遗传算法图像演化的结果不正确 [英] Incorrect results with genetic algorithm image evolution

问题描述

我正在尝试实施最初由

I'm attempting to implement a program originally created by Roger Alsing. I've done quite a bit of research on what other people have implemented. I decided to write my program in python, and use basic triangles as the shapes. When I run the program, it does not show improvement after more generations (The triangles tend to just disappear). I'm assuming something is wrong with my mutate function. Can anyone tell me why its producing less than satisfactory results?

我的代码:

import random
import copy
from PIL import Image, ImageDraw

optimal = Image.open("mona_lisa.png")
optimal = optimal.convert("RGBA")

size = width, height = optimal.size

num_shapes = 128

generations = 50000

def random_genome():
    elements = []

    for i in range(num_shapes):
        x = (random.randint(0, width), random.randint(0, height))
        y = (random.randint(0, width), random.randint(0, height))
        z = (random.randint(0, width), random.randint(0, height))
        r = random.randint(0, 255)
        g = random.randint(0, 255)
        b = random.randint(0, 255)
        alpha = random.randint(10, 255)

        elements.append([x, y, z, r, g, b, alpha])

    return elements

def render_daughter(dna):
    image = Image.new("RGBA", (width, height), "white")
    draw = ImageDraw.Draw(image)

    for item in dna:
        x = item[0]
        y = item[1]
        z = item[2]
        r = item[3]
        g = item[4]
        b = item[5]
        alpha = item[6]

        color = (r, g, b, alpha)

        draw.polygon([x, y, z], fill = color)

    return image

def mutate(dna):
    dna_copy = copy.deepcopy(dna)

    shape_index = random.randint(0, len(dna) - 1)
    roulette = random.random() * 2

    if roulette < 1:

        if roulette < 0.25:
            dna_copy[shape_index][3] = int(random.triangular(255, dna_copy[shape_index][3]))

        elif roulette < 0.5:
            dna_copy[shape_index][4] = int(random.triangular(255, dna_copy[shape_index][4]))

        elif roulette < 0.75:
            dna_copy[shape_index][5] = int(random.triangular(255, dna_copy[shape_index][5]))

        elif roulette < 1.0:
            dna_copy[shape_index][6] = int(0.00390625 * random.triangular(255, dna_copy[shape_index][6] * 255))

    else:

        if roulette < 1.25:
            dna_copy[shape_index][0] = (int(random.triangular(width, dna_copy[shape_index][0][0])), int(random.triangular(height, dna_copy[shape_index][0][1])))

        elif roulette < 1.5:
            dna_copy[shape_index][2] = (int(random.triangular(width, dna_copy[shape_index][3][0])), int(random.triangular(height, dna_copy[shape_index][4][1])))

        elif roulette < 1.75:
            dna_copy[shape_index][3] = (int(random.triangular(width, dna_copy[shape_index][4][0])), int(random.triangular(height, dna_copy[shape_index][5][1])))

    return dna_copy

def fitness(original, new):
    fitness = 0

    for x in range(0, width):
        for y in range(0, height):
            r1, g1, b1, a1 = original.getpixel((x, y))
            r2, g2, b2, a2 = new.getpixel((x, y))

            deltaRed = r1 - r2
            deltaGreen = g1 - g2
            deltaBlue = b1 - b2
            deltaAlpha = a1 - a2

            pixelFitness = deltaRed + deltaGreen + deltaBlue + deltaAlpha

            fitness += pixelFitness

    return fitness

def generate():
    mother = random_genome()
    best_genome = mother
    best_fitness = fitness(optimal, render_daughter(best_genome))


    for i in range(generations):
        daughter = copy.deepcopy(best_genome)
        daughter = mutate(daughter)

        daughter_fitness = fitness(optimal, render_daughter(daughter))

        if daughter_fitness < best_fitness:
            best_genome = daughter
            best_fitness = daughter_fitness

        if i % 50 == 0:
            print i

        if i % 1000 == 0:
            render_daughter(best_genome).save("iterations/output_" + str(i) + ".png")

if __name__ == "__main__":
    generate()

我正在使用的起始图像:

The beginning image I am using:

1000代后的输出图像:

The output image after 1,000 generations:

5,000代后输出图像:

Output image after 5,000 generations:

推荐答案

您正在检查新适应度是否小于当前适应度:

You're checking wheter the new fitness is smaller than the current fitness:

if daughter_fitness < best_fitness:

但是，您计算出的适应度可能为负:

The fitness you calculate, however, can be negative:

deltaRed = r1 - r2
deltaGreen = g1 - g2
deltaBlue = b1 - b2
deltaAlpha = a1 - a2

pixelFitness = deltaRed + deltaGreen + deltaBlue + deltaAlpha

fitness += pixelFitness

各种delta*变量可以是负数或正数；您的测试将偏爱负增量，从而增加最佳"图像的白度(r2，g2等的值越高，适应度越低，图像越白，直到它们全部达到255、255， 255.我不知道增加alpha会增加还是减少透明度.

The various delta* variables can be negative or positive; your test will favour negative deltas, increasing the whiteness of the "best" image (the higher values of r2, g2 etc, the lower the fitness, and the whiter the image, until they are all at 255, 255, 255. I don't know if increasing alpha increases or decreases the transparency).

因此，您应该获取差异的绝对值:

Thus, you should take the absolute value of the differences:

deltaRed = abs(r1 - r2)
deltaGreen = abs(g1 - g2)
deltaBlue = abs(b1 - b2)
deltaAlpha = abs(a1 - a2)

您还可以考虑平方和，或平方和的平方根(基本上将其转换为最小二乘拟合例程):

You could also consider the sum of the square, or square root of the sum of squares (which, basically, turns it into a least-squares fitting routine):

deltaRed = r1 - r2
deltaGreen = g1 - g2
deltaBlue = b1 - b2
deltaAlpha = a1 - a2

pixelFitness = math.sqrt(deltaRed**2 + deltaGreen**2 + deltaBlue**2 + deltaAlpha**2)

fitness += pixelFitness

最后，我注意到您的程序不适用于我.它位于mutate()函数的后半部分，在其中向x，y或z分配新值，但使用2以上的索引.random_genome()表明，您尝试改为访问颜色值，这些颜色值是整数，甚至尝试索引这些.

Finally, I noticed your program doesn't work for me. It's in the second half of your mutate() function, where you assign new values to x, y or z, but use indices above 2. random_genome() shows that you try to access colour values instead, which are integers, and even attempt to index those.

这会导致异常，所以我什至不知道如何使该程序运行.它要么一开始就没有运行过，要么没有正确粘贴.我已将其更改为

This leads to exceptions, so I don't even know how you could get this program running. It either never ran in the first place, or you didn't properly copy-paste. I've changed that to

if roulette < 1.25:
    dna_copy[shape_index][0] = (int(random.triangular(
        width, dna_copy[shape_index][0][0])), int(
            random.triangular(height, dna_copy[shape_index][0][1])))
elif roulette < 1.5:
    dna_copy[shape_index][1] = (int(random.triangular(
        width, dna_copy[shape_index][1][0])), int(
            random.triangular(height, dna_copy[shape_index][1][1])))
elif roulette < 1.75:
    dna_copy[shape_index][2] = (int(random.triangular(
        width, dna_copy[shape_index][2][0])), int(
            random.triangular(height, dna_copy[shape_index][2][1])))

这似乎可以满足您的要求.

which seems to do what you want.

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