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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). | |
| # | |
| # -------------------------------------------------------- | |
| # Building the graph based on retrieval results. | |
| # -------------------------------------------------------- | |
| import numpy as np | |
| def farthest_point_sampling(dist, N=None, dist_thresh=None): | |
| """Farthest point sampling. | |
| Args: | |
| dist: NxN distance matrix. | |
| N: Number of points to sample. | |
| dist_thresh: Distance threshold. Point sampling terminates once the | |
| maximum distance is below this threshold. | |
| Returns: | |
| indices: Indices of the sampled points. | |
| """ | |
| assert N is not None or dist_thresh is not None, "Either N or min_dist must be provided." | |
| if N is None: | |
| N = dist.shape[0] | |
| indices = [] | |
| distances = [0] | |
| indices.append(np.random.choice(dist.shape[0])) | |
| for i in range(1, N): | |
| d = dist[indices].min(axis=0) | |
| bst = d.argmax() | |
| bst_dist = d[bst] | |
| if dist_thresh is not None and bst_dist < dist_thresh: | |
| break | |
| indices.append(bst) | |
| distances.append(bst_dist) | |
| return np.array(indices), np.array(distances) | |
| def make_pairs_fps(sim_mat, Na=20, tokK=1, dist_thresh=None): | |
| dist_mat = 1 - sim_mat | |
| pairs = set() | |
| keyimgs_idx = np.array([]) | |
| if Na != 0: | |
| keyimgs_idx, _ = farthest_point_sampling(dist_mat, N=Na, dist_thresh=dist_thresh) | |
| # 1. Complete graph between key images | |
| for i in range(len(keyimgs_idx)): | |
| for j in range(i + 1, len(keyimgs_idx)): | |
| idx_i, idx_j = keyimgs_idx[i], keyimgs_idx[j] | |
| pairs.add((idx_i, idx_j)) | |
| # 2. Connect non-key images to the earest key image | |
| keyimg_dist_mat = dist_mat[:, keyimgs_idx] | |
| for i in range(keyimg_dist_mat.shape[0]): | |
| if i in keyimgs_idx: | |
| continue | |
| j = keyimg_dist_mat[i].argmax() | |
| i1, i2 = min(i, keyimgs_idx[j]), max(i, keyimgs_idx[j]) | |
| if i1 != i2 and (i1, i2) not in pairs: | |
| pairs.add((i1, i2)) | |
| # 3. Add some local connections (k-NN) for each view | |
| if tokK > 0: | |
| for i in range(dist_mat.shape[0]): | |
| idx = dist_mat[i].argsort()[:tokK] | |
| for j in idx: | |
| i1, i2 = min(i, j), max(i, j) | |
| if i1 != i2 and (i1, i2) not in pairs: | |
| pairs.add((i1, i2)) | |
| pairs = list(pairs) | |
| return pairs, keyimgs_idx | |