ForwardAdditive¶

class
menpofit.lk.
ForwardAdditive
(template, transform, residual, eps=1e10)[source]¶ Bases:
LucasKanade
Forward Additive (FA) LucasKanade algorithm.

run
(image, initial_shape, gt_shape=None, max_iters=20, return_costs=False)[source]¶ Execute the optimization algorithm.
 Parameters
image (menpo.image.Image) – The input test image.
initial_shape (menpo.shape.PointCloud) – The initial shape from which the optimization will start.
gt_shape (menpo.shape.PointCloud or
None
, optional) – The ground truth shape of the image. It is only needed in order to get passed in the optimization result object, which has the ability to compute the fitting error.max_iters (int, optional) – The maximum number of iterations. Note that the algorithm may converge, and thus stop, earlier.
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 the costs computation increases the computational cost of the fitting. The additional computation cost depends on the fitting method. Only use this option for research purposes.
 Returns
fitting_result (
LucasKanadeAlgorithmResult
) – The parametric iterative fitting result.

warped_images
(image, shapes)¶ 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
LucasKanadeResult
. 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.
