TransformChain¶

class menpo.transform.TransformChain(transforms)[source]¶

Bases: ComposableTransform

A chain of transforms that can be efficiently applied one after the other.

This class is the natural product of composition. Note that objects may know how to compose themselves more efficiently - such objects implement the ComposableTransform or VComposable interfaces.

Parameters:	transforms (list of `Transform`) – The list of transforms to be applied. Note that the first transform will be applied first - the result of which is fed into the second transform and so on until the chain is exhausted.

apply(x, batch_size=None, **kwargs)¶

Applies this transform to x.

If x is Transformable, 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

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`) – The `Transformable` object to be transformed. kwargs (dict) – Passed through to `_apply()`.
Returns:	transformed (`type(x)`) – The transformed object

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 and b 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. See composes_with for a description of how the mode of composition is decided.

Parameters:	transform (`Transform`) – Transform to be applied before `self`
Returns:	transform (`Transform` or `TransformChain`) – If the composition was native, a single new `Transform` will be returned. If not, a `TransformChain` 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 before `self`
Raises:	`ValueError` – If `transform` isn’t an instance of `composes_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 and b are left unchanged.

An attempt is made to perform native composition, but will fall back to a TransformChain as a last resort. See composes_with for a description of how the mode of composition is decided.

Parameters:	transform (`Transform`) – Transform to be applied after `self`
Returns:	transform (`Transform` or `TransformChain`) – If the composition was native, a single new `Transform` will be returned. If not, a `TransformChain` 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 after `self`
Raises:	`ValueError` – If `transform` isn’t an instance of `composes_inplace_with`

copy()¶

Generate an efficient copy of this object.

Note that Numpy arrays and other Copyable objects on self 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

composes_inplace_with¶

The Transform s that this transform composes inplace with natively (i.e. no TransformChain will be produced).

An attempt to compose inplace against any type that is not an instance of this property on this class will result in an Exception.

Type:	`Transform` or tuple of `Transform` s

composes_with¶

The Transform s that this transform composes with natively (i.e. no TransformChain will be produced).

If native composition is not possible, falls back to producing a TransformChain.

By default, this is the same list as composes_inplace_with.

Type:	`Transform` or tuple of `Transform` s

n_dims¶

The dimensionality of the data the transform operates on.

None if the transform is not dimension specific.

Type:	int or `None`

n_dims_output¶

The output of the data from the transform.

None if the output of the transform is not dimension specific.

Type:	int or `None`