死宅一枚。爬取5000张二次元妹子的图片，生成了头图。

接下来看看怎么实现的：

使用 Scrapy 框架爬取5000张二次元图

使用 opencv 批量格式化图片

将图片按照RGB值的均方根排序，实现效果

一、安装环境

1.安装 Scrapy 爬虫框架

pip install Scrapy

windows 安装可以 点击此处

2. 推荐使用 wheel 来安装 opencv 点击此处

3.安装 numpy 科学计算库

pip install numpy

4. 初始化一个 Scrapy 项目 acg

scrapy startproject acg

二、爬取图片

以下代码主要实现操作：

中间裁剪

统一大小

下载图片

重复抓取

/image.py

import scrapy

import urllib.request,urllib.parse

import numpy as np

import cv2

class acgimages(scrapy.Spider):

"""docstring for acgimages"""

name = 'images'

start_urls = [

"http://m.52dmtp.com/tupiandaquan/index_2.html"

]

count = 1

page = 2

def parse(self,response):

def imageSave(item,path):

try:

maxsize = 512

res = urllib.request.urlopen(item).read()

image = np.asarray(bytearray(res),dtype="uint8")

image = cv2.imdecode(image,cv2.IMREAD_COLOR)

height,width = image.shape[:2]

if height > width:

scalefactor = (maxsize*1.0) / width

res = cv2.resize(image,(int(width * scalefactor),(int(height * scalefactor))),interpolation = cv2.INTER_CUBIC)

cutImage = res[0:maxsize,0:maxsize]

if width >= height:

scalefactor = (maxsize*1.0) / height

res = cv2.resize(image,(int(width * scalefactor), int(height*scalefactor)), interpolation = cv2.INTER_CUBIC)

center_x = int(round(width*scalefactor*0.5))

cutImage = res[0:maxsize,int(center_x - maxsize/2):int(center_x + maxsize/2)]

cv2.imwrite(path,cutImage)

print('image is save in ' + path)

except:

print('image save error')

image_url = response.xpath("//div[@class='grid-wrap']//img/@src").extract()

for item in image_url:

item = item.split('?')[0]

item = urllib.parse.quote(item,safe='/:?=.')

if 'jpg' in item:

self.count = self.count + 1

path = 'img/'+ str(self.count) + ".jpg"

imageSave(item,path)

if 'png' in item:

self.count = self.count + 1

path = 'img/'+ str(self.count) + ".png"

imageSave(item,path)

if self.page < 1180:

self.page = self.page + 1

next_url = "http://m.52dmtp.com/tupiandaquan/index_%d.html"%self.page

yield scrapy.Request(next_url,callback = self.parse)

三、生成图片

1.创建一个图片数据库

2.导入图片

3.导入过程中

4.创建一个马赛克风格图片

5.打开原图

6.选择已经上传的数据库

7.生成完成

简单高效的平民玩法到此结束。

2017年12月10日 本来今天开开心心。被迫更新

一、安装依赖

1.安装图像处理库 pillow

pip install pillow

二、使用 python 做到马赛克拼图效果

写在前面，有人私信说 hsv(颜色空间)做效果会更好。大家可以尝试一下，这里的话只是对rgb的对比。主要效果差上面提到的程序一点，答主也在尽可能的优化代码，有更好的方法，欢迎私信评论。

核心部分：求像素平均值，尝试过了方差和均方根，个人对整体优化并不明显。

def get_avg_color(img):

width, height = img.size

pixels = img.load()

data = []

for x in range(width):

for y in range(height):

content = pixels[x, y]

data.append(content)

r = 0

g = 0

b = 0

count = 0

for x in range(len(data)):

r += data[x][0]

g += data[x][1]

b += data[x][2]

count += 1

rAvg = r / count

gAvg = g / count

bAvg = b / count

return (rAvg, gAvg, bAvg)

核心部分：对比图片块颜色与马赛克块平均颜色差值。很重要的一步就是，求出近似值数组后，取一个随机数，有效的解决马赛克块重复问题(不是不重复使用，因为这样需要的图片集会很大)。

def find_closiest(color, list_colors):

diff = 10000

cur_closer = []

arr_len = 0

for cur_color in list_colors:

n_diff = abs(color[0] - cur_color[0]) + abs(color[1] - cur_color[1]) + abs(color[2] -cur_color[2])

if n_diff < diff:

diff = n_diff

if len(cur_closer) <= 5:

cur_closer.append(cur_color)

else:

cur_closer[arr_len] = cur_color

arr_len += 1

if arr_len > 5:

arr_len = 0

index = random.randint(0,len(cur_closer) - 1)

return cur_closer[index]

核心部分： 图片分割并且按图片集名字对比就近的图片

def make_puzzle(img, color_list):

width, height = img.size

print("Width = {}, Height = {}".format(width,height))

background = Image.new('RGB', img.size, (255,255,255))

total_images = 0

for y1 in range(0, heigth, SLICE_SIZE):

for x1 in range(0, width, SLICE_SIZE):

y2 = y1 + SLICE_SIZE

x2 = x1 + SLICE_SIZE

new_img = img.crop((x1, y1, x2, y2))

color = get_avg_color(new_img)

close_img_name = find_closiest(color, color_list)

close_img_name = OUT_DIR + str(close_img_name) + '.jpg'

paste_img = Image.open(close_img_name)

total_images += 1

print("%s images \r" % total_images),

background.paste(paste_img, (x1, y1))

return background

全部代码 /main.py

import os

from PIL import Image,ImageOps

import argparse

import time

# 多进程，

from multiprocessing import Pool

import random

import math

# 少年，你不试试看hsv吗，据说效果更好

from colorsys import rgb_to_hsv

SLICE_SIZE = 60 # 马赛克大小

OUT_SIZE = 5000 # 图片大小(注意：要做到清晰，必须要大)

IN_DIR = "database/"

OUT_DIR = "output/"

def get_avg_color(img):

width, height = img.size

pixels = img.load()

data = []

for x in range(width):

for y in range(height):

content = pixels[x, y]

data.append(content)

r = 0

g = 0

b = 0

count = 0

for x in range(len(data)):

r += data[x][0]

g += data[x][1]

b += data[x][2]

count += 1

rAvg = r / count

gAvg = g / count

bAvg = b / count

return (rAvg, gAvg, bAvg)

def find_closiest(color, list_colors):

diff = 10000

cur_closer = []

arr_len = 0

for cur_color in list_colors:

n_diff = abs(color[0] - cur_color[0]) + abs(color[1] - cur_color[1]) + abs(color[2] -cur_color[2])

if n_diff < diff:

diff = n_diff

if len(cur_closer) <= 5:

cur_closer.append(cur_color)

else:

cur_closer[arr_len] = cur_color

arr_len += 1

if arr_len > 5:

arr_len = 0

index = random.randint(0,len(cur_closer) - 1)

return cur_closer[index]

def make_puzzle(img,color_list):

width, height = img.size

print("Width = {}, Height = {}".format(width,heigth))

background = Image.new('RGB', img.size, (255,255,255))

total_images = 0

for y1 in range(0, heigth, SLICE_SIZE):

for x1 in range(0, width, SLICE_SIZE):

y2 = y1 + SLICE_SIZE

x2 = x1 + SLICE_SIZE

new_img = img.crop((x1, y1, x2, y2))

color = get_avg_color(new_img)

close_img_name = find_closiest(color, color_list)

close_img_name = OUT_DIR + str(close_img_name) + '.jpg'

paste_img = Image.open(close_img_name)

total_images += 1

print("%s images \r" % total_images),

background.paste(paste_img, (x1, y1))

return background

def get_image_paths():

paths = []

for file_ in os.listdir(IN_DIR):

paths.append(IN_DIR + file_)

print("一共找到了%s" % len(paths) + "张图片")

return paths

def resize_pic(in_name,size):

img = Image.open(in_name)

img = ImageOps.fit(img, (size, size), Image.ANTIALIAS)

return img

def convert_image(path):

img = resize_pic(path,SLICE_SIZE)

color = get_avg_color(img)

img.save(str(OUT_DIR) + str(color) + ".jpg")

def convert_all_images():

paths = get_image_paths()

pool = Pool()

pool.map(convert_image, paths)

pool.close()

pool.join()

def read_img_db():

img_db = []

for file_ in os.listdir(OUT_DIR):

print(file_)

if file_ == 'None.jpg':

pass

else:

file_ = file_.split('.jpg')[0]

file_ = tuple(map(float, file_[1:-1].split(',')))

img_db.append(file_)

return img_db

if __name__ == '__main__':

parse = argparse.ArgumentParser()

parse.add_argument("-i",'--input',required=True,help='input image')

parse.add_argument("-d", "--db", type=str, required=True,help="source database")

parse.add_argument("-o", "--output", type=str, required=True,help="out directory")

parse.add_argument("-is",'--inputSize',type=str, required=False,help="inputSize")

parse.add_argument("-os",'--outputSize',type=str, required=False,help="outputSize")

args = parse.parse_args()

start_time = time.time()

args = parse.parse_args()

image = args.input

if args.db:

IN_DIR = args.db

if args.output:

OUT_DIR= args.output

if args.inputSize:

SLICE_SIZE = args.inputSize

if args.outputSize:

OUT_SIZE = args.outputSize

img = resize_pic(image,OUT_SIZE)

convert_all_images()

list_of_imgs = read_img_db()

# 为了添加图片信息量，合并两张图片

out = make_puzzle(img, list_of_imgs)

img = Image.blend(out, img, 0.5)

img.save('out.jpg')

print("耗时: %s" % (time.time() - start_time))

print("已完成")

命令行输入

python main.py -i test.jpg(你的图片) -d D:/acg/img/(你的图片集合) -o output/(你的马赛克图片输出位置)

当然，你还可以给你的 background 滤色

上传一张结果图(根据喜欢，精度可以更高)，知乎限制5m了

非洲小鸟，黑白对比度处理任然是败笔，建议还是 hsv

这个模型更适合颜色适配。

后续将会发布优化版本。

作者：今晚的风儿很喧嚣