public class opencv_video extends opencv_video
| Modifier and Type | Field and Description |
|---|---|
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(GpuMat img,
GpuMatVector pyramid,
Size winSize,
int maxLevel) |
static int |
buildOpticalFlowPyramid(GpuMat img,
GpuMatVector pyramid,
Size winSize,
int maxLevel,
boolean withDerivatives,
int pyrBorder,
int derivBorder,
boolean tryReuseInputImage) |
static int |
buildOpticalFlowPyramid(GpuMat img,
MatVector pyramid,
Size winSize,
int maxLevel) |
static int |
buildOpticalFlowPyramid(GpuMat img,
MatVector pyramid,
Size winSize,
int maxLevel,
boolean withDerivatives,
int pyrBorder,
int derivBorder,
boolean tryReuseInputImage) |
static int |
buildOpticalFlowPyramid(GpuMat img,
UMatVector pyramid,
Size winSize,
int maxLevel) |
static int |
buildOpticalFlowPyramid(GpuMat img,
UMatVector pyramid,
Size winSize,
int maxLevel,
boolean withDerivatives,
int pyrBorder,
int derivBorder,
boolean tryReuseInputImage) |
static int |
buildOpticalFlowPyramid(Mat img,
GpuMatVector pyramid,
Size winSize,
int maxLevel) |
static int |
buildOpticalFlowPyramid(Mat img,
GpuMatVector pyramid,
Size winSize,
int maxLevel,
boolean withDerivatives,
int pyrBorder,
int derivBorder,
boolean tryReuseInputImage) |
static int |
buildOpticalFlowPyramid(Mat img,
MatVector pyramid,
Size winSize,
int maxLevel) |
static int |
buildOpticalFlowPyramid(Mat img,
MatVector pyramid,
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(Mat img,
UMatVector pyramid,
Size winSize,
int maxLevel) |
static int |
buildOpticalFlowPyramid(Mat img,
UMatVector pyramid,
Size winSize,
int maxLevel,
boolean withDerivatives,
int pyrBorder,
int derivBorder,
boolean tryReuseInputImage) |
static int |
buildOpticalFlowPyramid(UMat img,
GpuMatVector pyramid,
Size winSize,
int maxLevel) |
static int |
buildOpticalFlowPyramid(UMat img,
GpuMatVector pyramid,
Size winSize,
int maxLevel,
boolean withDerivatives,
int pyrBorder,
int derivBorder,
boolean tryReuseInputImage) |
static int |
buildOpticalFlowPyramid(UMat img,
MatVector pyramid,
Size winSize,
int maxLevel) |
static int |
buildOpticalFlowPyramid(UMat img,
MatVector pyramid,
Size winSize,
int maxLevel,
boolean withDerivatives,
int pyrBorder,
int derivBorder,
boolean tryReuseInputImage) |
static int |
buildOpticalFlowPyramid(UMat img,
UMatVector pyramid,
Size winSize,
int maxLevel) |
static int |
buildOpticalFlowPyramid(UMat img,
UMatVector pyramid,
Size winSize,
int maxLevel,
boolean withDerivatives,
int pyrBorder,
int derivBorder,
boolean tryReuseInputImage) |
static void |
calcOpticalFlowFarneback(GpuMat prev,
GpuMat next,
GpuMat flow,
double pyr_scale,
int levels,
int winsize,
int iterations,
int poly_n,
double poly_sigma,
int flags) |
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)
\brief Computes a dense optical flow using the Gunnar Farneback's algorithm.
|
static void |
calcOpticalFlowFarneback(UMat prev,
UMat next,
UMat flow,
double pyr_scale,
int levels,
int winsize,
int iterations,
int poly_n,
double poly_sigma,
int flags) |
static void |
calcOpticalFlowPyrLK(GpuMat prevImg,
GpuMat nextImg,
GpuMat prevPts,
GpuMat nextPts,
GpuMat status,
GpuMat err) |
static void |
calcOpticalFlowPyrLK(GpuMat prevImg,
GpuMat nextImg,
GpuMat prevPts,
GpuMat nextPts,
GpuMat status,
GpuMat err,
Size winSize,
int maxLevel,
TermCriteria criteria,
int flags,
double minEigThreshold) |
static void |
calcOpticalFlowPyrLK(Mat prevImg,
Mat nextImg,
Mat prevPts,
Mat nextPts,
Mat status,
Mat err) |
static void |
calcOpticalFlowPyrLK(Mat prevImg,
Mat nextImg,
Mat prevPts,
Mat nextPts,
Mat status,
Mat err,
Size winSize,
int maxLevel,
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(UMat prevImg,
UMat nextImg,
UMat prevPts,
UMat nextPts,
UMat status,
UMat err) |
static void |
calcOpticalFlowPyrLK(UMat prevImg,
UMat nextImg,
UMat prevPts,
UMat nextPts,
UMat status,
UMat err,
Size winSize,
int maxLevel,
TermCriteria criteria,
int flags,
double minEigThreshold) |
static RotatedRect |
CamShift(GpuMat probImage,
Rect window,
TermCriteria criteria) |
static RotatedRect |
CamShift(Mat probImage,
Rect window,
TermCriteria criteria)
\brief Finds an object center, size, and orientation.
|
static RotatedRect |
CamShift(UMat probImage,
Rect window,
TermCriteria criteria) |
static double |
computeECC(GpuMat templateImage,
GpuMat inputImage) |
static double |
computeECC(GpuMat templateImage,
GpuMat inputImage,
GpuMat inputMask) |
static double |
computeECC(Mat templateImage,
Mat inputImage) |
static double |
computeECC(Mat templateImage,
Mat inputImage,
Mat inputMask)
\brief Computes the Enhanced Correlation Coefficient value between two images \cite EP08 .
|
static double |
computeECC(UMat templateImage,
UMat inputImage) |
static double |
computeECC(UMat templateImage,
UMat inputImage,
UMat inputMask) |
static BackgroundSubtractorKNN |
createBackgroundSubtractorKNN() |
static BackgroundSubtractorKNN |
createBackgroundSubtractorKNN(int history,
double dist2Threshold,
boolean detectShadows)
\brief Creates KNN Background Subtractor
|
static BackgroundSubtractorMOG2 |
createBackgroundSubtractorMOG2() |
static BackgroundSubtractorMOG2 |
createBackgroundSubtractorMOG2(int history,
double varThreshold,
boolean detectShadows)
\brief Creates MOG2 Background Subtractor
|
static Mat |
estimateRigidTransform(GpuMat src,
GpuMat dst,
boolean fullAffine)
Deprecated.
|
static Mat |
estimateRigidTransform(Mat src,
Mat dst,
boolean fullAffine)
Deprecated.
Use cv::estimateAffine2D, cv::estimateAffinePartial2D instead. If you are using this function
with images, extract points using cv::calcOpticalFlowPyrLK and then use the estimation functions.
|
static Mat |
estimateRigidTransform(UMat src,
UMat dst,
boolean fullAffine)
Deprecated.
|
static double |
findTransformECC(GpuMat templateImage,
GpuMat inputImage,
GpuMat warpMatrix) |
static double |
findTransformECC(GpuMat templateImage,
GpuMat inputImage,
GpuMat warpMatrix,
int motionType,
TermCriteria criteria,
GpuMat inputMask) |
static double |
findTransformECC(GpuMat templateImage,
GpuMat inputImage,
GpuMat warpMatrix,
int motionType,
TermCriteria criteria,
GpuMat inputMask,
int gaussFiltSize) |
static double |
findTransformECC(Mat templateImage,
Mat inputImage,
Mat warpMatrix) |
static double |
findTransformECC(Mat templateImage,
Mat inputImage,
Mat warpMatrix,
int motionType,
TermCriteria criteria,
Mat inputMask)
\overload
|
static double |
findTransformECC(Mat templateImage,
Mat inputImage,
Mat warpMatrix,
int motionType,
TermCriteria criteria,
Mat inputMask,
int gaussFiltSize)
\brief Finds the geometric transform (warp) between two images in terms of the ECC criterion \cite EP08 .
|
static double |
findTransformECC(UMat templateImage,
UMat inputImage,
UMat warpMatrix) |
static double |
findTransformECC(UMat templateImage,
UMat inputImage,
UMat warpMatrix,
int motionType,
TermCriteria criteria,
UMat inputMask) |
static double |
findTransformECC(UMat templateImage,
UMat inputImage,
UMat warpMatrix,
int motionType,
TermCriteria criteria,
UMat inputMask,
int gaussFiltSize) |
static int |
meanShift(GpuMat probImage,
Rect window,
TermCriteria criteria) |
static int |
meanShift(Mat probImage,
Rect window,
TermCriteria criteria)
\brief Finds an object on a back projection image.
|
static int |
meanShift(UMat probImage,
Rect window,
TermCriteria criteria) |
static Mat |
readOpticalFlow(BytePointer path)
\brief Read a .flo file
|
static Mat |
readOpticalFlow(String path) |
static boolean |
writeOpticalFlow(BytePointer path,
GpuMat flow) |
static boolean |
writeOpticalFlow(BytePointer path,
Mat flow)
\brief Write a .flo to disk
|
static boolean |
writeOpticalFlow(BytePointer path,
UMat flow) |
static boolean |
writeOpticalFlow(String path,
GpuMat flow) |
static boolean |
writeOpticalFlow(String path,
Mat flow) |
static boolean |
writeOpticalFlow(String path,
UMat flow) |
mappublic 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
@Namespace(value="cv") @ByVal public static RotatedRect CamShift(@ByVal Mat probImage, @ByRef Rect window, @ByVal 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 RotatedRect CamShift(@ByVal UMat probImage, @ByRef Rect window, @ByVal TermCriteria criteria)
@Namespace(value="cv") @ByVal public static RotatedRect CamShift(@ByVal GpuMat probImage, @ByRef Rect window, @ByVal TermCriteria criteria)
@Namespace(value="cv") public static int meanShift(@ByVal Mat probImage, @ByRef Rect window, @ByVal 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 UMat probImage, @ByRef Rect window, @ByVal TermCriteria criteria)
@Namespace(value="cv") public static int meanShift(@ByVal GpuMat probImage, @ByRef Rect window, @ByVal TermCriteria criteria)
@Namespace(value="cv") public static int buildOpticalFlowPyramid(@ByVal Mat img, @ByVal MatVector pyramid, @ByVal 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 Mat img, @ByVal MatVector pyramid, @ByVal Size winSize, int maxLevel)
@Namespace(value="cv") public static int buildOpticalFlowPyramid(@ByVal Mat img, @ByVal UMatVector pyramid, @ByVal 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 Mat img, @ByVal UMatVector pyramid, @ByVal Size winSize, int maxLevel)
@Namespace(value="cv") public static int buildOpticalFlowPyramid(@ByVal Mat img, @ByVal GpuMatVector pyramid, @ByVal 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 Mat img, @ByVal GpuMatVector pyramid, @ByVal Size winSize, int maxLevel)
@Namespace(value="cv") public static int buildOpticalFlowPyramid(@ByVal UMat img, @ByVal MatVector pyramid, @ByVal 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 UMat img, @ByVal MatVector pyramid, @ByVal Size winSize, int maxLevel)
@Namespace(value="cv") public static int buildOpticalFlowPyramid(@ByVal UMat img, @ByVal UMatVector pyramid, @ByVal 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 UMat img, @ByVal UMatVector pyramid, @ByVal Size winSize, int maxLevel)
@Namespace(value="cv") public static int buildOpticalFlowPyramid(@ByVal UMat img, @ByVal GpuMatVector pyramid, @ByVal 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 UMat img, @ByVal GpuMatVector pyramid, @ByVal Size winSize, int maxLevel)
@Namespace(value="cv") public static int buildOpticalFlowPyramid(@ByVal GpuMat img, @ByVal MatVector pyramid, @ByVal 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 GpuMat img, @ByVal MatVector pyramid, @ByVal Size winSize, int maxLevel)
@Namespace(value="cv") public static int buildOpticalFlowPyramid(@ByVal GpuMat img, @ByVal UMatVector pyramid, @ByVal 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 GpuMat img, @ByVal UMatVector pyramid, @ByVal Size winSize, int maxLevel)
@Namespace(value="cv") public static int buildOpticalFlowPyramid(@ByVal GpuMat img, @ByVal GpuMatVector pyramid, @ByVal 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 GpuMat img, @ByVal GpuMatVector pyramid, @ByVal Size winSize, int maxLevel)
@Namespace(value="cv") public static void calcOpticalFlowPyrLK(@ByVal Mat prevImg, @ByVal Mat nextImg, @ByVal Mat prevPts, @ByVal Mat nextPts, @ByVal Mat status, @ByVal Mat err, @ByVal(nullValue="cv::Size(21,21)") Size winSize, int maxLevel, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 30, 0.01)") 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 Some examples:
- 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 Mat prevImg, @ByVal Mat nextImg, @ByVal Mat prevPts, @ByVal Mat nextPts, @ByVal Mat status, @ByVal Mat err)
@Namespace(value="cv") public static void calcOpticalFlowPyrLK(@ByVal UMat prevImg, @ByVal UMat nextImg, @ByVal UMat prevPts, @ByVal UMat nextPts, @ByVal UMat status, @ByVal UMat err, @ByVal(nullValue="cv::Size(21,21)") Size winSize, int maxLevel, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 30, 0.01)") TermCriteria criteria, int flags, double minEigThreshold)
@Namespace(value="cv") public static void calcOpticalFlowPyrLK(@ByVal UMat prevImg, @ByVal UMat nextImg, @ByVal UMat prevPts, @ByVal UMat nextPts, @ByVal UMat status, @ByVal UMat err)
@Namespace(value="cv") public static void calcOpticalFlowPyrLK(@ByVal GpuMat prevImg, @ByVal GpuMat nextImg, @ByVal GpuMat prevPts, @ByVal GpuMat nextPts, @ByVal GpuMat status, @ByVal GpuMat err, @ByVal(nullValue="cv::Size(21,21)") Size winSize, int maxLevel, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 30, 0.01)") TermCriteria criteria, int flags, double minEigThreshold)
@Namespace(value="cv") public static void calcOpticalFlowPyrLK(@ByVal GpuMat prevImg, @ByVal GpuMat nextImg, @ByVal GpuMat prevPts, @ByVal GpuMat nextPts, @ByVal GpuMat status, @ByVal GpuMat err)
@Namespace(value="cv") public static void calcOpticalFlowFarneback(@ByVal Mat prev, @ByVal Mat next, @ByVal 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 \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.
The function finds an optical flow for each prev pixel using the \cite Farneback2003 algorithm so that
\[\texttt{prev} (y,x) \sim \texttt{next} ( y + \texttt{flow} (y,x)[1], x + \texttt{flow} (y,x)[0])\]\note Some examples:
- 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 UMat prev, @ByVal UMat next, @ByVal 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 GpuMat prev, @ByVal GpuMat next, @ByVal GpuMat flow, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags)
@Namespace(value="cv") @Deprecated @ByVal public static Mat estimateRigidTransform(@ByVal Mat src, @ByVal 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:
\[[A^*|b^*] = arg \min _{[A|b]} \sum _i \| \texttt{dst}[i] - A { \texttt{src}[i]}^T - b \| ^2\][A|b] can be either arbitrary (when fullAffine=true ) or have a form of
\[\begin{bmatrix} a_{11} & a_{12} & b_1 \\ -a_{12} & a_{11} & b_2 \end{bmatrix}\]estimateAffine2D, estimateAffinePartial2D, getAffineTransform, getPerspectiveTransform, findHomography@Namespace(value="cv") @Deprecated @ByVal public static Mat estimateRigidTransform(@ByVal UMat src, @ByVal UMat dst, @Cast(value="bool") boolean fullAffine)
@Namespace(value="cv") @Deprecated @ByVal public static Mat estimateRigidTransform(@ByVal GpuMat src, @ByVal GpuMat dst, @Cast(value="bool") boolean fullAffine)
@Namespace(value="cv") public static double computeECC(@ByVal Mat templateImage, @ByVal Mat inputImage, @ByVal(nullValue="cv::InputArray(cv::noArray())") Mat inputMask)
templateImage - single-channel template image; CV_8U or CV_32F array.inputImage - single-channel input image to be warped to provide an image similar to
templateImage, same type as templateImage.inputMask - An optional mask to indicate valid values of inputImage.
findTransformECC@Namespace(value="cv") public static double computeECC(@ByVal Mat templateImage, @ByVal Mat inputImage)
@Namespace(value="cv") public static double computeECC(@ByVal UMat templateImage, @ByVal UMat inputImage, @ByVal(nullValue="cv::InputArray(cv::noArray())") UMat inputMask)
@Namespace(value="cv") public static double computeECC(@ByVal UMat templateImage, @ByVal UMat inputImage)
@Namespace(value="cv") public static double computeECC(@ByVal GpuMat templateImage, @ByVal GpuMat inputImage, @ByVal(nullValue="cv::InputArray(cv::noArray())") GpuMat inputMask)
@Namespace(value="cv") public static double computeECC(@ByVal GpuMat templateImage, @ByVal GpuMat inputImage)
@Namespace(value="cv") public static double findTransformECC(@ByVal Mat templateImage, @ByVal Mat inputImage, @ByVal Mat warpMatrix, int motionType, @ByVal TermCriteria criteria, @ByVal Mat inputMask, int gaussFiltSize)
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 templateImage.warpMatrix - floating-point 2\times 3 or 3\times 3 mapping matrix (warp).motionType - parameter, specifying the type of motion:
- **MOTION_TRANSLATION** 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.
- **MOTION_EUCLIDEAN** sets a Euclidean (rigid) transformation as motion model; three
parameters are estimated; warpMatrix is 2\times 3.
- **MOTION_AFFINE** sets an affine motion model (DEFAULT); six parameters are estimated;
warpMatrix is 2\times 3.
- **MOTION_HOMOGRAPHY** sets a homography as a motion model; eight parameters are
estimated;\warpMatrix\ is 3\times 3.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.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.
computeECC, estimateAffine2D, estimateAffinePartial2D, findHomography@Namespace(value="cv") public static double findTransformECC(@ByVal UMat templateImage, @ByVal UMat inputImage, @ByVal UMat warpMatrix, int motionType, @ByVal TermCriteria criteria, @ByVal UMat inputMask, int gaussFiltSize)
@Namespace(value="cv") public static double findTransformECC(@ByVal GpuMat templateImage, @ByVal GpuMat inputImage, @ByVal GpuMat warpMatrix, int motionType, @ByVal TermCriteria criteria, @ByVal GpuMat inputMask, int gaussFiltSize)
@Namespace(value="cv") public static double findTransformECC(@ByVal Mat templateImage, @ByVal Mat inputImage, @ByVal Mat warpMatrix, int motionType, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 50, 0.001)") TermCriteria criteria, @ByVal(nullValue="cv::InputArray(cv::noArray())") Mat inputMask)
@Namespace(value="cv") public static double findTransformECC(@ByVal Mat templateImage, @ByVal Mat inputImage, @ByVal Mat warpMatrix)
@Namespace(value="cv") public static double findTransformECC(@ByVal UMat templateImage, @ByVal UMat inputImage, @ByVal UMat warpMatrix, int motionType, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 50, 0.001)") TermCriteria criteria, @ByVal(nullValue="cv::InputArray(cv::noArray())") UMat inputMask)
@Namespace(value="cv") public static double findTransformECC(@ByVal UMat templateImage, @ByVal UMat inputImage, @ByVal UMat warpMatrix)
@Namespace(value="cv") public static double findTransformECC(@ByVal GpuMat templateImage, @ByVal GpuMat inputImage, @ByVal GpuMat warpMatrix, int motionType, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 50, 0.001)") TermCriteria criteria, @ByVal(nullValue="cv::InputArray(cv::noArray())") GpuMat inputMask)
@Namespace(value="cv") public static double findTransformECC(@ByVal GpuMat templateImage, @ByVal GpuMat inputImage, @ByVal GpuMat warpMatrix)
@Namespace(value="cv") @ByVal public static Mat readOpticalFlow(@opencv_core.Str BytePointer path)
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).
@Namespace(value="cv") @ByVal public static Mat readOpticalFlow(@opencv_core.Str String path)
@Namespace(value="cv") @Cast(value="bool") public static boolean writeOpticalFlow(@opencv_core.Str BytePointer path, @ByVal Mat flow)
path - Path to the file to be writtenflow - 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).
@Namespace(value="cv") @Cast(value="bool") public static boolean writeOpticalFlow(@opencv_core.Str String path, @ByVal Mat flow)
@Namespace(value="cv") @Cast(value="bool") public static boolean writeOpticalFlow(@opencv_core.Str String path, @ByVal UMat flow)
@Namespace(value="cv") @Cast(value="bool") public static boolean writeOpticalFlow(@opencv_core.Str BytePointer path, @ByVal UMat flow)
@Namespace(value="cv") @Cast(value="bool") public static boolean writeOpticalFlow(@opencv_core.Str BytePointer path, @ByVal GpuMat flow)
@Namespace(value="cv") @Cast(value="bool") public static boolean writeOpticalFlow(@opencv_core.Str String path, @ByVal GpuMat flow)
@Namespace(value="cv") @opencv_core.Ptr public static 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 BackgroundSubtractorMOG2 createBackgroundSubtractorMOG2()
@Namespace(value="cv") @opencv_core.Ptr public static 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 BackgroundSubtractorKNN createBackgroundSubtractorKNN()
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