基于OPENCV的车牌识别系统源码（python课程设计）.zip

import cv2 import numpy as np ## from hyperlpr import * from aip import AipOcr ###百度AIP接口信息 APP_ID = API_KEY = SECRET_KEY= client = AipOcr(APP_ID,API_KEY,SECRET_KEY) #建立百度AI链接 client.setConnectionTimeoutInMillis(5000) client.setSocketTimeoutInMillis(5000) #车牌识别系统主函数 def lpr(filename): img = cv2.imread(filename) # 预处理，包括灰度处理，高斯滤波平滑处理，Sobel提取边界，图像二值化 gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) ## 灰度化处理 cv2.imshow('gray',gray_img) GaussianBlur_img = cv2.GaussianBlur(gray_img, (5, 5), 0) ##高斯模糊 cv2.imshow('Gaussian',GaussianBlur_img) # Sobel_img = cv2.Sobel(GaussianBlur_img, -1, 1, 0, ksize=3) ##提取边界 # #cv2.imshow('Soble',Sobel_img) canny = cv2.Canny(GaussianBlur_img,100,255) ##提取边界 cv2.imshow('Canny',canny) # # # ret, binary_img = cv2.threshold(canny, 127, 255, cv2.THRESH_BINARY) cv2.imshow('Binary',binary_img) # # # 形态学运算 kernel = np.ones((20, 20), np.uint8) # 先闭运算将车牌数字部分连接，再开运算将不是块状的或是较小的部分去掉 close_img = cv2.morphologyEx(binary_img, cv2.MORPH_CLOSE, kernel) open_img = cv2.morphologyEx(close_img, cv2.MORPH_OPEN, kernel) # kernel2 = np.ones((10, 10), np.uint8) # open_img2 = cv2.morphologyEx(open_img, cv2.MORPH_OPEN, kernel2) # 由于部分图像得到的轮廓边缘不整齐，因此再进行一次膨胀操作 element = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) dilation_img = cv2.dilate(open_img, element, iterations=3) ##膨胀操作 # 获取轮廓 contours, hierarchy = cv2.findContours(dilation_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) ##print(contours) # 测试边框识别结果 # cv2.drawContours(img, contours, -1, (0, 0, 255), 3) # cv2.imshow("lpr", img) # cv2.waitKey(0) # # 将轮廓规整为长方形 rectangles = [] mask = np.zeros(shape = img.shape ,dtype = np.uint8) for c in contours: ## print (c) x = [] y = [] for point in c: y.append(point[0][0]) x.append(point[0][1]) r = [min(y), min(x), max(y), max(x)] rectangles.append(r) ##print(rectangles) cv2.rectangle(mask,pt1=(min(y),min(x)),pt2=(max(y),max(x)),color = (0,0,255),thickness=3) cv2.imshow('Mask',mask) ##进一步细分筛选，排除干扰 new_rectangles = [] for i in rectangles: new_y = i[2]-i[0] new_x = i[3]-i[1] if new_y/new_x>=2 and new_y: new_rectangles.append(i) print(new_rectangles) # 颜色识别车牌区域 dist_r = [] max_mean = 0 mask2 = np.zeros(shape=img.shape, dtype = np.uint8) for r in new_rectangles: cv2.rectangle(mask2, pt1=(r[0], r[1]), pt2=(r[2], r[3]), color=(0, 0, 255), thickness=3) block = img[r[1]:r[3], r[0]:r[2]] hsv = cv2.cvtColor(block, cv2.COLOR_BGR2HSV) ##HSV low = np.array([100, 43, 46]) up = np.array([124, 255, 255]) result = cv2.inRange(hsv, low, up) # 用计算均值的方式找蓝色最多的区块 mean = cv2.mean(result) if mean[0] > max_mean: max_mean = mean[0] dist_r = r mask3 = np.zeros(shape=img.shape, dtype = np.uint8) cv2.rectangle(mask3, (dist_r[0]+3, dist_r[1]), (dist_r[2]-3, dist_r[3]), (0, 255, 0), 2) cv2.imshow('Mask3',mask3) cv2.imshow('Mask2', mask2) # cv2.rectangle(img, (dist_r[0]+3, dist_r[1]), (dist_r[2]-3, dist_r[3]), (0, 255, 0), 2) # cv2.putText(img, 'Car paste', (dist_r[0]+3, dist_r[1]-5), # cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0,0,255), 2) imgRoi = img[dist_r[1]:dist_r[3], dist_r[0]:dist_r[2]] cv2.imshow('key',imgRoi) image2 = get_file_content("Resources/car20.jpg") res = client.licensePlate(image2) # res =client.licensePlate(imgRoi) print("车牌号码："+res['words_result']['number']) print("车牌号码：" + res['words_result']['color']) # cv2.imshow("lpr", img) # if len(res)>0: # print(res[0][0]) cv2.waitKey(0) ##FloodFill算法 # def floodfiii(): def get_file_content(filePath): with open(filePath,'rb') as fp: return fp.read() # 主程序 lpr('Resources/car20.jpg')