车牌号识别（低级版）

本文主要是介绍车牌号识别（低级版），希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

import cv2
from matplotlib import pyplot as plt
import os
import numpy as np
from paddleocr import PaddleOCR, draw_ocr
from PIL import Image, ImageDraw, ImageFont# 利用paddelOCR进行文字扫描，并输出结果
def text_scan(img_path):ocr = PaddleOCR(use_angle_cls=True, use_gpu=False)# img_path = r'test image/license_plate1.jpg'result = ocr.ocr(img_path, cls=True)for line in result:# print(line)return result# 在图片中写入将车牌信息
def infor_write(img, rect, result):text = result[0][0][1][0]cv2img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # cv2和PIL中颜色的hex码的储存顺序不同pilimg = Image.fromarray(cv2img)# PIL图片上打印汉字draw = ImageDraw.Draw(pilimg)  # 图片上打印font = ImageFont.truetype("simhei.ttf", 20, encoding="utf-8")  # 参数1：字体文件路径，参数2：字体大小draw.text((rect[2], rect[1]), str(text), (0, 255, 0), font=font)  # 参数1：打印坐标，参数2：文本，参数3：字体颜色，参数4：字体# PIL图片转cv2 图片cv2charimg = cv2.cvtColor(np.array(pilimg), cv2.COLOR_RGB2BGR)return cv2charimgdef plt_show0(img):#cv2与plt的图像通道不同：cv2为[b,g,r];plt为[r,g,b]b,g,r=cv2.split(img)img=cv2.merge([r,g,b])plt.imshow(img)plt.show()
#plt显示灰度图片
def plt_show(img):plt.imshow(img,camp='gray')plt.show()# 图像去噪灰度处理
def gray_guss(img):img = cv2.GaussianBlur(img, (1, 1), 0)gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)return gray# 图像尺寸变换
def img_resize(img):a = 400 * img.shape[0] / img.shape[1]a = int(a)img = cv2.resize(img, (400, a))return img# Sobel检测,x方向上的边缘检测（增强边缘信息）
def Sobel_detec(img):Sobel_x = cv2.Sobel(img, cv2.CV_16S, 1, 0)absX = cv2.convertScaleAbs(Sobel_x)return absX# 寻找某区域最大外接矩形框4点坐标
def find_retangle(contour):y, x = [], []for p in contour:y.append(p[0][0])x.append(p[0][1])return [min(y), min(x), max(y), max(x)]# 寻找并定位车牌轮廓位置
def locate_license(img):blocks = []contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)for c in contours:x, y, w, h = cv2.boundingRect(c)r = find_retangle(c)a = (r[2] - r[0]) * (r[3] - r[1])s = (r[2] - r[0]) / (r[3] - r[1])print(w)if (w > (h * 3)) and (w < (h * 5)):blocks.append([r, a, s])# blocks.append([r, a, s])blocks = sorted(blocks, key=lambda b: b[1])[-3:]maxweight, maxindex = 0, -1for i in range(len(blocks)):b = oriimg[blocks[i][0][1]:blocks[i][0][3], blocks[i][0][0]:blocks[i][0][2]]hsv = cv2.cvtColor(b, cv2.COLOR_BGR2HSV)lower = np.array([70, 150, 50])upper = np.array([120, 255, 255])mask = cv2.inRange(hsv, lower, upper)w1 = 0for m in mask:w1 += m / 255w2 = 0for w in w1:w2 += wif w2 > maxweight:maxindex = imaxweight = w2print('blocks是', blocks[maxindex])print('blocks0是',blocks[maxindex][0])return blocks[maxindex][0]# 图像预处理+车牌轮廓位置检测
def fine_lisecenpts(img):# 图像去噪灰度处理guss = gray_guss(img)# Sobel检测，增强边缘信息sobel = Sobel_detec(guss)# 图像阈值化操作——获得二值化图ret, threshold = cv2.threshold(sobel, 0, 255, cv2.THRESH_OTSU)# # 对二值化图像进行边缘检测(可选，通过边缘检测后，最终进行形态学运算得到的轮廓面积更大)# threshold=cv2.Canny(threshold,threshold.shape[0],threshold.shape[1])# 形态学运算（从图像中提取对表达和描绘区域形状有意义的图像分量）——闭操作kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (30, 10))closing = cv2.morphologyEx(threshold, cv2.MORPH_CLOSE, kernelX, iterations=1)# 腐蚀（erode）和膨胀（dilate）kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (50, 1))kernelY = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 20))# x方向上进行闭操作（抑制暗细节）img = cv2.dilate(closing, kernelX)img = cv2.erode(img, kernelX)# y方向上进行开操作img = cv2.erode(img, kernelY)img = cv2.dilate(img, kernelY)# 进行中值滤波去噪Blur = cv2.medianBlur(img, 15)# 寻找轮廓rect = locate_license(Blur)print('rect是',rect)return rect, Blur# 车牌字符识别
def seg_char(rect_list, img):img = oriimg[rect_list[1]:rect_list[3], rect_list[0]:rect_list[2]]# 图像去噪灰度处理gray = gray_guss(img)# 图像阈值化操作-获得二值化图（可选）# ret,charimage=cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)# 图像进行闭运算k1 = np.ones((1, 1), np.uint8)close = cv2.morphologyEx(gray, cv2.MORPH_CLOSE, k1)cv2.imshow('close', close)cv2.imwrite(r"E:\ultralytics-20240216\21\img2\6.jpg", close)cv2.waitKey()res = text_scan(r"E:\ultralytics-20240216\21\img2\6.jpg")return res
def put_chinese_text(img, text, left_top):# 转换 cv2 img 为 PIL Imageimg_PIL = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))draw = ImageDraw.Draw(img_PIL)font = ImageFont.truetype('simhei.ttf', 30, encoding="utf-8")# 黄色文字fillColor = (255,255,0)position = left_topdraw.text(position, text, font=font, fill=fillColor)# 转换回 OpenCV 格式img_out = cv2.cvtColor(np.asarray(img_PIL),cv2.COLOR_RGB2BGR)return img_out# 主函数区
if __name__ == '__main__':img = cv2.imread(r"E:\ultralytics-20240216\21\img2\5.jpg")# 改变图像尺寸img = img_resize(img)oriimg = img.copy()# 寻找到车牌外轮廓矩形坐标print(1)rect, img = fine_lisecenpts(img)# 利用车牌轮廓坐标划分ROI区域用于字符识别，利用OCR识别车牌字符并返回字符串内容result = seg_char(rect, oriimg)print(result)print(rect)# 循环读取车牌字符串并写入到图片中text = result[0][0][1][0]# 获取文本所在的矩形位置left_top = tuple(rect[0:2])right_bottom = tuple(rect[2:4])# 在原始图像上绘制矩形（黄色框）cv2.rectangle(oriimg, left_top, right_bottom, (0, 255, 255), 2)# 在矩形旁边写入文本# 注意你可能需要根据实际情况调整文本的位置text_position = (right_bottom[0] + 1, right_bottom[1])oriimg = put_chinese_text(oriimg, text, text_position)# cv2.putText(oriimg, text, text_position, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2)cv2.imshow("Image with text", oriimg)cv2.waitKey(0)cv2.destroyAllWindows()