color-matcher
Description
color-matcher enables color transfer across images which comes in handy for automatic color-grading of photographs, paintings and film sequences as well as light-field and stopmotion corrections. The methods behind the mappings are based on the approach from Reinhard et al., an analytical solution to a Multi-Variate Gaussian Distribution (MVGD) transfer, the Monge-Kantorovich solution as proposed by Pitie et al. and classical histogram matching.
Results
| Source | Target | Result | |
|---|---|---|---|
| Photograph |  |
 |
 |
| Film sequence |  |
 |
 |
| Light-field correction |  |
 |
 |
| Paintings |  |
 |
 |
Installation
-
- via pip:
-
- install with
pip3 install color-matcher - type
color-matcher -hto the command line once installation finished
- install with
-
- from source:
-
- install Python from https://www.python.org/
- download the source using
git clone https://github.com/hahnec/color-matcher.git - go to the root directory
cd color-matcher - load dependencies
$ pip3 install -r requirements.txt - install with
python3 setup.py install - if installation ran smoothly, enter
color-matcher -hto the command line
CLI Usage
From the root directory of your downloaded repo, you can run the tool on the provided test data by
color-matcher -s './tests/data/scotland_house.png' -r './tests/data/scotland_plain.png'
on a UNIX system where the result is found at ./tests/data/. A windows equivalent of the above command is
color-matcher --src=".\\tests\\data\\scotland_house.png" --ref=".\\tests\\data\\scotland_plain.png"
Alternatively, you can specify the method or select your images manually with
color-matcher --win --method='hm-mkl-hm'
Note that batch processing is possible by passing a source directory, e.g., via
color-matcher -s './tests/data/' -r './tests/data/scotland_plain.png'
More information on optional arguments, can be found using the help parameter
color-matcher -h
API Usage
from color_matcher import ColorMatcher
from color_matcher.io_handler import load_img_file, save_img_file, FILE_EXTS
from color_matcher.normalizer import Normalizer
import os
img_ref = load_img_file('./tests/data/scotland_plain.png')
src_path = '.'
filenames = [os.path.join(src_path, f) for f in os.listdir(src_path)
if f.lower().endswith(FILE_EXTS)]
for i, fname in enumerate(filenames):
img_src = load_img_file(fname)
obj = ColorMatcher(src=img_src, ref=img_ref, method='mkl')
img_res = obj.main()
img_res = Normalizer(img_res).uint8_norm()
save_img_file(img_res, os.path.join(os.path.dirname(fname), str(i)+'.png'))
Citation
@misc{hahne2020plenopticam,
title={PlenoptiCam v1.0: A light-field imaging framework},
author={Christopher Hahne and Amar Aggoun},
year={2020},
eprint={2010.11687},
archivePrefix={arXiv},
primaryClass={eess.IV}
}
37 Dec 22, 2022
41 Dec 19, 2022
0 Feb 6, 2022
49 Dec 5, 2022
2 Oct 6, 2021
1 Dec 21, 2021
15 Nov 20, 2022
6.1k Jan 5, 2023
27 Aug 08, 2022
268 Dec 24, 2022
35 Jan 06, 2023
69 Dec 20, 2022
253 Jan 06, 2023
30 Oct 28, 2022
194 Dec 27, 2022
235 Dec 24, 2022
1 Jan 03, 2022
62 Dec 30, 2022
18 Jul 19, 2022
79 Jan 06, 2023
72 Dec 31, 2022
1 Jun 21, 2022
2.3k Jan 04, 2023
356 Dec 23, 2022
9 Dec 01, 2022
17 Jan 01, 2023
5.5k Jan 05, 2023
62 Dec 21, 2022