Image and video colorizer is package for automatic image and video colorization. Models are already trained.

Installation can be done in 5 easy steps

1. Install all requirements for Tensorflow and tensorflow itself with:

   pip install tensorflow-gpu
   

   if you use GPU device for computation otherwise:

   pip install tensorflow
   

2. Create virtual environment

   virtualenv -p python3 colorization_venv
   

3. Activate virtual environment

   source colorization_venv/bin/activate
   

4. Clone Image and video colorization package and move in it

   git clone https://github.com/PrimozGodec/ImageColorization.git
   cd ImageColorization
   

5. Install requirements

   pip install -r requirements.txt
   

6. You are done :)

In case you do not have a GPU device in your computer, please install Tensorflow for a CPU. Instructions are at the Tnesorflow website.

For automatic image colorizing follow those steps:

1. Copy images into /data/image/original directory

2. Run main.py script from src/image_colorization/ directory.

   python -m src.image_colorization.main --model <model name>
   

   Parameter --method is optional, if not present reg_full_model is default. It can be choose from this list:

   • reg_full_model (default)
   • reg_full_vgg_model
   • reg_part_model
   • class_weights_model
   • class_wo_weights_model

3. You can find colored images in /data/image/colorized directory.

on your GPU or CPU specifications. You will see progress bar that show you how far you are with colorization.

For automatic video colorizing follow those steps:

1. Copy images into /data/video/original directory

2. Run video_colorizer.py script from src/video_colorization/ directory.

   python -m src.video_colorization.video_colorizer
   

   Video colorizer is always using reg_full_model.

3. You can find colored videos in /data/video/colorized directory.

Colorization take few hours since there is a lot of images to color in a video and depends on your GPU or CPU specifications and length of a video. You will see progress bar that show you how far you are with colorization.