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import pickle |
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from glob import glob |
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from omegaconf import OmegaConf |
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from joblib import Parallel, delayed, parallel_backend |
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import torch |
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import numpy as np |
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from tqdm import tqdm |
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from preprocess.build import ProcessorBase |
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from preprocess.utils.label_convert import S3D_SCANNET as label_convert |
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from preprocess.utils.constant import * |
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PTS_LIMIT = 480000 |
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class S3DProcessor(ProcessorBase): |
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def record_splits(self, scan_ids): |
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split_dir = self.save_root / 'split' |
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split_dir.mkdir(exist_ok=True) |
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split = { |
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'train': [], |
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'val': [], |
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'test': []} |
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split['train'] = [scan_id[1] for scan_id in scan_ids if scan_id[0] == 'train'] |
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split['val'] = [scan_id[1] for scan_id in scan_ids if scan_id[0] == 'val'] |
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split['test'] = [scan_id[1] for scan_id in scan_ids if scan_id[0] == 'test'] |
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for _s, _c in split.items(): |
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with open(split_dir / f'{_s}_split.txt', 'w', encoding='utf-8') as fp: |
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fp.write('\n'.join(_c)) |
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def read_all_scans(self): |
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scan_ids = [] |
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for split in ['train', 'val', 'test']: |
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scan_paths = glob(str(self.data_root) + f'/{split}/*') |
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scan_ids.extend([(split, '_'.join(path.split('/')[-1].split('_')[:-2])) for path in scan_paths]) |
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return scan_ids |
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def process_point_cloud(self, scan_id, plydata, annotations): |
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vertices = plydata[0] |
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vertex_colors = (plydata[1][:,:3] + 1) / 2.0 * 255.0 |
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vertex_instance = - np.ones((vertices.shape[0])) |
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inst_to_label = {} |
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for _id, _box in enumerate(annotations['gt_boxes_upright_depth']): |
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if annotations['class'][_id] in [38, 39, 40]: |
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continue |
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centroid = _box[:3] |
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dimension = _box[3:6] |
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box_max = centroid + dimension/2 |
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box_min = centroid - dimension/2 |
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point_max_mask = np.all(vertices < box_max, axis=1) |
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point_min_mask = np.all(vertices > box_min, axis=1) |
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point_mask = np.logical_and(point_max_mask, point_min_mask) |
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vertex_instance[point_mask] = _id |
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inst_to_label[_id] = label_convert[annotations['class'][_id]] |
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center_points = np.mean(vertices, axis=0) |
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center_points[2] = np.min(vertices[:, 2]) |
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vertices = vertices - center_points |
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assert vertex_colors.shape == vertices.shape |
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assert vertex_colors.shape[0] == vertex_instance.shape[0] |
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if vertices.shape[0] > PTS_LIMIT: |
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pcd_idxs = np.random.choice(vertices.shape[0], size=PTS_LIMIT, replace=False) |
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vertices = vertices[pcd_idxs] |
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colors = colors[pcd_idxs] |
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vertex_instance = vertex_instance[pcd_idxs] |
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if self.check_key(self.output.pcd): |
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torch.save(inst_to_label, self.inst2label_path / f"{scan_id}.pth") |
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torch.save((vertices, vertex_colors, vertex_instance), self.pcd_path / f"{scan_id}.pth") |
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def scene_proc(self, scan_id): |
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split = scan_id[0] |
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scan_id = scan_id[1] |
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data_root = self.data_root / split |
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if not (data_root / f'{scan_id}_1cm_seg.pth').exists(): |
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return |
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if not (self.data_root.parent / 'anno_mask' / f'{scan_id}_1cm.bin').exists(): |
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return |
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plydata = torch.load(data_root / f'{scan_id}_1cm_seg.pth') |
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with open(self.data_root.parent / 'anno_mask' / f'{scan_id}_1cm.bin', 'rb') as f: |
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annotations = pickle.load(f) |
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self.process_point_cloud(scan_id, plydata, annotations) |
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def process_scans(self): |
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scan_ids = self.read_all_scans() |
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self.log_starting_info(len(scan_ids)) |
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if self.num_workers > 1: |
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with parallel_backend('multiprocessing', n_jobs=self.num_workers): |
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Parallel()(delayed(self.scene_proc)(scan_id) for scan_id in tqdm(scan_ids)) |
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else: |
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for scan_id in tqdm(scan_ids): |
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self.scene_proc(scan_id) |
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if __name__ == '__main__': |
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cfg = OmegaConf.create({ |
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'data_root': '/path/to/Structured3D/data_out/swin3d_new', |
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'save_root': '/output/path/to/Structured3D', |
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'num_workers': 1, |
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'output': { |
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'pcd': True, |
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} |
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}) |
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processor = S3DProcessor(cfg) |
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processor.process_scans() |
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