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Merge pull request #14109 from PedroFerreiradaCosta:adding_python_version_to_anisotropic_tutorial

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* Created python version of the code for the anisotropic image segmentation tutorial. Created python/cpp toggles for the markdown file.

* fix doxygen warnings
This commit is contained in:
Pedro Ferreira da Costa
2019-03-21 19:53:12 +00:00
committed by Alexander Alekhin
parent 865bd7abff
commit 9cef78b685
2 changed files with 135 additions and 6 deletions
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doc/tutorials/imgproc/anisotropic_image_segmentation/anisotropic_image_segmentation.markdown
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@@ -48,28 +48,65 @@ The orientation of an anisotropic image:
Coherency:
\f[C = \frac{\lambda_1 - \lambda_2}{\lambda_1 + \lambda_2}\f]
The coherency ranges from 0 to 1. For ideal local orientation (\f$\lambda_2\f$ = 0, \f$\lambda_1\f$ > 0) it is one, for an isotropic gray value structure (\f$\lambda_1\f$ = \f$\lambda_2\f$ > 0) it is zero.
The coherency ranges from 0 to 1. For ideal local orientation (\f$\lambda_2\f$ = 0, \f$\lambda_1\f$ > 0) it is one, for an isotropic gray value structure (\f$\lambda_1\f$ = \f$\lambda_2\f$ \> 0) it is zero.
Source code
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You can find source code in the `samples/cpp/tutorial_code/ImgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.cpp` of the OpenCV source code library.
@include cpp/tutorial_code/ImgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.cpp
@add_toggle_cpp
@include cpp/tutorial_code/ImgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.cpp
@end_toggle
@add_toggle_python
@include samples/python/tutorial_code/imgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.py
@end_toggle
Explanation
-----------
An anisotropic image segmentation algorithm consists of a gradient structure tensor calculation, an orientation calculation, a coherency calculation and an orientation and coherency thresholding:
@snippet samples/cpp/tutorial_code/ImgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.cpp main
@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.cpp main
@end_toggle
@add_toggle_python
@snippet samples/python/tutorial_code/imgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.py main
@end_toggle
A function calcGST() calculates orientation and coherency by using a gradient structure tensor. An input parameter w defines a window size:
@snippet samples/cpp/tutorial_code/ImgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.cpp calcGST
@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.cpp calcGST
@end_toggle
@add_toggle_python
@snippet samples/python/tutorial_code/imgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.py calcGST
@end_toggle
The below code applies a thresholds LowThr and HighThr to image orientation and a threshold C_Thr to image coherency calculated by the previous function. LowThr and HighThr define orientation range:
@snippet samples/cpp/tutorial_code/ImgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.cpp thresholding
@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.cpp thresholding
@end_toggle
@add_toggle_python
@snippet samples/python/tutorial_code/imgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.py thresholding
@end_toggle
And finally we combine thresholding results:
@snippet samples/cpp/tutorial_code/ImgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.cpp combining
@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.cpp combining
@end_toggle
@add_toggle_python
@snippet samples/python/tutorial_code/imgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.py combining
@end_toggle
Result
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