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