Forked from github.com/praywj/Interactive-Deep-Colorization-and-Compression
This originally was the code for the paper "Interactive Deep Colorization and Its Application for Image Compression"
by Xiao et al.
We expanded it with a few methods to choose the local cue points. Including a method, that worked better than the
approach used by Xiao et al.
We also added a GAN Compression system by Agustsson et al., instead of the original BPG compression.
- A Linux of choice, for the feeling of superiority.
- NVIDIA GPU for training, or CPU for running only with at least ~8 GB of RAM
- Tensorflow 2.X + python
The pretrained models are available here: models
They were trained on ~100.000 images of the ImageNet train set cropped to 256x256.
The file preprocess.py will preprocess the images used for training. For both the colorization and compression.
Adjust the code in main() to your needs before running.
Call dataset_prepare(set="train", max_img=100_000) with the number of images you want to use of this set. This
will symlink random images into res/set/original_img.
Note: This code was written with the ImageNet Dataset folder structure in mind. For other datasets, you might need
to adjust the code in other places as well.
preprocess_color and preprocess_grayscale will generate the required data for training, but also for some
recolorization and compression steps. You can choose what to generate and whether to overwrite as well. The random
crop uses the filename as a seed, to produce the same crop for color and grayscale images.
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