def estimate_alpha_rw(
image, trimap, preconditioner=None, laplacian_kwargs={}, cg_kwargs={}
):
"""
Estimate alpha from an input image and an input trimap using Learning Based Digital Matting as proposed by :cite:`grady2005random`.
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
preconditioner: function or scipy.sparse.linalg.LinearOperator
Function or sparse matrix that applies the preconditioner to a vector (default: jacobi)
laplacian_kwargs: dictionary
Arguments passed to the :code:`rw_laplacian` function
cg_kwargs: dictionary
Arguments passed to the :code:`cg` solver
Returns
----------------
alpha: numpy.ndarray
Estimated alpha matte
"""
if preconditioner is None:
preconditioner = jacobi
A, b = make_linear_system(rw_laplacian(image, **laplacian_kwargs), trimap)
x = cg(A, b, M=preconditioner(A), **cg_kwargs)
alpha = np.clip(x, 0, 1).reshape(trimap.shape)
return alpha
def estimate_alpha_lbdm(image,
trimap,
preconditioner=None,
laplacian_kwargs={},
cg_kwargs={}):
"""
Estimate alpha from an input image and an input trimap using Learning Based Digital Matting as proposed by :cite:`zheng2009learning`.
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
preconditioner: function or scipy.sparse.linalg.LinearOperator
Function or sparse matrix that applies the preconditioner to a vector (default: ichol)
laplacian_kwargs: dictionary
Arguments passed to the :code:`lbdm_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_lbdm(
... image,
... trimap,
... laplacian_kwargs={"epsilon": 1e-6},
... cg_kwargs={"maxiter":2000})
"""
if preconditioner is None:
preconditioner = ichol
sanity_check_image(image)
A, b = make_linear_system(lbdm_laplacian(image, **laplacian_kwargs),
trimap)
x = cg(A, b, M=preconditioner(A), **cg_kwargs)
alpha = np.clip(x, 0, 1).reshape(trimap.shape)
return alpha
def estimate_alpha_knn(image,
trimap,
preconditioner=None,
laplacian_kwargs={},
cg_kwargs={}):
"""
Estimate alpha from an input image and an input trimap using KNN Matting similar to :cite:`chen2013knn`.
See `knn_laplacian` for more details.
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
preconditioner: function or scipy.sparse.linalg.LinearOperator
Function or sparse matrix that applies the preconditioner to a vector (default: jacobi)
laplacian_kwargs: dictionary
Arguments passed to the :code:`knn_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_knn(
... image,
... trimap,
... laplacian_kwargs={"n_neighbors": [15, 10]},
... cg_kwargs={"maxiter":2000})
"""
if preconditioner is None:
preconditioner = jacobi
sanity_check_image(image)
A, b = make_linear_system(knn_laplacian(image, **laplacian_kwargs), trimap)
x = cg(A, b, M=preconditioner(A), **cg_kwargs)
alpha = np.clip(x, 0, 1).reshape(trimap.shape)
return alpha
def estimate_alpha_cf(image,
trimap,
preconditioner=None,
laplacian_kwargs={},
cg_kwargs={}):
"""
Estimate alpha from an input image and an input trimap using Closed-Form Alpha Matting as proposed by :cite:`levin2007closed`.
Parameters
-----------------
image: numpy.ndarray
Image with shape :math:`h \\times w \\times d` for which the foreground should be estimated
trimap: numpy.ndarray
Trimap with shape :math:`h \\times w \\times 1` of the image
preconditioner: function or scipy.sparse.linalg.LinearOperator
Function or sparse matrix that applies the preconditioner to a vector (default: ichol)
laplacian_kwargs: dictionary
Arguments passed to the :code:`cf_laplacian` function
cg_kwargs: dictionary
Arguments passed to the :code:`cg`
Returns
----------------
alpha: numpy.ndarray
Estimated alpha matte
"""
if preconditioner is None:
preconditioner = ichol
A, b = make_linear_system(cf_laplacian(image, **laplacian_kwargs), trimap)
x = cg(A, b, M=preconditioner(A), **cg_kwargs)
alpha = np.clip(x, 0, 1).reshape(trimap.shape)
return alpha