| public SetSVMDetector ( float detector ) : void | ||
| detector | float | The SVM detector |
| return | void | |
/// <summary>
/// Find the pedestrian in the image
/// </summary>
/// <param name="image">The image</param>
/// <param name="processingTime">The pedestrian detection time in milliseconds</param>
/// <returns>The region where pedestrians are detected</returns>
public static Rectangle[] Find(Mat image, bool tryUseCuda, bool tryUseOpenCL, out long processingTime)
{
Stopwatch watch;
Rectangle[] regions;
#if !(IOS || NETFX_CORE)
//check if there is a compatible Cuda device to run pedestrian detection
if (tryUseCuda && CudaInvoke.HasCuda)
{ //this is the Cuda version
using (CudaHOG des = new CudaHOG(new Size(64, 128), new Size(16, 16), new Size(8, 8), new Size(8, 8)))
{
des.SetSVMDetector(des.GetDefaultPeopleDetector());
watch = Stopwatch.StartNew();
using (GpuMat cudaBgr = new GpuMat(image))
using (GpuMat cudaBgra = new GpuMat())
using (VectorOfRect vr = new VectorOfRect())
{
CudaInvoke.CvtColor(cudaBgr, cudaBgra, ColorConversion.Bgr2Bgra);
des.DetectMultiScale(cudaBgra, vr);
regions = vr.ToArray();
}
}
}
else
#endif
{
//Many opencl functions require opencl compatible gpu devices.
//As of opencv 3.0-alpha, opencv will crash if opencl is enable and only opencv compatible cpu device is presented
//So we need to call CvInvoke.HaveOpenCLCompatibleGpuDevice instead of CvInvoke.HaveOpenCL (which also returns true on a system that only have cpu opencl devices).
CvInvoke.UseOpenCL = tryUseOpenCL && CvInvoke.HaveOpenCLCompatibleGpuDevice;
//this is the CPU/OpenCL version
using (HOGDescriptor des = new HOGDescriptor())
{
des.SetSVMDetector(HOGDescriptor.GetDefaultPeopleDetector());
//load the image to umat so it will automatically use opencl is available
UMat umat = image.ToUMat(AccessType.Read);
watch = Stopwatch.StartNew();
MCvObjectDetection[] results = des.DetectMultiScale(umat);
regions = new Rectangle[results.Length];
for (int i = 0; i < results.Length; i++)
regions[i] = results[i].Rect;
watch.Stop();
}
}
processingTime = watch.ElapsedMilliseconds;
return regions;
}
private void objectDetection(Image<Bgr, Byte> myImage)
{
using (HOGDescriptor des = new HOGDescriptor())
{
des.SetSVMDetector(HOGDescriptor.GetDefaultPeopleDetector());
regions = des.DetectMultiScale(myImage);
}
foreach (MCvObjectDetection pedestrian in regions)
{
myImage.Draw(pedestrian.Rect, new Bgr(Color.Red), 1);
}
}
/// <summary>
/// Find the pedestrian in the image
/// </summary>
/// <param name="image">The image</param>
/// <param name="processingTime">The pedestrian detection time in milliseconds</param>
/// <returns>The region where pedestrians are detected</returns>
public static Rectangle[] Find(Mat image, bool tryUseCuda, out long processingTime)
{
Stopwatch watch;
Rectangle[] regions;
#if !(__IOS__ || NETFX_CORE)
//check if there is a compatible Cuda device to run pedestrian detection
if (tryUseCuda && CudaInvoke.HasCuda)
{ //this is the Cuda version
using (CudaHOG des = new CudaHOG(new Size(64, 128), new Size(16, 16), new Size(8,8), new Size(8,8)))
{
des.SetSVMDetector(des.GetDefaultPeopleDetector());
watch = Stopwatch.StartNew();
using (GpuMat cudaBgr = new GpuMat(image))
using (GpuMat cudaBgra = new GpuMat() )
using (VectorOfRect vr = new VectorOfRect())
{
CudaInvoke.CvtColor(cudaBgr, cudaBgra, ColorConversion.Bgr2Bgra);
des.DetectMultiScale(cudaBgra, vr);
regions = vr.ToArray();
}
}
}
else
#endif
{
//this is the CPU/OpenCL version
using (HOGDescriptor des = new HOGDescriptor())
{
des.SetSVMDetector(HOGDescriptor.GetDefaultPeopleDetector());
//load the image to umat so it will automatically use opencl is available
UMat umat = image.ToUMat(AccessType.Read);
watch = Stopwatch.StartNew();
MCvObjectDetection[] results = des.DetectMultiScale(umat);
regions = new Rectangle[results.Length];
for (int i = 0; i < results.Length; i++)
regions[i] = results[i].Rect;
watch.Stop();
}
}
processingTime = watch.ElapsedMilliseconds;
return regions;
}
/// <summary>
/// Find the pedestrian in the image
/// </summary>
/// <param name="image">The image</param>
/// <param name="processingTime">The processing time in milliseconds</param>
/// <returns>The region where pedestrians are detected</returns>
public static Rectangle[] Find(IInputArray image, out long processingTime)
{
Stopwatch watch;
Rectangle[] regions;
using (InputArray iaImage = image.GetInputArray())
{
#if !(__IOS__ || NETFX_CORE)
//if the input array is a GpuMat
//check if there is a compatible Cuda device to run pedestrian detection
if (iaImage.Kind == InputArray.Type.CudaGpuMat)
{
//this is the Cuda version
using (CudaHOG des = new CudaHOG(new Size(64, 128), new Size(16, 16), new Size(8, 8), new Size(8, 8)))
{
des.SetSVMDetector(des.GetDefaultPeopleDetector());
watch = Stopwatch.StartNew();
using (GpuMat cudaBgra = new GpuMat())
using (VectorOfRect vr = new VectorOfRect())
{
CudaInvoke.CvtColor(image, cudaBgra, ColorConversion.Bgr2Bgra);
des.DetectMultiScale(cudaBgra, vr);
regions = vr.ToArray();
}
}
}
else
#endif
{
//this is the CPU/OpenCL version
using (HOGDescriptor des = new HOGDescriptor())
{
des.SetSVMDetector(HOGDescriptor.GetDefaultPeopleDetector());
watch = Stopwatch.StartNew();
MCvObjectDetection[] results = des.DetectMultiScale(image);
regions = new Rectangle[results.Length];
for (int i = 0; i < results.Length; i++)
regions[i] = results[i].Rect;
watch.Stop();
}
}
processingTime = watch.ElapsedMilliseconds;
return regions;
}
}
/// <summary>
/// Find the pedestrian in the image
/// </summary>
/// <param name="image">The image</param>
/// <param name="processingTime">The pedestrian detection time in milliseconds</param>
/// <returns>The image with pedestrian highlighted.</returns>
public static Image<Bgr, Byte> Find(Image<Bgr, Byte> image, out long processingTime)
{
Stopwatch watch;
Rectangle[] regions;
//check if there is a compatible GPU to run pedestrian detection
if (CudaInvoke.HasCuda)
{ //this is the GPU version
using (CudaHOG des = new CudaHOG(new Size(64, 128), new Size(16, 16), new Size(8, 8), new Size(8, 8)))
{
des.SetSVMDetector(des.GetDefaultPeopleDetector());
watch = Stopwatch.StartNew();
using (CudaImage<Bgr, Byte> gpuImg = new CudaImage<Bgr, byte>(image))
using (CudaImage<Bgra, Byte> gpuBgra = gpuImg.Convert<Bgra, Byte>())
using (VectorOfRect vr = new VectorOfRect())
{
CudaInvoke.CvtColor(gpuBgra, gpuBgra, ColorConversion.Bgr2Bgra);
des.DetectMultiScale(gpuBgra,vr);
regions = vr.ToArray();
}
}
}
else
{ //this is the CPU version
using (HOGDescriptor des = new HOGDescriptor())
{
des.SetSVMDetector(HOGDescriptor.GetDefaultPeopleDetector());
//load the image to umat so it will automatically use opencl is available
UMat umat = image.ToUMat();
watch = Stopwatch.StartNew();
//regions = des.DetectMultiScale(image);
MCvObjectDetection[] results = des.DetectMultiScale(umat);
regions = new Rectangle[results.Length];
for (int i = 0; i < results.Length; i++)
regions[i] = results[i].Rect;
}
}
watch.Stop();
processingTime = watch.ElapsedMilliseconds;
foreach (Rectangle pedestrain in regions)
{
image.Draw(pedestrain, new Bgr(Color.Red), 1);
}
return image;
}
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;
}
public void TestHOG2()
{
using (HOGDescriptor hog = new HOGDescriptor())
using (Image<Bgr, Byte> image = new Image<Bgr, byte>("lena.jpg"))
{
float[] pedestrianDescriptor = HOGDescriptor.GetDefaultPeopleDetector();
hog.SetSVMDetector(pedestrianDescriptor);
Stopwatch watch = Stopwatch.StartNew();
Rectangle[] rects = hog.DetectMultiScale(image);
watch.Stop();
Assert.AreEqual(0, rects.Length);
foreach (Rectangle rect in rects)
image.Draw(rect, new Bgr(Color.Red), 1);
Trace.WriteLine(String.Format("HOG detection time: {0} ms", watch.ElapsedMilliseconds));
//ImageViewer.Show(image, String.Format("Detection Time: {0}ms", watch.ElapsedMilliseconds));
}
}
public void TestGrabCut2()
{
Image<Bgr, Byte> img = new Image<Bgr, byte>("pedestrian.png");
HOGDescriptor desc = new HOGDescriptor();
desc.SetSVMDetector(HOGDescriptor.GetDefaultPeopleDetector());
Rectangle[] humanRegions = desc.DetectMultiScale(img);
Image<Gray, byte> pedestrianMask = new Image<Gray, byte>(img.Size);
foreach (Rectangle rect in humanRegions)
{
//generate the mask where 3 indicates forground and 2 indicates background
using (Image<Gray, byte> mask = img.GrabCut(rect, 2))
{
//get the mask of the forground
CvInvoke.cvCmpS(mask, 3, mask, Emgu.CV.CvEnum.CMP_TYPE.CV_CMP_EQ);
pedestrianMask._Or(mask);
}
}
}
public void TestGrabCut2()
{
Image<Bgr, Byte> img = EmguAssert.LoadImage<Bgr, Byte>("pedestrian.png");
HOGDescriptor desc = new HOGDescriptor();
desc.SetSVMDetector(HOGDescriptor.GetDefaultPeopleDetector());
MCvObjectDetection[] humanRegions = desc.DetectMultiScale(img);
Image<Gray, byte> pedestrianMask = new Image<Gray, byte>(img.Size);
foreach (MCvObjectDetection rect in humanRegions)
{
//generate the mask where 3 indicates foreground and 2 indicates background
using (Image<Gray, byte> mask = img.GrabCut(rect.Rect, 2))
{
//get the mask of the foreground
using (ScalarArray ia = new ScalarArray(3))
CvInvoke.Compare(mask, ia, mask, Emgu.CV.CvEnum.CmpType.Equal);
pedestrianMask._Or(mask);
}
}
}
public void TestHOG1()
{
using (HOGDescriptor hog = new HOGDescriptor())
using (Image<Bgr, Byte> image = EmguAssert.LoadImage<Bgr, Byte>("pedestrian.png"))
{
float[] pedestrianDescriptor = HOGDescriptor.GetDefaultPeopleDetector();
hog.SetSVMDetector(pedestrianDescriptor);
Stopwatch watch = Stopwatch.StartNew();
MCvObjectDetection[] rects = hog.DetectMultiScale(image);
watch.Stop();
EmguAssert.AreEqual(1, rects.Length);
foreach (MCvObjectDetection rect in rects)
image.Draw(rect.Rect, new Bgr(0, 0, 255), 1);
EmguAssert.WriteLine(String.Format("HOG detection time: {0} ms", watch.ElapsedMilliseconds));
//Emgu.CV.UI.ImageViewer.Show(image, String.Format("Detection Time: {0}ms", watch.ElapsedMilliseconds));
}
}