/// <summary>
/// Groups the object candidate rectangles.
/// </summary>
/// <param name="rectList"> Input/output vector of rectangles. Output vector includes retained and grouped rectangles.</param>
/// <param name="groupThreshold">Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it.</param>
/// <param name="eps"></param>
public static void GroupRectangles(IList <Rect> rectList, int groupThreshold, double eps = 0.2)
{
if (rectList == null)
{
throw new ArgumentNullException(nameof(rectList));
}
using (var rectListVec = new VectorOfRect(rectList))
{
NativeMethods.objdetect_groupRectangles1(rectListVec.CvPtr, groupThreshold, eps);
ClearAndAddRange(rectList, rectListVec.ToArray());
}
}
/// <summary>
/// Groups the object candidate rectangles.
/// </summary>
/// <param name="rectList"></param>
/// <param name="rejectLevels"></param>
/// <param name="levelWeights"></param>
/// <param name="groupThreshold"></param>
/// <param name="eps"></param>
public static void GroupRectangles(IList <Rect> rectList, out int[] rejectLevels, out double[] levelWeights, int groupThreshold, double eps = 0.2)
{
if (rectList == null)
{
throw new ArgumentNullException(nameof(rectList));
}
using (var rectListVec = new VectorOfRect(rectList))
using (var rejectLevelsVec = new VectorOfInt32())
using (var levelWeightsVec = new VectorOfDouble())
{
NativeMethods.objdetect_groupRectangles4(rectListVec.CvPtr, rejectLevelsVec.CvPtr, levelWeightsVec.CvPtr, groupThreshold, eps);
ClearAndAddRange(rectList, rectListVec.ToArray());
rejectLevels = rejectLevelsVec.ToArray();
levelWeights = levelWeightsVec.ToArray();
}
}
/// <summary>
///
/// </summary>
/// <param name="rectList"></param>
/// <param name="foundWeights"></param>
/// <param name="foundScales"></param>
/// <param name="detectThreshold"></param>
/// <param name="winDetSize"></param>
public static void GroupRectanglesMeanshift(IList <Rect> rectList, out double[] foundWeights,
out double[] foundScales, double detectThreshold = 0.0, Size?winDetSize = null)
{
if (rectList == null)
{
throw new ArgumentNullException(nameof(rectList));
}
Size winDetSize0 = winDetSize.GetValueOrDefault(new Size(64, 128));
using (var rectListVec = new VectorOfRect(rectList))
using (var foundWeightsVec = new VectorOfDouble())
using (var foundScalesVec = new VectorOfDouble())
{
NativeMethods.objdetect_groupRectangles_meanshift(
rectListVec.CvPtr, foundWeightsVec.CvPtr, foundScalesVec.CvPtr, detectThreshold, winDetSize0);
ClearAndAddRange(rectList, rectListVec.ToArray());
foundWeights = foundWeightsVec.ToArray();
foundScales = foundScalesVec.ToArray();
}
}
/// <summary>
/// Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
/// </summary>
/// <param name="image">Matrix of the type CV_8U containing an image where objects are detected.</param>
/// <param name="rejectLevels"></param>
/// <param name="levelWeights"></param>
/// <param name="scaleFactor">Parameter specifying how much the image size is reduced at each image scale.</param>
/// <param name="minNeighbors">Parameter specifying how many neighbors each candidate rectangle should have to retain it.</param>
/// <param name="flags">Parameter with the same meaning for an old cascade as in the function cvHaarDetectObjects.
/// It is not used for a new cascade.</param>
/// <param name="minSize">Minimum possible object size. Objects smaller than that are ignored.</param>
/// <param name="maxSize">Maximum possible object size. Objects larger than that are ignored.</param>
/// <param name="outputRejectLevels"></param>
/// <returns>Vector of rectangles where each rectangle contains the detected object.</returns>
public virtual Rect[] DetectMultiScale(
Mat image,
out int[] rejectLevels,
out double[] levelWeights,
double scaleFactor = 1.1,
int minNeighbors = 3,
HaarDetectionType flags = HaarDetectionType.Zero,
Size?minSize = null,
Size?maxSize = null,
bool outputRejectLevels = false)
{
if (disposed)
{
throw new ObjectDisposedException("CascadeClassifier");
}
if (image == null)
{
throw new ArgumentNullException(nameof(image));
}
image.ThrowIfDisposed();
Size minSize0 = minSize.GetValueOrDefault(new Size());
Size maxSize0 = maxSize.GetValueOrDefault(new Size());
using (var objectsVec = new VectorOfRect())
using (var rejectLevelsVec = new VectorOfInt32())
using (var levelWeightsVec = new VectorOfDouble())
{
NativeMethods.objdetect_CascadeClassifier_detectMultiScale(
ptr, image.CvPtr, objectsVec.CvPtr, rejectLevelsVec.CvPtr, levelWeightsVec.CvPtr,
scaleFactor, minNeighbors, (int)flags, minSize0, maxSize0, outputRejectLevels ? 1 : 0);
rejectLevels = rejectLevelsVec.ToArray();
levelWeights = levelWeightsVec.ToArray();
return(objectsVec.ToArray());
}
}
/// <summary>
/// Splits a motion history image into a few parts corresponding to separate independent motions
/// (for example, left hand, right hand).
/// </summary>
/// <param name="mhi">Motion history image.</param>
/// <param name="segmask">Image where the found mask should be stored, single-channel, 32-bit floating-point.</param>
/// <param name="boundingRects">Vector containing ROIs of motion connected components.</param>
/// <param name="timestamp">Current time in milliseconds or other units.</param>
/// <param name="segThresh">Segmentation threshold that is recommended to be equal to the interval between motion history “steps” or greater.</param>
public static void SegmentMotion(
InputArray mhi, OutputArray segmask,
out Rect[] boundingRects,
double timestamp, double segThresh)
{
if (mhi == null)
{
throw new ArgumentNullException("mhi");
}
if (segmask == null)
{
throw new ArgumentNullException("segmask");
}
mhi.ThrowIfDisposed();
segmask.ThrowIfNotReady();
using (var br = new VectorOfRect())
{
NativeMethods.video_segmentMotion(
mhi.CvPtr, segmask.CvPtr, br.CvPtr, timestamp, segThresh);
boundingRects = br.ToArray();
}
segmask.Fix();
}