In
Then I found out from the gaussian blur page of Wikipedia that:
Applying multiple, successive gaussian blurs to an image has the same
effect as applying a single, larger gaussian blur, whose radius is the
square root of the sum of the squares of the blur radii that were
actually applied. For example, applying successive gaussian blurs with
radii of 6 and 8 gives the same results as applying a single gaussian
blur of radius 10, since sqrt {6^{2}+8^{2}}=10.
So I thought that blur and singleBlur were the same in the following code:
cv::Mat firstLevel;
float sigma1, sigma2;
//intialize firstLevel, sigma1 and sigma2
cv::Mat blur = gaussianBlur(firstLevel, sigma1);
blur = gaussianBlur(blur, sigma2);
float singleSigma = std::sqrt(std::pow(sigma1,2)+std::pow(sigma2,2));
cv::Mat singleBlur = gaussianBlur(firstLevel, singleSigma);
cv::Mat diff = blur != singleBLur;
// Equal if no elements disagree
assert( cv::countNonZero(diff) == 0);
But this assert fails (and actually, for example, the first row of blur is different from the first one of singleBlur).
Why?
This UPDATE:
After different comments asking for more information, I'll update the answer.
What I'm trying to do is the real code:to parallelize this code. In particular, I'm focusing now on computing all the blurs at all levels in advance. The serial code (which works correctly) is the following:
vector<Mat> blurs ((par.numberOfScales+3)*levels, Mat());
cv::Mat octaveLayer = firstLevel;
int scaleCycles = par.numberOfScales+2;
//compute blurs at all layers (not parallelizable)
for(int i=0; i<levels; i++){
blurs[i*scaleCycles+1] = octaveLayer.clone();
for (int j = 1; j < scaleCycles; j++){
float sigma1 = par.sigmas[1]* sigma = par.sigmas[j]* sqrt(sigmaStep * sigmaStep - 1.0f);
blurs[j+1+i*scaleCycles] = gaussianBlur(blurs[j+i*scaleCycles], sigma);
if(j == par.numberOfScales)
octaveLayer = halfImage(blurs[j+1+i*scaleCycles]);
}
}
I'm sorry for the horrible indexes above, but I tried to respect the original code system (which is horrible, like starting counting from 1 instead of 0). The code above has scaleCycles=5 and levels=6, so 30 blurs are generated in total.
This is the "single blur" version, where first I compute the sigmas for each blur that has to be computed (following Wikipedia's formula) and then I apply the blur (notice that this is still serial and not parallelizable):
vector<Mat> singleBlurs ((par.numberOfScales+3)*levels, Mat());
vector<float> singleSigmas(scaleCycles);
float sigma2 = par.sigmas[2]* acc = 0;
for (int j = 1; j < scaleCycles; j++){
float sigma = par.sigmas[j]* sqrt(sigmaStep * sigmaStep - 1.0f);
acc += pow(sigma, 2);
singleSigmas[j] = sqrt(acc);
}
octaveLayer = firstLevel;
for(int i=0; i<levels; i++){
singleBlurs[i*scaleCycles+1] = octaveLayer.clone();
for (int j = 1; j < scaleCycles; j++){
float sigma = singleSigmas[j];
std::cout<<"j="<<j<<" sigma="<<sigma<<std::endl;
singleBlurs[j+1+i*scaleCycles] = gaussianBlur(singleBlurs[j+i*scaleCycles], sigma);
if(j == par.numberOfScales)
octaveLayer = halfImage(singleBlurs[j+1+i*scaleCycles]);
}
}
Of course the code above generates 30 blurs also with the same parameters of the previous version.
And then this is the code to see the difference between each signgleBlurs and blurs:
assert(blurs.size() == singleBlurs.size());
vector<Mat> blurDiffs(blurs.size());
for(int i=1; i<levels*scaleCycles; i++){
cv::Mat blur = gaussianBlur(firstLevel, sigma1);
blur = gaussianBlur(blur,sigma2);
cv::Mat singleBlur = gaussianBlur(firstLevel, std::sqrt(std::pow(sigma1,2)+std::pow(sigma2,2)));
equalMats(blur, singleBlur);
diff;
absdiff(blurs[i], singleBlurs[i], diff);
std::cout<<"i="<<i<<"diff rows="<<diff.rows<<" cols="<<diff.cols<<std::endl;
blurDiffs[i] = diff;
std::cout<<"blurs rows="<<blurs[i].rows<<" cols="<<blurs[i].cols<<std::endl;
std::cout<<"singleBlurs rows="<<singleBlurs[i].rows<<" cols="<<singleBlurs[i].cols<<std::endl;
std::cout<<"blurDiffs rows="<<blurDiffs[i].rows<<" cols="<<blurDiffs[i].cols<<std::endl;
namedWindow( "blueDiffs["+std::to_string(i)+"]", WINDOW_AUTOSIZE );// Create a window for display.
//imshow( "blueDiffs["+std::to_string(i)+"]", blurDiffs[i] ); // Show our image inside it.
//waitKey(0); // Wait for a keystroke in the window
Mat imageF_8UC3;
std::cout<<"converting..."<<std::endl;
blurDiffs[i].convertTo(imageF_8UC3, CV_8U, 255);
std::cout<<"converted"<<std::endl;
imwrite( "blurDiffs_"+std::to_string(i)+".jpg", imageF_8UC3);
}
Where:
Mat gaussianBlur(const Mat input, const float sigma)
{
Mat ret(input.rows, input.cols, input.type());
int size = (int)(2.0 * 3.0 * sigma + 1.0); if (size % 2 == 0) size++;
GaussianBlur(input, ret, Size(size, size), sigma, sigma, BORDER_REPLICATE);
return ret;
}
Now, what I've seen is that blurDiffs_1.jpg and blurDiffs_2.jpg are black, but suddendly from blurDiffs_3.jpg until the blurDiffs_29.jpg becomes whiter and whiter. For some reason, blurDiffs_30.jpg is almost completely black. The first (correct) version generates 1761 descriptors. The second (uncorrect) version generates >2.3k descriptors.
I don't think can't post the blurDiffs matrices because (especially the first ones) are very big and post's space is limited. I'll post some samples. I'll not post blurDiffs_1.jpg and blurDiffs_2.jpg because they're totally blacks. Notice that the others parameters are relevant.
This is because of halfImage the images become smaller and smaller (as expected).
partblurDiffs_3.jpg: of diff:
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blurDiffs_6.jpg:
blurDiffs_15.jpg:
blurDiffs_29.jpg:
If you need any more details, please let me know.
