2012-10-17 01:18:30 +02:00
|
|
|
'''
|
|
|
|
|
Simple "Square Detector" program.
|
|
|
|
|
|
|
|
|
|
Loads several images sequentially and tries to find squares in each image.
|
|
|
|
|
'''
|
|
|
|
|
|
|
|
|
|
import numpy as np
|
|
|
|
|
import cv2
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def angle_cos(p0, p1, p2):
|
|
|
|
|
d1, d2 = (p0-p1).astype('float'), (p2-p1).astype('float')
|
|
|
|
|
return abs( np.dot(d1, d2) / np.sqrt( np.dot(d1, d1)*np.dot(d2, d2) ) )
|
|
|
|
|
|
|
|
|
|
def find_squares(img):
|
|
|
|
|
img = cv2.GaussianBlur(img, (5, 5), 0)
|
|
|
|
|
squares = []
|
|
|
|
|
for gray in cv2.split(img):
|
|
|
|
|
for thrs in xrange(0, 255, 26):
|
|
|
|
|
if thrs == 0:
|
|
|
|
|
bin = cv2.Canny(gray, 0, 50, apertureSize=5)
|
|
|
|
|
bin = cv2.dilate(bin, None)
|
|
|
|
|
else:
|
|
|
|
|
retval, bin = cv2.threshold(gray, thrs, 255, cv2.THRESH_BINARY)
|
|
|
|
|
contours, hierarchy = cv2.findContours(bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
|
for cnt in contours:
|
|
|
|
|
cnt_len = cv2.arcLength(cnt, True)
|
|
|
|
|
cnt = cv2.approxPolyDP(cnt, 0.02*cnt_len, True)
|
|
|
|
|
if len(cnt) == 4 and cv2.contourArea(cnt) > 1000 and cv2.isContourConvex(cnt):
|
|
|
|
|
cnt = cnt.reshape(-1, 2)
|
|
|
|
|
max_cos = np.max([angle_cos( cnt[i], cnt[(i+1) % 4], cnt[(i+2) % 4] ) for i in xrange(4)])
|
|
|
|
|
if max_cos < 0.1:
|
|
|
|
|
squares.append(cnt)
|
|
|
|
|
return squares
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
|
|
from glob import glob
|
|
|
|
|
for fn in glob('../cpp/pic*.png'):
|
|
|
|
|
img = cv2.imread(fn)
|
|
|
|
|
squares = find_squares(img)
|
|
|
|
|
cv2.drawContours( img, squares, -1, (0, 255, 0), 3 )
|
|
|
|
|
cv2.imshow('squares', img)
|
|
|
|
|
ch = 0xFF & cv2.waitKey()
|
|
|
|
|
if ch == 27:
|
|
|
|
|
break
|
|
|
|
|
cv2.destroyAllWindows()
|
