def holistic_sampling_from_scale(aam, scale=0.35):
r"""
Function that generates a sampling reference mask given a scale value.
Parameters
----------
aam : :map:`AAM` or subclass
The trained AAM.
scale : `float`, optional
The scale value.
Returns
-------
true_positions : `ndarray` of `bool`
The array that has ``True`` for the points of the reference shape that
belong to the new mask.
boolean_image : `menpo.image.BooleanImage`
The boolean image of the mask.
"""
reference = aam.appearance_models[0].mean()
scaled_reference = reference.rescale(scale)
t = AlignmentUniformScale(scaled_reference.landmarks['source'].lms,
reference.landmarks['source'].lms)
new_indices = np.require(np.round(t.apply(
scaled_reference.mask.true_indices())), dtype=np.int)
modified_mask = deepcopy(reference.mask.pixels)
modified_mask[:] = False
modified_mask[:, new_indices[:, 0], new_indices[:, 1]] = True
true_positions = np.nonzero(
modified_mask[:, reference.mask.mask].ravel())[0]
return true_positions, BooleanImage(modified_mask[0])
def line_to_sparse(training_images,
sparse_shape,
rs,
group='PTS',
sd='draw_gaussian'):
ni, icp_transforms, reference_frame, n_landmarks, _n_align_points, _removed_transform, normalized_images, _rf_align, rs, [
align_t
] = rescale_images_to_reference_shape(training_images,
group,
rs,
sd='sample_gaussian')
path_to_db = '{}'.format(training_images[0].path.parent)
# Retrieve Results
mat = sio.loadmat('{}/result.mat'.format(path_to_db))
_u, _v = mat['u'], mat['v']
# Build Transforms
print(" - Build Transform")
transforms = []
for i in range(_u.shape[-1]):
transforms.append(OpticalFlowTransform(_u[:, :, i], _v[:, :, i]))
for i, n, t, norm, icpt, oft in zip(training_images, ni,
_removed_transform, normalized_images,
icp_transforms, transforms):
scale = AlignmentUniformScale(norm.landmarks[group].lms,
i.landmarks[group].lms)
pts = oft.apply(align_t.apply(sparse_shape))
i.landmarks['SPARSE'] = scale.apply(t.apply(_rf_align.apply(pts)))
return training_images
def test_align_2d_uniform_scale_set_h_matrix_raises_notimplemented_error():
scale = UniformScale(2.5, 2)
source = PointCloud(np.array([[0, 1], [1, 1], [-1, -5], [3, -5]]))
target = scale.apply(source)
# estimate the transform from source and source
estimate = AlignmentUniformScale(source, source)
# and set the target
estimate.set_h_matrix(scale.h_matrix)
def test_align_2d_uniform_scale_set_h_matrix_raises_notimplemented_error():
scale = UniformScale(2.5, 2)
source = PointCloud(np.array([[0, 1], [1, 1], [-1, -5], [3, -5]]))
target = scale.apply(source)
# estimate the transform from source and source
estimate = AlignmentUniformScale(source, source)
# and set the target
estimate.set_h_matrix(scale.h_matrix)
def test_align_2d_uniform_scale_set_target():
scale = UniformScale(2.5, 2)
source = PointCloud(np.array([[0, 1], [1, 1], [-1, -5], [3, -5]]))
target = scale.apply(source)
# estimate the transform from source and source
estimate = AlignmentUniformScale(source, source)
# and set the target
estimate.set_target(target)
# check the estimates is correct
assert_allclose(scale.h_matrix, estimate.h_matrix)
def test_align_2d_uniform_scale_set_target():
scale = UniformScale(2.5, 2)
source = PointCloud(np.array([[0, 1], [1, 1], [-1, -5], [3, -5]]))
target = scale.apply(source)
# estimate the transform from source and source
estimate = AlignmentUniformScale(source, source)
# and set the target
estimate.set_target(target)
# check the estimates is correct
assert_allclose(scale.h_matrix, estimate.h_matrix)
def holistic_sampling_from_scale(aam, scale=0.35):
reference = aam.appearance_models[0].mean()
scaled_reference = reference.rescale(scale)
t = AlignmentUniformScale(scaled_reference.landmarks['source'].lms,
reference.landmarks['source'].lms)
new_indices = np.require(np.round(t.apply(
scaled_reference.mask.true_indices())), dtype=np.int)
modified_mask = deepcopy(reference.mask.pixels)
modified_mask[:] = False
modified_mask[:, new_indices[:, 0], new_indices[:, 1]] = True
true_positions = np.nonzero(
modified_mask[:, reference.mask.mask].ravel())[0]
return true_positions, BooleanImage(modified_mask[0])
def homog_compose_before_alignment_nonuniformscale_test():
homog = Homogeneous(np.array([[0, 1, 0], [1, 0, 0], [0, 0, 1]]))
scale = UniformScale(2.5, 2)
source = PointCloud(np.array([[0, 1], [1, 1], [-1, -5], [3, -5]]))
target = scale.apply(source)
# estimate the transform from source and target
s = AlignmentUniformScale(source, target)
res = homog.compose_before(s)
assert (type(res) == Homogeneous)
def holistic_sampling_from_scale(aam, scale=0.35):
reference = aam.appearance_models[0].mean()
scaled_reference = reference.rescale(scale)
t = AlignmentUniformScale(scaled_reference.landmarks['source'].lms,
reference.landmarks['source'].lms)
new_indices = np.require(np.round(
t.apply(scaled_reference.mask.true_indices())),
dtype=np.int)
modified_mask = deepcopy(reference.mask.pixels)
modified_mask[:] = False
modified_mask[:, new_indices[:, 0], new_indices[:, 1]] = True
true_positions = np.nonzero(modified_mask[:,
reference.mask.mask].ravel())[0]
return true_positions, BooleanImage(modified_mask[0])
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 = AlignmentUniformScale(pc, reference_shape).as_vector()
return self.rescale(scale,
interpolator=interpolator,
round=round,
**kwargs)
def holistic_sampling_from_scale(aam, scale=0.35):
r"""
Function that generates a sampling reference mask given a scale value.
Parameters
----------
aam : :map:`AAM` or subclass
The trained AAM.
scale : `float`, optional
The scale value.
Returns
-------
true_positions : `ndarray` of `bool`
The array that has ``True`` for the points of the reference shape that
belong to the new mask.
boolean_image : `menpo.image.BooleanImage`
The boolean image of the mask.
"""
reference = aam.appearance_models[0].mean()
scaled_reference = reference.rescale(scale)
t = AlignmentUniformScale(scaled_reference.landmarks['source'],
reference.landmarks['source'])
new_indices = np.require(np.round(
t.apply(scaled_reference.mask.true_indices())),
dtype=np.int)
modified_mask = deepcopy(reference.mask.pixels)
modified_mask[:] = False
modified_mask[:, new_indices[:, 0], new_indices[:, 1]] = True
true_positions = np.nonzero(modified_mask[:,
reference.mask.mask].ravel())[0]
return true_positions, BooleanImage(modified_mask[0])