定位|车牌识别（车牌提取+调用百度api+OCR识别） python|图像识别|opencv

车牌识别过程

图片导入
图片灰度
车牌定位
车牌识别

分析

车牌的大小固定，宽高比在2~5.5之间

这是一个十分重要的训练数据。大致流程：

颜色查找（蓝色区域）
高斯模糊
灰度化
二阈值处理
边缘处理
闭运算
矩形筛选
车牌切割
车牌识别（调用百度aip实现）
调用百度aip请点击‘百度aip’进行注册获取对应的所需数据

颜色查找代码放在下方：

def color_find(img):#寻找蓝色,其他颜色均掩码 boundaries = [([86, 31, 4], [220, 88, 50])]#定义边界列表(lower[r, g, b], upper[r, g, b]) for (lower,upper) in boundaries:#遍历所有的边界 lower = np.array(lower, dtype = "uint8") upper = np.array(upper, dtype = "uint8") mask = cv2.inRange(img, lower, upper)#在指定边界内查找颜色并掩码 result_color_find = cv2.bitwise_and(img, img, mask = mask) return result_color_find

接下来是车牌识别的代码：

def pre_img(result_color_find,img):#对颜色处理后的车牌进行预处理 os.mkdir('license') img_gaus=cv2.GaussianBlur(result_color_find,(1,1),9)#高斯模糊 img_gray=cv2.cvtColor(img_gaus,cv2.COLOR_BGR2GRAY)#灰度化 ret,img_binary=cv2.threshold(img_gray,127,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)#二阈值处理 img_canny=cv2.Canny(img_binary, 200, 255)#边缘处理 kernel=cv2.getStructuringElement(cv2.MORPH_RECT,(50,20)) img_close=cv2.morphologyEx(img_canny,cv2.MORPH_CLOSE,kernel)#闭运算 contours,hierarchy=cv2.findContours(img_close,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) result_pre_img=[] for i in range(len(contours)): x,y,w,h=cv2.boundingRect(contours[i]) ratio=w/h if ratio > 2 and ratio < 5.5:#筛选出宽高比在2~5.5之间的矩形 cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),1) result=img[y:y+h,x:x+w]#预处理 pre_result=cv2.resize(result,(200,50)) result_pre_img.append(pre_result) for o in range(len(result_pre_img)): cv2.imwrite('license/license%d.jpg'%(o+1), result_pre_img[o]) return o+1

以上代码需调用：

import cv2
import numpy as np
import os
import shutil**

附上ui界面源码：
界面非原创设计，界面参考

import tkinter as tk from tkinter import filedialog from tkinter import * from tkinter import Button,Label,filedialog import License_import import cv2 from PIL import Image,ImageTk from aip import AipOcr import shutil import ossrcPath='' api='填写你的百度aip' api_id='这里写id' api_key='这里写apikey值' secret_key='这里写secretkey值' license_dir='license/' client = AipOcr(api_id, api_key, secret_key) result=[] window=tk.Tk() window.title('车牌识别(LPR)') window.geometry('1000x600') canvas_pic = tk.Canvas(window, width=700, height=600) canvas_pic.pack(side='right') canvas_plate = tk.Canvas(window, width=200, height=50) canvas_plate.place(x=10, y=320)def resize( resize_width, resize_height, pil_image): w, h = pil_image.size f1 = 1.0*resize_width/w f2 = 1.0*resize_height/h factor = min([f1, f2]) width = int(w*factor) height = int(h*factor) return pil_image.resize((width, height), Image.ANTIALIAS)def printcoords(): File = filedialog.askopenfilename(parent=window, title='请选择一张图片') global srcPath srcPath=File src = https://www.it610.com/article/Image.open(File) resizePic=resize(700,600,src) filename=ImageTk.PhotoImage(resizePic) canvas_pic.image=filename canvas_pic.create_image(350,300,image=filename)btn_choosePic = tk.Button(window, text='选择图片',command=printcoords).place(x=40, y=200)def location(): if os.path.exists('license'): shutil.rmtree('license') global pictureName img=cv2.imread(srcPath) result_file=License_import.color_find(img) for i in range(License_import.pre_img(result_file,img)): image = get_file_content(license_dir+'license%d.jpg'%(i+1)) src = https://www.it610.com/article/Image.open(license_dir+'license%d.jpg'%(i+1)) text=client.basicGeneral(image) for j in text.get('words_result'): var_returnTxt.set(j['words']) resizePic = resize(200, 50, src) filename = ImageTk.PhotoImage(resizePic) canvas_plate.image = filename canvas_plate.create_image(100, 25, image=filename)btn_location = tk.Button(window, text='开始识别', command=location).place(x=40, y=250) tk.Label(window, text='车牌定位结果: ', width=18).place(x=20, y= 300) tk.Label(window, text='车牌识别结果: ', width=18).place(x=20, y= 450) tk.Label(window, text='车牌识别步骤:\n' '1.请选择需要识别的图片\n' '2.请单击开始识别按钮\n' '3.车牌和识别结果将会被显示至下方\n\n\n' '该识别软件只能识别单个车牌\n' '如需识别多个车牌的图请裁剪图片后导入').place(x=0, y=18) var_returnTxt = tk.StringVar() var_returnTxt.set("等待识别......") tk.Label(window,textvariable=var_returnTxt,font=('Arial', 11),width=18, height=8).place(x=20, y=470)def get_file_content(filePath): with open(filePath, 'rb') as fp: return fp.read()if __name__ == '__main__': window.mainloop()

【定位|车牌识别（车牌提取+调用百度api+OCR识别）】附上效果图：