52 lines
1.4 KiB
Python
52 lines
1.4 KiB
Python
#!/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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fn = '../../../samples/data/pic6.png'
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img = cv2.imread(fn)
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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) |