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Open Source Computer Vision Library
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51 lines
1.4 KiB
51 lines
1.4 KiB
#!/usr/bin/env python |
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''' |
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Texture flow direction estimation. |
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Sample shows how cv2.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 |
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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/pic6.png') |
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
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h, w = img.shape[:2] |
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eigen = cv2.cornerEigenValsAndVecs(gray, 15, 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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vis = img.copy() |
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vis[:] = (192 + np.uint32(vis)) / 2 |
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d = 80 |
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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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eps = 0.05 |
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testTextureVectors = [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], |
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[-38, 70], [-79, 3], [0, 0], [0, 0], [-39, 69], [-79, -1], |
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[0, 0], [0, 0], [0, -79], [17, -78], [-48, -63], [65, -46], |
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[-69, -39], [-48, -63], [-45, 66]] |
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for i in range(len(textureVectors)): |
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self.assertLessEqual(cv2.norm(textureVectors[i] - testTextureVectors[i], cv2.NORM_L2), eps) |