git clone junyanz-interactive-deep-colorization_-_2017-05-18_18-34-06.bundle -b master
Richard Zhang*, Jun-Yan Zhu*, Phillip Isola, Xinyang Geng, Angela S. Lin, Tianhe Yu, and Alexei A. Efros.Real-Time User-Guided Image Colorization with Learned Deep Priors.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.
Clone this repo:
bashgit clone https://github.com/junyanz/interactive-deep-colorization ideepcolorcd ideepcolor
Download the reference model
We provide a "barebones" demo in iPython notebook, which does not require QT. We also provide our full GUI demo.
ipython notebookand click on
Run the UI:
python ideepcolor.py --gpu [GPU_ID]. Arguments are described below:
--win_size  GUI window size--gpu  GPU number--image_file ['./test_imgs/mortar_pestle.jpg'] path to the image file
image_filewas, along with the user input ab values.
We include an example usage of our Global Hints Network, applied to global histogram transfer. We show its usage in an iPython notebook.
./caffe_files to your
ipython notebook. Click on
sudo apt-get install python-opencv
sudo apt-get install python-qt4
sudo pip install qdarkstyle
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]