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项目作者： anibali

项目描述 ：
Human 3.6M 3D human pose dataset fetcher
高级语言： Python
项目地址: git://github.com/anibali/h36m-fetch.git
创建时间： 2018-03-20T00:24:14Z
项目社区：https://github.com/anibali/h36m-fetch
开源协议：Apache License 2.0
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Human3.6M dataset fetcher

Human3.6M 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
  • 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:

  1. $ docker-compose run --rm --user="$(id -u):$(id -g)" main python3 <script>

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”),
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.

Please read the
license agreement 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.