如何将所有分类结果打印到我的 GUI 上的文本小部件？
How can I print all the classification results to the Text widget on my GUI?
我有一个 GUI 可以将图像分类为正确或错误。我如何打印文本小部件中的所有结果，以便在单击用户控制按钮后打印出所有结果？当我单击该按钮时，我希望所有答案都打印在小部件中。但是，它只打印最后一个结果
这是我的代码
from tkinter import *
from PIL import Image,ImageTk
from tkinter import filedialog
from PIL import Image, ImageTk
import os
import cv2
import numpy as np
import time
class application(Tk):
def __init__(self,parent):
Tk.__init__(self,parent)
self.parent = parent
self.minsize(width=300,height=500)
self.initialize()
def initialize(self):
self.grid_columnconfigure(0,weight=1)
self.grid_columnconfigure(1,weight=1)
self.grid_columnconfigure(2,weight=1)
#Button widgets
###########################################################################################
#self.button1 = Button(self,text='Image Selector',bg= 'blue',width=15,height=2,command=self.browseFiles)
#self.button1.grid(column=0,row=1,sticky='W',pady=5)
self.button2 = Button(self,text='User control',bg= 'blue',width=15,height=2,command=self.Image_Classification)
self.button2.grid(column=0,row=1,sticky='W',pady=5,padx=150)
self.button3 = Button(self,text='Exit',bg= 'blue',width=10,height=2,command=self.destroy)
self.button3.grid(column=1,row=5,sticky='W',pady=5)
#self.button4 = Button(self,text='Method 2',width=10,height=2)
#self.button4.grid(column=0,row=2,sticky='W',pady=5)
#Text widget for inserting result
############################################################################################
self.text1 = Text(self,height=2,width=20,state=NORMAL)
self.text1.grid(column=2,row=1,sticky='E',padx=5)
#self.text1.configure(bg='cyan')
#self.canvas2 = Canvas(self,height=10,width=20,state=NORMAL)
#self.canvas2.grid(column=2,row=2,sticky='E',padx=5)
self.text3 = Text(self,height=2,width=20,state=NORMAL)
self.text3.grid(column=2,row=3,sticky='E',padx=5)
self.canvas = Canvas(self,width=230,height=180,state=NORMAL)
self.canvas.grid(column=0,row=4,sticky='W',padx=100,pady=10)
#self.canvas.configure(bg='green')
#Label widget
############################################################################################
self.label1 = Label(self,text="Mode", bg = 'red', width=10,height=2,anchor="center")
self.label1.grid(column=0,row=0,sticky='W',padx=120)
self.label2 = Label(self,text="Inspection",bg = 'red',width=20,height=2,anchor="center")
self.label2.grid(column=2,row=0,sticky='E',padx=50)
self.label3 = Label(self,text="Image",bg = 'red',width=10,height=2,anchor="center")
self.label3.grid(column=0,row=3,sticky='W',padx=120)
self.label4 = Label(self,text="Classification",bg = 'red',width=10,height=2,anchor="center")
self.label4.grid(column=1,row=1,sticky='E',pady=10)
#self.label5 = Label(self,text="Result 2(Black Spot)",width=30,height=2,anchor="e")
#self.label5.grid(column=1,row=2,sticky='E',pady=10)
self.label6 = Label(self,text="Time",bg = 'red',width=10,height=2,anchor="center")
self.label6.grid(column=1,row=3,sticky='E',pady=10)
'''def browseFiles(self):
self.filename = filedialog.askopenfilename(initialdir = '/',title = "Select a File",
filetypes = [("image",".jpeg"),("image",".png"),("image",".jpg")])
start=time.time()
self.imageFile = ImageTk.PhotoImage(file=self.filename)
self.canvas.create_image(50,10,image=self.imageFile,anchor=NW)
self.img = cv2.imread(self.filename)
self.hist = cv2.calcHist([self.img], [0], None, [256], [0,256])
self.mean_px = cv2.mean(self.img)
self.max_nums = max(self.hist); self.max_num = str(self.max_nums)
self.max_num_px= np.where(self.hist==self.max_nums)[0]+1; self.max_num_px = str(self.max_num_px)
self.min_nums = min(self.hist); self.min_num = str(self.min_nums)
self.min_num_px= np.where(self.hist==self.min_nums)[0]+1; self.min_num_px = str(self.min_num_px)
self.hist_2_num = round((self.max_nums*(2/5))[0])
self.hist_2_min_num = self.hist[self.hist>=self.hist_2_num][0]
self.hist_2_max_num = self.hist[self.hist>=self.hist_2_num][len(self.hist[self.hist>=self.hist_2_num])-1]
self.hist_2_min_val = np.where(self.hist==self.hist_2_min_num)[0][0]+1
self.hist_2_max_val = max(np.where(self.hist==self.hist[self.hist>=self.hist_2_num][len(self.hist[self.hist>=self.hist_2_num])-1])[0])
self.hist_2_diff = self.hist_2_max_val - self.hist_2_min_val
self.results = []
if self.hist_2_diff>50:
self.results.append('good')
else:
self.results.append('bad')
for choices in self.results:
self.text1 = Text(self,height=2,width=20,state=NORMAL)
self.text1.insert(END,str(choices))
self.text1.grid(column=2,row=1,sticky='E',padx=5)
end=time.time()
Time_elapsed='{:.3f}s'.format(end-start)
text3 = Text(self,height=2,width=20,state=NORMAL)
text3.insert(END,str(Time_elapsed))
text3.grid(column=2,row=3,sticky='E',padx=5)'''
def Image_Classification(self):
start=time.time()
for self.i in range(1,31):
for self.j in range(1,11):
self.filename = 'Path/Folder_'+str(self.i)+'/'+str(self.j)+'.jpg'
self.img = cv2.imread(self.filename)
self.hist = cv2.calcHist([self.img], [0], None, [256], [0,256])
self.mean_px = cv2.mean(self.img)
self.max_nums = max(self.hist); self.max_num = str(self.max_nums)
self.max_num_px= np.where(self.hist==self.max_nums)[0]+1; self.max_num_px = str(self.max_num_px)
self.min_nums = min(self.hist); self.min_num = str(self.min_nums)
self.min_num_px= np.where(self.hist==self.min_nums)[0]+1; self.min_num_px = str(self.min_num_px)
self.hist_2_num = round((self.max_nums*(2/5))[0])
self.hist_2_min_num = self.hist[self.hist>=self.hist_2_num][0]
self.hist_2_max_num = self.hist[self.hist>=self.hist_2_num][len(self.hist[self.hist>=self.hist_2_num])-1]
self.hist_2_min_val = np.where(self.hist==self.hist_2_min_num)[0][0]+1
self.hist_2_max_val = max(np.where(self.hist==self.hist[self.hist>=self.hist_2_num][len(self.hist[self.hist>=self.hist_2_num])-1])[0])
self.hist_2_diff = self.hist_2_max_val - self.hist_2_min_val
self.results = []
if self.hist_2_diff>50:
self.results.append('{}: good'.format(self.j))
print('{}: good'.format(self.j))
else:
self.results.append('{}: bad'.format(self.j))
print('{}: bad'.format(self.j))
for choices in self.results:
self.text1 = Text(self,height=2,width=20,state=NORMAL)
self.text1.insert(END,str(choices))
self.text1.grid(column=2,row=1,sticky='E',padx=5)
#self.text1.tag_config(background='blue')
self.j+=1
end=time.time()
Time_elapsed='{:.3f}s'.format(end-start)
text3 = Text(self,height=2,width=20,state=NORMAL)
text3.insert(END,str(Time_elapsed))
text3.grid(column=2,row=3,sticky='E',padx=5)
if __name__ == "__main__":
app = application(None)
#font.nametofont('TkDefaultFont').configure(size=10)
app['bg']='Cyan'
app.title("Trial")
app.mainloop()
我的成绩
预期结果
测试图片
您不应创建新的 Text 小部件来存储每次迭代的结果。由于您已经创建了 Text 小部件用于显示 initialize() 内的结果和经过的时间，您可以在 for 循环之后将结果插入到这些小部件中：
def Image_Classification(self):
self.results = []
start = time.time()
for self.i in range(1, 31):
for self.j in range(1, 11):
self.filename = 'Path/Folder_'+str(self.i)+'/'+str(self.j)+'.jpg'
self.img = cv2.imread(self.filename)
self.hist = cv2.calcHist([self.img], [0], None, [256], [0,256])
self.mean_px = cv2.mean(self.img)
self.max_nums = max(self.hist); self.max_num = str(self.max_nums)
self.max_num_px = np.where(self.hist==self.max_nums)[0]+1; self.max_num_px = str(self.max_num_px)
self.min_nums = min(self.hist); self.min_num = str(self.min_nums)
self.min_num_px = np.where(self.hist==self.min_nums)[0]+1; self.min_num_px = str(self.min_num_px)
self.hist_2_num = round((self.max_nums*(2/5))[0])
self.hist_2_min_num = self.hist[self.hist>=self.hist_2_num][0]
self.hist_2_max_num = self.hist[self.hist>=self.hist_2_num][len(self.hist[self.hist>=self.hist_2_num])-1]
self.hist_2_min_val = np.where(self.hist==self.hist_2_min_num)[0][0]+1
self.hist_2_max_val = max(np.where(self.hist==self.hist[self.hist>=self.hist_2_num][len(self.hist[self.hist>=self.hist_2_num])-1])[0])
self.hist_2_diff = self.hist_2_max_val - self.hist_2_min_val
result = 'good' if self.hist_2_diff > 50 else 'bad'
self.results.append(f'{self.i}.{self.j}: {result}')
end = time.time()
time_elapsed = f'{end-start:.3f}s'
self.text3.delete('1.0', END)
self.text3.insert(END, time_elapsed)
self.text1.delete('1.0', END)
for result in self.results:
self.text1.insert(END, f'{result}\n')
更新：为 self.text1 添加了滚动条：
def initialize(self):
...
self.text1 = Text(self, height=2, width=20, state=NORMAL)
self.text1.grid(column=2, row=1, sticky='nse', padx=5)
self.scrollbar1 = Scrollbar(self, orient=VERTICAL, command=self.text1.yview)
self.scrollbar1.grid(column=3, row=1, sticky='ns')
self.text1.config(yscrollcommand=self.scrollbar1.set)
...
我有一个 GUI 可以将图像分类为正确或错误。我如何打印文本小部件中的所有结果，以便在单击用户控制按钮后打印出所有结果？当我单击该按钮时，我希望所有答案都打印在小部件中。但是，它只打印最后一个结果
这是我的代码
from tkinter import *
from PIL import Image,ImageTk
from tkinter import filedialog
from PIL import Image, ImageTk
import os
import cv2
import numpy as np
import time
class application(Tk):
def __init__(self,parent):
Tk.__init__(self,parent)
self.parent = parent
self.minsize(width=300,height=500)
self.initialize()
def initialize(self):
self.grid_columnconfigure(0,weight=1)
self.grid_columnconfigure(1,weight=1)
self.grid_columnconfigure(2,weight=1)
#Button widgets
###########################################################################################
#self.button1 = Button(self,text='Image Selector',bg= 'blue',width=15,height=2,command=self.browseFiles)
#self.button1.grid(column=0,row=1,sticky='W',pady=5)
self.button2 = Button(self,text='User control',bg= 'blue',width=15,height=2,command=self.Image_Classification)
self.button2.grid(column=0,row=1,sticky='W',pady=5,padx=150)
self.button3 = Button(self,text='Exit',bg= 'blue',width=10,height=2,command=self.destroy)
self.button3.grid(column=1,row=5,sticky='W',pady=5)
#self.button4 = Button(self,text='Method 2',width=10,height=2)
#self.button4.grid(column=0,row=2,sticky='W',pady=5)
#Text widget for inserting result
############################################################################################
self.text1 = Text(self,height=2,width=20,state=NORMAL)
self.text1.grid(column=2,row=1,sticky='E',padx=5)
#self.text1.configure(bg='cyan')
#self.canvas2 = Canvas(self,height=10,width=20,state=NORMAL)
#self.canvas2.grid(column=2,row=2,sticky='E',padx=5)
self.text3 = Text(self,height=2,width=20,state=NORMAL)
self.text3.grid(column=2,row=3,sticky='E',padx=5)
self.canvas = Canvas(self,width=230,height=180,state=NORMAL)
self.canvas.grid(column=0,row=4,sticky='W',padx=100,pady=10)
#self.canvas.configure(bg='green')
#Label widget
############################################################################################
self.label1 = Label(self,text="Mode", bg = 'red', width=10,height=2,anchor="center")
self.label1.grid(column=0,row=0,sticky='W',padx=120)
self.label2 = Label(self,text="Inspection",bg = 'red',width=20,height=2,anchor="center")
self.label2.grid(column=2,row=0,sticky='E',padx=50)
self.label3 = Label(self,text="Image",bg = 'red',width=10,height=2,anchor="center")
self.label3.grid(column=0,row=3,sticky='W',padx=120)
self.label4 = Label(self,text="Classification",bg = 'red',width=10,height=2,anchor="center")
self.label4.grid(column=1,row=1,sticky='E',pady=10)
#self.label5 = Label(self,text="Result 2(Black Spot)",width=30,height=2,anchor="e")
#self.label5.grid(column=1,row=2,sticky='E',pady=10)
self.label6 = Label(self,text="Time",bg = 'red',width=10,height=2,anchor="center")
self.label6.grid(column=1,row=3,sticky='E',pady=10)
'''def browseFiles(self):
self.filename = filedialog.askopenfilename(initialdir = '/',title = "Select a File",
filetypes = [("image",".jpeg"),("image",".png"),("image",".jpg")])
start=time.time()
self.imageFile = ImageTk.PhotoImage(file=self.filename)
self.canvas.create_image(50,10,image=self.imageFile,anchor=NW)
self.img = cv2.imread(self.filename)
self.hist = cv2.calcHist([self.img], [0], None, [256], [0,256])
self.mean_px = cv2.mean(self.img)
self.max_nums = max(self.hist); self.max_num = str(self.max_nums)
self.max_num_px= np.where(self.hist==self.max_nums)[0]+1; self.max_num_px = str(self.max_num_px)
self.min_nums = min(self.hist); self.min_num = str(self.min_nums)
self.min_num_px= np.where(self.hist==self.min_nums)[0]+1; self.min_num_px = str(self.min_num_px)
self.hist_2_num = round((self.max_nums*(2/5))[0])
self.hist_2_min_num = self.hist[self.hist>=self.hist_2_num][0]
self.hist_2_max_num = self.hist[self.hist>=self.hist_2_num][len(self.hist[self.hist>=self.hist_2_num])-1]
self.hist_2_min_val = np.where(self.hist==self.hist_2_min_num)[0][0]+1
self.hist_2_max_val = max(np.where(self.hist==self.hist[self.hist>=self.hist_2_num][len(self.hist[self.hist>=self.hist_2_num])-1])[0])
self.hist_2_diff = self.hist_2_max_val - self.hist_2_min_val
self.results = []
if self.hist_2_diff>50:
self.results.append('good')
else:
self.results.append('bad')
for choices in self.results:
self.text1 = Text(self,height=2,width=20,state=NORMAL)
self.text1.insert(END,str(choices))
self.text1.grid(column=2,row=1,sticky='E',padx=5)
end=time.time()
Time_elapsed='{:.3f}s'.format(end-start)
text3 = Text(self,height=2,width=20,state=NORMAL)
text3.insert(END,str(Time_elapsed))
text3.grid(column=2,row=3,sticky='E',padx=5)'''
def Image_Classification(self):
start=time.time()
for self.i in range(1,31):
for self.j in range(1,11):
self.filename = 'Path/Folder_'+str(self.i)+'/'+str(self.j)+'.jpg'
self.img = cv2.imread(self.filename)
self.hist = cv2.calcHist([self.img], [0], None, [256], [0,256])
self.mean_px = cv2.mean(self.img)
self.max_nums = max(self.hist); self.max_num = str(self.max_nums)
self.max_num_px= np.where(self.hist==self.max_nums)[0]+1; self.max_num_px = str(self.max_num_px)
self.min_nums = min(self.hist); self.min_num = str(self.min_nums)
self.min_num_px= np.where(self.hist==self.min_nums)[0]+1; self.min_num_px = str(self.min_num_px)
self.hist_2_num = round((self.max_nums*(2/5))[0])
self.hist_2_min_num = self.hist[self.hist>=self.hist_2_num][0]
self.hist_2_max_num = self.hist[self.hist>=self.hist_2_num][len(self.hist[self.hist>=self.hist_2_num])-1]
self.hist_2_min_val = np.where(self.hist==self.hist_2_min_num)[0][0]+1
self.hist_2_max_val = max(np.where(self.hist==self.hist[self.hist>=self.hist_2_num][len(self.hist[self.hist>=self.hist_2_num])-1])[0])
self.hist_2_diff = self.hist_2_max_val - self.hist_2_min_val
self.results = []
if self.hist_2_diff>50:
self.results.append('{}: good'.format(self.j))
print('{}: good'.format(self.j))
else:
self.results.append('{}: bad'.format(self.j))
print('{}: bad'.format(self.j))
for choices in self.results:
self.text1 = Text(self,height=2,width=20,state=NORMAL)
self.text1.insert(END,str(choices))
self.text1.grid(column=2,row=1,sticky='E',padx=5)
#self.text1.tag_config(background='blue')
self.j+=1
end=time.time()
Time_elapsed='{:.3f}s'.format(end-start)
text3 = Text(self,height=2,width=20,state=NORMAL)
text3.insert(END,str(Time_elapsed))
text3.grid(column=2,row=3,sticky='E',padx=5)
if __name__ == "__main__":
app = application(None)
#font.nametofont('TkDefaultFont').configure(size=10)
app['bg']='Cyan'
app.title("Trial")
app.mainloop()
我的成绩
预期结果
测试图片
您不应创建新的 Text 小部件来存储每次迭代的结果。由于您已经创建了 Text 小部件用于显示 initialize() 内的结果和经过的时间，您可以在 for 循环之后将结果插入到这些小部件中：
def Image_Classification(self):
self.results = []
start = time.time()
for self.i in range(1, 31):
for self.j in range(1, 11):
self.filename = 'Path/Folder_'+str(self.i)+'/'+str(self.j)+'.jpg'
self.img = cv2.imread(self.filename)
self.hist = cv2.calcHist([self.img], [0], None, [256], [0,256])
self.mean_px = cv2.mean(self.img)
self.max_nums = max(self.hist); self.max_num = str(self.max_nums)
self.max_num_px = np.where(self.hist==self.max_nums)[0]+1; self.max_num_px = str(self.max_num_px)
self.min_nums = min(self.hist); self.min_num = str(self.min_nums)
self.min_num_px = np.where(self.hist==self.min_nums)[0]+1; self.min_num_px = str(self.min_num_px)
self.hist_2_num = round((self.max_nums*(2/5))[0])
self.hist_2_min_num = self.hist[self.hist>=self.hist_2_num][0]
self.hist_2_max_num = self.hist[self.hist>=self.hist_2_num][len(self.hist[self.hist>=self.hist_2_num])-1]
self.hist_2_min_val = np.where(self.hist==self.hist_2_min_num)[0][0]+1
self.hist_2_max_val = max(np.where(self.hist==self.hist[self.hist>=self.hist_2_num][len(self.hist[self.hist>=self.hist_2_num])-1])[0])
self.hist_2_diff = self.hist_2_max_val - self.hist_2_min_val
result = 'good' if self.hist_2_diff > 50 else 'bad'
self.results.append(f'{self.i}.{self.j}: {result}')
end = time.time()
time_elapsed = f'{end-start:.3f}s'
self.text3.delete('1.0', END)
self.text3.insert(END, time_elapsed)
self.text1.delete('1.0', END)
for result in self.results:
self.text1.insert(END, f'{result}\n')
更新：为 self.text1 添加了滚动条：
def initialize(self):
...
self.text1 = Text(self, height=2, width=20, state=NORMAL)
self.text1.grid(column=2, row=1, sticky='nse', padx=5)
self.scrollbar1 = Scrollbar(self, orient=VERTICAL, command=self.text1.yview)
self.scrollbar1.grid(column=3, row=1, sticky='ns')
self.text1.config(yscrollcommand=self.scrollbar1.set)
...