Genetic

import cv2
import numpy as np
import random
from operator import itemgetter
import time


def get_fitness_list(population, target):
    answer = []
    for img in population:
        fitness_ = fitness(img, target)
        answer.append(fitness_)
    return answer


def fitness(image, target):
    ans = 0
    for i in range(0, len(image)):
        for j in range(0, len(image[0])):
            ans += abs(int(image[i, j, 0]) - int(target[i, j, 0]))
            ans += abs(int(image[i, j, 1]) - int(target[i, j, 1]))
            ans += abs(int(image[i, j, 2]) - int(target[i, j, 2]))
    return ans


def change_matrix_in_random(image, max_change):
    copy = image.copy()

    for i in range(0, len(copy)):
        for j in range(0, len(copy[0])):
            if (np.random.uniform(0,1) > 0.5): #rate
                continue
            a = np.random.randint(-max_change, max_change)
            b = np.random.randint(-max_change, max_change)
            c = np.random.randint(-max_change, max_change)
            copy[i, j, 0] = max(min(copy[i, j, 0] + a, 255), 0)
            copy[i, j, 1] = max(min(copy[i, j, 1] + b, 255), 0)
            copy[i, j, 2] = max(min(copy[i, j, 2] + c, 255), 0)
    return copy


def get_rand_img(side_y, side_x):
    img = np.zeros((side_y, side_x, 3), np.uint8)
    for i in range(0, side_y):
        for j in range(0, side_x):
            img[i, j, 0] = random.randint(0, 255)
            img[i, j, 1] = random.randint(0, 255)
            img[i, j, 2] = random.randint(0, 255)
    return img


def select_best(population, target):
    fitness_list = get_fitness_list(population, target)
    arr_sort = []
    for i in range(GPS):
        arr_sort.append([fitness_list[i], population[i]])
    arr_sort = sorted(arr_sort, key=itemgetter(0))


    answer = []
    for i in range (PS):
        answer.append(arr_sort[i][1])
    return answer


def grow(population):
    answer = []
    for i in range (len(population)):
        for j in range(i+1, len(population)):
            child = crossingover(population[i], population[j])
            changed_child = change_matrix_in_random(child, 40)
            answer.append(changed_child)
    return answer

def crossingover(image1, image2):
    answer = image1.copy()
    for i in range(0, len(image1)):
        for j in range(0, len(image1[0])):
            change = np.random.randint(0,2)
            if change > 0:
                answer[i, j, 0] = image2[i, j, 0]
                answer[i, j, 1] = image2[i, j, 1]
                answer[i, j, 2] = image2[i, j, 2]
    return answer


def solve(image, target):
    copy = image.copy()

    population = []
    for i in range(PS):
        population.append(change_matrix_in_random(copy, 45))

    for iteration in range(8):
        population = grow(population)
        population = select_best(population, target)

    return population[0]


def main2(target):
    answer = get_rand_img(512, 512)

    for i in range(0, 512, 2):
        print(i)
        for j in range(0, 512, 2):
            print(1000 + j)
            solved = solve(answer[i:i+2], target[i:i+2])
            answer[i, j] = solved[0, 0]
            answer[i, j+1] = solved[0, 1]
            answer[i+1, j] = solved[1, 0]
            answer[i+1, j+1] = solved[1, 1]

    return answer

def main1():
    filepath = "image1.png"
    target = cv2.imread(filepath, cv2.IMREAD_COLOR)

    t = time.time()

    #cv2.namedWindow('target', cv2.WINDOW_NORMAL)
    #cv2.imshow('target', target)
    #cv2.waitKey(0)
    #cv2.destroyAllWindows()


    answer = main2(target)
    #answer = target
    #cv2.imshow("target", target)
    #cv2.imshow("answer", answer)

    #cv2.namedWindow('answer', cv2.WINDOW_NORMAL)
    #cv2.imshow('answer', answer)
    #cv2.waitKey(0)
    #cv2.destroyAllWindows()

    filepath = "C/Users/Insaf/PycharmProjects/genetic"

    cv2.imwrite('target.jpg', target)
    cv2.imwrite('result.jpg', answer)

    print("TIME ELAPSED: {}".format(time.time() - t))


PS = 3
GPS = (PS*(PS-1)//2)

main1()