Point Processing in Image Processing using Python-OpenCV

OpenCV is the huge open-source library for computer vision, machine learning, and image processing and now it plays a major role in real-time operation which is very important in today’s systems. By using it, one can process images and videos to identify objects, faces, or even the handwriting of a human.

Point processing in spatial domain

All the processing done on the pixel values. Point processing operations take the form –

s = T ( r )

Here, T is referred to as a grey level transformation function or a point processing operation, s refers to the processed image pixel value and r refers to the original image pixel value.

Image Negative:

s = (L-1) – r, where L= number of grey levels

Thresholding:



s = L-1 for r > threshold
s = 0 for r < threshold

Grey level slicing with background:

s = L-1 for a < r < b,
here a and b define some specific range of grey level
s = r otherwise.

Below is the implementation.

Original Input Image :

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import cv2
import numpy as np
  
   
# Image negative
img = cv2.imread('food.jpeg',0
  
# To ascertain total numbers of 
# rows and columns of the image,
# size of the image
m,n = img.shape
   
# To find the maximum grey level
# value in the image
L = img.max()
   
# Maximum grey level value  minus 
# the original image gives the
# negative image
img_neg = L-img
   
# convert the np array img_neg to 
# a png image
cv2.imwrite('Cameraman_Negative.png', img_neg)
   
# Thresholding without background 
# Let threshold =T
# Let pixel value in the original be denoted by r
# Let pixel value in the new image be denoted by s
# If r<T, s= 0
# If r>T, s=255
   
T = 150
  
# create a array of zeros
img_thresh = np.zeros((m,n), dtype = int
   
for i in range(m):
      
    for j in range(n):
          
        if img[i,j] <  T: 
            img_thresh[i,j]= 0
        else:
            img_thresh[i,j] = 255
   
   
# Convert array to png image
cv2.imwrite('Cameraman_Thresh.png', img_thresh)
   
# the lower threshold value
T1 = 100
  
# the upper threshold value
T2 = 180 
  
# create a array of zeros
img_thresh_back = np.zeros((m,n), dtype = int)
   
for i in range(m):
      
    for j in range(n):
          
        if T1 < img[i,j] < T2: 
            img_thresh_back[i,j]= 255
        else:
            img_thresh_back[i,j] = img[i,j]
  
# Convert array to  png image
cv2.imwrite('Cameraman_Thresh_Back.png', img_thresh_back)

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Output : Image Negative

Output : Image with Thresholding :

Output : Image with Grey Level Slicing with Background

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Ami Munshi is an Asst Professor with MPSTME, Mumbai (NMIMS University) with the Electronics and Telecommunications specialization Focus areas on GfG Application of Python3 libs for Data/Image compression, Encryption, Data Science and Analytics applications

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