && !HasUserValue( "mode.extent.uly" ) \
- && !HasUserValue( "mode.extent.lrx" ) \
- && !HasUserValue( "mode.extent.lry" );
-
- bool userRadius = !HasUserValue( "mode.radius.r" ) \
- && !HasUserValue( "mode.radius.cx" ) \
- && !HasUserValue( "mode.radius.cy" );
-
- // Update the sizes only if the user has not defined a size
- if (!HasUserValue("sizex") && !HasUserValue("sizey") )
+ ImageType::RegionType currentLargest;
+ currentLargest.SetSize( 0 , GetDefaultParameterInt("sizex") );
+ currentLargest.SetSize( 1 , GetDefaultParameterInt("sizey") );
+ currentLargest.SetIndex( 1 , GetDefaultParameterInt("starty") );
+ currentLargest.SetIndex( 0 , GetDefaultParameterInt("startx") );
+ // Update default only if largest has changed
+ if ( currentLargest != largestRegion )
{
- SetParameterInt("sizex",largestRegion.GetSize()[0]);
- SetParameterInt("sizey",largestRegion.GetSize()[1]);
-
- // Compute extent parameter with default sizex and sizey
- if ( GetParameterString( "mode" ) == "extent" && userExtent )
- ComputeExtentFromIndex( inImage, largestRegion );
-
- // Compute radius parameter with default sizex and sizey
- if ( GetParameterString( "mode" ) == "radius" && userRadius )
- ComputeRadiusFromIndex( inImage , largestRegion );
+ // Setting maximum value
+ SetMaximumParameterIntValue( "sizex" , largestRegion.GetSize(0) );
+ SetMaximumParameterIntValue( "sizey" , largestRegion.GetSize(1) );
+ SetMaximumParameterIntValue( "startx" ,
+ largestRegion.GetIndex(0) + largestRegion.GetSize(0) );
+ SetMaximumParameterIntValue( "starty" ,
+ largestRegion.GetIndex(1) + largestRegion.GetSize(1) );
+ // Setting default value
+ SetDefaultParameterInt( "sizex" , largestRegion.GetSize(0) );
+ SetDefaultParameterInt( "sizey" , largestRegion.GetSize(1) );
+ SetDefaultParameterInt( "startx" , largestRegion.GetIndex(0) );
+ SetDefaultParameterInt( "starty" , largestRegion.GetIndex(1) );
+ // Setting actual value
+ if ( !HasUserValue("sizex") )
+ SetParameterFloat( "sizex" ,
+ GetDefaultParameterFloat( "sizex" ) );
+ if ( !HasUserValue("sizey") )
+ SetParameterFloat( "sizey" ,
+ GetDefaultParameterFloat( "sizey" ) );
+ // Compute radius parameters default value
+ ComputeRadiusFromIndex( inImage , largestRegion );
+ // Compute extent parameters default value
+ ComputeExtentFromIndex( inImage, largestRegion );
}
-
unsigned int nbComponents = inImage->GetNumberOfComponentsPerPixel();
ListViewParameter *clParam =
dynamic_cast<ListViewParameter*>(GetParameterByKey("cl"));
@@ -296,55 +304,25 @@ private:
}
}
- // Put the limit of the index and the size relative the image
-
- SetMaximumParameterIntValue("sizex", largestRegion.GetSize(0));
- SetMaximumParameterIntValue("sizey", largestRegion.GetSize(1));
- SetMaximumParameterIntValue("startx", largestRegion.GetSize(0));
- SetMaximumParameterIntValue("starty", largestRegion.GetSize(1));
-
-
// Update the start and size parameter depending on the mode
- if ( GetParameterString("mode") == "extent" && !userExtent)
+ if ( GetParameterString("mode") == "extent" )
ComputeIndexFromExtent();
- if (GetParameterString("mode") == "radius" && !userRadius)
+ if (GetParameterString("mode") == "radius" )
ComputeIndexFromRadius();
-
- // Crop the roi region to be included in the largest possible
- // region
- if(!this->CropRegionOfInterest())
- {
- // Put the index of the ROI to origin and try to crop again
- SetParameterInt("startx",0);
- SetParameterInt("starty",0);
- this->CropRegionOfInterest();
- }
-
if(GetParameterString("mode")=="fit")
{
- this->SetParameterRole("startx",Role_Output);
- this->SetParameterRole("starty",Role_Output);
- this->SetParameterRole("sizex",Role_Output);
- this->SetParameterRole("sizey",Role_Output);
- this->DisableParameter("startx");
- this->DisableParameter("starty");
- this->DisableParameter("sizex");
- this->DisableParameter("sizey");
+ SetParameterRole("startx",Role_Output);
+ SetParameterRole("starty",Role_Output);
+ SetParameterRole("sizex",Role_Output);
+ SetParameterRole("sizey",Role_Output);
}
-
- else if(GetParameterString("mode")=="standard" ||
- GetParameterString("mode")=="extent" ||
- GetParameterString("mode")== "radius" )
+ else
{
- this->SetParameterRole("startx",Role_Input);
- this->SetParameterRole("starty",Role_Input);
- this->SetParameterRole("sizex",Role_Input);
- this->SetParameterRole("sizey",Role_Input);
- this->EnableParameter("startx");
- this->EnableParameter("starty");
- this->EnableParameter("sizex");
- this->EnableParameter("sizey");
+ SetParameterRole("startx",Role_Input);
+ SetParameterRole("starty",Role_Input);
+ SetParameterRole("sizex",Role_Input);
+ SetParameterRole("sizey",Role_Input);
}
}
@@ -355,6 +333,10 @@ private:
MandatoryOff("starty");
MandatoryOff("sizex");
MandatoryOff("sizey");
+ DisableParameter("startx");
+ DisableParameter("starty");
+ DisableParameter("sizex");
+ DisableParameter("sizey");
}
else
{
@@ -362,6 +344,10 @@ private:
MandatoryOn("starty");
MandatoryOn("sizex");
MandatoryOn("sizey");
+ EnableParameter("startx");
+ EnableParameter("starty");
+ EnableParameter("sizex");
+ EnableParameter("sizey");
}
if ( GetParameterString( "mode" ) == "fit" && HasValue( "mode.fit.im" ) )
@@ -384,18 +370,15 @@ private:
region.SetSize(1, GetParameterInt("sizey"));
region.SetIndex(0, GetParameterInt("startx"));
region.SetIndex(1, GetParameterInt("starty"));
- if ( HasValue("in") )
+ ImageType* inImage = GetParameterImage("in");
+ inImage->UpdateOutputInformation();
+ if (region.Crop(inImage->GetLargestPossibleRegion()))
{
- ImageType* inImage = GetParameterImage("in");
- inImage->UpdateOutputInformation();
- if (region.Crop(inImage->GetLargestPossibleRegion()))
- {
- SetParameterInt("sizex",region.GetSize(0));
- SetParameterInt("sizey",region.GetSize(1));
- SetParameterInt("startx",region.GetIndex(0));
- SetParameterInt("starty",region.GetIndex(1));
- return true;
- }
+ SetParameterInt("sizex",region.GetSize(0));
+ SetParameterInt("sizey",region.GetSize(1));
+ SetParameterInt("startx",region.GetIndex(0));
+ SetParameterInt("starty",region.GetIndex(1));
+ return true;
}
return false;
}
@@ -404,20 +387,14 @@ private:
ComputeIndexFromExtent()
{
assert( GetParameterString( "mode" ) == "extent" );
- int pixelValue = -1 ;
// Compute standard parameter depending on the unit chosen by the user
+ FloatVectorImageType::IndexType uli , lri;
if (GetParameterString( "mode.extent.unit" ) == "pxl" )
{
- pixelValue = std::round( GetParameterFloat( "mode.extent.ulx" ) );
- SetParameterInt( "startx", pixelValue);
- pixelValue = std::round( GetParameterFloat( "mode.extent.lrx" ) \
- - pixelValue ) + 1 ;
- SetParameterInt( "sizex", pixelValue);
- pixelValue = std::round( GetParameterFloat( "mode.extent.uly" ) );
- SetParameterInt( "starty", pixelValue);
- pixelValue = std::round( GetParameterFloat( "mode.extent.lry" ) \
- - pixelValue ) + 1 ;
- SetParameterInt( "sizey", pixelValue);
+ uli[0] = std::round( GetParameterFloat( "mode.extent.ulx" ) );
+ uli[1] = std::round( GetParameterFloat( "mode.extent.uly" ) );
+ lri[0] = std::round( GetParameterFloat( "mode.extent.lrx" ) );
+ lri[1] = std::round( GetParameterFloat( "mode.extent.lry" ) );
}
else if( GetParameterString( "mode.extent.unit" ) == "phy" )
{
@@ -426,20 +403,11 @@ private:
ulp[ 1 ] = GetParameterFloat( "mode.extent.uly" );
lrp[ 0 ] = GetParameterFloat( "mode.extent.lrx" );
lrp[ 1 ] = GetParameterFloat( "mode.extent.lry" );
-
ImageType * inImage = GetParameterImage("in");
- FloatVectorImageType::IndexType uli , lri;
inImage->TransformPhysicalPointToIndex(ulp,uli);
- inImage->TransformPhysicalPointToIndex(lrp,lri);
-
- SetParameterInt( "startx", uli[0]);
- SetParameterInt( "starty", uli[1]);
-
- SetParameterInt( "sizex", lri[0] - uli[0] + 1);
- SetParameterInt( "sizey", lri[1] - uli[1] + 1);
-
+ inImage->TransformPhysicalPointToIndex(lrp,lri);
}
- else if( GetParameterString( "mode.extent.unit" ) == "lonlat" )
+ else // if( GetParameterString( "mode.extent.unit" ) == "lonlat" )
{
RSTransformType::Pointer rsTransform = RSTransformType::New();
ImageType* inImage = GetParameterImage("in");
@@ -453,18 +421,13 @@ private:
lrp_in[ 1 ] = GetParameterFloat( "mode.extent.lry" );
ulp_out = rsTransform->TransformPoint(ulp_in);
lrp_out = rsTransform->TransformPoint(lrp_in);
-
- FloatVectorImageType::IndexType uli_out , lri_out;
- inImage->TransformPhysicalPointToIndex(ulp_out,uli_out);
- inImage->TransformPhysicalPointToIndex(lrp_out,lri_out);
-
- SetParameterInt( "startx", uli_out[0]);
- SetParameterInt( "starty", uli_out[1]);
-
- SetParameterInt( "sizex", lri_out[0] - uli_out[0] + 1);
- SetParameterInt( "sizey", lri_out[1] - uli_out[1] + 1);
+ inImage->TransformPhysicalPointToIndex(ulp_out,uli);
+ inImage->TransformPhysicalPointToIndex(lrp_out,lri);
}
- this->CropRegionOfInterest();
+ SetParameterInt( "startx", uli[0]);
+ SetParameterInt( "starty", uli[1]);
+ SetParameterInt( "sizex", lri[0] - uli[0] + 1);
+ SetParameterInt( "sizey", lri[1] - uli[1] + 1);
}
void
@@ -477,24 +440,24 @@ private:
lri[ 1 ] = largestRegion.GetSize()[1];
if ( GetParameterString( "mode.extent.unit" ) == "pxl" )
{
- SetParameterFloat("mode.extent.ulx", uli[0]);
- SetParameterFloat("mode.extent.uly", uli[1]);
- SetParameterFloat("mode.extent.lrx", lri[0]);
- SetParameterFloat("mode.extent.lry", lri[1]);
+ SetDefaultParameterFloat("mode.extent.ulx", uli[0]);
+ SetDefaultParameterFloat("mode.extent.uly", uli[1]);
+ SetDefaultParameterFloat("mode.extent.lrx", lri[0]);
+ SetDefaultParameterFloat("mode.extent.lry", lri[1]);
}
else if ( GetParameterString( "mode.extent.unit" ) == "phy" )
{
itk::Point<float, 2> ulp, lrp;
input->TransformIndexToPhysicalPoint(uli,ulp);
- SetParameterFloat("mode.extent.ulx",ulp[0]);
- SetParameterFloat("mode.extent.uly",ulp[1]);
+ SetDefaultParameterFloat("mode.extent.ulx",ulp[0]);
+ SetDefaultParameterFloat("mode.extent.uly",ulp[1]);
input->TransformIndexToPhysicalPoint(lri,lrp);
- SetParameterFloat("mode.extent.lrx",lrp[0]);
- SetParameterFloat("mode.extent.lry",lrp[1]);
+ SetDefaultParameterFloat("mode.extent.lrx",lrp[0]);
+ SetDefaultParameterFloat("mode.extent.lry",lrp[1]);
}
- else if ( GetParameterString( "mode.extent.unit" ) == "lonlat" )
+ else // if ( GetParameterString( "mode.extent.unit" ) == "lonlat" )
{
RSTransformType::Pointer rsTransform = RSTransformType::New();
rsTransform->SetInputKeywordList( input->GetImageKeywordlist() );
@@ -503,31 +466,42 @@ private:
itk::Point<float, 2> ulp_in, lrp_in , ulp_out , lrp_out;
input->TransformIndexToPhysicalPoint(uli,ulp_in);
ulp_out = rsTransform->TransformPoint( ulp_in );
- SetParameterFloat( "mode.extent.ulx" , ulp_out[ 0 ]);
- SetParameterFloat( "mode.extent.uly" , ulp_out[ 1 ]);
+ SetDefaultParameterFloat( "mode.extent.ulx" , ulp_out[ 0 ]);
+ SetDefaultParameterFloat( "mode.extent.uly" , ulp_out[ 1 ]);
input->TransformIndexToPhysicalPoint( lri , lrp_in );
lrp_out = rsTransform->TransformPoint( lrp_in );
- SetParameterFloat( "mode.extent.lrx" , lrp_out[ 0 ]);
- SetParameterFloat( "mode.extent.lry" , lrp_out[ 1 ]);
+ SetDefaultParameterFloat( "mode.extent.lrx" , lrp_out[ 0 ]);
+ SetDefaultParameterFloat( "mode.extent.lry" , lrp_out[ 1 ]);
}
+ if ( !HasUserValue( "mode.extent.ulx" ) )
+ SetParameterFloat( "mode.extent.ulx" ,
+ GetDefaultParameterFloat( "mode.extent.ulx" ) );
+ if ( !HasUserValue( "mode.extent.uly" ) )
+ SetParameterFloat( "mode.extent.uly" ,
+ GetDefaultParameterFloat( "mode.extent.uly" ) );
+ if ( !HasUserValue( "mode.extent.lrx" ) )
+ SetParameterFloat( "mode.extent.lrx" ,
+ GetDefaultParameterFloat( "mode.extent.lrx" ) );
+ if ( !HasUserValue( "mode.extent.lry" ) )
+ SetParameterFloat( "mode.extent.lry" ,
+ GetDefaultParameterFloat( "mode.extent.lry" ) );
}
void
ComputeIndexFromRadius()
{
- int pixelValue = -1;
+ FloatVectorImageType::SizeType radiusi ;
+ radiusi.Fill(0);
assert( GetParameterString( "mode" ) == "radius" );
- // First compute sizex sizey thanks to the radius
- if ( HasUserValue( "mode.radius.r" ) )
+ if ( HasValue( "mode.radius.r" ) )
{
if ( GetParameterString( "mode.radius.unitr" ) == "pxl" )
{
- pixelValue = std::floor( 2 * GetParameterFloat( "mode.radius.r" ) ) + 1;
- SetParameterInt( "sizey", pixelValue);
- SetParameterInt( "sizex", pixelValue);
+ radiusi[0] = std::floor( GetParameterFloat( "mode.radius.r" ) );
+ radiusi[1] = std::floor( GetParameterFloat( "mode.radius.r" ) );
}
- if ( GetParameterString( "mode.radius.unitr" ) == "phy" )
+ else //if ( GetParameterString( "mode.radius.unitr" ) == "phy" )
{
ImageType * inImage = GetParameterImage("in");
itk::Point<float, 2> radxp , radyp , ulp ;
@@ -540,64 +514,34 @@ private:
radyp[1] += GetParameterFloat( "mode.radius.r" );
bool lgtx = inImage->TransformPhysicalPointToIndex( radxp , radxi );
bool lgty = inImage->TransformPhysicalPointToIndex( radyp , radyi );
- FloatVectorImageType::IndexValueType maxR =
- std::min( inImage->GetLargestPossibleRegion().GetSize()[0] ,
- inImage->GetLargestPossibleRegion().GetSize()[1] );
- maxR = maxR / 2 - ( (maxR + 1) % 2 );
- if ( lgtx && lgty)
- {
- pixelValue = std::max( radxi[0] , radyi[1] );
- if ( maxR<pixelValue )
- {
- pixelValue = std::min( std::min( radxi[0] , radyi[1] ) , maxR );
- }
- }
- else if ( lgtx )
- {
- pixelValue = std::min( radxi[0] , maxR );
- }
- else if ( lgty )
- {
- pixelValue = std::min( radyi[1] , maxR );
- }
+ if ( lgtx )
+ radiusi[0] = radxp[0];
else
- {
- pixelValue = maxR;
- }
- SetParameterInt( "sizey", 2 * pixelValue + 1);
- SetParameterInt( "sizex", 2 * pixelValue + 1);
+ radiusi[0] = GetDefaultParameterInt( "sizex");
+ if ( lgty )
+ radiusi[1] = radyp[1];
+ else
+ radiusi[1] = GetDefaultParameterInt( "sizey");
}
}
-
- // Then compute startx and starty
- bool size = ( HasValue("sizex") && HasValue("sizey") );
- if ( size )
+ FloatVectorImageType::IndexType centeri ;
+ bool isIn(true);
+ if ( HasValue("sizex") && HasValue("sizey") )
{
- int radiusxi = GetParameterInt("sizex") / 2 ;
- int radiusyi = GetParameterInt("sizey") / 2 ;
-
- if ( GetParameterString( "mode.radius.unitc" ) == "pxl" && size )
+ if ( GetParameterString( "mode.radius.unitc" ) == "pxl" )
{
- pixelValue = std::round(GetParameterFloat( "mode.radius.cx" ));
- SetParameterInt( "startx", pixelValue - radiusxi);
- pixelValue = std::round(GetParameterFloat( "mode.radius.cy" ));
- SetParameterInt( "starty", pixelValue - radiusyi);
+ centeri[0] = std::round(GetParameterFloat( "mode.radius.cx" ));
+ centeri[1] = std::round(GetParameterFloat( "mode.radius.cy" ));
}
- if ( GetParameterString( "mode.radius.unitc" ) == "phy" && size )
+ else if ( GetParameterString( "mode.radius.unitc" ) == "phy" )
{
ImageType * inImage = GetParameterImage("in");
itk::Point<float, 2> centerp;
centerp[ 0 ] = GetParameterFloat( "mode.radius.cx" );
centerp[ 1 ] = GetParameterFloat( "mode.radius.cy" );
- FloatVectorImageType::IndexType centeri ;
- bool isIn = inImage->TransformPhysicalPointToIndex( centerp , centeri );
- if ( isIn )
- {
- SetParameterInt( "startx", centeri[0] - radiusxi);
- SetParameterInt( "starty", centeri[1] - radiusyi);
- }
+ isIn = inImage->TransformPhysicalPointToIndex( centerp , centeri );
}
- if ( GetParameterString( "mode.radius.unitc" ) == "lonlat" && size )
+ else // if ( GetParameterString( "mode.radius.unitc" ) == "lonlat" )
{
ImageType* inImage = GetParameterImage("in");
RSTransformType::Pointer rsTransform = RSTransformType::New();
@@ -608,16 +552,21 @@ private:
centerp_in[ 0 ] = GetParameterFloat( "mode.radius.cx" );
centerp_in[ 1 ] = GetParameterFloat( "mode.radius.cy" );
centerp_out = rsTransform->TransformPoint(centerp_in);
- FloatVectorImageType::IndexType centeri_out;
- bool isIn = inImage->TransformPhysicalPointToIndex( centerp_out ,
- centeri_out );
- if ( isIn )
- {
- SetParameterInt( "startx", centeri_out[0] - radiusxi);
- SetParameterInt( "starty", centeri_out[1] - radiusyi);
- }
+ isIn = inImage->TransformPhysicalPointToIndex( centerp_out ,
+ centeri );
}
}
+ if ( isIn )
+ {
+ SetParameterInt( "startx", centeri[0] - radiusi[0]);
+ SetParameterInt( "sizex", centeri[0] + radiusi[0] + 1 );
+ SetParameterInt( "starty", centeri[1] - radiusi[1]);
+ SetParameterInt( "sizey", centeri[1] + radiusi[1] + 1 );
+ }
+ else
+ {
+ // log
+ }
}
void
@@ -636,30 +585,36 @@ private:
if ( GetParameterString("mode.radius.unitr") == "pxl" )
{
int rad = std::min( centeri[ 0 ], centeri[ 1 ] );
- SetParameterFloat( "mode.radius.r" , rad);
+ SetDefaultParameterFloat( "mode.radius.r" , rad);
}
- if ( GetParameterString("mode.radius.unitr") == "phy" )
+ else // if ( GetParameterString("mode.radius.unitr") == "phy" )
{
itk::Point<float, 2> centerp , helpRxp, helpRyp;
input->TransformIndexToPhysicalPoint(centeri,centerp);
input->TransformIndexToPhysicalPoint(helpRxi,helpRxp);
input->TransformIndexToPhysicalPoint(helpRyi,helpRyp);
float rad = std::min( helpRxp[0] - helpRyp[0] , helpRyp[1] - helpRxp[1] );
- SetParameterFloat( "mode.radius.r" , rad);
+ SetDefaultParameterFloat( "mode.radius.r" , rad);
}
+
+ if ( !HasUserValue( "mode.radius.r" ) )
+ SetParameterFloat( "mode.radius.r" ,
+ GetDefaultParameterFloat( "mode.radius.r" ) );
+
+ // Center
if ( GetParameterString("mode.radius.unitc") == "pxl" )
{
- SetParameterFloat( "mode.radius.cx" , centeri[0]);
- SetParameterFloat( "mode.radius.cy" , centeri[1]) ;
+ SetDefaultParameterFloat( "mode.radius.cx" , centeri[0] );
+ SetDefaultParameterFloat( "mode.radius.cy" , centeri[1] );
}
- if ( GetParameterString("mode.radius.unitc") == "phy" )
+ else if ( GetParameterString("mode.radius.unitc") == "phy" )
{
- itk::Point<float, 2> centerp , helpRp;
+ itk::Point<float, 2> centerp ;
input->TransformIndexToPhysicalPoint(centeri,centerp);
- SetParameterFloat( "mode.radius.cx" , centerp[0]);
- SetParameterFloat( "mode.radius.cy" , centerp[1]) ;
+ SetDefaultParameterFloat( "mode.radius.cx" , centerp[0] );
+ SetDefaultParameterFloat( "mode.radius.cy" , centerp[1] );
}
- if ( GetParameterString("mode.radius.unitc") == "lonlat" )
+ else // if ( GetParameterString("mode.radius.unitc") == "lonlat" )
{
RSTransformType::Pointer rsTransform = RSTransformType::New();
rsTransform->SetInputKeywordList( input->GetImageKeywordlist() );
@@ -668,9 +623,15 @@ private:
itk::Point<float, 2> centerp_in, centerp_out;
input->TransformIndexToPhysicalPoint(centeri,centerp_in);
centerp_out = rsTransform->TransformPoint( centerp_in );
- SetParameterFloat( "mode.radius.cx" , centerp_out[ 0 ]);
- SetParameterFloat( "mode.radius.cy" , centerp_out[ 1 ]);
+ SetDefaultParameterFloat( "mode.radius.cx" , centerp_out[ 0 ]);
+ SetDefaultParameterFloat( "mode.radius.cy" , centerp_out[ 1 ]);
}
+ if ( !HasUserValue( "mode.radius.cx") )
+ SetParameterFloat( "mode.radius.cx" ,
+ GetDefaultParameterFloat( "mode.radius.cx" ) );
+ if ( !HasUserValue( "mode.radius.cy") )
+ SetParameterFloat( "mode.radius.cy" ,
+ GetDefaultParameterFloat( "mode.radius.cy" ) );
}
void
@@ -766,7 +727,7 @@ private:
// Setup the DEM Handler
otb::Wrapper::ElevationParametersHandler::SetupDEMHandlerFromElevationParameters(this,"elev");
- FloatVectorImageType::Pointer referencePtr = this->GetParameterImage("mode.fit.im");
+ FloatVectorImageType::Pointer referencePtr = GetParameterImage("mode.fit.im");
referencePtr->UpdateOutputInformation();
RSTransformType::Pointer rsTransform = RSTransformType::New();
@@ -826,7 +787,9 @@ private:
}
- this->CropRegionOfInterest();
+ if ( !CropRegionOfInterest() )
+ otbAppLogWARNING(<<"Could not extract the ROI as it is out of the "
+ "input image.");
ExtractROIFilterType::Pointer extractROIFilter = ExtractROIFilterType::New();
extractROIFilter->SetInput(inImage);
diff --git a/Modules/Applications/AppOpticalCalibration/test/CMakeLists.txt b/Modules/Applications/AppOpticalCalibration/test/CMakeLists.txt
index a3068e1a64d109373ccb799f839f62a9c74c03b1..7be8316cb8ba425920d426bf402e6640b3c06001 100644
--- a/Modules/Applications/AppOpticalCalibration/test/CMakeLists.txt
+++ b/Modules/Applications/AppOpticalCalibration/test/CMakeLists.txt
@@ -87,7 +87,7 @@ otb_test_application(NAME apTvRaOpticalCalibration_UnknownSensor
-acqui.sun.elev 62.7
-acqui.sun.azim 152.7
-acqui.view.elev 87.5
- -acqui.view.azim -77.0
+ -acqui.view.azim 283
-acqui.solarilluminations ${INPUTDATA}/apTvRaOpticalCalibrationUnknownSensorSolarIllumations2.txt
-atmo.rsr ${INPUTDATA}/apTvRaOpticalCalibrationUnknownSensorRSR.txt
-atmo.pressure 1013.0
diff --git a/Modules/Core/Common/include/otbLogger.h b/Modules/Core/Common/include/otbLogger.h
index ef420c7b2389a38af6be6d1f7cf19faaa4b7f1b5..0da8d382194a6815c31c19ccc21a3856a10fd383 100644
--- a/Modules/Core/Common/include/otbLogger.h
+++ b/Modules/Core/Common/include/otbLogger.h
@@ -59,8 +59,15 @@ public:
// Overwrite this to provide custom formatting of log entries
std::string BuildFormattedEntry(itk::Logger::PriorityLevelType, std::string const&) override;
+ /** Output logs about the RAM, caching and multi-threading settings */
void LogSetupInformation();
-
+
+ /** Return true if the LogSetupInformation has already been called*/
+ bool IsLogSetupInformationDone();
+
+ /** Set the flag m_LogSetupInfoDone to true */
+ void LogSetupInformationDone();
+
protected:
Logger();
virtual ~Logger() ITK_OVERRIDE;
@@ -71,6 +78,8 @@ private:
static Pointer CreateInstance();
+ bool m_LogSetupInfoDone;
+
}; // class Logger
} // namespace otb
diff --git a/Modules/Core/Common/src/otbLogger.cxx b/Modules/Core/Common/src/otbLogger.cxx
index b3730cf445a5816ca693da32de7d2a71f3aaba37..6678aa16320e4498b1a93310abbf346479467795 100644
--- a/Modules/Core/Common/src/otbLogger.cxx
+++ b/Modules/Core/Common/src/otbLogger.cxx
@@ -38,9 +38,6 @@ Logger::Pointer Logger::CreateInstance()
defaultOutput->SetStream(std::cout);
instance->AddLogOutput(defaultOutput);
-
- // Log setup information
- instance->LogSetupInformation();
return instance;
}
@@ -61,6 +58,8 @@ Logger::Logger()
this->SetTimeStampFormat(itk::LoggerBase::HUMANREADABLE);
this->SetHumanReadableFormat("%Y-%m-%d %H:%M:%S");
+
+ m_LogSetupInfoDone = false;
}
Logger::~Logger()
@@ -69,22 +68,29 @@ Logger::~Logger()
void Logger::LogSetupInformation()
{
- std::ostringstream oss;
-
- oss<<"Default RAM limit for OTB is "<<otb::ConfigurationManager::GetMaxRAMHint()<<" MB"<<std::endl;
- this->Info(oss.str());
- oss.str("");
- oss.clear();
-
- oss<<"GDAL maximum cache size is "<<GDALGetCacheMax64()/(1024*1024)<<" MB"<<std::endl;
- this->Info(oss.str());
- oss.str("");
- oss.clear();
-
- oss<<"OTB will use at most "<<itk::MultiThreader::GetGlobalDefaultNumberOfThreads()<<" threads"<<std::endl;
- this->Info(oss.str());
- oss.str("");
- oss.clear();
+ if (! IsLogSetupInformationDone())
+ {
+ std::ostringstream oss;
+
+ oss<<"Default RAM limit for OTB is "<<otb::ConfigurationManager::GetMaxRAMHint()<<" MB"<<std::endl;
+ this->Info(oss.str());
+ oss.str("");
+ oss.clear();
+
+ oss<<"GDAL maximum cache size is "<<GDALGetCacheMax64()/(1024*1024)<<" MB"<<std::endl;
+ this->Info(oss.str());
+ oss.str("");
+ oss.clear();
+
+ oss<<"OTB will use at most "<<itk::MultiThreader::GetGlobalDefaultNumberOfThreads()<<" threads"<<std::endl;
+ this->Info(oss.str());
+ oss.str("");
+ oss.clear();
+
+ // only switch the flag for the singleton, so that other instances can call
+ // LogSetupInformation() several times
+ Instance()->LogSetupInformationDone();
+ }
}
std::string Logger::BuildFormattedEntry(itk::Logger::PriorityLevelType level, std::string const & content)
@@ -116,4 +122,14 @@ std::string Logger::BuildFormattedEntry(itk::Logger::PriorityLevelType level, st
return s.str();
}
+bool Logger::IsLogSetupInformationDone()
+{
+ return m_LogSetupInfoDone;
+}
+
+void Logger::LogSetupInformationDone()
+{
+ m_LogSetupInfoDone = true;
+}
+
} // namespace otb
diff --git a/Modules/Core/Streaming/include/otbStreamingImageVirtualWriter.txx b/Modules/Core/Streaming/include/otbStreamingImageVirtualWriter.txx
index ec0ae5ae6d760b11ca6238deb582174752bb7f21..7336993ed392de64e980c05a7178fdc4cbe66ca7 100644
--- a/Modules/Core/Streaming/include/otbStreamingImageVirtualWriter.txx
+++ b/Modules/Core/Streaming/include/otbStreamingImageVirtualWriter.txx
@@ -183,6 +183,8 @@ void
StreamingImageVirtualWriter<TInputImage>
::GenerateData(void)
{
+ otb::Logger::Instance()->LogSetupInformation();
+
/**
* Prepare all the outputs. This may deallocate previous bulk data.
*/
diff --git a/Modules/Core/Streaming/include/otbStreamingManager.txx b/Modules/Core/Streaming/include/otbStreamingManager.txx
index 24e487a4e55998ee7d28b1be1a58cbda21ee8768..9d2f3ddbd8243ed40946058cafe96d6060d2926d 100644
--- a/Modules/Core/Streaming/include/otbStreamingManager.txx
+++ b/Modules/Core/Streaming/include/otbStreamingManager.txx
@@ -156,7 +156,7 @@ StreamingManager<TImage>::EstimateOptimalNumberOfDivisions(itk::DataObject * inp
unsigned int optimalNumberOfDivisions =
otb::PipelineMemoryPrintCalculator::EstimateOptimalNumberOfStreamDivisions(pipelineMemoryPrint, availableRAMInBytes);
- otbLogMacro(Info,<<"Estimated memory for full processing: "<<pipelineMemoryPrint * otb::PipelineMemoryPrintCalculator::ByteToMegabyte<<"MB (avail.: "<<availableRAMInBytes * otb::PipelineMemoryPrintCalculator::ByteToMegabyte<<" NB), optimal image partitioning: "<<optimalNumberOfDivisions<<" blocks");
+ otbLogMacro(Info,<<"Estimated memory for full processing: "<<pipelineMemoryPrint * otb::PipelineMemoryPrintCalculator::ByteToMegabyte<<"MB (avail.: "<<availableRAMInBytes * otb::PipelineMemoryPrintCalculator::ByteToMegabyte<<" MB), optimal image partitioning: "<<optimalNumberOfDivisions<<" blocks");
return optimalNumberOfDivisions;
}
diff --git a/Modules/Filtering/Statistics/include/otbStreamingStatisticsVectorImageFilter.txx b/Modules/Filtering/Statistics/include/otbStreamingStatisticsVectorImageFilter.txx
index af68ad38efab6be1f3465a7b5d2c9626fbc481d7..c721aa0a9b032bc563aff963c7c37f5dc8eedd4e 100644
--- a/Modules/Filtering/Statistics/include/otbStreamingStatisticsVectorImageFilter.txx
+++ b/Modules/Filtering/Statistics/include/otbStreamingStatisticsVectorImageFilter.txx
@@ -591,7 +591,7 @@ PersistentStreamingStatisticsVectorImageFilter<TInputImage, TPrecision>
{
for (unsigned int c = 0; c < threadSecondOrder.Cols(); ++c)
{
- threadSecondOrder(r, c) += vectorValue[r] * vectorValue[c];
+ threadSecondOrder(r, c) += static_cast<PrecisionType>(vectorValue[r]) * static_cast<PrecisionType>(vectorValue[c]);
}
}
threadSecondOrderComponent += vectorValue.GetSquaredNorm();
diff --git a/Modules/IO/ImageIO/include/otbImageFileReader.txx b/Modules/IO/ImageIO/include/otbImageFileReader.txx
index c8d4fb2d0f13c9a9add354d34fbff848b2397100..512be71058983155680a18eab313c6f6fbb3f51f 100644
--- a/Modules/IO/ImageIO/include/otbImageFileReader.txx
+++ b/Modules/IO/ImageIO/include/otbImageFileReader.txx
@@ -380,6 +380,17 @@ ImageFileReader<TOutputImage, ConvertPixelTraits>
spacing[i] = 1.0;
}
origin[i] = 0.5*spacing[i];
+ for (unsigned j = 0; j < TOutputImage::ImageDimension; ++j)
+ {
+ if (i == j)
+ {
+ direction[j][i] = 1.0;
+ }
+ else
+ {
+ direction[j][i] = 0.0;
+ }
+ }
}
}
diff --git a/Modules/IO/ImageIO/include/otbImageFileWriter.txx b/Modules/IO/ImageIO/include/otbImageFileWriter.txx
index 6cfff64f8ae1df38f26344110537bb1c47d0336c..f61c624d02470aa03296ffd03499c85a6c2749e1 100644
--- a/Modules/IO/ImageIO/include/otbImageFileWriter.txx
+++ b/Modules/IO/ImageIO/include/otbImageFileWriter.txx
@@ -279,6 +279,8 @@ ImageFileWriter<TInputImage>
itkExceptionMacro(<< "No input to writer");
}
+ otb::Logger::Instance()->LogSetupInformation();
+
/** Parse streaming modes */
if(m_FilenameHelper->StreamingTypeIsSet())
{
diff --git a/Modules/IO/ImageIO/include/otbMultiImageFileWriter.h b/Modules/IO/ImageIO/include/otbMultiImageFileWriter.h
index 5d2cf24f80ddbcf858d00becb3fbbd3489e5e9f9..d8b05f5c4bdaa69a60606dc5a5ed4db1ef47b416 100644
--- a/Modules/IO/ImageIO/include/otbMultiImageFileWriter.h
+++ b/Modules/IO/ImageIO/include/otbMultiImageFileWriter.h
@@ -41,6 +41,8 @@ namespace otb
* When the user gives a number of lines per strip or a tile size, the value
* is interpreted on the first input to deduce the number of streams. This
* number of streams is then used to split the other inputs.
+ *
+ * \ingroup OTBImageIO
*/
class OTBImageIO_EXPORT MultiImageFileWriter: public itk::ProcessObject
{
@@ -226,7 +228,11 @@ private:
bool m_IsObserving;
unsigned long m_ObserverID;
- /** Internal base wrapper class to handle each ImageFileWriter */
+ /** \class SinkBase
+ * Internal base wrapper class to handle each ImageFileWriter
+ *
+ * \ingroup OTBImageIO
+ */
class SinkBase
{
public:
@@ -248,6 +254,8 @@ private:
/** \class Sink
* Wrapper class for each ImageFileWriter
+ *
+ * \ingroup OTBImageIO
*/
template <class TImage>
class Sink : public SinkBase
diff --git a/Modules/IO/TestKernel/include/otbTestMain.h b/Modules/IO/TestKernel/include/otbTestMain.h
index ccb70a639cc5ec5a295d775affc2d5a510747c7d..7a833306b5ecfd8e6b434488a0932f3fdc2a5933 100644
--- a/Modules/IO/TestKernel/include/otbTestMain.h
+++ b/Modules/IO/TestKernel/include/otbTestMain.h
@@ -28,7 +28,7 @@
#include <iostream>
#include "itkMultiThreader.h"
-#include "itkMacro.h"
+#include "otbMacro.h"
#include "otbOGRDriversInit.h"
#include "otbTestHelper.h"
@@ -298,6 +298,7 @@ int main(int ac, char* av[])
}
else
{
+ otb::Logger::Instance()->LogSetupInformation();
MainFuncPointer f = j->second;
int result;
try
diff --git a/Modules/Learning/DimensionalityReductionLearning/include/otbAutoencoderModel.h b/Modules/Learning/DimensionalityReductionLearning/include/otbAutoencoderModel.h
index 7bf3231a9b8d372473fd5ed0f598d3f2fd7c7c06..5fe7ec2f27c9f91f21a2ce2417502f5d4beac54d 100644
--- a/Modules/Learning/DimensionalityReductionLearning/include/otbAutoencoderModel.h
+++ b/Modules/Learning/DimensionalityReductionLearning/include/otbAutoencoderModel.h
@@ -33,8 +33,9 @@
#endif
#include "otb_shark.h"
#include <shark/Algorithms/StoppingCriteria/AbstractStoppingCriterion.h>
-#include <shark/Models/FFNet.h>
-#include <shark/Models/Autoencoder.h>
+#include <shark/Models/LinearModel.h>
+#include <shark/Models/ConcatenatedModel.h>
+#include <shark/Models/NeuronLayers.h>
#if defined(__GNUC__) || defined(__clang__)
#pragma GCC diagnostic pop
#endif
@@ -76,9 +77,9 @@ public:
typedef typename Superclass::ConfidenceListSampleType ConfidenceListSampleType;
/// Neural network related typedefs
- typedef shark::Autoencoder<NeuronType,shark::LinearNeuron> OutAutoencoderType;
- typedef shark::Autoencoder<NeuronType,NeuronType> AutoencoderType;
- typedef shark::FFNet<NeuronType,shark::LinearNeuron> NetworkType;
+ typedef shark::ConcatenatedModel<shark::RealVector> ModelType;
+ typedef shark::LinearModel<shark::RealVector,NeuronType> LayerType;
+ typedef shark::LinearModel<shark::RealVector, shark::LinearNeuron> OutLayerType;
itkNewMacro(Self);
itkTypeMacro(AutoencoderModel, DimensionalityReductionModel);
@@ -127,18 +128,16 @@ public:
void Train() override;
- template <class T, class Autoencoder>
+ template <class T>
void TrainOneLayer(
shark::AbstractStoppingCriterion<T> & criterion,
- Autoencoder &,
unsigned int,
shark::Data<shark::RealVector> &,
std::ostream&);
- template <class T, class Autoencoder>
+ template <class T>
void TrainOneSparseLayer(
shark::AbstractStoppingCriterion<T> & criterion,
- Autoencoder &,
unsigned int,
shark::Data<shark::RealVector> &,
std::ostream&);
@@ -166,7 +165,9 @@ protected:
private:
/** Internal Network */
- NetworkType m_Net;
+ ModelType m_Encoder;
+ std::vector<LayerType> m_InLayers;
+ OutLayerType m_OutLayer;
itk::Array<unsigned int> m_NumberOfHiddenNeurons;
/** Training parameters */
unsigned int m_NumberOfIterations; // stop the training after a fixed number of iterations
diff --git a/Modules/Learning/DimensionalityReductionLearning/include/otbAutoencoderModel.txx b/Modules/Learning/DimensionalityReductionLearning/include/otbAutoencoderModel.txx
index 33f1c28e247c43f80ac28a1d608b1c15967c6a5e..e5a26e9ee3dc8cbf4918222f4b7b45bc93e925cb 100644
--- a/Modules/Learning/DimensionalityReductionLearning/include/otbAutoencoderModel.txx
+++ b/Modules/Learning/DimensionalityReductionLearning/include/otbAutoencoderModel.txx
@@ -34,18 +34,17 @@
#include "otbSharkUtils.h"
//include train function
#include <shark/ObjectiveFunctions/ErrorFunction.h>
-#include <shark/ObjectiveFunctions/SparseAutoencoderError.h>//the error function performing the regularisation of the hidden neurons
+//~ #include <shark/ObjectiveFunctions/SparseAutoencoderError.h>//the error function performing the regularisation of the hidden neurons
#include <shark/Algorithms/GradientDescent/Rprop.h>// the RProp optimization algorithm
#include <shark/ObjectiveFunctions/Loss/SquaredLoss.h> // squared loss used for regression
#include <shark/ObjectiveFunctions/Regularizer.h> //L2 regulariziation
-#include <shark/Models/ImpulseNoiseModel.h> //noise source to corrupt the inputs
-#include <shark/Models/ConcatenatedModel.h>//to concatenate the noise with the model
+//~ #include <shark/Models/ImpulseNoiseModel.h> //noise source to corrupt the inputs
#include <shark/Algorithms/StoppingCriteria/MaxIterations.h> //A simple stopping criterion that stops after a fixed number of iterations
#include <shark/Algorithms/StoppingCriteria/TrainingProgress.h> //Stops when the algorithm seems to converge, Tracks the progress of the training error over a period of time
-#include <shark/Algorithms/GradientDescent/SteepestDescent.h>
+#include <shark/Algorithms/GradientDescent/Adam.h>
#if defined(__GNUC__) || defined(__clang__)
#pragma GCC diagnostic pop
#endif
@@ -83,96 +82,56 @@ AutoencoderModel<TInputValue,NeuronType>
}
// Initialization of the feed forward neural network
- std::vector<size_t> layers;
- layers.push_back(shark::dataDimension(inputSamples));
+ m_Encoder = ModelType();
+ m_InLayers.clear();
+ size_t previousShape = shark::dataDimension(inputSamples);
for (unsigned int i = 0 ; i < m_NumberOfHiddenNeurons.Size(); ++i)
{
- layers.push_back(m_NumberOfHiddenNeurons[i]);
+ m_InLayers.push_back( LayerType(previousShape, m_NumberOfHiddenNeurons[i]) );
+ previousShape = m_NumberOfHiddenNeurons[i];
+ m_Encoder.add(&(m_InLayers.back()), true);
}
-
for (unsigned int i = std::max(0,static_cast<int>(m_NumberOfHiddenNeurons.Size()-1)) ; i > 0; --i)
{
- layers.push_back(m_NumberOfHiddenNeurons[i-1]);
- }
-
- layers.push_back(shark::dataDimension(inputSamples));
- m_Net.setStructure(layers);
- shark::initRandomNormal(m_Net,0.1);
-
- // Training of the first Autoencoder (first and last layer of the FF network)
- if (m_Epsilon > 0)
- {
- shark::TrainingProgress<> criterion(5,m_Epsilon);
-
- OutAutoencoderType net;
- // Shark doesn't allow to train a layer using a sparsity term AND a noisy input.
- if (m_Noise[0] != 0)
- {
- TrainOneLayer(criterion, net, 0, inputSamples, ofs);
- }
- else
- {
- TrainOneSparseLayer(criterion, net, 0, inputSamples, ofs);
- }
- criterion.reset();
+ m_InLayers.push_back( LayerType(previousShape, m_NumberOfHiddenNeurons[i-1]) );
+ previousShape = m_NumberOfHiddenNeurons[i-1];
}
- else
- {
- shark::MaxIterations<> criterion(m_NumberOfIterations);
+ m_OutLayer = OutLayerType(previousShape, shark::dataDimension(inputSamples));
- OutAutoencoderType net;
- // Shark doesn't allow to train a layer using a sparsity term AND a noisy input.
- if (m_Noise[0] != 0)
- {
- TrainOneLayer(criterion, net, 0, inputSamples, ofs);
- otbMsgDevMacro(<< "m_Noise " << m_Noise[0]);
- }
- else
- {
- TrainOneSparseLayer(criterion, net, 0, inputSamples, ofs);
- }
- criterion.reset();
- }
-
- // Training of the other autoencoders
- if (m_Epsilon > 0)
+ // Training of the autoencoders pairwise, starting from the first and last layers
+ for (unsigned int i = 0 ; i < m_NumberOfHiddenNeurons.Size(); ++i)
{
- shark::TrainingProgress<> criterion(5,m_Epsilon);
-
- for (unsigned int i = 1 ; i < m_NumberOfHiddenNeurons.Size(); ++i)
+ if (m_Epsilon > 0)
{
- AutoencoderType net;
+ shark::TrainingProgress<> criterion(5,m_Epsilon);
// Shark doesn't allow to train a layer using a sparsity term AND a noisy input.
if (m_Noise[i] != 0)
{
- TrainOneLayer(criterion, net, i, inputSamples, ofs);
+ TrainOneLayer(criterion, i, inputSamples, ofs);
}
else
{
- TrainOneSparseLayer(criterion, net, i, inputSamples, ofs);
+ TrainOneSparseLayer(criterion, i, inputSamples, ofs);
}
criterion.reset();
}
- }
- else
- {
- shark::MaxIterations<> criterion(m_NumberOfIterations);
-
- for (unsigned int i = 1 ; i < m_NumberOfHiddenNeurons.Size(); ++i)
+ else
{
- AutoencoderType net;
+ shark::MaxIterations<> criterion(m_NumberOfIterations);
// Shark doesn't allow to train a layer using a sparsity term AND a noisy input.
if (m_Noise[i] != 0)
{
- TrainOneLayer(criterion, net, i, inputSamples, ofs);
+ TrainOneLayer(criterion, i, inputSamples, ofs);
otbMsgDevMacro(<< "m_Noise " << m_Noise[0]);
}
else
{
- TrainOneSparseLayer( criterion, net, i, inputSamples, ofs);
+ TrainOneSparseLayer( criterion, i, inputSamples, ofs);
}
criterion.reset();
}
+ // encode the samples with the last encoder trained
+ inputSamples = m_InLayers[i](inputSamples);
}
if (m_NumberOfIterationsFineTuning > 0)
{
@@ -183,31 +142,37 @@ AutoencoderModel<TInputValue,NeuronType>
}
template <class TInputValue, class NeuronType>
-template <class T, class Autoencoder>
+template <class T>
void
AutoencoderModel<TInputValue,NeuronType>
::TrainOneLayer(
shark::AbstractStoppingCriterion<T> & criterion,
- Autoencoder & net,
unsigned int layer_index,
shark::Data<shark::RealVector> &samples,
std::ostream& File)
{
+ typedef shark::AbstractModel<shark::RealVector,shark::RealVector> BaseModelType;
+ ModelType net;
+ net.add(&(m_InLayers[layer_index]), true);
+ net.add( (layer_index ?
+ (BaseModelType*) &(m_InLayers[m_NumberOfHiddenNeurons.Size()*2 - 1 - layer_index]) :
+ (BaseModelType*) &m_OutLayer) , true);
+
otbMsgDevMacro(<< "Noise " << m_Noise[layer_index]);
std::size_t inputs = dataDimension(samples);
- net.setStructure(inputs, m_NumberOfHiddenNeurons[layer_index]);
initRandomUniform(net,-m_InitFactor*std::sqrt(1.0/inputs),m_InitFactor*std::sqrt(1.0/inputs));
- shark::ImpulseNoiseModel noise(inputs,m_Noise[layer_index],1.0); //set an input pixel with probability m_Noise to 0
- shark::ConcatenatedModel<shark::RealVector,shark::RealVector> model = noise>> net;
+ //~ shark::ImpulseNoiseModel noise(inputs,m_Noise[layer_index],1.0); //set an input pixel with probability m_Noise to 0
+ //~ shark::ConcatenatedModel<shark::RealVector,shark::RealVector> model = noise>> net;
shark::LabeledData<shark::RealVector,shark::RealVector> trainSet(samples,samples);//labels identical to inputs
shark::SquaredLoss<shark::RealVector> loss;
- shark::ErrorFunction error(trainSet, &model, &loss);
+ //~ shark::ErrorFunction error(trainSet, &model, &loss);
+ shark::ErrorFunction<> error(trainSet, &net, &loss);
- shark::TwoNormRegularizer regularizer(error.numberOfVariables());
+ shark::TwoNormRegularizer<> regularizer(error.numberOfVariables());
error.setRegularizer(m_Regularization[layer_index],®ularizer);
- shark::IRpropPlusFull optimizer;
+ shark::Adam<> optimizer;
error.init();
optimizer.init(error);
@@ -230,35 +195,37 @@ AutoencoderModel<TInputValue,NeuronType>
} while( !criterion.stop( optimizer.solution() ) );
net.setParameterVector(optimizer.solution().point);
- m_Net.setLayer(layer_index,net.encoderMatrix(),net.hiddenBias()); // Copy the encoder in the FF neural network
- m_Net.setLayer( m_NumberOfHiddenNeurons.Size()*2 - 1 - layer_index,net.decoderMatrix(),net.outputBias()); // Copy the decoder in the FF neural network
- samples = net.encode(samples);
}
template <class TInputValue, class NeuronType>
-template <class T, class Autoencoder>
+template <class T>
void AutoencoderModel<TInputValue,NeuronType>::TrainOneSparseLayer(
shark::AbstractStoppingCriterion<T> & criterion,
- Autoencoder & net,
unsigned int layer_index,
shark::Data<shark::RealVector> &samples,
std::ostream& File)
{
- //AutoencoderType net;
- std::size_t inputs = dataDimension(samples);
- net.setStructure(inputs, m_NumberOfHiddenNeurons[layer_index]);
+ typedef shark::AbstractModel<shark::RealVector,shark::RealVector> BaseModelType;
+ ModelType net;
+ net.add(&(m_InLayers[layer_index]), true);
+ net.add( (layer_index ?
+ (BaseModelType*) &(m_InLayers[m_NumberOfHiddenNeurons.Size()*2 - 1 - layer_index]) :
+ (BaseModelType*) &m_OutLayer) , true);
+ std::size_t inputs = dataDimension(samples);
shark::initRandomUniform(net,-m_InitFactor*std::sqrt(1.0/inputs),m_InitFactor*std::sqrt(1.0/inputs));
// Idea : set the initials value for the output weights higher than the input weights
shark::LabeledData<shark::RealVector,shark::RealVector> trainSet(samples,samples);//labels identical to inputs
shark::SquaredLoss<shark::RealVector> loss;
- shark::SparseAutoencoderError error(trainSet,&net, &loss, m_Rho[layer_index], m_Beta[layer_index]);
-
- shark::TwoNormRegularizer regularizer(error.numberOfVariables());
+ //~ shark::SparseAutoencoderError error(trainSet,&net, &loss, m_Rho[layer_index], m_Beta[layer_index]);
+ // SparseAutoencoderError doesn't exist anymore, for now use a plain ErrorFunction
+ shark::ErrorFunction<> error(trainSet, &net, &loss);
+
+ shark::TwoNormRegularizer<> regularizer(error.numberOfVariables());
error.setRegularizer(m_Regularization[layer_index],®ularizer);
- shark::IRpropPlusFull optimizer;
+ shark::Adam<> optimizer;
error.init();
optimizer.init(error);
@@ -279,9 +246,6 @@ void AutoencoderModel<TInputValue,NeuronType>::TrainOneSparseLayer(
File << "end layer" << std::endl;
}
net.setParameterVector(optimizer.solution().point);
- m_Net.setLayer(layer_index,net.encoderMatrix(),net.hiddenBias()); // Copy the encoder in the FF neural network
- m_Net.setLayer( m_NumberOfHiddenNeurons.Size()*2 - 1 - layer_index,net.decoderMatrix(),net.outputBias()); // Copy the decoder in the FF neural network
- samples = net.encode(samples);
}
template <class TInputValue, class NeuronType>
@@ -293,15 +257,23 @@ AutoencoderModel<TInputValue,NeuronType>
shark::Data<shark::RealVector> &samples,
std::ostream& File)
{
+ // create full network
+ ModelType net;
+ for (auto &layer : m_InLayers)
+ {
+ net.add(&layer, true);
+ }
+ net.add(&m_OutLayer, true);
+
//labels identical to inputs
shark::LabeledData<shark::RealVector,shark::RealVector> trainSet(samples,samples);
shark::SquaredLoss<shark::RealVector> loss;
- shark::ErrorFunction error(trainSet, &m_Net, &loss);
- shark::TwoNormRegularizer regularizer(error.numberOfVariables());
+ shark::ErrorFunction<> error(trainSet, &net, &loss);
+ shark::TwoNormRegularizer<> regularizer(error.numberOfVariables());
error.setRegularizer(m_Regularization[0],®ularizer);
- shark::IRpropPlusFull optimizer;
+ shark::Adam<> optimizer;
error.init();
optimizer.init(error);
otbMsgDevMacro(<<"Error before training : " << optimizer.solution().value);
@@ -326,7 +298,6 @@ AutoencoderModel<TInputValue,NeuronType>
try
{
this->Load(filename);
- m_Net.name();
}
catch(...)
{
@@ -350,22 +321,15 @@ AutoencoderModel<TInputValue,NeuronType>
{
otbMsgDevMacro(<< "saving model ...");
std::ofstream ofs(filename);
- ofs << m_Net.name() << std::endl; // the first line of the model file contains a key
+ ofs << "Autoencoder" << std::endl; // the first line of the model file contains a key
+ ofs << (m_InLayers.size() + 1) << std::endl; // second line is the number of encoders/decoders
shark::TextOutArchive oa(ofs);
- oa << m_Net;
- ofs.close();
-
- if (this->m_WriteWeights == true) // output the map vectors in a txt file
+ for (const auto &layer : m_InLayers)
{
- std::ofstream otxt(filename+".txt");
- for (unsigned int i = 0 ; i < m_Net.layerMatrices().size(); ++i)
- {
- otxt << "layer " << i << std::endl;
- otxt << m_Net.layerMatrix(i) << std::endl;
- otxt << m_Net.bias(i) << std::endl;
- otxt << std::endl;
- }
+ oa << layer;
}
+ oa << m_OutLayer;
+ ofs.close();
}
template <class TInputValue, class NeuronType>
@@ -373,23 +337,39 @@ void
AutoencoderModel<TInputValue,NeuronType>
::Load(const std::string & filename, const std::string & /*name*/)
{
- NetworkType net;
std::ifstream ifs(filename);
- char autoencoder[256];
- ifs.getline(autoencoder,256);
- std::string autoencoderstr(autoencoder);
-
- if (autoencoderstr != net.name()){
+ char buffer[256];
+ // check first line
+ ifs.getline(buffer,256);
+ std::string bufferStr(buffer);
+ if (bufferStr != "Autoencoder"){
itkExceptionMacro(<< "Error opening " << filename.c_str() );
}
+ // check second line
+ ifs.getline(buffer,256);
+ int nbLevels = boost::lexical_cast<int>(buffer);
+ if (nbLevels < 2 || nbLevels%2 == 1)
+ {
+ itkExceptionMacro(<< "Unexpected number of levels : "<<buffer );
+ }
+ m_InLayers.clear();
+ m_Encoder = ModelType();
shark::TextInArchive ia(ifs);
- ia >> m_Net;
+ for (int i=0 ; (i+1) < nbLevels ; i++)
+ {
+ LayerType layer;
+ ia >> layer;
+ m_InLayers.push_back(layer);
+ }
+ ia >> m_OutLayer;
ifs.close();
- // This gives us the dimension if we keep the encoder and decoder
- size_t feature_layer_index = m_Net.layerMatrices().size()/2;
- // number of neurons in the feature layer (second dimension of the first decoder weight matrix)
- this->SetDimension(m_Net.layerMatrix(feature_layer_index).size2());
+ for (int i=0 ; i < nbLevels/2 ; i++)
+ {
+ m_Encoder.add(&(m_InLayers[i]) ,true);
+ }
+
+ this->SetDimension( m_Encoder.outputShape()[0] );
}
template <class TInputValue, class NeuronType>
@@ -409,7 +389,7 @@ AutoencoderModel<TInputValue,NeuronType>
shark::Data<shark::RealVector> data = shark::createDataFromRange(features);
// features layer for a network containing the encoder and decoder part
- data = m_Net.evalLayer( m_Net.layerMatrices().size()/2-1 ,data);
+ data = m_Encoder(data);
TargetSampleType target;
target.SetSize(this->m_Dimension);
@@ -435,7 +415,7 @@ AutoencoderModel<TInputValue,NeuronType>
shark::Data<shark::RealVector> data = shark::createDataFromRange(features);
TargetSampleType target;
// features layer for a network containing the encoder and decoder part
- data = m_Net.evalLayer( m_Net.layerMatrices().size()/2-1 ,data);
+ data = m_Encoder(data);
unsigned int id = startIndex;
target.SetSize(this->m_Dimension);
diff --git a/Modules/Learning/DimensionalityReductionLearning/include/otbPCAModel.txx b/Modules/Learning/DimensionalityReductionLearning/include/otbPCAModel.txx
index 9f39326a21bc5f1980a49d80ecdaea55b42a450a..a387852fecc386d9c5f2a6c27c7bf39cd7a3649d 100644
--- a/Modules/Learning/DimensionalityReductionLearning/include/otbPCAModel.txx
+++ b/Modules/Learning/DimensionalityReductionLearning/include/otbPCAModel.txx
@@ -137,11 +137,11 @@ PCAModel<TInputValue>::Load(const std::string & filename, const std::string & /*
ifs.close();
if (this->m_Dimension ==0)
{
- this->m_Dimension = m_Encoder.outputSize();
+ this->m_Dimension = m_Encoder.outputShape()[0];
}
auto eigenvectors = m_Encoder.matrix();
- eigenvectors.resize(this->m_Dimension,m_Encoder.inputSize());
+ eigenvectors.resize(this->m_Dimension,m_Encoder.inputShape()[0]);
m_Encoder.setStructure(eigenvectors, m_Encoder.offset() );
}
diff --git a/Modules/Learning/LearningBase/otb-module.cmake b/Modules/Learning/LearningBase/otb-module.cmake
index afa2a339a1813cf16e5f6ea3700f079a36180dcd..c0af985032de6d4a2acd11988be1b9a177cf8219 100644
--- a/Modules/Learning/LearningBase/otb-module.cmake
+++ b/Modules/Learning/LearningBase/otb-module.cmake
@@ -28,7 +28,11 @@ otb_module(OTBLearningBase
OTBImageBase
OTBITK
- TEST_DEPENDS
+ OPTIONAL_DEPENDS
+ OTBShark
+
+ TEST_DEPENDS
+ OTBBoost
OTBTestKernel
OTBImageIO
diff --git a/Modules/Learning/LearningBase/test/CMakeLists.txt b/Modules/Learning/LearningBase/test/CMakeLists.txt
index d1d16c3e65801e606c6e6903538b65264a4483a6..48e28cc5cad320ffa41eee0659ff6979d0bf4457 100644
--- a/Modules/Learning/LearningBase/test/CMakeLists.txt
+++ b/Modules/Learning/LearningBase/test/CMakeLists.txt
@@ -32,6 +32,10 @@ otbKMeansImageClassificationFilterNew.cxx
otbMachineLearningModelTemplates.cxx
)
+if(OTB_USE_SHARK)
+ set(OTBLearningBaseTests ${OTBLearningBaseTests} otbSharkUtilsTests.cxx)
+endif()
+
add_executable(otbLearningBaseTestDriver ${OTBLearningBaseTests})
target_link_libraries(otbLearningBaseTestDriver ${OTBLearningBase-Test_LIBRARIES})
otb_module_target_label(otbLearningBaseTestDriver)
@@ -68,3 +72,7 @@ otb_add_test(NAME leTuDecisionTreeNew COMMAND otbLearningBaseTestDriver
otb_add_test(NAME leTuKMeansImageClassificationFilterNew COMMAND otbLearningBaseTestDriver
otbKMeansImageClassificationFilterNew)
+if(OTB_USE_SHARK)
+ otb_add_test(NAME leTuSharkNormalizeLabels COMMAND otbLearningBaseTestDriver
+ otbSharkNormalizeLabels)
+endif()
diff --git a/Modules/Learning/LearningBase/test/otbLearningBaseTestDriver.cxx b/Modules/Learning/LearningBase/test/otbLearningBaseTestDriver.cxx
index 5b38bf300dd4520c18e198b6e6643848cbdc937c..dc2d36b7943129ec6519ebbc4f194d1dd6078800 100644
--- a/Modules/Learning/LearningBase/test/otbLearningBaseTestDriver.cxx
+++ b/Modules/Learning/LearningBase/test/otbLearningBaseTestDriver.cxx
@@ -29,4 +29,7 @@ void RegisterTests()
REGISTER_TEST(otbSEMClassifierNew);
REGISTER_TEST(otbDecisionTreeNew);
REGISTER_TEST(otbKMeansImageClassificationFilterNew);
+#ifdef OTB_USE_SHARK
+ REGISTER_TEST(otbSharkNormalizeLabels);
+#endif
}
diff --git a/Modules/Learning/LearningBase/test/otbSharkUtilsTests.cxx b/Modules/Learning/LearningBase/test/otbSharkUtilsTests.cxx
new file mode 100644
index 0000000000000000000000000000000000000000..bc3783cb728b0f5ad0f6b2d43620b18ba7939e30
--- /dev/null
+++ b/Modules/Learning/LearningBase/test/otbSharkUtilsTests.cxx
@@ -0,0 +1,54 @@
+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ * https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "itkMacro.h"
+#include "otbSharkUtils.h"
+
+
+int otbSharkNormalizeLabels(int itkNotUsed(argc), char* itkNotUsed(argv) [])
+{
+ std::vector<unsigned int> inLabels = {2, 2, 3, 20, 1};
+ std::vector<unsigned int> expectedDictionary = {2, 3, 20, 1};
+ std::vector<unsigned int> expectedLabels = {0, 0, 1, 2, 3};
+
+ auto newLabels = inLabels;
+ std::vector<unsigned int> labelDict;
+ otb::Shark::NormalizeLabelsAndGetDictionary(newLabels, labelDict);
+
+ if(newLabels != expectedLabels)
+ {
+ std::cout << "Wrong new labels\n";
+ for(size_t i = 0; i<newLabels.size(); ++i)
+ std::cout << "Got " << newLabels[i] << " expected " << expectedLabels[i] << '\n';
+
+ return EXIT_FAILURE;
+ }
+
+ if(labelDict != expectedDictionary)
+ {
+ std::cout << "Wrong dictionary\n";
+ for(size_t i = 0; i<labelDict.size(); ++i)
+ std::cout << "Got " << labelDict[i] << " expected " << expectedDictionary[i] << '\n';
+
+ return EXIT_FAILURE;
+ }
+
+ return EXIT_SUCCESS;
+}
diff --git a/Modules/Learning/Sampling/src/otbSamplingRateCalculator.cxx b/Modules/Learning/Sampling/src/otbSamplingRateCalculator.cxx
index a7e8f2bc3ff14263ed453d6c46066e74d68c8356..86de38dfb0c5d8c596a1b0f73576c49b1d4200d3 100644
--- a/Modules/Learning/Sampling/src/otbSamplingRateCalculator.cxx
+++ b/Modules/Learning/Sampling/src/otbSamplingRateCalculator.cxx
@@ -233,7 +233,7 @@ SamplingRateCalculator
std::string::size_type pos5 = line.find_first_not_of(" \t", parts[2].begin() - line.begin());
std::string::size_type pos6 = line.find_last_not_of(" \t", parts[2].end() - line.begin() -1);
std::string::size_type pos7 = line.find_first_not_of(" \t", parts[3].begin() - line.begin());
- std::string::size_type pos8 = line.find_last_not_of(" \t", parts[3].end() - line.begin() -1);
+ std::string::size_type pos8 = line.find_last_not_of(" \t\r", parts[3].end() - line.begin() -1);
if (pos2 != std::string::npos && pos1 <= pos2 &&
pos4 != std::string::npos && pos3 <= pos4 &&
pos6 != std::string::npos && pos5 <= pos6 &&
@@ -336,7 +336,7 @@ SamplingRateCalculator
std::string::size_type pos1 = line.find_first_not_of(" \t", parts[0].begin() - line.begin());
std::string::size_type pos2 = line.find_last_not_of(" \t", parts[0].end() - line.begin() -1);
std::string::size_type pos3 = line.find_first_not_of(" \t", parts[1].begin() - line.begin());
- std::string::size_type pos4 = line.find_last_not_of(" \t", parts[1].end() - line.begin() -1);
+ std::string::size_type pos4 = line.find_last_not_of(" \t\r", parts[1].end() - line.begin() -1);
if (pos2 != std::string::npos && pos1 <= pos2 &&
pos4 != std::string::npos && pos3 <= pos4)
{
diff --git a/Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.h b/Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.h
index 7dd41d6eed22208d512ecf0e5d4eb5116f2bf5c0..41015ee9dc7f5f6bb6c3d1defbfad5ccb1c0c47b 100644
--- a/Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.h
@@ -33,7 +33,10 @@
#pragma GCC diagnostic ignored "-Wsign-compare"
#pragma GCC diagnostic ignored "-Wcast-align"
#pragma GCC diagnostic ignored "-Wunknown-pragmas"
+#pragma GCC diagnostic ignored "-Wheader-guard"
+#pragma GCC diagnostic ignored "-Wmissing-field-initializers"
#endif
+#include <shark/Models/Classifier.h>
#include "otb_shark.h"
#include "shark/Algorithms/Trainers/RFTrainer.h"
#if defined(__GNUC__) || defined(__clang__)
@@ -134,6 +137,10 @@ public:
/** If true, margin confidence value will be computed */
itkSetMacro(ComputeMargin, bool);
+ /** If true, class labels will be normalised in [0 ... nbClasses] */
+ itkGetMacro(NormalizeClassLabels, bool);
+ itkSetMacro(NormalizeClassLabels, bool);
+
protected:
/** Constructor */
SharkRandomForestsMachineLearningModel();
@@ -154,8 +161,10 @@ private:
SharkRandomForestsMachineLearningModel(const Self &); //purposely not implemented
void operator =(const Self&); //purposely not implemented
- shark::RFClassifier m_RFModel;
- shark::RFTrainer m_RFTrainer;
+ shark::RFClassifier<unsigned int> m_RFModel;
+ shark::RFTrainer<unsigned int> m_RFTrainer;
+ std::vector<unsigned int> m_ClassDictionary;
+ bool m_NormalizeClassLabels;
unsigned int m_NumberOfTrees;
unsigned int m_MTry;
diff --git a/Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.txx
index 207f1abdd77e4b5cfffd9bc5d104c4b40232f853..72c816069bebddc048a0f8af48f24579a55fa38b 100644
--- a/Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.txx
@@ -32,7 +32,6 @@
#pragma GCC diagnostic ignored "-Woverloaded-virtual"
#pragma GCC diagnostic ignored "-Wignored-qualifiers"
#endif
-#include <shark/Models/Converter.h>
#if defined(__GNUC__) || defined(__clang__)
#pragma GCC diagnostic pop
#endif
@@ -52,6 +51,7 @@ SharkRandomForestsMachineLearningModel<TInputValue,TOutputValue>
this->m_ConfidenceIndex = true;
this->m_IsRegressionSupported = false;
this->m_IsDoPredictBatchMultiThreaded = true;
+ this->m_NormalizeClassLabels = true;
}
@@ -76,13 +76,17 @@ SharkRandomForestsMachineLearningModel<TInputValue,TOutputValue>
Shark::ListSampleToSharkVector(this->GetInputListSample(), features);
Shark::ListSampleToSharkVector(this->GetTargetListSample(), class_labels);
+ if(m_NormalizeClassLabels)
+ {
+ Shark::NormalizeLabelsAndGetDictionary(class_labels, m_ClassDictionary);
+ }
shark::ClassificationDataset TrainSamples = shark::createLabeledDataFromRange(features,class_labels);
//Set parameters
m_RFTrainer.setMTry(m_MTry);
m_RFTrainer.setNTrees(m_NumberOfTrees);
m_RFTrainer.setNodeSize(m_NodeSize);
- m_RFTrainer.setOOBratio(m_OobRatio);
+ // m_RFTrainer.setOOBratio(m_OobRatio);
m_RFTrainer.train(m_RFModel, TrainSamples);
}
@@ -125,15 +129,20 @@ SharkRandomForestsMachineLearningModel<TInputValue,TOutputValue>
}
if (quality != ITK_NULLPTR)
{
- shark::RealVector probas = m_RFModel(samples);
+ shark::RealVector probas = m_RFModel.decisionFunction()(samples);
(*quality) = ComputeConfidence(probas, m_ComputeMargin);
}
- shark::ArgMaxConverter<shark::RFClassifier> amc;
- amc.decisionFunction() = m_RFModel;
- unsigned int res;
- amc.eval(samples, res);
+ unsigned int res{0};
+ m_RFModel.eval(samples, res);
TargetSampleType target;
- target[0] = static_cast<TOutputValue>(res);
+ if(m_NormalizeClassLabels)
+ {
+ target[0] = m_ClassDictionary[static_cast<TOutputValue>(res)];
+ }
+ else
+ {
+ target[0] = static_cast<TOutputValue>(res);
+ }
return target;
}
@@ -157,13 +166,13 @@ SharkRandomForestsMachineLearningModel<TInputValue,TOutputValue>
Shark::ListSampleRangeToSharkVector(input, features,startIndex,size);
shark::Data<shark::RealVector> inputSamples = shark::createDataFromRange(features);
- #ifdef _OPENMP
+#ifdef _OPENMP
omp_set_num_threads(itk::MultiThreader::GetGlobalDefaultNumberOfThreads());
- #endif
+#endif
if(quality != ITK_NULLPTR)
{
- shark::Data<shark::RealVector> probas = m_RFModel(inputSamples);
+ shark::Data<shark::RealVector> probas = m_RFModel.decisionFunction()(inputSamples);
unsigned int id = startIndex;
for(shark::RealVector && p : probas.elements())
{
@@ -175,14 +184,19 @@ SharkRandomForestsMachineLearningModel<TInputValue,TOutputValue>
}
}
- shark::ArgMaxConverter<shark::RFClassifier> amc;
- amc.decisionFunction() = m_RFModel;
- auto prediction = amc(inputSamples);
+ auto prediction = m_RFModel(inputSamples);
unsigned int id = startIndex;
for(const auto& p : prediction.elements())
{
TargetSampleType target;
- target[0] = static_cast<TOutputValue>(p);
+ if(m_NormalizeClassLabels)
+ {
+ target[0] = m_ClassDictionary[static_cast<TOutputValue>(p)];
+ }
+ else
+ {
+ target[0] = static_cast<TOutputValue>(p);
+ }
targets->SetMeasurementVector(id,target);
++id;
}
@@ -199,7 +213,18 @@ SharkRandomForestsMachineLearningModel<TInputValue,TOutputValue>
itkExceptionMacro(<< "Error opening " << filename.c_str() );
}
// Add comment with model file name
- ofs << "#" << m_RFModel.name() << std::endl;
+ ofs << "#" << m_RFModel.name();
+ if(m_NormalizeClassLabels) ofs << " with_dictionary";
+ ofs << std::endl;
+ if(m_NormalizeClassLabels)
+ {
+ ofs << m_ClassDictionary.size() << " ";
+ for(const auto& l : m_ClassDictionary)
+ {
+ ofs << l << " ";
+ }
+ ofs << std::endl;
+ }
shark::TextOutArchive oa(ofs);
m_RFModel.save(oa,0);
}
@@ -219,6 +244,10 @@ SharkRandomForestsMachineLearningModel<TInputValue,TOutputValue>
{
if( line.find( m_RFModel.name() ) == std::string::npos )
itkExceptionMacro( "The model file : " + filename + " cannot be read." );
+ if( line.find( "with_dictionary" ) == std::string::npos )
+ {
+ m_NormalizeClassLabels=false;
+ }
}
else
{
@@ -226,6 +255,18 @@ SharkRandomForestsMachineLearningModel<TInputValue,TOutputValue>
ifs.clear();
ifs.seekg( 0, std::ios::beg );
}
+ if(m_NormalizeClassLabels)
+ {
+ size_t nbLabels{0};
+ ifs >> nbLabels;
+ m_ClassDictionary.resize(nbLabels);
+ for(size_t i=0; i<nbLabels; ++i)
+ {
+ unsigned int label;
+ ifs >> label;
+ m_ClassDictionary[i]=label;
+ }
+ }
shark::TextInArchive ia( ifs );
m_RFModel.load( ia, 0 );
}
diff --git a/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.txx b/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.txx
index 9dd43948a719c9305dace0a6366ebfd40e4b3e24..1b08d538c943001279d9401f314d51e21e8dbf88 100644
--- a/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.txx
+++ b/Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.txx
@@ -55,6 +55,7 @@ SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
m_Normalized( false ), m_K(2), m_MaximumNumberOfIterations( 10 )
{
// Default set HardClusteringModel
+ this->m_ConfidenceIndex = true;
m_ClusteringModel = boost::make_shared<ClusteringModelType>( &m_Centroids );
}
@@ -174,7 +175,7 @@ SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
// Change quality measurement only if SoftClustering or other clustering method is used.
if( quality != ITK_NULLPTR )
{
- for( unsigned int qid = startIndex; qid < size; ++qid )
+ for( unsigned int qid = startIndex; qid < startIndex+size; ++qid )
{
quality->SetMeasurementVector( qid, static_cast<ConfidenceValueType>(1.) );
}
diff --git a/Modules/ThirdParty/Boost/otb-module-init.cmake b/Modules/ThirdParty/Boost/otb-module-init.cmake
index a5f58041fb0bf5001d123ff2e0f772a26cff6d34..0a07bdfdbc7a9d33a03464925c95667a036bea59 100644
--- a/Modules/ThirdParty/Boost/otb-module-init.cmake
+++ b/Modules/ThirdParty/Boost/otb-module-init.cmake
@@ -31,3 +31,8 @@ if (BUILD_TESTING)
message(STATUS "Found Boost components: unit_test_framework")
endif()
endif() #BUILD_TESTING
+
+if(WIN32)
+ # disable autolinking in boost
+ add_definitions( -DBOOST_ALL_NO_LIB )
+endif()
diff --git a/Modules/ThirdParty/Shark/include/otbSharkUtils.h b/Modules/ThirdParty/Shark/include/otbSharkUtils.h
index de3adf77401d0f131d2bd7d447627829b3df64ff..04c57b6d4e7f5a022b0c4fafa86ac41b134f690c 100644
--- a/Modules/ThirdParty/Shark/include/otbSharkUtils.h
+++ b/Modules/ThirdParty/Shark/include/otbSharkUtils.h
@@ -23,6 +23,7 @@
#include <stdexcept>
#include <string>
+#include <unordered_map>
#if defined(__GNUC__) || defined(__clang__)
#pragma GCC diagnostic push
@@ -127,6 +128,27 @@ template <class T> void ListSampleToSharkVector(const T * listSample, std::vecto
assert(listSample != nullptr);
ListSampleRangeToSharkVector(listSample,output,0, static_cast<unsigned int>(listSample->Size()));
}
+
+/** Shark assumes that labels are 0 ... (nbClasses-1). This function modifies the labels contained in the input vector and returns a vector with size = nbClasses which allows the translation from the normalised labels to the new ones oldLabel = dictionary[newLabel].
+*/
+template <typename T> void NormalizeLabelsAndGetDictionary(std::vector<T>& labels,
+ std::vector<T>& dictionary)
+{
+ std::unordered_map<T, T> dictMap;
+ T labelCount{0};
+ for(const auto& l : labels)
+ {
+ if(dictMap.find(l)==dictMap.end())
+ dictMap.insert({l, labelCount++});
+ }
+ dictionary.resize(labelCount);
+ for(auto& l : labels)
+ {
+ auto newLabel = dictMap[l];
+ dictionary[newLabel] = l;
+ l = newLabel;
+ }
+}
}
}
diff --git a/Modules/Visualization/Monteverdi/include/mvdMainWindow.h b/Modules/Visualization/Monteverdi/include/mvdMainWindow.h
index 3ae223763f6392aa498f81f9fcaf5776f3211a9f..5b7d6b66cf3a055353803aae37cf06821d58c8be 100644
--- a/Modules/Visualization/Monteverdi/include/mvdMainWindow.h
+++ b/Modules/Visualization/Monteverdi/include/mvdMainWindow.h
@@ -150,7 +150,7 @@ public slots:
/**
*/
- void ImportImages( const QStringList & filenames );
+ void ImportImages( const QStringList & filenames, bool enableOverviews );
/*-[ SIGNALS SECTION ]-----------------------------------------------------*/
diff --git a/Modules/Visualization/Monteverdi/src/main.cxx b/Modules/Visualization/Monteverdi/src/main.cxx
index e44dbb8459d0b239c8bc5c5283d5c41a6412d7ef..1765c73bec64c6ba02f19ef8ba06187c1289cf66 100644
--- a/Modules/Visualization/Monteverdi/src/main.cxx
+++ b/Modules/Visualization/Monteverdi/src/main.cxx
@@ -1,5 +1,6 @@
/*
* Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ * Copyright (C) 2017 CS Systemes d'Information (CS SI)
*
* This file is part of Orfeo Toolbox
*
@@ -31,6 +32,8 @@
//
// Qt includes (sorted by alphabetic order)
//// Must be included before system/custom includes.
+#include <QCoreApplication>
+#include <QFile>
#include <QPixmap>
#include <QSplashScreen>
@@ -48,6 +51,7 @@
//
// Monteverdi includes (sorted by alphabetic order)
+#include "mvdAlgorithm.h"
#include "mvdApplication.h"
#include "mvdMainWindow.h"
@@ -65,34 +69,39 @@ struct Flags
{
Flags() :
loadOTBApplications( false ),
- forceNoGLSL( false )
+ forceNoGLSL( false ),
+ forceNoOverviews( false )
{
}
bool loadOTBApplications: 1;
bool forceNoGLSL: 1;
+ bool forceNoOverviews: 1;
};
/*****************************************************************************/
/* FUNCTIONS DECLARATION */
/*****************************************************************************/
+void
+DisplayUsage( const char * );
+void
+AppendFromTextFile( QStringList &, const QString & );
/*****************************************************************************/
/* MAIN */
/*****************************************************************************/
int
-main( int argc, char* argv[] )
+main( int argc, char * argv[] )
{
QApplication qtApp( argc, argv );
- Flags flags;
//
// 0. Splash-screen.
#if USE_SPLASH_SCREEN
- QPixmap pixmap(QLatin1String( ":/images/application_splash" ));
- QSplashScreen splash(pixmap);
+ QPixmap pixmap( QLatin1String( ":/images/application_splash" ) );
+ QSplashScreen splash( pixmap );
splash.show();
qtApp.processEvents();//This is used to accept a click on the screen so that user can cancel the screen
#endif
@@ -100,24 +109,16 @@ main( int argc, char* argv[] )
//
// 0bis. Parse pre-initialization command-line arguments.
QStringList args( qtApp.arguments() );
+ Flags flags;
{
+ QStringList filenames;
+
for( QStringList::iterator it( args.begin() );
it!=args.end(); )
if( it->compare( "-h" )==0 ||
it->compare( "--help" )==0 )
{
- std::cout
- << mvd::ToLocalStdString(
- QCoreApplication::translate(
- PROJECT_NAME,
- "Usage: %1 [-h|--help] [-a|--applications] [<filename>...]\n"
- " -n, --no-glsl force OpenGL 1.x compatible rendering."
- " -a, --applications load OTB-applications from OTB_APPLICATIONS_PATH."
- " -h, --help display this help message.\n"
- )
- .arg( QFileInfo( argv[ 0 ] ).baseName() )
- )
- << std::endl;
+ DisplayUsage( argv[ 0 ] );
return ERROR_CODE_USAGE;
}
@@ -130,15 +131,44 @@ main( int argc, char* argv[] )
it = args.erase( it );
}
- else if(it->compare( "-n" )==0 ||
+ else if(it->compare( "-g" )==0 ||
it->compare( "--no-glsl" )==0 )
{
flags.forceNoGLSL = true;
it = args.erase( it );
}
+
+ else if(it->compare( "-o" )==0 ||
+ it->compare( "--no-overviews" )==0 )
+ {
+ flags.forceNoOverviews = true;
+
+ it = args.erase( it );
+ }
+
+ else if(it->compare( "-t" )==0 ||
+ it->compare( "--txt-file" )==0 )
+ {
+ it = args.erase( it );
+
+ if( it==args.end() ||
+ it->startsWith( '-' ) )
+ {
+ DisplayUsage( argv[ 0 ] );
+
+ return ERROR_CODE_USAGE;
+ }
+
+ AppendFromTextFile( filenames, *it );
+
+ it = args.erase( it );
+ }
+
else
++ it;
+
+ args << filenames;
}
//
@@ -205,14 +235,14 @@ main( int argc, char* argv[] )
#if USE_OTB_APPS
mainWindow.SetupOTBApplications();
#else // USE_OTB_APPS
- qWarning() << "OTB-applications support is not included in this build.";
+ qWarning() << "OTB-applications support is not included in this build.";
#endif // USE_OTB_APPS
//
// 6. Load command-line filenames.
args.pop_front();
- mainWindow.ImportImages( args );
+ mainWindow.ImportImages( args, !flags.forceNoOverviews );
//
// 6. Let's go: run the application and return exit code.
@@ -232,3 +262,63 @@ main( int argc, char* argv[] )
/*****************************************************************************/
/* FUNCTIONS IMPLEMENTATION */
/*****************************************************************************/
+void
+DisplayUsage( const char * argv0 )
+{
+ std::cout
+ << mvd::ToLocalStdString(
+ QCoreApplication::translate(
+ PROJECT_NAME,
+ "Usage: %1 "
+ "[-h|--help] "
+ "[-a|--applications] "
+ "[-g|--no-glsl] "
+ "[-o|--no-overviews] "
+ "[-t|--txt-file <filename>] "
+ "[<filename>...]\n"
+ " -a, --applications load OTB-applications from OTB_APPLICATIONS_PATH.\n"
+#if 0
+ " -f, --file load Monteverdi project file.\n"
+#endif
+ " -h, --help display this help message.\n"
+ " -g, --no-glsl force OpenGL 1.x compatible rendering.\n"
+ " -o, --no-overviews ignore build GDAL overviews step.\n"
+#if 0
+ " -O, --force-overviews force build GDAL overviews step.\n"
+#endif
+ " -t, --txt-file read layer filenames from text file.\n"
+#if 0
+ " -c, --csv-file read layer filenames & settings from CSV file.\n"
+ " -x, --xml-file read layer filenames & settings from XML file.\n"
+#endif
+ )
+ .arg( QFileInfo( argv0 ).baseName() )
+ )
+ << std::endl;
+}
+
+/*****************************************************************************/
+void
+AppendFromTextFile( QStringList & strings,
+ const QString & filename )
+{
+ QFile file( filename );
+
+ if( !file.open( QFile::ReadOnly | QFile::Text ) )
+ throw mvd::SystemError(
+ mvd::ToStdString(
+ QCoreApplication::translate( "mvd::", "Failed to open '%1'" )
+ .arg( filename )
+ )
+ );
+
+ QTextStream is( &file );
+
+ while( !is.atEnd() )
+ {
+ QString line( is.readLine() );
+
+ if( !line.isNull() )
+ strings << line;
+ }
+}
diff --git a/Modules/Visualization/Monteverdi/src/mvdMainWindow.cxx b/Modules/Visualization/Monteverdi/src/mvdMainWindow.cxx
index 9420452e321f19bb13b9ba6521c5b79ae9415280..f02f3537d0a550a288d5cd17b108c8df503d59d2 100644
--- a/Modules/Visualization/Monteverdi/src/mvdMainWindow.cxx
+++ b/Modules/Visualization/Monteverdi/src/mvdMainWindow.cxx
@@ -1,5 +1,6 @@
/*
* Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ * Copyright (C) 2017 CS Systemes d'Information (CS SI)
*
* This file is part of Orfeo Toolbox
*
@@ -1362,7 +1363,7 @@ MainWindow
/*****************************************************************************/
void
MainWindow
-::ImportImages( const QStringList & filenames )
+::ImportImages( const QStringList & filenames, bool enableOverviews )
{
if( filenames.isEmpty() )
return;
@@ -1376,7 +1377,8 @@ MainWindow
)
);
- if( !( value.isValid() ? value.toBool() : OVERVIEWS_ENABLED_DEFAULT ) ||
+ if( enableOverviews &&
+ ( value.isValid() ? value.toBool() : OVERVIEWS_ENABLED_DEFAULT ) &&
!BuildGDALOverviews( filenames ) )
return;
}
@@ -1809,7 +1811,8 @@ MainWindow
// Select filename.
QString caption(tr("Open file..."));
ImportImages(
- otb::GetOpenFileNames( this, caption )
+ otb::GetOpenFileNames( this, caption ),
+ true
);
}
diff --git a/Modules/Wrappers/ApplicationEngine/include/otbWrapperApplication.h b/Modules/Wrappers/ApplicationEngine/include/otbWrapperApplication.h
index 4748ab0c5ddc71a9a1ffff72b8ba89585d86bc8d..421ef9a4a71e239ba313477d0d9b9fa25fdff714 100644
--- a/Modules/Wrappers/ApplicationEngine/include/otbWrapperApplication.h
+++ b/Modules/Wrappers/ApplicationEngine/include/otbWrapperApplication.h
@@ -307,7 +307,7 @@ public:
*/
bool IsParameterMissing(const std::string &key) const;
- /* Set an default integer value, must used in the
+ /* Set a default integer value, must be used in the
* DoInit when setting a value by default
* for the parameter
*
@@ -319,7 +319,17 @@ public:
*/
void SetDefaultParameterInt(std::string parameter, int value);
- /* Set a default floating value, must used in the
+ /* Get the default integer value of a parameter
+ *
+ * Can be called for types :
+ * \li ParameterType_Int
+ * \li ParameterType_Float
+ * \li ParameterType_Radius
+ * \li ParameterType_Choice
+ */
+ int GetDefaultParameterInt(std::string parameter);
+
+ /* Set a default floating value, must be used in the
* DoInit when setting a value by default
* for the parameter
*
@@ -328,6 +338,13 @@ public:
*/
void SetDefaultParameterFloat(std::string parameter, float value);
+ /* Get the default floating value of a parameter
+ *
+ * Can be called for types :
+ * \li ParameterType_Float
+ */
+ float GetDefaultParameterFloat(std::string parameter);
+
/** Set a default pixel type for an output image parameter
*
* \param[in] parameter Name of the output image parameter
diff --git a/Modules/Wrappers/ApplicationEngine/include/otbWrapperNumericalParameter.h b/Modules/Wrappers/ApplicationEngine/include/otbWrapperNumericalParameter.h
index de97a3471b75a47f0a8cdcafa67b965b76e7d199..54871040f393428f3febbba17ab0374de14b1934 100644
--- a/Modules/Wrappers/ApplicationEngine/include/otbWrapperNumericalParameter.h
+++ b/Modules/Wrappers/ApplicationEngine/include/otbWrapperNumericalParameter.h
@@ -62,8 +62,8 @@ public:
/** Set the value */
void SetValue( ScalarType value)
{
- // TODO check minimum/maximum
- m_Value = value;
+ m_Value = ( value < m_MinimumValue ) ? m_MinimumValue :
+ ( value < m_MaximumValue ) ? value : m_MaximumValue ;
// Set Active only if the parameter is not automatically set
if (!GetAutomaticValue())
diff --git a/Modules/Wrappers/ApplicationEngine/src/otbWrapperApplication.cxx b/Modules/Wrappers/ApplicationEngine/src/otbWrapperApplication.cxx
index 31f46f3a019e6fe335ebcae5535b4b6284bc2f4a..cd34d9bddcb85596a19be8c969c11ad23dda56a5 100644
--- a/Modules/Wrappers/ApplicationEngine/src/otbWrapperApplication.cxx
+++ b/Modules/Wrappers/ApplicationEngine/src/otbWrapperApplication.cxx
@@ -645,6 +645,8 @@ int Application::ExecuteAndWriteOutput()
{
m_Chrono.Restart();
+ m_Logger->LogSetupInformation();
+
int status = this->Execute();
if (status == 0)
@@ -1040,6 +1042,37 @@ void Application::SetDefaultParameterInt(std::string parameter, int value)
}
}
+int Application::GetDefaultParameterInt(std::string parameter)
+{
+ Parameter* param = GetParameterByKey(parameter);
+ int ret = 0 ;
+ if (dynamic_cast<RadiusParameter*>(param))
+ {
+ RadiusParameter* paramRadius = dynamic_cast<RadiusParameter*>(param);
+ ret = paramRadius->GetDefaultValue();
+ }
+ else if (dynamic_cast<IntParameter*>(param))
+ {
+ IntParameter* paramInt = dynamic_cast<IntParameter*>(param);
+ ret = paramInt->GetDefaultValue();
+ }
+ else if (dynamic_cast<FloatParameter*>(param))
+ {
+ FloatParameter* paramFloat = dynamic_cast<FloatParameter*>(param);
+ ret = paramFloat->GetDefaultValue();
+ }
+ else if (dynamic_cast<RAMParameter*>(param))
+ {
+ RAMParameter* paramRAM = dynamic_cast<RAMParameter*>(param);
+ ret = paramRAM->GetDefaultValue();
+ }
+ else
+ {
+ // log
+ }
+ return ret;
+}
+
void Application::SetDefaultParameterFloat(std::string parameter, float value)
{
Parameter* param = GetParameterByKey(parameter);
@@ -1052,6 +1085,18 @@ void Application::SetDefaultParameterFloat(std::string parameter, float value)
}
}
+float Application::GetDefaultParameterFloat(std::string parameter)
+{
+ Parameter* param = GetParameterByKey(parameter);
+
+ if (dynamic_cast<FloatParameter*>(param))
+ {
+ FloatParameter* paramFloat = dynamic_cast<FloatParameter*>(param);
+ return paramFloat->GetDefaultValue();
+ }
+ return 0;
+}
+
void Application::SetDefaultOutputPixelType(std::string parameter, ImagePixelType type)
{
Parameter* param = GetParameterByKey(parameter);
diff --git a/SuperBuild/CMake/External_shark.cmake b/SuperBuild/CMake/External_shark.cmake
index ce8486db084935352b4266fc384f40be3604a29c..33934d4bb943dce00faceb9d6910534197a2342e 100644
--- a/SuperBuild/CMake/External_shark.cmake
+++ b/SuperBuild/CMake/External_shark.cmake
@@ -30,8 +30,8 @@ ADD_SUPERBUILD_CMAKE_VAR(SHARK BOOST_LIBRARYDIR)
ExternalProject_Add(SHARK
PREFIX SHARK
- URL "https://github.com/Shark-ML/Shark/archive/349f29bd71c370e0f88f7fc9aa66fa5c4768fcb0.zip"
- URL_MD5 d6e4310f943e8dda4a0151612b5c62ce
+ URL "https://github.com/Shark-ML/Shark/archive/2fd55e2b83f0666d05b403b291712668f4b76a13.zip"
+ URL_MD5 863bb5f0d94b01be5292867beb05a0bb
SOURCE_DIR ${SHARK_SB_SRC}
BINARY_DIR ${SHARK_SB_BUILD_DIR}
INSTALL_DIR ${SB_INSTALL_PREFIX}
@@ -45,6 +45,7 @@ ExternalProject_Add(SHARK
-DENABLE_HDF5:BOOL=OFF
-DENABLE_CBLAS:BOOL=OFF
-DENABLE_OPENMP:BOOL=${OTB_USE_OPENMP}
+ -DSHARK_INSTALL_LIB_DIR:STRING=lib/
${SHARK_SB_CONFIG}
CMAKE_COMMAND ${SB_CMAKE_COMMAND}
LOG_DOWNLOAD 1
diff --git a/SuperBuild/patches/SHARK/shark-2-ext-num-literals-all.diff b/SuperBuild/patches/SHARK/shark-2-ext-num-literals-all.diff
new file mode 100644
index 0000000000000000000000000000000000000000..0b964c1b9ada7aa4409f0f032285a70723caacfe
--- /dev/null
+++ b/SuperBuild/patches/SHARK/shark-2-ext-num-literals-all.diff
@@ -0,0 +1,13 @@
+diff -burN Shark.orig/CMakeLists.txt Shark/CMakeLists.txt
+--- Shark.orig/CMakeLists.txt 2018-02-05 18:04:58.012612932 +0100
++++ Shark/CMakeLists.txt 2018-02-05 18:20:50.032233165 +0100
+@@ -415,6 +415,9 @@
+ #####################################################################
+ # General Path settings
+ #####################################################################
++if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
++ add_definitions(-fext-numeric-literals)
++endif()
+ include_directories( ${shark_SOURCE_DIR}/include )
+ include_directories( ${shark_BINARY_DIR}/include )
+ add_subdirectory( include )
diff --git a/SuperBuild/patches/SHARK/shark-2-find-boost-all.diff b/SuperBuild/patches/SHARK/shark-2-find-boost-all.diff
deleted file mode 100644
index a97c1ac4afd1f56118fdba14cf7b993755bb5c00..0000000000000000000000000000000000000000
--- a/SuperBuild/patches/SHARK/shark-2-find-boost-all.diff
+++ /dev/null
@@ -1,16 +0,0 @@
-diff -burN Shark-349f29bd71c370e0f88f7fc9aa66fa5c4768fcb0.orig/CMakeLists.txt Shark-349f29bd71c370e0f88f7fc9aa66fa5c4768fcb0/CMakeLists.txt
---- Shark-349f29bd71c370e0f88f7fc9aa66fa5c4768fcb0.orig/CMakeLists.txt 2017-08-22 11:31:50.472052695 +0200
-+++ Shark-349f29bd71c370e0f88f7fc9aa66fa5c4768fcb0/CMakeLists.txt 2017-08-22 11:32:36.448358789 +0200
-@@ -141,10 +141,8 @@
-
- find_package(
- Boost 1.48.0 REQUIRED COMPONENTS
-- system date_time filesystem
-- program_options serialization thread
-- unit_test_framework
--)
-+ serialization
-+ )
-
- if(NOT Boost_FOUND)
- message(FATAL_ERROR "Please make sure Boost 1.48.0 is installed on your system")