An object-aware cartoonization method using CartoonGAN and Mask R-CNN.

We took inspiration from CartoonGAN which transforms images into Japanese anime style. It generates very pleasing scenery but presents objects too realistically. This is also the same case for a few of the other similar methods. So, we would like to propose an object-aware cartoonization method based on CartoonGAN with Mask R-CNN for object detection.

1. Clone this repository

2. Run setup from the repository root directory

   bash setup.sh

3. Start Python virtual environment

   source ./venv/bin/activate

4. Download pre-trained weights inside the root directory from here

Use UIControl.py to initiate the application. It provides a simple interactive way to input and transform image, as well as visualizing input and output images side by side. However, it does not mirror all the functionalities of the command line interface.

python UIControl.py

Use driver.py to perform image cartoonization. By default, it will apply shinkai style transfer with adaptive edge enhancement on images from input directory, the cartoonized images will be placed in output directory.

There are 4 styles available (from CartoonGAN):

• shinkai
• hayao
• hosoda
• paprika

There are 3 edge enhancement available:

• adaptive
• canny
• morph

There are many customization options available. As an example, the following command specified styles, edges, input image, and output directory:

python driver.py \
  --styles shinkai hayao \
	--edges adaptive canny \
	--input ./input/default.jpg \
	--output_dir ./output

To explore all available customization options, please use the following command to get detailed explainations:

python cartoonize.py --help

For each set of results:

• The top-left image is the original image
• The top-right image is the result from CartoonGAN
• The bottom-left image is the enhanced stroke mask
• The bottom-right image is the result of our application

• CartoonGAN-TensorFlow2
• Mask R-CNN for Object Detection and Segmentation using TensorFlow 2.0