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mindsponge.common.apply_to_point

mindsponge.common.apply_to_point(rotation, translation, point, extra_dims=0)

Rotate and translate the input coordinates.

$$\begin{array}{r}\begin{array}{rl}& rot\mathrm{\_}point=rotation\cdot point\\ & result=rot\mathrm{\_}point+translation\end{array}\end{array}$$

For specific multiplication and addition procedures, refer to the rots_mul_vecs and vecs_add apis.

Parameters

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

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

• point (Tensor) – Initial coordinate values $[(x,y,z)]$, where $x,y,z$ are Tensor and have the same shape.

• extra_dims (int) – Control whether to expand dims. default:0.

Returns

Tuple, the result of the coordinate transformation. Length is 3.

Supported Platforms:

Ascend GPU

Examples

>>> import numpy as np
>>> from mindsponge.common.geometry import apply_to_point
>>> from mindspore.common import Tensor
>>> from mindspore import dtype as mstype
>>> np.random.seed(1)
>>> rotation = []
>>> for i in range(9):
...     rotation.append(Tensor(np.random.rand(4),dtype=mstype.float32))
>>> translation = []
>>> for i in range(3):
...     translation.append(Tensor(np.random.rand(4),dtype=mstype.float32))
>>> point = []
>>> for i in range(3):
...     point.append(Tensor(np.random.rand(4),dtype=mstype.float32))
>>> out = apply_to_point(rotation, translation, point)
>>> print(out)
(Tensor(shape=[4], dtype=Float32, value= [ 1.02389336e+00,  1.12493467e+00,  2.54357845e-01,  1.25249946e+00]),
Tensor(shape=[4], dtype=Float32, value= [ 9.84841168e-01,  5.20081401e-01,  6.43978953e-01,  6.15328550e-01]),
Tensor(shape=[4], dtype=Float32, value= [ 8.62860143e-01,  9.11733627e-01,  1.09284782e+00,  1.44202101e+00]))