Merge branch 'release'

Author: thewtex

.gitignore

@@ -11,6 +11,7 @@ venv-*
 ITK-*
 libpython-not-needed-symbols-exported-by-interpreter
 tools/
+ninja/
 
 # back-up files when conflicts occur
 *.orig

docs/Makefile

@@ -3,7 +3,7 @@
 
 # You can set these variables from the command line.
 SPHINXOPTS    =
-SPHINXBUILD   = python -msphinx
+SPHINXBUILD   = sphinx-build
 SPHINXPROJ    = ITKPythonPackage
 SOURCEDIR     = .
 BUILDDIR      = _build
@@ -17,4 +17,4 @@ help:
 # Catch-all target: route all unknown targets to Sphinx using the new
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
 %: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/Quick_start_guide.rst

@@ -22,9 +22,11 @@ Here is a simple python script that reads an image, applies a median image filte
 
 .. literalinclude:: code/ReadMedianWrite.py
 
-There are two ways to instantiate filters with ITKPython:
+In `itk`, filters are optimized at compile time for each image pixel type and
+image dimension. There are two ways to instantiate these filters with the `itk`
+Python wrapping:
 
-- *Implicit (recommended)*: ITK type information is automatically detected from the data. Typed filter objects and images are implicitly created.
+- *Implicit (recommended)*: Type information is automatically detected from the data. Typed filter objects and images are implicitly created.
 
 .. literalinclude:: code/ImplicitInstantiation.py
    :lines: 8-
@@ -38,23 +40,23 @@ Explicit instantiation of a median image filter:
 
 Explicit instantiation of cast image filter:
 
-.. literalinclude:: code/CastImageFilter.py
-   :lines: 9-23
+.. literalinclude:: code/Cast.py
+   :lines: 10-18
 
 ITK Python types
 ................
 
-+---------------------+--------------------+
-| C++ type            | Python type        |
-+=====================+====================+
-| float               | itk.F              |
-+---------------------+--------------------+
-| double              | itk.D              |
-+---------------------+--------------------+
-| unsigned char       | itk.UC             |
-+---------------------+--------------------+
-| std::complex<float> | itk.complex[itk.F] |
-+---------------------+--------------------+
++---------------------+--------------------+--------------------+
+| C++ type            | Python type        | NumPy dtype        |
++=====================+====================+====================+
+| float               | itk.F              | np.float32         |
++---------------------+--------------------+--------------------+
+| double              | itk.D              | np.float64         |
++---------------------+--------------------+--------------------+
+| unsigned char       | itk.UC             | np.uint8           |
++---------------------+--------------------+--------------------+
+| std::complex<float> | itk.complex[itk.F] | np.complex64       |
++---------------------+--------------------+--------------------+
 
 This list is not exhaustive and is only presented to illustrate the type names. The complete list of types can be found in the `ITK Software Guide <https://itk.org/ItkSoftwareGuide.pdf>`_.
 
@@ -90,7 +92,7 @@ ITK filter parameters can be specified in the following ways:
 Mixing ITK and NumPy
 --------------------
 
-A common use case for using ITK in Python is to mingle NumPy and ITK operations on raster data. ITK provides a large number of I/O image formats and several sophisticated image processing algorithms not available in any other packages. The ability to intersperse that with numpy special purpose hacking provides a great tool for rapid prototyping.
+A common use case for using ITK in Python is to mingle NumPy and ITK operations on raster data. ITK provides a large number of I/O image formats and several sophisticated image processing algorithms not available in any other packages. The ability to intersperse that with the SciPy ecosystem provides a great tool for rapid prototyping.
 
 The following script shows how to integrate NumPy and ITK:
 
@@ -107,4 +109,4 @@ Similar functions are available to work with `itk.Matrix`, VNL vectors and matri
 Examples
 --------
 
-Examples can be found in the `ITKExamples project <https://itk.org/ITKExamples/src/index.html>`_.
+Examples can be found in the `ITKSphinxExamples project <https://itk.org/ITKExamples/src/index.html>`_.

docs/code/Cast.py

@@ -0,0 +1,18 @@
+#!/usr/bin/env python3
+
+import numpy as np
+import itk
+import sys
+
+input_filename = sys.argv[1]
+output_filename = sys.argv[2]
+
+image = itk.imread(input_filename)
+
+# Cast to an unsigned char pixel type
+cast_image = image.astype(itk.UC)
+
+# Equivalent
+cast_image = image.astype(np.uint8)
+
+itk.imwrite(cast_image, output_filename)

docs/code/CastImageFilter.py

@@ -1,4 +1,4 @@
-#!/usr/bin/env python
+#!/usr/bin/env python3
 
 import itk
 

docs/code/CompareITKTypes.py

@@ -1,4 +1,4 @@
-#!/usr/bin/env python
+#!/usr/bin/env python3
 
 import itk
 import sys

docs/code/CreateBaseline.py

@@ -1,19 +1,25 @@
-#!/usr/bin/env python
+#!/usr/bin/env python3
 
 import itk
 import sys
 
 input_filename = sys.argv[1]
 
+# An apriori ImageType
 PixelType = itk.F
 ImageType = itk.Image[PixelType,2]
+image = itk.imread(input_filename, PixelType)
+
+# An image type dynamically determined from the type on disk
+image = itk.imread(input_filename)
+ImageType = type(image)
 
 # Functional interface
-image = itk.imread(input_filename, PixelType)
+# The `ttype` keyword argument specifies the filter type.
 smoothed = itk.median_image_filter(image, ttype=(ImageType, ImageType))
 
 # Object-oriented interface
-reader = itk.ImageFileReader[ImageType].New(FileName=input_filename)
+reader = itk.ImageFileReader[ImageType].New(file_name=input_filename)
 median = itk.MedianImageFilter[ImageType, ImageType].New()
 median.SetInput(reader.GetOutput())
 median.Update()

docs/code/ExplicitInstantiation.py

@@ -1,4 +1,4 @@
-#!/usr/bin/env python
+#!/usr/bin/env python3
 
 import itk
 import sys
@@ -11,15 +11,13 @@
 #
 #   number_of_iterations
 #
-smoothed = itk.anti_alias_binary_image_filter(image,
-        number_of_iterations=3)
+smoothed = itk.anti_alias_binary_image_filter(image, number_of_iterations=3)
 
 # CamelCase keyword arguments:
 #
 #   NumberOfIterations
 #
-smoother = itk.AntiAliasBinaryImageFilter.New(image,
-        NumberOfIterations=3)
+smoother = itk.AntiAliasBinaryImageFilter.New(image, NumberOfIterations=3)
 smoother.Update()
 smoothed = smoother.GetOutput()
 

docs/code/FilterParameters.py

@@ -1,11 +1,12 @@
-#!/usr/bin/env python
+#!/usr/bin/env python3
 
 import itk
 import sys
 
 input_filename = sys.argv[1]
 
 image = itk.imread(input_filename)
+
 # Use ITK's functional, Pythonic interface. The filter type is implied by the
 # type of the input image. The filter is eagerly executed, and the output image
 # is directly returned.
@@ -21,9 +22,10 @@
 
 # Use itk.ImageFileReader instead of the wrapping function,
 # itk.imread to illustrate this example.
-reader = itk.ImageFileReader.New(FileName=input_filename)
+ImageType = itk.Image[itk.UC, 2]
+reader = itk.ImageFileReader[ImageType].New(FileName=input_filename)
 # Here we specify the filter input explicitly
-median = itk.MedianImageFilter.New(Input=reader.GetOutput())
+median = itk.MedianImageFilter.New(reader.GetOutput())
 # Same as above but shortened. Input does not have to be specified.
 median = itk.MedianImageFilter.New(reader.GetOutput())
 # Same as above. .GetOutput() does not have to be specified.