Multi-person Human Pose Estimation with HRNet in Pytorch

This is an unofficial implementation of the paper Deep High-Resolution Representation Learning for Human Pose Estimation.
The code is a simplified version of the official code with the ease-of-use in mind.

The code is fully compatible with the official pre-trained weights and the results are the same of the original implementation (only slight differences on gpu due to CUDA).

This repository provides:

• A simple HRNet implementation in Pytorch (>=1.0) - compatible with official weights.
• A simple class (SimpleHRNet) that loads the HRNet network for the human pose estimation, loads the pre-trained weights, and make human predictions on single images.
• Multi-person support with YOLOv3 (enabled by default).
• A reference code that runs a live demo reading frames from a webcam or a video file.

Class usage

import cv2
from SimpleHRNet import SimpleHRNet

model = SimpleHRNet(48, 17, "./weights/pose_hrnet_w48_384x288.pth")
image = cv2.imread("image.png", cv2.IMREAD_COLOR)

joints = hrnet.predict(image)

Running the live demo

From a connected camera

python scripts/live-demo.py --camera_id 0

From a saved video

python scripts/live-demo.py --filename video.mp4

Requirements

• Install the required packages
  pip install -r requirements.txt
• Download the official pre-trained weights from https://github.com/leoxiaobin/deep-high-resolution-net.pytorch
• For multi-person support:
  • Clone YOLOv3 in the folder ./models/detectors and change the folder name from PyTorch-YOLOv3 to yolo
  • Install YOLOv3 required packages
    pip install -r requirements.txt
  • Download the pre-trained weights running the script download_weights.sh from the weights folder
  • Your folders should look like:

    simple-HRNet
    ├── datasets                (unused)
    ├── misc                    (misc)
    ├── models                  (pytorch models)
    │  └── detectors            (people detectors)
    │    └── yolo               (PyTorch-YOLOv3 repository)
    │      ├── ...
    │      └── weights          (YOLOv3 weights)
    ├── weights                 (HRnet weights)
    └── scripts                 (scripts)