| | import json |
| | from glob import glob |
| | from omegaconf import OmegaConf |
| | from joblib import Parallel, delayed, parallel_backend |
| |
|
| | import torch |
| | import numpy as np |
| | import trimesh |
| | from tqdm import tqdm |
| | from scipy.spatial.transform import Rotation |
| |
|
| | from preprocess.build import ProcessorBase |
| | from preprocess.utils.label_convert import ARKITSCENE_SCANNET as label_convert |
| | from preprocess.utils.align_utils import compute_box_3d, calc_align_matrix, rotate_z_axis_by_degrees |
| | from preprocess.utils.constant import * |
| |
|
| |
|
| | class ARKitScenesProcessor(ProcessorBase): |
| | def record_splits(self, scan_ids): |
| | split_dir = self.save_root / 'split' |
| | split_dir.mkdir(exist_ok=True) |
| | if (split_dir / 'train_split.txt').exists() and (split_dir / 'val_split.txt').exists(): |
| | return |
| | split = { |
| | 'train': [], |
| | 'val': []} |
| | split['train'] = [scan_id[1] for scan_id in scan_ids if scan_id[0] == 'Training'] |
| | split['val'] = [scan_id[1] for scan_id in scan_ids if scan_id[0] == 'Validation'] |
| | for _s, _c in split.items(): |
| | with open(split_dir / f'{_s}_split.txt', 'w', encoding='utf-8') as fp: |
| | fp.write('\n'.join(_c)) |
| |
|
| | def read_all_scans(self): |
| | scan_ids = [] |
| | for split in ['Training', 'Validation']: |
| | scan_paths = glob(str(self.data_root) + f'/{split}/*') |
| | scan_ids.extend([(split, path.split('/')[-1]) for path in scan_paths]) |
| | return scan_ids |
| |
|
| | def process_point_cloud(self, scan_id, plydata, annotations): |
| | vertices = plydata.vertices |
| | vertex_colors = plydata.visual.vertex_colors |
| | vertex_colors = vertex_colors[:, :3] |
| |
|
| | vertex_instance = np.zeros((vertices.shape[0])) |
| | inst_to_label = {} |
| | bbox_list = [] |
| |
|
| | for _i, label_info in enumerate(annotations["data"]): |
| | obj_label = label_info["label"] |
| | object_id = _i + 1 |
| | rotation = np.array(label_info["segments"]["obbAligned"]["normalizedAxes"]).reshape(3, 3) |
| | r = Rotation.from_matrix(rotation) |
| |
|
| | transform = np.array(label_info["segments"]["obbAligned"]["centroid"]).reshape(-1, 3) |
| | scale = np.array(label_info["segments"]["obbAligned"]["axesLengths"]).reshape(-1, 3) |
| | trns = np.eye(4) |
| | trns[0:3, 3] = transform |
| | trns[0:3, 0:3] = rotation.T |
| | box_trimesh_fmt = trimesh.creation.box(scale.reshape(3,), trns) |
| | obj_containment = np.argwhere(box_trimesh_fmt.contains(vertices)) |
| |
|
| | vertex_instance[obj_containment] = object_id |
| | inst_to_label[object_id] = label_convert[obj_label] |
| |
|
| | box3d = compute_box_3d(scale.reshape(3).tolist(), transform, rotation) |
| | bbox_list.append(box3d) |
| | if len(bbox_list) == 0: |
| | return |
| |
|
| | align_angle = calc_align_matrix(bbox_list) |
| | vertices = rotate_z_axis_by_degrees(np.array(vertices), align_angle) |
| | if np.max(vertex_colors) <= 1: |
| | vertex_colors = vertex_colors * 255.0 |
| | center_points = np.mean(vertices, axis=0) |
| | center_points[2] = np.min(vertices[:, 2]) |
| | vertices = vertices - center_points |
| |
|
| | assert vertex_colors.shape == vertices.shape |
| | assert vertex_colors.shape[0] == vertex_instance.shape[0] |
| |
|
| | if self.check_key(self.output.pcd): |
| | torch.save(inst_to_label, self.inst2label_path / f"{scan_id}.pth") |
| | torch.save((vertices, vertex_colors, vertex_instance), self.pcd_path / f"{scan_id}.pth") |
| | np.save(self.pcd_path / f"{scan_id}_align_angle.npy", align_angle) |
| |
|
| | def scene_proc(self, scan_id): |
| | split = scan_id[0] |
| | scan_id = scan_id[1] |
| | data_root = self.data_root / split / scan_id |
| |
|
| | if not (data_root / f'{scan_id}_3dod_mesh.ply').exists(): |
| | return |
| | if not (data_root / f'{scan_id}_3dod_annotation.json').exists(): |
| | return |
| |
|
| | plydata = trimesh.load(data_root / f'{scan_id}_3dod_mesh.ply', process=False) |
| | with open((data_root / f'{scan_id}_3dod_annotation.json'), "r", encoding='utf-8') as f: |
| | annotations = json.load(f) |
| |
|
| | |
| | self.process_point_cloud(scan_id, plydata, annotations) |
| |
|
| | def process_scans(self): |
| | scan_ids = self.read_all_scans() |
| | self.log_starting_info(len(scan_ids)) |
| |
|
| | if self.num_workers > 1: |
| | with parallel_backend('multiprocessing', n_jobs=self.num_workers): |
| | Parallel()(delayed(self.scene_proc)(scan_id) for scan_id in tqdm(scan_ids)) |
| | else: |
| | for scan_id in tqdm(scan_ids): |
| | self.scene_proc(scan_id) |
| |
|
| |
|
| | if __name__ == '__main__': |
| | cfg = OmegaConf.create({ |
| | 'data_root': '/path/to/ARKitScenes', |
| | 'save_root': '/output/path/to/ARKitScenes', |
| | 'num_workers': 1, |
| | 'output': { |
| | 'pcd': True, |
| | } |
| | }) |
| | processor = ARKitScenesProcessor(cfg) |
| | processor.process_scans() |
| |
|
