298 lines
11 KiB
ReStructuredText
298 lines
11 KiB
ReStructuredText
Video Analysis
|
||
==============
|
||
|
||
.. highlight:: cpp
|
||
|
||
|
||
|
||
gpu::BroxOpticalFlow
|
||
--------------------
|
||
.. ocv:class:: gpu::BroxOpticalFlow
|
||
|
||
Class computing the optical flow for two images using Brox et al Optical Flow algorithm ([Brox2004]_). ::
|
||
|
||
class BroxOpticalFlow
|
||
{
|
||
public:
|
||
BroxOpticalFlow(float alpha_, float gamma_, float scale_factor_, int inner_iterations_, int outer_iterations_, int solver_iterations_);
|
||
|
||
//! Compute optical flow
|
||
//! frame0 - source frame (supports only CV_32FC1 type)
|
||
//! frame1 - frame to track (with the same size and type as frame0)
|
||
//! u - flow horizontal component (along x axis)
|
||
//! v - flow vertical component (along y axis)
|
||
void operator ()(const GpuMat& frame0, const GpuMat& frame1, GpuMat& u, GpuMat& v, Stream& stream = Stream::Null());
|
||
|
||
//! flow smoothness
|
||
float alpha;
|
||
|
||
//! gradient constancy importance
|
||
float gamma;
|
||
|
||
//! pyramid scale factor
|
||
float scale_factor;
|
||
|
||
//! number of lagged non-linearity iterations (inner loop)
|
||
int inner_iterations;
|
||
|
||
//! number of warping iterations (number of pyramid levels)
|
||
int outer_iterations;
|
||
|
||
//! number of linear system solver iterations
|
||
int solver_iterations;
|
||
|
||
GpuMat buf;
|
||
};
|
||
|
||
|
||
|
||
gpu::GoodFeaturesToTrackDetector_GPU
|
||
------------------------------------
|
||
.. ocv:class:: gpu::GoodFeaturesToTrackDetector_GPU
|
||
|
||
Class used for strong corners detection on an image. ::
|
||
|
||
class GoodFeaturesToTrackDetector_GPU
|
||
{
|
||
public:
|
||
explicit GoodFeaturesToTrackDetector_GPU(int maxCorners_ = 1000, double qualityLevel_ = 0.01, double minDistance_ = 0.0,
|
||
int blockSize_ = 3, bool useHarrisDetector_ = false, double harrisK_ = 0.04);
|
||
|
||
void operator ()(const GpuMat& image, GpuMat& corners, const GpuMat& mask = GpuMat());
|
||
|
||
int maxCorners;
|
||
double qualityLevel;
|
||
double minDistance;
|
||
|
||
int blockSize;
|
||
bool useHarrisDetector;
|
||
double harrisK;
|
||
|
||
void releaseMemory();
|
||
};
|
||
|
||
The class finds the most prominent corners in the image.
|
||
|
||
.. seealso:: :ocv:func:`goodFeaturesToTrack`
|
||
|
||
|
||
|
||
gpu::GoodFeaturesToTrackDetector_GPU::GoodFeaturesToTrackDetector_GPU
|
||
---------------------------------------------------------------------
|
||
Constructor.
|
||
|
||
.. ocv:function:: gpu::GoodFeaturesToTrackDetector_GPU::GoodFeaturesToTrackDetector_GPU(int maxCorners = 1000, double qualityLevel = 0.01, double minDistance = 0.0, int blockSize = 3, bool useHarrisDetector = false, double harrisK = 0.04)
|
||
|
||
:param maxCorners: Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.
|
||
|
||
:param qualityLevel: Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see :ocv:func:`gpu::cornerMinEigenVal` ) or the Harris function response (see :ocv:func:`gpu::cornerHarris` ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the ``qualityLevel=0.01`` , then all the corners with the quality measure less than 15 are rejected.
|
||
|
||
:param minDistance: Minimum possible Euclidean distance between the returned corners.
|
||
|
||
:param blockSize: Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See :ocv:func:`cornerEigenValsAndVecs` .
|
||
|
||
:param useHarrisDetector: Parameter indicating whether to use a Harris detector (see :ocv:func:`gpu::cornerHarris`) or :ocv:func:`gpu::cornerMinEigenVal`.
|
||
|
||
:param harrisK: Free parameter of the Harris detector.
|
||
|
||
|
||
|
||
gpu::GoodFeaturesToTrackDetector_GPU::operator ()
|
||
-------------------------------------------------
|
||
Finds the most prominent corners in the image.
|
||
|
||
.. ocv:function:: void gpu::GoodFeaturesToTrackDetector_GPU::operator ()(const GpuMat& image, GpuMat& corners, const GpuMat& mask = GpuMat())
|
||
|
||
:param image: Input 8-bit, single-channel image.
|
||
|
||
:param corners: Output vector of detected corners (it will be one row matrix with CV_32FC2 type).
|
||
|
||
:param mask: Optional region of interest. If the image is not empty (it needs to have the type ``CV_8UC1`` and the same size as ``image`` ), it specifies the region in which the corners are detected.
|
||
|
||
.. seealso:: :ocv:func:`goodFeaturesToTrack`
|
||
|
||
|
||
|
||
gpu::GoodFeaturesToTrackDetector_GPU::releaseMemory
|
||
---------------------------------------------------
|
||
Releases inner buffers memory.
|
||
|
||
.. ocv:function:: void gpu::GoodFeaturesToTrackDetector_GPU::releaseMemory()
|
||
|
||
|
||
|
||
gpu::FarnebackOpticalFlow
|
||
-------------------------
|
||
.. ocv:class:: gpu::FarnebackOpticalFlow
|
||
|
||
Class computing a dense optical flow using the Gunnar Farneback’s algorithm. ::
|
||
|
||
class CV_EXPORTS FarnebackOpticalFlow
|
||
{
|
||
public:
|
||
FarnebackOpticalFlow()
|
||
{
|
||
numLevels = 5;
|
||
pyrScale = 0.5;
|
||
fastPyramids = false;
|
||
winSize = 13;
|
||
numIters = 10;
|
||
polyN = 5;
|
||
polySigma = 1.1;
|
||
flags = 0;
|
||
}
|
||
|
||
int numLevels;
|
||
double pyrScale;
|
||
bool fastPyramids;
|
||
int winSize;
|
||
int numIters;
|
||
int polyN;
|
||
double polySigma;
|
||
int flags;
|
||
|
||
void operator ()(const GpuMat &frame0, const GpuMat &frame1, GpuMat &flowx, GpuMat &flowy, Stream &s = Stream::Null());
|
||
|
||
void releaseMemory();
|
||
|
||
private:
|
||
/* hidden */
|
||
};
|
||
|
||
|
||
|
||
gpu::FarnebackOpticalFlow::operator ()
|
||
--------------------------------------
|
||
Computes a dense optical flow using the Gunnar Farneback’s algorithm.
|
||
|
||
.. ocv:function:: void gpu::FarnebackOpticalFlow::operator ()(const GpuMat &frame0, const GpuMat &frame1, GpuMat &flowx, GpuMat &flowy, Stream &s = Stream::Null())
|
||
|
||
:param frame0: First 8-bit gray-scale input image
|
||
:param frame1: Second 8-bit gray-scale input image
|
||
:param flowx: Flow horizontal component
|
||
:param flowy: Flow vertical component
|
||
:param s: Stream
|
||
|
||
.. seealso:: :ocv:func:`calcOpticalFlowFarneback`
|
||
|
||
|
||
|
||
gpu::FarnebackOpticalFlow::releaseMemory
|
||
----------------------------------------
|
||
Releases unused auxiliary memory buffers.
|
||
|
||
.. ocv:function:: void gpu::FarnebackOpticalFlow::releaseMemory()
|
||
|
||
|
||
|
||
gpu::PyrLKOpticalFlow
|
||
---------------------
|
||
.. ocv:class:: gpu::PyrLKOpticalFlow
|
||
|
||
Class used for calculating an optical flow. ::
|
||
|
||
class PyrLKOpticalFlow
|
||
{
|
||
public:
|
||
PyrLKOpticalFlow();
|
||
|
||
void sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts,
|
||
GpuMat& status, GpuMat* err = 0);
|
||
|
||
void dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err = 0);
|
||
|
||
Size winSize;
|
||
int maxLevel;
|
||
int iters;
|
||
double derivLambda;
|
||
bool useInitialFlow;
|
||
float minEigThreshold;
|
||
bool getMinEigenVals;
|
||
|
||
void releaseMemory();
|
||
};
|
||
|
||
The class can calculate an optical flow for a sparse feature set or dense optical flow using the iterative Lucas-Kanade method with pyramids.
|
||
|
||
.. seealso:: :ocv:func:`calcOpticalFlowPyrLK`
|
||
|
||
|
||
|
||
gpu::PyrLKOpticalFlow::sparse
|
||
-----------------------------
|
||
Calculate an optical flow for a sparse feature set.
|
||
|
||
.. ocv:function:: void gpu::PyrLKOpticalFlow::sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts, GpuMat& status, GpuMat* err = 0)
|
||
|
||
:param prevImg: First 8-bit input image (supports both grayscale and color images).
|
||
|
||
:param nextImg: Second input image of the same size and the same type as ``prevImg`` .
|
||
|
||
:param prevPts: Vector of 2D points for which the flow needs to be found. It must be one row matrix with CV_32FC2 type.
|
||
|
||
:param nextPts: Output vector of 2D points (with single-precision floating-point coordinates) containing the calculated new positions of input features in the second image. When ``useInitialFlow`` is true, the vector must have the same size as in the input.
|
||
|
||
:param status: Output status vector (CV_8UC1 type). Each element of the vector is set to 1 if the flow for the corresponding features has been found. Otherwise, it is set to 0.
|
||
|
||
:param err: Output vector (CV_32FC1 type) that contains the difference between patches around the original and moved points or min eigen value if ``getMinEigenVals`` is checked. It can be NULL, if not needed.
|
||
|
||
.. seealso:: :ocv:func:`calcOpticalFlowPyrLK`
|
||
|
||
|
||
|
||
gpu::PyrLKOpticalFlow::dense
|
||
-----------------------------
|
||
Calculate dense optical flow.
|
||
|
||
.. ocv:function:: void gpu::PyrLKOpticalFlow::dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err = 0)
|
||
|
||
:param prevImg: First 8-bit grayscale input image.
|
||
|
||
:param nextImg: Second input image of the same size and the same type as ``prevImg`` .
|
||
|
||
:param u: Horizontal component of the optical flow of the same size as input images, 32-bit floating-point, single-channel
|
||
|
||
:param v: Vertical component of the optical flow of the same size as input images, 32-bit floating-point, single-channel
|
||
|
||
:param err: Output vector (CV_32FC1 type) that contains the difference between patches around the original and moved points or min eigen value if ``getMinEigenVals`` is checked. It can be NULL, if not needed.
|
||
|
||
|
||
|
||
gpu::PyrLKOpticalFlow::releaseMemory
|
||
------------------------------------
|
||
Releases inner buffers memory.
|
||
|
||
.. ocv:function:: void gpu::PyrLKOpticalFlow::releaseMemory()
|
||
|
||
|
||
|
||
gpu::interpolateFrames
|
||
----------------------
|
||
Interpolates frames (images) using provided optical flow (displacement field).
|
||
|
||
.. ocv:function:: void gpu::interpolateFrames(const GpuMat& frame0, const GpuMat& frame1, const GpuMat& fu, const GpuMat& fv, const GpuMat& bu, const GpuMat& bv, float pos, GpuMat& newFrame, GpuMat& buf, Stream& stream = Stream::Null())
|
||
|
||
:param frame0: First frame (32-bit floating point images, single channel).
|
||
|
||
:param frame1: Second frame. Must have the same type and size as ``frame0`` .
|
||
|
||
:param fu: Forward horizontal displacement.
|
||
|
||
:param fv: Forward vertical displacement.
|
||
|
||
:param bu: Backward horizontal displacement.
|
||
|
||
:param bv: Backward vertical displacement.
|
||
|
||
:param pos: New frame position.
|
||
|
||
:param newFrame: Output image.
|
||
|
||
:param buf: Temporary buffer, will have width x 6*height size, CV_32FC1 type and contain 6 GpuMat: occlusion masks for first frame, occlusion masks for second, interpolated forward horizontal flow, interpolated forward vertical flow, interpolated backward horizontal flow, interpolated backward vertical flow.
|
||
|
||
:param stream: Stream for the asynchronous version.
|
||
|
||
|
||
|
||
.. [Brox2004] T. Brox, A. Bruhn, N. Papenberg, J. Weickert. *High accuracy optical flow estimation based on a theory for warping*. ECCV 2004.
|