colorcorrect by Shunsuke Aihara - http://argmax.jp
date:
February 2012
description:
Imprement some of color correction algorithms
require:
python3.10 or later numpy>=2.0 Pillow>=10.0 a C++ compiler (the block based algorithms and the sampled ACE are in C++)
install:
% pip install . (a C++ compiler is needed; "python -m build" creates sdist and wheel)
test:
% pip install -e . % python -m unittest discover -s test -p "*_test.py"
impement algorithms:
gray world
colorcorrect.algorithm.gray_world
usage: image
max white
colorcorrect.algorithm.max_white
usage: image
stretch
colorcorrect.algorithm.stretch
usage: image
retinex
colorcorrect.algorithm.retinex
usage: image
retinex with adjust (gray world + retinex quadratic mapping)
colorcorrect.algorithm.retinex_with_adjust
usage: image
standard deviation weighted grey world
colorcorrect.algorithm.standard_deviation_weighted_grey_world
usage: image,subblock width(default:20), subblock height(default:20)
standard deviation and luminance weighted gray world
colorcorrect.algorithm.standard_deviation_and_luminance_weighted_gray_world
usage: image,subblock width(default:20), subblock height(default:20)
luminance weighted gray world
colorcorrect.algorithm.luminance_weighted_gray_world
usage: image,subblock width(default:20), subblock height(default:20)
(each block based algorithm also has a pure numpy twin with a _python suffix)
automatic color equalization
colorcorrect.algorithm.automatic_color_equalization
usage: image,slope(default:10),limit(default:1000),samples(default:500),
method(default:"fft"),levels(default:16),seed(default:None),workers(default:None),
min_distance(default:None),preserve_chroma(default:False)
method="fft" evaluates the full image sum of the paper with FFT
convolutions (exact up to the interpolation over `levels` grey
levels, deterministic, multi-threaded). method="sampling" is the
Monte-Carlo C++ implementation comparing every pixel with
`samples` random pixels; pass `seed` for reproducible output and
`min_distance` (default: half the mean sample spacing) to keep a
sample right next to a pixel from dominating it.
The saturation function is r(x) = clip(slope*x, -limit, limit) on
pixel differences in [-255, 255]; only slope/limit matters.
preserve_chroma=True runs ACE on the luminance only and keeps the
colour of every pixel (an extension of the paper); combined with
retinex_with_adjust it adds local contrast without desaturating:
>>> cca.automatic_color_equalization(cca.retinex_with_adjust(img), preserve_chroma=True)
All functions take and return (height, width, 3) uint8 numpy arrays and never modify their input.
sample usage:
>>> from PIL import Image
>>> import colorcorrect.algorithm as cca
>>> from colorcorrect.util import from_pil, to_pil
>>> img = Image.open('/path/to/image')
>>> to_pil(cca.stretch(from_pil(img))).show()
References:
Denis. Nikitenko, M. Wirth and K. Trudel, "Applicability Of White-Balancing Algorithms to Restoring Faded Colour Slides: An Empirical Evaluation.", Journal of Multimedia, vol. 3, no. 5, 2008. HK. Lam, OC. Au and CW. Wong, "Automatic white balancing using luminance component and standard deviation of RGB components", in Proc. ICASSP, 2004. E. Lam, "Combining gray world and retinex theory for automatic white balance in digital photography", in Proc. ISCE, 2005. A. Rizzi, C. Gatta and D. Marini, "A new algorithm for unsupervised global and local color correction.", Pattern Recognition Letters, vol. 24, no. 11, 2003. P. Getreuer, "Automatic Color Enhancement (ACE) and its Fast Implementation", Image Processing On Line, 2012. (FFT evaluation of ACE)
1 maintainer
Metadata
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Release files for colorcorrect 0.9.3
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