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Merge pull request #14109 from PedroFerreiradaCosta:adding_python_version_to_anisotropic_tutorial
* Created python version of the code for the anisotropic image segmentation tutorial. Created python/cpp toggles for the markdown file. * fix doxygen warnings
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Pedro Ferreira da Costa
Alexander Alekhin
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865bd7abff
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9cef78b685
@ -48,28 +48,65 @@ The orientation of an anisotropic image:
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Coherency:
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\f[C = \frac{\lambda_1 - \lambda_2}{\lambda_1 + \lambda_2}\f]
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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.
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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.
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Source code
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-----------
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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.
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@include cpp/tutorial_code/ImgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.cpp
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@add_toggle_cpp
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@include cpp/tutorial_code/ImgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.cpp
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@end_toggle
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@add_toggle_python
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@include samples/python/tutorial_code/imgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.py
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@end_toggle
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Explanation
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-----------
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An anisotropic image segmentation algorithm consists of a gradient structure tensor calculation, an orientation calculation, a coherency calculation and an orientation and coherency thresholding:
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@snippet samples/cpp/tutorial_code/ImgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.cpp main
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/ImgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.cpp main
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/imgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.py main
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@end_toggle
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A function calcGST() calculates orientation and coherency by using a gradient structure tensor. An input parameter w defines a window size:
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@snippet samples/cpp/tutorial_code/ImgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.cpp calcGST
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/ImgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.cpp calcGST
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/imgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.py calcGST
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@end_toggle
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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:
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@snippet samples/cpp/tutorial_code/ImgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.cpp thresholding
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/ImgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.cpp thresholding
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/imgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.py thresholding
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@end_toggle
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And finally we combine thresholding results:
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@snippet samples/cpp/tutorial_code/ImgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.cpp combining
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/ImgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.cpp combining
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/imgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.py combining
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@end_toggle
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Result
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------
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@ -0,0 +1,92 @@
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import cv2 as cv
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import numpy as np
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import argparse
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W = 52 # window size is WxW
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C_Thr = 0.43 # threshold for coherency
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LowThr = 35 # threshold1 for orientation, it ranges from 0 to 180
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HighThr = 57 # threshold2 for orientation, it ranges from 0 to 180
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## [calcGST]
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## [calcJ_header]
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## [calcGST_proto]
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def calcGST(inputIMG, w):
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## [calcGST_proto]
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img = inputIMG.astype(np.float32)
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# GST components calculation (start)
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# J = (J11 J12; J12 J22) - GST
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imgDiffX = cv.Sobel(img, cv.CV_32F, 1, 0, 3)
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imgDiffY = cv.Sobel(img, cv.CV_32F, 0, 1, 3)
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imgDiffXY = cv.multiply(imgDiffX, imgDiffY)
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## [calcJ_header]
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imgDiffXX = cv.multiply(imgDiffX, imgDiffX)
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imgDiffYY = cv.multiply(imgDiffY, imgDiffY)
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J11 = cv.boxFilter(imgDiffXX, cv.CV_32F, (w,w))
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J22 = cv.boxFilter(imgDiffYY, cv.CV_32F, (w,w))
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J12 = cv.boxFilter(imgDiffXY, cv.CV_32F, (w,w))
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# GST components calculations (stop)
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# eigenvalue calculation (start)
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# lambda1 = J11 + J22 + sqrt((J11-J22)^2 + 4*J12^2)
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# lambda2 = J11 + J22 - sqrt((J11-J22)^2 + 4*J12^2)
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tmp1 = J11 + J22
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tmp2 = J11 - J22
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tmp2 = cv.multiply(tmp2, tmp2)
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tmp3 = cv.multiply(J12, J12)
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tmp4 = np.sqrt(tmp2 + 4.0 * tmp3)
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lambda1 = tmp1 + tmp4 # biggest eigenvalue
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lambda2 = tmp1 - tmp4 # smallest eigenvalue
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# eigenvalue calculation (stop)
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# Coherency calculation (start)
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# Coherency = (lambda1 - lambda2)/(lambda1 + lambda2)) - measure of anisotropism
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# Coherency is anisotropy degree (consistency of local orientation)
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imgCoherencyOut = cv.divide(lambda1 - lambda2, lambda1 + lambda2)
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# Coherency calculation (stop)
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# orientation angle calculation (start)
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# tan(2*Alpha) = 2*J12/(J22 - J11)
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# Alpha = 0.5 atan2(2*J12/(J22 - J11))
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imgOrientationOut = cv.phase(J22 - J11, 2.0 * J12, angleInDegrees = True)
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imgOrientationOut = 0.5 * imgOrientationOut
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# orientation angle calculation (stop)
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return imgCoherencyOut, imgOrientationOut
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## [calcGST]
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parser = argparse.ArgumentParser(description='Code for Anisotropic image segmentation tutorial.')
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parser.add_argument('-i', '--input', help='Path to input image.', required=True)
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args = parser.parse_args()
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imgIn = cv.imread(args.input, cv.IMREAD_GRAYSCALE)
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if imgIn is None:
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print('Could not open or find the image: {}'.format(args.input))
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exit(0)
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## [main_extra]
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## [main]
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imgCoherency, imgOrientation = calcGST(imgIn, W)
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## [thresholding]
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_, imgCoherencyBin = cv.threshold(imgCoherency, C_Thr, 255, cv.THRESH_BINARY)
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_, imgOrientationBin = cv.threshold(imgOrientation, LowThr, HighThr, cv.THRESH_BINARY)
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## [thresholding]
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## [combining]
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imgBin = cv.bitwise_and(imgCoherencyBin, imgOrientationBin)
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## [combining]
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## [main]
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imgCoherency = cv.normalize(imgCoherency, None, alpha=0, beta=1, norm_type=cv.NORM_MINMAX, dtype=cv.CV_32F)
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imgOrientation = cv.normalize(imgOrientation, None, alpha=0, beta=1, norm_type=cv.NORM_MINMAX, dtype=cv.CV_32F)
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cv.imshow('result.jpg', np.uint8(0.5*(imgIn + imgBin)))
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cv.imshow('Coherency.jpg', imgCoherency)
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cv.imshow('Orientation.jpg', imgOrientation)
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cv.waitKey(0)
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## [main_extra]
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