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Open Source Computer Vision Library
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48 lines
1.6 KiB
48 lines
1.6 KiB
#!/usr/bin/env python |
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''' |
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Simple "Square Detector" program. |
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Loads several images sequentially and tries to find squares in each image. |
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''' |
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import numpy as np |
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import cv2 |
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def angle_cos(p0, p1, p2): |
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d1, d2 = (p0-p1).astype('float'), (p2-p1).astype('float') |
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return abs( np.dot(d1, d2) / np.sqrt( np.dot(d1, d1)*np.dot(d2, d2) ) ) |
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def find_squares(img): |
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img = cv2.GaussianBlur(img, (5, 5), 0) |
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squares = [] |
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for gray in cv2.split(img): |
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for thrs in xrange(0, 255, 26): |
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if thrs == 0: |
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bin = cv2.Canny(gray, 0, 50, apertureSize=5) |
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bin = cv2.dilate(bin, None) |
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else: |
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retval, bin = cv2.threshold(gray, thrs, 255, cv2.THRESH_BINARY) |
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bin, contours, hierarchy = cv2.findContours(bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) |
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for cnt in contours: |
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cnt_len = cv2.arcLength(cnt, True) |
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cnt = cv2.approxPolyDP(cnt, 0.02*cnt_len, True) |
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if len(cnt) == 4 and cv2.contourArea(cnt) > 1000 and cv2.isContourConvex(cnt): |
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cnt = cnt.reshape(-1, 2) |
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max_cos = np.max([angle_cos( cnt[i], cnt[(i+1) % 4], cnt[(i+2) % 4] ) for i in xrange(4)]) |
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if max_cos < 0.1: |
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squares.append(cnt) |
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return squares |
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if __name__ == '__main__': |
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from glob import glob |
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for fn in glob('../data/pic*.png'): |
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img = cv2.imread(fn) |
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squares = find_squares(img) |
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cv2.drawContours( img, squares, -1, (0, 255, 0), 3 ) |
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cv2.imshow('squares', img) |
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ch = 0xFF & cv2.waitKey() |
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if ch == 27: |
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break |
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cv2.destroyAllWindows()
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