有趣的Python图片制作之如何用QQ好友头像拼接出里昂

import requests,codecs,re,urllib,os,random,math
from PIL import Image
import numpy as np
import cv2 as cv

def simple_split(filepackage,size,littlesize): #简单拼接，参数为图片文件名，每行每列的size，小头像图片的大小
	row = size[0]
	col = size[1]
	bigimg = Image.new('RGBA',(littlesize*row,littlesize*col)) #结果图
	number = 0
	for i in range(row): #行
		for j in range(col): #列
			randpic = random.randint(1,friends_count)
			img = Image.open(filepackage+str(randpic)+'.png').convert('RGBA')
			img = img.resize((littlesize,littlesize))
			loc = (i*littlesize,j*littlesize,(i+1)*littlesize,(j+1)*littlesize)
			print(loc,number)
			number+=1
			bigimg.paste(img,loc)
	bigimg.save(resultSavePath)

import requests,codecs,re,urllib,os,random,math
from PIL import Image
import numpy as np
import cv2 as cv

txtpath = 'C:/Users/11037/Desktop/test/qqfriends.txt' #你从QQ邮箱中粘贴的文件
savepath = 'C:/Users/11037/Desktop/touxiang/' #头像存储位置

resultSavePath = 'C:/Users/11037/Desktop/result2.png'  #结果存储位置
modePath = 'C:/Users/11037/Desktop/leno.jpg'  #模板存储位置

friends_count = 0  #好友数量
all_mean_rgbs = []  #存储计算出的所有平均rgb值

def meanrbg(img): #计算图片平均rgb
	rgb = np.array(img)
	r = int(round(np.mean(rgb[:, :, 0])))
	g = int(round(np.mean(rgb[:, :, 1])))
	b = int(round(np.mean(rgb[:, :, 2])))
	return (r,g,b)

def gettouxiang(txtpath):#输入你的txt文件存储位置
	file = codecs.open(txtpath,'rb','utf-8')
	s = file.read()
	pattern = re.compile(r'\d+@qq.com')
	all_mail = pattern.findall(s) #正则表达式匹配所有的qq号
	all_link = [] #用于存储需要访问的链接
	url = 'http://qlogo.store.qq.com/qzone/'
	for mail in all_mail:
		qq = mail.replace('@qq.com','')
		l = url + qq +'/'+qq+'/100'
		all_link.append(l)
	i = 1
	for link in all_link:  #遍历链接，下载头像
		saveurl = savepath+str(i)+'.png'
		savaImg(link,saveurl)
		i +=1
		print('已下载',i)
	friends_count = len(all_link) #获取朋友头像数量
	return True

def savaImg(picurl,saveurl): #存储图片函数，picurl是图片的URL，saveurl是本地存储位置
	try:
		bytes = urllib.request.urlopen(picurl)
		file = open(saveurl,'wb')
		file.write(bytes.read())
		file.flush()
		file.close()
		return True
	except:
		print('worry')
		savaImg(picurl,saveurl)


def simple_split(filepackage,size,littlesize): #简单拼接，参数为图片文件名，每行每列的size，小头像图片的大小
	row = size[0]
	col = size[1]
	bigimg = Image.new('RGBA',(littlesize*row,littlesize*col))
	number = 0
	for i in range(row):
		for j in range(col):
			randpic = random.randint(1,friends_count)
			img = Image.open(filepackage+str(randpic)+'.png').convert('RGBA')
			img = img.resize((littlesize,littlesize))
			loc = (i*littlesize,j*littlesize,(i+1)*littlesize,(j+1)*littlesize)
			print(loc,number)
			number+=1
			bigimg.paste(img,loc)
	bigimg.save(resultSavePath)


def mode_split(filepackage,modepath,bigsize,littlesize): #以模板存储头像
	row = bigsize[0] #大图每行多少个小头像
	col = bigsize[1] #每列
	suitSize = (littlesize*row,littlesize*col) #大图最终的像素size
	bigImg = Image.open(modepath)
	bigImg = bigImg.resize(suitSize)
	resultImg = Image.new('RGBA',suitSize) 

	for i in range(row):
		for j in range(col):
			cutbox = (i*littlesize,j*littlesize,(i+1)*littlesize,(j+1)*littlesize) #模板剪切用于对比的某个区域
			cutImg = bigImg.crop(cutbox) #复制到cutImg中
			tmprgb = meanrbg(cutImg) 
			suitOne = mostSuitImg(tmprgb) + 1 #对比出最合适的头像

			img = Image.open(filepackage + str(suitOne) + '.png').convert('RGBA')
			img = img.resize((littlesize,littlesize))
			resultImg.paste(img,cutbox)
			print('已粘贴',cutbox)
	resultImg.save(resultSavePath) #存储


def mostSuitImg(tmprgb): #进行对比，找出最合适的头像
	global all_mean_rgbs
	minRange = 200000
	id = 0
	for rgb in all_mean_rgbs:
		tmp = (rgb[1][0]-tmprgb[2])**2+(rgb[1][1]-tmprgb[1])**2+(rgb[1][2]-tmprgb[1])**2
		if tmp<minRange:
			minRange = tmp
			id = rgb[0]
	return id


if __name__ == '__main__':
	# gettouxiang(txtpath)  #获取头像，如果已经获取就可以给注释掉了
	# simple_split(savepath,(20,20),30)  #简单拼接
	
	#模板拼接
	for i in range(1,friends_count+1):
		img = cv.imread(savepath+str(i)+'.png')
		rgb = meanrbg(img)
		all_mean_rgbs.append(rgb)
	all_mean_rgbs = list(enumerate(all_mean_rgbs)) #给列表增加一个索引
	
	mode_split(savepath,modePath,(50,80),20) #模板拼接