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| # -*- coding: UTF-8 -*- | |
| '''================================================= | |
| @Project -> File pram -> get_dataset | |
| @IDE PyCharm | |
| @Author [email protected] | |
| @Date 29/01/2024 14:40 | |
| ==================================================''' | |
| import os.path as osp | |
| import yaml | |
| from dataset.aachen import Aachen | |
| from dataset.twelve_scenes import TwelveScenes | |
| from dataset.seven_scenes import SevenScenes | |
| from dataset.cambridge_landmarks import CambridgeLandmarks | |
| from dataset.customdataset import CustomDataset | |
| from dataset.recdataset import RecDataset | |
| def get_dataset(dataset): | |
| if dataset in ['7Scenes', 'S']: | |
| return SevenScenes | |
| elif dataset in ['12Scenes', 'T']: | |
| return TwelveScenes | |
| elif dataset in ['Aachen', 'A']: | |
| return Aachen | |
| elif dataset in ['CambridgeLandmarks', 'C']: | |
| return CambridgeLandmarks | |
| else: | |
| return CustomDataset | |
| def compose_datasets(datasets, config, train=True, sample_ratio=None): | |
| sub_sets = [] | |
| for name in datasets: | |
| if name == 'S': | |
| ds_name = '7Scenes' | |
| elif name == 'T': | |
| ds_name = '12Scenes' | |
| elif name == 'A': | |
| ds_name = 'Aachen' | |
| elif name == 'R': | |
| ds_name = 'RobotCar-Seasons' | |
| elif name == 'C': | |
| ds_name = 'CambridgeLandmarks' | |
| else: | |
| ds_name = name | |
| # raise '{} dataset does not exist'.format(name) | |
| landmark_path = osp.join(config['landmark_path'], ds_name) | |
| dataset_path = osp.join(config['dataset_path'], ds_name) | |
| scene_config_path = 'configs/datasets/{:s}.yaml'.format(ds_name) | |
| with open(scene_config_path, 'r') as f: | |
| scene_config = yaml.load(f, Loader=yaml.Loader) | |
| DSet = get_dataset(dataset=ds_name) | |
| for scene in scene_config['scenes']: | |
| if sample_ratio is None: | |
| scene_sample_ratio = scene_config[scene]['training_sample_ratio'] if train else scene_config[scene][ | |
| 'eval_sample_ratio'] | |
| else: | |
| scene_sample_ratio = sample_ratio | |
| scene_set = DSet(landmark_path=landmark_path, | |
| dataset_path=dataset_path, | |
| scene=scene, | |
| seg_mode=scene_config[scene]['cluster_mode'], | |
| seg_method=scene_config[scene]['cluster_method'], | |
| n_class=scene_config[scene]['n_cluster'] + 1, # including invalid - 0 | |
| dataset=ds_name, | |
| train=train, | |
| nfeatures=config['max_keypoints'] if train else config['eval_max_keypoints'], | |
| min_inliers=config['min_inliers'], | |
| max_inliers=config['max_inliers'], | |
| random_inliers=config['random_inliers'], | |
| with_aug=config['with_aug'], | |
| jitter_params=config['jitter_params'], | |
| scale_params=config['scale_params'], | |
| image_dim=config['image_dim'], | |
| query_p3d_fn=osp.join(config['landmark_path'], ds_name, scene, | |
| 'point3D_query_n{:d}_{:s}_{:s}.npy'.format( | |
| scene_config[scene]['n_cluster'], | |
| scene_config[scene]['cluster_mode'], | |
| scene_config[scene]['cluster_method'])), | |
| query_info_path=osp.join(config['dataset_path'], ds_name, scene, | |
| 'queries_with_intrinsics.txt'), | |
| sample_ratio=scene_sample_ratio, | |
| ) | |
| sub_sets.append(scene_set) | |
| return RecDataset(sub_sets=sub_sets) | |