LSMI Dataset Version : 1.1

1.0 : LSMI dataset released. (Aug 05, 2021)

1.1 : Add option for saving sub-pair images for 3-illuminant scene (ex. _1,_12,_13) & saving subtracted image (ex. _2,_3,_23) (Feb 20, 2022)

[Paper] [Project site] [Download Dataset] [Video]

This is an official repository of "Large Scale Multi-Illuminant (LSMI) Dataset for Developing White Balance Algorithm under Mixed Illumination", which is accepted as a poster in ICCV 2021.

This repository provides

1. Preprocessing code of "Large Scale Multi Illuminant (LSMI) Dataset"
2. Code of Pixel-level illumination inference U-Net
3. Pre-trained model parameter for testing U-Net

If you use our code or dataset, please cite our paper:

@inproceedings{kim2021large,
  title={Large Scale Multi-Illuminant (LSMI) Dataset for Developing White Balance Algorithm Under Mixed Illumination},
  author={Kim, Dongyoung and Kim, Jinwoo and Nam, Seonghyeon and Lee, Dongwoo and Lee, Yeonkyung and Kang, Nahyup and Lee, Hyong-Euk and Yoo, ByungIn and Han, Jae-Joon and Kim, Seon Joo},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={2410--2419},
  year={2021}
}

Our running environment is as follows:

• Python version 3.8.3
• Pytorch version 1.7.0
• CUDA version 11.2

We provide a docker image, which supports all extra requirements (ex. dcraw,rawpy,tensorboard...), including specified version of python, pytorch, CUDA above.

You can download the docker image here.

The following instructions are assumed to run in a docker container that uses the docker image we provided.

In the docker container, clone this repository first.

git clone https://github.com/DY112/LSMI-dataset.git

You should first download the LSMI dataset from here.

The dataset is composed of 3 sub-folers named "galaxy", "nikon", "sony".

Folders named by each camera include several scenes, and each scene folder contains full-resolution RAW files and JPG files that is converted to sRGB color space.

Move all three folders to the root of cloned repository.

In each sub-folders, we provides metadata (meta.json), and train/val/test scene index (split.json).

In meta.json, we provides following informations.

• NumOfLights : Number of illuminants in the scene
• MCCCoord : Locations of Macbeth color chart
• Light1,2,3 : Normalized chromaticities of each illuminant (calculated through running 1_make_mixture_map.py)

0. Convert raw images to tiff files

   To convert original 1-channel bayer-pattern images to 3-channel RGB tiff images, run following code:

   python 0_cvt2tiff.py

   You should modify SOURCE and EXT variables properly.

   The converted tiff files are generated at the same location as the source file.

   This process uses DCRAW command, with '-h -D -4 -T' as options.

   There is no black level subtraction, saturated pixel clipping or else.

   You can change the parameters as appropriate for your purpose.

1. Make mixture map

   python 1_make_mixture_map.py

   Change the CAMERA variable properly to the target directory you want.

   This code does the following operations for each scene:

   • Subtract black level (no saturation clipping)
   • Use Macbeth Color Chart's achromatic patches, find each illuminant's chromaticities
   • Use green channel pixel values, calculate pixel level illuminant mixture map
   • Mask uncalculable pixel positions (which have 0 as value for all scene pairs) to ZERO_MASK

   After running this code, npy tpye mixture map data will be generated at each scene's directory.

   ⚠️ If you run this code with ZERO_MASK=-1, the full resolution mixture map may contains -1 for uncalculable pixels. You MUST replace this value appropriately before resizing to prevent this negative value from interpolating with other values.

2. Crop for train/test U-Net (Optional)

   python 2_preprocess_data.py

   This preprocessing code is written only for U-Net, so you can skip this step and freely process the full resolution LSMI set (tiff and npy files).

   The image and the mixture map are resized as a square with a length of the SIZE variable inside the code, and the ground-truth image is also generated.

   Note that the side of the image will be cropped to make the image shape square.

   If you don't want to crop the side of the image and just want to resize whole image anyway, use SQUARE_CROP=False

   We set the default test size to 256, and set train size to 512, and SQUARE_CROP=True.

   The new dataset is created in a folder with the name of the CAMERA_SIZE. (Ex. galaxy_512)

You can download pre-trained model parameter here.

Pre-trained model is trained on 512x512 data with random crop & random pixel level relighting augmentation method.

Locate downloaded models folder into SVWB_Unet.

• Test U-Net

  cd SVWB_Unet
  sh test.sh

• Train U-Net

  cd SVWB_Unet
  sh train.sh

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.