Class cv::cuda::SURF_CUDA#

Class used for extracting Speeded Up Robust Features (SURF) from an image. : View details

#include <opencv2/xfeatures2d/cuda.hpp>

Collaboration diagram for cv::cuda::SURF_CUDA:

Public Types#

enum KeypointLayout {
    X_ROW = 0,
    Y_ROW,
    LAPLACIAN_ROW,
    OCTAVE_ROW,
    SIZE_ROW,
    ANGLE_ROW,
    HESSIAN_ROW,
    ROWS_COUNT
}

Detailed Description#

Class used for extracting Speeded Up Robust Features (SURF) from an image. :

The class SURF_CUDA implements Speeded Up Robust Features descriptor. There is a fast multi-scale Hessian keypoint detector that can be used to find the keypoints (which is the default option). But the descriptors can also be computed for the user-specified keypoints. Only 8-bit grayscale images are supported.

The class SURF_CUDA can store results in the GPU and CPU memory. It provides functions to convert results between CPU and GPU version ( uploadKeypoints, downloadKeypoints, downloadDescriptors ). The format of CPU results is the same as SURF results. GPU results are stored in GpuMat. The keypoints matrix is \(\texttt{nFeatures} \times 7\) matrix with the CV_32FC1 type.

keypoints.ptr<float>(X_ROW)[i] contains x coordinate of the i-th feature.
keypoints.ptr<float>(Y_ROW)[i] contains y coordinate of the i-th feature.
keypoints.ptr<float>(LAPLACIAN_ROW)[i] contains the laplacian sign of the i-th feature.
keypoints.ptr<float>(OCTAVE_ROW)[i] contains the octave of the i-th feature.
keypoints.ptr<float>(SIZE_ROW)[i] contains the size of the i-th feature.
keypoints.ptr<float>(ANGLE_ROW)[i] contain orientation of the i-th feature.
keypoints.ptr<float>(HESSIAN_ROW)[i] contains the response of the i-th feature.

The descriptors matrix is \(\texttt{nFeatures} \times \texttt{descriptorSize}\) matrix with the CV_32FC1 type.

The class SURF_CUDA uses some buffers and provides access to it. All buffers can be safely released between function calls.

Member Enumeration Documentation#

enum KeypointLayout

X_ROW

Y_ROW

LAPLACIAN_ROW

OCTAVE_ROW

SIZE_ROW

ANGLE_ROW

HESSIAN_ROW

ROWS_COUNT

Constructor & Destructor Documentation#

SURF_CUDA()#

cv::cuda::SURF_CUDA::SURF_CUDA()

the default constructor

SURF_CUDA()#

cv::cuda::SURF_CUDA::SURF_CUDA(
double _hessianThreshold,
int _nOctaves = 4,
int _nOctaveLayers = 2,
bool _extended = false,
float _keypointsRatio = 0.01f,
bool _upright = false )

the full constructor taking all the necessary parameters

Member Function Documentation#

defaultNorm()#

int cv::cuda::SURF_CUDA::defaultNorm()

returns the default norm type

descriptorSize()#

int cv::cuda::SURF_CUDA::descriptorSize()

returns the descriptor size in float’s (64 or 128)

detect()#

void cv::cuda::SURF_CUDA::detect(
const GpuMat & img,
const GpuMat & mask,
GpuMat & keypoints )

Finds the keypoints using fast hessian detector used in SURF.

Parameters

img — Source image, currently supports only CV_8UC1 images.
mask — A mask image same size as src and of type CV_8UC1.
keypoints — Detected keypoints.

detectWithDescriptors()#

void cv::cuda::SURF_CUDA::detectWithDescriptors(
const GpuMat & img,
const GpuMat & mask,
GpuMat & keypoints,
GpuMat & descriptors,
bool useProvidedKeypoints = false )

Finds the keypoints and computes their descriptors using fast hessian detector used in SURF.

Parameters

img — Source image, currently supports only CV_8UC1 images.
mask — A mask image same size as src and of type CV_8UC1.
keypoints — Detected keypoints.
descriptors — Keypoint descriptors.
useProvidedKeypoints — Compute descriptors for the user-provided keypoints and recompute keypoints direction.

downloadDescriptors()#

void cv::cuda::SURF_CUDA::downloadDescriptors(
const GpuMat & descriptorsGPU,
std::vector< float > & descriptors )

download descriptors from device to host memory

downloadKeypoints()#

void cv::cuda::SURF_CUDA::downloadKeypoints(
const GpuMat & keypointsGPU,
std::vector< KeyPoint > & keypoints )

download keypoints from device to host memory

operator()()#

void cv::cuda::SURF_CUDA::operator()(
const GpuMat & img,
const GpuMat & mask,
GpuMat & keypoints )

finds the keypoints using fast hessian detector used in SURF supports CV_8UC1 images keypoints will have nFeature cols and 6 rows keypoints.ptr(X_ROW)[i] will contain x coordinate of i’th feature keypoints.ptr(Y_ROW)[i] will contain y coordinate of i’th feature keypoints.ptr(LAPLACIAN_ROW)[i] will contain laplacian sign of i’th feature keypoints.ptr(OCTAVE_ROW)[i] will contain octave of i’th feature keypoints.ptr(SIZE_ROW)[i] will contain size of i’th feature keypoints.ptr(ANGLE_ROW)[i] will contain orientation of i’th feature keypoints.ptr(HESSIAN_ROW)[i] will contain response of i’th feature

operator()()#

void cv::cuda::SURF_CUDA::operator()(
const GpuMat & img,
const GpuMat & mask,
GpuMat & keypoints,
GpuMat & descriptors,
bool useProvidedKeypoints = false )

finds the keypoints and computes their descriptors. Optionally it can compute descriptors for the user-provided keypoints and recompute keypoints direction

operator()()#

void cv::cuda::SURF_CUDA::operator()(
const GpuMat & img,
const GpuMat & mask,
std::vector< KeyPoint > & keypoints )

operator()()#

void cv::cuda::SURF_CUDA::operator()(
const GpuMat & img,
const GpuMat & mask,
std::vector< KeyPoint > & keypoints,
GpuMat & descriptors,
bool useProvidedKeypoints = false )

operator()()#

void cv::cuda::SURF_CUDA::operator()(
const GpuMat & img,
const GpuMat & mask,
std::vector< KeyPoint > & keypoints,
std::vector< float > & descriptors,
bool useProvidedKeypoints = false )

releaseMemory()#

void cv::cuda::SURF_CUDA::releaseMemory()

uploadKeypoints()#

void cv::cuda::SURF_CUDA::uploadKeypoints(
const std::vector< KeyPoint > & keypoints,
GpuMat & keypointsGPU )

upload host keypoints to device memory

create()#

static Ptr< SURF_CUDA > cv::cuda::SURF_CUDA::create(
double _hessianThreshold,
int _nOctaves = 4,
int _nOctaveLayers = 2,
bool _extended = false,
float _keypointsRatio = 0.01f,
bool _upright = false )

Parameters

_hessianThreshold — Threshold for hessian keypoint detector used in SURF.
_nOctaves — Number of pyramid octaves the keypoint detector will use.
_nOctaveLayers — Number of octave layers within each octave.
_extended — Extended descriptor flag (true - use extended 128-element descriptors; false - use 64-element descriptors).
_keypointsRatio — Limits a maximum number of features
_upright — Up-right or rotated features flag (true - do not compute orientation of features; false - compute orientation).

Member Data Documentation#

det#

GpuMat cv::cuda::SURF_CUDA::det

extended#

bool cv::cuda::SURF_CUDA::extended

hessianThreshold#

double cv::cuda::SURF_CUDA::hessianThreshold

keypointsRatio#

float cv::cuda::SURF_CUDA::keypointsRatio

max keypoints = min(keypointsRatio * img.size().area(), 65535)

mask1#

GpuMat cv::cuda::SURF_CUDA::mask1

maskSum#

GpuMat cv::cuda::SURF_CUDA::maskSum

maxPosBuffer#

GpuMat cv::cuda::SURF_CUDA::maxPosBuffer

nOctaveLayers#

int cv::cuda::SURF_CUDA::nOctaveLayers

nOctaves#

int cv::cuda::SURF_CUDA::nOctaves

sum#

GpuMat cv::cuda::SURF_CUDA::sum

trace#

GpuMat cv::cuda::SURF_CUDA::trace

upright#

bool cv::cuda::SURF_CUDA::upright

Source file#

The documentation for this class was generated from the following file:

opencv2/xfeatures2d/cuda.hpp