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| # -*- coding: UTF-8 -*- | |
| '''================================================= | |
| @Project -> File pram -> cambridge_landmarks | |
| @IDE PyCharm | |
| @Author fx221@cam.ac.uk | |
| @Date 29/01/2024 14:41 | |
| ==================================================''' | |
| import os.path as osp | |
| import numpy as np | |
| from colmap_utils.read_write_model import read_model | |
| import torchvision.transforms as tvt | |
| from dataset.basicdataset import BasicDataset | |
| class CambridgeLandmarks(BasicDataset): | |
| def __init__(self, landmark_path, scene, dataset_path, n_class, seg_mode, seg_method, dataset='CambridgeLandmarks', | |
| nfeatures=1024, | |
| query_p3d_fn=None, | |
| train=True, | |
| with_aug=False, | |
| min_inliers=0, | |
| max_inliers=4096, | |
| random_inliers=False, | |
| jitter_params=None, | |
| scale_params=None, | |
| image_dim=3, | |
| query_info_path=None, | |
| sample_ratio=1, | |
| ): | |
| self.landmark_path = osp.join(landmark_path, scene) | |
| self.dataset_path = osp.join(dataset_path, scene) | |
| self.n_class = n_class | |
| self.dataset = dataset + '/' + scene | |
| self.nfeatures = nfeatures | |
| self.with_aug = with_aug | |
| self.jitter_params = jitter_params | |
| self.scale_params = scale_params | |
| self.image_dim = image_dim | |
| self.train = train | |
| self.min_inliers = min_inliers | |
| self.max_inliers = max_inliers if max_inliers < nfeatures else nfeatures | |
| self.random_inliers = random_inliers | |
| self.image_prefix = '' | |
| train_transforms = [] | |
| if self.with_aug: | |
| train_transforms.append(tvt.ColorJitter( | |
| brightness=jitter_params['brightness'], | |
| contrast=jitter_params['contrast'], | |
| saturation=jitter_params['saturation'], | |
| hue=jitter_params['hue'])) | |
| if jitter_params['blur'] > 0: | |
| train_transforms.append(tvt.GaussianBlur(kernel_size=int(jitter_params['blur']))) | |
| self.train_transforms = tvt.Compose(train_transforms) | |
| if train: | |
| self.cameras, self.images, point3Ds = read_model(path=osp.join(self.landmark_path, '3D-models'), ext='.bin') | |
| self.name_to_id = {image.name: i for i, image in self.images.items() if len(self.images[i].point3D_ids) > 0} | |
| # only for testing of query images | |
| if not self.train: | |
| data = np.load(query_p3d_fn, allow_pickle=True)[()] | |
| self.img_p3d = data | |
| else: | |
| self.img_p3d = {} | |
| self.img_fns = [] | |
| with open(osp.join(self.dataset_path, 'dataset_train.txt' if train else 'dataset_test.txt'), 'r') as f: | |
| lines = f.readlines()[3:] # ignore the first 3 lines | |
| for l in lines: | |
| l = l.strip().split()[0] | |
| if train and l not in self.name_to_id.keys(): | |
| continue | |
| if not train and l not in self.img_p3d.keys(): | |
| continue | |
| self.img_fns.append(l) | |
| print('Load {} images from {} for {}...'.format(len(self.img_fns), | |
| self.dataset, 'training' if train else 'eval')) | |
| data = np.load(osp.join(self.landmark_path, | |
| 'point3D_cluster_n{:d}_{:s}_{:s}.npy'.format(n_class - 1, seg_mode, seg_method)), | |
| allow_pickle=True)[()] | |
| p3d_id = data['id'] | |
| seg_id = data['label'] | |
| self.p3d_seg = {p3d_id[i]: seg_id[i] for i in range(p3d_id.shape[0])} | |
| xyzs = data['xyz'] | |
| self.p3d_xyzs = {p3d_id[i]: xyzs[i] for i in range(p3d_id.shape[0])} | |
| # with open(osp.join(self.landmark_path, 'sc_mean_scale.txt'), 'r') as f: | |
| # lines = f.readlines() | |
| # for l in lines: | |
| # l = l.strip().split() | |
| # self.mean_xyz = np.array([float(v) for v in l[:3]]) | |
| # self.scale_xyz = np.array([float(v) for v in l[3:]]) | |
| if not train: | |
| self.query_info = self.read_query_info(path=query_info_path) | |
| self.nfeatures = nfeatures | |
| self.feature_dir = osp.join(self.landmark_path, 'feats') | |
| self.feats = {} | |