基于python车牌号识别_如何用Python提取和识别车牌号？

作为一个初步的概念方案，在此基础之上你希望进行进一步的完善吗？建议您从图像中提取出车牌号码，并将其传输至您的Tesseract系统中进行处理。通过查看代码中的注释部分来更好地理解我的意图

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)

#carry out an automatic thresholding technique in order to allow me to extract accurate boundary outlines from the picture

#need this to extract the name plate from the image.

Assign a threshold value to the variable thresh by utilizing OpenCV's adaptive thresholding function with a Gaussian-based approach on the grayscale image. The parameters include a maximum value of 255, a block size of 11 pixels and a constant of 2 for variance.

contours,h = cv2.findContours(thresh,1,2)

Once I have obtained the outlines from the contours list, I will determine which of them form rectangles.

The contours can be approximated as minimum polygons. Four-sided polygons are likely to be 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()

输出为牌照，如下所示：

现在把这个裁剪过的图像传给你的镶嵌画。在

^{pr2}$

下面是您共享的示例图像的输出。在

为了使车牌图像的透视转换呈现出边框矩形形状，并移除周围多余的边框后，在检测过程中所得结果会更加精确。如果您需要进一步的帮助，请随时告知我。

此代码不适合处理第二个图像，请考虑以下几点：因为当前筛选结果仅包含四边形，在这种情况下应用该代码可能会导致错误的结果。如能采纳此建议将不胜感激。