Ask Your Question
0

speed improvement cascade gpu

asked 2015-08-08 09:39:02 -0500

chaaar gravatar image

Hi, I just relate the improvement I got by chance, working with cascadeclassifier_gpu.cpp (v 2.4.10) I tried this file with a webcam video stream and one face picture in front of the cam. I was disappointed by the performance of my new gpu_2 against gpu_1. Gpu_1 computes at 5.5 fps, gpu_2 at 7.5 fps. In order to simplify the main code I wrote a function dealing with gpu code only. The result was astonishing, gpu_2 computes 10 x faster in average, between 40 and 100 fps average 70, same for gpu_1 between 30 and 40 fps, average 35. I precise that the high speed computing appears only when there is a detection, without the speed slow down at 7.5 for gpu 2, 5.5 for gpu_2 What is going on ? does anybody have an idea ? Regards

Linux 3.19.8-100.fc20.x86_64 #1 SMP nvidia driver 340.76 GPU_1 geforce 9500 GT 500M 32 cores GPU_2 geforce GT 720 1024M 192 cores

* part of the original code without the use of a function:

    (image.empty() ? frame : image).copyTo(frame_cpu);
    frame_gpu.upload(image.empty() ? frame : image);

    convertAndResize(frame_gpu, gray_gpu, resized_gpu, scaleFactor);
    convertAndResize(frame_cpu, gray_cpu, resized_cpu, scaleFactor);

    TickMeter tm;
    tm.start();
cascade_gpu.findLargestObject = findLargestObject;

 detections_num = cascade_gpu.detectMultiScale(resized_gpu, facesBuf_gpu, 1.2,
                                                      (filterRects || findLargestObject) ? 4 : 0);
    facesBuf_gpu.colRange(0, detections_num).download(faces_downloaded);

    resized_gpu.download(resized_cpu);
    for (int i = 0; i < detections_num; ++i)
    {
            rectangle(resized_cpu, faces_downloaded.ptr<cv::Rect>()[i], Scalar(255));
    }

    tm.stop();
    double detectionTime = tm.getTimeMilli();
    double fps = 1000 / detectionTime;

* the function created

static int getTargets (Mat &frame, CascadeClassifier_GPU &cascade_gpu, double scaleFactor, Rect* &faceRects, Mat &resized_cpu) { int detections_num=0; GpuMat facesBuf_gpu, frame_gpu, gray_gpu, resized_gpu; Mat faces_downloaded;

    frame_gpu.upload( frame );
    convertAndResize(frame_gpu, gray_gpu, resized_gpu, scaleFactor);

cascade_gpu.findLargestObject = true;

detections_num = cascade_gpu.detectMultiScale(resized_gpu, facesBuf_gpu, 1.2, 4);
    facesBuf_gpu.colRange(0, detections_num).download(faces_downloaded);

 resized_gpu.download(resized_cpu);

if( detections_num > 0)
    {
      faceRects = faces_downloaded.ptr<Rect>();
}
 return detections_num;

}

* main code modified

    (image.empty() ? frame : image).copyTo(frame_cpu);
    convertAndResize(frame_cpu, gray_cpu, resized_cpu, scaleFactor);
    TickMeter tm;
    tm.start();

 Rect *faceRects ;
 detections_num = getTargets(frame, cascade_gpu, scaleFactor, faceRects, resized_cpu);  

    for (int i = 0; i < detections_num; ++i)
    {
    rectangle(resized_cpu, faceRects[i], Scalar(255));
    }

    tm.stop();
edit retag flag offensive close merge delete

Comments

I was disappointed by the performance of my new gpu_2 against gpu_1. Gpu_1 computes at 5.5 fps, gpu_2 at 7.5 fps. --> Keep in mind that OpenCV2.4.10 is old and deprecated. Furthermore the CPU implementation of cascade classifiers has been updated exhaustively using parallel programming and multi core reasoning, while the GPU interface is still very basic. This might be the reason why your GPU processing is not that optimal.

StevenPuttemans gravatar imageStevenPuttemans ( 2015-08-10 02:40:25 -0500 )edit

1 answer

Sort by ยป oldest newest most voted
1

answered 2015-08-24 04:02:55 -0500

chaaar gravatar image

Thanks for your comment I will try the latest 3.0.

edit flag offensive delete link more

Question Tools

Stats

Asked: 2015-08-08 09:39:02 -0500

Seen: 190 times

Last updated: Aug 24 '15