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interface.py
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interface.py
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import tkinter as tk
from tkinter.filedialog import *
from tkinter import ttk
import cv2
from PIL import Image,ImageTk
from LPR import predict
#定义一个Interface类，继承ttk.Frame
class Interface(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")}
#Interface 构造函数
def __init__(self, win):
#初始化Interface ttk.Frame部分，将Interface置于实例中master之中
ttk.Frame.__init__(self, win)
#创建Frame子部件，并将各个子Frme部件置于实例类中
frame_left = ttk.Frame(self)
frame_right1 = ttk.Frame(self)
frame_right2 = ttk.Frame(self)
#窗口最大化
#win.state("zoomed")
#放置Interface
#padx,pady为 单元行间距
self.pack(fill=tk.BOTH, expand=tk.YES, padx="5", pady="5")
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)
#frame_left布局
ttk.Label(frame_left, text='原图：').pack(anchor="nw")
self.image_ctl = ttk.Label(frame_left)
self.image_ctl.pack(anchor="nw")
#frame_right1布局
#grid布局，指定行指定列，sticky 对齐方式
ttk.Label(frame_right1, text='车牌位置：').grid(column=0, row=0, sticky=tk.W)
self.roi_ctl = ttk.Label(frame_right1)
self.roi_ctl.grid(column=0, row=1, sticky=tk.W)
ttk.Label(frame_right1, text='识别结果：').grid(column=0, row=2, sticky=tk.W)
self.r_ctl = ttk.Label(frame_right1, text="")
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)
#frame_right2
from_pic_button = 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_pic_button.pack(anchor="se", pady="1")
self.predictor = predict.carpredictor()
def get_imgtk(self, img_bgr):
#bgr颜色空间转化为rgb颜色空间
img = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
#img 的变量类型为numpy.ndarray
#im 的变量类型为PIL.Image.Image
#image 的变量类型为PIL.ImageTk.PhotoImage
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
#改变尺寸大小
im = im.resize((wide, high), Image.ANTIALIAS)
imgtk = ImageTk.PhotoImage(image=im)
return imgtk
def from_pic(self):
self.thread_run = False
self.pic_path = askopenfilename(title="选择识别图片", filetypes=[("jpg图片", "*.jpg")])
if self.pic_path :
#np.fromfile(self.pic_path, dtype=np.uint8)读取的是一个numpy.ndarray , 但未进行编码处理
#需要结合cv2.imdecode()进行对数据数据的编码读取
#img_bgr = cv2.imdecode(np.fromfile(self.pic_path, dtype=np.uint8), cv2.IMREAD_COLOR)
img_bgr = cv2.imread(self.pic_path,cv2.IMREAD_COLOR )
#将cv2 format（numpy.ndarray） 格式转化为tkinter image
self.imgtk = self.get_imgtk(img_bgr)
self.image_ctl.configure(image=self.imgtk)
self.predictor.find_carplate(img_bgr)
if __name__ == '__main__':
win = tk.Tk()
win.title("车牌识别")
win.geometry("500x500")
interface = Interface(win)
win.mainloop()