Advanced 3D Body Pose Analysis
Live Demo Note: Live demo uses pre-rendered data from sample images and videos
This solution is in two parts
TensorFlow with CUDA for GPU acceleration Note that models used here are S.O.T.A. and computationally intensive thus requiring GPU with sufficient memory:
process.py
arguments:
--help show this help message
--image image file
--video video file
--json write results to json file
--round round coordiantes in json outputs
--minify minify json output
--verbose verbose logging
--model model used for predictions
--skipms skip time between frames in miliseconds
--plot plot output when processing image
--fov field-of-view in degrees
--batch process n detected people in parallel
--maxpeople limit processing to n people in the scene
--skeleton use specific skeleton definition standard
--augmentations how many variations of detection to run
--average run avarage on augmentation variations
--suppress suppress implausible poses
--minconfidence minimum detection confidence
--iou iou threshold for overlaps
Using default model and processing parameters
./process.py --model models/tiny --video media/BaseballPitchSlowMo.webm --json output.json
options: image:null video:media/BaseballPitchSlowMo.webm json:output.json verbose:1 model:models/tiny skipms:0 plot:0 fov:55 batch:64 maxpeople:1 skeleton: augmentations:1 average:1 suppress:1 round:1 minify:1 minconfidence:0.1 iou:0.7
loaded tensorflow 2.7.0
loaded cuda 11.2
loaded model: models/tiny in 27.8sec
loaded video: media/BaseballPitchSlowMo.webm frames: 720 resolution: 1080 x 1080
processed video: 720 frames in 67.8sec
results written to: output.json
/client
/media
Start compile TypeScript to JavaScript and run HTTP/HTTPS dev web server:
npm run dev
Start web browser and navigate to: