[Feature] Add a feature for the video demo (#392)

* Create demo.mp4

* Create video_demo.py

* Update large_image_demo.py

demo/large_image_demo.py

@@ -2,12 +2,12 @@
 """Perform MMYOLO inference on large images (as satellite imagery) as:
 
 ```shell
-wget -P checkpoint https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth syncbn_fast_8xb16-300e_coco/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth   syncbn_fast_8xb16-300e_coco/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth  # noqa: E501, E261.
+wget -P checkpoint https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth # noqa: E501, E261.
 
 python demo/large_image_demo.py \
     demo/large_image.jpg \
-    configs/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py \
-    checkpoint/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth
+    configs/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py \
+    checkpoint/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth
 ```
 """
 

demo/video_demo.py

@@ -0,0 +1,100 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""Perform MMYOLO inference on a video as:
+
+```shell
+wget -P checkpoint https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth # noqa: E501, E261.
+
+python demo/video_demo.py \
+    demo/video_demo.mp4 \
+    configs/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py \
+    checkpoint/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth \
+    --out demo_result.mp4
+```
+"""
+import argparse
+
+import cv2
+import mmcv
+from mmcv.transforms import Compose
+from mmdet.apis import inference_detector, init_detector
+from mmengine.utils import track_iter_progress
+
+from mmyolo.registry import VISUALIZERS
+from mmyolo.utils import register_all_modules
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='MMYOLO video demo')
+    parser.add_argument('video', help='Video file')
+    parser.add_argument('config', help='Config file')
+    parser.add_argument('checkpoint', help='Checkpoint file')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+    parser.add_argument(
+        '--score-thr', type=float, default=0.3, help='Bbox score threshold')
+    parser.add_argument('--out', type=str, help='Output video file')
+    parser.add_argument('--show', action='store_true', help='Show video')
+    parser.add_argument(
+        '--wait-time',
+        type=float,
+        default=1,
+        help='The interval of show (s), 0 is block')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    assert args.out or args.show, \
+        ('Please specify at least one operation (save/show the '
+         'video) with the argument "--out" or "--show"')
+
+    # register all modules in mmdet into the registries
+    register_all_modules()
+
+    # build the model from a config file and a checkpoint file
+    model = init_detector(args.config, args.checkpoint, device=args.device)
+
+    # build test pipeline
+    model.cfg.test_dataloader.dataset.pipeline[
+        0].type = 'mmdet.LoadImageFromNDArray'
+    test_pipeline = Compose(model.cfg.test_dataloader.dataset.pipeline)
+
+    # init visualizer
+    visualizer = VISUALIZERS.build(model.cfg.visualizer)
+    # the dataset_meta is loaded from the checkpoint and
+    # then pass to the model in init_detector
+    visualizer.dataset_meta = model.dataset_meta
+
+    video_reader = mmcv.VideoReader(args.video)
+    video_writer = None
+    if args.out:
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        video_writer = cv2.VideoWriter(
+            args.out, fourcc, video_reader.fps,
+            (video_reader.width, video_reader.height))
+
+    for frame in track_iter_progress(video_reader):
+        result = inference_detector(model, frame, test_pipeline=test_pipeline)
+        visualizer.add_datasample(
+            name='video',
+            image=frame,
+            data_sample=result,
+            draw_gt=False,
+            show=False,
+            pred_score_thr=args.score_thr)
+        frame = visualizer.get_image()
+
+        if args.show:
+            cv2.namedWindow('video', 0)
+            mmcv.imshow(frame, 'video', args.wait_time)
+        if args.out:
+            video_writer.write(frame)
+
+    if video_writer:
+        video_writer.release()
+    cv2.destroyAllWindows()
+
+
+if __name__ == '__main__':
+    main()