Along with 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.
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).
- Build MeshToPointcloudFPS and copy the compiled executable beside the python script in this repository as
FpsCpu. - Install python3 dependencies:
tqdm, numpy, joblib - Download
ShapeNetCore.v2.zipfrom the official website ~ 26.4 GB - Unzip it.
- In the script:
- Set
DATASET_PATHto the unzipped dataset directory. - Set
OUTPUT_PATHto an empty folder of your choice to store the processed data. - Set
TAXONOMY_PATHto the absolute path of thetaxonomy.jsonat the unzipped dataset directory.
- Set
- Set
n_jobsto the number of the CPU cores that you have on your system. - Run the script.
- Check the results at
OUTPUT_PATH(train6-2-2.h5,val6-2-2.h5, andtest6-2-2.h5).
The dtype for the dataset is np.float32 and np.int32 for the labels.
labels.id.txtatOUTPUT_PATHlists thesynidof the classes in the dataset.labels.names.txtatOUTPUT_PATHlists the resolved names of thesynids usingtaxonomy.json.labels.codes.jsonatOUTPUT_PATHholds the content of a python dictionary to convert class names (str) to class codes (int32).
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,)