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Specifications and Common Mistakes

- Specifications and Common Mistakes:

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mindsponge.common.quaternion_from_tensor

mindsponge.common.quaternion_from_tensor(tensor, normalize=False)

Take the input 'tensor' $[(xx,xy,xz,yx,yy,yz,zz)]$ to get the new 'quaternion', 'rotation', 'translation'.

$$\begin{array}{r}\begin{array}{rl}& tensor=[(xx,xy,xz,yx,yy,yz,zz)]\\ & quaternion=(xx,xy,xz,yx)\\ & translation=(yy,yz,zz)\end{array}\end{array}$$

Affine transformation is performed using the generated quaternion and translation. The process of affine transformation is referred to the quat_affine api.

Parameters

• tensor (Tensor) – An initial Tensor $[(xx,xy,xz,yx,yy,yz,zz)]$ . $[(xx,xy,xz,yx)]$ is the same with quaternion. $(yy,yz,zz)$ is the same with translation.

• normalize (bool) – Control whether to find the norm during quat_affine. Default: False.

Returns

• Tensor, new quaternion.Tensor of shape $(...,4)$ .

• Tuple, new rotation, $(xx,xy,xz,yx,yy,yz,zx,zy,zz)$, and xx and xy are Tensor and have the same shape.

• Tuple, translation vector $[(x,y,z)]$, where x, y and z are Tensor and have the same shape.

Supported Platforms:

Ascend GPU

Examples

>>> import numpy as np
>>> from mindsponge.common.geometry import quaternion_from_tensor
>>> from mindspore.common import Tensor
>>> tensor = Tensor(np.random.rand(7),dtype=mstype.float32)
>>> quaternion, rotation, translation = quaternion_from_tensor(tensor)
>>> print(quaternion)
[4.17021990e-01,  7.20324516e-01,  1.14374816e-04,  3.02332580e-01]
>>> print(rotation)
(Tensor(shape=[], dtype=Float32, value= 0.60137), Tensor(shape=[], dtype=Float32, value= -0.251994),
Tensor(shape=[], dtype=Float32, value= 0.435651), Tensor(shape=[], dtype=Float32, value= 0.252323),
Tensor(shape=[], dtype=Float32, value= -0.436365), Tensor(shape=[], dtype=Float32, value= -0.600713),
Tensor(shape=[], dtype=Float32, value= 0.43546), Tensor(shape=[], dtype=Float32, value= 0.600851),
Tensor(shape=[], dtype=Float32, value= -0.253555))
>>> print(translation)
(Tensor(shape=[], dtype=Float32, value= 0.146756),Tensor(shape=[], dtype=Float32, value= 0.0923386),
Tensor(shape=[], dtype=Float32, value= 0.18626))