| 13 | from utils.point_cloud_utils import load_ply_with_normals |
| 14 | |
| 15 | class FreeMaskPreprocessing(BasePreprocessing): |
| 16 | |
| 17 | FREEMASK_CLASS_IDS = (0, 1) |
| 18 | FREEMASK_CLASS_NAMES = ('background', 'foreground') |
| 19 | FREEMASK_COLOR_MAP = {0: (0, 0, 0), 1: (0, 0, 128)} |
| 20 | |
| 21 | def __init__( |
| 22 | self, |
| 23 | data_dir: str = "data/Datasets/ArKitScenes", |
| 24 | save_dir: str = "data/processed/unscene3d_arkit", |
| 25 | modes: tuple = ("train", "validation"), |
| 26 | n_jobs: int = 8, |
| 27 | freemask_dir: str = "data/Datasets/ArKitScenes"): |
| 28 | |
| 29 | super().__init__(data_dir, save_dir, modes, n_jobs) |
| 30 | |
| 31 | self.create_label_database() |
| 32 | self.freemask_base_path = Path(freemask_dir) |
| 33 | |
| 34 | for mode in self.modes: |
| 35 | trainval_split_dir = data_dir / Path("split") |
| 36 | with open(trainval_split_dir / f"{mode}.txt") as f: |
| 37 | split_file = f.read().split("\n")[:-1] |
| 38 | |
| 39 | scans_folder = "freemask" |
| 40 | filepaths = [] |
| 41 | for scene in split_file: |
| 42 | filepaths.append(self.data_dir / scans_folder / f'{scene}_cloud.npy') |
| 43 | self.files[mode] = natsorted(filepaths) |
| 44 | |
| 45 | def create_label_database(self): |
| 46 | label_database = {} |
| 47 | for row_id, class_id in enumerate(self.FREEMASK_CLASS_IDS): |
| 48 | label_database[class_id] = { |
| 49 | 'color': self.FREEMASK_COLOR_MAP[class_id], |
| 50 | 'name': self.FREEMASK_CLASS_NAMES[row_id], |
| 51 | 'validation': True |
| 52 | } |
| 53 | self._save_yaml(self.save_dir / "label_database.yaml", label_database) |
| 54 | return label_database |
| 55 | |
| 56 | def load_ply_cloud_with_normals(self, filepath): |
| 57 | |
| 58 | # load cloud |
| 59 | cloud = o3d.io.read_point_cloud(str(filepath)) |
| 60 | |
| 61 | # estimate normals |
| 62 | cloud.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30)) |
| 63 | |
| 64 | vertices = np.asarray(cloud.points) |
| 65 | normals = np.asarray(cloud.normals) |
| 66 | feats = np.asarray(cloud.colors) |
| 67 | feats = np.hstack((feats, normals)) |
| 68 | labels = np.zeros(len(vertices), dtype=np.int32) |
| 69 | |
| 70 | return vertices, feats, labels |
| 71 | |
| 72 | def process_file(self, filepath, mode): |
nothing calls this directly
no outgoing calls
no test coverage detected