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.
- expert_ensemble (subclass of