:param 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.
:param 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`` .
:param derivLambda:Relative weight of the spatial image derivatives impact to the optical flow estimation. If ``derivLambda=0`` , only the image intensity is used. If ``derivLambda=1`` , only derivatives are used. Any other values between 0 and 1 mean that both derivatives and the image intensity are used (in the corresponding proportions).
***OPTFLOW_USE_INITIAL_FLOW** Use initial estimations stored in ``nextPts`` . If the flag is not set, then ``prevPts`` is copied to ``nextPts`` and is considered as the initial estimate.
..cpp:function:: void calcOpticalFlowFarneback( InputArray prevImg, InputArray nextImg, InputOutputArray flow, double pyrScale, int levels, int winsize, int iterations, int polyN, double polySigma, int flags )
:param pyrScale:Parameter specifying the image scale (<1) to build pyramids for each image. ``pyrScale=0.5`` means a classical pyramid, where each next layer is twice smaller than the previous one.
:param 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.
:param 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.
:param polyN: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, ``polyN`` =5 or 7.
:param polySigma:Standard deviation of the Gaussian that is used to smooth derivatives used as a basis for the polynomial expansion. For ``polyN=5`` , you can set ``polySigma=1.1`` . For ``polyN=7`` , a good value would be ``polySigma=1.5`` .
:param flags:Operation flags that can be a combination of the following:
***OPTFLOW_FARNEBACK_GAUSSIAN** Use the Gaussian :math:`\texttt{winsize}\times\texttt{winsize}` filter instead of a box filter of the same size for optical flow estimation. Usually, this option gives 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.
..cpp:function:: Mat estimateRigidTransform( InputArray src, InputArray dst, bool fullAffine )
Computes an optimal affine transformation between two 2D point sets.
:param src:The first input 2D point set, stored in ``std::vector`` or ``Mat``, or an image, stored in ``Mat``
:param dst:The second input 2D point set of the same size and the same type as ``A``, or another image.
:param fullAffine:If true, the function finds an optimal affine transformation with no additional resrictions (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).
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
#.
or between 2 raster images. In this case, the function first finds some features in the ``src`` image and finds the corresponding features in ``dst`` image, after which the problem is reduced to the first case.
In the case of point sets, the problem is formulated in the following way. We need to find such 2x2 matrix *A* and 2x1 vector *b*, such that:
..math::
[A^*|b^*] = arg \min _{[A|b]} \sum _i \| \texttt{dst}[i] - A { \texttt{src}[i]}^T - b \| ^2
where ``src[i]`` and ``dst[i]`` are the i-th points in ``src`` and ``dst``, respectively
:math:`[A|b]` can be either arbitrary (when ``fullAffine=true`` ) or have form
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.
:param 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.
:param 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.
:param delta1, delta2:Minimum and maximum allowed difference between ``mhi`` values within a pixel neighorhood. That is, the function finds the minimum ( :math:`m(x,y)` ) and maximum ( :math:`M(x,y)` ) ``mhi`` values over :math:`3 \times 3` neighborhood of each pixel and marks the motion orientation at :math:`(x, y)` as valid only if
:cpp:func:`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.
:param mask:Mask image. It may be a conjunction of a valid gradient mask, also calculated by :cpp:func:`calcMotionGradient` , and the mask of a region whose direction needs to be calculated.
:cpp:func:`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()`` .
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
:cpp:func:`CamShift` , the search window size or orientation do not change during the search. You can simply pass the output of
:cpp:func:`calcBackProject` to this function. But better results can be obtained if you pre-filter the back projection and remove the noise (for example, by retrieving connected components with
:cpp:func:`findContours` , throwing away contours with small area (
:cpp:func:`contourArea` ), and rendering the remaining contours with
. However, you can modify ``transitionMatrix``,``controlMatrix`` , and ``measurementMatrix`` to get an extended Kalman filter functionality. See the OpenCV sample ``kalman.c`` .
