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Specifications and Common Mistakes

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mindsponge.common.pre_compose

mindsponge.common.pre_compose(quaternion, rotation, translation, update)[source]

Return a new QuatAffine which applies the transformation update first.

The process of obtaining the updated translation vector and rotation matrix is as follows:

\begin{array}{r} \begin{aligned} u p d a t e = (x x, x y, x z, y x, y y, y z) \\ v e c t o r_q u a t e r n i o n_u p d a t e = (x x, x y, x z) \\ x = (y x) \\ y = (y y) \\ z = (y z) \\ t r a n s_u p d a t e = (x, y, z) \\ n e w_q u a t e r n i o n = q u a t e r n i o n + v e c t o r_q u a t e r n i o n_u p d a t e * q u a t e r n i o n \\ r o t a t e d_t r a n s_u p d a t e = r o t a t i o n * t r a n s_u p d a t e \\ n e w_t r a n s l a t i o n = t r a n s l a t i o n + r o t a t e d_t r a n s_u p d a t e \end{aligned} \end{array}

vector_quaternion_update and quaternion are multiplied by the quat_multiply_by_vec function, Affine transformation is performed using the generated new_quaternion and new_translation. The process of affine transformation is referred to the quat_affine api.

Parameters

quaternion (Tensor) – The initial quaternion to be updated, shape $[(. . ., 4)]$ .
rotation (Tuple) – Rotation matrix, $(x x, x y, x z, y x, y y, y z, z x, z y, z z)$ , and xx and xy are Tensor and have the same shape.
translation (Tuple) – Translation vector $(x, y, z)$ , where x, y and z are Tensor and have the same shape.
update (Tensor) – The update-assisted matrix has shape $[(. . ., 6)]$ . 3-vector of x, y, and z such that the quaternion update is $(1, x, y, z)$ and zero for the 3-vector is the identity quaternion. 3-vector for translation concatenated.

Returns

Tensor, new quaternion.The updated Tensor tuple has shape $[(. . ., 4)]$ .
Tuple, the updated rotation matrix $(x x, x y, x z, y x, y y, y z, z x, z y, z z)$ , and xx and xy are Tensor and have the same shape.
Tuple, the updated 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 pre_compose
>>> from mindspore.common import Tensor
>>> from mindspore import dtype as mstype
>>> np.random.seed(1)
>>> quaternion = Tensor(np.random.rand(4),dtype=mstype.float32)
>>> update = Tensor(np.random.rand(6),dtype=mstype.float32)
>>> rotation = Tensor(np.random.rand(9),dtype=mstype.float32)
>>> translation = Tensor(np.random.rand(3),dtype=mstype.float32)
>>> quaternion, rotation, translation = pre_compose(quaternion,rotation,translation,update)
>>> print(quaternion)
[ 0.27905196  0.82475466 -0.05600705  0.48864394]
>>> print(rotation)
(Tensor(shape=[], dtype=Float32, value= 0.516181), Tensor(shape=[], dtype=Float32, value= -0.365098),
Tensor(shape=[], dtype=Float32, value= 0.774765), Tensor(shape=[], dtype=Float32, value= 0.18033),
Tensor(shape=[], dtype=Float32, value= -0.837986), Tensor(shape=[], dtype=Float32, value= -0.515034),
Tensor(shape=[], dtype=Float32, value= 0.837281), Tensor(shape=[], dtype=Float32, value= 0.405564),
Tensor(shape=[], dtype=Float32, value= -0.366714))
>>> print(translation)
(Tensor(shape=[], dtype=Float32, value= 0.724994), Tensor(shape=[], dtype=Float32, value= 1.47631),
Tensor(shape=[], dtype=Float32, value= 1.40978))