def adjustment_sample(filepath):
"""A sample function that illustrates how to use adjusters.
:param filepath: The absolute filepath of an input image.
:type filepath: str
:return: The evolved genome.
:rtype: Genome
"""
evolver = Evolver(get_image(filepath), adjusters=[STRONG_DARK_ADJUSTER])
return evolver.evolve()
def draw_shape_sample(filepath):
"""A sample function that illustrates how to use custom shape drawing methods.
:param filepath: The absolute filepath of an input image.
:type filepath: str
:return: The evolved genome.
:rtype: Genome
"""
evolver = Evolver(get_image(filepath), draw=add_square)
return evolver.evolve()
def standard_sample(filepath):
"""A standard sample function that illustrates how to use the module.
:param filepath: The absolute filepath of an input image.
:type filepath: str
:return: The evolved genome.
:rtype: Genome
"""
evolver = Evolver(get_image(filepath))
return evolver.evolve()
def error_metric_sample(filepath):
"""A sample function that illustrates how to use custom error metrics.
:param filepath: The absolute filepath of an input image.
:type filepath: str
:return: The evolved genome.
:rtype: Genome
"""
evolver = Evolver(get_image(filepath),
calculate_error=structural_similarity_error)
return evolver.evolve()
def load_genome_sample(filepath_source, filepath_genome):
"""A sample function that illustrates how to load previously saved genomes.
:param filepath_source: The absolute filepath of an input image.
:type filepath_source: str
:param filepath_genome: The filepath to load the pre-evolved genome.
:type filepath_genome: str
:return: The evolved genome.
:rtype: Genome
"""
evolver = Evolver(get_image(filepath_source),
saved_genome=load_genome(filepath_genome))
return evolver.evolve()
def save_genome_sample(filepath_source, filepath_to_save_to):
"""A sample function that illustrates how to save genomes.
:param filepath_source: The absolute filepath of an input image.
:type filepath_source: str
:param filepath_to_save_to: The filepath to save the genome.
:type filepath_to_save_to: str
:return: The evolved genome.
:rtype: Genome
"""
evolver = Evolver(get_image(filepath_source))
genome = evolver.evolve()
genome.save_genome(filepath_to_save_to)
return genome
def preprocess_sample(filepath):
"""A sample function that illustrates how to use preprocesses.
:param filepath: The absolute filepath of an input image.
:type filepath: str
:return: The evolved genome.
:rtype: Genome
"""
evolver = Evolver(get_image(filepath),
preprocesses=[
saturate_preprocess, contrast_preprocess,
smooth_preprocess
])
return evolver.evolve()
def callback_sample(filepath_source, filepath_csv):
"""A sample function that illustrates how to add callbacks to the evolve method.
:param filepath_source: The absolute filepath of an input image.
:type filepath_source: str
:param filepath_csv: The filepath to save CSV statistics to.
:type filepath_csv: str
:return: The evolved genome.
:rtype: Genome
"""
evolver = Evolver(get_image(filepath_source))
logger = callbacks.CSVLogger(filepath_csv)
return evolver.evolve(
callbacks=[callbacks.default_callback, logger.callback])
def save_image_sample(filepath_source, filepath_to_save_to):
"""A sample function that illustrates how to save images from genomes.
:param filepath_source: The absolute filepath of an input image.
:type filepath_source: str
:param filepath_to_save_to: The filepath to save the evolved image to.
:type filepath_to_save_to: str
:return: The evolved genome.
:rtype: Genome
"""
evolver = Evolver(get_image(filepath_source))
genome = evolver.evolve()
image = genome.render_scaled_image()
cv2.imwrite(filepath_to_save_to, image)
return genome
def main():
"""Run the application."""
parser = ArgumentParser(
description=f"Circlevolve CLI, version {__version__}")
parser.add_argument("image",
type=str,
help="Path to base image for evolution")
parser.add_argument("-square",
dest="square",
action='store_true',
help="Runs with squares instead of circles")
parser.add_argument("-debug",
dest="debug",
action='store_true',
help="Print statistics in console")
parser.add_argument(
"-ssim",
dest="ssim",
action='store_true',
help="Use structural-similarity instead of mean-squared-error")
parser.add_argument("-dark",
dest="dark",
action='store_true',
help="Optimize the algorithm for darker images")
group = parser.add_mutually_exclusive_group()
group.add_argument(
"-silent",
dest="output",
action='store_const',
const=2,
help="Disables viewing updates and statistics during evolution")
group.add_argument("-quiet",
dest="output",
action='store_const',
const=1,
help="Updates more rarely during evolution")
parser.add_argument("--saved-genome",
default=None,
type=str,
help="Path to saved genome for continued evolution")
parser.add_argument("--num-shapes",
default=1000,
type=int,
help="Number of shapes to draw with")
parser.add_argument("--num-generations",
default=5000,
type=int,
help="Number of generations to train")
args = parser.parse_args()
if args.saved_genome is not None:
saved_genome = load_genome(args.saved_genome)
else:
saved_genome = None
image_path = args.image + "_result.png"
genome_path = args.image + "_genome.pkl"
if args.square:
draw = drawers.add_square
else:
draw = drawers.add_circle
if args.ssim:
error = error_metrics.structural_similarity_error
else:
error = error_metrics.mean_squared_error
if args.dark:
adjustments = [adjusters.STRONG_DARK_ADJUSTER]
else:
adjustments = []
if args.output == 2:
silent = True
else:
silent = False
if args.output == 1:
callback_list = [callbacks.quiet_visual_callback]
if args.debug:
callback_list.append(callbacks.quiet_verbose_callback)
else:
callback_list = [callbacks.visual_callback]
if args.debug:
callback_list.append(callbacks.verbose_callback)
evolver = Evolver(get_image(args.image),
saved_genome=saved_genome,
num_shapes=args.num_shapes,
draw=draw,
calculate_error=error,
adjusters=adjustments)
print("evolve!")
genome = evolver.evolve(num_generations=args.num_generations,
silent=silent,
callbacks=callback_list)
image = genome.render_scaled_image()
imwrite(image_path, image)
genome.save_genome(genome_path)
print(f"Image saved at {image_path}\nGenome saved at {genome_path}")
show() # Keep matplotlib window open.