entropy for a gray image in opencv - c++

Question

I need code to find entropy of an image.
for(int i=0;i<grey_image.rows;i++)
{
for(int j=1;j<grey_image.cols;j++)
{
//cout<<i<<" "<<j<<" "<<(int)grey_image.at<uchar>(i,j)<<endl;
int a=(int)grey_image.at<uchar>(i,j);
int b=(int)grey_image.at<uchar>(i,j-1);
int x=a-b;
if(x<0)
x=0-x;
probability_array[x]++;
//grey_image.at<uchar>(i,j) = 255;
}
}
//calculating probability
int n=rows*cols;
for(int i=0;i<256;i++)
{
probability_array[i]/=n;
//cout<<probability_array[i]<<endl;
}
// galeleo team formula
float entropy=0;
for(int i=0;i<256;i++)
{
if (probability_array[i]>0)
{
float x=probability_array[i]*log(probability_array[i]);
entropy+=x;
}
}
return 0-entropy;
Actually I am using this to dump in a programmable camera to measure entropy. Now I want to use it in windows system. I am getting entropy of a gray image as zero.Please help me out. Where did I go wrong.

Answer 1

Without knowing what image are you using, we cannot know if a zero entropy result is not the right answer (as suggested by #Xocoatzin).
Besides, your code can benefit from some of the latest OpenCV features 😊: Here is a working implementation using OpenCV histograms and matrix expressions:
if (frame.channels()==3) cvtColor(frame,frame,CV_BGR2GRAY);
/// Establish the number of bins
int histSize = 256;
/// Set the ranges ( for B,G,R) )
float range[] = { 0, 256 } ;
const float* histRange = { range };
bool uniform = true; bool accumulate = false;
/// Compute the histograms:
calcHist( &frame, 1, 0, Mat(), hist, 1, &histSize, &histRange, uniform, accumulate );
hist /= frame.total();
hist += 1e-4; //prevent 0
Mat logP;
cv::log(hist,logP);
float entropy = -1*sum(hist.mul(logP)).val[0];
cout << entropy << endl;

Answer 2

here is what i m using, hope it helps; https://github.com/samidalati/OpenCV-Entropy you can find couple of methods to calculate the entropy of colored and grayscaled images using OpenCV
float entropy(Mat seq, Size size, int index)
{
int cnt = 0;
float entr = 0;
float total_size = size.height * size.width; //total size of all symbols in an image
for(int i=0;i<index;i++)
{
float sym_occur = seq.at<float>(0, i); //the number of times a sybmol has occured
if(sym_occur>0) //log of zero goes to infinity
{
cnt++;
entr += (sym_occur/total_size)*(log2(total_size/sym_occur));
}
}
cout<<"cnt: "<<cnt<<endl;
return entr;
}
// myEntropy calculates relative occurrence of different symbols within given input sequence using histogram
Mat myEntropy(Mat seq, int histSize)
{
float range[] = { 0, 256 } ;
const float* histRange = { range };
bool uniform = true; bool accumulate = false;
Mat hist;
/// Compute the histograms:
calcHist( &seq, 1, 0, Mat(), hist, 1, &histSize, &histRange, uniform, accumulate );
return hist;
}

Answer 3

enter code here
//Calculate Entropy of 2D histogram
double Sum_prob_1k = 0, Sum_prob_kl = 0, Sum_prob_ln_1k = 0, Sum_prob_ln_kl = 0;
for (int k = start; k < end; k++)
{
Sum_prob_1k = 0; Sum_prob_kl = 0;
Sum_prob_ln_1k = 0; Sum_prob_ln_kl = 0;
//i=1 need to be start = 1
for (int i = 1; i < k; i++)
{
Sum_prob_1k += HiGreyN[i];
if (HiGreyN[i] != 0)
Sum_prob_ln_1k += (HiGreyN[i] * System.Math.Log(HiGreyN[i]));
}
for (int i = k; i < end; i++)
{
Sum_prob_kl += HiGreyN[i];
if (HiGreyN[i] != 0)
Sum_prob_ln_kl += (HiGreyN[i] * System.Math.Log(HiGreyN[i]));
}
//Final equation of entropy for each K
EiGrey[k] = System.Math.Log(Sum_prob_1k) + System.Math.Log(Sum_prob_kl) -
(Sum_prob_ln_1k / Sum_prob_1k) - (Sum_prob_ln_kl / Sum_prob_kl);
if (EiGrey[k] < 0)
EiGrey[k] = 0;
}
//End calculating 2D Entropy