Large Scale Multi-Illuminant (LSMI) Dataset for Developing White Balance Algorithm under Mixed Illumination

Overview

Large Scale Multi-Illuminant (LSMI) Dataset for Developing White Balance Algorithm under Mixed Illumination (ICCV 2021)

스크린샷 2021-08-21 오후 3 30 22

Dataset License

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

About

[Project site] [Arxiv] [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

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

  1. 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.

  2. 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.

  3. 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 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
Owner
DongYoung Kim
Research Assistant of CIPLAB
DongYoung Kim
Code repository for our paper "Learning to Generate Scene Graph from Natural Language Supervision" in ICCV 2021

Scene Graph Generation from Natural Language Supervision This repository includes the Pytorch code for our paper "Learning to Generate Scene Graph fro

Yiwu Zhong 64 Dec 24, 2022
Code for "Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans" CVPR 2021 best paper candidate

News 05/17/2021 To make the comparison on ZJU-MoCap easier, we save quantitative and qualitative results of other methods at here, including Neural Vo

ZJU3DV 748 Jan 07, 2023
Code release for NeX: Real-time View Synthesis with Neural Basis Expansion

NeX: Real-time View Synthesis with Neural Basis Expansion Project Page | Video | Paper | COLAB | Shiny Dataset We present NeX, a new approach to novel

536 Dec 20, 2022
OneFlow is a performance-centered and open-source deep learning framework.

OneFlow OneFlow is a performance-centered and open-source deep learning framework. Latest News Version 0.5.0 is out! First class support for eager exe

OneFlow 4.2k Jan 07, 2023
High level network definitions with pre-trained weights in TensorFlow

TensorNets High level network definitions with pre-trained weights in TensorFlow (tested with 2.1.0 = TF = 1.4.0). Guiding principles Applicability.

Taehoon Lee 1k Dec 13, 2022
The open source code of SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation.

SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation(ICPR 2020) Overview This code is for the paper: Spatial Attention U-Net for Retinal V

Changlu Guo 151 Dec 28, 2022
MoCap-Solver: A Neural Solver for Optical Motion Capture Data

MoCap-Solver is a data-driven-based robust marker denoising method, which takes raw mocap markers as input and outputs corresponding clean markers and skeleton motions.

55 Dec 28, 2022
Implementation of Bagging and AdaBoost Algorithm

Bagging-and-AdaBoost Implementation of Bagging and AdaBoost Algorithm Dataset Red Wine Quality Data Sets For simplicity, we will have 2 classes of win

Zechen Ma 1 Nov 01, 2021
Trafffic prediction analysis using hybrid models - Machine Learning

Hybrid Machine learning Model Clone the Repository Create a new Directory as assests and download the model from the below link Model Link To Start th

1 Feb 08, 2022
Unified MultiWOZ evaluation scripts for the context-to-response task.

MultiWOZ Context-to-Response Evaluation Standardized and easy to use Inform, Success, BLEU ~ See the paper ~ Easy-to-use scripts for standardized eval

Tomáš Nekvinda 38 Dec 13, 2022
Checking fibonacci - Generating the Fibonacci sequence is a classic recursive problem

Fibonaaci Series Generating the Fibonacci sequence is a classic recursive proble

Moureen Caroline O 1 Feb 15, 2022
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud, CVPR 2019.

PointRCNN PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud Code release for the paper PointRCNN:3D Object Proposal Generation a

Shaoshuai Shi 1.5k Dec 27, 2022
(CVPR 2022 - oral) Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry

Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry Official implementation of the paper Multi-View Depth Est

Bae, Gwangbin 138 Dec 28, 2022
“英特尔创新大师杯”深度学习挑战赛 赛道3:CCKS2021中文NLP地址相关性任务

ccks2021-track3 CCKS2021中文NLP地址相关性任务-赛道三-冠军方案 团队:我的加菲鱼- wodejiafeiyu 初赛第二/复赛第一/决赛第一 前言 19年开始,陆陆续续参加了一些比赛,拿到过一些top,比较懒一直都没分享过,这次比较幸运又拿了top1,打算分享下 分类的任务

shaochenjie 131 Dec 31, 2022
This repository is for the preprint "A generative nonparametric Bayesian model for whole genomes"

BEAR Overview This repository contains code associated with the preprint A generative nonparametric Bayesian model for whole genomes (2021), which pro

Debora Marks Lab 10 Sep 18, 2022
Discord Multi Tool that focuses on design and easy usage

Multi-Tool-v1.0 Discord Multi Tool that focuses on design and easy usage Delete webhook Block all friends Spam webhook Modify webhook Webhook info Tok

Lodi#0001 24 May 23, 2022
Repo for "Event-Stream Representation for Human Gaits Identification Using Deep Neural Networks"

Summary This is the code for the paper Event-Stream Representation for Human Gaits Identification Using Deep Neural Networks by Yanxiang Wang, Xian Zh

zhangxian 54 Jan 03, 2023
PaddleRobotics is an open-source algorithm library for robots based on Paddle, including open-source parts such as human-robot interaction, complex motion control, environment perception, SLAM positioning, and navigation.

简体中文 | English PaddleRobotics paddleRobotics是基于paddle的机器人开源算法库集,包括人机交互、复杂运动控制、环境感知、slam定位导航等开源算法部分。 人机交互 主动多模交互技术TFVT-HRI 主动多模交互技术是通过视觉、语音、触摸传感器等输入机器人

185 Dec 26, 2022
This is a repository of our model for weakly-supervised video dense anticipation.

Introduction This is a repository of our model for weakly-supervised video dense anticipation. More results on GTEA, Epic-Kitchens etc. will come soon

2 Apr 09, 2022
The official implementation code of "PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense Reconstruction."

PlantStereo This is the official implementation code for the paper "PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense Reconstruction".

Wang Qingyu 14 Nov 28, 2022