mirror of
https://github.com/opencv/opencv.git
synced 2024-12-18 19:38:02 +08:00
96 lines
2.7 KiB
Python
96 lines
2.7 KiB
Python
#!/usr/bin/env python
|
|
|
|
'''
|
|
Simple "Square Detector" program.
|
|
|
|
Loads several images sequentially and tries to find squares in each image.
|
|
'''
|
|
|
|
# Python 2/3 compatibility
|
|
import sys
|
|
PY3 = sys.version_info[0] == 3
|
|
|
|
if PY3:
|
|
xrange = range
|
|
|
|
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)
|
|
bin, 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 and filterSquares(squares, cnt):
|
|
squares.append(cnt)
|
|
|
|
return squares
|
|
|
|
def intersectionRate(s1, s2):
|
|
area, intersection = cv2.intersectConvexConvex(np.array(s1), np.array(s2))
|
|
return 2 * area / (cv2.contourArea(np.array(s1)) + cv2.contourArea(np.array(s2)))
|
|
|
|
def filterSquares(squares, square):
|
|
|
|
for i in range(len(squares)):
|
|
if intersectionRate(squares[i], square) > 0.95:
|
|
return False
|
|
|
|
return True
|
|
|
|
from tests_common import NewOpenCVTests
|
|
|
|
class squares_test(NewOpenCVTests):
|
|
|
|
def test_squares(self):
|
|
|
|
img = self.get_sample('samples/data/pic1.png')
|
|
squares = find_squares(img)
|
|
|
|
testSquares = [
|
|
[[43, 25],
|
|
[43, 129],
|
|
[232, 129],
|
|
[232, 25]],
|
|
|
|
[[252, 87],
|
|
[324, 40],
|
|
[387, 137],
|
|
[315, 184]],
|
|
|
|
[[154, 178],
|
|
[196, 180],
|
|
[198, 278],
|
|
[154, 278]],
|
|
|
|
[[0, 0],
|
|
[400, 0],
|
|
[400, 300],
|
|
[0, 300]]
|
|
]
|
|
|
|
matches_counter = 0
|
|
for i in range(len(squares)):
|
|
for j in range(len(testSquares)):
|
|
if intersectionRate(squares[i], testSquares[j]) > 0.9:
|
|
matches_counter += 1
|
|
|
|
self.assertGreater(matches_counter / len(testSquares), 0.9)
|
|
self.assertLess( (len(squares) - matches_counter) / len(squares), 0.2) |