OpenCV 4.10.0-dev
Open Source Computer Vision
Loading...
Searching...
No Matches
cv::gapi::core Namespace Reference

This namespace contains G-API Operation Types for OpenCV Core module functionality. More...

Namespaces

namespace  cpu
 
namespace  fluid
 
namespace  gpu
 
namespace  ocl
 
namespace  plaidml
 

Typedefs

using GMat2 = std::tuple<GMat,GMat>
 
using GMat3 = std::tuple<GMat,GMat,GMat>
 
using GMat4 = std::tuple<GMat,GMat,GMat,GMat>
 
using GMatScalar = std::tuple<GMat, GScalar>
 
using GResize = cv::gapi::imgproc::GResize
 
using GResizeP = cv::gapi::imgproc::GResizeP
 

Functions

 G_TYPED_KERNEL (GAbsDiff,< GMat(GMat, GMat)>, "org.opencv.core.matrixop.absdiff")
 
 G_TYPED_KERNEL (GAbsDiffC,< GMat(GMat, GScalar)>, "org.opencv.core.matrixop.absdiffC")
 
 G_TYPED_KERNEL (GAdd,< GMat(GMat, GMat, int)>, "org.opencv.core.math.add")
 
 G_TYPED_KERNEL (GAddC,< GMat(GMat, GScalar, int)>, "org.opencv.core.math.addC")
 
 G_TYPED_KERNEL (GAddW,< GMat(GMat, double, GMat, double, double, int)>, "org.opencv.core.matrixop.addweighted")
 
 G_TYPED_KERNEL (GAnd,< GMat(GMat, GMat)>, "org.opencv.core.pixelwise.bitwise_and")
 
 G_TYPED_KERNEL (GAndS,< GMat(GMat, GScalar)>, "org.opencv.core.pixelwise.bitwise_andS")
 
 G_TYPED_KERNEL (GCmpEQ,< GMat(GMat, GMat)>, "org.opencv.core.pixelwise.compare.cmpEQ")
 
 G_TYPED_KERNEL (GCmpEQScalar,< GMat(GMat, GScalar)>, "org.opencv.core.pixelwise.compare.cmpEQScalar")
 
 G_TYPED_KERNEL (GCmpGE,< GMat(GMat, GMat)>, "org.opencv.core.pixelwise.compare.cmpGE")
 
 G_TYPED_KERNEL (GCmpGEScalar,< GMat(GMat, GScalar)>, "org.opencv.core.pixelwise.compare.cmpGEScalar")
 
 G_TYPED_KERNEL (GCmpGT,< GMat(GMat, GMat)>, "org.opencv.core.pixelwise.compare.cmpGT")
 
 G_TYPED_KERNEL (GCmpGTScalar,< GMat(GMat, GScalar)>, "org.opencv.core.pixelwise.compare.cmpGTScalar")
 
 G_TYPED_KERNEL (GCmpLE,< GMat(GMat, GMat)>, "org.opencv.core.pixelwise.compare.cmpLE")
 
 G_TYPED_KERNEL (GCmpLEScalar,< GMat(GMat, GScalar)>, "org.opencv.core.pixelwise.compare.cmpLEScalar")
 
 G_TYPED_KERNEL (GCmpLT,< GMat(GMat, GMat)>, "org.opencv.core.pixelwise.compare.cmpLT")
 
 G_TYPED_KERNEL (GCmpLTScalar,< GMat(GMat, GScalar)>, "org.opencv.core.pixelwise.compare.cmpLTScalar")
 
 G_TYPED_KERNEL (GCmpNE,< GMat(GMat, GMat)>, "org.opencv.core.pixelwise.compare.cmpNE")
 
 G_TYPED_KERNEL (GCmpNEScalar,< GMat(GMat, GScalar)>, "org.opencv.core.pixelwise.compare.cmpNEScalar")
 
 G_TYPED_KERNEL (GConcatHor,< GMat(GMat, GMat)>, "org.opencv.imgproc.transform.concatHor")
 
 G_TYPED_KERNEL (GConcatVert,< GMat(GMat, GMat)>, "org.opencv.imgproc.transform.concatVert")
 
 G_TYPED_KERNEL (GConvertTo,< GMat(GMat, int, double, double)>, "org.opencv.core.transform.convertTo")
 
 G_TYPED_KERNEL (GCountNonZero,< GOpaque< int >(GMat)>, "org.opencv.core.matrixop.countNonZero")
 
 G_TYPED_KERNEL (GCrop,< GMat(GMat, Rect)>, "org.opencv.core.transform.crop")
 
 G_TYPED_KERNEL (GDiv,< GMat(GMat, GMat, double, int)>, "org.opencv.core.math.div")
 
 G_TYPED_KERNEL (GDivC,< GMat(GMat, GScalar, double, int)>, "org.opencv.core.math.divC")
 
 G_TYPED_KERNEL (GDivRC,< GMat(GScalar, GMat, double, int)>, "org.opencv.core.math.divRC")
 
 G_TYPED_KERNEL (GFlip,< GMat(GMat, int)>, "org.opencv.core.transform.flip")
 
 G_TYPED_KERNEL (GInRange,< GMat(GMat, GScalar, GScalar)>, "org.opencv.core.matrixop.inrange")
 
 G_TYPED_KERNEL (GKMeans2D,< std::tuple< GOpaque< double >, GArray< int >, GArray< Point2f > >(GArray< Point2f >, int, GArray< int >, TermCriteria, int, KmeansFlags)>, "org.opencv.core.kmeans2D")
 
 G_TYPED_KERNEL (GKMeans3D,< std::tuple< GOpaque< double >, GArray< int >, GArray< Point3f > >(GArray< Point3f >, int, GArray< int >, TermCriteria, int, KmeansFlags)>, "org.opencv.core.kmeans3D")
 
 G_TYPED_KERNEL (GKMeansND,< std::tuple< GOpaque< double >, GMat, GMat >(GMat, int, GMat, TermCriteria, int, KmeansFlags)>, "org.opencv.core.kmeansND")
 
 G_TYPED_KERNEL (GKMeansNDNoInit,< std::tuple< GOpaque< double >, GMat, GMat >(GMat, int, TermCriteria, int, KmeansFlags)>, "org.opencv.core.kmeansNDNoInit")
 
 G_TYPED_KERNEL (GLUT,< GMat(GMat, Mat)>, "org.opencv.core.transform.LUT")
 
 G_TYPED_KERNEL (GMask,< GMat(GMat, GMat)>, "org.opencv.core.pixelwise.mask")
 
 G_TYPED_KERNEL (GMax,< GMat(GMat, GMat)>, "org.opencv.core.matrixop.max")
 
 G_TYPED_KERNEL (GMean,< GScalar(GMat)>, "org.opencv.core.math.mean")
 
 G_TYPED_KERNEL (GMerge3,< GMat(GMat, GMat, GMat)>, "org.opencv.core.transform.merge3")
 
 G_TYPED_KERNEL (GMerge4,< GMat(GMat, GMat, GMat, GMat)>, "org.opencv.core.transform.merge4")
 
 G_TYPED_KERNEL (GMin,< GMat(GMat, GMat)>, "org.opencv.core.matrixop.min")
 
 G_TYPED_KERNEL (GMul,< GMat(GMat, GMat, double, int)>, "org.opencv.core.math.mul")
 
 G_TYPED_KERNEL (GMulC,< GMat(GMat, GScalar, int)>, "org.opencv.core.math.mulC")
 
 G_TYPED_KERNEL (GMulCOld,< GMat(GMat, double, int)>, "org.opencv.core.math.mulCOld")
 
 G_TYPED_KERNEL (GMulS,< GMat(GMat, GScalar)>, "org.opencv.core.math.muls")
 
 G_TYPED_KERNEL (GNormalize,< GMat(GMat, double, double, int, int)>, "org.opencv.core.normalize")
 
 G_TYPED_KERNEL (GNormInf,< GScalar(GMat)>, "org.opencv.core.matrixop.norminf")
 
 G_TYPED_KERNEL (GNormL1,< GScalar(GMat)>, "org.opencv.core.matrixop.norml1")
 
 G_TYPED_KERNEL (GNormL2,< GScalar(GMat)>, "org.opencv.core.matrixop.norml2")
 
 G_TYPED_KERNEL (GNot,< GMat(GMat)>, "org.opencv.core.pixelwise.bitwise_not")
 
 G_TYPED_KERNEL (GOr,< GMat(GMat, GMat)>, "org.opencv.core.pixelwise.bitwise_or")
 
 G_TYPED_KERNEL (GOrS,< GMat(GMat, GScalar)>, "org.opencv.core.pixelwise.bitwise_orS")
 
 G_TYPED_KERNEL (GPhase,< GMat(GMat, GMat, bool)>, "org.opencv.core.math.phase")
 
 G_TYPED_KERNEL (GRemap,< GMat(GMat, Mat, Mat, int, int, Scalar)>, "org.opencv.core.transform.remap")
 
 G_TYPED_KERNEL (GSelect,< GMat(GMat, GMat, GMat)>, "org.opencv.core.pixelwise.select")
 
 G_TYPED_KERNEL (GSqrt,< GMat(GMat)>, "org.opencv.core.math.sqrt")
 
 G_TYPED_KERNEL (GSub,< GMat(GMat, GMat, int)>, "org.opencv.core.math.sub")
 
 G_TYPED_KERNEL (GSubC,< GMat(GMat, GScalar, int)>, "org.opencv.core.math.subC")
 
 G_TYPED_KERNEL (GSubRC,< GMat(GScalar, GMat, int)>, "org.opencv.core.math.subRC")
 
 G_TYPED_KERNEL (GSum,< GScalar(GMat)>, "org.opencv.core.matrixop.sum")
 
 G_TYPED_KERNEL (GThreshold,< GMat(GMat, GScalar, GScalar, int)>, "org.opencv.core.matrixop.threshold")
 
 G_TYPED_KERNEL (GTranspose,< GMat(GMat)>, "org.opencv.core.transpose")
 
 G_TYPED_KERNEL (GWarpAffine,< GMat(GMat, const Mat &, Size, int, int, const cv::Scalar &)>, "org.opencv.core.warpAffine")
 
 G_TYPED_KERNEL (GWarpPerspective,< GMat(GMat, const Mat &, Size, int, int, const cv::Scalar &)>, "org.opencv.core.warpPerspective")
 
 G_TYPED_KERNEL (GXor,< GMat(GMat, GMat)>, "org.opencv.core.pixelwise.bitwise_xor")
 
 G_TYPED_KERNEL (GXorS,< GMat(GMat, GScalar)>, "org.opencv.core.pixelwise.bitwise_xorS")
 
 G_TYPED_KERNEL_M (GCartToPolar,< GMat2(GMat, GMat, bool)>, "org.opencv.core.math.cartToPolar")
 
 G_TYPED_KERNEL_M (GIntegral,< GMat2(GMat, int, int)>, "org.opencv.core.matrixop.integral")
 
 G_TYPED_KERNEL_M (GPolarToCart,< GMat2(GMat, GMat, bool)>, "org.opencv.core.math.polarToCart")
 
 G_TYPED_KERNEL_M (GSplit3,< GMat3(GMat)>, "org.opencv.core.transform.split3")
 
 G_TYPED_KERNEL_M (GSplit4,< GMat4(GMat)>,"org.opencv.core.transform.split4")
 
 G_TYPED_KERNEL_M (GThresholdOT,< GMatScalar(GMat, GScalar, int)>, "org.opencv.core.matrixop.thresholdOT")
 

Detailed Description

This namespace contains G-API Operation Types for OpenCV Core module functionality.

Typedef Documentation

◆ GMat2

using cv::gapi::core::GMat2 = std::tuple<GMat,GMat>

◆ GMat3

using cv::gapi::core::GMat3 = std::tuple<GMat,GMat,GMat>

◆ GMat4

using cv::gapi::core::GMat4 = std::tuple<GMat,GMat,GMat,GMat>

◆ GMatScalar

using cv::gapi::core::GMatScalar = std::tuple<GMat, GScalar>

◆ GResize

using cv::gapi::core::GResize = cv::gapi::imgproc::GResize

◆ GResizeP

using cv::gapi::core::GResizeP = cv::gapi::imgproc::GResizeP

Function Documentation

◆ G_TYPED_KERNEL() [1/65]

cv::gapi::core::G_TYPED_KERNEL ( GAbsDiff ,
< GMat(GMat, GMat)> ,
"org.opencv.core.matrixop.absdiff"  )

◆ G_TYPED_KERNEL() [2/65]

cv::gapi::core::G_TYPED_KERNEL ( GAbsDiffC ,
< GMat(GMat, GScalar)> ,
"org.opencv.core.matrixop.absdiffC"  )

◆ G_TYPED_KERNEL() [3/65]

cv::gapi::core::G_TYPED_KERNEL ( GAdd ,
< GMat(GMat, GMat, int)> ,
"org.opencv.core.math.add"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [4/65]

cv::gapi::core::G_TYPED_KERNEL ( GAddC ,
< GMat(GMat, GScalar, int)> ,
"org.opencv.core.math.addC"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [5/65]

cv::gapi::core::G_TYPED_KERNEL ( GAddW ,
< GMat(GMat, double, GMat, double, double, int)> ,
"org.opencv.core.matrixop.addweighted"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [6/65]

cv::gapi::core::G_TYPED_KERNEL ( GAnd ,
< GMat(GMat, GMat)> ,
"org.opencv.core.pixelwise.bitwise_and"  )

◆ G_TYPED_KERNEL() [7/65]

cv::gapi::core::G_TYPED_KERNEL ( GAndS ,
< GMat(GMat, GScalar)> ,
"org.opencv.core.pixelwise.bitwise_andS"  )

◆ G_TYPED_KERNEL() [8/65]

cv::gapi::core::G_TYPED_KERNEL ( GCmpEQ ,
< GMat(GMat, GMat)> ,
"org.opencv.core.pixelwise.compare.cmpEQ"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [9/65]

cv::gapi::core::G_TYPED_KERNEL ( GCmpEQScalar ,
< GMat(GMat, GScalar)> ,
"org.opencv.core.pixelwise.compare.cmpEQScalar"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [10/65]

cv::gapi::core::G_TYPED_KERNEL ( GCmpGE ,
< GMat(GMat, GMat)> ,
"org.opencv.core.pixelwise.compare.cmpGE"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [11/65]

cv::gapi::core::G_TYPED_KERNEL ( GCmpGEScalar ,
< GMat(GMat, GScalar)> ,
"org.opencv.core.pixelwise.compare.cmpGEScalar"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [12/65]

cv::gapi::core::G_TYPED_KERNEL ( GCmpGT ,
< GMat(GMat, GMat)> ,
"org.opencv.core.pixelwise.compare.cmpGT"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [13/65]

cv::gapi::core::G_TYPED_KERNEL ( GCmpGTScalar ,
< GMat(GMat, GScalar)> ,
"org.opencv.core.pixelwise.compare.cmpGTScalar"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [14/65]

cv::gapi::core::G_TYPED_KERNEL ( GCmpLE ,
< GMat(GMat, GMat)> ,
"org.opencv.core.pixelwise.compare.cmpLE"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [15/65]

cv::gapi::core::G_TYPED_KERNEL ( GCmpLEScalar ,
< GMat(GMat, GScalar)> ,
"org.opencv.core.pixelwise.compare.cmpLEScalar"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [16/65]

cv::gapi::core::G_TYPED_KERNEL ( GCmpLT ,
< GMat(GMat, GMat)> ,
"org.opencv.core.pixelwise.compare.cmpLT"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [17/65]

cv::gapi::core::G_TYPED_KERNEL ( GCmpLTScalar ,
< GMat(GMat, GScalar)> ,
"org.opencv.core.pixelwise.compare.cmpLTScalar"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [18/65]

cv::gapi::core::G_TYPED_KERNEL ( GCmpNE ,
< GMat(GMat, GMat)> ,
"org.opencv.core.pixelwise.compare.cmpNE"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [19/65]

cv::gapi::core::G_TYPED_KERNEL ( GCmpNEScalar ,
< GMat(GMat, GScalar)> ,
"org.opencv.core.pixelwise.compare.cmpNEScalar"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [20/65]

cv::gapi::core::G_TYPED_KERNEL ( GConcatHor ,
< GMat(GMat, GMat)> ,
"org.opencv.imgproc.transform.concatHor"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [21/65]

cv::gapi::core::G_TYPED_KERNEL ( GConcatVert ,
< GMat(GMat, GMat)> ,
"org.opencv.imgproc.transform.concatVert"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [22/65]

cv::gapi::core::G_TYPED_KERNEL ( GConvertTo ,
< GMat(GMat, int, double, double)> ,
"org.opencv.core.transform.convertTo"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [23/65]

cv::gapi::core::G_TYPED_KERNEL ( GCountNonZero ,
< GOpaque< int >(GMat)> ,
"org.opencv.core.matrixop.countNonZero"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [24/65]

cv::gapi::core::G_TYPED_KERNEL ( GCrop ,
< GMat(GMat, Rect)> ,
"org.opencv.core.transform.crop"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [25/65]

cv::gapi::core::G_TYPED_KERNEL ( GDiv ,
< GMat(GMat, GMat, double, int)> ,
"org.opencv.core.math.div"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [26/65]

cv::gapi::core::G_TYPED_KERNEL ( GDivC ,
< GMat(GMat, GScalar, double, int)> ,
"org.opencv.core.math.divC"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [27/65]

cv::gapi::core::G_TYPED_KERNEL ( GDivRC ,
< GMat(GScalar, GMat, double, int)> ,
"org.opencv.core.math.divRC"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [28/65]

cv::gapi::core::G_TYPED_KERNEL ( GFlip ,
< GMat(GMat, int)> ,
"org.opencv.core.transform.flip"  )

◆ G_TYPED_KERNEL() [29/65]

cv::gapi::core::G_TYPED_KERNEL ( GInRange ,
< GMat(GMat, GScalar, GScalar)> ,
"org.opencv.core.matrixop.inrange"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [30/65]

cv::gapi::core::G_TYPED_KERNEL ( GKMeans2D ,
< std::tuple< GOpaque< double >, GArray< int >, GArray< Point2f > >(GArray< Point2f >, int, GArray< int >, TermCriteria, int, KmeansFlags)> ,
"org.opencv.core.kmeans2D"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [31/65]

cv::gapi::core::G_TYPED_KERNEL ( GKMeans3D ,
< std::tuple< GOpaque< double >, GArray< int >, GArray< Point3f > >(GArray< Point3f >, int, GArray< int >, TermCriteria, int, KmeansFlags)> ,
"org.opencv.core.kmeans3D"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [32/65]

cv::gapi::core::G_TYPED_KERNEL ( GKMeansND ,
< std::tuple< GOpaque< double >, GMat, GMat >(GMat, int, GMat, TermCriteria, int, KmeansFlags)> ,
"org.opencv.core.kmeansND"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [33/65]

cv::gapi::core::G_TYPED_KERNEL ( GKMeansNDNoInit ,
< std::tuple< GOpaque< double >, GMat, GMat >(GMat, int, TermCriteria, int, KmeansFlags)> ,
"org.opencv.core.kmeansNDNoInit"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [34/65]

cv::gapi::core::G_TYPED_KERNEL ( GLUT ,
< GMat(GMat, Mat)> ,
"org.opencv.core.transform.LUT"  )

◆ G_TYPED_KERNEL() [35/65]

cv::gapi::core::G_TYPED_KERNEL ( GMask ,
< GMat(GMat, GMat)> ,
"org.opencv.core.pixelwise.mask"  )

◆ G_TYPED_KERNEL() [36/65]

cv::gapi::core::G_TYPED_KERNEL ( GMax ,
< GMat(GMat, GMat)> ,
"org.opencv.core.matrixop.max"  )

◆ G_TYPED_KERNEL() [37/65]

cv::gapi::core::G_TYPED_KERNEL ( GMean ,
< GScalar(GMat)> ,
"org.opencv.core.math.mean"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [38/65]

cv::gapi::core::G_TYPED_KERNEL ( GMerge3 ,
< GMat(GMat, GMat, GMat)> ,
"org.opencv.core.transform.merge3"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [39/65]

cv::gapi::core::G_TYPED_KERNEL ( GMerge4 ,
< GMat(GMat, GMat, GMat, GMat)> ,
"org.opencv.core.transform.merge4"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [40/65]

cv::gapi::core::G_TYPED_KERNEL ( GMin ,
< GMat(GMat, GMat)> ,
"org.opencv.core.matrixop.min"  )

◆ G_TYPED_KERNEL() [41/65]

cv::gapi::core::G_TYPED_KERNEL ( GMul ,
< GMat(GMat, GMat, double, int)> ,
"org.opencv.core.math.mul"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [42/65]

cv::gapi::core::G_TYPED_KERNEL ( GMulC ,
< GMat(GMat, GScalar, int)> ,
"org.opencv.core.math.mulC"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [43/65]

cv::gapi::core::G_TYPED_KERNEL ( GMulCOld ,
< GMat(GMat, double, int)> ,
"org.opencv.core.math.mulCOld"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [44/65]

cv::gapi::core::G_TYPED_KERNEL ( GMulS ,
< GMat(GMat, GScalar)> ,
"org.opencv.core.math.muls"  )

◆ G_TYPED_KERNEL() [45/65]

cv::gapi::core::G_TYPED_KERNEL ( GNormalize ,
< GMat(GMat, double, double, int, int)> ,
"org.opencv.core.normalize"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [46/65]

cv::gapi::core::G_TYPED_KERNEL ( GNormInf ,
< GScalar(GMat)> ,
"org.opencv.core.matrixop.norminf"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [47/65]

cv::gapi::core::G_TYPED_KERNEL ( GNormL1 ,
< GScalar(GMat)> ,
"org.opencv.core.matrixop.norml1"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [48/65]

cv::gapi::core::G_TYPED_KERNEL ( GNormL2 ,
< GScalar(GMat)> ,
"org.opencv.core.matrixop.norml2"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [49/65]

cv::gapi::core::G_TYPED_KERNEL ( GNot ,
< GMat(GMat)> ,
"org.opencv.core.pixelwise.bitwise_not"  )

◆ G_TYPED_KERNEL() [50/65]

cv::gapi::core::G_TYPED_KERNEL ( GOr ,
< GMat(GMat, GMat)> ,
"org.opencv.core.pixelwise.bitwise_or"  )

◆ G_TYPED_KERNEL() [51/65]

cv::gapi::core::G_TYPED_KERNEL ( GOrS ,
< GMat(GMat, GScalar)> ,
"org.opencv.core.pixelwise.bitwise_orS"  )

◆ G_TYPED_KERNEL() [52/65]

cv::gapi::core::G_TYPED_KERNEL ( GPhase ,
< GMat(GMat, GMat, bool)> ,
"org.opencv.core.math.phase"  )

◆ G_TYPED_KERNEL() [53/65]

cv::gapi::core::G_TYPED_KERNEL ( GRemap ,
< GMat(GMat, Mat, Mat, int, int, Scalar)> ,
"org.opencv.core.transform.remap"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [54/65]

cv::gapi::core::G_TYPED_KERNEL ( GSelect ,
< GMat(GMat, GMat, GMat)> ,
"org.opencv.core.pixelwise.select"  )

◆ G_TYPED_KERNEL() [55/65]

cv::gapi::core::G_TYPED_KERNEL ( GSqrt ,
< GMat(GMat)> ,
"org.opencv.core.math.sqrt"  )

◆ G_TYPED_KERNEL() [56/65]

cv::gapi::core::G_TYPED_KERNEL ( GSub ,
< GMat(GMat, GMat, int)> ,
"org.opencv.core.math.sub"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [57/65]

cv::gapi::core::G_TYPED_KERNEL ( GSubC ,
< GMat(GMat, GScalar, int)> ,
"org.opencv.core.math.subC"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [58/65]

cv::gapi::core::G_TYPED_KERNEL ( GSubRC ,
< GMat(GScalar, GMat, int)> ,
"org.opencv.core.math.subRC"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [59/65]

cv::gapi::core::G_TYPED_KERNEL ( GSum ,
< GScalar(GMat)> ,
"org.opencv.core.matrixop.sum"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [60/65]

cv::gapi::core::G_TYPED_KERNEL ( GThreshold ,
< GMat(GMat, GScalar, GScalar, int)> ,
"org.opencv.core.matrixop.threshold"  )

◆ G_TYPED_KERNEL() [61/65]

cv::gapi::core::G_TYPED_KERNEL ( GTranspose ,
< GMat(GMat)> ,
"org.opencv.core.transpose"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [62/65]

cv::gapi::core::G_TYPED_KERNEL ( GWarpAffine ,
< GMat(GMat, const Mat &, Size, int, int, const cv::Scalar &)> ,
"org.opencv.core.warpAffine"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [63/65]

cv::gapi::core::G_TYPED_KERNEL ( GWarpPerspective ,
< GMat(GMat, const Mat &, Size, int, int, const cv::Scalar &)> ,
"org.opencv.core.warpPerspective"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL() [64/65]

cv::gapi::core::G_TYPED_KERNEL ( GXor ,
< GMat(GMat, GMat)> ,
"org.opencv.core.pixelwise.bitwise_xor"  )

◆ G_TYPED_KERNEL() [65/65]

cv::gapi::core::G_TYPED_KERNEL ( GXorS ,
< GMat(GMat, GScalar)> ,
"org.opencv.core.pixelwise.bitwise_xorS"  )

◆ G_TYPED_KERNEL_M() [1/6]

cv::gapi::core::G_TYPED_KERNEL_M ( GCartToPolar ,
< GMat2(GMat, GMat, bool)> ,
"org.opencv.core.math.cartToPolar"  )

◆ G_TYPED_KERNEL_M() [2/6]

cv::gapi::core::G_TYPED_KERNEL_M ( GIntegral ,
< GMat2(GMat, int, int)> ,
"org.opencv.core.matrixop.integral"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL_M() [3/6]

cv::gapi::core::G_TYPED_KERNEL_M ( GPolarToCart ,
< GMat2(GMat, GMat, bool)> ,
"org.opencv.core.math.polarToCart"  )

◆ G_TYPED_KERNEL_M() [4/6]

cv::gapi::core::G_TYPED_KERNEL_M ( GSplit3 ,
< GMat3(GMat)> ,
"org.opencv.core.transform.split3"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL_M() [5/6]

cv::gapi::core::G_TYPED_KERNEL_M ( GSplit4 ,
< GMat4(GMat)> ,
"org.opencv.core.transform.split4"  )
Here is the call graph for this function:

◆ G_TYPED_KERNEL_M() [6/6]

cv::gapi::core::G_TYPED_KERNEL_M ( GThresholdOT ,
< GMatScalar(GMat, GScalar, int)> ,
"org.opencv.core.matrixop.thresholdOT"  )
Here is the call graph for this function: