问如何用Python提取和识别车牌号码？

EN

import numpy as np
import cv2
import pytesseract
import matplotlib.pyplot as plt

img = cv2.imread('/home/muthu/Documents/3r9OQ.jpg')
#convert my image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#perform adaptive threshold so that I can extract proper contours from the image
#need this to extract the name plate from the image. 
thresh = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)
contours,h = cv2.findContours(thresh,1,2)

#once I have the contours list, i need to find the contours which form rectangles.
#the contours can be approximated to minimum polygons, polygons of size 4 are probably rectangles
largest_rectangle = [0,0]
for cnt in contours:
    approx = cv2.approxPolyDP(cnt,0.01*cv2.arcLength(cnt,True),True)
    if len(approx)==4: #polygons with 4 points is what I need.
        area = cv2.contourArea(cnt)
        if area > largest_rectangle[0]:
            #find the polygon which has the largest size.
            largest_rectangle = [cv2.contourArea(cnt), cnt, approx]

x,y,w,h = cv2.boundingRect(largest_rectangle[1])
#crop the rectangle to get the number plate.
roi=img[y:y+h,x:x+w]
#cv2.drawContours(img,[largest_rectangle[1]],0,(0,0,255),-1)
plt.imshow(roi, cmap = 'gray')
plt.show()

gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)
text = pytesseract.image_to_string(roi)
print text

from PIL import Image, ImageFilter
import pytesseract

img = Image.open('TcjXJ.jpg')
img2 = img.filter(ImageFilter.BLUR)
pixels = img2.load()
width, height = img2.size
x_ = []
y_ = []
for x in range(width):
    for y in range(height):
        if pixels[x, y] == (255, 255, 255):
            x_.append(x)
            y_.append(y)

img = img.crop((min(x_), min(y_),  max(x_), max(y_)))
text = pytesseract.image_to_string(img, lang='eng', config='-c tessedit_char_whitelist=ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789')
print(text)

TN 99 F 2378