| import pickle |
| from glob import glob |
| from omegaconf import OmegaConf |
| from joblib import Parallel, delayed, parallel_backend |
|
|
| import torch |
| import numpy as np |
| from tqdm import tqdm |
|
|
| from preprocess.build import ProcessorBase |
| from preprocess.utils.label_convert import S3D_SCANNET as label_convert |
| from preprocess.utils.constant import * |
|
|
|
|
| PTS_LIMIT = 480000 |
|
|
|
|
| class S3DProcessor(ProcessorBase): |
| def record_splits(self, scan_ids): |
| split_dir = self.save_root / 'split' |
| split_dir.mkdir(exist_ok=True) |
| split = { |
| 'train': [], |
| 'val': [], |
| 'test': []} |
| split['train'] = [scan_id[1] for scan_id in scan_ids if scan_id[0] == 'train'] |
| split['val'] = [scan_id[1] for scan_id in scan_ids if scan_id[0] == 'val'] |
| split['test'] = [scan_id[1] for scan_id in scan_ids if scan_id[0] == 'test'] |
| 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 ['train', 'val', 'test']: |
| scan_paths = glob(str(self.data_root) + f'/{split}/*') |
| scan_ids.extend([(split, '_'.join(path.split('/')[-1].split('_')[:-2])) for path in scan_paths]) |
| return scan_ids |
|
|
| def process_point_cloud(self, scan_id, plydata, annotations): |
| vertices = plydata[0] |
| vertex_colors = (plydata[1][:,:3] + 1) / 2.0 * 255.0 |
|
|
| vertex_instance = - np.ones((vertices.shape[0])) |
| inst_to_label = {} |
|
|
| for _id, _box in enumerate(annotations['gt_boxes_upright_depth']): |
| if annotations['class'][_id] in [38, 39, 40]: |
| continue |
| centroid = _box[:3] |
| dimension = _box[3:6] |
| box_max = centroid + dimension/2 |
| box_min = centroid - dimension/2 |
| point_max_mask = np.all(vertices < box_max, axis=1) |
| point_min_mask = np.all(vertices > box_min, axis=1) |
| point_mask = np.logical_and(point_max_mask, point_min_mask) |
| vertex_instance[point_mask] = _id |
| inst_to_label[_id] = label_convert[annotations['class'][_id]] |
|
|
| 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 vertices.shape[0] > PTS_LIMIT: |
| pcd_idxs = np.random.choice(vertices.shape[0], size=PTS_LIMIT, replace=False) |
| vertices = vertices[pcd_idxs] |
| colors = colors[pcd_idxs] |
| vertex_instance = vertex_instance[pcd_idxs] |
|
|
| 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") |
|
|
| def scene_proc(self, scan_id): |
| split = scan_id[0] |
| scan_id = scan_id[1] |
| data_root = self.data_root / split |
|
|
| if not (data_root / f'{scan_id}_1cm_seg.pth').exists(): |
| return |
| if not (self.data_root.parent / 'anno_mask' / f'{scan_id}_1cm.bin').exists(): |
| return |
| plydata = torch.load(data_root / f'{scan_id}_1cm_seg.pth') |
| with open(self.data_root.parent / 'anno_mask' / f'{scan_id}_1cm.bin', 'rb') as f: |
| annotations = pickle.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/Structured3D/data_out/swin3d_new', |
| 'save_root': '/output/path/to/Structured3D', |
| 'num_workers': 1, |
| 'output': { |
| 'pcd': True, |
| } |
| }) |
| processor = S3DProcessor(cfg) |
| processor.process_scans() |
|
|
