https://github.com/anibali/h36m-fetch

Human 3.6M 3D human pose dataset fetcher
https://github.com/anibali/h36m-fetch

dataset human-pose-estimation

Last synced: 18 days ago
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Human 3.6M 3D human pose dataset fetcher

• Host: GitHub
• URL: https://github.com/anibali/h36m-fetch
• Owner: anibali
• License: apache-2.0
• Created: 2018-03-20T00:24:14.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2023-03-19T23:20:27.000Z (over 1 year ago)
• Last Synced: 2024-10-12T18:54:18.408Z (about 1 month ago)
• Topics: dataset, human-pose-estimation
• Language: Python
• Homepage:
• Size: 61.5 KB
• Stars: 365
• Watchers: 10
• Forks: 48
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Human3.6M dataset fetcher

[Human3.6M](http://vision.imar.ro/human3.6m/description.php) is a 3D

human pose dataset containing 3.6 million human poses and corresponding

images. The scripts in this repository make it easy to download,

extract, and preprocess the images and annotations from Human3.6M.

**Please do not ask me for a copy of the Human3.6M dataset. I do not own

the data, nor do I have permission to redistribute it. Please visit

http://vision.imar.ro/human3.6m/ in order to request access and contact

the maintainers of the dataset.**

## Requirements

* Python 3

* [`axel`](https://github.com/axel-download-accelerator/axel)

* CDF

* ffmpeg 3.2.4

Alternatively, a Dockerfile is provided which has all of the

requirements set up. You can use it to run scripts like so:

```bash

$ docker-compose run --rm --user="$(id -u):$(id -g)" main python3 

```

## Usage

1. Firstly, you will need to create an account at

   http://vision.imar.ro/human3.6m/ to gain access to the dataset.

2. Once your account has been approved, log in and inspect your cookies

   to find your PHPSESSID.

3. Copy the configuration file `config.ini.example` to `config.ini`

   and fill in your PHPSESSID.

4. Use the `download_all.py` script to download the dataset,

   `extract_all.py` to extract the downloaded archives, and

   `process_all.py` to preprocess the dataset into an easier to use

   format.

## Frame sampling

Not all frames are selected during the preprocessing step. We assume

that the data will be used in the Protocol #2 setup (see

["Compositional Human Pose Regression"](https://arxiv.org/abs/1704.00159)),

so for subjects S9 and S11 every 64th frame is used. For the training

subjects (S1, S5, S6, S7, and S8), only "interesting" frames are used.

That is, near-duplicate frames during periods of low movement are

skipped.

You can edit `select_frame_indices_to_include()` in `process_all.py` to

change this behaviour.

## License

The code in this repository is licensed under the terms of the

[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).

Please read the

[license agreement](http://vision.imar.ro/human3.6m/eula.php) for the

Human3.6M dataset itself, which specifies citations you must make when

using the data in your own research. The file `metadata.xml` is directly

copied from the "Visualisation and large scale prediction software"

bundle from the Human3.6M website, and is subject to the same license

agreement.