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| # Copyright (C) 2024-present Naver Corporation. All rights reserved. | |
| # Licensed under CC BY-NC-SA 4.0 (non-commercial use only). | |
| # | |
| # -------------------------------------------------------- | |
| # InLoc dataloader | |
| # -------------------------------------------------------- | |
| import os | |
| import numpy as np | |
| import torch | |
| import PIL.Image | |
| import scipy.io | |
| import kapture | |
| from kapture.io.csv import kapture_from_dir | |
| from kapture_localization.utils.pairsfile import get_ordered_pairs_from_file | |
| from dust3r_visloc.datasets.utils import cam_to_world_from_kapture, get_resize_function, rescale_points3d | |
| from dust3r_visloc.datasets.base_dataset import BaseVislocDataset | |
| from dust3r.datasets.utils.transforms import ImgNorm | |
| from dust3r.utils.geometry import xy_grid, geotrf | |
| def read_alignments(path_to_alignment): | |
| aligns = {} | |
| with open(path_to_alignment, "r") as fid: | |
| while True: | |
| line = fid.readline() | |
| if not line: | |
| break | |
| if len(line) == 4: | |
| trans_nr = line[:-1] | |
| while line != 'After general icp:\n': | |
| line = fid.readline() | |
| line = fid.readline() | |
| p = [] | |
| for i in range(4): | |
| elems = line.split(' ') | |
| line = fid.readline() | |
| for e in elems: | |
| if len(e) != 0: | |
| p.append(float(e)) | |
| P = np.array(p).reshape(4, 4) | |
| aligns[trans_nr] = P | |
| return aligns | |
| class VislocInLoc(BaseVislocDataset): | |
| def __init__(self, root, pairsfile, topk=1): | |
| super().__init__() | |
| self.root = root | |
| self.topk = topk | |
| self.num_views = self.topk + 1 | |
| self.maxdim = None | |
| self.patch_size = None | |
| query_path = os.path.join(self.root, 'query') | |
| kdata_query = kapture_from_dir(query_path) | |
| assert kdata_query.records_camera is not None | |
| kdata_query_searchindex = {kdata_query.records_camera[(timestamp, sensor_id)]: (timestamp, sensor_id) | |
| for timestamp, sensor_id in kdata_query.records_camera.key_pairs()} | |
| self.query_data = {'path': query_path, 'kdata': kdata_query, 'searchindex': kdata_query_searchindex} | |
| map_path = os.path.join(self.root, 'mapping') | |
| kdata_map = kapture_from_dir(map_path) | |
| assert kdata_map.records_camera is not None and kdata_map.trajectories is not None | |
| kdata_map_searchindex = {kdata_map.records_camera[(timestamp, sensor_id)]: (timestamp, sensor_id) | |
| for timestamp, sensor_id in kdata_map.records_camera.key_pairs()} | |
| self.map_data = {'path': map_path, 'kdata': kdata_map, 'searchindex': kdata_map_searchindex} | |
| try: | |
| self.pairs = get_ordered_pairs_from_file(os.path.join(self.root, 'pairfiles/query', pairsfile + '.txt')) | |
| except Exception as e: | |
| # if using pairs from hloc | |
| self.pairs = {} | |
| with open(os.path.join(self.root, 'pairfiles/query', pairsfile + '.txt'), 'r') as fid: | |
| lines = fid.readlines() | |
| for line in lines: | |
| splits = line.rstrip("\n\r").split(" ") | |
| self.pairs.setdefault(splits[0].replace('query/', ''), []).append( | |
| (splits[1].replace('database/cutouts/', ''), 1.0) | |
| ) | |
| self.scenes = kdata_query.records_camera.data_list() | |
| self.aligns_DUC1 = read_alignments(os.path.join(self.root, 'mapping/DUC1_alignment/all_transformations.txt')) | |
| self.aligns_DUC2 = read_alignments(os.path.join(self.root, 'mapping/DUC2_alignment/all_transformations.txt')) | |
| def __len__(self): | |
| return len(self.scenes) | |
| def __getitem__(self, idx): | |
| assert self.maxdim is not None and self.patch_size is not None | |
| query_image = self.scenes[idx] | |
| map_images = [p[0] for p in self.pairs[query_image][:self.topk]] | |
| views = [] | |
| dataarray = [(query_image, self.query_data, False)] + [(map_image, self.map_data, True) | |
| for map_image in map_images] | |
| for idx, (imgname, data, should_load_depth) in enumerate(dataarray): | |
| imgpath, kdata, searchindex = map(data.get, ['path', 'kdata', 'searchindex']) | |
| timestamp, camera_id = searchindex[imgname] | |
| # for InLoc, SIMPLE_PINHOLE | |
| camera_params = kdata.sensors[camera_id].camera_params | |
| W, H, f, cx, cy = camera_params | |
| distortion = [0, 0, 0, 0] | |
| intrinsics = np.float32([(f, 0, cx), | |
| (0, f, cy), | |
| (0, 0, 1)]) | |
| if kdata.trajectories is not None and (timestamp, camera_id) in kdata.trajectories: | |
| cam_to_world = cam_to_world_from_kapture(kdata, timestamp, camera_id) | |
| else: | |
| cam_to_world = np.eye(4, dtype=np.float32) | |
| # Load RGB image | |
| rgb_image = PIL.Image.open(os.path.join(imgpath, 'sensors/records_data', imgname)).convert('RGB') | |
| rgb_image.load() | |
| W, H = rgb_image.size | |
| resize_func, to_resize, to_orig = get_resize_function(self.maxdim, self.patch_size, H, W) | |
| rgb_tensor = resize_func(ImgNorm(rgb_image)) | |
| view = { | |
| 'intrinsics': intrinsics, | |
| 'distortion': distortion, | |
| 'cam_to_world': cam_to_world, | |
| 'rgb': rgb_image, | |
| 'rgb_rescaled': rgb_tensor, | |
| 'to_orig': to_orig, | |
| 'idx': idx, | |
| 'image_name': imgname | |
| } | |
| # Load depthmap | |
| if should_load_depth: | |
| depthmap_filename = os.path.join(imgpath, 'sensors/records_data', imgname + '.mat') | |
| depthmap = scipy.io.loadmat(depthmap_filename) | |
| pt3d_cut = depthmap['XYZcut'] | |
| scene_id = imgname.replace('\\', '/').split('/')[1] | |
| if imgname.startswith('DUC1'): | |
| pts3d_full = geotrf(self.aligns_DUC1[scene_id], pt3d_cut) | |
| else: | |
| pts3d_full = geotrf(self.aligns_DUC2[scene_id], pt3d_cut) | |
| pts3d_valid = np.isfinite(pts3d_full.sum(axis=-1)) | |
| pts3d = pts3d_full[pts3d_valid] | |
| pts2d_int = xy_grid(W, H)[pts3d_valid] | |
| pts2d = pts2d_int.astype(np.float64) | |
| # nan => invalid | |
| pts3d_full[~pts3d_valid] = np.nan | |
| pts3d_full = torch.from_numpy(pts3d_full) | |
| view['pts3d'] = pts3d_full | |
| view["valid"] = pts3d_full.sum(dim=-1).isfinite() | |
| HR, WR = rgb_tensor.shape[1:] | |
| _, _, pts3d_rescaled, valid_rescaled = rescale_points3d(pts2d, pts3d, to_resize, HR, WR) | |
| pts3d_rescaled = torch.from_numpy(pts3d_rescaled) | |
| valid_rescaled = torch.from_numpy(valid_rescaled) | |
| view['pts3d_rescaled'] = pts3d_rescaled | |
| view["valid_rescaled"] = valid_rescaled | |
| views.append(view) | |
| return views | |