public class opencv_xfeatures2d extends opencv_xfeatures2d
| Modifier and Type | Class and Description |
|---|---|
static class |
opencv_xfeatures2d.AffineFeature2D
\brief Class implementing affine adaptation for key points.
|
static class |
opencv_xfeatures2d.BoostDesc
\brief Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in
\cite Trzcinski13a and \cite Trzcinski13b.
|
static class |
opencv_xfeatures2d.BriefDescriptorExtractor
\brief Class for computing BRIEF descriptors described in \cite calon2010 .
|
static class |
opencv_xfeatures2d.DAISY
\brief Class implementing DAISY descriptor, described in \cite Tola10
|
static class |
opencv_xfeatures2d.Elliptic_KeyPoint
\brief Elliptic region around an interest point.
|
static class |
opencv_xfeatures2d.FREAK
\addtogroup xfeatures2d_experiment
\{
|
static class |
opencv_xfeatures2d.HarrisLaplaceFeatureDetector
\brief Class implementing the Harris-Laplace feature detector as described in \cite Mikolajczyk2004.
|
static class |
opencv_xfeatures2d.LATCH
latch Class for computing the LATCH descriptor.
|
static class |
opencv_xfeatures2d.LUCID
\brief Class implementing the locally uniform comparison image descriptor, described in \cite LUCID
|
static class |
opencv_xfeatures2d.MSDDetector
\brief Class implementing the MSD (*Maximal Self-Dissimilarity*) keypoint detector, described in \cite Tombari14.
|
static class |
opencv_xfeatures2d.PCTSignatures
\brief Class implementing PCT (position-color-texture) signature extraction
as described in \cite KrulisLS16.
|
static class |
opencv_xfeatures2d.PCTSignaturesSQFD
\brief Class implementing Signature Quadratic Form Distance (SQFD).
|
static class |
opencv_xfeatures2d.SIFT
\addtogroup xfeatures2d_nonfree
\{
|
static class |
opencv_xfeatures2d.StarDetector
\brief The class implements the keypoint detector introduced by \cite Agrawal08, synonym of StarDetector.
|
static class |
opencv_xfeatures2d.SURF
\brief Class for extracting Speeded Up Robust Features from an image \cite Bay06 .
|
static class |
opencv_xfeatures2d.VGG
\brief Class implementing VGG (Oxford Visual Geometry Group) descriptor trained end to end
using "Descriptor Learning Using Convex Optimisation" (DLCO) aparatus described in \cite Simonyan14.
|
| Constructor and Description |
|---|
opencv_xfeatures2d() |
| Modifier and Type | Method and Description |
|---|---|
static void |
FASTForPointSet(opencv_core.GpuMat image,
opencv_core.KeyPointVector keypoints,
int threshold) |
static void |
FASTForPointSet(opencv_core.GpuMat image,
opencv_core.KeyPointVector keypoints,
int threshold,
boolean nonmaxSuppression,
int type) |
static void |
FASTForPointSet(opencv_core.Mat image,
opencv_core.KeyPointVector keypoints,
int threshold) |
static void |
FASTForPointSet(opencv_core.Mat image,
opencv_core.KeyPointVector keypoints,
int threshold,
boolean nonmaxSuppression,
int type)
\brief Estimates cornerness for prespecified KeyPoints using the FAST algorithm
|
static void |
FASTForPointSet(opencv_core.UMat image,
opencv_core.KeyPointVector keypoints,
int threshold) |
static void |
FASTForPointSet(opencv_core.UMat image,
opencv_core.KeyPointVector keypoints,
int threshold,
boolean nonmaxSuppression,
int type) |
static void |
matchGMS(opencv_core.Size size1,
opencv_core.Size size2,
opencv_core.KeyPointVector keypoints1,
opencv_core.KeyPointVector keypoints2,
opencv_core.DMatchVector matches1to2,
opencv_core.DMatchVector matchesGMS) |
static void |
matchGMS(opencv_core.Size size1,
opencv_core.Size size2,
opencv_core.KeyPointVector keypoints1,
opencv_core.KeyPointVector keypoints2,
opencv_core.DMatchVector matches1to2,
opencv_core.DMatchVector matchesGMS,
boolean withRotation,
boolean withScale,
double thresholdFactor)
\}
|
map@Namespace(value="cv::xfeatures2d")
public static void FASTForPointSet(@ByVal
opencv_core.Mat image,
@ByRef
opencv_core.KeyPointVector keypoints,
int threshold,
@Cast(value="bool")
boolean nonmaxSuppression,
int type)
image - grayscale image where keypoints (corners) are detected.keypoints - keypoints which should be tested to fit the FAST criteria. Keypoints not beeing
detected as corners are removed.threshold - threshold on difference between intensity of the central pixel and pixels of a
circle around this pixel.nonmaxSuppression - if true, non-maximum suppression is applied to detected corners
(keypoints).type - one of the three neighborhoods as defined in the paper:
FastFeatureDetector::TYPE_9_16, FastFeatureDetector::TYPE_7_12,
FastFeatureDetector::TYPE_5_8
Detects corners using the FAST algorithm by \cite Rosten06 .
@Namespace(value="cv::xfeatures2d")
public static void FASTForPointSet(@ByVal
opencv_core.Mat image,
@ByRef
opencv_core.KeyPointVector keypoints,
int threshold)
@Namespace(value="cv::xfeatures2d")
public static void FASTForPointSet(@ByVal
opencv_core.UMat image,
@ByRef
opencv_core.KeyPointVector keypoints,
int threshold,
@Cast(value="bool")
boolean nonmaxSuppression,
int type)
@Namespace(value="cv::xfeatures2d")
public static void FASTForPointSet(@ByVal
opencv_core.UMat image,
@ByRef
opencv_core.KeyPointVector keypoints,
int threshold)
@Namespace(value="cv::xfeatures2d")
public static void FASTForPointSet(@ByVal
opencv_core.GpuMat image,
@ByRef
opencv_core.KeyPointVector keypoints,
int threshold,
@Cast(value="bool")
boolean nonmaxSuppression,
int type)
@Namespace(value="cv::xfeatures2d")
public static void FASTForPointSet(@ByVal
opencv_core.GpuMat image,
@ByRef
opencv_core.KeyPointVector keypoints,
int threshold)
@Namespace(value="cv::xfeatures2d")
public static void matchGMS(@Const @ByRef
opencv_core.Size size1,
@Const @ByRef
opencv_core.Size size2,
@Const @ByRef
opencv_core.KeyPointVector keypoints1,
@Const @ByRef
opencv_core.KeyPointVector keypoints2,
@Const @ByRef
opencv_core.DMatchVector matches1to2,
@ByRef
opencv_core.DMatchVector matchesGMS,
@Cast(value="const bool")
boolean withRotation,
@Cast(value="const bool")
boolean withScale,
double thresholdFactor)
\addtogroup xfeatures2d_match \{
/** \brief GMS (Grid-based Motion Statistics) feature matching strategy by \cite Bian2017gms .
size1 - Input size of image1.size2 - Input size of image2.keypoints1 - Input keypoints of image1.keypoints2 - Input keypoints of image2.matches1to2 - Input 1-nearest neighbor matches.matchesGMS - Matches returned by the GMS matching strategy.withRotation - Take rotation transformation into account.withScale - Take scale transformation into account.thresholdFactor - The higher, the less matches.
\note
Since GMS works well when the number of features is large, we recommend to use the ORB feature and set FastThreshold to 0 to get as many as possible features quickly.
If matching results are not satisfying, please add more features. (We use 10000 for images with 640 X 480).
If your images have big rotation and scale changes, please set withRotation or withScale to true.@Namespace(value="cv::xfeatures2d")
public static void matchGMS(@Const @ByRef
opencv_core.Size size1,
@Const @ByRef
opencv_core.Size size2,
@Const @ByRef
opencv_core.KeyPointVector keypoints1,
@Const @ByRef
opencv_core.KeyPointVector keypoints2,
@Const @ByRef
opencv_core.DMatchVector matches1to2,
@ByRef
opencv_core.DMatchVector matchesGMS)
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