Interactive Deep Colorization

[Project Page] [Paper] [Demo Video] [Seminar Talk]

Real-Time User-Guided Image Colorization with Learned Deep Priors.
Richard Zhang*, Jun-Yan Zhu*, Phillip Isola, Xinyang Geng, Angela S. Lin, Tianhe Yu, and Alexei A. Efros.
In ACM Transactions on Graphics (SIGGRAPH 2017).
(*indicates equal contribution)

We first describe the system (0) Prerequisities and steps for (1) Getting started. We then describe the interactive colorization demo (2) Interactive Colorization (Local Hints Network). There are two demos: (a) a "barebones" version in iPython notebook and (b) the full GUI we used in our paper. We then provide an example of the (3) Global Hints Network.

(0) Prerequisites

Linux or OSX
Caffe
CPU or NVIDIA GPU + CUDA CuDNN.

(1) Getting Started

Clone this repo:

git clone https://github.com/junyanz/interactive-deep-colorization ideepcolor
cd ideepcolor

Download the reference model

bash ./models/fetch_models.sh

Install Caffe and 3rd party Python libraries (OpenCV, scikit-learn and scikit-image). See the Requirements for more details.

(2) Interactive Colorization (Local Hints Network)

We provide a "barebones" demo in iPython notebook, which does not require QT. We also provide our full GUI demo.

(2a) Barebones Interactive Colorization Demo

Run ipython notebook and click on DemoInteractiveColorization.ipynb.

If you need to convert the Notebook to an older version, use jupyter nbconvert --to notebook --nbformat 3 ./DemoInteractiveColorization.ipynb.

(2b) Full Demo GUI

Install Qt4 and QDarkStyle. (See Installation)
Run the UI: python ideepcolor.py --gpu [GPU_ID]. Arguments are described below:

--win_size    [512] GUI window size
--gpu         [0] GPU number
--image_file  ['./test_imgs/mortar_pestle.jpg'] path to the image file

User interactions

Adding points: Left-click somewhere on the input pad
Moving points: Left-click and hold on a point on the input pad, drag to desired location, and let go
Changing colors: For currently selected point, choose a recommended color (middle-left) or choose a color on the ab color gamut (top-left)
Removing points: Right-click on a point on the input pad
Changing patch size: Mouse wheel changes the patch size from 1x1 to 9x9
Load image: Click the load image button and choose desired image
Restart: Click on the restart button. All points on the pad will be removed.
Save result: Click on the save button. This will save the resulting colorization in a directory where the image_file was, along with the user input ab values.
Quit: Click on the quit button.

(3) Global Hints Network

We include an example usage of our Global Hints Network, applied to global histogram transfer. We show its usage in an iPython notebook.

Add ./caffe_files to your PYTHONPATH
Run ipython notebook. Click on ./DemoGlobalHistogramTransfer.ipynb

Installation

Install Caffe: see the Caffe installation and Ubuntu installation document. Please compile the Caffe with the python layer support (set WITH_PYTHON_LAYER=1 in the Makefile.config) and build Caffe python library by make pycaffe. You also need to add pycaffe to your PYTHONPATH. Use vi ~/.bashrc to edit the environment variables.

PYTHONPATH=/path/to/caffe/python:$PYTHONPATH
LD_LIBRARY_PATH=/path/to/caffe/build/lib:$LD_LIBRARY_PATH

Install scikit-image, scikit-learn, opencv, Qt4, and QDarkStyle pacakges:

# ./install/install_deps.sh
sudo pip install scikit-image
sudo pip install scikit-learn
sudo apt-get install python-opencv
sudo apt-get install python-qt4
sudo pip install qdarkstyle

For Conda users, type the following command lines:

# ./install/install_conda.sh
conda install -c anaconda protobuf  ## photobuf
conda install -c anaconda scikit-learn ## scikit-learn
conda install -c anaconda scikit-image  ## scikit-image
conda install -c menpo opencv   ## opencv
conda install pyqt=4.11 ## qt4
conda install -c auto qdarkstyle  ## qdarkstyle

Citation

If you use this code for your research, please cite our paper:

@article{zhang2017real,
  title={Real-Time User-Guided Image Colorization with Learned Deep Priors},
  author={Zhang, Richard and Zhu, Jun-Yan and Isola, Phillip and Geng, Xinyang and Lin, Angela S and Yu, Tianhe and Efros, Alexei A},
  journal={ACM Transactions on Graphics (TOG)},
  volume={9},
  number={4},
  year={2017},
  publisher={ACM}
}

Cat Paper Collection

One of the authors objects to the inclusion of this list, due to an allergy. Another author objects on the basis that cats are silly creatures and this is a serious, scientific paper. However, if you love cats, and love reading cool graphics, vision, and learning papers, please check out the Cat Paper Collection: [Github] [Webpage]

seokg/interactive-deep-colorization