使用OpenCL提升OpenCV图像处理性能 | speed up opencv image processing with OpenCL

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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 for compiling OpenCV:

WITH_OPENCL ON

example

Mat

#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

#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

• opencv-transparent-api

History

• 20190626: created.

Copyright

• Post author: kezunlin
• Post link: https://kezunlin.me/post/59afd8b3/
• Copyright Notice: All articles in this blog are licensed under CC BY-NC-SA 3.0 unless stating additionally.