# AppearanceWeightsNewton¶

class menpofit.aam.AppearanceWeightsNewton(aam_interface, n_iterations=3, compute_error=<function euclidean_bb_normalised_error>, alpha=0, bias=True)[source]

Bases: AppearanceWeights

Class for training a cascaded-regression Newton algorithm using Incremental Regularized Linear Regression (IRLRegression) given a trained AAM model. The algorithm uses the projection weights of the appearance vectors as features in the regression.

Parameters
• aam_interface (The AAM interface class from menpofit.aam.algorithm.lk.) –

Existing interfaces include:

• 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.

increment(images, gt_shapes, current_shapes, prefix='', verbose=False)

Method to increment the model with the set of current shapes.

Parameters
• images (list of menpo.image.Image) – The list of training images.

• gt_shapes (list of menpo.shape.PointCloud) – The list of ground truth shapes that correspond to the images.

• current_shapes (list of menpo.shape.PointCloud) – The list of current shapes that correspond to the images.

• prefix (str, optional) – The prefix to use when printing information.

• verbose (bool, optional) – If True, then information is printed during training.

Returns

current_shapes (list of menpo.shape.PointCloud) – The list of current shapes that correspond to the images.

project(J)

Projects a given vector or matrix onto the appearance subspace.

Type

ndarray

run(image, initial_shape, gt_shape=None, return_costs=False, **kwargs)

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 (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 (AAMAlgorithmResult) – The parametric iterative fitting result.

train(images, gt_shapes, current_shapes, prefix='', verbose=False)

Method to train the model given a set of initial shapes.

Parameters
• images (list of menpo.image.Image) – The list of training images.

• gt_shapes (list of menpo.shape.PointCloud) – The list of ground truth shapes that correspond to the images.

• current_shapes (list of menpo.shape.PointCloud) – The list of current shapes that correspond to the images, which will be used as initial shapes.

• prefix (str, optional) – The prefix to use when printing information.

• verbose (bool, optional) – If True, then information is printed during training.

Returns

current_shapes (list of menpo.shape.PointCloud) – The list of current shapes that correspond to the images.

property appearance_model

Returns the appearance model of the AAM.

Type

menpo.model.PCAModel

property transform

Returns the model driven differential transform object of the AAM, e.g. DifferentiablePiecewiseAffine or DifferentiableThinPlateSplines.

Type

subclass of DL and DX