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Open Source Computer Vision Library
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55 lines
1.2 KiB
55 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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Usage: |
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texture_flow.py [<image>] |
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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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def main(): |
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import sys |
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try: |
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fn = sys.argv[1] |
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except: |
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fn = 'starry_night.jpg' |
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img = cv.imread(cv.samples.findFile(fn)) |
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if img is None: |
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print('Failed to load image file:', fn) |
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sys.exit(1) |
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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, 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 = 12 |
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points = np.dstack( np.mgrid[d/2:w:d, d/2:h:d] ).reshape(-1, 2) |
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for x, y in np.int32(points): |
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vx, vy = np.int32(flow[y, x]*d) |
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cv.line(vis, (x-vx, y-vy), (x+vx, y+vy), (0, 0, 0), 1, cv.LINE_AA) |
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cv.imshow('input', img) |
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cv.imshow('flow', vis) |
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cv.waitKey() |
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print('Done') |
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if __name__ == '__main__': |
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print(__doc__) |
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main() |
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cv.destroyAllWindows()
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