You could be an artist with AI
[Live Demo] Note: Only frontend is available now
Or you can Run with Docker in minutes
This project can transform your casual sketch to beautiful painting/artwork using modern AI technology.
With Docker, you can quickly build and run the entire application in minutes 🐳
# 1. First, clone the repo
git clone https://github.com/mtobeiyf/sketch-to-art.git
cd sketch-to-art
# 2. Build Docker image
docker build -t sketch-to-art:dev .
# 3. Run!
docker run -it --rm -p 8080:8080 -p 5001:5001 -p 5002:5002 sketch-to-art:devThen, go to localhost:8080 and play with the demo! 🎉
To achieve the goal, there are mainly two steps in the pipeline:
- Reconstruct and generate real image from the sketch
- Arbitary style transfer to beautify the result with given result
The principle behind this is called Conditional Adversarial Networks, known as pix2pix, which is able to generate image based on the given image.
It became known to us with the appearance of Prisma app. Typically, we generate an individual model for each pre-defined style. Here, we want to go further by using any new picture as the style. So, we adopted the method, Universal Style Transfer via Feature Transforms proposed in NIPS2017, which enables us to perform arbitary style transfer.
The server side is powered by Python and Flask. You can see this simpler example.
Navigate to the server directory and all the files concerning the service and neural networks are there. The two main files:
app_pix.pyfor pix2pix translationapp_stylize.pyfor arbitrary style transfer
Make sure you have Python installed, and some packages are needed: tensorflow, keras, pillow, flask, gevent. You can use pip to install them:
pip install -r server/requirements.txt# Simply run with python
python app_xxx.pyAnd you could see the output indicating the port it's listening (5001 and 5002). Go to http://localhost:5001 and you should see the returned information.
You should installed:
# Clone the repo
git clone [email protected]:mtobeiyf/sketch-to-art.git
cd sketch-to-art
# Install dependencies
yarn # or npm install
# Run
yarn dev # or npm run dev
Open your favorite browser at http://localhost:8080, the site is there.
This is the final project of Digital Image Processing instructed by Prof. Jia Yan.
Xin Fu, Shuaibin Zhang, Tangbo Liu, Haoran Su
Copyright © 2018, Fing
Released under the MIT License.
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