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., the Monge-Kantorovich Linearization (MKL) as proposed by Pitie et al. and our analytical solution to a Multi-Variate Gaussian Distribution (MVGD) transfer in conjunction with classical histogram matching. As shown below our HM-MVGD-HM compound outperforms existing methods.

Results

	Source	Target	Result
Photograph
Film sequence
Light-field correction
Paintings

Installation

• via pip:

  1. install with pip3 install color-matcher
  2. type color-matcher -h to the command line once installation finished

• from source:

  1. install Python from https://www.python.org/
  2. download the source using git clone https://github.com/hahnec/color-matcher.git
  3. go to the root directory cd color-matcher
  4. load dependencies $ pip3 install -r requirements.txt
  5. install with python3 setup.py install
  6. if installation ran smoothly, enter color-matcher -h to 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)]

cm = ColorMatcher()
for i, fname in enumerate(filenames):
    img_src = load_img_file(fname)
    img_res = cm.transfer(src=img_src, ref=img_ref, method='mkl')
    img_res = Normalizer(img_res).uint8_norm()
    save_img_file(img_res, os.path.join(os.path.dirname(fname), str(i)+'.png'))

Experimental results

The above diagram illustrates light-field color consistency from Wasserstein metric W₁ and histogram distance D₂ where low values indicate higher similarity between source r and target z. These distance metrics are computed as follows

where f(k,  ⋅ ) and F(k,  ⋅ ) represent the Probability Density Function (PDF) and Cumulative Density Function (CDF) at intensity level k, respectively. More detailed information can be found in our IEEE paper.

Citation

@ARTICLE{plenopticam,
    author={Hahne, Christopher and Aggoun, Amar},
    journal={IEEE Transactions on Image Processing},
    title={PlenoptiCam v1.0: A Light-Field Imaging Framework},
    year={2021},
    volume={30},
    number={},
    pages={6757-6771},
    doi={10.1109/TIP.2021.3095671}
}

Author

Christopher Hahne