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https://github.com/Daniil-Osokin/lightweight-human-pose-estimation-3d-demo.pytorch

Real-time 3D multi-person pose estimation demo in PyTorch. OpenVINO backend can be used for fast inference on CPU.
https://github.com/Daniil-Osokin/lightweight-human-pose-estimation-3d-demo.pytorch

3d-human-pose cmu-panoptic computer-vision deep-learning human-pose-estimation keypoint-estimation lightweight multi-person-pose-estimation openvino pytorch real-time

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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.*





  




> 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 



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 



To run with OpenVINO, it is necessary to convert checkpoint to OpenVINO format:

1. Set OpenVINO environment variables:

    ```

	source /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 /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 



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`:

    ```

    python -m pip install nvidia-pyindex

    pip install nvidia-tensorrt==7.2.1.6

    ```

4. [Install](https://github.com/NVIDIA-AI-IOT/torch2trt) `torch2trt`.



Convert checkpoint to TensorRT format:

```

python scripts/convert_to_trt.py --checkpoint-path human-pose-estimation-3d.pth

```

> TensorRT does not support dynamic network input size reshape.

  Make sure you have set proper network input height, width with `--height` and `--width` options during conversion (if not, there will be no detections).

  Default values work for a usual video with 16:9 aspect ratio (1280x720, 1920x1080).

  You can check the network input size with `print(scaled_img.shape)` in the demo.py



To run the demo with TensorRT inference, pass `--use-tensorrt` option:

```

python demo.py --model human-pose-estimation-3d-trt.pth --use-tensorrt --video 0

```



I have observed ~10x network inference speedup on RTX 2060 (in comparison with default PyTorch 1.6.0+cu101 inference).