1. Fitter objects
  2. Fitting Methods

1. Fitter Objects

menpofit has specialised classes for performing a fitting process that are called Fitters. All Fitter objects are subclasses of one of the following:

  • MultiScaleNonParametricFitter: multi-scale non-parametric fitting method, i.e. a method that does not optimise over a parametric shape model but directly optimizes the coordinates of the landmarks
  • MultiScaleParametricFitter: multi-scale parametric fitting method, i.e. a method that optimises over the parameters of a statistical shape model

from menpofit.fitter.

The behaviour of Fitter classes can differ depending on the deformable model. For example, a Lucas-Kanade AAM fitter (LucasKanadeAAMFitter) assumes that you have trained an AAM model (assume the aam we trained in the Training section) and can be created as:

from menpofit.aam import LucasKanadeAAMFitter, WibergInverseCompositional
fitter = LucasKanadeAAMFitter(aam,
                              n_shape=[3, 20], n_appearance=[30, 150])

The constructor of the Fitter will set the active shape and appearance components based on n_shape and n_appearance respectively, and will also perform all the necessary pre-computations based on the selected algorithm.

However, there are deformable models that are directly defined through a Fitter object, which is responsible for training the model. SupervisedDescentFitter is a good example. The reason for that is that the fitting process is utilised during the building procedure, thus the functionality of a Fitter is required. Such models can be trained as:

from menpofit.sdm import SupervisedDescentFitter, NonParametricNewton
fitter = SupervisedDescentFitter(training_images,

Information about any Fitter object can be retrieved as:


2. Fitting Methods

All the deformable models that are currently implemented in mnepofit, which are the state-of-the-art approaches in current literature, aim to find a local optimum of the cost function that they try to optimise, given an initialization. The initialization can be seen as an initial estimation of the target shape. Fitter objects provide two functions for fitting a model to an image:

result = fitter.fit_from_shape(image, initial_shape, max_iters=20, gt_shape=None,
                               return_costs=False, **kwargs)


result = fitter.fit_from_bb(image, bounding_box, max_iters=20, gt_shape=None,
                            return_costs=False, **kwargs)

They only differ on the type of initialization. fit_from_shape expects a PointCloud as the initial_shape. On the other hand, the bounding_box argument of fit_from_bb is a PointDirectedGraph of 4 vertices that represents the initial bounding box. The bounding box is used in order to align the model's reference shape and use the resulting PointCloud as the initial shape. Such a bounding box can be retrieved using the detection methods of menpodetect. The rest of the options are:

max_iters (int or list of int)
Defines the maximum number of iterations. If int, then it specifies the maximum number of iterations over all scales. If list of int, then it specifies the maximum number of iterations per scale. Note that this does not apply on all deformable models. For example, it can control the number of iterations of a Lucas-Kanade optimization algorithm, but it does not affect the fitting of a cascaded-regression method (e.g. SDM) which has a predefined number of cascades (iterations).

gt_shape (PointCloud or None)
The ground truth shape associated to the image. This is only useful to compute the final fitting error. It is not used, of course, at any internal stage of the optimisation.

return_costs (bool)
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 the costs computation increases the computational cost of the fitting. The additional computation cost depends on the fitting method. Thus, this option should only be used for research purposes. Finally, this argument does not apply to all deformable models.

kwargs (dict)
Additional keyword arguments that can be passed to specific models.

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