基于python+OpenCV的车牌号码识别

article/2025/9/21 11:30:00

车牌识别行业已具备一定的市场规模，在电子警察、公路卡口、停车场、商业管理、汽修服务等领域已取得了部分应用。一个典型的车辆牌照识别系统一般包括以下4个部分：车辆图像获取、车牌定位、车牌字符分割和车牌字符识别

1、车牌定位的主要工作是从获取的车辆图像中找到汽车牌照所在位置，并把车牌从该区域中准确地分割出来
这里所采用的是利用车牌的颜色（黄色、蓝色、绿色）来进行定位

#定位车牌
def color_position(img,output_path):colors = [([26,43,46], [34,255,255]),  # 黄色([100,43,46], [124,255,255]),  # 蓝色([35, 43, 46], [77, 255, 255])  # 绿色]hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)for (lower, upper) in colors:lower = np.array(lower, dtype="uint8")  # 颜色下限upper = np.array(upper, dtype="uint8")  # 颜色上限# 根据阈值找到对应的颜色mask = cv2.inRange(hsv, lowerb=lower, upperb=upper)output = cv2.bitwise_and(img, img, mask=mask)k = mark_zone_color(output,output_path)if k==1:return 1# 展示图片#cv2.imshow("image", img)#cv2.imshow("image-color", output)#cv2.waitKey(0)return 0

在这里插入图片描述

2、将车牌提取出来

def mark_zone_color(src_img,output_img):#根据颜色在原始图像上标记#转灰度gray = cv2.cvtColor(src_img,cv2.COLOR_BGR2GRAY)#图像二值化ret,binary = cv2.threshold(gray,0,255,cv2.THRESH_BINARY)#轮廓检测x,contours,hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)#drawing = img#cv2.drawContours(drawing, contours, -1, (0, 0, 255), 3)  # 填充轮廓颜色#cv2.imshow('drawing', drawing)#cv2.waitKey(0)#print(contours)temp_contours = []  # 存储合理的轮廓car_plates=[]if len(contours)>0:for contour in contours:if cv2.contourArea(contour) > Min_Area:temp_contours.append(contour)car_plates = []for temp_contour in temp_contours:rect_tupple = cv2.minAreaRect(temp_contour)rect_width, rect_height = rect_tupple[1]if rect_width < rect_height:rect_width, rect_height = rect_height, rect_widthaspect_ratio = rect_width / rect_height# 车牌正常情况下宽高比在2 - 5.5之间if aspect_ratio > 2 and aspect_ratio < 5.5:car_plates.append(temp_contour)rect_vertices = cv2.boxPoints(rect_tupple)rect_vertices = np.int0(rect_vertices)if len(car_plates)==1:oldimg = cv2.drawContours(img, [rect_vertices], -1, (0, 0, 255), 2)#cv2.imshow("che pai ding wei", oldimg)# print(rect_tupple)break#把车牌号截取出来if len(car_plates)==1:for car_plate in car_plates:row_min,col_min = np.min(car_plate[:,0,:],axis=0)row_max,col_max = np.max(car_plate[:,0,:],axis=0)cv2.rectangle(img,(row_min,col_min),(row_max,col_max),(0,255,0),2)card_img = img[col_min:col_max,row_min:row_max,:]cv2.imshow("img",img)cv2.imwrite(output_img + '/' + 'card_img' + '.jpg',card_img)cv2.imshow("card_img.",card_img)cv2.waitKey(0)cv2.destroyAllWindows()return 1return 0