kts-o7 / fluid-art-and-galaxy-generation-using-machine-learning-and-image-processing Goto Github PK

A deep convolutional generative adversarial network (DCGAN) is trained on pictures of art. Images are procedurally created from the generative neural network by sampling the latent space. Information on the neural network architecture can be found here: https://arxiv.org/abs/1511.06434. Use the procedurally generated images to create mosaics.

• Keras, Tensorflow, Matplotlib, Numpy, PIL, Scikit-learn

Load a pretrained model into animated_mosaic.py and then run the script.

pretrained model for generating space tiles

generator (space_128_64).h5 will create images like the one below. The images are generated from a latent parameter space of 128 dimensions and the output images will be 64 x 64. This network was trained on images of galaxies from the [NASA image archive]

Thin film inference on the surface of a bubble

Fluid Art - 128x128px output from a 256 dimensional latent space trained on images of acrylic pour/fluid art after 10000 training epochs

1. Download your favorite images to a new directory
2. Change some parameters below like: directory, name, latent_dim, epochs, etc..
3. Run the code

from dcgan import DCGAN, create_dataset

if __name__ == '__main__':

    x_train, y_train = create_dataset(128,128, nSlices=150, resize=0.75, directory='space/')
    assert(x_train.shape[0]>0)

    x_train /= 255 

    dcgan = DCGAN(img_rows = x_train[0].shape[0],
                    img_cols = x_train[0].shape[1],
                    channels = x_train[0].shape[2], 
                    latent_dim=32,
                    name='nebula_32_128')
                    
    dcgan.train(x_train, epochs=10000, batch_size=32, save_interval=100)

The neural network will create outputs during the training process to the folder: images/. This process could take a while depending on your computer. For comparison the two pretrained models used 15000 training epochs which took ~1-2 hours on a GTX 1070.

The create_dataset function will cut random slices from images to create a new data set. This function requires you to put images in a new directory before hand

import matplotlib.pyplot as plt
import numpy as np

from dcgan import create_dataset 

# first resize the original image to 75% 
# then cut 100 random 128x128 subframes from each image in the directory 
x_train, y_train = create_dataset(128,128, nSlices=100, resize=0.75, directory='space/')

# scale RGB data between 0 and 1
x_train /= 255 

# plot results to make sure data looks good!
fig, axs = plt.subplots(4, 4)
for i in range(4):
    for j in range(4):
        axs[i,j].imshow( x_train[ np.random.randint(x_train.shape[0]) ] )
        axs[i,j].axis('off')
plt.show()