1 import random 2 from PIL import Image 3 from copy import deepcopy 4 5 6 img_chrome = Image.open('chrome.png') 7 h_chrome = img_chrome.size[1] 8 w_chrome = img_chrome.size[0] 9 10 11 img = Image.open('test1.png') 12 h = img.size[1] 13 w = img.size[0] 14 15 16 img_color = [] 17 img_color_chrome = [] 18 19 20 def info_pic(): # 获取图片信息 21 22 for x in range(w): 23 24 img_color_tmp = [] # 先获得每一列rgb信息 25 for y in range(h): 26 27 r, g, b = img.getpixel((x, y))[:3] 28 # print(r,g,b) 29 img_color_tmp.append([r, g, b]) 30 img_color.append(img_color_tmp) 31 32 print(img_color) 33 print(img.size) 34 return img_color, img.size 35 36 37 def info_chrome(): # 获取图片信息 38 39 for x in range(w_chrome): 40 41 img_color_tmp = [] # 先获得每一列rgb信息 42 for y in range(h_chrome): 43 44 r, g, b = img_chrome.getpixel((x, y))[:3] 45 # print(r,g,b) 46 img_color_tmp.append([r, g, b]) 47 img_color_chrome.append(img_color_tmp) 48 49 # print(img_color_chrome) 50 print(img_chrome.size) 51 return img_color_chrome, img_chrome.size 52 53 54 # info_chrome() 55 # print(img_color_chrome[10][10][0]) 56 57 population = [] 58 59 60 def initial_population(number=300): # 编码 61 62 for i in range(number): 63 chromosome = [] 64 for x in range(w): 65 row = [] 66 for y in range(h): 67 r = random.randint(0, 255) 68 g = random.randint(0, 255) 69 b = random.randint(0, 255) 70 row.append([r, g, b]) 71 chromosome.append(row) 72 population.append(chromosome) 73 print("随机基因初始化完成") 74 return population 75 76 # initial_population() 77 # info_chrome() 78 79 80 def fitness(chromosome): # 计算适应度函数 81 print("开始处理基因") 82 83 diff = 0 84 # for index1, item1 in enumerate(population): 85 86 for index2, item2 in enumerate(chromosome): # chromosome , index2表示图片的第几列 87 88 for index3, item3 in enumerate(item2): # row，index3表示某一列的第几行 89 90 for index4, item4 in enumerate(item3): # 像素值，index4表示rgb的位置 91 92 93 diff += abs(img_color_chrome[index2][index3][index4]-item4) 94 print(diff) 95 return diff 96 97 def population_fitness(population): 98 for index1, item1 in enumerate(population): 99 fitness(item1) 100 101 102 # fitness(population[1]) 103 104 class ga(object): 105 def __init__(self,population): 106 pass 107 108 def evolution(self, retain_rate=0.3, random_select_rate=0.5, mutation_rate=0.01): 109 """ 110 对当前种群依次进行选择、交叉并生成新一代种群，然后对新一代种群进行变异 111 """ 112 parents = self.selection(retain_rate, random_select_rate) 113 self.crossover(parents) 114 self.mutation(mutation_rate) 115 116 def selection(self, retain_rate, random_select_rate): 117 graded = [(fitness(chromosome), chromosome) for chromosome in population] 118 sort = [x[1] for x in sorted(graded)] # 正向排序 119 retain_length = int(len(sort) * retain_rate) 120 # 选出适应性强的个体，精英选择 121 parents = sort[:retain_length] 122 for chromosome in sort[retain_length:]: 123 if random.random() < random_select_rate: 124 parents.append(chromosome) 125 return parents 126 127 def crossover(self, parents,population): # 交叉 128 children = [] 129 # 需要繁殖的子代数量 130 target_number = len(population) - len(parents) 131 # 开始繁殖 132 while len(children) < target_number: 133 father = random.randint(0, len(parents) - 1) # 选择交叉父代 134 mother = random.randint(0, len(parents) - 1) # 选择交叉母代 135 if father != mother: 136 # 随机选取交叉点 137 cross_point_x = random.randint(0, w) 138 cross_point_y = random.randint(0, h) 139 140 new_gene = deepcopy(population[father][0][:cross_point_x]) 141 new_gene = [new_gene, 0] 142 new_gene[0][cross_point_x:] = deepcopy(population[mother][0][cross_point_x:]) 143 new_gene[0][cross_point_x][:cross_point_y] = deepcopy(population[father][0][cross_point_x][:cross_point_y]) 144 children.append(new_gene) 145 # 经过繁殖后，孩子和父母的数量与原始种群数量相等，在这里可以更新种群。 146 population = parents + children 147 148 def variation(genes, size): 149 rate = 0.5 150 print("开始变异") 151 for i, gene in enumerate(genes): 152 for x, row in enumerate(gene[0]): 153 for y, col in enumerate(row): 154 if random.randint(1, 100) / 100 <= rate: 155 # 图片由 r g b 三种颜色混合而成 变异就是改变他们的值 156 # a b c 分别对应 r_ g_ b_ 改变的值 可自行修改 157 # r g b 的最大值为255 158 # ------------------------------请修改这里-------------------------------------# 159 a = [-1, 1][random.randint(0, 1)] * random.randint(3, 10) 160 b = [-1, 1][random.randint(0, 1)] * random.randint(3, 10) 161 c = [-1, 1][random.randint(0, 1)] * random.randint(3, 10) 162 # ------------------------------请修改这里-------------------------------------# 163 genes[i][0][x][y][0] += a 164 genes[i][0][x][y][0] += b 165 genes[i][0][x][y][0] += c 166 genes[i][0][x][y][3] += a + b + c 167 print("变异结束") 168 169 def result(self): 170 ''' 171 获得近优值 172 ''' 173 graded = [(self.fitness_function(chromosome), chromosome) for chromosome in self.population] 174 graded = [x[1] for x in sorted(graded, reverse=True)] 175 # print('近优的x值为：', self.decode(graded[0]), 176 # '近优的y值为：', self.fitness_function(graded[0])) 177 # 将每一步迭代的结果存储到列表中 178 y_label.append(self.fitness_function(graded[0])) 179 return self.decode(graded[0]) 180 181 def iteration(self, iter_number): 182 for i in range(iter_number): 183 self.evolution() 184 self.result() 185 x_label.append(i)