怎么把yolov5的detect.py中的关于摄像头的代码提取出来

如果您想从 YOLOv5 的 `detect.py` 中提取关于摄像头的代码，可以按照以下步骤进行：

1. 打开 `detect.py` 文件，并找到 `parser.add_argument('--source', type=str, default='data/images', help='source')` 这一行代码。这一行代码定义了 `source` 参数，用于指定输入源，可以是图片、视频或摄像头。

2. 在 `detect.py` 文件中找到 `with torch.no_grad():` 开始的代码块。这一代码块包含了 YOLOv5 的主要逻辑。

3. 在代码块中找到 `if source.isnumeric(): # camera` 这一行代码。这一行代码判断输入源是否为摄像头。

4. 将该代码块中关于摄像头的代码提取出来，例如：

cap = cv2.VideoCapture(int(source))
while cap.isOpened():
    ... # 摄像头相关代码

5. 将提取出来的代码粘贴到您的项目中，并根据需要进行修改。注意，您需要导入必要的库，例如 `cv2`。

相关问题

怎么把yolov5 6.0的detect.py中的关于摄像头的代码提取出来

如果您想提取 YOLOv5 6.0 版本的 detect.py 中关于摄像头的代码，可以按照以下步骤进行：

1. 打开 detect.py 文件，找到以下代码：

# Camera inference
elif source.isnumeric():
    source = int(source)
    # Start streaming
    cap = cv2.VideoCapture(source)
    assert cap.isOpened(), f'Failed to open {source}'
    cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
    fps = cap.get(cv2.CAP_PROP_FPS)
    print(f'Using {source} with FPS {fps:.3f} [OK]')
    for path, img, im0s, vid_cap in dataset:
        if vid_path != path:
            vid_path, frames = path, []
            if isinstance(vid_cap, cv2.VideoCapture):
                frames = vid_cap.get(cv2.CAP_PROP_FRAME_COUNT)
            elif isinstance(vid_cap, int):
                frames = vid_cap
            else:
                assert False, f'Invalid video {path}'
        t1 = time_synchronized()
        # Get frames
        ret, frame = cap.read()
        if not ret:
            break
        assert frame is not None, 'Image Not Found '
        # Padded resize
        frame = letterbox(frame, new_shape=inp_shape)[0]
        # Normalize RGB
        frame = frame[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
        frame = np.ascontiguousarray(frame)
        # Inference
        img = torch.from_numpy(frame).to(device)
        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        # Inference
        t2 = time_synchronized()
        pred = model(img, augment=opt.augment)[0]

        # Apply NMS
        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
        t3 = time_synchronized()

        # Process detections
        for i, det in enumerate(pred):  # detections per image
            p, s, im0 = path, '', im0s.copy()

            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            if det is not None and len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                # Print results
                for c in det[:, -1].unique():
                    n = (det[:, -1] == c).sum()  # detections per class
                    s += f'{n} {names[int(c)]}s, '  # add to string

                # Write results
                for *xyxy, conf, cls in det:
                    if save_txt:  # Write to file
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                        with open(txt_path + '.txt', 'a') as f:
                            f.write(('%g ' * 5 + '\n') % (cls, *xywh))  # label format
                    if save_img or save_crop or save_patch:  # Add bbox to image
                        c = int(cls)  # integer class
                        label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
                        plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=3, **plot_kwargs)
                    if save_crop or save_patch:  # Save crop
                        #crop = im0[int(xyxy[1]):int(xyxy[3]), int(xyxy[0]):int(xyxy[2])]
                        save_file = str(Path(save_dir) / Path(p).stem) + f'_{i}_{names[c]}.jpg'
                        cv2.imwrite(save_file, crop)
            # Print results
            print(f'{s}Done. ({t3 - t1:.3f}s)')
            # Stream results
            if view_img:
                cv2.imshow(p, im0)
                if cv2.waitKey(1) == ord('q'):  # q to quit
                    raise StopIteration
    if save_txt or save_img:
        print(f"Results saved to {save_dir}")
        if platform == 'darwin':  # MacOS
            os.system(f'open {save_dir}')
    cap.release()

2. 将以上代码复制到一个新文件中

3. 删除以下代码：

elif source.isnumeric():
    source = int(source)
    # Start streaming
    cap = cv2.VideoCapture(source)
    assert cap.isOpened(), f'Failed to open {source}'
    cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
    fps = cap.get(cv2.CAP_PROP_FPS)
    print(f'Using {source} with FPS {fps:.3f} [OK]')

4. 将下面的代码留下来：

for path, img, im0s, vid_cap in dataset:
        if vid_path != path:
            vid_path, frames = path, []
            if isinstance(vid_cap, cv2.VideoCapture):
                frames = vid_cap.get(cv2.CAP_PROP_FRAME_COUNT)
            elif isinstance(vid_cap, int):
                frames = vid_cap
            else:
                assert False, f'Invalid video {path}'
        t1 = time_synchronized()
        # Get frames
        ret, frame = cap.read()
        if not ret:
            break
        assert frame is not None, 'Image Not Found '
        # Padded resize
        frame = letterbox(frame, new_shape=inp_shape)[0]
        # Normalize RGB
        frame = frame[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
        frame = np.ascontiguousarray(frame)
        # Inference
        img = torch.from_numpy(frame).to(device)
        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        # Inference
        t2 = time_synchronized()
        pred = model(img, augment=opt.augment)[0]

        # Apply NMS
        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
        t3 = time_synchronized()

        # Process detections
        for i, det in enumerate(pred):  # detections per image
            p, s, im0 = path, '', im0s.copy()

            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            if det is not None and len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                # Print results
                for c in det[:, -1].unique():
                    n = (det[:, -1] == c).sum()  # detections per class
                    s += f'{n} {names[int(c)]}s, '  # add to string

                # Write results
                for *xyxy, conf, cls in det:
                    if save_txt:  # Write to file
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                        with open(txt_path + '.txt', 'a') as f:
                            f.write(('%g ' * 5 + '\n') % (cls, *xywh))  # label format
                    if save_img or save_crop or save_patch:  # Add bbox to image
                        c = int(cls)  # integer class
                        label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
                        plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=3, **plot_kwargs)
                    if save_crop or save_patch:  # Save crop
                        #crop = im0[int(xyxy[1]):int(xyxy[3]), int(xyxy[0]):int(xyxy[2])]
                        save_file = str(Path(save_dir) / Path(p).stem) + f'_{i}_{names[c]}.jpg'
                        cv2.imwrite(save_file, crop)
            # Print results
            print(f'{s}Done. ({t3 - t1:.3f}s)')
            # Stream results
            if view_img:
                cv2.imshow(p, im0)
                if cv2.waitKey(1) == ord('q'):  # q to quit
                    raise StopIteration

这部分代码处理摄像头输入并进行推理。

5. 将提取出的代码保存到一个新文件中，例如 camera_detect.py。

现在您可以使用 camera_detect.py 进行摄像头推理。请确保在运行代码之前正确设置 YOLOv5 模型、类别和权重路径。