Upload 251 files

.gitattributes

@@ -40,3 +40,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 01_33.mp4 filter=lfs diff=lfs merge=lfs -text
 01_40.mov filter=lfs diff=lfs merge=lfs -text
 01_76.mp4 filter=lfs diff=lfs merge=lfs -text
+deep-high-resolution-net.pytorch/demo/hrnet-demo.gif filter=lfs diff=lfs merge=lfs -text

deep-high-resolution-net.pytorch/.amlignore

@@ -0,0 +1,6 @@
+## This file was auto generated by the Azure Machine Learning Studio. Please do not remove. 
+## Read more about the .amlignore file here: https://docs.microsoft.com/azure/machine-learning/how-to-save-write-experiment-files#storage-limits-of-experiment-snapshots
+        
+.ipynb_aml_checkpoints/ 
+*.amltmp 
+*.amltemp

deep-high-resolution-net.pytorch/.amlignore.amltmp

@@ -0,0 +1,6 @@
+## This file was auto generated by the Azure Machine Learning Studio. Please do not remove. 
+## Read more about the .amlignore file here: https://docs.microsoft.com/azure/machine-learning/how-to-save-write-experiment-files#storage-limits-of-experiment-snapshots
+        
+.ipynb_aml_checkpoints/ 
+*.amltmp 
+*.amltemp

deep-high-resolution-net.pytorch/.gitignore

@@ -0,0 +1,95 @@
+# IntelliJ project files
+.idea
+*.iml
+out
+gen
+
+### Vim template
+[._]*.s[a-w][a-z]
+[._]s[a-w][a-z]
+*.un~
+Session.vim
+.netrwhist
+*~
+
+### IPythonNotebook template
+# Temporary data
+.ipynb_checkpoints/
+
+### Python template
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+#lib/
+#lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+*.ipynb
+*.params
+*.json
+.vscode/
+
+lib/pycocotools/_mask.c
+lib/nms/cpu_nms.c
+
+output/*
+models/*
+log/*
+data/*
+external/
+
+draws/
+plot/
+

deep-high-resolution-net.pytorch/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2019 Leo Xiao
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

deep-high-resolution-net.pytorch/README.md

@@ -0,0 +1,276 @@
+# Deep High-Resolution Representation Learning for Human Pose Estimation (CVPR 2019)
+## News
+- [2021/04/12] Welcome to check out our recent work on bottom-up pose estimation (CVPR 2021) [HRNet-DEKR](https://github.com/HRNet/DEKR)!
+- [2020/07/05] [A very nice blog](https://towardsdatascience.com/overview-of-human-pose-estimation-neural-networks-hrnet-higherhrnet-architectures-and-faq-1954b2f8b249) from Towards Data Science introducing HRNet and HigherHRNet for human pose estimation.
+- [2020/03/13] A longer version is accepted by TPAMI: [Deep High-Resolution Representation Learning for Visual Recognition](https://arxiv.org/pdf/1908.07919.pdf). It includes more HRNet applications, and the codes are available: [semantic segmentation](https://github.com/HRNet/HRNet-Semantic-Segmentation),  [objection detection](https://github.com/HRNet/HRNet-Object-Detection),  [facial landmark detection](https://github.com/HRNet/HRNet-Facial-Landmark-Detection), and [image classification](https://github.com/HRNet/HRNet-Image-Classification).
+- [2020/02/01] We have added demo code for HRNet. Thanks [Alex Simes](https://github.com/alex9311). 
+- Visualization code for showing the pose estimation results. Thanks Depu!
+- [2019/08/27] HigherHRNet is now on [ArXiv](https://arxiv.org/abs/1908.10357), which is a bottom-up approach for human pose estimation powerd by HRNet. We will also release code and models at [Higher-HRNet-Human-Pose-Estimation](https://github.com/HRNet/Higher-HRNet-Human-Pose-Estimation), stay tuned!
+- Our new work [High-Resolution Representations for Labeling Pixels and Regions](https://arxiv.org/abs/1904.04514) is available at [HRNet](https://github.com/HRNet). Our HRNet has been applied to a wide range of vision tasks, such as [image classification](https://github.com/HRNet/HRNet-Image-Classification), [objection detection](https://github.com/HRNet/HRNet-Object-Detection), [semantic segmentation](https://github.com/HRNet/HRNet-Semantic-Segmentation) and [facial landmark](https://github.com/HRNet/HRNet-Facial-Landmark-Detection).
+
+## Introduction
+This is an official pytorch implementation of [*Deep High-Resolution Representation Learning for Human Pose Estimation*](https://arxiv.org/abs/1902.09212). 
+In this work, we are interested in the human pose estimation problem with a focus on learning reliable high-resolution representations. Most existing methods **recover high-resolution representations from low-resolution representations** produced by a high-to-low resolution network. Instead, our proposed network **maintains high-resolution representations** through the whole process.
+We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the mutli-resolution subnetworks **in parallel**. We conduct **repeated multi-scale fusions** such that each of the high-to-low resolution representations receives information from other parallel representations over and over, leading to rich high-resolution representations. As a result, the predicted keypoint heatmap is potentially more accurate and spatially more precise. We empirically demonstrate the effectiveness of our network through the superior pose estimation results over two benchmark datasets: the COCO keypoint detection dataset and the MPII Human Pose dataset. </br>
+
+![Illustrating the architecture of the proposed HRNet](/figures/hrnet.png)
+## Main Results
+### Results on MPII val
+| Arch               | Head | Shoulder | Elbow | Wrist |  Hip | Knee | Ankle | Mean | [email protected] |
+|--------------------|------|----------|-------|-------|------|------|-------|------|----------|
+| pose_resnet_50     | 96.4 |     95.3 |  89.0 |  83.2 | 88.4 | 84.0 |  79.6 | 88.5 |     34.0 |
+| pose_resnet_101    | 96.9 |     95.9 |  89.5 |  84.4 | 88.4 | 84.5 |  80.7 | 89.1 |     34.0 |
+| pose_resnet_152    | 97.0 |     95.9 |  90.0 |  85.0 | 89.2 | 85.3 |  81.3 | 89.6 |     35.0 |
+| **pose_hrnet_w32** | 97.1 |     95.9 |  90.3 |  86.4 | 89.1 | 87.1 |  83.3 | 90.3 |     37.7 |
+
+### Note:
+- Flip test is used.
+- Input size is 256x256
+- pose_resnet_[50,101,152] is our previous work of [*Simple Baselines for Human Pose Estimation and Tracking*](http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html)
+
+### Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset
+| Arch               | Input size | #Params | GFLOPs |    AP | Ap .5 | AP .75 | AP (M) | AP (L) |    AR | AR .5 | AR .75 | AR (M) | AR (L) |
+|--------------------|------------|---------|--------|-------|-------|--------|--------|--------|-------|-------|--------|--------|--------|
+| pose_resnet_50     |    256x192 | 34.0M   |    8.9 | 0.704 | 0.886 |  0.783 |  0.671 |  0.772 | 0.763 | 0.929 |  0.834 |  0.721 |  0.824 |
+| pose_resnet_50     |    384x288 | 34.0M   |   20.0 | 0.722 | 0.893 |  0.789 |  0.681 |  0.797 | 0.776 | 0.932 |  0.838 |  0.728 |  0.846 |
+| pose_resnet_101    |    256x192 | 53.0M   |   12.4 | 0.714 | 0.893 |  0.793 |  0.681 |  0.781 | 0.771 | 0.934 |  0.840 |  0.730 |  0.832 |
+| pose_resnet_101    |    384x288 | 53.0M   |   27.9 | 0.736 | 0.896 |  0.803 |  0.699 |  0.811 | 0.791 | 0.936 |  0.851 |  0.745 |  0.858 |
+| pose_resnet_152    |    256x192 | 68.6M   |   15.7 | 0.720 | 0.893 |  0.798 |  0.687 |  0.789 | 0.778 | 0.934 |  0.846 |  0.736 |  0.839 |
+| pose_resnet_152    |    384x288 | 68.6M   |   35.3 | 0.743 | 0.896 |  0.811 |  0.705 |  0.816 | 0.797 | 0.937 |  0.858 |  0.751 |  0.863 |
+| **pose_hrnet_w32** |    256x192 | 28.5M   |    7.1 | 0.744 | 0.905 |  0.819 |  0.708 |  0.810 | 0.798 | 0.942 |  0.865 |  0.757 |  0.858 |
+| **pose_hrnet_w32** |    384x288 | 28.5M   |   16.0 | 0.758 | 0.906 |  0.825 |  0.720 |  0.827 | 0.809 | 0.943 |  0.869 |  0.767 |  0.871 |
+| **pose_hrnet_w48** |    256x192 | 63.6M   |   14.6 | 0.751 | 0.906 |  0.822 |  0.715 |  0.818 | 0.804 | 0.943 |  0.867 |  0.762 |  0.864 |
+| **pose_hrnet_w48** |    384x288 | 63.6M   |   32.9 | 0.763 | 0.908 |  0.829 |  0.723 |  0.834 | 0.812 | 0.942 |  0.871 |  0.767 |  0.876 |
+
+### Note:
+- Flip test is used.
+- Person detector has person AP of 56.4 on COCO val2017 dataset.
+- pose_resnet_[50,101,152] is our previous work of [*Simple Baselines for Human Pose Estimation and Tracking*](http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html).
+- GFLOPs is for convolution and linear layers only.
+
+
+### Results on COCO test-dev2017 with detector having human AP of 60.9 on COCO test-dev2017 dataset
+| Arch               | Input size | #Params | GFLOPs |    AP | Ap .5 | AP .75 | AP (M) | AP (L) |    AR | AR .5 | AR .75 | AR (M) | AR (L) |
+|--------------------|------------|---------|--------|-------|-------|--------|--------|--------|-------|-------|--------|--------|--------|
+| pose_resnet_152    |    384x288 | 68.6M   |   35.3 | 0.737 | 0.919 |  0.828 |  0.713 |  0.800 | 0.790 | 0.952 |  0.856 |  0.748 |  0.849 |
+| **pose_hrnet_w48** |    384x288 | 63.6M   |   32.9 | 0.755 | 0.925 |  0.833 |  0.719 |  0.815 | 0.805 | 0.957 |  0.874 |  0.763 |  0.863 |
+| **pose_hrnet_w48\*** |    384x288 | 63.6M   |   32.9 | 0.770 | 0.927 |  0.845 |  0.734 |  0.831 | 0.820 | 0.960 |  0.886 |  0.778 |  0.877 |
+
+### Note:
+- Flip test is used.
+- Person detector has person AP of 60.9 on COCO test-dev2017 dataset.
+- pose_resnet_152 is our previous work of [*Simple Baselines for Human Pose Estimation and Tracking*](http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html).
+- GFLOPs is for convolution and linear layers only.
+- pose_hrnet_w48\* means using additional data from [AI challenger](https://challenger.ai/dataset/keypoint) for training.
+
+## Environment
+The code is developed using python 3.6 on Ubuntu 16.04. NVIDIA GPUs are needed. The code is developed and tested using 4 NVIDIA P100 GPU cards. Other platforms or GPU cards are not fully tested.
+
+## Quick start
+### Installation
+1. Install pytorch >= v1.0.0 following [official instruction](https://pytorch.org/).
+   **Note that if you use pytorch's version < v1.0.0, you should following the instruction at <https://github.com/Microsoft/human-pose-estimation.pytorch> to disable cudnn's implementations of BatchNorm layer. We encourage you to use higher pytorch's version(>=v1.0.0)**
+2. Clone this repo, and we'll call the directory that you cloned as ${POSE_ROOT}.
+3. Install dependencies:
+   ```
+   pip install -r requirements.txt
+   ```
+4. Make libs:
+   ```
+   cd ${POSE_ROOT}/lib
+   make
+   ```
+5. Install [COCOAPI](https://github.com/cocodataset/cocoapi):
+   ```
+   # COCOAPI=/path/to/clone/cocoapi
+   git clone https://github.com/cocodataset/cocoapi.git $COCOAPI
+   cd $COCOAPI/PythonAPI
+   # Install into global site-packages
+   make install
+   # Alternatively, if you do not have permissions or prefer
+   # not to install the COCO API into global site-packages
+   python3 setup.py install --user
+   ```
+   Note that instructions like # COCOAPI=/path/to/install/cocoapi indicate that you should pick a path where you'd like to have the software cloned and then set an environment variable (COCOAPI in this case) accordingly.
+4. Init output(training model output directory) and log(tensorboard log directory) directory:
+
+   ```
+   mkdir output 
+   mkdir log
+   ```
+
+   Your directory tree should look like this:
+
+   ```
+   ${POSE_ROOT}
+   ├── data
+   ├── experiments
+   ├── lib
+   ├── log
+   ├── models
+   ├── output
+   ├── tools 
+   ├── README.md
+   └── requirements.txt
+   ```
+
+6. Download pretrained models from our model zoo([GoogleDrive](https://drive.google.com/drive/folders/1hOTihvbyIxsm5ygDpbUuJ7O_tzv4oXjC?usp=sharing) or [OneDrive](https://1drv.ms/f/s!AhIXJn_J-blW231MH2krnmLq5kkQ))
+   ```
+   ${POSE_ROOT}
+    `-- models
+        `-- pytorch
+            |-- imagenet
+            |   |-- hrnet_w32-36af842e.pth
+            |   |-- hrnet_w48-8ef0771d.pth
+            |   |-- resnet50-19c8e357.pth
+            |   |-- resnet101-5d3b4d8f.pth
+            |   `-- resnet152-b121ed2d.pth
+            |-- pose_coco
+            |   |-- pose_hrnet_w32_256x192.pth
+            |   |-- pose_hrnet_w32_384x288.pth
+            |   |-- pose_hrnet_w48_256x192.pth
+            |   |-- pose_hrnet_w48_384x288.pth
+            |   |-- pose_resnet_101_256x192.pth
+            |   |-- pose_resnet_101_384x288.pth
+            |   |-- pose_resnet_152_256x192.pth
+            |   |-- pose_resnet_152_384x288.pth
+            |   |-- pose_resnet_50_256x192.pth
+            |   `-- pose_resnet_50_384x288.pth
+            `-- pose_mpii
+                |-- pose_hrnet_w32_256x256.pth
+                |-- pose_hrnet_w48_256x256.pth
+                |-- pose_resnet_101_256x256.pth
+                |-- pose_resnet_152_256x256.pth
+                `-- pose_resnet_50_256x256.pth
+
+   ```
+   
+### Data preparation
+**For MPII data**, please download from [MPII Human Pose Dataset](http://human-pose.mpi-inf.mpg.de/). The original annotation files are in matlab format. We have converted them into json format, you also need to download them from [OneDrive](https://1drv.ms/f/s!AhIXJn_J-blW00SqrairNetmeVu4) or [GoogleDrive](https://drive.google.com/drive/folders/1En_VqmStnsXMdldXA6qpqEyDQulnmS3a?usp=sharing).
+Extract them under {POSE_ROOT}/data, and make them look like this:
+```
+${POSE_ROOT}
+|-- data
+`-- |-- mpii
+    `-- |-- annot
+        |   |-- gt_valid.mat
+        |   |-- test.json
+        |   |-- train.json
+        |   |-- trainval.json
+        |   `-- valid.json
+        `-- images
+            |-- 000001163.jpg
+            |-- 000003072.jpg
+```
+
+**For COCO data**, please download from [COCO download](http://cocodataset.org/#download), 2017 Train/Val is needed for COCO keypoints training and validation. We also provide person detection result of COCO val2017 and test-dev2017 to reproduce our multi-person pose estimation results. Please download from [OneDrive](https://1drv.ms/f/s!AhIXJn_J-blWzzDXoz5BeFl8sWM-) or [GoogleDrive](https://drive.google.com/drive/folders/1fRUDNUDxe9fjqcRZ2bnF_TKMlO0nB_dk?usp=sharing).
+Download and extract them under {POSE_ROOT}/data, and make them look like this:
+```
+${POSE_ROOT}
+|-- data
+`-- |-- coco
+    `-- |-- annotations
+        |   |-- person_keypoints_train2017.json
+        |   `-- person_keypoints_val2017.json
+        |-- person_detection_results
+        |   |-- COCO_val2017_detections_AP_H_56_person.json
+        |   |-- COCO_test-dev2017_detections_AP_H_609_person.json
+        `-- images
+            |-- train2017
+            |   |-- 000000000009.jpg
+            |   |-- 000000000025.jpg
+            |   |-- 000000000030.jpg
+            |   |-- ... 
+            `-- val2017
+                |-- 000000000139.jpg
+                |-- 000000000285.jpg
+                |-- 000000000632.jpg
+                |-- ... 
+```
+
+### Training and Testing
+
+#### Testing on MPII dataset using model zoo's models([GoogleDrive](https://drive.google.com/drive/folders/1hOTihvbyIxsm5ygDpbUuJ7O_tzv4oXjC?usp=sharing) or [OneDrive](https://1drv.ms/f/s!AhIXJn_J-blW231MH2krnmLq5kkQ))
+ 
+
+```
+python tools/test.py \
+    --cfg experiments/mpii/hrnet/w32_256x256_adam_lr1e-3.yaml \
+    TEST.MODEL_FILE models/pytorch/pose_mpii/pose_hrnet_w32_256x256.pth
+```
+
+#### Training on MPII dataset
+
+```
+python tools/train.py \
+    --cfg experiments/mpii/hrnet/w32_256x256_adam_lr1e-3.yaml
+```
+
+#### Testing on COCO val2017 dataset using model zoo's models([GoogleDrive](https://drive.google.com/drive/folders/1hOTihvbyIxsm5ygDpbUuJ7O_tzv4oXjC?usp=sharing) or [OneDrive](https://1drv.ms/f/s!AhIXJn_J-blW231MH2krnmLq5kkQ))
+ 
+
+```
+python tools/test.py \
+    --cfg experiments/coco/hrnet/w32_256x192_adam_lr1e-3.yaml \
+    TEST.MODEL_FILE models/pytorch/pose_coco/pose_hrnet_w32_256x192.pth \
+    TEST.USE_GT_BBOX False
+```
+
+#### Training on COCO train2017 dataset
+
+```
+python tools/train.py \
+    --cfg experiments/coco/hrnet/w32_256x192_adam_lr1e-3.yaml \
+```
+
+### Visualization
+
+#### Visualizing predictions on COCO val
+
+```
+python visualization/plot_coco.py \
+    --prediction output/coco/w48_384x288_adam_lr1e-3/results/keypoints_val2017_results_0.json \
+    --save-path visualization/results
+
+```
+
+
+<img src="figures\visualization\coco\score_610_id_2685_000000002685.png" height="215"><img src="figures\visualization\coco\score_710_id_153229_000000153229.png" height="215"><img src="figures\visualization\coco\score_755_id_343561_000000343561.png" height="215">
+
+<img src="figures\visualization\coco\score_755_id_559842_000000559842.png" height="209"><img src="figures\visualization\coco\score_770_id_6954_000000006954.png" height="209"><img src="figures\visualization\coco\score_919_id_53626_000000053626.png" height="209">
+
+### Other applications
+Many other dense prediction tasks, such as segmentation, face alignment and object detection, etc. have been benefited by HRNet. More information can be found at [High-Resolution Networks](https://github.com/HRNet).
+
+### Other implementation
+[mmpose](https://github.com/open-mmlab/mmpose) </br>
+[ModelScope (中文）](https://modelscope.cn/models/damo/cv_hrnetv2w32_body-2d-keypoints_image/summary)</br>
+[timm](https://huggingface.co/docs/timm/main/en/models/hrnet)
+
+
+### Citation
+If you use our code or models in your research, please cite with:
+```
+@inproceedings{sun2019deep,
+  title={Deep High-Resolution Representation Learning for Human Pose Estimation},
+  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
+  booktitle={CVPR},
+  year={2019}
+}
+
+@inproceedings{xiao2018simple,
+    author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
+    title={Simple Baselines for Human Pose Estimation and Tracking},
+    booktitle = {European Conference on Computer Vision (ECCV)},
+    year = {2018}
+}
+
+@article{WangSCJDZLMTWLX19,
+  title={Deep High-Resolution Representation Learning for Visual Recognition},
+  author={Jingdong Wang and Ke Sun and Tianheng Cheng and 
+          Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and 
+          Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},
+  journal   = {TPAMI}
+  year={2019}
+}
+```

deep-high-resolution-net.pytorch/_config.yml

@@ -0,0 +1 @@
+theme: jekyll-theme-cayman

deep-high-resolution-net.pytorch/data/mpii/annot/.amlignore

@@ -0,0 +1,6 @@
+## This file was auto generated by the Azure Machine Learning Studio. Please do not remove. 
+## Read more about the .amlignore file here: https://docs.microsoft.com/azure/machine-learning/how-to-save-write-experiment-files#storage-limits-of-experiment-snapshots
+        
+.ipynb_aml_checkpoints/ 
+*.amltmp 
+*.amltemp

deep-high-resolution-net.pytorch/data/mpii/annot/.amlignore.amltmp

@@ -0,0 +1,6 @@
+## This file was auto generated by the Azure Machine Learning Studio. Please do not remove. 
+## Read more about the .amlignore file here: https://docs.microsoft.com/azure/machine-learning/how-to-save-write-experiment-files#storage-limits-of-experiment-snapshots
+        
+.ipynb_aml_checkpoints/ 
+*.amltmp 
+*.amltemp

deep-high-resolution-net.pytorch/data/mpii/annot/pose_train.json

@@ -0,0 +1,3 @@
+[{"center": [216.495849609375, 115.42289733886719], "scale": 0.4342059326171875, "joints": [[238.20614624023438, 158.84349060058594], [240.68731689453125, 130.30995178222656], [248.13084411621094, 100.53583526611328], [246.8902587890625, 100.53583526611328], [238.20614624023438, 129.06936645507812], [235.72496032714844, 158.84349060058594], [245.64967346191406, 99.29524230957031], [219.59732055664062, 79.44583129882812], [214.6349639892578, 78.20524597167969], [203.4696807861328, 72.00230407714844], [186.1014404296875, 103.01701354980469], [200.98849487304688, 89.37053680419922], [218.3567352294922, 75.72406768798828], [215.87554931640625, 76.96465301513672], [200.98849487304688, 89.37053680419922], [184.86085510253906, 100.53583526611328]], "image": "03_2_00010016.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [212.0, 131.0], "scale": 0.25, "joints": [[196, 106], [214, 127], [228, 154], [228, 154], [214, 127], [196, 106], [227, 156], [-1, -1], [-1, -1], [-1, -1], [-1, -1], [-1, -1], [-1, -1], [-1, -1], [-1, -1], [-1, -1]], "image": "01_19_00031809.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {"center": [194.73058319091797, 126.30073165893555], "scale": 0.4997070693969727, "joints": [[213.70680236816406, 176.2714385986328], [221.2972869873047, 145.90948486328125], [227.62269592285156, 116.8126220703125], [227.62269592285156, 116.8126220703125], [221.2972869873047, 144.6444091796875], [213.70680236816406, 176.2714385986328], [226.3576202392578, 115.54753875732422], [201.05599975585938, 91.51099395751953], [197.26075744628906, 88.9808349609375], [192.20042419433594, 76.33002471923828], [161.83847045898438, 106.69197082519531], [182.07977294921875, 99.10148620605469], [202.32107543945312, 90.24591827392578], [201.05599975585938, 90.24591827392578], [182.07977294921875, 101.63164520263672], [161.83847045898438, 106.69197082519531]], "image": "01_1_00007378.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [219.5, 92.0], "scale": 0.18, "joints": [[178, 88], [207, 77], [210, 110], [210, 110], [207, 77], [178, 88], [211, 105], [243, 108], [246, 105], [261, 92], [252, 74], [247, 86], [249, 107], [249, 107], [247, 86], [252, 74]], "image": "04_88_00150899.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [231.0, 105.5], "scale": 0.315, "joints": [[183, 137], [189, 103], [215, 117], [215, 117], [189, 103], [183, 137], [210, 114], [250, 98], [253, 96], [267, 80], [279, 74], [267, 82], [256, 99], [256, 99], [267, 82], [279, 74]], "image": "04_88_00150897.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [249.0, 121.0], "scale": 0.23, "joints": [[211, 98], [236, 110], [232, 142], [232, 142], [236, 110], [211, 98], [235, 138], [267, 142], [268, 139], [287, 134], [271, 113], [263, 133], [270, 144], [270, 144], [263, 133], [271, 113]], "image": "04_88_00150931.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [206.5, 114.0], "scale": 0.61, "joints": [[198, 175], [214, 152], [210, 122], [210, 122], [214, 152], [198, 175], [216, 124], [208, 92], [206, 88], [199, 74], [197, 53], [209, 65], [205, 85], [205, 85], [209, 65], [197, 53]], "image": "04_98_00166143.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [229.0, 74.5], "scale": 0.185, "joints": [[253, 56], [225, 67], [254, 82], [254, 82], [225, 67], [253, 56], [249, 83], [226, 91], [223, 93], [204, 93], [239, 64], [239, 81], [227, 90], [227, 90], [239, 81], [239, 64]], "image": "04_98_00166163.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [222.5, 128.5], "scale": 0.465, "joints": [[179, 174], [181, 141], [186, 107], [185, 106], [186, 141], [181, 175], [191, 105], [223, 91], [227, 91], [244, 82], [264, 104], [246, 98], [228, 90], [228, 90], [246, 98], [264, 101]], "image": "05_1_00009167.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [245.5, 123.5], "scale": 0.495, "joints": [[203, 173], [204, 147], [211, 120], [225, 123], [211, 151], [203, 173], [219, 118], [247, 105], [251, 102], [261, 93], [266, 74], [252, 83], [247, 98], [254, 107], [269, 108], [288, 102]], "image": "05_82_00134020.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [216.0, 97.0], "scale": 0.26, "joints": [[255, 74], [239, 91], [227, 117], [220, 106], [234, 86], [251, 71], [222, 109], [194, 116], [189, 114], [177, 110], [216, 107], [211, 123], [195, 122], [192, 86], [194, 97], [189, 109]], "image": "05_82_00134035.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [242.5, 132.0], "scale": 0.23, "joints": [[303, 152], [270, 141], [239, 133], [243, 118], [272, 134], [303, 152], [240, 127], [194, 116], [204, 120], [202, 119], [187, 109], [186, 133], [197, 117], [199, 121], [189, 141], [182, 155]], "image": "06_21_00037588.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [236.0, 125.0], "scale": 0.58, "joints": [[271, 183], [254, 168], [227, 147], [247, 135], [267, 163], [281, 176], [233, 136], [222, 105], [223, 102], [221, 87], [191, 67], [197, 87], [214, 99], [235, 98], [226, 113], [198, 111]], "image": "06_21_00037591.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [244.5, 113.5], "scale": 0.285, "joints": [[297, 88], [272, 104], [249, 125], [241, 104], [261, 95], [287, 85], [247, 115], [217, 127], [216, 128], [201, 133], [239, 134], [240, 142], [217, 142], [207, 117], [193, 114], [192, 136]], "image": "06_27_00048106.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [258.0, 133.5], "scale": 0.265, "joints": [[208, 160], [209, 129], [231, 110], [240, 117], [211, 131], [208, 160], [236, 110], [269, 111], [273, 112], [287, 112], [294, 107], [285, 107], [273, 110], [271, 117], [291, 112], [308, 115]], "image": "06_27_00048094.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [249.5, 103.0], "scale": 0.27, "joints": [[296, 110], [272, 114], [247, 127], [251, 108], [272, 107], [291, 98], [244, 116], [217, 114], [214, 112], [203, 102], [238, 130], [220, 130], [211, 130], [217, 109], [219, 89], [215, 76]], "image": "06_27_00048108.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [182.0, 127.5], "scale": 0.575, "joints": [[246, 185], [246, 151], [227, 115], [227, 115], [246, 151], [246, 185], [229, 111], [179, 88], [172, 91], [150, 96], [118, 70], [144, 71], [171, 85], [171, 100], [142, 100], [119, 99]], "image": "07_97_00141972.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [204.0, 159.5], "scale": 0.365, "joints": [[163, 134], [193, 126], [228, 123], [228, 123], [193, 126], [163, 134], [226, 124], [239, 154], [242, 156], [245, 165], [235, 196], [235, 178], [241, 161], [238, 161], [232, 171], [229, 185]], "image": "09_14_00027838.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [215.5, 166.0], "scale": 0.62, "joints": [[192, 104], [204, 124], [220, 152], [220, 152], [204, 124], [192, 104], [219, 157], [235, 190], [235, 193], [239, 199], [235, 228], [234, 213], [234, 198], [234, 198], [234, 213], [235, 228]], "image": "09_14_00027843.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [207.5, 163.5], "scale": 0.495, "joints": [[167, 114], [193, 121], [225, 136], [225, 136], [193, 121], [167, 114], [225, 143], [239, 172], [239, 175], [248, 188], [241, 213], [239, 197], [241, 179], [241, 179], [239, 197], [236, 206]], "image": "09_14_00027840.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [187.5, 118.5], "scale": 0.185, "joints": [[129, 100], [146, 113], [192, 137], [192, 137], [146, 113], [129, 100], [192, 133], [234, 132], [233, 129], [246, 114], [183, 118], [204, 121], [230, 130], [230, 130], [204, 121], [183, 118]], "image": "09_36_00058402.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [198.5, 146.5], "scale": 0.355, "joints": [[141, 111], [163, 130], [195, 151], [195, 151], [163, 130], [141, 111], [198, 149], [236, 172], [243, 174], [256, 182], [236, 154], [238, 157], [241, 167], [238, 174], [236, 165], [234, 155]], "image": "09_36_00058407.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [197.0, 149.5], "scale": 0.765, "joints": [[170, 73], [186, 101], [215, 131], [208, 136], [191, 102], [170, 73], [215, 141], [224, 171], [222, 179], [214, 197], [219, 226], [218, 206], [218, 178], [218, 178], [218, 206], [219, 226]], "image": "09_36_00058420.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [210.0, 133.0], "scale": 0.73, "joints": [[237, 198], [226, 170], [229, 131], [225, 132], [202, 165], [183, 193], [224, 132], [222, 85], [221, 86], [217, 60], [216, 130], [219, 109], [225, 83], [225, 83], [218, 206], [216, 130]], "image": "09_54_00082209.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [221.5, 132.0], "scale": 0.7, "joints": [[264, 194], [238, 167], [228, 135], [218, 133], [197, 163], [179, 202], [222, 131], [220, 91], [223, 80], [214, 62], [222, 93], [223, 89], [224, 84], [224, 84], [223, 89], [222, 93]], "image": "09_54_00082214.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [242.5, 88.5], "scale": 0.215, "joints": [[267, 62], [242, 85], [218, 108], [218, 108], [242, 85], [267, 67], [220, 110], [252, 107], [258, 103], [260, 89], [240, 81], [233, 90], [252, 99], [252, 99], [233, 90], [240, 81]], "image": "09_54_00082243.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [226.5, 111.0], "scale": 0.32, "joints": [[199, 79], [212, 111], [226, 143], [226, 143], [212, 111], [199, 79], [233, 142], [253, 119], [254, 114], [247, 99], [214, 116], [233, 114], [252, 116], [251, 118], [229, 123], [213, 121]], "image": "09_54_00082258.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [190.5, 131.0], "scale": 0.66, "joints": [[204, 197], [190, 166], [204, 136], [213, 136], [190, 166], [232, 183], [206, 134], [198, 94], [196, 86], [187, 65], [151, 109], [171, 108], [194, 93], [192, 89], [170, 101], [149, 107]], "image": "09_59_00090003.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [186.5, 132.0], "scale": 0.65, "joints": [[212, 197], [187, 168], [207, 140], [207, 140], [187, 168], [219, 188], [206, 135], [193, 96], [192, 86], [186, 67], [154, 109], [170, 105], [191, 93], [191, 93], [170, 105], [154, 109]], "image": "09_59_00090004.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [191.5, 104.0], "scale": 0.74, "joints": [[192, 138], [168, 112], [198, 104], [210, 104], [210, 143], [215, 178], [205, 99], [208, 58], [208, 50], [204, 30], [188, 54], [195, 54], [205, 53], [205, 53], [193, 54], [179, 49]], "image": "09_59_00090015.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [195.0, 109.5], "scale": 0.845, "joints": [[201, 138], [176, 114], [202, 97], [211, 97], [212, 142], [210, 194], [209, 93], [214, 53], [214, 45], [208, 25], [194, 40], [200, 43], [211, 45], [211, 45], [199, 46], [183, 40]], "image": "09_59_00090016.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [207.5, 108.0], "scale": 0.74, "joints": [[210, 182], [212, 140], [219, 105], [219, 105], [212, 140], [210, 182], [218, 101], [222, 62], [222, 54], [217, 34], [193, 37], [202, 46], [219, 56], [219, 56], [202, 46], [201, 37]], "image": "09_59_00090021.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [199.5, 139.0], "scale": 0.19, "joints": [[229, 158], [197, 146], [225, 130], [225, 130], [197, 146], [229, 158], [222, 127], [188, 120], [182, 122], [170, 128], [198, 147], [199, 132], [188, 121], [188, 121], [199, 132], [198, 147]], "image": "09_59_00090044.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [203.0, 145.5], "scale": 0.165, "joints": [[236, 150], [208, 147], [219, 130], [219, 130], [196, 154], [230, 162], [216, 129], [181, 134], [179, 137], [170, 146], [200, 159], [197, 141], [184, 133], [184, 133], [197, 141], [200, 159]], "image": "09_59_00090055.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [192.5, 131.5], "scale": 0.445, "joints": [[130, 87], [157, 112], [198, 103], [198, 103], [157, 112], [130, 87], [201, 108], [235, 134], [240, 134], [255, 138], [251, 176], [242, 159], [234, 136], [234, 136], [242, 159], [251, 176]], "image": "09_61_00092503.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [205.0, 128.0], "scale": 0.27, "joints": [[177, 107], [196, 137], [214, 101], [214, 101], [196, 137], [177, 107], [219, 105], [232, 135], [232, 144], [229, 155], [199, 138], [215, 135], [233, 138], [233, 138], [215, 135], [199, 138]], "image": "09_61_00092516.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [190.0, 152.0], "scale": 0.22, "joints": [[155, 161], [182, 169], [177, 131], [177, 131], [182, 169], [155, 161], [184, 130], [213, 151], [218, 155], [225, 166], [188, 174], [196, 157], [216, 155], [216, 155], [196, 157], [188, 174]], "image": "09_61_00092527.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [177.0, 125.0], "scale": 0.26, "joints": [[142, 141], [178, 142], [177, 101], [177, 101], [178, 142], [142, 141], [177, 99], [207, 129], [207, 136], [212, 151], [165, 138], [191, 139], [202, 133], [202, 133], [191, 139], [165, 138]], "image": "09_62_00093793.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [174.5, 131.0], "scale": 0.3, "joints": [[150, 109], [175, 129], [190, 101], [190, 101], [175, 129], [150, 109], [196, 102], [198, 135], [199, 142], [197, 161], [165, 124], [179, 131], [194, 139], [194, 139], [179, 131], [165, 124]], "image": "09_62_00093794.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [227.0, 140.0], "scale": 0.56, "joints": [[180, 194], [191, 162], [218, 145], [229, 145], [196, 165], [184, 196], [228, 141], [253, 114], [258, 110], [268, 101], [270, 85], [257, 84], [261, 109], [261, 109], [257, 84], [270, 85]], "image": "Budapest2021Diving10mPlatformWomenFinal_1_15_25591.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [239.0, 148.0], "scale": 0.46, "joints": [[177, 189], [192, 159], [218, 138], [229, 141], [200, 161], [183, 194], [228, 137], [263, 121], [266, 120], [277, 114], [293, 102], [277, 103], [263, 115], [266, 126], [283, 115], [295, 103]], "image": "Budapest2021Diving10mPlatformWomenFinal_1_15_25592.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [242.5, 154.5], "scale": 0.335, "joints": [[180, 184], [190, 149], [219, 129], [233, 137], [198, 153], [181, 188], [227, 129], [271, 130], [275, 131], [286, 140], [304, 135], [292, 121], [272, 125], [265, 136], [287, 135], [301, 132]], "image": "Budapest2021Diving10mPlatformWomenFinal_1_15_25593.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [231.5, 149.5], "scale": 0.305, "joints": [[176, 115], [208, 120], [244, 119], [236, 141], [211, 131], [175, 128], [247, 134], [265, 156], [269, 161], [272, 172], [280, 180], [288, 163], [279, 151], [254, 166], [240, 158], [255, 160]], "image": "Budapest2021Diving10mPlatformWomenFinal_1_15_25597.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [248.0, 130.0], "scale": 0.6, "joints": [[253, 70], [247, 100], [246, 137], [267, 132], [259, 104], [253, 70], [256, 141], [246, 172], [242, 176], [234, 190], [238, 140], [229, 148], [229, 165], [251, 176], [249, 166], [242, 158]], "image": "Budapest2021Diving10mPlatformWomenFinal_1_15_25600.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [237.0, 114.0], "scale": 0.3, "joints": [[208, 144], [228, 122], [251, 92], [238, 84], [226, 115], [205, 139], [248, 96], [265, 117], [267, 121], [269, 131], [233, 119], [251, 118], [266, 123], [266, 123], [251, 118], [233, 119]], "image": "Budapest2021Diving10mPlatformWomenFinal_1_15_25617.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [222.5, 111.5], "scale": 0.285, "joints": [[191, 117], [222, 107], [258, 94], [254, 83], [222, 100], [187, 114], [258, 99], [253, 125], [252, 132], [241, 140], [229, 112], [246, 119], [254, 128], [254, 128], [246, 119], [229, 112]], "image": "Budapest2021Diving10mPlatformWomenFinal_1_15_25618.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [204.5, 111.5], "scale": 0.635, "joints": [[232, 49], [243, 82], [216, 116], [203, 108], [231, 80], [219, 48], [214, 117], [183, 137], [177, 143], [166, 149], [166, 175], [172, 154], [176, 136], [176, 136], [172, 154], [166, 175]], "image": "Budapest2021Diving10mPlatformWomenFinal_2_9_17649.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [219.0, 133.0], "scale": 0.45, "joints": [[271, 178], [247, 148], [224, 127], [230, 110], [256, 140], [277, 167], [221, 117], [194, 99], [190, 95], [183, 88], [161, 111], [173, 104], [189, 97], [192, 93], [183, 106], [175, 114]], "image": "Budapest2021Diving10mPlatformWomenFinal_2_9_17659.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [238.0, 140.5], "scale": 0.615, "joints": [[215, 75], [230, 111], [240, 140], [220, 149], [230, 118], [232, 79], [236, 147], [245, 182], [245, 187], [250, 195], [249, 155], [252, 163], [253, 177], [236, 189], [240, 201], [256, 202]], "image": "Budapest2021Diving10mPlatformWomenFinal_2_9_17673.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [252.5, 162.0], "scale": 0.29, "joints": [[303, 143], [271, 161], [248, 187], [248, 178], [264, 155], [284, 133], [244, 181], [216, 171], [209, 166], [202, 156], [242, 191], [226, 183], [211, 170], [212, 165], [215, 159], [223, 153]], "image": "Budapest2021Diving10mPlatformWomenFinal_2_9_17680.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [175.5, 121.0], "scale": 0.84, "joints": [[180, 37], [175, 74], [172, 111], [172, 111], [175, 74], [180, 37], [176, 121], [173, 151], [173, 160], [173, 174], [172, 205], [172, 191], [171, 162], [171, 162], [172, 191], [172, 205]], "image": "Budapest2021Diving10mPlatformWomenFinal_3_1_03079.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [175.0, 130.5], "scale": 0.675, "joints": [[177, 63], [204, 89], [183, 123], [183, 123], [204, 89], [177, 63], [180, 132], [162, 156], [154, 163], [146, 171], [157, 198], [155, 176], [156, 160], [156, 160], [155, 176], [157, 198]], "image": "Budapest2021Diving10mPlatformWomenFinal_3_1_03095.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [177.5, 139.5], "scale": 0.605, "joints": [[177, 79], [209, 97], [186, 130], [186, 130], [209, 97], [177, 79], [181, 139], [164, 158], [154, 163], [146, 172], [156, 200], [156, 181], [155, 162], [155, 162], [156, 181], [156, 200]], "image": "Budapest2021Diving10mPlatformWomenFinal_3_1_03097.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [185.0, 162.0], "scale": 0.49, "joints": [[206, 105], [204, 141], [206, 168], [192, 165], [195, 143], [199, 113], [199, 164], [194, 191], [192, 200], [186, 210], [198, 174], [206, 190], [200, 203], [181, 199], [167, 194], [164, 211]], "image": "Budapest2021Diving10mPlatformWomenFinal_3_1_03124.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [187.5, 157.0], "scale": 0.46, "joints": [[219, 110], [212, 142], [208, 172], [204, 171], [207, 143], [216, 111], [204, 174], [187, 191], [181, 197], [172, 203], [156, 178], [171, 179], [186, 192], [186, 192], [171, 179], [156, 178]], "image": "Budapest2021Diving10mPlatformWomenFinal_3_1_03125.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [186.5, 153.5], "scale": 0.455, "joints": [[216, 108], [210, 141], [206, 173], [206, 173], [210, 141], [216, 108], [203, 175], [188, 190], [183, 195], [170, 199], [179, 195], [194, 198], [187, 195], [180, 185], [169, 178], [157, 178]], "image": "Budapest2021Diving10mPlatformWomenFinal_3_1_03126.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [164.0, 170.5], "scale": 0.785, "joints": [[188, 249], [170, 223], [147, 185], [170, 178], [180, 214], [188, 249], [156, 179], [157, 141], [159, 133], [160, 120], [151, 92], [140, 111], [147, 126], [171, 134], [172, 115], [164, 102]], "image": "Budapest2021Diving10mPlatformWomenFinal_3_7_14440.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]},
+
+]

deep-high-resolution-net.pytorch/data/mpii/annot/pose_train.json.amltmp

@@ -0,0 +1,3 @@
+[{"center": [216.495849609375, 115.42289733886719], "scale": 0.4342059326171875, "joints": [[238.20614624023438, 158.84349060058594], [240.68731689453125, 130.30995178222656], [248.13084411621094, 100.53583526611328], [246.8902587890625, 100.53583526611328], [238.20614624023438, 129.06936645507812], [235.72496032714844, 158.84349060058594], [245.64967346191406, 99.29524230957031], [219.59732055664062, 79.44583129882812], [214.6349639892578, 78.20524597167969], [203.4696807861328, 72.00230407714844], [186.1014404296875, 103.01701354980469], [200.98849487304688, 89.37053680419922], [218.3567352294922, 75.72406768798828], [215.87554931640625, 76.96465301513672], [200.98849487304688, 89.37053680419922], [184.86085510253906, 100.53583526611328]], "image": "03_2_00010016.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [212.0, 131.0], "scale": 0.25, "joints": [[196, 106], [214, 127], [228, 154], [228, 154], [214, 127], [196, 106], [227, 156], [-1, -1], [-1, -1], [-1, -1], [-1, -1], [-1, -1], [-1, -1], [-1, -1], [-1, -1], [-1, -1]], "image": "01_19_00031809.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {"center": [194.73058319091797, 126.30073165893555], "scale": 0.4997070693969727, "joints": [[213.70680236816406, 176.2714385986328], [221.2972869873047, 145.90948486328125], [227.62269592285156, 116.8126220703125], [227.62269592285156, 116.8126220703125], [221.2972869873047, 144.6444091796875], [213.70680236816406, 176.2714385986328], [226.3576202392578, 115.54753875732422], [201.05599975585938, 91.51099395751953], [197.26075744628906, 88.9808349609375], [192.20042419433594, 76.33002471923828], [161.83847045898438, 106.69197082519531], [182.07977294921875, 99.10148620605469], [202.32107543945312, 90.24591827392578], [201.05599975585938, 90.24591827392578], [182.07977294921875, 101.63164520263672], [161.83847045898438, 106.69197082519531]], "image": "01_1_00007378.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [219.5, 92.0], "scale": 0.18, "joints": [[178, 88], [207, 77], [210, 110], [210, 110], [207, 77], [178, 88], [211, 105], [243, 108], [246, 105], [261, 92], [252, 74], [247, 86], [249, 107], [249, 107], [247, 86], [252, 74]], "image": "04_88_00150899.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [231.0, 105.5], "scale": 0.315, "joints": [[183, 137], [189, 103], [215, 117], [215, 117], [189, 103], [183, 137], [210, 114], [250, 98], [253, 96], [267, 80], [279, 74], [267, 82], [256, 99], [256, 99], [267, 82], [279, 74]], "image": "04_88_00150897.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [249.0, 121.0], "scale": 0.23, "joints": [[211, 98], [236, 110], [232, 142], [232, 142], [236, 110], [211, 98], [235, 138], [267, 142], [268, 139], [287, 134], [271, 113], [263, 133], [270, 144], [270, 144], [263, 133], [271, 113]], "image": "04_88_00150931.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [206.5, 114.0], "scale": 0.61, "joints": [[198, 175], [214, 152], [210, 122], [210, 122], [214, 152], [198, 175], [216, 124], [208, 92], [206, 88], [199, 74], [197, 53], [209, 65], [205, 85], [205, 85], [209, 65], [197, 53]], "image": "04_98_00166143.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [229.0, 74.5], "scale": 0.185, "joints": [[253, 56], [225, 67], [254, 82], [254, 82], [225, 67], [253, 56], [249, 83], [226, 91], [223, 93], [204, 93], [239, 64], [239, 81], [227, 90], [227, 90], [239, 81], [239, 64]], "image": "04_98_00166163.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [222.5, 128.5], "scale": 0.465, "joints": [[179, 174], [181, 141], [186, 107], [185, 106], [186, 141], [181, 175], [191, 105], [223, 91], [227, 91], [244, 82], [264, 104], [246, 98], [228, 90], [228, 90], [246, 98], [264, 101]], "image": "05_1_00009167.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [245.5, 123.5], "scale": 0.495, "joints": [[203, 173], [204, 147], [211, 120], [225, 123], [211, 151], [203, 173], [219, 118], [247, 105], [251, 102], [261, 93], [266, 74], [252, 83], [247, 98], [254, 107], [269, 108], [288, 102]], "image": "05_82_00134020.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [216.0, 97.0], "scale": 0.26, "joints": [[255, 74], [239, 91], [227, 117], [220, 106], [234, 86], [251, 71], [222, 109], [194, 116], [189, 114], [177, 110], [216, 107], [211, 123], [195, 122], [192, 86], [194, 97], [189, 109]], "image": "05_82_00134035.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [242.5, 132.0], "scale": 0.23, "joints": [[303, 152], [270, 141], [239, 133], [243, 118], [272, 134], [303, 152], [240, 127], [194, 116], [204, 120], [202, 119], [187, 109], [186, 133], [197, 117], [199, 121], [189, 141], [182, 155]], "image": "06_21_00037588.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [236.0, 125.0], "scale": 0.58, "joints": [[271, 183], [254, 168], [227, 147], [247, 135], [267, 163], [281, 176], [233, 136], [222, 105], [223, 102], [221, 87], [191, 67], [197, 87], [214, 99], [235, 98], [226, 113], [198, 111]], "image": "06_21_00037591.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [244.5, 113.5], "scale": 0.285, "joints": [[297, 88], [272, 104], [249, 125], [241, 104], [261, 95], [287, 85], [247, 115], [217, 127], [216, 128], [201, 133], [239, 134], [240, 142], [217, 142], [207, 117], [193, 114], [192, 136]], "image": "06_27_00048106.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [258.0, 133.5], "scale": 0.265, "joints": [[208, 160], [209, 129], [231, 110], [240, 117], [211, 131], [208, 160], [236, 110], [269, 111], [273, 112], [287, 112], [294, 107], [285, 107], [273, 110], [271, 117], [291, 112], [308, 115]], "image": "06_27_00048094.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [249.5, 103.0], "scale": 0.27, "joints": [[296, 110], [272, 114], [247, 127], [251, 108], [272, 107], [291, 98], [244, 116], [217, 114], [214, 112], [203, 102], [238, 130], [220, 130], [211, 130], [217, 109], [219, 89], [215, 76]], "image": "06_27_00048108.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [182.0, 127.5], "scale": 0.575, "joints": [[246, 185], [246, 151], [227, 115], [227, 115], [246, 151], [246, 185], [229, 111], [179, 88], [172, 91], [150, 96], [118, 70], [144, 71], [171, 85], [171, 100], [142, 100], [119, 99]], "image": "07_97_00141972.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [204.0, 159.5], "scale": 0.365, "joints": [[163, 134], [193, 126], [228, 123], [228, 123], [193, 126], [163, 134], [226, 124], [239, 154], [242, 156], [245, 165], [235, 196], [235, 178], [241, 161], [238, 161], [232, 171], [229, 185]], "image": "09_14_00027838.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [215.5, 166.0], "scale": 0.62, "joints": [[192, 104], [204, 124], [220, 152], [220, 152], [204, 124], [192, 104], [219, 157], [235, 190], [235, 193], [239, 199], [235, 228], [234, 213], [234, 198], [234, 198], [234, 213], [235, 228]], "image": "09_14_00027843.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [207.5, 163.5], "scale": 0.495, "joints": [[167, 114], [193, 121], [225, 136], [225, 136], [193, 121], [167, 114], [225, 143], [239, 172], [239, 175], [248, 188], [241, 213], [239, 197], [241, 179], [241, 179], [239, 197], [236, 206]], "image": "09_14_00027840.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [187.5, 118.5], "scale": 0.185, "joints": [[129, 100], [146, 113], [192, 137], [192, 137], [146, 113], [129, 100], [192, 133], [234, 132], [233, 129], [246, 114], [183, 118], [204, 121], [230, 130], [230, 130], [204, 121], [183, 118]], "image": "09_36_00058402.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [198.5, 146.5], "scale": 0.355, "joints": [[141, 111], [163, 130], [195, 151], [195, 151], [163, 130], [141, 111], [198, 149], [236, 172], [243, 174], [256, 182], [236, 154], [238, 157], [241, 167], [238, 174], [236, 165], [234, 155]], "image": "09_36_00058407.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [197.0, 149.5], "scale": 0.765, "joints": [[170, 73], [186, 101], [215, 131], [208, 136], [191, 102], [170, 73], [215, 141], [224, 171], [222, 179], [214, 197], [219, 226], [218, 206], [218, 178], [218, 178], [218, 206], [219, 226]], "image": "09_36_00058420.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [210.0, 133.0], "scale": 0.73, "joints": [[237, 198], [226, 170], [229, 131], [225, 132], [202, 165], [183, 193], [224, 132], [222, 85], [221, 86], [217, 60], [216, 130], [219, 109], [225, 83], [225, 83], [218, 206], [216, 130]], "image": "09_54_00082209.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [221.5, 132.0], "scale": 0.7, "joints": [[264, 194], [238, 167], [228, 135], [218, 133], [197, 163], [179, 202], [222, 131], [220, 91], [223, 80], [214, 62], [222, 93], [223, 89], [224, 84], [224, 84], [223, 89], [222, 93]], "image": "09_54_00082214.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [242.5, 88.5], "scale": 0.215, "joints": [[267, 62], [242, 85], [218, 108], [218, 108], [242, 85], [267, 67], [220, 110], [252, 107], [258, 103], [260, 89], [240, 81], [233, 90], [252, 99], [252, 99], [233, 90], [240, 81]], "image": "09_54_00082243.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [226.5, 111.0], "scale": 0.32, "joints": [[199, 79], [212, 111], [226, 143], [226, 143], [212, 111], [199, 79], [233, 142], [253, 119], [254, 114], [247, 99], [214, 116], [233, 114], [252, 116], [251, 118], [229, 123], [213, 121]], "image": "09_54_00082258.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [190.5, 131.0], "scale": 0.66, "joints": [[204, 197], [190, 166], [204, 136], [213, 136], [190, 166], [232, 183], [206, 134], [198, 94], [196, 86], [187, 65], [151, 109], [171, 108], [194, 93], [192, 89], [170, 101], [149, 107]], "image": "09_59_00090003.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [186.5, 132.0], "scale": 0.65, "joints": [[212, 197], [187, 168], [207, 140], [207, 140], [187, 168], [219, 188], [206, 135], [193, 96], [192, 86], [186, 67], [154, 109], [170, 105], [191, 93], [191, 93], [170, 105], [154, 109]], "image": "09_59_00090004.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [191.5, 104.0], "scale": 0.74, "joints": [[192, 138], [168, 112], [198, 104], [210, 104], [210, 143], [215, 178], [205, 99], [208, 58], [208, 50], [204, 30], [188, 54], [195, 54], [205, 53], [205, 53], [193, 54], [179, 49]], "image": "09_59_00090015.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [195.0, 109.5], "scale": 0.845, "joints": [[201, 138], [176, 114], [202, 97], [211, 97], [212, 142], [210, 194], [209, 93], [214, 53], [214, 45], [208, 25], [194, 40], [200, 43], [211, 45], [211, 45], [199, 46], [183, 40]], "image": "09_59_00090016.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [207.5, 108.0], "scale": 0.74, "joints": [[210, 182], [212, 140], [219, 105], [219, 105], [212, 140], [210, 182], [218, 101], [222, 62], [222, 54], [217, 34], [193, 37], [202, 46], [219, 56], [219, 56], [202, 46], [201, 37]], "image": "09_59_00090021.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [199.5, 139.0], "scale": 0.19, "joints": [[229, 158], [197, 146], [225, 130], [225, 130], [197, 146], [229, 158], [222, 127], [188, 120], [182, 122], [170, 128], [198, 147], [199, 132], [188, 121], [188, 121], [199, 132], [198, 147]], "image": "09_59_00090044.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [203.0, 145.5], "scale": 0.165, "joints": [[236, 150], [208, 147], [219, 130], [219, 130], [196, 154], [230, 162], [216, 129], [181, 134], [179, 137], [170, 146], [200, 159], [197, 141], [184, 133], [184, 133], [197, 141], [200, 159]], "image": "09_59_00090055.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [192.5, 131.5], "scale": 0.445, "joints": [[130, 87], [157, 112], [198, 103], [198, 103], [157, 112], [130, 87], [201, 108], [235, 134], [240, 134], [255, 138], [251, 176], [242, 159], [234, 136], [234, 136], [242, 159], [251, 176]], "image": "09_61_00092503.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [205.0, 128.0], "scale": 0.27, "joints": [[177, 107], [196, 137], [214, 101], [214, 101], [196, 137], [177, 107], [219, 105], [232, 135], [232, 144], [229, 155], [199, 138], [215, 135], [233, 138], [233, 138], [215, 135], [199, 138]], "image": "09_61_00092516.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [190.0, 152.0], "scale": 0.22, "joints": [[155, 161], [182, 169], [177, 131], [177, 131], [182, 169], [155, 161], [184, 130], [213, 151], [218, 155], [225, 166], [188, 174], [196, 157], [216, 155], [216, 155], [196, 157], [188, 174]], "image": "09_61_00092527.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [177.0, 125.0], "scale": 0.26, "joints": [[142, 141], [178, 142], [177, 101], [177, 101], [178, 142], [142, 141], [177, 99], [207, 129], [207, 136], [212, 151], [165, 138], [191, 139], [202, 133], [202, 133], [191, 139], [165, 138]], "image": "09_62_00093793.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [174.5, 131.0], "scale": 0.3, "joints": [[150, 109], [175, 129], [190, 101], [190, 101], [175, 129], [150, 109], [196, 102], [198, 135], [199, 142], [197, 161], [165, 124], [179, 131], [194, 139], [194, 139], [179, 131], [165, 124]], "image": "09_62_00093794.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [227.0, 140.0], "scale": 0.56, "joints": [[180, 194], [191, 162], [218, 145], [229, 145], [196, 165], [184, 196], [228, 141], [253, 114], [258, 110], [268, 101], [270, 85], [257, 84], [261, 109], [261, 109], [257, 84], [270, 85]], "image": "Budapest2021Diving10mPlatformWomenFinal_1_15_25591.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [239.0, 148.0], "scale": 0.46, "joints": [[177, 189], [192, 159], [218, 138], [229, 141], [200, 161], [183, 194], [228, 137], [263, 121], [266, 120], [277, 114], [293, 102], [277, 103], [263, 115], [266, 126], [283, 115], [295, 103]], "image": "Budapest2021Diving10mPlatformWomenFinal_1_15_25592.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [242.5, 154.5], "scale": 0.335, "joints": [[180, 184], [190, 149], [219, 129], [233, 137], [198, 153], [181, 188], [227, 129], [271, 130], [275, 131], [286, 140], [304, 135], [292, 121], [272, 125], [265, 136], [287, 135], [301, 132]], "image": "Budapest2021Diving10mPlatformWomenFinal_1_15_25593.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [231.5, 149.5], "scale": 0.305, "joints": [[176, 115], [208, 120], [244, 119], [236, 141], [211, 131], [175, 128], [247, 134], [265, 156], [269, 161], [272, 172], [280, 180], [288, 163], [279, 151], [254, 166], [240, 158], [255, 160]], "image": "Budapest2021Diving10mPlatformWomenFinal_1_15_25597.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [248.0, 130.0], "scale": 0.6, "joints": [[253, 70], [247, 100], [246, 137], [267, 132], [259, 104], [253, 70], [256, 141], [246, 172], [242, 176], [234, 190], [238, 140], [229, 148], [229, 165], [251, 176], [249, 166], [242, 158]], "image": "Budapest2021Diving10mPlatformWomenFinal_1_15_25600.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [237.0, 114.0], "scale": 0.3, "joints": [[208, 144], [228, 122], [251, 92], [238, 84], [226, 115], [205, 139], [248, 96], [265, 117], [267, 121], [269, 131], [233, 119], [251, 118], [266, 123], [266, 123], [251, 118], [233, 119]], "image": "Budapest2021Diving10mPlatformWomenFinal_1_15_25617.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [222.5, 111.5], "scale": 0.285, "joints": [[191, 117], [222, 107], [258, 94], [254, 83], [222, 100], [187, 114], [258, 99], [253, 125], [252, 132], [241, 140], [229, 112], [246, 119], [254, 128], [254, 128], [246, 119], [229, 112]], "image": "Budapest2021Diving10mPlatformWomenFinal_1_15_25618.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [204.5, 111.5], "scale": 0.635, "joints": [[232, 49], [243, 82], [216, 116], [203, 108], [231, 80], [219, 48], [214, 117], [183, 137], [177, 143], [166, 149], [166, 175], [172, 154], [176, 136], [176, 136], [172, 154], [166, 175]], "image": "Budapest2021Diving10mPlatformWomenFinal_2_9_17649.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [219.0, 133.0], "scale": 0.45, "joints": [[271, 178], [247, 148], [224, 127], [230, 110], [256, 140], [277, 167], [221, 117], [194, 99], [190, 95], [183, 88], [161, 111], [173, 104], [189, 97], [192, 93], [183, 106], [175, 114]], "image": "Budapest2021Diving10mPlatformWomenFinal_2_9_17659.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [238.0, 140.5], "scale": 0.615, "joints": [[215, 75], [230, 111], [240, 140], [220, 149], [230, 118], [232, 79], [236, 147], [245, 182], [245, 187], [250, 195], [249, 155], [252, 163], [253, 177], [236, 189], [240, 201], [256, 202]], "image": "Budapest2021Diving10mPlatformWomenFinal_2_9_17673.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [252.5, 162.0], "scale": 0.29, "joints": [[303, 143], [271, 161], [248, 187], [248, 178], [264, 155], [284, 133], [244, 181], [216, 171], [209, 166], [202, 156], [242, 191], [226, 183], [211, 170], [212, 165], [215, 159], [223, 153]], "image": "Budapest2021Diving10mPlatformWomenFinal_2_9_17680.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [175.5, 121.0], "scale": 0.84, "joints": [[180, 37], [175, 74], [172, 111], [172, 111], [175, 74], [180, 37], [176, 121], [173, 151], [173, 160], [173, 174], [172, 205], [172, 191], [171, 162], [171, 162], [172, 191], [172, 205]], "image": "Budapest2021Diving10mPlatformWomenFinal_3_1_03079.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [175.0, 130.5], "scale": 0.675, "joints": [[177, 63], [204, 89], [183, 123], [183, 123], [204, 89], [177, 63], [180, 132], [162, 156], [154, 163], [146, 171], [157, 198], [155, 176], [156, 160], [156, 160], [155, 176], [157, 198]], "image": "Budapest2021Diving10mPlatformWomenFinal_3_1_03095.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [177.5, 139.5], "scale": 0.605, "joints": [[177, 79], [209, 97], [186, 130], [186, 130], [209, 97], [177, 79], [181, 139], [164, 158], [154, 163], [146, 172], [156, 200], [156, 181], [155, 162], [155, 162], [156, 181], [156, 200]], "image": "Budapest2021Diving10mPlatformWomenFinal_3_1_03097.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [185.0, 162.0], "scale": 0.49, "joints": [[206, 105], [204, 141], [206, 168], [192, 165], [195, 143], [199, 113], [199, 164], [194, 191], [192, 200], [186, 210], [198, 174], [206, 190], [200, 203], [181, 199], [167, 194], [164, 211]], "image": "Budapest2021Diving10mPlatformWomenFinal_3_1_03124.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [187.5, 157.0], "scale": 0.46, "joints": [[219, 110], [212, 142], [208, 172], [204, 171], [207, 143], [216, 111], [204, 174], [187, 191], [181, 197], [172, 203], [156, 178], [171, 179], [186, 192], [186, 192], [171, 179], [156, 178]], "image": "Budapest2021Diving10mPlatformWomenFinal_3_1_03125.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [186.5, 153.5], "scale": 0.455, "joints": [[216, 108], [210, 141], [206, 173], [206, 173], [210, 141], [216, 108], [203, 175], [188, 190], [183, 195], [170, 199], [179, 195], [194, 198], [187, 195], [180, 185], [169, 178], [157, 178]], "image": "Budapest2021Diving10mPlatformWomenFinal_3_1_03126.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, {"center": [164.0, 170.5], "scale": 0.785, "joints": [[188, 249], [170, 223], [147, 185], [170, 178], [180, 214], [188, 249], [156, 179], [157, 141], [159, 133], [160, 120], [151, 92], [140, 111], [147, 126], [171, 134], [172, 115], [164, 102]], "image": "Budapest2021Diving10mPlatformWomenFinal_3_7_14440.jpg", "joints_vis": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]},
+
+]