zhouzi180
86 lines
2.7 KiB
Markdown
86 lines
2.7 KiB
Markdown
# Tutorial for [Scoliosis1K](https://zhouzi180.github.io/Scoliosis1K)
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## Download the Scoliosis1K Dataset
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You can download the dataset from the [official website](https://zhouzi180.github.io/Scoliosis1K).
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The dataset is provided as four compressed files:
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* `Scoliosis1K-sil-raw.zip`
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* `Scoliosis1K-sil-pkl.zip`
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* `Scoliosis1K-pose-raw.zip`
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* `Scoliosis1K-pose-pkl.zip`
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We recommend using the provided pickle (`.pkl`) files for convenience.
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Decompress them with the following commands:
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```bash
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unzip -P <password> Scoliosis1K-sil-pkl.zip
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unzip -P <password> Scoliosis1K-pose-pkl.zip
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```
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> **Note**: The \<password\> can be obtained by signing the [release agreement](https://zhouzi180.github.io/Scoliosis1K/static/resources/Scoliosis1k_release_agreement.pdf) and sending it to **[12331257@mail.sustech.edu.cn](mailto:12331257@mail.sustech.edu.cn)**.
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### Dataset Structure
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After decompression, you will get the following structure:
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```
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├── Scoliosis1K-sil-pkl
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│ ├── 00000 # Identity
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│ │ ├── Positive # Class
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│ │ │ ├── 000_180 # View
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│ │ │ └── 000_180.pkl # Estimated Silhouette (PP-HumanSeg v2)
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│
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├── Scoliosis1K-pose-pkl
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│ ├── 00000 # Identity
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│ │ ├── Positive # Class
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│ │ │ ├── 000_180 # View
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│ │ │ └── 000_180.pkl # Estimated 2D Pose (ViTPose)
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```
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### Processing from RAW Dataset (optional)
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If you prefer, you can process the raw dataset into `.pkl` format.
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```bash
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# For silhouette raw data
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python datasets/pretreatment.py --input_path=<path_to_raw_silhouettes> --output_path=<output_path>
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# For pose raw data
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python datasets/pretreatment.py --input_path=<path_to_raw_pose> --output_path=<output_path> --pose --dataset=OUMVLP
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```
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---
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## Training and Testing
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Before training or testing, modify the `dataset_root` field in
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`configs/sconet/sconet_scoliosis1k.yaml`.
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Then run the following commands:
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```bash
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# Training
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CUDA_VISIBLE_DEVICES=0,1,2,3 \
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python -m torch.distributed.launch --nproc_per_node=4 \
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opengait/main.py --cfgs configs/sconet/sconet_scoliosis1k.yaml --phase train --log_to_file
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# Testing
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CUDA_VISIBLE_DEVICES=0,1,2,3 \
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python -m torch.distributed.launch --nproc_per_node=4 \
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opengait/main.py --cfgs configs/sconet/sconet_scoliosis1k.yaml --phase test --log_to_file
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```
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---
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## Pose-to-Heatmap Conversion
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*From our paper: **Pose as Clinical Prior: Learning Dual Representations for Scoliosis Screening (MICCAI 2025)***
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```bash
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CUDA_VISIBLE_DEVICES=0,1,2,3 \
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python -m torch.distributed.launch --nproc_per_node=4 \
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datasets/pretreatment_heatmap.py \
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--pose_data_path=<path_to_pose_pkl> \
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--save_root=<output_path> \
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--dataset_name=OUMVLP
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```
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