C# InputOutputArray.ThrowIfDisposed方法代碼示例

        /// <summary>
        /// finds intrinsic and extrinsic parameters of a stereo camera
        /// </summary>
        /// <param name="objectPoints">Vector of vectors of the calibration pattern points.</param>
        /// <param name="imagePoints1">Vector of vectors of the projections of the calibration pattern points, observed by the first camera.</param>
        /// <param name="imagePoints2">Vector of vectors of the projections of the calibration pattern points, observed by the second camera.</param>
        /// <param name="cameraMatrix1">Input/output first camera matrix</param>
        /// <param name="distCoeffs1">Input/output vector of distortion coefficients (k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]]) of 4, 5, or 8 elements. 
        /// The output vector length depends on the flags.</param>
        /// <param name="cameraMatrix2"> Input/output second camera matrix. The parameter is similar to cameraMatrix1 .</param>
        /// <param name="distCoeffs2">Input/output lens distortion coefficients for the second camera. The parameter is similar to distCoeffs1 .</param>
        /// <param name="imageSize">Size of the image used only to initialize intrinsic camera matrix.</param>
        /// <param name="R">Output rotation matrix between the 1st and the 2nd camera coordinate systems.</param>
        /// <param name="T">Output translation vector between the coordinate systems of the cameras.</param>
        /// <param name="E">Output essential matrix.</param>
        /// <param name="F">Output fundamental matrix.</param>
        /// <param name="criteria">Termination criteria for the iterative optimization algorithm.</param>
        /// <param name="flags">Different flags that may be zero or a combination of the CalibrationFlag values</param>
        /// <returns></returns>
        public static double StereoCalibrate(IEnumerable<InputArray> objectPoints,
                                             IEnumerable<InputArray> imagePoints1,
                                             IEnumerable<InputArray> imagePoints2,
                                             InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1,
                                             InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2,
                                             Size imageSize, OutputArray R,
                                             OutputArray T, OutputArray E, OutputArray F,
                                             TermCriteria? criteria = null,
                                             CalibrationFlag flags = CalibrationFlag.FixIntrinsic)
        {
            if (objectPoints == null)
                throw new ArgumentNullException("objectPoints");
            if (imagePoints1 == null)
                throw new ArgumentNullException("imagePoints1");
            if (imagePoints2 == null)
                throw new ArgumentNullException("imagePoints2");
            if (cameraMatrix1 == null)
                throw new ArgumentNullException("cameraMatrix1");
            if (distCoeffs1 == null)
                throw new ArgumentNullException("distCoeffs1");
            if (cameraMatrix2 == null)
                throw new ArgumentNullException("cameraMatrix2");
            if (distCoeffs2 == null)
                throw new ArgumentNullException("distCoeffs2");
            cameraMatrix1.ThrowIfDisposed();
            distCoeffs1.ThrowIfDisposed();
            cameraMatrix2.ThrowIfDisposed();
            distCoeffs2.ThrowIfDisposed();
            cameraMatrix1.ThrowIfNotReady();
            cameraMatrix2.ThrowIfNotReady();
            distCoeffs1.ThrowIfNotReady();
            distCoeffs2.ThrowIfNotReady();

            IntPtr[] opPtrs = EnumerableEx.SelectPtrs(objectPoints);
            IntPtr[] ip1Ptrs = EnumerableEx.SelectPtrs(imagePoints1);
            IntPtr[] ip2Ptrs = EnumerableEx.SelectPtrs(imagePoints2);

            TermCriteria criteria0 = criteria.GetValueOrDefault(
                new TermCriteria(CriteriaType.Iteration | CriteriaType.Epsilon, 30, 1e-6));

            double result =
                NativeMethods.calib3d_stereoCalibrate_InputArray(
                    opPtrs, opPtrs.Length,
                    ip1Ptrs, ip1Ptrs.Length, ip2Ptrs, ip2Ptrs.Length,
                    cameraMatrix1.CvPtr, distCoeffs1.CvPtr,
                    cameraMatrix2.CvPtr, distCoeffs2.CvPtr,
                    imageSize, ToPtr(R), ToPtr(T), ToPtr(E), ToPtr(F),
                    criteria0, (int)flags
                    );

            cameraMatrix1.Fix();
            distCoeffs1.Fix();
            cameraMatrix2.Fix();
            distCoeffs2.Fix();
            if (R != null)
                R.Fix();
            if (T != null)
                T.Fix();
            if (E != null)
                E.Fix();
            if (F != null)
                F.Fix();

            return result;
        }

 /// <summary>
 /// clusters the input data using k-Means algorithm
 /// </summary>
 /// <param name="data"></param>
 /// <param name="k"></param>
 /// <param name="bestLabels"></param>
 /// <param name="criteria"></param>
 /// <param name="attempts"></param>
 /// <param name="flags"></param>
 /// <param name="centers"></param>
 /// <returns></returns>
 public static double Kmeans(InputArray data, int k, InputOutputArray bestLabels,
     TermCriteria criteria, int attempts, KMeansFlag flags, OutputArray centers = null)
 {
     if (data == null)
         throw new ArgumentNullException("data");
     if (bestLabels == null)
         throw new ArgumentNullException("bestLabels");
     data.ThrowIfDisposed();
     bestLabels.ThrowIfDisposed();
     double ret = NativeMethods.core_kmeans(data.CvPtr, k, bestLabels.CvPtr, criteria, attempts, (int)flags, ToPtr(centers));
     bestLabels.Fix();
     if(centers != null)
         centers.Fix();
     return ret;
 }