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https://github.com/salehjg/Shapenet2_Preparation
A python script to convert and down-sample mesh data into pointclouds using FPS algorithm.
https://github.com/salehjg/Shapenet2_Preparation
data-preparation dataset farthest-point-sampling hdf5 python shapenet-dataset shapenetcore
Last synced: 2 months ago
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A python script to convert and down-sample mesh data into pointclouds using FPS algorithm.
- Host: GitHub
- URL: https://github.com/salehjg/Shapenet2_Preparation
- Owner: salehjg
- License: bsd-3-clause
- Created: 2021-08-01T19:02:42.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2021-08-11T17:22:28.000Z (over 3 years ago)
- Last Synced: 2024-08-01T03:43:45.205Z (5 months ago)
- Topics: data-preparation, dataset, farthest-point-sampling, hdf5, python, shapenet-dataset, shapenetcore
- Language: Python
- Homepage:
- Size: 7.81 KB
- Stars: 15
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- License: LICENSE.md
Awesome Lists containing this project
README
# ShapeNetCore.v2 Preparation
Along with [MeshToPointcloudFPS](https://github.com/salehjg/MeshToPointcloudFPS), this script is used to convert mesh files of the type `*.obj` to point clouds stored in HDF5 format. The script will also handle splitting the dataset into train-val-test with 60-20-20 % ratio.
# Data Conversion
The `MeshToPointcloudFPS` executable is responsible for data conversion from meshes `*.obj` to point clouds `*.h5` along with down-sampling the point clouds to the target number of points (`-n 1024`).
# Pre-requirements
1. Build [MeshToPointcloudFPS](https://github.com/salehjg/MeshToPointcloudFPS) and copy the compiled executable beside the python script in this repository as `FpsCpu`.
2. Install python3 dependencies: `tqdm, numpy, joblib`
3. Download `ShapeNetCore.v2.zip` from the official website ~ 26.4 GB
4. Unzip it.
5. In the script:
- Set `DATASET_PATH` to the unzipped dataset directory.
- Set `OUTPUT_PATH` to an empty folder of your choice to store the processed data.
- Set `TAXONOMY_PATH` to the absolute path of the `taxonomy.json` at the unzipped dataset directory.
6. Set `n_jobs` to the number of the CPU cores that you have on your system.
7. Run the script.
8. Check the results at `OUTPUT_PATH` (`train6-2-2.h5`, `val6-2-2.h5`, and `test6-2-2.h5`).
# Data Types
The `dtype` for the dataset is `np.float32` and `np.int32` for the labels.
# Data Class Names
* `labels.id.txt` at `OUTPUT_PATH` lists the `synid` of the classes in the dataset.
* `labels.names.txt` at `OUTPUT_PATH` lists the resolved names of the `synid`s using `taxonomy.json`.
* `labels.codes.json` at `OUTPUT_PATH` holds the content of a python dictionary to convert class names (`str`) to class codes (`int32`).
# Log
```
Listing *.h5 files...
Found class folders: 55
** Processing 03636649 ( lamp )
Concatenated class shape: (2318, 1024, 3)
** Processing 02691156 ( airplane )
Concatenated class shape: (4045, 1024, 3)
** Processing 02747177 ( ashcan )
Concatenated class shape: (343, 1024, 3)
** Processing 02773838 ( bag )
Concatenated class shape: (83, 1024, 3)
** Processing 02801938 ( basket )
Concatenated class shape: (113, 1024, 3)
** Processing 02808440 ( bathtub )
Concatenated class shape: (856, 1024, 3)
** Processing 02818832 ( bed )
Concatenated class shape: (233, 1024, 3)
** Processing 02828884 ( bench )
Concatenated class shape: (1813, 1024, 3)
** Processing 02843684 ( birdhouse )
Concatenated class shape: (73, 1024, 3)
** Processing 02871439 ( bookshelf )
Concatenated class shape: (452, 1024, 3)
** Processing 02876657 ( bottle )
Concatenated class shape: (498, 1024, 3)
** Processing 02880940 ( bowl )
Concatenated class shape: (186, 1024, 3)
** Processing 02924116 ( bus )
Concatenated class shape: (939, 1024, 3)
** Processing 02933112 ( cabinet )
Concatenated class shape: (1571, 1024, 3)
** Processing 02942699 ( camera )
Concatenated class shape: (113, 1024, 3)
** Processing 02946921 ( can )
Concatenated class shape: (108, 1024, 3)
** Processing 02954340 ( cap )
Concatenated class shape: (56, 1024, 3)
** Processing 02958343 ( car )
Concatenated class shape: (3513, 1024, 3)
** Processing 02992529 ( cellular telephone )
Concatenated class shape: (831, 1024, 3)
** Processing 03001627 ( chair )
Concatenated class shape: (6778, 1024, 3)
** Processing 03046257 ( clock )
Concatenated class shape: (651, 1024, 3)
** Processing 03085013 ( computer keyboard )
Concatenated class shape: (65, 1024, 3)
** Processing 03207941 ( dishwasher )
Concatenated class shape: (93, 1024, 3)
** Processing 03211117 ( display )
Concatenated class shape: (1093, 1024, 3)
** Processing 03261776 ( earphone )
Concatenated class shape: (73, 1024, 3)
** Processing 03325088 ( faucet )
Concatenated class shape: (744, 1024, 3)
** Processing 03337140 ( file )
Concatenated class shape: (298, 1024, 3)
** Processing 03467517 ( guitar )
Concatenated class shape: (797, 1024, 3)
** Processing 03513137 ( helmet )
Concatenated class shape: (162, 1024, 3)
** Processing 03593526 ( jar )
Concatenated class shape: (596, 1024, 3)
** Processing 03624134 ( knife )
Concatenated class shape: (424, 1024, 3)
** Processing 03642806 ( laptop )
Concatenated class shape: (460, 1024, 3)
** Processing 03691459 ( loudspeaker )
Concatenated class shape: (1597, 1024, 3)
** Processing 03710193 ( mailbox )
Concatenated class shape: (94, 1024, 3)
** Processing 03759954 ( microphone )
Concatenated class shape: (67, 1024, 3)
** Processing 03761084 ( microwave )
Concatenated class shape: (152, 1024, 3)
** Processing 03790512 ( motorcycle )
Concatenated class shape: (337, 1024, 3)
** Processing 03797390 ( mug )
Concatenated class shape: (214, 1024, 3)
** Processing 03928116 ( piano )
Concatenated class shape: (239, 1024, 3)
** Processing 03938244 ( pillow )
Concatenated class shape: (96, 1024, 3)
** Processing 03948459 ( pistol )
Concatenated class shape: (307, 1024, 3)
** Processing 03991062 ( pot )
Concatenated class shape: (602, 1024, 3)
** Processing 04004475 ( printer )
Concatenated class shape: (166, 1024, 3)
** Processing 04074963 ( remote control )
Concatenated class shape: (66, 1024, 3)
** Processing 04090263 ( rifle )
Concatenated class shape: (2373, 1024, 3)
** Processing 04099429 ( rocket )
Concatenated class shape: (85, 1024, 3)
** Processing 04225987 ( skateboard )
Concatenated class shape: (152, 1024, 3)
** Processing 04256520 ( sofa )
Concatenated class shape: (3173, 1024, 3)
** Processing 04330267 ( stove )
Concatenated class shape: (218, 1024, 3)
** Processing 04379243 ( table )
Concatenated class shape: (8436, 1024, 3)
** Processing 04401088 ( telephone )
Concatenated class shape: (1089, 1024, 3)
** Processing 04460130 ( tower )
Concatenated class shape: (133, 1024, 3)
** Processing 04468005 ( train )
Concatenated class shape: (389, 1024, 3)
** Processing 04530566 ( vessel )
Concatenated class shape: (1939, 1024, 3)
** Processing 04554684 ( washer )
Concatenated class shape: (169, 1024, 3)
## FINAL REPORT:
Train Set Data : (31535, 1024, 3)
Train Set Labels: (31535,)
Validation Set Data : (10468, 1024, 3)
Validation Set Labels: (10468,)
Test Set Data : (10468, 1024, 3)
Test Set Labels: (10468,)
```