jamesli1618 / angularGAN

Supporting code to the paper "Conditional GANs for Multi-Illuminant Color Constancy: Revolution or Yet Another Approach?"

Conditional GANs for Multi-Illuminant Color Constancy: Revolution or Yet Another Approach?

Supporting code to the paper
O Sidorov. Conditional GANs for Multi-Illuminant Color Constancy: Revolution or Yet Another Approach?

AngularGAN

The work presents an extension of the supervised image-to-image translation algorithm "pix2pix" by Isola et al. orriented specifically to the color constancy task.

AngularGAN inherits from this implementation of pix2pix in PyTorch. Therefore, you may follow original instruction for installation and dependincies. The new modules are implemented in Torch and do not require additional packages.

Datasets are below!

Getting started

• Put your data in datasets/facades in the format

-facedes/
    -test/
        -xxx.jpg
        -yyy.jpg
        -...
    -train/
        -zzz.jpg
        -...

where each image consist of couple of images A and B (input and output) concatenated along horizontal axis.

• Run visdom to open training visualization (optional)
• Run training (change parameter --model angular_gan_v2 to use v2)

chmod a+x run.sh
./run.sh

• Replace runtest.sh for testing (change parameter --model angular_gan_v2 to use v2)
  
  We thank autors of pix2pix for their excellent work!

Datasets

Tinted Multi-illuminant dataset

The MATLAB code generate_tinted_images.m allows to apply multi-illuimnant color cast to the input images. The tint maps are randomized and are not coherent between frames.
You can use the provided file real_illum_11346_Normalized.mat or create your own by simple normalization of the original illumination vectors as e_norm = e./norm(e).

GTAV Shadow Removal Dataset

The GTAV Shadow Removal Dataset of 5,723 image pairs with and without shadows may be acessed by the link.

Preview