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authors:
- name: "MMPose Contributors"
title: "OpenMMLab Pose Estimation Toolbox and Benchmark"
date-released: 2020-08-31
url: "https://github.com/open-mmlab/mmpose"
license: Apache-2.0
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MANIFEST.in 0 → 100755
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include requirements/*.txt
include mmpose/.mim/model-index.yml
recursive-include mmpose/.mim/configs *.py *.yml
recursive-include mmpose/.mim/tools *.py *.sh
recursive-include mmpose/.mim/demo *.py
README.md 0 → 100755
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<div align="center">
<img src="resources/mmpose-logo.png" width="450"/>
<div>&nbsp;</div>
<div align="center">
<b>OpenMMLab website</b>
<sup>
<a href="https://openmmlab.com">
<i>HOT</i>
</a>
</sup>
&nbsp;&nbsp;&nbsp;&nbsp;
<b>OpenMMLab platform</b>
<sup>
<a href="https://platform.openmmlab.com">
<i>TRY IT OUT</i>
</a>
</sup>
&nbsp;&nbsp;&nbsp;&nbsp;
<b>MMPose 1.0 Open Beta</b>
<sup>
<a href="https://mmpose.readthedocs.io/en/1.x/overview.html">
<i>JOIN</i>
</a>
</sup>
</div>
<div>&nbsp;</div>
[![Documentation](https://readthedocs.org/projects/mmpose/badge/?version=latest)](https://mmpose.readthedocs.io/en/latest/?badge=latest)
[![actions](https://github.com/open-mmlab/mmpose/workflows/build/badge.svg)](https://github.com/open-mmlab/mmpose/actions)
[![codecov](https://codecov.io/gh/open-mmlab/mmpose/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmpose)
[![PyPI](https://img.shields.io/pypi/v/mmpose)](https://pypi.org/project/mmpose/)
[![LICENSE](https://img.shields.io/github/license/open-mmlab/mmpose.svg)](https://github.com/open-mmlab/mmpose/blob/master/LICENSE)
[![Average time to resolve an issue](https://isitmaintained.com/badge/resolution/open-mmlab/mmpose.svg)](https://github.com/open-mmlab/mmpose/issues)
[![Percentage of issues still open](https://isitmaintained.com/badge/open/open-mmlab/mmpose.svg)](https://github.com/open-mmlab/mmpose/issues)
[📘Documentation](https://mmpose.readthedocs.io/en/v0.29.0/) |
[🛠️Installation](https://mmpose.readthedocs.io/en/v0.29.0/install.html) |
[👀Model Zoo](https://mmpose.readthedocs.io/en/v0.29.0/modelzoo.html) |
[📜Papers](https://mmpose.readthedocs.io/en/v0.29.0/papers/algorithms.html) |
[🆕Update News](https://mmpose.readthedocs.io/en/v0.29.0/changelog.html) |
[🤔Reporting Issues](https://github.com/open-mmlab/mmpose/issues/new/choose)
</div>
<div align="center">
English | [简体中文](README_CN.md)
</div>
## Introduction
MMPose is an open-source toolbox for pose estimation based on PyTorch.
It is a part of the [OpenMMLab project](https://github.com/open-mmlab).
The master branch works with **PyTorch 1.5+**.
https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-84f6-24eeddbf4d91.mp4
<details open>
<summary><b>Major Features</b></summary>
- **Support diverse tasks**
We support a wide spectrum of mainstream pose analysis tasks in current research community, including 2d multi-person human pose estimation, 2d hand pose estimation, 2d face landmark detection, 133 keypoint whole-body human pose estimation, 3d human mesh recovery, fashion landmark detection and animal pose estimation.
See [demo.md](demo/README.md) for more information.
- **Higher efficiency and higher accuracy**
MMPose implements multiple state-of-the-art (SOTA) deep learning models, including both top-down & bottom-up approaches. We achieve faster training speed and higher accuracy than other popular codebases, such as [HRNet](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch).
See [benchmark.md](docs/en/benchmark.md) for more information.
- **Support for various datasets**
The toolbox directly supports multiple popular and representative datasets, COCO, AIC, MPII, MPII-TRB, OCHuman etc.
See [data_preparation.md](docs/en/data_preparation.md) for more information.
- **Well designed, tested and documented**
We decompose MMPose into different components and one can easily construct a customized
pose estimation framework by combining different modules.
We provide detailed documentation and API reference, as well as unittests.
</details>
## What's New
- 2022-10-14: MMPose [v0.29.0](https://github.com/open-mmlab/mmpose/releases/tag/v0.29.0) is released. Major updates include:
- Support [DEKR](https://arxiv.org/abs/2104.02300) (CVPR'2021). See the [model page](/configs/body/2d_kpt_sview_rgb_img/dekr/coco/hrnet_coco.md)
- Support [CID](https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Contextual_Instance_Decoupling_for_Robust_Multi-Person_Pose_Estimation_CVPR_2022_paper.html) (CVPR'2022). See the [model page](/configs/body/2d_kpt_sview_rgb_img/cid/coco/hrnet_coco.md)
- 2022-09-01: **MMPose v1.0.0** beta has been released \[ [Code](https://github.com/open-mmlab/mmpose/tree/1.x) | [Docs](https://mmpose.readthedocs.io/en/1.x/) \]. Welcome to try it and your feedback will be greatly appreciated!
- 2022-02-28: MMPose model deployment is supported by [MMDeploy](https://github.com/open-mmlab/mmdeploy) v0.3.0
MMPose Webcam API is a simple yet powerful tool to develop interactive webcam applications with MMPose features.
- 2021-12-29: OpenMMLab Open Platform is online! Try our [pose estimation demo](https://platform.openmmlab.com/web-demo/demo/poseestimation)
## Installation
MMPose depends on [PyTorch](https://pytorch.org/) and [MMCV](https://github.com/open-mmlab/mmcv).
Below are quick steps for installation.
Please refer to [install.md](docs/en/install.md) for detailed installation guide.
```shell
conda create -n openmmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
conda activate openmmlab
pip3 install openmim
mim install mmcv-full
git clone https://github.com/open-mmlab/mmpose.git
cd mmpose
pip3 install -e .
```
## Getting Started
Please see [get_started.md](docs/en/get_started.md) for the basic usage of MMPose.
There are also tutorials:
- [learn about configs](docs/en/tutorials/0_config.md)
- [finetune model](docs/en/tutorials/1_finetune.md)
- [add new dataset](docs/en/tutorials/2_new_dataset.md)
- [customize data pipelines](docs/en/tutorials/3_data_pipeline.md)
- [add new modules](docs/en/tutorials/4_new_modules.md)
- [export a model to ONNX](docs/en/tutorials/5_export_model.md)
- [customize runtime settings](docs/en/tutorials/6_customize_runtime.md)
## Model Zoo
Results and models are available in the *README.md* of each method's config directory.
A summary can be found in the [Model Zoo](https://mmpose.readthedocs.io/en/latest/modelzoo.html) page.
<details open>
<summary><b>Supported algorithms:</b></summary>
- [x] [DeepPose](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#deeppose-cvpr-2014) (CVPR'2014)
- [x] [CPM](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#cpm-cvpr-2016) (CVPR'2016)
- [x] [Hourglass](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#hourglass-eccv-2016) (ECCV'2016)
- [x] [SimpleBaseline3D](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#simplebaseline3d-iccv-2017) (ICCV'2017)
- [x] [Associative Embedding](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#associative-embedding-nips-2017) (NeurIPS'2017)
- [x] [HMR](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#hmr-cvpr-2018) (CVPR'2018)
- [x] [SimpleBaseline2D](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#simplebaseline2d-eccv-2018) (ECCV'2018)
- [x] [HRNet](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#hrnet-cvpr-2019) (CVPR'2019)
- [x] [VideoPose3D](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#videopose3d-cvpr-2019) (CVPR'2019)
- [x] [HRNetv2](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#hrnetv2-tpami-2019) (TPAMI'2019)
- [x] [MSPN](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#mspn-arxiv-2019) (ArXiv'2019)
- [x] [SCNet](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#scnet-cvpr-2020) (CVPR'2020)
- [x] [HigherHRNet](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#higherhrnet-cvpr-2020) (CVPR'2020)
- [x] [RSN](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#rsn-eccv-2020) (ECCV'2020)
- [x] [InterNet](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#internet-eccv-2020) (ECCV'2020)
- [x] [VoxelPose](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#voxelpose-eccv-2020) (ECCV'2020)
- [x] [LiteHRNet](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#litehrnet-cvpr-2021) (CVPR'2021)
- [x] [ViPNAS](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#vipnas-cvpr-2021) (CVPR'2021)
- [x] [DEKR](https://mmpose.readthedocs.io/zh_CN/latest/papers/algorithms.html#dekr-cvpr-2021) (CVPR'2021)
- [x] [CID](https://mmpose.readthedocs.io/zh_CN/latest/papers/algorithms.html#cid-cvpr-2022) (CVPR'2022)
</details>
<details open>
<summary><b>Supported techniques:</b></summary>
- [x] [FPN](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#fpn-cvpr-2017) (CVPR'2017)
- [x] [FP16](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#fp16-arxiv-2017) (ArXiv'2017)
- [x] [Wingloss](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#wingloss-cvpr-2018) (CVPR'2018)
- [x] [AdaptiveWingloss](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#adaptivewingloss-iccv-2019) (ICCV'2019)
- [x] [DarkPose](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#darkpose-cvpr-2020) (CVPR'2020)
- [x] [UDP](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#udp-cvpr-2020) (CVPR'2020)
- [x] [Albumentations](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#albumentations-information-2020) (Information'2020)
- [x] [SoftWingloss](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#softwingloss-tip-2021) (TIP'2021)
- [x] [SmoothNet](/configs/_base_/filters/smoothnet_h36m.md) (arXiv'2021)
- [x] [RLE](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#rle-iccv-2021) (ICCV'2021)
</details>
<details open>
<summary><b>Supported <a href="https://mmpose.readthedocs.io/en/latest/datasets.html">datasets</a>:</b></summary>
- [x] [AFLW](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#aflw-iccvw-2011) \[[homepage](https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/aflw/)\] (ICCVW'2011)
- [x] [sub-JHMDB](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#jhmdb-iccv-2013) \[[homepage](http://jhmdb.is.tue.mpg.de/dataset)\] (ICCV'2013)
- [x] [COFW](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#cofw-iccv-2013) \[[homepage](http://www.vision.caltech.edu/xpburgos/ICCV13/)\] (ICCV'2013)
- [x] [MPII](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#mpii-cvpr-2014) \[[homepage](http://human-pose.mpi-inf.mpg.de/)\] (CVPR'2014)
- [x] [Human3.6M](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#human3-6m-tpami-2014) \[[homepage](http://vision.imar.ro/human3.6m/description.php)\] (TPAMI'2014)
- [x] [COCO](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#coco-eccv-2014) \[[homepage](http://cocodataset.org/)\] (ECCV'2014)
- [x] [CMU Panoptic](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#cmu-panoptic-iccv-2015) \[[homepage](http://domedb.perception.cs.cmu.edu/)\] (ICCV'2015)
- [x] [DeepFashion](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#deepfashion-cvpr-2016) \[[homepage](http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/LandmarkDetection.html)\] (CVPR'2016)
- [x] [DeepFashion2](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#deepfashion2-cvpr-2019) \[[homepage](https://github.com/switchablenorms/DeepFashion2)\] (CVPR'2019)
- [x] [300W](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#300w-imavis-2016) \[[homepage](https://ibug.doc.ic.ac.uk/resources/300-W/)\] (IMAVIS'2016)
- [x] [RHD](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#rhd-iccv-2017) \[[homepage](https://lmb.informatik.uni-freiburg.de/resources/datasets/RenderedHandposeDataset.en.html)\] (ICCV'2017)
- [x] [CMU Panoptic HandDB](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#cmu-panoptic-handdb-cvpr-2017) \[[homepage](http://domedb.perception.cs.cmu.edu/handdb.html)\] (CVPR'2017)
- [x] [AI Challenger](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#ai-challenger-arxiv-2017) \[[homepage](https://github.com/AIChallenger/AI_Challenger_2017)\] (ArXiv'2017)
- [x] [MHP](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#mhp-acm-mm-2018) \[[homepage](https://lv-mhp.github.io/dataset)\] (ACM MM'2018)
- [x] [WFLW](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#wflw-cvpr-2018) \[[homepage](https://wywu.github.io/projects/LAB/WFLW.html)\] (CVPR'2018)
- [x] [PoseTrack18](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#posetrack18-cvpr-2018) \[[homepage](https://posetrack.net/users/download.php)\] (CVPR'2018)
- [x] [OCHuman](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#ochuman-cvpr-2019) \[[homepage](https://github.com/liruilong940607/OCHumanApi)\] (CVPR'2019)
- [x] [CrowdPose](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#crowdpose-cvpr-2019) \[[homepage](https://github.com/Jeff-sjtu/CrowdPose)\] (CVPR'2019)
- [x] [MPII-TRB](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#mpii-trb-iccv-2019) \[[homepage](https://github.com/kennymckormick/Triplet-Representation-of-human-Body)\] (ICCV'2019)
- [x] [FreiHand](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#freihand-iccv-2019) \[[homepage](https://lmb.informatik.uni-freiburg.de/projects/freihand/)\] (ICCV'2019)
- [x] [Animal-Pose](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#animal-pose-iccv-2019) \[[homepage](https://sites.google.com/view/animal-pose/)\] (ICCV'2019)
- [x] [OneHand10K](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#onehand10k-tcsvt-2019) \[[homepage](https://www.yangangwang.com/papers/WANG-MCC-2018-10.html)\] (TCSVT'2019)
- [x] [Vinegar Fly](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#vinegar-fly-nature-methods-2019) \[[homepage](https://github.com/jgraving/DeepPoseKit-Data)\] (Nature Methods'2019)
- [x] [Desert Locust](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#desert-locust-elife-2019) \[[homepage](https://github.com/jgraving/DeepPoseKit-Data)\] (Elife'2019)
- [x] [Grévy’s Zebra](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#grevys-zebra-elife-2019) \[[homepage](https://github.com/jgraving/DeepPoseKit-Data)\] (Elife'2019)
- [x] [ATRW](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#atrw-acm-mm-2020) \[[homepage](https://cvwc2019.github.io/challenge.html)\] (ACM MM'2020)
- [x] [Halpe](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#halpe-cvpr-2020) \[[homepage](https://github.com/Fang-Haoshu/Halpe-FullBody/)\] (CVPR'2020)
- [x] [COCO-WholeBody](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#coco-wholebody-eccv-2020) \[[homepage](https://github.com/jin-s13/COCO-WholeBody/)\] (ECCV'2020)
- [x] [MacaquePose](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#macaquepose-biorxiv-2020) \[[homepage](http://www.pri.kyoto-u.ac.jp/datasets/macaquepose/index.html)\] (bioRxiv'2020)
- [x] [InterHand2.6M](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#interhand2-6m-eccv-2020) \[[homepage](https://mks0601.github.io/InterHand2.6M/)\] (ECCV'2020)
- [x] [AP-10K](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#ap-10k-neurips-2021) \[[homepage](https://github.com/AlexTheBad/AP-10K)\] (NeurIPS'2021)
- [x] [Horse-10](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#horse-10-wacv-2021) \[[homepage](http://www.mackenziemathislab.org/horse10)\] (WACV'2021)
</details>
<details open>
<summary><b>Supported backbones:</b></summary>
- [x] [AlexNet](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#alexnet-neurips-2012) (NeurIPS'2012)
- [x] [VGG](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#vgg-iclr-2015) (ICLR'2015)
- [x] [ResNet](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#resnet-cvpr-2016) (CVPR'2016)
- [x] [ResNext](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#resnext-cvpr-2017) (CVPR'2017)
- [x] [SEResNet](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#seresnet-cvpr-2018) (CVPR'2018)
- [x] [ShufflenetV1](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#shufflenetv1-cvpr-2018) (CVPR'2018)
- [x] [ShufflenetV2](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#shufflenetv2-eccv-2018) (ECCV'2018)
- [x] [MobilenetV2](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#mobilenetv2-cvpr-2018) (CVPR'2018)
- [x] [ResNetV1D](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#resnetv1d-cvpr-2019) (CVPR'2019)
- [x] [ResNeSt](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#resnest-arxiv-2020) (ArXiv'2020)
- [x] [Swin](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#swin-cvpr-2021) (CVPR'2021)
- [x] [HRFormer](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#hrformer-nips-2021) (NIPS'2021)
- [x] [PVT](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#pvt-iccv-2021) (ICCV'2021)
- [x] [PVTV2](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#pvtv2-cvmj-2022) (CVMJ'2022)
</details>
### Model Request
We will keep up with the latest progress of the community, and support more popular algorithms and frameworks. If you have any feature requests, please feel free to leave a comment in [MMPose Roadmap](https://github.com/open-mmlab/mmpose/issues/9).
### Benchmark
#### Accuracy and Training Speed
MMPose achieves superior of training speed and accuracy on the standard keypoint detection benchmarks like COCO. See more details at [benchmark.md](docs/en/benchmark.md).
#### Inference Speed
We summarize the model complexity and inference speed of major models in MMPose, including FLOPs, parameter counts and inference speeds on both CPU and GPU devices with different batch sizes. Please refer to [inference_speed_summary.md](docs/en/inference_speed_summary.md) for more details.
## Data Preparation
Please refer to [data_preparation.md](docs/en/data_preparation.md) for a general knowledge of data preparation.
## FAQ
Please refer to [FAQ](docs/en/faq.md) for frequently asked questions.
## Contributing
We appreciate all contributions to improve MMPose. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
## Acknowledgement
MMPose is an open source project that is contributed by researchers and engineers from various colleges and companies.
We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks.
We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new models.
## Citation
If you find this project useful in your research, please consider cite:
```bibtex
@misc{mmpose2020,
title={OpenMMLab Pose Estimation Toolbox and Benchmark},
author={MMPose Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmpose}},
year={2020}
}
```
## License
This project is released under the [Apache 2.0 license](LICENSE).
## Projects in OpenMMLab
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark
- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab Model Deployment Framework.
README_CN.md 0 → 100755
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<div align="center">
<img src="resources/mmpose-logo.png" width="450"/>
<div>&nbsp;</div>
<div align="center">
<b><font size="5">OpenMMLab 官网</font></b>
<sup>
<a href="https://openmmlab.com">
<i><font size="4">HOT</font></i>
</a>
</sup>
&nbsp;&nbsp;&nbsp;&nbsp;
<b><font size="5">OpenMMLab 开放平台</font></b>
<sup>
<a href="https://platform.openmmlab.com">
<i><font size="4">TRY IT OUT</font></i>
</a>
</sup>
&nbsp;&nbsp;&nbsp;&nbsp;
<b><font size="5">MMPose 1.0 公测</font></b>
<sup>
<a href="https://mmpose.readthedocs.io/en/1.x/overview.html">
<i><font size="4">JOIN</font></i>
</a>
</sup>
</div>
<div>&nbsp;</div>
[![Documentation](https://readthedocs.org/projects/mmpose/badge/?version=latest)](https://mmpose.readthedocs.io/en/latest/?badge=latest)
[![actions](https://github.com/open-mmlab/mmpose/workflows/build/badge.svg)](https://github.com/open-mmlab/mmpose/actions)
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[![LICENSE](https://img.shields.io/github/license/open-mmlab/mmpose.svg)](https://github.com/open-mmlab/mmpose/blob/master/LICENSE)
[![Average time to resolve an issue](https://isitmaintained.com/badge/resolution/open-mmlab/mmpose.svg)](https://github.com/open-mmlab/mmpose/issues)
[![Percentage of issues still open](https://isitmaintained.com/badge/open/open-mmlab/mmpose.svg)](https://github.com/open-mmlab/mmpose/issues)
[📘文档](https://mmpose.readthedocs.io/zh_CN/v0.29.0/) |
[🛠️安装](https://mmpose.readthedocs.io/zh_CN/v0.29.0/install.html) |
[👀模型库](https://mmpose.readthedocs.io/zh_CN/v0.29.0/modelzoo.html) |
[📜论文库](https://mmpose.readthedocs.io/zh_CN/v0.29.0/papers/algorithms.html) |
[🆕更新日志](https://mmpose.readthedocs.io/en/v0.29.0/changelog.html) |
[🤔报告问题](https://github.com/open-mmlab/mmpose/issues/new/choose)
</div>
<div align="center">
[English](README.md) | 简体中文
</div>
## 简介
MMPose 是一款基于 PyTorch 的姿态分析的开源工具箱，是 [OpenMMLab](http://openmmlab.org/) 项目的成员之一。
主分支代码目前支持 **PyTorch 1.5 以上**的版本。
https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-84f6-24eeddbf4d91.mp4
<details open>
<summary><b>主要特性</b></summary>
- **支持多种人体姿态分析相关任务**
MMPose 支持当前学界广泛关注的主流姿态分析任务：主要包括 2D多人姿态估计、2D手部姿态估计、2D人脸关键点检测、133关键点的全身人体姿态估计、3D人体形状恢复、服饰关键点检测、动物关键点检测等。
具体请参考 [功能演示](demo/README.md)。
- **更高的精度和更快的速度**
MMPose 复现了多种学界最先进的人体姿态分析模型，包括“自顶向下”和“自底向上”两大类算法。MMPose 相比于其他主流的代码库，具有更高的模型精度和训练速度。
具体请参考 [基准测试](docs/en/benchmark.md)（英文）。
- **支持多样的数据集**
MMPose 支持了很多主流数据集的准备和构建，如 COCO、AIC、MPII、MPII-TRB、OCHuman 等。 具体请参考 [数据集准备](docs/en/data_preparation.md)。
- **模块化设计**
MMPose 将统一的人体姿态分析框架解耦成不同的模块组件，通过组合不同的模块组件，用户可以便捷地构建自定义的人体姿态分析模型。
- **详尽的单元测试和文档**
MMPose 提供了详尽的说明文档，API 接口说明，全面的单元测试，以供社区参考。
</details>
## 最新进展
- 2022-10-14: MMPose [v0.29.0](https://github.com/open-mmlab/mmpose/releases/tag/v0.29.0) 已经发布，主要更新包括：
- 新增算法 [DEKR](https://arxiv.org/abs/2104.02300) (CVPR'2021). 详情请见 [模型页面](/configs/body/2d_kpt_sview_rgb_img/dekr/coco/hrnet_coco.md)
- 新增算法 [CID](https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Contextual_Instance_Decoupling_for_Robust_Multi-Person_Pose_Estimation_CVPR_2022_paper.html) (CVPR'2022). 详情请见 [模型页面](/configs/body/2d_kpt_sview_rgb_img/cid/coco/hrnet_coco.md)
- 2022-09-01: **MMPose v1.0.0** 公测版本已经发布 \[ [Code](https://github.com/open-mmlab/mmpose/tree/1.x) | [Docs](https://mmpose.readthedocs.io/en/1.x/) \]，欢迎尝试并提出宝贵意见
- 2022-02-28: [MMDeploy](https://github.com/open-mmlab/mmdeploy) v0.3.0 支持 MMPose 模型部署
- 2021-12-29: OpenMMLab 开放平台已经正式上线! 欢迎试用基于 MMPose 的[姿态估计 Demo](https://platform.openmmlab.com/web-demo/demo/poseestimation)
## 安装
MMPose 依赖 [PyTorch](https://pytorch.org/) 和 [MMCV](https://github.com/open-mmlab/mmcv)，以下是安装的简要步骤。
更详细的安装指南请参考 [install.md](docs/zh_cn/install.md)。
```shell
conda create -n openmmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
conda activate openmmlab
pip3 install openmim
mim install mmcv-full
git clone https://github.com/open-mmlab/mmpose.git
cd mmpose
pip3 install -e .
```
## 教程
请参考 [get_started.md](docs/zh_cn/get_started.md) 了解 MMPose 的基本使用。
MMPose 也提供了其他更详细的教程:
- [如何编写配置文件](docs/zh_cn/tutorials/0_config.md)
- [如何微调模型](docs/zh_cn/tutorials/1_finetune.md)
- [如何增加新数据集](docs/zh_cn/tutorials/2_new_dataset.md)
- [如何设计数据处理流程](docs/zh_cn/tutorials/3_data_pipeline.md)
- [如何增加新模块](docs/zh_cn/tutorials/4_new_modules.md)
- [如何导出模型为 onnx 格式](docs/zh_cn/tutorials/5_export_model.md)
- [如何自定义运行配置](docs/zh_cn/tutorials/6_customize_runtime.md)
- [如何使用摄像头应用接口（Webcam API）](docs/zh_cn/tutorials/7_webcam_api.md)
## 模型库
各个模型的结果和设置都可以在对应的 config（配置）目录下的 *README.md* 中查看。
整体的概况也可也在 [模型库](https://mmpose.readthedocs.io/zh_CN/latest/modelzoo.html) 页面中查看。
<details open>
<summary><b>支持的算法</b></summary>
- [x] [DeepPose](https://mmpose.readthedocs.io/zh_CN/latest/papers/algorithms.html#deeppose-cvpr-2014) (CVPR'2014)
- [x] [CPM](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#cpm-cvpr-2016) (CVPR'2016)
- [x] [Hourglass](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#hourglass-eccv-2016) (ECCV'2016)
- [x] [SimpleBaseline3D](https://mmpose.readthedocs.io/zh_CN/latest/papers/algorithms.html#simplebaseline3d-iccv-2017) (ICCV'2017)
- [x] [Associative Embedding](https://mmpose.readthedocs.io/zh_CN/latest/papers/algorithms.html#associative-embedding-nips-2017) (NeurIPS'2017)
- [x] [HMR](https://mmpose.readthedocs.io/zh_CN/latest/papers/algorithms.html#hmr-cvpr-2018) (CVPR'2018)
- [x] [SimpleBaseline2D](https://mmpose.readthedocs.io/zh_CN/latest/papers/algorithms.html#simplebaseline2d-eccv-2018) (ECCV'2018)
- [x] [HRNet](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#hrnet-cvpr-2019) (CVPR'2019)
- [x] [VideoPose3D](https://mmpose.readthedocs.io/zh_CN/latest/papers/algorithms.html#videopose3d-cvpr-2019) (CVPR'2019)
- [x] [HRNetv2](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#hrnetv2-tpami-2019) (TPAMI'2019)
- [x] [MSPN](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#mspn-arxiv-2019) (ArXiv'2019)
- [x] [SCNet](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#scnet-cvpr-2020) (CVPR'2020)
- [x] [HigherHRNet](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#higherhrnet-cvpr-2020) (CVPR'2020)
- [x] [RSN](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#rsn-eccv-2020) (ECCV'2020)
- [x] [InterNet](https://mmpose.readthedocs.io/zh_CN/latest/papers/algorithms.html#internet-eccv-2020) (ECCV'2020)
- [x] [VoxelPose](https://mmpose.readthedocs.io/zh_CN/latest/papers/algorithms.html#voxelpose-eccv-2020) (ECCV'2020)
- [x] [LiteHRNet](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#litehrnet-cvpr-2021) (CVPR'2021)
- [x] [ViPNAS](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#vipnas-cvpr-2021) (CVPR'2021)
- [x] [DEKR](https://mmpose.readthedocs.io/zh_CN/latest/papers/algorithms.html#dekr-cvpr-2021) (CVPR'2021)
- [x] [CID](https://mmpose.readthedocs.io/zh_CN/latest/papers/algorithms.html#cid-cvpr-2022) (CVPR'2022)
</details>
<details open>
<summary><b>支持的技术</b></summary>
- [x] [FPN](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#fpn-cvpr-2017) (CVPR'2017)
- [x] [FP16](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#fp16-arxiv-2017) (ArXiv'2017)
- [x] [Wingloss](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#wingloss-cvpr-2018) (CVPR'2018)
- [x] [AdaptiveWingloss](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#adaptivewingloss-iccv-2019) (ICCV'2019)
- [x] [DarkPose](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#darkpose-cvpr-2020) (CVPR'2020)
- [x] [UDP](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#udp-cvpr-2020) (CVPR'2020)
- [x] [Albumentations](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#albumentations-information-2020) (Information'2020)
- [x] [SoftWingloss](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#softwingloss-tip-2021) (TIP'2021)
- [x] [SmoothNet](/configs/_base_/filters/smoothnet_h36m.md) (arXiv'2021)
- [x] [RLE](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#rle-iccv-2021) (ICCV'2021)
</details>
<details open>
<summary><b><a href="https://mmpose.readthedocs.io/zh_CN/latest/datasets.html">支持的数据集</a></b></summary>
- [x] [AFLW](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#aflw-iccvw-2011) \[[homepage](https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/aflw/)\] (ICCVW'2011)
- [x] [sub-JHMDB](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#jhmdb-iccv-2013) \[[homepage](http://jhmdb.is.tue.mpg.de/dataset)\] (ICCV'2013)
- [x] [COFW](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#cofw-iccv-2013) \[[homepage](http://www.vision.caltech.edu/xpburgos/ICCV13/)\] (ICCV'2013)
- [x] [MPII](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#mpii-cvpr-2014) \[[homepage](http://human-pose.mpi-inf.mpg.de/)\] (CVPR'2014)
- [x] [Human3.6M](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#human3-6m-tpami-2014) \[[homepage](http://vision.imar.ro/human3.6m/description.php)\] (TPAMI'2014)
- [x] [COCO](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#coco-eccv-2014) \[[homepage](http://cocodataset.org/)\] (ECCV'2014)
- [x] [CMU Panoptic](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#cmu-panoptic-iccv-2015) (ICCV'2015)
- [x] [DeepFashion](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#deepfashion-cvpr-2016) \[[homepage](http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/LandmarkDetection.html)\] (CVPR'2016)
- [x] [DeepFashion2](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#deepfashion2-cvpr-2019) \[[homepage](https://github.com/switchablenorms/DeepFashion2)\] (CVPR'2019)
- [x] [300W](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#300w-imavis-2016) \[[homepage](https://ibug.doc.ic.ac.uk/resources/300-W/)\] (IMAVIS'2016)
- [x] [RHD](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#rhd-iccv-2017) \[[homepage](https://lmb.informatik.uni-freiburg.de/resources/datasets/RenderedHandposeDataset.en.html)\] (ICCV'2017)
- [x] [CMU Panoptic](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#cmu-panoptic-iccv-2015) \[[homepage](http://domedb.perception.cs.cmu.edu/)\] (ICCV'2015)
- [x] [AI Challenger](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#ai-challenger-arxiv-2017) \[[homepage](https://github.com/AIChallenger/AI_Challenger_2017)\] (ArXiv'2017)
- [x] [MHP](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#mhp-acm-mm-2018) \[[homepage](https://lv-mhp.github.io/dataset)\] (ACM MM'2018)
- [x] [WFLW](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#wflw-cvpr-2018) \[[homepage](https://wywu.github.io/projects/LAB/WFLW.html)\] (CVPR'2018)
- [x] [PoseTrack18](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#posetrack18-cvpr-2018) \[[homepage](https://posetrack.net/users/download.php)\] (CVPR'2018)
- [x] [OCHuman](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#ochuman-cvpr-2019) \[[homepage](https://github.com/liruilong940607/OCHumanApi)\] (CVPR'2019)
- [x] [CrowdPose](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#crowdpose-cvpr-2019) \[[homepage](https://github.com/Jeff-sjtu/CrowdPose)\] (CVPR'2019)
- [x] [MPII-TRB](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#mpii-trb-iccv-2019) \[[homepage](https://github.com/kennymckormick/Triplet-Representation-of-human-Body)\] (ICCV'2019)
- [x] [FreiHand](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#freihand-iccv-2019) \[[homepage](https://lmb.informatik.uni-freiburg.de/projects/freihand/)\] (ICCV'2019)
- [x] [Animal-Pose](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#animal-pose-iccv-2019) \[[homepage](https://sites.google.com/view/animal-pose/)\] (ICCV'2019)
- [x] [OneHand10K](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#onehand10k-tcsvt-2019) \[[homepage](https://www.yangangwang.com/papers/WANG-MCC-2018-10.html)\] (TCSVT'2019)
- [x] [Vinegar Fly](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#vinegar-fly-nature-methods-2019) \[[homepage](https://github.com/jgraving/DeepPoseKit-Data)\] (Nature Methods'2019)
- [x] [Desert Locust](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#desert-locust-elife-2019) \[[homepage](https://github.com/jgraving/DeepPoseKit-Data)\] (Elife'2019)
- [x] [Grévy’s Zebra](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#grevys-zebra-elife-2019) \[[homepage](https://github.com/jgraving/DeepPoseKit-Data)\] (Elife'2019)
- [x] [ATRW](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#atrw-acm-mm-2020) \[[homepage](https://cvwc2019.github.io/challenge.html)\] (ACM MM'2020)
- [x] [Halpe](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#halpe-cvpr-2020) \[[homepage](https://github.com/Fang-Haoshu/Halpe-FullBody/)\] (CVPR'2020)
- [x] [COCO-WholeBody](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#coco-wholebody-eccv-2020) \[[homepage](https://github.com/jin-s13/COCO-WholeBody/)\] (ECCV'2020)
- [x] [MacaquePose](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#macaquepose-biorxiv-2020) \[[homepage](http://www.pri.kyoto-u.ac.jp/datasets/macaquepose/index.html)\] (bioRxiv'2020)
- [x] [InterHand2.6M](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#interhand2-6m-eccv-2020) \[[homepage](https://mks0601.github.io/InterHand2.6M/)\] (ECCV'2020)
- [x] [AP-10K](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#ap-10k-neurips-2021) \[[homepage](https://github.com/AlexTheBad/AP-10K)\] (NeurIPS'2021)
- [x] [Horse-10](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#horse-10-wacv-2021) \[[homepage](http://www.mackenziemathislab.org/horse10)\] (WACV'2021)
</details>
<details>
<summary><b>支持的骨干网络</b></summary>
- [x] [AlexNet](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#alexnet-neurips-2012) (NeurIPS'2012)
- [x] [VGG](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#vgg-iclr-2015) (ICLR'2015)
- [x] [ResNet](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#resnet-cvpr-2016) (CVPR'2016)
- [x] [ResNext](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#resnext-cvpr-2017) (CVPR'2017)
- [x] [SEResNet](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#seresnet-cvpr-2018) (CVPR'2018)
- [x] [ShufflenetV1](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#shufflenetv1-cvpr-2018) (CVPR'2018)
- [x] [ShufflenetV2](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#shufflenetv2-eccv-2018) (ECCV'2018)
- [x] [MobilenetV2](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#mobilenetv2-cvpr-2018) (CVPR'2018)
- [x] [ResNetV1D](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#resnetv1d-cvpr-2019) (CVPR'2019)
- [x] [ResNeSt](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#resnest-arxiv-2020) (ArXiv'2020)
- [x] [Swin](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#swin-cvpr-2021) (CVPR'2021)
- [x] [HRFormer](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#hrformer-nips-2021) (NIPS'2021)
- [x] [PVT](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#pvt-iccv-2021) (ICCV'2021)
- [x] [PVTV2](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#pvtv2-cvmj-2022) (CVMJ'2022)
</details>
### 模型需求
我们将跟进学界的最新进展，并支持更多算法和框架。如果您对 MMPose 有任何功能需求，请随时在 [MMPose Roadmap](https://github.com/open-mmlab/mmpose/issues/9) 中留言。
### 基准测试
#### 训练精度和速度
MMPose 在主流关键点检测基准 COCO 上达到了优越的模型精度和训练速度。
详细信息可见 [基准测试](docs/en/benchmark.md)(英文)。
#### 推理速度
我们总结了 MMPose 中主要模型的复杂度信息和推理速度，包括模型的计算复杂度、参数数量，以及以不同的批处理大小在 CPU 和 GPU 上的推理速度。
详细信息可见 [模型推理速度](docs/zh_cn/inference_speed_summary.md)。
## 数据准备
请参考 [data_preparation.md](docs/en/data_preparation.md)（英文） 进行数据集准备。
## 常见问题
请参考 [FAQ](docs/zh_cn/faq.md) 了解其他用户的常见问题。
## 参与贡献
我们非常欢迎用户对于 MMPose 做出的任何贡献，可以参考 [CONTRIBUTION.md](.github/CONTRIBUTING.md) 文件了解更多细节。
## 致谢
MMPose 是一款由不同学校和公司共同贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。
我们希望该工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现现有算法并开发自己的新模型，从而不断为开源社区提供贡献。
## 引用
如果您觉得 MMPose 对您的研究有所帮助，请考虑引用它：
```bibtex
@misc{mmpose2020,
title={OpenMMLab Pose Estimation Toolbox and Benchmark},
author={MMPose Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmpose}},
year={2020}
}
```
## 许可证
该项目采用 [Apache 2.0 license](LICENSE) 开源协议。
## OpenMMLab的其他项目
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO 系列工具箱和基准测试
- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
## 欢迎加入 OpenMMLab 社区
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" height="400">
</div>
我们会在 OpenMMLab 社区为大家
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干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬
configs/_base_/datasets/300w.py 0 → 100755
View file @ 37c8cebc
dataset_info = dict(
dataset_name='300w',
paper_info=dict(
author='Sagonas, Christos and Antonakos, Epameinondas '
'and Tzimiropoulos, Georgios and Zafeiriou, Stefanos '
'and Pantic, Maja',
title='300 faces in-the-wild challenge: '
'Database and results',
container='Image and vision computing',
year='2016',
homepage='https://ibug.doc.ic.ac.uk/resources/300-W/',
),
keypoint_info={
0:
dict(
name='kpt-0', id=0, color=[255, 255, 255], type='', swap='kpt-16'),
1:
dict(
name='kpt-1', id=1, color=[255, 255, 255], type='', swap='kpt-15'),
2:
dict(
name='kpt-2', id=2, color=[255, 255, 255], type='', swap='kpt-14'),
3:
dict(
name='kpt-3', id=3, color=[255, 255, 255], type='', swap='kpt-13'),
4:
dict(
name='kpt-4', id=4, color=[255, 255, 255], type='', swap='kpt-12'),
5:
dict(
name='kpt-5', id=5, color=[255, 255, 255], type='', swap='kpt-11'),
6:
dict(
name='kpt-6', id=6, color=[255, 255, 255], type='', swap='kpt-10'),
7:
dict(name='kpt-7', id=7, color=[255, 255, 255], type='', swap='kpt-9'),
8:
dict(name='kpt-8', id=8, color=[255, 255, 255], type='', swap=''),
9:
dict(name='kpt-9', id=9, color=[255, 255, 255], type='', swap='kpt-7'),
10:
dict(
name='kpt-10', id=10, color=[255, 255, 255], type='',
swap='kpt-6'),
11:
dict(
name='kpt-11', id=11, color=[255, 255, 255], type='',
swap='kpt-5'),
12:
dict(
name='kpt-12', id=12, color=[255, 255, 255], type='',
swap='kpt-4'),
13:
dict(
name='kpt-13', id=13, color=[255, 255, 255], type='',
swap='kpt-3'),
14:
dict(
name='kpt-14', id=14, color=[255, 255, 255], type='',
swap='kpt-2'),
15:
dict(
name='kpt-15', id=15, color=[255, 255, 255], type='',
swap='kpt-1'),
16:
dict(
name='kpt-16', id=16, color=[255, 255, 255], type='',
swap='kpt-0'),
17:
dict(
name='kpt-17',
id=17,
color=[255, 255, 255],
type='',
swap='kpt-26'),
18:
dict(
name='kpt-18',
id=18,
color=[255, 255, 255],
type='',
swap='kpt-25'),
19:
dict(
name='kpt-19',
id=19,
color=[255, 255, 255],
type='',
swap='kpt-24'),
20:
dict(
name='kpt-20',
id=20,
color=[255, 255, 255],
type='',
swap='kpt-23'),
21:
dict(
name='kpt-21',
id=21,
color=[255, 255, 255],
type='',
swap='kpt-22'),
22:
dict(
name='kpt-22',
id=22,
color=[255, 255, 255],
type='',
swap='kpt-21'),
23:
dict(
name='kpt-23',
id=23,
color=[255, 255, 255],
type='',
swap='kpt-20'),
24:
dict(
name='kpt-24',
id=24,
color=[255, 255, 255],
type='',
swap='kpt-19'),
25:
dict(
name='kpt-25',
id=25,
color=[255, 255, 255],
type='',
swap='kpt-18'),
26:
dict(
name='kpt-26',
id=26,
color=[255, 255, 255],
type='',
swap='kpt-17'),
27:
dict(name='kpt-27', id=27, color=[255, 255, 255], type='', swap=''),
28:
dict(name='kpt-28', id=28, color=[255, 255, 255], type='', swap=''),
29:
dict(name='kpt-29', id=29, color=[255, 255, 255], type='', swap=''),
30:
dict(name='kpt-30', id=30, color=[255, 255, 255], type='', swap=''),
31:
dict(
name='kpt-31',
id=31,
color=[255, 255, 255],
type='',
swap='kpt-35'),
32:
dict(
name='kpt-32',
id=32,
color=[255, 255, 255],
type='',
swap='kpt-34'),
33:
dict(name='kpt-33', id=33, color=[255, 255, 255], type='', swap=''),
34:
dict(
name='kpt-34',
id=34,
color=[255, 255, 255],
type='',
swap='kpt-32'),
35:
dict(
name='kpt-35',
id=35,
color=[255, 255, 255],
type='',
swap='kpt-31'),
36:
dict(
name='kpt-36',
id=36,
color=[255, 255, 255],
type='',
swap='kpt-45'),
37:
dict(
name='kpt-37',
id=37,
color=[255, 255, 255],
type='',
swap='kpt-44'),
38:
dict(
name='kpt-38',
id=38,
color=[255, 255, 255],
type='',
swap='kpt-43'),
39:
dict(
name='kpt-39',
id=39,
color=[255, 255, 255],
type='',
swap='kpt-42'),
40:
dict(
name='kpt-40',
id=40,
color=[255, 255, 255],
type='',
swap='kpt-47'),
41:
dict(
name='kpt-41',
id=41,
color=[255, 255, 255],
type='',
swap='kpt-46'),
42:
dict(
name='kpt-42',
id=42,
color=[255, 255, 255],
type='',
swap='kpt-39'),
43:
dict(
name='kpt-43',
id=43,
color=[255, 255, 255],
type='',
swap='kpt-38'),
44:
dict(
name='kpt-44',
id=44,
color=[255, 255, 255],
type='',
swap='kpt-37'),
45:
dict(
name='kpt-45',
id=45,
color=[255, 255, 255],
type='',
swap='kpt-36'),
46:
dict(
name='kpt-46',
id=46,
color=[255, 255, 255],
type='',
swap='kpt-41'),
47:
dict(
name='kpt-47',
id=47,
color=[255, 255, 255],
type='',
swap='kpt-40'),
48:
dict(
name='kpt-48',
id=48,
color=[255, 255, 255],
type='',
swap='kpt-54'),
49:
dict(
name='kpt-49',
id=49,
color=[255, 255, 255],
type='',
swap='kpt-53'),
50:
dict(
name='kpt-50',
id=50,
color=[255, 255, 255],
type='',
swap='kpt-52'),
51:
dict(name='kpt-51', id=51, color=[255, 255, 255], type='', swap=''),
52:
dict(
name='kpt-52',
id=52,
color=[255, 255, 255],
type='',
swap='kpt-50'),
53:
dict(
name='kpt-53',
id=53,
color=[255, 255, 255],
type='',
swap='kpt-49'),
54:
dict(
name='kpt-54',
id=54,
color=[255, 255, 255],
type='',
swap='kpt-48'),
55:
dict(
name='kpt-55',
id=55,
color=[255, 255, 255],
type='',
swap='kpt-59'),
56:
dict(
name='kpt-56',
id=56,
color=[255, 255, 255],
type='',
swap='kpt-58'),
57:
dict(name='kpt-57', id=57, color=[255, 255, 255], type='', swap=''),
58:
dict(
name='kpt-58',
id=58,
color=[255, 255, 255],
type='',
swap='kpt-56'),
59:
dict(
name='kpt-59',
id=59,
color=[255, 255, 255],
type='',
swap='kpt-55'),
60:
dict(
name='kpt-60',
id=60,
color=[255, 255, 255],
type='',
swap='kpt-64'),
61:
dict(
name='kpt-61',
id=61,
color=[255, 255, 255],
type='',
swap='kpt-63'),
62:
dict(name='kpt-62', id=62, color=[255, 255, 255], type='', swap=''),
63:
dict(
name='kpt-63',
id=63,
color=[255, 255, 255],
type='',
swap='kpt-61'),
64:
dict(
name='kpt-64',
id=64,
color=[255, 255, 255],
type='',
swap='kpt-60'),
65:
dict(
name='kpt-65',
id=65,
color=[255, 255, 255],
type='',
swap='kpt-67'),
66:
dict(name='kpt-66', id=66, color=[255, 255, 255], type='', swap=''),
67:
dict(
name='kpt-67',
id=67,
color=[255, 255, 255],
type='',
swap='kpt-65'),
},
skeleton_info={},
joint_weights=[1.] * 68,
sigmas=[])
configs/_base_/datasets/aflw.py 0 → 100755
View file @ 37c8cebc
dataset_info = dict(
dataset_name='aflw',
paper_info=dict(
author='Koestinger, Martin and Wohlhart, Paul and '
'Roth, Peter M and Bischof, Horst',
title='Annotated facial landmarks in the wild: '
'A large-scale, real-world database for facial '
'landmark localization',
container='2011 IEEE international conference on computer '
'vision workshops (ICCV workshops)',
year='2011',
homepage='https://www.tugraz.at/institute/icg/research/'
'team-bischof/lrs/downloads/aflw/',
),
keypoint_info={
0:
dict(name='kpt-0', id=0, color=[255, 255, 255], type='', swap='kpt-5'),
1:
dict(name='kpt-1', id=1, color=[255, 255, 255], type='', swap='kpt-4'),
2:
dict(name='kpt-2', id=2, color=[255, 255, 255], type='', swap='kpt-3'),
3:
dict(name='kpt-3', id=3, color=[255, 255, 255], type='', swap='kpt-2'),
4:
dict(name='kpt-4', id=4, color=[255, 255, 255], type='', swap='kpt-1'),
5:
dict(name='kpt-5', id=5, color=[255, 255, 255], type='', swap='kpt-0'),
6:
dict(
name='kpt-6', id=6, color=[255, 255, 255], type='', swap='kpt-11'),
7:
dict(
name='kpt-7', id=7, color=[255, 255, 255], type='', swap='kpt-10'),
8:
dict(name='kpt-8', id=8, color=[255, 255, 255], type='', swap='kpt-9'),
9:
dict(name='kpt-9', id=9, color=[255, 255, 255], type='', swap='kpt-8'),
10:
dict(
name='kpt-10', id=10, color=[255, 255, 255], type='',
swap='kpt-7'),
11:
dict(
name='kpt-11', id=11, color=[255, 255, 255], type='',
swap='kpt-6'),
12:
dict(
name='kpt-12',
id=12,
color=[255, 255, 255],
type='',
swap='kpt-14'),
13:
dict(name='kpt-13', id=13, color=[255, 255, 255], type='', swap=''),
14:
dict(
name='kpt-14',
id=14,
color=[255, 255, 255],
type='',
swap='kpt-12'),
15:
dict(
name='kpt-15',
id=15,
color=[255, 255, 255],
type='',
swap='kpt-17'),
16:
dict(name='kpt-16', id=16, color=[255, 255, 255], type='', swap=''),
17:
dict(
name='kpt-17',
id=17,
color=[255, 255, 255],
type='',
swap='kpt-15'),
18:
dict(name='kpt-18', id=18, color=[255, 255, 255], type='', swap='')
},
skeleton_info={},
joint_weights=[1.] * 19,
sigmas=[])
configs/_base_/datasets/aic.py 0 → 100755
View file @ 37c8cebc
dataset_info = dict(
dataset_name='aic',
paper_info=dict(
author='Wu, Jiahong and Zheng, He and Zhao, Bo and '
'Li, Yixin and Yan, Baoming and Liang, Rui and '
'Wang, Wenjia and Zhou, Shipei and Lin, Guosen and '
'Fu, Yanwei and others',
title='Ai challenger: A large-scale dataset for going '
'deeper in image understanding',
container='arXiv',
year='2017',
homepage='https://github.com/AIChallenger/AI_Challenger_2017',
),
keypoint_info={
0:
dict(
name='right_shoulder',
id=0,
color=[255, 128, 0],
type='upper',
swap='left_shoulder'),
1:
dict(
name='right_elbow',
id=1,
color=[255, 128, 0],
type='upper',
swap='left_elbow'),
2:
dict(
name='right_wrist',
id=2,
color=[255, 128, 0],
type='upper',
swap='left_wrist'),
3:
dict(
name='left_shoulder',
id=3,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
4:
dict(
name='left_elbow',
id=4,
color=[0, 255, 0],
type='upper',
swap='right_elbow'),
5:
dict(
name='left_wrist',
id=5,
color=[0, 255, 0],
type='upper',
swap='right_wrist'),
6:
dict(
name='right_hip',
id=6,
color=[255, 128, 0],
type='lower',
swap='left_hip'),
7:
dict(
name='right_knee',
id=7,
color=[255, 128, 0],
type='lower',
swap='left_knee'),
8:
dict(
name='right_ankle',
id=8,
color=[255, 128, 0],
type='lower',
swap='left_ankle'),
9:
dict(
name='left_hip',
id=9,
color=[0, 255, 0],
type='lower',
swap='right_hip'),
10:
dict(
name='left_knee',
id=10,
color=[0, 255, 0],
type='lower',
swap='right_knee'),
11:
dict(
name='left_ankle',
id=11,
color=[0, 255, 0],
type='lower',
swap='right_ankle'),
12:
dict(
name='head_top',
id=12,
color=[51, 153, 255],
type='upper',
swap=''),
13:
dict(name='neck', id=13, color=[51, 153, 255], type='upper', swap='')
},
skeleton_info={
0:
dict(link=('right_wrist', 'right_elbow'), id=0, color=[255, 128, 0]),
1: dict(
link=('right_elbow', 'right_shoulder'), id=1, color=[255, 128, 0]),
2: dict(link=('right_shoulder', 'neck'), id=2, color=[51, 153, 255]),
3: dict(link=('neck', 'left_shoulder'), id=3, color=[51, 153, 255]),
4: dict(link=('left_shoulder', 'left_elbow'), id=4, color=[0, 255, 0]),
5: dict(link=('left_elbow', 'left_wrist'), id=5, color=[0, 255, 0]),
6: dict(link=('right_ankle', 'right_knee'), id=6, color=[255, 128, 0]),
7: dict(link=('right_knee', 'right_hip'), id=7, color=[255, 128, 0]),
8: dict(link=('right_hip', 'left_hip'), id=8, color=[51, 153, 255]),
9: dict(link=('left_hip', 'left_knee'), id=9, color=[0, 255, 0]),
10: dict(link=('left_knee', 'left_ankle'), id=10, color=[0, 255, 0]),
11: dict(link=('head_top', 'neck'), id=11, color=[51, 153, 255]),
12: dict(
link=('right_shoulder', 'right_hip'), id=12, color=[51, 153, 255]),
13:
dict(link=('left_shoulder', 'left_hip'), id=13, color=[51, 153, 255])
},
joint_weights=[
1., 1.2, 1.5, 1., 1.2, 1.5, 1., 1.2, 1.5, 1., 1.2, 1.5, 1., 1.
],
# 'https://github.com/AIChallenger/AI_Challenger_2017/blob/master/'
# 'Evaluation/keypoint_eval/keypoint_eval.py#L50'
# delta = 2 x sigma
sigmas=[
0.01388152, 0.01515228, 0.01057665, 0.01417709, 0.01497891, 0.01402144,
0.03909642, 0.03686941, 0.01981803, 0.03843971, 0.03412318, 0.02415081,
0.01291456, 0.01236173
])
configs/_base_/datasets/animalpose.py 0 → 100755
View file @ 37c8cebc
dataset_info = dict(
dataset_name='animalpose',
paper_info=dict(
author='Cao, Jinkun and Tang, Hongyang and Fang, Hao-Shu and '
'Shen, Xiaoyong and Lu, Cewu and Tai, Yu-Wing',
title='Cross-Domain Adaptation for Animal Pose Estimation',
container='The IEEE International Conference on '
'Computer Vision (ICCV)',
year='2019',
homepage='https://sites.google.com/view/animal-pose/',
),
keypoint_info={
0:
dict(
name='L_Eye', id=0, color=[0, 255, 0], type='upper', swap='R_Eye'),
1:
dict(
name='R_Eye',
id=1,
color=[255, 128, 0],
type='upper',
swap='L_Eye'),
2:
dict(
name='L_EarBase',
id=2,
color=[0, 255, 0],
type='upper',
swap='R_EarBase'),
3:
dict(
name='R_EarBase',
id=3,
color=[255, 128, 0],
type='upper',
swap='L_EarBase'),
4:
dict(name='Nose', id=4, color=[51, 153, 255], type='upper', swap=''),
5:
dict(name='Throat', id=5, color=[51, 153, 255], type='upper', swap=''),
6:
dict(
name='TailBase', id=6, color=[51, 153, 255], type='lower',
swap=''),
7:
dict(
name='Withers', id=7, color=[51, 153, 255], type='upper', swap=''),
8:
dict(
name='L_F_Elbow',
id=8,
color=[0, 255, 0],
type='upper',
swap='R_F_Elbow'),
9:
dict(
name='R_F_Elbow',
id=9,
color=[255, 128, 0],
type='upper',
swap='L_F_Elbow'),
10:
dict(
name='L_B_Elbow',
id=10,
color=[0, 255, 0],
type='lower',
swap='R_B_Elbow'),
11:
dict(
name='R_B_Elbow',
id=11,
color=[255, 128, 0],
type='lower',
swap='L_B_Elbow'),
12:
dict(
name='L_F_Knee',
id=12,
color=[0, 255, 0],
type='upper',
swap='R_F_Knee'),
13:
dict(
name='R_F_Knee',
id=13,
color=[255, 128, 0],
type='upper',
swap='L_F_Knee'),
14:
dict(
name='L_B_Knee',
id=14,
color=[0, 255, 0],
type='lower',
swap='R_B_Knee'),
15:
dict(
name='R_B_Knee',
id=15,
color=[255, 128, 0],
type='lower',
swap='L_B_Knee'),
16:
dict(
name='L_F_Paw',
id=16,
color=[0, 255, 0],
type='upper',
swap='R_F_Paw'),
17:
dict(
name='R_F_Paw',
id=17,
color=[255, 128, 0],
type='upper',
swap='L_F_Paw'),
18:
dict(
name='L_B_Paw',
id=18,
color=[0, 255, 0],
type='lower',
swap='R_B_Paw'),
19:
dict(
name='R_B_Paw',
id=19,
color=[255, 128, 0],
type='lower',
swap='L_B_Paw')
},
skeleton_info={
0: dict(link=('L_Eye', 'R_Eye'), id=0, color=[51, 153, 255]),
1: dict(link=('L_Eye', 'L_EarBase'), id=1, color=[0, 255, 0]),
2: dict(link=('R_Eye', 'R_EarBase'), id=2, color=[255, 128, 0]),
3: dict(link=('L_Eye', 'Nose'), id=3, color=[0, 255, 0]),
4: dict(link=('R_Eye', 'Nose'), id=4, color=[255, 128, 0]),
5: dict(link=('Nose', 'Throat'), id=5, color=[51, 153, 255]),
6: dict(link=('Throat', 'Withers'), id=6, color=[51, 153, 255]),
7: dict(link=('TailBase', 'Withers'), id=7, color=[51, 153, 255]),
8: dict(link=('Throat', 'L_F_Elbow'), id=8, color=[0, 255, 0]),
9: dict(link=('L_F_Elbow', 'L_F_Knee'), id=9, color=[0, 255, 0]),
10: dict(link=('L_F_Knee', 'L_F_Paw'), id=10, color=[0, 255, 0]),
11: dict(link=('Throat', 'R_F_Elbow'), id=11, color=[255, 128, 0]),
12: dict(link=('R_F_Elbow', 'R_F_Knee'), id=12, color=[255, 128, 0]),
13: dict(link=('R_F_Knee', 'R_F_Paw'), id=13, color=[255, 128, 0]),
14: dict(link=('TailBase', 'L_B_Elbow'), id=14, color=[0, 255, 0]),
15: dict(link=('L_B_Elbow', 'L_B_Knee'), id=15, color=[0, 255, 0]),
16: dict(link=('L_B_Knee', 'L_B_Paw'), id=16, color=[0, 255, 0]),
17: dict(link=('TailBase', 'R_B_Elbow'), id=17, color=[255, 128, 0]),
18: dict(link=('R_B_Elbow', 'R_B_Knee'), id=18, color=[255, 128, 0]),
19: dict(link=('R_B_Knee', 'R_B_Paw'), id=19, color=[255, 128, 0])
},
joint_weights=[
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.2, 1.2,
1.5, 1.5, 1.5, 1.5
],
# Note: The original paper did not provide enough information about
# the sigmas. We modified from 'https://github.com/cocodataset/'
# 'cocoapi/blob/master/PythonAPI/pycocotools/cocoeval.py#L523'
sigmas=[
0.025, 0.025, 0.026, 0.035, 0.035, 0.10, 0.10, 0.10, 0.107, 0.107,
0.107, 0.107, 0.087, 0.087, 0.087, 0.087, 0.089, 0.089, 0.089, 0.089
])
configs/_base_/datasets/ap10k.py 0 → 100755
View file @ 37c8cebc
dataset_info = dict(
dataset_name='ap10k',
paper_info=dict(
author='Yu, Hang and Xu, Yufei and Zhang, Jing and '
'Zhao, Wei and Guan, Ziyu and Tao, Dacheng',
title='AP-10K: A Benchmark for Animal Pose Estimation in the Wild',
container='35th Conference on Neural Information Processing Systems '
'(NeurIPS 2021) Track on Datasets and Bench-marks.',
year='2021',
homepage='https://github.com/AlexTheBad/AP-10K',
),
keypoint_info={
0:
dict(
name='L_Eye', id=0, color=[0, 255, 0], type='upper', swap='R_Eye'),
1:
dict(
name='R_Eye',
id=1,
color=[255, 128, 0],
type='upper',
swap='L_Eye'),
2:
dict(name='Nose', id=2, color=[51, 153, 255], type='upper', swap=''),
3:
dict(name='Neck', id=3, color=[51, 153, 255], type='upper', swap=''),
4:
dict(
name='Root of tail',
id=4,
color=[51, 153, 255],
type='lower',
swap=''),
5:
dict(
name='L_Shoulder',
id=5,
color=[51, 153, 255],
type='upper',
swap='R_Shoulder'),
6:
dict(
name='L_Elbow',
id=6,
color=[51, 153, 255],
type='upper',
swap='R_Elbow'),
7:
dict(
name='L_F_Paw',
id=7,
color=[0, 255, 0],
type='upper',
swap='R_F_Paw'),
8:
dict(
name='R_Shoulder',
id=8,
color=[0, 255, 0],
type='upper',
swap='L_Shoulder'),
9:
dict(
name='R_Elbow',
id=9,
color=[255, 128, 0],
type='upper',
swap='L_Elbow'),
10:
dict(
name='R_F_Paw',
id=10,
color=[0, 255, 0],
type='lower',
swap='L_F_Paw'),
11:
dict(
name='L_Hip',
id=11,
color=[255, 128, 0],
type='lower',
swap='R_Hip'),
12:
dict(
name='L_Knee',
id=12,
color=[255, 128, 0],
type='lower',
swap='R_Knee'),
13:
dict(
name='L_B_Paw',
id=13,
color=[0, 255, 0],
type='lower',
swap='R_B_Paw'),
14:
dict(
name='R_Hip', id=14, color=[0, 255, 0], type='lower',
swap='L_Hip'),
15:
dict(
name='R_Knee',
id=15,
color=[0, 255, 0],
type='lower',
swap='L_Knee'),
16:
dict(
name='R_B_Paw',
id=16,
color=[0, 255, 0],
type='lower',
swap='L_B_Paw'),
},
skeleton_info={
0: dict(link=('L_Eye', 'R_Eye'), id=0, color=[0, 0, 255]),
1: dict(link=('L_Eye', 'Nose'), id=1, color=[0, 0, 255]),
2: dict(link=('R_Eye', 'Nose'), id=2, color=[0, 0, 255]),
3: dict(link=('Nose', 'Neck'), id=3, color=[0, 255, 0]),
4: dict(link=('Neck', 'Root of tail'), id=4, color=[0, 255, 0]),
5: dict(link=('Neck', 'L_Shoulder'), id=5, color=[0, 255, 255]),
6: dict(link=('L_Shoulder', 'L_Elbow'), id=6, color=[0, 255, 255]),
7: dict(link=('L_Elbow', 'L_F_Paw'), id=6, color=[0, 255, 255]),
8: dict(link=('Neck', 'R_Shoulder'), id=7, color=[6, 156, 250]),
9: dict(link=('R_Shoulder', 'R_Elbow'), id=8, color=[6, 156, 250]),
10: dict(link=('R_Elbow', 'R_F_Paw'), id=9, color=[6, 156, 250]),
11: dict(link=('Root of tail', 'L_Hip'), id=10, color=[0, 255, 255]),
12: dict(link=('L_Hip', 'L_Knee'), id=11, color=[0, 255, 255]),
13: dict(link=('L_Knee', 'L_B_Paw'), id=12, color=[0, 255, 255]),
14: dict(link=('Root of tail', 'R_Hip'), id=13, color=[6, 156, 250]),
15: dict(link=('R_Hip', 'R_Knee'), id=14, color=[6, 156, 250]),
16: dict(link=('R_Knee', 'R_B_Paw'), id=15, color=[6, 156, 250]),
},
joint_weights=[
1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5,
1.5
],
sigmas=[
0.025, 0.025, 0.026, 0.035, 0.035, 0.079, 0.072, 0.062, 0.079, 0.072,
0.062, 0.107, 0.087, 0.089, 0.107, 0.087, 0.089
])
configs/_base_/datasets/atrw.py 0 → 100755
View file @ 37c8cebc
dataset_info = dict(
dataset_name='atrw',
paper_info=dict(
author='Li, Shuyuan and Li, Jianguo and Tang, Hanlin '
'and Qian, Rui and Lin, Weiyao',
title='ATRW: A Benchmark for Amur Tiger '
'Re-identification in the Wild',
container='Proceedings of the 28th ACM '
'International Conference on Multimedia',
year='2020',
homepage='https://cvwc2019.github.io/challenge.html',
),
keypoint_info={
0:
dict(
name='left_ear',
id=0,
color=[51, 153, 255],
type='upper',
swap='right_ear'),
1:
dict(
name='right_ear',
id=1,
color=[51, 153, 255],
type='upper',
swap='left_ear'),
2:
dict(name='nose', id=2, color=[51, 153, 255], type='upper', swap=''),
3:
dict(
name='right_shoulder',
id=3,
color=[255, 128, 0],
type='upper',
swap='left_shoulder'),
4:
dict(
name='right_front_paw',
id=4,
color=[255, 128, 0],
type='upper',
swap='left_front_paw'),
5:
dict(
name='left_shoulder',
id=5,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
6:
dict(
name='left_front_paw',
id=6,
color=[0, 255, 0],
type='upper',
swap='right_front_paw'),
7:
dict(
name='right_hip',
id=7,
color=[255, 128, 0],
type='lower',
swap='left_hip'),
8:
dict(
name='right_knee',
id=8,
color=[255, 128, 0],
type='lower',
swap='left_knee'),
9:
dict(
name='right_back_paw',
id=9,
color=[255, 128, 0],
type='lower',
swap='left_back_paw'),
10:
dict(
name='left_hip',
id=10,
color=[0, 255, 0],
type='lower',
swap='right_hip'),
11:
dict(
name='left_knee',
id=11,
color=[0, 255, 0],
type='lower',
swap='right_knee'),
12:
dict(
name='left_back_paw',
id=12,
color=[0, 255, 0],
type='lower',
swap='right_back_paw'),
13:
dict(name='tail', id=13, color=[51, 153, 255], type='lower', swap=''),
14:
dict(
name='center', id=14, color=[51, 153, 255], type='lower', swap=''),
},
skeleton_info={
0:
dict(link=('left_ear', 'nose'), id=0, color=[51, 153, 255]),
1:
dict(link=('right_ear', 'nose'), id=1, color=[51, 153, 255]),
2:
dict(link=('nose', 'center'), id=2, color=[51, 153, 255]),
3:
dict(
link=('left_shoulder', 'left_front_paw'), id=3, color=[0, 255, 0]),
4:
dict(link=('left_shoulder', 'center'), id=4, color=[0, 255, 0]),
5:
dict(
link=('right_shoulder', 'right_front_paw'),
id=5,
color=[255, 128, 0]),
6:
dict(link=('right_shoulder', 'center'), id=6, color=[255, 128, 0]),
7:
dict(link=('tail', 'center'), id=7, color=[51, 153, 255]),
8:
dict(link=('right_back_paw', 'right_knee'), id=8, color=[255, 128, 0]),
9:
dict(link=('right_knee', 'right_hip'), id=9, color=[255, 128, 0]),
10:
dict(link=('right_hip', 'tail'), id=10, color=[255, 128, 0]),
11:
dict(link=('left_back_paw', 'left_knee'), id=11, color=[0, 255, 0]),
12:
dict(link=('left_knee', 'left_hip'), id=12, color=[0, 255, 0]),
13:
dict(link=('left_hip', 'tail'), id=13, color=[0, 255, 0]),
},
joint_weights=[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
sigmas=[
0.0277, 0.0823, 0.0831, 0.0202, 0.0716, 0.0263, 0.0646, 0.0302, 0.0440,
0.0316, 0.0333, 0.0547, 0.0263, 0.0683, 0.0539
])
configs/_base_/datasets/campus.py 0 → 100755
View file @ 37c8cebc
dataset_info = dict(
dataset_name='campus',
paper_info=dict(
author='Belagiannis, Vasileios and Amin, Sikandar and Andriluka, '
'Mykhaylo and Schiele, Bernt and Navab, Nassir and Ilic, Slobodan',
title='3D Pictorial Structures for Multiple Human Pose Estimation',
container='IEEE Computer Society Conference on Computer Vision and '
'Pattern Recognition (CVPR)',
year='2014',
homepage='http://campar.in.tum.de/Chair/MultiHumanPose',
),
keypoint_info={
0:
dict(
name='right_ankle',
id=0,
color=[255, 128, 0],
type='lower',
swap='left_ankle'),
1:
dict(
name='right_knee',
id=1,
color=[255, 128, 0],
type='lower',
swap='left_knee'),
2:
dict(
name='right_hip',
id=2,
color=[255, 128, 0],
type='lower',
swap='left_hip'),
3:
dict(
name='left_hip',
id=3,
color=[0, 255, 0],
type='lower',
swap='right_hip'),
4:
dict(
name='left_knee',
id=4,
color=[0, 255, 0],
type='lower',
swap='right_knee'),
5:
dict(
name='left_ankle',
id=5,
color=[0, 255, 0],
type='lower',
swap='right_ankle'),
6:
dict(
name='right_wrist',
id=6,
color=[255, 128, 0],
type='upper',
swap='left_wrist'),
7:
dict(
name='right_elbow',
id=7,
color=[255, 128, 0],
type='upper',
swap='left_elbow'),
8:
dict(
name='right_shoulder',
id=8,
color=[255, 128, 0],
type='upper',
swap='left_shoulder'),
9:
dict(
name='left_shoulder',
id=9,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
10:
dict(
name='left_elbow',
id=10,
color=[0, 255, 0],
type='upper',
swap='right_elbow'),
11:
dict(
name='left_wrist',
id=11,
color=[0, 255, 0],
type='upper',
swap='right_wrist'),
12:
dict(
name='bottom_head',
id=12,
color=[51, 153, 255],
type='upper',
swap=''),
13:
dict(
name='top_head',
id=13,
color=[51, 153, 255],
type='upper',
swap=''),
},
skeleton_info={
0:
dict(link=('right_ankle', 'right_knee'), id=0, color=[255, 128, 0]),
1:
dict(link=('right_knee', 'right_hip'), id=1, color=[255, 128, 0]),
2:
dict(link=('left_hip', 'left_knee'), id=2, color=[0, 255, 0]),
3:
dict(link=('left_knee', 'left_ankle'), id=3, color=[0, 255, 0]),
4:
dict(link=('right_hip', 'left_hip'), id=4, color=[51, 153, 255]),
5:
dict(link=('right_wrist', 'right_elbow'), id=5, color=[255, 128, 0]),
6:
dict(
link=('right_elbow', 'right_shoulder'), id=6, color=[255, 128, 0]),
7:
dict(link=('left_shoulder', 'left_elbow'), id=7, color=[0, 255, 0]),
8:
dict(link=('left_elbow', 'left_wrist'), id=8, color=[0, 255, 0]),
9:
dict(link=('right_hip', 'right_shoulder'), id=9, color=[255, 128, 0]),
10:
dict(link=('left_hip', 'left_shoulder'), id=10, color=[0, 255, 0]),
11:
dict(
link=('right_shoulder', 'bottom_head'), id=11, color=[255, 128,
0]),
12:
dict(link=('left_shoulder', 'bottom_head'), id=12, color=[0, 255, 0]),
13:
dict(link=('bottom_head', 'top_head'), id=13, color=[51, 153, 255]),
},
joint_weights=[
1.5, 1.2, 1.0, 1.0, 1.2, 1.5, 1.5, 1.2, 1.0, 1.0, 1.2, 1.5, 1.0, 1.0
],
sigmas=[
0.089, 0.087, 0.107, 0.107, 0.087, 0.089, 0.062, 0.072, 0.079, 0.079,
0.072, 0.062, 0.026, 0.026
])
configs/_base_/datasets/coco.py 0 → 100755
View file @ 37c8cebc
dataset_info = dict(
dataset_name='coco',
paper_info=dict(
author='Lin, Tsung-Yi and Maire, Michael and '
'Belongie, Serge and Hays, James and '
'Perona, Pietro and Ramanan, Deva and '
r'Doll{\'a}r, Piotr and Zitnick, C Lawrence',
title='Microsoft coco: Common objects in context',
container='European conference on computer vision',
year='2014',
homepage='http://cocodataset.org/',
),
keypoint_info={
0:
dict(name='nose', id=0, color=[51, 153, 255], type='upper', swap=''),
1:
dict(
name='left_eye',
id=1,
color=[51, 153, 255],
type='upper',
swap='right_eye'),
2:
dict(
name='right_eye',
id=2,
color=[51, 153, 255],
type='upper',
swap='left_eye'),
3:
dict(
name='left_ear',
id=3,
color=[51, 153, 255],
type='upper',
swap='right_ear'),
4:
dict(
name='right_ear',
id=4,
color=[51, 153, 255],
type='upper',
swap='left_ear'),
5:
dict(
name='left_shoulder',
id=5,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
6:
dict(
name='right_shoulder',
id=6,
color=[255, 128, 0],
type='upper',
swap='left_shoulder'),
7:
dict(
name='left_elbow',
id=7,
color=[0, 255, 0],
type='upper',
swap='right_elbow'),
8:
dict(
name='right_elbow',
id=8,
color=[255, 128, 0],
type='upper',
swap='left_elbow'),
9:
dict(
name='left_wrist',
id=9,
color=[0, 255, 0],
type='upper',
swap='right_wrist'),
10:
dict(
name='right_wrist',
id=10,
color=[255, 128, 0],
type='upper',
swap='left_wrist'),
11:
dict(
name='left_hip',
id=11,
color=[0, 255, 0],
type='lower',
swap='right_hip'),
12:
dict(
name='right_hip',
id=12,
color=[255, 128, 0],
type='lower',
swap='left_hip'),
13:
dict(
name='left_knee',
id=13,
color=[0, 255, 0],
type='lower',
swap='right_knee'),
14:
dict(
name='right_knee',
id=14,
color=[255, 128, 0],
type='lower',
swap='left_knee'),
15:
dict(
name='left_ankle',
id=15,
color=[0, 255, 0],
type='lower',
swap='right_ankle'),
16:
dict(
name='right_ankle',
id=16,
color=[255, 128, 0],
type='lower',
swap='left_ankle')
},
skeleton_info={
0:
dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]),
1:
dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]),
2:
dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]),
3:
dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]),
4:
dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]),
5:
dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]),
6:
dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]),
7:
dict(
link=('left_shoulder', 'right_shoulder'),
id=7,
color=[51, 153, 255]),
8:
dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]),
9:
dict(
link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]),
10:
dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]),
11:
dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]),
12:
dict(link=('left_eye', 'right_eye'), id=12, color=[51, 153, 255]),
13:
dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]),
14:
dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]),
15:
dict(link=('left_eye', 'left_ear'), id=15, color=[51, 153, 255]),
16:
dict(link=('right_eye', 'right_ear'), id=16, color=[51, 153, 255]),
17:
dict(link=('left_ear', 'left_shoulder'), id=17, color=[51, 153, 255]),
18:
dict(
link=('right_ear', 'right_shoulder'), id=18, color=[51, 153, 255])
},
joint_weights=[
1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5,
1.5
],
sigmas=[
0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062,
0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089
])
configs/_base_/datasets/coco_wholebody.py 0 → 100755
View file @ 37c8cebc
dataset_info = dict(
dataset_name='coco_wholebody',
paper_info=dict(
author='Jin, Sheng and Xu, Lumin and Xu, Jin and '
'Wang, Can and Liu, Wentao and '
'Qian, Chen and Ouyang, Wanli and Luo, Ping',
title='Whole-Body Human Pose Estimation in the Wild',
container='Proceedings of the European '
'Conference on Computer Vision (ECCV)',
year='2020',
homepage='https://github.com/jin-s13/COCO-WholeBody/',
),
keypoint_info={
0:
dict(name='nose', id=0, color=[51, 153, 255], type='upper', swap=''),
1:
dict(
name='left_eye',
id=1,
color=[51, 153, 255],
type='upper',
swap='right_eye'),
2:
dict(
name='right_eye',
id=2,
color=[51, 153, 255],
type='upper',
swap='left_eye'),
3:
dict(
name='left_ear',
id=3,
color=[51, 153, 255],
type='upper',
swap='right_ear'),
4:
dict(
name='right_ear',
id=4,
color=[51, 153, 255],
type='upper',
swap='left_ear'),
5:
dict(
name='left_shoulder',
id=5,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
6:
dict(
name='right_shoulder',
id=6,
color=[255, 128, 0],
type='upper',
swap='left_shoulder'),
7:
dict(
name='left_elbow',
id=7,
color=[0, 255, 0],
type='upper',
swap='right_elbow'),
8:
dict(
name='right_elbow',
id=8,
color=[255, 128, 0],
type='upper',
swap='left_elbow'),
9:
dict(
name='left_wrist',
id=9,
color=[0, 255, 0],
type='upper',
swap='right_wrist'),
10:
dict(
name='right_wrist',
id=10,
color=[255, 128, 0],
type='upper',
swap='left_wrist'),
11:
dict(
name='left_hip',
id=11,
color=[0, 255, 0],
type='lower',
swap='right_hip'),
12:
dict(
name='right_hip',
id=12,
color=[255, 128, 0],
type='lower',
swap='left_hip'),
13:
dict(
name='left_knee',
id=13,
color=[0, 255, 0],
type='lower',
swap='right_knee'),
14:
dict(
name='right_knee',
id=14,
color=[255, 128, 0],
type='lower',
swap='left_knee'),
15:
dict(
name='left_ankle',
id=15,
color=[0, 255, 0],
type='lower',
swap='right_ankle'),
16:
dict(
name='right_ankle',
id=16,
color=[255, 128, 0],
type='lower',
swap='left_ankle'),
17:
dict(
name='left_big_toe',
id=17,
color=[255, 128, 0],
type='lower',
swap='right_big_toe'),
18:
dict(
name='left_small_toe',
id=18,
color=[255, 128, 0],
type='lower',
swap='right_small_toe'),
19:
dict(
name='left_heel',
id=19,
color=[255, 128, 0],
type='lower',
swap='right_heel'),
20:
dict(
name='right_big_toe',
id=20,
color=[255, 128, 0],
type='lower',
swap='left_big_toe'),
21:
dict(
name='right_small_toe',
id=21,
color=[255, 128, 0],
type='lower',
swap='left_small_toe'),
22:
dict(
name='right_heel',
id=22,
color=[255, 128, 0],
type='lower',
swap='left_heel'),
23:
dict(
name='face-0',
id=23,
color=[255, 255, 255],
type='',
swap='face-16'),
24:
dict(
name='face-1',
id=24,
color=[255, 255, 255],
type='',
swap='face-15'),
25:
dict(
name='face-2',
id=25,
color=[255, 255, 255],
type='',
swap='face-14'),
26:
dict(
name='face-3',
id=26,
color=[255, 255, 255],
type='',
swap='face-13'),
27:
dict(
name='face-4',
id=27,
color=[255, 255, 255],
type='',
swap='face-12'),
28:
dict(
name='face-5',
id=28,
color=[255, 255, 255],
type='',
swap='face-11'),
29:
dict(
name='face-6',
id=29,
color=[255, 255, 255],
type='',
swap='face-10'),
30:
dict(
name='face-7',
id=30,
color=[255, 255, 255],
type='',
swap='face-9'),
31:
dict(name='face-8', id=31, color=[255, 255, 255], type='', swap=''),
32:
dict(
name='face-9',
id=32,
color=[255, 255, 255],
type='',
swap='face-7'),
33:
dict(
name='face-10',
id=33,
color=[255, 255, 255],
type='',
swap='face-6'),
34:
dict(
name='face-11',
id=34,
color=[255, 255, 255],
type='',
swap='face-5'),
35:
dict(
name='face-12',
id=35,
color=[255, 255, 255],
type='',
swap='face-4'),
36:
dict(
name='face-13',
id=36,
color=[255, 255, 255],
type='',
swap='face-3'),
37:
dict(
name='face-14',
id=37,
color=[255, 255, 255],
type='',
swap='face-2'),
38:
dict(
name='face-15',
id=38,
color=[255, 255, 255],
type='',
swap='face-1'),
39:
dict(
name='face-16',
id=39,
color=[255, 255, 255],
type='',
swap='face-0'),
40:
dict(
name='face-17',
id=40,
color=[255, 255, 255],
type='',
swap='face-26'),
41:
dict(
name='face-18',
id=41,
color=[255, 255, 255],
type='',
swap='face-25'),
42:
dict(
name='face-19',
id=42,
color=[255, 255, 255],
type='',
swap='face-24'),
43:
dict(
name='face-20',
id=43,
color=[255, 255, 255],
type='',
swap='face-23'),
44:
dict(
name='face-21',
id=44,
color=[255, 255, 255],
type='',
swap='face-22'),
45:
dict(
name='face-22',
id=45,
color=[255, 255, 255],
type='',
swap='face-21'),
46:
dict(
name='face-23',
id=46,
color=[255, 255, 255],
type='',
swap='face-20'),
47:
dict(
name='face-24',
id=47,
color=[255, 255, 255],
type='',
swap='face-19'),
48:
dict(
name='face-25',
id=48,
color=[255, 255, 255],
type='',
swap='face-18'),
49:
dict(
name='face-26',
id=49,
color=[255, 255, 255],
type='',
swap='face-17'),
50:
dict(name='face-27', id=50, color=[255, 255, 255], type='', swap=''),
51:
dict(name='face-28', id=51, color=[255, 255, 255], type='', swap=''),
52:
dict(name='face-29', id=52, color=[255, 255, 255], type='', swap=''),
53:
dict(name='face-30', id=53, color=[255, 255, 255], type='', swap=''),
54:
dict(
name='face-31',
id=54,
color=[255, 255, 255],
type='',
swap='face-35'),
55:
dict(
name='face-32',
id=55,
color=[255, 255, 255],
type='',
swap='face-34'),
56:
dict(name='face-33', id=56, color=[255, 255, 255], type='', swap=''),
57:
dict(
name='face-34',
id=57,
color=[255, 255, 255],
type='',
swap='face-32'),
58:
dict(
name='face-35',
id=58,
color=[255, 255, 255],
type='',
swap='face-31'),
59:
dict(
name='face-36',
id=59,
color=[255, 255, 255],
type='',
swap='face-45'),
60:
dict(
name='face-37',
id=60,
color=[255, 255, 255],
type='',
swap='face-44'),
61:
dict(
name='face-38',
id=61,
color=[255, 255, 255],
type='',
swap='face-43'),
62:
dict(
name='face-39',
id=62,
color=[255, 255, 255],
type='',
swap='face-42'),
63:
dict(
name='face-40',
id=63,
color=[255, 255, 255],
type='',
swap='face-47'),
64:
dict(
name='face-41',
id=64,
color=[255, 255, 255],
type='',
swap='face-46'),
65:
dict(
name='face-42',
id=65,
color=[255, 255, 255],
type='',
swap='face-39'),
66:
dict(
name='face-43',
id=66,
color=[255, 255, 255],
type='',
swap='face-38'),
67:
dict(
name='face-44',
id=67,
color=[255, 255, 255],
type='',
swap='face-37'),
68:
dict(
name='face-45',
id=68,
color=[255, 255, 255],
type='',
swap='face-36'),
69:
dict(
name='face-46',
id=69,
color=[255, 255, 255],
type='',
swap='face-41'),
70:
dict(
name='face-47',
id=70,
color=[255, 255, 255],
type='',
swap='face-40'),
71:
dict(
name='face-48',
id=71,
color=[255, 255, 255],
type='',
swap='face-54'),
72:
dict(
name='face-49',
id=72,
color=[255, 255, 255],
type='',
swap='face-53'),
73:
dict(
name='face-50',
id=73,
color=[255, 255, 255],
type='',
swap='face-52'),
74:
dict(name='face-51', id=74, color=[255, 255, 255], type='', swap=''),
75:
dict(
name='face-52',
id=75,
color=[255, 255, 255],
type='',
swap='face-50'),
76:
dict(
name='face-53',
id=76,
color=[255, 255, 255],
type='',
swap='face-49'),
77:
dict(
name='face-54',
id=77,
color=[255, 255, 255],
type='',
swap='face-48'),
78:
dict(
name='face-55',
id=78,
color=[255, 255, 255],
type='',
swap='face-59'),
79:
dict(
name='face-56',
id=79,
color=[255, 255, 255],
type='',
swap='face-58'),
80:
dict(name='face-57', id=80, color=[255, 255, 255], type='', swap=''),
81:
dict(
name='face-58',
id=81,
color=[255, 255, 255],
type='',
swap='face-56'),
82:
dict(
name='face-59',
id=82,
color=[255, 255, 255],
type='',
swap='face-55'),
83:
dict(
name='face-60',
id=83,
color=[255, 255, 255],
type='',
swap='face-64'),
84:
dict(
name='face-61',
id=84,
color=[255, 255, 255],
type='',
swap='face-63'),
85:
dict(name='face-62', id=85, color=[255, 255, 255], type='', swap=''),
86:
dict(
name='face-63',
id=86,
color=[255, 255, 255],
type='',
swap='face-61'),
87:
dict(
name='face-64',
id=87,
color=[255, 255, 255],
type='',
swap='face-60'),
88:
dict(
name='face-65',
id=88,
color=[255, 255, 255],
type='',
swap='face-67'),
89:
dict(name='face-66', id=89, color=[255, 255, 255], type='', swap=''),
90:
dict(
name='face-67',
id=90,
color=[255, 255, 255],
type='',
swap='face-65'),
91:
dict(
name='left_hand_root',
id=91,
color=[255, 255, 255],
type='',
swap='right_hand_root'),
92:
dict(
name='left_thumb1',
id=92,
color=[255, 128, 0],
type='',
swap='right_thumb1'),
93:
dict(
name='left_thumb2',
id=93,
color=[255, 128, 0],
type='',
swap='right_thumb2'),
94:
dict(
name='left_thumb3',
id=94,
color=[255, 128, 0],
type='',
swap='right_thumb3'),
95:
dict(
name='left_thumb4',
id=95,
color=[255, 128, 0],
type='',
swap='right_thumb4'),
96:
dict(
name='left_forefinger1',
id=96,
color=[255, 153, 255],
type='',
swap='right_forefinger1'),
97:
dict(
name='left_forefinger2',
id=97,
color=[255, 153, 255],
type='',
swap='right_forefinger2'),
98:
dict(
name='left_forefinger3',
id=98,
color=[255, 153, 255],
type='',
swap='right_forefinger3'),
99:
dict(
name='left_forefinger4',
id=99,
color=[255, 153, 255],
type='',
swap='right_forefinger4'),
100:
dict(
name='left_middle_finger1',
id=100,
color=[102, 178, 255],
type='',
swap='right_middle_finger1'),
101:
dict(
name='left_middle_finger2',
id=101,
color=[102, 178, 255],
type='',
swap='right_middle_finger2'),
102:
dict(
name='left_middle_finger3',
id=102,
color=[102, 178, 255],
type='',
swap='right_middle_finger3'),
103:
dict(
name='left_middle_finger4',
id=103,
color=[102, 178, 255],
type='',
swap='right_middle_finger4'),
104:
dict(
name='left_ring_finger1',
id=104,
color=[255, 51, 51],
type='',
swap='right_ring_finger1'),
105:
dict(
name='left_ring_finger2',
id=105,
color=[255, 51, 51],
type='',
swap='right_ring_finger2'),
106:
dict(
name='left_ring_finger3',
id=106,
color=[255, 51, 51],
type='',
swap='right_ring_finger3'),
107:
dict(
name='left_ring_finger4',
id=107,
color=[255, 51, 51],
type='',
swap='right_ring_finger4'),
108:
dict(
name='left_pinky_finger1',
id=108,
color=[0, 255, 0],
type='',
swap='right_pinky_finger1'),
109:
dict(
name='left_pinky_finger2',
id=109,
color=[0, 255, 0],
type='',
swap='right_pinky_finger2'),
110:
dict(
name='left_pinky_finger3',
id=110,
color=[0, 255, 0],
type='',
swap='right_pinky_finger3'),
111:
dict(
name='left_pinky_finger4',
id=111,
color=[0, 255, 0],
type='',
swap='right_pinky_finger4'),
112:
dict(
name='right_hand_root',
id=112,
color=[255, 255, 255],
type='',
swap='left_hand_root'),
113:
dict(
name='right_thumb1',
id=113,
color=[255, 128, 0],
type='',
swap='left_thumb1'),
114:
dict(
name='right_thumb2',
id=114,
color=[255, 128, 0],
type='',
swap='left_thumb2'),
115:
dict(
name='right_thumb3',
id=115,
color=[255, 128, 0],
type='',
swap='left_thumb3'),
116:
dict(
name='right_thumb4',
id=116,
color=[255, 128, 0],
type='',
swap='left_thumb4'),
117:
dict(
name='right_forefinger1',
id=117,
color=[255, 153, 255],
type='',
swap='left_forefinger1'),
118:
dict(
name='right_forefinger2',
id=118,
color=[255, 153, 255],
type='',
swap='left_forefinger2'),
119:
dict(
name='right_forefinger3',
id=119,
color=[255, 153, 255],
type='',
swap='left_forefinger3'),
120:
dict(
name='right_forefinger4',
id=120,
color=[255, 153, 255],
type='',
swap='left_forefinger4'),
121:
dict(
name='right_middle_finger1',
id=121,
color=[102, 178, 255],
type='',
swap='left_middle_finger1'),
122:
dict(
name='right_middle_finger2',
id=122,
color=[102, 178, 255],
type='',
swap='left_middle_finger2'),
123:
dict(
name='right_middle_finger3',
id=123,
color=[102, 178, 255],
type='',
swap='left_middle_finger3'),
124:
dict(
name='right_middle_finger4',
id=124,
color=[102, 178, 255],
type='',
swap='left_middle_finger4'),
125:
dict(
name='right_ring_finger1',
id=125,
color=[255, 51, 51],
type='',
swap='left_ring_finger1'),
126:
dict(
name='right_ring_finger2',
id=126,
color=[255, 51, 51],
type='',
swap='left_ring_finger2'),
127:
dict(
name='right_ring_finger3',
id=127,
color=[255, 51, 51],
type='',
swap='left_ring_finger3'),
128:
dict(
name='right_ring_finger4',
id=128,
color=[255, 51, 51],
type='',
swap='left_ring_finger4'),
129:
dict(
name='right_pinky_finger1',
id=129,
color=[0, 255, 0],
type='',
swap='left_pinky_finger1'),
130:
dict(
name='right_pinky_finger2',
id=130,
color=[0, 255, 0],
type='',
swap='left_pinky_finger2'),
131:
dict(
name='right_pinky_finger3',
id=131,
color=[0, 255, 0],
type='',
swap='left_pinky_finger3'),
132:
dict(
name='right_pinky_finger4',
id=132,
color=[0, 255, 0],
type='',
swap='left_pinky_finger4')
},
skeleton_info={
0:
dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]),
1:
dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]),
2:
dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]),
3:
dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]),
4:
dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]),
5:
dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]),
6:
dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]),
7:
dict(
link=('left_shoulder', 'right_shoulder'),
id=7,
color=[51, 153, 255]),
8:
dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]),
9:
dict(
link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]),
10:
dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]),
11:
dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]),
12:
dict(link=('left_eye', 'right_eye'), id=12, color=[51, 153, 255]),
13:
dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]),
14:
dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]),
15:
dict(link=('left_eye', 'left_ear'), id=15, color=[51, 153, 255]),
16:
dict(link=('right_eye', 'right_ear'), id=16, color=[51, 153, 255]),
17:
dict(link=('left_ear', 'left_shoulder'), id=17, color=[51, 153, 255]),
18:
dict(
link=('right_ear', 'right_shoulder'), id=18, color=[51, 153, 255]),
19:
dict(link=('left_ankle', 'left_big_toe'), id=19, color=[0, 255, 0]),
20:
dict(link=('left_ankle', 'left_small_toe'), id=20, color=[0, 255, 0]),
21:
dict(link=('left_ankle', 'left_heel'), id=21, color=[0, 255, 0]),
22:
dict(
link=('right_ankle', 'right_big_toe'), id=22, color=[255, 128, 0]),
23:
dict(
link=('right_ankle', 'right_small_toe'),
id=23,
color=[255, 128, 0]),
24:
dict(link=('right_ankle', 'right_heel'), id=24, color=[255, 128, 0]),
25:
dict(
link=('left_hand_root', 'left_thumb1'), id=25, color=[255, 128,
0]),
26:
dict(link=('left_thumb1', 'left_thumb2'), id=26, color=[255, 128, 0]),
27:
dict(link=('left_thumb2', 'left_thumb3'), id=27, color=[255, 128, 0]),
28:
dict(link=('left_thumb3', 'left_thumb4'), id=28, color=[255, 128, 0]),
29:
dict(
link=('left_hand_root', 'left_forefinger1'),
id=29,
color=[255, 153, 255]),
30:
dict(
link=('left_forefinger1', 'left_forefinger2'),
id=30,
color=[255, 153, 255]),
31:
dict(
link=('left_forefinger2', 'left_forefinger3'),
id=31,
color=[255, 153, 255]),
32:
dict(
link=('left_forefinger3', 'left_forefinger4'),
id=32,
color=[255, 153, 255]),
33:
dict(
link=('left_hand_root', 'left_middle_finger1'),
id=33,
color=[102, 178, 255]),
34:
dict(
link=('left_middle_finger1', 'left_middle_finger2'),
id=34,
color=[102, 178, 255]),
35:
dict(
link=('left_middle_finger2', 'left_middle_finger3'),
id=35,
color=[102, 178, 255]),
36:
dict(
link=('left_middle_finger3', 'left_middle_finger4'),
id=36,
color=[102, 178, 255]),
37:
dict(
link=('left_hand_root', 'left_ring_finger1'),
id=37,
color=[255, 51, 51]),
38:
dict(
link=('left_ring_finger1', 'left_ring_finger2'),
id=38,
color=[255, 51, 51]),
39:
dict(
link=('left_ring_finger2', 'left_ring_finger3'),
id=39,
color=[255, 51, 51]),
40:
dict(
link=('left_ring_finger3', 'left_ring_finger4'),
id=40,
color=[255, 51, 51]),
41:
dict(
link=('left_hand_root', 'left_pinky_finger1'),
id=41,
color=[0, 255, 0]),
42:
dict(
link=('left_pinky_finger1', 'left_pinky_finger2'),
id=42,
color=[0, 255, 0]),
43:
dict(
link=('left_pinky_finger2', 'left_pinky_finger3'),
id=43,
color=[0, 255, 0]),
44:
dict(
link=('left_pinky_finger3', 'left_pinky_finger4'),
id=44,
color=[0, 255, 0]),
45:
dict(
link=('right_hand_root', 'right_thumb1'),
id=45,
color=[255, 128, 0]),
46:
dict(
link=('right_thumb1', 'right_thumb2'), id=46, color=[255, 128, 0]),
47:
dict(
link=('right_thumb2', 'right_thumb3'), id=47, color=[255, 128, 0]),
48:
dict(
link=('right_thumb3', 'right_thumb4'), id=48, color=[255, 128, 0]),
49:
dict(
link=('right_hand_root', 'right_forefinger1'),
id=49,
color=[255, 153, 255]),
50:
dict(
link=('right_forefinger1', 'right_forefinger2'),
id=50,
color=[255, 153, 255]),
51:
dict(
link=('right_forefinger2', 'right_forefinger3'),
id=51,
color=[255, 153, 255]),
52:
dict(
link=('right_forefinger3', 'right_forefinger4'),
id=52,
color=[255, 153, 255]),
53:
dict(
link=('right_hand_root', 'right_middle_finger1'),
id=53,
color=[102, 178, 255]),
54:
dict(
link=('right_middle_finger1', 'right_middle_finger2'),
id=54,
color=[102, 178, 255]),
55:
dict(
link=('right_middle_finger2', 'right_middle_finger3'),
id=55,
color=[102, 178, 255]),
56:
dict(
link=('right_middle_finger3', 'right_middle_finger4'),
id=56,
color=[102, 178, 255]),
57:
dict(
link=('right_hand_root', 'right_ring_finger1'),
id=57,
color=[255, 51, 51]),
58:
dict(
link=('right_ring_finger1', 'right_ring_finger2'),
id=58,
color=[255, 51, 51]),
59:
dict(
link=('right_ring_finger2', 'right_ring_finger3'),
id=59,
color=[255, 51, 51]),
60:
dict(
link=('right_ring_finger3', 'right_ring_finger4'),
id=60,
color=[255, 51, 51]),
61:
dict(
link=('right_hand_root', 'right_pinky_finger1'),
id=61,
color=[0, 255, 0]),
62:
dict(
link=('right_pinky_finger1', 'right_pinky_finger2'),
id=62,
color=[0, 255, 0]),
63:
dict(
link=('right_pinky_finger2', 'right_pinky_finger3'),
id=63,
color=[0, 255, 0]),
64:
dict(
link=('right_pinky_finger3', 'right_pinky_finger4'),
id=64,
color=[0, 255, 0])
},
joint_weights=[1.] * 133,
# 'https://github.com/jin-s13/COCO-WholeBody/blob/master/'
# 'evaluation/myeval_wholebody.py#L175'
sigmas=[
0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062,
0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089, 0.068, 0.066, 0.066,
0.092, 0.094, 0.094, 0.042, 0.043, 0.044, 0.043, 0.040, 0.035, 0.031,
0.025, 0.020, 0.023, 0.029, 0.032, 0.037, 0.038, 0.043, 0.041, 0.045,
0.013, 0.012, 0.011, 0.011, 0.012, 0.012, 0.011, 0.011, 0.013, 0.015,
0.009, 0.007, 0.007, 0.007, 0.012, 0.009, 0.008, 0.016, 0.010, 0.017,
0.011, 0.009, 0.011, 0.009, 0.007, 0.013, 0.008, 0.011, 0.012, 0.010,
0.034, 0.008, 0.008, 0.009, 0.008, 0.008, 0.007, 0.010, 0.008, 0.009,
0.009, 0.009, 0.007, 0.007, 0.008, 0.011, 0.008, 0.008, 0.008, 0.01,
0.008, 0.029, 0.022, 0.035, 0.037, 0.047, 0.026, 0.025, 0.024, 0.035,
0.018, 0.024, 0.022, 0.026, 0.017, 0.021, 0.021, 0.032, 0.02, 0.019,
0.022, 0.031, 0.029, 0.022, 0.035, 0.037, 0.047, 0.026, 0.025, 0.024,
0.035, 0.018, 0.024, 0.022, 0.026, 0.017, 0.021, 0.021, 0.032, 0.02,
0.019, 0.022, 0.031
])
configs/_base_/datasets/coco_wholebody_face.py 0 → 100755
View file @ 37c8cebc
dataset_info = dict(
dataset_name='coco_wholebody_face',
paper_info=dict(
author='Jin, Sheng and Xu, Lumin and Xu, Jin and '
'Wang, Can and Liu, Wentao and '
'Qian, Chen and Ouyang, Wanli and Luo, Ping',
title='Whole-Body Human Pose Estimation in the Wild',
container='Proceedings of the European '
'Conference on Computer Vision (ECCV)',
year='2020',
homepage='https://github.com/jin-s13/COCO-WholeBody/',
),
keypoint_info={
0:
dict(
name='face-0',
id=0,
color=[255, 255, 255],
type='',
swap='face-16'),
1:
dict(
name='face-1',
id=1,
color=[255, 255, 255],
type='',
swap='face-15'),
2:
dict(
name='face-2',
id=2,
color=[255, 255, 255],
type='',
swap='face-14'),
3:
dict(
name='face-3',
id=3,
color=[255, 255, 255],
type='',
swap='face-13'),
4:
dict(
name='face-4',
id=4,
color=[255, 255, 255],
type='',
swap='face-12'),
5:
dict(
name='face-5',
id=5,
color=[255, 255, 255],
type='',
swap='face-11'),
6:
dict(
name='face-6',
id=6,
color=[255, 255, 255],
type='',
swap='face-10'),
7:
dict(
name='face-7', id=7, color=[255, 255, 255], type='',
swap='face-9'),
8:
dict(name='face-8', id=8, color=[255, 255, 255], type='', swap=''),
9:
dict(
name='face-9', id=9, color=[255, 255, 255], type='',
swap='face-7'),
10:
dict(
name='face-10',
id=10,
color=[255, 255, 255],
type='',
swap='face-6'),
11:
dict(
name='face-11',
id=11,
color=[255, 255, 255],
type='',
swap='face-5'),
12:
dict(
name='face-12',
id=12,
color=[255, 255, 255],
type='',
swap='face-4'),
13:
dict(
name='face-13',
id=13,
color=[255, 255, 255],
type='',
swap='face-3'),
14:
dict(
name='face-14',
id=14,
color=[255, 255, 255],
type='',
swap='face-2'),
15:
dict(
name='face-15',
id=15,
color=[255, 255, 255],
type='',
swap='face-1'),
16:
dict(
name='face-16',
id=16,
color=[255, 255, 255],
type='',
swap='face-0'),
17:
dict(
name='face-17',
id=17,
color=[255, 255, 255],
type='',
swap='face-26'),
18:
dict(
name='face-18',
id=18,
color=[255, 255, 255],
type='',
swap='face-25'),
19:
dict(
name='face-19',
id=19,
color=[255, 255, 255],
type='',
swap='face-24'),
20:
dict(
name='face-20',
id=20,
color=[255, 255, 255],
type='',
swap='face-23'),
21:
dict(
name='face-21',
id=21,
color=[255, 255, 255],
type='',
swap='face-22'),
22:
dict(
name='face-22',
id=22,
color=[255, 255, 255],
type='',
swap='face-21'),
23:
dict(
name='face-23',
id=23,
color=[255, 255, 255],
type='',
swap='face-20'),
24:
dict(
name='face-24',
id=24,
color=[255, 255, 255],
type='',
swap='face-19'),
25:
dict(
name='face-25',
id=25,
color=[255, 255, 255],
type='',
swap='face-18'),
26:
dict(
name='face-26',
id=26,
color=[255, 255, 255],
type='',
swap='face-17'),
27:
dict(name='face-27', id=27, color=[255, 255, 255], type='', swap=''),
28:
dict(name='face-28', id=28, color=[255, 255, 255], type='', swap=''),
29:
dict(name='face-29', id=29, color=[255, 255, 255], type='', swap=''),
30:
dict(name='face-30', id=30, color=[255, 255, 255], type='', swap=''),
31:
dict(
name='face-31',
id=31,
color=[255, 255, 255],
type='',
swap='face-35'),
32:
dict(
name='face-32',
id=32,
color=[255, 255, 255],
type='',
swap='face-34'),
33:
dict(name='face-33', id=33, color=[255, 255, 255], type='', swap=''),
34:
dict(
name='face-34',
id=34,
color=[255, 255, 255],
type='',
swap='face-32'),
35:
dict(
name='face-35',
id=35,
color=[255, 255, 255],
type='',
swap='face-31'),
36:
dict(
name='face-36',
id=36,
color=[255, 255, 255],
type='',
swap='face-45'),
37:
dict(
name='face-37',
id=37,
color=[255, 255, 255],
type='',
swap='face-44'),
38:
dict(
name='face-38',
id=38,
color=[255, 255, 255],
type='',
swap='face-43'),
39:
dict(
name='face-39',
id=39,
color=[255, 255, 255],
type='',
swap='face-42'),
40:
dict(
name='face-40',
id=40,
color=[255, 255, 255],
type='',
swap='face-47'),
41:
dict(
name='face-41',
id=41,
color=[255, 255, 255],
type='',
swap='face-46'),
42:
dict(
name='face-42',
id=42,
color=[255, 255, 255],
type='',
swap='face-39'),
43:
dict(
name='face-43',
id=43,
color=[255, 255, 255],
type='',
swap='face-38'),
44:
dict(
name='face-44',
id=44,
color=[255, 255, 255],
type='',
swap='face-37'),
45:
dict(
name='face-45',
id=45,
color=[255, 255, 255],
type='',
swap='face-36'),
46:
dict(
name='face-46',
id=46,
color=[255, 255, 255],
type='',
swap='face-41'),
47:
dict(
name='face-47',
id=47,
color=[255, 255, 255],
type='',
swap='face-40'),
48:
dict(
name='face-48',
id=48,
color=[255, 255, 255],
type='',
swap='face-54'),
49:
dict(
name='face-49',
id=49,
color=[255, 255, 255],
type='',
swap='face-53'),
50:
dict(
name='face-50',
id=50,
color=[255, 255, 255],
type='',
swap='face-52'),
51:
dict(name='face-51', id=52, color=[255, 255, 255], type='', swap=''),
52:
dict(
name='face-52',
id=52,
color=[255, 255, 255],
type='',
swap='face-50'),
53:
dict(
name='face-53',
id=53,
color=[255, 255, 255],
type='',
swap='face-49'),
54:
dict(
name='face-54',
id=54,
color=[255, 255, 255],
type='',
swap='face-48'),
55:
dict(
name='face-55',
id=55,
color=[255, 255, 255],
type='',
swap='face-59'),
56:
dict(
name='face-56',
id=56,
color=[255, 255, 255],
type='',
swap='face-58'),
57:
dict(name='face-57', id=57, color=[255, 255, 255], type='', swap=''),
58:
dict(
name='face-58',
id=58,
color=[255, 255, 255],
type='',
swap='face-56'),
59:
dict(
name='face-59',
id=59,
color=[255, 255, 255],
type='',
swap='face-55'),
60:
dict(
name='face-60',
id=60,
color=[255, 255, 255],
type='',
swap='face-64'),
61:
dict(
name='face-61',
id=61,
color=[255, 255, 255],
type='',
swap='face-63'),
62:
dict(name='face-62', id=62, color=[255, 255, 255], type='', swap=''),
63:
dict(
name='face-63',
id=63,
color=[255, 255, 255],
type='',
swap='face-61'),
64:
dict(
name='face-64',
id=64,
color=[255, 255, 255],
type='',
swap='face-60'),
65:
dict(
name='face-65',
id=65,
color=[255, 255, 255],
type='',
swap='face-67'),
66:
dict(name='face-66', id=66, color=[255, 255, 255], type='', swap=''),
67:
dict(
name='face-67',
id=67,
color=[255, 255, 255],
type='',
swap='face-65')
},
skeleton_info={},
joint_weights=[1.] * 68,
# 'https://github.com/jin-s13/COCO-WholeBody/blob/master/'
# 'evaluation/myeval_wholebody.py#L177'
sigmas=[
0.042, 0.043, 0.044, 0.043, 0.040, 0.035, 0.031, 0.025, 0.020, 0.023,
0.029, 0.032, 0.037, 0.038, 0.043, 0.041, 0.045, 0.013, 0.012, 0.011,
0.011, 0.012, 0.012, 0.011, 0.011, 0.013, 0.015, 0.009, 0.007, 0.007,
0.007, 0.012, 0.009, 0.008, 0.016, 0.010, 0.017, 0.011, 0.009, 0.011,
0.009, 0.007, 0.013, 0.008, 0.011, 0.012, 0.010, 0.034, 0.008, 0.008,
0.009, 0.008, 0.008, 0.007, 0.010, 0.008, 0.009, 0.009, 0.009, 0.007,
0.007, 0.008, 0.011, 0.008, 0.008, 0.008, 0.01, 0.008
])
configs/_base_/datasets/coco_wholebody_hand.py 0 → 100755
View file @ 37c8cebc
dataset_info = dict(
dataset_name='coco_wholebody_hand',
paper_info=dict(
author='Jin, Sheng and Xu, Lumin and Xu, Jin and '
'Wang, Can and Liu, Wentao and '
'Qian, Chen and Ouyang, Wanli and Luo, Ping',
title='Whole-Body Human Pose Estimation in the Wild',
container='Proceedings of the European '
'Conference on Computer Vision (ECCV)',
year='2020',
homepage='https://github.com/jin-s13/COCO-WholeBody/',
),
keypoint_info={
0:
dict(name='wrist', id=0, color=[255, 255, 255], type='', swap=''),
1:
dict(name='thumb1', id=1, color=[255, 128, 0], type='', swap=''),
2:
dict(name='thumb2', id=2, color=[255, 128, 0], type='', swap=''),
3:
dict(name='thumb3', id=3, color=[255, 128, 0], type='', swap=''),
4:
dict(name='thumb4', id=4, color=[255, 128, 0], type='', swap=''),
5:
dict(
name='forefinger1', id=5, color=[255, 153, 255], type='', swap=''),
6:
dict(
name='forefinger2', id=6, color=[255, 153, 255], type='', swap=''),
7:
dict(
name='forefinger3', id=7, color=[255, 153, 255], type='', swap=''),
8:
dict(
name='forefinger4', id=8, color=[255, 153, 255], type='', swap=''),
9:
dict(
name='middle_finger1',
id=9,
color=[102, 178, 255],
type='',
swap=''),
10:
dict(
name='middle_finger2',
id=10,
color=[102, 178, 255],
type='',
swap=''),
11:
dict(
name='middle_finger3',
id=11,
color=[102, 178, 255],
type='',
swap=''),
12:
dict(
name='middle_finger4',
id=12,
color=[102, 178, 255],
type='',
swap=''),
13:
dict(
name='ring_finger1', id=13, color=[255, 51, 51], type='', swap=''),
14:
dict(
name='ring_finger2', id=14, color=[255, 51, 51], type='', swap=''),
15:
dict(
name='ring_finger3', id=15, color=[255, 51, 51], type='', swap=''),
16:
dict(
name='ring_finger4', id=16, color=[255, 51, 51], type='', swap=''),
17:
dict(name='pinky_finger1', id=17, color=[0, 255, 0], type='', swap=''),
18:
dict(name='pinky_finger2', id=18, color=[0, 255, 0], type='', swap=''),
19:
dict(name='pinky_finger3', id=19, color=[0, 255, 0], type='', swap=''),
20:
dict(name='pinky_finger4', id=20, color=[0, 255, 0], type='', swap='')
},
skeleton_info={
0:
dict(link=('wrist', 'thumb1'), id=0, color=[255, 128, 0]),
1:
dict(link=('thumb1', 'thumb2'), id=1, color=[255, 128, 0]),
2:
dict(link=('thumb2', 'thumb3'), id=2, color=[255, 128, 0]),
3:
dict(link=('thumb3', 'thumb4'), id=3, color=[255, 128, 0]),
4:
dict(link=('wrist', 'forefinger1'), id=4, color=[255, 153, 255]),
5:
dict(link=('forefinger1', 'forefinger2'), id=5, color=[255, 153, 255]),
6:
dict(link=('forefinger2', 'forefinger3'), id=6, color=[255, 153, 255]),
7:
dict(link=('forefinger3', 'forefinger4'), id=7, color=[255, 153, 255]),
8:
dict(link=('wrist', 'middle_finger1'), id=8, color=[102, 178, 255]),
9:
dict(
link=('middle_finger1', 'middle_finger2'),
id=9,
color=[102, 178, 255]),
10:
dict(
link=('middle_finger2', 'middle_finger3'),
id=10,
color=[102, 178, 255]),
11:
dict(
link=('middle_finger3', 'middle_finger4'),
id=11,
color=[102, 178, 255]),
12:
dict(link=('wrist', 'ring_finger1'), id=12, color=[255, 51, 51]),
13:
dict(
link=('ring_finger1', 'ring_finger2'), id=13, color=[255, 51, 51]),
14:
dict(
link=('ring_finger2', 'ring_finger3'), id=14, color=[255, 51, 51]),
15:
dict(
link=('ring_finger3', 'ring_finger4'), id=15, color=[255, 51, 51]),
16:
dict(link=('wrist', 'pinky_finger1'), id=16, color=[0, 255, 0]),
17:
dict(
link=('pinky_finger1', 'pinky_finger2'), id=17, color=[0, 255, 0]),
18:
dict(
link=('pinky_finger2', 'pinky_finger3'), id=18, color=[0, 255, 0]),
19:
dict(
link=('pinky_finger3', 'pinky_finger4'), id=19, color=[0, 255, 0])
},
joint_weights=[1.] * 21,
sigmas=[
0.029, 0.022, 0.035, 0.037, 0.047, 0.026, 0.025, 0.024, 0.035, 0.018,
0.024, 0.022, 0.026, 0.017, 0.021, 0.021, 0.032, 0.02, 0.019, 0.022,
0.031
])
configs/_base_/datasets/cofw.py 0 → 100755
View file @ 37c8cebc
dataset_info = dict(
dataset_name='cofw',
paper_info=dict(
author='Burgos-Artizzu, Xavier P and Perona, '
r'Pietro and Doll{\'a}r, Piotr',
title='Robust face landmark estimation under occlusion',
container='Proceedings of the IEEE international '
'conference on computer vision',
year='2013',
homepage='http://www.vision.caltech.edu/xpburgos/ICCV13/',
),
keypoint_info={
0:
dict(name='kpt-0', id=0, color=[255, 255, 255], type='', swap='kpt-1'),
1:
dict(name='kpt-1', id=1, color=[255, 255, 255], type='', swap='kpt-0'),
2:
dict(name='kpt-2', id=2, color=[255, 255, 255], type='', swap='kpt-3'),
3:
dict(name='kpt-3', id=3, color=[255, 255, 255], type='', swap='kpt-2'),
4:
dict(name='kpt-4', id=4, color=[255, 255, 255], type='', swap='kpt-6'),
5:
dict(name='kpt-5', id=5, color=[255, 255, 255], type='', swap='kpt-7'),
6:
dict(name='kpt-6', id=6, color=[255, 255, 255], type='', swap='kpt-4'),
7:
dict(name='kpt-7', id=7, color=[255, 255, 255], type='', swap='kpt-5'),
8:
dict(name='kpt-8', id=8, color=[255, 255, 255], type='', swap='kpt-9'),
9:
dict(name='kpt-9', id=9, color=[255, 255, 255], type='', swap='kpt-8'),
10:
dict(
name='kpt-10',
id=10,
color=[255, 255, 255],
type='',
swap='kpt-11'),
11:
dict(
name='kpt-11',
id=11,
color=[255, 255, 255],
type='',
swap='kpt-10'),
12:
dict(
name='kpt-12',
id=12,
color=[255, 255, 255],
type='',
swap='kpt-14'),
13:
dict(
name='kpt-13',
id=13,
color=[255, 255, 255],
type='',
swap='kpt-15'),
14:
dict(
name='kpt-14',
id=14,
color=[255, 255, 255],
type='',
swap='kpt-12'),
15:
dict(
name='kpt-15',
id=15,
color=[255, 255, 255],
type='',
swap='kpt-13'),
16:
dict(
name='kpt-16',
id=16,
color=[255, 255, 255],
type='',
swap='kpt-17'),
17:
dict(
name='kpt-17',
id=17,
color=[255, 255, 255],
type='',
swap='kpt-16'),
18:
dict(
name='kpt-18',
id=18,
color=[255, 255, 255],
type='',
swap='kpt-19'),
19:
dict(
name='kpt-19',
id=19,
color=[255, 255, 255],
type='',
swap='kpt-18'),
20:
dict(name='kpt-20', id=20, color=[255, 255, 255], type='', swap=''),
21:
dict(name='kpt-21', id=21, color=[255, 255, 255], type='', swap=''),
22:
dict(
name='kpt-22',
id=22,
color=[255, 255, 255],
type='',
swap='kpt-23'),
23:
dict(
name='kpt-23',
id=23,
color=[255, 255, 255],
type='',
swap='kpt-22'),
24:
dict(name='kpt-24', id=24, color=[255, 255, 255], type='', swap=''),
25:
dict(name='kpt-25', id=25, color=[255, 255, 255], type='', swap=''),
26:
dict(name='kpt-26', id=26, color=[255, 255, 255], type='', swap=''),
27:
dict(name='kpt-27', id=27, color=[255, 255, 255], type='', swap=''),
28:
dict(name='kpt-28', id=28, color=[255, 255, 255], type='', swap='')
},
skeleton_info={},
joint_weights=[1.] * 29,
sigmas=[])
configs/_base_/datasets/crowdpose.py 0 → 100755
View file @ 37c8cebc
dataset_info = dict(
dataset_name='crowdpose',
paper_info=dict(
author='Li, Jiefeng and Wang, Can and Zhu, Hao and '
'Mao, Yihuan and Fang, Hao-Shu and Lu, Cewu',
title='CrowdPose: Efficient Crowded Scenes Pose Estimation '
'and A New Benchmark',
container='Proceedings of IEEE Conference on Computer '
'Vision and Pattern Recognition (CVPR)',
year='2019',
homepage='https://github.com/Jeff-sjtu/CrowdPose',
),
keypoint_info={
0:
dict(
name='left_shoulder',
id=0,
color=[51, 153, 255],
type='upper',
swap='right_shoulder'),
1:
dict(
name='right_shoulder',
id=1,
color=[51, 153, 255],
type='upper',
swap='left_shoulder'),
2:
dict(
name='left_elbow',
id=2,
color=[51, 153, 255],
type='upper',
swap='right_elbow'),
3:
dict(
name='right_elbow',
id=3,
color=[51, 153, 255],
type='upper',
swap='left_elbow'),
4:
dict(
name='left_wrist',
id=4,
color=[51, 153, 255],
type='upper',
swap='right_wrist'),
5:
dict(
name='right_wrist',
id=5,
color=[0, 255, 0],
type='upper',
swap='left_wrist'),
6:
dict(
name='left_hip',
id=6,
color=[255, 128, 0],
type='lower',
swap='right_hip'),
7:
dict(
name='right_hip',
id=7,
color=[0, 255, 0],
type='lower',
swap='left_hip'),
8:
dict(
name='left_knee',
id=8,
color=[255, 128, 0],
type='lower',
swap='right_knee'),
9:
dict(
name='right_knee',
id=9,
color=[0, 255, 0],
type='lower',
swap='left_knee'),
10:
dict(
name='left_ankle',
id=10,
color=[255, 128, 0],
type='lower',
swap='right_ankle'),
11:
dict(
name='right_ankle',
id=11,
color=[0, 255, 0],
type='lower',
swap='left_ankle'),
12:
dict(
name='top_head', id=12, color=[255, 128, 0], type='upper',
swap=''),
13:
dict(name='neck', id=13, color=[0, 255, 0], type='upper', swap='')
},
skeleton_info={
0:
dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]),
1:
dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]),
2:
dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]),
3:
dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]),
4:
dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]),
5:
dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]),
6:
dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]),
7:
dict(
link=('left_shoulder', 'right_shoulder'),
id=7,
color=[51, 153, 255]),
8:
dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]),
9:
dict(
link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]),
10:
dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]),
11:
dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]),
12:
dict(link=('top_head', 'neck'), id=12, color=[51, 153, 255]),
13:
dict(link=('right_shoulder', 'neck'), id=13, color=[51, 153, 255]),
14:
dict(link=('left_shoulder', 'neck'), id=14, color=[51, 153, 255])
},
joint_weights=[
0.2, 0.2, 0.2, 1.3, 1.5, 0.2, 1.3, 1.5, 0.2, 0.2, 0.5, 0.2, 0.2, 0.5
],
sigmas=[
0.079, 0.079, 0.072, 0.072, 0.062, 0.062, 0.107, 0.107, 0.087, 0.087,
0.089, 0.089, 0.079, 0.079
])
configs/_base_/datasets/deepfashion2.py 0 → 100755
View file @ 37c8cebc
colors = dict(
sss=[255, 128, 0], # short_sleeve_shirt
lss=[255, 0, 128], # long_sleeved_shirt
sso=[128, 0, 255], # short_sleeved_outwear
lso=[0, 128, 255], # long_sleeved_outwear
vest=[0, 128, 128], # vest
sling=[0, 0, 128], # sling
shorts=[128, 128, 128], # shorts
trousers=[128, 0, 128], # trousers
skirt=[64, 128, 128], # skirt
ssd=[64, 64, 128], # short_sleeved_dress
lsd=[128, 64, 0], # long_sleeved_dress
vd=[128, 64, 255], # vest_dress
sd=[128, 64, 0], # sling_dress
)
dataset_info = dict(
dataset_name='deepfashion2',
paper_info=dict(
author='Yuying Ge and Ruimao Zhang and Lingyun Wu '
'and Xiaogang Wang and Xiaoou Tang and Ping Luo',
title='DeepFashion2: A Versatile Benchmark for '
'Detection, Pose Estimation, Segmentation and '
'Re-Identification of Clothing Images',
container='Proceedings of IEEE Conference on Computer '
'Vision and Pattern Recognition (CVPR)',
year='2019',
homepage='https://github.com/switchablenorms/DeepFashion2',
),
keypoint_info={
# short_sleeved_shirt
0:
dict(name='sss_kpt1', id=0, color=colors['sss'], type='', swap=''),
1:
dict(
name='sss_kpt2',
id=1,
color=colors['sss'],
type='',
swap='sss_kpt6'),
2:
dict(
name='sss_kpt3',
id=2,
color=colors['sss'],
type='',
swap='sss_kpt5'),
3:
dict(name='sss_kpt4', id=3, color=colors['sss'], type='', swap=''),
4:
dict(
name='sss_kpt5',
id=4,
color=colors['sss'],
type='',
swap='sss_kpt3'),
5:
dict(
name='sss_kpt6',
id=5,
color=colors['sss'],
type='',
swap='sss_kpt2'),
6:
dict(
name='sss_kpt7',
id=6,
color=colors['sss'],
type='',
swap='sss_kpt25'),
7:
dict(
name='sss_kpt8',
id=7,
color=colors['sss'],
type='',
swap='sss_kpt24'),
8:
dict(
name='sss_kpt9',
id=8,
color=colors['sss'],
type='',
swap='sss_kpt23'),
9:
dict(
name='sss_kpt10',
id=9,
color=colors['sss'],
type='',
swap='sss_kpt22'),
10:
dict(
name='sss_kpt11',
id=10,
color=colors['sss'],
type='',
swap='sss_kpt21'),
11:
dict(
name='sss_kpt12',
id=11,
color=colors['sss'],
type='',
swap='sss_kpt20'),
12:
dict(
name='sss_kpt13',
id=12,
color=colors['sss'],
type='',
swap='sss_kpt19'),
13:
dict(
name='sss_kpt14',
id=13,
color=colors['sss'],
type='',
swap='sss_kpt18'),
14:
dict(
name='sss_kpt15',
id=14,
color=colors['sss'],
type='',
swap='sss_kpt17'),
15:
dict(name='sss_kpt16', id=15, color=colors['sss'], type='', swap=''),
16:
dict(
name='sss_kpt17',
id=16,
color=colors['sss'],
type='',
swap='sss_kpt15'),
17:
dict(
name='sss_kpt18',
id=17,
color=colors['sss'],
type='',
swap='sss_kpt14'),
18:
dict(
name='sss_kpt19',
id=18,
color=colors['sss'],
type='',
swap='sss_kpt13'),
19:
dict(
name='sss_kpt20',
id=19,
color=colors['sss'],
type='',
swap='sss_kpt12'),
20:
dict(
name='sss_kpt21',
id=20,
color=colors['sss'],
type='',
swap='sss_kpt11'),
21:
dict(
name='sss_kpt22',
id=21,
color=colors['sss'],
type='',
swap='sss_kpt10'),
22:
dict(
name='sss_kpt23',
id=22,
color=colors['sss'],
type='',
swap='sss_kpt9'),
23:
dict(
name='sss_kpt24',
id=23,
color=colors['sss'],
type='',
swap='sss_kpt8'),
24:
dict(
name='sss_kpt25',
id=24,
color=colors['sss'],
type='',
swap='sss_kpt7'),
# long_sleeved_shirt
25:
dict(name='lss_kpt1', id=25, color=colors['lss'], type='', swap=''),
26:
dict(
name='lss_kpt2',
id=26,
color=colors['lss'],
type='',
swap='lss_kpt6'),
27:
dict(
name='lss_kpt3',
id=27,
color=colors['lss'],
type='',
swap='lss_kpt5'),
28:
dict(name='lss_kpt4', id=28, color=colors['lss'], type='', swap=''),
29:
dict(
name='lss_kpt5',
id=29,
color=colors['lss'],
type='',
swap='lss_kpt3'),
30:
dict(
name='lss_kpt6',
id=30,
color=colors['lss'],
type='',
swap='lss_kpt2'),
31:
dict(
name='lss_kpt7',
id=31,
color=colors['lss'],
type='',
swap='lss_kpt33'),
32:
dict(
name='lss_kpt8',
id=32,
color=colors['lss'],
type='',
swap='lss_kpt32'),
33:
dict(
name='lss_kpt9',
id=33,
color=colors['lss'],
type='',
swap='lss_kpt31'),
34:
dict(
name='lss_kpt10',
id=34,
color=colors['lss'],
type='',
swap='lss_kpt30'),
35:
dict(
name='lss_kpt11',
id=35,
color=colors['lss'],
type='',
swap='lss_kpt29'),
36:
dict(
name='lss_kpt12',
id=36,
color=colors['lss'],
type='',
swap='lss_kpt28'),
37:
dict(
name='lss_kpt13',
id=37,
color=colors['lss'],
type='',
swap='lss_kpt27'),
38:
dict(
name='lss_kpt14',
id=38,
color=colors['lss'],
type='',
swap='lss_kpt26'),
39:
dict(
name='lss_kpt15',
id=39,
color=colors['lss'],
type='',
swap='lss_kpt25'),
40:
dict(
name='lss_kpt16',
id=40,
color=colors['lss'],
type='',
swap='lss_kpt24'),
41:
dict(
name='lss_kpt17',
id=41,
color=colors['lss'],
type='',
swap='lss_kpt23'),
42:
dict(
name='lss_kpt18',
id=42,
color=colors['lss'],
type='',
swap='lss_kpt22'),
43:
dict(
name='lss_kpt19',
id=43,
color=colors['lss'],
type='',
swap='lss_kpt21'),
44:
dict(name='lss_kpt20', id=44, color=colors['lss'], type='', swap=''),
45:
dict(
name='lss_kpt21',
id=45,
color=colors['lss'],
type='',
swap='lss_kpt19'),
46:
dict(
name='lss_kpt22',
id=46,
color=colors['lss'],
type='',
swap='lss_kpt18'),
47:
dict(
name='lss_kpt23',
id=47,
color=colors['lss'],
type='',
swap='lss_kpt17'),
48:
dict(
name='lss_kpt24',
id=48,
color=colors['lss'],
type='',
swap='lss_kpt16'),
49:
dict(
name='lss_kpt25',
id=49,
color=colors['lss'],
type='',
swap='lss_kpt15'),
50:
dict(
name='lss_kpt26',
id=50,
color=colors['lss'],
type='',
swap='lss_kpt14'),
51:
dict(
name='lss_kpt27',
id=51,
color=colors['lss'],
type='',
swap='lss_kpt13'),
52:
dict(
name='lss_kpt28',
id=52,
color=colors['lss'],
type='',
swap='lss_kpt12'),
53:
dict(
name='lss_kpt29',
id=53,
color=colors['lss'],
type='',
swap='lss_kpt11'),
54:
dict(
name='lss_kpt30',
id=54,
color=colors['lss'],
type='',
swap='lss_kpt10'),
55:
dict(
name='lss_kpt31',
id=55,
color=colors['lss'],
type='',
swap='lss_kpt9'),
56:
dict(
name='lss_kpt32',
id=56,
color=colors['lss'],
type='',
swap='lss_kpt8'),
57:
dict(
name='lss_kpt33',
id=57,
color=colors['lss'],
type='',
swap='lss_kpt7'),
# short_sleeved_outwear
58:
dict(name='sso_kpt1', id=58, color=colors['sso'], type='', swap=''),
59:
dict(
name='sso_kpt2',
id=59,
color=colors['sso'],
type='',
swap='sso_kpt26'),
60:
dict(
name='sso_kpt3',
id=60,
color=colors['sso'],
type='',
swap='sso_kpt5'),
61:
dict(
name='sso_kpt4',
id=61,
color=colors['sso'],
type='',
swap='sso_kpt6'),
62:
dict(
name='sso_kpt5',
id=62,
color=colors['sso'],
type='',
swap='sso_kpt3'),
63:
dict(
name='sso_kpt6',
id=63,
color=colors['sso'],
type='',
swap='sso_kpt4'),
64:
dict(
name='sso_kpt7',
id=64,
color=colors['sso'],
type='',
swap='sso_kpt25'),
65:
dict(
name='sso_kpt8',
id=65,
color=colors['sso'],
type='',
swap='sso_kpt24'),
66:
dict(
name='sso_kpt9',
id=66,
color=colors['sso'],
type='',
swap='sso_kpt23'),
67:
dict(
name='sso_kpt10',
id=67,
color=colors['sso'],
type='',
swap='sso_kpt22'),
68:
dict(
name='sso_kpt11',
id=68,
color=colors['sso'],
type='',
swap='sso_kpt21'),
69:
dict(
name='sso_kpt12',
id=69,
color=colors['sso'],
type='',
swap='sso_kpt20'),
70:
dict(
name='sso_kpt13',
id=70,
color=colors['sso'],
type='',
swap='sso_kpt19'),
71:
dict(
name='sso_kpt14',
id=71,
color=colors['sso'],
type='',
swap='sso_kpt18'),
72:
dict(
name='sso_kpt15',
id=72,
color=colors['sso'],
type='',
swap='sso_kpt17'),
73:
dict(
name='sso_kpt16',
id=73,
color=colors['sso'],
type='',
swap='sso_kpt29'),
74:
dict(
name='sso_kpt17',
id=74,
color=colors['sso'],
type='',
swap='sso_kpt15'),
75:
dict(
name='sso_kpt18',
id=75,
color=colors['sso'],
type='',
swap='sso_kpt14'),
76:
dict(
name='sso_kpt19',
id=76,
color=colors['sso'],
type='',
swap='sso_kpt13'),
77:
dict(
name='sso_kpt20',
id=77,
color=colors['sso'],
type='',
swap='sso_kpt12'),
78:
dict(
name='sso_kpt21',
id=78,
color=colors['sso'],
type='',
swap='sso_kpt11'),
79:
dict(
name='sso_kpt22',
id=79,
color=colors['sso'],
type='',
swap='sso_kpt10'),
80:
dict(
name='sso_kpt23',
id=80,
color=colors['sso'],
type='',
swap='sso_kpt9'),
81:
dict(
name='sso_kpt24',
id=81,
color=colors['sso'],
type='',
swap='sso_kpt8'),
82:
dict(
name='sso_kpt25',
id=82,
color=colors['sso'],
type='',
swap='sso_kpt7'),
83:
dict(
name='sso_kpt26',
id=83,
color=colors['sso'],
type='',
swap='sso_kpt2'),
84:
dict(
name='sso_kpt27',
id=84,
color=colors['sso'],
type='',
swap='sso_kpt30'),
85:
dict(
name='sso_kpt28',
id=85,
color=colors['sso'],
type='',
swap='sso_kpt31'),
86:
dict(
name='sso_kpt29',
id=86,
color=colors['sso'],
type='',
swap='sso_kpt16'),
87:
dict(
name='sso_kpt30',
id=87,
color=colors['sso'],
type='',
swap='sso_kpt27'),
88:
dict(
name='sso_kpt31',
id=88,
color=colors['sso'],
type='',
swap='sso_kpt28'),
# long_sleeved_outwear
89:
dict(name='lso_kpt1', id=89, color=colors['lso'], type='', swap=''),
90:
dict(
name='lso_kpt2',
id=90,
color=colors['lso'],
type='',
swap='lso_kpt6'),
91:
dict(
name='lso_kpt3',
id=91,
color=colors['lso'],
type='',
swap='lso_kpt5'),
92:
dict(
name='lso_kpt4',
id=92,
color=colors['lso'],
type='',
swap='lso_kpt34'),
93:
dict(
name='lso_kpt5',
id=93,
color=colors['lso'],
type='',
swap='lso_kpt3'),
94:
dict(
name='lso_kpt6',
id=94,
color=colors['lso'],
type='',
swap='lso_kpt2'),
95:
dict(
name='lso_kpt7',
id=95,
color=colors['lso'],
type='',
swap='lso_kpt33'),
96:
dict(
name='lso_kpt8',
id=96,
color=colors['lso'],
type='',
swap='lso_kpt32'),
97:
dict(
name='lso_kpt9',
id=97,
color=colors['lso'],
type='',
swap='lso_kpt31'),
98:
dict(
name='lso_kpt10',
id=98,
color=colors['lso'],
type='',
swap='lso_kpt30'),
99:
dict(
name='lso_kpt11',
id=99,
color=colors['lso'],
type='',
swap='lso_kpt29'),
100:
dict(
name='lso_kpt12',
id=100,
color=colors['lso'],
type='',
swap='lso_kpt28'),
101:
dict(
name='lso_kpt13',
id=101,
color=colors['lso'],
type='',
swap='lso_kpt27'),
102:
dict(
name='lso_kpt14',
id=102,
color=colors['lso'],
type='',
swap='lso_kpt26'),
103:
dict(
name='lso_kpt15',
id=103,
color=colors['lso'],
type='',
swap='lso_kpt25'),
104:
dict(
name='lso_kpt16',
id=104,
color=colors['lso'],
type='',
swap='lso_kpt24'),
105:
dict(
name='lso_kpt17',
id=105,
color=colors['lso'],
type='',
swap='lso_kpt23'),
106:
dict(
name='lso_kpt18',
id=106,
color=colors['lso'],
type='',
swap='lso_kpt22'),
107:
dict(
name='lso_kpt19',
id=107,
color=colors['lso'],
type='',
swap='lso_kpt21'),
108:
dict(
name='lso_kpt20',
id=108,
color=colors['lso'],
type='',
swap='lso_kpt37'),
109:
dict(
name='lso_kpt21',
id=109,
color=colors['lso'],
type='',
swap='lso_kpt19'),
110:
dict(
name='lso_kpt22',
id=110,
color=colors['lso'],
type='',
swap='lso_kpt18'),
111:
dict(
name='lso_kpt23',
id=111,
color=colors['lso'],
type='',
swap='lso_kpt17'),
112:
dict(
name='lso_kpt24',
id=112,
color=colors['lso'],
type='',
swap='lso_kpt16'),
113:
dict(
name='lso_kpt25',
id=113,
color=colors['lso'],
type='',
swap='lso_kpt15'),
114:
dict(
name='lso_kpt26',
id=114,
color=colors['lso'],
type='',
swap='lso_kpt14'),
115:
dict(
name='lso_kpt27',
id=115,
color=colors['lso'],
type='',
swap='lso_kpt13'),
116:
dict(
name='lso_kpt28',
id=116,
color=colors['lso'],
type='',
swap='lso_kpt12'),
117:
dict(
name='lso_kpt29',
id=117,
color=colors['lso'],
type='',
swap='lso_kpt11'),
118:
dict(
name='lso_kpt30',
id=118,
color=colors['lso'],
type='',
swap='lso_kpt10'),
119:
dict(
name='lso_kpt31',
id=119,
color=colors['lso'],
type='',
swap='lso_kpt9'),
120:
dict(
name='lso_kpt32',
id=120,
color=colors['lso'],
type='',
swap='lso_kpt8'),
121:
dict(
name='lso_kpt33',
id=121,
color=colors['lso'],
type='',
swap='lso_kpt7'),
122:
dict(
name='lso_kpt34',
id=122,
color=colors['lso'],
type='',
swap='lso_kpt4'),
123:
dict(
name='lso_kpt35',
id=123,
color=colors['lso'],
type='',
swap='lso_kpt38'),
124:
dict(
name='lso_kpt36',
id=124,
color=colors['lso'],
type='',
swap='lso_kpt39'),
125:
dict(
name='lso_kpt37',
id=125,
color=colors['lso'],
type='',
swap='lso_kpt20'),
126:
dict(
name='lso_kpt38',
id=126,
color=colors['lso'],
type='',
swap='lso_kpt35'),
127:
dict(
name='lso_kpt39',
id=127,
color=colors['lso'],
type='',
swap='lso_kpt36'),
# vest
128:
dict(name='vest_kpt1', id=128, color=colors['vest'], type='', swap=''),
129:
dict(
name='vest_kpt2',
id=129,
color=colors['vest'],
type='',
swap='vest_kpt6'),
130:
dict(
name='vest_kpt3',
id=130,
color=colors['vest'],
type='',
swap='vest_kpt5'),
131:
dict(name='vest_kpt4', id=131, color=colors['vest'], type='', swap=''),
132:
dict(
name='vest_kpt5',
id=132,
color=colors['vest'],
type='',
swap='vest_kpt3'),
133:
dict(
name='vest_kpt6',
id=133,
color=colors['vest'],
type='',
swap='vest_kpt2'),
134:
dict(
name='vest_kpt7',
id=134,
color=colors['vest'],
type='',
swap='vest_kpt15'),
135:
dict(
name='vest_kpt8',
id=135,
color=colors['vest'],
type='',
swap='vest_kpt14'),
136:
dict(
name='vest_kpt9',
id=136,
color=colors['vest'],
type='',
swap='vest_kpt13'),
137:
dict(
name='vest_kpt10',
id=137,
color=colors['vest'],
type='',
swap='vest_kpt12'),
138:
dict(
name='vest_kpt11', id=138, color=colors['vest'], type='', swap=''),
139:
dict(
name='vest_kpt12',
id=139,
color=colors['vest'],
type='',
swap='vest_kpt10'),
140:
dict(
name='vest_kpt13', id=140, color=colors['vest'], type='', swap=''),
141:
dict(
name='vest_kpt14',
id=141,
color=colors['vest'],
type='',
swap='vest_kpt8'),
142:
dict(
name='vest_kpt15',
id=142,
color=colors['vest'],
type='',
swap='vest_kpt7'),
# sling
143:
dict(
name='sling_kpt1', id=143, color=colors['sling'], type='',
swap=''),
144:
dict(
name='sling_kpt2',
id=144,
color=colors['sling'],
type='',
swap='sling_kpt6'),
145:
dict(
name='sling_kpt3',
id=145,
color=colors['sling'],
type='',
swap='sling_kpt5'),
146:
dict(
name='sling_kpt4', id=146, color=colors['sling'], type='',
swap=''),
147:
dict(
name='sling_kpt5',
id=147,
color=colors['sling'],
type='',
swap='sling_kpt3'),
148:
dict(
name='sling_kpt6',
id=148,
color=colors['sling'],
type='',
swap='sling_kpt2'),
149:
dict(
name='sling_kpt7',
id=149,
color=colors['sling'],
type='',
swap='sling_kpt15'),
150:
dict(
name='sling_kpt8',
id=150,
color=colors['sling'],
type='',
swap='sling_kpt14'),
151:
dict(
name='sling_kpt9',
id=151,
color=colors['sling'],
type='',
swap='sling_kpt13'),
152:
dict(
name='sling_kpt10',
id=152,
color=colors['sling'],
type='',
swap='sling_kpt12'),
153:
dict(
name='sling_kpt11',
id=153,
color=colors['sling'],
type='',
swap=''),
154:
dict(
name='sling_kpt12',
id=154,
color=colors['sling'],
type='',
swap='sling_kpt10'),
155:
dict(
name='sling_kpt13',
id=155,
color=colors['sling'],
type='',
swap='sling_kpt9'),
156:
dict(
name='sling_kpt14',
id=156,
color=colors['sling'],
type='',
swap='sling_kpt8'),
157:
dict(
name='sling_kpt15',
id=157,
color=colors['sling'],
type='',
swap='sling_kpt7'),
# shorts
158:
dict(
name='shorts_kpt1',
id=158,
color=colors['shorts'],
type='',
swap='shorts_kpt3'),
159:
dict(
name='shorts_kpt2',
id=159,
color=colors['shorts'],
type='',
swap=''),
160:
dict(
name='shorts_kpt3',
id=160,
color=colors['shorts'],
type='',
swap='shorts_kpt1'),
161:
dict(
name='shorts_kpt4',
id=161,
color=colors['shorts'],
type='',
swap='shorts_kpt10'),
162:
dict(
name='shorts_kpt5',
id=162,
color=colors['shorts'],
type='',
swap='shorts_kpt9'),
163:
dict(
name='shorts_kpt6',
id=163,
color=colors['shorts'],
type='',
swap='shorts_kpt8'),
164:
dict(
name='shorts_kpt7',
id=164,
color=colors['shorts'],
type='',
swap=''),
165:
dict(
name='shorts_kpt8',
id=165,
color=colors['shorts'],
type='',
swap='shorts_kpt6'),
166:
dict(
name='shorts_kpt9',
id=166,
color=colors['shorts'],
type='',
swap='shorts_kpt5'),
167:
dict(
name='shorts_kpt10',
id=167,
color=colors['shorts'],
type='',
swap='shorts_kpt4'),
# trousers
168:
dict(
name='trousers_kpt1',
id=168,
color=colors['trousers'],
type='',
swap='trousers_kpt3'),
169:
dict(
name='trousers_kpt2',
id=169,
color=colors['trousers'],
type='',
swap=''),
170:
dict(
name='trousers_kpt3',
id=170,
color=colors['trousers'],
type='',
swap='trousers_kpt1'),
171:
dict(
name='trousers_kpt4',
id=171,
color=colors['trousers'],
type='',
swap='trousers_kpt14'),
172:
dict(
name='trousers_kpt5',
id=172,
color=colors['trousers'],
type='',
swap='trousers_kpt13'),
173:
dict(
name='trousers_kpt6',
id=173,
color=colors['trousers'],
type='',
swap='trousers_kpt12'),
174:
dict(
name='trousers_kpt7',
id=174,
color=colors['trousers'],
type='',
swap='trousers_kpt11'),
175:
dict(
name='trousers_kpt8',
id=175,
color=colors['trousers'],
type='',
swap='trousers_kpt10'),
176:
dict(
name='trousers_kpt9',
id=176,
color=colors['trousers'],
type='',
swap=''),
177:
dict(
name='trousers_kpt10',
id=177,
color=colors['trousers'],
type='',
swap='trousers_kpt8'),
178:
dict(
name='trousers_kpt11',
id=178,
color=colors['trousers'],
type='',
swap='trousers_kpt7'),
179:
dict(
name='trousers_kpt12',
id=179,
color=colors['trousers'],
type='',
swap='trousers_kpt6'),
180:
dict(
name='trousers_kpt13',
id=180,
color=colors['trousers'],
type='',
swap='trousers_kpt5'),
181:
dict(
name='trousers_kpt14',
id=181,
color=colors['trousers'],
type='',
swap='trousers_kpt4'),
# skirt
182:
dict(
name='skirt_kpt1',
id=182,
color=colors['skirt'],
type='',
swap='skirt_kpt3'),
183:
dict(
name='skirt_kpt2', id=183, color=colors['skirt'], type='',
swap=''),
184:
dict(
name='skirt_kpt3',
id=184,
color=colors['skirt'],
type='',
swap='skirt_kpt1'),
185:
dict(
name='skirt_kpt4',
id=185,
color=colors['skirt'],
type='',
swap='skirt_kpt8'),
186:
dict(
name='skirt_kpt5',
id=186,
color=colors['skirt'],
type='',
swap='skirt_kpt7'),
187:
dict(
name='skirt_kpt6', id=187, color=colors['skirt'], type='',
swap=''),
188:
dict(
name='skirt_kpt7',
id=188,
color=colors['skirt'],
type='',
swap='skirt_kpt5'),
189:
dict(
name='skirt_kpt8',
id=189,
color=colors['skirt'],
type='',
swap='skirt_kpt4'),
# short_sleeved_dress
190:
dict(name='ssd_kpt1', id=190, color=colors['ssd'], type='', swap=''),
191:
dict(
name='ssd_kpt2',
id=191,
color=colors['ssd'],
type='',
swap='ssd_kpt6'),
192:
dict(
name='ssd_kpt3',
id=192,
color=colors['ssd'],
type='',
swap='ssd_kpt5'),
193:
dict(name='ssd_kpt4', id=193, color=colors['ssd'], type='', swap=''),
194:
dict(
name='ssd_kpt5',
id=194,
color=colors['ssd'],
type='',
swap='ssd_kpt3'),
195:
dict(
name='ssd_kpt6',
id=195,
color=colors['ssd'],
type='',
swap='ssd_kpt2'),
196:
dict(
name='ssd_kpt7',
id=196,
color=colors['ssd'],
type='',
swap='ssd_kpt29'),
197:
dict(
name='ssd_kpt8',
id=197,
color=colors['ssd'],
type='',
swap='ssd_kpt28'),
198:
dict(
name='ssd_kpt9',
id=198,
color=colors['ssd'],
type='',
swap='ssd_kpt27'),
199:
dict(
name='ssd_kpt10',
id=199,
color=colors['ssd'],
type='',
swap='ssd_kpt26'),
200:
dict(
name='ssd_kpt11',
id=200,
color=colors['ssd'],
type='',
swap='ssd_kpt25'),
201:
dict(
name='ssd_kpt12',
id=201,
color=colors['ssd'],
type='',
swap='ssd_kpt24'),
202:
dict(
name='ssd_kpt13',
id=202,
color=colors['ssd'],
type='',
swap='ssd_kpt23'),
203:
dict(
name='ssd_kpt14',
id=203,
color=colors['ssd'],
type='',
swap='ssd_kpt22'),
204:
dict(
name='ssd_kpt15',
id=204,
color=colors['ssd'],
type='',
swap='ssd_kpt21'),
205:
dict(
name='ssd_kpt16',
id=205,
color=colors['ssd'],
type='',
swap='ssd_kpt20'),
206:
dict(
name='ssd_kpt17',
id=206,
color=colors['ssd'],
type='',
swap='ssd_kpt19'),
207:
dict(name='ssd_kpt18', id=207, color=colors['ssd'], type='', swap=''),
208:
dict(
name='ssd_kpt19',
id=208,
color=colors['ssd'],
type='',
swap='ssd_kpt17'),
209:
dict(
name='ssd_kpt20',
id=209,
color=colors['ssd'],
type='',
swap='ssd_kpt16'),
210:
dict(
name='ssd_kpt21',
id=210,
color=colors['ssd'],
type='',
swap='ssd_kpt15'),
211:
dict(
name='ssd_kpt22',
id=211,
color=colors['ssd'],
type='',
swap='ssd_kpt14'),
212:
dict(
name='ssd_kpt23',
id=212,
color=colors['ssd'],
type='',
swap='ssd_kpt13'),
213:
dict(
name='ssd_kpt24',
id=213,
color=colors['ssd'],
type='',
swap='ssd_kpt12'),
214:
dict(
name='ssd_kpt25',
id=214,
color=colors['ssd'],
type='',
swap='ssd_kpt11'),
215:
dict(
name='ssd_kpt26',
id=215,
color=colors['ssd'],
type='',
swap='ssd_kpt10'),
216:
dict(
name='ssd_kpt27',
id=216,
color=colors['ssd'],
type='',
swap='ssd_kpt9'),
217:
dict(
name='ssd_kpt28',
id=217,
color=colors['ssd'],
type='',
swap='ssd_kpt8'),
218:
dict(
name='ssd_kpt29',
id=218,
color=colors['ssd'],
type='',
swap='ssd_kpt7'),
# long_sleeved_dress
219:
dict(name='lsd_kpt1', id=219, color=colors['lsd'], type='', swap=''),
220:
dict(
name='lsd_kpt2',
id=220,
color=colors['lsd'],
type='',
swap='lsd_kpt6'),
221:
dict(
name='lsd_kpt3',
id=221,
color=colors['lsd'],
type='',
swap='lsd_kpt5'),
222:
dict(name='lsd_kpt4', id=222, color=colors['lsd'], type='', swap=''),
223:
dict(
name='lsd_kpt5',
id=223,
color=colors['lsd'],
type='',
swap='lsd_kpt3'),
224:
dict(
name='lsd_kpt6',
id=224,
color=colors['lsd'],
type='',
swap='lsd_kpt2'),
225:
dict(
name='lsd_kpt7',
id=225,
color=colors['lsd'],
type='',
swap='lsd_kpt37'),
226:
dict(
name='lsd_kpt8',
id=226,
color=colors['lsd'],
type='',
swap='lsd_kpt36'),
227:
dict(
name='lsd_kpt9',
id=227,
color=colors['lsd'],
type='',
swap='lsd_kpt35'),
228:
dict(
name='lsd_kpt10',
id=228,
color=colors['lsd'],
type='',
swap='lsd_kpt34'),
229:
dict(
name='lsd_kpt11',
id=229,
color=colors['lsd'],
type='',
swap='lsd_kpt33'),
230:
dict(
name='lsd_kpt12',
id=230,
color=colors['lsd'],
type='',
swap='lsd_kpt32'),
231:
dict(
name='lsd_kpt13',
id=231,
color=colors['lsd'],
type='',
swap='lsd_kpt31'),
232:
dict(
name='lsd_kpt14',
id=232,
color=colors['lsd'],
type='',
swap='lsd_kpt30'),
233:
dict(
name='lsd_kpt15',
id=233,
color=colors['lsd'],
type='',
swap='lsd_kpt29'),
234:
dict(
name='lsd_kpt16',
id=234,
color=colors['lsd'],
type='',
swap='lsd_kpt28'),
235:
dict(
name='lsd_kpt17',
id=235,
color=colors['lsd'],
type='',
swap='lsd_kpt27'),
236:
dict(
name='lsd_kpt18',
id=236,
color=colors['lsd'],
type='',
swap='lsd_kpt26'),
237:
dict(
name='lsd_kpt19',
id=237,
color=colors['lsd'],
type='',
swap='lsd_kpt25'),
238:
dict(
name='lsd_kpt20',
id=238,
color=colors['lsd'],
type='',
swap='lsd_kpt24'),
239:
dict(
name='lsd_kpt21',
id=239,
color=colors['lsd'],
type='',
swap='lsd_kpt23'),
240:
dict(name='lsd_kpt22', id=240, color=colors['lsd'], type='', swap=''),
241:
dict(
name='lsd_kpt23',
id=241,
color=colors['lsd'],
type='',
swap='lsd_kpt21'),
242:
dict(
name='lsd_kpt24',
id=242,
color=colors['lsd'],
type='',
swap='lsd_kpt20'),
243:
dict(
name='lsd_kpt25',
id=243,
color=colors['lsd'],
type='',
swap='lsd_kpt19'),
244:
dict(
name='lsd_kpt26',
id=244,
color=colors['lsd'],
type='',
swap='lsd_kpt18'),
245:
dict(
name='lsd_kpt27',
id=245,
color=colors['lsd'],
type='',
swap='lsd_kpt17'),
246:
dict(
name='lsd_kpt28',
id=246,
color=colors['lsd'],
type='',
swap='lsd_kpt16'),
247:
dict(
name='lsd_kpt29',
id=247,
color=colors['lsd'],
type='',
swap='lsd_kpt15'),
248:
dict(
name='lsd_kpt30',
id=248,
color=colors['lsd'],
type='',
swap='lsd_kpt14'),
249:
dict(
name='lsd_kpt31',
id=249,
color=colors['lsd'],
type='',
swap='lsd_kpt13'),
250:
dict(
name='lsd_kpt32',
id=250,
color=colors['lsd'],
type='',
swap='lsd_kpt12'),
251:
dict(
name='lsd_kpt33',
id=251,
color=colors['lsd'],
type='',
swap='lsd_kpt11'),
252:
dict(
name='lsd_kpt34',
id=252,
color=colors['lsd'],
type='',
swap='lsd_kpt10'),
253:
dict(
name='lsd_kpt35',
id=253,
color=colors['lsd'],
type='',
swap='lsd_kpt9'),
254:
dict(
name='lsd_kpt36',
id=254,
color=colors['lsd'],
type='',
swap='lsd_kpt8'),
255:
dict(
name='lsd_kpt37',
id=255,
color=colors['lsd'],
type='',
swap='lsd_kpt7'),
# vest_dress
256:
dict(name='vd_kpt1', id=256, color=colors['vd'], type='', swap=''),
257:
dict(
name='vd_kpt2',
id=257,
color=colors['vd'],
type='',
swap='vd_kpt6'),
258:
dict(
name='vd_kpt3',
id=258,
color=colors['vd'],
type='',
swap='vd_kpt5'),
259:
dict(name='vd_kpt4', id=259, color=colors['vd'], type='', swap=''),
260:
dict(
name='vd_kpt5',
id=260,
color=colors['vd'],
type='',
swap='vd_kpt3'),
261:
dict(
name='vd_kpt6',
id=261,
color=colors['vd'],
type='',
swap='vd_kpt2'),
262:
dict(
name='vd_kpt7',
id=262,
color=colors['vd'],
type='',
swap='vd_kpt19'),
263:
dict(
name='vd_kpt8',
id=263,
color=colors['vd'],
type='',
swap='vd_kpt18'),
264:
dict(
name='vd_kpt9',
id=264,
color=colors['vd'],
type='',
swap='vd_kpt17'),
265:
dict(
name='vd_kpt10',
id=265,
color=colors['vd'],
type='',
swap='vd_kpt16'),
266:
dict(
name='vd_kpt11',
id=266,
color=colors['vd'],
type='',
swap='vd_kpt15'),
267:
dict(
name='vd_kpt12',
id=267,
color=colors['vd'],
type='',
swap='vd_kpt14'),
268:
dict(name='vd_kpt13', id=268, color=colors['vd'], type='', swap=''),
269:
dict(
name='vd_kpt14',
id=269,
color=colors['vd'],
type='',
swap='vd_kpt12'),
270:
dict(
name='vd_kpt15',
id=270,
color=colors['vd'],
type='',
swap='vd_kpt11'),
271:
dict(
name='vd_kpt16',
id=271,
color=colors['vd'],
type='',
swap='vd_kpt10'),
272:
dict(
name='vd_kpt17',
id=272,
color=colors['vd'],
type='',
swap='vd_kpt9'),
273:
dict(
name='vd_kpt18',
id=273,
color=colors['vd'],
type='',
swap='vd_kpt8'),
274:
dict(
name='vd_kpt19',
id=274,
color=colors['vd'],
type='',
swap='vd_kpt7'),
# sling_dress
275:
dict(name='sd_kpt1', id=275, color=colors['sd'], type='', swap=''),
276:
dict(
name='sd_kpt2',
id=276,
color=colors['sd'],
type='',
swap='sd_kpt6'),
277:
dict(
name='sd_kpt3',
id=277,
color=colors['sd'],
type='',
swap='sd_kpt5'),
278:
dict(name='sd_kpt4', id=278, color=colors['sd'], type='', swap=''),
279:
dict(
name='sd_kpt5',
id=279,
color=colors['sd'],
type='',
swap='sd_kpt3'),
280:
dict(
name='sd_kpt6',
id=280,
color=colors['sd'],
type='',
swap='sd_kpt2'),
281:
dict(
name='sd_kpt7',
id=281,
color=colors['sd'],
type='',
swap='sd_kpt19'),
282:
dict(
name='sd_kpt8',
id=282,
color=colors['sd'],
type='',
swap='sd_kpt18'),
283:
dict(
name='sd_kpt9',
id=283,
color=colors['sd'],
type='',
swap='sd_kpt17'),
284:
dict(
name='sd_kpt10',
id=284,
color=colors['sd'],
type='',
swap='sd_kpt16'),
285:
dict(
name='sd_kpt11',
id=285,
color=colors['sd'],
type='',
swap='sd_kpt15'),
286:
dict(
name='sd_kpt12',
id=286,
color=colors['sd'],
type='',
swap='sd_kpt14'),
287:
dict(name='sd_kpt13', id=287, color=colors['sd'], type='', swap=''),
288:
dict(
name='sd_kpt14',
id=288,
color=colors['sd'],
type='',
swap='sd_kpt12'),
289:
dict(
name='sd_kpt15',
id=289,
color=colors['sd'],
type='',
swap='sd_kpt11'),
290:
dict(
name='sd_kpt16',
id=290,
color=colors['sd'],
type='',
swap='sd_kpt10'),
291:
dict(
name='sd_kpt17',
id=291,
color=colors['sd'],
type='',
swap='sd_kpt9'),
292:
dict(
name='sd_kpt18',
id=292,
color=colors['sd'],
type='',
swap='sd_kpt8'),
293:
dict(
name='sd_kpt19',
id=293,
color=colors['sd'],
type='',
swap='sd_kpt7'),
},
skeleton_info={
# short_sleeved_shirt
0:
dict(link=('sss_kpt1', 'sss_kpt2'), id=0, color=[255, 128, 0]),
1:
dict(link=('sss_kpt2', 'sss_kpt7'), id=1, color=[255, 128, 0]),
2:
dict(link=('sss_kpt7', 'sss_kpt8'), id=2, color=[255, 128, 0]),
3:
dict(link=('sss_kpt8', 'sss_kpt9'), id=3, color=[255, 128, 0]),
4:
dict(link=('sss_kpt9', 'sss_kpt10'), id=4, color=[255, 128, 0]),
5:
dict(link=('sss_kpt10', 'sss_kpt11'), id=5, color=[255, 128, 0]),
6:
dict(link=('sss_kpt11', 'sss_kpt12'), id=6, color=[255, 128, 0]),
7:
dict(link=('sss_kpt12', 'sss_kpt13'), id=7, color=[255, 128, 0]),
8:
dict(link=('sss_kpt13', 'sss_kpt14'), id=8, color=[255, 128, 0]),
9:
dict(link=('sss_kpt14', 'sss_kpt15'), id=9, color=[255, 128, 0]),
10:
dict(link=('sss_kpt15', 'sss_kpt16'), id=10, color=[255, 128, 0]),
11:
dict(link=('sss_kpt16', 'sss_kpt17'), id=11, color=[255, 128, 0]),
12:
dict(link=('sss_kpt17', 'sss_kpt18'), id=12, color=[255, 128, 0]),
13:
dict(link=('sss_kpt18', 'sss_kpt19'), id=13, color=[255, 128, 0]),
14:
dict(link=('sss_kpt19', 'sss_kpt20'), id=14, color=[255, 128, 0]),
15:
dict(link=('sss_kpt20', 'sss_kpt21'), id=15, color=[255, 128, 0]),
16:
dict(link=('sss_kpt21', 'sss_kpt22'), id=16, color=[255, 128, 0]),
17:
dict(link=('sss_kpt22', 'sss_kpt23'), id=17, color=[255, 128, 0]),
18:
dict(link=('sss_kpt23', 'sss_kpt24'), id=18, color=[255, 128, 0]),
19:
dict(link=('sss_kpt24', 'sss_kpt25'), id=19, color=[255, 128, 0]),
20:
dict(link=('sss_kpt25', 'sss_kpt6'), id=20, color=[255, 128, 0]),
21:
dict(link=('sss_kpt6', 'sss_kpt1'), id=21, color=[255, 128, 0]),
22:
dict(link=('sss_kpt2', 'sss_kpt3'), id=22, color=[255, 128, 0]),
23:
dict(link=('sss_kpt3', 'sss_kpt4'), id=23, color=[255, 128, 0]),
24:
dict(link=('sss_kpt4', 'sss_kpt5'), id=24, color=[255, 128, 0]),
25:
dict(link=('sss_kpt5', 'sss_kpt6'), id=25, color=[255, 128, 0]),
# long_sleeve_shirt
26:
dict(link=('lss_kpt1', 'lss_kpt2'), id=26, color=[255, 0, 128]),
27:
dict(link=('lss_kpt2', 'lss_kpt7'), id=27, color=[255, 0, 128]),
28:
dict(link=('lss_kpt7', 'lss_kpt8'), id=28, color=[255, 0, 128]),
29:
dict(link=('lss_kpt8', 'lss_kpt9'), id=29, color=[255, 0, 128]),
30:
dict(link=('lss_kpt9', 'lss_kpt10'), id=30, color=[255, 0, 128]),
31:
dict(link=('lss_kpt10', 'lss_kpt11'), id=31, color=[255, 0, 128]),
32:
dict(link=('lss_kpt11', 'lss_kpt12'), id=32, color=[255, 0, 128]),
33:
dict(link=('lss_kpt12', 'lss_kpt13'), id=33, color=[255, 0, 128]),
34:
dict(link=('lss_kpt13', 'lss_kpt14'), id=34, color=[255, 0, 128]),
35:
dict(link=('lss_kpt14', 'lss_kpt15'), id=35, color=[255, 0, 128]),
36:
dict(link=('lss_kpt15', 'lss_kpt16'), id=36, color=[255, 0, 128]),
37:
dict(link=('lss_kpt16', 'lss_kpt17'), id=37, color=[255, 0, 128]),
38:
dict(link=('lss_kpt17', 'lss_kpt18'), id=38, color=[255, 0, 128]),
39:
dict(link=('lss_kpt18', 'lss_kpt19'), id=39, color=[255, 0, 128]),
40:
dict(link=('lss_kpt19', 'lss_kpt20'), id=40, color=[255, 0, 128]),
41:
dict(link=('lss_kpt20', 'lss_kpt21'), id=41, color=[255, 0, 128]),
42:
dict(link=('lss_kpt21', 'lss_kpt22'), id=42, color=[255, 0, 128]),
43:
dict(link=('lss_kpt22', 'lss_kpt23'), id=43, color=[255, 0, 128]),
44:
dict(link=('lss_kpt23', 'lss_kpt24'), id=44, color=[255, 0, 128]),
45:
dict(link=('lss_kpt24', 'lss_kpt25'), id=45, color=[255, 0, 128]),
46:
dict(link=('lss_kpt25', 'lss_kpt26'), id=46, color=[255, 0, 128]),
47:
dict(link=('lss_kpt26', 'lss_kpt27'), id=47, color=[255, 0, 128]),
48:
dict(link=('lss_kpt27', 'lss_kpt28'), id=48, color=[255, 0, 128]),
49:
dict(link=('lss_kpt28', 'lss_kpt29'), id=49, color=[255, 0, 128]),
50:
dict(link=('lss_kpt29', 'lss_kpt30'), id=50, color=[255, 0, 128]),
51:
dict(link=('lss_kpt30', 'lss_kpt31'), id=51, color=[255, 0, 128]),
52:
dict(link=('lss_kpt31', 'lss_kpt32'), id=52, color=[255, 0, 128]),
53:
dict(link=('lss_kpt32', 'lss_kpt33'), id=53, color=[255, 0, 128]),
54:
dict(link=('lss_kpt33', 'lss_kpt6'), id=54, color=[255, 0, 128]),
55:
dict(link=('lss_kpt6', 'lss_kpt5'), id=55, color=[255, 0, 128]),
56:
dict(link=('lss_kpt5', 'lss_kpt4'), id=56, color=[255, 0, 128]),
57:
dict(link=('lss_kpt4', 'lss_kpt3'), id=57, color=[255, 0, 128]),
58:
dict(link=('lss_kpt3', 'lss_kpt2'), id=58, color=[255, 0, 128]),
59:
dict(link=('lss_kpt6', 'lss_kpt1'), id=59, color=[255, 0, 128]),
# short_sleeved_outwear
60:
dict(link=('sso_kpt1', 'sso_kpt4'), id=60, color=[128, 0, 255]),
61:
dict(link=('sso_kpt4', 'sso_kpt7'), id=61, color=[128, 0, 255]),
62:
dict(link=('sso_kpt7', 'sso_kpt8'), id=62, color=[128, 0, 255]),
63:
dict(link=('sso_kpt8', 'sso_kpt9'), id=63, color=[128, 0, 255]),
64:
dict(link=('sso_kpt9', 'sso_kpt10'), id=64, color=[128, 0, 255]),
65:
dict(link=('sso_kpt10', 'sso_kpt11'), id=65, color=[128, 0, 255]),
66:
dict(link=('sso_kpt11', 'sso_kpt12'), id=66, color=[128, 0, 255]),
67:
dict(link=('sso_kpt12', 'sso_kpt13'), id=67, color=[128, 0, 255]),
68:
dict(link=('sso_kpt13', 'sso_kpt14'), id=68, color=[128, 0, 255]),
69:
dict(link=('sso_kpt14', 'sso_kpt15'), id=69, color=[128, 0, 255]),
70:
dict(link=('sso_kpt15', 'sso_kpt16'), id=70, color=[128, 0, 255]),
71:
dict(link=('sso_kpt16', 'sso_kpt31'), id=71, color=[128, 0, 255]),
72:
dict(link=('sso_kpt31', 'sso_kpt30'), id=72, color=[128, 0, 255]),
73:
dict(link=('sso_kpt30', 'sso_kpt2'), id=73, color=[128, 0, 255]),
74:
dict(link=('sso_kpt2', 'sso_kpt3'), id=74, color=[128, 0, 255]),
75:
dict(link=('sso_kpt3', 'sso_kpt4'), id=75, color=[128, 0, 255]),
76:
dict(link=('sso_kpt1', 'sso_kpt6'), id=76, color=[128, 0, 255]),
77:
dict(link=('sso_kpt6', 'sso_kpt25'), id=77, color=[128, 0, 255]),
78:
dict(link=('sso_kpt25', 'sso_kpt24'), id=78, color=[128, 0, 255]),
79:
dict(link=('sso_kpt24', 'sso_kpt23'), id=79, color=[128, 0, 255]),
80:
dict(link=('sso_kpt23', 'sso_kpt22'), id=80, color=[128, 0, 255]),
81:
dict(link=('sso_kpt22', 'sso_kpt21'), id=81, color=[128, 0, 255]),
82:
dict(link=('sso_kpt21', 'sso_kpt20'), id=82, color=[128, 0, 255]),
83:
dict(link=('sso_kpt20', 'sso_kpt19'), id=83, color=[128, 0, 255]),
84:
dict(link=('sso_kpt19', 'sso_kpt18'), id=84, color=[128, 0, 255]),
85:
dict(link=('sso_kpt18', 'sso_kpt17'), id=85, color=[128, 0, 255]),
86:
dict(link=('sso_kpt17', 'sso_kpt29'), id=86, color=[128, 0, 255]),
87:
dict(link=('sso_kpt29', 'sso_kpt28'), id=87, color=[128, 0, 255]),
88:
dict(link=('sso_kpt28', 'sso_kpt27'), id=88, color=[128, 0, 255]),
89:
dict(link=('sso_kpt27', 'sso_kpt26'), id=89, color=[128, 0, 255]),
90:
dict(link=('sso_kpt26', 'sso_kpt5'), id=90, color=[128, 0, 255]),
91:
dict(link=('sso_kpt5', 'sso_kpt6'), id=91, color=[128, 0, 255]),
# long_sleeved_outwear
92:
dict(link=('lso_kpt1', 'lso_kpt2'), id=92, color=[0, 128, 255]),
93:
dict(link=('lso_kpt2', 'lso_kpt7'), id=93, color=[0, 128, 255]),
94:
dict(link=('lso_kpt7', 'lso_kpt8'), id=94, color=[0, 128, 255]),
95:
dict(link=('lso_kpt8', 'lso_kpt9'), id=95, color=[0, 128, 255]),
96:
dict(link=('lso_kpt9', 'lso_kpt10'), id=96, color=[0, 128, 255]),
97:
dict(link=('lso_kpt10', 'lso_kpt11'), id=97, color=[0, 128, 255]),
98:
dict(link=('lso_kpt11', 'lso_kpt12'), id=98, color=[0, 128, 255]),
99:
dict(link=('lso_kpt12', 'lso_kpt13'), id=99, color=[0, 128, 255]),
100:
dict(link=('lso_kpt13', 'lso_kpt14'), id=100, color=[0, 128, 255]),
101:
dict(link=('lso_kpt14', 'lso_kpt15'), id=101, color=[0, 128, 255]),
102:
dict(link=('lso_kpt15', 'lso_kpt16'), id=102, color=[0, 128, 255]),
103:
dict(link=('lso_kpt16', 'lso_kpt17'), id=103, color=[0, 128, 255]),
104:
dict(link=('lso_kpt17', 'lso_kpt18'), id=104, color=[0, 128, 255]),
105:
dict(link=('lso_kpt18', 'lso_kpt19'), id=105, color=[0, 128, 255]),
106:
dict(link=('lso_kpt19', 'lso_kpt20'), id=106, color=[0, 128, 255]),
107:
dict(link=('lso_kpt20', 'lso_kpt39'), id=107, color=[0, 128, 255]),
108:
dict(link=('lso_kpt39', 'lso_kpt38'), id=108, color=[0, 128, 255]),
109:
dict(link=('lso_kpt38', 'lso_kpt4'), id=109, color=[0, 128, 255]),
110:
dict(link=('lso_kpt4', 'lso_kpt3'), id=110, color=[0, 128, 255]),
111:
dict(link=('lso_kpt3', 'lso_kpt2'), id=111, color=[0, 128, 255]),
112:
dict(link=('lso_kpt1', 'lso_kpt6'), id=112, color=[0, 128, 255]),
113:
dict(link=('lso_kpt6', 'lso_kpt33'), id=113, color=[0, 128, 255]),
114:
dict(link=('lso_kpt33', 'lso_kpt32'), id=114, color=[0, 128, 255]),
115:
dict(link=('lso_kpt32', 'lso_kpt31'), id=115, color=[0, 128, 255]),
116:
dict(link=('lso_kpt31', 'lso_kpt30'), id=116, color=[0, 128, 255]),
117:
dict(link=('lso_kpt30', 'lso_kpt29'), id=117, color=[0, 128, 255]),
118:
dict(link=('lso_kpt29', 'lso_kpt28'), id=118, color=[0, 128, 255]),
119:
dict(link=('lso_kpt28', 'lso_kpt27'), id=119, color=[0, 128, 255]),
120:
dict(link=('lso_kpt27', 'lso_kpt26'), id=120, color=[0, 128, 255]),
121:
dict(link=('lso_kpt26', 'lso_kpt25'), id=121, color=[0, 128, 255]),
122:
dict(link=('lso_kpt25', 'lso_kpt24'), id=122, color=[0, 128, 255]),
123:
dict(link=('lso_kpt24', 'lso_kpt23'), id=123, color=[0, 128, 255]),
124:
dict(link=('lso_kpt23', 'lso_kpt22'), id=124, color=[0, 128, 255]),
125:
dict(link=('lso_kpt22', 'lso_kpt21'), id=125, color=[0, 128, 255]),
126:
dict(link=('lso_kpt21', 'lso_kpt37'), id=126, color=[0, 128, 255]),
127:
dict(link=('lso_kpt37', 'lso_kpt36'), id=127, color=[0, 128, 255]),
128:
dict(link=('lso_kpt36', 'lso_kpt35'), id=128, color=[0, 128, 255]),
129:
dict(link=('lso_kpt35', 'lso_kpt34'), id=129, color=[0, 128, 255]),
130:
dict(link=('lso_kpt34', 'lso_kpt5'), id=130, color=[0, 128, 255]),
131:
dict(link=('lso_kpt5', 'lso_kpt6'), id=131, color=[0, 128, 255]),
# vest
132:
dict(link=('vest_kpt1', 'vest_kpt2'), id=132, color=[0, 128, 128]),
133:
dict(link=('vest_kpt2', 'vest_kpt7'), id=133, color=[0, 128, 128]),
134:
dict(link=('vest_kpt7', 'vest_kpt8'), id=134, color=[0, 128, 128]),
135:
dict(link=('vest_kpt8', 'vest_kpt9'), id=135, color=[0, 128, 128]),
136:
dict(link=('vest_kpt9', 'vest_kpt10'), id=136, color=[0, 128, 128]),
137:
dict(link=('vest_kpt10', 'vest_kpt11'), id=137, color=[0, 128, 128]),
138:
dict(link=('vest_kpt11', 'vest_kpt12'), id=138, color=[0, 128, 128]),
139:
dict(link=('vest_kpt12', 'vest_kpt13'), id=139, color=[0, 128, 128]),
140:
dict(link=('vest_kpt13', 'vest_kpt14'), id=140, color=[0, 128, 128]),
141:
dict(link=('vest_kpt14', 'vest_kpt15'), id=141, color=[0, 128, 128]),
142:
dict(link=('vest_kpt15', 'vest_kpt6'), id=142, color=[0, 128, 128]),
143:
dict(link=('vest_kpt6', 'vest_kpt1'), id=143, color=[0, 128, 128]),
144:
dict(link=('vest_kpt2', 'vest_kpt3'), id=144, color=[0, 128, 128]),
145:
dict(link=('vest_kpt3', 'vest_kpt4'), id=145, color=[0, 128, 128]),
146:
dict(link=('vest_kpt4', 'vest_kpt5'), id=146, color=[0, 128, 128]),
147:
dict(link=('vest_kpt5', 'vest_kpt6'), id=147, color=[0, 128, 128]),
# sling
148:
dict(link=('sling_kpt1', 'sling_kpt2'), id=148, color=[0, 0, 128]),
149:
dict(link=('sling_kpt2', 'sling_kpt8'), id=149, color=[0, 0, 128]),
150:
dict(link=('sling_kpt8', 'sling_kpt9'), id=150, color=[0, 0, 128]),
151:
dict(link=('sling_kpt9', 'sling_kpt10'), id=151, color=[0, 0, 128]),
152:
dict(link=('sling_kpt10', 'sling_kpt11'), id=152, color=[0, 0, 128]),
153:
dict(link=('sling_kpt11', 'sling_kpt12'), id=153, color=[0, 0, 128]),
154:
dict(link=('sling_kpt12', 'sling_kpt13'), id=154, color=[0, 0, 128]),
155:
dict(link=('sling_kpt13', 'sling_kpt14'), id=155, color=[0, 0, 128]),
156:
dict(link=('sling_kpt14', 'sling_kpt6'), id=156, color=[0, 0, 128]),
157:
dict(link=('sling_kpt2', 'sling_kpt7'), id=157, color=[0, 0, 128]),
158:
dict(link=('sling_kpt6', 'sling_kpt15'), id=158, color=[0, 0, 128]),
159:
dict(link=('sling_kpt2', 'sling_kpt3'), id=159, color=[0, 0, 128]),
160:
dict(link=('sling_kpt3', 'sling_kpt4'), id=160, color=[0, 0, 128]),
161:
dict(link=('sling_kpt4', 'sling_kpt5'), id=161, color=[0, 0, 128]),
162:
dict(link=('sling_kpt5', 'sling_kpt6'), id=162, color=[0, 0, 128]),
163:
dict(link=('sling_kpt1', 'sling_kpt6'), id=163, color=[0, 0, 128]),
# shorts
164:
dict(
link=('shorts_kpt1', 'shorts_kpt4'), id=164, color=[128, 128,
128]),
165:
dict(
link=('shorts_kpt4', 'shorts_kpt5'), id=165, color=[128, 128,
128]),
166:
dict(
link=('shorts_kpt5', 'shorts_kpt6'), id=166, color=[128, 128,
128]),
167:
dict(
link=('shorts_kpt6', 'shorts_kpt7'), id=167, color=[128, 128,
128]),
168:
dict(
link=('shorts_kpt7', 'shorts_kpt8'), id=168, color=[128, 128,
128]),
169:
dict(
link=('shorts_kpt8', 'shorts_kpt9'), id=169, color=[128, 128,
128]),
170:
dict(
link=('shorts_kpt9', 'shorts_kpt10'),
id=170,
color=[128, 128, 128]),
171:
dict(
link=('shorts_kpt10', 'shorts_kpt3'),
id=171,
color=[128, 128, 128]),
172:
dict(
link=('shorts_kpt3', 'shorts_kpt2'), id=172, color=[128, 128,
128]),
173:
dict(
link=('shorts_kpt2', 'shorts_kpt1'), id=173, color=[128, 128,
128]),
# trousers
174:
dict(
link=('trousers_kpt1', 'trousers_kpt4'),
id=174,
color=[128, 0, 128]),
175:
dict(
link=('trousers_kpt4', 'trousers_kpt5'),
id=175,
color=[128, 0, 128]),
176:
dict(
link=('trousers_kpt5', 'trousers_kpt6'),
id=176,
color=[128, 0, 128]),
177:
dict(
link=('trousers_kpt6', 'trousers_kpt7'),
id=177,
color=[128, 0, 128]),
178:
dict(
link=('trousers_kpt7', 'trousers_kpt8'),
id=178,
color=[128, 0, 128]),
179:
dict(
link=('trousers_kpt8', 'trousers_kpt9'),
id=179,
color=[128, 0, 128]),
180:
dict(
link=('trousers_kpt9', 'trousers_kpt10'),
id=180,
color=[128, 0, 128]),
181:
dict(
link=('trousers_kpt10', 'trousers_kpt11'),
id=181,
color=[128, 0, 128]),
182:
dict(
link=('trousers_kpt11', 'trousers_kpt12'),
id=182,
color=[128, 0, 128]),
183:
dict(
link=('trousers_kpt12', 'trousers_kpt13'),
id=183,
color=[128, 0, 128]),
184:
dict(
link=('trousers_kpt13', 'trousers_kpt14'),
id=184,
color=[128, 0, 128]),
185:
dict(
link=('trousers_kpt14', 'trousers_kpt3'),
id=185,
color=[128, 0, 128]),
186:
dict(
link=('trousers_kpt3', 'trousers_kpt2'),
id=186,
color=[128, 0, 128]),
187:
dict(
link=('trousers_kpt2', 'trousers_kpt1'),
id=187,
color=[128, 0, 128]),
# skirt
188:
dict(link=('skirt_kpt1', 'skirt_kpt4'), id=188, color=[64, 128, 128]),
189:
dict(link=('skirt_kpt4', 'skirt_kpt5'), id=189, color=[64, 128, 128]),
190:
dict(link=('skirt_kpt5', 'skirt_kpt6'), id=190, color=[64, 128, 128]),
191:
dict(link=('skirt_kpt6', 'skirt_kpt7'), id=191, color=[64, 128, 128]),
192:
dict(link=('skirt_kpt7', 'skirt_kpt8'), id=192, color=[64, 128, 128]),
193:
dict(link=('skirt_kpt8', 'skirt_kpt3'), id=193, color=[64, 128, 128]),
194:
dict(link=('skirt_kpt3', 'skirt_kpt2'), id=194, color=[64, 128, 128]),
195:
dict(link=('skirt_kpt2', 'skirt_kpt1'), id=195, color=[64, 128, 128]),
# short_sleeved_dress
196:
dict(link=('ssd_kpt1', 'ssd_kpt2'), id=196, color=[64, 64, 128]),
197:
dict(link=('ssd_kpt2', 'ssd_kpt7'), id=197, color=[64, 64, 128]),
198:
dict(link=('ssd_kpt7', 'ssd_kpt8'), id=198, color=[64, 64, 128]),
199:
dict(link=('ssd_kpt8', 'ssd_kpt9'), id=199, color=[64, 64, 128]),
200:
dict(link=('ssd_kpt9', 'ssd_kpt10'), id=200, color=[64, 64, 128]),
201:
dict(link=('ssd_kpt10', 'ssd_kpt11'), id=201, color=[64, 64, 128]),
202:
dict(link=('ssd_kpt11', 'ssd_kpt12'), id=202, color=[64, 64, 128]),
203:
dict(link=('ssd_kpt12', 'ssd_kpt13'), id=203, color=[64, 64, 128]),
204:
dict(link=('ssd_kpt13', 'ssd_kpt14'), id=204, color=[64, 64, 128]),
205:
dict(link=('ssd_kpt14', 'ssd_kpt15'), id=205, color=[64, 64, 128]),
206:
dict(link=('ssd_kpt15', 'ssd_kpt16'), id=206, color=[64, 64, 128]),
207:
dict(link=('ssd_kpt16', 'ssd_kpt17'), id=207, color=[64, 64, 128]),
208:
dict(link=('ssd_kpt17', 'ssd_kpt18'), id=208, color=[64, 64, 128]),
209:
dict(link=('ssd_kpt18', 'ssd_kpt19'), id=209, color=[64, 64, 128]),
210:
dict(link=('ssd_kpt19', 'ssd_kpt20'), id=210, color=[64, 64, 128]),
211:
dict(link=('ssd_kpt20', 'ssd_kpt21'), id=211, color=[64, 64, 128]),
212:
dict(link=('ssd_kpt21', 'ssd_kpt22'), id=212, color=[64, 64, 128]),
213:
dict(link=('ssd_kpt22', 'ssd_kpt23'), id=213, color=[64, 64, 128]),
214:
dict(link=('ssd_kpt23', 'ssd_kpt24'), id=214, color=[64, 64, 128]),
215:
dict(link=('ssd_kpt24', 'ssd_kpt25'), id=215, color=[64, 64, 128]),
216:
dict(link=('ssd_kpt25', 'ssd_kpt26'), id=216, color=[64, 64, 128]),
217:
dict(link=('ssd_kpt26', 'ssd_kpt27'), id=217, color=[64, 64, 128]),
218:
dict(link=('ssd_kpt27', 'ssd_kpt28'), id=218, color=[64, 64, 128]),
219:
dict(link=('ssd_kpt28', 'ssd_kpt29'), id=219, color=[64, 64, 128]),
220:
dict(link=('ssd_kpt29', 'ssd_kpt6'), id=220, color=[64, 64, 128]),
221:
dict(link=('ssd_kpt6', 'ssd_kpt5'), id=221, color=[64, 64, 128]),
222:
dict(link=('ssd_kpt5', 'ssd_kpt4'), id=222, color=[64, 64, 128]),
223:
dict(link=('ssd_kpt4', 'ssd_kpt3'), id=223, color=[64, 64, 128]),
224:
dict(link=('ssd_kpt3', 'ssd_kpt2'), id=224, color=[64, 64, 128]),
225:
dict(link=('ssd_kpt6', 'ssd_kpt1'), id=225, color=[64, 64, 128]),
# long_sleeved_dress
226:
dict(link=('lsd_kpt1', 'lsd_kpt2'), id=226, color=[128, 64, 0]),
227:
dict(link=('lsd_kpt2', 'lsd_kpt7'), id=228, color=[128, 64, 0]),
228:
dict(link=('lsd_kpt7', 'lsd_kpt8'), id=228, color=[128, 64, 0]),
229:
dict(link=('lsd_kpt8', 'lsd_kpt9'), id=229, color=[128, 64, 0]),
230:
dict(link=('lsd_kpt9', 'lsd_kpt10'), id=230, color=[128, 64, 0]),
231:
dict(link=('lsd_kpt10', 'lsd_kpt11'), id=231, color=[128, 64, 0]),
232:
dict(link=('lsd_kpt11', 'lsd_kpt12'), id=232, color=[128, 64, 0]),
233:
dict(link=('lsd_kpt12', 'lsd_kpt13'), id=233, color=[128, 64, 0]),
234:
dict(link=('lsd_kpt13', 'lsd_kpt14'), id=234, color=[128, 64, 0]),
235:
dict(link=('lsd_kpt14', 'lsd_kpt15'), id=235, color=[128, 64, 0]),
236:
dict(link=('lsd_kpt15', 'lsd_kpt16'), id=236, color=[128, 64, 0]),
237:
dict(link=('lsd_kpt16', 'lsd_kpt17'), id=237, color=[128, 64, 0]),
238:
dict(link=('lsd_kpt17', 'lsd_kpt18'), id=238, color=[128, 64, 0]),
239:
dict(link=('lsd_kpt18', 'lsd_kpt19'), id=239, color=[128, 64, 0]),
240:
dict(link=('lsd_kpt19', 'lsd_kpt20'), id=240, color=[128, 64, 0]),
241:
dict(link=('lsd_kpt20', 'lsd_kpt21'), id=241, color=[128, 64, 0]),
242:
dict(link=('lsd_kpt21', 'lsd_kpt22'), id=242, color=[128, 64, 0]),
243:
dict(link=('lsd_kpt22', 'lsd_kpt23'), id=243, color=[128, 64, 0]),
244:
dict(link=('lsd_kpt23', 'lsd_kpt24'), id=244, color=[128, 64, 0]),
245:
dict(link=('lsd_kpt24', 'lsd_kpt25'), id=245, color=[128, 64, 0]),
246:
dict(link=('lsd_kpt25', 'lsd_kpt26'), id=246, color=[128, 64, 0]),
247:
dict(link=('lsd_kpt26', 'lsd_kpt27'), id=247, color=[128, 64, 0]),
248:
dict(link=('lsd_kpt27', 'lsd_kpt28'), id=248, color=[128, 64, 0]),
249:
dict(link=('lsd_kpt28', 'lsd_kpt29'), id=249, color=[128, 64, 0]),
250:
dict(link=('lsd_kpt29', 'lsd_kpt30'), id=250, color=[128, 64, 0]),
251:
dict(link=('lsd_kpt30', 'lsd_kpt31'), id=251, color=[128, 64, 0]),
252:
dict(link=('lsd_kpt31', 'lsd_kpt32'), id=252, color=[128, 64, 0]),
253:
dict(link=('lsd_kpt32', 'lsd_kpt33'), id=253, color=[128, 64, 0]),
254:
dict(link=('lsd_kpt33', 'lsd_kpt34'), id=254, color=[128, 64, 0]),
255:
dict(link=('lsd_kpt34', 'lsd_kpt35'), id=255, color=[128, 64, 0]),
256:
dict(link=('lsd_kpt35', 'lsd_kpt36'), id=256, color=[128, 64, 0]),
257:
dict(link=('lsd_kpt36', 'lsd_kpt37'), id=257, color=[128, 64, 0]),
258:
dict(link=('lsd_kpt37', 'lsd_kpt6'), id=258, color=[128, 64, 0]),
259:
dict(link=('lsd_kpt6', 'lsd_kpt5'), id=259, color=[128, 64, 0]),
260:
dict(link=('lsd_kpt5', 'lsd_kpt4'), id=260, color=[128, 64, 0]),
261:
dict(link=('lsd_kpt4', 'lsd_kpt3'), id=261, color=[128, 64, 0]),
262:
dict(link=('lsd_kpt3', 'lsd_kpt2'), id=262, color=[128, 64, 0]),
263:
dict(link=('lsd_kpt6', 'lsd_kpt1'), id=263, color=[128, 64, 0]),
# vest_dress
264:
dict(link=('vd_kpt1', 'vd_kpt2'), id=264, color=[128, 64, 255]),
265:
dict(link=('vd_kpt2', 'vd_kpt7'), id=265, color=[128, 64, 255]),
266:
dict(link=('vd_kpt7', 'vd_kpt8'), id=266, color=[128, 64, 255]),
267:
dict(link=('vd_kpt8', 'vd_kpt9'), id=267, color=[128, 64, 255]),
268:
dict(link=('vd_kpt9', 'vd_kpt10'), id=268, color=[128, 64, 255]),
269:
dict(link=('vd_kpt10', 'vd_kpt11'), id=269, color=[128, 64, 255]),
270:
dict(link=('vd_kpt11', 'vd_kpt12'), id=270, color=[128, 64, 255]),
271:
dict(link=('vd_kpt12', 'vd_kpt13'), id=271, color=[128, 64, 255]),
272:
dict(link=('vd_kpt13', 'vd_kpt14'), id=272, color=[128, 64, 255]),
273:
dict(link=('vd_kpt14', 'vd_kpt15'), id=273, color=[128, 64, 255]),
274:
dict(link=('vd_kpt15', 'vd_kpt16'), id=274, color=[128, 64, 255]),
275:
dict(link=('vd_kpt16', 'vd_kpt17'), id=275, color=[128, 64, 255]),
276:
dict(link=('vd_kpt17', 'vd_kpt18'), id=276, color=[128, 64, 255]),
277:
dict(link=('vd_kpt18', 'vd_kpt19'), id=277, color=[128, 64, 255]),
278:
dict(link=('vd_kpt19', 'vd_kpt6'), id=278, color=[128, 64, 255]),
279:
dict(link=('vd_kpt6', 'vd_kpt5'), id=279, color=[128, 64, 255]),
280:
dict(link=('vd_kpt5', 'vd_kpt4'), id=280, color=[128, 64, 255]),
281:
dict(link=('vd_kpt4', 'vd_kpt3'), id=281, color=[128, 64, 255]),
282:
dict(link=('vd_kpt3', 'vd_kpt2'), id=282, color=[128, 64, 255]),
283:
dict(link=('vd_kpt6', 'vd_kpt1'), id=283, color=[128, 64, 255]),
# sling_dress
284:
dict(link=('sd_kpt1', 'sd_kpt2'), id=284, color=[128, 64, 0]),
285:
dict(link=('sd_kpt2', 'sd_kpt8'), id=285, color=[128, 64, 0]),
286:
dict(link=('sd_kpt8', 'sd_kpt9'), id=286, color=[128, 64, 0]),
287:
dict(link=('sd_kpt9', 'sd_kpt10'), id=287, color=[128, 64, 0]),
288:
dict(link=('sd_kpt10', 'sd_kpt11'), id=288, color=[128, 64, 0]),
289:
dict(link=('sd_kpt11', 'sd_kpt12'), id=289, color=[128, 64, 0]),
290:
dict(link=('sd_kpt12', 'sd_kpt13'), id=290, color=[128, 64, 0]),
291:
dict(link=('sd_kpt13', 'sd_kpt14'), id=291, color=[128, 64, 0]),
292:
dict(link=('sd_kpt14', 'sd_kpt15'), id=292, color=[128, 64, 0]),
293:
dict(link=('sd_kpt15', 'sd_kpt16'), id=293, color=[128, 64, 0]),
294:
dict(link=('sd_kpt16', 'sd_kpt17'), id=294, color=[128, 64, 0]),
295:
dict(link=('sd_kpt17', 'sd_kpt18'), id=295, color=[128, 64, 0]),
296:
dict(link=('sd_kpt18', 'sd_kpt6'), id=296, color=[128, 64, 0]),
297:
dict(link=('sd_kpt6', 'sd_kpt5'), id=297, color=[128, 64, 0]),
298:
dict(link=('sd_kpt5', 'sd_kpt4'), id=298, color=[128, 64, 0]),
299:
dict(link=('sd_kpt4', 'sd_kpt3'), id=299, color=[128, 64, 0]),
300:
dict(link=('sd_kpt3', 'sd_kpt2'), id=300, color=[128, 64, 0]),
301:
dict(link=('sd_kpt2', 'sd_kpt7'), id=301, color=[128, 64, 0]),
302:
dict(link=('sd_kpt6', 'sd_kpt19'), id=302, color=[128, 64, 0]),
303:
dict(link=('sd_kpt6', 'sd_kpt1'), id=303, color=[128, 64, 0]),
},
joint_weights=[1.] * 294,
sigmas=[])
configs/_base_/datasets/deepfashion_full.py 0 → 100755
View file @ 37c8cebc
dataset_info = dict(
dataset_name='deepfashion_full',
paper_info=dict(
author='Liu, Ziwei and Luo, Ping and Qiu, Shi '
'and Wang, Xiaogang and Tang, Xiaoou',
title='DeepFashion: Powering Robust Clothes Recognition '
'and Retrieval with Rich Annotations',
container='Proceedings of IEEE Conference on Computer '
'Vision and Pattern Recognition (CVPR)',
year='2016',
homepage='http://mmlab.ie.cuhk.edu.hk/projects/'
'DeepFashion/LandmarkDetection.html',
),
keypoint_info={
0:
dict(
name='left collar',
id=0,
color=[255, 255, 255],
type='',
swap='right collar'),
1:
dict(
name='right collar',
id=1,
color=[255, 255, 255],
type='',
swap='left collar'),
2:
dict(
name='left sleeve',
id=2,
color=[255, 255, 255],
type='',
swap='right sleeve'),
3:
dict(
name='right sleeve',
id=3,
color=[255, 255, 255],
type='',
swap='left sleeve'),
4:
dict(
name='left waistline',
id=0,
color=[255, 255, 255],
type='',
swap='right waistline'),
5:
dict(
name='right waistline',
id=1,
color=[255, 255, 255],
type='',
swap='left waistline'),
6:
dict(
name='left hem',
id=2,
color=[255, 255, 255],
type='',
swap='right hem'),
7:
dict(
name='right hem',
id=3,
color=[255, 255, 255],
type='',
swap='left hem'),
},
skeleton_info={},
joint_weights=[1.] * 8,
sigmas=[])
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