Python realizes stitching pictures

Recently I was writing a paper on Convolutional Neural Networks. There are many experimental results that need to be sorted out. I originally used Meitu Xiuxiu for image stitching, but found that there were too many repeated operations and the stitching effect was not good. I thought of writing a script in python. To achieve, see a simple example:

Horizontal stitching

First, I need to splice the deformation of the same picture into one line, the code is as follows:

import os
from PIL import Image

UNIT_SIZE =229 #The size of a single image is 229*229
TARGET_WIDTH =6* UNIT_SIZE #The horizontal length after splicing is 6*229

path ="C:/Users/zm/Desktop/FinalResult/Other-Reconstruction/2"
images =[] #Store the names of all images first
for root, dirs, files in os.walk(path):for f in files :
 images.append(f)for i inrange(len(images)/6): #6 images as a group
 imagefile =[]
 j =0for j inrange(6):
 imagefile.append(Image.open(path+'/'+images[i*6+j])) 
 target = Image.new('RGB',(TARGET_WIDTH, UNIT_SIZE)) 
 left =0
 right = UNIT_SIZE
 for image in imagefile:  
 target.paste(image,(left,0, right, UNIT_SIZE))#Copy image to the specified location of target
 left += UNIT_SIZE #left is the abscissa of the upper left corner, increasing sequentially
 right += UNIT_SIZE #right is the abscissa of the lower right, increasing sequentially
 quality_value =100 #quality to specify the quality of the generated image, the range is 0-100
 target.save(path+'/result/'+os.path.splitext(images[i*6+j])[0]+'.jpg', quality = quality_value)
 imagefile =[]

Vertical stitching

After stitching each type of pictures into a row of six pictures, stitch these pictures together vertically. There are 3 kinds of images in total, so there are 3 rows

import os
from PIL import Image

UNIT_SIZE =229 #Image height
TARGET_WIDTH =6* UNIT_SIZE #There are 6 images in a row, so 6*229 so wide

path ="C:/Users/zm/Desktop/FinalResult/Other-Reconstruction/2/result"
imagefile =[]for root, dirs, files in os.walk(path):for f in files :
 imagefile.append(Image.open(path+'/'+f)) 
target = Image.new('RGB',(TARGET_WIDTH, UNIT_SIZE*3)) #The size of the final stitched image is(229*3)*(229*6)
left =0
right = UNIT_SIZE
for image in imagefile:  
 target.paste(image,(0, left, TARGET_WIDTH, right))
 left += UNIT_SIZE #Stitching from top to bottom, the ordinate of the upper left corner increases
 right += UNIT_SIZE　#The ordinate in the lower left corner also increases
 quality_value =100
 target.save(path+'/result.jpg', quality = quality_value)

Simultaneous horizontal and vertical stitching

The pictures that need to be processed today are as follows: on the left is the folder where the pictures are located, and the pictures in each folder are shown on the right as →_→, which need to be spliced into 2*5 pictures.

The script written is as follows:

import os
from PIL import Image

UNIT_SIZE =229 # the size of image 
def pinjie(images,num):
 target = Image.new('RGB',(UNIT_SIZE*5, UNIT_SIZE*2)) # result is 2*5
 leftone =0
 lefttwo =0
 rightone = UNIT_SIZE
 righttwo = UNIT_SIZE
 for i inrange(len(images)):if(i%2==0):
 target.paste(images[i],(leftone,0, rightone, UNIT_SIZE))
 leftone += UNIT_SIZE #Move the upper left corner of the first line to the right
 rightone += UNIT_SIZE #Move right bottom corner
 else:
 target.paste(images[i],(lefttwo, UNIT_SIZE, righttwo, UNIT_SIZE*2))
 lefttwo += UNIT_SIZE #Move the upper left corner of the second line to the right
 righttwo += UNIT_SIZE #Move right bottom corner
 quality_value =100
 target.save(path+dirlist[num]+'.jpg', quality = quality_value)

path ="C:/Users/laojbdao/Desktop/FinalResult/result4/different_distribution/"
dirlist =[] # all dir name
for root, dirs, files in os.walk(path):for dir in dirs :
 dirlist.append(dir)

num =0for dir in dirlist:
 images =[] # images in each folder
 for root, dirs, files in os.walk(path+dir): # traverse each folder
 print path+dir+''for file in files:
 images.append(Image.open(path+dir+'/'+file))pinjie(images,num)
 num +=1
 images =[]

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The above is the whole content of this article, I hope it will be helpful to everyone's study.