maheshkkumar/estimate_illum

Illumination Estimation and Relighting

This repository contains the source code for Single Indoor Scene Image Illumination Estimation and Relighting.

Setup

To setup the required environent to run the experiments, you could do it in two ways.

Option 1: Using pip

In a new python environment created using either conda or virtualenv, use the following command to install the required packages in the requirements.txt file.

pip install -r requirements.txt

Option 2: Using conda

This option allows you use conda to setup all the dependencies by using the environment.yml file.

conda create -n illum_est
conda activate illum_est
conda env update -f environment.yml

Dataset

All our experiments use the Multi-illumination dataset for both illumination estimation and relighting. The dataset is split into train, test, and val text files and can be found under data folder.

Training

To train the all illumination estimation models, set the model flag in train.sh to the appropriate architecture name (i.e., original, vgg, resnet18, or unet).

bash train.sh

To train the baseline image relighting modele for left-right relighting along with illumination estimation.

bash train_relighting_baseline.sh

Additionally, to train the baseline random relighting model use --random_relight boolean flag in train_relighting_baseline.sh script.

To train the best model for relighting.

bash train_relighting.sh

Note: Make necessary changes in the training/eval .sh files for specific training/evaluation.

Evaluation

To evaluate the model use the appropriate evaluation script. For illumination estimation models:

bash eval.sh

For image relighting models:

bash eval_relighting.sh

Acknowledgements

Some of the code is borrowed from Multi-illumination.