ModifiedAlternatingForwardCompositional¶
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class
menpofit.aam.
ModifiedAlternatingForwardCompositional
(aam_interface, eps=1e-05)¶ Bases:
ModifiedAlternating
Modified Alternating Forward Compositional (MAFC) Gauss-Newton algorithm
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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 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.
Returns: fitting_result (
AAMAlgorithmResult
) – The parametric iterative fitting result.
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appearance_model
¶ Returns the appearance model of the AAM.
Type: menpo.model.PCAModel
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template
¶ Returns the template of the AAM (usually the mean of the appearance model).
Type: menpo.image.Image or subclass
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transform
¶ Returns the model driven differential transform object of the AAM, e.g.
DifferentiablePiecewiseAffine
orDifferentiableThinPlateSplines
.Type: subclass of DL
andDX
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