# pose-tensorflow

**Repository Path**: duanlei199653/pose-tensorflow

## Basic Information

- **Project Name**: pose-tensorflow
- **Description**:  Human Pose estimation with TensorFlow framework 
- **Primary Language**: C++
- **License**: LGPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-08-26
- **Last Updated**: 2021-11-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Human Pose Estimation with TensorFlow

![](images/teaser.png)

Here you can find the implementation of the Human Body Pose Estimation algorithm,
presented in the [DeeperCut](http://arxiv.org/abs/1605.03170) and [ArtTrack](http://arxiv.org/abs/1612.01465) papers:

**Eldar Insafutdinov, Leonid Pishchulin, Bjoern Andres, Mykhaylo Andriluka and Bernt Schiele
DeeperCut:  A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model.
In _European Conference on Computer Vision (ECCV)_, 2016**

**Eldar Insafutdinov, Mykhaylo Andriluka, Leonid Pishchulin, Siyu Tang, Evgeny Levinkov, Bjoern Andres and Bernt Schiele
ArtTrack: Articulated Multi-person Tracking in the Wild.
In _Conference on Computer Vision and Pattern Recognition (CVPR)_, 2017**

<p align="center">
    <a href="https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/"><img width="50%" src="images/mpilogo-inf-narrow.png"></a>
</p>

For more information visit http://pose.mpi-inf.mpg.de

## Prerequisites

The implementation is in Python 3 and TensorFlow. We recommended using `conda` to install the dependencies.
First, create a Python 3.6 environment:

```bash
conda create -n py36 python=3.6
conda activate py36
```

Then, install basic dependencies with conda:

```bash
conda install numpy scikit-image pillow scipy pyyaml matplotlib cython
```

Install TensorFlow and remaining packages with pip:
```bash
pip install tensorflow-gpu easydict munkres
```

When running training or prediction scripts, please make sure to set the environment variable
`TF_CUDNN_USE_AUTOTUNE` to 0 (see [this ticket](https://github.com/tensorflow/tensorflow/issues/5048)
for explanation).

If your machine has multiple GPUs, you can select which GPU you want to run on
by setting the environment variable, eg. `CUDA_VISIBLE_DEVICES=0`.

## Demo code

Single-Person (if there is only one person in the image)

```
# Download pre-trained model files
$ cd models/mpii
$ ./download_models.sh
$ cd -

# Run demo of single person pose estimation
$ TF_CUDNN_USE_AUTOTUNE=0 python3 demo/singleperson.py
```

Multiple People

```
# Compile dependencies
$ ./compile.sh

# Download pre-trained model files
$ cd models/coco
$ ./download_models.sh
$ cd -

# Run demo of multi person pose estimation
$ TF_CUDNN_USE_AUTOTUNE=0 python3 demo/demo_multiperson.py
```

## Training models

Please follow these [instructions](models/README.md)

## Citation
Please cite ArtTrack and DeeperCut in your publications if it helps your research:

    @inproceedings{insafutdinov2017cvpr,
	    title = {ArtTrack: Articulated Multi-person Tracking in the Wild},
	    booktitle = {CVPR'17},
	    url = {http://arxiv.org/abs/1612.01465},
	    author = {Eldar Insafutdinov and Mykhaylo Andriluka and Leonid Pishchulin and Siyu Tang and Evgeny Levinkov and Bjoern Andres and Bernt Schiele}
    }

    @article{insafutdinov2016eccv,
        title = {DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model},
	    booktitle = {ECCV'16},
        url = {http://arxiv.org/abs/1605.03170},
        author = {Eldar Insafutdinov and Leonid Pishchulin and Bjoern Andres and Mykhaylo Andriluka and Bernt Schiele}
    }