- Ubuntu 16.04
- Pillow
- PyTorch >= 1.1.0
- Numpy
- gdown (for fetching pretrained models)
git clone https://github.com/minhmanho/deep_preset.git
cd deep_preset
Positive Pair-wise Loss (PPL) could improve Deep Preset in directly stylizing photos; however, it became worse in predicting preset, as described in our paper. Therefore, depending on your needs, please download Deep Preset with PPL for directly stylizing photos
./models/fetch_model_wPPL.sh
Or Deep Preset without PPL for preset prediction.
./models/fetch_model_woPPL.sh
Run our Deep Preset to stylize photos as:
CUDA_VISIBLE_DEVICES=0 python run.py \
--content ./data/content/ \
--style ./data/style/ \
--out ./data/out/ \
--ckpt ./models/dp_wPPL.pth.tar \
--size 512x512
Where --size is for the photo size [Width]x[Height], which should be divisible by 16.
Besides, --size set as 352x352 will activate the preset prediction.
In case of only preset prediction needed, please add --p as:
CUDA_VISIBLE_DEVICES=0 python run.py \
--content ./data/content/ \
--style ./data/style/ \
--out ./data/out/ \
--ckpt ./models/dp_woPPL.pth.tar \
--p
After processing, the predicted preset will be stored as a JSON file revealing how Lightroom settings are adjusted, as follows:
{
"Highlights2012": -23,
"Shadows2012": 4,
"BlueHue": -8,
"Sharpness": 19,
"Clarity2012": -2
...
}
I planned to share the dataset and Lightroom Add-ons publicly. However, I don't have much time for it these days. Uploading the whole dataset is inefficient since it is about ~500GB. You can download the training images with natural colors labeled as "0" at this Google Drive Afterward, you will need to script Lightroom (or create a Plugin/Add-ons) to generate other styles.
(You can refer to these ugly lines of code)
local catalog = LrApplication.activeCatalog()
local photos = catalog:getTargetPhotos()
local catalog_folder = LrPathUtils.parent(catalog:getPath())
pfolder = "<folder containing json presets>"
outfolder = "<out folder>"
for i=1,500,1 do
pdir = pfolder .. tostring(i) .. ".json"
local file = io.open(pdir, 'r')
if file then
local contents = file:read( "*a" )
p = json.decode(contents);
io.close( file )
else
p = nil
end
for j, photo in ipairs(photos) do
catalog:withWriteAccessDo ("Apply preset", function()
local tmp = LrApplication.addDevelopPresetForPlugin( _PLUGIN, "Preset " .. tostring(i), p)
photo:applyDevelopPreset (tmp, _PLUGIN)
end)
end
local _out = outfolder .. tostring(i)
LrFileUtils.createDirectory(_out)
exportImage.exportPhotos(photos, _out)
Please check Adobe Lightroom software development kit (SDK) for more details.
If you find this work useful, please consider citing:
@InProceedings{Ho_2021_WACV,
author = {Ho, Man M. and Zhou, Jinjia},
title = {Deep Preset: Blending and Retouching Photos With Color Style Transfer},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {2021},
pages = {2113-2121}
}
We would like to thank:
-
Do Khang for the photos of Duyen To.
-
digantamisra98 for the Unofficial PyTorch Implementation of EvoNorm
Liu, Hanxiao, Andrew Brock, Karen Simonyan, and Quoc V. Le. "Evolving Normalization-Activation Layers."
arXiv preprint arXiv:2004.02967 (2020).
- and Richard Zhang for the BlurPool.
Zhang, Richard. "Making convolutional networks shift-invariant again."
ICML (2019).
Our code and trained models are for non-commercial uses and research purposes only.
If you have any questions, feel free to contact me (maintainer) at [email protected]
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent