yolov5调用外接摄像头检测

要调用外接摄像头进行目标检测，首先需要确保你的摄像头能够在你的电脑上正常工作。你可以使用OpenCV库来读取摄像头的视频流。

以下是一个基本的代码示例，可以使用YoloV5模型检测摄像头捕获的视频流：

import cv2
import torch
from models.experimental import attempt_load
from utils.general import non_max_suppression, scale_coords
from utils.torch_utils import select_device

# 加载模型
weights = 'yolov5s.pt'
device = select_device('0')
model = attempt_load(weights, map_location=device)

# 设置阈值和IOU阈值
conf_thres = 0.25
iou_thres = 0.45

# 读取摄像头
cap = cv2.VideoCapture(0)

while True:
    # 读取视频帧
    ret, frame = cap.read()
    if not ret:
        break

    # 将帧转换为模型需要的格式
    img = frame[:, :, ::-1].transpose(2, 0, 1)
    img = torch.from_numpy(img).float().div(255.0).unsqueeze(0)

    # 将图像输入模型并运行推理
    with torch.no_grad():
        detections = model(img, augment=False)[0]
        detections = non_max_suppression(detections, conf_thres, iou_thres, classes=None, agnostic=False)

    # 处理检测结果并绘制边界框
    for detection in detections:
        if detection is not None:
            detection = scale_coords(img.shape[2:], detection[:, :4], frame.shape).round()
            for *xyxy, conf, cls in detection:
                label = f'{model.names[int(cls)]} {conf:.2f}'
                cv2.rectangle(frame, (int(xyxy[0]), int(xyxy[1])), (int(xyxy[2]), int(xyxy[3])), (255, 0, 0), 2)
                cv2.putText(frame, label, (int(xyxy[0]), int(xyxy[1]) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)

    # 显示检测结果
    cv2.imshow('YOLOv5', frame)
    if cv2.waitKey(1) == ord('q'):
        break

# 释放资源
cap.release()
cv2.destroyAllWindows()

这个代码示例使用了YoloV5模型来检测摄像头捕获的视频流，并将检测结果绘制在视频帧上。你可以根据自己的需求进行修改和调整。