下方注释的是爬取图片的爬虫

爬取的图片集放在同级目录image文件夹下，每一个像素点都是一个图片

import os

import sys

import time

import math

import numpy as np

from PIL import Image, ImageOps

from multiprocessing import Pool

from colorsys import rgb_to_hsv, hsv_to_rgb

from typing import List, Tuple, Union

class mosaic(object):

"""定义计算图片的平均hsv值

"""

def __init__(self, IN_DIR: str, OUT_DIR: str, SLICE_SIZE: int, REPATE: int,OUT_SIZE: int) -> None:

self.IN_DIR = IN_DIR # 原始的图像素材所在文件夹

self.OUT_DIR = OUT_DIR # 输出素材的文件夹, 这些都是计算过hsv和经过resize之后的图像

self.SLICE_SIZE = SLICE_SIZE # 图像放缩后的大小

self.REPATE = REPATE # 同一张图片可以重复使用的次数

self.OUT_SIZE = OUT_SIZE # 最终图片输出的大小

def resize_pic(self, in_name: str, size: int) -> Image:

"""转换图像大小

"""

img = Image.open(in_name)

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

return img

def get_avg_color(self, img: Image) -> Tuple[float, float, float]:

"""计算图像的平均hsv

"""

width, height = img.size

pixels = img.load()

if type(pixels) is not int:

data = [] # 存储图像像素的值

for x in range(width):

for y in range(height):

cpixel = pixels[x, y] # 获得每一个像素的值

data.append(cpixel)

h = 0

s = 0

v = 0

count = 0

for x in range(len(data)):

r = data[x][0]

g = data[x][1]

b = data[x][2] # 得到一个点的GRB三色

count += 1

hsv = rgb_to_hsv(r / 255.0, g / 255.0, b / 255.0)

h += hsv[0]

s += hsv[1]

v += hsv[2]

hAvg = round(h / count, 3)

sAvg = round(s / count, 3)

vAvg = round(v / count, 3)

if count > 0: # 像素点的个数大于0

return (hAvg, sAvg, vAvg)

else:

raise IOError("读取图片数据失败")

else:

raise IOError("PIL 读取图片数据失败")

class create_image_db(mosaic):

"""创建所需要的数据

"""

def __init__(self, IN_DIR: str, OUT_DIR: str, SLICE_SIZE: int, REPATE: int,

OUT_SIZE: int) -> None:

super(create_image_db, self).__init__(IN_DIR, OUT_DIR, SLICE_SIZE,

REPATE, OUT_SIZE)

def make_dir(self) -> None:

os.makedirs(os.path.dirname(self.OUT_DIR), exist_ok=True) # 没有就创建文件夹

def get_image_paths(self) -> List[str]:

"""获取文件夹内图像的地址

"""

paths = []

suffixs = ['png', 'jpg']

for file_ in os.listdir(self.IN_DIR):

# suffix = file_.split('.')[-1] # 获得文件后缀

# if suffix in suffixs: # 通过后缀判断是否是图片

if file_.endswith('jpg'):

paths.append(os.path.join(self.IN_DIR,file_)) # 添加图像路径

else:

print("非图片:%s" % file_)

if len(paths) > 0:

print("一共找到了%s" % len(paths) + "张图片")

else:

raise IOError("未找到任何图片")

return paths

def convert_image(self, path):

"""转换图像大小, 同时计算一个图像的平均hsv值.

"""

try:

img = self.resize_pic(path, self.SLICE_SIZE)

color = self.get_avg_color(img)

img.save(str(self.OUT_DIR) + str(color) + ".png")

except:

pass

def convert_all_images(self) -> None:

"""将所有图像进行转换

"""

self.make_dir()

paths = self.get_image_paths()

print("正在生成马赛克块...")

pool = Pool() # 多进程处理

pool.map(self.convert_image, paths) # 对已有的图像进行处理, 转换为对应的色块

pool.close()

pool.join()

class create_mosaic(mosaic):

"""创建马赛克图片

"""

def __init__(self, IN_DIR: str, OUT_DIR: str, SLICE_SIZE: int, REPATE: int,

OUT_SIZE: int) -> None:

super(create_mosaic, self).__init__(IN_DIR, OUT_DIR, SLICE_SIZE, REPATE,

OUT_SIZE)

def read_img_db(self) -> List[List[Union[float, int]]]:

"""读取所有的图片

"""

img_db = [] # 存储color_list

for file_ in os.listdir(self.OUT_DIR):

if file_ == 'None.png':

pass

else:

file_ = file_.split('.png')[0] # 获得文件名

file_ = file_[1:-1].split(',') # 获得hsv三个值

file_ = [float(i) for i in file_]

file_.append(0) # 最后一位计算图像使用次数

img_db.append(file_)

return img_db

def find_closiest(self, color: Tuple[float, float, float],

list_colors: List[List[Union[float, int]]]) -> str:

"""寻找与像素块颜色最接近的图像

"""

FAR = 10000000

for cur_color in list_colors: # list_color是图像库中所以图像的平均hsv颜色

n_diff = np.sum((color - np.absolute(cur_color[:3]))**2)

if cur_color[3] <= self.REPATE: # 同一个图片使用次数不能太多

if n_diff < FAR: # 修改最接近的颜色

FAR = n_diff

cur_closer = cur_color

cur_closer[3] += 1

return "({}, {}, {})".format(cur_closer[0], cur_closer[1],

cur_closer[2]) # 返回hsv颜色

def make_puzzle(self, img: str) -> bool:

"""制作拼图

"""

img = self.resize_pic(img, self.OUT_SIZE) # 读取图片并修改大小

color_list = self.read_img_db() # 获取所有的颜色的list

width, height = img.size # 获得图片的宽度和高度

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

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

(255, 255, 255)) # 创建一个空白的背景, 之后向里面填充图片

total_images = math.floor(

(width * height) / (self.SLICE_SIZE * self.SLICE_SIZE)) # 需要多少小图片

now_images = 0 # 用来计算完成度

for y1 in range(0, height, self.SLICE_SIZE):

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

try:

# 计算当前位置

y2 = y1 + self.SLICE_SIZE

x2 = x1 + self.SLICE_SIZE

# 截取图像的一小块, 并计算平均hsv

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

color = self.get_avg_color(new_img)

# 找到最相似颜色的照片

close_img_name = self.find_closiest(color, color_list)

close_img_name = self.OUT_DIR + str(

close_img_name) + '.png' # 图片的地址

paste_img = Image.open(close_img_name)

# 计算完成度

now_images += 1

now_done = math.floor((now_images / total_images) * 100)

r = '\r[{}{}]{}%'.format("#" * now_done,

" " * (100 - now_done), now_done)

sys.stdout.write(r)

sys.stdout.flush()

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

except IOError:

print('创建马赛克块失败')

# 保持最后的结果

background.save('out_without_background.jpg')

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

img.save('out_with_background.jpg')

return True

if __name__ == "__main__":

filePath = os.path.dirname(os.path.abspath(__file__)) # 获取当前的路径

start_time = time.time() # 程序开始运行时间, 记录一共运行了多久

# 创建马赛克块, 创建素材库

createdb = create_image_db(IN_DIR=os.path.join(filePath, 'images/'),

OUT_DIR=os.path.join(filePath, 'outputImages/'),

SLICE_SIZE=100,

REPATE=20,

OUT_SIZE=8000)

createdb.convert_all_images()

# 创建拼图 (这里使用绝对路径)

createM = create_mosaic(IN_DIR=os.path.join(filePath, 'images/'),

OUT_DIR=os.path.join(filePath, 'outputImages/'),

SLICE_SIZE=100,

REPATE=20,

OUT_SIZE=8000)

out = createM.make_puzzle(img=os.path.join(filePath, '微信图片_20201027102845.jpg'))

# 打印时间

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

print("已完成")

# 爬图片

# from urllib import request

# from common import Common

# import datetime

#

# driver = Common.get_driver('chrome')

# driver.get('https://pic.sogou.com/pics?ie=utf8&p=40230504&interV=kKIOkrELjboMmLkEkrsTkKIMkLELjboImLkEk74TkKIMkrELjbkRmLkEmrELjbgRmLkEkLYTLlDpFEPQjb0E0vcGwOVFmVnmMz7lWaIPjf9QxeVPz6R7zOMTKVeRFTbxHUwElKJ6wu9DxqRLzO1H1qR7zOM%3D_923767100&query=%E9%A3%9E%E8%BD%A6')

# end_time = datetime.datetime.now() + datetime.timedelta(seconds=15)

# while True:

# driver.execute_script("window.scrollTo(0,document.body.scrollHeight)")

# time.sleep(0.2)

# if datetime.datetime.now() > end_time:

# time.sleep(3)

# break

# f = 0

# for i in driver.find_elements_by_xpath('//img[@src]'):

# try:

# url = i.get_attribute('src')

# request.urlretrieve(url, f'images\\sougou{f}.jpg')

# print(f)

# f+=1

# except:

# print('出错')