| 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 |
|
|
|
|
| |
| 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 |
|
|
|
|
| |
| def upsample(motion, last_framerate, new_framerate): |
| step = int(new_framerate / last_framerate) |
| assert step >= 1 |
|
|
| |
| 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)) |
| |
| 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) |
|
|
