Create a grayscale or colour image with predefined size image_height and image_width using fully connected neural networks.
The generation of images only requires python numpy and matplotlib.
Medium article can be found here.
You can either have a look at the jupyter notebook nb_random_art.ipynb if you want to understand the algorithm and check out several settings of the method.
For fast image generation is is recommended to use the python main programm file random_art.py
numpy==1.15.3
matplotlib==3.0.0
seaborn==0.9.0
For the random_art.py programm argparse is used to define several input parameters:
parser = argparse.ArgumentParser(description="Generate random art with a deep neural network")
parser.add_argument("-img_height", metavar="", type=int, default=512,
help="Image height of created random art. Default is 512")
parser.add_argument("-img_width", metavar="", type=int, default=512,
help="Image width of created random art. Default is 512")
parser.add_argument("-colormode", metavar="", type=str, default="RGB",
help="How image color should be generated. Options are ['BW', 'RGB', 'CMYK', 'HSV', 'HSL']. By default this
value is 'RGB'")
parser.add_argument("-alpha", metavar="", type=str, default="False",
help="Whether or not to add a alpha channel for the image. Default is False")
parser.add_argument("-n_images", metavar="", type=int, default=1,
help="Number of images to generate. Default is 1")
parser.add_argument("-n_depth", metavar="", type=int, default=10,
help="Number of layers for the neural network. Default is 10")
parser.add_argument("-n_size", metavar="", type=int, default=15,
help="Number of neurons in each hidden layer. Default is 15")
parser.add_argument("-activation", metavar="", type=str, default="tanh",
help="Activation function to apply on the hidden layers. Default is 'tanh'")
parser.add_argument("-z1", metavar="", type=float, default=-0.618,
help="Input variable 1 to insert determinism into the random art. The value should be between -1 and 1. Default
is -0.618")
parser.add_argument("-z2", metavar="", type=float, default=+0.618,
help="Input variable 2 to insert determinism into the random art. The value should be between -1 and 1. Default
is +0.618")
args = parser.parse_args()So in order to create 1 RGB image of size 400x500, no alpha channel, a dense net with 15 layers, each laying having 15 neurons, type in following command in the shell:
python random_art.py -img_height 400 -img_width 500 -colormode RGB -alpha False -n_images 1 -n_depth 15 -n_size 15
Following commands were used [default params were used] to get the images stored in the result subdirectory:
python random_art.py -img_height 512 -image_width 512 -colormode BW -alpha False -n_images 5
E.g, leading to following 2 random images (resized in order to have next to each other):
python random_art.py -img_height 512 -img_width 512 -colormode RGB -alpha False -n_images 10
E.g, leading to following 2 random images (resized in order to have next to each other):
python random_art.py -img_height 512 -img_width 512 -colormode CYMYK -alpha False -n_images 5
E.g, leading to following 2 random images (resized in order to have next to each other):
python random_art.py -img_height 512 -img_width 512 -colormode HSV -alpha False -n_images 5
E.g, leading to following 2 random images (resized in order to have next to each other):
python random_art.py -img_height 512 -img_width 512 -colormode HSL -alpha False -n_images 5
E.g, leading to following 2 random images (resized in order to have next to each other):
You can try out different input arguments (larger networks and neurons, different actiation functions etc..) as suggested in the Jupyter Notebook, to see what images will be created.
For example the following images are created by deeper neural nets.
Image 1: n_depth=15 and n_size=25, Image 2: n_depth=25 and n_size=45:
Code under MIT License.