如何在python中进行实时摄像机对象检测

import cv2

# 导入深度学习模型和配置文件
model_weights = "path/to/model/weights"
model_config = "path/to/model/config"
model_labels = "path/to/model/labels"

# 加载模型和配置文件
net = cv2.dnn.readNetFromDarknet(model_config, model_weights)
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# 初始化摄像机
cap = cv2.VideoCapture(0)

while True:
    # 读取一帧图像
    ret, frame = cap.read()
    
    # 对图像进行对象检测
    blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
    net.setInput(blob)
    outs = net.forward(output_layers)
    
    # 处理每个检测到的对象
    for out in outs:
        for detection in out:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]
            if confidence > 0.5:
                # 检测到一个对象，绘制框和标签
                # ...
    
    # 显示处理后的图像
    cv2.imshow("Object Detection", frame)
    
    # 按下'q'键退出循环
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# 释放摄像机和关闭窗口
cap.release()
cv2.destroyAllWindows()