如何修改yolov5-7.0中detect.py源码，将未识别出来的图片保存到新文件夹

要修改yolov5-7.0中detect.py源码，将未识别出来的图片保存到新文件夹，可以按照以下步骤进行操作：

1. 在detect.py源码中找到以下代码段：

# Process detections
    for i, det in enumerate(detections):  # detections per image
        if webcam:  # batch_size >= 1
            p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()
        else:
            p, s, im0 = path, '', im0s

        gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
        if len(det):
            # Rescale boxes from img_size to im0 size
            det[:, :4] = scale_coords(img_size, 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' * (n > 1)}, "  # add to string

            # Write results
            for *xyxy, conf, cls in det:
                if save_img or view_img:  # 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)
        print(f'{s}Done. ({t2 - t1:.3f}s)')

        # Stream results
        if view_img:
            cv2.imshow(str(p), im0)
            cv2.waitKey(1)  # 1 millisecond

        # Save results (image with detections)
        if save_img:
            if dataset.mode == 'images':
                cv2.imwrite(save_path / p, im0)
            else:  # 'video'
                if vid_path != save_path:  # new video
                    vid_path = save_path / Path(p).stem + suffix
                    if vid_writer is not None:
                        vid_writer.release()  # release previous video writer
                    fourcc = 'mp4v'  # output video codec
                    fps = vid_cap.get(cv2.CAP_PROP_FPS)
                    w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                    h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                    vid_writer = cv2.VideoWriter(str(vid_path), cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
                vid_writer.write(im0)

    if save_txt or save_img:
        print(f"Results saved to {colorstr('bold', save_path)}")
        if platform == 'darwin' and not update:  # MacOS
            os.system('open ' + str(save_path))

2. 在上述代码段中添加以下代码段，用于将未识别出来的图片保存到新文件夹中：

        # Save images without detections
        if save_img:
            if dataset.mode == 'images':
                if len(det) == 0:
                    cv2.imwrite(save_path_no_detection / p, im0)
            else:  # 'video'
                if len(det) == 0:
                    if vid_path_no_detection != save_path_no_detection:  # new video
                        vid_path_no_detection = save_path_no_detection / Path(p).stem + suffix
                        if vid_writer_no_detection is not None:
                            vid_writer_no_detection.release()  # release previous video writer
                        fourcc = 'mp4v'  # output video codec
                        fps = vid_cap.get(cv2.CAP_PROP_FPS)
                        w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                        h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                        vid_writer_no_detection = cv2.VideoWriter(str(vid_path_no_detection), cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
                    vid_writer_no_detection.write(im0)

3. 保存detect.py源码并重新运行程序即可看到未识别出来的图片被保存到新文件夹中。