毕业设计 python opencv实现车牌识别 界面，pythonopencv,主要代码参考http

毕业设计 python opencv实现车牌识别 界面，pythonopencv,主要代码参考http

主要代码参考https://blog.csdn.net/wzh191920/article/details/79589506

GitHub：https://github.com/yinghualuowu

答辩通过了，补完~

这里主要是用两种方法进行定位识别

# -*- coding: utf-8 -*-__author__ = ‘樱花落舞‘import tkinter as tkfrom tkinter.filedialog import *from tkinter import ttkimport img_function as predictimport cv2from PIL import Image, ImageTkimport threadingimport timeimport img_mathimport tracebackimport debugimport configfrom threading import Threadclass ThreadWithReturnValue(Thread):    def __init__(self, group=None, target=None, name=None, args=(), kwargs=None, *, daemon=None):        Thread.__init__(self, group, target, name, args, kwargs, daemon=daemon)        self._return1 = None        self._return2 = None        self._return3 = None    def run(self):        if self._target is not None:            self._return1,self._return2,self._return3 = self._target(*self._args, **self._kwargs)    def join(self):        Thread.join(self)        return self._return1,self._return2,self._return3class Surface(ttk.Frame):    pic_path = ""    viewhigh = 600    viewwide = 600    update_time = 0    thread = None    thread_run = False    camera = None    color_transform = {"green": ("绿牌", "#55FF55"), "yello": ("黄牌", "#FFFF00"), "blue": ("蓝牌", "#6666FF")}    def __init__(self, win):        ttk.Frame.__init__(self, win)        frame_left = ttk.Frame(self)        frame_right1 = ttk.Frame(self)        frame_right2 = ttk.Frame(self)        win.title("车牌识别")        win.state("zoomed")        self.pack(fill=tk.BOTH, expand=tk.YES, padx="10", pady="10")        frame_left.pack(side=LEFT, expand=1, fill=BOTH)        frame_right1.pack(side=TOP, expand=1, fill=tk.Y)        frame_right2.pack(side=RIGHT, expand=0)        ttk.Label(frame_left, text=‘原图：‘).pack(anchor="nw")        ttk.Label(frame_right1, text=‘形状定位车牌位置：‘).grid(column=0, row=0, sticky=tk.W)        from_pic_ctl = ttk.Button(frame_right2, text="来自图片", width=20, command=self.from_pic)        from_vedio_ctl = ttk.Button(frame_right2, text="来自摄像头", width=20, command=self.from_vedio)        from_img_pre = ttk.Button(frame_right2, text="查看形状预处理图像", width=20,command = self.show_img_pre)        self.image_ctl = ttk.Label(frame_left)        self.image_ctl.pack(anchor="nw")        self.roi_ctl = ttk.Label(frame_right1)        self.roi_ctl.grid(column=0, row=1, sticky=tk.W)        ttk.Label(frame_right1, text=‘形状定位识别结果：‘).grid(column=0, row=2, sticky=tk.W)        self.r_ctl = ttk.Label(frame_right1, text="",font=(‘Times‘,‘20‘))        self.r_ctl.grid(column=0, row=3, sticky=tk.W)        self.color_ctl = ttk.Label(frame_right1, text="", width="20")        self.color_ctl.grid(column=0, row=4, sticky=tk.W)        from_vedio_ctl.pack(anchor="se", pady="5")        from_pic_ctl.pack(anchor="se", pady="5")        from_img_pre.pack(anchor="se", pady="5")        ttk.Label(frame_right1, text=‘颜色定位车牌位置：‘).grid(column=0, row=5, sticky=tk.W)        self.roi_ct2 = ttk.Label(frame_right1)        self.roi_ct2.grid(column=0, row=6, sticky=tk.W)        ttk.Label(frame_right1, text=‘颜色定位识别结果：‘).grid(column=0, row=7, sticky=tk.W)        self.r_ct2 = ttk.Label(frame_right1, text="",font=(‘Times‘,‘20‘))        self.r_ct2.grid(column=0, row=8, sticky=tk.W)        self.color_ct2 = ttk.Label(frame_right1, text="", width="20")        self.color_ct2.grid(column=0, row=9, sticky=tk.W)        self.predictor = predict.CardPredictor()        self.predictor.train_svm()    def get_imgtk(self, img_bgr):        img = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)        im = Image.fromarray(img)        imgtk = ImageTk.PhotoImage(image=im)        wide = imgtk.width()        high = imgtk.height()        if wide > self.viewwide or high > self.viewhigh:            wide_factor = self.viewwide / wide            high_factor = self.viewhigh / high            factor = min(wide_factor, high_factor)            wide = int(wide * factor)            if wide <= 0: wide = 1            high = int(high * factor)            if high <= 0: high = 1            im = im.resize((wide, high), Image.ANTIALIAS)            imgtk = ImageTk.PhotoImage(image=im)        return imgtk    def show_roi1(self, r, roi, color):        if r:            roi = cv2.cvtColor(roi, cv2.COLOR_BGR2RGB)            roi = Image.fromarray(roi)            self.imgtk_roi = ImageTk.PhotoImage(image=roi)            self.roi_ctl.configure(image=self.imgtk_roi, state=‘enable‘)            self.r_ctl.configure(text=str(r))            self.update_time = time.time()            try:                c = self.color_transform[color]                self.color_ctl.configure(text=c[0], background=c[1], state=‘enable‘)            except:                self.color_ctl.configure(state=‘disabled‘)        elif self.update_time + 8 < time.time():            self.roi_ctl.configure(state=‘disabled‘)            self.r_ctl.configure(text="")            self.color_ctl.configure(state=‘disabled‘)    def show_roi2(self, r, roi, color):        if r:            roi = cv2.cvtColor(roi, cv2.COLOR_BGR2RGB)            roi = Image.fromarray(roi)            self.imgtk_roi = ImageTk.PhotoImage(image=roi)            self.roi_ct2.configure(image=self.imgtk_roi, state=‘enable‘)            self.r_ct2.configure(text=str(r))            self.update_time = time.time()            try:                c = self.color_transform[color]                self.color_ct2.configure(text=c[0], background=c[1], state=‘enable‘)            except:                self.color_ct2.configure(state=‘disabled‘)        elif self.update_time + 8 < time.time():            self.roi_ct2.configure(state=‘disabled‘)            self.r_ct2.configure(text="")            self.color_ct2.configure(state=‘disabled‘)    def show_img_pre(self):        filename = config.get_name()        if filename.any() == True:            debug.img_show(filename)    def from_vedio(self):        if self.thread_run:            return        if self.camera is None:            self.camera = cv2.VideoCapture(0)            if not self.camera.isOpened():                mBox.showwarning(‘警告‘, ‘摄像头打开失败！‘)                self.camera = None                return        self.thread = threading.Thread(target=self.vedio_thread, args=(self,))        self.thread.setDaemon(True)        self.thread.start()        self.thread_run = True    def from_pic(self):        self.thread_run = False        self.pic_path = askopenfilename(title="选择识别图片", filetypes=[("jpg图片", "*.jpg"), ("png图片", "*.png")])        if self.pic_path:            img_bgr = img_math.img_read(self.pic_path)            first_img, oldimg = self.predictor.img_first_pre(img_bgr)            self.imgtk = self.get_imgtk(img_bgr)            self.image_ctl.configure(image=self.imgtk)            th1 = ThreadWithReturnValue(target=self.predictor.img_color_contours,args=(first_img,oldimg))            th2 = ThreadWithReturnValue(target=self.predictor.img_only_color,args=(oldimg,oldimg,first_img))            th1.start()            th2.start()            r_c, roi_c, color_c = th1.join()            r_color,roi_color,color_color = th2.join()            print(r_c,r_color)            self.show_roi2(r_color, roi_color, color_color)            self.show_roi1(r_c, roi_c, color_c)    @staticmethod    def vedio_thread(self):        self.thread_run = True        predict_time = time.time()        while self.thread_run:            _, img_bgr = self.camera.read()            self.imgtk = self.get_imgtk(img_bgr)            self.image_ctl.configure(image=self.imgtk)            if time.time() - predict_time > 2:                r, roi, color = self.predictor(img_bgr)                self.show_roi(r, roi, color)                predict_time = time.time()        print("run end")def close_window():    print("destroy")    if surface.thread_run:        surface.thread_run = False        surface.thread.join(2.0)    win.destroy()if __name__ == ‘__main__‘:    win = tk.Tk()    surface = Surface(win)    # close,退出输出destroy    win.protocol(‘WM_DELETE_WINDOW‘, close_window)    # 进入消息循环    win.mainloop()

毕业设计 python opencv实现车牌识别 界面