Add mutil-stream saving feature(ultralytics#3864)

detect.py

@@ -76,14 +76,16 @@ def run(weights='yolov5s.pt',  # model.pt path(s)
         modelc = load_classifier(name='resnet50', n=2)  # initialize
         modelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval()
 
-    # Set Dataloader
-    vid_path, vid_writer = None, None
+    # Dataloader
     if webcam:
         view_img = check_imshow()
         cudnn.benchmark = True  # set True to speed up constant image size inference
         dataset = LoadStreams(source, img_size=imgsz, stride=stride)
+        bs = len(dataset)  # batch_size
     else:
         dataset = LoadImages(source, img_size=imgsz, stride=stride)
+        bs = 1  # batch_size
+    vid_path, vid_writer = [None] * bs, [None] * bs
 
     # Run inference
     if device.type != 'cpu':
@@ -158,19 +160,19 @@ def run(weights='yolov5s.pt',  # model.pt path(s)
                 if dataset.mode == 'image':
                     cv2.imwrite(save_path, im0)
                 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_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 += '.mp4'
-                        vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
-                    vid_writer.write(im0)
+                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
+                    vid_writer[i].write(im0)
 
     if save_txt or save_img:
         s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''