C# OpenCvSharp.CvTermCriteria类代码示例

 public CvLevMarq()
 {
     Mask = PrevParam = Param = J = Err = JtJ = JtJN = JtErr = JtJV = JtJW = null;
     LambdaLg10 = 0; 
     State = LevMarqState.Done;
     Criteria = new CvTermCriteria(0, 0);
     Iters = 0;
     CompleteSymmFlag = false;
 }

    // Use this for initialization
    void Start()
    {
        cascade = CvHaarClassifierCascade.FromFile(@"./Assets/haarcascade_frontalface_alt.xml");
        capture = Cv.CreateCameraCapture(0);
        Cv.SetCaptureProperty(capture, CaptureProperty.FrameWidth, CAPTURE_WIDTH);
        Cv.SetCaptureProperty(capture, CaptureProperty.FrameHeight, CAPTURE_HEIGHT);
        IplImage frame = Cv.QueryFrame(capture);
        Cv.NamedWindow("FaceDetect");

        CvSVM svm = new CvSVM ();
          	CvTermCriteria criteria = new CvTermCriteria (CriteriaType.Epsilon, 1000, double.Epsilon);
          	CvSVMParams param = new CvSVMParams (CvSVM.C_SVC, CvSVM.RBF, 10.0, 8.0, 1.0, 10.0, 0.5, 0.1, null, criteria);
    }

        void Awake()
        {
            _cap = new CvCapture(0);

            _capImage = _cap.QueryFrame();
            _capRgbImage = new IplImage(_capImage.Width, _capImage.Height, BitDepth.U8, 3);
            Debug.Log(string.Format("Capture info : size{0}", _capImage.Size));
               	_capGrayImage0 = new IplImage(_capImage.Size, BitDepth.U8, 1);
            _capGrayImage1 = new IplImage(_capImage.Size, BitDepth.U8, 1);
            _pyramidImage0 = new IplImage(new CvSize(_capImage.Width + 8, _capImage.Height/3), BitDepth.U8, 1);
            _pyramidImage1 = new IplImage(new CvSize(_capImage.Width + 8, _capImage.Height/3), BitDepth.U8, 1);
            _eigImage = new IplImage(_capImage.Size, BitDepth.F32, 1);
            _tmpImage = new IplImage(_capImage.Size, BitDepth.F32, 1);
            Cv.ConvertImage(_capImage, _capGrayImage0, 0);
            width = _capImage.Width;
            height = _capImage.Height;

            _opticalFlowWinSize = new CvSize(opticalFlowWinSize, opticalFlowWinSize);
            _opticalFlowCrit = new CvTermCriteria(CriteriaType.Iteration | CriteriaType.Epsilon, ofCritIterations, ofCritError);

            _prevTime = _currTime = UnityEngine.Time.time;
        }

        /// <summary>
        /// ベクトル集合を，与えられたクラスタ数に分割する.
        /// 入力サンプルを各クラスタに分類するために cluster_count 個のクラスタの中心を求める k-means 法を実装する．
        /// 出力 labels(i) は，配列 samples のi番目の行のサンプルが属するクラスタのインデックスを表す． 
        /// </summary>
        /// <param name="samples">浮動小数点型の入力サンプル行列．1行あたり一つのサンプル.</param>
        /// <param name="samplesType"></param>
        /// <param name="clusterCount">集合を分割するクラスタ数</param>
        /// <param name="labels">出力の整数ベクトル．すべてのサンプルについて，それぞれがどのクラスタに属しているかが保存されている.</param>
        /// <param name="termcrit">最大繰り返し数と(または)，精度（1ループでの各クラスタ中心位置移動距離）の指定</param>
        /// <param name="attemps"></param>
        /// <param name="rng"></param>
        /// <param name="flag"></param>
        /// <param name="centers"></param>
        /// <param name="compactness"></param>
        /// <returns></returns>
#else
        /// <summary>
        /// Splits set of vectors by given number of clusters
        /// </summary>
        /// <param name="samples">Floating-point matrix of input samples, one row per sample. </param>
        /// <param name="samplesType"></param>
        /// <param name="clusterCount">Number of clusters to split the set by. </param>
        /// <param name="labels">Output integer vector storing cluster indices for every sample. </param>
        /// <param name="termcrit">Specifies maximum number of iterations and/or accuracy (distance the centers move by between the subsequent iterations). </param>
        /// <param name="attemps"></param>
        /// <param name="rng"></param>
        /// <param name="flag"></param>
        /// <param name="centers"></param>
        /// <param name="compactness"></param>
        /// <returns></returns>
#endif
        public static int KMeans2(Array samples, MatrixType samplesType, int clusterCount, int[] labels, CvTermCriteria termcrit, int attemps, CvRNG rng, KMeansFlag flag, CvArr centers, out double compactness)
        {
            if (samples == null)
                throw new ArgumentNullException("samples");

            using (CvMat samplesMat = new CvMat(labels.Length, 1, samplesType, samples, false))
            using (CvMat labelsMat = new CvMat(labels.Length, 1, MatrixType.S32C1, labels, false))
            {
                return KMeans2(samplesMat, clusterCount, labelsMat, termcrit, attemps, rng, flag, centers, out compactness);
            }
        }

 public static extern int cvCamShift(IntPtr prob_image, CvRect window, CvTermCriteria criteria, IntPtr comp, ref CvBox2D box);

 public static extern void cvPyrMeanShiftFiltering(IntPtr src, IntPtr dst, double sp, double sr, int max_level, CvTermCriteria termcrit);

 public static extern int cvKMeans2(IntPtr samples, int cluster_count, IntPtr labels, CvTermCriteria termcrit,
     int attempts, ref UInt64 rng, [MarshalAs(UnmanagedType.I4)] KMeansFlag flags, IntPtr _centers, out double compactness);

 public static extern void cvSnakeImage(IntPtr image, IntPtr points, int length,
     [MarshalAs(UnmanagedType.LPArray)] float[] alpha, [MarshalAs(UnmanagedType.LPArray)] float[] beta, [MarshalAs(UnmanagedType.LPArray)] float[] gamma,
     int coeffUsage, CvSize win, CvTermCriteria criteria, [MarshalAs(UnmanagedType.Bool)] bool calc_gradient);

 public static extern void cvStereoCalibrate(IntPtr object_points, IntPtr image_points1, IntPtr image_points2, IntPtr npoints,
                        IntPtr camera_matrix1, IntPtr dist_coeffs1, IntPtr camera_matrix2, IntPtr dist_coeffs2,
                        CvSize image_size, IntPtr R, IntPtr T, IntPtr E, IntPtr F,
                        CvTermCriteria term_crit, CalibrationFlag flags);

        /// <summary>
        /// ベクトル集合を，与えられたクラスタ数に分割する.
        /// 入力サンプルを各クラスタに分類するために cluster_count 個のクラスタの中心を求める k-means 法を実装する．
        /// 出力 labels(i) は，配列 samples のi番目の行のサンプルが属するクラスタのインデックスを表す． 
        /// </summary>
        /// <param name="samples">浮動小数点型の入力サンプル行列．1行あたり一つのサンプル.</param>
        /// <param name="cluster_count">集合を分割するクラスタ数</param>
        /// <param name="labels">出力の整数ベクトル．すべてのサンプルについて，それぞれがどのクラスタに属しているかが保存されている.</param>
        /// <param name="termcrit">最大繰り返し数と(または)，精度（1ループでの各クラスタ中心位置移動距離）の指定</param>
        /// <param name="attemps"></param>
        /// <param name="rng"></param>
        /// <param name="flag"></param>
        /// <param name="_centers"></param>
        /// <param name="compactness"></param>
        /// <returns></returns>
#else
        /// <summary>
        /// Splits set of vectors by given number of clusters
        /// </summary>
        /// <param name="samples">Floating-point matrix of input samples, one row per sample. </param>
        /// <param name="cluster_count">Number of clusters to split the set by. </param>
        /// <param name="labels">Output integer vector storing cluster indices for every sample. </param>
        /// <param name="termcrit">Specifies maximum number of iterations and/or accuracy (distance the centers move by between the subsequent iterations). </param>
        /// <param name="attemps"></param>
        /// <param name="rng"></param>
        /// <param name="flag"></param>
        /// <param name="_centers"></param>
        /// <param name="compactness"></param>
        /// <returns></returns>
#endif
        public static int KMeans2(CvArr samples, int cluster_count, CvArr labels, CvTermCriteria termcrit, int attemps, CvRNG rng, KMeansFlag flag, CvArr _centers, out double compactness)
        {
            if (samples == null)
                throw new ArgumentNullException("samples");
            if (labels == null)
                throw new ArgumentNullException("labels");

            if (rng == null)
            {
                rng = new CvRNG();
            }
            IntPtr centersPtr = (_centers != null) ? _centers.CvPtr : IntPtr.Zero;

            UInt64 rngValue = rng.Seed;
            int result = CvInvoke.cvKMeans2(samples.CvPtr, cluster_count, labels.CvPtr, termcrit, attemps, ref rngValue, flag, centersPtr, out compactness);
            rng.Seed = rngValue;

            return result;
        }

 public static extern void cvCalcEigenObjects(int nObjects, CvCallback input, CvCallback output, [MarshalAs(UnmanagedType.I4)] EigenObjectsIOFlag ioFlags,
                  int ioBufSize, IntPtr userData, ref CvTermCriteria calcLimit, IntPtr avg, [In] float[] eigVals);

        /// <summary>
        /// ツリーから輪郭を復元する
        /// </summary>
        /// <param name="storage">復元した輪郭の出力先</param>
        /// <param name="criteria">復元を止める基準</param>
        /// <returns></returns>
#else
        /// <summary>
        /// Restores contour from tree.
        /// </summary>
        /// <param name="storage">Container for the reconstructed contour. </param>
        /// <param name="criteria">Criteria, where to stop reconstruction. </param>
        /// <returns></returns>
#endif
        public CvSeq ContourFromContourTree(CvMemStorage storage, CvTermCriteria criteria)
        {
            return Cv.ContourFromContourTree(this, storage, criteria);
        }

        /// <summary>
        /// Находит углы в изображении шахматной доски, установленном с помощью SetImage
        /// </summary>
        /// <param name="corners">Найденные углы</param>
        /// <returns>Показывает соответствуют-ли найленные углы шаблону CornersPattern</returns>
        public bool FindCorners(out CvPoint2D32f[] corners)
        {
            bool result;
            int numCorners;
            ChessboardFlag flags = ChessboardFlag.AdaptiveThresh | ChessboardFlag.NormalizeImage | ChessboardFlag.FilterQuads;
            CvTermCriteria criteria = new CvTermCriteria(CriteriaType.Iteration | CriteriaType.Epsilon, MaxIterations, Epsilon);

            // Находим углы
            result = chessBoard.FindChessboardCorners(CornersPattern, out corners, out numCorners, flags);

            // Уточнаем положение углов
            chessBoard.CvtColor(grayChessBoard, ColorConversion.RgbToGray);
            grayChessBoard.FindCornerSubPix(corners, corners.Length, new CvSize(11, 11), new CvSize(-1, -1), criteria);

            return result;
        }

        /// <summary>
        /// コーナー位置を高精度化する.
        /// </summary>
        /// <param name="image">入力画像</param>
        /// <param name="corners">コーナーの初期座標が入力され，高精度化された座標が出力される.</param>
        /// <param name="count">コーナーの数</param>
        /// <param name="win">検索ウィンドウの半分のサイズ．（例）win=(5,5) ならば 5*2+1 × 5*2+1 = 11 × 11 が探索ウィンドウして使われる.</param>
        /// <param name="zeroZone">総和を計算する際に含まれない，探索領域の中心に存在する総和対象外領域の半分のサイズ．この値は，自己相関行列において発生しうる特異点を避けるために用いられる． 値が (-1,-1) の場合は，そのようなサイズはないということを意味する</param>
        /// <param name="criteria">コーナー座標の高精度化のための繰り返し処理の終了条件．コーナー位置の高精度化の繰り返し処理は，規定回数に達するか，目標精度に達したときに終了する．</param>
#else
        /// <summary>
        /// Iterates to find the sub-pixel accurate location of corners, or radial saddle points.
        /// </summary>
        /// <param name="image">Input image. </param>
        /// <param name="corners">Initial coordinates of the input corners and refined coordinates on output. </param>
        /// <param name="count">Number of corners. </param>
        /// <param name="win">Half sizes of the search window.</param>
        /// <param name="zeroZone">Half size of the dead region in the middle of the search zone over which the summation in formulae below is not done. It is used sometimes to avoid possible singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such size. </param>
        /// <param name="criteria">Criteria for termination of the iterative process of corner refinement. That is, the process of corner position refinement stops either after certain number of iteration or when a required accuracy is achieved. The criteria may specify either of or both the maximum number of iteration and the required accuracy. </param>
#endif
        public static void FindCornerSubPix(CvArr image, CvPoint2D32f[] corners, int count, CvSize win, CvSize zeroZone, CvTermCriteria criteria)
        {
            if (image == null)
                throw new ArgumentNullException("image");
            if (corners == null)
                throw new ArgumentNullException("corners");

            NativeMethods.cvFindCornerSubPix(image.CvPtr, corners, count, win, zeroZone, criteria);
        }

 public static extern int cvMeanShift(IntPtr prob_image, CvRect window, CvTermCriteria criteria, IntPtr comp);