/// <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> /// Returns a list of the leading edge ID connected to each triangle. /// The function gives one edge ID for each triangle. /// </summary> /// <returns>Output vector.</returns> public int[] GetLeadingEdgeList() { ThrowIfDisposed(); using var leadingEdgeList = new VectorOfInt32(); NativeMethods.HandleException( NativeMethods.imgproc_Subdiv2D_getLeadingEdgeList(ptr, leadingEdgeList.CvPtr)); GC.KeepAlive(this); return(leadingEdgeList.ToArray()); }
/// <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> /// 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()); } }