Image Thresholding in Python - An Easy and Quick Guide

Hey, there fellow coder! Today we are going to understand what Image Thresholding is and how to implement the same in the python programming language.

Let’s get right into the concept!

Also read: Edge Detection in Images using Python.

What is Image Thresholding?

Thresholding is defined as a process of dividing an image into two parts namely: “foreground” and “background”. It is mostly used in various Image processing tasks, allows greater image recognition and segmentation, etc.

Different Types of Thresholding Techniques

One can implement various threshold techniques which are named and described below:

S.No.	Thresholding Technique Name	Function name	Description
1	Binary Thresholding	cv2.THRESH_BINARY	1. (pixel intensity) > set threshold : 255 (white) 2. Else set to 0 (black).
2	Binary Inverted Thresholding	cv2.THRESH_BINARY_INV	The opposite case of cv2.THRESH_BINARY.
3	Tozero Thresholding	cv2.THRESH_TOZERO	1. (pixel intensity) < set threshold value : 0 (black) 2. Else set it to white
4	Tozero Inverted Thresholding	cv2.THRESH_TOZERO_INV	The opposite case of cv2.THRESH_TOZERO
5	Truncated Thresholding	cv2.THRESH_TRUNC	1. ( pixel intensity ) > threshold: Truncated to the threshold. 2. The pixel values are set to be the same as the threshold. 3. All other values remain the same.

Various Thresholding Techniques

Also read: Python: Converting Images to Pencil Sketch

The Full Code

Since the thresholding methods have direct functions for implementation, we can directly look at the code implementation of the thresholding method. I hope you understand the coding implementation of the same.

import cv2  
import numpy as np

img = cv2.imread('lori.jpg')  
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) 

ret, thresh_hold = cv2.threshold(img, 100, 255, cv2.THRESH_BINARY) 
ret, thresh_hold1 = cv2.threshold(img, 100, 255, cv2.THRESH_BINARY_INV) 
ret, thresh_hold2 = cv2.threshold(img, 100, 255, cv2.THRESH_TOZERO) 
ret, thresh_hold3 = cv2.threshold(img, 100, 255, cv2.THRESH_TOZERO_INV) 
ret, thresh_hold4 = cv2.threshold(img, 100, 255, cv2.THRESH_TRUNC)   

thresh_hold = cv2.resize(thresh_hold, (960, 540))    
cv2.imshow('Binary Threshold Image', thresh_hold) 

thresh_hold1 = cv2.resize(thresh_hold1, (960, 540))    
cv2.imshow('Binary Threshold Inverted Image', thresh_hold1) 

thresh_hold2 = cv2.resize(thresh_hold2, (960, 540))    
cv2.imshow('Threshold Tozero Image', thresh_hold2) 

thresh_hold3 = cv2.resize(thresh_hold3, (960, 540))    
cv2.imshow('ThresholdTozero Inverted output', thresh_hold3) 

thresh_hold4= cv2.resize(thresh_hold4, (960, 540))    
cv2.imshow('Truncated Threshold output', thresh_hold4) 

if cv2.waitKey(0) & 0xff == 25:  
    cv2.destroyAllWindows()