Automatic Image Colorization

Overview

This is a TensorFlow implementation of the Residual Encoder Network based on Automatic Colorization and the pre-trained VGG16 model from https://github.com/machrisaa/tensorflow-vgg

For latest TensorFlow with estimator support, check 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

How to use

Download pre-trained VGG16 model vgg16.npy to vgg folder
Option 1: Use pre-trained residual encoder model
- Download model here
- 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

More example output images can be found in sample_output_images folder.

References

Contributing

See CONTRIBUTING.md

License

GNU GPL 3.0 for personal or research use. COMMERCIAL USE PROHIBITED.