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‎.gitignore

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*.caffemodel
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.c9
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.idea
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test_imgs
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#
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*~
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.fuse*
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*.npy
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*_log
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*.caffemodel
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*.pyc
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*.DS_Store
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logger.txt
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cache/
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results/
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checkpoints/
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user_test/
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# luarocks build files
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*.src.rock
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*.zip
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*.tar.gz
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*.t7
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# Caffe models
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*.caffemodel
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*.solverstate
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# Object files
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*.o
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*.os
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*.ko
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*.obj
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*.elf
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# Precompiled Headers
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*.gch
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*.pch
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# Libraries
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*.lib
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*.a
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*.la
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*.lo
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*.def
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*.exp
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# Shared objects (inc. Windows DLLs)
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*.dll
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*.so
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*.so.*
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*.dylib
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# Executables
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*.exe
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*.out
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*.app
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*.i*86
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*.x86_64
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*.hex
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.ipynb_checkpoints/

‎DemoGlobalHistogramTransfer.ipynb

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‎DemoInteractiveColorization.ipynb

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‎LICENSE

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The MIT License (MIT)
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Copyright (c) 2017 Jun-Yan Zhu and Richard Zhang
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎LICENSE_igan

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The MIT License (MIT)
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Copyright (c) 2016 Jun-Yan Zhu
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎README.md

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# Interactive Deep Colorization
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### [[Project Page]](https://richzhang.github.io/ideepcolor/) [[Paper]]() [[Demo Video]](https://youtu.be/eL5ilZgM89Q) [[Talk]](https://www.youtube.com/watch?v=FTzcFsz2xqw&feature=youtu.be&t=992)
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<img src='imgs/demo.gif' width=600>
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<!-- This repository contains a Python implementation for user-guided image colorization technique described in the following paper: -->
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[Richard Zhang](https://richzhang.github.io/)\*, [Jun-Yan Zhu](https://people.eecs.berkeley.edu/~junyanz/)\*, [Phillip Isola](http://people.eecs.berkeley.edu/~isola/), [Xinyang Geng](http://young-geng.xyz/), Angela S. Lin, Tianhe Yu, and [Alexei A. Efros](https://people.eecs.berkeley.edu/~efros/).
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**Real-Time User-Guided Image Colorization with Learned Deep Priors.**
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In ACM Transactions on Graphics (SIGGRAPH 2017).
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(\*indicates equal contribution)
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We first describe the system <b>(0) Prerequisities</b> and steps for <b>(1) Getting started</b>. We then describe the interactive colorization demo <b>(2) Interactive Colorization (Local Hints Network)</b>. 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 <b>(3) Global Hints Network</b>.
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<!-- <img src='https://richzhang.github.io/ideepcolor/index_files/imagenet_showcase_small.jpg' width=800> -->
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### (0) Prerequisites
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- Linux or OSX
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- [Caffe](http://caffe.berkeleyvision.org/installation.html)
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- CPU or NVIDIA GPU + CUDA CuDNN.
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### (1) Getting Started
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- Clone this repo:
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```bash
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git clone https://github.com/junyanz/interactive-deep-colorization ideepcolor
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cd ideepcolor
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```
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- Download the reference model
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```
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bash ./models/fetch_reference_model.sh
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```
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- Install [Caffe](http://caffe.berkeleyvision.org/installation.html) and Python libraries ([OpenCV](http://opencv.org/))
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### (2) Interactive Colorization (Local Hints Network)
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<img src='imgs/teaser_v3.jpg' width=800>
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We provide a "barebones" demo in iPython notebook, which does not require QT. We also provide our full GUI demo.
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#### 2(a) Barebones Interactive Colorization Demo
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- Run `ipython notebook` and click on [`DemoInteractiveColorization.ipynb`](./DemoInteractiveColorization.ipynb).
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#### 2(b) Full Demo GUI
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- Install [Qt4](https://wiki.python.org/moin/PyQt4) and [QDarkStyle](https://github.com/ColinDuquesnoy/QDarkStyleSheet). (See [Requirements](## (A) Requirements))
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- Run the UI: `bash python ideepcolor.py --gpu [GPU_ID]`. Arguments are described below:
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```
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--win_size [512] GUI window size
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--gpu [0] GPU number
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--image_file ['./tesT_imgs/mortar_pester.jpg'] path to the image file
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```
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- User interactions
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<img src='./imgs/pad.jpg' width=800>
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- <b>Adding points</b>: Left-click somewhere on the input pad
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- <b>Moving points</b>: Left-click and hold on a point on the input pad, drag to desired location, and let go
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- <b>Changing colors</b>: For currently selected point, choose a recommended color (middle-left) or choose a color on the ab color gamut (top-left)
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- <b>Removing points</b>: Right-click on a point on the input pad
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- <b>Changing patch size</b>: Mouse wheel changes the patch size from 1x1 to 9x9
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- <b>Load image</b>: Click the load image button and choose desired image
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- <b>Restart</b>: Click on the restart button. All points on the pad will be removed.
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- <b>Save result</b>: 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.
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- <b>Quit</b>: Click on the quit button.
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### (3) Global Hints Network
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<img src='https://richzhang.github.io/ideepcolor/index_files/lab_all_figures45k_small.jpg' width=800>
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We include an example usage of our Global Hints Network, applied to global histogram transfer. We show its usage in an iPython notebook.
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- Add `./caffe_files` to your `PYTHONPATH`
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- Run `ipython notebook`. Click on [`./DemoGlobalHistogramTransfer.ipynb`](./DemoGlobalHistogramTransfer.ipynb)`
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## (A) Requirements
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- Caffe (See Caffe installation [document](http://caffe.berkeleyvision.org/installation.html))
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- OpenCV
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```
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sudo apt-get install python-opencv
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```
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- Qt4
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```
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sudo apt-get install python-qt4
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```
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- QDarkStyle
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```
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sudo pip install qdarkstyle
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```

‎caffe_files/__init__.py

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