In this blog, we will implement two features:
- Combine all avatars into a larger image
- Combine all avatars into a large image with a certain template
Again, all the running results are given first:
code implementation
1, code required library
import requests,codecs,re,urllib,os,random,math from PIL import Image import numpy as np import cv2 as cv
2、Code Explanation
This blog will not explain how to get your friend's avatar, if you need it, you can refer to this blog post:
Python Crawler - Get friend info from QQ mailbox and crawl avatars.
Now that we have all of our friends' avatars, let's start with a collection of all of them.
2.1. Merge small avatars into larger ones
For this, it is directly to each small avatar paste in the big picture on the line, the use of Image's paste function can be solved. For the order of the paste can be directly in accordance with the following chart one by one paste:
So, the code is given directly:
def simple_split(filepackage,size,littlesize): # Simple splicing, parameters are image file name, size of each row and column, size of small avatar image
row = size[0]
col = size[1]
bigimg = ('RGBA',(littlesize*row,littlesize*col)) #Results Chart
number = 0
for i in range(row): # rows
for j in range(col): # columns
randpic = (1,friends_count)
img = (filepackage+str(randpic)+'.png').convert('RGBA')
img = ((littlesize,littlesize))
loc = (i*littlesize,j*littlesize,(i+1)*littlesize,(j+1)*littlesize)
print(loc,number)
number+=1
(img,loc)
(resultSavePath)
Since we don't have a lot of friends, we just randomly choose one friend avatar at a time to post, so there are a lot of duplicate avatars that end up appearing if you have a high density.
Let's show you a picture of what I ended up with:
2.2, to a certain picture as a template splicing picture
Since it's not clear if there are any third party libraries that can do it directly, all I did was build my own little wheel.
Thoughts:
Divide the template into small A x B images, let's describe its position as pic[i][j], then get the average RGB value of each small image, compare the average RGB value of pic[i][j] with that of your friend's avatar, find the closest avatar, and then insert that avatar at pic[i][j] of the image.
The thought process is still relatively simple I guess 😀
The next step is to realize it:
The code is given as comments in many places, so I won't go into it and give the code directly:
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/' # The file you pasted from your QQ mailbox
savepath = 'C:/Users/11037/Desktop/touxiang/' # Avatar storage location
resultSavePath = 'C:/Users/11037/Desktop/' # Where results are stored
modePath = 'C:/Users/11037/Desktop/' # Template storage location
friends_count = 0 # of friends
all_mean_rgbs = [] # Stores all average rgb values calculated
def meanrbg(img): # Calculate the average rgb of an image
rgb = (img)
r = int(round((rgb[:, :, 0])))
g = int(round((rgb[:, :, 1])))
b = int(round((rgb[:, :, 2])))
return (r,g,b)
def gettouxiang(txtpath):# Enter the location where your txt file is stored
file = (txtpath,'rb','utf-8')
s = ()
pattern = (r'\d+@')
all_mail = (s) #Regular Expression Match All qq Numbers
all_link = [] # Used to store links that need to be accessed
url = '/qzone/'
for mail in all_mail:
qq = ('@','')
l = url + qq +'/'+qq+'/100'
all_link.append(l)
i = 1
for link in all_link: # Traverse the link and download the avatar
saveurl = savepath+str(i)+'.png'
savaImg(link,saveurl)
i +=1
print('Downloaded',i)
friends_count = len(all_link) # Get the number of friends' avatars
return True
def savaImg(picurl,saveurl): # store image function, picurl is the URL of the image, saveurl is the local storage location
try:
bytes = (picurl)
file = open(saveurl,'wb')
(())
()
()
return True
except:
print('worry')
savaImg(picurl,saveurl)
def simple_split(filepackage,size,littlesize): # Simple splicing, parameters are image file name, size of each row and column, size of small avatar image
row = size[0]
col = size[1]
bigimg = ('RGBA',(littlesize*row,littlesize*col))
number = 0
for i in range(row):
for j in range(col):
randpic = (1,friends_count)
img = (filepackage+str(randpic)+'.png').convert('RGBA')
img = ((littlesize,littlesize))
loc = (i*littlesize,j*littlesize,(i+1)*littlesize,(j+1)*littlesize)
print(loc,number)
number+=1
(img,loc)
(resultSavePath)
def mode_split(filepackage,modepath,bigsize,littlesize): #Store avatars as templates
row = bigsize[0] #How many small avatars per line of the large image
col = bigsize[1] # Each column
suitSize = (littlesize*row,littlesize*col) #The final pixel size of the big picture.
bigImg = (modepath)
bigImg = (suitSize)
resultImg = ('RGBA',suitSize)
for i in range(row):
for j in range(col):
cutbox = (i*littlesize,j*littlesize,(i+1)*littlesize,(j+1)*littlesize) # Template clipping is used to compare an area
cutImg = (cutbox) #Copy to cutImg
tmprgb = meanrbg(cutImg)
suitOne = mostSuitImg(tmprgb) + 1 #Compare the best avatars
img = (filepackage + str(suitOne) + '.png').convert('RGBA')
img = ((littlesize,littlesize))
(img,cutbox)
print('Pasted',cutbox)
(resultSavePath) #Storage
def mostSuitImg(tmprgb): #Compare and contrast to find the most suitable avatar
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) # get the avatar, if you've already got it, you can comment it out.
# simple_split(savepath,(20,20),30) #simple_split
# Template splicing
for i in range(1,friends_count+1):
img = (savepath+str(i)+'.png')
rgb = meanrbg(img)
all_mean_rgbs.append(rgb)
all_mean_rgbs = list(enumerate(all_mean_rgbs)) # Add an index to the list
mode_split(savepath,modePath,(50,80),20) #stencil splice
To show you the final result:
It's still all good at this point, right? Ha ha.
Give Lyon's template and final results again:
Add [modified Leon]:
I default to naming each avatar with a number, which can be easy to follow.
At the same time, the above code is encapsulated, many functions can be used independently to meet different functions. You can read the code to rewrite their own needs to achieve the function, for example, the above my default avatar image are square, you if the picture has a rectangular change under the code can also meet.
Theoretically, the more avatars you have of your friends, the smaller the difference between the produced image and the template. Take the function mode_split for example, the bigger the bigsize you set, the clearer your image will be.
To this interesting Python image production of how to use QQ friends avatars spliced Leon article is introduced to this, more related python friends avatars splicing content please search for my previous articles or continue to browse the following related articles I hope that you will support me more in the future!