|
public DetectMultiScale ( Byte>.Image |
||
| image | Byte>.Image | The image where the objects are to be detected from |
| scaleFactor | double | The factor by which the search window is scaled between the subsequent scans, for example, 1.1 means increasing window by 10% |
| minNeighbors | int | Minimum number (minus 1) of neighbor rectangles that makes up an object. All the groups of a smaller number of rectangles than min_neighbors-1 are rejected. If min_neighbors is 0, the function does not any grouping at all and returns all the detected candidate rectangles, which may be useful if the user wants to apply a customized grouping procedure |
| minSize | Minimum window size. Use Size.Empty for default, where it is set to the size of samples the classifier has been trained on (~20x20 for face detection) | |
| maxSize | Maxumum window size. Use Size.Empty for default, where the parameter will be ignored. | |
| Результат | System.Drawing.Rectangle[] | |
public Rectangle[] DetectMultiScale(Image<Gray, Byte> image, double scaleFactor, int minNeighbors, Size minSize, Size maxSize)
{
using (MemStorage stor = new MemStorage())
{
Seq<Rectangle> rectangles = new Seq<Rectangle>(stor);
CvInvoke.CvCascadeClassifierDetectMultiScale(_ptr, image, rectangles, scaleFactor, minNeighbors, 0, minSize, maxSize);
return rectangles.ToArray();
}
}
//:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
//::::::::::::Detection of the hand in a gray image::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
public List<Object> Detection(Image<Gray, Byte> frame)
{
List<Object> listReturn = new List<object>(2);
haar = new CascadeClassifier(@"C:\Users\America\Documents\MySystemV1\classifier\cascade.xml");
if (frame != null)
{
System.Drawing.Rectangle[] hands = haar.DetectMultiScale(frame, 1.1, 4, new System.Drawing.Size(frame.Width / 8, frame.Height / 8), new System.Drawing.Size(frame.Width / 3, frame.Height / 3));
foreach (System.Drawing.Rectangle roi in hands)
{
roi.Inflate(-5, 17); // Make the roi bigger, becuse we dont obteined the tootal length of the fingers, in some cases.
frame.Draw(roi, new Gray (255), 3);
}
if (hands.Count() == 0)
{
Rectangle[] noDetection= new Rectangle[]{};
//noDetection[0] = Rectangle.Empty;
listReturn.Add(noDetection);
}
else
{
listReturn.Add(hands);
}
}
listReturn.Add(frame);
return listReturn;
//Regresa los dos valores si el frame es diferente de null, lo cual se supone que siempre es cierto, por que eso se toma en cuenta desde data poll
}//finaliza detection()
