/// <summary>
/// Finds lines in the input image.
/// This is the output of the default parameters of the algorithm on the above shown image.
/// </summary>
/// <param name="image">A grayscale (CV_8UC1) input image. </param>
/// <param name="lines">A vector of Vec4i or Vec4f elements specifying the beginning and ending point of a line.
/// Where Vec4i/Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly oriented depending on the gradient.</param>
/// <param name="width">Vector of widths of the regions, where the lines are found. E.g. Width of line.</param>
/// <param name="prec">Vector of precisions with which the lines are found.</param>
/// <param name="nfa">Vector containing number of false alarms in the line region,
/// with precision of 10%. The bigger the value, logarithmically better the detection.</param>
public virtual void Detect(InputArray image, out Vec4f[] lines,
out double[] width, out double[] prec, out double[] nfa)
{
if (image == null)
{
throw new ArgumentNullException("nameof(image)");
}
image.ThrowIfDisposed();
using (var linesVec = new VectorOfVec4f())
using (var widthVec = new VectorOfDouble())
using (var precVec = new VectorOfDouble())
using (var nfaVec = new VectorOfDouble())
{
NativeMethods.imgproc_LineSegmentDetector_detect_vector(ptr, image.CvPtr,
linesVec.CvPtr, widthVec.CvPtr, precVec.CvPtr, nfaVec.CvPtr);
lines = linesVec.ToArray();
width = widthVec.ToArray();
prec = precVec.ToArray();
nfa = nfaVec.ToArray();
}
GC.KeepAlive(image);
}
/// <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 = 0,
Size?minSize = null,
Size?maxSize = null,
bool outputRejectLevels = false)
{
ThrowIfDisposed();
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_detectMultiScale2(
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>
/// 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="image">Input image CV_8UC1 or CV_8UC3 with a single letter</param>
/// <param name="classes">The classifier returns the character class categorical label, or list of class labels, to which the input image corresponds</param>
/// <param name="confidences">The classifier returns the probability of the input image corresponding to each classes in out_class</param>
public void Eval(InputArray image, out int[] classes, out double[] confidences)
{
if (image == null)
{
throw new ArgumentNullException("nameof(image)");
}
image.ThrowIfDisposed();
using (var vecClass = new VectorOfInt32())
using (var vecConfd = new VectorOfDouble())
{
NativeMethods.text_OCRHMMDecoder_ClassifierCallback_eval(ptr, image.CvPtr, vecClass.CvPtr, vecConfd.CvPtr);
classes = vecClass.ToArray();
confidences = vecConfd.ToArray();
}
GC.KeepAlive(image);
}
/// <summary>
/// Groups the object candidate rectangles.
/// </summary>
/// <param name="rectList"></param>
/// <param name="groupThreshold"></param>
/// <param name="eps"></param>
/// <param name="weights"></param>
/// <param name="levelWeights"></param>
public static void GroupRectangles(IList <Rect> rectList, int groupThreshold, double eps, out int[] weights, out double[] levelWeights)
{
if (rectList == null)
{
throw new ArgumentNullException(nameof(rectList));
}
using var rectListVec = new VectorOfRect(rectList);
using var weightsVec = new VectorOfInt32();
using var levelWeightsVec = new VectorOfDouble();
NativeMethods.HandleException(
NativeMethods.objdetect_groupRectangles3(
rectListVec.CvPtr, groupThreshold, eps, weightsVec.CvPtr, levelWeightsVec.CvPtr));
ClearAndAddRange(rectList, rectListVec.ToArray());
weights = weightsVec.ToArray();
levelWeights = levelWeightsVec.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 = 0,
Size?minSize = null,
Size?maxSize = null,
bool outputRejectLevels = false)
{
if (disposed)
{
throw new ObjectDisposedException("CascadeClassifier");
}
if (image == null)
{
throw new ArgumentNullException("nameof(image)");
}
_stopWatch.Start();
image.ThrowIfDisposed();
_stopWatch.Stop();
Debug.Log("FF_throw: " + _stopWatch.ElapsedMilliseconds + "ms");
_stopWatch.Reset();
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_detectMultiScale2(
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>
///
/// </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();
}
}
