This is an AI that gives real-time feedback to the user of how they're performing a body movement (like, workout, dance, etc) against a benchmark video.
You can give the benchmark video as a pre-saved file, and the user video as either a pre-saved file or with the webcam feed.
Its built using mediapipe in the backend, so we get a pretty high FPS (around 15 on MacBook Pro 16) when running on CPU only, as opposed to 2-3 FPS when the same application was built with tf-pose-estimation.
from move_comparison import compare_positions
benchmark_video = 'dance_videos/benchmark_dance.mp4'
user_video = 'dance_videos/right_dance.mp4' # replace with 0 for webcam
compare_positions(benchmark_video, user_video)
You can create your own moves with the create_moves.py file. It essentially opens the webcam, lets you do your move, and when you're done, you can press Q to save it with the supplied name: create_move('Move 1')
streamlit run streamlit_app.py
In console:
1. gcloud services enable containerregistry.googleapis.com
2. git clone https://github.com/PrashantSaikia/Body-Movement-Comparison-with-Mediapipe.git
3. cd Body-Movement-Comparison-with-Mediapipe/
4. docker build -t app .
5. docker tag app gcr.io/dance-comparison/app
6. docker push gcr.io/dance-comparison/app
Then create a Kubernetes cluster via the UI, create a deployment with the image pushed, and set the port to 8501.
If you get Pod errors: Error with exit code 2, Pod errors: CrashLoopBackOff or Does not have minimum availability, wait for a couple of minutes, wait for the pods to become available, refresh and check again.