| OpenCV 5.0.0alpha Open Source Computer Vision

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Namespaces | Classes | Typedefs | Enumerations | Functions
cv::gapi Namespace Reference
G-API framework » G-API Graph Compilation Arguments | G-API framework » G-API Data Types » G-API Metadata Descriptors | G-API framework » G-API Main Classes | G-API framework » G-API Serialization functionality

## Namespaces
namespace | calib3d
| This namespace contains G-API Operation Types for Stereo and related functionality.
namespace | compound
namespace | core
| This namespace contains G-API Operation Types for OpenCV Core module functionality.
namespace | cpu
| This namespace contains G-API CPU backend functions, structures, and symbols.
namespace | fluid
| This namespace contains G-API Fluid backend functions, structures, and symbols.
namespace | ie
| This namespace contains G-API OpenVINO backend functions, structures, and symbols.
namespace | imgproc
| This namespace contains G-API Operation Types for OpenCV ImgProc module functionality.
namespace | nn
namespace | oak
namespace | ocl
| This namespace contains G-API OpenCL backend functions, structures, and symbols.
namespace | onnx
| This namespace contains G-API ONNX Runtime backend functions, structures, and symbols.
namespace | ot
| This namespace contains G-API Operation Types for VAS Object Tracking module functionality.
namespace | ov
| This namespace contains G-API OpenVINO 2.0 backend functions, structures, and symbols.
namespace | own
| This namespace contains G-API own data structures used in its standalone mode build.
namespace | plaidml
| This namespace contains G-API PlaidML backend functions, structures, and symbols.
namespace | python
| This namespace contains G-API Python backend functions, structures, and symbols.
namespace | render
| This namespace contains G-API CPU rendering backend functions, structures, and symbols. See G-API Drawing and composition functionality for details.
namespace | s11n
| This namespace contains G-API serialization and deserialization functions and data structures.
namespace | streaming
| This namespace contains G-API functions, structures, and symbols related to the Streaming execution mode.
namespace | video
| This namespace contains G-API Operations and functions for video-oriented algorithms, like optical flow and background subtraction.
namespace | wip
| This namespace contains experimental G-API functionality, functions or structures in this namespace are subjects to change or removal in the future releases. This namespace also contains functions which API is not stabilized yet.

## Classes
struct | Generic
| Generic network type: input and output layers are configured dynamically at runtime. More...
struct | GNetPackage
| A container class for network configurations. Similar to GKernelPackage. Use cv::gapi::networks() to construct this object. More...
struct | KalmanParams
| Structure for the Kalman filter's initialization parameters. More...
struct | use_only
| cv::gapi::use_only() is a special combinator which hints G-API to use only kernels specified in cv::GComputation::compile() (and not to extend kernels available by default with that package). More...

## Typedefs
using | GKernelPackage = cv::GKernelPackage

## Enumerations
enum class | StereoOutputFormat { DEPTH_FLOAT16 , DEPTH_FLOAT32 , DISPARITY_FIXED16_11_5 , DISPARITY_FIXED16_12_4 , DEPTH_16F = DEPTH_FLOAT16 , DEPTH_32F = DEPTH_FLOAT32 , DISPARITY_16Q_10_5 = DISPARITY_FIXED16_11_5 , DISPARITY_16Q_11_4 = DISPARITY_FIXED16_12_4 }

## Functions
GMat | absDiff (const GMat &src1, const GMat &src2)
| Calculates the per-element absolute difference between two matrices.
GMat | absDiffC (const GMat &src, const GScalar &c)
| Calculates absolute value of matrix elements.
GMat | add (const GMat &src1, const GMat &src2, int ddepth=-1)
| Calculates the per-element sum of two matrices.
GMat | addC (const GMat &src1, const GScalar &c, int ddepth=-1)
| Calculates the per-element sum of matrix and given scalar.
GMat | addC (const GScalar &c, const GMat &src1, int ddepth=-1)
| This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
GMat | addWeighted (const GMat &src1, double alpha, const GMat &src2, double beta, double gamma, int ddepth=-1)
| Calculates the weighted sum of two matrices.
GMat | BackgroundSubtractor (const GMat &src, const cv::gapi::video::BackgroundSubtractorParams &bsParams)
| Gaussian Mixture-based or K-nearest neighbours-based Background/Foreground Segmentation Algorithm. The operation generates a foreground mask.
GMat | BayerGR2RGB (const GMat &src_gr)
| Converts an image from BayerGR color space to RGB. The function converts an input image from BayerGR color space to RGB. The conventional ranges for G, R, and B channel values are 0 to 255.
GMat | BGR2Gray (const GMat &src)
| Converts an image from BGR color space to gray-scaled.
GMat | BGR2I420 (const GMat &src)
| Converts an image from BGR color space to I420 color space.
GMat | BGR2LUV (const GMat &src)
| Converts an image from BGR color space to LUV color space.
GMat | BGR2RGB (const GMat &src)
| Converts an image from BGR color space to RGB color space.
GMat | BGR2YUV (const GMat &src)
| Converts an image from BGR color space to YUV color space.
GMat | bilateralFilter (const GMat &src, int d, double sigmaColor, double sigmaSpace, int borderType=BORDER_DEFAULT)
| Applies the bilateral filter to an image.
cv::GRunArg | bind (cv::GRunArgP &out)
| Wraps output GRunArgsP available during graph execution to GRunArgs which can be serialized.
cv::GRunArgsP | bind (cv::GRunArgs &out_args)
| Wraps deserialized output GRunArgs to GRunArgsP which can be used by GCompiled.
GMat | bitwise_and (const GMat &src1, const GMat &src2)
| computes bitwise conjunction of the two matrixes (src1 & src2) Calculates the per-element bit-wise logical conjunction of two matrices of the same size.
GMat | bitwise_and (const GMat &src1, const GScalar &src2)
GMat | bitwise_not (const GMat &src)
| Inverts every bit of an array.
GMat | bitwise_or (const GMat &src1, const GMat &src2)
| computes bitwise disjunction of the two matrixes (src1 | src2) Calculates the per-element bit-wise logical disjunction of two matrices of the same size.
GMat | bitwise_or (const GMat &src1, const GScalar &src2)
GMat | bitwise_xor (const GMat &src1, const GMat &src2)
| computes bitwise logical "exclusive or" of the two matrixes (src1 ^ src2) Calculates the per-element bit-wise logical "exclusive or" of two matrices of the same size.
GMat | bitwise_xor (const GMat &src1, const GScalar &src2)
GMat | blur (const GMat &src, const Size &ksize, const Point &anchor=Point(-1,-1), int borderType=BORDER_DEFAULT, const Scalar &borderValue=Scalar(0))
| Blurs an image using the normalized box filter.
GOpaque< Rect > | boundingRect (const GArray< Point2f > &src)
GOpaque< Rect > | boundingRect (const GArray< Point2i > &src)
GOpaque< Rect > | boundingRect (const GMat &src)
| Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image.
GMat | boxFilter (const GMat &src, int dtype, const Size &ksize, const Point &anchor=Point(-1,-1), bool normalize=true, int borderType=BORDER_DEFAULT, const Scalar &borderValue=Scalar(0))
| Blurs an image using the box filter.
std::tuple< GArray< GMat >, GScalar > | buildOpticalFlowPyramid (const GMat &img, const Size &winSize, const GScalar &maxLevel, bool withDerivatives=true, int pyrBorder=BORDER_REFLECT_101, int derivBorder=BORDER_CONSTANT, bool tryReuseInputImage=true)
| Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
std::tuple< GArray< Point2f >, GArray< uchar >, GArray< float > > | calcOpticalFlowPyrLK (const GArray< GMat > &prevPyr, const GArray< GMat > &nextPyr, const GArray< Point2f > &prevPts, const GArray< Point2f > &predPts, const Size &winSize=Size(21, 21), const GScalar &maxLevel=3, const TermCriteria &criteria=TermCriteria(TermCriteria::COUNT|TermCriteria::EPS, 30, 0.01), int flags=0, double minEigThresh=1e-4)
std::tuple< GArray< Point2f >, GArray< uchar >, GArray< float > > | calcOpticalFlowPyrLK (const GMat &prevImg, const GMat &nextImg, const GArray< Point2f > &prevPts, const GArray< Point2f > &predPts, const Size &winSize=Size(21, 21), const GScalar &maxLevel=3, const TermCriteria &criteria=TermCriteria(TermCriteria::COUNT|TermCriteria::EPS, 30, 0.01), int flags=0, double minEigThresh=1e-4)
| Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.
GMat | Canny (const GMat &image, double threshold1, double threshold2, int apertureSize=3, bool L2gradient=false)
| Finds edges in an image using the Canny algorithm.
std::tuple< GMat, GMat > | cartToPolar (const GMat &x, const GMat &y, bool angleInDegrees=false)
| Calculates the magnitude and angle of 2D vectors.
GMat | cmpEQ (const GMat &src1, const GMat &src2)
| Performs the per-element comparison of two matrices checking if elements from first matrix are equal to elements in second.
GMat | cmpEQ (const GMat &src1, const GScalar &src2)
GMat | cmpGE (const GMat &src1, const GMat &src2)
| Performs the per-element comparison of two matrices checking if elements from first matrix are greater or equal compare to elements in second.
GMat | cmpGE (const GMat &src1, const GScalar &src2)
GMat | cmpGT (const GMat &src1, const GMat &src2)
| Performs the per-element comparison of two matrices checking if elements from first matrix are greater compare to elements in second.
GMat | cmpGT (const GMat &src1, const GScalar &src2)
GMat | cmpLE (const GMat &src1, const GMat &src2)
| Performs the per-element comparison of two matrices checking if elements from first matrix are less or equal compare to elements in second.
GMat | cmpLE (const GMat &src1, const GScalar &src2)
GMat | cmpLT (const GMat &src1, const GMat &src2)
| Performs the per-element comparison of two matrices checking if elements from first matrix are less than elements in second.
GMat | cmpLT (const GMat &src1, const GScalar &src2)
GMat | cmpNE (const GMat &src1, const GMat &src2)
| Performs the per-element comparison of two matrices checking if elements from first matrix are not equal to elements in second.
GMat | cmpNE (const GMat &src1, const GScalar &src2)
template<typename... Ps>
cv::GKernelPackage | combine (const cv::GKernelPackage &a, const cv::GKernelPackage &b, Ps &&... rest)
| Combines multiple G-API kernel packages into one.
cv::GKernelPackage | combine (const cv::GKernelPackage &lhs, const cv::GKernelPackage &rhs)
GMat | concatHor (const GMat &src1, const GMat &src2)
| Applies horizontal concatenation to given matrices.
GMat | concatHor (const std::vector< GMat > &v)
GMat | concatVert (const GMat &src1, const GMat &src2)
| Applies vertical concatenation to given matrices.
GMat | concatVert (const std::vector< GMat > &v)
GMat | convertTo (const GMat &src, int rdepth, double alpha=1, double beta=0)
| Converts a matrix to another data depth with optional scaling.
GFrame | copy (const GFrame &in)
| Makes a copy of the input frame. Note that this copy may be not real (no actual data copied). Use this function to maintain graph contracts, e.g when graph's input needs to be passed directly to output, like in Streaming mode.
GMat | copy (const GMat &in)
| Makes a copy of the input image. Note that this copy may be not real (no actual data copied). Use this function to maintain graph contracts, e.g when graph's input needs to be passed directly to output, like in Streaming mode.
GOpaque< int > | countNonZero (const GMat &src)
| Counts non-zero array elements.
GMat | crop (const GMat &src, const Rect &rect)
| Crops a 2D matrix.
template<>
cv::GComputation | deserialize (const std::vector< char > &bytes)
| Deserialize GComputation from a byte array.
template<>
cv::GMetaArgs | deserialize (const std::vector< char > &bytes)
| Deserialize GMetaArgs from a byte array.
template<>
cv::GRunArgs | deserialize (const std::vector< char > &bytes)
| Deserialize GRunArgs from a byte array.
template<>
std::vector< std::string > | deserialize (const std::vector< char > &bytes)
| Deserialize std::vector<std::string> from a byte array.
template<typename T , typename... Types>
std::enable_if< std::is_same< T, GCompileArgs >::value, GCompileArgs >::type | deserialize (const std::vector< char > &bytes)
| Deserialize GCompileArgs which types were specified in the template from a byte array.
template<typename T , typename AtLeastOneAdapterT , typename... AdapterTypes>
std::enable_if< std::is_same< T, GRunArgs >::value, GRunArgs >::type | deserialize (const std::vector< char > &bytes)
| Deserialize GRunArgs including RMat and MediaFrame objects if any from a byte array.
GMat | dilate (const GMat &src, const Mat &kernel, const Point &anchor=Point(-1,-1), int iterations=1, int borderType=BORDER_CONSTANT, const Scalar &borderValue=morphologyDefaultBorderValue())
| Dilates an image by using a specific structuring element.
GMat | dilate3x3 (const GMat &src, int iterations=1, int borderType=BORDER_CONSTANT, const Scalar &borderValue=morphologyDefaultBorderValue())
| Dilates an image by using 3 by 3 rectangular structuring element.
GMat | div (const GMat &src1, const GMat &src2, double scale, int ddepth=-1)
| Performs per-element division of two matrices.
GMat | divC (const GMat &src, const GScalar &divisor, double scale, int ddepth=-1)
| Divides matrix by scalar.
GMat | divRC (const GScalar &divident, const GMat &src, double scale, int ddepth=-1)
| Divides scalar by matrix.
GMat | equalizeHist (const GMat &src)
GMat | erode (const GMat &src, const Mat &kernel, const Point &anchor=Point(-1,-1), int iterations=1, int borderType=BORDER_CONSTANT, const Scalar &borderValue=morphologyDefaultBorderValue())
| Erodes an image by using a specific structuring element.
GMat | erode3x3 (const GMat &src, int iterations=1, int borderType=BORDER_CONSTANT, const Scalar &borderValue=morphologyDefaultBorderValue())
| Erodes an image by using 3 by 3 rectangular structuring element.
GMat | filter2D (const GMat &src, int ddepth, const Mat &kernel, const Point &anchor=Point(-1,-1), const Scalar &delta=Scalar(0), int borderType=BORDER_DEFAULT, const Scalar &borderValue=Scalar(0))
| Convolves an image with the kernel.
GArray< GArray< Point > > | findContours (const GMat &src, const RetrievalModes mode, const ContourApproximationModes method)
GArray< GArray< Point > > | findContours (const GMat &src, const RetrievalModes mode, const ContourApproximationModes method, const GOpaque< Point > &offset)
| Finds contours in a binary image.
std::tuple< GArray< GArray< Point > >, GArray< Vec4i > > | findContoursH (const GMat &src, const RetrievalModes mode, const ContourApproximationModes method)
std::tuple< GArray< GArray< Point > >, GArray< Vec4i > > | findContoursH (const GMat &src, const RetrievalModes mode, const ContourApproximationModes method, const GOpaque< Point > &offset)
| Finds contours and their hierarchy in a binary image.
GOpaque< Vec4f > | fitLine2D (const GArray< Point2d > &src, const DistanceTypes distType, const double param=0., const double reps=0., const double aeps=0.)
GOpaque< Vec4f > | fitLine2D (const GArray< Point2f > &src, const DistanceTypes distType, const double param=0., const double reps=0., const double aeps=0.)
GOpaque< Vec4f > | fitLine2D (const GArray< Point2i > &src, const DistanceTypes distType, const double param=0., const double reps=0., const double aeps=0.)
GOpaque< Vec4f > | fitLine2D (const GMat &src, const DistanceTypes distType, const double param=0., const double reps=0., const double aeps=0.)
| Fits a line to a 2D point set.
GOpaque< Vec6f > | fitLine3D (const GArray< Point3d > &src, const DistanceTypes distType, const double param=0., const double reps=0., const double aeps=0.)
GOpaque< Vec6f > | fitLine3D (const GArray< Point3f > &src, const DistanceTypes distType, const double param=0., const double reps=0., const double aeps=0.)
GOpaque< Vec6f > | fitLine3D (const GArray< Point3i > &src, const DistanceTypes distType, const double param=0., const double reps=0., const double aeps=0.)
GOpaque< Vec6f > | fitLine3D (const GMat &src, const DistanceTypes distType, const double param=0., const double reps=0., const double aeps=0.)
| Fits a line to a 3D point set.
GMat | flip (const GMat &src, int flipCode)
| Flips a 2D matrix around vertical, horizontal, or both axes.
GMat | gaussianBlur (const GMat &src, const Size &ksize, double sigmaX, double sigmaY=0, int borderType=BORDER_DEFAULT, const Scalar &borderValue=Scalar(0))
| Blurs an image using a Gaussian filter.
template<typename T >
cv::util::optional< T > | getCompileArg (const cv::GCompileArgs &args)
| Retrieves particular compilation argument by its type from cv::GCompileArgs.
GArray< Point2f > | goodFeaturesToTrack (const GMat &image, int maxCorners, double qualityLevel, double minDistance, const Mat &mask=Mat(), int blockSize=3, bool useHarrisDetector=false, double k=0.04)
| Determines strong corners on an image.
GMat | I4202BGR (const GMat &src)
| Converts an image from I420 color space to BGR color space.
GMat | I4202RGB (const GMat &src)
| Converts an image from I420 color space to BGR color space.
template<typename Net , typename... Args>
Net::Result | infer (Args &&... args)
| Calculates response for the specified network (template parameter) given the input data.
template<typename T = Generic>
cv::GInferListOutputs | infer (const std::string &tag, const cv::GArray< cv::Rect > &rois, const cv::GInferInputs &inputs)
| Calculates responses for the specified network for every region in the source image.
template<typename T = Generic>
cv::GInferOutputs | infer (const std::string &tag, const cv::GInferInputs &inputs)
| Calculates response for generic network.
template<typename T = Generic>
cv::GInferOutputs | infer (const std::string &tag, const cv::GOpaque< cv::Rect > &roi, const cv::GInferInputs &inputs)
| Calculates response for the generic network for the specified region in the source image. Currently expects a single-input network only.
template<typename Net , typename... Args>
Net::ResultL | infer (cv::GArray< cv::Rect > roi, Args &&... args)
| Calculates responses for the specified network (template parameter) for every region in the source image.
template<typename Net , typename T >
Net::Result | infer (cv::GOpaque< cv::Rect > roi, T in)
| Calculates response for the specified network (template parameter) for the specified region in the source image. Currently expects a single-input network only.
template<typename T = Generic, typename Input >
std::enable_if< cv::detail::accepted_infer_types< Input >::value, cv::GInferListOutputs >::type | infer2 (const std::string &tag, const Input &in, const cv::GInferListInputs &inputs)
| Calculates responses for the specified network for every region in the source image, extended version.
template<typename Net , typename T , typename... Args>
Net::ResultL | infer2 (T image, cv::GArray< Args >... args)
| Calculates responses for the specified network (template parameter) for every region in the source image, extended version.
GMat | inRange (const GMat &src, const GScalar &threshLow, const GScalar &threshUp)
| Applies a range-level threshold to each matrix element.
std::tuple< GMat, GMat > | integral (const GMat &src, int sdepth=-1, int sqdepth=-1)
| Calculates the integral of an image.
void | island (const std::string &name, GProtoInputArgs &&ins, GProtoOutputArgs &&outs)
| Define an tagged island (subgraph) within a computation.
GMat | KalmanFilter (const GMat &measurement, const GOpaque< bool > &haveMeasurement, const cv::gapi::KalmanParams &kfParams)
GMat | KalmanFilter (const GMat &measurement, const GOpaque< bool > &haveMeasurement, const GMat &control, const cv::gapi::KalmanParams &kfParams)
| Standard Kalman filter algorithm http://en.wikipedia.org/wiki/Kalman_filter.
template<typename... KK>
GKernelPackage | kernels ()
| Create a kernel package object containing kernels and transformations specified in variadic template argument.
template<typename... FF>
GKernelPackage | kernels (FF &... functors)
std::tuple< GOpaque< double >, GArray< int >, GArray< Point2f > > | kmeans (const GArray< Point2f > &data, const int K, const GArray< int > &bestLabels, const TermCriteria &criteria, const int attempts, const KmeansFlags flags)
std::tuple< GOpaque< double >, GArray< int >, GArray< Point3f > > | kmeans (const GArray< Point3f > &data, const int K, const GArray< int > &bestLabels, const TermCriteria &criteria, const int attempts, const KmeansFlags flags)
std::tuple< GOpaque< double >, GMat, GMat > | kmeans (const GMat &data, const int K, const GMat &bestLabels, const TermCriteria &criteria, const int attempts, const KmeansFlags flags)
| Finds centers of clusters and groups input samples around the clusters.
std::tuple< GOpaque< double >, GMat, GMat > | kmeans (const GMat &data, const int K, const TermCriteria &criteria, const int attempts, const KmeansFlags flags)
GMat | Laplacian (const GMat &src, int ddepth, int ksize=1, double scale=1, double delta=0, int borderType=BORDER_DEFAULT)
| Calculates the Laplacian of an image.
GMat | LUT (const GMat &src, const Mat &lut)
| Performs a look-up table transform of a matrix.
GMat | LUV2BGR (const GMat &src)
| Converts an image from LUV color space to BGR color space.
GMat | mask (const GMat &src, const GMat &mask)
| Applies a mask to a matrix.
GMat | max (const GMat &src1, const GMat &src2)
| Calculates per-element maximum of two matrices.
GScalar | mean (const GMat &src)
| Calculates an average (mean) of matrix elements.
GMat | medianBlur (const GMat &src, int ksize)
| Blurs an image using the median filter.
GMat | merge3 (const GMat &src1, const GMat &src2, const GMat &src3)
| Creates one 3-channel matrix out of 3 single-channel ones.
GMat | merge4 (const GMat &src1, const GMat &src2, const GMat &src3, const GMat &src4)
| Creates one 4-channel matrix out of 4 single-channel ones.
GMat | min (const GMat &src1, const GMat &src2)
| Calculates per-element minimum of two matrices.
GMat | morphologyEx (const GMat &src, const MorphTypes op, const Mat &kernel, const Point &anchor=Point(-1,-1), const int iterations=1, const BorderTypes borderType=BORDER_CONSTANT, const Scalar &borderValue=morphologyDefaultBorderValue())
| Performs advanced morphological transformations.
GMat | mul (const GMat &src1, const GMat &src2, double scale=1.0, int ddepth=-1)
| Calculates the per-element scaled product of two matrices.
GMat | mulC (const GMat &src, const GScalar &multiplier, int ddepth=-1)
| This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
GMat | mulC (const GMat &src, double multiplier, int ddepth=-1)
| Multiplies matrix by scalar.
GMat | mulC (const GScalar &multiplier, const GMat &src, int ddepth=-1)
| This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
template<typename... Args>
cv::gapi::GNetPackage | networks (Args &&... args)
GMat | normalize (const GMat &src, double alpha, double beta, int norm_type, int ddepth=-1)
| Normalizes the norm or value range of an array.
GScalar | normInf (const GMat &src)
| Calculates the absolute infinite norm of a matrix.
GScalar | normL1 (const GMat &src)
| Calculates the absolute L1 norm of a matrix.
GScalar | normL2 (const GMat &src)
| Calculates the absolute L2 norm of a matrix.
GMat | NV12toBGR (const GMat &src_y, const GMat &src_uv)
| Converts an image from NV12 (YUV420p) color space to BGR. The function converts an input image from NV12 color space to RGB. The conventional ranges for Y, U, and V channel values are 0 to 255.
GMatP | NV12toBGRp (const GMat &src_y, const GMat &src_uv)
| Converts an image from NV12 (YUV420p) color space to BGR. The function converts an input image from NV12 color space to BGR. The conventional ranges for Y, U, and V channel values are 0 to 255.
GMat | NV12toGray (const GMat &src_y, const GMat &src_uv)
| Converts an image from NV12 (YUV420p) color space to gray-scaled. The function converts an input image from NV12 color space to gray-scaled. The conventional ranges for Y, U, and V channel values are 0 to 255.
GMat | NV12toRGB (const GMat &src_y, const GMat &src_uv)
| Converts an image from NV12 (YUV420p) color space to RGB. The function converts an input image from NV12 color space to RGB. The conventional ranges for Y, U, and V channel values are 0 to 255.
GMatP | NV12toRGBp (const GMat &src_y, const GMat &src_uv)
| Converts an image from NV12 (YUV420p) color space to RGB. The function converts an input image from NV12 color space to RGB. The conventional ranges for Y, U, and V channel values are 0 to 255.
bool | operator!= (const GBackend &lhs, const GBackend &rhs)
cv::gapi::GNetPackage & | operator+= (cv::gapi::GNetPackage &lhs, const cv::gapi::GNetPackage &rhs)
GArray< Rect > | parseSSD (const GMat &in, const GOpaque< Size > &inSz, const float confidenceThreshold, const bool alignmentToSquare, const bool filterOutOfBounds)
| Parses output of SSD network.
std::tuple< GArray< Rect >, GArray< int > > | parseSSD (const GMat &in, const GOpaque< Size > &inSz, const float confidenceThreshold=0.5f, const int filterLabel=-1)
| Parses output of SSD network.
std::tuple< GArray< Rect >, GArray< int > > | parseYolo (const GMat &in, const GOpaque< Size > &inSz, const float confidenceThreshold=0.5f, const float nmsThreshold=0.5f, const std::vector< float > &anchors=nn::parsers::GParseYolo::defaultAnchors())
| Parses output of Yolo network.
GMat | phase (const GMat &x, const GMat &y, bool angleInDegrees=false)
| Calculates the rotation angle of 2D vectors.
std::tuple< GMat, GMat > | polarToCart (const GMat &magnitude, const GMat &angle, bool angleInDegrees=false)
| Calculates x and y coordinates of 2D vectors from their magnitude and angle.
GMat | remap (const GMat &src, const Mat &map1, const Mat &map2, int interpolation, int borderMode=BORDER_CONSTANT, const Scalar &borderValue=Scalar())
| Applies a generic geometrical transformation to an image.
GMat | resize (const GMat &src, const Size &dsize, double fx=0, double fy=0, int interpolation=INTER_LINEAR)
| Resizes an image.
GMatP | resizeP (const GMatP &src, const Size &dsize, int interpolation=cv::INTER_LINEAR)
| Resizes a planar image.
GMat | RGB2Gray (const GMat &src)
| Converts an image from RGB color space to gray-scaled.
GMat | RGB2Gray (const GMat &src, float rY, float gY, float bY)
GMat | RGB2HSV (const GMat &src)
| Converts an image from RGB color space to HSV. The function converts an input image from RGB color space to HSV. The conventional ranges for R, G, and B channel values are 0 to 255.
GMat | RGB2I420 (const GMat &src)
| Converts an image from RGB color space to I420 color space.
GMat | RGB2Lab (const GMat &src)
| Converts an image from RGB color space to Lab color space.
GMat | RGB2YUV (const GMat &src)
| Converts an image from RGB color space to YUV color space.
GMat | RGB2YUV422 (const GMat &src)
| Converts an image from RGB color space to YUV422. The function converts an input image from RGB color space to YUV422. The conventional ranges for R, G, and B channel values are 0 to 255.
GMat | select (const GMat &src1, const GMat &src2, const GMat &mask)
| Select values from either first or second of input matrices by given mask. The function set to the output matrix either the value from the first input matrix if corresponding value of mask matrix is 255, or value from the second input matrix (if value of mask matrix set to 0).
GMat | sepFilter (const GMat &src, int ddepth, const Mat &kernelX, const Mat &kernelY, const Point &anchor, const Scalar &delta, int borderType=BORDER_DEFAULT, const Scalar &borderValue=Scalar(0))
| Applies a separable linear filter to a matrix(image).
std::vector< char > | serialize (const cv::GCompileArgs &ca)
std::vector< char > | serialize (const cv::GComputation &c)
| Serialize a graph represented by GComputation into an array of bytes.
std::vector< char > | serialize (const cv::GMetaArgs &ma)
std::vector< char > | serialize (const cv::GRunArgs &ra)
std::vector< char > | serialize (const std::vector< std::string > &vs)
GMat | Sobel (const GMat &src, int ddepth, int dx, int dy, int ksize=3, double scale=1, double delta=0, int borderType=BORDER_DEFAULT, const Scalar &borderValue=Scalar(0))
| Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
std::tuple< GMat, GMat > | SobelXY (const GMat &src, int ddepth, int order, int ksize=3, double scale=1, double delta=0, int borderType=BORDER_DEFAULT, const Scalar &borderValue=Scalar(0))
| Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
std::tuple< GMat, GMat, GMat > | split3 (const GMat &src)
| Divides a 3-channel matrix into 3 single-channel matrices.
std::tuple< GMat, GMat, GMat, GMat > | split4 (const GMat &src)
| Divides a 4-channel matrix into 4 single-channel matrices.
GMat | sqrt (const GMat &src)
| Calculates a square root of array elements.
GMat | stereo (const GMat &left, const GMat &right, const StereoOutputFormat of=StereoOutputFormat::DEPTH_FLOAT32)
| Computes disparity/depth map for the specified stereo-pair. The function computes disparity or depth map depending on passed StereoOutputFormat argument.
GMat | sub (const GMat &src1, const GMat &src2, int ddepth=-1)
| Calculates the per-element difference between two matrices.
GMat | subC (const GMat &src, const GScalar &c, int ddepth=-1)
| Calculates the per-element difference between matrix and given scalar.
GMat | subRC (const GScalar &c, const GMat &src, int ddepth=-1)
| Calculates the per-element difference between given scalar and the matrix.
GScalar | sum (const GMat &src)
| Calculates sum of all matrix elements.
std::tuple< GMat, GScalar > | threshold (const GMat &src, const GScalar &maxval, int type)
GMat | threshold (const GMat &src, const GScalar &thresh, const GScalar &maxval, int type)
| Applies a fixed-level threshold to each matrix element.
GMat | transpose (const GMat &src)
| Transposes a matrix.
GMat | warpAffine (const GMat &src, const Mat &M, const Size &dsize, int flags=cv::INTER_LINEAR, int borderMode=cv::BORDER_CONSTANT, const Scalar &borderValue=Scalar())
| Applies an affine transformation to an image.
GMat | warpPerspective (const GMat &src, const Mat &M, const Size &dsize, int flags=cv::INTER_LINEAR, int borderMode=cv::BORDER_CONSTANT, const Scalar &borderValue=Scalar())
| Applies a perspective transformation to an image.
GMat | YUV2BGR (const GMat &src)
| Converts an image from YUV color space to BGR color space.
GMat | YUV2RGB (const GMat &src)
| Converts an image from YUV color space to RGB. The function converts an input image from YUV color space to RGB. The conventional ranges for Y, U, and V channel values are 0 to 255.

## Typedef Documentation

## ◆ GKernelPackage

using cv::gapi::GKernelPackage = cv::GKernelPackage

## Enumeration Type Documentation

## ◆ StereoOutputFormat

enum class cv::gapi::StereoOutputFormat
| strong

The enum specified format of result that you get from cv::gapi::stereo.

Enumerator
DEPTH_FLOAT16 | Floating point 16 bit value, CV_16FC1. This identifier is deprecated, use DEPTH_16F instead.
DEPTH_FLOAT32 | Floating point 32 bit value, CV_32FC1 This identifier is deprecated, use DEPTH_16F instead.
DISPARITY_FIXED16_11_5 | 16 bit signed: first bit for sign, 10 bits for integer part, 5 bits for fractional part. This identifier is deprecated, use DISPARITY_16Q_10_5 instead.
DISPARITY_FIXED16_12_4 | 16 bit signed: first bit for sign, 11 bits for integer part, 4 bits for fractional part. This identifier is deprecated, use DISPARITY_16Q_11_4 instead.
DEPTH_16F | Same as DEPTH_FLOAT16.
DEPTH_32F | Same as DEPTH_FLOAT32.
DISPARITY_16Q_10_5 | Same as DISPARITY_FIXED16_11_5.
DISPARITY_16Q_11_4 | Same as DISPARITY_FIXED16_12_4.

## Function Documentation

## ◆ combine() [1/2]

template<typename... Ps>

cv::GKernelPackage cv::gapi::combine | ( | const cv::GKernelPackage & | a,
|  | const cv::GKernelPackage & | b,
|  | Ps &&... | rest )

Python:
| cv.gapi.combine( | lhs, rhs | ) -> | retval

Combines multiple G-API kernel packages into one.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

This function successively combines the passed kernel packages using a right fold. Calling `combine(a, b, c)` is equal to `combine(a, combine(b, c))`.

Returns
  The resulting kernel package

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## ◆ combine() [2/2]

cv::GKernelPackage cv::gapi::combine | ( | const cv::GKernelPackage & | lhs,
|  | const cv::GKernelPackage & | rhs )

Python:
| cv.gapi.combine( | lhs, rhs | ) -> | retval

## ◆ deserialize() [1/3]

template<>

cv::GMetaArgs cv::gapi::deserialize | ( | const std::vector< char > & | bytes | ) |
| inline

Deserialize GMetaArgs from a byte array.

Check different overloads for more examples.

Parameters

  bytes | serialized vector of bytes.

Returns
  deserialized GMetaArgs object.

## ◆ deserialize() [2/3]

template<>

cv::GRunArgs cv::gapi::deserialize | ( | const std::vector< char > & | bytes | ) |
| inline

Deserialize GRunArgs from a byte array.

Check different overloads for more examples.

Parameters

  bytes | serialized vector of bytes.

Returns
  deserialized GRunArgs object.

## ◆ deserialize() [3/3]

template<>

std::vector< std::string > cv::gapi::deserialize | ( | const std::vector< char > & | bytes | ) |
| inline

Deserialize std::vector<std::string> from a byte array.

Check different overloads for more examples.

Parameters

  bytes | serialized vector of bytes.

Returns
  deserialized std::vector<std::string> object.

## ◆ equalizeHist()

GMat cv::gapi::equalizeHist | ( | const GMat & | src | ) |

Python:
| cv.gapi.equalizeHist( | src | ) -> | retval

gapi_feature

The function equalizes the histogram of the input image using the following algorithm:

- Calculate the histogram \(H\) for src .
- Normalize the histogram so that the sum of histogram bins is 255.
- Compute the integral of the histogram:

  \[H'_i = \sum _{0 \le j < i} H(j)\]

- Transform the image using \(H'\) as a look-up table: \(\texttt{dst}(x,y) = H'(\texttt{src}(x,y))\)

The algorithm normalizes the brightness and increases the contrast of the image.

Note
  - The returned image is of the same size and type as input.
  - Function textual ID is "org.opencv.imgproc.equalizeHist"

Parameters

  src | Source 8-bit single channel image.

## ◆ getCompileArg()

template<typename T >

cv::util::optional< T > cv::gapi::getCompileArg | ( | const cv::GCompileArgs & | args | ) |
| inline

Retrieves particular compilation argument by its type from cv::GCompileArgs.

## ◆ infer() [1/6]

template<typename Net , typename... Args>

Net::Result cv::gapi::infer | ( | Args &&... | args | ) |

Python:
| cv.gapi.infer( | name, inputs | ) -> | retval
| cv.gapi.infer( | name, roi, inputs | ) -> | retval
| cv.gapi.infer( | name, rois, inputs | ) -> | retval

Calculates response for the specified network (template parameter) given the input data.

Template Parameters

  A | network type defined with G_API_NET() macro.

Parameters

  args | network's input parameters as specified in G_API_NET() macro.

Returns
  an object of return type as defined in G_API_NET(). If a network has multiple return values (defined with a tuple), a tuple of objects of appropriate type is returned.

See also
  G_API_NET()

## ◆ infer() [2/6]

template<typename T = Generic>

cv::GInferListOutputs cv::gapi::infer | ( | const std::string & | tag,
|  | const cv::GArray< cv::Rect > & | rois,
|  | const cv::GInferInputs & | inputs )

Python:
| cv.gapi.infer( | name, inputs | ) -> | retval
| cv.gapi.infer( | name, roi, inputs | ) -> | retval
| cv.gapi.infer( | name, rois, inputs | ) -> | retval

Calculates responses for the specified network for every region in the source image.

Parameters

  tag | a network tag
  rois | a list of rectangles describing regions of interest in the source image. Usually an output of object detector or tracker.
  inputs | networks's inputs

Returns
  a cv::GInferListOutputs

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## ◆ infer() [3/6]

template<typename T = Generic>

cv::GInferOutputs cv::gapi::infer | ( | const std::string & | tag,
|  | const cv::GInferInputs & | inputs )

Python:
| cv.gapi.infer( | name, inputs | ) -> | retval
| cv.gapi.infer( | name, roi, inputs | ) -> | retval
| cv.gapi.infer( | name, rois, inputs | ) -> | retval

Calculates response for generic network.

Parameters

  tag | a network tag
  inputs | networks's inputs

Returns
  a GInferOutputs

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## ◆ infer() [4/6]

template<typename T = Generic>

cv::GInferOutputs cv::gapi::infer | ( | const std::string & | tag,
|  | const cv::GOpaque< cv::Rect > & | roi,
|  | const cv::GInferInputs & | inputs )

Python:
| cv.gapi.infer( | name, inputs | ) -> | retval
| cv.gapi.infer( | name, roi, inputs | ) -> | retval
| cv.gapi.infer( | name, rois, inputs | ) -> | retval

Calculates response for the generic network for the specified region in the source image. Currently expects a single-input network only.

Parameters

  tag | a network tag
  roi | a an object describing the region of interest in the source image. May be calculated in the same graph dynamically.
  inputs | networks's inputs

Returns
  a cv::GInferOutputs

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## ◆ infer() [5/6]

template<typename Net , typename... Args>

Net::ResultL cv::gapi::infer | ( | cv::GArray< cv::Rect > | roi,
|  | Args &&... | args )

Python:
| cv.gapi.infer( | name, inputs | ) -> | retval
| cv.gapi.infer( | name, roi, inputs | ) -> | retval
| cv.gapi.infer( | name, rois, inputs | ) -> | retval

Calculates responses for the specified network (template parameter) for every region in the source image.

Template Parameters

  A | network type defined with G_API_NET() macro.

Parameters

  roi | a list of rectangles describing regions of interest in the source image. Usually an output of object detector or tracker.
  args | network's input parameters as specified in G_API_NET() macro. NOTE: verified to work reliably with 1-input topologies only.

Returns
  a list of objects of return type as defined in G_API_NET(). If a network has multiple return values (defined with a tuple), a tuple of GArray<> objects is returned with the appropriate types inside.

See also
  G_API_NET()

## ◆ infer() [6/6]

template<typename Net , typename T >

Net::Result cv::gapi::infer | ( | cv::GOpaque< cv::Rect > | roi,
|  | T | in )

Python:
| cv.gapi.infer( | name, inputs | ) -> | retval
| cv.gapi.infer( | name, roi, inputs | ) -> | retval
| cv.gapi.infer( | name, rois, inputs | ) -> | retval

Calculates response for the specified network (template parameter) for the specified region in the source image. Currently expects a single-input network only.

Template Parameters

  A | network type defined with G_API_NET() macro.

Parameters

  in | input image where to take ROI from.
  roi | an object describing the region of interest in the source image. May be calculated in the same graph dynamically.

Returns
  an object of return type as defined in G_API_NET(). If a network has multiple return values (defined with a tuple), a tuple of objects of appropriate type is returned.

See also
  G_API_NET()

## ◆ infer2() [1/2]

template<typename T = Generic, typename Input >

std::enable_if< cv::detail::accepted_infer_types< Input >::value, cv::GInferListOutputs >::type cv::gapi::infer2 | ( | const std::string & | tag,
|  | const Input & | in,
|  | const cv::GInferListInputs & | inputs )

Python:
| cv.gapi.infer2( | name, in_, inputs | ) -> | retval

Calculates responses for the specified network for every region in the source image, extended version.

Parameters

  tag | a network tag
  in | a source image containing regions of interest.
  inputs | networks's inputs

Returns
  a cv::GInferListOutputs

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## ◆ infer2() [2/2]

template<typename Net , typename T , typename... Args>

Net::ResultL cv::gapi::infer2 | ( | T | image,
|  | cv::GArray< Args >... | args )

Python:
| cv.gapi.infer2( | name, in_, inputs | ) -> | retval

Calculates responses for the specified network (template parameter) for every region in the source image, extended version.

Template Parameters

  A | network type defined with G_API_NET() macro.

Parameters

  image | A source image containing regions of interest
  args | GArray<> objects of cv::Rect or cv::GMat, one per every network input: If a cv::GArray<cv::Rect> is passed, the appropriate regions are taken from `image` and preprocessed to this particular network input; If a cv::GArray<cv::GMat> is passed, the underlying data traited as tensor (no automatic preprocessing happen).

Returns
  a list of objects of return type as defined in G_API_NET(). If a network has multiple return values (defined with a tuple), a tuple of GArray<> objects is returned with the appropriate types inside.

See also
  G_API_NET()

## ◆ island()

void cv::gapi::island | ( | const std::string & | name,
|  | GProtoInputArgs && | ins,
|  | GProtoOutputArgs && | outs )

Define an tagged island (subgraph) within a computation.

Declare an Island tagged with `name` and defined from `ins` to `outs` (exclusively, as ins/outs are data objects, and regioning is done on operations level). Throws if any operation between `ins` and `outs` are already assigned to another island.

Islands allow to partition graph into subgraphs, fine-tuning the way it is scheduled by the underlying executor.

Parameters

  name | name of the Island to create
  ins | vector of input data objects where the subgraph begins
  outs | vector of output data objects where the subgraph ends.

The way how an island is defined is similar to how cv::GComputation is defined on input/output data objects. Same rules apply here as well – if there's no functional dependency between inputs and outputs or there's not enough input data objects were specified to properly calculate all outputs, an exception is thrown.

Use cv::GIn() / cv::GOut() to specify input/output vectors.

## ◆ kmeans() [1/4]

std::tuple< GOpaque< double >, GArray< int >, GArray< Point2f > > cv::gapi::kmeans | ( | const GArray< Point2f > & | data,
|  | const int | K,
|  | const GArray< int > & | bestLabels,
|  | const TermCriteria & | criteria,
|  | const int | attempts,
|  | const KmeansFlags | flags )

Python:
| cv.gapi.kmeans( | data, K, bestLabels, criteria, attempts, flags | ) -> | retval
| cv.gapi.kmeans( | data, K, criteria, attempts, flags | ) -> | retval

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Note
  Function textual ID is "org.opencv.core.kmeans2D"

## ◆ kmeans() [2/4]

std::tuple< GOpaque< double >, GArray< int >, GArray< Point3f > > cv::gapi::kmeans | ( | const GArray< Point3f > & | data,
|  | const int | K,
|  | const GArray< int > & | bestLabels,
|  | const TermCriteria & | criteria,
|  | const int | attempts,
|  | const KmeansFlags | flags )

Python:
| cv.gapi.kmeans( | data, K, bestLabels, criteria, attempts, flags | ) -> | retval
| cv.gapi.kmeans( | data, K, criteria, attempts, flags | ) -> | retval

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Note
  Function textual ID is "org.opencv.core.kmeans3D"

## ◆ kmeans() [3/4]

std::tuple< GOpaque< double >, GMat, GMat > cv::gapi::kmeans | ( | const GMat & | data,
|  | const int | K,
|  | const GMat & | bestLabels,
|  | const TermCriteria & | criteria,
|  | const int | attempts,
|  | const KmeansFlags | flags )

Python:
| cv.gapi.kmeans( | data, K, bestLabels, criteria, attempts, flags | ) -> | retval
| cv.gapi.kmeans( | data, K, criteria, attempts, flags | ) -> | retval

Finds centers of clusters and groups input samples around the clusters.

The function kmeans implements a k-means algorithm that finds the centers of K clusters and groups the input samples around the clusters. As an output, \(\texttt{bestLabels}_i\) contains a 0-based cluster index for the \(i^{th}\) sample.

Note
  - Function textual ID is "org.opencv.core.kmeansND"
  - In case of an N-dimentional points' set given, input GMat can have the following traits: 2 dimensions, a single row or column if there are N channels, or N columns if there is a single channel. Mat should have CV_32F depth.
  - Although, if GMat with height != 1, width != 1, channels != 1 given as data, n-dimensional samples are considered given in amount of A, where A = height, n = width * channels.
  - In case of GMat given as data:
    - the output labels are returned as 1-channel GMat with sizes width = 1, height = A, where A is samples amount, or width = bestLabels.width, height = bestLabels.height if bestLabels given;
    - the cluster centers are returned as 1-channel GMat with sizes width = n, height = K, where n is samples' dimentionality and K is clusters' amount.
  - As one of possible usages, if you want to control the initial labels for each attempt by yourself, you can utilize just the core of the function. To do that, set the number of attempts to 1, initialize labels each time using a custom algorithm, pass them with the ( flags = KMEANS_USE_INITIAL_LABELS ) flag, and then choose the best (most-compact) clustering.

Parameters

  data | Data for clustering. An array of N-Dimensional points with float coordinates is needed. Function can take GArray<Point2f>, GArray<Point3f> for 2D and 3D cases or GMat for any dimentionality and channels.
  K | Number of clusters to split the set by.
  bestLabels | Optional input integer array that can store the supposed initial cluster indices for every sample. Used when ( flags = KMEANS_USE_INITIAL_LABELS ) flag is set.
  criteria | The algorithm termination criteria, that is, the maximum number of iterations and/or the desired accuracy. The accuracy is specified as criteria.epsilon. As soon as each of the cluster centers moves by less than criteria.epsilon on some iteration, the algorithm stops.
  attempts | Flag to specify the number of times the algorithm is executed using different initial labellings. The algorithm returns the labels that yield the best compactness (see the first function return value).
  flags | Flag that can take values of cv::KmeansFlags .

Returns
  - Compactness measure that is computed as

    \[\sum _i \| \texttt{samples} _i - \texttt{centers} _{ \texttt{labels} _i} \| ^2\]

    after every attempt. The best (minimum) value is chosen and the corresponding labels and the compactness value are returned by the function.
  - Integer array that stores the cluster indices for every sample.
  - Array of the cluster centers.

## ◆ kmeans() [4/4]

std::tuple< GOpaque< double >, GMat, GMat > cv::gapi::kmeans | ( | const GMat & | data,
|  | const int | K,
|  | const TermCriteria & | criteria,
|  | const int | attempts,
|  | const KmeansFlags | flags )

Python:
| cv.gapi.kmeans( | data, K, bestLabels, criteria, attempts, flags | ) -> | retval
| cv.gapi.kmeans( | data, K, criteria, attempts, flags | ) -> | retval

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Note
  - Function textual ID is "org.opencv.core.kmeansNDNoInit"
  - KMEANS_USE_INITIAL_LABELS flag must not be set while using this overload.

## ◆ networks()

template<typename... Args>

cv::gapi::GNetPackage cv::gapi::networks | ( | Args &&... | args | ) |

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## ◆ operator!=()

bool cv::gapi::operator!= | ( | const GBackend & | lhs,
|  | const GBackend & | rhs )
| inline

## ◆ operator+=()

cv::gapi::GNetPackage & cv::gapi::operator+= | ( | cv::gapi::GNetPackage & | lhs,
|  | const cv::gapi::GNetPackage & | rhs )
| inline

## ◆ parseSSD() [1/2]

GArray< Rect > cv::gapi::parseSSD | ( | const GMat & | in,
|  | const GOpaque< Size > & | inSz,
|  | const float | confidenceThreshold,
|  | const bool | alignmentToSquare,
|  | const bool | filterOutOfBounds )

Python:
| cv.gapi.parseSSD( | in_, inSz[, confidenceThreshold[, filterLabel]] | ) -> | retval
| cv.gapi.parseSSD( | in_, inSz, confidenceThreshold, alignmentToSquare, filterOutOfBounds | ) -> | retval

Parses output of SSD network.

Extracts detection information (box, confidence) from SSD output and filters it by given confidence and by going out of bounds.

Note
  Function textual ID is "org.opencv.nn.parsers.parseSSD"

Parameters

  in | Input CV_32F tensor with {1,1,N,7} dimensions.
  inSz | Size to project detected boxes to (size of the input image).
  confidenceThreshold | If confidence of the detection is smaller than confidence threshold, detection is rejected.
  alignmentToSquare | If provided true, bounding boxes are extended to squares. The center of the rectangle remains unchanged, the side of the square is the larger side of the rectangle.
  filterOutOfBounds | If provided true, out-of-frame boxes are filtered.

Returns
  a vector of detected bounding boxes.

## ◆ parseSSD() [2/2]

std::tuple< GArray< Rect >, GArray< int > > cv::gapi::parseSSD | ( | const GMat & | in,
|  | const GOpaque< Size > & | inSz,
|  | const float | confidenceThreshold = 0.5f,
|  | const int | filterLabel = -1 )

Python:
| cv.gapi.parseSSD( | in_, inSz[, confidenceThreshold[, filterLabel]] | ) -> | retval
| cv.gapi.parseSSD( | in_, inSz, confidenceThreshold, alignmentToSquare, filterOutOfBounds | ) -> | retval

Parses output of SSD network.

Extracts detection information (box, confidence, label) from SSD output and filters it by given confidence and label.

Note
  Function textual ID is "org.opencv.nn.parsers.parseSSD_BL"

Parameters

  in | Input CV_32F tensor with {1,1,N,7} dimensions.
  inSz | Size to project detected boxes to (size of the input image).
  confidenceThreshold | If confidence of the detection is smaller than confidence threshold, detection is rejected.
  filterLabel | If provided (!= -1), only detections with given label will get to the output.

Returns
  a tuple with a vector of detected boxes and a vector of appropriate labels.

## ◆ parseYolo()

std::tuple< GArray< Rect >, GArray< int > > cv::gapi::parseYolo | ( | const GMat & | in,
|  | const GOpaque< Size > & | inSz,
|  | const float | confidenceThreshold = 0.5f,
|  | const float | nmsThreshold = 0.5f,
|  | const std::vector< float > & | anchors = nn::parsers::GParseYolo::defaultAnchors() )

Python:
| cv.gapi.parseYolo( | in_, inSz[, confidenceThreshold[, nmsThreshold[, anchors]]] | ) -> | retval

Parses output of Yolo network.

Extracts detection information (box, confidence, label) from Yolo output, filters it by given confidence and performs non-maximum suppression for overlapping boxes.

Note
  Function textual ID is "org.opencv.nn.parsers.parseYolo"

Parameters

  in | Input CV_32F tensor with {1,13,13,N} dimensions, N should satisfy: \[\texttt{N} = (\texttt{num_classes} + \texttt{5}) * \texttt{5},\] where num_classes - a number of classes Yolo network was trained with.
  inSz | Size to project detected boxes to (size of the input image).
  confidenceThreshold | If confidence of the detection is smaller than confidence threshold, detection is rejected.
  nmsThreshold | Non-maximum suppression threshold which controls minimum relative box intersection area required for rejecting the box with a smaller confidence. If 1.f, nms is not performed and no boxes are rejected.
  anchors | Anchors Yolo network was trained with.

Note
  The default anchor values are specified for YOLO v2 Tiny as described in Intel Open Model Zoo documentation.

Returns
  a tuple with a vector of detected boxes and a vector of appropriate labels.

## ◆ stereo()

GMat cv::gapi::stereo | ( | const GMat & | left,
|  | const GMat & | right,
|  | const StereoOutputFormat | of = StereoOutputFormat::DEPTH_FLOAT32 )

Computes disparity/depth map for the specified stereo-pair. The function computes disparity or depth map depending on passed StereoOutputFormat argument.

Parameters

  left | 8-bit single-channel left image of CV_8UC1 type.
  right | 8-bit single-channel right image of CV_8UC1 type.
  of | enum to specified output kind: depth or disparity and corresponding type

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## ◆ transpose()

GMat cv::gapi::transpose | ( | const GMat & | src | ) |

Python:
| cv.gapi.transpose( | src | ) -> | retval

Transposes a matrix.

The function transposes the matrix:

\[\texttt{dst} (i,j) = \texttt{src} (j,i)\]

Note
  - Function textual ID is "org.opencv.core.transpose"
  - No complex conjugation is done in case of a complex matrix. It should be done separately if needed.

Parameters

  src | input array.