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Add tum facade dataset (isl-org#613)
added dataset classes for the TUM-Facade dataset (https://github.com/OloOcki/tum-facade ) and updated the init file --------- Co-authored-by: photolap <[email protected]> Co-authored-by: Sameer Sheorey <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,238 @@ | ||
| import glob | ||
| from pathlib import Path | ||
| import logging | ||
| import numpy as np | ||
| import open3d as o3d | ||
| from ..utils import DATASET | ||
| from .base_dataset import BaseDataset, BaseDatasetSplit | ||
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| log = logging.getLogger(__name__) | ||
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| class TUMFacade(BaseDataset): | ||
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| def __init__(self, | ||
| dataset_path, | ||
| info_path=None, | ||
| name='TUM_Facade', | ||
| cache_dir='./logs/cache', | ||
| use_cache=False, | ||
| use_global=False, | ||
| **kwargs): | ||
| """Dataset classes for the TUM-Facade dataset. Semantic segmentation | ||
| annotations over TUM-MLS-2016 point cloud data. | ||
| Website: https://mediatum.ub.tum.de/node?id=1636761 | ||
| Code: https://github.com/OloOcki/tum-facade | ||
| Download: | ||
| - Original: https://dataserv.ub.tum.de/index.php/s/m1636761.003 | ||
| - Processed: https://tumde-my.sharepoint.com/:f:/g/personal/olaf_wysocki_tum_de/EjA8B_KGDyFEulRzmq-CG1QBBL4dZ7z5PoHeI8zMD0JxIQ?e=9MrMcl | ||
| Data License: CC BY-NC-SA 4.0 | ||
| Citation: | ||
| - Paper: Wysocki, O. and Hoegner, L. and Stilla, U., TUM-FAÇADE: | ||
| Reviewing and enriching point cloud benchmarks for façade | ||
| segmentation, ISPRS 2022 | ||
| - Dataset: Wysocki, Olaf and Tan, Yue and Zhang, Jiarui and | ||
| Stilla, Uwe, TUM-FACADE dataset, TU Munich, 2023 | ||
| README file from processed dataset website: | ||
| The dataset split is provided in the following folder structure | ||
| -->tum-facade | ||
| -->pointclouds | ||
| -->annotatedGlobalCRS | ||
| -->test_files | ||
| -->training_files | ||
| -->validation_files | ||
| -->annotatedLocalCRS | ||
| -->test_files | ||
| -->training_files | ||
| -->validation_file | ||
| The indivisual point clouds are compressed as .7z files and are | ||
| stored in the .pcd format. | ||
| To make use of the dataset split in open3D-ML, all the point cloud | ||
| files have to be unpacked with 7Zip. The folder structure itself | ||
| must not be modified, else the reading functionalities in open3D-ML | ||
| are not going to work. As a path to the dataset, the path to the | ||
| 'tum-facade' folder must be set. | ||
| The dataset is split in the following way (10.08.2023): | ||
| Testing : Building Nr. 23 | ||
| Training : Buildings Nr. 57, Nr.58, Nr. 60 | ||
| Validation : Buildings Nr. 22, Nr.59, Nr. 62, Nr. 81 | ||
| Initialize the function by passing the dataset and other details. | ||
| Args: | ||
| dataset_path: The path to the dataset to use. | ||
| info_path: The path to the file that includes information about | ||
| the dataset. This is default to dataset path if nothing is | ||
| provided. | ||
| name: The name of the dataset (TUM_Facade in this case). | ||
| cache_dir: The directory where the cache is stored. | ||
| use_cache: Indicates if the dataset should be cached. | ||
| use_global: Inidcates if the dataset should be used in a local or | ||
| the global CRS | ||
| Returns: | ||
| class: The corresponding class. | ||
| """ | ||
| super().__init__( | ||
| dataset_path=dataset_path, | ||
| info_path=info_path, | ||
| name=name, | ||
| cache_dir=cache_dir, | ||
| use_cache=use_cache, | ||
| use_global=use_global, # Diese habe ich selbst hinzugefügt | ||
| **kwargs) | ||
| cfg = self.cfg | ||
| self.name = cfg.name | ||
| self.dataset_path = cfg.dataset_path | ||
| self.label_to_names = self.get_label_to_names() | ||
| self.use_global = cfg.use_global | ||
| if info_path is None: | ||
| self.info_path = dataset_path | ||
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| if self.use_global: | ||
| # Finding all the training files | ||
| self.trainFiles = glob.glob( | ||
| str( | ||
| Path(cfg.dataset_path) / 'pointclouds' / | ||
| 'annotatedGlobalCRS' / 'training_files' / '*.pcd')) | ||
| # Finding all the validation Files | ||
| self.valFiles = glob.glob( | ||
| str( | ||
| Path(cfg.dataset_path) / 'pointclouds' / | ||
| 'annotatedGlobalCRS' / 'validation_files' / '*.pcd')) | ||
| # Finding all the test files | ||
| self.testFiles = glob.glob( | ||
| str( | ||
| Path(cfg.dataset_path) / 'pointclouds' / | ||
| 'annotatedGlobalCRS' / 'test_files' / '*.pcd')) | ||
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| elif not self.use_global: | ||
| # Finding all the training files | ||
| self.trainFiles = glob.glob( | ||
| str( | ||
| Path(cfg.dataset_path) / 'pointclouds' / | ||
| 'annotatedLocalCRS' / 'training_files' / '*.pcd')) | ||
| # Finding all the validation Files | ||
| self.valFiles = glob.glob( | ||
| str( | ||
| Path(cfg.dataset_path) / 'pointclouds' / | ||
| 'annotatedLocalCRS' / 'validation_files' / '*.pcd')) | ||
| # Finding all the test files | ||
| self.testFiles = glob.glob( | ||
| str( | ||
| Path(cfg.dataset_path) / 'pointclouds' / | ||
| 'annotatedLocalCRS' / 'test_files' / '*.pcd')) | ||
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| else: | ||
| raise ValueError( | ||
| "Invalid specification! use_global must either be True or False!" | ||
| ) | ||
|
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| @staticmethod | ||
| def get_label_to_names(): # | ||
| """Returns a label to names dictionary object. | ||
| Returns: | ||
| A dict where keys are label numbers and values are the corresponding | ||
| names. | ||
| """ | ||
| label_to_names = { | ||
| 0: 'not_assigned', | ||
| 1: 'wall', | ||
| 2: 'window', | ||
| 3: 'door', | ||
| 4: 'balcony', | ||
| 5: 'molding', | ||
| 6: 'deco', | ||
| 7: 'column', | ||
| 8: 'arch', | ||
| 9: 'drainpipe', | ||
| 10: 'stairs', | ||
| 11: 'ground_surface', | ||
| 12: 'terrain', | ||
| 13: 'roof', | ||
| 14: 'blinds', | ||
| 15: 'outer_ceiling_surface', | ||
| 16: 'interior', | ||
| 17: 'other' | ||
| } | ||
| return label_to_names | ||
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| def get_split(self, split): | ||
| return TUMFacadeSplit(self, split=split) | ||
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| def get_split_list(self, split): | ||
| """Returns the list of data splits available. | ||
| Args: | ||
| split: A string identifying the dataset split that is usually one of | ||
| 'training', 'test', 'validation', or 'all'. | ||
| Returns: | ||
| A dataset split object providing the requested subset of the data. | ||
| Raises: | ||
| ValueError: Indicates that the split name passed is incorrect. The | ||
| split name should be one of 'training', 'test', 'validation', or | ||
| 'all'. | ||
| """ | ||
| if split in ['train', 'training']: | ||
| return self.trainFiles | ||
| elif split in ['test', 'testing']: | ||
| return self.testFiles | ||
| elif split in ['val', 'validation']: | ||
| return self.valFiles | ||
| elif split in ['all']: | ||
| return self.trainFiles + self.valFiles + self.testFiles | ||
| else: | ||
| raise ValueError("Invalid split {}".format(split)) | ||
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| def is_tested(self, attr): | ||
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| pass | ||
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| def save_test_result(self, results, attr): | ||
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| pass | ||
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| class TUMFacadeSplit(BaseDatasetSplit): | ||
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| def __init__(self, dataset, split='train'): | ||
| super().__init__(dataset, split=split) | ||
| log.info("Found {} pointclouds for {}".format(len(self.path_list), | ||
| split)) | ||
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| def __len__(self): | ||
| return len(self.path_list) | ||
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| def get_data(self, idx): | ||
| pc_path = self.path_list[idx] | ||
| data = o3d.t.io.read_point_cloud(pc_path).point | ||
| points = data["positions"].numpy() | ||
| points = np.float32(points) | ||
| labels = data['classification'].numpy().astype(np.int32).reshape((-1,)) | ||
| data = {'point': points, 'feat': None, 'label': labels} | ||
| return data | ||
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| def get_attr(self, idx): | ||
| pc_path = Path(self.path_list[idx]) | ||
| pc_path = str(pc_path) | ||
| name = pc_path.replace('.txt', '') | ||
| parts = name.split("/") | ||
| name = parts[-1] | ||
| split = self.split | ||
| attr = {'idx': idx, 'name': name, 'path': pc_path, 'split': split} | ||
| return attr | ||
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| DATASET._register_module(TUMFacade) |