ParametricRegressorTrainer¶
- class menpo.fit.regression.trainer.ParametricRegressorTrainer(appearance_model, transform, reference_shape, regression_type=<function mlr at 0x7f5c2da62cf8>, regression_features=<function weights at 0x7f5c2da628c0>, update='compositional', noise_std=0.04, rotation=False, n_perturbations=10, interpolator='scipy')[source]¶
Bases: RegressorTrainer
Class for training a Parametric Regressor.
Parameters: appearance_model : PCAModel
The appearance model to be used.
transform : Affine
The transform used for warping.
reference_shape : PointCloud
The reference shape that will be used.
regression_type : function, optional
A function that defines the regression technique to be used. Examples of such closures can be found in Functions
regression_features : None or function, optional
The parametric features that are used during the regression.
If None, the reconstruction appearance weights will be used as feature.
If string or function, the feature representation will be computed using one of the function in:
If string, the feature representation will be extracted by executing a parametric feature function.
Note that this feature type can only be one of the parametric feature functions defined Parametric Features.
patch_shape : tuple, optional
The shape of the patches that will be extracted.
update : ‘compositional’ or ‘additive’
Defines the way to update the warp.
noise_std : float, optional
The standard deviation of the gaussian noise used to produce the training shapes.
rotation : boolean, optional
Specifies whether ground truth in-plane rotation is to be used to produce the training shapes.
n_perturbations : int, optional
Defines the number of perturbations that will be applied to the training shapes.
interpolator : string
Specifies the interpolator used in warping.
- delta_ps(gt_shape, perturbed_shape)[source]¶
Method to generate the delta_ps for the regression.
Parameters: gt_shape : PointCloud
The ground truth shape.
perturbed_shape : PointCloud
The perturbed shape.
- features(image, shape)[source]¶
Method that extracts the features for the regression, which in this case are patch based.
Parameters: image : MaskedImage
The current image.
shape : PointCloud
The current shape.
- perturb_shapes(gt_shape)¶
Perturbs the given shapes. The number of perturbations is defined by n_perturbations.
Parameters: gt_shape : PointCloud list
List of the shapes that correspond to the images. will be perturbed.
Returns: perturbed_shapes : PointCloud list
List of the perturbed shapes.
- train(images, shapes, perturbed_shapes=None, verbose=False, **kwargs)¶
Trains a Regressor given a list of landmarked images.
Parameters: images : list of MaskedImage
The set of landmarked images from which to train the regressor.
shapes : PointCloud list
List of the shapes that correspond to the images.
perturbed_shapes : PointCloud list, optional
List of the perturbed shapes used for the regressor training.
verbose : boolean, optional
Flag that controls information and progress printing.
Returns: regressor : :map:`Regressor`
A regressor object.
Raises: ValueError :
The number of shapes must be equal to the number of images.
ValueError :
The number of perturbed shapes must be equal or multiple to the number of images.