How to do by element processing on OpenCV Mat and GpuMat effectively? - c++

Question

I am using OpenCV C++ for image processing. I want to do some fast processing on Mat and GpuMat by element.
For example, I have to apply a complexed function to every element of the Mat or GpuMat. Currently, I am accessing each element of a Mat by looping as below:
// C++ Example 1: a and b are Mat
for (int i = 0; i < 512; i++) {
for (int j = 0; j < 512; j++) {
double sPixel = s.at<double>(512 * i + j);
if (sPixel >= 0 && sPixel <= 1) {
a.at<double>(512 * i + j) = double(1);
} else if (sPixel > 1) {
b.at<double>(512 * i + j) = double(1);
}
}
}
// C++ Example 2: f, x are Mat
for (int i = 0; i < 512; i++) {
for (int j = 0; j < 512; j++) {
f.at<double>(512 * i + j) = (1 / (2 * sigma)) * (1 + cos(pi * x.at<double>(512 * i + j) / sigma));
}
}
However, I think this method is slow because there are no actual relations among elements of Mat, if the by element calculation is done parallelly it would be better.
On the other hand, I cannot access elements of GpuMat. If I download and upload data between Mat and GpuMat frequently, it would be extremely slow and the advantage of using GPU does not exist.
So my question is:
What are some improved ways to do by element processing on Mat and GpuMat?
Especially those provided by OpenCV itself.
How to do by element processing on GpuMat?

Answer 1

You just use built-in openCV functions that do per-element operations. E.g. you have overloaded matrix operators for addition, subtraction of matrices or matrices and scalars, functions for element-wise multiplication, division, absolute difference, trigonometric functions, powers, roots etc. They usually have the same name as the standard library math functions. Just search the docs. For comparing matrix elements like in your first example, use matrix expressions.
This is really the same as the point 1. You have to check the functions that openCV provide and divide your operation into steps that might be executed with those function. E.g. here is the nice list of such functions:
http://docs.opencv.org/2.4/modules/gpu/doc/per_element_operations.html
http://docs.opencv.org/trunk/d8/d34/group__cudaarithm__elem.html
If the above functions are not enough for you, avoid accessing pixels by using at() method as this is extremely inefficent and not recommended when iterating through all the pixels. Use the ptr() function instead to access whole rows.
Here is the example how can you transform your calculations using the above techniques:
//first example
b = (s > 1);
a = (s >= 0).mul(s <= 1);
//second example
f = (1 / (2*sigma)) * ((1 + cos_mat) / sigma);
There is no per-element cos() function in openCV, but if you want performance, you can implement cosine as Taylor series, which will equal a couple of per-element multiplications and subtractions/additions, and obtain the cos_mat matrix that way. You can find an example here:
http://answers.opencv.org/question/55602/sine-or-cosine-of-every-element-in-mat-c/