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