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How can you use K-Means clustering to posterize an image using c++?

asked 2014-02-05 19:17:51 -0500

humungousDust2 gravatar image

updated 2016-01-22 07:01:17 -0500

Hi all,

I'm trying to posterize an image, i.e. reduce the number of colours in an image, but I'm not having much luck.

I've found the following Python code from OpenCV's documentation, which uses K-Means:

import numpy as np
import cv2

img = cv2.imread('home.jpg')
Z = img.reshape((-1,3))

# convert to np.float32
Z = np.float32(Z)

# define criteria, number of clusters(K) and apply kmeans()
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
K = 8
ret,label,center=cv2.kmeans(Z,K,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)

# Now convert back into uint8, and make original image
center = np.uint8(center)
res = center[label.flatten()]
res2 = res.reshape((img.shape))

cv2.imshow('res2',res2)
cv2.waitKey(0)
cv2.destroyAllWindows()

My problem is that I only know C/C++. Would someone please help me out by converting this to the C++ equivalent?

Thanks in advance.

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answered 2014-02-06 02:11:12 -0500

Viatorus gravatar image

You are looking for clustering. Have look here: http://stackoverflow.com/a/10242156/1611317

#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"

using namespace cv;

int main( int argc, char** argv )
{
  Mat src = imread( argv[1], 1 );
  Mat samples(src.rows * src.cols, 3, CV_32F);
  for( int y = 0; y < src.rows; y++ )
    for( int x = 0; x < src.cols; x++ )
      for( int z = 0; z < 3; z++)
        samples.at<float>(y + x*src.rows, z) = src.at<Vec3b>(y,x)[z];


  int clusterCount = 15;
  Mat labels;
  int attempts = 5;
  Mat centers;
  kmeans(samples, clusterCount, labels, TermCriteria(CV_TERMCRIT_ITER|CV_TERMCRIT_EPS, 10000, 0.0001), attempts, KMEANS_PP_CENTERS, centers );


  Mat new_image( src.size(), src.type() );
  for( int y = 0; y < src.rows; y++ )
    for( int x = 0; x < src.cols; x++ )
    { 
      int cluster_idx = labels.at<int>(y + x*src.rows,0);
      new_image.at<Vec3b>(y,x)[0] = centers.at<float>(cluster_idx, 0);
      new_image.at<Vec3b>(y,x)[1] = centers.at<float>(cluster_idx, 1);
      new_image.at<Vec3b>(y,x)[2] = centers.at<float>(cluster_idx, 2);
    }
  imshow( "clustered image", new_image );
  waitKey( 0 );
}
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Comments

Perfect, thank you very much. This was exactly what I was looking for.

humungousDust2 gravatar imagehumungousDust2 ( 2014-02-09 20:05:30 -0500 )edit

Thank you for this, you saved my day !1

Hemang gravatar imageHemang ( 2017-01-26 18:21:52 -0500 )edit
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Asked: 2014-02-05 19:17:51 -0500

Seen: 13,318 times

Last updated: Feb 06 '14