Upload segementing_Letter_Using_CV2.py

segementing_Letter_Using_CV2.py

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+import cv2
+from local_utils import detect_lp
+from get_plate import get_plate
+#for test
+# from transfer import load_model
+# from preprocessing import preprocess_image
+# import glob
+# import matplotlib.pyplot as plt
+# from os.path import splitext,basename
+# import matplotlib.gridspec as gridspec
+
+
+# 1. see what it looks like in different types: plate_image, gray, blur, binary,thre_mor
+def DiffImage(LpImg):
+	if (len(LpImg)): #check if there is at least one license image
+    # Scales, calculates absolute values, and converts the result to 8-bit.
+		plate_image = cv2.convertScaleAbs(LpImg[0], alpha=(255.0))
+	    
+	    # convert to grayscale and blur the image
+		# 
+		gray = cv2.cvtColor(plate_image, cv2.COLOR_BGR2GRAY)
+		blur = cv2.GaussianBlur(gray,(7,7),0)
+	    
+	    # Applied inversed thresh_binary 
+		binary = cv2.threshold(blur, 180, 255,
+	                         cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
+	    
+		kernel3 = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
+		thre_mor = cv2.morphologyEx(binary, cv2.MORPH_DILATE, kernel3)
+		
+	return plate_image,gray,blur,binary,thre_mor
+	
+
+
+# Create sort_contours() function to grab the contour of each digit from left to right
+def sort_contours(cnts,reverse = False):
+    i = 0
+    boundingBoxes = [cv2.boundingRect(c) for c in cnts]
+    (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),
+                                        key=lambda b: b[1][i], reverse=reverse))
+    return cnts
+
+
+def get_Crop_Letter(wpod_net,image):
+
+	vehicle, LpImg,cor = get_plate(wpod_net,image)
+	plate_image,gray,blur,binary,thre_mor=DiffImage(LpImg)
+	cont, _  = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+	#print(len(cont))
+	# creat a copy version "test_roi" of plat_image to draw bounding box
+	test_roi = plate_image.copy()
+
+	# Initialize a list which will be used to append charater image
+	crop_characters = []
+
+	# define standard width and height of character
+	digit_w, digit_h = 30, 60
+
+	for c in sort_contours(cont):
+		(x, y, w, h) = cv2.boundingRect(c)
+		ratio = h/w
+		if 1<=ratio<=5: # Only select contour with defined ratio
+			if h/plate_image.shape[0]>=0.5: # Select contour which has the height larger than XXX% of the plate
+	            # Draw bounding box arroung digit number
+				cv2.rectangle(test_roi, (x, y), (x + w, y + h), (0, 255,0), 2 )
+
+	            # Sperate number and gibe prediction
+				curr_num = thre_mor[y:y+h,x:x+w]
+				curr_num = cv2.resize(curr_num, dsize=(digit_w, digit_h))
+				_, curr_num = cv2.threshold(curr_num, 200, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+				crop_characters.append(curr_num)
+	#print("Detect {} numbers!".format(len(crop_characters)))
+	return test_roi,crop_characters
+	
+
+