deep-photo-styletransfer

Code and data for paper "Deep Photo Style Transfer"

Disclaimer

This software is published for academic and non-commercial use only.

Setup

This code is based on torch. It has been tested on Ubuntu 14.04 LTS.

Dependencies:

• Torch (with matio-ffi and loadcaffe)
• Matlab or Octave

CUDA backend:

• CUDA
• cudnn

Download VGG-19:

sh models/download_models.sh

Compile cuda_utils.cu (Adjust PREFIX and NVCC_PREFIX in makefile for your machine):

make clean && make

Usage

Quick start

To generate all results (in examples/) using the provided scripts, simply run

run('gen_laplacian/gen_laplacian.m')

in Matlab or Octave and then

python gen_all.py

in Python. The final output will be in examples/final_results/.

Basic usage

1. Given input and style images with semantic segmentation masks, put them in examples/ respectively. They will have the following filename form: examples/input/in .png , examples/style/tar .png and examples/segmentation/in .png , examples/segmentation/tar .png ;
2. Compute the matting Laplacian matrix using gen_laplacian/gen_laplacian.m in Matlab. The output matrix will have the following filename form: gen_laplacian/Input_Laplacian_3x3_1e-7_CSR .mat ;

Note: Please make sure that the content image resolution is consistent for Matting Laplacian computation in Matlab and style transfer in Torch, otherwise the result won't be correct.

3. Run the following script to generate segmented intermediate result:

th neuralstyle_seg.lua -content_image  -style_image