RegularisedLandmarkMeanShift

class menpofit.clm.RegularisedLandmarkMeanShift(expert_ensemble, shape_model, kernel_covariance=10, eps=1e-05)

Bases: GradientDescentCLMAlgorithm

Regularized Landmark Mean-Shift (RLMS) algorithm.

Parameters

• expert_ensemble (subclass of ExpertEnsemble) – The ensemble of experts object, e.g. CorrelationFilterExpertEnsemble.

• shape_model (subclass of PDM, optional) – The shape model object, e.g. OrthoPDM.

• kernel_covariance (int or float, optional) – The covariance of the kernel.

• eps (float, optional) – Value for checking the convergence of the optimization.

References

1

J.M. Saragih, S. Lucey, and J. F. Cohn. “Deformable model fitting by regularized landmark mean-shift”, International Journal of Computer Vision (IJCV), 91(2): 200-215, 2011.

run(image, initial_shape, gt_shape=None, max_iters=20, return_costs=False, map_inference=False)

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 this argument currently has no effect and will raise a warning if set to ``True``. This is because it is not possible to evaluate the cost function of this algorithm.

• map_inference (bool, optional) – If True, then the solution will be given after performing MAP inference.

Returns

fitting_result (ParametricIterativeResult) – The parametric iterative fitting result.