mirrors/opencv_contrib
1
0
Fork 0
Code Pull Requests Packages Releases Activity

vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e

Browse Source
This commit is contained in:
Gitea Mirror Bot
2026-08-22 00:11:13 +08:00
commit 12022378a3
3872 changed files with 2513409 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/cudafeatures2d/include/opencv2/cudafeatures2d.hpp
+595
View File
@@ -0,0 +1,595 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_CUDAFEATURES2D_HPP
#define OPENCV_CUDAFEATURES2D_HPP
#ifndef __cplusplus
# error cudafeatures2d.hpp header must be compiled as C++
#endif
#include "opencv2/core/cuda.hpp"
#include "opencv2/features.hpp"
#include "opencv2/cudafilters.hpp"
/**
@addtogroup cuda
@{
@defgroup cudafeatures2d Feature Detection and Description
@}
*/
namespace cv { namespace cuda {
//! @addtogroup cudafeatures2d
//! @{
////////////////////////// Corners Detection ///////////////////////////
/** @brief Base class for Cornerness Criteria computation. :
*/
class CV_EXPORTS_W CornernessCriteria : public Algorithm
{
public:
/** @brief Computes the cornerness criteria at each image pixel.
@param src Source image.
@param dst Destination image containing cornerness values. It will have the same size as src and
CV_32FC1 type.
@param stream Stream for the asynchronous version.
*/
CV_WRAP virtual void compute(InputArray src, OutputArray dst, Stream& stream = Stream::Null()) = 0;
};
/** @brief Creates implementation for Harris cornerness criteria.
@param srcType Input source type. Only CV_8UC1 and CV_32FC1 are supported for now.
@param blockSize Neighborhood size.
@param ksize Aperture parameter for the Sobel operator.
@param k Harris detector free parameter.
@param borderType Pixel extrapolation method. Only BORDER_REFLECT101 and BORDER_REPLICATE are
supported for now.
@sa cornerHarris
*/
CV_EXPORTS_W Ptr<CornernessCriteria> createHarrisCorner(int srcType, int blockSize, int ksize, double k, int borderType = BORDER_REFLECT101);
/** @brief Creates implementation for the minimum eigen value of a 2x2 derivative covariation matrix (the
cornerness criteria).
@param srcType Input source type. Only CV_8UC1 and CV_32FC1 are supported for now.
@param blockSize Neighborhood size.
@param ksize Aperture parameter for the Sobel operator.
@param borderType Pixel extrapolation method. Only BORDER_REFLECT101 and BORDER_REPLICATE are
supported for now.
@sa cornerMinEigenVal
*/
CV_EXPORTS_W Ptr<CornernessCriteria> createMinEigenValCorner(int srcType, int blockSize, int ksize, int borderType = BORDER_REFLECT101);
////////////////////////// Corners Detection ///////////////////////////
/** @brief Base class for Corners Detector. :
*/
class CV_EXPORTS_W CornersDetector : public Algorithm
{
public:
/** @brief Determines strong corners on an image.
@param image Input 8-bit or floating-point 32-bit, single-channel image.
@param corners Output vector of detected corners (1-row matrix with CV_32FC2 type with corners
positions).
@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.
@param stream Stream for the asynchronous version.
*/
CV_WRAP virtual void detect(InputArray image, OutputArray corners, InputArray mask = noArray(), Stream& stream = Stream::Null()) = 0;
CV_WRAP virtual void setMaxCorners(int maxCorners) = 0;
CV_WRAP virtual void setMinDistance(double minDistance) = 0;
};
/** @brief Creates implementation for cuda::CornersDetector .
@param srcType Input source type. Only CV_8UC1 and CV_32FC1 are supported for now.
@param maxCorners Maximum number of corners to return. If there are more corners than are found,
the strongest of them is returned.
@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 cornerMinEigenVal ) or the Harris function response (see 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.
@param minDistance Minimum possible Euclidean distance between the returned corners.
@param blockSize Size of an average block for computing a derivative covariation matrix over each
pixel neighborhood. See cornerEigenValsAndVecs .
@param useHarrisDetector Parameter indicating whether to use a Harris detector (see cornerHarris)
or cornerMinEigenVal.
@param harrisK Free parameter of the Harris detector.
*/
CV_EXPORTS_W Ptr<CornersDetector> createGoodFeaturesToTrackDetector(int srcType, int maxCorners = 1000, double qualityLevel = 0.01, double minDistance = 0.0,
int blockSize = 3, bool useHarrisDetector = false, double harrisK = 0.04);
//
// DescriptorMatcher
//
/** @brief Abstract base class for matching keypoint descriptors.
It has two groups of match methods: for matching descriptors of an image with another image or with
an image set.
*/
class CV_EXPORTS_W DescriptorMatcher : public cv::Algorithm
{
public:
//
// Factories
//
/** @brief Brute-force descriptor matcher.
For each descriptor in the first set, this matcher finds the closest descriptor in the second set
by trying each one. This descriptor matcher supports masking permissible matches of descriptor
sets.
@param normType One of NORM_L1, NORM_L2, NORM_HAMMING. L1 and L2 norms are
preferable choices for SIFT and SURF descriptors, NORM_HAMMING should be used with ORB, BRISK and
BRIEF).
*/
CV_WRAP static Ptr<cuda::DescriptorMatcher> createBFMatcher(int normType = cv::NORM_L2);
//
// Utility
//
/** @brief Returns true if the descriptor matcher supports masking permissible matches.
*/
CV_WRAP virtual bool isMaskSupported() const = 0;
//
// Descriptor collection
//
/** @brief Adds descriptors to train a descriptor collection.
If the collection is not empty, the new descriptors are added to existing train descriptors.
@param descriptors Descriptors to add. Each descriptors[i] is a set of descriptors from the same
train image.
*/
CV_WRAP virtual void add(const std::vector<GpuMat>& descriptors) = 0;
/** @brief Returns a constant link to the train descriptor collection.
*/
CV_WRAP virtual const std::vector<GpuMat>& getTrainDescriptors() const = 0;
/** @brief Clears the train descriptor collection.
*/
CV_WRAP virtual void clear() CV_OVERRIDE = 0;
/** @brief Returns true if there are no train descriptors in the collection.
*/
CV_WRAP virtual bool empty() const CV_OVERRIDE = 0;
/** @brief Trains a descriptor matcher.
Trains a descriptor matcher (for example, the flann index). In all methods to match, the method
train() is run every time before matching.
*/
CV_WRAP virtual void train() = 0;
//
// 1 to 1 match
//
/** @brief Finds the best match for each descriptor from a query set (blocking version).
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Matches. If a query descriptor is masked out in mask , no match is added for this
descriptor. So, matches size may be smaller than the query descriptors count.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
In the first variant of this method, the train descriptors are passed as an input argument. In the
second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is
used. Optional mask (or masks) can be passed to specify which query and training descriptors can be
matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if
mask.at\<uchar\>(i,j) is non-zero.
*/
CV_WRAP virtual void match(InputArray queryDescriptors, InputArray trainDescriptors,
CV_OUT std::vector<DMatch>& matches,
InputArray mask = noArray()) = 0;
/** @overload
*/
CV_WRAP virtual void match(InputArray queryDescriptors,
CV_OUT std::vector<DMatch>& matches,
const std::vector<GpuMat>& masks = std::vector<GpuMat>()) = 0;
/** @brief Finds the best match for each descriptor from a query set (asynchronous version).
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Matches array stored in GPU memory. Internal representation is not defined.
Use DescriptorMatcher::matchConvert method to retrieve results in standard representation.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
@param stream CUDA stream.
In the first variant of this method, the train descriptors are passed as an input argument. In the
second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is
used. Optional mask (or masks) can be passed to specify which query and training descriptors can be
matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if
mask.at\<uchar\>(i,j) is non-zero.
*/
CV_WRAP virtual void matchAsync(InputArray queryDescriptors, InputArray trainDescriptors,
OutputArray matches,
InputArray mask = noArray(),
Stream& stream = Stream::Null()) = 0;
/** @overload
*/
CV_WRAP virtual void matchAsync(InputArray queryDescriptors,
OutputArray matches,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(),
Stream& stream = Stream::Null()) = 0;
/** @brief Converts matches array from internal representation to standard matches vector.
The method is supposed to be used with DescriptorMatcher::matchAsync to get final result.
Call this method only after DescriptorMatcher::matchAsync is completed (ie. after synchronization).
@param gpu_matches Matches, returned from DescriptorMatcher::matchAsync.
@param matches Vector of DMatch objects.
*/
CV_WRAP virtual void matchConvert(InputArray gpu_matches,
CV_OUT std::vector<DMatch>& matches) = 0;
//
// knn match
//
/** @brief Finds the k best matches for each descriptor from a query set (blocking version).
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Matches. Each matches[i] is k or less matches for the same query descriptor.
@param k Count of best matches found per each query descriptor or less if a query descriptor has
less than k possible matches in total.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
These extended variants of DescriptorMatcher::match methods find several best matches for each query
descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match
for the details about query and train descriptors.
*/
CV_WRAP virtual void knnMatch(InputArray queryDescriptors, InputArray trainDescriptors,
CV_OUT std::vector<std::vector<DMatch> >& matches,
int k,
InputArray mask = noArray(),
bool compactResult = false) = 0;
/** @overload
*/
CV_WRAP virtual void knnMatch(InputArray queryDescriptors,
CV_OUT std::vector<std::vector<DMatch> >& matches,
int k,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(),
bool compactResult = false) = 0;
/** @brief Finds the k best matches for each descriptor from a query set (asynchronous version).
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Matches array stored in GPU memory. Internal representation is not defined.
Use DescriptorMatcher::knnMatchConvert method to retrieve results in standard representation.
@param k Count of best matches found per each query descriptor or less if a query descriptor has
less than k possible matches in total.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
@param stream CUDA stream.
These extended variants of DescriptorMatcher::matchAsync methods find several best matches for each query
descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::matchAsync
for the details about query and train descriptors.
*/
CV_WRAP virtual void knnMatchAsync(InputArray queryDescriptors, InputArray trainDescriptors,
OutputArray matches,
int k,
InputArray mask = noArray(),
Stream& stream = Stream::Null()) = 0;
/** @overload
*/
CV_WRAP virtual void knnMatchAsync(InputArray queryDescriptors,
OutputArray matches,
int k,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(),
Stream& stream = Stream::Null()) = 0;
/** @brief Converts matches array from internal representation to standard matches vector.
The method is supposed to be used with DescriptorMatcher::knnMatchAsync to get final result.
Call this method only after DescriptorMatcher::knnMatchAsync is completed (ie. after synchronization).
@param gpu_matches Matches, returned from DescriptorMatcher::knnMatchAsync.
@param matches Vector of DMatch objects.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
*/
CV_WRAP virtual void knnMatchConvert(InputArray gpu_matches,
CV_OUT std::vector< std::vector<DMatch> >& matches,
bool compactResult = false) = 0;
//
// radius match
//
/** @brief For each query descriptor, finds the training descriptors not farther than the specified distance (blocking version).
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Found matches.
@param maxDistance Threshold for the distance between matched descriptors. Distance means here
metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured
in Pixels)!
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
For each query descriptor, the methods find such training descriptors that the distance between the
query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are
returned in the distance increasing order.
*/
CV_WRAP virtual void radiusMatch(InputArray queryDescriptors, InputArray trainDescriptors,
CV_OUT std::vector<std::vector<DMatch> >& matches,
float maxDistance,
InputArray mask = noArray(),
bool compactResult = false) = 0;
/** @overload
*/
CV_WRAP virtual void radiusMatch(InputArray queryDescriptors,
CV_OUT std::vector<std::vector<DMatch> >& matches,
float maxDistance,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(),
bool compactResult = false) = 0;
/** @brief For each query descriptor, finds the training descriptors not farther than the specified distance (asynchronous version).
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Matches array stored in GPU memory. Internal representation is not defined.
Use DescriptorMatcher::radiusMatchConvert method to retrieve results in standard representation.
@param maxDistance Threshold for the distance between matched descriptors. Distance means here
metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured
in Pixels)!
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
@param stream CUDA stream.
For each query descriptor, the methods find such training descriptors that the distance between the
query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are
returned in the distance increasing order.
*/
CV_WRAP virtual void radiusMatchAsync(InputArray queryDescriptors, InputArray trainDescriptors,
OutputArray matches,
float maxDistance,
InputArray mask = noArray(),
Stream& stream = Stream::Null()) = 0;
/** @overload
*/
CV_WRAP virtual void radiusMatchAsync(InputArray queryDescriptors,
OutputArray matches,
float maxDistance,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(),
Stream& stream = Stream::Null()) = 0;
/** @brief Converts matches array from internal representation to standard matches vector.
The method is supposed to be used with DescriptorMatcher::radiusMatchAsync to get final result.
Call this method only after DescriptorMatcher::radiusMatchAsync is completed (ie. after synchronization).
@param gpu_matches Matches, returned from DescriptorMatcher::radiusMatchAsync.
@param matches Vector of DMatch objects.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
*/
CV_WRAP virtual void radiusMatchConvert(InputArray gpu_matches,
CV_OUT std::vector< std::vector<DMatch> >& matches,
bool compactResult = false) = 0;
};
//
// Feature2DAsync
//
/** @brief Abstract base class for CUDA asynchronous 2D image feature detectors and descriptor extractors.
*/
class CV_EXPORTS_W Feature2DAsync : public cv::Feature2D
{
public:
CV_WRAP virtual ~Feature2DAsync();
/** @brief Detects keypoints in an image.
@param image Image.
@param keypoints The detected keypoints.
@param mask Mask specifying where to look for keypoints (optional). It must be a 8-bit integer
matrix with non-zero values in the region of interest.
@param stream CUDA stream.
*/
CV_WRAP virtual void detectAsync(InputArray image,
OutputArray keypoints,
InputArray mask = noArray(),
Stream& stream = Stream::Null());
/** @brief Computes the descriptors for a set of keypoints detected in an image.
@param image Image.
@param keypoints Input collection of keypoints.
@param descriptors Computed descriptors. Row j is the descriptor for j-th keypoint.
@param stream CUDA stream.
*/
CV_WRAP virtual void computeAsync(InputArray image,
OutputArray keypoints,
OutputArray descriptors,
Stream& stream = Stream::Null());
/** Detects keypoints and computes the descriptors. */
CV_WRAP virtual void detectAndComputeAsync(InputArray image,
InputArray mask,
OutputArray keypoints,
OutputArray descriptors,
bool useProvidedKeypoints = false,
Stream& stream = Stream::Null());
/** Converts keypoints array from internal representation to standard vector. */
CV_WRAP virtual void convert(InputArray gpu_keypoints,
CV_OUT std::vector<KeyPoint>& keypoints) = 0;
};
//
// FastFeatureDetector
//
/** @brief Wrapping class for feature detection using the FAST method.
*/
class CV_EXPORTS_W FastFeatureDetector : public Feature2DAsync
{
public:
static const int LOCATION_ROW = 0;
static const int RESPONSE_ROW = 1;
static const int ROWS_COUNT = 2;
static const int FEATURE_SIZE = 7;
CV_WRAP static Ptr<cuda::FastFeatureDetector> create(int threshold=10,
bool nonmaxSuppression=true,
int type=cv::FastFeatureDetector::TYPE_9_16,
int max_npoints = 5000);
CV_WRAP virtual void setThreshold(int threshold) = 0;
CV_WRAP virtual void setMaxNumPoints(int max_npoints) = 0;
CV_WRAP virtual int getMaxNumPoints() const = 0;
};
//
// ORB
//
/** @brief Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor
*
* @sa cv::ORB
*/
class CV_EXPORTS_W ORB : public Feature2DAsync
{
public:
static const int X_ROW = 0;
static const int Y_ROW = 1;
static const int RESPONSE_ROW = 2;
static const int ANGLE_ROW = 3;
static const int OCTAVE_ROW = 4;
static const int SIZE_ROW = 5;
static const int ROWS_COUNT = 6;
CV_WRAP static Ptr<cuda::ORB> create(int nfeatures=500,
float scaleFactor=1.2f,
int nlevels=8,
int edgeThreshold=31,
int firstLevel=0,
int WTA_K=2,
int scoreType=cv::ORB::HARRIS_SCORE,
int patchSize=31,
int fastThreshold=20,
bool blurForDescriptor=false);
CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0;
CV_WRAP virtual int getMaxFeatures() const = 0;
CV_WRAP virtual void setScaleFactor(double scaleFactor) = 0;
CV_WRAP virtual double getScaleFactor() const = 0;
CV_WRAP virtual void setNLevels(int nlevels) = 0;
CV_WRAP virtual int getNLevels() const = 0;
CV_WRAP virtual void setEdgeThreshold(int edgeThreshold) = 0;
CV_WRAP virtual int getEdgeThreshold() const = 0;
CV_WRAP virtual void setFirstLevel(int firstLevel) = 0;
CV_WRAP virtual int getFirstLevel() const = 0;
CV_WRAP virtual void setWTA_K(int wta_k) = 0;
CV_WRAP virtual int getWTA_K() const = 0;
CV_WRAP virtual void setScoreType(int scoreType) = 0;
CV_WRAP virtual int getScoreType() const = 0;
CV_WRAP virtual void setPatchSize(int patchSize) = 0;
CV_WRAP virtual int getPatchSize() const = 0;
CV_WRAP virtual void setFastThreshold(int fastThreshold) = 0;
CV_WRAP virtual int getFastThreshold() const = 0;
//! if true, image will be blurred before descriptors calculation
CV_WRAP virtual void setBlurForDescriptor(bool blurForDescriptor) = 0;
CV_WRAP virtual bool getBlurForDescriptor() const = 0;
};
//! @}
}} // namespace cv { namespace cuda {
#endif /* OPENCV_CUDAFEATURES2D_HPP */