FullyParametricProjectOutOPP¶

class
menpofit.sdm.
FullyParametricProjectOutOPP
(patch_features=<function no_op>, patch_shape=(17, 17), n_iterations=3, shape_model_cls=<class 'menpofit.modelinstance.OrthoPDM'>, appearance_model_cls=<class 'menpo.model.pca.PCAVectorModel'>, compute_error=<function euclidean_bb_normalised_error>, bias=True)[source]¶ Bases:
ParametricAppearanceProjectOut
Class for training a cascadedregression algorithm that employs parametric shape and appearance models using Multivariate Linear Regression with Orthogonal Procrustes Problem reconstructions (
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).
shape_model_cls (subclass of
PDM
, optional) – The class to be used for building the shape model. The most common choice isOrthoPDM
.appearance_model_cls (menpo.model.PCAVectorModel or subclass) – The class to be used for building the appearance model.
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.

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.

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 (
ParametricIterativeResult
) – The result of the fitting procedure.

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.