Muthukrishnan

这里有一个关于如何解决问题的粗略想法。你可以在上面建造。您需要从图像中提取车牌号,然后将图像发送到您的镶嵌。阅读代码注释以了解我要做的事情。

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()

输出是如下所附的车牌号:

现在把这张剪掉的照片传给你的镶嵌画。

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

我得到下面的输出为您共享的示例图像。

如果透视将车牌图像转换为边框矩形,并删除周围的额外边框,解析将更准确。如果你也需要帮助,请告诉我。

如果按原样使用,上面的代码对第二幅图像不起作用,因为我正在将搜索过滤到有4条边的多边形。希望你有这个想法。