(min_threshold, M, data)
| 72 | M = int(np.max(edges[:, 1])) |
| 73 | |
| 74 | def delete_thereshold(min_threshold, M, data): |
| 75 | valid_frames = np.bincount(data, minlength=M) |
| 76 | valid_indices = valid_frames > min_threshold |
| 77 | wrong_indices = valid_frames <= min_threshold |
| 78 | num_valid_frames = np.sum(valid_indices) |
| 79 | frame_index = np.zeros(M, dtype=int) |
| 80 | frame_index[valid_indices] = np.arange(num_valid_frames) |
| 81 | frame_index[wrong_indices] = -1 |
| 82 | max_frame = np.argmax(valid_frames) |
| 83 | return max_frame, num_valid_frames, frame_index |
| 84 | |
| 85 | # delete and reindex the frames that contains zero landmarks |
| 86 | max_frame, N, indices_frame = delete_thereshold(0, N, edges[:,0]-1) |
no outgoing calls
no test coverage detected