Armour / Automatic Image Colorization
Licence: gpl-3.0
🎨 Automatic Image Colorization using TensorFlow based on Residual Encoder Network
Stars: ✠130
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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 inconfig.py)
-
Option 2: Train your own model!
- Change the
batch_sizeandtraining_itersif you want. - Change
training_dirto your directory that has all your training jpg images - Run
python train.py
- Change the
-
Test
- Change
testing_dirto your directory that has all your testing jpg images - Run
python test.py
- Change
Examples
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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.
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].