# 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