example.py

import numpy
import imageio
import gari
import pygad
import matplotlib.pyplot

"""
GARI (Genetic Algorithm for Reproducing Images) is a Python project that uses the PyGAD library for reproducing images using the genetic algorithm. GARI reproduces a single image using Genetic Algorithm (GA) by evolving pixel values.

For implementing the genetic algorithm, the PyGAD library is used. Check its documentation here: https://pygad.readthedocs.io

This project works with both color and gray images without any modifications.

This project is implemented using Python 3.5 by Ahmed Fawzy Gad.
Contact info:
ahmed.f.gad@gmail.com
https://www.linkedin.com/in/ahmedfgad/
"""

# Reading target image to be reproduced using Genetic Algorithm (GA).
target_im = imageio.imread('fruit.jpg')
target_im = numpy.asarray(target_im/255, dtype=numpy.float)

# Target image after enconding. Value encoding is used.
target_chromosome = gari.img2chromosome(target_im)

def fitness_fun(solution, solution_idx):
    """
    Calculating the fitness value for a solution in the population.
    The fitness value is calculated using the sum of absolute difference between genes values in the original and reproduced chromosomes.
    
    solution: Current solution in the population to calculate its fitness.
    solution_idx: Index of the solution within the population.
    """

    fitness = numpy.sum(numpy.abs(target_chromosome-solution))

    # Negating the fitness value to make it increasing rather than decreasing.
    fitness = numpy.sum(target_chromosome) - fitness
    return fitness

def callback(ga_instance):
    print("Generation = {gen}".format(gen=ga_instance.generations_completed))
    print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution()[1]))

    if ga_instance.generations_completed % 500 == 0:
        matplotlib.pyplot.imsave('solution_'+str(ga_instance.generations_completed)+'.png', gari.chromosome2img(ga_instance.best_solution()[0], target_im.shape))

ga_instance = pygad.GA(num_generations=20000,
                       num_parents_mating=10,
                       fitness_func=fitness_fun,
                       sol_per_pop=20,
                       num_genes=target_im.size,
                       init_range_low=0.0,
                       init_range_high=1.0,
                       mutation_percent_genes=0.01,
                       mutation_type="random",
                       mutation_by_replacement=True,
                       random_mutation_min_val=0.0,
                       random_mutation_max_val=1.0,
                       callback_generation=callback)

ga_instance.run()

# After the generations complete, some plots are showed that summarize the how the outputs/fitenss values evolve over generations.
ga_instance.plot_result()

# Returning the details of the best solution.
solution, solution_fitness, solution_idx = ga_instance.best_solution()
print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))

if ga_instance.best_solution_generation != -1:
    print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation))

result = gari.chromosome2img(solution, target_im.shape)
matplotlib.pyplot.imshow(result)
matplotlib.pyplot.title("PyGAD & GARI for Reproducing Images")
matplotlib.pyplot.show()