Alternating Forward Compositional (AFC) Gauss-Newton algorithm.
run(image, initial_shape, gt_shape=None, max_iters=20, return_costs=False, map_inference=False)¶
Execute the optimization algorithm.
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.
map_inference (bool, optional) – If
True, then the solution will be given after performing MAP inference.
AAMAlgorithmResult) – The parametric iterative fitting result.
Returns the appearance model of the AAM.
Returns the template of the AAM (usually the mean of the appearance model).
menpo.image.Image or subclass