dataset_tools.md 13 KB
Dataset Tools
Animal Pose
@InProceedings{Cao_2019_ICCV,
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},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}
For Animal-Pose dataset, the images and annotations can be downloaded from official website. The script tools/dataset_converters/parse_animalpose_dataset.py converts raw annotations into the format compatible with MMPose. The pre-processed annotation files are available. If you would like to generate the annotations by yourself, please follow:
- Download the raw images and annotations and extract them under
$MMPOSE/data. Make them look like this:
mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
│── animalpose
│
│-- VOC2012
│ │-- Annotations
│ │-- ImageSets
│ │-- JPEGImages
│ │-- SegmentationClass
│ │-- SegmentationObject
│
│-- animalpose_image_part2
│ │-- cat
│ │-- cow
│ │-- dog
│ │-- horse
│ │-- sheep
│
│-- PASCAL2011_animal_annotation
│ │-- cat
│ │ |-- 2007_000528_1.xml
│ │ |-- 2007_000549_1.xml
│ │ │-- ...
│ │-- cow
│ │-- dog
│ │-- horse
│ │-- sheep
│
│-- annimalpose_anno2
│ │-- cat
│ │ |-- ca1.xml
│ │ |-- ca2.xml
│ │ │-- ...
│ │-- cow
│ │-- dog
│ │-- horse
│ │-- sheep
- Run command
python tools/dataset_converters/parse_animalpose_dataset.py
The generated annotation files are put in $MMPOSE/data/animalpose/annotations.
The official dataset does not provide the official train/val/test set split. We choose the images from PascalVOC for train & val. In total, we have 3608 images and 5117 annotations for train+val, where 2798 images with 4000 annotations are used for training, and 810 images with 1117 annotations are used for validation. Those images from other sources (1000 images with 1000 annotations) are used for testing.
COFW
@inproceedings{burgos2013robust,
title={Robust face landmark estimation under occlusion},
author={Burgos-Artizzu, Xavier P and Perona, Pietro and Doll{\'a}r, Piotr},
booktitle={Proceedings of the IEEE international conference on computer vision},
pages={1513--1520},
year={2013}
}
For COFW data, please download from COFW Dataset (Color Images).
Move COFW_train_color.mat and COFW_test_color.mat to $MMPOSE/data/cofw/ and make them look like:
mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
│── cofw
|── COFW_train_color.mat
|── COFW_test_color.mat
Run pip install h5py first to install the dependency, then run the following script under $MMPOSE:
python tools/dataset_converters/parse_cofw_dataset.py
And you will get
mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
│── cofw
|── COFW_train_color.mat
|── COFW_test_color.mat
|── annotations
| |── cofw_train.json
| |── cofw_test.json
|── images
|── 000001.jpg
|── 000002.jpg
DeepposeKit
@article{graving2019deepposekit,
title={DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning},
author={Graving, Jacob M and Chae, Daniel and Naik, Hemal and Li, Liang and Koger, Benjamin and Costelloe, Blair R and Couzin, Iain D},
journal={Elife},
volume={8},
pages={e47994},
year={2019},
publisher={eLife Sciences Publications Limited}
}
For Vinegar Fly, Desert Locust, and Grévy’s Zebra dataset, the annotations files can be downloaded from DeepPoseKit-Data. The script tools/dataset_converters/parse_deepposekit_dataset.py converts raw annotations into the format compatible with MMPose. The pre-processed annotation files are available at vinegar_fly_annotations, locust_annotations, and zebra_annotations. If you would like to generate the annotations by yourself, please follows:
- Download the raw images and annotations and extract them under
$MMPOSE/data. Make them look like this:
mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
|
|── DeepPoseKit-Data
| `── datasets
| |── fly
| | |── annotation_data_release.h5
| | |── skeleton.csv
| | |── ...
| |
| |── locust
| | |── annotation_data_release.h5
| | |── skeleton.csv
| | |── ...
| |
| `── zebra
| |── annotation_data_release.h5
| |── skeleton.csv
| |── ...
|
│── fly
`-- images
│-- 0.jpg
│-- 1.jpg
│-- ...
Note that the images can be downloaded from vinegar_fly_images, locust_images, and zebra_images.
- Run command
python tools/dataset_converters/parse_deepposekit_dataset.py
The generated annotation files are put in $MMPOSE/data/fly/annotations, $MMPOSE/data/locust/annotations, and $MMPOSE/data/zebra/annotations.
Since the official dataset does not provide the test set, we randomly select 90% images for training, and the rest (10%) for evaluation.
Macaque
@article{labuguen2020macaquepose,
title={MacaquePose: A novel ‘in the wild’macaque monkey pose dataset for markerless motion capture},
author={Labuguen, Rollyn and Matsumoto, Jumpei and Negrete, Salvador and Nishimaru, Hiroshi and Nishijo, Hisao and Takada, Masahiko and Go, Yasuhiro and Inoue, Ken-ichi and Shibata, Tomohiro},
journal={bioRxiv},
year={2020},
publisher={Cold Spring Harbor Laboratory}
}
For MacaquePose dataset, images and annotations can be downloaded from download. The script tools/dataset_converters/parse_macaquepose_dataset.py converts raw annotations into the format compatible with MMPose. The pre-processed macaque_annotations are available. If you would like to generate the annotations by yourself, please follows:
- Download the raw images and annotations and extract them under
$MMPOSE/data. Make them look like this:
mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
│── macaque
│-- annotations.csv
│-- images
│ │-- 01418849d54b3005.jpg
│ │-- 0142d1d1a6904a70.jpg
│ │-- 01ef2c4c260321b7.jpg
│ │-- 020a1c75c8c85238.jpg
│ │-- 020b1506eef2557d.jpg
│ │-- ...
- Run command
python tools/dataset_converters/parse_macaquepose_dataset.py
The generated annotation files are put in $MMPOSE/data/macaque/annotations.
Since the official dataset does not provide the test set, we randomly select 12500 images for training, and the rest for evaluation.
Human3.6M
@article{h36m_pami,
author = {Ionescu, Catalin and Papava, Dragos and Olaru, Vlad and Sminchisescu, Cristian},
title = {Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
publisher = {IEEE Computer Society},
volume = {36},
number = {7},
pages = {1325-1339},
month = {jul},
year = {2014}
}
For Human3.6M, please download from the official website and place the files under $MMPOSE/data/h36m.
Then run the preprocessing script:
python tools/dataset_converters/preprocess_h36m.py --metadata {path to metadata.xml} --original data/h36m
This will extract camera parameters and pose annotations at full framerate (50 FPS) and downsampled framerate (10 FPS). The processed data should have the following structure:
mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
├── h36m
├── annotation_body3d
| ├── cameras.pkl
| ├── fps50
| | ├── h36m_test.npz
| | ├── h36m_train.npz
| | ├── joint2d_rel_stats.pkl
| | ├── joint2d_stats.pkl
| | ├── joint3d_rel_stats.pkl
| | `── joint3d_stats.pkl
| `── fps10
| ├── h36m_test.npz
| ├── h36m_train.npz
| ├── joint2d_rel_stats.pkl
| ├── joint2d_stats.pkl
| ├── joint3d_rel_stats.pkl
| `── joint3d_stats.pkl
`── images
├── S1
| ├── S1_Directions_1.54138969
| | ├── S1_Directions_1.54138969_00001.jpg
| | ├── S1_Directions_1.54138969_00002.jpg
| | ├── ...
| ├── ...
├── S5
├── S6
├── S7
├── S8
├── S9
`── S11
After that, the annotations need to be transformed into COCO format which is compatible with MMPose. Please run:
python tools/dataset_converters/h36m_to_coco.py
MPII
@inproceedings{andriluka14cvpr,
author = {Mykhaylo Andriluka and Leonid Pishchulin and Peter Gehler and Schiele, Bernt},
title = {2D Human Pose Estimation: New Benchmark and State of the Art Analysis},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2014},
month = {June}
}
During training and inference for MPII, the prediction result will be saved as '.mat' format by default. We also provide a tool to convert this .mat to more readable .json format.
python tools/dataset_converters/mat2json ${PRED_MAT_FILE} ${GT_JSON_FILE} ${OUTPUT_PRED_JSON_FILE}
For example,
python tools/dataset/mat2json work_dirs/res50_mpii_256x256/pred.mat data/mpii/annotations/mpii_val.json pred.json