File block.hpp: Use Device layer instead.
File border_interpolate.hpp: Use Device layer instead.
File color.hpp: Use Device layer instead.
File common.hpp: Use Device layer instead.
File core.hpp: Use <opencv2/gapi/ocl/core.hpp> instead.
File cuda_types.hpp: Use Device layer instead.
Member cv::Algorithm::write (const Ptr< FileStorage > &fs, const String &name=String()) const
Member cv::aruco::calibrateCameraAruco (InputArrayOfArrays corners, InputArray ids, InputArray counter, const Ptr< Board > &board, Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, OutputArray stdDeviationsIntrinsics, OutputArray stdDeviationsExtrinsics, OutputArray perViewErrors, int flags=0, const TermCriteria &criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON)): Use Board::matchImagePoints and cv::solvePnP
Member cv::aruco::calibrateCameraAruco (InputArrayOfArrays corners, InputArray ids, InputArray counter, const Ptr< Board > &board, Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs=noArray(), OutputArrayOfArrays tvecs=noArray(), int flags=0, const TermCriteria &criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON)): Use Board::matchImagePoints and cv::solvePnP
Member cv::aruco::calibrateCameraCharuco (InputArrayOfArrays charucoCorners, InputArrayOfArrays charucoIds, const Ptr< CharucoBoard > &board, Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, OutputArray stdDeviationsIntrinsics, OutputArray stdDeviationsExtrinsics, OutputArray perViewErrors, int flags=0, const TermCriteria &criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON)): Use CharucoBoard::matchImagePoints and cv::solvePnP
Member cv::aruco::calibrateCameraCharuco (InputArrayOfArrays charucoCorners, InputArrayOfArrays charucoIds, const Ptr< CharucoBoard > &board, Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs=noArray(), OutputArrayOfArrays tvecs=noArray(), int flags=0, const TermCriteria &criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON)): Use CharucoBoard::matchImagePoints and cv::solvePnP
Member cv::aruco::detectCharucoDiamond (InputArray image, InputArrayOfArrays markerCorners, InputArray markerIds, float squareMarkerLengthRate, OutputArrayOfArrays diamondCorners, OutputArray diamondIds, InputArray cameraMatrix=noArray(), InputArray distCoeffs=noArray(), Ptr< Dictionary > dictionary=makePtr< Dictionary >(getPredefinedDictionary(PredefinedDictionaryType::DICT_4X4_50))): Use CharucoDetector::detectDiamonds
Member cv::aruco::detectMarkers (InputArray image, const Ptr< Dictionary > &dictionary, OutputArrayOfArrays corners, OutputArray ids, const Ptr< DetectorParameters > &parameters=makePtr< DetectorParameters >(), OutputArrayOfArrays rejectedImgPoints=noArray()): Use class ArucoDetector::detectMarkers
Member cv::aruco::drawCharucoDiamond (const Ptr< Dictionary > &dictionary, Vec4i ids, int squareLength, int markerLength, OutputArray img, int marginSize=0, int borderBits=1): Use CharucoBoard::generateImage()
Member cv::aruco::drawPlanarBoard (const Ptr< Board > &board, Size outSize, OutputArray img, int marginSize, int borderBits): Use Board::generateImage
Struct cv::aruco::EstimateParameters: Use Board::matchImagePoints and cv::solvePnP
Member cv::aruco::estimatePoseBoard (InputArrayOfArrays corners, InputArray ids, const Ptr< Board > &board, InputArray cameraMatrix, InputArray distCoeffs, InputOutputArray rvec, InputOutputArray tvec, bool useExtrinsicGuess=false): Use Board::matchImagePoints and cv::solvePnP
Member cv::aruco::estimatePoseCharucoBoard (InputArray charucoCorners, InputArray charucoIds, const Ptr< CharucoBoard > &board, InputArray cameraMatrix, InputArray distCoeffs, InputOutputArray rvec, InputOutputArray tvec, bool useExtrinsicGuess=false): Use CharucoBoard::matchImagePoints and cv::solvePnP
Member cv::aruco::estimatePoseSingleMarkers (InputArrayOfArrays corners, float markerLength, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvecs, OutputArray tvecs, OutputArray objPoints=noArray(), const Ptr< EstimateParameters > &estimateParameters=makePtr< EstimateParameters >()): Use cv::solvePnP
Member cv::aruco::getBoardObjectAndImagePoints (const Ptr< Board > &board, InputArrayOfArrays detectedCorners, InputArray detectedIds, OutputArray objPoints, OutputArray imgPoints): Use Board::matchImagePoints
Member cv::aruco::interpolateCornersCharuco (InputArrayOfArrays markerCorners, InputArray markerIds, InputArray image, const Ptr< CharucoBoard > &board, OutputArray charucoCorners, OutputArray charucoIds, InputArray cameraMatrix=noArray(), InputArray distCoeffs=noArray(), int minMarkers=2): Use CharucoDetector::detectBoard
Member cv::aruco::PatternPositionType: Use Board::matchImagePoints and cv::solvePnP
Member cv::aruco::refineDetectedMarkers (InputArray image, const Ptr< Board > &board, InputOutputArrayOfArrays detectedCorners, InputOutputArray detectedIds, InputOutputArrayOfArrays rejectedCorners, InputArray cameraMatrix=noArray(), InputArray distCoeffs=noArray(), float minRepDistance=10.f, float errorCorrectionRate=3.f, bool checkAllOrders=true, OutputArray recoveredIdxs=noArray(), const Ptr< DetectorParameters > &parameters=makePtr< DetectorParameters >()): Use class ArucoDetector::refineDetectedMarkers
Member cv::aruco::testCharucoCornersCollinear (const Ptr< CharucoBoard > &board, InputArray charucoIds): Use CharucoBoard::checkCharucoCornersCollinear
Member cv::convertFp16 (InputArray src, OutputArray dst): Use Mat::convertTo with CV_16F instead.
Member cv::createStitcher (bool try_use_gpu=false): use Stitcher::create
Member cv::createStitcherScans (bool try_use_gpu=false): use Stitcher::create
Member cv::dnn::getInferenceEngineBackendType ()
Member cv::dnn::Layer::finalize (const std::vector< Mat * > &input, std::vector< Mat > &output): Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead
Member cv::dnn::Layer::finalize (const std::vector< Mat > &inputs, std::vector< Mat > &outputs): Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead
Member cv::dnn::Layer::finalize (const std::vector< Mat > &inputs): Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead
Member cv::dnn::Layer::forward (std::vector< Mat * > &input, std::vector< Mat > &output, std::vector< Mat > &internals): Use Layer::forward(InputArrayOfArrays, OutputArrayOfArrays, OutputArrayOfArrays) instead
Member cv::dnn::Layer::run (const std::vector< Mat > &inputs, std::vector< Mat > &outputs, std::vector< Mat > &internals): This method will be removed in the future release.
Member cv::dnn::LSTMLayer::setProduceCellOutput (bool produce=false)=0: Use flag use_timestamp_dim in LayerParams.
Member cv::dnn::LSTMLayer::setUseTimstampsDim (bool use=true)=0: Use flag produce_cell_output in LayerParams.
Member cv::dnn::LSTMLayer::setWeights (const Mat &Wh, const Mat &Wx, const Mat &b)=0: Use LayerParams::blobs instead.
Member cv::dnn::Net::getLayer (const String &layerName) const: Use int getLayerId(const String &layer)
Member cv::dnn::Net::getLayer (const LayerId &layerId) const: to be removed
Member cv::dnn::Net::LayerId: Use getLayerId() with int result.
Member cv::dnn::setInferenceEngineBackendType (const cv::String &newBackendType)
Member cv::error (const Exception &exc): drop this version
Member cv::estimateRigidTransform (InputArray src, InputArray dst, bool fullAffine): Use cv::estimateAffine2D, cv::estimateAffinePartial2D instead. If you are using this function with images, extract points using cv::calcOpticalFlowPyrLK and then use the estimation functions.
Member cv::FileNode::FileNode (const FileStorage *fs, size_t blockIdx, size_t ofs)
Member cv::getThreadNum (): Current implementation doesn't corresponding to this documentation.
Member cv::linearPolar (InputArray src, OutputArray dst, Point2f center, double maxRadius, int flags): This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags)
Member cv::logPolar (InputArray src, OutputArray dst, Point2f center, double M, int flags): This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG);
Member cv::ocl::Context::Context (int dtype)
Member cv::ocl::Context::create ()
Member cv::ocl::Context::create (int dtype)
Member cv::ocl::initializeContextFromHandle (Context &ctx, void *platform, void *context, void *device)
Class cv::ocl::Platform
Member cv::ocl::Platform::getDefault ()
Member cv::ovis::updateTexture (const String &name, InputArray image): use setMaterialProperty
Member cv::text::loadOCRHMMClassifierCNN (const String &filename): use loadOCRHMMClassifier instead
Member cv::text::loadOCRHMMClassifierNM (const String &filename): loadOCRHMMClassifier instead
Member cv::TLSDataAccumulator< T >::gather (std::vector< T * > &data) const: replaced by detachData()
Member cv::TLSDataContainer::gatherData (std::vector< void * > &data) const: use detachData() instead
Member cv::v_signmask (const v_reg< _Tp, n > &a): v_signmask depends on a lane count heavily and therefore isn't universal enough
Member CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
Member CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API
File datamov_utils.hpp: Use Device layer instead.
File dynamic_smem.hpp: Use Device layer instead.
File emulation.hpp: Use Device layer instead.
File filters.hpp: Use Device layer instead.
File funcattrib.hpp: Use Device layer instead.
File functional.hpp: Use Device layer instead.
Member G_TYPED_KERNEL_M: This macro is deprecated in favor of G_TYPED_KERNEL that is used for declaring any G-API Operation.
File ggpukernel.hpp: Use <opencv2/gapi/ocl/goclkernel.hpp> instead.
File imgproc.hpp: Use <opencv2/gapi/ocl/imgproc.hpp> instead.
File limits.hpp: Use Device layer instead.
File reduce.hpp: Use Device layer instead.
File saturate_cast.hpp: Use Device layer instead.
File scan.hpp: Use Device layer instead.
File simd_functions.hpp: Use Device layer instead.
File transform.hpp: Use Device layer instead.
File type_traits.hpp: Use Device layer instead.
File utility.hpp: Use Device layer instead.
File vec_distance.hpp: Use Device layer instead.
File vec_math.hpp: Use Device layer instead.
File vec_traits.hpp: Use Device layer instead.
File warp.hpp: Use Device layer instead.
File warp_reduce.hpp: Use Device layer instead.
File warp_shuffle.hpp: Use Device layer instead.