Awesome

Hand in Depth

Single view depth image based hand detection and pose estimation.

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Introduction

This repo contains the code for:

HandMap: Robust hand pose estimation via intermediate dense guidance map supervision, in ECCV2018. [Webpage] [BibTex]

This repo also contains following up work for real-time tracking: [Webpage].

If you use code in this repo for your research, please cite the above mentioned paper.

The code in this repo is written and maintained by Xiaokun Wu.

Pose estimation

<a name="pose-estimation"></a> Please check code/README.md for the pose estimation part of this project.

Tracking

<a name="tracking"></a> If you happen to have a Intel® RealSense™ depth camera, you can also take a look at code/camera/README.md for the tracking part of this project.

Code for real-time capture, detection, and tracking is provided there, which is an application of the pose estimation part and produces the teaser figure above.

Quick start

<a name="quick-start"></a> Tested on Windows10 (with Anaconda), July 15, 2019. Linux is similar and should be easier.

1. Setting up:

// create a virtual env using conda
export PYTHON_VERSION=3.6
conda create -n hand python=$PYTHON_VERSION numpy pip
source activate hand

// install tensorflow, change to 'tensorflow' package if you do not want/have GPU
pip install --upgrade tensorflow-gpu

// clone this repo
git clone https://github.com/xkunwu/depth-hand.git
cd depth-hand/code
pip install -r requirements.txt

2. Prepare data (using my favorite model 'super_edt2m' as example):

• Download from [Baidu Cloud Storage] into '$HOME/data/univue/output', so you should find '$HOME/data/univue/output/hands17/prepared/annotation', and '$HOME/data/univue/output/hands17/log/log-super_edt2m-180222-112534/model.ckpt.index', and others.
• Create a softlink folder (shortcut in Windows) '$HOME/data/univue/output/hands17/log/blinks/super_edt2m/', which should point to '$HOME/data/univue/output/hands17/log/log-super_edt2m-180222-112534/'.

Note: you should change '$HOME' to a meaningful path in Windows.

3. Test using pretrained model:

python -m train.evaluate --data_root=$HOME/data --out_root=$HOME/data/univue/output --model_name=super_edt2m

There should be no errors, and you are ready to go. Check the details in README.md in the pose estimation part if you want to train your own model.

4. Live detection using depth camera:

python -m camera.capture --data_root=$HOME/data --out_root=$HOME/data/univue/output --model_name=super_edt2m