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123 lines
4.8 KiB
ReStructuredText
123 lines
4.8 KiB
ReStructuredText
Feature Detection
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=================
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.. highlight:: cpp
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cuda::CornernessCriteria
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------------------------
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.. ocv:class:: cuda::CornernessCriteria : public Algorithm
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Base class for Cornerness Criteria computation. ::
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class CV_EXPORTS CornernessCriteria : public Algorithm
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{
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public:
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virtual void compute(InputArray src, OutputArray dst, Stream& stream = Stream::Null()) = 0;
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};
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cuda::CornernessCriteria::compute
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---------------------------------
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Computes the cornerness criteria at each image pixel.
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.. ocv:function:: void cuda::CornernessCriteria::compute(InputArray src, OutputArray dst, Stream& stream = Stream::Null())
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:param src: Source image.
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:param dst: Destination image containing cornerness values. It will have the same size as ``src`` and ``CV_32FC1`` type.
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:param stream: Stream for the asynchronous version.
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cuda::createHarrisCorner
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------------------------
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Creates implementation for Harris cornerness criteria.
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.. ocv:function:: Ptr<CornernessCriteria> cuda::createHarrisCorner(int srcType, int blockSize, int ksize, double k, int borderType = BORDER_REFLECT101)
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:param srcType: Input source type. Only ``CV_8UC1`` and ``CV_32FC1`` are supported for now.
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:param blockSize: Neighborhood size.
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:param ksize: Aperture parameter for the Sobel operator.
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:param k: Harris detector free parameter.
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:param borderType: Pixel extrapolation method. Only ``BORDER_REFLECT101`` and ``BORDER_REPLICATE`` are supported for now.
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.. seealso:: :ocv:func:`cornerHarris`
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cuda::createMinEigenValCorner
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-----------------------------
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Creates implementation for the minimum eigen value of a 2x2 derivative covariation matrix (the cornerness criteria).
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.. ocv:function:: Ptr<CornernessCriteria> cuda::createMinEigenValCorner(int srcType, int blockSize, int ksize, int borderType = BORDER_REFLECT101)
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:param srcType: Input source type. Only ``CV_8UC1`` and ``CV_32FC1`` are supported for now.
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:param blockSize: Neighborhood size.
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:param ksize: Aperture parameter for the Sobel operator.
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:param borderType: Pixel extrapolation method. Only ``BORDER_REFLECT101`` and ``BORDER_REPLICATE`` are supported for now.
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.. seealso:: :ocv:func:`cornerMinEigenVal`
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cuda::CornersDetector
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---------------------
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.. ocv:class:: cuda::CornersDetector : public Algorithm
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Base class for Corners Detector. ::
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class CV_EXPORTS CornersDetector : public Algorithm
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{
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public:
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virtual void detect(InputArray image, OutputArray corners, InputArray mask = noArray()) = 0;
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};
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cuda::CornersDetector::detect
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-----------------------------
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Determines strong corners on an image.
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.. ocv:function:: void cuda::CornersDetector::detect(InputArray image, OutputArray corners, InputArray mask = noArray())
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:param image: Input 8-bit or floating-point 32-bit, single-channel image.
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:param corners: Output vector of detected corners (1-row matrix with CV_32FC2 type with corners positions).
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: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.
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cuda::createGoodFeaturesToTrackDetector
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---------------------------------------
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Creates implementation for :ocv:class:`cuda::CornersDetector` .
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.. ocv:function:: Ptr<CornersDetector> cuda::createGoodFeaturesToTrackDetector(int srcType, int maxCorners = 1000, double qualityLevel = 0.01, double minDistance = 0.0, int blockSize = 3, bool useHarrisDetector = false, double harrisK = 0.04)
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:param srcType: Input source type. Only ``CV_8UC1`` and ``CV_32FC1`` are supported for now.
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:param maxCorners: Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.
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: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:`cornerMinEigenVal` ) or the Harris function response (see :ocv:func:`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.
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:param minDistance: Minimum possible Euclidean distance between the returned corners.
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:param blockSize: Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See :ocv:func:`cornerEigenValsAndVecs` .
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:param useHarrisDetector: Parameter indicating whether to use a Harris detector (see :ocv:func:`cornerHarris`) or :ocv:func:`cornerMinEigenVal`.
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:param harrisK: Free parameter of the Harris detector.
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.. seealso:: :ocv:func:`goodFeaturesToTrack`
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