//=============================Feature Descriptor (HOG) Data Training Tanaman=============================
public static Rectangle[] findObjects(Image <Bgr, Byte> image, out long processingTime, Size winSize, string dataFile)
{
Stopwatch watch;
Rectangle[] regions;
if (GpuInvoke.HasCuda)
{
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
{
using (HOGDescriptor des = new HOGDescriptor(winSize, blockSize, blockStride, cellSize, nbins, 1, -1, 0.2, true))
{
des.SetSVMDetector(GetDataObjects(dataFile));
watch = Stopwatch.StartNew();
regions = des.DetectMultiScale(image);
}
}
watch.Stop();
processingTime = watch.ElapsedMilliseconds;
return(regions);
}
public static Image <Bgr, byte> detectFace(Image <Bgr, byte> image, out int detections, out List <PointF> positions)
{
Image <Bgr, byte> copyImage = new Image <Bgr, byte>(image.Bitmap); //Copy the image into a new one
Person_Detector.facePositions.Clear();
using (GpuImage <Bgr, Byte> gpuImage = new GpuImage <Bgr, byte>(image))
using (GpuImage <Gray, Byte> gpuGray = gpuImage.Convert <Gray, Byte>()) //The cascade classifier only takes gray for some reason
{
faces = face.DetectMultiScale(gpuGray, 1.1, 10, Size.Empty);
//Draws rectanges to the face detection positions
foreach (Rectangle f in faces)
{
copyImage.Draw(f, new Bgr(Color.Red), 4);
Person_Detector.facePositions.Add(new PointF(f.Location.X + (f.Width / 2), f.Location.Y + (f.Height / 2)));
}
detections = faces.Length;
positions = facePositions;
}
return(copyImage);
}
public static void UsingGPU(Image <Bgr, Byte> image, GpuCascadeClassifier face, GpuCascadeClassifier eye, List <Rectangle> faces, List <Rectangle> eyes, out long detectionTime)
{
watch = Stopwatch.StartNew();
using (GpuImage <Bgr, Byte> gpuImage = new GpuImage <Bgr, byte>(image))
using (GpuImage <Gray, Byte> gpuGray = gpuImage.Convert <Gray, Byte>())
{
Rectangle[] faceRegion = face.DetectMultiScale(gpuGray, 1.1, 10, Size.Empty);
faces.AddRange(faceRegion);
foreach (Rectangle f in faceRegion)
{
using (GpuImage <Gray, Byte> faceImg = gpuGray.GetSubRect(f))
{
//For some reason a clone is required.
//Might be a bug of GpuCascadeClassifier in opencv
using (GpuImage <Gray, Byte> clone = faceImg.Clone())
{
Rectangle[] eyeRegion = eye.DetectMultiScale(clone, 1.1, 10, Size.Empty);
foreach (Rectangle e in eyeRegion)
{
Rectangle eyeRect = e;
eyeRect.Offset(f.X, f.Y);
eyes.Add(eyeRect);
}
}
}
}
}
watch.Stop();
detectionTime = watch.ElapsedMilliseconds;
}
public static Rectangle[] Detect(Image <Bgr, Byte> image, string cascadeFile,
double scaleFactor = 1.3, int minNeighbors = 10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE detectionType = Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
int minSize = 20, int maxSize = 0)
{
string cascadeFilePath = CascadeManager.GetCascade(cascadeFile);
Size minimumSize;
if (minSize == 0)
{
minimumSize = Size.Empty;
}
else
{
minimumSize = new Size(minSize, minSize);
}
Size maximumSize;
if (maxSize == 0)
{
maximumSize = Size.Empty;
}
else
{
maximumSize = new Size(maxSize, maxSize);
}
if (GpuInvoke.HasCuda)
{
using (GpuCascadeClassifier cascade = new GpuCascadeClassifier(cascadeFilePath))
using (GpuImage <Bgr, Byte> gpuImage = new GpuImage <Bgr, byte>(image))
using (GpuImage <Gray, Byte> gpuGray = gpuImage.Convert <Gray, Byte>())
{
return(cascade.DetectMultiScale(gpuGray, scaleFactor, minNeighbors, minimumSize));
}
}
else
{
using (HaarCascade cascade = new HaarCascade(cascadeFilePath))
using (Image <Gray, Byte> gray = image.Convert <Gray, Byte>())
{
gray._EqualizeHist();
MCvAvgComp[] detected = cascade.Detect(gray,
scaleFactor, minNeighbors,
detectionType,
minimumSize, maximumSize);
return((from x in detected
select x.rect).ToArray());
}
}
}
public static Rectangle[] Detect(Image<Bgr, Byte> image, string cascadeFile,
double scaleFactor = 1.3, int minNeighbors = 10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE detectionType = Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
int minSize = 20, int maxSize = 0)
{
string cascadeFilePath = CascadeManager.GetCascade(cascadeFile);
Size minimumSize;
if (minSize == 0)
{
minimumSize = Size.Empty;
}
else
{
minimumSize = new Size(minSize, minSize);
}
Size maximumSize;
if (maxSize == 0)
{
maximumSize = Size.Empty;
}
else
{
maximumSize = new Size(maxSize, maxSize);
}
if (GpuInvoke.HasCuda)
{
using (GpuCascadeClassifier cascade = new GpuCascadeClassifier(cascadeFilePath))
using (GpuImage<Bgr, Byte> gpuImage = new GpuImage<Bgr, byte>(image))
using (GpuImage<Gray, Byte> gpuGray = gpuImage.Convert<Gray, Byte>())
{
return cascade.DetectMultiScale(gpuGray, scaleFactor, minNeighbors, minimumSize);
}
}
else
{
using (HaarCascade cascade = new HaarCascade(cascadeFilePath))
using (Image<Gray, Byte> gray = image.Convert<Gray, Byte>())
{
gray._EqualizeHist();
MCvAvgComp[] detected = cascade.Detect(gray,
scaleFactor, minNeighbors,
detectionType,
minimumSize, maximumSize);
return (from x in detected
select x.rect).ToArray();
}
}
}
public IImage GetResult(IImage source, PrepareImageParameter parameter)
{
if (source is Image <Bgr, byte> == false)
{
return(null);
}
using (var image = new GpuImage <Bgr, byte>((Image <Bgr, byte>)source))
{
return(image.Convert <Gray, byte>());
}
}
static void Run()
{
Image <Bgr, Byte> image = new Image <Bgr, byte>("pedestrian.png");
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())
{
des.SetSVMDetector(HOGDescriptor.GetDefaultPeopleDetector());
watch = Stopwatch.StartNew();
regions = des.DetectMultiScale(image);
}
}
watch.Stop();
foreach (Rectangle pedestrain in regions)
{
image.Draw(pedestrain, new Bgr(Color.Red), 1);
}
ImageViewer.Show(
image,
String.Format("Pedestrain detection using {0} in {1} milliseconds.",
GpuInvoke.HasCuda ? "GPU" : "CPU",
watch.ElapsedMilliseconds));
}
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;
}
public static void Detect(Image <Bgr, Byte> image, String faceFileName, List <Rectangle> faces, out long detectionTime)
{
Stopwatch watch;
if (GpuInvoke.HasCuda)
{
using (GpuCascadeClassifier face = new GpuCascadeClassifier(faceFileName))
{
watch = Stopwatch.StartNew();
using (GpuImage <Bgr, Byte> gpuImage = new GpuImage <Bgr, byte>(image))
using (GpuImage <Gray, Byte> gpuGray = gpuImage.Convert <Gray, Byte>())
{
Rectangle[] faceRegion = face.DetectMultiScale(gpuGray, 1.1, 10, Size.Empty);
faces.AddRange(faceRegion);
}
watch.Stop();
}
}
else
{
//Read the HaarCascade objects
using (CascadeClassifier face = new CascadeClassifier(faceFileName))
{
watch = Stopwatch.StartNew();
using (Image <Gray, Byte> gray = image.Convert <Gray, Byte>()) //Convert it to Grayscale
{
//normalizes brightness and increases contrast of the image
gray._EqualizeHist();
//Detect the faces from the gray scale image and store the locations as rectangle
//The first dimensional is the channel
//The second dimension is the index of the rectangle in the specific channel
Rectangle[] facesDetected = face.DetectMultiScale(
gray,
1.1,
10,
new Size(20, 20),
Size.Empty);
faces.AddRange(facesDetected);
}
watch.Stop();
}
}
detectionTime = watch.ElapsedMilliseconds;
}
public static void Detect(Image<Bgr, Byte> image, String faceFileName, List<Rectangle> faces, out long detectionTime)
{
Stopwatch watch;
if (GpuInvoke.HasCuda)
{
using (GpuCascadeClassifier face = new GpuCascadeClassifier(faceFileName))
{
watch = Stopwatch.StartNew();
using (GpuImage<Bgr, Byte> gpuImage = new GpuImage<Bgr, byte>(image))
using (GpuImage<Gray, Byte> gpuGray = gpuImage.Convert<Gray, Byte>())
{
Rectangle[] faceRegion = face.DetectMultiScale(gpuGray, 1.1, 10, Size.Empty);
faces.AddRange(faceRegion);
}
watch.Stop();
}
}
else
{
//Read the HaarCascade objects
using (CascadeClassifier face = new CascadeClassifier(faceFileName))
{
watch = Stopwatch.StartNew();
using (Image<Gray, Byte> gray = image.Convert<Gray, Byte>()) //Convert it to Grayscale
{
//normalizes brightness and increases contrast of the image
gray._EqualizeHist();
//Detect the faces from the gray scale image and store the locations as rectangle
//The first dimensional is the channel
//The second dimension is the index of the rectangle in the specific channel
Rectangle[] facesDetected = face.DetectMultiScale(
gray,
1.1,
10,
new Size(20, 20),
Size.Empty);
faces.AddRange(facesDetected);
}
watch.Stop();
}
}
detectionTime = watch.ElapsedMilliseconds;
}
public static Image <Bgr, Byte> findPerson(Image <Bgr, Byte> image, out int detections, out List <PointF> positions)
{
//If the gpu has nvidia CUDA
if (GpuInvoke.HasCuda)
{
imageToProcess = new Image <Bgr, byte>(image.Bitmap); //Value is coppied so the refference of the input image is not changed outside of this class
Person_Detector.positions.Clear();
using (GpuHOGDescriptor descriptor = new GpuHOGDescriptor())
{
descriptor.SetSVMDetector(GpuHOGDescriptor.GetDefaultPeopleDetector());
using (GpuImage <Bgr, Byte> gpuImage = new GpuImage <Bgr, byte>(imageToProcess)) //Create gpuImage from image
{
using (GpuImage <Bgra, Byte> bgraImage = gpuImage.Convert <Bgra, Byte>())
{
regeions = descriptor.DetectMultiScale(bgraImage); //Returns all detected regions in a rectangle array
}
}
}
}
else
{
using (HOGDescriptor des = new HOGDescriptor())
{
regeions = des.DetectMultiScale(imageToProcess);
}
}
detections = regeions.Length;
//Draws detected rectangles onto the image being returned
foreach (Rectangle ped in regeions)
{
imageToProcess.Draw(ped, new Bgr(Color.Red), 5);
imageToProcess.Draw(new Cross2DF(new PointF(ped.Location.X + (ped.Width / 2), ped.Location.Y + (ped.Height / 2)), 30, 30), new Bgr(Color.Green), 3);
Person_Detector.positions.Add(new PointF(ped.Location.X + (ped.Width / 2), ped.Location.Y + (ped.Height / 2))); //Sets the putput variable
}
positions = Person_Detector.positions;
return(imageToProcess);
}
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);
}
/// <summary>
/// Find the pedestrian in the image
/// </summary>
/// <param name="imageFileName">The name of 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> source, out long processingTime)
{
Image <Bgr, Byte> image = source.Copy();
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())
{
des.SetSVMDetector(HOGDescriptor.GetDefaultPeopleDetector());
watch = Stopwatch.StartNew();
regions = des.DetectMultiScale(image);
}
}
watch.Stop();
processingTime = watch.ElapsedMilliseconds;
foreach (Rectangle pedestrain in regions)
{
image.Draw(pedestrain, new Bgr(Color.Red), 1);
}
return(image);
}
private void DetectFace(Image <Bgr, Byte> image, List <Rectangle> faces)
{
#if !IOS
if (GpuInvoke.HasCuda)
{
using (GpuCascadeClassifier face = new GpuCascadeClassifier(_faceFileName))
{
using (GpuImage <Bgr, Byte> gpuImage = new GpuImage <Bgr, byte>(image))
using (GpuImage <Gray, Byte> gpuGray = gpuImage.Convert <Gray, Byte>())
{
Rectangle[] faceRegion = face.DetectMultiScale(gpuGray, 1.1, 10, Size.Empty);
faces.AddRange(faceRegion);
}
}
}
else
#endif
{
//Read the HaarCascade objects
using (CascadeClassifier face = new CascadeClassifier(_faceFileName))
{
using (Image <Gray, Byte> gray = image.Convert <Gray, Byte>()) //Convert it to Grayscale
{
//normalizes brightness and increases contrast of the image
gray._EqualizeHist();
//Detect the faces from the gray scale image and store the locations as rectangle
//The first dimensional is the channel
//The second dimension is the index of the rectangle in the specific channel
Rectangle[] facesDetected = face.DetectMultiScale(
gray,
1.1,
10,
new Size(20, 20),
Size.Empty);
faces.AddRange(facesDetected);
}
}
}
}
/// <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(Image <Bgr, Byte> image, out long processingTime)
{
Stopwatch watch;
Rectangle[] regions;
#if !IOS
//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
#endif
{ //this is the CPU version
using (HOGDescriptor des = new HOGDescriptor())
{
des.SetSVMDetector(HOGDescriptor.GetDefaultPeopleDetector());
watch = Stopwatch.StartNew();
regions = des.DetectMultiScale(image);
}
}
watch.Stop();
processingTime = watch.ElapsedMilliseconds;
return(regions);
}
//=================================================== 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 static Image<Bgr, Byte> DetectFeatures()
//{
// Stopwatch watch = Stopwatch.StartNew();
// Rectangle[] regions = GetBodies("Untitled7.jpg");
// MCvAvgComp[][] facesDetected = GetFaces("Untitled7.jpg");
// Image<Bgr, Byte> image = new Image<Bgr, byte>("Untitled7.jpg");
// foreach (Rectangle pedestrain in regions)
// {
// image.Draw(pedestrain, new Bgr(Color.Red), 1);
// }
// foreach (MCvAvgComp f in facesDetected[0])
// {
// //draw the face detected in the 0th (gray) channel with blue color
// image.Draw(f.rect, new Bgr(Color.Blue), 2);
// }
// return image;
//}
// Body Function
public static Rectangle[] GetBodies(string fileName)
{
Image <Bgr, Byte> image = new Image <Bgr, byte>(fileName);
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())
{
des.SetSVMDetector(HOGDescriptor.GetDefaultPeopleDetector());
watch = Stopwatch.StartNew();
regions = des.DetectMultiScale(image);
}
}
watch.Stop();
return(regions);
}
public static void Detect(Image<Bgr, Byte> image, String faceFileName, String eyeFileName, List<Rectangle> faces, List<Rectangle> eyes, out long detectionTime)
{
Stopwatch watch;
if (GpuInvoke.HasCuda)
{
using (GpuCascadeClassifier face = new GpuCascadeClassifier(faceFileName))
using (GpuCascadeClassifier eye = new GpuCascadeClassifier(eyeFileName))
{
watch = Stopwatch.StartNew();
using (GpuImage<Bgr, Byte> gpuImage = new GpuImage<Bgr, byte>(image))
using (GpuImage<Gray, Byte> gpuGray = gpuImage.Convert<Gray, Byte>())
{
Rectangle[] faceRegion = face.DetectMultiScale(gpuGray, 1.1, 10, Size.Empty);
faces.AddRange(faceRegion);
foreach (Rectangle f in faceRegion)
{
using (GpuImage<Gray, Byte> faceImg = gpuGray.GetSubRect(f))
{
//For some reason a clone is required.
//Might be a bug of GpuCascadeClassifier in opencv
using (GpuImage<Gray, Byte> clone = faceImg.Clone())
{
Rectangle[] eyeRegion = eye.DetectMultiScale(clone, 1.1, 10, Size.Empty);
foreach (Rectangle e in eyeRegion)
{
Rectangle eyeRect = e;
eyeRect.Offset(f.X, f.Y);
eyes.Add(eyeRect);
}
}
}
}
}
watch.Stop();
}
}
else
{
//Read the HaarCascade objects
using (CascadeClassifier face = new CascadeClassifier(faceFileName))
using (CascadeClassifier eye = new CascadeClassifier(eyeFileName))
{
watch = Stopwatch.StartNew();
using (Image<Gray, Byte> gray = image.Convert<Gray, Byte>()) //Convert it to Grayscale
{
//normalizes brightness and increases contrast of the image
gray._EqualizeHist();
//Detect the faces from the gray scale image and store the locations as rectangle
//The first dimensional is the channel
//The second dimension is the index of the rectangle in the specific channel
Rectangle[] facesDetected = face.DetectMultiScale(
gray,
1.1,
10,
new Size(20, 20),
Size.Empty);
faces.AddRange(facesDetected);
foreach (Rectangle f in facesDetected)
{
//Set the region of interest on the faces
gray.ROI = f;
Rectangle[] eyesDetected = eye.DetectMultiScale(
gray,
1.1,
10,
new Size(20, 20),
Size.Empty);
gray.ROI = Rectangle.Empty;
foreach (Rectangle e in eyesDetected)
{
Rectangle eyeRect = e;
eyeRect.Offset(f.X, f.Y);
eyes.Add(eyeRect);
}
}
}
watch.Stop();
}
}
detectionTime = watch.ElapsedMilliseconds;
}
public static void Detect(Image <Bgr, Byte> image, String faceFileName, String eyeFileName, List <Rectangle> faces, List <Rectangle> eyes, out long detectionTime)
{
Stopwatch watch;
if (GpuInvoke.HasCuda)
{
using (GpuCascadeClassifier face = new GpuCascadeClassifier(faceFileName))
using (GpuCascadeClassifier eye = new GpuCascadeClassifier(eyeFileName))
{
watch = Stopwatch.StartNew();
using (GpuImage <Bgr, Byte> gpuImage = new GpuImage <Bgr, byte>(image))
using (GpuImage <Gray, Byte> gpuGray = gpuImage.Convert <Gray, Byte>())
{
Rectangle[] faceRegion = face.DetectMultiScale(gpuGray, 1.4, 4, Size.Empty);
faces.AddRange(faceRegion);
foreach (Rectangle f in faceRegion)
{
using (GpuImage <Gray, Byte> faceImg = gpuGray.GetSubRect(f))
{
//For some reason a clone is required.
//Might be a bug of GpuCascadeClassifier in opencv
using (GpuImage <Gray, Byte> clone = faceImg.Clone(null))
{
Rectangle[] eyeRegion = eye.DetectMultiScale(clone, 1.4, 4, Size.Empty);
foreach (Rectangle e in eyeRegion)
{
//Rectangle eyeRect = e;
//eyeRect.Offset(f.X, f.Y);
//eyes.Add(eyeRect);
}
}
}
}
}
watch.Stop();
}
}
else
{
//Read the HaarCascade objects
using (CascadeClassifier face = new CascadeClassifier(faceFileName))
using (CascadeClassifier eye = new CascadeClassifier(eyeFileName))
{
watch = Stopwatch.StartNew();
using (Image <Gray, Byte> gray = image.Convert <Gray, Byte>()) //Convert it to Grayscale
{
//normalizes brightness and increases contrast of the image
gray._EqualizeHist();
//Detect the faces from the gray scale image and store the locations as rectangle
//The first dimensional is the channel
//The second dimension is the index of the rectangle in the specific channel
Rectangle[] facesDetected = face.DetectMultiScale(
gray,
1.3,
4,
new Size(20, 20),
Size.Empty);
faces.AddRange(facesDetected);
foreach (Rectangle f in facesDetected)
{
//Set the region of interest on the faces
gray.ROI = f;
Rectangle[] eyesDetected = eye.DetectMultiScale(
gray,
1.4,
10,
new Size(20, 20),
Size.Empty);
gray.ROI = Rectangle.Empty;
foreach (Rectangle e in eyesDetected)
{
//Rectangle eyeRect = e;
//eyeRect.Offset(f.X, f.Y);
//eyes.Add(eyeRect);
}
}
}
watch.Stop();
}
}
detectionTime = watch.ElapsedMilliseconds;
}
public void classify(BitmapSource frame)
{
Console.WriteLine(relativeURI);
//byte[] classifiedImage = frame;
//WriteableBitmap frameImage = new WriteableBitmap(frameWidth, frameHeight, 96, 96, PixelFormats.Bgr32, null);
//BitmapSource frameImage = BitmapSource.Create(frameWidth, frameHeight, 96, 96, PixelFormats.Bgr32, null, frame, stride);
/*
resultsPtr = CvInvoke.cvHaarDetectObjects(
Marshal.GetIUnknownForObject(frame),
classifier,
resultsPtr,
1.1,
3,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new System.Drawing.Size(0,0),
new System.Drawing.Size(0,0)
);
Console.WriteLine("Classified?!? Pointer below: ");
Console.WriteLine(resultsPtr.ToString());
*/
//return classifiedImage;
Console.WriteLine(" - - - Converting Bitmap...");
System.Drawing.Bitmap bitmapFrame;
using (MemoryStream outStream = new MemoryStream())
{
BitmapEncoder enc = new BmpBitmapEncoder();
enc.Frames.Add(BitmapFrame.Create(frame));
enc.Save(outStream);
bitmapFrame = new System.Drawing.Bitmap(outStream);
}
Console.WriteLine(" - - - Bitmap converted!");
Image<Bgr, Byte> image = new Image<Bgr, Byte>(bitmapFrame);
Console.WriteLine(" - - - Image set");
Console.WriteLine(" - - - Check CUDA...");
if (GpuInvoke.HasCuda)
{
Console.WriteLine(" - - - Has CUDA!");
using (GpuCascadeClassifier target = new GpuCascadeClassifier(classifierURI))
{
using (GpuImage<Bgr, Byte> gpuImage = new GpuImage<Bgr, byte>(image))
using (GpuImage<Gray, Byte> gpuGray = gpuImage.Convert<Gray, Byte>())
{
Console.WriteLine(" - - - Detecting!");
Rectangle[] targetSet = target.DetectMultiScale(gpuGray, 1.1, 10, System.Drawing.Size.Empty);
Console.WriteLine(" - - - Detected :D :D :D Printing rectangle set: ");
foreach (Rectangle f in targetSet)
{
Console.WriteLine("Rectangle found at: " + f.ToString());
//draw the face detected in the 0th (gray) channel with blue color
image.Draw(f, new Bgr(System.Drawing.Color.Blue), 2);
}
Console.WriteLine(" - - - DONE");
}
}
}
else
{
using (HOGDescriptor des = new HOGDescriptor())
{
//des.SetSVMDetector
}
Console.WriteLine(" - - - No CUDA :( ");
Console.WriteLine(" - - - Devices available: " + GpuInvoke.GetCudaEnabledDeviceCount());
}
}
public void classify(BitmapSource frame)
{
Console.WriteLine(relativeURI);
//byte[] classifiedImage = frame;
//WriteableBitmap frameImage = new WriteableBitmap(frameWidth, frameHeight, 96, 96, PixelFormats.Bgr32, null);
//BitmapSource frameImage = BitmapSource.Create(frameWidth, frameHeight, 96, 96, PixelFormats.Bgr32, null, frame, stride);
/*
* resultsPtr = CvInvoke.cvHaarDetectObjects(
* Marshal.GetIUnknownForObject(frame),
* classifier,
* resultsPtr,
* 1.1,
* 3,
* Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
* new System.Drawing.Size(0,0),
* new System.Drawing.Size(0,0)
* );
*
* Console.WriteLine("Classified?!? Pointer below: ");
* Console.WriteLine(resultsPtr.ToString());
*/
//return classifiedImage;
Console.WriteLine(" - - - Converting Bitmap...");
System.Drawing.Bitmap bitmapFrame;
using (MemoryStream outStream = new MemoryStream())
{
BitmapEncoder enc = new BmpBitmapEncoder();
enc.Frames.Add(BitmapFrame.Create(frame));
enc.Save(outStream);
bitmapFrame = new System.Drawing.Bitmap(outStream);
}
Console.WriteLine(" - - - Bitmap converted!");
Image <Bgr, Byte> image = new Image <Bgr, Byte>(bitmapFrame);
Console.WriteLine(" - - - Image set");
Console.WriteLine(" - - - Check CUDA...");
if (GpuInvoke.HasCuda)
{
Console.WriteLine(" - - - Has CUDA!");
using (GpuCascadeClassifier target = new GpuCascadeClassifier(classifierURI))
{
using (GpuImage <Bgr, Byte> gpuImage = new GpuImage <Bgr, byte>(image))
using (GpuImage <Gray, Byte> gpuGray = gpuImage.Convert <Gray, Byte>())
{
Console.WriteLine(" - - - Detecting!");
Rectangle[] targetSet = target.DetectMultiScale(gpuGray, 1.1, 10, System.Drawing.Size.Empty);
Console.WriteLine(" - - - Detected :D :D :D Printing rectangle set: ");
foreach (Rectangle f in targetSet)
{
Console.WriteLine("Rectangle found at: " + f.ToString());
//draw the face detected in the 0th (gray) channel with blue color
image.Draw(f, new Bgr(System.Drawing.Color.Blue), 2);
}
Console.WriteLine(" - - - DONE");
}
}
}
else
{
using (HOGDescriptor des = new HOGDescriptor())
{
//des.SetSVMDetector
}
Console.WriteLine(" - - - No CUDA :( ");
Console.WriteLine(" - - - Devices available: " + GpuInvoke.GetCudaEnabledDeviceCount());
}
}
private void DetectFaceAndEyes(Image <Bgr, byte> image, List <Rectangle> faces, List <Rectangle> eyes)
{
#if !IOS
if (GpuInvoke.HasCuda)
{
using (var face = new GpuCascadeClassifier(_faceFileName))
using (var eye = new GpuCascadeClassifier(_eyeFileName))
{
using (var gpuImage = new GpuImage <Bgr, byte>(image))
using (var gpuGray = gpuImage.Convert <Gray, byte>())
{
var faceRegion = face.DetectMultiScale(gpuGray, 1.1, 10, Size.Empty);
faces.AddRange(faceRegion);
foreach (var f in faceRegion)
{
using (var faceImg = gpuGray.GetSubRect(f))
{
//For some reason a clone is required.
//Might be a bug of GpuCascadeClassifier in opencv
using (var clone = faceImg.Clone())
{
var eyeRegion = eye.DetectMultiScale(clone, 1.1, 10, Size.Empty);
foreach (var e in eyeRegion)
{
var eyeRect = e;
eyeRect.Offset(f.X, f.Y);
eyes.Add(eyeRect);
}
}
}
}
}
}
}
else
#endif
using (var face = new CascadeClassifier(_faceFileName))
using (var eye = new CascadeClassifier(_eyeFileName))
{
using (var gray = image.Convert <Gray, byte>()) //Convert it to Grayscale
{
//normalizes brightness and increases contrast of the image
gray._EqualizeHist();
//Detect the faces from the gray scale image and store the locations as rectangle
//The first dimensional is the channel
//The second dimension is the index of the rectangle in the specific channel
var facesDetected = face.DetectMultiScale(
gray,
1.1,
10,
new Size(20, 20),
Size.Empty);
faces.AddRange(facesDetected);
foreach (var f in facesDetected)
{
//Set the region of interest on the faces
gray.ROI = f;
var eyesDetected = eye.DetectMultiScale(
gray,
1.1,
10,
new Size(20, 20),
Size.Empty);
gray.ROI = Rectangle.Empty;
foreach (var e in eyesDetected)
{
var eyeRect = e;
eyeRect.Offset(f.X, f.Y);
eyes.Add(eyeRect);
}
}
}
}
}
// public static void detectFaceGPU(Image<Bgr, Byte> image, String faceFileName, String eyesFileName, List<Rectangle> facesList, List<Rectangle> eyesList, out long detectionTime)
public static void detectFaceGPU(Image<Bgr, Byte> image, String faceFileName, String eyesFileName, List<Rectangle> facesList, out Rectangle eyeL, out Rectangle eyeR, out long detectionTime)
{
Stopwatch watch;
Rectangle eyeLtmp = new Rectangle(), eyeRtmp = new Rectangle();
using (GpuCascadeClassifier faceCascade = new GpuCascadeClassifier(faceFileName))
using (GpuCascadeClassifier eyesCascade = new GpuCascadeClassifier(eyesFileName))
{
watch = Stopwatch.StartNew();
using (GpuImage<Bgr, Byte> gpuImage = new GpuImage<Bgr,byte>(image))
using (GpuImage<Gray, Byte> grayImage = gpuImage.Convert<Gray, Byte>())
{
Rectangle[] facesRegion = faceCascade.DetectMultiScale(grayImage, 1.1, 10, new Size(image.Width / 8, image.Height / 8));
facesList.AddRange(facesRegion);
foreach (Rectangle f in facesRegion)
{
#region ROI dla oczu
Point eyesBoxTopLeft = new Point(f.X, f.Top + f.Height / 4);
Size eyesBoxArea = new Size(f.Width, f.Height / 4);
Rectangle eyesBox = new Rectangle(eyesBoxTopLeft, eyesBoxArea);
Size rightEyeBoxArea = new Size(eyesBox.Width / 2, eyesBox.Height);
Rectangle rightEyeBox = new Rectangle(eyesBoxTopLeft, rightEyeBoxArea);
Size leftEyeBoxArea = new Size(eyesBox.Width / 2, eyesBox.Height);
Rectangle leftEyeBox = new Rectangle(new Point(eyesBoxTopLeft.X + leftEyeBoxArea.Width, eyesBoxTopLeft.Y), leftEyeBoxArea);
#endregion
#region Prawe oko
using (GpuImage<Gray, Byte> faceImg = grayImage.GetSubRect(rightEyeBox))
{
using (GpuImage<Gray, Byte> clone = faceImg.Clone())
{
Rectangle[] eyeRegion = eyesCascade.DetectMultiScale(clone, 1.1, 10, new Size(30, 30));
foreach (Rectangle e in eyeRegion)
{
Rectangle eyeRect = e;
eyeRect.Offset(rightEyeBox.X, rightEyeBox.Y);
eyeRtmp = eyeRect;
}
}
}
#endregion
#region Lewe oko
using (GpuImage<Gray, Byte> faceImg = grayImage.GetSubRect(leftEyeBox))
{
using (GpuImage<Gray, Byte> clone = faceImg.Clone())
{
Rectangle[] eyeRegion = eyesCascade.DetectMultiScale(clone, 1.1, 10, new Size(30, 30));
foreach (Rectangle e in eyeRegion)
{
Rectangle eyeRect = e;
eyeRect.Offset(leftEyeBox.X, leftEyeBox.Y);
eyeLtmp = eyeRect;
}
}
}
#endregion
}
}
watch.Stop();
}
detectionTime = watch.ElapsedMilliseconds;
eyeL = eyeLtmp;
eyeR = eyeRtmp;
}
static void Run()
{
Image <Bgr, Byte> image = new Image <Bgr, byte>("lena.jpg"); //Read the files as an 8-bit Bgr image
Stopwatch watch;
String faceFileName = "haarcascade_frontalface_default.xml";
String eyeFileName = "haarcascade_eye.xml";
if (GpuInvoke.HasCuda)
{
using (GpuCascadeClassifier face = new GpuCascadeClassifier(faceFileName))
using (GpuCascadeClassifier eye = new GpuCascadeClassifier(eyeFileName))
{
watch = Stopwatch.StartNew();
using (GpuImage <Bgr, Byte> gpuImage = new GpuImage <Bgr, byte>(image))
using (GpuImage <Gray, Byte> gpuGray = gpuImage.Convert <Gray, Byte>())
{
Rectangle[] faceRegion = face.DetectMultiScale(gpuGray, 1.1, 10, Size.Empty);
foreach (Rectangle f in faceRegion)
{
//draw the face detected in the 0th (gray) channel with blue color
image.Draw(f, new Bgr(Color.Blue), 2);
using (GpuImage <Gray, Byte> faceImg = gpuGray.GetSubRect(f))
{
//For some reason a clone is required.
//Might be a bug of GpuCascadeClassifier in opencv
using (GpuImage <Gray, Byte> clone = faceImg.Clone())
{
Rectangle[] eyeRegion = eye.DetectMultiScale(clone, 1.1, 10, Size.Empty);
foreach (Rectangle e in eyeRegion)
{
Rectangle eyeRect = e;
eyeRect.Offset(f.X, f.Y);
image.Draw(eyeRect, new Bgr(Color.Red), 2);
}
}
}
}
}
watch.Stop();
}
}
else
{
//Read the HaarCascade objects
using (HaarCascade face = new HaarCascade(faceFileName))
using (HaarCascade eye = new HaarCascade(eyeFileName))
{
watch = Stopwatch.StartNew();
using (Image <Gray, Byte> gray = image.Convert <Gray, Byte>()) //Convert it to Grayscale
{
//normalizes brightness and increases contrast of the image
gray._EqualizeHist();
//Detect the faces from the gray scale image and store the locations as rectangle
//The first dimensional is the channel
//The second dimension is the index of the rectangle in the specific channel
MCvAvgComp[] facesDetected = face.Detect(
gray,
1.1,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
foreach (MCvAvgComp f in facesDetected)
{
//draw the face detected in the 0th (gray) channel with blue color
image.Draw(f.rect, new Bgr(Color.Blue), 2);
//Set the region of interest on the faces
gray.ROI = f.rect;
MCvAvgComp[] eyesDetected = eye.Detect(
gray,
1.1,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
gray.ROI = Rectangle.Empty;
foreach (MCvAvgComp e in eyesDetected)
{
Rectangle eyeRect = e.rect;
eyeRect.Offset(f.rect.X, f.rect.Y);
image.Draw(eyeRect, new Bgr(Color.Red), 2);
}
}
}
watch.Stop();
}
}
//display the image
ImageViewer.Show(image, String.Format(
"Completed face and eye detection using {0} in {1} milliseconds",
GpuInvoke.HasCuda ? "GPU": "CPU",
watch.ElapsedMilliseconds));
}
static void Run()
{
Image<Bgr, Byte> image = new Image<Bgr, byte>("lena.jpg"); //Read the files as an 8-bit Bgr image
Stopwatch watch;
String faceFileName = "haarcascade_frontalface_default.xml";
String eyeFileName = "haarcascade_eye.xml";
if (GpuInvoke.HasCuda)
{
using (GpuCascadeClassifier face = new GpuCascadeClassifier(faceFileName))
using (GpuCascadeClassifier eye = new GpuCascadeClassifier(eyeFileName))
{
watch = Stopwatch.StartNew();
using (GpuImage<Bgr, Byte> gpuImage = new GpuImage<Bgr, byte>(image))
using (GpuImage<Gray, Byte> gpuGray = gpuImage.Convert<Gray, Byte>())
{
Rectangle[] faceRegion = face.DetectMultiScale(gpuGray, 1.1, 10, Size.Empty);
foreach (Rectangle f in faceRegion)
{
//draw the face detected in the 0th (gray) channel with blue color
image.Draw(f, new Bgr(Color.Blue), 2);
using (GpuImage<Gray, Byte> faceImg = gpuGray.GetSubRect(f))
{
//For some reason a clone is required.
//Might be a bug of GpuCascadeClassifier in opencv
using (GpuImage<Gray, Byte> clone = faceImg.Clone())
{
Rectangle[] eyeRegion = eye.DetectMultiScale(clone, 1.1, 10, Size.Empty);
foreach (Rectangle e in eyeRegion)
{
Rectangle eyeRect = e;
eyeRect.Offset(f.X, f.Y);
image.Draw(eyeRect, new Bgr(Color.Red), 2);
}
}
}
}
}
watch.Stop();
}
}
else
{
//Read the HaarCascade objects
using(HaarCascade face = new HaarCascade(faceFileName))
using(HaarCascade eye = new HaarCascade(eyeFileName))
{
watch = Stopwatch.StartNew();
using (Image<Gray, Byte> gray = image.Convert<Gray, Byte>()) //Convert it to Grayscale
{
//normalizes brightness and increases contrast of the image
gray._EqualizeHist();
//Detect the faces from the gray scale image and store the locations as rectangle
//The first dimensional is the channel
//The second dimension is the index of the rectangle in the specific channel
MCvAvgComp[] facesDetected = face.Detect(
gray,
1.1,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
foreach (MCvAvgComp f in facesDetected)
{
//draw the face detected in the 0th (gray) channel with blue color
image.Draw(f.rect, new Bgr(Color.Blue), 2);
//Set the region of interest on the faces
gray.ROI = f.rect;
MCvAvgComp[] eyesDetected = eye.Detect(
gray,
1.1,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
gray.ROI = Rectangle.Empty;
foreach (MCvAvgComp e in eyesDetected)
{
Rectangle eyeRect = e.rect;
eyeRect.Offset(f.rect.X, f.rect.Y);
image.Draw(eyeRect, new Bgr(Color.Red), 2);
}
}
}
watch.Stop();
}
}
//display the image
ImageViewer.Show(image, String.Format(
"Completed face and eye detection using {0} in {1} milliseconds",
GpuInvoke.HasCuda ? "GPU": "CPU",
watch.ElapsedMilliseconds));
}
private static Rectangle[] DetectFace(Image<Bgr, Byte> image, string faceFileName)
{
try
{
if (GpuInvoke.HasCuda)
{
using (GpuCascadeClassifier face = new GpuCascadeClassifier(faceFileName))
{
using (GpuImage<Bgr, Byte> gpuImage = new GpuImage<Bgr, byte>(image))
using (GpuImage<Gray, Byte> gpuGray = gpuImage.Convert<Gray, Byte>())
{
Rectangle[] faceRegion = face.DetectMultiScale(gpuGray, 1.1, 10, Size.Empty);
return faceRegion;
}
}
}
else
{
//Read the HaarCascade objects
using (CascadeClassifier face = new CascadeClassifier(faceFileName))
{
using (Image<Gray, Byte> gray = image.Convert<Gray, Byte>()) //Convert it to Grayscale
{
//normalizes brightness and increases contrast of the image
gray._EqualizeHist();
//Detect the faces from the gray scale image and store the locations as rectangle
//The first dimensional is the channel
//The second dimension is the index of the rectangle in the specific channel
Rectangle[] facesDetected = face.DetectMultiScale(
gray,
1.1,
10,
new Size(filterWidth, filterHeight),
Size.Empty);
return facesDetected;
}
}
}
}
catch (Exception ex)
{
throw ex;
}
}