yolov5使用电脑摄像头进行视频

### 使用YOLOv5与电脑摄像头进行实时视频物体检测 为了实现实时视频物体检测，可以利用Python中的`cv2.VideoCapture()`函数来获取来自电脑摄像头的数据流，并将其传递给预训练好的YOLOv5模型来进行预测。下面是一个简单的例子，展示了如何设置环境并执行这一过程[^1]。 #### 安装依赖库 首先需要安装必要的软件包，包括PyTorch、OpenCV以及ultralytics/yolov5仓库中提供的工具集： ```bash pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113 pip install opencv-python-headless git clone https://github.com/ultralytics/yolov5.git cd yolov5 pip install -r requirements.txt ``` #### 编写代码实现 接下来编写一段脚本来读取摄像头帧数据并对每一帧应用YOLOv5模型进行推理操作： ```python import cv2 from pathlib import Path import torch from models.experimental import attempt_load from utils.general import non_max_suppression, scale_coords from utils.torch_utils import select_device from utils.datasets import letterbox def detect_objects(model, img0): stride = int(model.stride.max()) # model stride imgsz = check_img_size(640, s=stride) # check image size names = model.module.names if hasattr(model, 'module') else model.names # get class names colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] # Padded resize img = letterbox(img0, new_shape=imgsz)[0] # Convert img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 img = np.ascontiguousarray(img) img = torch.from_numpy(img).to(device) img = img.float() # uint8 to fp16/32 img /= 255.0 # 0 - 255 to 0.0 - 1.0 if img.ndimension() == 3: img = img.unsqueeze(0) pred = model(img, augment=False)[0] # Apply NMS pred = non_max_suppression(pred, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False) detections = [] for det in pred: # detections per image if len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_coords(img.shape[2:], det[:, :4], img0.shape).round() for *xyxy, conf, cls in reversed(det): label = f'{names[int(cls)]} {conf:.2f}' plot_one_box(xyxy, img0, label=label, color=colors[int(cls)], line_thickness=3) detection_info = { "label": names[int(cls)], "confidence": float(conf), "bounding_box": [int(x.item()) for x in xyxy], } detections.append(detection_info) return img0, detections if __name__ == '__main__': weights_path = 'yolov5s.pt' # path/to/custom_weights.pt or yolov5s.pt etc. device = select_device('') model = attempt_load(weights_path, map_location=device) # load FP32 model cap = cv2.VideoCapture(0) while True: ret, frame = cap.read() result_frame, results = detect_objects(model, frame.copy()) cv2.imshow('Detection', result_frame) key = cv2.waitKey(1) & 0xFF if key == ord("q"): break cap.release() cv2.destroyAllWindows() ``` 此段程序会打开默认的网络摄像机设备（编号通常为0），持续捕获图像帧并通过调用`detect_objects`方法完成对象识别工作。最后，在屏幕上显示带有标注框的结果画面直到按下键盘上的Q键为止。