:param corners:Initial coordinates of the input corners; refined coordinates on output
:param count:Number of corners
:param win:Half of the side length of the search window. For example, if ``win`` =(5,5), then a :math:`5*2+1 \times 5*2+1 = 11 \times 11` search window would be used
:param zero_zone:Half of the size of the dead region in the middle of the search zone over which the summation in the formula below is not done. It is used sometimes to avoid possible singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such size
:param criteria:Criteria for termination of the iterative process of corner refinement. That is, the process of corner position refinement stops either after a certain number of iterations or when a required accuracy is achieved. The ``criteria`` may specify either of or both the maximum number of iteration and the required accuracy
The function iterates to find the sub-pixel accurate location of corners, or radial saddle points, as shown in on the picture below.
..image:: ../pics/cornersubpix.png
Sub-pixel accurate corner locator is based on the observation that every vector from the center
:math:`q`
to a point
:math:`p`
located within a neighborhood of
:math:`q`
is orthogonal to the image gradient at
:math:`p`
subject to image and measurement noise. Consider the expression:
..math::
\epsilon _i = {DI_{p_i}}^T \cdot (q - p_i)
where
:math:`{DI_{p_i}}`
is the image gradient at the one of the points
:math:`p_i`
in a neighborhood of
:math:`q`
. The value of
:math:`q`
is to be found such that
:math:`\epsilon_i`
is minimized. A system of equations may be set up with
..cfunction:: CvSeq* cvHoughLines2( CvArr* image, void* storage, int method, double rho, double theta, int threshold, double param1=0, double param2=0 )
Finds lines in a binary image using a Hough transform.
:param image:The 8-bit, single-channel, binary source image. In the case of a probabilistic method, the image is modified by the function
:param storage:The storage for the lines that are detected. It can
be a memory storage (in this case a sequence of lines is created in
the storage and returned by the function) or single row/single column
matrix (CvMat*) of a particular type (see below) to which the lines'
parameters are written. The matrix header is modified by the function
so its ``cols`` or ``rows`` will contain the number of lines
detected. If ``storage`` is a matrix and the actual number
of lines exceeds the matrix size, the maximum possible number of lines
is returned (in the case of standard hough transform the lines are sorted
by the accumulator value)
:param method:The Hough transform variant, one of the following:
***CV_HOUGH_STANDARD** classical or standard Hough transform. Every line is represented by two floating-point numbers :math:`(\rho, \theta)` , where :math:`\rho` is a distance between (0,0) point and the line, and :math:`\theta` is the angle between x-axis and the normal to the line. Thus, the matrix must be (the created sequence will be) of ``CV_32FC2`` type
***CV_HOUGH_PROBABILISTIC** probabilistic Hough transform (more efficient in case if picture contains a few long linear segments). It returns line segments rather than the whole line. Each segment is represented by starting and ending points, and the matrix must be (the created sequence will be) of ``CV_32SC4`` type
***CV_HOUGH_MULTI_SCALE** multi-scale variant of the classical Hough transform. The lines are encoded the same way as ``CV_HOUGH_STANDARD``
:param rho:Distance resolution in pixel-related units
:param theta:Angle resolution measured in radians
:param threshold:Threshold parameter. A line is returned by the function if the corresponding accumulator value is greater than ``threshold``
:param param1:The first method-dependent parameter:
* For the classical Hough transform it is not used (0).
* For the probabilistic Hough transform it is the minimum line length.
* For the multi-scale Hough transform it is the divisor for the distance resolution :math:`\rho` . (The coarse distance resolution will be :math:`\rho` and the accurate resolution will be :math:`(\rho / \texttt{param1})` ).
:param param2:The second method-dependent parameter:
* For the classical Hough transform it is not used (0).
* For the probabilistic Hough transform it is the maximum gap between line segments lying on the same line to treat them as a single line segment (i.e. to join them).
* For the multi-scale Hough transform it is the divisor for the angle resolution :math:`\theta` . (The coarse angle resolution will be :math:`\theta` and the accurate resolution will be :math:`(\theta / \texttt{param2})` ).
The function implements a few variants of the Hough transform for line detection.
**Example. Detecting lines with Hough transform.**
::
/* This is a standalone program. Pass an image name as a first parameter
of the program. Switch between standard and probabilistic Hough transform
..cfunction:: int cvSampleLine( const CvArr* image CvPoint pt1 CvPoint pt2 void* buffer int connectivity=8 )
Reads the raster line to the buffer.
:param image:Image to sample the line from
:param pt1:Starting line point
:param pt2:Ending line point
:param buffer:Buffer to store the line points; must have enough size to store :math:`max( |\texttt{pt2.x} - \texttt{pt1.x}|+1, |\texttt{pt2.y} - \texttt{pt1.y}|+1 )`
points in the case of an 8-connected line and :math:`(|\texttt{pt2.x}-\texttt{pt1.x}|+|\texttt{pt2.y}-\texttt{pt1.y}|+1)`
in the case of a 4-connected line
:param connectivity:The line connectivity, 4 or 8
The function implements a particular application of line iterators. The function reads all of the image points lying on the line between
``pt1``
and
``pt2``
, including the end points, and stores them into the buffer.
