def test_uniformscale3d_from_vector():
scale = 2
h**o = np.array([[scale, 0, 0, 0],
[0, scale, 0, 0],
[0, 0, scale, 0],
[0, 0, 0, 1]])
uniform_scale = UniformScale(1, 3)
tr = uniform_scale.from_vector(scale)
assert_equal(tr.h_matrix, h**o)
def test_uniformscale2d_update_from_vector():
# make a uniform scale of 1, 2 dimensional
uniform_scale = UniformScale(1, 2)
new_scale = 2
h**o = np.array([[new_scale, 0, 0],
[0, new_scale, 0],
[0, 0, 1]])
uniform_scale.from_vector_inplace(new_scale)
assert_equal(uniform_scale.h_matrix, h**o)
def rescale_to_reference_shape(self, reference_shape, group=None,
label='all', interpolator='scipy',
round='ceil', **kwargs):
r"""
Return a copy of this image, rescaled so that the scale of a
particular group of landmarks matches the scale of the passed
reference landmarks.
Parameters
----------
reference_shape: :class:`menpo.shape.pointcloud`
The reference shape to which the landmarks scale will be matched
against.
group : string, Optional
The key of the landmark set that should be used. If None,
and if there is only one set of landmarks, this set will be used.
Default: None
label: string, Optional
The label of of the landmark manager that you wish to use. If
'all' all landmarks in the group are used.
Default: 'all'
interpolator : 'scipy' or 'c', optional
The interpolator that should be used to perform the warp.
round: {'ceil', 'floor', 'round'}
Rounding function to be applied to floating point shapes.
Default: 'ceil'
kwargs : dict
Passed through to the interpolator. See `menpo.interpolation`
for details.
Returns
-------
rescaled_image : type(self)
A copy of this image, rescaled.
"""
pc = self.landmarks[group][label].lms
scale = UniformScale.align(pc, reference_shape).as_vector()
return self.rescale(scale, interpolator=interpolator,
round=round, **kwargs)
def __init__(self, shape_models, appearance_models, transform_cls,
feature_type, reference_shape, downscale, patch_size,
interpolator):
self.shape_models = shape_models
self.appearance_models = appearance_models
self.transform_cls = transform_cls
self.feature_type = feature_type
self.reference_shape = reference_shape
self.downscale = downscale
self.patch_size = patch_size
self.interpolator = interpolator
if len(appearance_models) > 1:
difference = UniformScale.align(
appearance_models[0].mean.landmarks['source'].lms,
appearance_models[1].mean.landmarks['source'].lms).as_vector()
if difference == 1:
self.scaled_reference_frames = False
else:
self.scaled_reference_frames = True
else:
self.downscale = None
self.scaled_reference_frames = None
def test_uniformscale_identity_3d():
assert_allclose(UniformScale.identity(3).h_matrix, np.eye(4))
