Source code for menpofit.atm.fitter

from menpofit import checks
from menpofit.fitter import MultiScaleParametricFitter

from .algorithm import InverseCompositional


[docs]class LucasKanadeATMFitter(MultiScaleParametricFitter): r""" Class for defining an ATM fitter using the Lucas-Kanade optimization. Parameters ---------- atm : :map:`ATM` or `subclass` The trained ATM model. lk_algorithm_cls : `class`, optional The Lukas-Kanade optimisation algorithm that will get applied. The possible algorithms are: =========================== ============== ============= Class Warp Direction Warp Update =========================== ============== ============= :map:`ForwardCompositional` Forward Compositional :map:`InverseCompositional` Inverse =========================== ============== ============= n_shape : `int` or `float` or `list` of those or ``None``, optional The number of shape components that will be used. If `int`, then it defines the exact number of active components. If `float`, then it defines the percentage of variance to keep. If `int` or `float`, then the provided value will be applied for all scales. If `list`, then it defines a value per scale. If ``None``, then all the available components will be used. Note that this simply sets the active components without trimming the unused ones. Also, the available components may have already been trimmed to `max_shape_components` during training. sampling : `list` of `int` or `ndarray` or ``None`` It defines a sampling mask per scale. If `int`, then it defines the sub-sampling step of the sampling mask. If `ndarray`, then it explicitly defines the sampling mask. If ``None``, then no sub-sampling is applied. """ def __init__(self, atm, lk_algorithm_cls=InverseCompositional, n_shape=None, sampling=None): # Store model self._model = atm # Check parameters checks.set_models_components(atm.shape_models, n_shape) self._sampling = checks.check_sampling(sampling, atm.n_scales) # Get list of algorithm objects per scale interfaces = atm.build_fitter_interfaces(self._sampling) algorithms = [lk_algorithm_cls(interface) for interface in interfaces] # Call superclass super(LucasKanadeATMFitter, self).__init__( scales=atm.scales, reference_shape=atm.reference_shape, holistic_features=atm.holistic_features, algorithms=algorithms) @property def atm(self): r""" The trained ATM model. :type: :map:`ATM` or `subclass` """ return self._model
[docs] def warped_images(self, image, shapes): r""" Given an input test image and a list of shapes, it warps the image into the shapes. This is useful for generating the warped images of a fitting procedure stored within a :map:`MultiScaleParametricIterativeResult`. Parameters ---------- image : `menpo.image.Image` or `subclass` The input image to be warped. shapes : `list` of `menpo.shape.PointCloud` The list of shapes in which the image will be warped. The shapes are obtained during the iterations of a fitting procedure. Returns ------- warped_images : `list` of `menpo.image.MaskedImage` or `ndarray` The warped images. """ return self.algorithms[-1].interface.warped_images(image=image, shapes=shapes)
def __str__(self): # Compute scale info strings scales_info = [] lvl_str_tmplt = r""" - Scale {} - {} active shape components - {} similarity transform components""" for k, s in enumerate(self.scales): scales_info.append(lvl_str_tmplt.format( s, self.atm.shape_models[k].n_active_components, self.atm.shape_models[k].n_global_parameters)) scales_info = '\n'.join(scales_info) cls_str = r"""{class_title} - Scales: {scales} {scales_info} """.format(class_title=self.algorithms[0].__str__(), scales=self.scales, scales_info=scales_info) return self.atm.__str__() + cls_str