def extract_char(img):
image = DetectPlates.extract(img)
img_gray, thresh = Preprocess.preprocess(image)
listOfPossibleChars = []
imgContours, contours, npaHierarchy = cv2.findContours(
thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
def sort_contours(cnts, method="left-to-right"):
# initialize the reverse flag and sort index
reverse = False
i = 0
# handle if we need to sort in reverse
if method == "right-to-left" or method == "bottom-to-top":
reverse = True
# handle if we are sorting against the y-coordinate rather than
# the x-coordinate of the bounding box
if method == "top-to-bottom" or method == "bottom-to-top":
i = 1
# construct the list of bounding boxes and sort them from top to
# bottom
boundingBoxes = [cv2.boundingRect(c) for c in cnts]
(cnts, boundingBoxes) = zip(*sorted(
zip(cnts, boundingBoxes), key=lambda b: b[1][i], reverse=reverse))
# return the list of sorted contours and bounding boxes
return (cnts, boundingBoxes)
(contours, boundingBoxes) = sort_contours(contours)
for i in range(len(contours)):
#check xem contours tim duoc co kha nang la char hay khong
if checkIfPossibleChar_(contours[i]):
listOfPossibleChars.append(contours[i])
idx = 0
for i in range(len(contours)):
# tra ve vi tri chieu rong, cao cho moi duowng vien
x, y, w, h = cv2.boundingRect(contours[i])
#crop and save
idx += 1
new_img = image[y - 2:y + h + 2, x - 1:x + w + 1]
img_gray = cv2.cvtColor(new_img, cv2.COLOR_BGR2GRAY)
#new_img = 1- new_img
_, img_threshold = cv2.threshold(img_gray, 150, 255, cv2.THRESH_BINARY)
new_img = img_threshold
import Preprocess
import cv2
import DetectPlates
import DetectChars
import argparse
import predict
ap = argparse.ArgumentParser()
ap.add_argument('-i', '--image', help="fill the path of image")
args = vars(ap.parse_args())
img = cv2.imread(args['image'])
DetectPlates.extract(img)
DetectChars.extract_char(img)
# Main.py
import cv2
import numpy as np
import os
import DetectChars
import DetectPlates
import PossiblePlate
# module level variables ##########################################################################
SCALAR_BLACK = (0.0, 0.0, 0.0)
SCALAR_WHITE = (255.0, 255.0, 255.0)
SCALAR_YELLOW = (0.0, 255.0, 255.0)
SCALAR_GREEN = (0.0, 255.0, 0.0)
