public static Image<Bgr, Byte> Find(Image<Bgr, Byte> image, out long processingTime)
{
Stopwatch watch;
Rectangle[] regions;
if (GpuInvoke.HasCuda)
{
using (GpuHOGDescriptor des = new GpuHOGDescriptor())
{
des.SetSVMDetector(GpuHOGDescriptor.GetDefaultPeopleDetector());
watch = Stopwatch.StartNew();
using (GpuImage<Bgr, Byte> gpuImage = new GpuImage<Bgr, byte>(image))
{
using (GpuImage<Bgra, Byte> gpuGpraImage = gpuImage.Convert<Bgra, Byte>())
{
regions = des.DetectMultiScale(gpuGpraImage);
}
}
}
}
else
{
using (HOGDescriptor des = new HOGDescriptor())
{
des.SetSVMDetector(HOGDescriptor.GetDefaultPeopleDetector());
watch = Stopwatch.StartNew();
regions = des.DetectMultiScale(image);
}
}
watch.Stop();
processingTime = watch.ElapsedMilliseconds;
foreach (Rectangle rect in regions)
{
image.Draw(rect, new Bgr(Color.Red),1 );
}
return image;
}
//=================================================== Feature Descriptor (HOG) Data Training Kursi ===========================================
public static Rectangle[] FindObject(Image<Bgr, Byte> image, out long processingTime, Size winSizeObject, string dataFile)
{
Stopwatch watch;
Rectangle[] regions;
//check if there is a compatible GPU to run pedestrian detection
if (GpuInvoke.HasCuda)
{ //this is the GPU version
using (GpuHOGDescriptor des = new GpuHOGDescriptor())
{
des.SetSVMDetector(GpuHOGDescriptor.GetDefaultPeopleDetector());
watch = Stopwatch.StartNew();
using (GpuImage<Bgr, Byte> gpuImg = new GpuImage<Bgr, byte>(image))
using (GpuImage<Bgra, Byte> gpuBgra = gpuImg.Convert<Bgra, Byte>())
{
regions = des.DetectMultiScale(gpuBgra);
}
}
}
else
{ //this is the CPU version
using (HOGDescriptor des = new HOGDescriptor(winSizeObject, blockSize, blockStride, cellSize, nbins, 1, -1, 0.2, true))
{
des.SetSVMDetector(GetDataObjects(dataFile));
//des.SetSVMDetector(GetData2());
watch = Stopwatch.StartNew();
regions = des.DetectMultiScale(image);
}
}
watch.Stop();
processingTime = watch.ElapsedMilliseconds;
return regions;
}
