代码解释 # Save results (image with detections) if save_img: if dataset.mode == 'image': cv2.imwrite(save_path, im0) print(f" The image with the result is saved in: {save_path}") else: # 'video' or 'stream' if vid_path != save_path: # new video vid_path = save_path if isinstance(vid_writer, cv2.VideoWriter): vid_writer.release() # release previous video writer if vid_cap: # video 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)) else: # stream fps, w, h = 30, im0.shape[1], im0.shape[0] save_path += '.mp4' vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) vid_writer.write(im0)

这段代码是用来保存带有检测结果的图像或视频的。如果`save_img`为真，那么就会保存一张带有检测结果的图像。如果数据集模式是'image'，那么就会将这个图像保存到指定的路径`save_path`中。如果数据集模式是'video'或者'stream'，那么就会将这个带有检测结果的图像写入到视频文件中。如果`vid_path`与`save_path`不同，说明需要保存一个新的视频文件，就需要释放之前的视频写入器`vid_writer`。如果当前正在处理的是视频，就会获取视频的帧率、宽度和高度；如果是实时流，就会默认使用30帧每秒、当前图像的宽度和高度，并将文件类型设置为.mp4。最后，将带有检测结果的图像写入到视频文件`vid_writer`中。

相关问题

# Save results (image with detections) if save_img: if dataset.mode == 'image': cv2.imwrite(save_path, im0) else: # 'video' or 'stream' if vid_path[i] != save_path: # new video vid_path[i] = save_path if isinstance(vid_writer[i], cv2.VideoWriter): vid_writer[i].release() # release previous video writer if vid_cap: # video 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)) else: # stream fps, w, h = 30, im0.shape[1], im0.shape[0] save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) vid_writer[i].write(im0)

这段代码是YOLOv5中的一部分，用于在检测完成后保存检测结果。如果设置了保存图像的选项，则会将带有检测结果的图像保存到指定的目录中。如果数据集模式为'image'，则将保存单个图像，否则将保存视频或流。如果是保存视频，则会检查是否有新的视频需要保存。如果有，会释放之前的视频写入器，然后创建一个新的视频写入器。如果是保存流，则会将帧率、宽度和高度设为默认值。然后，将保存路径更改为具有'.mp4'后缀的路径，以强制结果视频使用MP4格式。最后，通过视频写入器将带有检测结果的图像写入到结果视频中。

代码解释# Process detections for i, det in enumerate(pred): # detections per image if webcam: # batch_size >= 1 p, s, im0 = path[i], '%g: ' % i, im0s[i].copy() else: p, s, im0 = path, '', im0s save_path = str(Path(out) / Path(p).name) s += '%gx%g ' % img.shape[2:] # print string 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 += '%g %ss, ' % (n, names[int(c)]) # 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(save_path[:save_path.rfind('.')] + '.txt', 'a') as file: file.write(('%g ' * 5 + '\n') % (cls, *xywh)) # label format if save_img or view_img: # Add bbox to image label = '%s %.2f' % (names[int(cls)], conf) if label is not None: if (label.split())[0] == 'person': people_coords.append(xyxy) # plot_one_box(xyxy, im0, line_thickness=3) plot_dots_on_people(xyxy, im0) # Plot lines connecting people distancing(people_coords, im0, dist_thres_lim=(100, 150)) # Print time (inference + NMS) print('%sDone. (%.3fs)' % (s, t2 - t1)) # Stream results if 1: ui.showimg(im0) if cv2.waitKey(1) == ord('q'): # q to quit raise StopIteration # Save results (image with detections) if save_img: if dataset.mode == 'images': cv2.imwrite(save_path, im0) else: if vid_path != save_path: # new video vid_path = save_path if isinstance(vid_writer, cv2.VideoWriter): vid_writer.release() # release previous video writer 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(save_path, cv2.VideoWriter_fourcc(*opt.fourcc), fps, (w, h)) vid_writer.write(im0)

这段代码是目标检测算法的输出结果处理部分。主要包括以下几个步骤：

1. 对每张图片的检测结果进行处理，包括将检测框从输入图像的尺寸缩放到输出图像的尺寸，并将结果写入文本文件中。

2. 对每个类别的检测结果统计数量，并将数量和类别名称添加到输出字符串中。

3. 对每个检测到的目标绘制边界框，并在边界框上标注类别和置信度。

4. 如果检测到的目标是人，则将其坐标保存在列表中，并在图像上绘制点和连线进行社交距离监测。

5. 将处理后的图像展示出来，并将图像保存到文件中。