def noisy_align(source, target, noise_std=0.04, rotation=False):
r"""
Constructs and perturbs the optimal similarity transform between source
to the target by adding white noise to its weights.
Parameters
----------
source: :class:`menpo.shape.PointCloud`
The source pointcloud instance used in the alignment
target: :class:`menpo.shape.PointCloud`
The target pointcloud instance used in the alignment
noise_std: float
The standard deviation of the white noise
Default: 0.04
rotation: boolean
If False the second parameter of the Similarity,
which captures captures inplane rotations, is set to 0.
Default:False
Returns
-------
noisy_transform : :class: `menpo.transform.Similarity`
The noisy Similarity Transform
"""
transform = AlignmentSimilarity(source, target, rotation=rotation)
parameters = transform.as_vector()
parameter_range = np.hstack((parameters[:2], target.range()))
noise = (parameter_range * noise_std *
np.random.randn(transform.n_parameters))
return Similarity.init_identity(source.n_dims).from_vector(parameters + noise)
def noisy_align(source, target, noise_std=0.04, rotation=False):
r"""
Constructs and perturbs the optimal similarity transform between source
to the target by adding white noise to its weights.
Parameters
----------
source: :class:`menpo.shape.PointCloud`
The source pointcloud instance used in the alignment
target: :class:`menpo.shape.PointCloud`
The target pointcloud instance used in the alignment
noise_std: float
The standard deviation of the white noise
Default: 0.04
rotation: boolean
If False the second parameter of the Similarity,
which captures captures inplane rotations, is set to 0.
Default:False
Returns
-------
noisy_transform : :class: `menpo.transform.Similarity`
The noisy Similarity Transform
"""
transform = AlignmentSimilarity(source, target, rotation=rotation)
parameters = transform.as_vector()
parameter_range = np.hstack((parameters[:2], target.range()))
noise = (parameter_range * noise_std *
np.random.randn(transform.n_parameters))
return Similarity.identity(source.n_dims).from_vector(parameters + noise)
