Skip to content
Related Articles

Related Articles

Improve Article
Point Processing in Image Processing using Python-OpenCV
  • Last Updated : 10 May, 2020

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


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 :

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
            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
            img_thresh_back[i,j] = img[i,j]
# Convert array to  png image
cv2.imwrite('Cameraman_Thresh_Back.png', img_thresh_back)

Output : Image Negative

Output : Image with Thresholding :

Output : Image with Grey Level Slicing with Background

 Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.  

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course

My Personal Notes arrow_drop_up
Recommended Articles
Page :