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 skew_shape(pointcloud, theta, phi):
r"""
Method that skews the provided pointcloud.
Parameters
----------
pointcloud : `menpo.shape.PointCloud`
The shape to distort.
theta : `float`
The skew angle over x axis (tan(theta)).
phi : `float`
The skew angle over y axis (tan(phi)).
Returns
-------
skewed_shape : `menpo.shape.PointCloud`
The skewed (distorted) pointcloud.
"""
rotate_ccw = Similarity.init_identity(pointcloud.n_dims)
# Create skew matrix
h_matrix = np.ones((3, 3))
h_matrix[0, 1] = np.tan(theta * np.pi / 180.)
h_matrix[1, 0] = np.tan(phi * np.pi / 180.)
h_matrix[:2, 2] = 0.
h_matrix[2, :2] = 0.
r = Affine(h_matrix)
t = Translation(-pointcloud.centre(), skip_checks=True)
# Translate to origin, rotate counter-clockwise, then translate back
rotate_ccw.compose_before_inplace(t)
rotate_ccw.compose_before_inplace(r)
rotate_ccw.compose_before_inplace(t.pseudoinverse())
return rotate_ccw.apply(pointcloud)
def _recursive_procrustes(self):
r"""
Recursively calculates a procrustes alignment.
"""
global mean_pointcloud, PointCloud, Similarity
if mean_pointcloud is None or PointCloud is None or Similarity is None:
from menpo.shape import mean_pointcloud, PointCloud
from menpo.transform import Similarity
if self.n_iterations > self.max_iterations:
return False
new_tgt = mean_pointcloud([PointCloud(t.aligned_source().points,
copy=False)
for t in self.transforms])
# rescale the new_target to be the same size as the original about
# it's centre
rescale = Similarity.init_identity(new_tgt.n_dims)
s = UniformScale(self.initial_target_scale / new_tgt.norm(),
self.n_dims, skip_checks=True)
t = Translation(-new_tgt.centre(), skip_checks=True)
rescale.compose_before_inplace(t)
rescale.compose_before_inplace(s)
rescale.compose_before_inplace(t.pseudoinverse())
rescale.apply_inplace(new_tgt)
# check to see if we have converged yet
delta_target = np.linalg.norm(self.target.points - new_tgt.points)
if delta_target < 1e-6:
return True
else:
self.n_iterations += 1
for t in self.transforms:
t.set_target(new_tgt)
self.target = new_tgt
return self._recursive_procrustes()
def test_similarity_2d_from_vector():
params = np.array([0.2, 0.1, 1, 2])
h**o = np.array([[params[0] + 1, -params[1], params[2]],
[params[1], params[0] + 1, params[3]], [0, 0, 1]])
sim = Similarity.init_identity(2).from_vector(params)
assert_almost_equal(sim.h_matrix, h**o)
def test_similarity_2d_from_vector():
params = np.array([0.2, 0.1, 1, 2])
h**o = np.array([[params[0] + 1, -params[1], params[2]],
[params[1], params[0] + 1, params[3]],
[0, 0, 1]])
sim = Similarity.init_identity(2).from_vector(params)
assert_equal(sim.h_matrix, h**o)
def test_similarity_identity_3d():
assert_allclose(Similarity.init_identity(3).h_matrix, np.eye(4))
def test_similarity_identity_3d():
assert_allclose(Similarity.init_identity(3).h_matrix,
np.eye(4))