CONTENTS

Foreground Estimation

pymatting.estimate_foreground_cf

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Signature

estimate_foreground_cf( image, alpha, regularization=1e-5, rtol=1e-5, neighbors=[(-1, 0), (1, 0), (0, -1), (0, 1)], return_background=False, foreground_guess=None, background_guess=None, ichol_kwargs={}, cg_kwargs={})

Function Description

Estimates the foreground of an image given alpha matte and image.
This method is based on the publication [LLW07].

Parameters

  • image (numpy.ndarray)
      Input image with shape \(h \times w \times d\)
  • alpha (numpy.ndarray)
      Input alpha matte with shape \(h \times w\)
  • regularization (float)
      Regularization strength \(\epsilon\), defaults to \(10^{-5}\)
  • neighbors (list of tuples of ints)
      List of relative positions that define the neighborhood of a pixel
  • return_background (bool)
      Whether to return the estimated background in addition to the foreground
  • foreground_guess (numpy.ndarray)
      An initial guess for the foreground image in order to accelerate convergence. Using input image by default.
  • background_guess (numpy.ndarray)
      An initial guess for the background image. Using input image by default.
  • ichol_kwargs (dictionary)
      Keyword arguments for the incomplete Cholesky preconditioner
  • cg_kwargs (dictionary)
      Keyword arguments for the conjugate gradient descent solver

Returns

  • F (numpy.ndarray)
      Extracted foreground
  • B (numpy.ndarray)
      Extracted background (not returned by default)

Example

>>> from pymatting import * >>> image = load_image("data/lemur/lemur.png", "RGB") >>> alpha = load_image("data/lemur/lemur_alpha.png", "GRAY") >>> F = estimate_foreground_cf(image, alpha, return_background=False) >>> F, B = estimate_foreground_cf(image, alpha, return_background=True)

See Also

    stack_images: This function can be used to place the foreground on a new background.

    pymatting.estimate_foreground_ml

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    Signature

    estimate_foreground_ml( image, alpha, regularization=1e-5, n_small_iterations=10, n_big_iterations=2, small_size=32, return_background=False, gradient_weight=1.0)

    Function Description

    Estimates the foreground of an image given its alpha matte.
    See [GUCH20] for reference.

    Parameters

    • image (numpy.ndarray)
        Input image with shape \(h \times w \times d\)
    • alpha (numpy.ndarray)
        Input alpha matte shape \(h \times w\)
    • regularization (float)
        Regularization strength \(\epsilon\), defaults to \(10^{-5}\). Higher regularization results in smoother colors.
    • n_small_iterations (int)
        Number of iterations performed on small scale, defaults to \(10\)
    • n_big_iterations (int)
        Number of iterations performed on large scale, defaults to \(2\)
    • small_size (int)
        Threshold that determines at which size n_small_iterations should be used
    • return_background (bool)
        Whether to return the estimated background in addition to the foreground
    • gradient_weight (float)
        Larger values enforce smoother foregrounds, defaults to \(1\)

    Returns

    • F (numpy.ndarray)
        Extracted foreground
    • B (numpy.ndarray)
        Extracted background

    Example

    >>> from pymatting import * >>> image = load_image("data/lemur/lemur.png", "RGB") >>> alpha = load_image("data/lemur/lemur_alpha.png", "GRAY") >>> F = estimate_foreground_ml(image, alpha, return_background=False) >>> F, B = estimate_foreground_ml(image, alpha, return_background=True)

    See Also

      stack_images: This function can be used to place the foreground on a new background.

      pymatting.estimate_foreground_ml_cupy

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      Signature

      estimate_foreground_ml_cupy( input_image, input_alpha, regularization=1e-5, n_small_iterations=10, n_big_iterations=2, small_size=32, block_size=(32, 32), return_background=False)

      Function Description

      See the estimate_foreground method for documentation.

      pymatting.estimate_foreground_ml_pyopencl

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      Signature

      estimate_foreground_ml_pyopencl( input_image, input_alpha, regularization=1e-5, n_small_iterations=10, n_big_iterations=2, small_size=32, return_background=False)

      Function Description

      See the estimate_foreground method for documentation.