Skip to content
Related Articles

Related Articles

Improve Article

Impact of Image Flattening

  • Last Updated : 22 Jan, 2021

Flattening is a technique that is used to convert multi-dimensional arrays into a 1-D array, it is generally used in Deep Learning while feeding the 1-D array information to the classification model.

What is the need for Flattening of an Image?

Multi-Dimensional arrays take more amount of memory while 1-D arrays take less memory, which is the most important reason why we flatten the Image Array before processing/feeding the information to our model. In most cases, we will be dealing with a dataset which contains a large amount of images thus flattening helps in decreasing the memory as well as reducing the time to train the model.

Step 1: Importing the necessary libraries

Python3




import numpy as np
import pandas as pd
import cv2 as cv
from google.colab.patches import cv2_imshow
from skimage import io
from PIL import Image
import matplotlib.pylab as plt
from numpy import array
from sys import getsizeof

Step 2: Fetching a random image through web



Python3




#Fetching the url and showing the image using cv2_imshow
for url in urls:
  image = io.imread(url)
  cv2_imshow(image)
  print('\n')




Step 3: Transforming the image into a multi-dimensional array

Python3




#Getting the multi-dimensional array from the image
array1 = array(image)
#Memory occupied by the multi-dimensional array
size1 = getsizeof(array1)
print(array1)

Step 4: Now Flattening the multi-dimensional array using flatten() function

Python3




#Using Flatten function on array 1 to convert the multi-dimensional 
# array to 1-D array
array2 = array1.flatten()
#Memory occupied by array 2
size2 = getsizeof(array2)
#displaying the 1-D array
print(array2)

Step5: Results of Flattening

Python3




#Print's the two different size's of the array
print(f"Size of Multidimensional Image : {size1}")
print(f"Size of Flattened Image : {size2}")
difference = size1 - size2
#Print's the difference of memory between the size of Multidimensional & 1-D array
print("Size difference in the images: ", difference)
Size of Multidimensional Image : 1324928 
Size of Flattened Image : 1324896
Size difference in the images: 32

Step 6: Full Code

Python3




#importing libraries
import numpy as np
import pandas as pd
import cv2 as cv
from google.colab.patches import cv2_imshow
from skimage import io
from PIL import Image
import matplotlib.pylab as plt
from numpy import array
from sys import getsizeof
  
#Fetching the url and showing the image using cv2_imshow
for url in urls:
  image = io.imread(url)
  cv2_imshow(image)
  print('\n')
  
#Getting the multi-dimensional array from the image
array1 = array(image)
#Memory occupied by the multi-dimensional array
size1 = getsizeof(array1)
print(array1)
  
#Using Flatten function on array 1 to convert the multi-dimensional 
# array to 1-D array
array2 = array1.flatten()
#Memory occupied by array 2
size2 = getsizeof(array2)
#displaying the 1-D array
print(array2)
  
#Print's the two different size's of the array
print(f"Size of Multidimensional Image : {size1}")
print(f"Size of Flattened Image : {size2}")
difference = size1 - size2
#Print's the difference of memory between the size of Multidimensional & 1-D array
print(difference)


Conclusion:

After running the whole code we see that there is not a major difference in memory used in the multi-dimensional image array and the flattened array. Then people may ask why we are doing the flattening when the effect is negligible. In a large datasets when we are dealing with thousands of images the net amount of the memory saved due to all the images accumulates to be pretty big.

Attention reader! Don’t stop learning now. Get hold of all the important Machine Learning Concepts with the Machine Learning Foundation Course at a student-friendly price and become industry ready.




My Personal Notes arrow_drop_up
Recommended Articles
Page :