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Real-time 3D multi-person pose estimation demo in PyTorch. OpenVINO backend can be used for fast inference on CPU.
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# Real-time 3D Multi-person Pose Estimation Demo
This repository contains 3D multi-person pose estimation demo in PyTorch. Intel OpenVINO™ backend can be used for fast inference on CPU. This demo is based on [Lightweight OpenPose](https://arxiv.org/pdf/1811.12004.pdf) and [Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB](https://arxiv.org/pdf/1712.03453.pdf) papers. It detects 2D coordinates of up to 18 types of keypoints: ears, eyes, nose, neck, shoulders, elbows, wrists, hips, knees, and ankles, as well as their 3D coordinates. It was trained on [MS COCO](http://cocodataset.org/#home) and [CMU Panoptic](http://domedb.perception.cs.cmu.edu/) datasets and achieves 100 mm MPJPE (mean per joint position error) on CMU Panoptic subset. *This repository significantly overlaps with https://github.com/opencv/open_model_zoo/, however contains just the necessary code for 3D human pose estimation demo.*
<p align="center">
<img src="data/human_pose_estimation_3d_demo.jpg" />
</p>
> The major part of this work was done by [Mariia Ageeva](https://github.com/marrmar), when she was the :top::rocket::fire: intern at Intel.
## Table of Contents
* [Requirements](#requirements)
* [Prerequisites](#prerequisites)
* [Pre-trained model](#pre-trained-model)
* [Running](#running)
* [Inference with OpenVINO](#inference-openvino)
* [Inference with TensorRT](#inference-tensorrt)
## Requirements
* Python 3.5 (or above)
* CMake 3.10 (or above)
* C++ Compiler (g++ or MSVC)
* OpenCV 4.0 (or above)
> [Optional] [Intel OpenVINO](https://software.intel.com/en-us/openvino-toolkit) for fast inference on CPU.
> [Optional] [NVIDIA TensorRT](https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html) for fast inference on Jetson.
## Prerequisites
1. Install requirements:
```
pip install -r requirements.txt
```
2. Build `pose_extractor` module:
```
python setup.py build_ext
```
3. Add build folder to `PYTHONPATH`:
```
export PYTHONPATH=pose_extractor/build/:$PYTHONPATH
```
## Pre-trained model <a name="pre-trained-model"/>
Pre-trained model is available at [Google Drive](https://drive.google.com/file/d/1niBUbUecPhKt3GyeDNukobL4OQ3jqssH/view?usp=sharing).
## Running
To run the demo, pass path to the pre-trained checkpoint and camera id (or path to video file):
```
python demo.py --model human-pose-estimation-3d.pth --video 0
```
> Camera can capture scene under different view angles, so for correct scene visualization, please pass camera extrinsics and focal length with `--extrinsics` and `--fx` options correspondingly (extrinsics sample format can be found in data folder). In case no camera parameters provided, demo will use the default ones.
## Inference with OpenVINO <a name="inference-openvino"/>
To run with OpenVINO, it is necessary to convert checkpoint to OpenVINO format:
1. Set OpenVINO environment variables:
```
source <OpenVINO_INSTALL_DIR>/bin/setupvars.sh
```
2. Convert checkpoint to ONNX:
```
python scripts/convert_to_onnx.py --checkpoint-path human-pose-estimation-3d.pth
```
3. Convert to OpenVINO format:
```
python <OpenVINO_INSTALL_DIR>/deployment_tools/model_optimizer/mo.py --input_model human-pose-estimation-3d.onnx --input=data --mean_values=data[128.0,128.0,128.0] --scale_values=data[255.0,255.0,255.0] --output=features,heatmaps,pafs
```
To run the demo with OpenVINO inference, pass `--use-openvino` option and specify device to infer on:
```
python demo.py --model human-pose-estimation-3d.xml --device CPU --use-openvino --video 0
```
## Inference with TensorRT <a name="inference-tensorrt"/>
To run with TensorRT, it is necessary to install it properly. Please, follow the [official guide](https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html), these steps work for me:
1. [Install](https://developer.nvidia.com/cuda-downloads) CUDA 11.1.
2. [Install](https://developer.nvidia.com/cudnn) cuDNN 8 (runtime library, then developer).
3. Install `nvidia-tensorrt`:
```
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matched fp:832808a2112f9a6f, topic:deep-learning, topic:pytorch
matched fp:832808a2112f9a6f, topic:computer-vision, name:pose estimation, desc:pose estimation