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 __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！")
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(unet, img_src_path)
img_src_copy, Lic_img, prebox = 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
prebox = np.array(prebox)
if get_iou(prebox, gtbox) > 0.7:
iou_right += 1
Lic_pred = cnn_predict(
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)
#print('print the result')
#print(Lic_pred)
if Lic_pred:
for i, lic_pred in enumerate(Lic_pred):
if i == 0:
text = lic_pred[1]
text = text[0:2] + text[3:]
#print(text)
elif i == 1:
text = lic_pred[1]
text = text[0:2] + text[3:]
#print(text)
elif i == 2:
text = lic_pred[1]
text = text[0:2] + text[3:]