Code and data for paper "Deep Photo Harmonization". (ArXiv TBD!)

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

This code is based on torch. It has been tested on Ubuntu 16.04 LTS.

Dependencies:

• Torch (with 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

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

python gen_all.py

in Python and then

run('filt_cnn_artifact.m')

in Matlab or Octave. The final output will be in results/.

Here are some results from our algorithm (from left to right are original painting, naive composite and our output):

• Our torch implementation is based on Justin Johnson's code;
• Histogram loss is inspired by Risser et al.

Feel free to contact me if there is any question (Fujun Luan [email protected]).