normalize histogram in c++ - function normalize in openCV - c++

Question

I need to normalize the histogram of an image f which mean to applicated an transformation of histogram from image in order to extend the range of value of f to all available values.
the norm(fmin) = Vmin ( minimal value we want to reach) and normal(fmin) = Vmax ( maximal value we want to reach)
I have this formula too
the goal is to have the same result that the function normalize which openCV gives.
Mat normalize(Mat image, float minValue, float maxValue)
{
Mat res = image.clone();
assert(minValue <= maxValue);
float Fmax = 0;
float Fmin = 0;
for(int i = 0; i < res.rows; i++)
{
for(int j = 0; j < res.cols; j++)
{
float x = res.at<float>(i,j);
if(i < minValue)
{
Fmin = i;
}
if( i > maxValue)
{
Fmax = i;
}
res.at<float>(i,j) = (x - Fmin) * ((maxValue - minValue) / (Fmax - Fmin)) + minValue;
}
}
return res;
}
I have this error : !!! Warning, saved image values not between 0 and 1.
!!! Warning, saved image values not between 0 and 1.
I think I didn't understand how to calculate fmin/ fmax

Answer 1

So, as I explained in my comment, there are some mistakes, here's the corrected version. You need to run the double loop twice, once to find the min-max, and a second time to apply the formula. There were also errors in the comparisons:
cv::Mat normalize(cv::Mat image, float minValue, float maxValue)
{
cv::Mat res = image.clone();
assert(minValue <= maxValue);
// 1) find min and max values
float Fmax = 0.0f;
float Fmin = 1.0f; // set it to 1, not 0
for (int i = 0; i < res.rows; i++)
{
float* pixels = res.ptr<float>(i); // this is quicker
for (int j = 0; j < res.cols; j++)
{
float x = pixels[j];
if (x < Fmin) // compare x and Fmin, not i and minValue
{
Fmin = x;
}
if (x > Fmax) // compare x and Fmax, not i and maxValue
{
Fmax = x;
}
}
}
// 1 color image => don't normalize + avoid crash
if (Fmin >= Fmax)
return res;
// 2) normalize using your formula
for (int i = 0; i < res.rows; i++)
{
float* pixels = res.ptr<float>(i);
for (int j = 0; j < res.cols; j++)
{
pixels[j] = (pixels[j] - Fmin) * ((maxValue - minValue) / (Fmax - Fmin)) + minValue;
}
}
return res;
}
If your source image is a grayscale image in 8 bit, you can convert it like that:
cv::Mat floatImage;
grayImage.convertTo(floatImage, CV_32F, 1.0 / 255, 0);
floatImage = normalize(floatImage, 0, 1.0f);
floatImage.convertTo(grayImage, CV_8UC1, 255.0, 0);
Also, if you use cv::minMaxLoc, your normalize function can be made shorter =>
cv::Mat normalize(cv::Mat image, float minValue, float maxValue)
{
cv::Mat res = image.clone();
assert(minValue <= maxValue);
// 1) find min and max values
double Fmax;
double Fmin;
cv::minMaxLoc(image, &Fmin, &Fmax);
if (Fmin >= Fmax)
return res;
// 2) normalize using your formula
for (int i = 0; i < res.rows; i++)
{
float* pixels = res.ptr<float>(i);
for (int j = 0; j < res.cols; j++)
{
pixels[j] = (pixels[j] - Fmin) * ((maxValue - minValue) / (Fmax - Fmin)) + minValue;
}
}
return res;
}