如何解决如何在 tkinter 中并排显示图像？
我有这个对象检测程序，用户提供一个图像并选择一个模型进行检测，它显示在 tkinter 上，如图所示：
我正在尝试创建一个名为 compare 的命令，用户按下它，选择一个图像并并排显示两张图片。我可以说它需要尝试，除非避免任何错误。此外，我正在尝试制作一个命令重置，它使窗口返回到它的起点。我的代码是这样的：
import numpy as np
import argparse
import cv2
import tkinter as tk
from tkinter import filedialog
from PIL import ImageTk,Image
from tkinter import messageBox
def object_det_MobileNetSSD():
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i","--image",default=img1)
ap.add_argument("-p","--prototxt",default="MobileNetSSD_deploy.prototxt.txt")
ap.add_argument("-m","--model",default="MobileNetSSD_deploy.caffemodel")
ap.add_argument("-c","--confidence",type=float,default=0.2)
args = vars(ap.parse_args())
# initialize the list of class labels MobileNet SSD was trained to
# detect,then generate a set of bounding Box colors for each class
CLASSES = ["background","aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","diningtable","dog","horse","motorbike","person","pottedplant","sheep","sofa","train","tvmonitor"]
COLORS = np.random.uniform(0,255,size=(len(CLASSES),3))
# load our serialized model from disk
print("[INFO] loading model...")
model = cv2.dnn.readNetFromCaffe(args["prototxt"],args["model"])
# load the input image and construct an input blob for the image
# by resizing to a fixed 300x300 pixels and then normalizing it
# (note: normalization is done via the authors of the MobileNet SSD
# implementation)
image = cv2.imread(args["image"])
(h,w) = image.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(image,(300,300)),0.007843,300),127.5)
# pass the blob through the network and obtain the detections and
# predictions
print("[INFO] computing object detections...")
model.setInput(blob)
detections = model.forward()
# loop over the detections
for i in np.arange(0,detections.shape[2]):
# extract the confidence (i.e.,probability) associated with the
# prediction
confidence = detections[0,i,2]
# filter out weak detections by ensuring the `confidence` is
# greater than the minimum confidence
if confidence > args["confidence"]:
# extract the index of the class label from the `detections`,# then compute the (x,y)-coordinates of the bounding Box for
# the object
idx = int(detections[0,1])
Box = detections[0,3:7] * np.array([w,h,w,h])
(startX,startY,endX,endY) = Box.astype("int")
# display the prediction
label = "{}: {:.2f}%".format(CLASSES[idx],confidence * 100)
print("[INFO] {}".format(label))
cv2.rectangle(image,(startX,startY),(endX,endY),COLORS[idx],2)
y = startY - 15 if startY - 15 > 15 else startY + 15
cv2.putText(image,label,y),cv2.FONT_HERShey_SIMPLEX,0.5,2)
image = Image.fromarray(cv2.cvtColor(image,cv2.COLOR_BGR2RGB))
width,height = image.size
if int(width)<int(height): img = image.resize((400,555),Image.ANTIALIAS)
else: img = image.resize((900,Image.ANTIALIAS)
img = ImageTk.PhotoImage(img)
panel.config(image=img)
panel.image=img
def object_det_Yolo():
config_file="ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt"
frozen_model="frozen_inference_graph.pb"
model=cv2.dnn_DetectionModel(frozen_model,config_file)
Labels=[]
file_name="labels.txt"
with open(file_name,"rt") as fpt:
Labels= fpt.read().rstrip("\n").split("\n")
model.setInputSize(320,320)
model.setInputScale(1.0/127.5)
model.setInputMean((127.5,127.5,127.5))
model.setInputSwapRB(True)
img=cv2.imread(img1)
ClassIndex,confidence,bBox=model.detect(img,confThreshold=0.5)
for ClassInd,conf,Boxes in zip(ClassIndex.flatten(),confidence.flatten(),bBox):
cv2.rectangle(img,Boxes,color=(0,0),thickness=2)
cv2.putText(img,Labels[ClassInd-1].upper(),(Boxes[0]+10,Boxes[1]+30),cv2.FONT_HERShey_COMPLEX,1,(0,100,2)
img = Image.fromarray(cv2.cvtColor(img,cv2.COLOR_BGR2RGB))
width,height = img.size
if int(width)<int(height): img = img.resize((400,Image.ANTIALIAS)
else: img = img.resize((900,Image.ANTIALIAS)
img = ImageTk.PhotoImage(img)
panel.config(image=img)
panel.image=img
def compare():
pass
def reset():
pass
def check_checkBox1() :
global val1
val1=is_checked1.get()
if val1== 1:
is_checked2.set(0)
return val1
def check_checkBox2():
global val2
val2=is_checked2.get()
if val2 == 1:
is_checked1.set(0)
return val2
def model_choice():
check_checkBox1()
check_checkBox2()
if val1==1: object_det_MobileNetSSD()
elif val2==1: object_det_Yolo()
else:
messageBox.showerror("Error","Choose a model")
def callback():
global img1
img1= filedialog.askopenfilename()
model_choice()
root=tk.Tk()
root.title("Object Detection")
root.resizable(False,False)
is_checked1 = tk.Intvar()
is_checked2 = tk.Intvar()
is_checked3 = tk.Intvar()
window_height = 670
window_width = 1000
screen_width = root.winfo_screenwidth()
screen_height = root.winfo_screenheight()
x_cordinate = int((screen_width/2) - (window_width/2))
y_cordinate = int((screen_height/2) - (window_height/2))
root.geometry("{}x{}+{}+{}".format(window_width,window_height,x_cordinate-45,y_cordinate-45))
root.configure(background='grey')
menubar=tk.Menu(root)
root.config(menu=menubar)
selection=tk.Menu(menubar,tearoff=0)
menubar.add_cascade(label="Detection Models",menu=selection)
selection.add_checkbutton(label="MobileNet",onvalue = 1,offvalue = 0,variable = is_checked1,command = check_checkBox1)
selection.add_checkbutton(label="YOLO",variable = is_checked2,command = check_checkBox2)
commandbar=tk.Menu(menubar)
selection2=tk.Menu(menubar,tearoff=0)
menubar.add_cascade(label="Commands",menu=selection2)
selection2.add_command(label="Compare",command=compare)
selection2.add_command(label="Reset",command=reset)
w1=tk.Label(root,text="Object detection",font = "Impact 36",bg ='lightgray',width = 900,borderwidth=4,relief="solid")
w1.pack(fill="x")
panel = tk.Label(root,bg="grey")
panel.pack()
button=tk.Button(root,text="Choose image",font = "Impact 17",fg = "lightgray",highlightbackground="lightgray",bg ="black",command=callback)
button.pack()
button.place(x=390,y=625)
root.mainloop()
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