Commit 60f840a4 authored by Guillaume Pasero's avatar Guillaume Pasero
Browse files

DOC: new recipe for Python wrappings (WIP)

parent b79555cc
......@@ -203,165 +203,28 @@ available in the ``otb-bin`` OSGeo4W package, and the environment is
configured automatically so you don’t need to tweak
``OTB_APPLICATION_PATH``.
In the ``otbApplication`` module, two main classes can be manipulated :
- ``Registry``, which provides access to the list of available
applications, and can create applications.
- ``Application``, the base class for all applications. This allows to
interact with an application instance created by the ``Registry``.
Here is one example of how to use Python to run the ``Smoothing``
application, changing the algorithm at each iteration.
Once your environment is set, you can use OTB applications from Python, just
like this small example:
.. code-block:: python
# Example on the use of the Smoothing application
#
# We will use sys.argv to retrieve arguments from the command line.
# Here, the script will accept an image file as first argument,
# and the basename of the output files, without extension.
from sys import argv
# The python module providing access to OTB applications is otbApplication
import otbApplication
# otbApplication.Registry can tell you what application are available
print "Available applications: "
print str( otbApplication.Registry.GetAvailableApplications() )
import otbApplication as otb
# Let's create the application with codename "Smoothing"
app = otbApplication.Registry.CreateApplication("Smoothing")
# We print the keys of all its parameter
print app.GetParametersKeys()
# First, we set the input image filename
app.SetParameterString("in", argv[1])
# The smoothing algorithm can be set with the "type" parameter key
# and can take 3 values: 'mean', 'gaussian', 'anidif'
for type in ['mean', 'gaussian', 'anidif']:
print 'Running with ' + type + ' smoothing type'
# Here we configure the smoothing algorithm
app.SetParameterString("type", type)
# Set the output filename, using the algorithm to differentiate the outputs
app.SetParameterString("out", argv[2] + type + ".tif")
# This will execute the application and save the output file
app.ExecuteAndWriteOutput()
Numpy array processing
----------------------
Input and output images to any OTB application in the form of numpy array is now possible in OTB python wrapping.
The python wrapping only exposes OTB ApplicationEngine module which allow to access existing C++ applications.
Due to blissful nature of ApplicationEngine's loading mechanism no specific wrapping is required for each application.
Numpy extension to Python wrapping allows data exchange to application as an array rather than a disk file.
Ofcourse, it is possible to load an image from file and then convert to numpy array or just provide a file as earlier via
Application.SetParameterString(...).
This bridge that completes numpy and OTB makes it easy to plug OTB into any image processing chain via python code that uses
GIS/Image processing tools such as GDAL, GRASS GIS, OSSIM that can deal with numpy.
Below code reads an input image using python pillow (PIL) and convert it to numpy array. This numpy array is
used an input to the application via *SetImageFromNumpyArray(...)* method.
The application used in this example is ExtractROI. After extracting
a small area the output image is taken as numpy array with *GetImageFromNumpyArray(...)* method thus avoid wiriting
output to a temporary file.
::
import sys
import os
import numpy as np
import otbApplication
from PIL import Image as PILImage
pilimage = PILImage.open('poupees.jpg')
npimage = np.asarray(pilimage)
inshow(pilimage)
ExtractROI = otbApplication.Registry.CreateApplication('ExtractROI')
ExtractROI.SetImageFromNumpyArray('in', npimage)
ExtractROI.SetParameterInt('startx', 140)
ExtractROI.SetParameterInt('starty', 120)
ExtractROI.SetParameterInt('sizex', 150)
ExtractROI.SetParameterInt('sizey', 150)
ExtractROI.Execute()
ExtractOutput = ExtractROI.GetImageAsNumpyArray('out')
output_pil_image = PILImage.fromarray(np.uint8(ExtractOutput))
imshow(output_pil_image)
In-memory connection
--------------------
Applications are often use as parts of larger processing
chains. Chaining applications currently requires to write/read back
images between applications, resulting in heavy I/O operations and a
significant amount of time dedicated to writing temporary files.
Since OTB 5.8, it is possible to connect an output image parameter
from one application to the input image parameter of the next
parameter. This results in the wiring of the internal ITK/OTB
pipelines together, allowing to perform image streaming between the
applications. There is therefore no more writing of temporary
images. The last application of the processing chain is responsible
for writing the final result images.
In-memory connection between applications is available both at the C++
API level and using the python bindings to the application presented
in the `Python interface`_ section.
Here is a Python code sample connecting several applications together:
.. code-block:: python
import otbApplication as otb
app1 = otb.Registry.CreateApplication("Smoothing")
app2 = otb.Registry.CreateApplication("Smoothing")
app3 = otb.Registry.CreateApplication("Smoothing")
app4 = otb.Registry.CreateApplication("ConcatenateImages")
app1.IN = argv[1]
app1.Execute()
# Connection between app1.out and app2.in
app2.SetParameterInputImage("in",app1.GetParameterOutputImage("out"))
# Execute call is mandatory to wire the pipeline and expose the
# application output. It does not write image
app2.Execute()
app3.IN = argv[1]
# Execute call is mandatory to wire the pipeline and expose the
# application output. It does not write image
app3.Execute()
# Connection between app2.out, app3.out and app4.il using images list
app4.AddImageToParameterInputImageList("il",app2.GetParameterOutputImage("out"));
app4.AddImageToParameterInputImageList("il",app3.GetParameterOutputImage("out"));
app = otb.Registry.CreateApplication("Smoothing")
app4.OUT = argv[2]
# We set its parameters
app.SetParameterString("in", "my_input_image.tif")
app.SetParameterString("type", "mean")
app.SetParameterString("out", "my_output_image.tif")
# Call to ExecuteAndWriteOutput() both wires the pipeline and
# actually writes the output, only necessary for last application of
# the chain.
app4.ExecuteAndWriteOutput()
# This will execute the application and save the output file
app.ExecuteAndWriteOutput()
**Note:** Streaming will only work properly if the application internal
implementation does not break it, for instance by using an internal
writer to write intermediate data. In this case, execution should
still be correct, but some intermediate data will be read or written.
For more information about this Python interface, check the recipe section.
QGIS interface
--------------
......
......@@ -18,4 +18,5 @@ and demonstrate how the can be applied.
recipes/pbclassif.rst
recipes/featextract.rst
recipes/stereo.rst
recipes/python.rst
OTB processing in Python
========================
Basics
------
In the ``otbApplication`` module, two main classes can be manipulated :
- ``Registry``, which provides access to the list of available
applications, and can create applications.
- ``Application``, the base class for all applications. This allows to
interact with an application instance created by the ``Registry``.
Here is one example of how to use Python to run the ``Smoothing``
application, changing the algorithm at each iteration.
.. code-block:: python
# Example on the use of the Smoothing application
#
# We will use sys.argv to retrieve arguments from the command line.
# Here, the script will accept an image file as first argument,
# and the basename of the output files, without extension.
from sys import argv
# The python module providing access to OTB applications is otbApplication
import otbApplication
# otbApplication.Registry can tell you what application are available
print "Available applications: "
print str( otbApplication.Registry.GetAvailableApplications() )
# Let's create the application with codename "Smoothing"
app = otbApplication.Registry.CreateApplication("Smoothing")
# We print the keys of all its parameter
print app.GetParametersKeys()
# First, we set the input image filename
app.SetParameterString("in", argv[1])
# The smoothing algorithm can be set with the "type" parameter key
# and can take 3 values: 'mean', 'gaussian', 'anidif'
for type in ['mean', 'gaussian', 'anidif']:
print 'Running with ' + type + ' smoothing type'
# Here we configure the smoothing algorithm
app.SetParameterString("type", type)
# Set the output filename, using the algorithm to differentiate the outputs
app.SetParameterString("out", argv[2] + type + ".tif")
# This will execute the application and save the output file
app.ExecuteAndWriteOutput()
Numpy array processing
----------------------
Input and output images to any OTB application in the form of numpy array is now possible in OTB python wrapping.
The python wrapping only exposes OTB ApplicationEngine module which allow to access existing C++ applications.
Due to blissful nature of ApplicationEngine's loading mechanism no specific wrapping is required for each application.
Numpy extension to Python wrapping allows data exchange to application as an array rather than a disk file.
Ofcourse, it is possible to load an image from file and then convert to numpy array or just provide a file as earlier via
Application.SetParameterString(...).
This bridge that completes numpy and OTB makes it easy to plug OTB into any image processing chain via python code that uses
GIS/Image processing tools such as GDAL, GRASS GIS, OSSIM that can deal with numpy.
Below code reads an input image using python pillow (PIL) and convert it to numpy array. This numpy array is
used an input to the application via *SetImageFromNumpyArray(...)* method.
The application used in this example is ExtractROI. After extracting
a small area the output image is taken as numpy array with *GetImageFromNumpyArray(...)* method thus avoid wiriting
output to a temporary file.
::
import sys
import os
import numpy as np
import otbApplication
from PIL import Image as PILImage
pilimage = PILImage.open('poupees.jpg')
npimage = np.asarray(pilimage)
inshow(pilimage)
ExtractROI = otbApplication.Registry.CreateApplication('ExtractROI')
ExtractROI.SetImageFromNumpyArray('in', npimage)
ExtractROI.SetParameterInt('startx', 140)
ExtractROI.SetParameterInt('starty', 120)
ExtractROI.SetParameterInt('sizex', 150)
ExtractROI.SetParameterInt('sizey', 150)
ExtractROI.Execute()
ExtractOutput = ExtractROI.GetImageAsNumpyArray('out')
output_pil_image = PILImage.fromarray(np.uint8(ExtractOutput))
imshow(output_pil_image)
In-memory connection
--------------------
Applications are often use as parts of larger processing
chains. Chaining applications currently requires to write/read back
images between applications, resulting in heavy I/O operations and a
significant amount of time dedicated to writing temporary files.
Since OTB 5.8, it is possible to connect an output image parameter
from one application to the input image parameter of the next
parameter. This results in the wiring of the internal ITK/OTB
pipelines together, allowing to perform image streaming between the
applications. There is therefore no more writing of temporary
images. The last application of the processing chain is responsible
for writing the final result images.
In-memory connection between applications is available both at the C++
API level and using the python bindings.
Here is a Python code sample connecting several applications together:
.. code-block:: python
import otbApplication as otb
app1 = otb.Registry.CreateApplication("Smoothing")
app2 = otb.Registry.CreateApplication("Smoothing")
app3 = otb.Registry.CreateApplication("Smoothing")
app4 = otb.Registry.CreateApplication("ConcatenateImages")
app1.IN = argv[1]
app1.Execute()
# Connection between app1.out and app2.in
app2.SetParameterInputImage("in",app1.GetParameterOutputImage("out"))
# Execute call is mandatory to wire the pipeline and expose the
# application output. It does not write image
app2.Execute()
app3.IN = argv[1]
# Execute call is mandatory to wire the pipeline and expose the
# application output. It does not write image
app3.Execute()
# Connection between app2.out, app3.out and app4.il using images list
app4.AddImageToParameterInputImageList("il",app2.GetParameterOutputImage("out"));
app4.AddImageToParameterInputImageList("il",app3.GetParameterOutputImage("out"));
app4.OUT = argv[2]
# Call to ExecuteAndWriteOutput() both wires the pipeline and
# actually writes the output, only necessary for last application of
# the chain.
app4.ExecuteAndWriteOutput()
**Note:** Streaming will only work properly if the application internal
implementation does not break it, for instance by using an internal
writer to write intermediate data. In this case, execution should
still be correct, but some intermediate data will be read or written.
Corner cases
------------
There are a few corner cases to be aware of when using Python wrappers. They are
often limitations, that one day may be solved by future developments. If it
happens, this documentation will report the OTB version that fixes the issue.
Calling UpdateParameters()
^^^^^^^^^^^^^^^^^^^^^^^^^^
These wrappers are made as a mirror of the C++ API, so there is a function
``UpdateParameters()``. Its role is to update parameters that depend on others.
It is called at least once at the beginning of ``Execute()``.
In command line and GUI launchers, this functions gets called each time a
parameter of the application is modified. In Python, this mechanism is not
automated: there are cases where you may have to call it yourself.
Let's take an example with the application ``PolygonClassStatictics``. In this
application, the choices available in the parameter ``field`` depend on the list
of fields actually present in the vector file ``vec``. If you try to set the
parameters ``vec`` and ``field``, you will get an error:
.. code-block:: python
import otbApplication as otb
app = otb.Registry.CreateApplication("PolygonClassStatistics")
app.SetParameterString("vec","../../src/OTB-Data/Input/Classification/variousVectors.sqlite")
app.SetParameterString("field", "label")
::
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/gpasero/Projet_OTB/build/OTB/lib/otb/python/otbApplication.py", line 897, in SetParameterString
def SetParameterString(self, *args): return _otbApplication.Application_SetParameterString(self, *args)
RuntimeError: Exception thrown in otbApplication Application_SetParameterString: /home/gpasero/Projet_OTB/src/OTB/Modules/Wrappers/ApplicationEngine/src/otbWrapperListViewParameter.cxx:141:
itk::ERROR: ListViewParameter(0x149da10): Cannot find label
The error says that the choice ``label`` is not recognized, because ``UpdateParameters()``
was not called after setting the vector file. The solution is to call it before
setting the ``field`` parameter:
.. code-block:: python
app.UpdateParameters()
app.SetParameterString("field", "label")
No metadata in Numpy arrays
^^^^^^^^^^^^^^^^^^^^^^^^^^^
TODO
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