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