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Python | Thresholding techniques using OpenCV | Set-2 (Adaptive Thresholding)

  • Difficulty Level : Medium
  • Last Updated : 06 May, 2019

Prerequisite: Simple Thresholding using OpenCV

In the previous post, Simple Thresholding was explained with different types of thresholding techniques. Another Thresholding technique is Adaptive Thresholding. In Simple Thresholding, a global value of threshold was used which remained constant throughout. So, a constant threshold value won’t help in the case of variable lighting conditions in different areas. Adaptive thresholding is the method where the threshold value is calculated for smaller regions. This leads to different threshold values for different regions with respect to the change in lighting. We use cv2.adaptiveThreshold for this.

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Syntax: cv2.adaptiveThreshold(source, maxVal, adaptiveMethod, thresholdType, blocksize, constant)



Parameters:
-> source: Input Image array(Single-channel, 8-bit or floating-point)
-> maxVal: Maximum value that can be assigned to a pixel.
-> adaptiveMethod: Adaptive method decides how threshold value is calculated.

 cv2.ADAPTIVE_THRESH_MEAN_C: Threshold Value = (Mean of the neighbourhood area values – constant value). In other words, it is the mean of the blockSize√óblockSize neighborhood of a point minus constant.

cv2.ADAPTIVE_THRESH_GAUSSIAN_C: Threshold Value = (Gaussian-weighted sum of the neighbourhood values – constant value). In other words, it is a weighted sum of the blockSize√óblockSize neighborhood of a point minus constant.

-> thresholdType: The type of thresholding to be applied.
-> blockSize: Size of a pixel neighborhood that is used to calculate a threshold value.
-> constant: A constant value that is subtracted from the mean or weighted sum of the neighbourhood pixels.

Below is the Python implementation :




# Python program to illustrate 
# adaptive thresholding type on an image
       
# organizing imports 
import cv2 
import numpy as np 
   
# path to input image is specified and  
# image is loaded with imread command 
image1 = cv2.imread('input1.jpg'
   
# cv2.cvtColor is applied over the
# image input with applied parameters
# to convert the image in grayscale 
img = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
   
# applying different thresholding 
# techniques on the input image
thresh1 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
                                          cv2.THRESH_BINARY, 199, 5)
  
thresh2 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                                          cv2.THRESH_BINARY, 199, 5)
  
# the window showing output images
# with the corresponding thresholding 
# techniques applied to the input image
cv2.imshow('Adaptive Mean', thresh1)
cv2.imshow('Adaptive Gaussian', thresh2)
  
     
# De-allocate any associated memory usage  
if cv2.waitKey(0) & 0xff == 27
    cv2.destroyAllWindows() 


Input Image:

Output:




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