Inference hrnet

Inferencing the deep-high-resolution-net.pytoch without using Docker.

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

python inference.py --cfg inference-config.yaml \
    --videoFile ../../multi_people.mp4 \
    --writeBoxFrames \
    --outputDir output \
    TEST.MODEL_FILE ../models/pytorch/pose_coco/pose_hrnet_w32_256x192.pth 

The above command will create a video under output directory and a lot of pose image under output/pose directory. Even with usage of GPU (GTX1080 in my case), the person detection will take nearly 0.06 sec, the person pose match will take nearly 0.07 sec. In total. inference time per frame will be 0.13 sec, nearly 10fps. So if you prefer a real-time (fps >= 20) pose estimation then you should try other approach.

Result

Some output image is as:

Fig: 1 person inference

Fig: 3 person inference

Fig: 3 person inference