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															@@ -50,16 +50,9 @@ def recognize(image): 
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															     image = image[corner_right_down[1]:corner_left_up[1],corner_right_down[0]:corner_left_up[0]] 
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															     image = image[corner_right_down[1]:corner_left_up[1],corner_right_down[0]:corner_left_up[0]] 
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															-    # hsv = cv2.cvtColor(image.copy(), cv2.COLOR_BGR2HSV) 
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															-    # hsv = cv2.GaussianBlur(hsv, (1, 1), 0) 
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															-    # _, thresh = cv2.threshold(hsv[:, :, 1], lower, upper, cv2.THRESH_BINARY) 
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															-    # cnts, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) 
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															-    # cv2.drawContours(image, cnts, -1, (0, 255, 0), 2) 
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															     image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 
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															     image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 
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															-    thresh = cv2.threshold(image, lower, upper, cv2.THRESH_BINARY)[0] 
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															-    # thresh = cv2.adaptiveThreshold(image,upper,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,3,2) 
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															+    thresh = cv2.adaptiveThreshold(image,upper,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,3,2) 
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															     binary = np.zeros(image.shape) 
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															     binary = np.zeros(image.shape) 
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															     binary[image < thresh] = 1 
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															     binary[image < thresh] = 1 
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															@@ -81,5 +74,5 @@ def recognize(image): 
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															 if  __name__ == "__main__": 
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															 if  __name__ == "__main__": 
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															     while True: 
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															     while True: 
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															-        image = cv2.imread('falling_ball/image.png') 
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															+        image = cv2.imread('falling_ball/image3.png') 
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															         print(recognize(image)) 
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															         print(recognize(image)) 
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