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camera.py
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camera.py
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import cv2
import torch
import tkinter as tk
from PIL import Image, ImageTk
from torchvision import transforms
from torchvision import models
import torch.nn as nn
# 加载模型
model = models.resnet18(pretrained=False)
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 5) # 假设您有3个类别
model.load_state_dict(torch.load('model_weights_resnet18.pth'))
model.eval()
# 定义转换
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
# 类别
classes = ['bird','dog','other','snake']
# 初始化摄像头
cap = cv2.VideoCapture(0)
def update_frame():
# 捕获一帧图像
ret, frame = cap.read()
if not ret:
print("Failed to grab frame")
return
# 将捕获的帧转换为Tkinter格式
cv2image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA)
img = Image.fromarray(cv2image)
imgtk = ImageTk.PhotoImage(image=img)
video_label.imgtk = imgtk
video_label.configure(image=imgtk)
# 转换图像以适应模型输入
img_for_pred = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
img_t = transform(img_for_pred)
batch_t = torch.unsqueeze(img_t, 0)
# 进行预测
out = model(batch_t)
_, index = torch.max(out, 1)
# 更新预测结果
result_var.set(f'Prediction: {classes[index[0]]}')
# 每50毫秒刷新一次界面
root.after(50, update_frame)
# 创建UI
root = tk.Tk()
root.title("Real-time Image Classification and Video Stream")
# 用于显示视频帧的Label
video_label = tk.Label(root)
video_label.pack()
# 用于显示预测结果的Label
result_var = tk.StringVar()
result_label = tk.Label(root, textvariable=result_var, font=('Helvetica', 20))
result_label.pack()
# 开始更新帧
update_frame()
# 启动UI循环
root.mainloop()
# 释放资源
cap.release()
cv2.destroyAllWindows()