:param circles:Output vector of found circles. Each vector is encoded as a 3-element floating-point vector :math:`(x, y, radius)` .
:param circleStorage:In C function this is a memory storage that will contain the output sequence of found circles.
:param method:Detection method to use. Currently, the only implemented method is ``CV_HOUGH_GRADIENT`` , which is basically *21HT* , described in [Yuen90]_.
:param dp:Inverse ratio of the accumulator resolution to the image resolution. For example, if ``dp=1`` , the accumulator has the same resolution as the input image. If ``dp=2`` , the accumulator has half as big width and height.
:param markers:Input/output 32-bit single-channel image (map) of markers. It should have the same size as ``image`` .
The function implements one of the variants
of watershed, non-parametric marker-based segmentation algorithm,
described in [Meyer92]_. Before passing the image to the
function, you have to roughly outline the desired regions in the image ``markers`` with positive (
:math:`>0` ) indices. So, every region is
represented as one or more connected components with the pixel values
1, 2, 3, and so on. Such markers can be retrieved from a binary mask
using
:ocv:func:`findContours` and
:ocv:func:`drawContours` (see the ``watershed.cpp`` demo).
The markers are "seeds" of the future image
regions. All the other pixels in ``markers`` , whose relation to the
outlined regions is not known and should be defined by the algorithm,
should be set to 0's. In the function output, each pixel in
markers is set to a value of the "seed" components or to -1 at
boundaries between the regions.
..note:: Every two neighbor connected components are not necessarily separated by a watershed boundary (-1's pixels); for example, when such tangent components exist in the initial marker image. Visual demonstration and usage example of the function can be found in the OpenCV samples directory (see the ``watershed.cpp`` demo).
The function implements one of the variants of watershed, non-parametric marker-based segmentation algorithm, described in [Meyer92]_.
..seealso:::ocv:func:`findContours`
Before passing the image to the function, you have to roughly outline the desired regions in the image ``markers`` with positive (``>0``) indices. So, every region is represented as one or more connected components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary mask using :ocv:func:`findContours` and :ocv:func:`drawContours` (see the ``watershed.cpp`` demo). The markers are "seeds" of the future image regions. All the other pixels in ``markers`` , whose relation to the outlined regions is not known and should be defined by the algorithm, should be set to 0's. In the function output, each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the regions.
Visual demonstration and usage example of the function can be found in the OpenCV samples directory (see the ``watershed.cpp`` demo).
..note:: Any two neighbor connected components are not necessarily separated by a watershed boundary (-1's pixels); for example, they can touch each other in the initial marker image passed to the function.
@ -51,7 +51,9 @@ Computes a dense optical flow using the Gunnar Farneback's algorithm.
..ocv:function:: void calcOpticalFlowFarneback( InputArray prevImg, InputArray nextImg, InputOutputArray flow, double pyrScale, int levels, int winsize, int iterations, int polyN, double polySigma, int flags )
Vadim Pisarevsky
14 years ago