A neural network to smartly create coherent terminal colorschemes based on an image, which might then be used as background.
say we have a pixel with the rgb value of (255, 120, 70). to display this information in a format that the neural network can understand, we have to convert it to 3 floats. each consist of the color/255 (max value).
255/255 = 1.00000000
120/255 = 0.47058823
70/255 = 0.27450980
so there you have it. our pixel is represented by 1.00000000 0.47058823 0.27450980
Done with data that wasn't in the training set
####Normal Way (wallpaper -> colorscheme)
####Reverse Way (colorscheme -> wallpaper) (Still WIP to generate backgrounds)
With a background generated from this website
With a background generated from this website
- Turn the input of the network between [-1, 1] so that the training is faster
- Get more data for the training
- Test multiple color extracters and parameters for the training
- Build the network in the opposite direction, from colorscheme to wallpaper
- A procedural wallpaper generation/fetcher based on colors (for when the network is used in the opposite way)
- Easy wrappers for all the mini-tools written