Image Processing

Image is made of pixels. Thanks to the extensive Python modules such as numpy and openCV, image can be converted into numbers, manipulated and subsequently reproduced into a different outcome. This blogpost explores the processing time taken to produce the same outcome upon using different techniques.

The experiments are carried out using Cloudera Machine Learning (CML) on Kubernetes platform powered by Openshift 4.8 with the hardware specification as described below. CML is embedded with workbench and Jupyterlab notebook IDE for data scientist to do coding, EDA, etc. It's always a good practice using IDE to explore the performance output, e.g. whether the code is fully/under utilizing the allocated CPU resource or becomes "noisy neighbour" in the multi-tenant Kubernetes platform.

The following experiments are carried out using the Jupyterlab notebook. Here's the link to download the complete notebook.

• Declare the variables.

  #Variables Declaration
   
  import time
  import cv2
  import numpy as np
  from numpy import asarray
  import psutil
  import os
  import matplotlib.pylab as plt
  import sys
  from PIL import Image
   
  new_color = 5,3,192,255
  old_color = 0, 0, 0, 0
  width, height = 988,988
  img = Image.open('me.png')
  ori_img_array = asarray(img)

• Display image details based on the image file.

  #Show image details
   
  plt.imshow(img)
  print("Source Image format:", img.format)
  print("Image size:", img.size)
  print("Image mode:", img.mode)
  numpyraw = asarray(img)  
  #np.set_printoptions(threshold=sys.maxsize)
  np.save('bbb.npy',numpyraw)
  print("Values of original image:\n",numpyraw[0:1,0:3])  

  Source Image format: PNG Image size: (998, 998) Image mode: RGBA Values of original image: [[[0 0 0 0] [0 0 0 0] [0 0 0 0]]]

• Display image details using numpy raw integers!

  #Show image using numpy integers
   
  numpyraw = np.load('bbb.npy')
  print("Some contents of numpy file:\n",numpyraw[0:1,0:3])
  plt.imshow(numpyraw)

Some contents of numpy file:
 [[[0 0 0 0]
  [0 0 0 0]
  [0 0 0 0]]]
<matplotlib.image.AxesImage at 0x7ff82611d4f0>

• Check out the time taken to change the background colour using putpixel() function.

  #Replace colour with specific pixel number using putpixel() fn
   
  start=time.time()
  imgtmp = img
  width, height = imgtmp.size
  pix = img.load()
  for x in range(0, width):
          for y in range(0, height):
              if pix[x,y] == old_color:
                  imgtmp.putpixel((x, y), new_color)
  plt.imshow(imgtmp)
  tampered_img_array = asarray(imgtmp)
  print("Values of original image:\n",ori_img_array[0:1,0:3])    
  print("\nValues of tampered image:\n",tampered_img_array[0:1,0:3])
   
  end = time.time()
  print("Time Taken:{}".format(end - start))

  Values of original image: [[[0 0 0 0] [0 0 0 0] [0 0 0 0]]]

  Values of tampered image: [[[ 5 3 192 255] [ 5 3 192 255] [ 5 3 192 255]]] Time Taken:1.7054669857025146

• Check out the time taken to change the background colour using the typical Python loop.

  #Replace colour with specific numpy array
   
  start = time.time()
  numpyraw = np.load('bbb.npy')
  for x in range(0, width):
          for y in range(0, height):
              if (numpyraw[x][y] == [0, 0, 0, 0]).all() :
                  numpyraw[x][y] = [5,3,192,255]
  print("Values of original image:\n",ori_img_array[0:1,0:3])    
  print("\nValues of tampered image:\n",numpyraw[0:1,0:3])
  plt.imshow(numpyraw)
   
  end = time.time()
  print("Time Taken:{}".format(end - start))

  Values of original image: [[[0 0 0 0] [0 0 0 0] [0 0 0 0]]]

  Values of tampered image: [[[ 5 3 192 255] [ 5 3 192 255] [ 5 3 192 255]]] Time Taken:8.568198680877686

• Check out the time taken to change the background colour using openCV module.

  # Use cv2 to convert background
   
  start = time.time()
  #edges = cv2.Canny(ori_img_array, 100, 200)
  #plt.imshow(edges)
  masked = np.copy(ori_img_array)
  lower = np.array([0,0,0,0])  
  upper = np.array([0,0,0,0])
   
  mask = cv2.inRange(masked, lower, upper)
  plt.imshow(mask)
  masked[mask != 0] = [5,3,192,255]
  plt.imshow(masked)
   
  end = time.time()
  print("Time Taken:{}".format(end - start))

  Time Taken:0.11178255081176758

Conclusion: Different techniques take varied processing time and utilize CPU resource differently to produce the same output.