| 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() |
|
|
