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#!/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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# Python 2/3 compatibility
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import sys
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PY3 = sys.version_info[0] == 3
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if PY3:
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xrange = range
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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 and filterSquares(squares, cnt):
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squares.append(cnt)
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return squares
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def intersectionRate(s1, s2):
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area, intersection = cv2.intersectConvexConvex(np.array(s1), np.array(s2))
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return 2 * area / (cv2.contourArea(np.array(s1)) + cv2.contourArea(np.array(s2)))
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def filterSquares(squares, square):
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for i in range(len(squares)):
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if intersectionRate(squares[i], square) > 0.95:
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return False
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return True
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from tests_common import NewOpenCVTests
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class squares_test(NewOpenCVTests):
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def test_squares(self):
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img = self.get_sample('samples/data/pic1.png')
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squares = find_squares(img)
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testSquares = [
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[[43, 25],
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[43, 129],
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[232, 129],
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[232, 25]],
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[[252, 87],
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[324, 40],
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[387, 137],
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[315, 184]],
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[[154, 178],
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[196, 180],
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[198, 278],
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[154, 278]],
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[[0, 0],
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[400, 0],
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[400, 300],
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[0, 300]]
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]
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matches_counter = 0
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for i in range(len(squares)):
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for j in range(len(testSquares)):
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if intersectionRate(squares[i], testSquares[j]) > 0.9:
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matches_counter += 1
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self.assertGreater(matches_counter / len(testSquares), 0.9)
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self.assertLess( (len(squares) - matches_counter) / len(squares), 0.2)
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