Ahzyuan
105 lines
3.6 KiB
Markdown
105 lines
3.6 KiB
Markdown
# OUMVLP
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Step1: Download URL: http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html
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Step2: Unzip the dataset, you will get a structure directory like:
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```
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python datasets/OUMVLP/extractor.py --input_path Path_of_OUMVLP-base --output_path Path_of_OUMVLP-raw --password Given_Password
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```
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- Original
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```
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OUMVLP-raw
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Silhouette_000-00 (view-sequence)
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00001 (subject)
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0001.png (frame)
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0002.png (frame)
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......
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00002
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0001.png (frame)
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0002.png (frame)
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......
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......
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Silhouette_000-01
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00001
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0001.png (frame)
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0002.png (frame)
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......
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00002
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0001.png (frame)
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0002.png (frame)
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......
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......
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Silhouette_015-00
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......
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Silhouette_015-01
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......
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......
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```
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Step3-1 : To rearrange directory of OUMVLP dataset(for silhouette), turning to id-type-view structure, Run
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```
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python datasets/OUMVLP/rearrange_OUMVLP.py --input_path Path_of_OUMVLP-raw --output_path Path_of_OUMVLP-silu-rearranged
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```
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Step3-2 : To rearrange directory of OUMVLP dataset(for pose), turning to id-type-view structure, Run
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```
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python datasets/OUMVLP/rearrange_OUMVLP_pose.py --input_path Path_of_OUMVLP-pose --output_path Path_of_OUMVLP-pose-rearranged
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```
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Step4-1: Transforming images to pickle file, run
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```
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python datasets/pretreatment.py --input_path Path_of_OUMVLP-silu-rearranged --output_path Path_of_OUMVLP-pkl
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```
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Step4-2: Transforming pose txts to pickle file, run
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> [!IMPORTANT]
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> Before extracting pose pkls, **you need to possess the pose selection index files** ([Why](https://github.com/ShiqiYu/OpenGait/pull/280)). Here are two ways to get it:
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> 1. `Approach 1`: Directly download it:
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> - Open [Download Link](https://drive.google.com/drive/folders/1gkXdrVtNuGbU5wd8lWoPfAo_qYpokm52?usp=sharing), choose `AlphaPose` or `OpenPose` version
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> - Find a suitable location to unzip it, like `<somewhere>/OUMVLP/Pose/match_idx`.
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> - Move the zip file into the `match_idx` dir and unzip it there.
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> - You will finally get the index root: `<somewhere>/OUMVLP/Pose/match_idx/AlphaPose`
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> *(Here we take `AlphaPose` version as an example, this path is what we call `Path_of_OUMVLP-pose-index` below)*
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>
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> 2. `Approach 2`: Run the following command to generate it by yourself (**rearranged silhouette dataset is needed**):
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>
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> ```bash
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> python datasets/OUMVLP/pose_index_extractor.py \
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> -p Path_of_OUMVLP-pose-rearranged \
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> -s Path_of_OUMVLP-silu-rearranged \
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> -o Path_of_OUMVLP-pose-index
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> ```
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```bash
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python datasets/pretreatment.py \
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--input_path Path_of_OUMVLP-pose-rearranged \
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--output_path Path_of_OUMVLP-pose-pkl \
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--pose \
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--dataset OUMVLP \
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--oumvlp_index_dir Path_of_OUMVLP-pose-index
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```
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gernerate the 17 Number of Pose Points Format from 18 Number of Pose Points
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```
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python datasets/OUMVLP/rearrange_OUMVLP_pose.py --input_path Path_of_OUMVLP-pose18 --output_path Path_of_OUMVLP-pose17
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```
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- Processed
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```
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OUMVLP-pkl
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00001 (subject)
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00 (sequence)
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000 (view)
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000.pkl (contains all frames)
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015 (view)
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015.pkl (contains all frames)
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...
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01 (sequence)
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000 (view)
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000.pkl (contains all frames)
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015 (view)
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015.pkl (contains all frames)
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......
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00002 (subject)
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......
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......
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```
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