基于u-net，cv2以及cnn的中文车牌定位，矫正和端到端识别软件，其中unet和cv2用于车牌定位和矫正，cnn进行车牌识别

# -*- coding:utf-8 -*- # author: DuanshengLiu import cv2 import numpy as np from tkinter import * from tkinter.filedialog import askopenfilename from PIL import Image, ImageTk from tensorflow import keras from core import locate_and_correct from Unet import unet_predict from CNN import cnn_predict class Window: def __init__(self, win, ww, wh): self.win = win self.ww = ww self.wh = wh self.win.geometry("%dx%d+%d+%d" % (ww, wh, 200, 50)) # 界面启动时的初始位置 self.win.title("车牌定位，矫正和识别软件---by DuanshengLiu") self.img_src_path = None self.label_src = Label(self.win, text='原图:', font=('微软雅黑', 13)).place(x=0, y=0) self.label_lic1 = Label(self.win, text='车牌区域1:', font=('微软雅黑', 13)).place(x=615, y=0) self.label_pred1 = Label(self.win, text='识别结果1:', font=('微软雅黑', 13)).place(x=615, y=85) self.label_lic2 = Label(self.win, text='车牌区域2:', font=('微软雅黑', 13)).place(x=615, y=180) self.label_pred2 = Label(self.win, text='识别结果2:', font=('微软雅黑', 13)).place(x=615, y=265) self.label_lic3 = Label(self.win, text='车牌区域3:', font=('微软雅黑', 13)).place(x=615, y=360) self.label_pred3 = Label(self.win, text='识别结果3:', font=('微软雅黑', 13)).place(x=615, y=445) self.can_src = Canvas(self.win, width=512, height=512, bg='white', relief='solid', borderwidth=1) # 原图画布 self.can_src.place(x=50, y=0) self.can_lic1 = Canvas(self.win, width=245, height=85, bg='white', relief='solid', borderwidth=1) # 车牌区域1画布 self.can_lic1.place(x=710, y=0) self.can_pred1 = Canvas(self.win, width=245, height=65, bg='white', relief='solid', borderwidth=1) # 车牌识别1画布 self.can_pred1.place(x=710, y=90) self.can_lic2 = Canvas(self.win, width=245, height=85, bg='white', relief='solid', borderwidth=1) # 车牌区域2画布 self.can_lic2.place(x=710, y=175) self.can_pred2 = Canvas(self.win, width=245, height=65, bg='white', relief='solid', borderwidth=1) # 车牌识别2画布 self.can_pred2.place(x=710, y=265) self.can_lic3 = Canvas(self.win, width=245, height=85, bg='white', relief='solid', borderwidth=1) # 车牌区域3画布 self.can_lic3.place(x=710, y=350) self.can_pred3 = Canvas(self.win, width=245, height=65, bg='white', relief='solid', borderwidth=1) # 车牌识别3画布 self.can_pred3.place(x=710, y=440) self.button1 = Button(self.win, text='选择文件', width=10, height=1, command=self.load_show_img) # 选择文件按钮 self.button1.place(x=680, y=wh - 30) self.button2 = Button(self.win, text='识别车牌', width=10, height=1, command=self.display) # 识别车牌按钮 self.button2.place(x=780, y=wh - 30) self.button3 = Button(self.win, text='清空所有', width=10, height=1, command=self.clear) # 清空所有按钮 self.button3.place(x=880, y=wh - 30) self.unet = keras.models.load_model('unet.h5') self.cnn = keras.models.load_model('cnn.h5') print('正在启动中,请稍等...') cnn_predict(self.cnn, [np.zeros((80, 240, 3))]) print("已启动,开始识别吧！") def load_show_img(self): self.clear() sv = StringVar() sv.set(askopenfilename()) self.img_src_path = Entry(self.win, state='readonly', text=sv).get() # 获取到所打开的图片 img_open = Image.open(self.img_src_path) if img_open.size[0] * img_open.size[1] > 240 * 80: img_open = img_open.resize((512, 512), Image.ANTIALIAS) self.img_Tk = ImageTk.PhotoImage(img_open) self.can_src.create_image(258, 258, image=self.img_Tk, anchor='center') def display(self): if self.img_src_path == None: # 还没选择图片就进行预测 self.can_pred1.create_text(32, 15, text='请选择图片', anchor='nw', font=('黑体', 28)) else: img_src = cv2.imdecode(np.fromfile(self.img_src_path, dtype=np.uint8), -1) # 从中文路径读取时用 h, w = img_src.shape[0], img_src.shape[1] if h * w <= 240 * 80 and 2 <= w / h <= 5: # 满足该条件说明可能整个图片就是一张车牌,无需定位,直接识别即可 lic = cv2.resize(img_src, dsize=(240, 80), interpolation=cv2.INTER_AREA)[:, :, :3] # 直接resize为(240,80) img_src_copy, Lic_img = img_src, [lic] else: # 否则就需通过unet对img_src原图预测,得到img_mask,实现车牌定位,然后进行识别 img_src, img_mask = unet_predict(self.unet, self.img_src_path) img_src_copy, Lic_img = locate_and_correct(img_src, img_mask) # 利用core.py中的locate_and_correct函数进行车牌定位和矫正 Lic_pred = cnn_predict(self.cnn, Lic_img) # 利用cnn进行车牌的识别预测,Lic_pred中存的是元祖(车牌图片,识别结果) if Lic_pred: img = Image.fromarray(img_src_copy[:, :, ::-1]) # img_src_copy[:, :, ::-1]将BGR转为RGB self.img_Tk = ImageTk.PhotoImage(img) self.can_src.delete('all') # 显示前,先清空画板 self.can_src.create_image(258, 258, image=self.img_Tk, anchor='center') # img_src_copy上绘制出了定位的车牌轮廓,将其显示在画板上 for i, lic_pred in enumerate(Lic_pred): if i == 0: self.lic_Tk1 = ImageTk.PhotoImage(Image.fromarray(lic_pred[0][:, :, ::-1])) self.can_lic1.create_image(5, 5, image=self.lic_Tk1, anchor='nw') self.can_pred1.create_text(35, 15, text=lic_pred[1], anchor='nw', font=('黑体', 28)) elif i == 1: self.lic_Tk2 = ImageTk.PhotoImage(Image.fromarray(lic_pred[0][:, :, ::-1])) self.can_lic2.create_image(5, 5, image=self.lic_Tk2, anchor='nw') self.can_pred2.create_text(40, 15, text=lic_pred[1], anchor='nw', font=('黑体', 28)) elif i == 2: self.lic_Tk3 = ImageTk.PhotoImage(Image.fromarray(lic_pred[0][:, :, ::-1])) self.can_lic3.create_image(5, 5, image=self.lic_Tk3, anchor='nw') self.can_pred3.create_text(40, 15, text=lic_pred[1], anchor='nw', font=('黑体', 28)) else: # Lic_pred为空说明未能识别 self.can_pred1.create_text(47, 15, text='未能识别', anchor='nw', font=('黑体', 27)) def clear(self): self.can_src.delete('all') self.can_lic1.delete('all') self.can_lic2.delete('all') self.can_lic3.delete('all') self.can_pred1.delete('all') self.can_pred2.delete('all') self.can_pred3.delete('all') self.img_src_path = None def closeEvent(): # 关闭前清除session(),防止'NoneType' object is not callable keras.backend.clear_session() sys.exit() if __name__ == '__main__': win = Tk() ww = 1000 # 窗口宽设定1000 wh = 600 # 窗口高设定600 Window(win, ww, wh) win.protocol("WM_DELETE_WINDOW", Window.closeEvent) win.mainloop()