我目前正在做一个机器学习项目，我正在尝试制作一个基本的Tkinter UI，以从执行手势识别的文件中调用函数。
当前，我的GUI上有一个按钮，要求手势识别文件中的“主要”功能，每次单击该按钮时，它都会运行该功能，但是GUI变得无响应，因此我无法单击其他任何东西。我想知道这是否与没有任何回报有关？这将停止ui主循环。
我要构建的功能是让UI运行Camera.py中的“主要功能”（我将在下面链接），然后为该主要功能的三个选项分别分配一个按钮“按1可以训练模型，按2可以检测手势”等。而不是手动输入。
我当前的UI代码是
import numpy as np
import matplotlib as plt
from sys import exit
from tkinter import *
from tkinter import ttk
import tkinter.simpledialog as sd
import camera as cam
modelobject = cam.Yolo_Model()
def runTraining():
modelobject.main()
def getUserInput():
options = {'minvalue':1, 'maxvalue':4}
userInput = sd.askinteger('User Input', 'Select an Option', **options)
print((f'You entered {userInput}'))
window=Tk()
window.title("Gesture Recognition")
window.geometry('600x350')
btn1 = ttk.Button(window, text="Train the Model", command=lambda: runTraining())
btn1.grid(column=0, row=0)
btn2 = ttk.Button(window, text="Detect Hand Gesture", command=lambda: getUserInput())
btn2.grid(column=1, row=0)
btn3 = ttk.Button(window, text="Clear Images Stored From Training")
btn3.grid(column=2, row=0)
def close_window():
exit(0)
window.destroy()
exitbtn = ttk.Button(window, text="Exit")
exitbtn.grid(column=0, row=4)
exitbtn.config(command=close_window)
window.mainloop()
我要在UI中运行并调用函数的文件的代码是
import numpy as np
import os
from numpy import expand_dims
import cv2
from trainer import predicter
from keras.preprocessing.image import img_to_array
import time
from im_to_txt import m
from random import random
import shutil
class Yolo_Model:
# Sets the intial parameters used within the definition of the class.
def __init__(self):
# Enables the video capture
self.cap = cv2.VideoCapture(0)
# Sets the temporary directory used for the captured frame
self.filename = "img.jpg"
# Sets the destination of the training directory
self.train_dir = "new_training"
try:
os.mkdir(self.train_dir)
except:
print("directory exists")
def func(self, curr):
boo = ""
image = cv2.imread(self.filename)
os.remove(self.filename)
print("Enter 'T' if the label is correct or 'F' is the label is incorrect!")
boo = input(curr[0] + "\n")
try:
if boo == 'T':
name = str(random()) + '.jpg'
name = os.path.join(self.train_dir, name)
try:
cv2.imwrite(name, image)
except:
print("Could not save the image to " + str(name))
m(name, curr)
image = cv2
except ValueError as e:
print(e)
print("Value not recognised")
def model_detector(self):
while (self.cap.isOpened()):
ret, frame = self.cap.read()
if ret == True:
# time.sleep(1)
frame = cv2.flip(frame, +1)
cv2.imshow('window', frame)
try:
cv2.imwrite(self.filename, frame)
except:
print("Could not save the image to img.jpg")
output = predicter(0.6, self.filename)
os.remove(self.filename)
# os.remove(photo_filename)
while (len(output) >= 5):
curr = output[0:6]
output = output[6:]
label = curr[0]
try:
image = cv2.rectangle(frame, (curr[1], curr[4]), (curr[3], curr[2]), (0, 0, 255), 3)
image = cv2.putText(frame, label, (curr[1], (curr[2] - 10)), cv2.FONT_HERSHEY_SIMPLEX, 1,
(0, 255, 0), 2)
except:
print("Could not annote the image. Please ensure the co-ordinates are correct")
cv2.imshow('window', image)
# Used to detstroy the image capture window
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
# This function is used to train the system. It detects a gesture being shown by the user and applies
# a label. It then prompts the user, asking if the label identified was correct. If yes, the yolo format
# text file is created, needed for training, and the image is saved into the training directory. The user
# is also shown the detected image with the label and bounding box.
def model_trainer(self):
# Constant loop for capturing frames for the webcam.
while (True):
# Captures the image and frame number.
ret, frame = self.cap.read()
if ret == True:
# Additional sleep timer can be used for waiting in between capturing frames. Turned
# off for real time detection.
# time.sleep(1)
# Flips the frame so it appears the right way for the user.
frame = cv2.flip(frame, +1)
# Shows the user the frame that has been captured by the camera. This is constantly
# updated each time a new frame is captured.
cv2.imshow('window', frame)
# Try and except block used incase the system cannot write the captured frame. The frame
# is saved so it can be used by the prediction file.
try:
cv2.imwrite(self.filename, frame)
except:
print("Could not save the image to img.jpg")
# The predict method from trainer.py is called. This will return the prediction of the captured
# image along with the the parameters needed for the boudnding box and prediction score. The parameter
# 0.6 corrosponds to threshold of how confident the model is about the label applied. Any lower and the
# prediction will be discounted. 0.6 is lower than normal to allow for additonal classifcation to be made
# during training.
output = predicter(0.6, self.filename)
# If the length of the array returned from the model prediction is greater than 5,i.e. a prediction was made
# then the first 6 values, corrosponding to the length of one prediction, will be upacked until there are no more
# predictions from that frame.
while (len(output) >= 5):
# The first 6 values corrosponding to the first prediction are extracted
curr = output[0:6]
output = output[6:]
# The predicted label is the first value within the returned array
label = curr[0]
# A try block is used incase the given parameters for the bounding box are incorrect or cannot be labeled
# within the image.
try:
image = cv2.rectangle(frame, (curr[1], curr[4]), (curr[3], curr[2]), (0, 0, 255), 3)
image = cv2.putText(frame, label, (curr[1], (curr[2] - 10)), cv2.FONT_HERSHEY_SIMPLEX, 1,
(0, 255, 0), 2)
except:
print("Could not annote the image. Please ensure the co-ordinates are correct")
cv2.imshow('window', image)
# This tunction is called to check if the predicted label was correct and if so use the image for training.
self.func(curr)
# Used to destroy the image capture window
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
# Adds additional ethics and security in removing the training directory
# from the system.
def removeFiles(self):
# Try and except used incase the training directory could not be located.
try:
# Removes the training directory.
shutil.rmtree(self.train_dir, ignore_errors=True)
# Throws an OS error as the file cannot be found, printing user instructions on the error.
except OSError as e:
print("Error: ")
print(e)
# The main menu for the user to select the options for the system. This menu calls the functions
# used for training, detecting, and removing stored images.
def main(self):
menu = input(
"Press 1 to train the model: \nWarning training will store images of you!!\nPress 2 to detect the hand gesture: \nPress 3 to remove any images stored during training\n")
if menu == '1':
while (True):
self.model_trainer()
elif menu == '2':
while (True):
self.model_detector()
elif menu == '3':
self.removeFiles()
# The final else statement catches the program should a unexpected value be entered.
else:
print("Error. Correct option not selected")
# Automatically calls the main method within the Yolo_Model class
if __name__ == "__main__":
model = Yolo_Model()
# Call the main method
model.main()
print("Closing application")
# Try and except should the camera not have been used within the system.
try:
# Stop the frame capture
cap.release()
# Destroy the all the CV2 windows
cv2.destroyAllWindows()
except:
print("Image capture not used")
为了澄清，在运行UI时，窗口会弹出，然后，当我单击当前分配给从camera.py运行“ main”的按钮时，它会运行，但是UI变得无响应，所以我必须从控制台完成此过程，这就是我要用UI功能替换的过程。
我希望这很清楚，而且由于我不是很有经验，我缺少一个明显的解决方法。 干杯
答案 0 :(得分：0)
只要您的runTraining（）函数正在运行，您的UI就会一直无响应。 您必须在单独的线程或进程中运行长时间运行的任务。如果要从长期运行的任务中更改UI元素，则必须设置一个在主循环中轮询的队列，并且可以在主循环的上下文中更改UI。
这里是一个示例：http://zetcode.com/articles/tkinterlongruntask/
另请参阅Python Tkinter: How do I make my GUI responsive as long as a thread runs?（评论中有一些指向相关问题的链接）
下面是一个示例，该示例直接使用PySimpleGui而不是TKinter：https://github.com/PySimpleGUI/PySimpleGUI/blob/master/DemoPrograms/Demo_Multithreaded_Long_Tasks.py