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mindsponge.common.invert_point

mindsponge.common.invert_point(transformed_point, rotation, translation, extra_dims=0, stack=False, use_numpy=False)

The inverse transformation of a rigid body group transformation with respect to a point coordinate, that is, the inverse transformation of apply to point Make rotational translation changes on coordinates with the transpose of the rotation matrix $(xx,xy,xz,yx,yy,yz,zx,zy,zz)$ and the translation vector $(x,y,z)$ translation.

First, the initial coordinates are translated, and then the transpose of the rotation matrix is multiplied by rot_point to get the final coordinates.

$$\begin{array}{r}\begin{array}{rl}& ro{t}_{p}oint=transforme{d}_{p}oint-translation\\ & result=rotatio{n}^t\ast ro{t}_{p}oint\end{array}\end{array}$$

The specific procedures of vector subtraction, transpose and multiplication can be referred to the api of vecs_sub, invert_rots, rots_mul_vecs etc.

Parameters

• transformed_point (Tuple) – The initial coordinates of the input have shape $(x,y,z)$, where x, y and z are Tensor and have the same shape.

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

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

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

• stack (bool) – Control whether to transform to tuple. Default: False.

• use_numpy (bool) – Control whether to use numpy. Default: False.

Returns

Tuple, the transformed coordinate of invert point.Length is 3.

Supported Platforms:

Ascend GPU

Examples

>>> from mindsponge.common.geometry import invert_point
>>> from mindspore.common import Tensor
>>> from mindspore import dtype as mstype
>>> transformed_point = (1, 2, 3)
>>> rotation = (1, 2, 3, 4, 5, 6, 7, 8, 9)
>>> translation = (1, 0.5, -1)
>>> output= invert_point(transformed_point, rotation, translation)
>>> print(output)
(Tensor(shape=[], dtype=Float32, value = 34), Tensor(shape=[], dtype=Float32, value = 39.5),
 Tensor(shape=[], dtype=Float32, value = 45))