Super Saiyan Effects

We can apply fun effects on videos with pose estimation results, like Super Saiyan transformation.

https://user-images.githubusercontent.com/11788150/150138076-2192079f-068a-4d43-bf27-2f1fd708cabc.mp4

Instruction

Get started

Launch the demo from the mmpose root directory:

python tools/webcam/run_webcam.py --config tools/webcam/configs/supersaiyan/saiyan.py

Hotkeys

Hotkey	Function
s	Toggle the Super Saiyan effect on/off.
h	Show help information.
m	Show the monitoring information.
q	Exit.

Note that the demo will automatically save the output video into a file record.mp4.

Configuration

Choose a detection model

Users can choose detection models from the MMDetection Model Zoo. Just set the model_config and model_checkpoint in the detector node accordingly, and the model will be automatically downloaded and loaded.

# 'DetectorNode':
# This node performs object detection from the frame image using an
# MMDetection model.
dict(
    type='DetectorNode',
    name='Detector',
    model_config='demo/mmdetection_cfg/mask_rcnn_r50_fpn_2x_coco.py',
    model_checkpoint='https://download.openmmlab.com/'
    'mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/'
    'mask_rcnn_r50_fpn_2x_coco_bbox_mAP-0.392'
    '__segm_mAP-0.354_20200505_003907-3e542a40.pth',
    input_buffer='_input_',  # `_input_` is a runner-reserved buffer
    output_buffer='det_result'),

Choose a or more pose models

In this demo we use two top-down pose estimation models for humans and animals respectively. Users can choose models from the MMPose Model Zoo. To apply different pose models on different instance types, you can add multiple pose estimator nodes with cls_names set accordingly.

# 'TopDownPoseEstimatorNode':
# This node performs keypoint detection from the frame image using an
# MMPose top-down model. Detection results is needed.
dict(
    type='TopDownPoseEstimatorNode',
    name='Human Pose Estimator',
    model_config='configs/wholebody/2d_kpt_sview_rgb_img/'
    'topdown_heatmap/coco-wholebody/'
    'vipnas_mbv3_coco_wholebody_256x192_dark.py',
    model_checkpoint='https://openmmlab-share.oss-cn-hangz'
    'hou.aliyuncs.com/mmpose/top_down/vipnas/vipnas_mbv3_co'
    'co_wholebody_256x192_dark-e2158108_20211205.pth',
    cls_names=['person'],
    input_buffer='det_result',
    output_buffer='human_pose')

Run the demo without GPU

If you don't have GPU and CUDA in your device, the demo can run with only CPU by setting device='cpu' in all model nodes. For example:

dict(
    type='DetectorNode',
    name='Detector',
    model_config='demo/mmdetection_cfg/mask_rcnn_r50_fpn_2x_coco.py',
    model_checkpoint='https://download.openmmlab.com/'
    'mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/'
    'mask_rcnn_r50_fpn_2x_coco_bbox_mAP-0.392'
    '__segm_mAP-0.354_20200505_003907-3e542a40.pth',
    device='cpu',
    input_buffer='_input_',  # `_input_` is a runner-reserved buffer
    output_buffer='det_result'),

Debug webcam and display

You can launch the webcam runner with a debug config:

python tools/webcam/run_webcam.py --config tools/webcam/configs/examples/test_camera.py