| import os | |
| from tqdm import tqdm | |
| import torch | |
| from torch.utils.data import DataLoader | |
| from logger import Logger, Visualizer | |
| import imageio | |
| from scipy.spatial import ConvexHull | |
| import numpy as np | |
| from sync_batchnorm import DataParallelWithCallback | |
| def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, | |
| use_relative_movement=False, use_relative_jacobian=False): | |
| if adapt_movement_scale: | |
| source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume | |
| driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume | |
| adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area) | |
| else: | |
| adapt_movement_scale = 1 | |
| kp_new = {k: v for k, v in kp_driving.items()} | |
| if use_relative_movement: | |
| kp_value_diff = (kp_driving['value'] - kp_driving_initial['value']) | |
| kp_value_diff *= adapt_movement_scale | |
| kp_new['value'] = kp_value_diff + kp_source['value'] | |
| if use_relative_jacobian: | |
| jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) | |
| kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) | |
| return kp_new | |