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Demo

Outline

Modify configs through script arguments

When running demos using our provided scripts, you may specify --cfg-options to in-place modify the config.

  • Update config keys of dict.

The config options can be specified following the order of the dict keys in the original config.
For example, --cfg-options model.backbone.norm_eval=False changes the all BN modules in model backbones to train mode.

  • Update keys inside a list of configs.

Some config dicts are composed as a list in your config. For example, the training pipeline data.train.pipeline is normally a list
e.g. [dict(type='SampleFrames'), ...]. If you want to change 'SampleFrames' to 'DenseSampleFrames' in the pipeline,
you may specify --cfg-options data.train.pipeline.0.type=DenseSampleFrames.

  • Update values of list/tuples.

If the value to be updated is a list or a tuple. For example, the config file normally sets workflow=[('train', 1)]. If you want to
change this key, you may specify --cfg-options workflow="[(train,1),(val,1)]". Note that the quotation mark \" is necessary to
support list/tuple data types, and that NO white space is allowed inside the quotation marks in the specified value.

Video demo

We provide a demo script to predict the recognition result using a single video. In order to get predict results in range [0, 1], make sure to set model['test_cfg'] = dict(average_clips='prob') in config file.

python demo/demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${VIDEO_FILE} {LABEL_FILE} [--use-frames] \
    [--device ${DEVICE_TYPE}] [--fps {FPS}] [--font-scale {FONT_SCALE}] [--font-color {FONT_COLOR}] \
    [--target-resolution ${TARGET_RESOLUTION}] [--resize-algorithm {RESIZE_ALGORITHM}] [--out-filename {OUT_FILE}]

Optional arguments:

  • --use-frames: If specified, the demo will take rawframes as input. Otherwise, it will take a video as input.
  • DEVICE_TYPE: Type of device to run the demo. Allowed values are cuda device like cuda:0 or cpu. If not specified, it will be set to cuda:0.
  • FPS: FPS value of the output video when using rawframes as input. If not specified, it will be set to 30.
  • FONT_SCALE: Font scale of the label added in the video. If not specified, it will be 0.5.
  • FONT_COLOR: Font color of the label added in the video. If not specified, it will be white.
  • TARGET_RESOLUTION: Resolution(desired_width, desired_height) for resizing the frames before output when using a video as input. If not specified, it will be None and the frames are resized by keeping the existing aspect ratio.
  • RESIZE_ALGORITHM: Resize algorithm used for resizing. If not specified, it will be set to bicubic.
  • OUT_FILE: Path to the output file which can be a video format or gif format. If not specified, it will be set to None and does not generate the output file.

Examples:

Assume that you are located at $MMACTION2 and have already downloaded the checkpoints to the directory checkpoints/,
or use checkpoint url from configs/ to directly load corresponding checkpoint, which will be automatically saved in $HOME/.cache/torch/checkpoints.

  1. Recognize a video file as input by using a TSN model on cuda by default.

    ```shell

    The demo.mp4 and label_map_k400.txt are both from Kinetics-400

    python demo/demo.py configs/recognition/tsn/tsn_r50_video_inference_1x1x3_100e_kinetics400_rgb.py \
    checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth \
    demo/demo.mp4 tools/data/kinetics/label_map_k400.txt
    ```

  2. Recognize a video file as input by using a TSN model on cuda by default, loading checkpoint from url.

    ```shell

    The demo.mp4 and label_map_k400.txt are both from Kinetics-400

    python demo/demo.py configs/recognition/tsn/tsn_r50_video_inference_1x1x3_100e_kinetics400_rgb.py \
    https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth \
    demo/demo.mp4 tools/data/kinetics/label_map_k400.txt
    ```

  3. Recognize a list of rawframes as input by using a TSN model on cpu.

    shell python demo/demo.py configs/recognition/tsn/tsn_r50_inference_1x1x3_100e_kinetics400_rgb.py \ checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth \ PATH_TO_FRAMES/ LABEL_FILE --use-frames --device cpu

  4. Recognize a video file as input by using a TSN model and then generate an mp4 file.

    ```shell

    The demo.mp4 and label_map_k400.txt are both from Kinetics-400

    python demo/demo.py configs/recognition/tsn/tsn_r50_video_inference_1x1x3_100e_kinetics400_rgb.py \
    checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth \
    demo/demo.mp4 tools/data/kinetics/label_map_k400.txt --out-filename demo/demo_out.mp4
    ```

  5. Recognize a list of rawframes as input by using a TSN model and then generate a gif file.

    shell python demo/demo.py configs/recognition/tsn/tsn_r50_inference_1x1x3_100e_kinetics400_rgb.py \ checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth \ PATH_TO_FRAMES/ LABEL_FILE --use-frames --out-filename demo/demo_out.gif

  6. Recognize a video file as input by using a TSN model, then generate an mp4 file with a given resolution and resize algorithm.

    ```shell

    The demo.mp4 and label_map_k400.txt are both from Kinetics-400

    python demo/demo.py configs/recognition/tsn/tsn_r50_video_inference_1x1x3_100e_kinetics400_rgb.py \
    checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth \
    demo/demo.mp4 tools/data/kinetics/label_map_k400.txt --target-resolution 340 256 --resize-algorithm bilinear \
    --out-filename demo/demo_out.mp4
    ```

    ```shell

    The demo.mp4 and label_map_k400.txt are both from Kinetics-400

    If either dimension is set to -1, the frames are resized by keeping the existing aspect ratio

    For --target-resolution 170 -1, original resolution (340, 256) -> target resolution (170, 128)

    python demo/demo.py configs/recognition/tsn/tsn_r50_video_inference_1x1x3_100e_kinetics400_rgb.py \
    checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth \
    demo/demo.mp4 tools/data/kinetics/label_map_k400.txt --target-resolution 170 -1 --resize-algorithm bilinear \
    --out-filename demo/demo_out.mp4
    ```

  7. Recognize a video file as input by using a TSN model, then generate an mp4 file with a label in a red color and fontscale 1.

    ```shell

    The demo.mp4 and label_map_k400.txt are both from Kinetics-400

    python demo/demo.py configs/recognition/tsn/tsn_r50_video_inference_1x1x3_100e_kinetics400_rgb.py \
    checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth \
    demo/demo.mp4 tools/data/kinetics/label_map_k400.txt --font-scale 1 --font-color red \
    --out-filename demo/demo_out.mp4
    ```

  8. Recognize a list of rawframes as input by using a TSN model and then generate an mp4 file with 24 fps.

    shell python demo/demo.py configs/recognition/tsn/tsn_r50_inference_1x1x3_100e_kinetics400_rgb.py \ checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth \ PATH_TO_FRAMES/ LABEL_FILE --use-frames --fps 24 --out-filename demo/demo_out.gif

SpatioTemporal Action Detection Video Demo

We provide a demo script to predict the SpatioTemporal Action Detection result using a single video.

python demo/demo_spatiotemporal_det.py --video ${VIDEO_FILE} \
    [--config ${SPATIOTEMPORAL_ACTION_DETECTION_CONFIG_FILE}] \
    [--checkpoint ${SPATIOTEMPORAL_ACTION_DETECTION_CHECKPOINT}] \
    [--det-config ${HUMAN_DETECTION_CONFIG_FILE}] \
    [--det-checkpoint ${HUMAN_DETECTION_CHECKPOINT}] \
    [--det-score-thr ${HUMAN_DETECTION_SCORE_THRESHOLD}] \
    [--action-score-thr ${ACTION_DETECTION_SCORE_THRESHOLD}] \
    [--label-map ${LABEL_MAP}] \
    [--device ${DEVICE}] \
    [--out-filename ${OUTPUT_FILENAME}] \
    [--predict-stepsize ${PREDICT_STEPSIZE}] \
    [--output-stepsize ${OUTPUT_STEPSIZE}] \
    [--output-fps ${OUTPUT_FPS}]

Optional arguments:

  • SPATIOTEMPORAL_ACTION_DETECTION_CONFIG_FILE: The spatiotemporal action detection config file path.
  • SPATIOTEMPORAL_ACTION_DETECTION_CHECKPOINT: The spatiotemporal action detection checkpoint URL.
  • HUMAN_DETECTION_CONFIG_FILE: The human detection config file path.
  • HUMAN_DETECTION_CHECKPOINT: The human detection checkpoint URL.
  • HUMAN_DETECTION_SCORE_THRE: The score threshold for human detection. Default: 0.9.
  • ACTION_DETECTION_SCORE_THRESHOLD: The score threshold for action detection. Default: 0.5.
  • LABEL_MAP: The label map used. Default: tools/data/ava/label_map.txt.
  • DEVICE: Type of device to run the demo. Allowed values are cuda device like cuda:0 or cpu. Default: cuda:0.
  • OUTPUT_FILENAME: Path to the output file which is a video format. Default: demo/stdet_demo.mp4.
  • PREDICT_STEPSIZE: Make a prediction per N frames. Default: 8.
  • OUTPUT_STEPSIZE: Output 1 frame per N frames in the input video. Note that PREDICT_STEPSIZE % OUTPUT_STEPSIZE == 0. Default: 4.
  • OUTPUT_FPS: The FPS of demo video output. Default: 6.

Examples:

Assume that you are located at $MMACTION2 .

  1. Use the Faster RCNN as the human detector, SlowOnly-8x8-R101 as the action detector. Making predictions per 8 frames, and output 1 frame per 4 frames to the output video. The FPS of the output video is 4.
python demo/demo_spatiotemporal_det.py --video demo/demo.mp4 \
    --config configs/detection/ava/slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb.py \
    --checkpoint https://download.openmmlab.com/mmaction/detection/ava/slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb/slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb_20201217-16378594.pth \
    --det-config demo/faster_rcnn_r50_fpn_2x_coco.py \
    --det-checkpoint http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth \
    --det-score-thr 0.9 \
    --action-score-thr 0.5 \
    --label-map tools/data/ava/label_map.txt \
    --predict-stepsize 8 \
    --output-stepsize 4 \
    --output-fps 6

Video GradCAM Demo

We provide a demo script to visualize GradCAM results using a single video.

python demo/demo_gradcam.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${VIDEO_FILE} [--use-frames] \
    [--device ${DEVICE_TYPE}] [--target-layer-name ${TARGET_LAYER_NAME}] [--fps {FPS}] \
    [--target-resolution ${TARGET_RESOLUTION}] [--resize-algorithm {RESIZE_ALGORITHM}] [--out-filename {OUT_FILE}]
  • --use-frames: If specified, the demo will take rawframes as input. Otherwise, it will take a video as input.
  • DEVICE_TYPE: Type of device to run the demo. Allowed values are cuda device like cuda:0 or cpu. If not specified, it will be set to cuda:0.
  • FPS: FPS value of the output video when using rawframes as input. If not specified, it will be set to 30.
  • OUT_FILE: Path to the output file which can be a video format or gif format. If not specified, it will be set to None and does not generate the output file.
  • TARGET_LAYER_NAME: Layer name to generate GradCAM localization map.
  • TARGET_RESOLUTION: Resolution(desired_width, desired_height) for resizing the frames before output when using a video as input. If not specified, it will be None and the frames are resized by keeping the existing aspect ratio.
  • RESIZE_ALGORITHM: Resize algorithm used for resizing. If not specified, it will be set to bilinear.

Examples:

Assume that you are located at $MMACTION2 and have already downloaded the checkpoints to the directory checkpoints/,
or use checkpoint url from configs/ to directly load corresponding checkpoint, which will be automatically saved in $HOME/.cache/torch/checkpoints.

  1. Get GradCAM results of a I3D model, using a video file as input and then generate an gif file with 10 fps.

    shell python demo/demo_gradcam.py configs/recognition/i3d/i3d_r50_video_inference_32x2x1_100e_kinetics400_rgb.py \ checkpoints/i3d_r50_video_32x2x1_100e_kinetics400_rgb_20200826-e31c6f52.pth demo/demo.mp4 \ --target-layer-name backbone/layer4/1/relu --fps 10 \ --out-filename demo/demo_gradcam.gif

  2. Get GradCAM results of a TSM model, using a video file as input and then generate an gif file, loading checkpoint from url.

    shell python demo/demo_gradcam.py configs/recognition/tsm/tsm_r50_video_inference_1x1x8_100e_kinetics400_rgb.py \ https://download.openmmlab.com/mmaction/recognition/tsm/tsm_r50_video_1x1x8_100e_kinetics400_rgb/tsm_r50_video_1x1x8_100e_kinetics400_rgb_20200702-a77f4328.pth \ demo/demo.mp4 --target-layer-name backbone/layer4/1/relu --out-filename demo/demo_gradcam_tsm.gif

Webcam demo

We provide a demo script to implement real-time action recognition from web camera. In order to get predict results in range [0, 1], make sure to set model.['test_cfg'] = dict(average_clips='prob') in config file.

python demo/webcam_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${LABEL_FILE} \
    [--device ${DEVICE_TYPE}] [--camera-id ${CAMERA_ID}] [--threshold ${THRESHOLD}] \
    [--average-size ${AVERAGE_SIZE}] [--drawing-fps ${DRAWING_FPS}] [--inference-fps ${INFERENCE_FPS}]

Optional arguments:

  • DEVICE_TYPE: Type of device to run the demo. Allowed values are cuda device like cuda:0 or cpu. If not specified, it will be set to cuda:0.
  • CAMERA_ID: ID of camera device If not specified, it will be set to 0.
  • THRESHOLD: Threshold of prediction score for action recognition. Only label with score higher than the threshold will be shown. If not specified, it will be set to 0.
  • AVERAGE_SIZE: Number of latest clips to be averaged for prediction. If not specified, it will be set to 1.
  • DRAWING_FPS: Upper bound FPS value of the output drawing. If not specified, it will be set to 20.
  • INFERENCE_FPS: Upper bound FPS value of the output drawing. If not specified, it will be set to 4.

:::{note}
If your hardware is good enough, increasing the value of DRAWING_FPS and INFERENCE_FPS will get a better experience.
:::

Examples:

Assume that you are located at $MMACTION2 and have already downloaded the checkpoints to the directory checkpoints/,
or use checkpoint url from configs/ to directly load corresponding checkpoint, which will be automatically saved in $HOME/.cache/torch/checkpoints.

  1. Recognize the action from web camera as input by using a TSN model on cpu, averaging the score per 5 times
    and outputting result labels with score higher than 0.2.

    shell python demo/webcam_demo.py configs/recognition/tsn/tsn_r50_video_inference_1x1x3_100e_kinetics400_rgb.py \ checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth tools/data/kinetics/label_map_k400.txt --average-size 5 \ --threshold 0.2 --device cpu

  2. Recognize the action from web camera as input by using a TSN model on cpu, averaging the score per 5 times
    and outputting result labels with score higher than 0.2, loading checkpoint from url.

    shell python demo/webcam_demo.py configs/recognition/tsn/tsn_r50_video_inference_1x1x3_100e_kinetics400_rgb.py \ https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth \ tools/data/kinetics/label_map_k400.txt --average-size 5 --threshold 0.2 --device cpu

  3. Recognize the action from web camera as input by using a I3D model on gpu by default, averaging the score per 5 times
    and outputting result labels with score higher than 0.2.

    shell python demo/webcam_demo.py configs/recognition/i3d/i3d_r50_video_inference_32x2x1_100e_kinetics400_rgb.py \ checkpoints/i3d_r50_32x2x1_100e_kinetics400_rgb_20200614-c25ef9a4.pth tools/data/kinetics/label_map_k400.txt \ --average-size 5 --threshold 0.2

:::{note}
Considering the efficiency difference for users' hardware, Some modifications might be done to suit the case.
Users can change:

1). SampleFrames step (especially the number of clip_len and num_clips) of test_pipeline in the config file, like --cfg-options data.test.pipeline.0.num_clips=3.
2). Change to the suitable Crop methods like TenCrop, ThreeCrop, CenterCrop, etc. in test_pipeline of the config file, like --cfg-options data.test.pipeline.4.type=CenterCrop.
3). Change the number of --average-size. The smaller, the faster.
:::

Long video demo

We provide a demo script to predict different labels using a single long video. In order to get predict results in range [0, 1], make sure to set test_cfg = dict(average_clips='prob') in config file.

python demo/long_video_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${VIDEO_FILE} ${LABEL_FILE} \
    ${OUT_FILE} [--input-step ${INPUT_STEP}] [--device ${DEVICE_TYPE}] [--threshold ${THRESHOLD}]

Optional arguments:

  • OUT_FILE: Path to the output, either video or json file
  • INPUT_STEP: Input step for sampling frames, which can help to get more spare input. If not specified , it will be set to 1.
  • DEVICE_TYPE: Type of device to run the demo. Allowed values are cuda device like cuda:0 or cpu. If not specified, it will be set to cuda:0.
  • THRESHOLD: Threshold of prediction score for action recognition. Only label with score higher than the threshold will be shown. If not specified, it will be set to 0.01.
  • STRIDE: By default, the demo generates a prediction for each single frame, which might cost lots of time. To speed up, you can set the argument STRIDE and then the demo will generate a prediction every STRIDE x sample_length frames (sample_length indicates the size of temporal window from which you sample frames, which equals to clip_len x frame_interval). For example, if the sample_length is 64 frames and you set STRIDE to 0.5, predictions will be generated every 32 frames. If set as 0, predictions will be generated for each frame. The desired value of STRIDE is (0, 1], while it also works for STRIDE > 1 (the generated predictions will be too sparse). Default: 0.
  • LABEL_COLOR: Font Color of the labels in (B, G, R). Default is white, that is (256, 256, 256).
  • MSG_COLOR: Font Color of the messages in (B, G, R). Default is gray, that is (128, 128, 128).

Examples:

Assume that you are located at $MMACTION2 and have already downloaded the checkpoints to the directory checkpoints/,
or use checkpoint url from configs/ to directly load corresponding checkpoint, which will be automatically saved in $HOME/.cache/torch/checkpoints.

  1. Predict different labels in a long video by using a TSN model on cpu, with 3 frames for input steps (that is, random sample one from each 3 frames)
    and outputting result labels with score higher than 0.2.

    shell python demo/long_video_demo.py configs/recognition/tsn/tsn_r50_video_inference_1x1x3_100e_kinetics400_rgb.py \ checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth PATH_TO_LONG_VIDEO tools/data/kinetics/label_map_k400.txt PATH_TO_SAVED_VIDEO \ --input-step 3 --device cpu --threshold 0.2

  2. Predict different labels in a long video by using a TSN model on cpu, with 3 frames for input steps (that is, random sample one from each 3 frames)
    and outputting result labels with score higher than 0.2, loading checkpoint from url.

    shell python demo/long_video_demo.py configs/recognition/tsn/tsn_r50_video_inference_1x1x3_100e_kinetics400_rgb.py \ https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth \ PATH_TO_LONG_VIDEO tools/data/kinetics/label_map_k400.txt PATH_TO_SAVED_VIDEO --input-step 3 --device cpu --threshold 0.2

  3. Predict different labels in a long video from web by using a TSN model on cpu, with 3 frames for input steps (that is, random sample one from each 3 frames)
    and outputting result labels with score higher than 0.2, loading checkpoint from url.

    shell python demo/long_video_demo.py configs/recognition/tsn/tsn_r50_video_inference_1x1x3_100e_kinetics400_rgb.py \ https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth \ https://www.learningcontainer.com/wp-content/uploads/2020/05/sample-mp4-file.mp4 \ tools/data/kinetics/label_map_k400.txt PATH_TO_SAVED_VIDEO --input-step 3 --device cpu --threshold 0.2

  4. Predict different labels in a long video by using a I3D model on gpu, with input_step=1, threshold=0.01 as default and print the labels in cyan.

    shell python demo/long_video_demo.py configs/recognition/i3d/i3d_r50_video_inference_32x2x1_100e_kinetics400_rgb.py \ checkpoints/i3d_r50_256p_32x2x1_100e_kinetics400_rgb_20200801-7d9f44de.pth PATH_TO_LONG_VIDEO tools/data/kinetics/label_map_k400.txt PATH_TO_SAVED_VIDEO \ --label-color 255 255 0

  5. Predict different labels in a long video by using a I3D model on gpu and save the results as a json file

    shell python demo/long_video_demo.py configs/recognition/i3d/i3d_r50_video_inference_32x2x1_100e_kinetics400_rgb.py \ checkpoints/i3d_r50_256p_32x2x1_100e_kinetics400_rgb_20200801-7d9f44de.pth PATH_TO_LONG_VIDEO tools/data/kinetics/label_map_k400.txt ./results.json

SpatioTemporal Action Detection Webcam Demo

We provide a demo script to implement real-time spatio-temporal action detection from a web camera.

python demo/webcam_demo_spatiotemporal_det.py \
    [--config ${SPATIOTEMPORAL_ACTION_DETECTION_CONFIG_FILE}] \
    [--checkpoint ${SPATIOTEMPORAL_ACTION_DETECTION_CHECKPOINT}] \
    [--action-score-thr ${ACTION_DETECTION_SCORE_THRESHOLD}] \
    [--det-config ${HUMAN_DETECTION_CONFIG_FILE}] \
    [--det-checkpoint ${HUMAN_DETECTION_CHECKPOINT}] \
    [--det-score-thr ${HUMAN_DETECTION_SCORE_THRESHOLD}] \
    [--input-video] ${INPUT_VIDEO} \
    [--label-map ${LABEL_MAP}] \
    [--device ${DEVICE}] \
    [--output-fps ${OUTPUT_FPS}] \
    [--out-filename ${OUTPUT_FILENAME}] \
    [--show] \
    [--display-height] ${DISPLAY_HEIGHT} \
    [--display-width] ${DISPLAY_WIDTH} \
    [--predict-stepsize ${PREDICT_STEPSIZE}] \
    [--clip-vis-length] ${CLIP_VIS_LENGTH}

Optional arguments:

  • SPATIOTEMPORAL_ACTION_DETECTION_CONFIG_FILE: The spatiotemporal action detection config file path.
  • SPATIOTEMPORAL_ACTION_DETECTION_CHECKPOINT: The spatiotemporal action detection checkpoint path or URL.
  • ACTION_DETECTION_SCORE_THRESHOLD: The score threshold for action detection. Default: 0.4.
  • HUMAN_DETECTION_CONFIG_FILE: The human detection config file path.
  • HUMAN_DETECTION_CHECKPOINT: The human detection checkpoint URL.
  • HUMAN_DETECTION_SCORE_THRE: The score threshold for human detection. Default: 0.9.
  • INPUT_VIDEO: The webcam id or video path of the source. Default: 0.
  • LABEL_MAP: The label map used. Default: tools/data/ava/label_map.txt.
  • DEVICE: Type of device to run the demo. Allowed values are cuda device like cuda:0 or cpu. Default: cuda:0.
  • OUTPUT_FPS: The FPS of demo video output. Default: 15.
  • OUTPUT_FILENAME: Path to the output file which is a video format. Default: None.
  • --show: Whether to show predictions with cv2.imshow.
  • DISPLAY_HEIGHT: The height of the display frame. Default: 0.
  • DISPLAY_WIDTH: The width of the display frame. Default: 0. If DISPLAY_HEIGHT <= 0 and DISPLAY_WIDTH <= 0, the display frame and input video share the same shape.
  • PREDICT_STEPSIZE: Make a prediction per N frames. Default: 8.
  • CLIP_VIS_LENGTH: The number of the draw frames for each clip. In other words, for each clip, there are at most CLIP_VIS_LENGTH frames to be draw around the keyframe. DEFAULT: 8.

Tips to get a better experience for webcam demo:

  • How to choose --output-fps?

  • --output-fps should be almost equal to read thread fps.

  • Read thread fps is printed by logger in format DEBUG:__main__:Read Thread: {duration} ms, {fps} fps

  • How to choose --predict-stepsize?

  • It's related to how to choose human detector and spatio-temporval model.

  • Overall, the duration of read thread for each task should be greater equal to that of model inference.
  • The durations for read/inference are both printed by logger.
  • Larger --predict-stepsize leads to larger duration for read thread.
  • In order to fully take the advantage of computation resources, decrease the value of --predict-stepsize.

Examples:

Assume that you are located at $MMACTION2 .

  1. Use the Faster RCNN as the human detector, SlowOnly-8x8-R101 as the action detector. Making predictions per 40 frames, and FPS of the output is 20. Show predictions with cv2.imshow.
python demo/webcam_demo_spatiotemporal_det.py \
    --input-video 0 \
    --config configs/detection/ava/slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb.py \
    --checkpoint https://download.openmmlab.com/mmaction/detection/ava/slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb/slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb_20201217-16378594.pth \
    --det-config demo/faster_rcnn_r50_fpn_2x_coco.py \
    --det-checkpoint http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth \
    --det-score-thr 0.9 \
    --action-score-thr 0.5 \
    --label-map tools/data/ava/label_map.txt \
    --predict-stepsize 40 \
    --output-fps 20 \
    --show

Skeleton-based Action Recognition Demo

We provide a demo script to predict the skeleton-based action recognition result using a single video.

python demo/demo_skeleton.py ${VIDEO_FILE} ${OUT_FILENAME} \
    [--config ${SKELETON_BASED_ACTION_RECOGNITION_CONFIG_FILE}] \
    [--checkpoint ${SKELETON_BASED_ACTION_RECOGNITION_CHECKPOINT}] \
    [--det-config ${HUMAN_DETECTION_CONFIG_FILE}] \
    [--det-checkpoint ${HUMAN_DETECTION_CHECKPOINT}] \
    [--det-score-thr ${HUMAN_DETECTION_SCORE_THRESHOLD}] \
    [--pose-config ${HUMAN_POSE_ESTIMATION_CONFIG_FILE}] \
    [--pose-checkpoint ${HUMAN_POSE_ESTIMATION_CHECKPOINT}] \
    [--label-map ${LABEL_MAP}] \
    [--device ${DEVICE}] \
    [--short-side] ${SHORT_SIDE}

Optional arguments:

  • SKELETON_BASED_ACTION_RECOGNITION_CONFIG_FILE: The skeleton-based action recognition config file path.
  • SKELETON_BASED_ACTION_RECOGNITION_CHECKPOINT: The skeleton-based action recognition checkpoint path or URL.
  • HUMAN_DETECTION_CONFIG_FILE: The human detection config file path.
  • HUMAN_DETECTION_CHECKPOINT: The human detection checkpoint URL.
  • HUMAN_DETECTION_SCORE_THRE: The score threshold for human detection. Default: 0.9.
  • HUMAN_POSE_ESTIMATION_CONFIG_FILE: The human pose estimation config file path (trained on COCO-Keypoint).
  • HUMAN_POSE_ESTIMATION_CHECKPOINT: The human pose estimation checkpoint URL (trained on COCO-Keypoint).
  • LABEL_MAP: The label map used. Default: tools/data/ava/label_map.txt.
  • DEVICE: Type of device to run the demo. Allowed values are cuda device like cuda:0 or cpu. Default: cuda:0.
  • SHORT_SIDE: The short side used for frame extraction. Default: 480.

Examples:

Assume that you are located at $MMACTION2 .

  1. Use the Faster RCNN as the human detector, HRNetw32 as the pose estimator, PoseC3D-NTURGB+D-120-Xsub-keypoint as the skeleton-based action recognizer.
python demo/demo_skeleton.py demo/ntu_sample.avi demo/skeleton_demo.mp4 \
    --config configs/skeleton/posec3d/slowonly_r50_u48_240e_ntu120_xsub_keypoint.py \
    --checkpoint https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_r50_u48_240e_ntu120_xsub_keypoint/slowonly_r50_u48_240e_ntu120_xsub_keypoint-6736b03f.pth \
    --det-config demo/faster_rcnn_r50_fpn_2x_coco.py \
    --det-checkpoint http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth \
    --det-score-thr 0.9 \
    --pose-config demo/hrnet_w32_coco_256x192.py \
    --pose-checkpoint https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth \
    --label-map tools/data/skeleton/label_map_ntu120.txt
  1. Use the Faster RCNN as the human detector, HRNetw32 as the pose estimator, STGCN-NTURGB+D-60-Xsub-keypoint as the skeleton-based action recognizer.
python demo/demo_skeleton.py demo/ntu_sample.avi demo/skeleton_demo.mp4 \
    --config configs/skeleton/stgcn/stgcn_80e_ntu60_xsub_keypoint.py \
    --checkpoint https://download.openmmlab.com/mmaction/skeleton/stgcn/stgcn_80e_ntu60_xsub_keypoint/stgcn_80e_ntu60_xsub_keypoint-e7bb9653.pth \
    --det-config demo/faster_rcnn_r50_fpn_2x_coco.py \
    --det-checkpoint http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth \
    --det-score-thr 0.9 \
    --pose-config demo/hrnet_w32_coco_256x192.py \
    --pose-checkpoint https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth \
    --label-map tools/data/skeleton/label_map_ntu120.txt

Video Structuralize Demo

We provide a demo script to to predict the skeleton-based and rgb-based action recognition and spatio-temporal action detection result using a single video.

python demo/demo_video_structuralize.py
    [--rgb-stdet-config ${RGB_BASED_SPATIO_TEMPORAL_ACTION_DETECTION_CONFIG_FILE}] \
    [--rgb-stdet-checkpoint ${RGB_BASED_SPATIO_TEMPORAL_ACTION_DETECTION_CHECKPOINT}] \
    [--skeleton-stdet-checkpoint ${SKELETON_BASED_SPATIO_TEMPORAL_ACTION_DETECTION_CHECKPOINT}] \
    [--det-config ${HUMAN_DETECTION_CONFIG_FILE}] \
    [--det-checkpoint ${HUMAN_DETECTION_CHECKPOINT}] \
    [--pose-config ${HUMAN_POSE_ESTIMATION_CONFIG_FILE}] \
    [--pose-checkpoint ${HUMAN_POSE_ESTIMATION_CHECKPOINT}] \
    [--skeleton-config ${SKELETON_BASED_ACTION_RECOGNITION_CONFIG_FILE}] \
    [--skeleton-checkpoint ${SKELETON_BASED_ACTION_RECOGNITION_CHECKPOINT}] \
    [--rgb-config ${RGB_BASED_ACTION_RECOGNITION_CONFIG_FILE}] \
    [--rgb-checkpoint ${RGB_BASED_ACTION_RECOGNITION_CHECKPOINT}] \
    [--use-skeleton-stdet ${USE_SKELETON_BASED_SPATIO_TEMPORAL_DETECTION_METHOD}] \
    [--use-skeleton-recog ${USE_SKELETON_BASED_ACTION_RECOGNITION_METHOD}] \
    [--det-score-thr ${HUMAN_DETECTION_SCORE_THRE}] \
    [--action-score-thr ${ACTION_DETECTION_SCORE_THRE}] \
    [--video ${VIDEO_FILE}] \
    [--label-map-stdet ${LABEL_MAP_FOR_SPATIO_TEMPORAL_ACTION_DETECTION}] \
    [--device ${DEVICE}] \
    [--out-filename ${OUTPUT_FILENAME}] \
    [--predict-stepsize ${PREDICT_STEPSIZE}] \
    [--output-stepsize ${OUTPU_STEPSIZE}] \
    [--output-fps ${OUTPUT_FPS}] \
    [--cfg-options]

Optional arguments:

  • RGB_BASED_SPATIO_TEMPORAL_ACTION_DETECTION_CONFIG_FILE: The rgb-based spatio temoral action detection config file path.
  • RGB_BASED_SPATIO_TEMPORAL_ACTION_DETECTION_CHECKPOINT: The rgb-based spatio temoral action detection checkpoint path or URL.
  • SKELETON_BASED_SPATIO_TEMPORAL_ACTION_DETECTION_CHECKPOINT: The skeleton-based spatio temoral action detection checkpoint path or URL.
  • HUMAN_DETECTION_CONFIG_FILE: The human detection config file path.
  • HUMAN_DETECTION_CHECKPOINT: The human detection checkpoint URL.
  • HUMAN_POSE_ESTIMATION_CONFIG_FILE: The human pose estimation config file path (trained on COCO-Keypoint).
  • HUMAN_POSE_ESTIMATION_CHECKPOINT: The human pose estimation checkpoint URL (trained on COCO-Keypoint).
  • SKELETON_BASED_ACTION_RECOGNITION_CONFIG_FILE: The skeleton-based action recognition config file path.
  • SKELETON_BASED_ACTION_RECOGNITION_CHECKPOINT: The skeleton-based action recognition checkpoint path or URL.
  • RGB_BASED_ACTION_RECOGNITION_CONFIG_FILE: The rgb-based action recognition config file path.
  • RGB_BASED_ACTION_RECOGNITION_CHECKPOINT: The rgb-based action recognition checkpoint path or URL.
  • USE_SKELETON_BASED_SPATIO_TEMPORAL_DETECTION_METHOD: Use skeleton-based spatio temporal action detection method.
  • USE_SKELETON_BASED_ACTION_RECOGNITION_METHOD: Use skeleton-based action recognition method.
  • HUMAN_DETECTION_SCORE_THRE: The score threshold for human detection. Default: 0.9.
  • ACTION_DETECTION_SCORE_THRE: The score threshold for action detection. Default: 0.4.
  • LABEL_MAP_FOR_SPATIO_TEMPORAL_ACTION_DETECTION: The label map for spatio temporal action detection used. Default: tools/data/ava/label_map.txt.
  • LABEL_MAP: The label map for action recognition. Default: tools/data/kinetics/label_map_k400.txt.
  • DEVICE: Type of device to run the demo. Allowed values are cuda device like cuda:0 or cpu. Default: cuda:0.
  • OUTPUT_FILENAME: Path to the output file which is a video format. Default: demo/test_stdet_recognition_output.mp4.
  • PREDICT_STEPSIZE: Make a prediction per N frames. Default: 8.
  • OUTPUT_STEPSIZE: Output 1 frame per N frames in the input video. Note that PREDICT_STEPSIZE % OUTPUT_STEPSIZE == 0. Default: 1.
  • OUTPUT_FPS: The FPS of demo video output. Default: 24.

Examples:

Assume that you are located at $MMACTION2 .

  1. Use the Faster RCNN as the human detector, HRNetw32 as the pose estimator, PoseC3D as the skeleton-based action recognizer and the skeleton-based spatio temporal action detector. Making action detection predictions per 8 frames, and output 1 frame per 1 frame to the output video. The FPS of the output video is 24.
python demo/demo_video_structuralize.py
    --skeleton-stdet-checkpoint https://download.openmmlab.com/mmaction/skeleton/posec3d/posec3d_ava.pth \
    --det-config demo/faster_rcnn_r50_fpn_2x_coco.py \
    --det-checkpoint http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth \
    --pose-config demo/hrnet_w32_coco_256x192.py
    --pose-checkpoint https://download.openmmlab.com/mmpose/top_down/hrnet/
    hrnet_w32_coco_256x192-c78dce93_20200708.pth \
    --skeleton-config configs/skeleton/posec3d/slowonly_r50_u48_240e_ntu120_xsub_keypoint.py \
    --skeleton-checkpoint https://download.openmmlab.com/mmaction/skeleton/posec3d/
    posec3d_k400.pth \
    --use-skeleton-stdet \
    --use-skeleton-recog \
    --label-map-stdet tools/data/ava/label_map.txt \
    --label-map tools/data/kinetics/label_map_k400.txt
  1. Use the Faster RCNN as the human detector, TSN-R50-1x1x3 as the rgb-based action recognizer, SlowOnly-8x8-R101 as the rgb-based spatio temporal action detector. Making action detection predictions per 8 frames, and output 1 frame per 1 frame to the output video. The FPS of the output video is 24.
python demo/demo_video_structuralize.py
    --rgb-stdet-config configs/detection/ava/slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb.py \
    --rgb-stdet-checkpoint  https://download.openmmlab.com/mmaction/detection/ava/slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb/slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb_20201217-16378594.pth \
    --det-config demo/faster_rcnn_r50_fpn_2x_coco.py \
    --det-checkpoint http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth \
    --rgb-config configs/recognition/tsn/
    tsn_r50_video_inference_1x1x3_100e_kinetics400_rgb.py \
    --rgb-checkpoint https://download.openmmlab.com/mmaction/recognition/
    tsn/tsn_r50_1x1x3_100e_kinetics400_rgb/
    tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth \
    --label-map-stdet tools/data/ava/label_map.txt \
    --label-map tools/data/kinetics/label_map_k400.txt
  1. Use the Faster RCNN as the human detector, HRNetw32 as the pose estimator, PoseC3D as the skeleton-based action recognizer, SlowOnly-8x8-R101 as the rgb-based spatio temporal action detector. Making action detection predictions per 8 frames, and output 1 frame per 1 frame to the output video. The FPS of the output video is 24.
python demo/demo_video_structuralize.py
    --rgb-stdet-config configs/detection/ava/slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb.py \
    --rgb-stdet-checkpoint  https://download.openmmlab.com/mmaction/detection/ava/slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb/slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb_20201217-16378594.pth \
    --det-config demo/faster_rcnn_r50_fpn_2x_coco.py \
    --det-checkpoint http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth \
    --pose-config demo/hrnet_w32_coco_256x192.py
    --pose-checkpoint https://download.openmmlab.com/mmpose/top_down/hrnet/
    hrnet_w32_coco_256x192-c78dce93_20200708.pth \
    --skeleton-config configs/skeleton/posec3d/slowonly_r50_u48_240e_ntu120_xsub_keypoint.py \
    --skeleton-checkpoint https://download.openmmlab.com/mmaction/skeleton/posec3d/
    posec3d_k400.pth \
    --use-skeleton-recog \
    --label-map-stdet tools/data/ava/label_map.txt \
    --label-map tools/data/kinetics/label_map_k400.txt
  1. Use the Faster RCNN as the human detector, HRNetw32 as the pose estimator, TSN-R50-1x1x3 as the rgb-based action recognizer, PoseC3D as the skeleton-based spatio temporal action detector. Making action detection predictions per 8 frames, and output 1 frame per 1 frame to the output video. The FPS of the output video is 24.
python demo/demo_video_structuralize.py
    --skeleton-stdet-checkpoint https://download.openmmlab.com/mmaction/skeleton/posec3d/posec3d_ava.pth \
    --det-config demo/faster_rcnn_r50_fpn_2x_coco.py \
    --det-checkpoint http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth \
    --pose-config demo/hrnet_w32_coco_256x192.py
    --pose-checkpoint https://download.openmmlab.com/mmpose/top_down/hrnet/
    hrnet_w32_coco_256x192-c78dce93_20200708.pth \
    --skeleton-config configs/skeleton/posec3d/slowonly_r50_u48_240e_ntu120_xsub_keypoint.py \
    --rgb-config configs/recognition/tsn/
    tsn_r50_video_inference_1x1x3_100e_kinetics400_rgb.py \
    --rgb-checkpoint https://download.openmmlab.com/mmaction/recognition/
    tsn/tsn_r50_1x1x3_100e_kinetics400_rgb/
    tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth \
    --use-skeleton-stdet \
    --label-map-stdet tools/data/ava/label_map.txt \
    --label-map tools/data/kinetics/label_map_k400.txt