from menpo.base import name_of_callable
from menpofit import checks
from menpofit.fitter import MultiScaleParametricFitter
from .algorithm import AlternatingRegularisedLandmarkMeanShift
from .result import UnifiedAAMCLMResult
[docs]class UnifiedAAMCLMFitter(MultiScaleParametricFitter):
r"""
Class defining a Unified AAM - CLM fitter.
.. note:: When using a method with a parametric shape model, the first step
is to **reconstruct the initial shape** using the shape model. The
generated reconstructed shape is then used as initialisation for
the iterative optimisation. This step takes place at each scale
and it is not considered as an iteration, thus it is not counted
for the provided `max_iters`.
Parameters
----------
unified_aam_clm : :map:`UnifiedAAMCLM` or subclass
The trained unified AAM-CLM model.
algorithm_cls : `class`, optional
The unified optimisation algorithm that will get applied. The
possible algorithms are:
============================================== =====================
Class Method
============================================== =====================
:map:`ProjectOutRegularisedLandmarkMeanShift` Project-Out IC + RLMS
:map:`AlternatingRegularisedLandmarkMeanShift` Alternating IC + RLMS
============================================== =====================
n_shape : `int` or `float` or `list` of those or ``None``, optional
The number of shape components that will be used. If `int`, then it
defines the exact number of active components. If `float`, then it
defines the percentage of variance to keep. If `int` or `float`, then
the provided value will be applied for all scales. If `list`, then it
defines a value per scale. If ``None``, then all the available
components will be used. Note that this simply sets the active
components without trimming the unused ones. Also, the available
components may have already been trimmed to `max_shape_components`
during training.
n_appearance : `int` or `float` or `list` of those or ``None``, optional
The number of appearance components that will be used. If `int`, then it
defines the exact number of active components. If `float`, then it
defines the percentage of variance to keep. If `int` or `float`, then
the provided value will be applied for all scales. If `list`, then it
defines a value per scale. If ``None``, then all the available
components will be used. Note that this simply sets the active
components without trimming the unused ones. Also, the available
components may have already been trimmed to `max_appearance_components`
during training.
sampling : `list` of `int` or `ndarray` or ``None``
It defines a sampling mask per scale. If `int`, then it defines the
sub-sampling step of the sampling mask. If `ndarray`, then it
explicitly defines the sampling mask. If ``None``, then no
sub-sampling is applied.
"""
def __init__(self, unified_aam_clm,
algorithm_cls=AlternatingRegularisedLandmarkMeanShift,
n_shape=None, n_appearance=None, sampling=None):
self._model = unified_aam_clm
# Check parameters
checks.set_models_components(self._model.shape_models, n_shape)
checks.set_models_components(self._model.appearance_models,
n_appearance)
self._sampling = checks.check_sampling(sampling, self._model.n_scales)
# Get list of algorithm objects per scale
interfaces = unified_aam_clm.build_fitter_interfaces(self._sampling)
algorithms = [algorithm_cls(interface,
self._model.expert_ensembles[k],
self._model.patch_shape[k],
self._model.response_covariance)
for k, interface in enumerate(interfaces)]
# Call superclass
super(UnifiedAAMCLMFitter, self).__init__(
scales=self._model.scales,
reference_shape=self._model.reference_shape,
holistic_features=self._model.holistic_features,
algorithms=algorithms)
@property
def unified_aam_clm(self):
r"""
The trained unified AAM-CLM model.
:type: :map:`UnifiedAAMCLM` or `subclass`
"""
return self._model
[docs] def appearance_reconstructions(self, appearance_parameters,
n_iters_per_scale):
r"""
Method that generates the appearance reconstructions given a set of
appearance parameters. This is to be combined with a
:map:`UnifiedAAMCLMResult` object, in order to generate the
appearance reconstructions of a fitting procedure.
Parameters
----------
appearance_parameters : `list` of ``(n_params,)`` `ndarray`
A set of appearance parameters per fitting iteration. It can be
retrieved as a property of an :map:`UnifiedAAMCLMResult` object.
n_iters_per_scale : `list` of `int`
The number of iterations per scale. This is necessary in order to
figure out which appearance parameters correspond to the model of
each scale. It can be retrieved as a property of a
:map:`UnifiedAAMCLMResult` object.
Returns
-------
appearance_reconstructions : `list` of `menpo.image.Image`
`List` of the appearance reconstructions that correspond to the
provided parameters.
"""
return self.unified_aam_clm.appearance_reconstructions(
appearance_parameters=appearance_parameters,
n_iters_per_scale=n_iters_per_scale)
[docs] def warped_images(self, image, shapes):
r"""
Given an input test image and a list of shapes, it warps the image
into the shapes. This is useful for generating the warped images of a
fitting procedure stored within an :map:`UnifiedAAMCLMResult`.
Parameters
----------
image : `menpo.image.Image` or `subclass`
The input image to be warped.
shapes : `list` of `menpo.shape.PointCloud`
The list of shapes in which the image will be warped. The shapes
are obtained during the iterations of a fitting procedure.
Returns
-------
warped_images : `list` of `menpo.image.MaskedImage` or `ndarray`
The warped images.
"""
return self.algorithms[-1].interface.warped_images(image=image,
shapes=shapes)
@property
def response_covariance(self):
r"""
Returns the covariance value of the desired Gaussian response used to
train the ensemble of experts.
:type: `int`
"""
return self._model.response_covariance
def _fitter_result(self, image, algorithm_results, affine_transforms,
scale_transforms, gt_shape=None):
r"""
Function the creates the multi-scale fitting result object.
Parameters
----------
image : `menpo.image.Image` or subclass
The image that was fitted.
algorithm_results : `list` of :map:`AAMAlgorithmResult` or subclass
The list of fitting result per scale.
affine_transforms : `list` of `menpo.transform.Affine`
The list of affine transforms per scale that are the inverses of the
transformations introduced by the rescale wrt the reference shape as
well as the feature extraction.
scale_transforms : `list` of `menpo.shape.Scale`
The list of inverse scaling transforms per scale.
gt_shape : `menpo.shape.PointCloud`, optional
The ground truth shape associated to the image.
Returns
-------
fitting_result : :map:`UnifiedAAMCLMResult` or subclass
The multi-scale fitting result containing the result of the fitting
procedure.
"""
return UnifiedAAMCLMResult(
results=algorithm_results, scales=self.scales,
affine_transforms=affine_transforms,
scale_transforms=scale_transforms, image=image, gt_shape=gt_shape)
def __str__(self):
# Compute scale info strings
scales_info = []
lvl_str_tmplt = r""" - Scale {}
- {} active shape components
- {} similarity transform components
- {} active appearance components"""
for k, s in enumerate(self.scales):
scales_info.append(lvl_str_tmplt.format(
s,
self._model.shape_models[k].model.n_active_components,
self._model.shape_models[k].n_global_parameters,
self._model.appearance_models[k].n_active_components))
scales_info = '\n'.join(scales_info)
cls_str = r"""{class_title}
- Scales: {scales}
{scales_info}
""".format(class_title=name_of_callable(self.algorithms[0]),
scales=self.scales,
scales_info=scales_info)
return self._model.__str__() + cls_str