select nearest_neighbor views based on unique_frame_index
(args, train_set, threshold, unique_frame_index)
| 390 | return unique_frame_index |
| 391 | |
| 392 | def select_nearest_neighbor_views(args, train_set, threshold, unique_frame_index): |
| 393 | ''' select nearest_neighbor views based on unique_frame_index ''' |
| 394 | # Frustum near-far threshold |
| 395 | K_APPROX, FRUSTUM_APPROX, W, H = camera_frustum_initializer(args) |
| 396 | |
| 397 | # compute how many unique views in the train set |
| 398 | keyframe_idx = 0 |
| 399 | frame_index = [] |
| 400 | |
| 401 | for i in range(len(train_set)): |
| 402 | if i % 200 == 0: |
| 403 | print ('Image {:d} / {:d}'.format(i, len(train_set))) |
| 404 | |
| 405 | if keyframe_idx == i: |
| 406 | continue |
| 407 | |
| 408 | # compute frustum overlap |
| 409 | for j in unique_frame_index: |
| 410 | overlap_2p = compute_frustums_overlap(train_set.poses[j].reshape(3,4), train_set.poses[i].reshape(3,4), FRUSTUM_APPROX, K_APPROX, W, H) |
| 411 | if overlap_2p > threshold: |
| 412 | frame_index.append(i) |
| 413 | break |
| 414 | frame_index = np.array(frame_index) |
| 415 | frame_index = frame_index[::5] |
| 416 | print('train set selected', len(frame_index)) |
| 417 | train_set.c_imgs = list(train_set.c_imgs[i] for i in frame_index) |
| 418 | train_set.d_imgs = list(train_set.d_imgs[i] for i in frame_index) |
| 419 | train_set.poses = train_set.poses[frame_index] |
| 420 | return |
| 421 | |
| 422 | def load_7Scenes_dataloader(args): |
| 423 | ''' Data loader for Pose Regression Network ''' |
nothing calls this directly
no test coverage detected