https://github.com/Armour/Automatic-Image-Colorization

🎨 Automatic Image Colorization using TensorFlow based on Residual Encoder Network
https://github.com/Armour/Automatic-Image-Colorization

auto-colorization colorization gpu image-colorization jarvis python3 residual-encoder-network tensorflow

Last synced: about 2 months ago
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🎨 Automatic Image Colorization using TensorFlow based on Residual Encoder Network

• Host: GitHub
• URL: https://github.com/Armour/Automatic-Image-Colorization
• Owner: Armour
• License: gpl-3.0
• Created: 2016-12-08T23:10:26.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2022-09-08T21:58:21.000Z (about 2 years ago)
• Last Synced: 2024-05-09T11:31:52.850Z (4 months ago)
• Topics: auto-colorization, colorization, gpu, image-colorization, jarvis, python3, residual-encoder-network, tensorflow
• Language: Python
• Homepage: http://tinyclouds.org/colorize/
• Size: 38.8 MB
• Stars: 169
• Watchers: 11
• Forks: 45
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • Contributing: .github/CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: .github/CODE_OF_CONDUCT.md

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README

        
# Automatic Image Colorization

[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat)](http://makeapullrequest.com)

[![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)

[![Template from jarvis](https://img.shields.io/badge/Hi-Jarvis-ff69b4.svg)](https://github.com/Armour/Jarvis)

## Overview

This is a TensorFlow implementation of the Residual Encoder Network based on [Automatic Colorization](http://tinyclouds.org/colorize/) and the pre-trained VGG16 model from [https://github.com/machrisaa/tensorflow-vgg](https://github.com/machrisaa/tensorflow-vgg)

**For latest TensorFlow with [estimator](https://www.tensorflow.org/guide/estimators) support, check [tf-1.12](https://github.com/Armour/Automatic-Image-Colorization/tree/tf-1.12) branch. (still under development, the training code is working now)**

## Structure

* `config.py`: config variables like batch size, training_iters and so on

* `image_helper.py`: all functions related to image manipulation

* `read_input.py`: all functions related to input

* `residual_encoder.py`: the residual encoder model

* `common.py`: the common part for training and testing, which is mainly the workflow for this model

* `train.py`: train the residual encoder model using TensorFlow built-in AdamOptimizer

* `test.py`: test your own images and save the output images

## TensorFlow graph

![residual_encoder](images/residual_encoder.png)

## How to use

* Download pre-trained VGG16 model [vgg16.npy](https://mega.nz/#!YU1FWJrA!O1ywiCS2IiOlUCtCpI6HTJOMrneN-Qdv3ywQP5poecM) to vgg folder

* Option 1: Use pre-trained residual encoder model

  * Download model [here](https://github.com/Armour/Automatic-Image-Colorization/releases/tag/2.0)

  * Unzip all files to `summary_path` (you can change this path in `config.py`)

* Option 2: Train your own model!

  1. Change the `batch_size` and `training_iters` if you want.

  2. Change `training_dir` to your directory that has all your training jpg images

  3. Run `python train.py`

* Test

  1. Change `testing_dir` to your directory that has all your testing jpg images

  2. Run `python test.py`

## Examples

* ![1](images/1.png)

* ![2](images/2.png)

* ![3](images/3.png)

* ![4](images/4.png)

* ![5](images/5.png)

* ![6](images/6.png)

* ![7](images/7.png)

* ![8](images/8.png)

* ![9](images/9.png)

* ![10](images/10.png)

* ![11](images/11.png)

* ![12](images/12.png)

* More example output images can be found in [sample_output_images](https://github.com/Armour/Automatic-Image-Colorization/blob/master/sample_output_images) folder.

## References

* [Automatic Colorization](http://tinyclouds.org/colorize/)

* [pavelgonchar/colornet](https://github.com/pavelgonchar/colornet)

* [raghavgupta0296/ColourNet](https://github.com/raghavgupta0296/ColourNet)

* [pretrained VGG16 npy file](https://github.com/machrisaa/tensorflow-vgg)

## Contributing

See [CONTRIBUTING.md](https://github.com/Armour/Automatic-Image-Colorization/blob/master/.github/CONTRIBUTING.md)

## License

[GNU GPL 3.0](https://github.com/Armour/Automatic-Image-Colorization/blob/master/LICENSE) for personal or research use. COMMERCIAL USE PROHIBITED.