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Open Source Computer Vision Library
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47 lines
1.2 KiB
47 lines
1.2 KiB
#!/usr/bin/env python |
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''' |
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Texture flow direction estimation. |
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Sample shows how cv.cornerEigenValsAndVecs function can be used |
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to estimate image texture flow direction. |
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''' |
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# Python 2/3 compatibility |
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from __future__ import print_function |
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import numpy as np |
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import cv2 as cv |
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import sys |
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from tests_common import NewOpenCVTests |
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class texture_flow_test(NewOpenCVTests): |
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def test_texture_flow(self): |
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img = self.get_sample('samples/data/chessboard.png') |
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gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY) |
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h, w = img.shape[:2] |
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eigen = cv.cornerEigenValsAndVecs(gray, 5, 3) |
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eigen = eigen.reshape(h, w, 3, 2) # [[e1, e2], v1, v2] |
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flow = eigen[:,:,2] |
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d = 300 |
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eps = d / 30 |
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points = np.dstack( np.mgrid[d/2:w:d, d/2:h:d] ).reshape(-1, 2) |
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textureVectors = [] |
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for x, y in np.int32(points): |
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textureVectors.append(np.int32(flow[y, x]*d)) |
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for i in range(len(textureVectors)): |
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self.assertTrue(cv.norm(textureVectors[i], cv.NORM_L2) < eps |
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or abs(cv.norm(textureVectors[i], cv.NORM_L2) - d) < eps) |
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if __name__ == '__main__': |
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NewOpenCVTests.bootstrap()
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