Python Implementation of Generative Adversarial Networks for Extrapolation of Corrosion in Automobile Images

This repository is provided as a tutorial for the rust level and texture maps prediction and application to cars' fender.

The repository consists of the following four folders:

ds:

train contains the training set used for the Rust Level Network (RLN) and Rust Texture Network (RTN) training
test contains the images that were lately acquired for a comparison with the models' predicted maps

target-cars:

img contains car images.
mask contains the masks for each car image.
annotation holds three points along the fender of each car (the start of the fender, its end, and an arbitrary point in between) and the estimated radius in pixels.

samples:

maps contains examples of maps generated by our RLN and RTN models.
cars contains the results of the generated maps applied to the target cars.

src:

utils is a set of functions used for the rust map application.
main is a back-to-back pipeline for our application, from the RLN and RTN predictions until the rust maps application in a car.
model contains our RLN and RTN generator and discriminator architectures.
data contains a function that wraps our dataset

weights:

this folder contain the trained weights of our RLN and RTN generator and discriminator models

The file src/main.py is the executable file of this project.