/// <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="weights"></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">Relative difference between sides of the rectangles to merge them into a group.</param>
public static void GroupRectangles(IList <Rect> rectList, out int[] weights, int groupThreshold, double eps = 0.2)
{
if (rectList == null)
{
throw new ArgumentNullException(nameof(rectList));
}
using (var rectListVec = new VectorOfRect(rectList))
using (var weightsVec = new VectorOfInt32())
{
NativeMethods.objdetect_groupRectangles2(rectListVec.CvPtr, weightsVec.CvPtr, groupThreshold, eps);
ClearAndAddRange(rectList, rectListVec.ToArray());
weights = weightsVec.ToArray();
}
}
/// <summary>
/// select the 512 "best description pairs"
/// </summary>
/// <param name="images">grayscale images set</param>
/// <param name="keypoints">set of detected keypoints</param>
/// <param name="corrThresh">correlation threshold</param>
/// <param name="verbose">print construction information</param>
/// <returns>list of best pair indexes</returns>
public int[] SelectPairs(IEnumerable <Mat> images, out KeyPoint[][] keypoints,
double corrThresh = 0.7, bool verbose = true)
{
if (images == null)
{
throw new ArgumentNullException(nameof(images));
}
IntPtr[] imagesPtrs = EnumerableEx.SelectPtrs(images);
using (var outVec = new VectorOfInt32())
using (var keypointsVec = new VectorOfVectorKeyPoint())
{
NativeMethods.features2d_FREAK_selectPairs(ptr, imagesPtrs, imagesPtrs.Length,
keypointsVec.CvPtr, corrThresh, verbose ? 1 : 0, outVec.CvPtr);
keypoints = keypointsVec.ToArray();
return(outVec.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());
}
}
