speed up opencv image processing with OpenCL

speed up opencv image processing with OpenCL

Guide

OpenCL is a framework for writing programs that execute on these heterogenous platforms. The developers of an OpenCL library utilize all OpenCL compatible devices (CPUs, GPUs, DSPs, FPGAs etc) they find on a computer / device and assign the right tasks to the right processor.
Keep in mind that as a user of OpenCV library you are not developing any OpenCL library. In fact you are not even a user of the OpenCL library because all the details are hidden behind the transparent API/TAPI.

config

cmake config by default:

WITH_OPENCL ON

example

Mat

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#include "opencv2/opencv.hpp"
using namespace cv;

int main(int argc, char** argv)
{
Mat img, gray;
img = imread("image.jpg", IMREAD_COLOR);

cvtColor(img, gray, COLOR_BGR2GRAY);
GaussianBlur(gray, gray,Size(7, 7), 1.5);
Canny(gray, gray, 0, 50);

imshow("edges", gray);
waitKey();
return 0;
}

UMat

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#include "opencv2/opencv.hpp"
using namespace cv;

int main(int argc, char** argv)
{
UMat img, gray;
imread("image.jpg", IMREAD_COLOR).copyTo(img);

cvtColor(img, gray, COLOR_BGR2GRAY);
GaussianBlur(gray, gray,Size(7, 7), 1.5);
Canny(gray, gray, 0, 50);

imshow("edges", gray);
waitKey();
return 0;
}

UMat with transparent API/TAPI

Reference

History

  • 20190626: created.