Realize arbitrary image style transfer through Multi-pooling decomposition and reconstruction based on adaptive principal component extraction and color transformation (PCECT) algorithm
PyTorch implementation for photorealistic style transfer that does not need any further post-processing steps; e.g. from winter to spring, from summer to autumn, etc. this is one of the best pre-trained models available today to achieve end-to-end style stylization
The code was written by Raeyi.
- PyTorch >= 0.4.1
- Check the requirements.txt
pip install -r requirements.txt
- Clone this repo:
git clone https://github.com/Raeyi/multipooling-AdaPECT.git
cd multipooling-AdaPECT
- Pretrained models can be found in the
./Pretrained_model
- Prepare image dataset
- Images can be found in DPST repo
- You can find the entire content and style images (with paired segmentation label maps) in the following link DPST images. input folder has the content images and the style folder has the style images. Every segmention map can be found in the segmentation folder.
- To make a new dataset with label pairs, please follow the instruction of PhotoWCT repo
- Put the content and style images with their segment label pairs (if available) into the
example
folder accordingly.- Currently there are several example images so that you can execute the code as soon as you clone this repo.
- Images can be found in DPST repo
- Finally, test the model:
python LapSobGaus_transfer.py --option_unpool sum -a --content ./examples/content/coco --style ./examples/style/coco --content_segment ./examples/content_segment --style_segment ./examples/style_segment/ --output ./outputs/ --verbose --image_size 512
The test results will be saved to ./outputs
by default.
--content
: FOLDER-PATH-TO-CONTENT-IMAGES--content_segment
: FOLDER-PATH-TO-CONTENT-SEGMENT-LABEL-IMAGES--style
: FOLDER-PATH-TO-STYLE-IMAGES--style_segment
: FOLDER-PATH-TO-STYLE-SEGMENT-LABEL-IMAGES--output
: FOLDER-PATH-TO-OUTPUT-IMAGES--image_size
: output image size--alpha
: alpha determines the blending ratio between content and stylized features--option_unpool
: two versions of our model (sum, cat5)-e
,--transfer_at_encoder
: stylize at the encoder module-d
,--transfer_at_decoder
: stylize at the decoder module-s
,--transfer_at_skip
: stylize at the skipped high frequency components-a
,--transfer_all
: stylize and save for every composition; i.e. power set of {-e,-d,-s})--cpu
: run on CPU--verbose
- DPST: "Deep Photo Style Transfer" | Paper | Code
- PhotoWCT: "A Closed-form Solution to Photorealistic Image Stylization" | Paper | Code
- PhotoWCT (full): PhotoWCT + post processing
- WCT2:Photorealistic Style Transfer via Wavelet Transforms | paper | supplementary materials
- Only for
option_unpool = sum
version - Full stylization
python transfer.py --option_unpool sum -e -s --content ./examples/content --style ./examples/style --content_segment ./examples/content_segment --style_segment ./examples/style_segment/ --output ./outputs/ --verbose --image_size 512
- Low-frequency-only stylization
python transfer.py --option_unpool sum -e --content ./examples/content --style ./examples/style --content_segment ./examples/content_segment --style_segment ./examples/style_segment/ --output ./outputs/ --verbose --image_size 512
option_unpool = cat5
version
python transfer.py --option_unpool cat5 -a --content ./examples/content --style ./examples/style --content_segment ./examples/content_segment --style_segment ./examples/style_segment/ --output ./outputs/ --verbose --image_size 512
- Our implementation is highly inspired from NVIDIA's PhotoWCT Code.
If you find this work useful for your research, please cite:
@inproceedings{yoo2019photorealistic,
title={Photorealistic Style Transfer via Wavelet Transforms},
author={Yoo, Jaejun and Uh, Youngjung and Chun, Sanghyuk and Kang, Byeongkyu and Ha, Jung-Woo},
booktitle = {International Conference on Computer Vision (ICCV)},
year={2019}
}
Feel free to contact me if there is any question (Jaejun Yoo [email protected]).
Copyright (c) 2019 NAVER Corp.
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