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add 300wlp
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‎configs/_base_/datasets/300w_LP.py‎

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‎configs/_base_/datasets/300wlp.py‎

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dataset_info = dict(
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dataset_name='300wlp',
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paper_info=dict(
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author='Xiangyu Zhu1, and Zhen Lei1 '
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'and Xiaoming Liu2, and Hailin Shi1 '
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'and Stan Z. Li1',
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title='300 faces in-the-wild challenge: '
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'Database and results',
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container='Image and vision computing',
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year='2016',
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homepage='http://www.cbsr.ia.ac.cn/users/xiangyuzhu/'
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'projects/3DDFA/main.htm',
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),
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keypoint_info={
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0: dict(name='kpt-0', id=0, color=[255, 0, 0], type='', swap=''),
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1: dict(name='kpt-1', id=1, color=[255, 0, 0], type='', swap=''),
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2: dict(name='kpt-2', id=2, color=[255, 0, 0], type='', swap=''),
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3: dict(name='kpt-3', id=3, color=[255, 0, 0], type='', swap=''),
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4: dict(name='kpt-4', id=4, color=[255, 0, 0], type='', swap=''),
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5: dict(name='kpt-5', id=5, color=[255, 0, 0], type='', swap=''),
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6: dict(name='kpt-6', id=6, color=[255, 0, 0], type='', swap=''),
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7: dict(name='kpt-7', id=7, color=[255, 0, 0], type='', swap=''),
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8: dict(name='kpt-8', id=8, color=[255, 0, 0], type='', swap=''),
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9: dict(name='kpt-9', id=9, color=[255, 0, 0], type='', swap=''),
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10: dict(name='kpt-10', id=10, color=[255, 0, 0], type='', swap=''),
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11: dict(name='kpt-11', id=11, color=[255, 0, 0], type='', swap=''),
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12: dict(name='kpt-12', id=12, color=[255, 0, 0], type='', swap=''),
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13: dict(name='kpt-13', id=13, color=[255, 0, 0], type='', swap=''),
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14: dict(name='kpt-14', id=14, color=[255, 0, 0], type='', swap=''),
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15: dict(name='kpt-15', id=15, color=[255, 0, 0], type='', swap=''),
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16: dict(name='kpt-16', id=16, color=[255, 0, 0], type='', swap=''),
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17: dict(name='kpt-17', id=17, color=[255, 0, 0], type='', swap=''),
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18: dict(name='kpt-18', id=18, color=[255, 0, 0], type='', swap=''),
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19: dict(name='kpt-19', id=19, color=[255, 0, 0], type='', swap=''),
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20: dict(name='kpt-20', id=20, color=[255, 0, 0], type='', swap=''),
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21: dict(name='kpt-21', id=21, color=[255, 0, 0], type='', swap=''),
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22: dict(name='kpt-22', id=22, color=[255, 0, 0], type='', swap=''),
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23: dict(name='kpt-23', id=23, color=[255, 0, 0], type='', swap=''),
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24: dict(name='kpt-24', id=24, color=[255, 0, 0], type='', swap=''),
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25: dict(name='kpt-25', id=25, color=[255, 0, 0], type='', swap=''),
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26: dict(name='kpt-26', id=26, color=[255, 0, 0], type='', swap=''),
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27: dict(name='kpt-27', id=27, color=[255, 0, 0], type='', swap=''),
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28: dict(name='kpt-28', id=28, color=[255, 0, 0], type='', swap=''),
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29: dict(name='kpt-29', id=29, color=[255, 0, 0], type='', swap=''),
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30: dict(name='kpt-30', id=30, color=[255, 0, 0], type='', swap=''),
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31: dict(name='kpt-31', id=31, color=[255, 0, 0], type='', swap=''),
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32: dict(name='kpt-32', id=32, color=[255, 0, 0], type='', swap=''),
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33: dict(name='kpt-33', id=33, color=[255, 0, 0], type='', swap=''),
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34: dict(name='kpt-34', id=34, color=[255, 0, 0], type='', swap=''),
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35: dict(name='kpt-35', id=35, color=[255, 0, 0], type='', swap=''),
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36: dict(name='kpt-36', id=36, color=[255, 0, 0], type='', swap=''),
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37: dict(name='kpt-37', id=37, color=[255, 0, 0], type='', swap=''),
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38: dict(name='kpt-38', id=38, color=[255, 0, 0], type='', swap=''),
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39: dict(name='kpt-39', id=39, color=[255, 0, 0], type='', swap=''),
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40: dict(name='kpt-40', id=40, color=[255, 0, 0], type='', swap=''),
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41: dict(name='kpt-41', id=41, color=[255, 0, 0], type='', swap=''),
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42: dict(name='kpt-42', id=42, color=[255, 0, 0], type='', swap=''),
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43: dict(name='kpt-43', id=43, color=[255, 0, 0], type='', swap=''),
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44: dict(name='kpt-44', id=44, color=[255, 0, 0], type='', swap=''),
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45: dict(name='kpt-45', id=45, color=[255, 0, 0], type='', swap=''),
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46: dict(name='kpt-46', id=46, color=[255, 0, 0], type='', swap=''),
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47: dict(name='kpt-47', id=47, color=[255, 0, 0], type='', swap=''),
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48: dict(name='kpt-48', id=48, color=[255, 0, 0], type='', swap=''),
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49: dict(name='kpt-49', id=49, color=[255, 0, 0], type='', swap=''),
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50: dict(name='kpt-50', id=50, color=[255, 0, 0], type='', swap=''),
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51: dict(name='kpt-51', id=51, color=[255, 0, 0], type='', swap=''),
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52: dict(name='kpt-52', id=52, color=[255, 0, 0], type='', swap=''),
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53: dict(name='kpt-53', id=53, color=[255, 0, 0], type='', swap=''),
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54: dict(name='kpt-54', id=54, color=[255, 0, 0], type='', swap=''),
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55: dict(name='kpt-55', id=55, color=[255, 0, 0], type='', swap=''),
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56: dict(name='kpt-56', id=56, color=[255, 0, 0], type='', swap=''),
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57: dict(name='kpt-57', id=57, color=[255, 0, 0], type='', swap=''),
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58: dict(name='kpt-58', id=58, color=[255, 0, 0], type='', swap=''),
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59: dict(name='kpt-59', id=59, color=[255, 0, 0], type='', swap=''),
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60: dict(name='kpt-60', id=60, color=[255, 0, 0], type='', swap=''),
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61: dict(name='kpt-61', id=61, color=[255, 0, 0], type='', swap=''),
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62: dict(name='kpt-62', id=62, color=[255, 0, 0], type='', swap=''),
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63: dict(name='kpt-63', id=63, color=[255, 0, 0], type='', swap=''),
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64: dict(name='kpt-64', id=64, color=[255, 0, 0], type='', swap=''),
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65: dict(name='kpt-65', id=65, color=[255, 0, 0], type='', swap=''),
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66: dict(name='kpt-66', id=66, color=[255, 0, 0], type='', swap=''),
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67: dict(name='kpt-67', id=67, color=[255, 0, 0], type='', swap=''),
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},
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skeleton_info={},
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joint_weights=[1.] * 68,
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sigmas=[])

‎configs/face_2d_keypoint/topdown_heatmap/300wlp/hrnetv2_300wlp.md‎

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<details>
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<summary align="right"><a href="http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/main.htm">300WLP (IEEE'2017)</a></summary>
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```bibtex
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@article{zhu2017face,
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title={Face alignment in full pose range: A 3d total solution},
@@ -38,7 +37,6 @@ Results on 300W-LP dataset
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The model is trained on 300W-LP train.
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| Arch | Input Size | NME<sub>*full*</sub> | NME<sub>*test*</sub> | ckpt | log |
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| :----------------------------------------------------------- | :--------: | :------------------: | :------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
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| Arch | Input Size | NME<sub>*full*</sub> | NME<sub>*test*</sub> | ckpt | log |
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| :------------------------------------------------- | :--------: | :------------------: | :------------------: | :------------------------------------------------: | :------------------------------------------------: |
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| [pose_hrnetv2_w18](/configs/face_2d_keypoint/topdown_heatmap/300wlp/td-hm_hrnetv2-w18_8xb64-60e_300wlp-256x256.py) | 256x256 | 0.0413 | 0.04125 | [ckpt](https://download.openmmlab.com/mmpose/v1/face_2d_keypoint/topdown_heatmap/300wlp/hrnetv2_w18_300wlp_256x256-fb433d21_20230922.pth) | [log](https://download.openmmlab.com/mmpose/v1/face_2d_keypoint/topdown_heatmap/300wlp/hrnetv2_w18_300wlp_256x256-fb433d21_20230922.json) |
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‎mmpose/datasets/datasets/face/__init__.py‎

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from .coco_wholebody_face_dataset import CocoWholeBodyFaceDataset
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from .cofw_dataset import COFWDataset
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from .face_300w_dataset import Face300WDataset
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from .face_300wlp_dataset import Face300WLPDataset
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from .lapa_dataset import LapaDataset
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from .wflw_dataset import WFLWDataset
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from .face_300wlp_dataset import Face300WLPDataset
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__all__ = [
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'Face300WDataset', 'WFLWDataset', 'AFLWDataset', 'COFWDataset',
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'CocoWholeBodyFaceDataset', 'LapaDataset', 'Face300WLPDataset'

‎mmpose/datasets/datasets/face/face_300wlp_dataset.py‎

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# Copyright (c) OpenMMLab. All rights reserved.
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import os.path as osp
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from typing import Optional
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import numpy as np
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from mmpose.registry import DATASETS
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from mmpose.structures.bbox import bbox_cs2xyxy
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from ..base import BaseCocoStyleDataset
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image. Default: 1000.
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"""
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METAINFO: dict = dict(from_file='configs/_base_/datasets/300w_LP.py')
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METAINFO: dict = dict(from_file='configs/_base_/datasets/300wlp.py')

‎projects/just_dance/configs/_base_‎

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../../../configs/_base_
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../../../configs/_base_

‎tests/test_datasets/test_datasets/test_face_datasets/test_face_300wlp_dataset.py‎

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# Copyright (c) OpenMMLab. All rights reserved.
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from unittest import TestCase
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import numpy as np
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from mmpose.datasets.datasets.face import Face300WLPDataset
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data_mode='bottomup',
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test_mode=True,
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filter_cfg=dict(bbox_score_thr=0.3))
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‎tools/dataset_converters/300wlp2coco.py‎

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# Copyright (c) OpenMMLab. All rights reserved.
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import json
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import os
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import os.path as osp
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# split 300w-lp data
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def split_data(file_img, train_path, val_path,
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test_path, shuffle=True, ratio1=0.8, ratio2=0.1):
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def split_data(file_img,
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train_path,
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val_path,
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test_path,
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shuffle=True,
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ratio1=0.8,
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ratio2=0.1):
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img_list = os.listdir(file_img)
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if shuffle:
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np.random.shuffle(img_list)
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print(f'done {out_file}')
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if __name__ == "__main__":
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if __name__ == '__main__':
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# 1.Move all images to one folder
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# 2.split 300W-LP data
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# 3.convert json
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os.makedirs(anno_path_json)
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for tv in ['val', 'test', 'train']:
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print(f'processing {tv}')
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convert_300WLP_to_coco(root_path, tv, anno_path_json + '/' + f'face_landmarks_300wlp_{tv}.json')
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convert_300WLP_to_coco(
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root_path, tv,
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anno_path_json + '/' + f'face_landmarks_300wlp_{tv}.json')

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