vasu12360's profile - activity

How to install TBB after compilation on odroid xu4

I am working on odroid ux4 which comprise of Mali-T628.

When I run my simple canny edge detection algorithum for cpu and opencl on my laptop opencl came to be 2-2.5 times faster than cpu version.

However when i run the same code on odroid opencl code is much slower than cpu.

This is not only with the case of canny but also with many other algorithums like haar detection and many more.

can anyone tell me what can be the problem and how to solve it.

code:

#include <iostream>
#include <string>
#include <iterator>
//Program for testing CPU and openCL performance when canny algorithum is applied on the Image.
/*

1 GPU devices are detected.
name                 : Mali-T628
available            : 1
imageSupport         : 1
OpenCL_C_Version     : OpenCL C 1.1 

UMat X Canny Time: 5.28345
Mat X Canny Time: 4.39179

*/

#include <opencv2/opencv.hpp>
#include <opencv2/core/ocl.hpp>
//#include <CL/cl.h>
using namespace std;

int main(){
     if (!cv::ocl::haveOpenCL())
        {
            cout << "OpenCL is not avaiable..." << endl;
            return 0;
        }
        cv::ocl::Context context;
        if (!context.create(cv::ocl::Device::TYPE_GPU))
        {
            cout << "Failed creating the context..." << endl;
            return 0;
        }

//   cv::ocl::setUseOpenCL(true);
    // In OpenCV 3.0.0 beta, only a single device is detected.
        cout << context.ndevices() << " GPU devices are detected." << endl;


    for (int i = 0; i < context.ndevices(); i++)   
        {
        cv::ocl::Device device = context.device(i);
        cout << "name                 : " << device.name() << endl;
        cout << "available            : " << device.available() << endl;
        cout << "imageSupport         : " << device.imageSupport() << endl;
        cout << "OpenCL_C_Version     : " << device.OpenCL_C_Version() << endl;
        cout << endl;
//    break;
        }
 //   cv::ocl::Device(context.device(0));
    double tic, toc;

    cv::Mat mat_src = cv::imread("/home/vasu/opencv_opencl/DSC_0231.JPG", 0);
    cv::Mat mat_dst;
    cv::UMat umat_src = mat_src.getUMat(cv::ACCESS_READ, cv::USAGE_ALLOCATE_DEVICE_MEMORY);
    cv::UMat umat_dst;
if(!mat_src.data)
{
cout<<"no data available";
}
else
{
    cv::imshow("Input",mat_src);
    cv::waitKey(0);
}

    tic = (double)cv::getTickCount();
    for(int i = 0 ; i<100; i++){
        cv::Canny(umat_src, umat_dst, 0, 50);
    }
    toc = (double)cv::getTickCount();
    cout<<"UMat X Canny Time: "<<(double)((toc-tic)/cv::getTickFrequency())<<endl;

    tic = (double)cv::getTickCount();
        for(int i = 0 ; i<100; i++){
                cv::Canny(mat_src, mat_dst, 0, 50);
        }
        toc = (double)cv::getTickCount();
        cout<<"Mat X Canny Time: "<<(double)((toc-tic)/cv::getTickFrequency())<<endl;

    return 0;


}

have been recently hired by a startup and they have given me the task to make opencv program as much computationally optimised as possible using cuda and opencl and record the performance of the program(code) in different scenario.

I have worked on opencv for 3-4 months but have not worked with cuda or opencl built opencv nor I have prior knowledge of cuda or opencl .

Though there are a few examples of cuda and ocl opencv functions, i dont want to just copy and exchange simple functions with cuda functions....i want to aquire deep knowledge of where to use cuda or opencl and were to use cpu for better optimisation.

Can anyone suggest me how to take steps in this field and how to pursue further? Further if we use standalone cuda or opencl libraries instead of built opencl lib is there any case when anyone get better result in former case P.S: I am using opencv 3.2 with c++

hey rupert , could u submit that workaround of yours as i am stuck in the same problem

from what i have seen watershed is only importing sketcher from common. In sketcher problem might be most probably in onmouse function, in which "cv2.EVENT_LBUTTONDOWN" is working perfectly fine as i have checked it myself it seems problem exist in "self.prev_pt and flags & cv2.EVENT_FLAG_LBUTTON". see if that helps

that's exactly what i am doing

yep i am quite sure about that

i have added it in the question part

i have recently updated in ubuntu 16.04 ,and now i am facing a lot of problem to run opencv can someone give me proper and clear instruction/steps on how to install opencv on 16.04

i would need to train 200-500 image

opencv 2.4.13 , python 2.7

but how do i store trained model in memory.I will rely appreciate if you can guide me through this or suggest me some links which can help me to implement LBPH as i am not much proficient in opencv

I am working on a face recognition program using cv2.createLBPHFaceRecognizer() on python given at: http://hanzratech.in/2015/02/03/face-...

I am able to add new faces in database however, I want to save the trained data in a file so that i don't have to retrain same database how could i do so.