public class opencv_video extends opencv_video
| Modifier and Type | Class and Description |
|---|---|
static class |
opencv_video.BackgroundSubtractor
\addtogroup video_motion
\{
|
static class |
opencv_video.BackgroundSubtractorKNN
\brief K-nearest neighbours - based Background/Foreground Segmentation Algorithm.
|
static class |
opencv_video.BackgroundSubtractorMOG2
\brief Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
|
static class |
opencv_video.CvKalman |
static class |
opencv_video.DenseOpticalFlow |
static class |
opencv_video.DualTVL1OpticalFlow
\brief "Dual TV L1" Optical Flow Algorithm.
|
static class |
opencv_video.FarnebackOpticalFlow
\brief Class computing a dense optical flow using the Gunnar Farneback's algorithm.
|
static class |
opencv_video.KalmanFilter
\brief Kalman filter class.
|
static class |
opencv_video.SparseOpticalFlow
\brief Base interface for sparse optical flow algorithms.
|
static class |
opencv_video.SparsePyrLKOpticalFlow
\brief Class used for calculating a sparse optical flow.
|
opencv_video.AbstractCvKalman| Modifier and Type | Field and Description |
|---|---|
static int |
CV_LKFLOW_GET_MIN_EIGENVALS |
static int |
CV_LKFLOW_INITIAL_GUESSES |
static int |
CV_LKFLOW_PYR_A_READY
optical flow
|
static int |
CV_LKFLOW_PYR_B_READY |
static int |
MOTION_AFFINE
enum cv::
|
static int |
MOTION_EUCLIDEAN
enum cv::
|
static int |
MOTION_HOMOGRAPHY
enum cv::
|
static int |
MOTION_TRANSLATION
enum cv::
|
static int |
OPTFLOW_FARNEBACK_GAUSSIAN
enum cv::
|
static int |
OPTFLOW_LK_GET_MIN_EIGENVALS
enum cv::
|
static int |
OPTFLOW_USE_INITIAL_FLOW
enum cv::
|
| Constructor and Description |
|---|
opencv_video() |
| Modifier and Type | Method and Description |
|---|---|
static int |
buildOpticalFlowPyramid(opencv_core.GpuMat img,
opencv_core.GpuMatVector pyramid,
opencv_core.Size winSize,
int maxLevel) |
static int |
buildOpticalFlowPyramid(opencv_core.GpuMat img,
opencv_core.GpuMatVector pyramid,
opencv_core.Size winSize,
int maxLevel,
boolean withDerivatives,
int pyrBorder,
int derivBorder,
boolean tryReuseInputImage) |
static int |
buildOpticalFlowPyramid(opencv_core.GpuMat img,
opencv_core.MatVector pyramid,
opencv_core.Size winSize,
int maxLevel) |
static int |
buildOpticalFlowPyramid(opencv_core.GpuMat img,
opencv_core.MatVector pyramid,
opencv_core.Size winSize,
int maxLevel,
boolean withDerivatives,
int pyrBorder,
int derivBorder,
boolean tryReuseInputImage) |
static int |
buildOpticalFlowPyramid(opencv_core.GpuMat img,
opencv_core.UMatVector pyramid,
opencv_core.Size winSize,
int maxLevel) |
static int |
buildOpticalFlowPyramid(opencv_core.GpuMat img,
opencv_core.UMatVector pyramid,
opencv_core.Size winSize,
int maxLevel,
boolean withDerivatives,
int pyrBorder,
int derivBorder,
boolean tryReuseInputImage) |
static int |
buildOpticalFlowPyramid(opencv_core.Mat img,
opencv_core.GpuMatVector pyramid,
opencv_core.Size winSize,
int maxLevel) |
static int |
buildOpticalFlowPyramid(opencv_core.Mat img,
opencv_core.GpuMatVector pyramid,
opencv_core.Size winSize,
int maxLevel,
boolean withDerivatives,
int pyrBorder,
int derivBorder,
boolean tryReuseInputImage) |
static int |
buildOpticalFlowPyramid(opencv_core.Mat img,
opencv_core.MatVector pyramid,
opencv_core.Size winSize,
int maxLevel) |
static int |
buildOpticalFlowPyramid(opencv_core.Mat img,
opencv_core.MatVector pyramid,
opencv_core.Size winSize,
int maxLevel,
boolean withDerivatives,
int pyrBorder,
int derivBorder,
boolean tryReuseInputImage)
\brief Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
|
static int |
buildOpticalFlowPyramid(opencv_core.Mat img,
opencv_core.UMatVector pyramid,
opencv_core.Size winSize,
int maxLevel) |
static int |
buildOpticalFlowPyramid(opencv_core.Mat img,
opencv_core.UMatVector pyramid,
opencv_core.Size winSize,
int maxLevel,
boolean withDerivatives,
int pyrBorder,
int derivBorder,
boolean tryReuseInputImage) |
static int |
buildOpticalFlowPyramid(opencv_core.UMat img,
opencv_core.GpuMatVector pyramid,
opencv_core.Size winSize,
int maxLevel) |
static int |
buildOpticalFlowPyramid(opencv_core.UMat img,
opencv_core.GpuMatVector pyramid,
opencv_core.Size winSize,
int maxLevel,
boolean withDerivatives,
int pyrBorder,
int derivBorder,
boolean tryReuseInputImage) |
static int |
buildOpticalFlowPyramid(opencv_core.UMat img,
opencv_core.MatVector pyramid,
opencv_core.Size winSize,
int maxLevel) |
static int |
buildOpticalFlowPyramid(opencv_core.UMat img,
opencv_core.MatVector pyramid,
opencv_core.Size winSize,
int maxLevel,
boolean withDerivatives,
int pyrBorder,
int derivBorder,
boolean tryReuseInputImage) |
static int |
buildOpticalFlowPyramid(opencv_core.UMat img,
opencv_core.UMatVector pyramid,
opencv_core.Size winSize,
int maxLevel) |
static int |
buildOpticalFlowPyramid(opencv_core.UMat img,
opencv_core.UMatVector pyramid,
opencv_core.Size winSize,
int maxLevel,
boolean withDerivatives,
int pyrBorder,
int derivBorder,
boolean tryReuseInputImage) |
static void |
calcOpticalFlowFarneback(opencv_core.GpuMat prev,
opencv_core.GpuMat next,
opencv_core.GpuMat flow,
double pyr_scale,
int levels,
int winsize,
int iterations,
int poly_n,
double poly_sigma,
int flags) |
static void |
calcOpticalFlowFarneback(opencv_core.Mat prev,
opencv_core.Mat next,
opencv_core.Mat flow,
double pyr_scale,
int levels,
int winsize,
int iterations,
int poly_n,
double poly_sigma,
int flags)
\brief Computes a dense optical flow using the Gunnar Farneback's algorithm.
|
static void |
calcOpticalFlowFarneback(opencv_core.UMat prev,
opencv_core.UMat next,
opencv_core.UMat flow,
double pyr_scale,
int levels,
int winsize,
int iterations,
int poly_n,
double poly_sigma,
int flags) |
static void |
calcOpticalFlowPyrLK(opencv_core.GpuMat prevImg,
opencv_core.GpuMat nextImg,
opencv_core.GpuMat prevPts,
opencv_core.GpuMat nextPts,
opencv_core.GpuMat status,
opencv_core.GpuMat err) |
static void |
calcOpticalFlowPyrLK(opencv_core.GpuMat prevImg,
opencv_core.GpuMat nextImg,
opencv_core.GpuMat prevPts,
opencv_core.GpuMat nextPts,
opencv_core.GpuMat status,
opencv_core.GpuMat err,
opencv_core.Size winSize,
int maxLevel,
opencv_core.TermCriteria criteria,
int flags,
double minEigThreshold) |
static void |
calcOpticalFlowPyrLK(opencv_core.Mat prevImg,
opencv_core.Mat nextImg,
opencv_core.Mat prevPts,
opencv_core.Mat nextPts,
opencv_core.Mat status,
opencv_core.Mat err) |
static void |
calcOpticalFlowPyrLK(opencv_core.Mat prevImg,
opencv_core.Mat nextImg,
opencv_core.Mat prevPts,
opencv_core.Mat nextPts,
opencv_core.Mat status,
opencv_core.Mat err,
opencv_core.Size winSize,
int maxLevel,
opencv_core.TermCriteria criteria,
int flags,
double minEigThreshold)
\brief Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with
pyramids.
|
static void |
calcOpticalFlowPyrLK(opencv_core.UMat prevImg,
opencv_core.UMat nextImg,
opencv_core.UMat prevPts,
opencv_core.UMat nextPts,
opencv_core.UMat status,
opencv_core.UMat err) |
static void |
calcOpticalFlowPyrLK(opencv_core.UMat prevImg,
opencv_core.UMat nextImg,
opencv_core.UMat prevPts,
opencv_core.UMat nextPts,
opencv_core.UMat status,
opencv_core.UMat err,
opencv_core.Size winSize,
int maxLevel,
opencv_core.TermCriteria criteria,
int flags,
double minEigThreshold) |
static opencv_core.RotatedRect |
CamShift(opencv_core.GpuMat probImage,
opencv_core.Rect window,
opencv_core.TermCriteria criteria) |
static opencv_core.RotatedRect |
CamShift(opencv_core.Mat probImage,
opencv_core.Rect window,
opencv_core.TermCriteria criteria)
\brief Finds an object center, size, and orientation.
|
static opencv_core.RotatedRect |
CamShift(opencv_core.UMat probImage,
opencv_core.Rect window,
opencv_core.TermCriteria criteria) |
static opencv_video.BackgroundSubtractorKNN |
createBackgroundSubtractorKNN() |
static opencv_video.BackgroundSubtractorKNN |
createBackgroundSubtractorKNN(int history,
double dist2Threshold,
boolean detectShadows)
\brief Creates KNN Background Subtractor
|
static opencv_video.BackgroundSubtractorMOG2 |
createBackgroundSubtractorMOG2() |
static opencv_video.BackgroundSubtractorMOG2 |
createBackgroundSubtractorMOG2(int history,
double varThreshold,
boolean detectShadows)
\brief Creates MOG2 Background Subtractor
|
static opencv_video.DualTVL1OpticalFlow |
createOptFlow_DualTVL1()
\brief Creates instance of cv::DenseOpticalFlow
|
static void |
cvCalcOpticalFlowFarneback(opencv_core.CvArr prev,
opencv_core.CvArr next,
opencv_core.CvArr flow,
double pyr_scale,
int levels,
int winsize,
int iterations,
int poly_n,
double poly_sigma,
int flags) |
static void |
cvCalcOpticalFlowPyrLK(opencv_core.CvArr prev,
opencv_core.CvArr curr,
opencv_core.CvArr prev_pyr,
opencv_core.CvArr curr_pyr,
float[] prev_features,
float[] curr_features,
int count,
opencv_core.CvSize win_size,
int level,
byte[] status,
float[] track_error,
opencv_core.CvTermCriteria criteria,
int flags) |
static void |
cvCalcOpticalFlowPyrLK(opencv_core.CvArr prev,
opencv_core.CvArr curr,
opencv_core.CvArr prev_pyr,
opencv_core.CvArr curr_pyr,
FloatBuffer prev_features,
FloatBuffer curr_features,
int count,
opencv_core.CvSize win_size,
int level,
ByteBuffer status,
FloatBuffer track_error,
opencv_core.CvTermCriteria criteria,
int flags) |
static void |
cvCalcOpticalFlowPyrLK(opencv_core.CvArr prev,
opencv_core.CvArr curr,
opencv_core.CvArr prev_pyr,
opencv_core.CvArr curr_pyr,
opencv_core.CvPoint2D32f prev_features,
opencv_core.CvPoint2D32f curr_features,
int count,
opencv_core.CvSize win_size,
int level,
org.bytedeco.javacpp.BytePointer status,
org.bytedeco.javacpp.FloatPointer track_error,
opencv_core.CvTermCriteria criteria,
int flags) |
static int |
cvCamShift(opencv_core.CvArr prob_image,
opencv_core.CvRect window,
opencv_core.CvTermCriteria criteria,
opencv_imgproc.CvConnectedComp comp) |
static int |
cvCamShift(opencv_core.CvArr prob_image,
opencv_core.CvRect window,
opencv_core.CvTermCriteria criteria,
opencv_imgproc.CvConnectedComp comp,
opencv_core.CvBox2D box)
\
Tracking *
\
|
static opencv_video.CvKalman |
cvCreateKalman(int dynam_params,
int measure_params) |
static opencv_video.CvKalman |
cvCreateKalman(int dynam_params,
int measure_params,
int control_params) |
static int |
cvEstimateRigidTransform(opencv_core.CvArr A,
opencv_core.CvArr B,
opencv_core.CvMat M,
int full_affine) |
static opencv_core.CvMat |
cvKalmanCorrect(opencv_video.CvKalman kalman,
opencv_core.CvMat measurement) |
static opencv_core.CvMat |
cvKalmanPredict(opencv_video.CvKalman kalman) |
static opencv_core.CvMat |
cvKalmanPredict(opencv_video.CvKalman kalman,
opencv_core.CvMat control) |
static opencv_core.CvMat |
cvKalmanUpdateByMeasurement(opencv_video.CvKalman arg1,
opencv_core.CvMat arg2) |
static opencv_core.CvMat |
cvKalmanUpdateByTime(opencv_video.CvKalman arg1,
opencv_core.CvMat arg2) |
static int |
cvMeanShift(opencv_core.CvArr prob_image,
opencv_core.CvRect window,
opencv_core.CvTermCriteria criteria,
opencv_imgproc.CvConnectedComp comp) |
static void |
cvReleaseKalman(opencv_video.CvKalman kalman) |
static void |
cvReleaseKalman(org.bytedeco.javacpp.PointerPointer kalman) |
static opencv_core.Mat |
estimateRigidTransform(opencv_core.GpuMat src,
opencv_core.GpuMat dst,
boolean fullAffine) |
static opencv_core.Mat |
estimateRigidTransform(opencv_core.GpuMat src,
opencv_core.GpuMat dst,
boolean fullAffine,
int ransacMaxIters,
double ransacGoodRatio,
int ransacSize0) |
static opencv_core.Mat |
estimateRigidTransform(opencv_core.Mat src,
opencv_core.Mat dst,
boolean fullAffine)
\brief Computes an optimal affine transformation between two 2D point sets.
|
static opencv_core.Mat |
estimateRigidTransform(opencv_core.Mat src,
opencv_core.Mat dst,
boolean fullAffine,
int ransacMaxIters,
double ransacGoodRatio,
int ransacSize0) |
static opencv_core.Mat |
estimateRigidTransform(opencv_core.UMat src,
opencv_core.UMat dst,
boolean fullAffine) |
static opencv_core.Mat |
estimateRigidTransform(opencv_core.UMat src,
opencv_core.UMat dst,
boolean fullAffine,
int ransacMaxIters,
double ransacGoodRatio,
int ransacSize0) |
static double |
findTransformECC(opencv_core.GpuMat templateImage,
opencv_core.GpuMat inputImage,
opencv_core.GpuMat warpMatrix) |
static double |
findTransformECC(opencv_core.GpuMat templateImage,
opencv_core.GpuMat inputImage,
opencv_core.GpuMat warpMatrix,
int motionType,
opencv_core.TermCriteria criteria,
opencv_core.GpuMat inputMask) |
static double |
findTransformECC(opencv_core.Mat templateImage,
opencv_core.Mat inputImage,
opencv_core.Mat warpMatrix) |
static double |
findTransformECC(opencv_core.Mat templateImage,
opencv_core.Mat inputImage,
opencv_core.Mat warpMatrix,
int motionType,
opencv_core.TermCriteria criteria,
opencv_core.Mat inputMask)
\brief Finds the geometric transform (warp) between two images in terms of the ECC criterion \cite EP08 .
|
static double |
findTransformECC(opencv_core.UMat templateImage,
opencv_core.UMat inputImage,
opencv_core.UMat warpMatrix) |
static double |
findTransformECC(opencv_core.UMat templateImage,
opencv_core.UMat inputImage,
opencv_core.UMat warpMatrix,
int motionType,
opencv_core.TermCriteria criteria,
opencv_core.UMat inputMask) |
static int |
meanShift(opencv_core.GpuMat probImage,
opencv_core.Rect window,
opencv_core.TermCriteria criteria) |
static int |
meanShift(opencv_core.Mat probImage,
opencv_core.Rect window,
opencv_core.TermCriteria criteria)
\brief Finds an object on a back projection image.
|
static int |
meanShift(opencv_core.UMat probImage,
opencv_core.Rect window,
opencv_core.TermCriteria criteria) |
mappublic static final int CV_LKFLOW_PYR_A_READY
public static final int CV_LKFLOW_PYR_B_READY
public static final int CV_LKFLOW_INITIAL_GUESSES
public static final int CV_LKFLOW_GET_MIN_EIGENVALS
public static final int OPTFLOW_USE_INITIAL_FLOW
public static final int OPTFLOW_LK_GET_MIN_EIGENVALS
public static final int OPTFLOW_FARNEBACK_GAUSSIAN
public static final int MOTION_TRANSLATION
public static final int MOTION_EUCLIDEAN
public static final int MOTION_AFFINE
public static final int MOTION_HOMOGRAPHY
public static void cvCalcOpticalFlowPyrLK(@Const
opencv_core.CvArr prev,
@Const
opencv_core.CvArr curr,
opencv_core.CvArr prev_pyr,
opencv_core.CvArr curr_pyr,
@Const
opencv_core.CvPoint2D32f prev_features,
opencv_core.CvPoint2D32f curr_features,
int count,
@ByVal
opencv_core.CvSize win_size,
int level,
@Cast(value="char*")
org.bytedeco.javacpp.BytePointer status,
org.bytedeco.javacpp.FloatPointer track_error,
@ByVal
opencv_core.CvTermCriteria criteria,
int flags)
public static void cvCalcOpticalFlowPyrLK(@Const
opencv_core.CvArr prev,
@Const
opencv_core.CvArr curr,
opencv_core.CvArr prev_pyr,
opencv_core.CvArr curr_pyr,
@Cast(value="const CvPoint2D32f*")
FloatBuffer prev_features,
@Cast(value="CvPoint2D32f*")
FloatBuffer curr_features,
int count,
@ByVal
opencv_core.CvSize win_size,
int level,
@Cast(value="char*")
ByteBuffer status,
FloatBuffer track_error,
@ByVal
opencv_core.CvTermCriteria criteria,
int flags)
public static void cvCalcOpticalFlowPyrLK(@Const
opencv_core.CvArr prev,
@Const
opencv_core.CvArr curr,
opencv_core.CvArr prev_pyr,
opencv_core.CvArr curr_pyr,
@Cast(value="const CvPoint2D32f*")
float[] prev_features,
@Cast(value="CvPoint2D32f*")
float[] curr_features,
int count,
@ByVal
opencv_core.CvSize win_size,
int level,
@Cast(value="char*")
byte[] status,
float[] track_error,
@ByVal
opencv_core.CvTermCriteria criteria,
int flags)
public static int cvEstimateRigidTransform(@Const
opencv_core.CvArr A,
@Const
opencv_core.CvArr B,
opencv_core.CvMat M,
int full_affine)
public static void cvCalcOpticalFlowFarneback(@Const
opencv_core.CvArr prev,
@Const
opencv_core.CvArr next,
opencv_core.CvArr flow,
double pyr_scale,
int levels,
int winsize,
int iterations,
int poly_n,
double poly_sigma,
int flags)
public static int cvCamShift(@Const
opencv_core.CvArr prob_image,
@ByVal
opencv_core.CvRect window,
@ByVal
opencv_core.CvTermCriteria criteria,
opencv_imgproc.CvConnectedComp comp,
opencv_core.CvBox2D box)
public static int cvCamShift(@Const
opencv_core.CvArr prob_image,
@ByVal
opencv_core.CvRect window,
@ByVal
opencv_core.CvTermCriteria criteria,
opencv_imgproc.CvConnectedComp comp)
public static int cvMeanShift(@Const
opencv_core.CvArr prob_image,
@ByVal
opencv_core.CvRect window,
@ByVal
opencv_core.CvTermCriteria criteria,
opencv_imgproc.CvConnectedComp comp)
public static opencv_video.CvKalman cvCreateKalman(int dynam_params, int measure_params, int control_params)
public static opencv_video.CvKalman cvCreateKalman(int dynam_params, int measure_params)
public static void cvReleaseKalman(@Cast(value="CvKalman**")
org.bytedeco.javacpp.PointerPointer kalman)
public static void cvReleaseKalman(@ByPtrPtr
opencv_video.CvKalman kalman)
@Const public static opencv_core.CvMat cvKalmanPredict(opencv_video.CvKalman kalman, @Const opencv_core.CvMat control)
@Const public static opencv_core.CvMat cvKalmanPredict(opencv_video.CvKalman kalman)
@Const public static opencv_core.CvMat cvKalmanCorrect(opencv_video.CvKalman kalman, @Const opencv_core.CvMat measurement)
@Const public static opencv_core.CvMat cvKalmanUpdateByTime(opencv_video.CvKalman arg1, opencv_core.CvMat arg2)
@Const public static opencv_core.CvMat cvKalmanUpdateByMeasurement(opencv_video.CvKalman arg1, opencv_core.CvMat arg2)
@Namespace(value="cv") @ByVal public static opencv_core.RotatedRect CamShift(@ByVal opencv_core.Mat probImage, @ByRef opencv_core.Rect window, @ByVal opencv_core.TermCriteria criteria)
probImage - Back projection of the object histogram. See calcBackProject.window - Initial search window.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.
\note - (Python) A sample explaining the camshift tracking algorithm can be found at opencv_source_code/samples/python/camshift.py
@Namespace(value="cv") @ByVal public static opencv_core.RotatedRect CamShift(@ByVal opencv_core.UMat probImage, @ByRef opencv_core.Rect window, @ByVal opencv_core.TermCriteria criteria)
@Namespace(value="cv") @ByVal public static opencv_core.RotatedRect CamShift(@ByVal opencv_core.GpuMat probImage, @ByRef opencv_core.Rect window, @ByVal opencv_core.TermCriteria criteria)
@Namespace(value="cv")
public static int meanShift(@ByVal
opencv_core.Mat probImage,
@ByRef
opencv_core.Rect window,
@ByVal
opencv_core.TermCriteria criteria)
probImage - Back projection of the object histogram. See calcBackProject for details.window - Initial search window.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.
@Namespace(value="cv")
public static int meanShift(@ByVal
opencv_core.UMat probImage,
@ByRef
opencv_core.Rect window,
@ByVal
opencv_core.TermCriteria criteria)
@Namespace(value="cv")
public static int meanShift(@ByVal
opencv_core.GpuMat probImage,
@ByRef
opencv_core.Rect window,
@ByVal
opencv_core.TermCriteria criteria)
@Namespace(value="cv")
public static int buildOpticalFlowPyramid(@ByVal
opencv_core.Mat img,
@ByVal
opencv_core.MatVector pyramid,
@ByVal
opencv_core.Size winSize,
int maxLevel,
@Cast(value="bool")
boolean withDerivatives,
int pyrBorder,
int derivBorder,
@Cast(value="bool")
boolean tryReuseInputImage)
img - 8-bit input image.pyramid - output pyramid.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.maxLevel - 0-based maximal pyramid level number.withDerivatives - set to precompute gradients for the every pyramid level. If pyramid is
constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.pyrBorder - the border mode for pyramid layers.derivBorder - the border mode for gradients.tryReuseInputImage - put ROI of input image into the pyramid if possible. You can pass false
to force data copying.@Namespace(value="cv")
public static int buildOpticalFlowPyramid(@ByVal
opencv_core.Mat img,
@ByVal
opencv_core.MatVector pyramid,
@ByVal
opencv_core.Size winSize,
int maxLevel)
@Namespace(value="cv")
public static int buildOpticalFlowPyramid(@ByVal
opencv_core.Mat img,
@ByVal
opencv_core.UMatVector pyramid,
@ByVal
opencv_core.Size winSize,
int maxLevel,
@Cast(value="bool")
boolean withDerivatives,
int pyrBorder,
int derivBorder,
@Cast(value="bool")
boolean tryReuseInputImage)
@Namespace(value="cv")
public static int buildOpticalFlowPyramid(@ByVal
opencv_core.Mat img,
@ByVal
opencv_core.UMatVector pyramid,
@ByVal
opencv_core.Size winSize,
int maxLevel)
@Namespace(value="cv")
public static int buildOpticalFlowPyramid(@ByVal
opencv_core.Mat img,
@ByVal
opencv_core.GpuMatVector pyramid,
@ByVal
opencv_core.Size winSize,
int maxLevel,
@Cast(value="bool")
boolean withDerivatives,
int pyrBorder,
int derivBorder,
@Cast(value="bool")
boolean tryReuseInputImage)
@Namespace(value="cv")
public static int buildOpticalFlowPyramid(@ByVal
opencv_core.Mat img,
@ByVal
opencv_core.GpuMatVector pyramid,
@ByVal
opencv_core.Size winSize,
int maxLevel)
@Namespace(value="cv")
public static int buildOpticalFlowPyramid(@ByVal
opencv_core.UMat img,
@ByVal
opencv_core.MatVector pyramid,
@ByVal
opencv_core.Size winSize,
int maxLevel,
@Cast(value="bool")
boolean withDerivatives,
int pyrBorder,
int derivBorder,
@Cast(value="bool")
boolean tryReuseInputImage)
@Namespace(value="cv")
public static int buildOpticalFlowPyramid(@ByVal
opencv_core.UMat img,
@ByVal
opencv_core.MatVector pyramid,
@ByVal
opencv_core.Size winSize,
int maxLevel)
@Namespace(value="cv")
public static int buildOpticalFlowPyramid(@ByVal
opencv_core.UMat img,
@ByVal
opencv_core.UMatVector pyramid,
@ByVal
opencv_core.Size winSize,
int maxLevel,
@Cast(value="bool")
boolean withDerivatives,
int pyrBorder,
int derivBorder,
@Cast(value="bool")
boolean tryReuseInputImage)
@Namespace(value="cv")
public static int buildOpticalFlowPyramid(@ByVal
opencv_core.UMat img,
@ByVal
opencv_core.UMatVector pyramid,
@ByVal
opencv_core.Size winSize,
int maxLevel)
@Namespace(value="cv")
public static int buildOpticalFlowPyramid(@ByVal
opencv_core.UMat img,
@ByVal
opencv_core.GpuMatVector pyramid,
@ByVal
opencv_core.Size winSize,
int maxLevel,
@Cast(value="bool")
boolean withDerivatives,
int pyrBorder,
int derivBorder,
@Cast(value="bool")
boolean tryReuseInputImage)
@Namespace(value="cv")
public static int buildOpticalFlowPyramid(@ByVal
opencv_core.UMat img,
@ByVal
opencv_core.GpuMatVector pyramid,
@ByVal
opencv_core.Size winSize,
int maxLevel)
@Namespace(value="cv")
public static int buildOpticalFlowPyramid(@ByVal
opencv_core.GpuMat img,
@ByVal
opencv_core.MatVector pyramid,
@ByVal
opencv_core.Size winSize,
int maxLevel,
@Cast(value="bool")
boolean withDerivatives,
int pyrBorder,
int derivBorder,
@Cast(value="bool")
boolean tryReuseInputImage)
@Namespace(value="cv")
public static int buildOpticalFlowPyramid(@ByVal
opencv_core.GpuMat img,
@ByVal
opencv_core.MatVector pyramid,
@ByVal
opencv_core.Size winSize,
int maxLevel)
@Namespace(value="cv")
public static int buildOpticalFlowPyramid(@ByVal
opencv_core.GpuMat img,
@ByVal
opencv_core.UMatVector pyramid,
@ByVal
opencv_core.Size winSize,
int maxLevel,
@Cast(value="bool")
boolean withDerivatives,
int pyrBorder,
int derivBorder,
@Cast(value="bool")
boolean tryReuseInputImage)
@Namespace(value="cv")
public static int buildOpticalFlowPyramid(@ByVal
opencv_core.GpuMat img,
@ByVal
opencv_core.UMatVector pyramid,
@ByVal
opencv_core.Size winSize,
int maxLevel)
@Namespace(value="cv")
public static int buildOpticalFlowPyramid(@ByVal
opencv_core.GpuMat img,
@ByVal
opencv_core.GpuMatVector pyramid,
@ByVal
opencv_core.Size winSize,
int maxLevel,
@Cast(value="bool")
boolean withDerivatives,
int pyrBorder,
int derivBorder,
@Cast(value="bool")
boolean tryReuseInputImage)
@Namespace(value="cv")
public static int buildOpticalFlowPyramid(@ByVal
opencv_core.GpuMat img,
@ByVal
opencv_core.GpuMatVector pyramid,
@ByVal
opencv_core.Size winSize,
int maxLevel)
@Namespace(value="cv")
public static void calcOpticalFlowPyrLK(@ByVal
opencv_core.Mat prevImg,
@ByVal
opencv_core.Mat nextImg,
@ByVal
opencv_core.Mat prevPts,
@ByVal
opencv_core.Mat nextPts,
@ByVal
opencv_core.Mat status,
@ByVal
opencv_core.Mat err,
@ByVal(nullValue="cv::Size(21,21)")
opencv_core.Size winSize,
int maxLevel,
@ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 30, 0.01)")
opencv_core.TermCriteria criteria,
int flags,
double minEigThreshold)
prevImg - first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.nextImg - second input image or pyramid of the same size and the same type as prevImg.prevPts - vector of 2D points for which the flow needs to be found; point coordinates must be
single-precision floating-point numbers.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.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.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).winSize - size of the search window at each pyramid level.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.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.flags - operation flags:
- **OPTFLOW_USE_INITIAL_FLOW** uses initial estimations, stored in nextPts; if the flag is
not set, then prevPts is copied to nextPts and is considered the initial estimate.
- **OPTFLOW_LK_GET_MIN_EIGENVALS** 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.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.
\note
- An example using the Lucas-Kanade optical flow algorithm can be found at opencv_source_code/samples/cpp/lkdemo.cpp - (Python) An example using the Lucas-Kanade optical flow algorithm can be found at opencv_source_code/samples/python/lk_track.py - (Python) An example using the Lucas-Kanade tracker for homography matching can be found at opencv_source_code/samples/python/lk_homography.py
@Namespace(value="cv")
public static void calcOpticalFlowPyrLK(@ByVal
opencv_core.Mat prevImg,
@ByVal
opencv_core.Mat nextImg,
@ByVal
opencv_core.Mat prevPts,
@ByVal
opencv_core.Mat nextPts,
@ByVal
opencv_core.Mat status,
@ByVal
opencv_core.Mat err)
@Namespace(value="cv")
public static void calcOpticalFlowPyrLK(@ByVal
opencv_core.UMat prevImg,
@ByVal
opencv_core.UMat nextImg,
@ByVal
opencv_core.UMat prevPts,
@ByVal
opencv_core.UMat nextPts,
@ByVal
opencv_core.UMat status,
@ByVal
opencv_core.UMat err,
@ByVal(nullValue="cv::Size(21,21)")
opencv_core.Size winSize,
int maxLevel,
@ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 30, 0.01)")
opencv_core.TermCriteria criteria,
int flags,
double minEigThreshold)
@Namespace(value="cv")
public static void calcOpticalFlowPyrLK(@ByVal
opencv_core.UMat prevImg,
@ByVal
opencv_core.UMat nextImg,
@ByVal
opencv_core.UMat prevPts,
@ByVal
opencv_core.UMat nextPts,
@ByVal
opencv_core.UMat status,
@ByVal
opencv_core.UMat err)
@Namespace(value="cv")
public static void calcOpticalFlowPyrLK(@ByVal
opencv_core.GpuMat prevImg,
@ByVal
opencv_core.GpuMat nextImg,
@ByVal
opencv_core.GpuMat prevPts,
@ByVal
opencv_core.GpuMat nextPts,
@ByVal
opencv_core.GpuMat status,
@ByVal
opencv_core.GpuMat err,
@ByVal(nullValue="cv::Size(21,21)")
opencv_core.Size winSize,
int maxLevel,
@ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 30, 0.01)")
opencv_core.TermCriteria criteria,
int flags,
double minEigThreshold)
@Namespace(value="cv")
public static void calcOpticalFlowPyrLK(@ByVal
opencv_core.GpuMat prevImg,
@ByVal
opencv_core.GpuMat nextImg,
@ByVal
opencv_core.GpuMat prevPts,
@ByVal
opencv_core.GpuMat nextPts,
@ByVal
opencv_core.GpuMat status,
@ByVal
opencv_core.GpuMat err)
@Namespace(value="cv")
public static void calcOpticalFlowFarneback(@ByVal
opencv_core.Mat prev,
@ByVal
opencv_core.Mat next,
@ByVal
opencv_core.Mat flow,
double pyr_scale,
int levels,
int winsize,
int iterations,
int poly_n,
double poly_sigma,
int flags)
prev - first 8-bit single-channel input image.next - second input image of the same size and the same type as prev.flow - computed flow image that has the same size as prev and type CV_32FC2.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.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.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.iterations - number of iterations the algorithm does at each pyramid level.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.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.flags - operation flags that can be a combination of the following:
- **OPTFLOW_USE_INITIAL_FLOW** uses the input flow as an initial flow approximation.
- **OPTFLOW_FARNEBACK_GAUSSIAN** uses the Gaussian \f$\texttt{winsize}\times\texttt{winsize}\f$
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.
The function finds an optical flow for each prev pixel using the \cite Farneback2003 algorithm so that
\f[\texttt{prev} (y,x) \sim \texttt{next} ( y + \texttt{flow} (y,x)[1], x + \texttt{flow} (y,x)[0])\f]
\note
- An example using the optical flow algorithm described by Gunnar Farneback can be found at opencv_source_code/samples/cpp/fback.cpp - (Python) An example using the optical flow algorithm described by Gunnar Farneback can be found at opencv_source_code/samples/python/opt_flow.py
@Namespace(value="cv")
public static void calcOpticalFlowFarneback(@ByVal
opencv_core.UMat prev,
@ByVal
opencv_core.UMat next,
@ByVal
opencv_core.UMat flow,
double pyr_scale,
int levels,
int winsize,
int iterations,
int poly_n,
double poly_sigma,
int flags)
@Namespace(value="cv")
public static void calcOpticalFlowFarneback(@ByVal
opencv_core.GpuMat prev,
@ByVal
opencv_core.GpuMat next,
@ByVal
opencv_core.GpuMat flow,
double pyr_scale,
int levels,
int winsize,
int iterations,
int poly_n,
double poly_sigma,
int flags)
@Namespace(value="cv") @ByVal public static opencv_core.Mat estimateRigidTransform(@ByVal opencv_core.Mat src, @ByVal opencv_core.Mat dst, @Cast(value="bool") boolean fullAffine)
src - First input 2D point set stored in std::vector or Mat, or an image stored in Mat.dst - Second input 2D point set of the same size and the same type as A, or another image.fullAffine - If true, the function finds an optimal affine transformation with no additional
restrictions (6 degrees of freedom). Otherwise, the class of transformations to choose from is
limited to combinations of translation, rotation, and uniform scaling (4 degrees of freedom).
The function finds an optimal affine transform *[A|b]* (a 2 x 3 floating-point matrix) that approximates best the affine transformation between:
Two point sets Two raster images. In this case, the function first finds some features in the src image and finds the corresponding features in dst image. After that, the problem is reduced to the first case. In case of point sets, the problem is formulated as follows: you need to find a 2x2 matrix *A* and 2x1 vector *b* so that:
\f[[A^*|b^*] = arg \min _{[A|b]} \sum _i \| \texttt{dst}[i] - A { \texttt{src}[i]}^T - b \| ^2\f] where src[i] and dst[i] are the i-th points in src and dst, respectively \f$[A|b]\f$ can be either arbitrary (when fullAffine=true ) or have a form of \f[\begin{bmatrix} a_{11} & a_{12} & b_1 \\ -a_{12} & a_{11} & b_2 \end{bmatrix}\f] when fullAffine=false.
\sa estimateAffine2D, estimateAffinePartial2D, getAffineTransform, getPerspectiveTransform, findHomography
@Namespace(value="cv") @ByVal public static opencv_core.Mat estimateRigidTransform(@ByVal opencv_core.UMat src, @ByVal opencv_core.UMat dst, @Cast(value="bool") boolean fullAffine)
@Namespace(value="cv") @ByVal public static opencv_core.Mat estimateRigidTransform(@ByVal opencv_core.GpuMat src, @ByVal opencv_core.GpuMat dst, @Cast(value="bool") boolean fullAffine)
@Namespace(value="cv") @ByVal public static opencv_core.Mat estimateRigidTransform(@ByVal opencv_core.Mat src, @ByVal opencv_core.Mat dst, @Cast(value="bool") boolean fullAffine, int ransacMaxIters, double ransacGoodRatio, int ransacSize0)
@Namespace(value="cv") @ByVal public static opencv_core.Mat estimateRigidTransform(@ByVal opencv_core.UMat src, @ByVal opencv_core.UMat dst, @Cast(value="bool") boolean fullAffine, int ransacMaxIters, double ransacGoodRatio, int ransacSize0)
@Namespace(value="cv") @ByVal public static opencv_core.Mat estimateRigidTransform(@ByVal opencv_core.GpuMat src, @ByVal opencv_core.GpuMat dst, @Cast(value="bool") boolean fullAffine, int ransacMaxIters, double ransacGoodRatio, int ransacSize0)
@Namespace(value="cv")
public static double findTransformECC(@ByVal
opencv_core.Mat templateImage,
@ByVal
opencv_core.Mat inputImage,
@ByVal
opencv_core.Mat warpMatrix,
int motionType,
@ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 50, 0.001)")
opencv_core.TermCriteria criteria,
@ByVal(nullValue="cv::InputArray(cv::noArray())")
opencv_core.Mat inputMask)
templateImage - single-channel template image; CV_8U or CV_32F array.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 temlateImage.warpMatrix - floating-point \f$2\times 3\f$ or \f$3\times 3\f$ mapping matrix (warp).motionType - parameter, specifying the type of motion:
- **MOTION_TRANSLATION** sets a translational motion model; warpMatrix is \f$2\times 3\f$ with
the first \f$2\times 2\f$ part being the unity matrix and the rest two parameters being
estimated.
- **MOTION_EUCLIDEAN** sets a Euclidean (rigid) transformation as motion model; three
parameters are estimated; warpMatrix is \f$2\times 3\f$.
- **MOTION_AFFINE** sets an affine motion model (DEFAULT); six parameters are estimated;
warpMatrix is \f$2\times 3\f$.
- **MOTION_HOMOGRAPHY** sets a homography as a motion model; eight parameters are
estimated;\warpMatrix\ is \f$3\times 3\f$.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.inputMask - An optional mask to indicate valid values of inputImage.
The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion (\cite EP08), that is
\f[\texttt{warpMatrix} = \texttt{warpMatrix} = \arg\max_{W} \texttt{ECC}(\texttt{templateImage}(x,y),\texttt{inputImage}(x',y'))\f]
where
\f[\begin{bmatrix} x' \\ y' \end{bmatrix} = W \cdot \begin{bmatrix} x \\ y \\ 1 \end{bmatrix}\f]
(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 \f$3\times 3\f$ 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.
\sa estimateAffine2D, estimateAffinePartial2D, findHomography
@Namespace(value="cv")
public static double findTransformECC(@ByVal
opencv_core.Mat templateImage,
@ByVal
opencv_core.Mat inputImage,
@ByVal
opencv_core.Mat warpMatrix)
@Namespace(value="cv")
public static double findTransformECC(@ByVal
opencv_core.UMat templateImage,
@ByVal
opencv_core.UMat inputImage,
@ByVal
opencv_core.UMat warpMatrix,
int motionType,
@ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 50, 0.001)")
opencv_core.TermCriteria criteria,
@ByVal(nullValue="cv::InputArray(cv::noArray())")
opencv_core.UMat inputMask)
@Namespace(value="cv")
public static double findTransformECC(@ByVal
opencv_core.UMat templateImage,
@ByVal
opencv_core.UMat inputImage,
@ByVal
opencv_core.UMat warpMatrix)
@Namespace(value="cv")
public static double findTransformECC(@ByVal
opencv_core.GpuMat templateImage,
@ByVal
opencv_core.GpuMat inputImage,
@ByVal
opencv_core.GpuMat warpMatrix,
int motionType,
@ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 50, 0.001)")
opencv_core.TermCriteria criteria,
@ByVal(nullValue="cv::InputArray(cv::noArray())")
opencv_core.GpuMat inputMask)
@Namespace(value="cv")
public static double findTransformECC(@ByVal
opencv_core.GpuMat templateImage,
@ByVal
opencv_core.GpuMat inputImage,
@ByVal
opencv_core.GpuMat warpMatrix)
@Namespace(value="cv") @opencv_core.Ptr public static opencv_video.DualTVL1OpticalFlow createOptFlow_DualTVL1()
@Namespace(value="cv") @opencv_core.Ptr public static opencv_video.BackgroundSubtractorMOG2 createBackgroundSubtractorMOG2(int history, double varThreshold, @Cast(value="bool") boolean detectShadows)
history - Length of the history.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.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.@Namespace(value="cv") @opencv_core.Ptr public static opencv_video.BackgroundSubtractorMOG2 createBackgroundSubtractorMOG2()
@Namespace(value="cv") @opencv_core.Ptr public static opencv_video.BackgroundSubtractorKNN createBackgroundSubtractorKNN(int history, double dist2Threshold, @Cast(value="bool") boolean detectShadows)
history - Length of the history.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.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.@Namespace(value="cv") @opencv_core.Ptr public static opencv_video.BackgroundSubtractorKNN createBackgroundSubtractorKNN()
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