python 实现图像检测界面

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python 实现图像检测界面

python 实现图像检测界面

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 tk

from tkinter.filedialog import *

from tkinter import ttk

import img_function as predict

import cv2

from PIL import Image, ImageTk

import threading

import time

import img_math

import traceback

import debug

import config

from threading import Thread

class 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._return3

class 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="", pady="")

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")

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',''))

self.id(column=0, row=3, sticky=tk.W)

from_vedio_ctl.pack(anchor="se", pady="")

from_pic_ctl.pack(anchor="se", pady="")

from_img_pre.pack(anchor="se", pady="")

ttk.Label(frame_right1, text='颜色定位车牌位置:').grid(column=0, row=5, 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',''))

self.id(column=0, row=8, sticky=tk.W)

self.predictor = predict.CardPredictor()

ain_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.figure(text=str(r))

self.update_time = time.time()

try:

c = lor_transform[color]

except:

elif self.update_time + 8 < time.time():

self.figure(text="")

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.figure(text=str(r))

self.update_time = time.time()

try:

c = lor_transform[color]

except:

elif self.update_time + 8 < time.time():

self.figure(text="")

def show_img_pre(self):

filename = _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 = _imgtk(img_bgr)

self.figure(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 = ad()

self.imgtk = _imgtk(img_bgr)

self.figure(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()

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