ForwardCompositional(template, transform, residual, eps=1e-10)¶
Forward Compositional (FC) Lucas-Kanade algorithm
template (menpo.image.Image or subclass) – The image template.
residual (class subclass, optional) –
The residual that will get applied. All possible residuals are:
Sum of Squared Differences
Sum of Squared Differences on Fourier domain
Enhanced Correlation Coefficient
eps (float, optional) – Value for checking the convergence of the optimization.
run(image, initial_shape, gt_shape=None, max_iters=20, return_costs=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 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.
LucasKanadeAlgorithmResult) – The parametric iterative fitting result.
Given an input test image and a list of shapes, it warps the image into the shapes. This is useful for generating the warped images of a fitting procedure stored within a
image (menpo.image.Image or subclass) – The input image to be warped.
shapes (list of menpo.shape.PointCloud) – The list of shapes in which the image will be warped. The shapes are obtained during the iterations of a fitting procedure.
warped_images (list of menpo.image.MaskedImage or ndarray) – The warped images.