Aesthetic-Driven Image Enhancement by Adversarial Learning
This is the demo code for the paper Aesthetic-Driven Image Enhancement by Adversarial Learning by Deng et al. at ACMMM 2018. (ArXiv).
The code is based on: [1] https://github.com/soumith/dcgan.torch [2] https://github.com/qassemoquab/stnbhwd
Prerequisite
1. Install torch7 (lua)
git clone https://github.com/torch/distro.git ~/torch --recursive
cd ~/torch; bash install-deps;
./install.sh;
source ~/.bashrc;
2. Install the standard STN package
git clone https://github.com/qassemoquab/stnbhwd.git; cd stnbhwd && luarocks make stnbhwd-scm-1.rockspec;
3. Copy the customized STN files to the torch folder path.
cp ./stn/*.lua ~/torch/install/share/lua/5.1/stn/
4. Other lua dependencies
luarocks install cudnn; # (The current version requires cudnn 5.0)
luarocks install tds;
luarocks intsall nngraph;
luarocks install dpnn;
luarocks install matio; # (Make sure that the shared libraries (libmatio.so or libmatio.dylib) are in your library path, e.g., sudo apt-get install libmatio2)
luarocks install https://raw.githubusercontent.com/szym/display/master/display-scm-0.rockspec
5. Display UI
Optionally, for displaying images during training and generation, we will use the display package. Start the server with: th -ldisplay.start Open this URL in your browser: http://localhost:8000
Download pre-trained models and images:
You may download the models and our images (from AVA dataset) and extract to the repective directories from Google_Drive or Baidu Pan
To run enhancement for an input image:
cd ./ACMMM_2018_release;
./run_single_image.sh
(For CPU mode, use "--gpu 0" in ./run_single_image.sh.
Note that you might need to download "model_best_cpu.t7" and put it in the "./ACMMM_2018_release/checkpoints/" folder.)
To run training (~11 GB of GPU memory required):
cd ./ACMMM_2018_release;
./train_my_own.sh