openCV形态学操作实现简单的车牌识别

import cv2
import numpy as np
from numpy.ma.testutils import approx

if __name__ == '__main__':
    img = cv2.imread('./image/car2.jpg')
    # img = cv2.resize(img, (img.shape[1] // 4, img.shape[0] // 4))
    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

    gaussian_blue = cv2.GaussianBlur(gray,(3,3),sigmaX=0)

    # #算出其轮廓
    canny = cv2.Canny(gaussian_blue,300,400)
    #
    #
    # #二值化操作
    ret,binary = cv2.threshold(canny,127,255,cv2.THRESH_BINARY)
    cv2.imshow('binary', binary)
    #
    #形态学操作，为了获取一个大块儿的矩形
    dilate = cv2.dilate(binary,np.ones(shape=[10,10],dtype=np.uint8))
    cv2.imshow('dilate', dilate)


    kernel = np.ones((5,5),np.uint8)
    erode = cv2.erode(dilate, kernel=kernel, iterations=1)
    cv2.imshow('erode', erode)

    # close_img = cv2.morphologyEx(binary,cv2.MORPH_CLOSE,kernel)
    # cv2.imshow('close', close_img)
    # open_img = cv2.morphologyEx(close_img,cv2.MORPH_OPEN,kernel)
    # cv2.imshow('open', open_img)


    List,hierarchy = cv2.findContours(erode,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
    List = sorted(List,key=cv2.contourArea,reverse=True)[:30]

    car_pai = None
    for c in List:
        peri = cv2.arcLength(c,True) #获取轮廓周长
        approx = cv2.approxPolyDP(c,0.01 * peri,True) #将轮廓转变为一个近似的多边形

        if len(approx) == 4:
            car_pai = approx
            break

    cv2.drawContours(img,[car_pai],-1,(0,255,0),3)
    cv2.imshow('img',img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

实现效果如下图：