Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification




  • Paper topic: Images
  • Software type: Code
  • Able to run a replicability test: True
  • Replicability score: 5
  • Software language: Other
  • License: CC BY-NC-SA 4.0
  • Build mechanism: Not applicable (python, Matlab..)
  • Dependencies: torch / torch-nn
  • Documentation score {0,1,2}: 1
  • Reviewer: Nicolas Bonneel <nicolas.bonneel@liris.cnrs.fr>
  • Time spent for the test (build->first run, timeout at 100min): 30min

Source code information


The short LUA code comes with a downloadable pretrained network (the data to retrain the network is from http://places.csail.mit.edu/ but the code to train the network is not provided). I used the Ubuntu for Windows framework (with Windows 10) to run the code, which was simpler here.
I still needed to perform a number of steps:
- dos2unix download_model.sh to prevent including the \r in the download URL
- sudo apt-get install cmake and sudo apt-get install libreadline-dev for dependencies
- installing torch with nn was slightly harder than the instructions on the torch website claim:
git clone https://github.com/torch/distro.git ~/torch --recursive
cd ~/torch
then on line 178 of install--deps  you need to change
sudo apt-get install -y python-software-properties
to sudo apt-get install -y software-properties-common since python-software-properties is not supported anymore.
Then you can run bash install-deps and ./install.sh
But then you still need to install nn:
sudo apt install luarocks
sudo ~/torch/install/bin/luarocks install torch
sudo ~/torch/install/bin/luarocks install nn
and finally run the colorize script, not directly with "th" but using:
~/torch/install/bin/th colorize.lua ansel_colorado_1941.png out.png
Then everything works fine, runs smoothly (counts 2-3 minutes on the CPU for the only example image provided, which exactly replicate the landscape result in the teaser).

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