Affine¶
-
class
menpo.transform.
Affine
(h_matrix, copy=True, skip_checks=False)[source]¶ Bases:
Homogeneous
Base class for all
n
-dimensional affine transformations. Provides methods to break the transform down into its constituent scale/rotation/translation, to view the homogeneous matrix equivalent, and to chain this transform with other affine transformations.Parameters: - h_matrix (
(n_dims + 1, n_dims + 1)
ndarray) – The homogeneous matrix of the affine transformation. - copy (bool, optional) – If
False
avoid copyingh_matrix
for performance. - skip_checks (bool, optional) – If
True
avoid sanity checks onh_matrix
for performance.
-
apply
(x, batch_size=None, **kwargs)¶ Applies this transform to
x
.If
x
isTransformable
,x
will be handed this transform object to transform itself non-destructively (a transformed copy of the object will be returned).If not,
x
is assumed to be an ndarray. The transformation will be non-destructive, returning the transformed version.Any
kwargs
will be passed to the specific transform_apply()
method.Parameters: - x (
Transformable
or(n_points, n_dims)
ndarray) – The array or object to be transformed. - batch_size (int, optional) – If not
None
, this determines how many items from the numpy array will be passed through the transform at a time. This is useful for operations that require large intermediate matrices to be computed. - kwargs (dict) – Passed through to
_apply()
.
Returns: transformed (
type(x)
) – The transformed object or array- x (
-
apply_inplace
(x, **kwargs)¶ Applies this transform to a
Transformable
x
destructively.Any
kwargs
will be passed to the specific transform_apply()
method.Parameters: - x (
Transformable
) – TheTransformable
object to be transformed. - kwargs (dict) – Passed through to
_apply()
.
Returns: transformed (
type(x)
) – The transformed object- x (
-
as_vector
(**kwargs)¶ Returns a flattened representation of the object as a single vector.
Returns: vector ((N,) ndarray) – The core representation of the object, flattened into a single vector. Note that this is always a view back on to the original object, but is not writable.
-
compose_after
(transform)¶ A
Transform
that represents this transform composed after the given transform:c = a.compose_after(b) c.apply(p) == a.apply(b.apply(p))
a
andb
are left unchanged.This corresponds to the usual mathematical formalism for the compose operator,
o
.An attempt is made to perform native composition, but will fall back to a
TransformChain
as a last resort. Seecomposes_with
for a description of how the mode of composition is decided.Parameters: transform ( Transform
) – Transform to be applied beforeself
Returns: transform ( Transform
orTransformChain
) – If the composition was native, a single newTransform
will be returned. If not, aTransformChain
is returned instead.
-
compose_after_inplace
(transform)¶ Update
self
so that it represents this transform composed after the given transform:a_orig = a.copy() a.compose_after_inplace(b) a.apply(p) == a_orig.apply(b.apply(p))
a
is permanently altered to be the result of the composition.b
is left unchanged.Parameters: transform ( composes_inplace_with
) – Transform to be applied beforeself
Raises: ValueError
– Iftransform
isn’t an instance ofcomposes_inplace_with
-
compose_before
(transform)¶ A
Transform
that represents this transform composed before the given transform:c = a.compose_before(b) c.apply(p) == b.apply(a.apply(p))
a
andb
are left unchanged.An attempt is made to perform native composition, but will fall back to a
TransformChain
as a last resort. Seecomposes_with
for a description of how the mode of composition is decided.Parameters: transform ( Transform
) – Transform to be applied afterself
Returns: transform ( Transform
orTransformChain
) – If the composition was native, a single newTransform
will be returned. If not, aTransformChain
is returned instead.
-
compose_before_inplace
(transform)¶ Update
self
so that it represents this transform composed before the given transform:a_orig = a.copy() a.compose_before_inplace(b) a.apply(p) == b.apply(a_orig.apply(p))
a
is permanently altered to be the result of the composition.b
is left unchanged.Parameters: transform ( composes_inplace_with
) – Transform to be applied afterself
Raises: ValueError
– Iftransform
isn’t an instance ofcomposes_inplace_with
-
copy
()¶ Generate an efficient copy of this object.
Note that Numpy arrays and other
Copyable
objects onself
will be deeply copied. Dictionaries and sets will be shallow copied, and everything else will be assigned (no copy will be made).Classes that store state other than numpy arrays and immutable types should overwrite this method to ensure all state is copied.
Returns: type(self)
– A copy of this object
-
decompose
()[source]¶ Decompose this transform into discrete Affine Transforms.
Useful for understanding the effect of a complex composite transform.
Returns: transforms (list of DiscreteAffine
) – Equivalent to this affine transform, such that:reduce(lambda x,y: x.chain(y), self.decompose()) == self
-
from_vector
(vector)¶ Build a new instance of the object from its vectorized state.
self
is used to fill out the missing state required to rebuild a full object from it’s standardized flattened state. This is the default implementation, which is adeepcopy
of the object followed by a call tofrom_vector_inplace()
. This method can be overridden for a performance benefit if desired.Parameters: vector ( (n_parameters,)
ndarray) – Flattened representation of the object.Returns: transform ( Homogeneous
) – An new instance of this class.
-
from_vector_inplace
(p)[source]¶ Updates this Affine in-place from the new parameters. See from_vector for details of the parameter format
-
has_nan_values
()¶ Tests if the vectorized form of the object contains
nan
values or not. This is particularly useful for objects with unknown values that have been mapped tonan
values.Returns: has_nan_values (bool) – If the vectorized object contains nan
values.
-
classmethod
init_identity
(n_dims)[source]¶ Creates an identity matrix Affine transform.
Parameters: n_dims (int) – The number of dimensions. Returns: identity ( Affine
) – The identity matrix transform.
-
pseudoinverse
()¶ The pseudoinverse of the transform - that is, the transform that results from swapping source and target, or more formally, negating the transforms parameters. If the transform has a true inverse this is returned instead.
Type: Homogeneous
-
pseudoinverse_vector
(vector)¶ The vectorized pseudoinverse of a provided vector instance. Syntactic sugar for:
self.from_vector(vector).pseudoinverse().as_vector()
Can be much faster than the explict call as object creation can be entirely avoided in some cases.
Parameters: vector ( (n_parameters,)
ndarray) – A vectorized version ofself
Returns: pseudoinverse_vector ( (n_parameters,)
ndarray) – The pseudoinverse of the vector provided
-
set_h_matrix
(value, copy=True, skip_checks=False)¶ Updates
h_matrix
, optionally performing sanity checks.Note that it won’t always be possible to manually specify the
h_matrix
through this method, specifically if changing theh_matrix
could change the nature of the transform. Seeh_matrix_is_mutable
for how you can discover if theh_matrix
is allowed to be set for a given class.Parameters: - value (ndarray) – The new homogeneous matrix to set.
- copy (bool, optional) – If
False
, do not copy the h_matrix. Useful for performance. - skip_checks (bool, optional) – If
True
, skip checking. Useful for performance.
Raises: NotImplementedError
– Ifh_matrix_is_mutable
returnsFalse
.
-
composes_with
¶ Any Homogeneous can compose with any other Homogeneous.
-
h_matrix
¶ The homogeneous matrix defining this transform.
Type: (n_dims + 1, n_dims + 1)
ndarray
-
h_matrix_is_mutable
¶ True
iffset_h_matrix()
is permitted on this type of transform.If this returns
False
calls toset_h_matrix()
will raise aNotImplementedError
.Type: bool
-
has_true_inverse
¶ The pseudoinverse is an exact inverse.
Type: True
-
linear_component
¶ The linear component of this affine transform.
Type: (n_dims, n_dims)
ndarray
-
n_dims
¶ The dimensionality of the data the transform operates on.
Type: int
-
n_dims_output
¶ The output of the data from the transform.
Type: int
-
n_parameters
¶ n_dims * (n_dims + 1)
parameters - every element of the matrix but the homogeneous part.Type: int Examples
2D Affine: 6 parameters:
[p1, p3, p5] [p2, p4, p6]
3D Affine: 12 parameters:
[p1, p4, p7, p10] [p2, p5, p8, p11] [p3, p6, p9, p12]
-
translation_component
¶ The translation component of this affine transform.
Type: (n_dims,)
ndarray
- h_matrix (