This is a project that transforming human pictures into cartoon images, and also capable of transforming cartoon images into human images.
The methods applied is CycleGAN. CycleGAN is primarily used to transform domain of an image into another domain. In this project, we trained two generators and two discriminators to apply cyclegan. Because the training time takes too long, our model does not perform very well on human-cartoon transformation. However, given more time, we will train more and make generator better for transformation.
This project originated from the project of "weather classification". At the end of last project, we wanted to use cycleGAN to transformed rainy and foggy images into cloudy images. However, we failed on implementing cycleGAN algorithm. We re-tried CycleGAN on human-cartoon-transformation to better understand the logic of cycleGAN. There are many updated imporved methods on image style transfer.
Our group intends to classify weather images first, and then convert severe weather images into clear images that are easier to identify. Our project has a wide range of application scenarios like autonomous driving. Specifically, Severe weather phenomena have various negative effects on transportation. As a source of information for vehicle sensors, the state of the environment is directly influenced by weather conditions. For camera-led multi-sensor fusion system which is one of the mainstream, it is particularly important to recognize the weather through images and obtain surrounding information based on clear pictures.
For one try of testing, we used yoturb video depend on each weather condition. Because we are using our model to real-world, we need to see how our model perform on the real-world weather classification at driving.