We have released our dataset for public using. The dataset can be downloaded through following links:
Sketch-image pairs: https://cloudstor.aarnet.edu.au/plus/s/rMSBYCjEZJ70ab2
Sketch with control color blocks: https://cloudstor.aarnet.edu.au/plus/s/ixj8XS0rMmUqq0Z
It is the original implementation of the journal article: Auto-painter: Cartoon image generation from sketch by using conditional Wasserstein generative adversarial networks https://www.sciencedirect.com/science/article/pii/S0925231218306209?via%3Dihub
This project mean to make an end-to-end network for the sketch of cartoon to have color automatically.
More results can be seen here: https://irfanicmll.github.io/work-page/
Try our demo here: http://103.202.133.77:10086/
Since the lab's server has temporarily expired, the demo is now unavailable. You can see the demo video and train your own model.
New model has been updated!~ The performance is much better than in the orginal paper! See the demo video:https://youtu.be/g9rf-YFGgbg
Have a try~
The pre-train model can be download here: https://cloudstor.aarnet.edu.au/plus/s/LvyREKsiaH47Aa6
My homepage: https://irfanicmll.github.io/
Welcome to contact me~
python3.5
tensorflow1.4
Vgg model from:https://github.com/machrisaa/tensorflow-vgg(optional, if you use the loss_f)
Color images: Collected on the Internet
Sketch: Generated from the preprocessing/gen_sketch/sketch.py
Put you orginal data in the folder preprocessing/gen_sketch/pic_org
Run the sketch.py and you will get the training set in the preprocessing/gen_sketch/pic_sketch folder
Download the pre-train weight of Vgg16, and put the model and the pretrian weight uder the folder of training&test/my_vgg
Run the training command as:
python auto-painter.py --mode train --input_dir $TRAINING_SET --output_dir $OUTPUT --checkpoint None
Run the testing command as:
python auto-painter.py --mode test --input_dir $TESTING_SET --output_dir $OUTPUT_TEST --checkpoint $OUTPUT