//
// C++: int meanShift(Mat probImage, Rect& window, TermCriteria criteria)
//
//javadoc: meanShift(probImage, window, criteria)
public static int meanShift(Mat probImage, Rect window, TermCriteria criteria)
{
if (probImage != null)
{
probImage.ThrowIfDisposed();
}
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
double[] window_out = new double[4];
int retVal = video_Video_meanShift_10(probImage.nativeObj, window.x, window.y, window.width, window.height, window_out, criteria.type, criteria.maxCount, criteria.epsilon);
if (window != null)
{
window.x = (int)window_out[0]; window.y = (int)window_out[1]; window.width = (int)window_out[2]; window.height = (int)window_out[3];
}
return(retVal);
#else
return(-1);
#endif
}
//
// C++: RotatedRect CamShift(Mat probImage, Rect& window, TermCriteria criteria)
//
//javadoc: CamShift(probImage, window, criteria)
public static RotatedRect CamShift(Mat probImage, Rect window, TermCriteria criteria)
{
if (probImage != null)
{
probImage.ThrowIfDisposed();
}
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS) && !UNITY_EDITOR) || UNITY_5
double[] window_out = new double[4];
double[] tmpArray = new double[5];
video_Video_CamShift_10(probImage.nativeObj, window.x, window.y, window.width, window.height, window_out, criteria.type, criteria.maxCount, criteria.epsilon, tmpArray);
RotatedRect retVal = new RotatedRect(tmpArray);
if (window != null)
{
window.x = (int)window_out[0]; window.y = (int)window_out[1]; window.width = (int)window_out[2]; window.height = (int)window_out[3];
}
return(retVal);
#else
return(null);
#endif
}
//
// C++: BOWKMeansTrainer(int clusterCount, TermCriteria termcrit = TermCriteria(), int attempts = 3, int flags = KMEANS_PP_CENTERS)
//
//javadoc: BOWKMeansTrainer::BOWKMeansTrainer(clusterCount, termcrit, attempts, flags)
public BOWKMeansTrainer(int clusterCount, TermCriteria termcrit, int attempts, int flags) :
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
base(features2d_BOWKMeansTrainer_BOWKMeansTrainer_10(clusterCount, termcrit.type, termcrit.maxCount, termcrit.epsilon, attempts, flags))
#else
base(IntPtr.Zero)
//
// C++: EM::EM(int nclusters = EM::DEFAULT_NCLUSTERS, int covMatType = EM::COV_MAT_DIAGONAL, TermCriteria termCrit = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, EM::DEFAULT_MAX_ITERS, FLT_EPSILON))
//
/**
* <p>The constructor of the class</p>
*
* @param nclusters The number of mixture components in the Gaussian mixture
* model. Default value of the parameter is <code>EM.DEFAULT_NCLUSTERS=5</code>.
* Some of EM implementation could determine the optimal number of mixtures
* within a specified value range, but that is not the case in ML yet.
* @param covMatType Constraint on covariance matrices which defines type of
* matrices. Possible values are:
* <ul>
* <li> EM.COV_MAT_SPHERICAL A scaled identity matrix <em>mu_k * I</em>.
* There is the only parameter <em>mu_k</em> to be estimated for each matrix.
* The option may be used in special cases, when the constraint is relevant, or
* as a first step in the optimization (for example in case when the data is
* preprocessed with PCA). The results of such preliminary estimation may be
* passed again to the optimization procedure, this time with <code>covMatType=EM.COV_MAT_DIAGONAL</code>.
* <li> EM.COV_MAT_DIAGONAL A diagonal matrix with positive diagonal
* elements. The number of free parameters is <code>d</code> for each matrix.
* This is most commonly used option yielding good estimation results.
* <li> EM.COV_MAT_GENERIC A symmetric positively defined matrix. The number
* of free parameters in each matrix is about <em>d^2/2</em>. It is not
* recommended to use this option, unless there is pretty accurate initial
* estimation of the parameters and/or a huge number of training samples.
* </ul>
* @param termCrit The termination criteria of the EM algorithm. The EM
* algorithm can be terminated by the number of iterations <code>termCrit.maxCount</code>
* (number of M-steps) or when relative change of likelihood logarithm is less
* than <code>termCrit.epsilon</code>. Default maximum number of iterations is
* <code>EM.DEFAULT_MAX_ITERS=100</code>.
*
* @see <a href="http://docs.opencv.org/modules/ml/doc/expectation_maximization.html#em-em">org.opencv.ml.EM.EM</a>
*/
public EM(int nclusters, int covMatType, TermCriteria termCrit)
:
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS) && !UNITY_EDITOR) || UNITY_5
base(ml_EM_EM_10(nclusters, covMatType, termCrit.type, termCrit.maxCount, termCrit.epsilon))
#else
base(IntPtr.Zero)
//javadoc: calibrate(objectPoints, imagePoints, size, K, xi, D, rvecs, tvecs, flags, criteria)
public static double calibrate(Mat objectPoints, Mat imagePoints, Size size, Mat K, Mat xi, Mat D, List <Mat> rvecs, List <Mat> tvecs, int flags, TermCriteria criteria)
{
if (objectPoints != null)
{
objectPoints.ThrowIfDisposed();
}
if (imagePoints != null)
{
imagePoints.ThrowIfDisposed();
}
if (K != null)
{
K.ThrowIfDisposed();
}
if (xi != null)
{
xi.ThrowIfDisposed();
}
if (D != null)
{
D.ThrowIfDisposed();
}
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS) && !UNITY_EDITOR) || UNITY_5
Mat rvecs_mat = new Mat();
Mat tvecs_mat = new Mat();
double retVal = ccalib_Ccalib_calibrate_11(objectPoints.nativeObj, imagePoints.nativeObj, size.width, size.height, K.nativeObj, xi.nativeObj, D.nativeObj, rvecs_mat.nativeObj, tvecs_mat.nativeObj, flags, criteria.type, criteria.maxCount, criteria.epsilon);
Converters.Mat_to_vector_Mat(rvecs_mat, rvecs);
rvecs_mat.release();
Converters.Mat_to_vector_Mat(tvecs_mat, tvecs);
tvecs_mat.release();
return(retVal);
#else
return(-1);
#endif
}
//javadoc: stereoCalibrate(objectPoints, imagePoints1, imagePoints2, imageSize1, imageSize2, K1, xi1, D1, K2, xi2, D2, rvec, tvec, rvecsL, tvecsL, flags, criteria)
public static double stereoCalibrate(List <Mat> objectPoints, List <Mat> imagePoints1, List <Mat> imagePoints2, Size imageSize1, Size imageSize2, Mat K1, Mat xi1, Mat D1, Mat K2, Mat xi2, Mat D2, Mat rvec, Mat tvec, List <Mat> rvecsL, List <Mat> tvecsL, int flags, TermCriteria criteria)
{
if (K1 != null)
{
K1.ThrowIfDisposed();
}
if (xi1 != null)
{
xi1.ThrowIfDisposed();
}
if (D1 != null)
{
D1.ThrowIfDisposed();
}
if (K2 != null)
{
K2.ThrowIfDisposed();
}
if (xi2 != null)
{
xi2.ThrowIfDisposed();
}
if (D2 != null)
{
D2.ThrowIfDisposed();
}
if (rvec != null)
{
rvec.ThrowIfDisposed();
}
if (tvec != null)
{
tvec.ThrowIfDisposed();
}
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS) && !UNITY_EDITOR) || UNITY_5
Mat objectPoints_mat = Converters.vector_Mat_to_Mat(objectPoints);
Mat imagePoints1_mat = Converters.vector_Mat_to_Mat(imagePoints1);
Mat imagePoints2_mat = Converters.vector_Mat_to_Mat(imagePoints2);
Mat rvecsL_mat = new Mat();
Mat tvecsL_mat = new Mat();
double retVal = ccalib_Ccalib_stereoCalibrate_11(objectPoints_mat.nativeObj, imagePoints1_mat.nativeObj, imagePoints2_mat.nativeObj, imageSize1.width, imageSize1.height, imageSize2.width, imageSize2.height, K1.nativeObj, xi1.nativeObj, D1.nativeObj, K2.nativeObj, xi2.nativeObj, D2.nativeObj, rvec.nativeObj, tvec.nativeObj, rvecsL_mat.nativeObj, tvecsL_mat.nativeObj, flags, criteria.type, criteria.maxCount, criteria.epsilon);
Converters.Mat_to_vector_Mat(objectPoints_mat, objectPoints);
objectPoints_mat.release();
Converters.Mat_to_vector_Mat(imagePoints1_mat, imagePoints1);
imagePoints1_mat.release();
Converters.Mat_to_vector_Mat(imagePoints2_mat, imagePoints2);
imagePoints2_mat.release();
Converters.Mat_to_vector_Mat(rvecsL_mat, rvecsL);
rvecsL_mat.release();
Converters.Mat_to_vector_Mat(tvecsL_mat, tvecsL);
tvecsL_mat.release();
return(retVal);
#else
return(-1);
#endif
}
//
// C++: void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, vector_Point2f prevPts, vector_Point2f& nextPts, vector_uchar& status, vector_float& err, Size winSize = Size(21,21), int maxLevel = 3, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), int flags = 0, double minEigThreshold = 1e-4)
//
//javadoc: calcOpticalFlowPyrLK(prevImg, nextImg, prevPts, nextPts, status, err, winSize, maxLevel, criteria, flags, minEigThreshold)
public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel, TermCriteria criteria, int flags, double minEigThreshold)
{
if (prevImg != null)
{
prevImg.ThrowIfDisposed();
}
if (nextImg != null)
{
nextImg.ThrowIfDisposed();
}
if (prevPts != null)
{
prevPts.ThrowIfDisposed();
}
if (nextPts != null)
{
nextPts.ThrowIfDisposed();
}
if (status != null)
{
status.ThrowIfDisposed();
}
if (err != null)
{
err.ThrowIfDisposed();
}
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat prevPts_mat = prevPts;
Mat nextPts_mat = nextPts;
Mat status_mat = status;
Mat err_mat = err;
video_Video_calcOpticalFlowPyrLK_10(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj, winSize.width, winSize.height, maxLevel, criteria.type, criteria.maxCount, criteria.epsilon, flags, minEigThreshold);
return;
#else
return;
#endif
}
//
// C++: double findTransformECC(Mat templateImage, Mat inputImage, Mat& warpMatrix, int motionType = MOTION_AFFINE, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 0.001), Mat inputMask = Mat())
//
//javadoc: findTransformECC(templateImage, inputImage, warpMatrix, motionType, criteria, inputMask)
public static double findTransformECC(Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType, TermCriteria criteria, Mat inputMask)
{
if (templateImage != null)
{
templateImage.ThrowIfDisposed();
}
if (inputImage != null)
{
inputImage.ThrowIfDisposed();
}
if (warpMatrix != null)
{
warpMatrix.ThrowIfDisposed();
}
if (inputMask != null)
{
inputMask.ThrowIfDisposed();
}
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
double retVal = video_Video_findTransformECC_10(templateImage.nativeObj, inputImage.nativeObj, warpMatrix.nativeObj, motionType, criteria.type, criteria.maxCount, criteria.epsilon, inputMask.nativeObj);
return(retVal);
#else
return(-1);
#endif
}