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313 lines
12 KiB
ReStructuredText
313 lines
12 KiB
ReStructuredText
Hough Transform
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===============
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.. highlight:: cpp
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gpu::HoughLinesDetector
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-----------------------
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.. ocv:class:: gpu::HoughLinesDetector : public Algorithm
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Base class for lines detector algorithm. ::
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class CV_EXPORTS HoughLinesDetector : public Algorithm
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{
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public:
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virtual void detect(InputArray src, OutputArray lines) = 0;
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virtual void downloadResults(InputArray d_lines, OutputArray h_lines, OutputArray h_votes = noArray()) = 0;
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virtual void setRho(float rho) = 0;
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virtual float getRho() const = 0;
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virtual void setTheta(float theta) = 0;
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virtual float getTheta() const = 0;
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virtual void setThreshold(int threshold) = 0;
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virtual int getThreshold() const = 0;
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virtual void setDoSort(bool doSort) = 0;
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virtual bool getDoSort() const = 0;
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virtual void setMaxLines(int maxLines) = 0;
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virtual int getMaxLines() const = 0;
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};
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gpu::HoughLinesDetector::detect
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-------------------------------
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Finds lines in a binary image using the classical Hough transform.
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.. ocv:function:: void gpu::HoughLinesDetector::detect(InputArray src, OutputArray lines)
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:param src: 8-bit, single-channel binary source image.
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:param lines: Output vector of lines. Each line is represented by a two-element vector :math:`(\rho, \theta)` . :math:`\rho` is the distance from the coordinate origin :math:`(0,0)` (top-left corner of the image). :math:`\theta` is the line rotation angle in radians ( :math:`0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}` ).
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.. seealso:: :ocv:func:`HoughLines`
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gpu::HoughLinesDetector::downloadResults
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----------------------------------------
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Downloads results from :ocv:func:`gpu::HoughLinesDetector::detect` to host memory.
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.. ocv:function:: void gpu::HoughLinesDetector::downloadResults(InputArray d_lines, OutputArray h_lines, OutputArray h_votes = noArray())
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:param d_lines: Result of :ocv:func:`gpu::HoughLinesDetector::detect` .
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:param h_lines: Output host array.
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:param h_votes: Optional output array for line's votes.
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gpu::createHoughLinesDetector
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-----------------------------
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Creates implementation for :ocv:class:`gpu::HoughLinesDetector` .
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.. ocv:function:: Ptr<HoughLinesDetector> gpu::createHoughLinesDetector(float rho, float theta, int threshold, bool doSort = false, int maxLines = 4096)
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:param rho: Distance resolution of the accumulator in pixels.
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:param theta: Angle resolution of the accumulator in radians.
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:param threshold: Accumulator threshold parameter. Only those lines are returned that get enough votes ( :math:`>\texttt{threshold}` ).
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:param doSort: Performs lines sort by votes.
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:param maxLines: Maximum number of output lines.
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gpu::HoughSegmentDetector
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-------------------------
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.. ocv:class:: gpu::HoughSegmentDetector : public Algorithm
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Base class for line segments detector algorithm. ::
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class CV_EXPORTS HoughSegmentDetector : public Algorithm
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{
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public:
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virtual void detect(InputArray src, OutputArray lines) = 0;
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virtual void setRho(float rho) = 0;
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virtual float getRho() const = 0;
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virtual void setTheta(float theta) = 0;
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virtual float getTheta() const = 0;
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virtual void setMinLineLength(int minLineLength) = 0;
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virtual int getMinLineLength() const = 0;
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virtual void setMaxLineGap(int maxLineGap) = 0;
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virtual int getMaxLineGap() const = 0;
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virtual void setMaxLines(int maxLines) = 0;
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virtual int getMaxLines() const = 0;
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};
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gpu::HoughSegmentDetector::detect
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---------------------------------
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Finds line segments in a binary image using the probabilistic Hough transform.
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.. ocv:function:: void gpu::HoughSegmentDetector::detect(InputArray src, OutputArray lines)
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:param src: 8-bit, single-channel binary source image.
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:param lines: Output vector of lines. Each line is represented by a 4-element vector :math:`(x_1, y_1, x_2, y_2)` , where :math:`(x_1,y_1)` and :math:`(x_2, y_2)` are the ending points of each detected line segment.
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.. seealso:: :ocv:func:`HoughLinesP`
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gpu::createHoughSegmentDetector
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-------------------------------
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Creates implementation for :ocv:class:`gpu::HoughSegmentDetector` .
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.. ocv:function:: Ptr<HoughSegmentDetector> gpu::createHoughSegmentDetector(float rho, float theta, int minLineLength, int maxLineGap, int maxLines = 4096)
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:param rho: Distance resolution of the accumulator in pixels.
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:param theta: Angle resolution of the accumulator in radians.
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:param minLineLength: Minimum line length. Line segments shorter than that are rejected.
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:param maxLineGap: Maximum allowed gap between points on the same line to link them.
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:param maxLines: Maximum number of output lines.
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gpu::HoughCirclesDetector
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-------------------------
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.. ocv:class:: gpu::HoughCirclesDetector : public Algorithm
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Base class for circles detector algorithm. ::
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class CV_EXPORTS HoughCirclesDetector : public Algorithm
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{
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public:
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virtual void detect(InputArray src, OutputArray circles) = 0;
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virtual void setDp(float dp) = 0;
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virtual float getDp() const = 0;
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virtual void setMinDist(float minDist) = 0;
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virtual float getMinDist() const = 0;
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virtual void setCannyThreshold(int cannyThreshold) = 0;
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virtual int getCannyThreshold() const = 0;
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virtual void setVotesThreshold(int votesThreshold) = 0;
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virtual int getVotesThreshold() const = 0;
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virtual void setMinRadius(int minRadius) = 0;
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virtual int getMinRadius() const = 0;
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virtual void setMaxRadius(int maxRadius) = 0;
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virtual int getMaxRadius() const = 0;
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virtual void setMaxCircles(int maxCircles) = 0;
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virtual int getMaxCircles() const = 0;
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};
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gpu::HoughCirclesDetector::detect
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---------------------------------
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Finds circles in a grayscale image using the Hough transform.
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.. ocv:function:: void gpu::HoughCirclesDetector::detect(InputArray src, OutputArray circles)
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:param src: 8-bit, single-channel grayscale input image.
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:param circles: Output vector of found circles. Each vector is encoded as a 3-element floating-point vector :math:`(x, y, radius)` .
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.. seealso:: :ocv:func:`HoughCircles`
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gpu::createHoughCirclesDetector
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-------------------------------
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Creates implementation for :ocv:class:`gpu::HoughCirclesDetector` .
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.. ocv:function:: Ptr<HoughCirclesDetector> gpu::createHoughCirclesDetector(float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles = 4096)
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: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.
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:param minDist: Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed.
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:param cannyThreshold: The higher threshold of the two passed to Canny edge detector (the lower one is twice smaller).
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:param votesThreshold: The accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected.
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:param minRadius: Minimum circle radius.
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:param maxRadius: Maximum circle radius.
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:param maxCircles: Maximum number of output circles.
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gpu::GeneralizedHough
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---------------------
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.. ocv:class:: gpu::GeneralizedHough : public Algorithm
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Base class for generalized hough transform. ::
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class CV_EXPORTS GeneralizedHough : public Algorithm
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{
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public:
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static Ptr<GeneralizedHough> create(int method);
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virtual void setTemplate(InputArray templ, int cannyThreshold = 100, Point templCenter = Point(-1, -1)) = 0;
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virtual void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter = Point(-1, -1)) = 0;
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virtual void detect(InputArray image, OutputArray positions, int cannyThreshold = 100) = 0;
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virtual void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions) = 0;
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virtual void downloadResults(InputArray d_positions, OutputArray h_positions, OutputArray h_votes = noArray()) = 0;
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};
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Finds arbitrary template in the grayscale image using Generalized Hough Transform.
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gpu::GeneralizedHough::create
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-----------------------------
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Creates implementation for :ocv:class:`gpu::GeneralizedHough` .
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.. ocv:function:: Ptr<GeneralizedHough> gpu::GeneralizedHough::create(int method)
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:param method: Combination of flags ( ``cv::GeneralizedHough::GHT_POSITION`` , ``cv::GeneralizedHough::GHT_SCALE`` , ``cv::GeneralizedHough::GHT_ROTATION`` ) specifying transformation to find.
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For full affine transformations (move + scale + rotation) [Guil1999]_ algorithm is used, otherwise [Ballard1981]_ algorithm is used.
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gpu::GeneralizedHough::setTemplate
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----------------------------------
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Set template to search.
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.. ocv:function:: void gpu::GeneralizedHough::setTemplate(InputArray templ, int cannyThreshold = 100, Point templCenter = Point(-1, -1))
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.. ocv:function:: void gpu::GeneralizedHough::setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter = Point(-1, -1))
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:param templ: Template image. Canny edge detector will be applied to extract template edges.
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:param cannyThreshold: Threshold value for Canny edge detector.
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:param templCenter: Center for rotation. By default image center will be used.
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:param edges: Edge map for template image.
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:param dx: First derivative of template image in the vertical direction. Support only ``CV_32S`` type.
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:param dy: First derivative of template image in the horizontal direction. Support only ``CV_32S`` type.
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gpu::GeneralizedHough::detect
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-----------------------------
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Finds template (set by :ocv:func:`gpu::GeneralizedHough::setTemplate` ) in the grayscale image.
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.. ocv:function:: void gpu::GeneralizedHough::detect(InputArray image, OutputArray positions, int cannyThreshold = 100)
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.. ocv:function:: void gpu::GeneralizedHough::detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions)
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:param templ: Input image. Canny edge detector will be applied to extract template edges.
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:param positions: Output vector of found objects. Each vector is encoded as a 4-element floating-point vector :math:`(x, y, scale, angle)` .
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:param cannyThreshold: Threshold value for Canny edge detector.
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:param edges: Edge map for input image.
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:param dx: First derivative of input image in the vertical direction. Support only ``CV_32S`` type.
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:param dy: First derivative of input image in the horizontal direction. Support only ``CV_32S`` type.
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gpu::GeneralizedHough::downloadResults
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--------------------------------------
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Downloads results from :ocv:func:`gpu::GeneralizedHough::detect` to host memory.
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.. ocv:function:: void gpu::GeneralizedHough::downloadResult(InputArray d_positions, OutputArray h_positions, OutputArray h_votes = noArray())
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:param d_lines: Result of :ocv:func:`gpu::GeneralizedHough::detect` .
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:param h_lines: Output host array.
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:param h_votes: Optional output array for votes. Each vector is encoded as a 3-element integer-point vector :math:`(position_votes, scale_votes, angle_votes)` .
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.. [Ballard1981] Ballard, D.H. (1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 (2): 111-122.
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.. [Guil1999] Guil, N., González-Linares, J.M. and Zapata, E.L. (1999). Bidimensional shape detection using an invariant approach. Pattern Recognition 32 (6): 1025-1038.
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