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| from typing import Dict, List | |
| import numpy as np | |
| import torch | |
| from torch import Tensor | |
| import mGPT.utils.geometry_conver as geometry_conver | |
| def lengths_to_mask(lengths: List[int], | |
| device: torch.device, | |
| max_len: int = None) -> Tensor: | |
| lengths = torch.tensor(lengths, device=device) | |
| max_len = max_len if max_len else max(lengths) | |
| mask = torch.arange(max_len, device=device).expand( | |
| len(lengths), max_len) < lengths.unsqueeze(1) | |
| return mask | |
| def detach_to_numpy(tensor): | |
| return tensor.detach().cpu().numpy() | |
| def remove_padding(tensors, lengths): | |
| return [ | |
| tensor[:tensor_length] | |
| for tensor, tensor_length in zip(tensors, lengths) | |
| ] | |
| def nfeats_of(rottype): | |
| if rottype in ["rotvec", "axisangle"]: | |
| return 3 | |
| elif rottype in ["rotquat", "quaternion"]: | |
| return 4 | |
| elif rottype in ["rot6d", "6drot", "rotation6d"]: | |
| return 6 | |
| elif rottype in ["rotmat"]: | |
| return 9 | |
| else: | |
| return TypeError("This rotation type doesn't have features.") | |
| def axis_angle_to(newtype, rotations): | |
| if newtype in ["matrix"]: | |
| rotations = geometry_conver.axis_angle_to_matrix(rotations) | |
| return rotations | |
| elif newtype in ["rotmat"]: | |
| rotations = geometry_conver.axis_angle_to_matrix(rotations) | |
| rotations = matrix_to("rotmat", rotations) | |
| return rotations | |
| elif newtype in ["rot6d", "6drot", "rotation6d"]: | |
| rotations = geometry_conver.axis_angle_to_matrix(rotations) | |
| rotations = matrix_to("rot6d", rotations) | |
| return rotations | |
| elif newtype in ["rotquat", "quaternion"]: | |
| rotations = geometry_conver.axis_angle_to_quaternion(rotations) | |
| return rotations | |
| elif newtype in ["rotvec", "axisangle"]: | |
| return rotations | |
| else: | |
| raise NotImplementedError | |
| def matrix_to(newtype, rotations): | |
| if newtype in ["matrix"]: | |
| return rotations | |
| if newtype in ["rotmat"]: | |
| rotations = rotations.reshape((*rotations.shape[:-2], 9)) | |
| return rotations | |
| elif newtype in ["rot6d", "6drot", "rotation6d"]: | |
| rotations = geometry_conver.matrix_to_rotation_6d(rotations) | |
| return rotations | |
| elif newtype in ["rotquat", "quaternion"]: | |
| rotations = geometry_conver.matrix_to_quaternion(rotations) | |
| return rotations | |
| elif newtype in ["rotvec", "axisangle"]: | |
| rotations = geometry_conver.matrix_to_axis_angle(rotations) | |
| return rotations | |
| else: | |
| raise NotImplementedError | |
| def to_matrix(oldtype, rotations): | |
| if oldtype in ["matrix"]: | |
| return rotations | |
| if oldtype in ["rotmat"]: | |
| rotations = rotations.reshape((*rotations.shape[:-2], 3, 3)) | |
| return rotations | |
| elif oldtype in ["rot6d", "6drot", "rotation6d"]: | |
| rotations = geometry_conver.rotation_6d_to_matrix(rotations) | |
| return rotations | |
| elif oldtype in ["rotquat", "quaternion"]: | |
| rotations = geometry_conver.quaternion_to_matrix(rotations) | |
| return rotations | |
| elif oldtype in ["rotvec", "axisangle"]: | |
| rotations = geometry_conver.axis_angle_to_matrix(rotations) | |
| return rotations | |
| else: | |
| raise NotImplementedError | |
| # TODO: use a real subsampler.. | |
| def subsample(num_frames, last_framerate, new_framerate): | |
| step = int(last_framerate / new_framerate) | |
| assert step >= 1 | |
| frames = np.arange(0, num_frames, step) | |
| return frames | |
| # TODO: use a real upsampler.. | |
| def upsample(motion, last_framerate, new_framerate): | |
| step = int(new_framerate / last_framerate) | |
| assert step >= 1 | |
| # Alpha blending => interpolation | |
| alpha = np.linspace(0, 1, step + 1) | |
| last = np.einsum("l,...->l...", 1 - alpha, motion[:-1]) | |
| new = np.einsum("l,...->l...", alpha, motion[1:]) | |
| chuncks = (last + new)[:-1] | |
| output = np.concatenate(chuncks.swapaxes(1, 0)) | |
| # Don't forget the last one | |
| output = np.concatenate((output, motion[[-1]])) | |
| return output | |
| if __name__ == "__main__": | |
| motion = np.arange(105) | |
| submotion = motion[subsample(len(motion), 100.0, 12.5)] | |
| newmotion = upsample(submotion, 12.5, 100) | |
| print(newmotion) | |