Python implementation of image matting method proposed in A. Levin D. Lischinski and Y. Weiss. A Closed Form Solution to Natural Image Matting. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2006, New York

The repository also contains implementation of background/foreground reconstruction method proposed in Levin, Anat, Dani Lischinski, and Yair Weiss. "A closed-form solution to natural image matting." IEEE Transactions on Pattern Analysis and Machine Intelligence 30.2 (2008): 228-242.

• python 3.5+ (Though it should run on 2.7)
• scipy
• numpy
• opencv-python

Clone this repository and install the closed-form-matting package via pip.

git clone https://github.com/MarcoForte/closed-form-matting.git
cd closed-form-matting/
pip install .

CLI inerface:

# Scribbles input
closed-form-matting ./testdata/source.png -s ./testdata/scribbles.png  -o output_alpha.png

# Trimap input
closed-form-matting ./testdata/source.png -t ./testdata/trimap.png  -o output_alpha.png

# Add flag --solve-fg to compute foreground color and output RGBA image instead
# of alpha.

Python interface:

import closed_form_matting
...
# For scribles input
alpha = closed_form_matting.closed_form_matting_with_scribbles(image, scribbles)

# For trimap input
alpha = closed_form_matting.closed_form_matting_with_trimap(image, trimap)

# For prior with confidence
alpha = closed_form_matting.closed_form_matting_with_prior(
    image, prior, prior_confidence, optional_const_mask)

# To get Matting Laplacian for image
laplacian = closed_form_matting.compute_laplacian(image, optional_const_mask)

CLI interface (requires opencv-python):

solve-foreground-background image.png alpha.png foreground.png background.png

Python interface:

from closed_form_matting import solve_foreground_background
...
foreground, background = solve_foreground_background(image, alpha)

Original image	Scribbled image	Output alpha	Output foreground

The computation is generally faster than the matlab version thanks to more vectorization. Note. The computed laplacian is slightly different due to array ordering in numpy being different than in matlab. To get same laplacian as in matlab change,

indsM = np.arange(h*w).reshape((h, w)) ravelImg = img.reshape(h*w, d) to indsM = np.arange(h*w).reshape((h, w), order='F') ravelImg = img.reshape(h*w, d, , order='F'). Again note that this will result in incorrect alpha if the D_s, b_s orderings are not also changed to order='F'F.

For more information see the original paper http://www.wisdom.weizmann.ac.il/~levina/papers/Matting-Levin-Lischinski-Weiss-CVPR06.pdf The original matlab code is here http://www.wisdom.weizmann.ac.il/~levina/matting.tar.gz

The code is free for academic/research purpose. Use at your own risk and we are not responsible for any loss resulting from this code. Feel free to submit pull request for bug fixes.