from functools import partial
from menpo.feature import no_op
from menpofit.result import MultiScaleNonParametricIterativeResult
from menpofit.error import euclidean_bb_normalised_error
from menpofit.math import (IIRLRegression, IRLRegression, PCRRegression,
OptimalLinearRegression, OPPRegression)
from .base import (BaseSupervisedDescentAlgorithm,
compute_non_parametric_delta_x, features_per_image,
features_per_patch, update_non_parametric_estimates,
print_non_parametric_info, fit_non_parametric_shape)
class NonParametricSDAlgorithm(BaseSupervisedDescentAlgorithm):
r"""
Abstract class for training a non-parametric cascaded-regression Supervised
Descent algorithm.
"""
def __init__(self):
super(NonParametricSDAlgorithm, self).__init__()
self.regressors = []
@property
def _multi_scale_fitter_result(self):
# The result class to be used by a multi-scale fitter
return MultiScaleNonParametricIterativeResult
def _compute_delta_x(self, gt_shapes, current_shapes):
return compute_non_parametric_delta_x(gt_shapes, current_shapes)
def _update_estimates(self, estimated_delta_x, delta_x, gt_x,
current_shapes):
update_non_parametric_estimates(estimated_delta_x, delta_x, gt_x,
current_shapes)
def _compute_training_features(self, images, gt_shapes, current_shapes,
prefix='', verbose=False):
return features_per_image(images, current_shapes, self.patch_shape,
self.patch_features, prefix=prefix,
verbose=verbose)
def _compute_test_features(self, image, current_shape):
return features_per_patch(image, current_shape,
self.patch_shape, self.patch_features)
def run(self, image, initial_shape, gt_shape=None, return_costs=False,
**kwargs):
r"""
Run the algorithm to an image given an initial shape.
Parameters
----------
image : `menpo.image.Image` or subclass
The image to be fitted.
initial_shape : `menpo.shape.PointCloud`
The initial shape from which the fitting procedure will start.
gt_shape : class : `menpo.shape.PointCloud` or ``None``, optional
The ground truth shape associated to the image.
return_costs : `bool`, optional
If ``True``, then the cost function values will be computed
during the fitting procedure. Then these cost values will be
assigned to the returned `fitting_result`. *Note that this
argument currently has no effect and will raise a warning if set
to ``True``. This is because it is not possible to evaluate the
cost function of this algorithm.*
Returns
-------
fitting_result: :map:`NonParametricIterativeResult`
The result of the fitting procedure.
"""
return fit_non_parametric_shape(image, initial_shape, self,
gt_shape=gt_shape,
return_costs=return_costs)
def _print_regression_info(self, template_shape, gt_shapes, n_perturbations,
delta_x, estimated_delta_x, level_index,
prefix=''):
print_non_parametric_info(template_shape, gt_shapes, n_perturbations,
delta_x, estimated_delta_x, level_index,
self._compute_error, prefix=prefix)
[docs]class NonParametricNewton(NonParametricSDAlgorithm):
r"""
Class for training a non-parametric cascaded-regression algorithm using
Incremental Regularized Linear Regression (:map:`IRLRegression`).
Parameters
----------
patch_features : `callable`, optional
The features to be extracted from the patches of an image.
patch_shape : `(int, int)`, optional
The shape of the extracted patches.
n_iterations : `int`, optional
The number of iterations (cascades).
compute_error : `callable`, optional
The function to be used for computing the fitting error when training
each cascade.
alpha : `float`, optional
The regularization parameter.
bias : `bool`, optional
Flag that controls whether to use a bias term.
"""
def __init__(self, patch_features=no_op, patch_shape=(17, 17),
n_iterations=3, compute_error=euclidean_bb_normalised_error,
alpha=0, bias=True):
super(NonParametricNewton, self).__init__()
self._regressor_cls = partial(IRLRegression, alpha=alpha, bias=bias)
self.patch_shape = patch_shape
self.patch_features = patch_features
self.n_iterations = n_iterations
self._compute_error = compute_error
[docs]class NonParametricGaussNewton(NonParametricSDAlgorithm):
r"""
Class for training a non-parametric cascaded-regression algorithm using
Indirect Incremental Regularized Linear Regression (:map:`IIRLRegression`).
Parameters
----------
patch_features : `callable`, optional
The features to be extracted from the patches of an image.
patch_shape : `(int, int)`, optional
The shape of the extracted patches.
n_iterations : `int`, optional
The number of iterations (cascades).
compute_error : `callable`, optional
The function to be used for computing the fitting error when training
each cascade.
alpha : `float`, optional
The regularization parameter.
bias : `bool`, optional
Flag that controls whether to use a bias term.
alpha2 : `float`, optional
The regularization parameter of the Hessian matrix.
"""
def __init__(self, patch_features=no_op, patch_shape=(17, 17),
n_iterations=3, compute_error=euclidean_bb_normalised_error,
alpha=0, bias=True, alpha2=0):
super(NonParametricGaussNewton, self).__init__()
self._regressor_cls = partial(IIRLRegression, alpha=alpha, bias=bias,
alpha2=alpha2)
self.patch_shape = patch_shape
self.patch_features = patch_features
self.n_iterations = n_iterations
self._compute_error = compute_error
[docs]class NonParametricPCRRegression(NonParametricSDAlgorithm):
r"""
Class for training a non-parametric cascaded-regression algorithm using
Principal Component Regression (:map:`PCRRegression`).
Parameters
----------
patch_features : `callable`, optional
The features to be extracted from the patches of an image.
patch_shape : `(int, int)`, optional
The shape of the extracted patches.
n_iterations : `int`, optional
The number of iterations (cascades).
compute_error : `callable`, optional
The function to be used for computing the fitting error when training
each cascade.
variance : `float` or ``None``, optional
The SVD variance.
bias : `bool`, optional
Flag that controls whether to use a bias term.
"""
def __init__(self, patch_features=no_op, patch_shape=(17, 17),
n_iterations=3, compute_error=euclidean_bb_normalised_error,
variance=None, bias=True):
super(NonParametricPCRRegression, self).__init__()
self._regressor_cls = partial(PCRRegression, variance=variance,
bias=bias)
self.patch_shape = patch_shape
self.patch_features = patch_features
self.n_iterations = n_iterations
self._compute_error = compute_error
[docs]class NonParametricOptimalRegression(NonParametricSDAlgorithm):
r"""
Class for training a non-parametric cascaded-regression algorithm using
Multivariate Linear Regression with optimal reconstructions
(:map:`OptimalLinearRegression`).
Parameters
----------
patch_features : `callable`, optional
The features to be extracted from the patches of an image.
patch_shape : `(int, int)`, optional
The shape of the extracted patches.
n_iterations : `int`, optional
The number of iterations (cascades).
compute_error : `callable`, optional
The function to be used for computing the fitting error when training
each cascade.
variance : `float` or ``None``, optional
The SVD variance.
bias : `bool`, optional
Flag that controls whether to use a bias term.
"""
def __init__(self, patch_features=no_op, patch_shape=(17, 17),
n_iterations=3, compute_error=euclidean_bb_normalised_error,
variance=None, bias=True):
super(NonParametricOptimalRegression, self).__init__()
self._regressor_cls = partial(OptimalLinearRegression,
variance=variance, bias=bias)
self.patch_shape = patch_shape
self.patch_features = patch_features
self.n_iterations = n_iterations
self._compute_error = compute_error
[docs]class NonParametricOPPRegression(NonParametricSDAlgorithm):
r"""
Class for training a non-parametric cascaded-regression algorithm using
Multivariate Linear Regression with Orthogonal Procrustes Problem
reconstructions (:map:`OPPRegression`).
Parameters
----------
patch_features : `callable`, optional
The features to be extracted from the patches of an image.
patch_shape : `(int, int)`, optional
The shape of the extracted patches.
n_iterations : `int`, optional
The number of iterations (cascades).
compute_error : `callable`, optional
The function to be used for computing the fitting error when training
each cascade.
bias : `bool`, optional
Flag that controls whether to use a bias term.
"""
def __init__(self, patch_features=no_op, patch_shape=(17, 17),
n_iterations=3, compute_error=euclidean_bb_normalised_error,
bias=True):
super(NonParametricOPPRegression, self).__init__()
self._regressor_cls = partial(OPPRegression, bias=bias)
self.patch_shape = patch_shape
self.patch_features = patch_features
self.n_iterations = n_iterations
self._compute_error = compute_error