Initial

squares.py

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+import cv2
+import numpy as np
+
+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):
+    squares = []
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    # cv2.imshow("gray", gray)
+
+    gaussian = cv2.GaussianBlur(gray, (5, 5), 0)
+
+    thresh = cv2.adaptiveThreshold(gaussian, 255, 1, 1, 11, 2)
+    #temp,bin = cv2.threshold(gaussian, 80, 255, cv2.THRESH_BINARY)
+    cv2.imshow("thresh", thresh)
+
+    contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+
+    cv2.drawContours( gray, contours, -1, (0, 255, 0), 3 )
+
+    #cv2.imshow('contours', gray)
+    for cnt in contours:
+        cnt_len = cv2.arcLength(cnt, True)
+        cnt = cv2.approxPolyDP(cnt, 0.02*cnt_len, True)
+        if cv2.contourArea(cnt) > 1000 :
+            #cnt = cnt.reshape(-1, 2)
+            #max_cos = np.max([angle_cos( cnt[i], cnt[(i+1) % 4], cnt[(i+2) % 4] ) for i in range(4)])
+            #if max_cos < 0.1:
+            squares.append(cnt)
+    return squares
+
+if __name__ == '__main__':
+    img = cv2.imread('equip/main_screen.jpg')
+
+    #cv2.imshow("origin", img)
+
+    squares = find_squares(img)
+    print("Find %d squres" % len(squares))
+    cv2.drawContours( img, squares, -1, (0, 255, 0), 3 )
+    cv2.imshow('squares', img)
+
+    cv2.waitKey()