This demo code is meant to be run on a video and includes a person detector. Nvidia-docker and GPUs are required. It only expects there to be one person in each frame of video, though the code could easily be extended to support multiple people.

Prep

1. Download the researchers' pretrained pose estimator from google drive to this directory under models/
2. Put the video file you'd like to infer on in this directory under videos
3. build the docker container in this directory with ./build-docker.sh (this can take time because it involves compiling opencv)
4. update the inference-config.yaml file to reflect the number of GPUs you have available

Running the Model

Start your docker container with:

nvidia-docker run --rm -it \
  -v $(pwd)/output:/output \
  -v $(pwd)/videos:/videos \
  -v $(pwd)/models:/models \
  -w /pose_root \
  hrnet_demo_inference \
  /bin/bash

Once the container is running, you can run inference with:

python tools/inference.py \
  --cfg inference-config.yaml \
  --videoFile /videos/my-video.mp4 \
  --inferenceFps 10 \
  --writeBoxFrames \
  TEST.MODEL_FILE \
  /models/pytorch/pose_coco/pose_hrnet_w32_384x288.pth

The command above will output frames with boxes, frames with poses, a video with poses, and a csv with the keypoint coordinates for each frame.

Original source for demo video above is licensed for Free for commercial use No attribution required by Pixabay