Creamas offers some ready made objects to start producing images using genetic programming (GP), which can be found from :py~creamas.domains.image.gp package. They are coupled with image evaluation features from :py~creamas.domains.image.features module. The functionality is based on DEAP library.
Below is a simple example of creating images using ~creamas.domains.image.gp.generator.GPImageGenerator.
from creamas.domains.image.features import ImageEntropyFeature
from creamas.domains.image.gp import tools
from creamas.domains.image import gp
# Create a primitive set for DEAP.
pset = tools.create_super_pset(bw=True)
# Create a DEAP toolbox with some reasonable default values for quick testing.
toolbox = tools.create_toolbox(pset)
# Choose a feature which should be maximised and create an evaluation function for it
# (Remember that evaluation function should also return possible framing for the artifact.
feat = ImageEntropyFeature()
def evaluate_func(artifact):
# Filter out uninteresting images
if gp.GPImageArtifact.png_compression_ratio(artifact) <= 0.08:
return 0.0, None
return feat(artifact), None
# Create a GP image generator, which adds 'name' as created artifacts' creators.
# If each agent has its own generator, 'name' should be different for each agent.
# Using e.g. util.sanitize_agent_name(agent.name) is a good choice.
gpgen = gp.GPImageGenerator('name', toolbox, pset, 40, 20, evaluate_func, (32, 32))
# Run evolution with given parameters and return the best artifact
arts = gpgen.generate()
art = arts[0][0]
img = art.obj
# Save the artifact as an image and the function used to generate it. Functions can be used again to generate the
# image from same function in different sizes, or to induce succesive GP runs.
gp.GPImageArtifact.save(art, 'test_image.png', pset, shape=(400, 400), string_file='test_image.txt')For example, the code above has been used to generate the following image:
Check out GP documentation<domains> for details on how to use the functionality.