Large Scale Multi-Illuminant (LSMI) Dataset for Developing White Balance Algorithm under Mixed Illumination (ICCV 2021)
Dataset License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
About
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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
- Preprocessing code of “Large Scale Multi Illuminant (LSMI) Dataset”
- Code of Pixel-level illumination inference U-Net
- Pre-trained model parameter for testing U-Net
Requirements
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.
Getting Started
Clone this repo
In the docker container, clone this repository first.
git clone https://github.com/DY112/LSMI-dataset.git
Download the LSMI dataset
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.
Preprocess the LSMI dataset
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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.
-
Make mixture map
python 1_make_mixture_map.py
Change the CAMERA variable properly to the target directory you want.
.npy tpye mixture map data will be generated at each scene’s directory.
-
Crop
python 2_preprocess_data.py
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.
We set the size to 256 to test the U-Net, and 512 for train the U-Net.
Here, to test the pre-trained U-Net, set size to 256.
The new dataset is created in a folder with the name of the CAMERA_SIZE. (Ex. galaxy_256)
Use U-Net for pixel-level AWB
You can download pre-trained model parameter here.
Pre-trained model is trained on 512×512 data with random crop & random pixel level relighting augmentation method.
Locate downloaded models folder into SVWB_Unet.
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Test U-Net
cd SVWB_Unet sh test.sh -
Train U-Net
cd SVWB_Unet sh train.sh
