RegularisedLandmarkMeanShift(expert_ensemble, shape_model, kernel_covariance=10, eps=1e-05)¶
Regularized Landmark Mean-Shift (RLMS) algorithm.
expert_ensemble (subclass of
ExpertEnsemble) – The ensemble of experts object, e.g.
kernel_covariance (int or float, optional) – The covariance of the kernel.
eps (float, optional) – Value for checking the convergence of the optimization.
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.
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.
ParametricIterativeResult) – The parametric iterative fitting result.