//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.video;
import java.util.ArrayList;
import java.util.List;
import org.opencv.core.Mat;
import org.opencv.core.MatOfByte;
import org.opencv.core.MatOfFloat;
import org.opencv.core.MatOfPoint2f;
import org.opencv.core.Rect;
import org.opencv.core.RotatedRect;
import org.opencv.core.Size;
import org.opencv.core.TermCriteria;
import org.opencv.utils.Converters;
import org.opencv.video.BackgroundSubtractorKNN;
import org.opencv.video.BackgroundSubtractorMOG2;
// C++: class Video
public class Video {
private static final int
CV_LKFLOW_INITIAL_GUESSES = 4,
CV_LKFLOW_GET_MIN_EIGENVALS = 8;
// C++: enum <unnamed>
public static final int
OPTFLOW_USE_INITIAL_FLOW = 4,
OPTFLOW_LK_GET_MIN_EIGENVALS = 8,
OPTFLOW_FARNEBACK_GAUSSIAN = 256,
MOTION_TRANSLATION = 0,
MOTION_EUCLIDEAN = 1,
MOTION_AFFINE = 2,
MOTION_HOMOGRAPHY = 3;
// C++: enum MODE (cv.detail.TrackerSamplerCSC.MODE)
public static final int
TrackerSamplerCSC_MODE_INIT_POS = 1,
TrackerSamplerCSC_MODE_INIT_NEG = 2,
TrackerSamplerCSC_MODE_TRACK_POS = 3,
TrackerSamplerCSC_MODE_TRACK_NEG = 4,
TrackerSamplerCSC_MODE_DETECT = 5;
//
// C++: Ptr_BackgroundSubtractorMOG2 cv::createBackgroundSubtractorMOG2(int history = 500, double varThreshold = 16, bool detectShadows = true)
//
/**
* Creates MOG2 Background Subtractor
*
* @param history Length of the history.
* @param varThreshold Threshold on the squared Mahalanobis distance between the pixel and the model
* to decide whether a pixel is well described by the background model. This parameter does not
* affect the background update.
* @param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the
* speed a bit, so if you do not need this feature, set the parameter to false.
* @return automatically generated
*/
public static BackgroundSubtractorMOG2 createBackgroundSubtractorMOG2(int history, double varThreshold, boolean detectShadows) {
return BackgroundSubtractorMOG2.__fromPtr__(createBackgroundSubtractorMOG2_0(history, varThreshold, detectShadows));
}
/**
* Creates MOG2 Background Subtractor
*
* @param history Length of the history.
* @param varThreshold Threshold on the squared Mahalanobis distance between the pixel and the model
* to decide whether a pixel is well described by the background model. This parameter does not
* affect the background update.
* speed a bit, so if you do not need this feature, set the parameter to false.
* @return automatically generated
*/
public static BackgroundSubtractorMOG2 createBackgroundSubtractorMOG2(int history, double varThreshold) {
return BackgroundSubtractorMOG2.__fromPtr__(createBackgroundSubtractorMOG2_1(history, varThreshold));
}
/**
* Creates MOG2 Background Subtractor
*
* @param history Length of the history.
* to decide whether a pixel is well described by the background model. This parameter does not
* affect the background update.
* speed a bit, so if you do not need this feature, set the parameter to false.
* @return automatically generated
*/
public static BackgroundSubtractorMOG2 createBackgroundSubtractorMOG2(int history) {
return BackgroundSubtractorMOG2.__fromPtr__(createBackgroundSubtractorMOG2_2(history));
}
/**
* Creates MOG2 Background Subtractor
*
* to decide whether a pixel is well described by the background model. This parameter does not
* affect the background update.
* speed a bit, so if you do not need this feature, set the parameter to false.
* @return automatically generated
*/
public static BackgroundSubtractorMOG2 createBackgroundSubtractorMOG2() {
return BackgroundSubtractorMOG2.__fromPtr__(createBackgroundSubtractorMOG2_3());
}
//
// C++: Ptr_BackgroundSubtractorKNN cv::createBackgroundSubtractorKNN(int history = 500, double dist2Threshold = 400.0, bool detectShadows = true)
//
/**
* Creates KNN Background Subtractor
*
* @param history Length of the history.
* @param dist2Threshold Threshold on the squared distance between the pixel and the sample to decide
* whether a pixel is close to that sample. This parameter does not affect the background update.
* @param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the
* speed a bit, so if you do not need this feature, set the parameter to false.
* @return automatically generated
*/
public static BackgroundSubtractorKNN createBackgroundSubtractorKNN(int history, double dist2Threshold, boolean detectShadows) {
return BackgroundSubtractorKNN.__fromPtr__(createBackgroundSubtractorKNN_0(history, dist2Threshold, detectShadows));
}
/**
* Creates KNN Background Subtractor
*
* @param history Length of the history.
* @param dist2Threshold Threshold on the squared distance between the pixel and the sample to decide
* whether a pixel is close to that sample. This parameter does not affect the background update.
* speed a bit, so if you do not need this feature, set the parameter to false.
* @return automatically generated
*/
public static BackgroundSubtractorKNN createBackgroundSubtractorKNN(int history, double dist2Threshold) {
return BackgroundSubtractorKNN.__fromPtr__(createBackgroundSubtractorKNN_1(history, dist2Threshold));
}
/**
* Creates KNN Background Subtractor
*
* @param history Length of the history.
* whether a pixel is close to that sample. This parameter does not affect the background update.
* speed a bit, so if you do not need this feature, set the parameter to false.
* @return automatically generated
*/
public static BackgroundSubtractorKNN createBackgroundSubtractorKNN(int history) {
return BackgroundSubtractorKNN.__fromPtr__(createBackgroundSubtractorKNN_2(history));
}
/**
* Creates KNN Background Subtractor
*
* whether a pixel is close to that sample. This parameter does not affect the background update.
* speed a bit, so if you do not need this feature, set the parameter to false.
* @return automatically generated
*/
public static BackgroundSubtractorKNN createBackgroundSubtractorKNN() {
return BackgroundSubtractorKNN.__fromPtr__(createBackgroundSubtractorKNN_3());
}
//
// C++: RotatedRect cv::CamShift(Mat probImage, Rect& window, TermCriteria criteria)
//
/**
* Finds an object center, size, and orientation.
*
* @param probImage Back projection of the object histogram. See calcBackProject.
* @param window Initial search window.
* @param criteria Stop criteria for the underlying meanShift.
* returns
* (in old interfaces) Number of iterations CAMSHIFT took to converge
* The function implements the CAMSHIFT object tracking algorithm CITE: Bradski98 . First, it finds an
* object center using meanShift and then adjusts the window size and finds the optimal rotation. The
* function returns the rotated rectangle structure that includes the object position, size, and
* orientation. The next position of the search window can be obtained with RotatedRect::boundingRect()
*
* See the OpenCV sample camshiftdemo.c that tracks colored objects.
*
* <b>Note:</b>
* <ul>
* <li>
* (Python) A sample explaining the camshift tracking algorithm can be found at
* opencv_source_code/samples/python/camshift.py
* </li>
* </ul>
* @return automatically generated
*/
public static RotatedRect CamShift(Mat probImage, Rect window, TermCriteria criteria) {
double[] window_out = new double[4];
RotatedRect retVal = new RotatedRect(CamShift_0(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;
}
//
// C++: int cv::meanShift(Mat probImage, Rect& window, TermCriteria criteria)
//
/**
* Finds an object on a back projection image.
*
* @param probImage Back projection of the object histogram. See calcBackProject for details.
* @param window Initial search window.
* @param criteria Stop criteria for the iterative search algorithm.
* returns
* : Number of iterations CAMSHIFT took to converge.
* The function implements the iterative object search algorithm. It takes the input back projection of
* an object and the initial position. The mass center in window of the back projection image is
* computed and the search window center shifts to the mass center. The procedure is repeated until the
* specified number of iterations criteria.maxCount is done or until the window center shifts by less
* than criteria.epsilon. The algorithm is used inside CamShift and, unlike CamShift , the search
* window size or orientation do not change during the search. You can simply pass the output of
* calcBackProject to this function. But better results can be obtained if you pre-filter the back
* projection and remove the noise. For example, you can do this by retrieving connected components
* with findContours , throwing away contours with small area ( contourArea ), and rendering the
* remaining contours with drawContours.
* @return automatically generated
*/
public static int meanShift(Mat probImage, Rect window, TermCriteria criteria) {
double[] window_out = new double[4];
int retVal = meanShift_0(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;
}
//
// C++: int cv::buildOpticalFlowPyramid(Mat img, vector_Mat& pyramid, Size winSize, int maxLevel, bool withDerivatives = true, int pyrBorder = BORDER_REFLECT_101, int derivBorder = BORDER_CONSTANT, bool tryReuseInputImage = true)
//
/**
* Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
*
* @param img 8-bit input image.
* @param pyramid output pyramid.
* @param winSize window size of optical flow algorithm. Must be not less than winSize argument of
* calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels.
* @param maxLevel 0-based maximal pyramid level number.
* @param withDerivatives set to precompute gradients for the every pyramid level. If pyramid is
* constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.
* @param pyrBorder the border mode for pyramid layers.
* @param derivBorder the border mode for gradients.
* @param tryReuseInputImage put ROI of input image into the pyramid if possible. You can pass false
* to force data copying.
* @return number of levels in constructed pyramid. Can be less than maxLevel.
*/
public static int buildOpticalFlowPyramid(Mat img, List<Mat> pyramid, Size winSize, int maxLevel, boolean withDerivatives, int pyrBorder, int derivBorder, boolean tryReuseInputImage) {
Mat pyramid_mat = new Mat();
int retVal = buildOpticalFlowPyramid_0(img.nativeObj, pyramid_mat.nativeObj, winSize.width, winSize.height, maxLevel, withDerivatives, pyrBorder, derivBorder, tryReuseInputImage);
Converters.Mat_to_vector_Mat(pyramid_mat, pyramid);
pyramid_mat.release();
return retVal;
}
/**
* Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
*
* @param img 8-bit input image.
* @param pyramid output pyramid.
* @param winSize window size of optical flow algorithm. Must be not less than winSize argument of
* calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels.
* @param maxLevel 0-based maximal pyramid level number.
* @param withDerivatives set to precompute gradients for the every pyramid level. If pyramid is
* constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.
* @param pyrBorder the border mode for pyramid layers.
* @param derivBorder the border mode for gradients.
* to force data copying.
* @return number of levels in constructed pyramid. Can be less than maxLevel.
*/
public static int buildOpticalFlowPyramid(Mat img, List<Mat> pyramid, Size winSize, int maxLevel, boolean withDerivatives, int pyrBorder, int derivBorder) {
Mat pyramid_mat = new Mat();
int retVal = buildOpticalFlowPyramid_1(img.nativeObj, pyramid_mat.nativeObj, winSize.width, winSize.height, maxLevel, withDerivatives, pyrBorder, derivBorder);
Converters.Mat_to_vector_Mat(pyramid_mat, pyramid);
pyramid_mat.release();
return retVal;
}
/**
* Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
*
* @param img 8-bit input image.
* @param pyramid output pyramid.
* @param winSize window size of optical flow algorithm. Must be not less than winSize argument of
* calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels.
* @param maxLevel 0-based maximal pyramid level number.
* @param withDerivatives set to precompute gradients for the every pyramid level. If pyramid is
* constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.
* @param pyrBorder the border mode for pyramid layers.
* to force data copying.
* @return number of levels in constructed pyramid. Can be less than maxLevel.
*/
public static int buildOpticalFlowPyramid(Mat img, List<Mat> pyramid, Size winSize, int maxLevel, boolean withDerivatives, int pyrBorder) {
Mat pyramid_mat = new Mat();
int retVal = buildOpticalFlowPyramid_2(img.nativeObj, pyramid_mat.nativeObj, winSize.width, winSize.height, maxLevel, withDerivatives, pyrBorder);
Converters.Mat_to_vector_Mat(pyramid_mat, pyramid);
pyramid_mat.release();
return retVal;
}
/**
* Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
*
* @param img 8-bit input image.
* @param pyramid output pyramid.
* @param winSize window size of optical flow algorithm. Must be not less than winSize argument of
* calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels.
* @param maxLevel 0-based maximal pyramid level number.
* @param withDerivatives set to precompute gradients for the every pyramid level. If pyramid is
* constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.
* to force data copying.
* @return number of levels in constructed pyramid. Can be less than maxLevel.
*/
public static int buildOpticalFlowPyramid(Mat img, List<Mat> pyramid, Size winSize, int maxLevel, boolean withDerivatives) {
Mat pyramid_mat = new Mat();
int retVal = buildOpticalFlowPyramid_3(img.nativeObj, pyramid_mat.nativeObj, winSize.width, winSize.height, maxLevel, withDerivatives);
Converters.Mat_to_vector_Mat(pyramid_mat, pyramid);
pyramid_mat.release();
return retVal;
}
/**
* Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
*
* @param img 8-bit input image.
* @param pyramid output pyramid.
* @param winSize window size of optical flow algorithm. Must be not less than winSize argument of
* calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels.
* @param maxLevel 0-based maximal pyramid level number.
* constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.
* to force data copying.
* @return number of levels in constructed pyramid. Can be less than maxLevel.
*/
public static int buildOpticalFlowPyramid(Mat img, List<Mat> pyramid, Size winSize, int maxLevel) {
Mat pyramid_mat = new Mat();
int retVal = buildOpticalFlowPyramid_4(img.nativeObj, pyramid_mat.nativeObj, winSize.width, winSize.height, maxLevel);
Converters.Mat_to_vector_Mat(pyramid_mat, pyramid);
pyramid_mat.release();
return retVal;
}
//
// C++: void cv::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)
//
/**
* Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with
* pyramids.
*
* @param prevImg first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
* @param nextImg second input image or pyramid of the same size and the same type as prevImg.
* @param prevPts vector of 2D points for which the flow needs to be found; point coordinates must be
* single-precision floating-point numbers.
* @param nextPts output vector of 2D points (with single-precision floating-point coordinates)
* containing the calculated new positions of input features in the second image; when
* OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input.
* @param status output status vector (of unsigned chars); each element of the vector is set to 1 if
* the flow for the corresponding features has been found, otherwise, it is set to 0.
* @param err output vector of errors; each element of the vector is set to an error for the
* corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't
* found then the error is not defined (use the status parameter to find such cases).
* @param winSize size of the search window at each pyramid level.
* @param maxLevel 0-based maximal pyramid level number; if set to 0, pyramids are not used (single
* level), if set to 1, two levels are used, and so on; if pyramids are passed to input then
* algorithm will use as many levels as pyramids have but no more than maxLevel.
* @param criteria parameter, specifying the termination criteria of the iterative search algorithm
* (after the specified maximum number of iterations criteria.maxCount or when the search window
* moves by less than criteria.epsilon.
* @param flags operation flags:
* <ul>
* <li>
* <b>OPTFLOW_USE_INITIAL_FLOW</b> uses initial estimations, stored in nextPts; if the flag is
* not set, then prevPts is copied to nextPts and is considered the initial estimate.
* </li>
* <li>
* <b>OPTFLOW_LK_GET_MIN_EIGENVALS</b> use minimum eigen values as an error measure (see
* minEigThreshold description); if the flag is not set, then L1 distance between patches
* around the original and a moved point, divided by number of pixels in a window, is used as a
* error measure.
* </li>
* </ul>
* @param minEigThreshold the algorithm calculates the minimum eigen value of a 2x2 normal matrix of
* optical flow equations (this matrix is called a spatial gradient matrix in CITE: Bouguet00), divided
* by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding
* feature is filtered out and its flow is not processed, so it allows to remove bad points and get a
* performance boost.
*
* The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See
* CITE: Bouguet00 . The function is parallelized with the TBB library.
*
* <b>Note:</b> Some examples:
*
* <ul>
* <li>
* An example using the Lucas-Kanade optical flow algorithm can be found at
* opencv_source_code/samples/cpp/lkdemo.cpp
* </li>
* <li>
* (Python) An example using the Lucas-Kanade optical flow algorithm can be found at
* opencv_source_code/samples/python/lk_track.py
* </li>
* <li>
* (Python) An example using the Lucas-Kanade tracker for homography matching can be found at
* opencv_source_code/samples/python/lk_homography.py
* </li>
* </ul>
*/
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) {
Mat prevPts_mat = prevPts;
Mat nextPts_mat = nextPts;
Mat status_mat = status;
Mat err_mat = err;
calcOpticalFlowPyrLK_0(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);
}
/**
* Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with
* pyramids.
*
* @param prevImg first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
* @param nextImg second input image or pyramid of the same size and the same type as prevImg.
* @param prevPts vector of 2D points for which the flow needs to be found; point coordinates must be
* single-precision floating-point numbers.
* @param nextPts output vector of 2D points (with single-precision floating-point coordinates)
* containing the calculated new positions of input features in the second image; when
* OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input.
* @param status output status vector (of unsigned chars); each element of the vector is set to 1 if
* the flow for the corresponding features has been found, otherwise, it is set to 0.
* @param err output vector of errors; each element of the vector is set to an error for the
* corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't
* found then the error is not defined (use the status parameter to find such cases).
* @param winSize size of the search window at each pyramid level.
* @param maxLevel 0-based maximal pyramid level number; if set to 0, pyramids are not used (single
* level), if set to 1, two levels are used, and so on; if pyramids are passed to input then
* algorithm will use as many levels as pyramids have but no more than maxLevel.
* @param criteria parameter, specifying the termination criteria of the iterative search algorithm
* (after the specified maximum number of iterations criteria.maxCount or when the search window
* moves by less than criteria.epsilon.
* @param flags operation flags:
* <ul>
* <li>
* <b>OPTFLOW_USE_INITIAL_FLOW</b> uses initial estimations, stored in nextPts; if the flag is
* not set, then prevPts is copied to nextPts and is considered the initial estimate.
* </li>
* <li>
* <b>OPTFLOW_LK_GET_MIN_EIGENVALS</b> use minimum eigen values as an error measure (see
* minEigThreshold description); if the flag is not set, then L1 distance between patches
* around the original and a moved point, divided by number of pixels in a window, is used as a
* error measure.
* </li>
* </ul>
* optical flow equations (this matrix is called a spatial gradient matrix in CITE: Bouguet00), divided
* by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding
* feature is filtered out and its flow is not processed, so it allows to remove bad points and get a
* performance boost.
*
* The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See
* CITE: Bouguet00 . The function is parallelized with the TBB library.
*
* <b>Note:</b> Some examples:
*
* <ul>
* <li>
* An example using the Lucas-Kanade optical flow algorithm can be found at
* opencv_source_code/samples/cpp/lkdemo.cpp
* </li>
* <li>
* (Python) An example using the Lucas-Kanade optical flow algorithm can be found at
* opencv_source_code/samples/python/lk_track.py
* </li>
* <li>
* (Python) An example using the Lucas-Kanade tracker for homography matching can be found at
* opencv_source_code/samples/python/lk_homography.py
* </li>
* </ul>
*/
public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel, TermCriteria criteria, int flags) {
Mat prevPts_mat = prevPts;
Mat nextPts_mat = nextPts;
Mat status_mat = status;
Mat err_mat = err;
calcOpticalFlowPyrLK_1(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);
}
/**
* Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with
* pyramids.
*
* @param prevImg first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
* @param nextImg second input image or pyramid of the same size and the same type as prevImg.
* @param prevPts vector of 2D points for which the flow needs to be found; point coordinates must be
* single-precision floating-point numbers.
* @param nextPts output vector of 2D points (with single-precision floating-point coordinates)
* containing the calculated new positions of input features in the second image; when
* OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input.
* @param status output status vector (of unsigned chars); each element of the vector is set to 1 if
* the flow for the corresponding features has been found, otherwise, it is set to 0.
* @param err output vector of errors; each element of the vector is set to an error for the
* corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't
* found then the error is not defined (use the status parameter to find such cases).
* @param winSize size of the search window at each pyramid level.
* @param maxLevel 0-based maximal pyramid level number; if set to 0, pyramids are not used (single
* level), if set to 1, two levels are used, and so on; if pyramids are passed to input then
* algorithm will use as many levels as pyramids have but no more than maxLevel.
* @param criteria parameter, specifying the termination criteria of the iterative search algorithm
* (after the specified maximum number of iterations criteria.maxCount or when the search window
* moves by less than criteria.epsilon.
* <ul>
* <li>
* <b>OPTFLOW_USE_INITIAL_FLOW</b> uses initial estimations, stored in nextPts; if the flag is
* not set, then prevPts is copied to nextPts and is considered the initial estimate.
* </li>
* <li>
* <b>OPTFLOW_LK_GET_MIN_EIGENVALS</b> use minimum eigen values as an error measure (see
* minEigThreshold description); if the flag is not set, then L1 distance between patches
* around the original and a moved point, divided by number of pixels in a window, is used as a
* error measure.
* </li>
* </ul>
* optical flow equations (this matrix is called a spatial gradient matrix in CITE: Bouguet00), divided
* by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding
* feature is filtered out and its flow is not processed, so it allows to remove bad points and get a
* performance boost.
*
* The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See
* CITE: Bouguet00 . The function is parallelized with the TBB library.
*
* <b>Note:</b> Some examples:
*
* <ul>
* <li>
* An example using the Lucas-Kanade optical flow algorithm can be found at
* opencv_source_code/samples/cpp/lkdemo.cpp
* </li>
* <li>
* (Python) An example using the Lucas-Kanade optical flow algorithm can be found at
* opencv_source_code/samples/python/lk_track.py
* </li>
* <li>
* (Python) An example using the Lucas-Kanade tracker for homography matching can be found at
* opencv_source_code/samples/python/lk_homography.py
* </li>
* </ul>
*/
public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel, TermCriteria criteria) {
Mat prevPts_mat = prevPts;
Mat nextPts_mat = nextPts;
Mat status_mat = status;
Mat err_mat = err;
calcOpticalFlowPyrLK_2(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);
}
/**
* Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with
* pyramids.
*
* @param prevImg first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
* @param nextImg second input image or pyramid of the same size and the same type as prevImg.
* @param prevPts vector of 2D points for which the flow needs to be found; point coordinates must be
* single-precision floating-point numbers.
* @param nextPts output vector of 2D points (with single-precision floating-point coordinates)
* containing the calculated new positions of input features in the second image; when
* OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input.
* @param status output status vector (of unsigned chars); each element of the vector is set to 1 if
* the flow for the corresponding features has been found, otherwise, it is set to 0.
* @param err output vector of errors; each element of the vector is set to an error for the
* corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't
* found then the error is not defined (use the status parameter to find such cases).
* @param winSize size of the search window at each pyramid level.
* @param maxLevel 0-based maximal pyramid level number; if set to 0, pyramids are not used (single
* level), if set to 1, two levels are used, and so on; if pyramids are passed to input then
* algorithm will use as many levels as pyramids have but no more than maxLevel.
* (after the specified maximum number of iterations criteria.maxCount or when the search window
* moves by less than criteria.epsilon.
* <ul>
* <li>
* <b>OPTFLOW_USE_INITIAL_FLOW</b> uses initial estimations, stored in nextPts; if the flag is
* not set, then prevPts is copied to nextPts and is considered the initial estimate.
* </li>
* <li>
* <b>OPTFLOW_LK_GET_MIN_EIGENVALS</b> use minimum eigen values as an error measure (see
* minEigThreshold description); if the flag is not set, then L1 distance between patches
* around the original and a moved point, divided by number of pixels in a window, is used as a
* error measure.
* </li>
* </ul>
* optical flow equations (this matrix is called a spatial gradient matrix in CITE: Bouguet00), divided
* by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding
* feature is filtered out and its flow is not processed, so it allows to remove bad points and get a
* performance boost.
*
* The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See
* CITE: Bouguet00 . The function is parallelized with the TBB library.
*
* <b>Note:</b> Some examples:
*
* <ul>
* <li>
* An example using the Lucas-Kanade optical flow algorithm can be found at
* opencv_source_code/samples/cpp/lkdemo.cpp
* </li>
* <li>
* (Python) An example using the Lucas-Kanade optical flow algorithm can be found at
* opencv_source_code/samples/python/lk_track.py
* </li>
* <li>
* (Python) An example using the Lucas-Kanade tracker for homography matching can be found at
* opencv_source_code/samples/python/lk_homography.py
* </li>
* </ul>
*/
public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel) {
Mat prevPts_mat = prevPts;
Mat nextPts_mat = nextPts;
Mat status_mat = status;
Mat err_mat = err;
calcOpticalFlowPyrLK_3(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj, winSize.width, winSize.height, maxLevel);
}
/**
* Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with
* pyramids.
*
* @param prevImg first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
* @param nextImg second input image or pyramid of the same size and the same type as prevImg.
* @param prevPts vector of 2D points for which the flow needs to be found; point coordinates must be
* single-precision floating-point numbers.
* @param nextPts output vector of 2D points (with single-precision floating-point coordinates)
* containing the calculated new positions of input features in the second image; when
* OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input.
* @param status output status vector (of unsigned chars); each element of the vector is set to 1 if
* the flow for the corresponding features has been found, otherwise, it is set to 0.
* @param err output vector of errors; each element of the vector is set to an error for the
* corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't
* found then the error is not defined (use the status parameter to find such cases).
* @param winSize size of the search window at each pyramid level.
* level), if set to 1, two levels are used, and so on; if pyramids are passed to input then
* algorithm will use as many levels as pyramids have but no more than maxLevel.
* (after the specified maximum number of iterations criteria.maxCount or when the search window
* moves by less than criteria.epsilon.
* <ul>
* <li>
* <b>OPTFLOW_USE_INITIAL_FLOW</b> uses initial estimations, stored in nextPts; if the flag is
* not set, then prevPts is copied to nextPts and is considered the initial estimate.
* </li>
* <li>
* <b>OPTFLOW_LK_GET_MIN_EIGENVALS</b> use minimum eigen values as an error measure (see
* minEigThreshold description); if the flag is not set, then L1 distance between patches
* around the original and a moved point, divided by number of pixels in a window, is used as a
* error measure.
* </li>
* </ul>
* optical flow equations (this matrix is called a spatial gradient matrix in CITE: Bouguet00), divided
* by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding
* feature is filtered out and its flow is not processed, so it allows to remove bad points and get a
* performance boost.
*
* The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See
* CITE: Bouguet00 . The function is parallelized with the TBB library.
*
* <b>Note:</b> Some examples:
*
* <ul>
* <li>
* An example using the Lucas-Kanade optical flow algorithm can be found at
* opencv_source_code/samples/cpp/lkdemo.cpp
* </li>
* <li>
* (Python) An example using the Lucas-Kanade optical flow algorithm can be found at
* opencv_source_code/samples/python/lk_track.py
* </li>
* <li>
* (Python) An example using the Lucas-Kanade tracker for homography matching can be found at
* opencv_source_code/samples/python/lk_homography.py
* </li>
* </ul>
*/
public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize) {
Mat prevPts_mat = prevPts;
Mat nextPts_mat = nextPts;
Mat status_mat = status;
Mat err_mat = err;
calcOpticalFlowPyrLK_4(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj, winSize.width, winSize.height);
}
/**
* Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with
* pyramids.
*
* @param prevImg first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
* @param nextImg second input image or pyramid of the same size and the same type as prevImg.
* @param prevPts vector of 2D points for which the flow needs to be found; point coordinates must be
* single-precision floating-point numbers.
* @param nextPts output vector of 2D points (with single-precision floating-point coordinates)
* containing the calculated new positions of input features in the second image; when
* OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input.
* @param status output status vector (of unsigned chars); each element of the vector is set to 1 if
* the flow for the corresponding features has been found, otherwise, it is set to 0.
* @param err output vector of errors; each element of the vector is set to an error for the
* corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't
* found then the error is not defined (use the status parameter to find such cases).
* level), if set to 1, two levels are used, and so on; if pyramids are passed to input then
* algorithm will use as many levels as pyramids have but no more than maxLevel.
* (after the specified maximum number of iterations criteria.maxCount or when the search window
* moves by less than criteria.epsilon.
* <ul>
* <li>
* <b>OPTFLOW_USE_INITIAL_FLOW</b> uses initial estimations, stored in nextPts; if the flag is
* not set, then prevPts is copied to nextPts and is considered the initial estimate.
* </li>
* <li>
* <b>OPTFLOW_LK_GET_MIN_EIGENVALS</b> use minimum eigen values as an error measure (see
* minEigThreshold description); if the flag is not set, then L1 distance between patches
* around the original and a moved point, divided by number of pixels in a window, is used as a
* error measure.
* </li>
* </ul>
* optical flow equations (this matrix is called a spatial gradient matrix in CITE: Bouguet00), divided
* by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding
* feature is filtered out and its flow is not processed, so it allows to remove bad points and get a
* performance boost.
*
* The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See
* CITE: Bouguet00 . The function is parallelized with the TBB library.
*
* <b>Note:</b> Some examples:
*
* <ul>
* <li>
* An example using the Lucas-Kanade optical flow algorithm can be found at
* opencv_source_code/samples/cpp/lkdemo.cpp
* </li>
* <li>
* (Python) An example using the Lucas-Kanade optical flow algorithm can be found at
* opencv_source_code/samples/python/lk_track.py
* </li>
* <li>
* (Python) An example using the Lucas-Kanade tracker for homography matching can be found at
* opencv_source_code/samples/python/lk_homography.py
* </li>
* </ul>
*/
public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err) {
Mat prevPts_mat = prevPts;
Mat nextPts_mat = nextPts;
Mat status_mat = status;
Mat err_mat = err;
calcOpticalFlowPyrLK_5(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj);
}
//
// C++: void cv::calcOpticalFlowFarneback(Mat prev, Mat next, Mat& flow, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags)
//
/**
* Computes a dense optical flow using the Gunnar Farneback's algorithm.
*
* @param prev first 8-bit single-channel input image.
* @param next second input image of the same size and the same type as prev.
* @param flow computed flow image that has the same size as prev and type CV_32FC2.
* @param pyr_scale parameter, specifying the image scale (<1) to build pyramids for each image;
* pyr_scale=0.5 means a classical pyramid, where each next layer is twice smaller than the previous
* one.
* @param levels number of pyramid layers including the initial image; levels=1 means that no extra
* layers are created and only the original images are used.
* @param winsize averaging window size; larger values increase the algorithm robustness to image
* noise and give more chances for fast motion detection, but yield more blurred motion field.
* @param iterations number of iterations the algorithm does at each pyramid level.
* @param poly_n size of the pixel neighborhood used to find polynomial expansion in each pixel;
* larger values mean that the image will be approximated with smoother surfaces, yielding more
* robust algorithm and more blurred motion field, typically poly_n =5 or 7.
* @param poly_sigma standard deviation of the Gaussian that is used to smooth derivatives used as a
* basis for the polynomial expansion; for poly_n=5, you can set poly_sigma=1.1, for poly_n=7, a
* good value would be poly_sigma=1.5.
* @param flags operation flags that can be a combination of the following:
* <ul>
* <li>
* <b>OPTFLOW_USE_INITIAL_FLOW</b> uses the input flow as an initial flow approximation.
* </li>
* <li>
* <b>OPTFLOW_FARNEBACK_GAUSSIAN</b> uses the Gaussian \(\texttt{winsize}\times\texttt{winsize}\)
* filter instead of a box filter of the same size for optical flow estimation; usually, this
* option gives z more accurate flow than with a box filter, at the cost of lower speed;
* normally, winsize for a Gaussian window should be set to a larger value to achieve the same
* level of robustness.
* </li>
* </ul>
*
* The function finds an optical flow for each prev pixel using the CITE: Farneback2003 algorithm so that
*
* \(\texttt{prev} (y,x) \sim \texttt{next} ( y + \texttt{flow} (y,x)[1], x + \texttt{flow} (y,x)[0])\)
*
* <b>Note:</b> Some examples:
*
* <ul>
* <li>
* An example using the optical flow algorithm described by Gunnar Farneback can be found at
* opencv_source_code/samples/cpp/fback.cpp
* </li>
* <li>
* (Python) An example using the optical flow algorithm described by Gunnar Farneback can be
* found at opencv_source_code/samples/python/opt_flow.py
* </li>
* </ul>
*/
public static void calcOpticalFlowFarneback(Mat prev, Mat next, Mat flow, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags) {
calcOpticalFlowFarneback_0(prev.nativeObj, next.nativeObj, flow.nativeObj, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags);
}
//
// C++: double cv::computeECC(Mat templateImage, Mat inputImage, Mat inputMask = Mat())
//
/**
* Computes the Enhanced Correlation Coefficient value between two images CITE: EP08 .
*
* @param templateImage single-channel template image; CV_8U or CV_32F array.
* @param inputImage single-channel input image to be warped to provide an image similar to
* templateImage, same type as templateImage.
* @param inputMask An optional mask to indicate valid values of inputImage.
*
* SEE:
* findTransformECC
* @return automatically generated
*/
public static double computeECC(Mat templateImage, Mat inputImage, Mat inputMask) {
return computeECC_0(templateImage.nativeObj, inputImage.nativeObj, inputMask.nativeObj);
}
/**
* Computes the Enhanced Correlation Coefficient value between two images CITE: EP08 .
*
* @param templateImage single-channel template image; CV_8U or CV_32F array.
* @param inputImage single-channel input image to be warped to provide an image similar to
* templateImage, same type as templateImage.
*
* SEE:
* findTransformECC
* @return automatically generated
*/
public static double computeECC(Mat templateImage, Mat inputImage) {
return computeECC_1(templateImage.nativeObj, inputImage.nativeObj);
}
//
// C++: double cv::findTransformECC(Mat templateImage, Mat inputImage, Mat& warpMatrix, int motionType, TermCriteria criteria, Mat inputMask, int gaussFiltSize)
//
/**
* Finds the geometric transform (warp) between two images in terms of the ECC criterion CITE: EP08 .
*
* @param templateImage single-channel template image; CV_8U or CV_32F array.
* @param inputImage single-channel input image which should be warped with the final warpMatrix in
* order to provide an image similar to templateImage, same type as templateImage.
* @param warpMatrix floating-point \(2\times 3\) or \(3\times 3\) mapping matrix (warp).
* @param motionType parameter, specifying the type of motion:
* <ul>
* <li>
* <b>MOTION_TRANSLATION</b> sets a translational motion model; warpMatrix is \(2\times 3\) with
* the first \(2\times 2\) part being the unity matrix and the rest two parameters being
* estimated.
* </li>
* <li>
* <b>MOTION_EUCLIDEAN</b> sets a Euclidean (rigid) transformation as motion model; three
* parameters are estimated; warpMatrix is \(2\times 3\).
* </li>
* <li>
* <b>MOTION_AFFINE</b> sets an affine motion model (DEFAULT); six parameters are estimated;
* warpMatrix is \(2\times 3\).
* </li>
* <li>
* <b>MOTION_HOMOGRAPHY</b> sets a homography as a motion model; eight parameters are
* estimated;\{@code warpMatrix\} is \(3\times 3\).
* </li>
* </ul>
* @param criteria parameter, specifying the termination criteria of the ECC algorithm;
* criteria.epsilon defines the threshold of the increment in the correlation coefficient between two
* iterations (a negative criteria.epsilon makes criteria.maxcount the only termination criterion).
* Default values are shown in the declaration above.
* @param inputMask An optional mask to indicate valid values of inputImage.
* @param gaussFiltSize An optional value indicating size of gaussian blur filter; (DEFAULT: 5)
*
* The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion
* (CITE: EP08), that is
*
* \(\texttt{warpMatrix} = \arg\max_{W} \texttt{ECC}(\texttt{templateImage}(x,y),\texttt{inputImage}(x',y'))\)
*
* where
*
* \(\begin{bmatrix} x' \\ y' \end{bmatrix} = W \cdot \begin{bmatrix} x \\ y \\ 1 \end{bmatrix}\)
*
* (the equation holds with homogeneous coordinates for homography). It returns the final enhanced
* correlation coefficient, that is the correlation coefficient between the template image and the
* final warped input image. When a \(3\times 3\) matrix is given with motionType =0, 1 or 2, the third
* row is ignored.
*
* Unlike findHomography and estimateRigidTransform, the function findTransformECC implements an
* area-based alignment that builds on intensity similarities. In essence, the function updates the
* initial transformation that roughly aligns the images. If this information is missing, the identity
* warp (unity matrix) is used as an initialization. Note that if images undergo strong
* displacements/rotations, an initial transformation that roughly aligns the images is necessary
* (e.g., a simple euclidean/similarity transform that allows for the images showing the same image
* content approximately). Use inverse warping in the second image to take an image close to the first
* one, i.e. use the flag WARP_INVERSE_MAP with warpAffine or warpPerspective. See also the OpenCV
* sample image_alignment.cpp that demonstrates the use of the function. Note that the function throws
* an exception if algorithm does not converges.
*
* SEE:
* computeECC, estimateAffine2D, estimateAffinePartial2D, findHomography
* @return automatically generated
*/
public static double findTransformECC(Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType, TermCriteria criteria, Mat inputMask, int gaussFiltSize) {
return findTransformECC_0(templateImage.nativeObj, inputImage.nativeObj, warpMatrix.nativeObj, motionType, criteria.type, criteria.maxCount, criteria.epsilon, inputMask.nativeObj, gaussFiltSize);
}
//
// C++: double cv::findTransformECC(Mat templateImage, Mat inputImage, Mat& warpMatrix, int motionType = MOTION_AFFINE, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 0.001), Mat inputMask = Mat())
//
public static double findTransformECC(Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType, TermCriteria criteria, Mat inputMask) {
return findTransformECC_1(templateImage.nativeObj, inputImage.nativeObj, warpMatrix.nativeObj, motionType, criteria.type, criteria.maxCount, criteria.epsilon, inputMask.nativeObj);
}
public static double findTransformECC(Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType, TermCriteria criteria) {
return findTransformECC_2(templateImage.nativeObj, inputImage.nativeObj, warpMatrix.nativeObj, motionType, criteria.type, criteria.maxCount, criteria.epsilon);
}
public static double findTransformECC(Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType) {
return findTransformECC_3(templateImage.nativeObj, inputImage.nativeObj, warpMatrix.nativeObj, motionType);
}
public static double findTransformECC(Mat templateImage, Mat inputImage, Mat warpMatrix) {
return findTransformECC_4(templateImage.nativeObj, inputImage.nativeObj, warpMatrix.nativeObj);
}
//
// C++: Mat cv::readOpticalFlow(String path)
//
/**
* Read a .flo file
*
* @param path Path to the file to be loaded
*
* The function readOpticalFlow loads a flow field from a file and returns it as a single matrix.
* Resulting Mat has a type CV_32FC2 - floating-point, 2-channel. First channel corresponds to the
* flow in the horizontal direction (u), second - vertical (v).
* @return automatically generated
*/
public static Mat readOpticalFlow(String path) {
return new Mat(readOpticalFlow_0(path));
}
//
// C++: bool cv::writeOpticalFlow(String path, Mat flow)
//
/**
* Write a .flo to disk
*
* @param path Path to the file to be written
* @param flow Flow field to be stored
*
* The function stores a flow field in a file, returns true on success, false otherwise.
* The flow field must be a 2-channel, floating-point matrix (CV_32FC2). First channel corresponds
* to the flow in the horizontal direction (u), second - vertical (v).
* @return automatically generated
*/
public static boolean writeOpticalFlow(String path, Mat flow) {
return writeOpticalFlow_0(path, flow.nativeObj);
}
// C++: Ptr_BackgroundSubtractorMOG2 cv::createBackgroundSubtractorMOG2(int history = 500, double varThreshold = 16, bool detectShadows = true)
private static native long createBackgroundSubtractorMOG2_0(int history, double varThreshold, boolean detectShadows);
private static native long createBackgroundSubtractorMOG2_1(int history, double varThreshold);
private static native long createBackgroundSubtractorMOG2_2(int history);
private static native long createBackgroundSubtractorMOG2_3();
// C++: Ptr_BackgroundSubtractorKNN cv::createBackgroundSubtractorKNN(int history = 500, double dist2Threshold = 400.0, bool detectShadows = true)
private static native long createBackgroundSubtractorKNN_0(int history, double dist2Threshold, boolean detectShadows);
private static native long createBackgroundSubtractorKNN_1(int history, double dist2Threshold);
private static native long createBackgroundSubtractorKNN_2(int history);
private static native long createBackgroundSubtractorKNN_3();
// C++: RotatedRect cv::CamShift(Mat probImage, Rect& window, TermCriteria criteria)
private static native double[] CamShift_0(long probImage_nativeObj, int window_x, int window_y, int window_width, int window_height, double[] window_out, int criteria_type, int criteria_maxCount, double criteria_epsilon);
// C++: int cv::meanShift(Mat probImage, Rect& window, TermCriteria criteria)
private static native int meanShift_0(long probImage_nativeObj, int window_x, int window_y, int window_width, int window_height, double[] window_out, int criteria_type, int criteria_maxCount, double criteria_epsilon);
// C++: int cv::buildOpticalFlowPyramid(Mat img, vector_Mat& pyramid, Size winSize, int maxLevel, bool withDerivatives = true, int pyrBorder = BORDER_REFLECT_101, int derivBorder = BORDER_CONSTANT, bool tryReuseInputImage = true)
private static native int buildOpticalFlowPyramid_0(long img_nativeObj, long pyramid_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel, boolean withDerivatives, int pyrBorder, int derivBorder, boolean tryReuseInputImage);
private static native int buildOpticalFlowPyramid_1(long img_nativeObj, long pyramid_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel, boolean withDerivatives, int pyrBorder, int derivBorder);
private static native int buildOpticalFlowPyramid_2(long img_nativeObj, long pyramid_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel, boolean withDerivatives, int pyrBorder);
private static native int buildOpticalFlowPyramid_3(long img_nativeObj, long pyramid_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel, boolean withDerivatives);
private static native int buildOpticalFlowPyramid_4(long img_nativeObj, long pyramid_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel);
// C++: void cv::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)
private static native void calcOpticalFlowPyrLK_0(long prevImg_nativeObj, long nextImg_nativeObj, long prevPts_mat_nativeObj, long nextPts_mat_nativeObj, long status_mat_nativeObj, long err_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel, int criteria_type, int criteria_maxCount, double criteria_epsilon, int flags, double minEigThreshold);
private static native void calcOpticalFlowPyrLK_1(long prevImg_nativeObj, long nextImg_nativeObj, long prevPts_mat_nativeObj, long nextPts_mat_nativeObj, long status_mat_nativeObj, long err_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel, int criteria_type, int criteria_maxCount, double criteria_epsilon, int flags);
private static native void calcOpticalFlowPyrLK_2(long prevImg_nativeObj, long nextImg_nativeObj, long prevPts_mat_nativeObj, long nextPts_mat_nativeObj, long status_mat_nativeObj, long err_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel, int criteria_type, int criteria_maxCount, double criteria_epsilon);
private static native void calcOpticalFlowPyrLK_3(long prevImg_nativeObj, long nextImg_nativeObj, long prevPts_mat_nativeObj, long nextPts_mat_nativeObj, long status_mat_nativeObj, long err_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel);
private static native void calcOpticalFlowPyrLK_4(long prevImg_nativeObj, long nextImg_nativeObj, long prevPts_mat_nativeObj, long nextPts_mat_nativeObj, long status_mat_nativeObj, long err_mat_nativeObj, double winSize_width, double winSize_height);
private static native void calcOpticalFlowPyrLK_5(long prevImg_nativeObj, long nextImg_nativeObj, long prevPts_mat_nativeObj, long nextPts_mat_nativeObj, long status_mat_nativeObj, long err_mat_nativeObj);
// C++: void cv::calcOpticalFlowFarneback(Mat prev, Mat next, Mat& flow, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags)
private static native void calcOpticalFlowFarneback_0(long prev_nativeObj, long next_nativeObj, long flow_nativeObj, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags);
// C++: double cv::computeECC(Mat templateImage, Mat inputImage, Mat inputMask = Mat())
private static native double computeECC_0(long templateImage_nativeObj, long inputImage_nativeObj, long inputMask_nativeObj);
private static native double computeECC_1(long templateImage_nativeObj, long inputImage_nativeObj);
// C++: double cv::findTransformECC(Mat templateImage, Mat inputImage, Mat& warpMatrix, int motionType, TermCriteria criteria, Mat inputMask, int gaussFiltSize)
private static native double findTransformECC_0(long templateImage_nativeObj, long inputImage_nativeObj, long warpMatrix_nativeObj, int motionType, int criteria_type, int criteria_maxCount, double criteria_epsilon, long inputMask_nativeObj, int gaussFiltSize);
// C++: double cv::findTransformECC(Mat templateImage, Mat inputImage, Mat& warpMatrix, int motionType = MOTION_AFFINE, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 0.001), Mat inputMask = Mat())
private static native double findTransformECC_1(long templateImage_nativeObj, long inputImage_nativeObj, long warpMatrix_nativeObj, int motionType, int criteria_type, int criteria_maxCount, double criteria_epsilon, long inputMask_nativeObj);
private static native double findTransformECC_2(long templateImage_nativeObj, long inputImage_nativeObj, long warpMatrix_nativeObj, int motionType, int criteria_type, int criteria_maxCount, double criteria_epsilon);
private static native double findTransformECC_3(long templateImage_nativeObj, long inputImage_nativeObj, long warpMatrix_nativeObj, int motionType);
private static native double findTransformECC_4(long templateImage_nativeObj, long inputImage_nativeObj, long warpMatrix_nativeObj);
// C++: Mat cv::readOpticalFlow(String path)
private static native long readOpticalFlow_0(String path);
// C++: bool cv::writeOpticalFlow(String path, Mat flow)
private static native boolean writeOpticalFlow_0(String path, long flow_nativeObj);
}
