
OpenCV 2.4.4  
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java.lang.Object org.opencv.video.Video
public class Video
Field Summary  

static int 
OPTFLOW_FARNEBACK_GAUSSIAN

static int 
OPTFLOW_LK_GET_MIN_EIGENVALS

static int 
OPTFLOW_USE_INITIAL_FLOW

Constructor Summary  

Video()

Method Summary  

static int 
buildOpticalFlowPyramid(Mat img,
java.util.List<Mat> pyramid,
Size winSize,
int maxLevel)
Constructs the image pyramid which can be passed to "calcOpticalFlowPyrLK". 
static int 
buildOpticalFlowPyramid(Mat img,
java.util.List<Mat> pyramid,
Size winSize,
int maxLevel,
boolean withDerivatives,
int pyrBorder,
int derivBorder,
boolean tryReuseInputImage)
Constructs the image pyramid which can be passed to "calcOpticalFlowPyrLK". 
static double 
calcGlobalOrientation(Mat orientation,
Mat mask,
Mat mhi,
double timestamp,
double duration)
Calculates a global motion orientation in a selected region. 
static void 
calcMotionGradient(Mat mhi,
Mat mask,
Mat orientation,
double delta1,
double delta2)
Calculates a gradient orientation of a motion history image. 
static void 
calcMotionGradient(Mat mhi,
Mat mask,
Mat orientation,
double delta1,
double delta2,
int apertureSize)
Calculates a gradient orientation of a motion history image. 
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)
Computes a dense optical flow using the Gunnar Farneback's algorithm. 
static void 
calcOpticalFlowPyrLK(Mat prevImg,
Mat nextImg,
MatOfPoint2f prevPts,
MatOfPoint2f nextPts,
MatOfByte status,
MatOfFloat err)
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids. 
static void 
calcOpticalFlowPyrLK(Mat prevImg,
Mat nextImg,
MatOfPoint2f prevPts,
MatOfPoint2f nextPts,
MatOfByte status,
MatOfFloat err,
Size winSize,
int maxLevel)
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids. 
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)
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids. 
static void 
calcOpticalFlowSF(Mat from,
Mat to,
Mat flow,
int layers,
int averaging_block_size,
int max_flow)
Calculate an optical flow using "SimpleFlow" algorithm. 
static void 
calcOpticalFlowSF(Mat from,
Mat to,
Mat flow,
int layers,
int averaging_block_size,
int max_flow,
double sigma_dist,
double sigma_color,
int postprocess_window,
double sigma_dist_fix,
double sigma_color_fix,
double occ_thr,
int upscale_averaging_radius,
double upscale_sigma_dist,
double upscale_sigma_color,
double speed_up_thr)
Calculate an optical flow using "SimpleFlow" algorithm. 
static RotatedRect 
CamShift(Mat probImage,
Rect window,
TermCriteria criteria)
Finds an object center, size, and orientation. 
static Mat 
estimateRigidTransform(Mat src,
Mat dst,
boolean fullAffine)
Computes an optimal affine transformation between two 2D point sets. 
static int 
meanShift(Mat probImage,
Rect window,
TermCriteria criteria)
Finds an object on a back projection image. 
static void 
segmentMotion(Mat mhi,
Mat segmask,
MatOfRect boundingRects,
double timestamp,
double segThresh)
Splits a motion history image into a few parts corresponding to separate independent motions (for example, left hand, right hand). 
static void 
updateMotionHistory(Mat silhouette,
Mat mhi,
double timestamp,
double duration)
Updates the motion history image by a moving silhouette. 
Methods inherited from class java.lang.Object 

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait 
Field Detail 

public static final int OPTFLOW_FARNEBACK_GAUSSIAN
public static final int OPTFLOW_LK_GET_MIN_EIGENVALS
public static final int OPTFLOW_USE_INITIAL_FLOW
Constructor Detail 

public Video()
Method Detail 

public static int buildOpticalFlowPyramid(Mat img, java.util.List<Mat> pyramid, Size winSize, int maxLevel)
Constructs the image pyramid which can be passed to "calcOpticalFlowPyrLK".
img
 8bit 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
 0based maximal pyramid level number.public static int buildOpticalFlowPyramid(Mat img, java.util.List<Mat> pyramid, Size winSize, int maxLevel, boolean withDerivatives, int pyrBorder, int derivBorder, boolean tryReuseInputImage)
Constructs the image pyramid which can be passed to "calcOpticalFlowPyrLK".
img
 8bit 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
 0based 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.
:return: number of levels in constructed pyramid. Can be less than
maxLevel
.
public static double calcGlobalOrientation(Mat orientation, Mat mask, Mat mhi, double timestamp, double duration)
Calculates a global motion orientation in a selected region.
The function calculates an average motion direction in the selected region
and returns the angle between 0 degrees and 360 degrees. The average
direction is computed from the weighted orientation histogram, where a recent
motion has a larger weight and the motion occurred in the past has a smaller
weight, as recorded in mhi
.
orientation
 Motion gradient orientation image calculated by the
function "calcMotionGradient".mask
 Mask image. It may be a conjunction of a valid gradient mask,
also calculated by "calcMotionGradient", and the mask of a region whose
direction needs to be calculated.mhi
 Motion history image calculated by "updateMotionHistory".timestamp
 Timestamp passed to "updateMotionHistory".duration
 Maximum duration of a motion track in milliseconds, passed to
"updateMotionHistory".public static void calcMotionGradient(Mat mhi, Mat mask, Mat orientation, double delta1, double delta2)
Calculates a gradient orientation of a motion history image.
The function calculates a gradient orientation at each pixel (x, y) as:
orientation(x,y)= arctan((dmhi/dy)/(dmhi/dx))
In fact, "fastAtan2" and "phase" are used so that the computed angle is
measured in degrees and covers the full range 0..360. Also, the
mask
is filled to indicate pixels where the computed angle is
valid.
mhi
 Motion history singlechannel floatingpoint image.mask
 Output mask image that has the type CV_8UC1
and the
same size as mhi
. Its nonzero elements mark pixels where the
motion gradient data is correct.orientation
 Output motion gradient orientation image that has the same
type and the same size as mhi
. Each pixel of the image is a
motion orientation, from 0 to 360 degrees.delta1
 Minimal (or maximal) allowed difference between
mhi
values within a pixel neighborhood.delta2
 Maximal (or minimal) allowed difference between
mhi
values within a pixel neighborhood. That is, the function
finds the minimum (m(x,y)) and maximum (M(x,y))
mhi
values over 3 x 3 neighborhood of each pixel and
marks the motion orientation at (x, y) as valid only if
min(delta1, delta2) <= M(x,y)m(x,y) <= max(delta1, delta2).
public static void calcMotionGradient(Mat mhi, Mat mask, Mat orientation, double delta1, double delta2, int apertureSize)
Calculates a gradient orientation of a motion history image.
The function calculates a gradient orientation at each pixel (x, y) as:
orientation(x,y)= arctan((dmhi/dy)/(dmhi/dx))
In fact, "fastAtan2" and "phase" are used so that the computed angle is
measured in degrees and covers the full range 0..360. Also, the
mask
is filled to indicate pixels where the computed angle is
valid.
mhi
 Motion history singlechannel floatingpoint image.mask
 Output mask image that has the type CV_8UC1
and the
same size as mhi
. Its nonzero elements mark pixels where the
motion gradient data is correct.orientation
 Output motion gradient orientation image that has the same
type and the same size as mhi
. Each pixel of the image is a
motion orientation, from 0 to 360 degrees.delta1
 Minimal (or maximal) allowed difference between
mhi
values within a pixel neighborhood.delta2
 Maximal (or minimal) allowed difference between
mhi
values within a pixel neighborhood. That is, the function
finds the minimum (m(x,y)) and maximum (M(x,y))
mhi
values over 3 x 3 neighborhood of each pixel and
marks the motion orientation at (x, y) as valid only if
min(delta1, delta2) <= M(x,y)m(x,y) <= max(delta1, delta2).
apertureSize
 Aperture size of the "Sobel" operator.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)
Computes a dense optical flow using the Gunnar Farneback's algorithm.
The function finds an optical flow for each prev
pixel using the
[Farneback2003] algorithm so that
prev(y,x) ~ next(y + flow(y,x)[1], x + flow(y,x)[0])
prev
 first 8bit singlechannel 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:
flow
as an
initial flow approximation.
winsize
for a Gaussian window
should be set to a larger value to achieve the same level of robustness.
public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err)
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.
The function implements a sparse iterative version of the LucasKanade optical flow in pyramids. See [Bouguet00]. The function is parallelized with the TBB library.
prevImg
 first 8bit 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 singleprecision floatingpoint numbers.nextPts
 output vector of 2D points (with singleprecision
floatingpoint 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).public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel)
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.
The function implements a sparse iterative version of the LucasKanade optical flow in pyramids. See [Bouguet00]. The function is parallelized with the TBB library.
prevImg
 first 8bit 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 singleprecision floatingpoint numbers.nextPts
 output vector of 2D points (with singleprecision
floatingpoint 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
 0based 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
.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)
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.
The function implements a sparse iterative version of the LucasKanade optical flow in pyramids. See [Bouguet00]. The function is parallelized with the TBB library.
prevImg
 first 8bit 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 singleprecision floatingpoint numbers.nextPts
 output vector of 2D points (with singleprecision
floatingpoint 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
 0based 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:
nextPts
; if the flag is not set, then prevPts
is
copied to nextPts
and is considered the initial estimate.
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 [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.public static void calcOpticalFlowSF(Mat from, Mat to, Mat flow, int layers, int averaging_block_size, int max_flow)
Calculate an optical flow using "SimpleFlow" algorithm.
See [Tao2012]. And site of project  http://graphics.berkeley.edu/papers/TaoSAN201205/.
from
 a fromto
 a toflow
 a flowlayers
 Number of layersaveraging_block_size
 Size of block through which we sum up when
calculate cost function for pixelmax_flow
 maximal flow that we search at each levelpublic static void calcOpticalFlowSF(Mat from, Mat to, Mat flow, int layers, int averaging_block_size, int max_flow, double sigma_dist, double sigma_color, int postprocess_window, double sigma_dist_fix, double sigma_color_fix, double occ_thr, int upscale_averaging_radius, double upscale_sigma_dist, double upscale_sigma_color, double speed_up_thr)
Calculate an optical flow using "SimpleFlow" algorithm.
See [Tao2012]. And site of project  http://graphics.berkeley.edu/papers/TaoSAN201205/.
from
 a fromto
 a toflow
 a flowlayers
 Number of layersaveraging_block_size
 Size of block through which we sum up when
calculate cost function for pixelmax_flow
 maximal flow that we search at each levelsigma_dist
 vector smooth spatial sigma parametersigma_color
 vector smooth color sigma parameterpostprocess_window
 window size for postprocess cross bilateral filtersigma_dist_fix
 spatial sigma for postprocess cross bilateralf filtersigma_color_fix
 color sigma for postprocess cross bilateral filterocc_thr
 threshold for detecting occlusionsupscale_averaging_radius
 a upscale_averaging_radiusupscale_sigma_dist
 spatial sigma for bilateral upscale operationupscale_sigma_color
 color sigma for bilateral upscale operationspeed_up_thr
 threshold to detect point with irregular flow  where
flow should be recalculated after upscalepublic static RotatedRect CamShift(Mat probImage, Rect window, TermCriteria criteria)
Finds an object center, size, and orientation.
The function implements the CAMSHIFT object tracking algorithm [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.
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
public static Mat estimateRigidTransform(Mat src, Mat dst, boolean fullAffine)
Computes an optimal affine transformation between two 2D point sets.
The function finds an optimal affine transform *[Ab]* (a 2 x 3
floatingpoint matrix) that approximates best the affine transformation
between:
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:
[A^*b^*] = arg min _([Ab]) sum _idst[i]  A (src[i])^T  b ^2
where src[i]
and dst[i]
are the ith points in
src
and dst
, respectively
[Ab] can be either arbitrary (when fullAffine=true
) or
have a form of
a_11 a_12 b_1 a_12 a_11 b_2
when fullAffine=false
.
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 (5 degrees of
freedom).Calib3d.findHomography(org.opencv.core.MatOfPoint2f, org.opencv.core.MatOfPoint2f, int, double, org.opencv.core.Mat)
,
Imgproc.getAffineTransform(org.opencv.core.MatOfPoint2f, org.opencv.core.MatOfPoint2f)
,
Imgproc.getPerspectiveTransform(org.opencv.core.Mat, org.opencv.core.Mat)
public static int meanShift(Mat probImage, Rect window, TermCriteria criteria)
Finds an object on a back projection image.
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 prefilter 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".
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.
public static void segmentMotion(Mat mhi, Mat segmask, MatOfRect boundingRects, double timestamp, double segThresh)
Splits a motion history image into a few parts corresponding to separate independent motions (for example, left hand, right hand).
The function finds all of the motion segments and marks them in
segmask
with individual values (1,2,...). It also computes a
vector with ROIs of motion connected components. After that the motion
direction for every component can be calculated with "calcGlobalOrientation"
using the extracted mask of the particular component.
mhi
 Motion history image.segmask
 Image where the found mask should be stored, singlechannel,
32bit floatingpoint.boundingRects
 Vector containing ROIs of motion connected components.timestamp
 Current time in milliseconds or other units.segThresh
 Segmentation threshold that is recommended to be equal to
the interval between motion history "steps" or greater.public static void updateMotionHistory(Mat silhouette, Mat mhi, double timestamp, double duration)
Updates the motion history image by a moving silhouette.
The function updates the motion history image as follows:
mhi(x,y)= timestamp if silhouette(x,y) != 0; 0 if silhouette(x,y) = 0 and mhi <(timestamp  duration); mhi(x,y) otherwise
That is, MHI pixels where the motion occurs are set to the current
timestamp
, while the pixels where the motion happened last time
a long time ago are cleared.
The function, together with "calcMotionGradient" and "calcGlobalOrientation",
implements a motion templates technique described in [Davis97] and
[Bradski00].
See also the OpenCV sample motempl.c
that demonstrates the use
of all the motion template functions.
silhouette
 Silhouette mask that has nonzero pixels where the motion
occurs.mhi
 Motion history image that is updated by the function
(singlechannel, 32bit floatingpoint).timestamp
 Current time in milliseconds or other units.duration
 Maximal duration of the motion track in the same units as
timestamp
.

OpenCV 2.4.4 Documentation  
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