def estimate_alpha_lkm(image, trimap, laplacian_kwargs={}, cg_kwargs={}):
"""
Estimate alpha from an input image and an input trimap as described in Fast Matting Using Large Kernel Matting Laplacian Matrices by :cite:`he2010fast`.
Parameters
----------
image: numpy.ndarray
Image with shape :math:`h \\times w \\times d` for which the alpha matte should be estimated
trimap: numpy.ndarray
Trimap with shape :math:`h \\times w` of the image
laplacian_kwargs: dictionary
Arguments passed to the :code:`lkm_laplacian` function
cg_kwargs: dictionary
Arguments passed to the :code:`cg` solver
Returns
-------
alpha: numpy.ndarray
Estimated alpha matte
Example
-------
>>> from pymatting import *
>>> image = load_image("data/lemur/lemur.png", "RGB")
>>> trimap = load_image("data/lemur/lemur_trimap.png", "GRAY")
>>> alpha = estimate_alpha_lkm(
... image,
... trimap,
... laplacian_kwargs={"epsilon": 1e-6, "radius": 15},
... cg_kwargs={"maxiter":2000})
"""
sanity_check_image(image)
L_matvec, diag_L = lkm_laplacian(image, **laplacian_kwargs)
is_fg, is_bg, is_known, is_unknown = trimap_split(trimap)
lambda_value = 100.0
c = lambda_value * is_known
b = lambda_value * is_fg
inv_diag_A = 1.0 / (diag_L + c)
def A_matvec(x):
return L_matvec(x) + c * x
def jacobi(x):
return inv_diag_A * x
x = cg(A_matvec, b, M=jacobi, **cg_kwargs)
alpha = np.clip(x, 0, 1).reshape(trimap.shape)
return alpha
def estimate_alpha_lkm(image, trimap, laplacian_kwargs={}, cg_kwargs={}):
"""
Estimate alpha from an input image and an input trimap using Learning Based Digital Matting as proposed by :cite:`he2010fast`.
Parameters
-----------------
image: numpy.ndarray
Image with shape :math:`h \\times w \\times d` for which the alpha matte should be estimated
trimap: numpy.ndarray
Trimap with shape :math:`h \\times w \\times 1` of the image
laplacian_kwargs: dictionary
Arguments passed to the :code:`lkm_laplacian` function
cg_kwargs: dictionary
Arguments passed to the :code:`cg` solver
Returns
----------------
alpha: numpy.ndarray
Estimated alpha matte
"""
L_matvec, diag_L = lkm_laplacian(image, **laplacian_kwargs)
is_fg, is_bg, is_known, is_unknown = trimap_split(trimap)
lambda_value = 100.0
c = lambda_value * is_known
b = lambda_value * is_fg
inv_diag_A = 1.0 / (diag_L + c)
def A_matvec(x):
return L_matvec(x) + c * x
def jacobi(x):
return inv_diag_A * x
x = cg(A_matvec, b, M=jacobi, **cg_kwargs)
alpha = np.clip(x, 0, 1).reshape(trimap.shape)
return alpha