我正在尝试创建一个tkinter窗口，用户按下一个按钮，然后输入一个img让程序扫描它。我想要的是图像出现在tkinter窗口上，并且程序不要结束，而是继续，这样用户就可以输入另一个图像。我的输出是这样的：
https://prnt.sc/w837e2
https://prnt.sc/w838c2
https://prnt.sc/w83c69
tk窗口被破坏，显示的只是输出。此外，当文件对话框打开并且用户没有输入图像时，tkwindow也会关闭。
这是代码：
import numpy as np
import argparse
import cv2
import tkinter as tk
from tkinter import filedialog
def search_image():
global image1
image1 = filedialog.askopenfilename()
root.destroy()
return image1
root = tk.Tk()
root.geometry('1200x900-100-100')
root.resizable(False, False)
root.title('YOLO')
w = tk.Label(root, text = "IMAGE-DETECTION-YOLO", font = "Arial 36", bg ='lightgray', width = 900)
w.pack()
button = tk.Button(root, text = "CHOOSE", font = "Arial 36", command = search_image)
button.pack()
root.mainloop()
#######after that is code for the detection model############################
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", default=image1,
help="path to input image")
ap.add_argument("-p", "--prototxt", default="MobileNetSSD_deploy.prototxt.txt",
help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", default="MobileNetSSD_deploy.caffemodel",
help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.2,
help="minimum probability to filter weak detections")
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, 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, 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, 0, i, 1])
box = detections[0, 0, i, 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, (startX, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
# show the output image
cv2.imshow("Output", image)
cv2.imwrite('image_detected.jpg',image)
cv2.waitKey(0)
您应该将检测放在一个函数中，并在选择图像文件后调用该函数。还可以使用Pillow模块转换结果图像，并使用Label显示结果图像。
import numpy as np
import argparse
import cv2
import tkinter as tk
from tkinter import filedialog
from PIL import Image, ImageTk
def detect_objects(image1):
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", default=image1,
help="path to input image")
ap.add_argument("-p", "--prototxt", default="MobileNetSSD_deploy.prototxt.txt",
help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", default="MobileNetSSD_deploy.caffemodel",
help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.2,
help="minimum probability to filter weak detections")
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, 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, 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, 0, i, 1])
box = detections[0, 0, i, 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, (startX, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
# show the output image
image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
tkimage = ImageTk.PhotoImage(image)
result.config(image=tkimage)
result.iamge = tkimage
def search_image():
image1 = filedialog.askopenfilename()
if image1:
detect_objects(image1)
root = tk.Tk()
root.geometry('1200x900-100-100')
root.resizable(False, False)
root.title('YOLO')
w = tk.Label(root, text = "IMAGE-DETECTION-YOLO", font = "Arial 36", bg ='lightgray', width = 900)
w.pack()
button = tk.Button(root, text = "CHOOSE", font = "Arial 36", command = search_image)
button.pack()
# label to show the result
result = tk.Label(root)
result.pack()
root.mainloop()