Skip to content

Latest commit

 

History

History
133 lines (86 loc) · 3.02 KB

README.md

File metadata and controls

133 lines (86 loc) · 3.02 KB
Raw

ExpandNet

Create HDR images from LDR inputs:

| (left) LDR input | (middle) Exposures taken from the predicted HDR | (right) Tone mapped HDR |

Training and inference code for:

ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content

Demetris Marnerides, Thomas Bashford-Rogers, Jonathan Hatchett and Kurt Debattista

Paper was presented at Eurographics 2018 and published in Computer Graphics Forum.

(arxiv version)

Prerequisites

Requires the PyTorch library along with OpenCV. Python >= 3.6 supported only.

First follow the instructions for installing PyTorch.

To install OpenCV use:

conda install opencv3 -c menpo

NOTE There might be training issues when using latest versions of OpenCV for the tone mapping operators (issue #14)

Usage

The expand.py script accepts an LDR image as input, and creates an HDR Image prediction, (.hdr or .exr file if --use_exr flag is used).

python expand.py  ldr_input.jpg

Can also do batch processing:

python expand.py  *.jpg

Or take whole directory as input:

python expand.py  path/to/ldr_dir

To put results in a separate directory:

python expand.py  *.jpg --out results/

Results can be resized and filenames can be tagged:

python expand.py test.jpg --resize True --height 960 --width 540 --tag my-tag

If a GPU is available and Pytorch detects it then the conversion will happen on the GPU. To force CPU usage (slower) set --use_gpu False.

The network operates on patches to limit memory usage when expanding high resolution images. The flag --patch_size adjusts the size.

The resulting HDR Image can be tone mapped using the TMOs provided by OpenCV, or using the exposure operator.

python expand.py  test.jpg --tone_map reinhard

Testing on video

Use --video True (along with a tone mapper --tone_map) to test video conversion.

WARNING This is completely new and experimental. Very slow and memory consuming! Should only test short low-res clips. The model is not designed for videos thus flickering occurs which is reduced in the code by smoothing luminance percentile curves.

In the following example, on the left is the LDR video input and on the right is the (reinhard) tone mapped prediction.

Any tips to improve this are welcome!

Training

To train:

python train.py

For more training configuration and settings please use:

python train.py --help

Viewing HDR Images

There is a very easy to use online viewer at openhdr.org which also has tone mapping functionality.

Contact: [email protected]