I have two pictures in grayscale of the same size MxN, img1 and img2. When I use function cv::mean() on these pictures, it returns one values. After that, I produce two column vectors of these pictures and get a matrix of size MNx2, samples. Then i use function cv:: calcCovarMatrix(). This function should return covariation matrix and matrix of mean values on each columns. But values of mean matrix of function cv:: calcCovarMatrix(). are differ from values returned from cv::mean() function.
Here's my code.
Mat img1 = imread( "sample.jpeg", 0 );
Mat img2 = imread( "sample3.jpeg", 0 );
cout<<mean(img1)<<endl;
cout<<mean(img2)<<endl;
results are:
[127.303, 0, 0, 0]
[161.606, 0, 0, 0]
after that I use function cv::calcCovarMatrix():
int all_pixels = img1.cols*img1.rows;
Mat_<float> samples;
samples.create(all_pixels, 2);
int counter = 0;
for(int i = 0; i < img1.rows; i++){
for(int j = 0; j < img1.cols; j++){
samples.at<float>(0,counter) = img1.at<uchar>(i,j);
samples.at<float>(1,counter) = img2.at<uchar>(i,j);
counter++;
}
}
Mat cov, mu;
calcCovarMatrix(samples, cov, mu, COVAR_NORMAL | COVAR_ROWS);
cout << "mu: " << endl;
cout << mu << endl;
it returns :
mu:
[63.65198211624441, 63.65911574764034]
Why these values are not the same as in cv::mean()?
I tried another way to copy data, and not I have different values of covariances and mean for different ways of copying data
When I used this new way to copy data, I get different results of covariance.
double av1 = mean(img1)[0];
double av2 = mean(img2)[0];
cout<<"mean1 = "<<av1<<", mean2 = "<<av2<<endl;
gives me:
mean1 = 127.303, mean2 = 161.606
and then I have two sample arrays, samplesOld and samplesNew.
Old way of copying data:
int all_pixels = img1.cols*img1.rows;
cout<<"all_pixels = "<<all_pixels<<endl;
Mat_<float> samplesOld;
samplesOld.create(all_pixels, 2);
int counter = 0;
for(int i = 0; i < img1.rows; i++){
for(int j = 0; j < img1.cols; j++){
samplesOld.at<float>(0,counter) = (img1.at<uchar>(i,j));
samplesOld.at<float>(1,counter) = (img2.at<uchar>(i,j) );
counter++;
}
}
New way of copying data:
Mat samplesNew;
samplesNew.push_back(img1.reshape(1,1)); // img1, flattened, goes on the 1st row
samplesNew.push_back(img2.reshape(1,1)); // img2 on the 2nd, etc.
samplesNew.convertTo(samplesNew, CV_32F);
transpose(samplesNew,samplesNew);
After that I have new arrays for covariances of old and new array samples arrays
Mat covOld, muOld;
cv::calcCovarMatrix(samplesOld, covOld, muOld, COVAR_NORMAL | COVAR_ROWS);
Mat covNew, muNew;
cv::calcCovarMatrix(samplesNew, covNew, muNew, COVAR_NORMAL | COVAR_ROWS);
covOld = covOld / (samplesOld.rows - 1);
covNew = covNew / (samplesNew.rows - 1);
cout << "\ncovOld: " << endl;
cout << covOld << endl;
cout << "covNew: " << endl;
cout << covNew << endl;
This gives me
covOld:
[7215.396208855592, 7157.354438304864;
7157.354438304864, 7215.581732969795]
covNew:
[6327.103578936067, 1127.485398832948;
1127.485398832948, 5500.445588341399]
and this
cout << "\nmuOld: " << endl;
cout << muOld << endl;
cout << "muNew: " << endl;
cout << muNew << endl;
gives me values:
muOld:
[63.65198211624441, 63.65911574764034]
muNew:
[127.3026130153999, 161.606000993542]
Here in muNew values are same as returned by mean() function. But I don't understand, why on my arrays samplesNew and samplesOld which are of same size calcCovarMatrix() returns different values?
