org.opencv.video.Video
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OpenCV 2.4.2 | |||||||
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java.lang.Object
public class Video
| Field Summary | |
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static int |
OPTFLOW_FARNEBACK_GAUSSIAN
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static int |
OPTFLOW_LK_GET_MIN_EIGENVALS
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static int |
OPTFLOW_USE_INITIAL_FLOW
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| Constructor Summary | |
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Video()
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| Method Summary | |
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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 Lucas-Kanade 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 Lucas-Kanade 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 Lucas-Kanade method with pyramids. |
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 |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
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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 |
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public Video()
| Method Detail |
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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 - 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.
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 - 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.
: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 single-channel floating-point image.mask - Output mask image that has the type CV_8UC1 and the
same size as mhi. Its non-zero 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 single-channel floating-point image.mask - Output mask image that has the type CV_8UC1 and the
same size as mhi. Its non-zero 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 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:
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 Lucas-Kanade method with pyramids.
The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See [Bouguet00]. The function is parallelized with the TBB library.
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. The
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. 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 a
error for the corresponding feature. A 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 Lucas-Kanade method with pyramids.
The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See [Bouguet00]. The function is parallelized with the TBB library.
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. The
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. 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 a
error for the corresponding feature. A 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.
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 Lucas-Kanade method with pyramids.
The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See [Bouguet00]. The function is parallelized with the TBB library.
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. The
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. 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 a
error for the corresponding feature. A 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:
nextPts. If the flag is not set, then prevPts is
copied to nextPts and is considered as 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 computes a 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 then minEigThreshold then a corresponding
feature is filtered out and its flow is not computed. So it allows to remove
bad points earlier and speed up the computation.
public 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 *[A|b]* (a 2 x 3
floating-point 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 _([A|b]) sum _i|dst[i] - A (src[i])^T - b| ^2
where src[i] and dst[i] are the i-th points in
src and dst, respectively
[A|b] 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 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".
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, single-channel,
32-bit floating-point.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 non-zero pixels where the motion
occurs.mhi - Motion history image that is updated by the function
(single-channel, 32-bit floating-point).timestamp - Current time in milliseconds or other units.duration - Maximal duration of the motion track in the same units as
timestamp.
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Official OpenCV 2.4 Documentation | |||||||
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