OpenCV - Adaptive Threshold

In simple thresholding, the threshold value is global, i.e., it is same for all the pixels in the image. Adaptive thresholding is the method where the threshold value is calculated for smaller regions and therefore, there will be different threshold values for different regions.
In OpenCV, you can perform Adaptive threshold operation on an image using the method adaptiveThreshold() of the Imgproc class. Following is the syntax of this method.
adaptiveThreshold(src, dst, maxValue, adaptiveMethod, thresholdType, blockSize, C)

This method accepts the following parameters −
src − An object of the class Mat representing the source (input) image.
src − An object of the class Mat representing the source (input) image.
dst − An object of the class Mat representing the destination (output) image.
dst − An object of the class Mat representing the destination (output) image.
maxValue − A variable of double type representing the value that is to be given if pixel value is more than the threshold value.
maxValue − A variable of double type representing the value that is to be given if pixel value is more than the threshold value.
adaptiveMethod − A variable of integer the type representing the adaptive method to be used. This will be either of the following two values

ADAPTIVE_THRESH_MEAN_C − threshold value is the mean of neighborhood area.
ADAPTIVE_THRESH_GAUSSIAN_C − threshold value is the weighted sum of neighborhood values where weights are a Gaussian window.


adaptiveMethod − A variable of integer the type representing the adaptive method to be used. This will be either of the following two values
ADAPTIVE_THRESH_MEAN_C − threshold value is the mean of neighborhood area.
ADAPTIVE_THRESH_MEAN_C − threshold value is the mean of neighborhood area.
ADAPTIVE_THRESH_GAUSSIAN_C − threshold value is the weighted sum of neighborhood values where weights are a Gaussian window.
ADAPTIVE_THRESH_GAUSSIAN_C − threshold value is the weighted sum of neighborhood values where weights are a Gaussian window.
thresholdType − A variable of integer type representing the type of threshold to be used.
thresholdType − A variable of integer type representing the type of threshold to be used.
blockSize − A variable of the integer type representing size of the pixelneighborhood used to calculate the threshold value.
blockSize − A variable of the integer type representing size of the pixelneighborhood used to calculate the threshold value.
C − A variable of double type representing the constant used in the both methods (subtracted from the mean or weighted mean).
C − A variable of double type representing the constant used in the both methods (subtracted from the mean or weighted mean).
The following program demonstrates how to perform Adaptive threshold operation on an image in OpenCV. Here we are choosing adaptive threshold of type binary and ADAPTIVE_THRESH_MEAN_C for threshold method.
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;

public class AdaptiveThresh {
   public static void main(String args[]) throws Exception {
      // Loading the OpenCV core library
      System.loadLibrary( Core.NATIVE_LIBRARY_NAME );

      // Reading the Image from the file and storing it in to a Matrix object
      String file ="E:/OpenCV/chap14/thresh_input.jpg";
      
      // Reading the image
      Mat src = Imgcodecs.imread(file,0);

      // Creating an empty matrix to store the result
      Mat dst = new Mat();

      Imgproc.adaptiveThreshold(src, dst, 125, Imgproc.ADAPTIVE_THRESH_MEAN_C,
         Imgproc.THRESH_BINARY, 11, 12);

      // Writing the image
      Imgcodecs.imwrite("E:/OpenCV/chap14/Adaptivemean_thresh_binary.jpg", dst);

      System.out.println("Image Processed");
   } 
}
Assume that following is the input image thresh_input.jpg specified in the above program.
On executing the program, you will get the following output −
Image Processed

If you open the specified path, you can observe the output image as follows −
In addition to the ADAPTIVE_THRESH_MEAN_C as the adaptive method and THRESH_BINARY as the threshold type as demonstrated in the previous example, we can choose more combinations of these two values.
Imgproc.adaptiveThreshold(src, dst, 125, Imgproc.ADAPTIVE_THRESH_MEAN_C, 
   Imgproc.THRESH_BINARY, 11, 12);

Following are the values representing various combinations of values for the parameters adaptiveMethod and thresholdType and their respective outputs.

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