如何解决在 tkinter 上需要关于对象检测程序的帮助
我正在尝试制作一个 tkinter 窗口,用户按下按钮,然后用户输入一个 img 供程序扫描它。我想要的是图像出现在 tkinter 窗口上,并且程序不结束并继续,以便用户输入另一个图像。我的输出是这个:
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,tk 窗口也会关闭。
代码如下:
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",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),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)
# show the output image
cv2.imshow("Output",image)
cv2.imwrite('image_detected.jpg',image)
cv2.waitKey(0)
解决方法
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),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)
# 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",command = search_image)
button.pack()
# label to show the result
result = tk.Label(root)
result.pack()
root.mainloop()
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