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//! # Core functionality //! # Basic structures //! # C structures and operations //! # Connections with C++ //! # Operations on arrays //! # XML/YAML Persistence //! # Clustering //! # Utility and system functions and macros //! # SSE utilities //! # NEON utilities //! # Softfloat support //! # Utility functions for OpenCV samples //! # OpenGL interoperability //! # Intel IPP Asynchronous C/C++ Converters //! # Optimization Algorithms //! # DirectX interoperability //! # Eigen support //! # OpenCL support //! # Intel VA-API/OpenCL (CL-VA) interoperability //! # Hardware Acceleration Layer //! # Functions //! # Interface //! # Universal intrinsics //! # Private implementation helpers use std::os::raw::{c_char, c_void}; use libc::{ptrdiff_t, size_t}; use crate::{Error, Result, core, sys, types}; pub const ACCESS_FAST: i32 = 1<<26; pub const ACCESS_MASK: i32 = 3<<24; pub const ACCESS_READ: i32 = 1<<24; pub const ACCESS_RW: i32 = 3<<24; pub const ACCESS_WRITE: i32 = 1<<25; /// `iiiiii|abcdefgh|iiiiiii` with some specified `i` pub const BORDER_CONSTANT: i32 = 0; /// same as BORDER_REFLECT_101 pub const BORDER_DEFAULT: i32 = 4; /// do not look outside of ROI pub const BORDER_ISOLATED: i32 = 16; /// `fedcba|abcdefgh|hgfedcb` pub const BORDER_REFLECT: i32 = 2; /// same as BORDER_REFLECT_101 pub const BORDER_REFLECT101: i32 = 4; /// `gfedcb|abcdefgh|gfedcba` pub const BORDER_REFLECT_101: i32 = 4; /// `aaaaaa|abcdefgh|hhhhhhh` pub const BORDER_REPLICATE: i32 = 1; /// `uvwxyz|abcdefgh|ijklmno` pub const BORDER_TRANSPARENT: i32 = 5; /// `cdefgh|abcdefgh|abcdefg` pub const BORDER_WRAP: i32 = 3; /// incorrect input align pub const BadAlign: i32 = -21; pub const BadAlphaChannel: i32 = -18; /// input COI is not supported pub const BadCOI: i32 = -24; pub const BadCallBack: i32 = -22; pub const BadDataPtr: i32 = -12; /// input image depth is not supported by the function pub const BadDepth: i32 = -17; /// image size is invalid pub const BadImageSize: i32 = -10; pub const BadModelOrChSeq: i32 = -14; pub const BadNumChannel1U: i32 = -16; /// bad number of channels, for example, some functions accept only single channel matrices. pub const BadNumChannels: i32 = -15; /// offset is invalid pub const BadOffset: i32 = -11; /// number of dimensions is out of range pub const BadOrder: i32 = -19; /// incorrect input origin pub const BadOrigin: i32 = -20; /// incorrect input roi pub const BadROISize: i32 = -25; /// image step is wrong, this may happen for a non-continuous matrix. pub const BadStep: i32 = -13; pub const BadTileSize: i32 = -23; /// src1 is equal to src2. pub const CMP_EQ: i32 = 0; /// src1 is greater than or equal to src2. pub const CMP_GE: i32 = 2; /// src1 is greater than src2. pub const CMP_GT: i32 = 1; /// src1 is less than or equal to src2. pub const CMP_LE: i32 = 4; /// src1 is less than src2. pub const CMP_LT: i32 = 3; /// src1 is unequal to src2. pub const CMP_NE: i32 = 5; pub const COVAR_COLS: i32 = 16; pub const COVAR_NORMAL: i32 = 1; pub const COVAR_ROWS: i32 = 8; pub const COVAR_SCALE: i32 = 4; pub const COVAR_SCRAMBLED: i32 = 0; pub const COVAR_USE_AVG: i32 = 2; pub const CPU_AVX: i32 = 10; pub const CPU_AVX2: i32 = 11; /// Skylake-X with AVX-512F/CD/BW/DQ/VL pub const CPU_AVX512_SKX: i32 = 256; pub const CPU_AVX_512BW: i32 = 14; pub const CPU_AVX_512CD: i32 = 15; pub const CPU_AVX_512DQ: i32 = 16; pub const CPU_AVX_512ER: i32 = 17; pub const CPU_AVX_512F: i32 = 13; pub const CPU_AVX_512IFMA: i32 = 18; pub const CPU_AVX_512IFMA512: i32 = 18; pub const CPU_AVX_512PF: i32 = 19; pub const CPU_AVX_512VBMI: i32 = 20; pub const CPU_AVX_512VL: i32 = 21; pub const CPU_FMA3: i32 = 12; pub const CPU_FP16: i32 = 9; pub const CPU_MAX_FEATURE: i32 = 512; pub const CPU_MMX: i32 = 1; pub const CPU_NEON: i32 = 100; pub const CPU_POPCNT: i32 = 8; pub const CPU_SSE: i32 = 2; pub const CPU_SSE2: i32 = 3; pub const CPU_SSE3: i32 = 4; pub const CPU_SSE4_1: i32 = 6; pub const CPU_SSE4_2: i32 = 7; pub const CPU_SSSE3: i32 = 5; pub const CPU_VSX: i32 = 200; pub const CPU_VSX3: i32 = 201; pub const CV_16S: i32 = 3; pub const CV_16U: i32 = 2; pub const CV_32F: i32 = 5; pub const CV_32S: i32 = 4; pub const CV_64F: i32 = 6; pub const CV_8S: i32 = 1; pub const CV_8U: i32 = 0; pub const CV_CN_MAX: i32 = 512; pub const CV_CN_SHIFT: i32 = 3; pub const CV_CPU_AVX: i32 = 10; pub const CV_CPU_AVX2: i32 = 11; pub const CV_CPU_AVX512_SKX: i32 = 256; pub const CV_CPU_AVX_512BW: i32 = 14; pub const CV_CPU_AVX_512CD: i32 = 15; pub const CV_CPU_AVX_512DQ: i32 = 16; pub const CV_CPU_AVX_512ER: i32 = 17; pub const CV_CPU_AVX_512F: i32 = 13; pub const CV_CPU_AVX_512IFMA: i32 = 18; /// deprecated pub const CV_CPU_AVX_512IFMA512: i32 = 18; pub const CV_CPU_AVX_512PF: i32 = 19; pub const CV_CPU_AVX_512VBMI: i32 = 20; pub const CV_CPU_AVX_512VL: i32 = 21; pub const CV_CPU_FMA3: i32 = 12; pub const CV_CPU_FP16: i32 = 9; pub const CV_CPU_MMX: i32 = 1; pub const CV_CPU_NEON: i32 = 100; pub const CV_CPU_NONE: i32 = 0; pub const CV_CPU_POPCNT: i32 = 8; pub const CV_CPU_SSE: i32 = 2; pub const CV_CPU_SSE2: i32 = 3; pub const CV_CPU_SSE3: i32 = 4; pub const CV_CPU_SSE4_1: i32 = 6; pub const CV_CPU_SSE4_2: i32 = 7; pub const CV_CPU_SSSE3: i32 = 5; pub const CV_CPU_VSX: i32 = 200; pub const CV_CPU_VSX3: i32 = 201; pub const CV_HAL_BORDER_CONSTANT: i32 = 0; pub const CV_HAL_BORDER_ISOLATED: i32 = 16; pub const CV_HAL_BORDER_REFLECT: i32 = 2; pub const CV_HAL_BORDER_REFLECT_101: i32 = 4; pub const CV_HAL_BORDER_REPLICATE: i32 = 1; pub const CV_HAL_BORDER_TRANSPARENT: i32 = 5; pub const CV_HAL_BORDER_WRAP: i32 = 3; pub const CV_HAL_CMP_EQ: i32 = 0; pub const CV_HAL_CMP_GE: i32 = 2; pub const CV_HAL_CMP_GT: i32 = 1; pub const CV_HAL_CMP_LE: i32 = 4; pub const CV_HAL_CMP_LT: i32 = 3; pub const CV_HAL_CMP_NE: i32 = 5; pub const CV_HAL_DFT_COMPLEX_OUTPUT: i32 = 16; pub const CV_HAL_DFT_INVERSE: i32 = 1; pub const CV_HAL_DFT_IS_CONTINUOUS: i32 = 512; pub const CV_HAL_DFT_IS_INPLACE: i32 = 1024; pub const CV_HAL_DFT_REAL_OUTPUT: i32 = 32; pub const CV_HAL_DFT_ROWS: i32 = 4; pub const CV_HAL_DFT_SCALE: i32 = 2; pub const CV_HAL_DFT_STAGE_COLS: i32 = 128; pub const CV_HAL_DFT_TWO_STAGE: i32 = 64; pub const CV_HAL_ERROR_NOT_IMPLEMENTED: i32 = 1; pub const CV_HAL_ERROR_OK: i32 = 0; pub const CV_HAL_ERROR_UNKNOWN: i32 = -1; pub const CV_HAL_GEMM_1_T: i32 = 1; pub const CV_HAL_GEMM_2_T: i32 = 2; pub const CV_HAL_GEMM_3_T: i32 = 4; pub const CV_HAL_SVD_FULL_UV: i32 = 8; pub const CV_HAL_SVD_MODIFY_A: i32 = 4; pub const CV_HAL_SVD_NO_UV: i32 = 1; pub const CV_HAL_SVD_SHORT_UV: i32 = 2; pub const CV_HARDWARE_MAX_FEATURE: i32 = 512; pub const CV_MAJOR_VERSION: i32 = 3; pub const CV_MAT_CONT_FLAG_SHIFT: i32 = 14; pub const CV_MINOR_VERSION: i32 = 4; pub const CV_SUBMAT_FLAG_SHIFT: i32 = 15; pub const CV_SUBMINOR_VERSION: i32 = 6; pub const CV_VERSION_MAJOR: i32 = 3; pub const CV_VERSION_MINOR: i32 = 4; pub const CV_VERSION_REVISION: i32 = 6; pub const CV_VERSION_STATUS: &'static str = ""; pub const DCT_INVERSE: i32 = 1; pub const DCT_ROWS: i32 = 4; pub const DECOMP_CHOLESKY: i32 = 3; pub const DECOMP_EIG: i32 = 2; pub const DECOMP_LU: i32 = 0; pub const DECOMP_NORMAL: i32 = 16; pub const DECOMP_QR: i32 = 4; pub const DECOMP_SVD: i32 = 1; pub const DFT_COMPLEX_INPUT: i32 = 64; pub const DFT_COMPLEX_OUTPUT: i32 = 16; pub const DFT_INVERSE: i32 = 1; pub const DFT_REAL_OUTPUT: i32 = 32; pub const DFT_ROWS: i32 = 4; pub const DFT_SCALE: i32 = 2; pub const FILLED: i32 = -1; pub const FLAGS_EXPAND_SAME_NAMES: i32 = 0x02; pub const FLAGS_MAPPING: i32 = 0x01; pub const FLAGS_NONE: i32 = 0; /// normal size serif font pub const FONT_HERSHEY_COMPLEX: i32 = 3; /// smaller version of FONT_HERSHEY_COMPLEX pub const FONT_HERSHEY_COMPLEX_SMALL: i32 = 5; /// normal size sans-serif font (more complex than FONT_HERSHEY_SIMPLEX) pub const FONT_HERSHEY_DUPLEX: i32 = 2; /// small size sans-serif font pub const FONT_HERSHEY_PLAIN: i32 = 1; /// more complex variant of FONT_HERSHEY_SCRIPT_SIMPLEX pub const FONT_HERSHEY_SCRIPT_COMPLEX: i32 = 7; /// hand-writing style font pub const FONT_HERSHEY_SCRIPT_SIMPLEX: i32 = 6; /// normal size sans-serif font pub const FONT_HERSHEY_SIMPLEX: i32 = 0; /// normal size serif font (more complex than FONT_HERSHEY_COMPLEX) pub const FONT_HERSHEY_TRIPLEX: i32 = 4; /// flag for italic font pub const FONT_ITALIC: i32 = 16; pub const Formatter_FMT_C: i32 = 5; pub const Formatter_FMT_CSV: i32 = 2; pub const Formatter_FMT_DEFAULT: i32 = 0; pub const Formatter_FMT_MATLAB: i32 = 1; pub const Formatter_FMT_NUMPY: i32 = 4; pub const Formatter_FMT_PYTHON: i32 = 3; /// transposes src1 pub const GEMM_1_T: i32 = 1; /// transposes src2 pub const GEMM_2_T: i32 = 2; /// transposes src3 pub const GEMM_3_T: i32 = 4; /// GPU API call error pub const GpuApiCallError: i32 = -217; /// no CUDA support pub const GpuNotSupported: i32 = -216; pub const Hamming_normType: i32 = 6; /// image header is NULL pub const HeaderIsNull: i32 = -9; pub const IMPL_IPP: i32 = 0+1; pub const IMPL_OPENCL: i32 = 0+2; pub const IMPL_PLAIN: i32 = 0; pub const KMEANS_PP_CENTERS: i32 = 2; pub const KMEANS_RANDOM_CENTERS: i32 = 0; pub const KMEANS_USE_INITIAL_LABELS: i32 = 1; /// 4-connected line pub const LINE_4: i32 = 4; /// 8-connected line pub const LINE_8: i32 = 8; /// antialiased line pub const LINE_AA: i32 = 16; /// Debug message. Disabled in the "Release" build. pub const LOG_LEVEL_DEBUG: i32 = 5; /// Error message pub const LOG_LEVEL_ERROR: i32 = 2; /// Fatal (critical) error (unrecoverable internal error) pub const LOG_LEVEL_FATAL: i32 = 1; /// Info message pub const LOG_LEVEL_INFO: i32 = 4; /// for using in setLogVevel() call pub const LOG_LEVEL_SILENT: i32 = 0; /// Verbose (trace) messages. Requires verbosity level. Disabled in the "Release" build. pub const LOG_LEVEL_VERBOSE: i32 = 6; /// Warning message pub const LOG_LEVEL_WARNING: i32 = 3; pub const MaskIsTiled: i32 = -26; pub const Mat_AUTO_STEP: usize = 0; pub const Mat_DEPTH_MASK: i32 = 7; pub const Mat_MAGIC_MASK: i32 = 0xFFFF0000; pub const Mat_MAGIC_VAL: i32 = 0x42FF0000; pub const Mat_TYPE_MASK: i32 = 0x00000FFF; pub const NORM_HAMMING: i32 = 6; pub const NORM_HAMMING2: i32 = 7; pub const NORM_INF: i32 = 1; pub const NORM_L1: i32 = 2; pub const NORM_L2: i32 = 4; pub const NORM_L2SQR: i32 = 5; /// flag pub const NORM_MINMAX: i32 = 32; /// flag pub const NORM_RELATIVE: i32 = 8; /// bit-mask which can be used to separate norm type from norm flags pub const NORM_TYPE_MASK: i32 = 7; pub const OPENCV_ABI_COMPATIBILITY: i32 = 300; /// OpenCL API call error pub const OpenCLApiCallError: i32 = -220; pub const OpenCLDoubleNotSupported: i32 = -221; /// OpenCL initialization error pub const OpenCLInitError: i32 = -222; pub const OpenCLNoAMDBlasFft: i32 = -223; /// OpenGL API call error pub const OpenGlApiCallError: i32 = -219; /// no OpenGL support pub const OpenGlNotSupported: i32 = -218; /// indicates that the input samples are stored as matrix columns pub const PCA_DATA_AS_COL: i32 = 1; /// indicates that the input samples are stored as matrix rows pub const PCA_DATA_AS_ROW: i32 = 0; pub const PCA_USE_AVG: i32 = 2; pub const Param_ALGORITHM: i32 = 6; pub const Param_BOOLEAN: i32 = 1; pub const Param_FLOAT: i32 = 7; pub const Param_INT: i32 = 0; pub const Param_MAT: i32 = 4; pub const Param_MAT_VECTOR: i32 = 5; pub const Param_REAL: i32 = 2; pub const Param_SCALAR: i32 = 12; pub const Param_STRING: i32 = 3; pub const Param_UCHAR: i32 = 11; pub const Param_UINT64: i32 = 9; pub const Param_UNSIGNED_INT: i32 = 8; /// the output is the mean vector of all rows/columns of the matrix. pub const REDUCE_AVG: i32 = 1; /// the output is the maximum (column/row-wise) of all rows/columns of the matrix. pub const REDUCE_MAX: i32 = 2; /// the output is the minimum (column/row-wise) of all rows/columns of the matrix. pub const REDUCE_MIN: i32 = 3; /// the output is the sum of all rows/columns of the matrix. pub const REDUCE_SUM: i32 = 0; pub const RNG_NORMAL: i32 = 1; pub const RNG_UNIFORM: i32 = 0; /// Rotate 180 degrees clockwise pub const ROTATE_180: i32 = 1; /// Rotate 90 degrees clockwise pub const ROTATE_90_CLOCKWISE: i32 = 0; /// Rotate 270 degrees clockwise pub const ROTATE_90_COUNTERCLOCKWISE: i32 = 2; /// there are multiple maxima for target function - the arbitrary one is returned pub const SOLVELP_MULTI: i32 = 1; /// there is only one maximum for target function pub const SOLVELP_SINGLE: i32 = 0; /// problem is unbounded (target function can achieve arbitrary high values) pub const SOLVELP_UNBOUNDED: i32 = -2; /// problem is unfeasible (there are no points that satisfy all the constraints imposed) pub const SOLVELP_UNFEASIBLE: i32 = -1; /// each matrix row is sorted in the ascending pub const SORT_ASCENDING: i32 = 0; /// each matrix row is sorted in the pub const SORT_DESCENDING: i32 = 16; /// each matrix column is sorted pub const SORT_EVERY_COLUMN: i32 = 1; /// each matrix row is sorted independently pub const SORT_EVERY_ROW: i32 = 0; pub const SVD_FULL_UV: i32 = 4; pub const SVD_MODIFY_A: i32 = 1; pub const SVD_NO_UV: i32 = 2; pub const SparseMat_HASH_BIT: i32 = 0x80000000; pub const SparseMat_HASH_SCALE: i32 = 0x5bd1e995; pub const SparseMat_MAX_DIM: i32 = 32; /// assertion failed pub const StsAssert: i32 = -215; /// tracing pub const StsAutoTrace: i32 = -8; /// pseudo error for back trace pub const StsBackTrace: i32 = -1; /// function arg/param is bad pub const StsBadArg: i32 = -5; /// flag is wrong or not supported pub const StsBadFlag: i32 = -206; /// unsupported function pub const StsBadFunc: i32 = -6; /// bad format of mask (neither 8uC1 nor 8sC1) pub const StsBadMask: i32 = -208; /// an allocated block has been corrupted pub const StsBadMemBlock: i32 = -214; /// bad CvPoint pub const StsBadPoint: i32 = -207; /// the input/output structure size is incorrect pub const StsBadSize: i32 = -201; /// division by zero pub const StsDivByZero: i32 = -202; /// unknown /unspecified error pub const StsError: i32 = -2; /// incorrect filter offset value pub const StsFilterOffsetErr: i32 = -31; /// incorrect filter structure content pub const StsFilterStructContentErr: i32 = -29; /// in-place operation is not supported pub const StsInplaceNotSupported: i32 = -203; /// internal error (bad state) pub const StsInternal: i32 = -3; /// incorrect transform kernel content pub const StsKernelStructContentErr: i32 = -30; /// iteration didn't converge pub const StsNoConv: i32 = -7; /// insufficient memory pub const StsNoMem: i32 = -4; /// the requested function/feature is not implemented pub const StsNotImplemented: i32 = -213; /// null pointer pub const StsNullPtr: i32 = -27; /// request can't be completed pub const StsObjectNotFound: i32 = -204; /// everything is ok pub const StsOk: i32 = 0; /// some of parameters are out of range pub const StsOutOfRange: i32 = -211; /// invalid syntax/structure of the parsed file pub const StsParseError: i32 = -212; /// formats of input/output arrays differ pub const StsUnmatchedFormats: i32 = -205; /// sizes of input/output structures do not match pub const StsUnmatchedSizes: i32 = -209; /// the data format/type is not supported by the function pub const StsUnsupportedFormat: i32 = -210; /// incorrect vector length pub const StsVecLengthErr: i32 = -28; pub const TEST_CUSTOM: i32 = 0; pub const TEST_EQ: i32 = 1; pub const TEST_GE: i32 = 5; pub const TEST_GT: i32 = 6; pub const TEST_LE: i32 = 3; pub const TEST_LT: i32 = 4; pub const TEST_NE: i32 = 2; pub const TYPE_FUN: i32 = 0+3; pub const TYPE_GENERAL: i32 = 0; pub const TYPE_MARKER: i32 = 0+1; pub const TYPE_WRAPPER: i32 = 0+2; /// the maximum number of iterations or elements to compute pub const TermCriteria_COUNT: i32 = 1; /// the desired accuracy or change in parameters at which the iterative algorithm stops pub const TermCriteria_EPS: i32 = 2; /// ditto pub const TermCriteria_MAX_ITER: i32 = 1; pub const UMatData_ASYNC_CLEANUP: i32 = 128; pub const UMatData_COPY_ON_MAP: i32 = 1; pub const UMatData_DEVICE_COPY_OBSOLETE: i32 = 4; pub const UMatData_DEVICE_MEM_MAPPED: i32 = 64; pub const UMatData_HOST_COPY_OBSOLETE: i32 = 2; pub const UMatData_TEMP_COPIED_UMAT: i32 = 24; pub const UMatData_TEMP_UMAT: i32 = 8; pub const UMatData_USER_ALLOCATED: i32 = 32; pub const USAGE_ALLOCATE_DEVICE_MEMORY: i32 = 1 << 1; pub const USAGE_ALLOCATE_HOST_MEMORY: i32 = 1 << 0; pub const USAGE_ALLOCATE_SHARED_MEMORY: i32 = 1 << 2; pub const USAGE_DEFAULT: i32 = 0; pub const _InputArray_KIND_SHIFT: i32 = 16; pub const __UMAT_USAGE_FLAGS_32BIT: i32 = 0x7fffffff; #[repr(C)] #[derive(Debug)] pub enum FLAGS { FLAGS_NONE = FLAGS_NONE as isize, FLAGS_MAPPING = FLAGS_MAPPING as isize, FLAGS_EXPAND_SAME_NAMES = FLAGS_EXPAND_SAME_NAMES as isize, } #[repr(C)] #[derive(Debug)] pub enum IMPL { IMPL_PLAIN = IMPL_PLAIN as isize, IMPL_IPP = IMPL_IPP as isize, IMPL_OPENCL = IMPL_OPENCL as isize, } #[repr(C)] #[derive(Debug)] pub enum TYPE { TYPE_GENERAL = TYPE_GENERAL as isize, TYPE_MARKER = TYPE_MARKER as isize, TYPE_WRAPPER = TYPE_WRAPPER as isize, TYPE_FUN = TYPE_FUN as isize, } #[repr(C)] #[derive(Debug)] pub enum UMatUsageFlags { USAGE_DEFAULT = USAGE_DEFAULT as isize, USAGE_ALLOCATE_HOST_MEMORY = USAGE_ALLOCATE_HOST_MEMORY as isize, USAGE_ALLOCATE_DEVICE_MEMORY = USAGE_ALLOCATE_DEVICE_MEMORY as isize, USAGE_ALLOCATE_SHARED_MEMORY = USAGE_ALLOCATE_SHARED_MEMORY as isize, __UMAT_USAGE_FLAGS_32BIT = __UMAT_USAGE_FLAGS_32BIT as isize, } pub type Vec8i = core::Vec8<i32>; pub type Vec6d = core::Vec6<f64>; pub type Vec6f = core::Vec6<f32>; pub type Vec6i = core::Vec6<i32>; pub type Vec4d = core::Vec4<f64>; pub type Vec4f = core::Vec4<f32>; pub type Vec4i = core::Vec4<i32>; pub type Vec4w = core::Vec4<u16>; pub type Vec4s = core::Vec4<i16>; pub type Vec4b = core::Vec4<u8>; pub type Vec3d = core::Vec3<f64>; pub type Vec3f = core::Vec3<f32>; pub type Vec3i = core::Vec3<i32>; pub type Vec3w = core::Vec3<u16>; pub type Vec3s = core::Vec3<i16>; pub type Vec3b = core::Vec3<u8>; pub type Vec2d = core::Vec2<f64>; pub type Size2d = core::Size_<f64>; pub type Point2d = core::Point_<f64>; pub type Rect2d = core::Rect_<f64>; pub type Vec2f = core::Vec2<f32>; pub type Size2f = core::Size_<f32>; pub type Point2f = core::Point_<f32>; pub type Rect2f = core::Rect_<f32>; pub type Size2l = core::Size_<i64>; pub type Point2l = core::Point_<i64>; pub type Vec2i = core::Vec2<i32>; pub type Size2i = core::Size_<i32>; pub type Point2i = core::Point_<i32>; pub type Rect2i = core::Rect_<i32>; pub type Size = core::Size_<i32>; pub type Point = core::Point_<i32>; pub type Rect = core::Rect_<i32>; pub type Vec2w = core::Vec2<u16>; pub type Vec2s = core::Vec2<i16>; pub type Vec2b = core::Vec2<u8>; pub type Scalar = core::Scalar_<f64>; /// Class for matching keypoint descriptors /// /// query descriptor index, train descriptor index, train image index, and distance between /// descriptors. #[repr(C)] #[derive(Copy,Clone,Debug,PartialEq)] pub struct DMatch { pub query_idx: i32, pub train_idx: i32, pub img_idx: i32, pub distance: f32, } /// Data structure for salient point detectors. /// /// The class instance stores a keypoint, i.e. a point feature found by one of many available keypoint /// detectors, such as Harris corner detector, #FAST, %StarDetector, %SURF, %SIFT etc. /// /// The keypoint is characterized by the 2D position, scale (proportional to the diameter of the /// neighborhood that needs to be taken into account), orientation and some other parameters. The /// keypoint neighborhood is then analyzed by another algorithm that builds a descriptor (usually /// represented as a feature vector). The keypoints representing the same object in different images /// can then be matched using %KDTree or another method. #[repr(C)] #[derive(Copy,Clone,Debug,PartialEq)] pub struct KeyPoint { pub pt: core::Point2f, pub size: f32, pub angle: f32, pub response: f32, pub octave: i32, pub class_id: i32, } /// struct returned by cv::moments /// /// The spatial moments <span lang='latex'>\texttt{Moments::m}_{ji}</span> are computed as: /// /// <div lang='latex'>\texttt{m} _{ji}= \sum _{x,y} \left ( \texttt{array} (x,y) \cdot x^j \cdot y^i \right )</div> /// /// The central moments <span lang='latex'>\texttt{Moments::mu}_{ji}</span> are computed as: /// /// <div lang='latex'>\texttt{mu} _{ji}= \sum _{x,y} \left ( \texttt{array} (x,y) \cdot (x - \bar{x} )^j \cdot (y - \bar{y} )^i \right )</div> /// /// where <span lang='latex'>(\bar{x}, \bar{y})</span> is the mass center: /// /// <div lang='latex'>\bar{x} = \frac{\texttt{m}_{10}}{\texttt{m}_{00}} , \; \bar{y} = \frac{\texttt{m}_{01}}{\texttt{m}_{00}}</div> /// /// The normalized central moments <span lang='latex'>\texttt{Moments::nu}_{ij}</span> are computed as: /// /// <div lang='latex'>\texttt{nu} _{ji}= \frac{\texttt{mu}_{ji}}{\texttt{m}_{00}^{(i+j)/2+1}} .</div> /// /// /// Note: /// <span lang='latex'>\texttt{mu}_{00}=\texttt{m}_{00}</span>, <span lang='latex'>\texttt{nu}_{00}=1</span> /// <span lang='latex'>\texttt{nu}_{10}=\texttt{mu}_{10}=\texttt{mu}_{01}=\texttt{mu}_{10}=0</span> , hence the values are not /// stored. /// /// The moments of a contour are defined in the same way but computed using the Green's formula (see /// <http://en.wikipedia.org/wiki/Green_theorem>). So, due to a limited raster resolution, the moments /// computed for a contour are slightly different from the moments computed for the same rasterized /// contour. /// /// /// Note: /// Since the contour moments are computed using Green formula, you may get seemingly odd results for /// contours with self-intersections, e.g. a zero area (m00) for butterfly-shaped contours. #[repr(C)] #[derive(Copy,Clone,Debug,PartialEq)] pub struct Moments { pub m00: f64, pub m10: f64, pub m01: f64, pub m20: f64, pub m11: f64, pub m02: f64, pub m30: f64, pub m21: f64, pub m12: f64, pub m03: f64, pub mu20: f64, pub mu11: f64, pub mu02: f64, pub mu30: f64, pub mu21: f64, pub mu12: f64, pub mu03: f64, pub nu20: f64, pub nu11: f64, pub nu02: f64, pub nu30: f64, pub nu21: f64, pub nu12: f64, pub nu03: f64, } /// proxy for hal::Cholesky pub fn cholesky(a: &mut f64, astep: size_t, m: i32, b: &mut f64, bstep: size_t, n: i32) -> Result<bool> { unsafe { sys::cv_Cholesky_double_X_size_t_int_double_X_size_t_int(a, astep, m, b, bstep, n) }.into_result() } /// proxy for hal::Cholesky pub fn cholesky_f32(a: &mut f32, astep: size_t, m: i32, b: &mut f32, bstep: size_t, n: i32) -> Result<bool> { unsafe { sys::cv_Cholesky_float_X_size_t_int_float_X_size_t_int(a, astep, m, b, bstep, n) }.into_result() } /// Performs a look-up table transform of an array. /// /// The function LUT fills the output array with values from the look-up table. Indices of the entries /// are taken from the input array. That is, the function processes each element of src as follows: /// <div lang='latex'>\texttt{dst} (I) \leftarrow \texttt{lut(src(I) + d)}</div> /// where /// <div lang='latex'>d = \fork{0}{if \(\texttt{src}\) has depth \(\texttt{CV_8U}\)}{128}{if \(\texttt{src}\) has depth \(\texttt{CV_8S}\)}</div> /// ## Parameters /// * src: input array of 8-bit elements. /// * lut: look-up table of 256 elements; in case of multi-channel input array, the table should /// either have a single channel (in this case the same table is used for all channels) or the same /// number of channels as in the input array. /// * dst: output array of the same size and number of channels as src, and the same depth as lut. /// ## See also /// convertScaleAbs, Mat::convertTo pub fn lut(src: &core::Mat, lut: &core::Mat, dst: &mut core::Mat) -> Result<()> { unsafe { sys::cv_LUT_Mat_Mat_Mat(src.as_raw_Mat(), lut.as_raw_Mat(), dst.as_raw_Mat()) }.into_result() } /// proxy for hal::LU pub fn lu(a: &mut f64, astep: size_t, m: i32, b: &mut f64, bstep: size_t, n: i32) -> Result<i32> { unsafe { sys::cv_LU_double_X_size_t_int_double_X_size_t_int(a, astep, m, b, bstep, n) }.into_result() } /// proxy for hal::LU pub fn lu_f32(a: &mut f32, astep: size_t, m: i32, b: &mut f32, bstep: size_t, n: i32) -> Result<i32> { unsafe { sys::cv_LU_float_X_size_t_int_float_X_size_t_int(a, astep, m, b, bstep, n) }.into_result() } /// Calculates the Mahalanobis distance between two vectors. /// /// The function cv::Mahalanobis calculates and returns the weighted distance between two vectors: /// <div lang='latex'>d( \texttt{vec1} , \texttt{vec2} )= \sqrt{\sum_{i,j}{\texttt{icovar(i,j)}\cdot(\texttt{vec1}(I)-\texttt{vec2}(I))\cdot(\texttt{vec1(j)}-\texttt{vec2(j)})} }</div> /// The covariance matrix may be calculated using the #calcCovarMatrix function and then inverted using /// the invert function (preferably using the #DECOMP_SVD method, as the most accurate). /// ## Parameters /// * v1: first 1D input vector. /// * v2: second 1D input vector. /// * icovar: inverse covariance matrix. pub fn mahalanobis(v1: &core::Mat, v2: &core::Mat, icovar: &core::Mat) -> Result<f64> { unsafe { sys::cv_Mahalanobis_Mat_Mat_Mat(v1.as_raw_Mat(), v2.as_raw_Mat(), icovar.as_raw_Mat()) }.into_result() } /// wrap PCA::backProject pub fn pca_back_project(data: &core::Mat, mean: &core::Mat, eigenvectors: &core::Mat, result: &mut core::Mat) -> Result<()> { unsafe { sys::cv_PCABackProject_Mat_Mat_Mat_Mat(data.as_raw_Mat(), mean.as_raw_Mat(), eigenvectors.as_raw_Mat(), result.as_raw_Mat()) }.into_result() } /// wrap PCA::operator() and add eigenvalues output parameter pub fn pca_compute_values_variance(data: &core::Mat, mean: &mut core::Mat, eigenvectors: &mut core::Mat, eigenvalues: &mut core::Mat, retained_variance: f64) -> Result<()> { unsafe { sys::cv_PCACompute_Mat_Mat_Mat_Mat_double(data.as_raw_Mat(), mean.as_raw_Mat(), eigenvectors.as_raw_Mat(), eigenvalues.as_raw_Mat(), retained_variance) }.into_result() } /// wrap PCA::operator() and add eigenvalues output parameter /// /// ## C++ default parameters /// * max_components: 0 pub fn pca_compute_values(data: &core::Mat, mean: &mut core::Mat, eigenvectors: &mut core::Mat, eigenvalues: &mut core::Mat, max_components: i32) -> Result<()> { unsafe { sys::cv_PCACompute_Mat_Mat_Mat_Mat_int(data.as_raw_Mat(), mean.as_raw_Mat(), eigenvectors.as_raw_Mat(), eigenvalues.as_raw_Mat(), max_components) }.into_result() } /// wrap PCA::operator() pub fn pca_compute_variance(data: &core::Mat, mean: &mut core::Mat, eigenvectors: &mut core::Mat, retained_variance: f64) -> Result<()> { unsafe { sys::cv_PCACompute_Mat_Mat_Mat_double(data.as_raw_Mat(), mean.as_raw_Mat(), eigenvectors.as_raw_Mat(), retained_variance) }.into_result() } /// wrap PCA::operator() /// /// ## C++ default parameters /// * max_components: 0 pub fn pca_compute(data: &core::Mat, mean: &mut core::Mat, eigenvectors: &mut core::Mat, max_components: i32) -> Result<()> { unsafe { sys::cv_PCACompute_Mat_Mat_Mat_int(data.as_raw_Mat(), mean.as_raw_Mat(), eigenvectors.as_raw_Mat(), max_components) }.into_result() } /// wrap PCA::project pub fn pca_project(data: &core::Mat, mean: &core::Mat, eigenvectors: &core::Mat, result: &mut core::Mat) -> Result<()> { unsafe { sys::cv_PCAProject_Mat_Mat_Mat_Mat(data.as_raw_Mat(), mean.as_raw_Mat(), eigenvectors.as_raw_Mat(), result.as_raw_Mat()) }.into_result() } /// Computes the Peak Signal-to-Noise Ratio (PSNR) image quality metric. /// /// This function calculates the Peak Signal-to-Noise Ratio (PSNR) image quality metric in decibels (dB), between two input arrays src1 and src2. Arrays must have depth CV_8U. /// /// The PSNR is calculated as follows: /// /// <div lang='latex'> /// \texttt{PSNR} = 10 \cdot \log_{10}{\left( \frac{R^2}{MSE} \right) } /// </div> /// /// where R is the maximum integer value of depth CV_8U (255) and MSE is the mean squared error between the two arrays. /// /// ## Parameters /// * src1: first input array. /// * src2: second input array of the same size as src1. pub fn psnr(src1: &core::Mat, src2: &core::Mat) -> Result<f64> { unsafe { sys::cv_PSNR_Mat_Mat(src1.as_raw_Mat(), src2.as_raw_Mat()) }.into_result() } /// wrap SVD::backSubst pub fn sv_back_subst(w: &core::Mat, u: &core::Mat, vt: &core::Mat, rhs: &core::Mat, dst: &mut core::Mat) -> Result<()> { unsafe { sys::cv_SVBackSubst_Mat_Mat_Mat_Mat_Mat(w.as_raw_Mat(), u.as_raw_Mat(), vt.as_raw_Mat(), rhs.as_raw_Mat(), dst.as_raw_Mat()) }.into_result() } /// wrap SVD::compute /// /// ## C++ default parameters /// * flags: 0 pub fn sv_decomp(src: &core::Mat, w: &mut core::Mat, u: &mut core::Mat, vt: &mut core::Mat, flags: i32) -> Result<()> { unsafe { sys::cv_SVDecomp_Mat_Mat_Mat_Mat_int(src.as_raw_Mat(), w.as_raw_Mat(), u.as_raw_Mat(), vt.as_raw_Mat(), flags) }.into_result() } /// Calculates the per-element absolute difference between two arrays or between an array and a scalar. /// /// The function cv::absdiff calculates: /// Absolute difference between two arrays when they have the same /// size and type: /// <div lang='latex'>\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{src2}(I)|)</div> /// Absolute difference between an array and a scalar when the second /// array is constructed from Scalar or has as many elements as the /// number of channels in `src1`: /// <div lang='latex'>\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{src2} |)</div> /// Absolute difference between a scalar and an array when the first /// array is constructed from Scalar or has as many elements as the /// number of channels in `src2`: /// <div lang='latex'>\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1} - \texttt{src2}(I) |)</div> /// where I is a multi-dimensional index of array elements. In case of /// multi-channel arrays, each channel is processed independently. /// /// Note: Saturation is not applied when the arrays have the depth CV_32S. /// You may even get a negative value in the case of overflow. /// ## Parameters /// * src1: first input array or a scalar. /// * src2: second input array or a scalar. /// * dst: output array that has the same size and type as input arrays. /// ## See also /// cv::abs(const Mat&) pub fn absdiff(src1: &core::Mat, src2: &core::Mat, dst: &mut core::Mat) -> Result<()> { unsafe { sys::cv_absdiff_Mat_Mat_Mat(src1.as_raw_Mat(), src2.as_raw_Mat(), dst.as_raw_Mat()) }.into_result() } /// Calculates the weighted sum of two arrays. /// /// The function addWeighted calculates the weighted sum of two arrays as follows: /// <div lang='latex'>\texttt{dst} (I)= \texttt{saturate} ( \texttt{src1} (I)* \texttt{alpha} + \texttt{src2} (I)* \texttt{beta} + \texttt{gamma} )</div> /// where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each /// channel is processed independently. /// The function can be replaced with a matrix expression: /// ```ignore{.cpp} /// dst = src1*alpha + src2*beta + gamma; /// ``` /// /// /// Note: Saturation is not applied when the output array has the depth CV_32S. You may even get /// result of an incorrect sign in the case of overflow. /// ## Parameters /// * src1: first input array. /// * alpha: weight of the first array elements. /// * src2: second input array of the same size and channel number as src1. /// * beta: weight of the second array elements. /// * gamma: scalar added to each sum. /// * dst: output array that has the same size and number of channels as the input arrays. /// * dtype: optional depth of the output array; when both input arrays have the same depth, dtype /// can be set to -1, which will be equivalent to src1.depth(). /// ## See also /// add, subtract, scaleAdd, Mat::convertTo /// /// ## C++ default parameters /// * dtype: -1 pub fn add_weighted(src1: &core::Mat, alpha: f64, src2: &core::Mat, beta: f64, gamma: f64, dst: &mut core::Mat, dtype: i32) -> Result<()> { unsafe { sys::cv_addWeighted_Mat_double_Mat_double_double_Mat_int(src1.as_raw_Mat(), alpha, src2.as_raw_Mat(), beta, gamma, dst.as_raw_Mat(), dtype) }.into_result() } /// Calculates the per-element sum of two arrays or an array and a scalar. /// /// The function add calculates: /// - Sum of two arrays when both input arrays have the same size and the same number of channels: /// <div lang='latex'>\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0</div> /// - Sum of an array and a scalar when src2 is constructed from Scalar or has the same number of /// elements as `src1.channels()`: /// <div lang='latex'>\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2} ) \quad \texttt{if mask}(I) \ne0</div> /// - Sum of a scalar and an array when src1 is constructed from Scalar or has the same number of /// elements as `src2.channels()`: /// <div lang='latex'>\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} + \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0</div> /// where `I` is a multi-dimensional index of array elements. In case of multi-channel arrays, each /// channel is processed independently. /// /// The first function in the list above can be replaced with matrix expressions: /// ```ignore{.cpp} /// dst = src1 + src2; /// dst += src1; // equivalent to add(dst, src1, dst); /// ``` /// /// The input arrays and the output array can all have the same or different depths. For example, you /// can add a 16-bit unsigned array to a 8-bit signed array and store the sum as a 32-bit /// floating-point array. Depth of the output array is determined by the dtype parameter. In the second /// and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can /// be set to the default -1. In this case, the output array will have the same depth as the input /// array, be it src1, src2 or both. /// /// Note: Saturation is not applied when the output array has the depth CV_32S. You may even get /// result of an incorrect sign in the case of overflow. /// ## Parameters /// * src1: first input array or a scalar. /// * src2: second input array or a scalar. /// * dst: output array that has the same size and number of channels as the input array(s); the /// depth is defined by dtype or src1/src2. /// * mask: optional operation mask - 8-bit single channel array, that specifies elements of the /// output array to be changed. /// * dtype: optional depth of the output array (see the discussion below). /// ## See also /// subtract, addWeighted, scaleAdd, Mat::convertTo /// /// ## C++ default parameters /// * mask: noArray() /// * dtype: -1 pub fn add(src1: &core::Mat, src2: &core::Mat, dst: &mut core::Mat, mask: &core::Mat, dtype: i32) -> Result<()> { unsafe { sys::cv_add_Mat_Mat_Mat_Mat_int(src1.as_raw_Mat(), src2.as_raw_Mat(), dst.as_raw_Mat(), mask.as_raw_Mat(), dtype) }.into_result() } /// Aligns a buffer size to the specified number of bytes. /// /// The function returns the minimum number that is greater than or equal to sz and is divisible by n : /// <div lang='latex'>\texttt{(sz + n-1) & -n}</div> /// ## Parameters /// * sz: Buffer size to align. /// * n: Alignment size that must be a power of two. pub fn align_size(sz: size_t, n: i32) -> Result<size_t> { unsafe { sys::cv_alignSize_size_t_int(sz, n) }.into_result() } /// naive nearest neighbor finder /// /// see http://en.wikipedia.org/wiki/Nearest_neighbor_search /// @todo document /// /// ## C++ default parameters /// * norm_type: NORM_L2 /// * k: 0 /// * mask: noArray() /// * update: 0 /// * crosscheck: false pub fn batch_distance(src1: &core::Mat, src2: &core::Mat, dist: &mut core::Mat, dtype: i32, nidx: &mut core::Mat, norm_type: i32, k: i32, mask: &core::Mat, update: i32, crosscheck: bool) -> Result<()> { unsafe { sys::cv_batchDistance_Mat_Mat_Mat_int_Mat_int_int_Mat_int_bool(src1.as_raw_Mat(), src2.as_raw_Mat(), dist.as_raw_Mat(), dtype, nidx.as_raw_Mat(), norm_type, k, mask.as_raw_Mat(), update, crosscheck) }.into_result() } /// computes bitwise conjunction of the two arrays (dst = src1 & src2) /// Calculates the per-element bit-wise conjunction of two arrays or an /// array and a scalar. /// /// The function cv::bitwise_and calculates the per-element bit-wise logical conjunction for: /// Two arrays when src1 and src2 have the same size: /// <div lang='latex'>\texttt{dst} (I) = \texttt{src1} (I) \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0</div> /// An array and a scalar when src2 is constructed from Scalar or has /// the same number of elements as `src1.channels()`: /// <div lang='latex'>\texttt{dst} (I) = \texttt{src1} (I) \wedge \texttt{src2} \quad \texttt{if mask} (I) \ne0</div> /// A scalar and an array when src1 is constructed from Scalar or has /// the same number of elements as `src2.channels()`: /// <div lang='latex'>\texttt{dst} (I) = \texttt{src1} \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0</div> /// In case of floating-point arrays, their machine-specific bit /// representations (usually IEEE754-compliant) are used for the operation. /// In case of multi-channel arrays, each channel is processed /// independently. In the second and third cases above, the scalar is first /// converted to the array type. /// ## Parameters /// * src1: first input array or a scalar. /// * src2: second input array or a scalar. /// * dst: output array that has the same size and type as the input /// arrays. /// * mask: optional operation mask, 8-bit single channel array, that /// specifies elements of the output array to be changed. /// /// ## C++ default parameters /// * mask: noArray() pub fn bitwise_and(src1: &core::Mat, src2: &core::Mat, dst: &mut core::Mat, mask: &core::Mat) -> Result<()> { unsafe { sys::cv_bitwise_and_Mat_Mat_Mat_Mat(src1.as_raw_Mat(), src2.as_raw_Mat(), dst.as_raw_Mat(), mask.as_raw_Mat()) }.into_result() } /// Inverts every bit of an array. /// /// The function cv::bitwise_not calculates per-element bit-wise inversion of the input /// array: /// <div lang='latex'>\texttt{dst} (I) = \neg \texttt{src} (I)</div> /// In case of a floating-point input array, its machine-specific bit /// representation (usually IEEE754-compliant) is used for the operation. In /// case of multi-channel arrays, each channel is processed independently. /// ## Parameters /// * src: input array. /// * dst: output array that has the same size and type as the input /// array. /// * mask: optional operation mask, 8-bit single channel array, that /// specifies elements of the output array to be changed. /// /// ## C++ default parameters /// * mask: noArray() pub fn bitwise_not(src: &core::Mat, dst: &mut core::Mat, mask: &core::Mat) -> Result<()> { unsafe { sys::cv_bitwise_not_Mat_Mat_Mat(src.as_raw_Mat(), dst.as_raw_Mat(), mask.as_raw_Mat()) }.into_result() } /// Calculates the per-element bit-wise disjunction of two arrays or an /// array and a scalar. /// /// The function cv::bitwise_or calculates the per-element bit-wise logical disjunction for: /// Two arrays when src1 and src2 have the same size: /// <div lang='latex'>\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0</div> /// An array and a scalar when src2 is constructed from Scalar or has /// the same number of elements as `src1.channels()`: /// <div lang='latex'>\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} \quad \texttt{if mask} (I) \ne0</div> /// A scalar and an array when src1 is constructed from Scalar or has /// the same number of elements as `src2.channels()`: /// <div lang='latex'>\texttt{dst} (I) = \texttt{src1} \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0</div> /// In case of floating-point arrays, their machine-specific bit /// representations (usually IEEE754-compliant) are used for the operation. /// In case of multi-channel arrays, each channel is processed /// independently. In the second and third cases above, the scalar is first /// converted to the array type. /// ## Parameters /// * src1: first input array or a scalar. /// * src2: second input array or a scalar. /// * dst: output array that has the same size and type as the input /// arrays. /// * mask: optional operation mask, 8-bit single channel array, that /// specifies elements of the output array to be changed. /// /// ## C++ default parameters /// * mask: noArray() pub fn bitwise_or(src1: &core::Mat, src2: &core::Mat, dst: &mut core::Mat, mask: &core::Mat) -> Result<()> { unsafe { sys::cv_bitwise_or_Mat_Mat_Mat_Mat(src1.as_raw_Mat(), src2.as_raw_Mat(), dst.as_raw_Mat(), mask.as_raw_Mat()) }.into_result() } /// Calculates the per-element bit-wise "exclusive or" operation on two /// arrays or an array and a scalar. /// /// The function cv::bitwise_xor calculates the per-element bit-wise logical "exclusive-or" /// operation for: /// Two arrays when src1 and src2 have the same size: /// <div lang='latex'>\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0</div> /// An array and a scalar when src2 is constructed from Scalar or has /// the same number of elements as `src1.channels()`: /// <div lang='latex'>\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} \quad \texttt{if mask} (I) \ne0</div> /// A scalar and an array when src1 is constructed from Scalar or has /// the same number of elements as `src2.channels()`: /// <div lang='latex'>\texttt{dst} (I) = \texttt{src1} \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0</div> /// In case of floating-point arrays, their machine-specific bit /// representations (usually IEEE754-compliant) are used for the operation. /// In case of multi-channel arrays, each channel is processed /// independently. In the 2nd and 3rd cases above, the scalar is first /// converted to the array type. /// ## Parameters /// * src1: first input array or a scalar. /// * src2: second input array or a scalar. /// * dst: output array that has the same size and type as the input /// arrays. /// * mask: optional operation mask, 8-bit single channel array, that /// specifies elements of the output array to be changed. /// /// ## C++ default parameters /// * mask: noArray() pub fn bitwise_xor(src1: &core::Mat, src2: &core::Mat, dst: &mut core::Mat, mask: &core::Mat) -> Result<()> { unsafe { sys::cv_bitwise_xor_Mat_Mat_Mat_Mat(src1.as_raw_Mat(), src2.as_raw_Mat(), dst.as_raw_Mat(), mask.as_raw_Mat()) }.into_result() } /// Computes the source location of an extrapolated pixel. /// /// The function computes and returns the coordinate of a donor pixel corresponding to the specified /// extrapolated pixel when using the specified extrapolation border mode. For example, if you use /// cv::BORDER_WRAP mode in the horizontal direction, cv::BORDER_REFLECT_101 in the vertical direction and /// want to compute value of the "virtual" pixel Point(-5, 100) in a floating-point image img , it /// looks like: /// ```ignore{.cpp} /// float val = img.at<float>(borderInterpolate(100, img.rows, cv::BORDER_REFLECT_101), /// borderInterpolate(-5, img.cols, cv::BORDER_WRAP)); /// ``` /// /// Normally, the function is not called directly. It is used inside filtering functions and also in /// copyMakeBorder. /// ## Parameters /// * p: 0-based coordinate of the extrapolated pixel along one of the axes, likely \<0 or \>= len /// * len: Length of the array along the corresponding axis. /// * borderType: Border type, one of the #BorderTypes, except for #BORDER_TRANSPARENT and /// #BORDER_ISOLATED . When borderType==#BORDER_CONSTANT , the function always returns -1, regardless /// of p and len. /// /// ## See also /// copyMakeBorder pub fn border_interpolate(p: i32, len: i32, border_type: i32) -> Result<i32> { unsafe { sys::cv_borderInterpolate_int_int_int(p, len, border_type) }.into_result() } /// Calculates the covariance matrix of a set of vectors. /// /// The function cv::calcCovarMatrix calculates the covariance matrix and, optionally, the mean vector of /// the set of input vectors. /// ## Parameters /// * samples: samples stored as separate matrices /// * nsamples: number of samples /// * covar: output covariance matrix of the type ctype and square size. /// * mean: input or output (depending on the flags) array as the average value of the input vectors. /// * flags: operation flags as a combination of #CovarFlags /// * ctype: type of the matrixl; it equals 'CV_64F' by default. /// ## See also /// PCA, mulTransposed, Mahalanobis /// @todo InputArrayOfArrays /// /// ## Overloaded parameters /// /// /// Note: use #COVAR_ROWS or #COVAR_COLS flag /// * samples: samples stored as rows/columns of a single matrix. /// * covar: output covariance matrix of the type ctype and square size. /// * mean: input or output (depending on the flags) array as the average value of the input vectors. /// * flags: operation flags as a combination of #CovarFlags /// * ctype: type of the matrixl; it equals 'CV_64F' by default. /// /// ## C++ default parameters /// * ctype: CV_64F pub fn calc_covar_matrix(samples: &core::Mat, covar: &mut core::Mat, mean: &mut core::Mat, flags: i32, ctype: i32) -> Result<()> { unsafe { sys::cv_calcCovarMatrix_Mat_Mat_Mat_int_int(samples.as_raw_Mat(), covar.as_raw_Mat(), mean.as_raw_Mat(), flags, ctype) }.into_result() } /// Calculates the magnitude and angle of 2D vectors. /// /// The function cv::cartToPolar calculates either the magnitude, angle, or both /// for every 2D vector (x(I),y(I)): /// <div lang='latex'>\begin{array}{l} \texttt{magnitude} (I)= \sqrt{\texttt{x}(I)^2+\texttt{y}(I)^2} , \\ \texttt{angle} (I)= \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))[ \cdot180 / \pi ] \end{array}</div> /// /// The angles are calculated with accuracy about 0.3 degrees. For the point /// (0,0), the angle is set to 0. /// ## Parameters /// * x: array of x-coordinates; this must be a single-precision or /// double-precision floating-point array. /// * y: array of y-coordinates, that must have the same size and same type as x. /// * magnitude: output array of magnitudes of the same size and type as x. /// * angle: output array of angles that has the same size and type as /// x; the angles are measured in radians (from 0 to 2\*Pi) or in degrees (0 to 360 degrees). /// * angleInDegrees: a flag, indicating whether the angles are measured /// in radians (which is by default), or in degrees. /// ## See also /// Sobel, Scharr /// /// ## C++ default parameters /// * angle_in_degrees: false pub fn cart_to_polar(x: &core::Mat, y: &core::Mat, magnitude: &mut core::Mat, angle: &mut core::Mat, angle_in_degrees: bool) -> Result<()> { unsafe { sys::cv_cartToPolar_Mat_Mat_Mat_Mat_bool(x.as_raw_Mat(), y.as_raw_Mat(), magnitude.as_raw_Mat(), angle.as_raw_Mat(), angle_in_degrees) }.into_result() } /// Returns true if the specified feature is supported by the host hardware. /// /// The function returns true if the host hardware supports the specified feature. When user calls /// setUseOptimized(false), the subsequent calls to checkHardwareSupport() will return false until /// setUseOptimized(true) is called. This way user can dynamically switch on and off the optimized code /// in OpenCV. /// ## Parameters /// * feature: The feature of interest, one of cv::CpuFeatures pub fn check_hardware_support(feature: i32) -> Result<bool> { unsafe { sys::cv_checkHardwareSupport_int(feature) }.into_result() } /// Checks every element of an input array for invalid values. /// /// The function cv::checkRange checks that every array element is neither NaN nor infinite. When minVal \> /// -DBL_MAX and maxVal \< DBL_MAX, the function also checks that each value is between minVal and /// maxVal. In case of multi-channel arrays, each channel is processed independently. If some values /// are out of range, position of the first outlier is stored in pos (when pos != NULL). Then, the /// function either returns false (when quiet=true) or throws an exception. /// ## Parameters /// * a: input array. /// * quiet: a flag, indicating whether the functions quietly return false when the array elements /// are out of range or they throw an exception. /// * pos: optional output parameter, when not NULL, must be a pointer to array of src.dims /// elements. /// * minVal: inclusive lower boundary of valid values range. /// * maxVal: exclusive upper boundary of valid values range. /// /// ## C++ default parameters /// * quiet: true /// * pos: 0 /// * min_val: -DBL_MAX /// * max_val: DBL_MAX pub fn check_range(a: &core::Mat, quiet: bool, pos: &mut core::Point, min_val: f64, max_val: f64) -> Result<bool> { unsafe { sys::cv_checkRange_Mat_bool_Point_X_double_double(a.as_raw_Mat(), quiet, pos, min_val, max_val) }.into_result() } /// Performs the per-element comparison of two arrays or an array and scalar value. /// /// The function compares: /// Elements of two arrays when src1 and src2 have the same size: /// <div lang='latex'>\texttt{dst} (I) = \texttt{src1} (I) \,\texttt{cmpop}\, \texttt{src2} (I)</div> /// Elements of src1 with a scalar src2 when src2 is constructed from /// Scalar or has a single element: /// <div lang='latex'>\texttt{dst} (I) = \texttt{src1}(I) \,\texttt{cmpop}\, \texttt{src2}</div> /// src1 with elements of src2 when src1 is constructed from Scalar or /// has a single element: /// <div lang='latex'>\texttt{dst} (I) = \texttt{src1} \,\texttt{cmpop}\, \texttt{src2} (I)</div> /// When the comparison result is true, the corresponding element of output /// array is set to 255. The comparison operations can be replaced with the /// equivalent matrix expressions: /// ```ignore{.cpp} /// Mat dst1 = src1 >= src2; /// Mat dst2 = src1 < 8; /// ... /// ``` /// /// ## Parameters /// * src1: first input array or a scalar; when it is an array, it must have a single channel. /// * src2: second input array or a scalar; when it is an array, it must have a single channel. /// * dst: output array of type ref CV_8U that has the same size and the same number of channels as /// the input arrays. /// * cmpop: a flag, that specifies correspondence between the arrays (cv::CmpTypes) /// ## See also /// checkRange, min, max, threshold pub fn compare(src1: &core::Mat, src2: &core::Mat, dst: &mut core::Mat, cmpop: i32) -> Result<()> { unsafe { sys::cv_compare_Mat_Mat_Mat_int(src1.as_raw_Mat(), src2.as_raw_Mat(), dst.as_raw_Mat(), cmpop) }.into_result() } /// Copies the lower or the upper half of a square matrix to its another half. /// /// The function cv::completeSymm copies the lower or the upper half of a square matrix to /// its another half. The matrix diagonal remains unchanged: /// - <span lang='latex'>\texttt{m}_{ij}=\texttt{m}_{ji}</span> for <span lang='latex'>i > j</span> if /// lowerToUpper=false /// - <span lang='latex'>\texttt{m}_{ij}=\texttt{m}_{ji}</span> for <span lang='latex'>i < j</span> if /// lowerToUpper=true /// /// ## Parameters /// * m: input-output floating-point square matrix. /// * lowerToUpper: operation flag; if true, the lower half is copied to /// the upper half. Otherwise, the upper half is copied to the lower half. /// ## See also /// flip, transpose /// /// ## C++ default parameters /// * lower_to_upper: false pub fn complete_symm(m: &mut core::Mat, lower_to_upper: bool) -> Result<()> { unsafe { sys::cv_completeSymm_Mat_bool(m.as_raw_Mat(), lower_to_upper) }.into_result() } /// Converts an array to half precision floating number. /// /// This function converts FP32 (single precision floating point) from/to FP16 (half precision floating point). CV_16S format is used to represent FP16 data. /// There are two use modes (src -> dst): CV_32F -> CV_16S and CV_16S -> CV_32F. The input array has to have type of CV_32F or /// CV_16S to represent the bit depth. If the input array is neither of them, the function will raise an error. /// The format of half precision floating point is defined in IEEE 754-2008. /// /// ## Parameters /// * src: input array. /// * dst: output array. pub fn convert_fp16(src: &core::Mat, dst: &mut core::Mat) -> Result<()> { unsafe { sys::cv_convertFp16_Mat_Mat(src.as_raw_Mat(), dst.as_raw_Mat()) }.into_result() } /// Scales, calculates absolute values, and converts the result to 8-bit. /// /// On each element of the input array, the function convertScaleAbs /// performs three operations sequentially: scaling, taking an absolute /// value, conversion to an unsigned 8-bit type: /// <div lang='latex'>\texttt{dst} (I)= \texttt{saturate\_cast<uchar>} (| \texttt{src} (I)* \texttt{alpha} + \texttt{beta} |)</div> /// In case of multi-channel arrays, the function processes each channel /// independently. When the output is not 8-bit, the operation can be /// emulated by calling the Mat::convertTo method (or by using matrix /// expressions) and then by calculating an absolute value of the result. /// For example: /// ```ignore{.cpp} /// Mat_<float> A(30,30); /// randu(A, Scalar(-100), Scalar(100)); /// Mat_<float> B = A*5 + 3; /// B = abs(B); /// // Mat_<float> B = abs(A*5+3) will also do the job, /// // but it will allocate a temporary matrix /// ``` /// /// ## Parameters /// * src: input array. /// * dst: output array. /// * alpha: optional scale factor. /// * beta: optional delta added to the scaled values. /// ## See also /// Mat::convertTo, cv::abs(const Mat&) /// /// ## C++ default parameters /// * alpha: 1 /// * beta: 0 pub fn convert_scale_abs(src: &core::Mat, dst: &mut core::Mat, alpha: f64, beta: f64) -> Result<()> { unsafe { sys::cv_convertScaleAbs_Mat_Mat_double_double(src.as_raw_Mat(), dst.as_raw_Mat(), alpha, beta) }.into_result() } /// Forms a border around an image. /// /// The function copies the source image into the middle of the destination image. The areas to the /// left, to the right, above and below the copied source image will be filled with extrapolated /// pixels. This is not what filtering functions based on it do (they extrapolate pixels on-fly), but /// what other more complex functions, including your own, may do to simplify image boundary handling. /// /// The function supports the mode when src is already in the middle of dst . In this case, the /// function does not copy src itself but simply constructs the border, for example: /// /// ```ignore{.cpp} /// // let border be the same in all directions /// int border=2; /// // constructs a larger image to fit both the image and the border /// Mat gray_buf(rgb.rows + border*2, rgb.cols + border*2, rgb.depth()); /// // select the middle part of it w/o copying data /// Mat gray(gray_canvas, Rect(border, border, rgb.cols, rgb.rows)); /// // convert image from RGB to grayscale /// cvtColor(rgb, gray, COLOR_RGB2GRAY); /// // form a border in-place /// copyMakeBorder(gray, gray_buf, border, border, /// border, border, BORDER_REPLICATE); /// // now do some custom filtering ... /// ... /// ``` /// /// /// Note: When the source image is a part (ROI) of a bigger image, the function will try to use the /// pixels outside of the ROI to form a border. To disable this feature and always do extrapolation, as /// if src was not a ROI, use borderType | #BORDER_ISOLATED. /// /// ## Parameters /// * src: Source image. /// * dst: Destination image of the same type as src and the size Size(src.cols+left+right, /// src.rows+top+bottom) . /// * top: /// * bottom: /// * left: /// * right: Parameter specifying how many pixels in each direction from the source image rectangle /// to extrapolate. For example, top=1, bottom=1, left=1, right=1 mean that 1 pixel-wide border needs /// to be built. /// * borderType: Border type. See borderInterpolate for details. /// * value: Border value if borderType==BORDER_CONSTANT . /// /// ## See also /// borderInterpolate /// /// ## C++ default parameters /// * value: Scalar() pub fn copy_make_border(src: &core::Mat, dst: &mut core::Mat, top: i32, bottom: i32, left: i32, right: i32, border_type: i32, value: core::Scalar) -> Result<()> { unsafe { sys::cv_copyMakeBorder_Mat_Mat_int_int_int_int_int_Scalar(src.as_raw_Mat(), dst.as_raw_Mat(), top, bottom, left, right, border_type, value) }.into_result() } /// Counts non-zero array elements. /// /// The function returns the number of non-zero elements in src : /// <div lang='latex'>\sum _{I: \; \texttt{src} (I) \ne0 } 1</div> /// ## Parameters /// * src: single-channel array. /// ## See also /// mean, meanStdDev, norm, minMaxLoc, calcCovarMatrix pub fn count_non_zero(src: &core::Mat) -> Result<i32> { unsafe { sys::cv_countNonZero_Mat(src.as_raw_Mat()) }.into_result() } /// Computes the cube root of an argument. /// /// The function cubeRoot computes <span lang='latex'>\sqrt[3]{\texttt{val}}</span>. Negative arguments are handled correctly. /// NaN and Inf are not handled. The accuracy approaches the maximum possible accuracy for /// single-precision data. /// ## Parameters /// * val: A function argument. pub fn cube_root(val: f32) -> Result<f32> { unsafe { sys::cv_cubeRoot_float(val) }.into_result() } pub fn cv_abs(x: i8) -> Result<i32> { unsafe { sys::cv_cv_abs_schar(x) }.into_result() } pub fn cv_abs_1(x: u16) -> Result<i32> { unsafe { sys::cv_cv_abs_ushort(x) }.into_result() } /// Performs a forward or inverse discrete Cosine transform of 1D or 2D array. /// /// The function cv::dct performs a forward or inverse discrete Cosine transform (DCT) of a 1D or 2D /// floating-point array: /// * Forward Cosine transform of a 1D vector of N elements: /// <div lang='latex'>Y = C^{(N)} \cdot X</div> /// where /// <div lang='latex'>C^{(N)}_{jk}= \sqrt{\alpha_j/N} \cos \left ( \frac{\pi(2k+1)j}{2N} \right )</div> /// and /// <span lang='latex'>\alpha_0=1</span>, <span lang='latex'>\alpha_j=2</span> for *j \> 0*. /// * Inverse Cosine transform of a 1D vector of N elements: /// <div lang='latex'>X = \left (C^{(N)} \right )^{-1} \cdot Y = \left (C^{(N)} \right )^T \cdot Y</div> /// (since <span lang='latex'>C^{(N)}</span> is an orthogonal matrix, <span lang='latex'>C^{(N)} \cdot \left(C^{(N)}\right)^T = I</span> ) /// * Forward 2D Cosine transform of M x N matrix: /// <div lang='latex'>Y = C^{(N)} \cdot X \cdot \left (C^{(N)} \right )^T</div> /// * Inverse 2D Cosine transform of M x N matrix: /// <div lang='latex'>X = \left (C^{(N)} \right )^T \cdot X \cdot C^{(N)}</div> /// /// The function chooses the mode of operation by looking at the flags and size of the input array: /// * If (flags & #DCT_INVERSE) == 0 , the function does a forward 1D or 2D transform. Otherwise, it /// is an inverse 1D or 2D transform. /// * If (flags & #DCT_ROWS) != 0 , the function performs a 1D transform of each row. /// * If the array is a single column or a single row, the function performs a 1D transform. /// * If none of the above is true, the function performs a 2D transform. /// /// /// Note: Currently dct supports even-size arrays (2, 4, 6 ...). For data analysis and approximation, you /// can pad the array when necessary. /// Also, the function performance depends very much, and not monotonically, on the array size (see /// getOptimalDFTSize ). In the current implementation DCT of a vector of size N is calculated via DFT /// of a vector of size N/2 . Thus, the optimal DCT size N1 \>= N can be calculated as: /// ```ignore /// size_t getOptimalDCTSize(size_t N) { return 2*getOptimalDFTSize((N+1)/2); } /// N1 = getOptimalDCTSize(N); /// ``` /// /// ## Parameters /// * src: input floating-point array. /// * dst: output array of the same size and type as src . /// * flags: transformation flags as a combination of cv::DftFlags (DCT_*) /// ## See also /// dft , getOptimalDFTSize , idct /// /// ## C++ default parameters /// * flags: 0 pub fn dct(src: &core::Mat, dst: &mut core::Mat, flags: i32) -> Result<()> { unsafe { sys::cv_dct_Mat_Mat_int(src.as_raw_Mat(), dst.as_raw_Mat(), flags) }.into_result() } /// Returns string of cv::Mat depth value: CV_8U -> "CV_8U" or "<invalid depth>" pub fn depth_to_string(depth: i32) -> Result<String> { unsafe { sys::cv_depthToString_int(depth) }.into_result().map(crate::templ::receive_string) } pub fn check_failed__mat_channels(v: i32, ctx: &core::CheckContext) -> Result<()> { unsafe { sys::cv_detail_check_failed_MatChannels_int_CheckContext(v, ctx.as_raw_CheckContext()) }.into_result() } pub fn check_failed__mat_channels_1(v1: i32, v2: i32, ctx: &core::CheckContext) -> Result<()> { unsafe { sys::cv_detail_check_failed_MatChannels_int_int_CheckContext(v1, v2, ctx.as_raw_CheckContext()) }.into_result() } pub fn check_failed__mat_depth(v: i32, ctx: &core::CheckContext) -> Result<()> { unsafe { sys::cv_detail_check_failed_MatDepth_int_CheckContext(v, ctx.as_raw_CheckContext()) }.into_result() } pub fn check_failed__mat_depth_1(v1: i32, v2: i32, ctx: &core::CheckContext) -> Result<()> { unsafe { sys::cv_detail_check_failed_MatDepth_int_int_CheckContext(v1, v2, ctx.as_raw_CheckContext()) }.into_result() } pub fn check_failed__mat_type(v: i32, ctx: &core::CheckContext) -> Result<()> { unsafe { sys::cv_detail_check_failed_MatType_int_CheckContext(v, ctx.as_raw_CheckContext()) }.into_result() } pub fn check_failed__mat_type_1(v1: i32, v2: i32, ctx: &core::CheckContext) -> Result<()> { unsafe { sys::cv_detail_check_failed_MatType_int_int_CheckContext(v1, v2, ctx.as_raw_CheckContext()) }.into_result() } pub fn check_failed_auto(v: f64, ctx: &core::CheckContext) -> Result<()> { unsafe { sys::cv_detail_check_failed_auto_double_CheckContext(v, ctx.as_raw_CheckContext()) }.into_result() } pub fn check_failed_auto_1(v1: f64, v2: f64, ctx: &core::CheckContext) -> Result<()> { unsafe { sys::cv_detail_check_failed_auto_double_double_CheckContext(v1, v2, ctx.as_raw_CheckContext()) }.into_result() } pub fn check_failed_auto_2(v: f32, ctx: &core::CheckContext) -> Result<()> { unsafe { sys::cv_detail_check_failed_auto_float_CheckContext(v, ctx.as_raw_CheckContext()) }.into_result() } pub fn check_failed_auto_3(v1: f32, v2: f32, ctx: &core::CheckContext) -> Result<()> { unsafe { sys::cv_detail_check_failed_auto_float_float_CheckContext(v1, v2, ctx.as_raw_CheckContext()) }.into_result() } pub fn check_failed_auto_4(v: i32, ctx: &core::CheckContext) -> Result<()> { unsafe { sys::cv_detail_check_failed_auto_int_CheckContext(v, ctx.as_raw_CheckContext()) }.into_result() } pub fn check_failed_auto_5(v1: i32, v2: i32, ctx: &core::CheckContext) -> Result<()> { unsafe { sys::cv_detail_check_failed_auto_int_int_CheckContext(v1, v2, ctx.as_raw_CheckContext()) }.into_result() } pub fn check_failed_auto_6(v: size_t, ctx: &core::CheckContext) -> Result<()> { unsafe { sys::cv_detail_check_failed_auto_size_t_CheckContext(v, ctx.as_raw_CheckContext()) }.into_result() } pub fn check_failed_auto_7(v1: size_t, v2: size_t, ctx: &core::CheckContext) -> Result<()> { unsafe { sys::cv_detail_check_failed_auto_size_t_size_t_CheckContext(v1, v2, ctx.as_raw_CheckContext()) }.into_result() } /// Returns the determinant of a square floating-point matrix. /// /// The function cv::determinant calculates and returns the determinant of the /// specified matrix. For small matrices ( mtx.cols=mtx.rows\<=3 ), the /// direct method is used. For larger matrices, the function uses LU /// factorization with partial pivoting. /// /// For symmetric positively-determined matrices, it is also possible to use /// eigen decomposition to calculate the determinant. /// ## Parameters /// * mtx: input matrix that must have CV_32FC1 or CV_64FC1 type and /// square size. /// ## See also /// trace, invert, solve, eigen, @ref MatrixExpressions pub fn determinant(mtx: &core::Mat) -> Result<f64> { unsafe { sys::cv_determinant_Mat(mtx.as_raw_Mat()) }.into_result() } /// Performs a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array. /// /// The function cv::dft performs one of the following: /// * Forward the Fourier transform of a 1D vector of N elements: /// <div lang='latex'>Y = F^{(N)} \cdot X,</div> /// where <span lang='latex'>F^{(N)}_{jk}=\exp(-2\pi i j k/N)</span> and <span lang='latex'>i=\sqrt{-1}</span> /// * Inverse the Fourier transform of a 1D vector of N elements: /// <div lang='latex'>\begin{array}{l} X'= \left (F^{(N)} \right )^{-1} \cdot Y = \left (F^{(N)} \right )^* \cdot y \\ X = (1/N) \cdot X, \end{array}</div> /// where <span lang='latex'>F^*=\left(\textrm{Re}(F^{(N)})-\textrm{Im}(F^{(N)})\right)^T</span> /// * Forward the 2D Fourier transform of a M x N matrix: /// <div lang='latex'>Y = F^{(M)} \cdot X \cdot F^{(N)}</div> /// * Inverse the 2D Fourier transform of a M x N matrix: /// <div lang='latex'>\begin{array}{l} X'= \left (F^{(M)} \right )^* \cdot Y \cdot \left (F^{(N)} \right )^* \\ X = \frac{1}{M \cdot N} \cdot X' \end{array}</div> /// /// In case of real (single-channel) data, the output spectrum of the forward Fourier transform or input /// spectrum of the inverse Fourier transform can be represented in a packed format called *CCS* /// (complex-conjugate-symmetrical). It was borrowed from IPL (Intel\* Image Processing Library). Here /// is how 2D *CCS* spectrum looks: /// <div lang='latex'>\begin{bmatrix} Re Y_{0,0} & Re Y_{0,1} & Im Y_{0,1} & Re Y_{0,2} & Im Y_{0,2} & \cdots & Re Y_{0,N/2-1} & Im Y_{0,N/2-1} & Re Y_{0,N/2} \\ Re Y_{1,0} & Re Y_{1,1} & Im Y_{1,1} & Re Y_{1,2} & Im Y_{1,2} & \cdots & Re Y_{1,N/2-1} & Im Y_{1,N/2-1} & Re Y_{1,N/2} \\ Im Y_{1,0} & Re Y_{2,1} & Im Y_{2,1} & Re Y_{2,2} & Im Y_{2,2} & \cdots & Re Y_{2,N/2-1} & Im Y_{2,N/2-1} & Im Y_{1,N/2} \\ \hdotsfor{9} \\ Re Y_{M/2-1,0} & Re Y_{M-3,1} & Im Y_{M-3,1} & \hdotsfor{3} & Re Y_{M-3,N/2-1} & Im Y_{M-3,N/2-1}& Re Y_{M/2-1,N/2} \\ Im Y_{M/2-1,0} & Re Y_{M-2,1} & Im Y_{M-2,1} & \hdotsfor{3} & Re Y_{M-2,N/2-1} & Im Y_{M-2,N/2-1}& Im Y_{M/2-1,N/2} \\ Re Y_{M/2,0} & Re Y_{M-1,1} & Im Y_{M-1,1} & \hdotsfor{3} & Re Y_{M-1,N/2-1} & Im Y_{M-1,N/2-1}& Re Y_{M/2,N/2} \end{bmatrix}</div> /// /// In case of 1D transform of a real vector, the output looks like the first row of the matrix above. /// /// So, the function chooses an operation mode depending on the flags and size of the input array: /// * If #DFT_ROWS is set or the input array has a single row or single column, the function /// performs a 1D forward or inverse transform of each row of a matrix when #DFT_ROWS is set. /// Otherwise, it performs a 2D transform. /// * If the input array is real and #DFT_INVERSE is not set, the function performs a forward 1D or /// 2D transform: /// * When #DFT_COMPLEX_OUTPUT is set, the output is a complex matrix of the same size as /// input. /// * When #DFT_COMPLEX_OUTPUT is not set, the output is a real matrix of the same size as /// input. In case of 2D transform, it uses the packed format as shown above. In case of a /// single 1D transform, it looks like the first row of the matrix above. In case of /// multiple 1D transforms (when using the #DFT_ROWS flag), each row of the output matrix /// looks like the first row of the matrix above. /// * If the input array is complex and either #DFT_INVERSE or #DFT_REAL_OUTPUT are not set, the /// output is a complex array of the same size as input. The function performs a forward or /// inverse 1D or 2D transform of the whole input array or each row of the input array /// independently, depending on the flags DFT_INVERSE and DFT_ROWS. /// * When #DFT_INVERSE is set and the input array is real, or it is complex but #DFT_REAL_OUTPUT /// is set, the output is a real array of the same size as input. The function performs a 1D or 2D /// inverse transformation of the whole input array or each individual row, depending on the flags /// #DFT_INVERSE and #DFT_ROWS. /// /// If #DFT_SCALE is set, the scaling is done after the transformation. /// /// Unlike dct , the function supports arrays of arbitrary size. But only those arrays are processed /// efficiently, whose sizes can be factorized in a product of small prime numbers (2, 3, and 5 in the /// current implementation). Such an efficient DFT size can be calculated using the getOptimalDFTSize /// method. /// /// The sample below illustrates how to calculate a DFT-based convolution of two 2D real arrays: /// ```ignore /// void convolveDFT(InputArray A, InputArray B, OutputArray C) /// { /// // reallocate the output array if needed /// C.create(abs(A.rows - B.rows)+1, abs(A.cols - B.cols)+1, A.type()); /// Size dftSize; /// // calculate the size of DFT transform /// dftSize.width = getOptimalDFTSize(A.cols + B.cols - 1); /// dftSize.height = getOptimalDFTSize(A.rows + B.rows - 1); /// /// // allocate temporary buffers and initialize them with 0's /// Mat tempA(dftSize, A.type(), Scalar::all(0)); /// Mat tempB(dftSize, B.type(), Scalar::all(0)); /// /// // copy A and B to the top-left corners of tempA and tempB, respectively /// Mat roiA(tempA, Rect(0,0,A.cols,A.rows)); /// A.copyTo(roiA); /// Mat roiB(tempB, Rect(0,0,B.cols,B.rows)); /// B.copyTo(roiB); /// /// // now transform the padded A & B in-place; /// // use "nonzeroRows" hint for faster processing /// dft(tempA, tempA, 0, A.rows); /// dft(tempB, tempB, 0, B.rows); /// /// // multiply the spectrums; /// // the function handles packed spectrum representations well /// mulSpectrums(tempA, tempB, tempA); /// /// // transform the product back from the frequency domain. /// // Even though all the result rows will be non-zero, /// // you need only the first C.rows of them, and thus you /// // pass nonzeroRows == C.rows /// dft(tempA, tempA, DFT_INVERSE + DFT_SCALE, C.rows); /// /// // now copy the result back to C. /// tempA(Rect(0, 0, C.cols, C.rows)).copyTo(C); /// /// // all the temporary buffers will be deallocated automatically /// } /// ``` /// /// To optimize this sample, consider the following approaches: /// * Since nonzeroRows != 0 is passed to the forward transform calls and since A and B are copied to /// the top-left corners of tempA and tempB, respectively, it is not necessary to clear the whole /// tempA and tempB. It is only necessary to clear the tempA.cols - A.cols ( tempB.cols - B.cols) /// rightmost columns of the matrices. /// * This DFT-based convolution does not have to be applied to the whole big arrays, especially if B /// is significantly smaller than A or vice versa. Instead, you can calculate convolution by parts. /// To do this, you need to split the output array C into multiple tiles. For each tile, estimate /// which parts of A and B are required to calculate convolution in this tile. If the tiles in C are /// too small, the speed will decrease a lot because of repeated work. In the ultimate case, when /// each tile in C is a single pixel, the algorithm becomes equivalent to the naive convolution /// algorithm. If the tiles are too big, the temporary arrays tempA and tempB become too big and /// there is also a slowdown because of bad cache locality. So, there is an optimal tile size /// somewhere in the middle. /// * If different tiles in C can be calculated in parallel and, thus, the convolution is done by /// parts, the loop can be threaded. /// /// All of the above improvements have been implemented in #matchTemplate and #filter2D . Therefore, by /// using them, you can get the performance even better than with the above theoretically optimal /// implementation. Though, those two functions actually calculate cross-correlation, not convolution, /// so you need to "flip" the second convolution operand B vertically and horizontally using flip . /// /// Note: /// * An example using the discrete fourier transform can be found at /// opencv_source_code/samples/cpp/dft.cpp /// * (Python) An example using the dft functionality to perform Wiener deconvolution can be found /// at opencv_source/samples/python/deconvolution.py /// * (Python) An example rearranging the quadrants of a Fourier image can be found at /// opencv_source/samples/python/dft.py /// ## Parameters /// * src: input array that could be real or complex. /// * dst: output array whose size and type depends on the flags . /// * flags: transformation flags, representing a combination of the #DftFlags /// * nonzeroRows: when the parameter is not zero, the function assumes that only the first /// nonzeroRows rows of the input array (#DFT_INVERSE is not set) or only the first nonzeroRows of the /// output array (#DFT_INVERSE is set) contain non-zeros, thus, the function can handle the rest of the /// rows more efficiently and save some time; this technique is very useful for calculating array /// cross-correlation or convolution using DFT. /// ## See also /// dct , getOptimalDFTSize , mulSpectrums, filter2D , matchTemplate , flip , cartToPolar , /// magnitude , phase /// /// ## C++ default parameters /// * flags: 0 /// * nonzero_rows: 0 pub fn dft(src: &core::Mat, dst: &mut core::Mat, flags: i32, nonzero_rows: i32) -> Result<()> { unsafe { sys::cv_dft_Mat_Mat_int_int(src.as_raw_Mat(), dst.as_raw_Mat(), flags, nonzero_rows) }.into_result() } /// Get OpenCV type from DirectX type /// ## Parameters /// * iD3DFORMAT: - enum D3DTYPE for D3D9 /// ## Returns /// OpenCV type or -1 if there is no equivalent pub fn get_type_from_d3d_format(i_d3_dformat: i32) -> Result<i32> { unsafe { sys::cv_directx_getTypeFromD3DFORMAT_int(i_d3_dformat) }.into_result() } /// Get OpenCV type from DirectX type /// ## Parameters /// * iDXGI_FORMAT: - enum DXGI_FORMAT for D3D10/D3D11 /// ## Returns /// OpenCV type or -1 if there is no equivalent pub fn get_type_from_dxgi_format(i_dxgi_format: i32) -> Result<i32> { unsafe { sys::cv_directx_getTypeFromDXGI_FORMAT_int(i_dxgi_format) }.into_result() } /// Integer division with result round up. /// /// Use this function instead of `ceil((float)a / b)` expressions. /// /// ## See also /// alignSize pub fn div_up(a: i32, b: u32) -> Result<i32> { unsafe { sys::cv_divUp_int_unsigned_int(a, b) }.into_result() } /// Integer division with result round up. /// /// Use this function instead of `ceil((float)a / b)` expressions. /// /// ## See also /// alignSize /// /// ## Overloaded parameters pub fn duv_up_u(a: size_t, b: u32) -> Result<size_t> { unsafe { sys::cv_divUp_size_t_unsigned_int(a, b) }.into_result() } /// Performs per-element division of two arrays or a scalar by an array. /// /// The function cv::divide divides one array by another: /// <div lang='latex'>\texttt{dst(I) = saturate(src1(I)*scale/src2(I))}</div> /// or a scalar by an array when there is no src1 : /// <div lang='latex'>\texttt{dst(I) = saturate(scale/src2(I))}</div> /// /// When src2(I) is zero, dst(I) will also be zero. Different channels of /// multi-channel arrays are processed independently. /// /// /// Note: Saturation is not applied when the output array has the depth CV_32S. You may even get /// result of an incorrect sign in the case of overflow. /// ## Parameters /// * src1: first input array. /// * src2: second input array of the same size and type as src1. /// * scale: scalar factor. /// * dst: output array of the same size and type as src2. /// * dtype: optional depth of the output array; if -1, dst will have depth src2.depth(), but in /// case of an array-by-array division, you can only pass -1 when src1.depth()==src2.depth(). /// ## See also /// multiply, add, subtract /// /// ## C++ default parameters /// * scale: 1 /// * dtype: -1 pub fn divide_mat(src1: &core::Mat, src2: &core::Mat, dst: &mut core::Mat, scale: f64, dtype: i32) -> Result<()> { unsafe { sys::cv_divide_Mat_Mat_Mat_double_int(src1.as_raw_Mat(), src2.as_raw_Mat(), dst.as_raw_Mat(), scale, dtype) }.into_result() } /// Performs per-element division of two arrays or a scalar by an array. /// /// The function cv::divide divides one array by another: /// <div lang='latex'>\texttt{dst(I) = saturate(src1(I)*scale/src2(I))}</div> /// or a scalar by an array when there is no src1 : /// <div lang='latex'>\texttt{dst(I) = saturate(scale/src2(I))}</div> /// /// When src2(I) is zero, dst(I) will also be zero. Different channels of /// multi-channel arrays are processed independently. /// /// /// Note: Saturation is not applied when the output array has the depth CV_32S. You may even get /// result of an incorrect sign in the case of overflow. /// ## Parameters /// * src1: first input array. /// * src2: second input array of the same size and type as src1. /// * scale: scalar factor. /// * dst: output array of the same size and type as src2. /// * dtype: optional depth of the output array; if -1, dst will have depth src2.depth(), but in /// case of an array-by-array division, you can only pass -1 when src1.depth()==src2.depth(). /// ## See also /// multiply, add, subtract /// /// ## Overloaded parameters /// /// ## C++ default parameters /// * dtype: -1 pub fn divide(scale: f64, src2: &core::Mat, dst: &mut core::Mat, dtype: i32) -> Result<()> { unsafe { sys::cv_divide_double_Mat_Mat_int(scale, src2.as_raw_Mat(), dst.as_raw_Mat(), dtype) }.into_result() } /// Calculates eigenvalues and eigenvectors of a non-symmetric matrix (real eigenvalues only). /// /// /// Note: Assumes real eigenvalues. /// /// The function calculates eigenvalues and eigenvectors (optional) of the square matrix src: /// ```ignore /// src*eigenvectors.row(i).t() = eigenvalues.at<srcType>(i)*eigenvectors.row(i).t() /// ``` /// /// /// ## Parameters /// * src: input matrix (CV_32FC1 or CV_64FC1 type). /// * eigenvalues: output vector of eigenvalues (type is the same type as src). /// * eigenvectors: output matrix of eigenvectors (type is the same type as src). The eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding eigenvalues. /// ## See also /// eigen pub fn eigen_non_symmetric(src: &core::Mat, eigenvalues: &mut core::Mat, eigenvectors: &mut core::Mat) -> Result<()> { unsafe { sys::cv_eigenNonSymmetric_Mat_Mat_Mat(src.as_raw_Mat(), eigenvalues.as_raw_Mat(), eigenvectors.as_raw_Mat()) }.into_result() } /// Calculates eigenvalues and eigenvectors of a symmetric matrix. /// /// The function cv::eigen calculates just eigenvalues, or eigenvalues and eigenvectors of the symmetric /// matrix src: /// ```ignore /// src*eigenvectors.row(i).t() = eigenvalues.at<srcType>(i)*eigenvectors.row(i).t() /// ``` /// /// /// /// Note: Use cv::eigenNonSymmetric for calculation of real eigenvalues and eigenvectors of non-symmetric matrix. /// /// ## Parameters /// * src: input matrix that must have CV_32FC1 or CV_64FC1 type, square size and be symmetrical /// (src ^T^ == src). /// * eigenvalues: output vector of eigenvalues of the same type as src; the eigenvalues are stored /// in the descending order. /// * eigenvectors: output matrix of eigenvectors; it has the same size and type as src; the /// eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding /// eigenvalues. /// ## See also /// eigenNonSymmetric, completeSymm , PCA /// /// ## C++ default parameters /// * eigenvectors: noArray() pub fn eigen(src: &core::Mat, eigenvalues: &mut core::Mat, eigenvectors: &mut core::Mat) -> Result<bool> { unsafe { sys::cv_eigen_Mat_Mat_Mat(src.as_raw_Mat(), eigenvalues.as_raw_Mat(), eigenvectors.as_raw_Mat()) }.into_result() } /// same as cv::error, but does not return pub fn error_no_return(_code: i32, _err: &str, _func: &str, _file: &str, _line: i32) -> Result<()> { string_arg!(_err); string_arg!(_func); string_arg!(_file); unsafe { sys::cv_errorNoReturn_int_String_const_char_X_const_char_X_int(_code, _err.as_ptr(), _func.as_ptr(), _file.as_ptr(), _line) }.into_result() } pub fn error(_code: i32, _err: &str, _func: &str, _file: &str, _line: i32) -> Result<()> { string_arg!(_err); string_arg!(_func); string_arg!(_file); unsafe { sys::cv_error_int_String_const_char_X_const_char_X_int(_code, _err.as_ptr(), _func.as_ptr(), _file.as_ptr(), _line) }.into_result() } /// Calculates the exponent of every array element. /// /// The function cv::exp calculates the exponent of every element of the input /// array: /// <div lang='latex'>\texttt{dst} [I] = e^{ src(I) }</div> /// /// The maximum relative error is about 7e-6 for single-precision input and /// less than 1e-10 for double-precision input. Currently, the function /// converts denormalized values to zeros on output. Special values (NaN, /// Inf) are not handled. /// ## Parameters /// * src: input array. /// * dst: output array of the same size and type as src. /// ## See also /// log , cartToPolar , polarToCart , phase , pow , sqrt , magnitude pub fn exp(src: &core::Mat, dst: &mut core::Mat) -> Result<()> { unsafe { sys::cv_exp_Mat_Mat(src.as_raw_Mat(), dst.as_raw_Mat()) }.into_result() } /// Extracts a single channel from src (coi is 0-based index) /// ## Parameters /// * src: input array /// * dst: output array /// * coi: index of channel to extract /// ## See also /// mixChannels, split pub fn extract_channel(src: &core::Mat, dst: &mut core::Mat, coi: i32) -> Result<()> { unsafe { sys::cv_extractChannel_Mat_Mat_int(src.as_raw_Mat(), dst.as_raw_Mat(), coi) }.into_result() } /// Calculates the angle of a 2D vector in degrees. /// /// The function fastAtan2 calculates the full-range angle of an input 2D vector. The angle is measured /// in degrees and varies from 0 to 360 degrees. The accuracy is about 0.3 degrees. /// ## Parameters /// * x: x-coordinate of the vector. /// * y: y-coordinate of the vector. pub fn fast_atan2(y: f32, x: f32) -> Result<f32> { unsafe { sys::cv_fastAtan2_float_float(y, x) }.into_result() } /// Returns the list of locations of non-zero pixels /// /// Given a binary matrix (likely returned from an operation such /// as threshold(), compare(), >, ==, etc, return all of /// the non-zero indices as a cv::Mat or std::vector<cv::Point> (x,y) /// For example: /// ```ignore{.cpp} /// cv::Mat binaryImage; // input, binary image /// cv::Mat locations; // output, locations of non-zero pixels /// cv::findNonZero(binaryImage, locations); /// /// // access pixel coordinates /// Point pnt = locations.at<Point>(i); /// ``` /// /// or /// ```ignore{.cpp} /// cv::Mat binaryImage; // input, binary image /// vector<Point> locations; // output, locations of non-zero pixels /// cv::findNonZero(binaryImage, locations); /// /// // access pixel coordinates /// Point pnt = locations[i]; /// ``` /// /// ## Parameters /// * src: single-channel array (type CV_8UC1) /// * idx: the output array, type of cv::Mat or std::vector<Point>, corresponding to non-zero indices in the input pub fn find_non_zero(src: &core::Mat, idx: &mut core::Mat) -> Result<()> { unsafe { sys::cv_findNonZero_Mat_Mat(src.as_raw_Mat(), idx.as_raw_Mat()) }.into_result() } /// Flips a 2D array around vertical, horizontal, or both axes. /// /// The function cv::flip flips the array in one of three different ways (row /// and column indices are 0-based): /// <div lang='latex'>\texttt{dst} _{ij} = /// \left\{ /// \begin{array}{l l} /// \texttt{src} _{\texttt{src.rows}-i-1,j} & if\; \texttt{flipCode} = 0 \\ /// \texttt{src} _{i, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} > 0 \\ /// \texttt{src} _{ \texttt{src.rows} -i-1, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} < 0 \\ /// \end{array} /// \right.</div> /// The example scenarios of using the function are the following: /// Vertical flipping of the image (flipCode == 0) to switch between /// top-left and bottom-left image origin. This is a typical operation /// in video processing on Microsoft Windows\* OS. /// Horizontal flipping of the image with the subsequent horizontal /// shift and absolute difference calculation to check for a /// vertical-axis symmetry (flipCode \> 0). /// Simultaneous horizontal and vertical flipping of the image with /// the subsequent shift and absolute difference calculation to check /// for a central symmetry (flipCode \< 0). /// Reversing the order of point arrays (flipCode \> 0 or /// flipCode == 0). /// ## Parameters /// * src: input array. /// * dst: output array of the same size and type as src. /// * flipCode: a flag to specify how to flip the array; 0 means /// flipping around the x-axis and positive value (for example, 1) means /// flipping around y-axis. Negative value (for example, -1) means flipping /// around both axes. /// ## See also /// transpose , repeat , completeSymm pub fn flip(src: &core::Mat, dst: &mut core::Mat, flip_code: i32) -> Result<()> { unsafe { sys::cv_flip_Mat_Mat_int(src.as_raw_Mat(), dst.as_raw_Mat(), flip_code) }.into_result() } /// Performs generalized matrix multiplication. /// /// The function cv::gemm performs generalized matrix multiplication similar to the /// gemm functions in BLAS level 3. For example, /// `gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T)` /// corresponds to /// <div lang='latex'>\texttt{dst} = \texttt{alpha} \cdot \texttt{src1} ^T \cdot \texttt{src2} + \texttt{beta} \cdot \texttt{src3} ^T</div> /// /// In case of complex (two-channel) data, performed a complex matrix /// multiplication. /// /// The function can be replaced with a matrix expression. For example, the /// above call can be replaced with: /// ```ignore{.cpp} /// dst = alpha*src1.t()*src2 + beta*src3.t(); /// ``` /// /// ## Parameters /// * src1: first multiplied input matrix that could be real(CV_32FC1, /// CV_64FC1) or complex(CV_32FC2, CV_64FC2). /// * src2: second multiplied input matrix of the same type as src1. /// * alpha: weight of the matrix product. /// * src3: third optional delta matrix added to the matrix product; it /// should have the same type as src1 and src2. /// * beta: weight of src3. /// * dst: output matrix; it has the proper size and the same type as /// input matrices. /// * flags: operation flags (cv::GemmFlags) /// ## See also /// mulTransposed , transform /// /// ## C++ default parameters /// * flags: 0 pub fn gemm(src1: &core::Mat, src2: &core::Mat, alpha: f64, src3: &core::Mat, beta: f64, dst: &mut core::Mat, flags: i32) -> Result<()> { unsafe { sys::cv_gemm_Mat_Mat_double_Mat_double_Mat_int(src1.as_raw_Mat(), src2.as_raw_Mat(), alpha, src3.as_raw_Mat(), beta, dst.as_raw_Mat(), flags) }.into_result() } /// Returns full configuration time cmake output. /// /// Returned value is raw cmake output including version control system revision, compiler version, /// compiler flags, enabled modules and third party libraries, etc. Output format depends on target /// architecture. pub fn get_build_information() -> Result<String> { unsafe { sys::cv_getBuildInformation() }.into_result().map(crate::templ::receive_string) } /// Returns list of CPU features enabled during compilation. /// /// Returned value is a string containing space separated list of CPU features with following markers: /// /// - no markers - baseline features /// - prefix `*` - features enabled in dispatcher /// - suffix `?` - features enabled but not available in HW /// /// Example: `SSE SSE2 SSE3 *SSE4.1 *SSE4.2 *FP16 *AVX *AVX2 *AVX512-SKX?` pub fn get_cpu_features_line() -> Result<String> { unsafe { sys::cv_getCPUFeaturesLine() }.into_result().map(crate::templ::receive_string_mut) } /// Returns the number of CPU ticks. /// /// The function returns the current number of CPU ticks on some architectures (such as x86, x64, /// PowerPC). On other platforms the function is equivalent to getTickCount. It can also be used for /// very accurate time measurements, as well as for RNG initialization. Note that in case of multi-CPU /// systems a thread, from which getCPUTickCount is called, can be suspended and resumed at another CPU /// with its own counter. So, theoretically (and practically) the subsequent calls to the function do /// not necessary return the monotonously increasing values. Also, since a modern CPU varies the CPU /// frequency depending on the load, the number of CPU clocks spent in some code cannot be directly /// converted to time units. Therefore, getTickCount is generally a preferable solution for measuring /// execution time. pub fn get_cpu_tick_count() -> Result<i64> { unsafe { sys::cv_getCPUTickCount() }.into_result() } pub fn get_elem_size(_type: i32) -> Result<size_t> { unsafe { sys::cv_getElemSize_int(_type) }.into_result() } /// Returns feature name by ID /// /// Returns empty string if feature is not defined pub fn get_hardware_feature_name(feature: i32) -> Result<String> { unsafe { sys::cv_getHardwareFeatureName_int(feature) }.into_result().map(crate::templ::receive_string_mut) } /// Returns the number of threads used by OpenCV for parallel regions. /// /// Always returns 1 if OpenCV is built without threading support. /// /// The exact meaning of return value depends on the threading framework used by OpenCV library: /// - `TBB` - The number of threads, that OpenCV will try to use for parallel regions. If there is /// any tbb::thread_scheduler_init in user code conflicting with OpenCV, then function returns /// default number of threads used by TBB library. /// - `OpenMP` - An upper bound on the number of threads that could be used to form a new team. /// - `Concurrency` - The number of threads, that OpenCV will try to use for parallel regions. /// - `GCD` - Unsupported; returns the GCD thread pool limit (512) for compatibility. /// - `C=` - The number of threads, that OpenCV will try to use for parallel regions, if before /// called setNumThreads with threads \> 0, otherwise returns the number of logical CPUs, /// available for the process. /// ## See also /// setNumThreads, getThreadNum pub fn get_num_threads() -> Result<i32> { unsafe { sys::cv_getNumThreads() }.into_result() } /// Returns the number of logical CPUs available for the process. pub fn get_number_of_cpus() -> Result<i32> { unsafe { sys::cv_getNumberOfCPUs() }.into_result() } /// Returns the optimal DFT size for a given vector size. /// /// DFT performance is not a monotonic function of a vector size. Therefore, when you calculate /// convolution of two arrays or perform the spectral analysis of an array, it usually makes sense to /// pad the input data with zeros to get a bit larger array that can be transformed much faster than the /// original one. Arrays whose size is a power-of-two (2, 4, 8, 16, 32, ...) are the fastest to process. /// Though, the arrays whose size is a product of 2's, 3's, and 5's (for example, 300 = 5\*5\*3\*2\*2) /// are also processed quite efficiently. /// /// The function cv::getOptimalDFTSize returns the minimum number N that is greater than or equal to vecsize /// so that the DFT of a vector of size N can be processed efficiently. In the current implementation N /// = 2 ^p^ \* 3 ^q^ \* 5 ^r^ for some integer p, q, r. /// /// The function returns a negative number if vecsize is too large (very close to INT_MAX ). /// /// While the function cannot be used directly to estimate the optimal vector size for DCT transform /// (since the current DCT implementation supports only even-size vectors), it can be easily processed /// as getOptimalDFTSize((vecsize+1)/2)\*2. /// ## Parameters /// * vecsize: vector size. /// ## See also /// dft , dct , idft , idct , mulSpectrums pub fn get_optimal_dft_size(vecsize: i32) -> Result<i32> { unsafe { sys::cv_getOptimalDFTSize_int(vecsize) }.into_result() } /// Returns the index of the currently executed thread within the current parallel region. Always /// returns 0 if called outside of parallel region. /// /// **Deprecated**: Current implementation doesn't corresponding to this documentation. /// /// /// The exact meaning of the return value depends on the threading framework used by OpenCV library: /// - `TBB` - Unsupported with current 4.1 TBB release. Maybe will be supported in future. /// - `OpenMP` - The thread number, within the current team, of the calling thread. /// - `Concurrency` - An ID for the virtual processor that the current context is executing on (0 /// for master thread and unique number for others, but not necessary 1,2,3,...). /// - `GCD` - System calling thread's ID. Never returns 0 inside parallel region. /// - `C=` - The index of the current parallel task. /// ## See also /// setNumThreads, getNumThreads #[deprecated = "Current implementation doesn't corresponding to this documentation."] pub fn get_thread_num() -> Result<i32> { unsafe { sys::cv_getThreadNum() }.into_result() } /// Returns the number of ticks. /// /// The function returns the number of ticks after the certain event (for example, when the machine was /// turned on). It can be used to initialize RNG or to measure a function execution time by reading the /// tick count before and after the function call. /// ## See also /// getTickFrequency, TickMeter pub fn get_tick_count() -> Result<i64> { unsafe { sys::cv_getTickCount() }.into_result() } /// Returns the number of ticks per second. /// /// The function returns the number of ticks per second. That is, the following code computes the /// execution time in seconds: /// ```ignore /// double t = (double)getTickCount(); /// // do something ... /// t = ((double)getTickCount() - t)/getTickFrequency(); /// ``` /// /// ## See also /// getTickCount, TickMeter pub fn get_tick_frequency() -> Result<f64> { unsafe { sys::cv_getTickFrequency() }.into_result() } /// Returns major library version pub fn get_version_major() -> Result<i32> { unsafe { sys::cv_getVersionMajor() }.into_result() } /// Returns minor library version pub fn get_version_minor() -> Result<i32> { unsafe { sys::cv_getVersionMinor() }.into_result() } /// Returns revision field of the library version pub fn get_version_revision() -> Result<i32> { unsafe { sys::cv_getVersionRevision() }.into_result() } /// Returns library version string /// /// For example "3.4.1-dev". /// /// ## See also /// getMajorVersion, getMinorVersion, getRevisionVersion pub fn get_version_string() -> Result<String> { unsafe { sys::cv_getVersionString() }.into_result().map(crate::templ::receive_string_mut) } /// /// ## C++ default parameters /// * recursive: false pub fn glob(pattern: &str, result: &mut types::VectorOfString, recursive: bool) -> Result<()> { string_arg!(mut pattern); unsafe { sys::cv_glob_String_VectorOfString_bool(pattern.as_ptr() as _, result.as_raw_VectorOfString(), recursive) }.into_result() } pub fn have_open_vx() -> Result<bool> { unsafe { sys::cv_haveOpenVX() }.into_result() } /// Applies horizontal concatenation to given matrices. /// /// The function horizontally concatenates two or more cv::Mat matrices (with the same number of rows). /// ```ignore{.cpp} /// cv::Mat matArray[] = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)), /// cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)), /// cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),}; /// /// cv::Mat out; /// cv::hconcat( matArray, 3, out ); /// //out: /// //[1, 2, 3; /// // 1, 2, 3; /// // 1, 2, 3; /// // 1, 2, 3] /// ``` /// /// ## Parameters /// * src: input array or vector of matrices. all of the matrices must have the same number of rows and the same depth. /// * nsrc: number of matrices in src. /// * dst: output array. It has the same number of rows and depth as the src, and the sum of cols of the src. /// ## See also /// cv::vconcat(const Mat*, size_t, OutputArray), cv::vconcat(InputArrayOfArrays, OutputArray) and cv::vconcat(InputArray, InputArray, OutputArray) /// /// ## Overloaded parameters /// /// ```ignore{.cpp} /// cv::Mat_<float> A = (cv::Mat_<float>(3, 2) << 1, 4, /// 2, 5, /// 3, 6); /// cv::Mat_<float> B = (cv::Mat_<float>(3, 2) << 7, 10, /// 8, 11, /// 9, 12); /// /// cv::Mat C; /// cv::hconcat(A, B, C); /// //C: /// //[1, 4, 7, 10; /// // 2, 5, 8, 11; /// // 3, 6, 9, 12] /// ``` /// /// * src1: first input array to be considered for horizontal concatenation. /// * src2: second input array to be considered for horizontal concatenation. /// * dst: output array. It has the same number of rows and depth as the src1 and src2, and the sum of cols of the src1 and src2. pub fn hconcat_2(src1: &core::Mat, src2: &core::Mat, dst: &mut core::Mat) -> Result<()> { unsafe { sys::cv_hconcat_Mat_Mat_Mat(src1.as_raw_Mat(), src2.as_raw_Mat(), dst.as_raw_Mat()) }.into_result() } /// Applies horizontal concatenation to given matrices. /// /// The function horizontally concatenates two or more cv::Mat matrices (with the same number of rows). /// ```ignore{.cpp} /// cv::Mat matArray[] = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)), /// cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)), /// cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),}; /// /// cv::Mat out; /// cv::hconcat( matArray, 3, out ); /// //out: /// //[1, 2, 3; /// // 1, 2, 3; /// // 1, 2, 3; /// // 1, 2, 3] /// ``` /// /// ## Parameters /// * src: input array or vector of matrices. all of the matrices must have the same number of rows and the same depth. /// * nsrc: number of matrices in src. /// * dst: output array. It has the same number of rows and depth as the src, and the sum of cols of the src. /// ## See also /// cv::vconcat(const Mat*, size_t, OutputArray), cv::vconcat(InputArrayOfArrays, OutputArray) and cv::vconcat(InputArray, InputArray, OutputArray) /// /// ## Overloaded parameters /// /// ```ignore{.cpp} /// std::vector<cv::Mat> matrices = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)), /// cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)), /// cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),}; /// /// cv::Mat out; /// cv::hconcat( matrices, out ); /// //out: /// //[1, 2, 3; /// // 1, 2, 3; /// // 1, 2, 3; /// // 1, 2, 3] /// ``` /// /// * src: input array or vector of matrices. all of the matrices must have the same number of rows and the same depth. /// * dst: output array. It has the same number of rows and depth as the src, and the sum of cols of the src. /// same depth. pub fn hconcat(src: &types::VectorOfMat, dst: &mut core::Mat) -> Result<()> { unsafe { sys::cv_hconcat_VectorOfMat_Mat(src.as_raw_VectorOfMat(), dst.as_raw_Mat()) }.into_result() } /// Calculates the inverse Discrete Cosine Transform of a 1D or 2D array. /// /// idct(src, dst, flags) is equivalent to dct(src, dst, flags | DCT_INVERSE). /// ## Parameters /// * src: input floating-point single-channel array. /// * dst: output array of the same size and type as src. /// * flags: operation flags. /// ## See also /// dct, dft, idft, getOptimalDFTSize /// /// ## C++ default parameters /// * flags: 0 pub fn idct(src: &core::Mat, dst: &mut core::Mat, flags: i32) -> Result<()> { unsafe { sys::cv_idct_Mat_Mat_int(src.as_raw_Mat(), dst.as_raw_Mat(), flags) }.into_result() } /// Calculates the inverse Discrete Fourier Transform of a 1D or 2D array. /// /// idft(src, dst, flags) is equivalent to dft(src, dst, flags | #DFT_INVERSE) . /// /// Note: None of dft and idft scales the result by default. So, you should pass #DFT_SCALE to one of /// dft or idft explicitly to make these transforms mutually inverse. /// ## See also /// dft, dct, idct, mulSpectrums, getOptimalDFTSize /// ## Parameters /// * src: input floating-point real or complex array. /// * dst: output array whose size and type depend on the flags. /// * flags: operation flags (see dft and #DftFlags). /// * nonzeroRows: number of dst rows to process; the rest of the rows have undefined content (see /// the convolution sample in dft description. /// /// ## C++ default parameters /// * flags: 0 /// * nonzero_rows: 0 pub fn idft(src: &core::Mat, dst: &mut core::Mat, flags: i32, nonzero_rows: i32) -> Result<()> { unsafe { sys::cv_idft_Mat_Mat_int_int(src.as_raw_Mat(), dst.as_raw_Mat(), flags, nonzero_rows) }.into_result() } /// Checks if array elements lie between the elements of two other arrays. /// /// The function checks the range as follows: /// * For every element of a single-channel input array: /// <div lang='latex'>\texttt{dst} (I)= \texttt{lowerb} (I)_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb} (I)_0</div> /// * For two-channel arrays: /// <div lang='latex'>\texttt{dst} (I)= \texttt{lowerb} (I)_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb} (I)_0 \land \texttt{lowerb} (I)_1 \leq \texttt{src} (I)_1 \leq \texttt{upperb} (I)_1</div> /// * and so forth. /// /// That is, dst (I) is set to 255 (all 1 -bits) if src (I) is within the /// specified 1D, 2D, 3D, ... box and 0 otherwise. /// /// When the lower and/or upper boundary parameters are scalars, the indexes /// (I) at lowerb and upperb in the above formulas should be omitted. /// ## Parameters /// * src: first input array. /// * lowerb: inclusive lower boundary array or a scalar. /// * upperb: inclusive upper boundary array or a scalar. /// * dst: output array of the same size as src and CV_8U type. pub fn in_range(src: &core::Mat, lowerb: &core::Mat, upperb: &core::Mat, dst: &mut core::Mat) -> Result<()> { unsafe { sys::cv_inRange_Mat_Mat_Mat_Mat(src.as_raw_Mat(), lowerb.as_raw_Mat(), upperb.as_raw_Mat(), dst.as_raw_Mat()) }.into_result() } /// Inserts a single channel to dst (coi is 0-based index) /// ## Parameters /// * src: input array /// * dst: output array /// * coi: index of channel for insertion /// ## See also /// mixChannels, merge pub fn insert_channel(src: &core::Mat, dst: &mut core::Mat, coi: i32) -> Result<()> { unsafe { sys::cv_insertChannel_Mat_Mat_int(src.as_raw_Mat(), dst.as_raw_Mat(), coi) }.into_result() } pub fn get_flags() -> Result<core::FLAGS> { unsafe { sys::cv_instr_getFlags() }.into_result() } pub fn reset_trace() -> Result<()> { unsafe { sys::cv_instr_resetTrace() }.into_result() } pub fn set_flags(mode_flags: core::FLAGS) -> Result<()> { unsafe { sys::cv_instr_setFlags_FLAGS(mode_flags) }.into_result() } pub fn set_flags_1(mode_flags: i32) -> Result<()> { unsafe { sys::cv_instr_setFlags_int(mode_flags) }.into_result() } pub fn set_use_instrumentation(flag: bool) -> Result<()> { unsafe { sys::cv_instr_setUseInstrumentation_bool(flag) }.into_result() } pub fn use_instrumentation() -> Result<bool> { unsafe { sys::cv_instr_useInstrumentation() }.into_result() } /// Finds the inverse or pseudo-inverse of a matrix. /// /// The function cv::invert inverts the matrix src and stores the result in dst /// . When the matrix src is singular or non-square, the function calculates /// the pseudo-inverse matrix (the dst matrix) so that norm(src\*dst - I) is /// minimal, where I is an identity matrix. /// /// In case of the #DECOMP_LU method, the function returns non-zero value if /// the inverse has been successfully calculated and 0 if src is singular. /// /// In case of the #DECOMP_SVD method, the function returns the inverse /// condition number of src (the ratio of the smallest singular value to the /// largest singular value) and 0 if src is singular. The SVD method /// calculates a pseudo-inverse matrix if src is singular. /// /// Similarly to #DECOMP_LU, the method #DECOMP_CHOLESKY works only with /// non-singular square matrices that should also be symmetrical and /// positively defined. In this case, the function stores the inverted /// matrix in dst and returns non-zero. Otherwise, it returns 0. /// /// ## Parameters /// * src: input floating-point M x N matrix. /// * dst: output matrix of N x M size and the same type as src. /// * flags: inversion method (cv::DecompTypes) /// ## See also /// solve, SVD /// /// ## C++ default parameters /// * flags: DECOMP_LU pub fn invert(src: &core::Mat, dst: &mut core::Mat, flags: i32) -> Result<f64> { unsafe { sys::cv_invert_Mat_Mat_int(src.as_raw_Mat(), dst.as_raw_Mat(), flags) }.into_result() } pub fn get_ipp_error_location() -> Result<String> { unsafe { sys::cv_ipp_getIppErrorLocation() }.into_result().map(crate::templ::receive_string_mut) } pub fn get_ipp_features() -> Result<u64> { unsafe { sys::cv_ipp_getIppFeatures() }.into_result() } pub fn get_ipp_status() -> Result<i32> { unsafe { sys::cv_ipp_getIppStatus() }.into_result() } pub fn get_ipp_version() -> Result<String> { unsafe { sys::cv_ipp_getIppVersion() }.into_result().map(crate::templ::receive_string_mut) } pub fn set_use_ipp_ne(flag: bool) -> Result<()> { unsafe { sys::cv_ipp_setUseIPP_NE_bool(flag) }.into_result() } pub fn set_use_ipp__not_exact(flag: bool) -> Result<()> { unsafe { sys::cv_ipp_setUseIPP_NotExact_bool(flag) }.into_result() } pub fn set_use_ipp(flag: bool) -> Result<()> { unsafe { sys::cv_ipp_setUseIPP_bool(flag) }.into_result() } pub fn use_ipp() -> Result<bool> { unsafe { sys::cv_ipp_useIPP() }.into_result() } pub fn use_ipp_ne() -> Result<bool> { unsafe { sys::cv_ipp_useIPP_NE() }.into_result() } pub fn use_ipp__not_exact() -> Result<bool> { unsafe { sys::cv_ipp_useIPP_NotExact() }.into_result() } /// Finds centers of clusters and groups input samples around the clusters. /// /// The function kmeans implements a k-means algorithm that finds the centers of cluster_count clusters /// and groups the input samples around the clusters. As an output, <span lang='latex'>\texttt{bestLabels}_i</span> contains a /// 0-based cluster index for the sample stored in the <span lang='latex'>i^{th}</span> row of the samples matrix. /// /// /// Note: /// * (Python) An example on K-means clustering can be found at /// opencv_source_code/samples/python/kmeans.py /// ## Parameters /// * data: Data for clustering. An array of N-Dimensional points with float coordinates is needed. /// Examples of this array can be: /// * Mat points(count, 2, CV_32F); /// * Mat points(count, 1, CV_32FC2); /// * Mat points(1, count, CV_32FC2); /// * std::vector\<cv::Point2f\> points(sampleCount); /// * K: Number of clusters to split the set by. /// * bestLabels: Input/output integer array that stores the cluster indices for every sample. /// * 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 last /// function parameter). /// * flags: Flag that can take values of cv::KmeansFlags /// * centers: Output matrix of the cluster centers, one row per each cluster center. /// ## Returns /// The function returns the compactness measure that is computed as /// <div lang='latex'>\sum _i \| \texttt{samples} _i - \texttt{centers} _{ \texttt{labels} _i} \| ^2</div> /// after every attempt. The best (minimum) value is chosen and the corresponding labels and the /// compactness value are returned by the function. Basically, you can use only the core of the /// function, 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. /// /// ## C++ default parameters /// * centers: noArray() pub fn kmeans(data: &core::Mat, k: i32, best_labels: &mut core::Mat, criteria: &core::TermCriteria, attempts: i32, flags: i32, centers: &mut core::Mat) -> Result<f64> { unsafe { sys::cv_kmeans_Mat_int_Mat_TermCriteria_int_int_Mat(data.as_raw_Mat(), k, best_labels.as_raw_Mat(), criteria.as_raw_TermCriteria(), attempts, flags, centers.as_raw_Mat()) }.into_result() } /// Calculates the natural logarithm of every array element. /// /// The function cv::log calculates the natural logarithm of every element of the input array: /// <div lang='latex'>\texttt{dst} (I) = \log (\texttt{src}(I)) </div> /// /// Output on zero, negative and special (NaN, Inf) values is undefined. /// /// ## Parameters /// * src: input array. /// * dst: output array of the same size and type as src . /// ## See also /// exp, cartToPolar, polarToCart, phase, pow, sqrt, magnitude pub fn log(src: &core::Mat, dst: &mut core::Mat) -> Result<()> { unsafe { sys::cv_log_Mat_Mat(src.as_raw_Mat(), dst.as_raw_Mat()) }.into_result() } /// Calculates the magnitude of 2D vectors. /// /// The function cv::magnitude calculates the magnitude of 2D vectors formed /// from the corresponding elements of x and y arrays: /// <div lang='latex'>\texttt{dst} (I) = \sqrt{\texttt{x}(I)^2 + \texttt{y}(I)^2}</div> /// ## Parameters /// * x: floating-point array of x-coordinates of the vectors. /// * y: floating-point array of y-coordinates of the vectors; it must /// have the same size as x. /// * magnitude: output array of the same size and type as x. /// ## See also /// cartToPolar, polarToCart, phase, sqrt pub fn magnitude(x: &core::Mat, y: &core::Mat, magnitude: &mut core::Mat) -> Result<()> { unsafe { sys::cv_magnitude_Mat_Mat_Mat(x.as_raw_Mat(), y.as_raw_Mat(), magnitude.as_raw_Mat()) }.into_result() } /// Calculates per-element maximum of two arrays or an array and a scalar. /// /// The function cv::max calculates the per-element maximum of two arrays: /// <div lang='latex'>\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{src2} (I))</div> /// or array and a scalar: /// <div lang='latex'>\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{value} )</div> /// ## Parameters /// * src1: first input array. /// * src2: second input array of the same size and type as src1 . /// * dst: output array of the same size and type as src1. /// ## See also /// min, compare, inRange, minMaxLoc, @ref MatrixExpressions pub fn max(src1: &core::Mat, src2: &core::Mat, dst: &mut core::Mat) -> Result<()> { unsafe { sys::cv_max_Mat_Mat_Mat(src1.as_raw_Mat(), src2.as_raw_Mat(), dst.as_raw_Mat()) }.into_result() } /// Calculates per-element maximum of two arrays or an array and a scalar. /// /// The function cv::max calculates the per-element maximum of two arrays: /// <div lang='latex'>\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{src2} (I))</div> /// or array and a scalar: /// <div lang='latex'>\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{value} )</div> /// ## Parameters /// * src1: first input array. /// * src2: second input array of the same size and type as src1 . /// * dst: output array of the same size and type as src1. /// ## See also /// min, compare, inRange, minMaxLoc, @ref MatrixExpressions /// /// ## Overloaded parameters /// /// needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) pub fn max_umat(src1: &core::UMat, src2: &core::UMat, dst: &mut core::UMat) -> Result<()> { unsafe { sys::cv_max_UMat_UMat_UMat(src1.as_raw_UMat(), src2.as_raw_UMat(), dst.as_raw_UMat()) }.into_result() } /// Calculates a mean and standard deviation of array elements. /// /// The function cv::meanStdDev calculates the mean and the standard deviation M /// of array elements independently for each channel and returns it via the /// output parameters: /// <div lang='latex'>\begin{array}{l} N = \sum _{I, \texttt{mask} (I) \ne 0} 1 \\ \texttt{mean} _c = \frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \texttt{src} (I)_c}{N} \\ \texttt{stddev} _c = \sqrt{\frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \left ( \texttt{src} (I)_c - \texttt{mean} _c \right )^2}{N}} \end{array}</div> /// When all the mask elements are 0's, the function returns /// mean=stddev=Scalar::all(0). /// /// Note: The calculated standard deviation is only the diagonal of the /// complete normalized covariance matrix. If the full matrix is needed, you /// can reshape the multi-channel array M x N to the single-channel array /// M\*N x mtx.channels() (only possible when the matrix is continuous) and /// then pass the matrix to calcCovarMatrix . /// ## Parameters /// * src: input array that should have from 1 to 4 channels so that the results can be stored in /// Scalar_ 's. /// * mean: output parameter: calculated mean value. /// * stddev: output parameter: calculated standard deviation. /// * mask: optional operation mask. /// ## See also /// countNonZero, mean, norm, minMaxLoc, calcCovarMatrix /// /// ## C++ default parameters /// * mask: noArray() pub fn mean_std_dev(src: &core::Mat, mean: &mut core::Mat, stddev: &mut core::Mat, mask: &core::Mat) -> Result<()> { unsafe { sys::cv_meanStdDev_Mat_Mat_Mat_Mat(src.as_raw_Mat(), mean.as_raw_Mat(), stddev.as_raw_Mat(), mask.as_raw_Mat()) }.into_result() } /// Calculates an average (mean) of array elements. /// /// The function cv::mean calculates the mean value M of array elements, /// independently for each channel, and return it: /// <div lang='latex'>\begin{array}{l} N = \sum _{I: \; \texttt{mask} (I) \ne 0} 1 \\ M_c = \left ( \sum _{I: \; \texttt{mask} (I) \ne 0}{ \texttt{mtx} (I)_c} \right )/N \end{array}</div> /// When all the mask elements are 0's, the function returns Scalar::all(0) /// ## Parameters /// * src: input array that should have from 1 to 4 channels so that the result can be stored in /// Scalar_ . /// * mask: optional operation mask. /// ## See also /// countNonZero, meanStdDev, norm, minMaxLoc /// /// ## C++ default parameters /// * mask: noArray() pub fn mean(src: &core::Mat, mask: &core::Mat) -> Result<core::Scalar> { unsafe { sys::cv_mean_Mat_Mat(src.as_raw_Mat(), mask.as_raw_Mat()) }.into_result() } /// Creates one multi-channel array out of several single-channel ones. /// /// The function cv::merge merges several arrays to make a single multi-channel array. That is, each /// element of the output array will be a concatenation of the elements of the input arrays, where /// elements of i-th input array are treated as mv[i].channels()-element vectors. /// /// The function cv::split does the reverse operation. If you need to shuffle channels in some other /// advanced way, use cv::mixChannels. /// /// The following example shows how to merge 3 single channel matrices into a single 3-channel matrix. /// @snippet snippets/core_merge.cpp example /// /// ## Parameters /// * mv: input array of matrices to be merged; all the matrices in mv must have the same /// size and the same depth. /// * count: number of input matrices when mv is a plain C array; it must be greater than zero. /// * dst: output array of the same size and the same depth as mv[0]; The number of channels will /// be equal to the parameter count. /// ## See also /// mixChannels, split, Mat::reshape /// /// ## Overloaded parameters /// /// * mv: input vector of matrices to be merged; all the matrices in mv must have the same /// size and the same depth. /// * dst: output array of the same size and the same depth as mv[0]; The number of channels will /// be the total number of channels in the matrix array. pub fn merge(mv: &types::VectorOfMat, dst: &mut core::Mat) -> Result<()> { unsafe { sys::cv_merge_VectorOfMat_Mat(mv.as_raw_VectorOfMat(), dst.as_raw_Mat()) }.into_result() } /// Finds the global minimum and maximum in an array /// /// The function cv::minMaxIdx finds the minimum and maximum element values and their positions. The /// extremums are searched across the whole array or, if mask is not an empty array, in the specified /// array region. The function does not work with multi-channel arrays. If you need to find minimum or /// maximum elements across all the channels, use Mat::reshape first to reinterpret the array as /// single-channel. Or you may extract the particular channel using either extractImageCOI , or /// mixChannels , or split . In case of a sparse matrix, the minimum is found among non-zero elements /// only. /// /// Note: When minIdx is not NULL, it must have at least 2 elements (as well as maxIdx), even if src is /// a single-row or single-column matrix. In OpenCV (following MATLAB) each array has at least 2 /// dimensions, i.e. single-column matrix is Mx1 matrix (and therefore minIdx/maxIdx will be /// (i1,0)/(i2,0)) and single-row matrix is 1xN matrix (and therefore minIdx/maxIdx will be /// (0,j1)/(0,j2)). /// ## Parameters /// * src: input single-channel array. /// * minVal: pointer to the returned minimum value; NULL is used if not required. /// * maxVal: pointer to the returned maximum value; NULL is used if not required. /// * minIdx: pointer to the returned minimum location (in nD case); NULL is used if not required; /// Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element /// in each dimension are stored there sequentially. /// * maxIdx: pointer to the returned maximum location (in nD case). NULL is used if not required. /// * mask: specified array region /// /// ## C++ default parameters /// * max_val: 0 /// * min_idx: 0 /// * max_idx: 0 /// * mask: noArray() pub fn min_max_idx(src: &core::Mat, min_val: &mut f64, max_val: &mut f64, min_idx: &mut i32, max_idx: &mut i32, mask: &core::Mat) -> Result<()> { unsafe { sys::cv_minMaxIdx_Mat_double_X_double_X_int_X_int_X_Mat(src.as_raw_Mat(), min_val, max_val, min_idx, max_idx, mask.as_raw_Mat()) }.into_result() } /// Finds the global minimum and maximum in an array. /// /// The function cv::minMaxLoc finds the minimum and maximum element values and their positions. The /// extremums are searched across the whole array or, if mask is not an empty array, in the specified /// array region. /// /// The function do not work with multi-channel arrays. If you need to find minimum or maximum /// elements across all the channels, use Mat::reshape first to reinterpret the array as /// single-channel. Or you may extract the particular channel using either extractImageCOI , or /// mixChannels , or split . /// ## Parameters /// * src: input single-channel array. /// * minVal: pointer to the returned minimum value; NULL is used if not required. /// * maxVal: pointer to the returned maximum value; NULL is used if not required. /// * minLoc: pointer to the returned minimum location (in 2D case); NULL is used if not required. /// * maxLoc: pointer to the returned maximum location (in 2D case); NULL is used if not required. /// * mask: optional mask used to select a sub-array. /// ## See also /// max, min, compare, inRange, extractImageCOI, mixChannels, split, Mat::reshape /// /// ## C++ default parameters /// * max_val: 0 /// * min_loc: 0 /// * max_loc: 0 /// * mask: noArray() pub fn min_max_loc(src: &core::Mat, min_val: &mut f64, max_val: &mut f64, min_loc: &mut core::Point, max_loc: &mut core::Point, mask: &core::Mat) -> Result<()> { unsafe { sys::cv_minMaxLoc_Mat_double_X_double_X_Point_X_Point_X_Mat(src.as_raw_Mat(), min_val, max_val, min_loc, max_loc, mask.as_raw_Mat()) }.into_result() } /// Calculates per-element minimum of two arrays or an array and a scalar. /// /// The function cv::min calculates the per-element minimum of two arrays: /// <div lang='latex'>\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{src2} (I))</div> /// or array and a scalar: /// <div lang='latex'>\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{value} )</div> /// ## Parameters /// * src1: first input array. /// * src2: second input array of the same size and type as src1. /// * dst: output array of the same size and type as src1. /// ## See also /// max, compare, inRange, minMaxLoc pub fn min(src1: &core::Mat, src2: &core::Mat, dst: &mut core::Mat) -> Result<()> { unsafe { sys::cv_min_Mat_Mat_Mat(src1.as_raw_Mat(), src2.as_raw_Mat(), dst.as_raw_Mat()) }.into_result() } /// Calculates per-element minimum of two arrays or an array and a scalar. /// /// The function cv::min calculates the per-element minimum of two arrays: /// <div lang='latex'>\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{src2} (I))</div> /// or array and a scalar: /// <div lang='latex'>\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{value} )</div> /// ## Parameters /// * src1: first input array. /// * src2: second input array of the same size and type as src1. /// * dst: output array of the same size and type as src1. /// ## See also /// max, compare, inRange, minMaxLoc /// /// ## Overloaded parameters /// /// needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) pub fn min_umat(src1: &core::UMat, src2: &core::UMat, dst: &mut core::UMat) -> Result<()> { unsafe { sys::cv_min_UMat_UMat_UMat(src1.as_raw_UMat(), src2.as_raw_UMat(), dst.as_raw_UMat()) }.into_result() } /// Copies specified channels from input arrays to the specified channels of /// output arrays. /// /// The function cv::mixChannels provides an advanced mechanism for shuffling image channels. /// /// cv::split,cv::merge,cv::extractChannel,cv::insertChannel and some forms of cv::cvtColor are partial cases of cv::mixChannels. /// /// In the example below, the code splits a 4-channel BGRA image into a 3-channel BGR (with B and R /// channels swapped) and a separate alpha-channel image: /// ```ignore{.cpp} /// Mat bgra( 100, 100, CV_8UC4, Scalar(255,0,0,255) ); /// Mat bgr( bgra.rows, bgra.cols, CV_8UC3 ); /// Mat alpha( bgra.rows, bgra.cols, CV_8UC1 ); /// /// // forming an array of matrices is a quite efficient operation, /// // because the matrix data is not copied, only the headers /// Mat out[] = { bgr, alpha }; /// // bgra[0] -> bgr[2], bgra[1] -> bgr[1], /// // bgra[2] -> bgr[0], bgra[3] -> alpha[0] /// int from_to[] = { 0,2, 1,1, 2,0, 3,3 }; /// mixChannels( &bgra, 1, out, 2, from_to, 4 ); /// ``` /// /// /// Note: Unlike many other new-style C++ functions in OpenCV (see the introduction section and /// Mat::create ), cv::mixChannels requires the output arrays to be pre-allocated before calling the /// function. /// ## Parameters /// * src: input array or vector of matrices; all of the matrices must have the same size and the /// same depth. /// * nsrcs: number of matrices in `src`. /// * dst: output array or vector of matrices; all the matrices **must be allocated**; their size and /// depth must be the same as in `src[0]`. /// * ndsts: number of matrices in `dst`. /// * fromTo: array of index pairs specifying which channels are copied and where; fromTo[k\*2] is /// a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in /// dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to /// src[0].channels()-1, the second input image channels are indexed from src[0].channels() to /// src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image /// channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is /// filled with zero . /// * npairs: number of index pairs in `fromTo`. /// ## See also /// split, merge, extractChannel, insertChannel, cvtColor /// /// ## Overloaded parameters /// /// * src: input array or vector of matrices; all of the matrices must have the same size and the /// same depth. /// * dst: output array or vector of matrices; all the matrices **must be allocated**; their size and /// depth must be the same as in src[0]. /// * fromTo: array of index pairs specifying which channels are copied and where; fromTo[k\*2] is /// a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in /// dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to /// src[0].channels()-1, the second input image channels are indexed from src[0].channels() to /// src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image /// channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is /// filled with zero . pub fn mix_channels(src: &types::VectorOfMat, dst: &mut types::VectorOfMat, from_to: &types::VectorOfint) -> Result<()> { unsafe { sys::cv_mixChannels_VectorOfMat_VectorOfMat_VectorOfint(src.as_raw_VectorOfMat(), dst.as_raw_VectorOfMat(), from_to.as_raw_VectorOfint()) }.into_result() } /// Performs the per-element multiplication of two Fourier spectrums. /// /// The function cv::mulSpectrums performs the per-element multiplication of the two CCS-packed or complex /// matrices that are results of a real or complex Fourier transform. /// /// The function, together with dft and idft , may be used to calculate convolution (pass conjB=false ) /// or correlation (pass conjB=true ) of two arrays rapidly. When the arrays are complex, they are /// simply multiplied (per element) with an optional conjugation of the second-array elements. When the /// arrays are real, they are assumed to be CCS-packed (see dft for details). /// ## Parameters /// * a: first input array. /// * b: second input array of the same size and type as src1 . /// * c: output array of the same size and type as src1 . /// * flags: operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that /// each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a `0` as value. /// * conjB: optional flag that conjugates the second input array before the multiplication (true) /// or not (false). /// /// ## C++ default parameters /// * conj_b: false pub fn mul_spectrums(a: &core::Mat, b: &core::Mat, c: &mut core::Mat, flags: i32, conj_b: bool) -> Result<()> { unsafe { sys::cv_mulSpectrums_Mat_Mat_Mat_int_bool(a.as_raw_Mat(), b.as_raw_Mat(), c.as_raw_Mat(), flags, conj_b) }.into_result() } /// Calculates the product of a matrix and its transposition. /// /// The function cv::mulTransposed calculates the product of src and its /// transposition: /// <div lang='latex'>\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} )^T ( \texttt{src} - \texttt{delta} )</div> /// if aTa=true , and /// <div lang='latex'>\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} ) ( \texttt{src} - \texttt{delta} )^T</div> /// otherwise. The function is used to calculate the covariance matrix. With /// zero delta, it can be used as a faster substitute for general matrix /// product A\*B when B=A' /// ## Parameters /// * src: input single-channel matrix. Note that unlike gemm, the /// function can multiply not only floating-point matrices. /// * dst: output square matrix. /// * aTa: Flag specifying the multiplication ordering. See the /// description below. /// * delta: Optional delta matrix subtracted from src before the /// multiplication. When the matrix is empty ( delta=noArray() ), it is /// assumed to be zero, that is, nothing is subtracted. If it has the same /// size as src , it is simply subtracted. Otherwise, it is "repeated" (see /// repeat ) to cover the full src and then subtracted. Type of the delta /// matrix, when it is not empty, must be the same as the type of created /// output matrix. See the dtype parameter description below. /// * scale: Optional scale factor for the matrix product. /// * dtype: Optional type of the output matrix. When it is negative, /// the output matrix will have the same type as src . Otherwise, it will be /// type=CV_MAT_DEPTH(dtype) that should be either CV_32F or CV_64F . /// ## See also /// calcCovarMatrix, gemm, repeat, reduce /// /// ## C++ default parameters /// * delta: noArray() /// * scale: 1 /// * dtype: -1 pub fn mul_transposed(src: &core::Mat, dst: &mut core::Mat, a_ta: bool, delta: &core::Mat, scale: f64, dtype: i32) -> Result<()> { unsafe { sys::cv_mulTransposed_Mat_Mat_bool_Mat_double_int(src.as_raw_Mat(), dst.as_raw_Mat(), a_ta, delta.as_raw_Mat(), scale, dtype) }.into_result() } /// Calculates the per-element scaled product of two arrays. /// /// The function multiply calculates the per-element product of two arrays: /// /// <div lang='latex'>\texttt{dst} (I)= \texttt{saturate} ( \texttt{scale} \cdot \texttt{src1} (I) \cdot \texttt{src2} (I))</div> /// /// There is also a @ref MatrixExpressions -friendly variant of the first function. See Mat::mul . /// /// For a not-per-element matrix product, see gemm . /// /// /// Note: Saturation is not applied when the output array has the depth /// CV_32S. You may even get result of an incorrect sign in the case of /// overflow. /// ## Parameters /// * src1: first input array. /// * src2: second input array of the same size and the same type as src1. /// * dst: output array of the same size and type as src1. /// * scale: optional scale factor. /// * dtype: optional depth of the output array /// ## See also /// add, subtract, divide, scaleAdd, addWeighted, accumulate, accumulateProduct, accumulateSquare, /// Mat::convertTo /// /// ## C++ default parameters /// * scale: 1 /// * dtype: -1 pub fn multiply(src1: &core::Mat, src2: &core::Mat, dst: &mut core::Mat, scale: f64, dtype: i32) -> Result<()> { unsafe { sys::cv_multiply_Mat_Mat_Mat_double_int(src1.as_raw_Mat(), src2.as_raw_Mat(), dst.as_raw_Mat(), scale, dtype) }.into_result() } pub fn norm_l1(a: &f32, b: &f32, n: i32) -> Result<f32> { unsafe { sys::cv_normL1_const_float_X_const_float_X_int(a, b, n) }.into_result() } pub fn norm_l2(a: &u8, b: &u8, n: i32) -> Result<i32> { unsafe { sys::cv_normL1_const_uchar_X_const_uchar_X_int(a, b, n) }.into_result() } pub fn norm_l2_sqr(a: &f32, b: &f32, n: i32) -> Result<f32> { unsafe { sys::cv_normL2Sqr_const_float_X_const_float_X_int(a, b, n) }.into_result() } /// Calculates an absolute difference norm or a relative difference norm. /// /// This version of cv::norm calculates the absolute difference norm /// or the relative difference norm of arrays src1 and src2. /// The type of norm to calculate is specified using #NormTypes. /// /// ## Parameters /// * src1: first input array. /// * src2: second input array of the same size and the same type as src1. /// * normType: type of the norm (see #NormTypes). /// * mask: optional operation mask; it must have the same size as src1 and CV_8UC1 type. /// /// ## C++ default parameters /// * norm_type: NORM_L2 /// * mask: noArray() pub fn norm_with_type(src1: &core::Mat, src2: &core::Mat, norm_type: i32, mask: &core::Mat) -> Result<f64> { unsafe { sys::cv_norm_Mat_Mat_int_Mat(src1.as_raw_Mat(), src2.as_raw_Mat(), norm_type, mask.as_raw_Mat()) }.into_result() } /// Calculates the absolute norm of an array. /// /// This version of #norm calculates the absolute norm of src1. The type of norm to calculate is specified using #NormTypes. /// /// As example for one array consider the function <span lang='latex'>r(x)= \begin{pmatrix} x \\ 1-x \end{pmatrix}, x \in [-1;1]</span>. /// The <span lang='latex'> L_{1}, L_{2} </span> and <span lang='latex'> L_{\infty} </span> norm for the sample value <span lang='latex'>r(-1) = \begin{pmatrix} -1 \\ 2 \end{pmatrix}</span> /// is calculated as follows /// \f{align*} /// \| r(-1) \|_{L_1} &= |-1| + |2| = 3 \\ /// \| r(-1) \|_{L_2} &= \sqrt{(-1)^{2} + (2)^{2}} = \sqrt{5} \\ /// \| r(-1) \|_{L_\infty} &= \max(|-1|,|2|) = 2 /// \f} /// and for <span lang='latex'>r(0.5) = \begin{pmatrix} 0.5 \\ 0.5 \end{pmatrix}</span> the calculation is /// \f{align*} /// \| r(0.5) \|_{L_1} &= |0.5| + |0.5| = 1 \\ /// \| r(0.5) \|_{L_2} &= \sqrt{(0.5)^{2} + (0.5)^{2}} = \sqrt{0.5} \\ /// \| r(0.5) \|_{L_\infty} &= \max(|0.5|,|0.5|) = 0.5. /// \f} /// The following graphic shows all values for the three norm functions <span lang='latex'>\| r(x) \|_{L_1}, \| r(x) \|_{L_2}</span> and <span lang='latex'>\| r(x) \|_{L_\infty}</span>. /// It is notable that the <span lang='latex'> L_{1} </span> norm forms the upper and the <span lang='latex'> L_{\infty} </span> norm forms the lower border for the example function <span lang='latex'> r(x) </span>. ///  /// /// When the mask parameter is specified and it is not empty, the norm is /// /// If normType is not specified, #NORM_L2 is used. /// calculated only over the region specified by the mask. /// /// Multi-channel input arrays are treated as single-channel arrays, that is, /// the results for all channels are combined. /// /// Hamming norms can only be calculated with CV_8U depth arrays. /// /// ## Parameters /// * src1: first input array. /// * normType: type of the norm (see #NormTypes). /// * mask: optional operation mask; it must have the same size as src1 and CV_8UC1 type. /// /// ## C++ default parameters /// * norm_type: NORM_L2 /// * mask: noArray() pub fn norm(src1: &core::Mat, norm_type: i32, mask: &core::Mat) -> Result<f64> { unsafe { sys::cv_norm_Mat_int_Mat(src1.as_raw_Mat(), norm_type, mask.as_raw_Mat()) }.into_result() } /// Normalizes the norm or value range of an array. /// /// The function cv::normalize normalizes scale and shift the input array elements so that /// <div lang='latex'>\| \texttt{dst} \| _{L_p}= \texttt{alpha}</div> /// (where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that /// <div lang='latex'>\min _I \texttt{dst} (I)= \texttt{alpha} , \, \, \max _I \texttt{dst} (I)= \texttt{beta}</div> /// /// when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be /// normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this /// sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or /// min-max but modify the whole array, you can use norm and Mat::convertTo. /// /// In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this, /// the range transformation for sparse matrices is not allowed since it can shift the zero level. /// /// Possible usage with some positive example data: /// ```ignore{.cpp} /// vector<double> positiveData = { 2.0, 8.0, 10.0 }; /// vector<double> normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax; /// /// // Norm to probability (total count) /// // sum(numbers) = 20.0 /// // 2.0 0.1 (2.0/20.0) /// // 8.0 0.4 (8.0/20.0) /// // 10.0 0.5 (10.0/20.0) /// normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1); /// /// // Norm to unit vector: ||positiveData|| = 1.0 /// // 2.0 0.15 /// // 8.0 0.62 /// // 10.0 0.77 /// normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_L2); /// /// // Norm to max element /// // 2.0 0.2 (2.0/10.0) /// // 8.0 0.8 (8.0/10.0) /// // 10.0 1.0 (10.0/10.0) /// normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_INF); /// /// // Norm to range [0.0;1.0] /// // 2.0 0.0 (shift to left border) /// // 8.0 0.75 (6.0/8.0) /// // 10.0 1.0 (shift to right border) /// normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX); /// ``` /// /// /// ## Parameters /// * src: input array. /// * dst: output array of the same size as src . /// * alpha: norm value to normalize to or the lower range boundary in case of the range /// normalization. /// * beta: upper range boundary in case of the range normalization; it is not used for the norm /// normalization. /// * norm_type: normalization type (see cv::NormTypes). /// * dtype: when negative, the output array has the same type as src; otherwise, it has the same /// number of channels as src and the depth =CV_MAT_DEPTH(dtype). /// * mask: optional operation mask. /// ## See also /// norm, Mat::convertTo, SparseMat::convertTo /// /// ## C++ default parameters /// * alpha: 1 /// * beta: 0 /// * norm_type: NORM_L2 /// * dtype: -1 /// * mask: noArray() pub fn normalize(src: &core::Mat, dst: &mut core::Mat, alpha: f64, beta: f64, norm_type: i32, dtype: i32, mask: &core::Mat) -> Result<()> { unsafe { sys::cv_normalize_Mat_Mat_double_double_int_int_Mat(src.as_raw_Mat(), dst.as_raw_Mat(), alpha, beta, norm_type, dtype, mask.as_raw_Mat()) }.into_result() } /// Parallel data processor /// /// ## C++ default parameters /// * nstripes: -1. pub fn parallel_for_(range: &core::Range, body: &dyn core::ParallelLoopBody, nstripes: f64) -> Result<()> { unsafe { sys::cv_parallel_for__Range_ParallelLoopBody_double(range.as_raw_Range(), body.as_raw_ParallelLoopBody(), nstripes) }.into_result() } /// converts NaN's to the given number /// /// ## C++ default parameters /// * val: 0 pub fn patch_na_ns(a: &mut core::Mat, val: f64) -> Result<()> { unsafe { sys::cv_patchNaNs_Mat_double(a.as_raw_Mat(), val) }.into_result() } /// Performs the perspective matrix transformation of vectors. /// /// The function cv::perspectiveTransform transforms every element of src by /// treating it as a 2D or 3D vector, in the following way: /// <div lang='latex'>(x, y, z) \rightarrow (x'/w, y'/w, z'/w)</div> /// where /// <div lang='latex'>(x', y', z', w') = \texttt{mat} \cdot \begin{bmatrix} x & y & z & 1 \end{bmatrix}</div> /// and /// <div lang='latex'>w = \fork{w'}{if \(w' \ne 0\)}{\infty}{otherwise}</div> /// /// Here a 3D vector transformation is shown. In case of a 2D vector /// transformation, the z component is omitted. /// /// /// Note: The function transforms a sparse set of 2D or 3D vectors. If you /// want to transform an image using perspective transformation, use /// warpPerspective . If you have an inverse problem, that is, you want to /// compute the most probable perspective transformation out of several /// pairs of corresponding points, you can use getPerspectiveTransform or /// findHomography . /// ## Parameters /// * src: input two-channel or three-channel floating-point array; each /// element is a 2D/3D vector to be transformed. /// * dst: output array of the same size and type as src. /// * m: 3x3 or 4x4 floating-point transformation matrix. /// ## See also /// transform, warpPerspective, getPerspectiveTransform, findHomography pub fn perspective_transform(src: &core::Mat, dst: &mut core::Mat, m: &core::Mat) -> Result<()> { unsafe { sys::cv_perspectiveTransform_Mat_Mat_Mat(src.as_raw_Mat(), dst.as_raw_Mat(), m.as_raw_Mat()) }.into_result() } /// Calculates the rotation angle of 2D vectors. /// /// The function cv::phase calculates the rotation angle of each 2D vector that /// is formed from the corresponding elements of x and y : /// <div lang='latex'>\texttt{angle} (I) = \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))</div> /// /// The angle estimation accuracy is about 0.3 degrees. When x(I)=y(I)=0 , /// the corresponding angle(I) is set to 0. /// ## Parameters /// * x: input floating-point array of x-coordinates of 2D vectors. /// * y: input array of y-coordinates of 2D vectors; it must have the /// same size and the same type as x. /// * angle: output array of vector angles; it has the same size and /// same type as x . /// * angleInDegrees: when true, the function calculates the angle in /// degrees, otherwise, they are measured in radians. /// /// ## C++ default parameters /// * angle_in_degrees: false pub fn phase(x: &core::Mat, y: &core::Mat, angle: &mut core::Mat, angle_in_degrees: bool) -> Result<()> { unsafe { sys::cv_phase_Mat_Mat_Mat_bool(x.as_raw_Mat(), y.as_raw_Mat(), angle.as_raw_Mat(), angle_in_degrees) }.into_result() } /// Calculates x and y coordinates of 2D vectors from their magnitude and angle. /// /// The function cv::polarToCart calculates the Cartesian coordinates of each 2D /// vector represented by the corresponding elements of magnitude and angle: /// <div lang='latex'>\begin{array}{l} \texttt{x} (I) = \texttt{magnitude} (I) \cos ( \texttt{angle} (I)) \\ \texttt{y} (I) = \texttt{magnitude} (I) \sin ( \texttt{angle} (I)) \\ \end{array}</div> /// /// The relative accuracy of the estimated coordinates is about 1e-6. /// ## Parameters /// * magnitude: input floating-point array of magnitudes of 2D vectors; /// it can be an empty matrix (=Mat()), in this case, the function assumes /// that all the magnitudes are =1; if it is not empty, it must have the /// same size and type as angle. /// * angle: input floating-point array of angles of 2D vectors. /// * x: output array of x-coordinates of 2D vectors; it has the same /// size and type as angle. /// * y: output array of y-coordinates of 2D vectors; it has the same /// size and type as angle. /// * angleInDegrees: when true, the input angles are measured in /// degrees, otherwise, they are measured in radians. /// ## See also /// cartToPolar, magnitude, phase, exp, log, pow, sqrt /// /// ## C++ default parameters /// * angle_in_degrees: false pub fn polar_to_cart(magnitude: &core::Mat, angle: &core::Mat, x: &mut core::Mat, y: &mut core::Mat, angle_in_degrees: bool) -> Result<()> { unsafe { sys::cv_polarToCart_Mat_Mat_Mat_Mat_bool(magnitude.as_raw_Mat(), angle.as_raw_Mat(), x.as_raw_Mat(), y.as_raw_Mat(), angle_in_degrees) }.into_result() } /// Raises every array element to a power. /// /// The function cv::pow raises every element of the input array to power : /// <div lang='latex'>\texttt{dst} (I) = \fork{\texttt{src}(I)^{power}}{if \(\texttt{power}\) is integer}{|\texttt{src}(I)|^{power}}{otherwise}</div> /// /// So, for a non-integer power exponent, the absolute values of input array /// elements are used. However, it is possible to get true values for /// negative values using some extra operations. In the example below, /// computing the 5th root of array src shows: /// ```ignore{.cpp} /// Mat mask = src < 0; /// pow(src, 1./5, dst); /// subtract(Scalar::all(0), dst, dst, mask); /// ``` /// /// For some values of power, such as integer values, 0.5 and -0.5, /// specialized faster algorithms are used. /// /// Special values (NaN, Inf) are not handled. /// ## Parameters /// * src: input array. /// * power: exponent of power. /// * dst: output array of the same size and type as src. /// ## See also /// sqrt, exp, log, cartToPolar, polarToCart pub fn pow(src: &core::Mat, power: f64, dst: &mut core::Mat) -> Result<()> { unsafe { sys::cv_pow_Mat_double_Mat(src.as_raw_Mat(), power, dst.as_raw_Mat()) }.into_result() } /// Fills the array with normally distributed random numbers. /// /// The function cv::randn fills the matrix dst with normally distributed random numbers with the specified /// mean vector and the standard deviation matrix. The generated random numbers are clipped to fit the /// value range of the output array data type. /// ## Parameters /// * dst: output array of random numbers; the array must be pre-allocated and have 1 to 4 channels. /// * mean: mean value (expectation) of the generated random numbers. /// * stddev: standard deviation of the generated random numbers; it can be either a vector (in /// which case a diagonal standard deviation matrix is assumed) or a square matrix. /// ## See also /// RNG, randu pub fn randn(dst: &mut core::Mat, mean: &core::Mat, stddev: &core::Mat) -> Result<()> { unsafe { sys::cv_randn_Mat_Mat_Mat(dst.as_raw_Mat(), mean.as_raw_Mat(), stddev.as_raw_Mat()) }.into_result() } /// Generates a single uniformly-distributed random number or an array of random numbers. /// /// Non-template variant of the function fills the matrix dst with uniformly-distributed /// random numbers from the specified range: /// <div lang='latex'>\texttt{low} _c \leq \texttt{dst} (I)_c < \texttt{high} _c</div> /// ## Parameters /// * dst: output array of random numbers; the array must be pre-allocated. /// * low: inclusive lower boundary of the generated random numbers. /// * high: exclusive upper boundary of the generated random numbers. /// ## See also /// RNG, randn, theRNG pub fn randu(dst: &mut core::Mat, low: &core::Mat, high: &core::Mat) -> Result<()> { unsafe { sys::cv_randu_Mat_Mat_Mat(dst.as_raw_Mat(), low.as_raw_Mat(), high.as_raw_Mat()) }.into_result() } /// Reduces a matrix to a vector. /// /// The function #reduce reduces the matrix to a vector by treating the matrix rows/columns as a set of /// 1D vectors and performing the specified operation on the vectors until a single row/column is /// obtained. For example, the function can be used to compute horizontal and vertical projections of a /// raster image. In case of #REDUCE_MAX and #REDUCE_MIN , the output image should have the same type as the source one. /// In case of #REDUCE_SUM and #REDUCE_AVG , the output may have a larger element bit-depth to preserve accuracy. /// And multi-channel arrays are also supported in these two reduction modes. /// /// The following code demonstrates its usage for a single channel matrix. /// @snippet snippets/core_reduce.cpp example /// /// And the following code demonstrates its usage for a two-channel matrix. /// @snippet snippets/core_reduce.cpp example2 /// /// ## Parameters /// * src: input 2D matrix. /// * dst: output vector. Its size and type is defined by dim and dtype parameters. /// * dim: dimension index along which the matrix is reduced. 0 means that the matrix is reduced to /// a single row. 1 means that the matrix is reduced to a single column. /// * rtype: reduction operation that could be one of #ReduceTypes /// * dtype: when negative, the output vector will have the same type as the input matrix, /// otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()). /// ## See also /// repeat /// /// ## C++ default parameters /// * dtype: -1 pub fn reduce(src: &core::Mat, dst: &mut core::Mat, dim: i32, rtype: i32, dtype: i32) -> Result<()> { unsafe { sys::cv_reduce_Mat_Mat_int_int_int(src.as_raw_Mat(), dst.as_raw_Mat(), dim, rtype, dtype) }.into_result() } /// Fills the output array with repeated copies of the input array. /// /// The function cv::repeat duplicates the input array one or more times along each of the two axes: /// <div lang='latex'>\texttt{dst} _{ij}= \texttt{src} _{i\mod src.rows, \; j\mod src.cols }</div> /// The second variant of the function is more convenient to use with @ref MatrixExpressions. /// ## Parameters /// * src: input array to replicate. /// * ny: Flag to specify how many times the `src` is repeated along the /// vertical axis. /// * nx: Flag to specify how many times the `src` is repeated along the /// horizontal axis. /// * dst: output array of the same type as `src`. /// ## See also /// cv::reduce /// /// ## Overloaded parameters /// /// * src: input array to replicate. /// * ny: Flag to specify how many times the `src` is repeated along the /// vertical axis. /// * nx: Flag to specify how many times the `src` is repeated along the /// horizontal axis. pub fn repeat(src: &core::Mat, ny: i32, nx: i32) -> Result<core::Mat> { unsafe { sys::cv_repeat_Mat_int_int(src.as_raw_Mat(), ny, nx) }.into_result().map(|ptr| core::Mat { ptr }) } /// Fills the output array with repeated copies of the input array. /// /// The function cv::repeat duplicates the input array one or more times along each of the two axes: /// <div lang='latex'>\texttt{dst} _{ij}= \texttt{src} _{i\mod src.rows, \; j\mod src.cols }</div> /// The second variant of the function is more convenient to use with @ref MatrixExpressions. /// ## Parameters /// * src: input array to replicate. /// * ny: Flag to specify how many times the `src` is repeated along the /// vertical axis. /// * nx: Flag to specify how many times the `src` is repeated along the /// horizontal axis. /// * dst: output array of the same type as `src`. /// ## See also /// cv::reduce pub fn repeat_to(src: &core::Mat, ny: i32, nx: i32, dst: &mut core::Mat) -> Result<()> { unsafe { sys::cv_repeat_Mat_int_int_Mat(src.as_raw_Mat(), ny, nx, dst.as_raw_Mat()) }.into_result() } /// Rotates a 2D array in multiples of 90 degrees. /// The function cv::rotate rotates the array in one of three different ways: /// Rotate by 90 degrees clockwise (rotateCode = ROTATE_90_CLOCKWISE). /// Rotate by 180 degrees clockwise (rotateCode = ROTATE_180). /// Rotate by 270 degrees clockwise (rotateCode = ROTATE_90_COUNTERCLOCKWISE). /// ## Parameters /// * src: input array. /// * dst: output array of the same type as src. The size is the same with ROTATE_180, /// and the rows and cols are switched for ROTATE_90_CLOCKWISE and ROTATE_90_COUNTERCLOCKWISE. /// * rotateCode: an enum to specify how to rotate the array; see the enum #RotateFlags /// ## See also /// transpose , repeat , completeSymm, flip, RotateFlags pub fn rotate(src: &core::Mat, dst: &mut core::Mat, rotate_code: i32) -> Result<()> { unsafe { sys::cv_rotate_Mat_Mat_int(src.as_raw_Mat(), dst.as_raw_Mat(), rotate_code) }.into_result() } /// Round first value up to the nearest multiple of second value. /// /// Use this function instead of `ceil((float)a / b) * b` expressions. /// /// ## See also /// divUp pub fn round_up(a: i32, b: u32) -> Result<i32> { unsafe { sys::cv_roundUp_int_unsigned_int(a, b) }.into_result() } /// Round first value up to the nearest multiple of second value. /// /// Use this function instead of `ceil((float)a / b) * b` expressions. /// /// ## See also /// divUp /// /// ## Overloaded parameters pub fn round_up_1(a: size_t, b: u32) -> Result<size_t> { unsafe { sys::cv_roundUp_size_t_unsigned_int(a, b) }.into_result() } /// Override search data path by adding new search location /// /// Use this only to override default behavior /// Passed paths are used in LIFO order. /// /// ## Parameters /// * path: Path to used samples data pub fn add_samples_data_search_path(path: &str) -> Result<()> { string_arg!(path); unsafe { sys::cv_samples_addSamplesDataSearchPath_String(path.as_ptr()) }.into_result() } /// Append samples search data sub directory /// /// General usage is to add OpenCV modules name (`<opencv_contrib>/modules/<name>/samples/data` -> `<name>/samples/data` + `modules/<name>/samples/data`). /// Passed subdirectories are used in LIFO order. /// /// ## Parameters /// * subdir: samples data sub directory pub fn add_samples_data_search_sub_directory(subdir: &str) -> Result<()> { string_arg!(subdir); unsafe { sys::cv_samples_addSamplesDataSearchSubDirectory_String(subdir.as_ptr()) }.into_result() } /// /// ## C++ default parameters /// * silent_mode: false pub fn find_file_or_keep(relative_path: &str, silent_mode: bool) -> Result<String> { string_arg!(relative_path); unsafe { sys::cv_samples_findFileOrKeep_String_bool(relative_path.as_ptr(), silent_mode) }.into_result().map(crate::templ::receive_string_mut) } /// Try to find requested data file /// /// Search directories: /// /// 1. Directories passed via `addSamplesDataSearchPath()` /// 2. OPENCV_SAMPLES_DATA_PATH_HINT environment variable /// 3. OPENCV_SAMPLES_DATA_PATH environment variable /// If parameter value is not empty and nothing is found then stop searching. /// 4. Detects build/install path based on: /// a. current working directory (CWD) /// b. and/or binary module location (opencv_core/opencv_world, doesn't work with static linkage) /// 5. Scan `<source>/{,data,samples/data}` directories if build directory is detected or the current directory is in source tree. /// 6. Scan `<install>/share/OpenCV` directory if install directory is detected. /// /// @see cv::utils::findDataFile /// /// ## Parameters /// * relative_path: Relative path to data file /// * required: Specify "file not found" handling. /// If true, function prints information message and raises cv::Exception. /// If false, function returns empty result /// * silentMode: Disables messages /// ## Returns /// Returns path (absolute or relative to the current directory) or empty string if file is not found /// /// ## C++ default parameters /// * required: true /// * silent_mode: false pub fn find_file(relative_path: &str, required: bool, silent_mode: bool) -> Result<String> { string_arg!(relative_path); unsafe { sys::cv_samples_findFile_String_bool_bool(relative_path.as_ptr(), required, silent_mode) }.into_result().map(crate::templ::receive_string_mut) } /// Calculates the sum of a scaled array and another array. /// /// The function scaleAdd is one of the classical primitive linear algebra operations, known as DAXPY /// or SAXPY in [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms). It calculates /// the sum of a scaled array and another array: /// <div lang='latex'>\texttt{dst} (I)= \texttt{scale} \cdot \texttt{src1} (I) + \texttt{src2} (I)</div> /// The function can also be emulated with a matrix expression, for example: /// ```ignore{.cpp} /// Mat A(3, 3, CV_64F); /// ... /// A.row(0) = A.row(1)*2 + A.row(2); /// ``` /// /// ## Parameters /// * src1: first input array. /// * alpha: scale factor for the first array. /// * src2: second input array of the same size and type as src1. /// * dst: output array of the same size and type as src1. /// ## See also /// add, addWeighted, subtract, Mat::dot, Mat::convertTo pub fn scale_add(src1: &core::Mat, alpha: f64, src2: &core::Mat, dst: &mut core::Mat) -> Result<()> { unsafe { sys::cv_scaleAdd_Mat_double_Mat_Mat(src1.as_raw_Mat(), alpha, src2.as_raw_Mat(), dst.as_raw_Mat()) }.into_result() } /// Sets/resets the break-on-error mode. /// /// When the break-on-error mode is set, the default error handler issues a hardware exception, which /// can make debugging more convenient. /// /// \return the previous state pub fn set_break_on_error(flag: bool) -> Result<bool> { unsafe { sys::cv_setBreakOnError_bool(flag) }.into_result() } /// Initializes a scaled identity matrix. /// /// The function cv::setIdentity initializes a scaled identity matrix: /// <div lang='latex'>\texttt{mtx} (i,j)= \fork{\texttt{value}}{ if \(i=j\)}{0}{otherwise}</div> /// /// The function can also be emulated using the matrix initializers and the /// matrix expressions: /// ```ignore /// Mat A = Mat::eye(4, 3, CV_32F)*5; /// // A will be set to [[5, 0, 0], [0, 5, 0], [0, 0, 5], [0, 0, 0]] /// ``` /// /// ## Parameters /// * mtx: matrix to initialize (not necessarily square). /// * s: value to assign to diagonal elements. /// ## See also /// Mat::zeros, Mat::ones, Mat::setTo, Mat::operator= /// /// ## C++ default parameters /// * s: Scalar(1) pub fn set_identity(mtx: &mut core::Mat, s: core::Scalar) -> Result<()> { unsafe { sys::cv_setIdentity_Mat_Scalar(mtx.as_raw_Mat(), s) }.into_result() } /// OpenCV will try to set the number of threads for the next parallel region. /// /// If threads == 0, OpenCV will disable threading optimizations and run all it's functions /// sequentially. Passing threads \< 0 will reset threads number to system default. This function must /// be called outside of parallel region. /// /// OpenCV will try to run its functions with specified threads number, but some behaviour differs from /// framework: /// * `TBB` - User-defined parallel constructions will run with the same threads number, if /// another is not specified. If later on user creates his own scheduler, OpenCV will use it. /// * `OpenMP` - No special defined behaviour. /// * `Concurrency` - If threads == 1, OpenCV will disable threading optimizations and run its /// functions sequentially. /// * `GCD` - Supports only values \<= 0. /// * `C=` - No special defined behaviour. /// ## Parameters /// * nthreads: Number of threads used by OpenCV. /// ## See also /// getNumThreads, getThreadNum pub fn set_num_threads(nthreads: i32) -> Result<()> { unsafe { sys::cv_setNumThreads_int(nthreads) }.into_result() } /// Sets state of default random number generator. /// /// The function cv::setRNGSeed sets state of default random number generator to custom value. /// ## Parameters /// * seed: new state for default random number generator /// ## See also /// RNG, randu, randn pub fn set_rng_seed(seed: i32) -> Result<()> { unsafe { sys::cv_setRNGSeed_int(seed) }.into_result() } pub fn set_use_open_vx(flag: bool) -> Result<()> { unsafe { sys::cv_setUseOpenVX_bool(flag) }.into_result() } /// Enables or disables the optimized code. /// /// The function can be used to dynamically turn on and off optimized dispatched code (code that uses SSE4.2, AVX/AVX2, /// and other instructions on the platforms that support it). It sets a global flag that is further /// checked by OpenCV functions. Since the flag is not checked in the inner OpenCV loops, it is only /// safe to call the function on the very top level in your application where you can be sure that no /// other OpenCV function is currently executed. /// /// By default, the optimized code is enabled unless you disable it in CMake. The current status can be /// retrieved using useOptimized. /// ## Parameters /// * onoff: The boolean flag specifying whether the optimized code should be used (onoff=true) /// or not (onoff=false). pub fn set_use_optimized(onoff: bool) -> Result<()> { unsafe { sys::cv_setUseOptimized_bool(onoff) }.into_result() } /// Finds the real roots of a cubic equation. /// /// The function solveCubic finds the real roots of a cubic equation: /// * if coeffs is a 4-element vector: /// <div lang='latex'>\texttt{coeffs} [0] x^3 + \texttt{coeffs} [1] x^2 + \texttt{coeffs} [2] x + \texttt{coeffs} [3] = 0</div> /// * if coeffs is a 3-element vector: /// <div lang='latex'>x^3 + \texttt{coeffs} [0] x^2 + \texttt{coeffs} [1] x + \texttt{coeffs} [2] = 0</div> /// /// The roots are stored in the roots array. /// ## Parameters /// * coeffs: equation coefficients, an array of 3 or 4 elements. /// * roots: output array of real roots that has 1 or 3 elements. /// ## Returns /// number of real roots. It can be 0, 1 or 2. pub fn solve_cubic(coeffs: &core::Mat, roots: &mut core::Mat) -> Result<i32> { unsafe { sys::cv_solveCubic_Mat_Mat(coeffs.as_raw_Mat(), roots.as_raw_Mat()) }.into_result() } /// Solve given (non-integer) linear programming problem using the Simplex Algorithm (Simplex Method). /// /// What we mean here by "linear programming problem" (or LP problem, for short) can be formulated as: /// /// <div lang='latex'>\mbox{Maximize } c\cdot x\\ /// \mbox{Subject to:}\\ /// Ax\leq b\\ /// x\geq 0</div> /// /// Where <span lang='latex'>c</span> is fixed `1`-by-`n` row-vector, <span lang='latex'>A</span> is fixed `m`-by-`n` matrix, <span lang='latex'>b</span> is fixed `m`-by-`1` /// column vector and <span lang='latex'>x</span> is an arbitrary `n`-by-`1` column vector, which satisfies the constraints. /// /// Simplex algorithm is one of many algorithms that are designed to handle this sort of problems /// efficiently. Although it is not optimal in theoretical sense (there exist algorithms that can solve /// any problem written as above in polynomial time, while simplex method degenerates to exponential /// time for some special cases), it is well-studied, easy to implement and is shown to work well for /// real-life purposes. /// /// The particular implementation is taken almost verbatim from **Introduction to Algorithms, third /// edition** by T. H. Cormen, C. E. Leiserson, R. L. Rivest and Clifford Stein. In particular, the /// Bland's rule <http://en.wikipedia.org/wiki/Bland%27s_rule> is used to prevent cycling. /// /// ## Parameters /// * Func: This row-vector corresponds to <span lang='latex'>c</span> in the LP problem formulation (see above). It should /// contain 32- or 64-bit floating point numbers. As a convenience, column-vector may be also submitted, /// in the latter case it is understood to correspond to <span lang='latex'>c^T</span>. /// * Constr: `m`-by-`n+1` matrix, whose rightmost column corresponds to <span lang='latex'>b</span> in formulation above /// and the remaining to <span lang='latex'>A</span>. It should contain 32- or 64-bit floating point numbers. /// * z: The solution will be returned here as a column-vector - it corresponds to <span lang='latex'>c</span> in the /// formulation above. It will contain 64-bit floating point numbers. /// ## Returns /// One of cv::SolveLPResult pub fn solve_lp(func: &core::Mat, constr: &core::Mat, z: &mut core::Mat) -> Result<i32> { unsafe { sys::cv_solveLP_Mat_Mat_Mat(func.as_raw_Mat(), constr.as_raw_Mat(), z.as_raw_Mat()) }.into_result() } /// Finds the real or complex roots of a polynomial equation. /// /// The function cv::solvePoly finds real and complex roots of a polynomial equation: /// <div lang='latex'>\texttt{coeffs} [n] x^{n} + \texttt{coeffs} [n-1] x^{n-1} + ... + \texttt{coeffs} [1] x + \texttt{coeffs} [0] = 0</div> /// ## Parameters /// * coeffs: array of polynomial coefficients. /// * roots: output (complex) array of roots. /// * maxIters: maximum number of iterations the algorithm does. /// /// ## C++ default parameters /// * max_iters: 300 pub fn solve_poly(coeffs: &core::Mat, roots: &mut core::Mat, max_iters: i32) -> Result<f64> { unsafe { sys::cv_solvePoly_Mat_Mat_int(coeffs.as_raw_Mat(), roots.as_raw_Mat(), max_iters) }.into_result() } /// Solves one or more linear systems or least-squares problems. /// /// The function cv::solve solves a linear system or least-squares problem (the /// latter is possible with SVD or QR methods, or by specifying the flag /// #DECOMP_NORMAL ): /// <div lang='latex'>\texttt{dst} = \arg \min _X \| \texttt{src1} \cdot \texttt{X} - \texttt{src2} \|</div> /// /// If #DECOMP_LU or #DECOMP_CHOLESKY method is used, the function returns 1 /// if src1 (or <span lang='latex'>\texttt{src1}^T\texttt{src1}</span> ) is non-singular. Otherwise, /// it returns 0. In the latter case, dst is not valid. Other methods find a /// pseudo-solution in case of a singular left-hand side part. /// /// /// Note: If you want to find a unity-norm solution of an under-defined /// singular system <span lang='latex'>\texttt{src1}\cdot\texttt{dst}=0</span> , the function solve /// will not do the work. Use SVD::solveZ instead. /// /// ## Parameters /// * src1: input matrix on the left-hand side of the system. /// * src2: input matrix on the right-hand side of the system. /// * dst: output solution. /// * flags: solution (matrix inversion) method (#DecompTypes) /// ## See also /// invert, SVD, eigen /// /// ## C++ default parameters /// * flags: DECOMP_LU pub fn solve(src1: &core::Mat, src2: &core::Mat, dst: &mut core::Mat, flags: i32) -> Result<bool> { unsafe { sys::cv_solve_Mat_Mat_Mat_int(src1.as_raw_Mat(), src2.as_raw_Mat(), dst.as_raw_Mat(), flags) }.into_result() } /// Sorts each row or each column of a matrix. /// /// The function cv::sortIdx sorts each matrix row or each matrix column in the /// ascending or descending order. So you should pass two operation flags to /// get desired behaviour. Instead of reordering the elements themselves, it /// stores the indices of sorted elements in the output array. For example: /// ```ignore /// Mat A = Mat::eye(3,3,CV_32F), B; /// sortIdx(A, B, SORT_EVERY_ROW + SORT_ASCENDING); /// // B will probably contain /// // (because of equal elements in A some permutations are possible): /// // [[1, 2, 0], [0, 2, 1], [0, 1, 2]] /// ``` /// /// ## Parameters /// * src: input single-channel array. /// * dst: output integer array of the same size as src. /// * flags: operation flags that could be a combination of cv::SortFlags /// ## See also /// sort, randShuffle pub fn sort_idx(src: &core::Mat, dst: &mut core::Mat, flags: i32) -> Result<()> { unsafe { sys::cv_sortIdx_Mat_Mat_int(src.as_raw_Mat(), dst.as_raw_Mat(), flags) }.into_result() } /// Sorts each row or each column of a matrix. /// /// The function cv::sort sorts each matrix row or each matrix column in /// ascending or descending order. So you should pass two operation flags to /// get desired behaviour. If you want to sort matrix rows or columns /// lexicographically, you can use STL std::sort generic function with the /// proper comparison predicate. /// /// ## Parameters /// * src: input single-channel array. /// * dst: output array of the same size and type as src. /// * flags: operation flags, a combination of #SortFlags /// ## See also /// sortIdx, randShuffle pub fn sort(src: &core::Mat, dst: &mut core::Mat, flags: i32) -> Result<()> { unsafe { sys::cv_sort_Mat_Mat_int(src.as_raw_Mat(), dst.as_raw_Mat(), flags) }.into_result() } /// Divides a multi-channel array into several single-channel arrays. /// /// The function cv::split splits a multi-channel array into separate single-channel arrays: /// <div lang='latex'>\texttt{mv} [c](I) = \texttt{src} (I)_c</div> /// If you need to extract a single channel or do some other sophisticated channel permutation, use /// mixChannels . /// /// The following example demonstrates how to split a 3-channel matrix into 3 single channel matrices. /// @snippet snippets/core_split.cpp example /// /// ## Parameters /// * src: input multi-channel array. /// * mvbegin: output array; the number of arrays must match src.channels(); the arrays themselves are /// reallocated, if needed. /// ## See also /// merge, mixChannels, cvtColor /// /// ## Overloaded parameters /// /// * m: input multi-channel array. /// * mv: output vector of arrays; the arrays themselves are reallocated, if needed. pub fn split(m: &core::Mat, mv: &mut types::VectorOfMat) -> Result<()> { unsafe { sys::cv_split_Mat_VectorOfMat(m.as_raw_Mat(), mv.as_raw_VectorOfMat()) }.into_result() } /// Calculates a square root of array elements. /// /// The function cv::sqrt calculates a square root of each input array element. /// In case of multi-channel arrays, each channel is processed /// independently. The accuracy is approximately the same as of the built-in /// std::sqrt . /// ## Parameters /// * src: input floating-point array. /// * dst: output array of the same size and type as src. pub fn sqrt(src: &core::Mat, dst: &mut core::Mat) -> Result<()> { unsafe { sys::cv_sqrt_Mat_Mat(src.as_raw_Mat(), dst.as_raw_Mat()) }.into_result() } /// Calculates the per-element difference between two arrays or array and a scalar. /// /// The function subtract calculates: /// - Difference between two arrays, when both input arrays have the same size and the same number of /// channels: /// <div lang='latex'>\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0</div> /// - Difference between an array and a scalar, when src2 is constructed from Scalar or has the same /// number of elements as `src1.channels()`: /// <div lang='latex'>\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2} ) \quad \texttt{if mask}(I) \ne0</div> /// - Difference between a scalar and an array, when src1 is constructed from Scalar or has the same /// number of elements as `src2.channels()`: /// <div lang='latex'>\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} - \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0</div> /// - The reverse difference between a scalar and an array in the case of `SubRS`: /// <div lang='latex'>\texttt{dst}(I) = \texttt{saturate} ( \texttt{src2} - \texttt{src1}(I) ) \quad \texttt{if mask}(I) \ne0</div> /// where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each /// channel is processed independently. /// /// The first function in the list above can be replaced with matrix expressions: /// ```ignore{.cpp} /// dst = src1 - src2; /// dst -= src1; // equivalent to subtract(dst, src1, dst); /// ``` /// /// The input arrays and the output array can all have the same or different depths. For example, you /// can subtract to 8-bit unsigned arrays and store the difference in a 16-bit signed array. Depth of /// the output array is determined by dtype parameter. In the second and third cases above, as well as /// in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this /// case the output array will have the same depth as the input array, be it src1, src2 or both. /// /// Note: Saturation is not applied when the output array has the depth CV_32S. You may even get /// result of an incorrect sign in the case of overflow. /// ## Parameters /// * src1: first input array or a scalar. /// * src2: second input array or a scalar. /// * dst: output array of the same size and the same number of channels as the input array. /// * mask: optional operation mask; this is an 8-bit single channel array that specifies elements /// of the output array to be changed. /// * dtype: optional depth of the output array /// ## See also /// add, addWeighted, scaleAdd, Mat::convertTo /// /// ## C++ default parameters /// * mask: noArray() /// * dtype: -1 pub fn subtract(src1: &core::Mat, src2: &core::Mat, dst: &mut core::Mat, mask: &core::Mat, dtype: i32) -> Result<()> { unsafe { sys::cv_subtract_Mat_Mat_Mat_Mat_int(src1.as_raw_Mat(), src2.as_raw_Mat(), dst.as_raw_Mat(), mask.as_raw_Mat(), dtype) }.into_result() } /// Calculates the sum of array elements. /// /// The function cv::sum calculates and returns the sum of array elements, /// independently for each channel. /// ## Parameters /// * src: input array that must have from 1 to 4 channels. /// ## See also /// countNonZero, mean, meanStdDev, norm, minMaxLoc, reduce pub fn sum(src: &core::Mat) -> Result<core::Scalar> { unsafe { sys::cv_sum_Mat(src.as_raw_Mat()) }.into_result() } /// Swaps two matrices pub fn swap(a: &mut core::Mat, b: &mut core::Mat) -> Result<()> { unsafe { sys::cv_swap_Mat_Mat(a.as_raw_Mat(), b.as_raw_Mat()) }.into_result() } /// Swaps two matrices /// /// ## Overloaded parameters pub fn swap_umat(a: &mut core::UMat, b: &mut core::UMat) -> Result<()> { unsafe { sys::cv_swap_UMat_UMat(a.as_raw_UMat(), b.as_raw_UMat()) }.into_result() } /// /// ## C++ default parameters /// * suffix: 0 pub fn tempfile(suffix: &str) -> Result<String> { string_arg!(suffix); unsafe { sys::cv_tempfile_const_char_X(suffix.as_ptr()) }.into_result().map(crate::templ::receive_string_mut) } /// Returns the trace of a matrix. /// /// The function cv::trace returns the sum of the diagonal elements of the /// matrix mtx . /// <div lang='latex'>\mathrm{tr} ( \texttt{mtx} ) = \sum _i \texttt{mtx} (i,i)</div> /// ## Parameters /// * mtx: input matrix. pub fn trace(mtx: &core::Mat) -> Result<core::Scalar> { unsafe { sys::cv_trace_Mat(mtx.as_raw_Mat()) }.into_result() } /// Performs the matrix transformation of every array element. /// /// The function cv::transform performs the matrix transformation of every /// element of the array src and stores the results in dst : /// <div lang='latex'>\texttt{dst} (I) = \texttt{m} \cdot \texttt{src} (I)</div> /// (when m.cols=src.channels() ), or /// <div lang='latex'>\texttt{dst} (I) = \texttt{m} \cdot [ \texttt{src} (I); 1]</div> /// (when m.cols=src.channels()+1 ) /// /// Every element of the N -channel array src is interpreted as N -element /// vector that is transformed using the M x N or M x (N+1) matrix m to /// M-element vector - the corresponding element of the output array dst . /// /// The function may be used for geometrical transformation of /// N -dimensional points, arbitrary linear color space transformation (such /// as various kinds of RGB to YUV transforms), shuffling the image /// channels, and so forth. /// ## Parameters /// * src: input array that must have as many channels (1 to 4) as /// m.cols or m.cols-1. /// * dst: output array of the same size and depth as src; it has as /// many channels as m.rows. /// * m: transformation 2x2 or 2x3 floating-point matrix. /// ## See also /// perspectiveTransform, getAffineTransform, estimateAffine2D, warpAffine, warpPerspective pub fn transform(src: &core::Mat, dst: &mut core::Mat, m: &core::Mat) -> Result<()> { unsafe { sys::cv_transform_Mat_Mat_Mat(src.as_raw_Mat(), dst.as_raw_Mat(), m.as_raw_Mat()) }.into_result() } /// Transposes a matrix. /// /// The function cv::transpose transposes the matrix src : /// <div lang='latex'>\texttt{dst} (i,j) = \texttt{src} (j,i)</div> /// /// Note: No complex conjugation is done in case of a complex matrix. It /// should be done separately if needed. /// ## Parameters /// * src: input array. /// * dst: output array of the same type as src. pub fn transpose(src: &core::Mat, dst: &mut core::Mat) -> Result<()> { unsafe { sys::cv_transpose_Mat_Mat(src.as_raw_Mat(), dst.as_raw_Mat()) }.into_result() } /// Returns string of cv::Mat depth value: CV_8UC3 -> "CV_8UC3" or "<invalid type>" pub fn type_to_string(_type: i32) -> Result<String> { unsafe { sys::cv_typeToString_int(_type) }.into_result().map(crate::templ::receive_string) } pub fn use_open_vx() -> Result<bool> { unsafe { sys::cv_useOpenVX() }.into_result() } /// Returns the status of optimized code usage. /// /// The function returns true if the optimized code is enabled. Otherwise, it returns false. pub fn use_optimized() -> Result<bool> { unsafe { sys::cv_useOptimized() }.into_result() } pub fn dump_input_array_of_arrays(argument: &types::VectorOfMat) -> Result<String> { unsafe { sys::cv_utils_dumpInputArrayOfArrays_VectorOfMat(argument.as_raw_VectorOfMat()) }.into_result().map(crate::templ::receive_string_mut) } pub fn dump_input_array(argument: &core::Mat) -> Result<String> { unsafe { sys::cv_utils_dumpInputArray_Mat(argument.as_raw_Mat()) }.into_result().map(crate::templ::receive_string_mut) } pub fn dump_input_output_array_of_arrays(argument: &mut types::VectorOfMat) -> Result<String> { unsafe { sys::cv_utils_dumpInputOutputArrayOfArrays_VectorOfMat(argument.as_raw_VectorOfMat()) }.into_result().map(crate::templ::receive_string_mut) } pub fn dump_input_output_array(argument: &mut core::Mat) -> Result<String> { unsafe { sys::cv_utils_dumpInputOutputArray_Mat(argument.as_raw_Mat()) }.into_result().map(crate::templ::receive_string_mut) } pub fn get_thread_id() -> Result<i32> { unsafe { sys::cv_utils_getThreadID() }.into_result() } /// Converts VASurfaceID object to OutputArray. /// ## Parameters /// * display: - VADisplay object. /// * surface: - source VASurfaceID object. /// * size: - size of image represented by VASurfaceID object. /// * dst: - destination OutputArray. pub fn convert_from_va_surface(display: &mut c_void, surface: u32, size: core::Size, dst: &mut core::Mat) -> Result<()> { unsafe { sys::cv_va_intel_convertFromVASurface_void_X_unsigned_int_Size_Mat(display, surface, size, dst.as_raw_Mat()) }.into_result() } /// Converts InputArray to VASurfaceID object. /// ## Parameters /// * display: - VADisplay object. /// * src: - source InputArray. /// * surface: - destination VASurfaceID object. /// * size: - size of image represented by VASurfaceID object. pub fn convert_to_va_surface(display: &mut c_void, src: &core::Mat, surface: u32, size: core::Size) -> Result<()> { unsafe { sys::cv_va_intel_convertToVASurface_void_X_Mat_unsigned_int_Size(display, src.as_raw_Mat(), surface, size) }.into_result() } /// Applies vertical concatenation to given matrices. /// /// The function vertically concatenates two or more cv::Mat matrices (with the same number of cols). /// ```ignore{.cpp} /// cv::Mat matArray[] = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)), /// cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)), /// cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),}; /// /// cv::Mat out; /// cv::vconcat( matArray, 3, out ); /// //out: /// //[1, 1, 1, 1; /// // 2, 2, 2, 2; /// // 3, 3, 3, 3] /// ``` /// /// ## Parameters /// * src: input array or vector of matrices. all of the matrices must have the same number of cols and the same depth. /// * nsrc: number of matrices in src. /// * dst: output array. It has the same number of cols and depth as the src, and the sum of rows of the src. /// ## See also /// cv::hconcat(const Mat*, size_t, OutputArray), cv::hconcat(InputArrayOfArrays, OutputArray) and cv::hconcat(InputArray, InputArray, OutputArray) /// /// ## Overloaded parameters /// /// ```ignore{.cpp} /// cv::Mat_<float> A = (cv::Mat_<float>(3, 2) << 1, 7, /// 2, 8, /// 3, 9); /// cv::Mat_<float> B = (cv::Mat_<float>(3, 2) << 4, 10, /// 5, 11, /// 6, 12); /// /// cv::Mat C; /// cv::vconcat(A, B, C); /// //C: /// //[1, 7; /// // 2, 8; /// // 3, 9; /// // 4, 10; /// // 5, 11; /// // 6, 12] /// ``` /// /// * src1: first input array to be considered for vertical concatenation. /// * src2: second input array to be considered for vertical concatenation. /// * dst: output array. It has the same number of cols and depth as the src1 and src2, and the sum of rows of the src1 and src2. pub fn vconcat_2(src1: &core::Mat, src2: &core::Mat, dst: &mut core::Mat) -> Result<()> { unsafe { sys::cv_vconcat_Mat_Mat_Mat(src1.as_raw_Mat(), src2.as_raw_Mat(), dst.as_raw_Mat()) }.into_result() } /// Applies vertical concatenation to given matrices. /// /// The function vertically concatenates two or more cv::Mat matrices (with the same number of cols). /// ```ignore{.cpp} /// cv::Mat matArray[] = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)), /// cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)), /// cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),}; /// /// cv::Mat out; /// cv::vconcat( matArray, 3, out ); /// //out: /// //[1, 1, 1, 1; /// // 2, 2, 2, 2; /// // 3, 3, 3, 3] /// ``` /// /// ## Parameters /// * src: input array or vector of matrices. all of the matrices must have the same number of cols and the same depth. /// * nsrc: number of matrices in src. /// * dst: output array. It has the same number of cols and depth as the src, and the sum of rows of the src. /// ## See also /// cv::hconcat(const Mat*, size_t, OutputArray), cv::hconcat(InputArrayOfArrays, OutputArray) and cv::hconcat(InputArray, InputArray, OutputArray) /// /// ## Overloaded parameters /// /// ```ignore{.cpp} /// std::vector<cv::Mat> matrices = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)), /// cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)), /// cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),}; /// /// cv::Mat out; /// cv::vconcat( matrices, out ); /// //out: /// //[1, 1, 1, 1; /// // 2, 2, 2, 2; /// // 3, 3, 3, 3] /// ``` /// /// * src: input array or vector of matrices. all of the matrices must have the same number of cols and the same depth /// * dst: output array. It has the same number of cols and depth as the src, and the sum of rows of the src. /// same depth. pub fn vconcat(src: &types::VectorOfMat, dst: &mut core::Mat) -> Result<()> { unsafe { sys::cv_vconcat_VectorOfMat_Mat(src.as_raw_VectorOfMat(), dst.as_raw_Mat()) }.into_result() } // Generating impl for trait cv::Algorithm (trait) /// This is a base class for all more or less complex algorithms in OpenCV /// /// especially for classes of algorithms, for which there can be multiple implementations. The examples /// are stereo correspondence (for which there are algorithms like block matching, semi-global block /// matching, graph-cut etc.), background subtraction (which can be done using mixture-of-gaussians /// models, codebook-based algorithm etc.), optical flow (block matching, Lucas-Kanade, Horn-Schunck /// etc.). /// /// Here is example of SimpleBlobDetector use in your application via Algorithm interface: /// @snippet snippets/core_various.cpp Algorithm pub trait Algorithm { #[inline(always)] fn as_raw_Algorithm(&self) -> *mut c_void; /// Clears the algorithm state fn clear(&mut self) -> Result<()> { unsafe { sys::cv_Algorithm_clear(self.as_raw_Algorithm()) }.into_result() } /// Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read fn empty(&self) -> Result<bool> { unsafe { sys::cv_Algorithm_empty_const(self.as_raw_Algorithm()) }.into_result() } /// Saves the algorithm to a file. /// In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs). fn save(&self, filename: &str) -> Result<()> { string_arg!(filename); unsafe { sys::cv_Algorithm_save_const_String(self.as_raw_Algorithm(), filename.as_ptr()) }.into_result() } /// Returns the algorithm string identifier. /// This string is used as top level xml/yml node tag when the object is saved to a file or string. fn get_default_name(&self) -> Result<String> { unsafe { sys::cv_Algorithm_getDefaultName_const(self.as_raw_Algorithm()) }.into_result().map(crate::templ::receive_string_mut) } } // boxed class cv::AutoLock pub struct AutoLock { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::AutoLock { fn drop(&mut self) { unsafe { sys::cv_AutoLock_delete(self.ptr) }; } } impl core::AutoLock { #[inline(always)] pub fn as_raw_AutoLock(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for AutoLock {} impl AutoLock { } // Generating impl for trait cv::BufferPoolController (trait) pub trait BufferPoolController { #[inline(always)] fn as_raw_BufferPoolController(&self) -> *mut c_void; fn get_reserved_size(&self) -> Result<size_t> { unsafe { sys::cv_BufferPoolController_getReservedSize_const(self.as_raw_BufferPoolController()) }.into_result() } fn get_max_reserved_size(&self) -> Result<size_t> { unsafe { sys::cv_BufferPoolController_getMaxReservedSize_const(self.as_raw_BufferPoolController()) }.into_result() } fn set_max_reserved_size(&mut self, size: size_t) -> Result<()> { unsafe { sys::cv_BufferPoolController_setMaxReservedSize_size_t(self.as_raw_BufferPoolController(), size) }.into_result() } fn free_all_reserved_buffers(&mut self) -> Result<()> { unsafe { sys::cv_BufferPoolController_freeAllReservedBuffers(self.as_raw_BufferPoolController()) }.into_result() } } // boxed class cv::CommandLineParser /// Designed for command line parsing /// /// The sample below demonstrates how to use CommandLineParser: /// ```ignore /// CommandLineParser parser(argc, argv, keys); /// parser.about("Application name v1.0.0"); /// /// if (parser.has("help")) /// { /// parser.printMessage(); /// return 0; /// } /// /// int N = parser.get<int>("N"); /// double fps = parser.get<double>("fps"); /// String path = parser.get<String>("path"); /// /// use_time_stamp = parser.has("timestamp"); /// /// String img1 = parser.get<String>(0); /// String img2 = parser.get<String>(1); /// /// int repeat = parser.get<int>(2); /// /// if (!parser.check()) /// { /// parser.printErrors(); /// return 0; /// } /// ``` /// /// /// ### Keys syntax /// /// The keys parameter is a string containing several blocks, each one is enclosed in curly braces and /// describes one argument. Each argument contains three parts separated by the `|` symbol: /// /// -# argument names is a space-separated list of option synonyms (to mark argument as positional, prefix it with the `@` symbol) /// -# default value will be used if the argument was not provided (can be empty) /// -# help message (can be empty) /// /// For example: /// /// ```ignore{.cpp} /// const String keys = /// "{help h usage ? | | print this message }" /// "{@image1 | | image1 for compare }" /// "{@image2 |<none>| image2 for compare }" /// "{@repeat |1 | number }" /// "{path |. | path to file }" /// "{fps | -1.0 | fps for output video }" /// "{N count |100 | count of objects }" /// "{ts timestamp | | use time stamp }" /// ; /// } /// ``` /// /// /// Note that there are no default values for `help` and `timestamp` so we can check their presence using the `has()` method. /// Arguments with default values are considered to be always present. Use the `get()` method in these cases to check their /// actual value instead. /// /// String keys like `get<String>("@image1")` return the empty string `""` by default - even with an empty default value. /// Use the special `<none>` default value to enforce that the returned string must not be empty. (like in `get<String>("@image2")`) /// /// ### Usage /// /// For the described keys: /// /// ```ignore{.sh} /// # Good call (3 positional parameters: image1, image2 and repeat; N is 200, ts is true) /// $ ./app -N=200 1.png 2.jpg 19 -ts /// /// # Bad call /// $ ./app -fps=aaa /// ERRORS: /// Parameter 'fps': can not convert: [aaa] to [double] /// ``` pub struct CommandLineParser { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::CommandLineParser { fn drop(&mut self) { unsafe { sys::cv_CommandLineParser_delete(self.ptr) }; } } impl core::CommandLineParser { #[inline(always)] pub fn as_raw_CommandLineParser(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for CommandLineParser {} impl CommandLineParser { /// Copy constructor pub fn new(parser: &core::CommandLineParser) -> Result<core::CommandLineParser> { unsafe { sys::cv_CommandLineParser_CommandLineParser_CommandLineParser(parser.as_raw_CommandLineParser()) }.into_result().map(|ptr| core::CommandLineParser { ptr }) } /// Returns application path /// /// This method returns the path to the executable from the command line (`argv[0]`). /// /// For example, if the application has been started with such a command: /// ```ignore{.sh} /// $ ./bin/my-executable /// ``` /// /// this method will return `./bin`. pub fn get_path_to_application(&self) -> Result<String> { unsafe { sys::cv_CommandLineParser_getPathToApplication_const(self.as_raw_CommandLineParser()) }.into_result().map(crate::templ::receive_string_mut) } /// Check if field was provided in the command line /// /// ## Parameters /// * name: argument name to check pub fn has(&self, name: &str) -> Result<bool> { string_arg!(name); unsafe { sys::cv_CommandLineParser_has_const_String(self.as_raw_CommandLineParser(), name.as_ptr()) }.into_result() } /// Check for parsing errors /// /// Returns false if error occurred while accessing the parameters (bad conversion, missing arguments, /// etc.). Call @ref printErrors to print error messages list. pub fn check(&self) -> Result<bool> { unsafe { sys::cv_CommandLineParser_check_const(self.as_raw_CommandLineParser()) }.into_result() } /// Set the about message /// /// The about message will be shown when @ref printMessage is called, right before arguments table. pub fn about(&mut self, message: &str) -> Result<()> { string_arg!(message); unsafe { sys::cv_CommandLineParser_about_String(self.as_raw_CommandLineParser(), message.as_ptr()) }.into_result() } /// Print help message /// /// This method will print standard help message containing the about message and arguments description. /// /// ## See also /// about pub fn print_message(&self) -> Result<()> { unsafe { sys::cv_CommandLineParser_printMessage_const(self.as_raw_CommandLineParser()) }.into_result() } /// Print list of errors occurred /// /// ## See also /// check pub fn print_errors(&self) -> Result<()> { unsafe { sys::cv_CommandLineParser_printErrors_const(self.as_raw_CommandLineParser()) }.into_result() } } // boxed class cv::ConjGradSolver /// This class is used to perform the non-linear non-constrained minimization of a function /// with known gradient, /// /// defined on an *n*-dimensional Euclidean space, using the **Nonlinear Conjugate Gradient method**. /// The implementation was done based on the beautifully clear explanatory article [An Introduction to /// the Conjugate Gradient Method Without the Agonizing /// Pain](http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf) by Jonathan Richard /// Shewchuk. The method can be seen as an adaptation of a standard Conjugate Gradient method (see, for /// example <http://en.wikipedia.org/wiki/Conjugate_gradient_method>) for numerically solving the /// systems of linear equations. /// /// It should be noted, that this method, although deterministic, is rather a heuristic method and /// therefore may converge to a local minima, not necessary a global one. What is even more disastrous, /// most of its behaviour is ruled by gradient, therefore it essentially cannot distinguish between /// local minima and maxima. Therefore, if it starts sufficiently near to the local maximum, it may /// converge to it. Another obvious restriction is that it should be possible to compute the gradient of /// a function at any point, thus it is preferable to have analytic expression for gradient and /// computational burden should be born by the user. /// /// The latter responsibility is accompilished via the getGradient method of a /// MinProblemSolver::Function interface (which represents function being optimized). This method takes /// point a point in *n*-dimensional space (first argument represents the array of coordinates of that /// point) and comput its gradient (it should be stored in the second argument as an array). /// /// /// Note: class ConjGradSolver thus does not add any new methods to the basic MinProblemSolver interface. /// /// /// Note: term criteria should meet following condition: /// ```ignore /// termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0 /// // or /// termcrit.type == TermCriteria::MAX_ITER) && termcrit.maxCount > 0 /// ``` pub struct ConjGradSolver { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::ConjGradSolver { fn drop(&mut self) { unsafe { sys::cv_ConjGradSolver_delete(self.ptr) }; } } impl core::ConjGradSolver { #[inline(always)] pub fn as_raw_ConjGradSolver(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for ConjGradSolver {} impl core::Algorithm for ConjGradSolver { #[inline(always)] fn as_raw_Algorithm(&self) -> *mut c_void { self.ptr } } impl core::MinProblemSolver for ConjGradSolver { #[inline(always)] fn as_raw_MinProblemSolver(&self) -> *mut c_void { self.ptr } } impl ConjGradSolver { /// This function returns the reference to the ready-to-use ConjGradSolver object. /// /// All the parameters are optional, so this procedure can be called even without parameters at /// all. In this case, the default values will be used. As default value for terminal criteria are /// the only sensible ones, MinProblemSolver::setFunction() should be called upon the obtained /// object, if the function was not given to create(). Otherwise, the two ways (submit it to /// create() or miss it out and call the MinProblemSolver::setFunction()) are absolutely equivalent /// (and will drop the same errors in the same way, should invalid input be detected). /// ## Parameters /// * f: Pointer to the function that will be minimized, similarly to the one you submit via /// MinProblemSolver::setFunction. /// * termcrit: Terminal criteria to the algorithm, similarly to the one you submit via /// MinProblemSolver::setTermCriteria. /// /// ## C++ default parameters /// * f: Ptr<ConjGradSolver::Function>() /// * termcrit: TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001) pub fn create(f: &types::PtrOfFunction, termcrit: &core::TermCriteria) -> Result<types::PtrOfConjGradSolver> { unsafe { sys::cv_ConjGradSolver_create_PtrOfFunction_TermCriteria(f.as_raw_PtrOfFunction(), termcrit.as_raw_TermCriteria()) }.into_result().map(|ptr| types::PtrOfConjGradSolver { ptr }) } } impl DMatch { pub fn default() -> Result<core::DMatch> { unsafe { sys::cv_DMatch_DMatch() }.into_result() } pub fn new(_query_idx: i32, _train_idx: i32, _distance: f32) -> Result<core::DMatch> { unsafe { sys::cv_DMatch_DMatch_int_int_float(_query_idx, _train_idx, _distance) }.into_result() } pub fn new_index(_query_idx: i32, _train_idx: i32, _img_idx: i32, _distance: f32) -> Result<core::DMatch> { unsafe { sys::cv_DMatch_DMatch_int_int_int_float(_query_idx, _train_idx, _img_idx, _distance) }.into_result() } } // Generating impl for trait cv::DownhillSolver (trait) /// This class is used to perform the non-linear non-constrained minimization of a function, /// /// defined on an `n`-dimensional Euclidean space, using the **Nelder-Mead method**, also known as /// **downhill simplex method**. The basic idea about the method can be obtained from /// <http://en.wikipedia.org/wiki/Nelder-Mead_method>. /// /// It should be noted, that this method, although deterministic, is rather a heuristic and therefore /// may converge to a local minima, not necessary a global one. It is iterative optimization technique, /// which at each step uses an information about the values of a function evaluated only at `n+1` /// points, arranged as a *simplex* in `n`-dimensional space (hence the second name of the method). At /// each step new point is chosen to evaluate function at, obtained value is compared with previous /// ones and based on this information simplex changes it's shape , slowly moving to the local minimum. /// Thus this method is using *only* function values to make decision, on contrary to, say, Nonlinear /// Conjugate Gradient method (which is also implemented in optim). /// /// Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when the /// function values at the vertices of simplex are within termcrit.epsilon range or simplex becomes so /// small that it can enclosed in a box with termcrit.epsilon sides, whatever comes first, for some /// defined by user positive integer termcrit.maxCount and positive non-integer termcrit.epsilon. /// /// /// Note: DownhillSolver is a derivative of the abstract interface /// cv::MinProblemSolver, which in turn is derived from the Algorithm interface and is used to /// encapsulate the functionality, common to all non-linear optimization algorithms in the optim /// module. /// /// /// Note: term criteria should meet following condition: /// ```ignore /// termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0 /// ``` pub trait DownhillSolver: core::MinProblemSolver { #[inline(always)] fn as_raw_DownhillSolver(&self) -> *mut c_void; /// Returns the initial step that will be used in downhill simplex algorithm. /// /// ## Parameters /// * step: Initial step that will be used in algorithm. Note, that although corresponding setter /// accepts column-vectors as well as row-vectors, this method will return a row-vector. /// @see DownhillSolver::setInitStep fn get_init_step(&self, step: &mut core::Mat) -> Result<()> { unsafe { sys::cv_DownhillSolver_getInitStep_const_Mat(self.as_raw_DownhillSolver(), step.as_raw_Mat()) }.into_result() } /// Sets the initial step that will be used in downhill simplex algorithm. /// /// Step, together with initial point (givin in DownhillSolver::minimize) are two `n`-dimensional /// vectors that are used to determine the shape of initial simplex. Roughly said, initial point /// determines the position of a simplex (it will become simplex's centroid), while step determines the /// spread (size in each dimension) of a simplex. To be more precise, if <span lang='latex'>s,x_0\in\mathbb{R}^n</span> are /// the initial step and initial point respectively, the vertices of a simplex will be: /// <span lang='latex'>v_0:=x_0-\frac{1}{2} s</span> and <span lang='latex'>v_i:=x_0+s_i</span> for <span lang='latex'>i=1,2,\dots,n</span> where <span lang='latex'>s_i</span> denotes /// projections of the initial step of *n*-th coordinate (the result of projection is treated to be /// vector given by <span lang='latex'>s_i:=e_i\cdot\left<e_i\cdot s\right></span>, where <span lang='latex'>e_i</span> form canonical basis) /// /// ## Parameters /// * step: Initial step that will be used in algorithm. Roughly said, it determines the spread /// (size in each dimension) of an initial simplex. fn set_init_step(&mut self, step: &core::Mat) -> Result<()> { unsafe { sys::cv_DownhillSolver_setInitStep_Mat(self.as_raw_DownhillSolver(), step.as_raw_Mat()) }.into_result() } } impl dyn DownhillSolver + '_ { /// This function returns the reference to the ready-to-use DownhillSolver object. /// /// All the parameters are optional, so this procedure can be called even without parameters at /// all. In this case, the default values will be used. As default value for terminal criteria are /// the only sensible ones, MinProblemSolver::setFunction() and DownhillSolver::setInitStep() /// should be called upon the obtained object, if the respective parameters were not given to /// create(). Otherwise, the two ways (give parameters to createDownhillSolver() or miss them out /// and call the MinProblemSolver::setFunction() and DownhillSolver::setInitStep()) are absolutely /// equivalent (and will drop the same errors in the same way, should invalid input be detected). /// ## Parameters /// * f: Pointer to the function that will be minimized, similarly to the one you submit via /// MinProblemSolver::setFunction. /// * initStep: Initial step, that will be used to construct the initial simplex, similarly to the one /// you submit via MinProblemSolver::setInitStep. /// * termcrit: Terminal criteria to the algorithm, similarly to the one you submit via /// MinProblemSolver::setTermCriteria. /// /// ## C++ default parameters /// * f: Ptr<MinProblemSolver::Function>() /// * init_step: Mat_<double>(1,1,0.0) /// * termcrit: TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001) pub fn create(f: &types::PtrOfFunction, init_step: &core::Mat, termcrit: &core::TermCriteria) -> Result<types::PtrOfDownhillSolver> { unsafe { sys::cv_DownhillSolver_create_PtrOfFunction_Mat_TermCriteria(f.as_raw_PtrOfFunction(), init_step.as_raw_Mat(), termcrit.as_raw_TermCriteria()) }.into_result().map(|ptr| types::PtrOfDownhillSolver { ptr }) } } // Generating impl for trait cv::Formatted (trait) /// @todo document pub trait Formatted { #[inline(always)] fn as_raw_Formatted(&self) -> *mut c_void; fn next(&mut self) -> Result<String> { unsafe { sys::cv_Formatted_next(self.as_raw_Formatted()) }.into_result().map(crate::templ::receive_string) } fn reset(&mut self) -> Result<()> { unsafe { sys::cv_Formatted_reset(self.as_raw_Formatted()) }.into_result() } } // Generating impl for trait cv::Formatter (trait) /// @todo document pub trait Formatter { #[inline(always)] fn as_raw_Formatter(&self) -> *mut c_void; fn format(&self, mtx: &core::Mat) -> Result<types::PtrOfFormatted> { unsafe { sys::cv_Formatter_format_const_Mat(self.as_raw_Formatter(), mtx.as_raw_Mat()) }.into_result().map(|ptr| types::PtrOfFormatted { ptr }) } /// /// ## C++ default parameters /// * p: 8 fn set32f_precision(&mut self, p: i32) -> Result<()> { unsafe { sys::cv_Formatter_set32fPrecision_int(self.as_raw_Formatter(), p) }.into_result() } /// /// ## C++ default parameters /// * p: 16 fn set64f_precision(&mut self, p: i32) -> Result<()> { unsafe { sys::cv_Formatter_set64fPrecision_int(self.as_raw_Formatter(), p) }.into_result() } /// /// ## C++ default parameters /// * ml: true fn set_multiline(&mut self, ml: bool) -> Result<()> { unsafe { sys::cv_Formatter_setMultiline_bool(self.as_raw_Formatter(), ml) }.into_result() } } impl dyn Formatter + '_ { /// /// ## C++ default parameters /// * fmt: FMT_DEFAULT pub fn get(fmt: i32) -> Result<types::PtrOfFormatter> { unsafe { sys::cv_Formatter_get_int(fmt) }.into_result().map(|ptr| types::PtrOfFormatter { ptr }) } } // boxed class cv::Hamming /// replaced with CV_Assert(expr) in Debug configuration pub struct Hamming { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::Hamming { fn drop(&mut self) { unsafe { sys::cv_Hamming_delete(self.ptr) }; } } impl core::Hamming { #[inline(always)] pub fn as_raw_Hamming(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for Hamming {} impl KeyPoint { /// the default constructor pub fn default() -> Result<core::KeyPoint> { unsafe { sys::cv_KeyPoint_KeyPoint() }.into_result() } /// ## Parameters /// * _pt: x & y coordinates of the keypoint /// * _size: keypoint diameter /// * _angle: keypoint orientation /// * _response: keypoint detector response on the keypoint (that is, strength of the keypoint) /// * _octave: pyramid octave in which the keypoint has been detected /// * _class_id: object id /// /// ## C++ default parameters /// * _angle: -1 /// * _response: 0 /// * _octave: 0 /// * _class_id: -1 pub fn new_point(_pt: core::Point2f, _size: f32, _angle: f32, _response: f32, _octave: i32, _class_id: i32) -> Result<core::KeyPoint> { unsafe { sys::cv_KeyPoint_KeyPoint_Point2f_float_float_float_int_int(_pt, _size, _angle, _response, _octave, _class_id) }.into_result() } /// ## Parameters /// * x: x-coordinate of the keypoint /// * y: y-coordinate of the keypoint /// * _size: keypoint diameter /// * _angle: keypoint orientation /// * _response: keypoint detector response on the keypoint (that is, strength of the keypoint) /// * _octave: pyramid octave in which the keypoint has been detected /// * _class_id: object id /// /// ## C++ default parameters /// * _angle: -1 /// * _response: 0 /// * _octave: 0 /// * _class_id: -1 pub fn new_coords(x: f32, y: f32, _size: f32, _angle: f32, _response: f32, _octave: i32, _class_id: i32) -> Result<core::KeyPoint> { unsafe { sys::cv_KeyPoint_KeyPoint_float_float_float_float_float_int_int(x, y, _size, _angle, _response, _octave, _class_id) }.into_result() } pub fn hash(self) -> Result<size_t> { unsafe { sys::cv_KeyPoint_hash_const(self) }.into_result() } /// This method converts vector of keypoints to vector of points or the reverse, where each keypoint is /// assigned the same size and the same orientation. /// /// ## Parameters /// * keypoints: Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB /// * points2f: Array of (x,y) coordinates of each keypoint /// * keypointIndexes: Array of indexes of keypoints to be converted to points. (Acts like a mask to /// convert only specified keypoints) /// /// ## C++ default parameters /// * keypoint_indexes: std::vector<int>() pub fn convert_from(keypoints: &types::VectorOfKeyPoint, points2f: &mut types::VectorOfPoint2f, keypoint_indexes: &types::VectorOfint) -> Result<()> { unsafe { sys::cv_KeyPoint_convert_VectorOfKeyPoint_VectorOfPoint2f_VectorOfint(keypoints.as_raw_VectorOfKeyPoint(), points2f.as_raw_VectorOfPoint2f(), keypoint_indexes.as_raw_VectorOfint()) }.into_result() } /// ## Parameters /// * points2f: Array of (x,y) coordinates of each keypoint /// * keypoints: Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB /// * size: keypoint diameter /// * response: keypoint detector response on the keypoint (that is, strength of the keypoint) /// * octave: pyramid octave in which the keypoint has been detected /// * class_id: object id /// /// ## C++ default parameters /// * size: 1 /// * response: 1 /// * octave: 0 /// * class_id: -1 pub fn convert_to(points2f: &types::VectorOfPoint2f, keypoints: &mut types::VectorOfKeyPoint, size: f32, response: f32, octave: i32, class_id: i32) -> Result<()> { unsafe { sys::cv_KeyPoint_convert_VectorOfPoint2f_VectorOfKeyPoint_float_float_int_int(points2f.as_raw_VectorOfPoint2f(), keypoints.as_raw_VectorOfKeyPoint(), size, response, octave, class_id) }.into_result() } /// This method computes overlap for pair of keypoints. Overlap is the ratio between area of keypoint /// regions' intersection and area of keypoint regions' union (considering keypoint region as circle). /// If they don't overlap, we get zero. If they coincide at same location with same size, we get 1. /// ## Parameters /// * kp1: First keypoint /// * kp2: Second keypoint pub fn overlap(kp1: core::KeyPoint, kp2: core::KeyPoint) -> Result<f32> { unsafe { sys::cv_KeyPoint_overlap_KeyPoint_KeyPoint(kp1, kp2) }.into_result() } } // boxed class cv::LDA /// Linear Discriminant Analysis /// @todo document this class pub struct LDA { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::LDA { fn drop(&mut self) { unsafe { sys::cv_LDA_delete(self.ptr) }; } } impl core::LDA { #[inline(always)] pub fn as_raw_LDA(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for LDA {} impl LDA { /// constructor /// Initializes a LDA with num_components (default 0). /// /// ## C++ default parameters /// * num_components: 0 pub fn new(num_components: i32) -> Result<core::LDA> { unsafe { sys::cv_LDA_LDA_int(num_components) }.into_result().map(|ptr| core::LDA { ptr }) } /// Initializes and performs a Discriminant Analysis with Fisher's /// Optimization Criterion on given data in src and corresponding labels /// in labels. If 0 (or less) number of components are given, they are /// automatically determined for given data in computation. /// /// ## C++ default parameters /// * num_components: 0 pub fn new_with_data(src: &types::VectorOfMat, labels: &core::Mat, num_components: i32) -> Result<core::LDA> { unsafe { sys::cv_LDA_LDA_VectorOfMat_Mat_int(src.as_raw_VectorOfMat(), labels.as_raw_Mat(), num_components) }.into_result().map(|ptr| core::LDA { ptr }) } /// Serializes this object to a given filename. pub fn save(&self, filename: &str) -> Result<()> { string_arg!(filename); unsafe { sys::cv_LDA_save_const_String(self.as_raw_LDA(), filename.as_ptr()) }.into_result() } /// Deserializes this object from a given filename. pub fn load(&mut self, filename: &str) -> Result<()> { string_arg!(filename); unsafe { sys::cv_LDA_load_String(self.as_raw_LDA(), filename.as_ptr()) }.into_result() } /// Compute the discriminants for data in src (row aligned) and labels. pub fn compute(&mut self, src: &types::VectorOfMat, labels: &core::Mat) -> Result<()> { unsafe { sys::cv_LDA_compute_VectorOfMat_Mat(self.as_raw_LDA(), src.as_raw_VectorOfMat(), labels.as_raw_Mat()) }.into_result() } /// Projects samples into the LDA subspace. /// src may be one or more row aligned samples. pub fn project(&mut self, src: &core::Mat) -> Result<core::Mat> { unsafe { sys::cv_LDA_project_Mat(self.as_raw_LDA(), src.as_raw_Mat()) }.into_result().map(|ptr| core::Mat { ptr }) } /// Reconstructs projections from the LDA subspace. /// src may be one or more row aligned projections. pub fn reconstruct(&mut self, src: &core::Mat) -> Result<core::Mat> { unsafe { sys::cv_LDA_reconstruct_Mat(self.as_raw_LDA(), src.as_raw_Mat()) }.into_result().map(|ptr| core::Mat { ptr }) } /// Returns the eigenvectors of this LDA. pub fn eigenvectors(&self) -> Result<core::Mat> { unsafe { sys::cv_LDA_eigenvectors_const(self.as_raw_LDA()) }.into_result().map(|ptr| core::Mat { ptr }) } /// Returns the eigenvalues of this LDA. pub fn eigenvalues(&self) -> Result<core::Mat> { unsafe { sys::cv_LDA_eigenvalues_const(self.as_raw_LDA()) }.into_result().map(|ptr| core::Mat { ptr }) } pub fn subspace_project(w: &core::Mat, mean: &core::Mat, src: &core::Mat) -> Result<core::Mat> { unsafe { sys::cv_LDA_subspaceProject_Mat_Mat_Mat(w.as_raw_Mat(), mean.as_raw_Mat(), src.as_raw_Mat()) }.into_result().map(|ptr| core::Mat { ptr }) } pub fn subspace_reconstruct(w: &core::Mat, mean: &core::Mat, src: &core::Mat) -> Result<core::Mat> { unsafe { sys::cv_LDA_subspaceReconstruct_Mat_Mat_Mat(w.as_raw_Mat(), mean.as_raw_Mat(), src.as_raw_Mat()) }.into_result().map(|ptr| core::Mat { ptr }) } } // boxed class cv::Mat /// n-dimensional dense array class \anchor CVMat_Details /// /// The class Mat represents an n-dimensional dense numerical single-channel or multi-channel array. It /// can be used to store real or complex-valued vectors and matrices, grayscale or color images, voxel /// volumes, vector fields, point clouds, tensors, histograms (though, very high-dimensional histograms /// may be better stored in a SparseMat ). The data layout of the array `M` is defined by the array /// `M.step[]`, so that the address of element <span lang='latex'>(i_0,...,i_{M.dims-1})</span>, where <span lang='latex'>0\leq i_k<M.size[k]</span>, is /// computed as: /// <div lang='latex'>addr(M_{i_0,...,i_{M.dims-1}}) = M.data + M.step[0]*i_0 + M.step[1]*i_1 + ... + M.step[M.dims-1]*i_{M.dims-1}</div> /// In case of a 2-dimensional array, the above formula is reduced to: /// <div lang='latex'>addr(M_{i,j}) = M.data + M.step[0]*i + M.step[1]*j</div> /// Note that `M.step[i] >= M.step[i+1]` (in fact, `M.step[i] >= M.step[i+1]*M.size[i+1]` ). This means /// that 2-dimensional matrices are stored row-by-row, 3-dimensional matrices are stored plane-by-plane, /// and so on. M.step[M.dims-1] is minimal and always equal to the element size M.elemSize() . /// /// So, the data layout in Mat is fully compatible with CvMat, IplImage, and CvMatND types from OpenCV /// 1.x. It is also compatible with the majority of dense array types from the standard toolkits and /// SDKs, such as Numpy (ndarray), Win32 (independent device bitmaps), and others, that is, with any /// array that uses *steps* (or *strides*) to compute the position of a pixel. Due to this /// compatibility, it is possible to make a Mat header for user-allocated data and process it in-place /// using OpenCV functions. /// /// There are many different ways to create a Mat object. The most popular options are listed below: /// /// - Use the create(nrows, ncols, type) method or the similar Mat(nrows, ncols, type[, fillValue]) /// constructor. A new array of the specified size and type is allocated. type has the same meaning as /// in the cvCreateMat method. For example, CV_8UC1 means a 8-bit single-channel array, CV_32FC2 /// means a 2-channel (complex) floating-point array, and so on. /// ```ignore /// // make a 7x7 complex matrix filled with 1+3j. /// Mat M(7,7,CV_32FC2,Scalar(1,3)); /// // and now turn M to a 100x60 15-channel 8-bit matrix. /// // The old content will be deallocated /// M.create(100,60,CV_8UC(15)); /// ``` /// /// As noted in the introduction to this chapter, create() allocates only a new array when the shape /// or type of the current array are different from the specified ones. /// /// - Create a multi-dimensional array: /// ```ignore /// // create a 100x100x100 8-bit array /// int sz[] = {100, 100, 100}; /// Mat bigCube(3, sz, CV_8U, Scalar::all(0)); /// ``` /// /// It passes the number of dimensions =1 to the Mat constructor but the created array will be /// 2-dimensional with the number of columns set to 1. So, Mat::dims is always \>= 2 (can also be 0 /// when the array is empty). /// /// - Use a copy constructor or assignment operator where there can be an array or expression on the /// right side (see below). As noted in the introduction, the array assignment is an O(1) operation /// because it only copies the header and increases the reference counter. The Mat::clone() method can /// be used to get a full (deep) copy of the array when you need it. /// /// - Construct a header for a part of another array. It can be a single row, single column, several /// rows, several columns, rectangular region in the array (called a *minor* in algebra) or a /// diagonal. Such operations are also O(1) because the new header references the same data. You can /// actually modify a part of the array using this feature, for example: /// ```ignore /// // add the 5-th row, multiplied by 3 to the 3rd row /// M.row(3) = M.row(3) + M.row(5)*3; /// // now copy the 7-th column to the 1-st column /// // M.col(1) = M.col(7); // this will not work /// Mat M1 = M.col(1); /// M.col(7).copyTo(M1); /// // create a new 320x240 image /// Mat img(Size(320,240),CV_8UC3); /// // select a ROI /// Mat roi(img, Rect(10,10,100,100)); /// // fill the ROI with (0,255,0) (which is green in RGB space); /// // the original 320x240 image will be modified /// roi = Scalar(0,255,0); /// ``` /// /// Due to the additional datastart and dataend members, it is possible to compute a relative /// sub-array position in the main *container* array using locateROI(): /// ```ignore /// Mat A = Mat::eye(10, 10, CV_32S); /// // extracts A columns, 1 (inclusive) to 3 (exclusive). /// Mat B = A(Range::all(), Range(1, 3)); /// // extracts B rows, 5 (inclusive) to 9 (exclusive). /// // that is, C \~ A(Range(5, 9), Range(1, 3)) /// Mat C = B(Range(5, 9), Range::all()); /// Size size; Point ofs; /// C.locateROI(size, ofs); /// // size will be (width=10,height=10) and the ofs will be (x=1, y=5) /// ``` /// /// As in case of whole matrices, if you need a deep copy, use the `clone()` method of the extracted /// sub-matrices. /// /// - Make a header for user-allocated data. It can be useful to do the following: /// -# Process "foreign" data using OpenCV (for example, when you implement a DirectShow\* filter or /// a processing module for gstreamer, and so on). For example: /// ```ignore /// void process_video_frame(const unsigned char* pixels, /// int width, int height, int step) /// { /// Mat img(height, width, CV_8UC3, pixels, step); /// GaussianBlur(img, img, Size(7,7), 1.5, 1.5); /// } /// ``` /// /// -# Quickly initialize small matrices and/or get a super-fast element access. /// ```ignore /// double m[3][3] = {{a, b, c}, {d, e, f}, {g, h, i}}; /// Mat M = Mat(3, 3, CV_64F, m).inv(); /// ``` /// /// . /// Partial yet very common cases of this *user-allocated data* case are conversions from CvMat and /// IplImage to Mat. For this purpose, there is function cv::cvarrToMat taking pointers to CvMat or /// IplImage and the optional flag indicating whether to copy the data or not. /// @snippet samples/cpp/image.cpp iplimage /// /// - Use MATLAB-style array initializers, zeros(), ones(), eye(), for example: /// ```ignore /// // create a double-precision identity matrix and add it to M. /// M += Mat::eye(M.rows, M.cols, CV_64F); /// ``` /// /// /// - Use a comma-separated initializer: /// ```ignore /// // create a 3x3 double-precision identity matrix /// Mat M = (Mat_<double>(3,3) << 1, 0, 0, 0, 1, 0, 0, 0, 1); /// ``` /// /// With this approach, you first call a constructor of the Mat class with the proper parameters, and /// then you just put `<< operator` followed by comma-separated values that can be constants, /// variables, expressions, and so on. Also, note the extra parentheses required to avoid compilation /// errors. /// /// Once the array is created, it is automatically managed via a reference-counting mechanism. If the /// array header is built on top of user-allocated data, you should handle the data by yourself. The /// array data is deallocated when no one points to it. If you want to release the data pointed by a /// array header before the array destructor is called, use Mat::release(). /// /// The next important thing to learn about the array class is element access. This manual already /// described how to compute an address of each array element. Normally, you are not required to use the /// formula directly in the code. If you know the array element type (which can be retrieved using the /// method Mat::type() ), you can access the element <span lang='latex'>M_{ij}</span> of a 2-dimensional array as: /// ```ignore /// M.at<double>(i,j) += 1.f; /// ``` /// /// assuming that `M` is a double-precision floating-point array. There are several variants of the method /// at for a different number of dimensions. /// /// If you need to process a whole row of a 2D array, the most efficient way is to get the pointer to /// the row first, and then just use the plain C operator [] : /// ```ignore /// // compute sum of positive matrix elements /// // (assuming that M is a double-precision matrix) /// double sum=0; /// for(int i = 0; i < M.rows; i++) /// { /// const double* Mi = M.ptr<double>(i); /// for(int j = 0; j < M.cols; j++) /// sum += std::max(Mi[j], 0.); /// } /// ``` /// /// Some operations, like the one above, do not actually depend on the array shape. They just process /// elements of an array one by one (or elements from multiple arrays that have the same coordinates, /// for example, array addition). Such operations are called *element-wise*. It makes sense to check /// whether all the input/output arrays are continuous, namely, have no gaps at the end of each row. If /// yes, process them as a long single row: /// ```ignore /// // compute the sum of positive matrix elements, optimized variant /// double sum=0; /// int cols = M.cols, rows = M.rows; /// if(M.isContinuous()) /// { /// cols *= rows; /// rows = 1; /// } /// for(int i = 0; i < rows; i++) /// { /// const double* Mi = M.ptr<double>(i); /// for(int j = 0; j < cols; j++) /// sum += std::max(Mi[j], 0.); /// } /// ``` /// /// In case of the continuous matrix, the outer loop body is executed just once. So, the overhead is /// smaller, which is especially noticeable in case of small matrices. /// /// Finally, there are STL-style iterators that are smart enough to skip gaps between successive rows: /// ```ignore /// // compute sum of positive matrix elements, iterator-based variant /// double sum=0; /// MatConstIterator_<double> it = M.begin<double>(), it_end = M.end<double>(); /// for(; it != it_end; ++it) /// sum += std::max(*it, 0.); /// ``` /// /// The matrix iterators are random-access iterators, so they can be passed to any STL algorithm, /// including std::sort(). /// /// /// Note: Matrix Expressions and arithmetic see MatExpr pub struct Mat { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::Mat { fn drop(&mut self) { unsafe { sys::cv_Mat_delete(self.ptr) }; } } impl core::Mat { #[inline(always)] pub fn as_raw_Mat(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for Mat {} impl Mat { pub fn flags(&self) -> Result<i32> { unsafe { sys::cv_Mat_flags_const(self.as_raw_Mat()) }.into_result() } /// the matrix dimensionality, >= 2 pub fn dims(&self) -> Result<i32> { unsafe { sys::cv_Mat_dims_const(self.as_raw_Mat()) }.into_result() } /// the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions pub fn rows(&self) -> Result<i32> { unsafe { sys::cv_Mat_rows_const(self.as_raw_Mat()) }.into_result() } /// the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions pub fn cols(&self) -> Result<i32> { unsafe { sys::cv_Mat_cols_const(self.as_raw_Mat()) }.into_result() } /// pointer to the data pub fn data_mut(&mut self) -> Result<&mut u8> { unsafe { sys::cv_Mat_data(self.as_raw_Mat()) }.into_result().and_then(|x| unsafe { x.as_mut() }.ok_or_else(|| Error::new(core::StsNullPtr, format!("Function returned Null pointer")))) } /// pointer to the data pub unsafe fn set_data(&mut self, val: &mut u8) -> Result<()> { { sys::cv_Mat_set_data_uchar_X(self.as_raw_Mat(), val) }.into_result() } /// helper fields used in locateROI and adjustROI pub fn datastart(&self) -> Result<&u8> { unsafe { sys::cv_Mat_datastart_const(self.as_raw_Mat()) }.into_result().and_then(|x| unsafe { x.as_ref() }.ok_or_else(|| Error::new(core::StsNullPtr, format!("Function returned Null pointer")))) } pub fn dataend(&self) -> Result<&u8> { unsafe { sys::cv_Mat_dataend_const(self.as_raw_Mat()) }.into_result().and_then(|x| unsafe { x.as_ref() }.ok_or_else(|| Error::new(core::StsNullPtr, format!("Function returned Null pointer")))) } pub fn datalimit(&self) -> Result<&u8> { unsafe { sys::cv_Mat_datalimit_const(self.as_raw_Mat()) }.into_result().and_then(|x| unsafe { x.as_ref() }.ok_or_else(|| Error::new(core::StsNullPtr, format!("Function returned Null pointer")))) } pub fn mat_size(&self) -> Result<core::MatSize> { unsafe { sys::cv_Mat_size_const(self.as_raw_Mat()) }.into_result().map(|ptr| core::MatSize { ptr }) } pub fn mat_step(&self) -> Result<core::MatStep> { unsafe { sys::cv_Mat_step_const(self.as_raw_Mat()) }.into_result().map(|ptr| core::MatStep { ptr }) } /// These are various constructors that form a matrix. As noted in the AutomaticAllocation, often /// the default constructor is enough, and the proper matrix will be allocated by an OpenCV function. /// The constructed matrix can further be assigned to another matrix or matrix expression or can be /// allocated with Mat::create . In the former case, the old content is de-referenced. pub fn new() -> Result<core::Mat> { unsafe { sys::cv_Mat_Mat() }.into_result().map(|ptr| core::Mat { ptr }) } /// ## Parameters /// * rows: Number of rows in a 2D array. /// * cols: Number of columns in a 2D array. /// * type: Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or /// CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. pub unsafe fn new_rows_cols(rows: i32, cols: i32, _type: i32) -> Result<core::Mat> { { sys::cv_Mat_Mat_int_int_int(rows, cols, _type) }.into_result().map(|ptr| core::Mat { ptr }) } /// ## Parameters /// * size: 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the /// number of columns go in the reverse order. /// * type: Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or /// CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. pub unsafe fn new_size(size: core::Size, _type: i32) -> Result<core::Mat> { { sys::cv_Mat_Mat_Size_int(size, _type) }.into_result().map(|ptr| core::Mat { ptr }) } /// ## Parameters /// * rows: Number of rows in a 2D array. /// * cols: Number of columns in a 2D array. /// * type: Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or /// CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. /// * s: An optional value to initialize each matrix element with. To set all the matrix elements to /// the particular value after the construction, use the assignment operator /// Mat::operator=(const Scalar& value) . pub fn new_rows_cols_with_default(rows: i32, cols: i32, _type: i32, s: core::Scalar) -> Result<core::Mat> { unsafe { sys::cv_Mat_Mat_int_int_int_Scalar(rows, cols, _type, s) }.into_result().map(|ptr| core::Mat { ptr }) } /// ## Parameters /// * size: 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the /// number of columns go in the reverse order. /// * type: Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or /// CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. /// * s: An optional value to initialize each matrix element with. To set all the matrix elements to /// the particular value after the construction, use the assignment operator /// Mat::operator=(const Scalar& value) . pub fn new_size_with_default(size: core::Size, _type: i32, s: core::Scalar) -> Result<core::Mat> { unsafe { sys::cv_Mat_Mat_Size_int_Scalar(size, _type, s) }.into_result().map(|ptr| core::Mat { ptr }) } /// ## Parameters /// * sizes: Array of integers specifying an n-dimensional array shape. /// * type: Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or /// CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. pub unsafe fn new_nd(sizes: &types::VectorOfint, _type: i32) -> Result<core::Mat> { { sys::cv_Mat_Mat_VectorOfint_int(sizes.as_raw_VectorOfint(), _type) }.into_result().map(|ptr| core::Mat { ptr }) } /// ## Parameters /// * sizes: Array of integers specifying an n-dimensional array shape. /// * type: Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or /// CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. /// * s: An optional value to initialize each matrix element with. To set all the matrix elements to /// the particular value after the construction, use the assignment operator /// Mat::operator=(const Scalar& value) . pub fn new_nd_with_default(sizes: &types::VectorOfint, _type: i32, s: core::Scalar) -> Result<core::Mat> { unsafe { sys::cv_Mat_Mat_VectorOfint_int_Scalar(sizes.as_raw_VectorOfint(), _type, s) }.into_result().map(|ptr| core::Mat { ptr }) } /// ## Parameters /// * m: Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied /// by these constructors. Instead, the header pointing to m data or its sub-array is constructed and /// associated with it. The reference counter, if any, is incremented. So, when you modify the matrix /// formed using such a constructor, you also modify the corresponding elements of m . If you want to /// have an independent copy of the sub-array, use Mat::clone() . pub fn copy(m: &core::Mat) -> Result<core::Mat> { unsafe { sys::cv_Mat_Mat_Mat(m.as_raw_Mat()) }.into_result().map(|ptr| core::Mat { ptr }) } /// ## Parameters /// * rows: Number of rows in a 2D array. /// * cols: Number of columns in a 2D array. /// * type: Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or /// CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. /// * data: Pointer to the user data. Matrix constructors that take data and step parameters do not /// allocate matrix data. Instead, they just initialize the matrix header that points to the specified /// data, which means that no data is copied. This operation is very efficient and can be used to /// process external data using OpenCV functions. The external data is not automatically deallocated, so /// you should take care of it. /// * step: Number of bytes each matrix row occupies. The value should include the padding bytes at /// the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed /// and the actual step is calculated as cols*elemSize(). See Mat::elemSize. /// /// ## C++ default parameters /// * step: AUTO_STEP pub fn new_rows_cols_with_data(rows: i32, cols: i32, _type: i32, data: &mut c_void, step: size_t) -> Result<core::Mat> { unsafe { sys::cv_Mat_Mat_int_int_int_void_X_size_t(rows, cols, _type, data, step) }.into_result().map(|ptr| core::Mat { ptr }) } /// ## Parameters /// * size: 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the /// number of columns go in the reverse order. /// * type: Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or /// CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. /// * data: Pointer to the user data. Matrix constructors that take data and step parameters do not /// allocate matrix data. Instead, they just initialize the matrix header that points to the specified /// data, which means that no data is copied. This operation is very efficient and can be used to /// process external data using OpenCV functions. The external data is not automatically deallocated, so /// you should take care of it. /// * step: Number of bytes each matrix row occupies. The value should include the padding bytes at /// the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed /// and the actual step is calculated as cols*elemSize(). See Mat::elemSize. /// /// ## C++ default parameters /// * step: AUTO_STEP pub fn new_size_with_data(size: core::Size, _type: i32, data: &mut c_void, step: size_t) -> Result<core::Mat> { unsafe { sys::cv_Mat_Mat_Size_int_void_X_size_t(size, _type, data, step) }.into_result().map(|ptr| core::Mat { ptr }) } /// ## Parameters /// * sizes: Array of integers specifying an n-dimensional array shape. /// * type: Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or /// CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. /// * data: Pointer to the user data. Matrix constructors that take data and step parameters do not /// allocate matrix data. Instead, they just initialize the matrix header that points to the specified /// data, which means that no data is copied. This operation is very efficient and can be used to /// process external data using OpenCV functions. The external data is not automatically deallocated, so /// you should take care of it. /// * steps: Array of ndims-1 steps in case of a multi-dimensional array (the last step is always /// set to the element size). If not specified, the matrix is assumed to be continuous. /// /// ## C++ default parameters /// * steps: 0 pub fn new_nd_with_data(sizes: &types::VectorOfint, _type: i32, data: &mut c_void, steps: &[size_t]) -> Result<core::Mat> { unsafe { sys::cv_Mat_Mat_VectorOfint_int_void_X_const_size_t_X(sizes.as_raw_VectorOfint(), _type, data, steps.as_ptr()) }.into_result().map(|ptr| core::Mat { ptr }) } /// ## Parameters /// * m: Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied /// by these constructors. Instead, the header pointing to m data or its sub-array is constructed and /// associated with it. The reference counter, if any, is incremented. So, when you modify the matrix /// formed using such a constructor, you also modify the corresponding elements of m . If you want to /// have an independent copy of the sub-array, use Mat::clone() . /// * rowRange: Range of the m rows to take. As usual, the range start is inclusive and the range /// end is exclusive. Use Range::all() to take all the rows. /// * colRange: Range of the m columns to take. Use Range::all() to take all the columns. /// /// ## C++ default parameters /// * col_range: Range::all() pub fn rowscols(m: &core::Mat, row_range: &core::Range, col_range: &core::Range) -> Result<core::Mat> { unsafe { sys::cv_Mat_Mat_Mat_Range_Range(m.as_raw_Mat(), row_range.as_raw_Range(), col_range.as_raw_Range()) }.into_result().map(|ptr| core::Mat { ptr }) } /// ## Parameters /// * m: Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied /// by these constructors. Instead, the header pointing to m data or its sub-array is constructed and /// associated with it. The reference counter, if any, is incremented. So, when you modify the matrix /// formed using such a constructor, you also modify the corresponding elements of m . If you want to /// have an independent copy of the sub-array, use Mat::clone() . /// * roi: Region of interest. pub fn roi(m: &core::Mat, roi: core::Rect) -> Result<core::Mat> { unsafe { sys::cv_Mat_Mat_Mat_Rect(m.as_raw_Mat(), roi) }.into_result().map(|ptr| core::Mat { ptr }) } /// ## Parameters /// * m: Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied /// by these constructors. Instead, the header pointing to m data or its sub-array is constructed and /// associated with it. The reference counter, if any, is incremented. So, when you modify the matrix /// formed using such a constructor, you also modify the corresponding elements of m . If you want to /// have an independent copy of the sub-array, use Mat::clone() . /// * ranges: Array of selected ranges of m along each dimensionality. pub fn ranges(m: &core::Mat, ranges: &types::VectorOfRange) -> Result<core::Mat> { unsafe { sys::cv_Mat_Mat_Mat_VectorOfRange(m.as_raw_Mat(), ranges.as_raw_VectorOfRange()) }.into_result().map(|ptr| core::Mat { ptr }) } /// retrieve UMat from Mat /// /// ## C++ default parameters /// * usage_flags: USAGE_DEFAULT pub fn get_umat(&self, access_flags: i32, usage_flags: core::UMatUsageFlags) -> Result<core::UMat> { unsafe { sys::cv_Mat_getUMat_const_int_UMatUsageFlags(self.as_raw_Mat(), access_flags, usage_flags) }.into_result().map(|ptr| core::UMat { ptr }) } /// Creates a matrix header for the specified matrix row. /// /// The method makes a new header for the specified matrix row and returns it. This is an O(1) /// operation, regardless of the matrix size. The underlying data of the new matrix is shared with the /// original matrix. Here is the example of one of the classical basic matrix processing operations, /// axpy, used by LU and many other algorithms: /// ```ignore /// inline void matrix_axpy(Mat& A, int i, int j, double alpha) /// { /// A.row(i) += A.row(j)*alpha; /// } /// ``` /// /// /// Note: In the current implementation, the following code does not work as expected: /// ```ignore /// Mat A; /// ... /// A.row(i) = A.row(j); // will not work /// ``` /// /// This happens because A.row(i) forms a temporary header that is further assigned to another header. /// Remember that each of these operations is O(1), that is, no data is copied. Thus, the above /// assignment is not true if you may have expected the j-th row to be copied to the i-th row. To /// achieve that, you should either turn this simple assignment into an expression or use the /// Mat::copyTo method: /// ```ignore /// Mat A; /// ... /// // works, but looks a bit obscure. /// A.row(i) = A.row(j) + 0; /// // this is a bit longer, but the recommended method. /// A.row(j).copyTo(A.row(i)); /// ``` /// /// ## Parameters /// * y: A 0-based row index. pub fn row(&self, y: i32) -> Result<core::Mat> { unsafe { sys::cv_Mat_row_const_int(self.as_raw_Mat(), y) }.into_result().map(|ptr| core::Mat { ptr }) } /// Creates a matrix header for the specified matrix column. /// /// The method makes a new header for the specified matrix column and returns it. This is an O(1) /// operation, regardless of the matrix size. The underlying data of the new matrix is shared with the /// original matrix. See also the Mat::row description. /// ## Parameters /// * x: A 0-based column index. pub fn col(&self, x: i32) -> Result<core::Mat> { unsafe { sys::cv_Mat_col_const_int(self.as_raw_Mat(), x) }.into_result().map(|ptr| core::Mat { ptr }) } /// Creates a matrix header for the specified row span. /// /// The method makes a new header for the specified row span of the matrix. Similarly to Mat::row and /// Mat::col , this is an O(1) operation. /// ## Parameters /// * startrow: An inclusive 0-based start index of the row span. /// * endrow: An exclusive 0-based ending index of the row span. pub fn row_bounds(&self, startrow: i32, endrow: i32) -> Result<core::Mat> { unsafe { sys::cv_Mat_rowRange_const_int_int(self.as_raw_Mat(), startrow, endrow) }.into_result().map(|ptr| core::Mat { ptr }) } /// ## Parameters /// * r: Range structure containing both the start and the end indices. pub fn row_range(&self, r: &core::Range) -> Result<core::Mat> { unsafe { sys::cv_Mat_rowRange_const_Range(self.as_raw_Mat(), r.as_raw_Range()) }.into_result().map(|ptr| core::Mat { ptr }) } /// Creates a matrix header for the specified column span. /// /// The method makes a new header for the specified column span of the matrix. Similarly to Mat::row and /// Mat::col , this is an O(1) operation. /// ## Parameters /// * startcol: An inclusive 0-based start index of the column span. /// * endcol: An exclusive 0-based ending index of the column span. pub fn col_bounds(&self, startcol: i32, endcol: i32) -> Result<core::Mat> { unsafe { sys::cv_Mat_colRange_const_int_int(self.as_raw_Mat(), startcol, endcol) }.into_result().map(|ptr| core::Mat { ptr }) } /// ## Parameters /// * r: Range structure containing both the start and the end indices. pub fn col_range(&self, r: &core::Range) -> Result<core::Mat> { unsafe { sys::cv_Mat_colRange_const_Range(self.as_raw_Mat(), r.as_raw_Range()) }.into_result().map(|ptr| core::Mat { ptr }) } /// Extracts a diagonal from a matrix /// /// The method makes a new header for the specified matrix diagonal. The new matrix is represented as a /// single-column matrix. Similarly to Mat::row and Mat::col, this is an O(1) operation. /// ## Parameters /// * d: index of the diagonal, with the following values: /// - `d=0` is the main diagonal. /// - `d<0` is a diagonal from the lower half. For example, d=-1 means the diagonal is set /// immediately below the main one. /// - `d>0` is a diagonal from the upper half. For example, d=1 means the diagonal is set /// immediately above the main one. /// For example: /// ```ignore /// Mat m = (Mat_<int>(3,3) << /// 1,2,3, /// 4,5,6, /// 7,8,9); /// Mat d0 = m.diag(0); /// Mat d1 = m.diag(1); /// Mat d_1 = m.diag(-1); /// ``` /// /// The resulting matrices are /// ```ignore /// d0 = /// [1; /// 5; /// 9] /// d1 = /// [2; /// 6] /// d_1 = /// [4; /// 8] /// ``` /// /// ## C++ default parameters /// * d: 0 pub fn diag(&self, d: i32) -> Result<core::Mat> { unsafe { sys::cv_Mat_diag_const_int(self.as_raw_Mat(), d) }.into_result().map(|ptr| core::Mat { ptr }) } /// creates a diagonal matrix /// /// The method creates a square diagonal matrix from specified main diagonal. /// ## Parameters /// * d: One-dimensional matrix that represents the main diagonal. pub fn diag_new_mat(d: &core::Mat) -> Result<core::Mat> { unsafe { sys::cv_Mat_diag_Mat(d.as_raw_Mat()) }.into_result().map(|ptr| core::Mat { ptr }) } /// Creates a full copy of the array and the underlying data. /// /// The method creates a full copy of the array. The original step[] is not taken into account. So, the /// array copy is a continuous array occupying total()*elemSize() bytes. pub fn clone(&self) -> Result<core::Mat> { unsafe { sys::cv_Mat_clone_const(self.as_raw_Mat()) }.into_result().map(|ptr| core::Mat { ptr }) } /// Copies the matrix to another one. /// /// The method copies the matrix data to another matrix. Before copying the data, the method invokes : /// ```ignore /// m.create(this->size(), this->type()); /// ``` /// /// so that the destination matrix is reallocated if needed. While m.copyTo(m); works flawlessly, the /// function does not handle the case of a partial overlap between the source and the destination /// matrices. /// /// When the operation mask is specified, if the Mat::create call shown above reallocates the matrix, /// the newly allocated matrix is initialized with all zeros before copying the data. /// ## Parameters /// * m: Destination matrix. If it does not have a proper size or type before the operation, it is /// reallocated. pub fn copy_to(&self, m: &mut core::Mat) -> Result<()> { unsafe { sys::cv_Mat_copyTo_const_Mat(self.as_raw_Mat(), m.as_raw_Mat()) }.into_result() } /// ## Parameters /// * m: Destination matrix. If it does not have a proper size or type before the operation, it is /// reallocated. /// * mask: Operation mask of the same size as \*this. Its non-zero elements indicate which matrix /// elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels. pub fn copy_to_masked(&self, m: &mut core::Mat, mask: &core::Mat) -> Result<()> { unsafe { sys::cv_Mat_copyTo_const_Mat_Mat(self.as_raw_Mat(), m.as_raw_Mat(), mask.as_raw_Mat()) }.into_result() } /// Converts an array to another data type with optional scaling. /// /// The method converts source pixel values to the target data type. saturate_cast\<\> is applied at /// the end to avoid possible overflows: /// /// <div lang='latex'>m(x,y) = saturate \_ cast<rType>( \alpha (*this)(x,y) + \beta )</div> /// ## Parameters /// * m: output matrix; if it does not have a proper size or type before the operation, it is /// reallocated. /// * rtype: desired output matrix type or, rather, the depth since the number of channels are the /// same as the input has; if rtype is negative, the output matrix will have the same type as the input. /// * alpha: optional scale factor. /// * beta: optional delta added to the scaled values. /// /// ## C++ default parameters /// * alpha: 1 /// * beta: 0 pub fn convert_to(&self, m: &mut core::Mat, rtype: i32, alpha: f64, beta: f64) -> Result<()> { unsafe { sys::cv_Mat_convertTo_const_Mat_int_double_double(self.as_raw_Mat(), m.as_raw_Mat(), rtype, alpha, beta) }.into_result() } /// Provides a functional form of convertTo. /// /// This is an internally used method called by the @ref MatrixExpressions engine. /// ## Parameters /// * m: Destination array. /// * type: Desired destination array depth (or -1 if it should be the same as the source type). /// /// ## C++ default parameters /// * _type: -1 pub fn assign_to(&self, m: &mut core::Mat, _type: i32) -> Result<()> { unsafe { sys::cv_Mat_assignTo_const_Mat_int(self.as_raw_Mat(), m.as_raw_Mat(), _type) }.into_result() } /// Sets all or some of the array elements to the specified value. /// /// This is an advanced variant of the Mat::operator=(const Scalar& s) operator. /// ## Parameters /// * value: Assigned scalar converted to the actual array type. /// * mask: Operation mask of the same size as \*this. Its non-zero elements indicate which matrix /// elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels /// /// ## C++ default parameters /// * mask: noArray() pub fn set_to(&mut self, value: &core::Mat, mask: &core::Mat) -> Result<core::Mat> { unsafe { sys::cv_Mat_setTo_Mat_Mat(self.as_raw_Mat(), value.as_raw_Mat(), mask.as_raw_Mat()) }.into_result().map(|ptr| core::Mat { ptr }) } /// Changes the shape and/or the number of channels of a 2D matrix without copying the data. /// /// The method makes a new matrix header for \*this elements. The new matrix may have a different size /// and/or different number of channels. Any combination is possible if: /// * No extra elements are included into the new matrix and no elements are excluded. Consequently, /// the product rows\*cols\*channels() must stay the same after the transformation. /// * No data is copied. That is, this is an O(1) operation. Consequently, if you change the number of /// rows, or the operation changes the indices of elements row in some other way, the matrix must be /// continuous. See Mat::isContinuous . /// /// For example, if there is a set of 3D points stored as an STL vector, and you want to represent the /// points as a 3xN matrix, do the following: /// ```ignore /// std::vector<Point3f> vec; /// ... /// Mat pointMat = Mat(vec). // convert vector to Mat, O(1) operation /// reshape(1). // make Nx3 1-channel matrix out of Nx1 3-channel. /// // Also, an O(1) operation /// t(); // finally, transpose the Nx3 matrix. /// // This involves copying all the elements /// ``` /// /// ## Parameters /// * cn: New number of channels. If the parameter is 0, the number of channels remains the same. /// * rows: New number of rows. If the parameter is 0, the number of rows remains the same. /// /// ## C++ default parameters /// * rows: 0 pub fn reshape(&self, cn: i32, rows: i32) -> Result<core::Mat> { unsafe { sys::cv_Mat_reshape_const_int_int(self.as_raw_Mat(), cn, rows) }.into_result().map(|ptr| core::Mat { ptr }) } pub fn reshape_nd(&self, cn: i32, newshape: &types::VectorOfint) -> Result<core::Mat> { unsafe { sys::cv_Mat_reshape_const_int_VectorOfint(self.as_raw_Mat(), cn, newshape.as_raw_VectorOfint()) }.into_result().map(|ptr| core::Mat { ptr }) } /// Computes a cross-product of two 3-element vectors. /// /// The method computes a cross-product of two 3-element vectors. The vectors must be 3-element /// floating-point vectors of the same shape and size. The result is another 3-element vector of the /// same shape and type as operands. /// ## Parameters /// * m: Another cross-product operand. pub fn cross(&self, m: &core::Mat) -> Result<core::Mat> { unsafe { sys::cv_Mat_cross_const_Mat(self.as_raw_Mat(), m.as_raw_Mat()) }.into_result().map(|ptr| core::Mat { ptr }) } /// Computes a dot-product of two vectors. /// /// The method computes a dot-product of two matrices. If the matrices are not single-column or /// single-row vectors, the top-to-bottom left-to-right scan ordering is used to treat them as 1D /// vectors. The vectors must have the same size and type. If the matrices have more than one channel, /// the dot products from all the channels are summed together. /// ## Parameters /// * m: another dot-product operand. pub fn dot(&self, m: &core::Mat) -> Result<f64> { unsafe { sys::cv_Mat_dot_const_Mat(self.as_raw_Mat(), m.as_raw_Mat()) }.into_result() } /// Allocates new array data if needed. /// /// This is one of the key Mat methods. Most new-style OpenCV functions and methods that produce arrays /// call this method for each output array. The method uses the following algorithm: /// /// -# If the current array shape and the type match the new ones, return immediately. Otherwise, /// de-reference the previous data by calling Mat::release. /// -# Initialize the new header. /// -# Allocate the new data of total()\*elemSize() bytes. /// -# Allocate the new, associated with the data, reference counter and set it to 1. /// /// Such a scheme makes the memory management robust and efficient at the same time and helps avoid /// extra typing for you. This means that usually there is no need to explicitly allocate output arrays. /// That is, instead of writing: /// ```ignore /// Mat color; /// ... /// Mat gray(color.rows, color.cols, color.depth()); /// cvtColor(color, gray, COLOR_BGR2GRAY); /// ``` /// /// you can simply write: /// ```ignore /// Mat color; /// ... /// Mat gray; /// cvtColor(color, gray, COLOR_BGR2GRAY); /// ``` /// /// because cvtColor, as well as the most of OpenCV functions, calls Mat::create() for the output array /// internally. /// ## Parameters /// * rows: New number of rows. /// * cols: New number of columns. /// * type: New matrix type. pub unsafe fn create_rows_cols(&mut self, rows: i32, cols: i32, _type: i32) -> Result<()> { { sys::cv_Mat_create_int_int_int(self.as_raw_Mat(), rows, cols, _type) }.into_result() } /// ## Parameters /// * size: Alternative new matrix size specification: Size(cols, rows) /// * type: New matrix type. pub unsafe fn create_size(&mut self, size: core::Size, _type: i32) -> Result<()> { { sys::cv_Mat_create_Size_int(self.as_raw_Mat(), size, _type) }.into_result() } /// ## Parameters /// * sizes: Array of integers specifying a new array shape. /// * type: New matrix type. pub unsafe fn create_nd(&mut self, sizes: &types::VectorOfint, _type: i32) -> Result<()> { { sys::cv_Mat_create_VectorOfint_int(self.as_raw_Mat(), sizes.as_raw_VectorOfint(), _type) }.into_result() } /// Increments the reference counter. /// /// The method increments the reference counter associated with the matrix data. If the matrix header /// points to an external data set (see Mat::Mat ), the reference counter is NULL, and the method has no /// effect in this case. Normally, to avoid memory leaks, the method should not be called explicitly. It /// is called implicitly by the matrix assignment operator. The reference counter increment is an atomic /// operation on the platforms that support it. Thus, it is safe to operate on the same matrices /// asynchronously in different threads. pub fn addref(&mut self) -> Result<()> { unsafe { sys::cv_Mat_addref(self.as_raw_Mat()) }.into_result() } /// Decrements the reference counter and deallocates the matrix if needed. /// /// The method decrements the reference counter associated with the matrix data. When the reference /// counter reaches 0, the matrix data is deallocated and the data and the reference counter pointers /// are set to NULL's. If the matrix header points to an external data set (see Mat::Mat ), the /// reference counter is NULL, and the method has no effect in this case. /// /// This method can be called manually to force the matrix data deallocation. But since this method is /// automatically called in the destructor, or by any other method that changes the data pointer, it is /// usually not needed. The reference counter decrement and check for 0 is an atomic operation on the /// platforms that support it. Thus, it is safe to operate on the same matrices asynchronously in /// different threads. pub fn release(&mut self) -> Result<()> { unsafe { sys::cv_Mat_release(self.as_raw_Mat()) }.into_result() } /// internal use function, consider to use 'release' method instead; deallocates the matrix data pub fn deallocate(&mut self) -> Result<()> { unsafe { sys::cv_Mat_deallocate(self.as_raw_Mat()) }.into_result() } /// Reserves space for the certain number of rows. /// /// The method reserves space for sz rows. If the matrix already has enough space to store sz rows, /// nothing happens. If the matrix is reallocated, the first Mat::rows rows are preserved. The method /// emulates the corresponding method of the STL vector class. /// ## Parameters /// * sz: Number of rows. pub fn reserve(&mut self, sz: size_t) -> Result<()> { unsafe { sys::cv_Mat_reserve_size_t(self.as_raw_Mat(), sz) }.into_result() } /// Reserves space for the certain number of bytes. /// /// The method reserves space for sz bytes. If the matrix already has enough space to store sz bytes, /// nothing happens. If matrix has to be reallocated its previous content could be lost. /// ## Parameters /// * sz: Number of bytes. pub fn reserve_buffer(&mut self, sz: size_t) -> Result<()> { unsafe { sys::cv_Mat_reserveBuffer_size_t(self.as_raw_Mat(), sz) }.into_result() } /// Changes the number of matrix rows. /// /// The methods change the number of matrix rows. If the matrix is reallocated, the first /// min(Mat::rows, sz) rows are preserved. The methods emulate the corresponding methods of the STL /// vector class. /// ## Parameters /// * sz: New number of rows. pub fn resize(&mut self, sz: size_t) -> Result<()> { unsafe { sys::cv_Mat_resize_size_t(self.as_raw_Mat(), sz) }.into_result() } /// ## Parameters /// * sz: New number of rows. /// * s: Value assigned to the newly added elements. pub fn resize_with_default(&mut self, sz: size_t, s: core::Scalar) -> Result<()> { unsafe { sys::cv_Mat_resize_size_t_Scalar(self.as_raw_Mat(), sz, s) }.into_result() } /// ## Parameters /// * m: Added line(s). pub fn push_back(&mut self, m: &core::Mat) -> Result<()> { unsafe { sys::cv_Mat_push_back_Mat(self.as_raw_Mat(), m.as_raw_Mat()) }.into_result() } /// Removes elements from the bottom of the matrix. /// /// The method removes one or more rows from the bottom of the matrix. /// ## Parameters /// * nelems: Number of removed rows. If it is greater than the total number of rows, an exception /// is thrown. /// /// ## C++ default parameters /// * nelems: 1 pub fn pop_back(&mut self, nelems: size_t) -> Result<()> { unsafe { sys::cv_Mat_pop_back_size_t(self.as_raw_Mat(), nelems) }.into_result() } /// Locates the matrix header within a parent matrix. /// /// After you extracted a submatrix from a matrix using Mat::row, Mat::col, Mat::rowRange, /// Mat::colRange, and others, the resultant submatrix points just to the part of the original big /// matrix. However, each submatrix contains information (represented by datastart and dataend /// fields) that helps reconstruct the original matrix size and the position of the extracted /// submatrix within the original matrix. The method locateROI does exactly that. /// ## Parameters /// * wholeSize: Output parameter that contains the size of the whole matrix containing *this* /// as a part. /// * ofs: Output parameter that contains an offset of *this* inside the whole matrix. pub fn locate_roi(&self, whole_size: &mut core::Size, ofs: &mut core::Point) -> Result<()> { unsafe { sys::cv_Mat_locateROI_const_Size_Point(self.as_raw_Mat(), whole_size, ofs) }.into_result() } /// Adjusts a submatrix size and position within the parent matrix. /// /// The method is complimentary to Mat::locateROI . The typical use of these functions is to determine /// the submatrix position within the parent matrix and then shift the position somehow. Typically, it /// can be required for filtering operations when pixels outside of the ROI should be taken into /// account. When all the method parameters are positive, the ROI needs to grow in all directions by the /// specified amount, for example: /// ```ignore /// A.adjustROI(2, 2, 2, 2); /// ``` /// /// In this example, the matrix size is increased by 4 elements in each direction. The matrix is shifted /// by 2 elements to the left and 2 elements up, which brings in all the necessary pixels for the /// filtering with the 5x5 kernel. /// /// adjustROI forces the adjusted ROI to be inside of the parent matrix that is boundaries of the /// adjusted ROI are constrained by boundaries of the parent matrix. For example, if the submatrix A is /// located in the first row of a parent matrix and you called A.adjustROI(2, 2, 2, 2) then A will not /// be increased in the upward direction. /// /// The function is used internally by the OpenCV filtering functions, like filter2D , morphological /// operations, and so on. /// ## Parameters /// * dtop: Shift of the top submatrix boundary upwards. /// * dbottom: Shift of the bottom submatrix boundary downwards. /// * dleft: Shift of the left submatrix boundary to the left. /// * dright: Shift of the right submatrix boundary to the right. /// ## See also /// copyMakeBorder pub fn adjust_roi(&mut self, dtop: i32, dbottom: i32, dleft: i32, dright: i32) -> Result<core::Mat> { unsafe { sys::cv_Mat_adjustROI_int_int_int_int(self.as_raw_Mat(), dtop, dbottom, dleft, dright) }.into_result().map(|ptr| core::Mat { ptr }) } /// Reports whether the matrix is continuous or not. /// /// The method returns true if the matrix elements are stored continuously without gaps at the end of /// each row. Otherwise, it returns false. Obviously, 1x1 or 1xN matrices are always continuous. /// Matrices created with Mat::create are always continuous. But if you extract a part of the matrix /// using Mat::col, Mat::diag, and so on, or constructed a matrix header for externally allocated data, /// such matrices may no longer have this property. /// /// The continuity flag is stored as a bit in the Mat::flags field and is computed automatically when /// you construct a matrix header. Thus, the continuity check is a very fast operation, though /// theoretically it could be done as follows: /// ```ignore /// // alternative implementation of Mat::isContinuous() /// bool myCheckMatContinuity(const Mat& m) /// { /// //return (m.flags & Mat::CONTINUOUS_FLAG) != 0; /// return m.rows == 1 || m.step == m.cols*m.elemSize(); /// } /// ``` /// /// The method is used in quite a few of OpenCV functions. The point is that element-wise operations /// (such as arithmetic and logical operations, math functions, alpha blending, color space /// transformations, and others) do not depend on the image geometry. Thus, if all the input and output /// arrays are continuous, the functions can process them as very long single-row vectors. The example /// below illustrates how an alpha-blending function can be implemented: /// ```ignore /// template<typename T> /// void alphaBlendRGBA(const Mat& src1, const Mat& src2, Mat& dst) /// { /// const float alpha_scale = (float)std::numeric_limits<T>::max(), /// inv_scale = 1.f/alpha_scale; /// /// CV_Assert( src1.type() == src2.type() && /// src1.type() == CV_MAKETYPE(traits::Depth<T>::value, 4) && /// src1.size() == src2.size()); /// Size size = src1.size(); /// dst.create(size, src1.type()); /// /// // here is the idiom: check the arrays for continuity and, /// // if this is the case, /// // treat the arrays as 1D vectors /// if( src1.isContinuous() && src2.isContinuous() && dst.isContinuous() ) /// { /// size.width *= size.height; /// size.height = 1; /// } /// size.width *= 4; /// /// for( int i = 0; i < size.height; i++ ) /// { /// // when the arrays are continuous, /// // the outer loop is executed only once /// const T* ptr1 = src1.ptr<T>(i); /// const T* ptr2 = src2.ptr<T>(i); /// T* dptr = dst.ptr<T>(i); /// /// for( int j = 0; j < size.width; j += 4 ) /// { /// float alpha = ptr1[j+3]*inv_scale, beta = ptr2[j+3]*inv_scale; /// dptr[j] = saturate_cast<T>(ptr1[j]*alpha + ptr2[j]*beta); /// dptr[j+1] = saturate_cast<T>(ptr1[j+1]*alpha + ptr2[j+1]*beta); /// dptr[j+2] = saturate_cast<T>(ptr1[j+2]*alpha + ptr2[j+2]*beta); /// dptr[j+3] = saturate_cast<T>((1 - (1-alpha)*(1-beta))*alpha_scale); /// } /// } /// } /// ``` /// /// This approach, while being very simple, can boost the performance of a simple element-operation by /// 10-20 percents, especially if the image is rather small and the operation is quite simple. /// /// Another OpenCV idiom in this function, a call of Mat::create for the destination array, that /// allocates the destination array unless it already has the proper size and type. And while the newly /// allocated arrays are always continuous, you still need to check the destination array because /// Mat::create does not always allocate a new matrix. pub fn is_continuous(&self) -> Result<bool> { unsafe { sys::cv_Mat_isContinuous_const(self.as_raw_Mat()) }.into_result() } /// returns true if the matrix is a submatrix of another matrix pub fn is_submatrix(&self) -> Result<bool> { unsafe { sys::cv_Mat_isSubmatrix_const(self.as_raw_Mat()) }.into_result() } /// Returns the matrix element size in bytes. /// /// The method returns the matrix element size in bytes. For example, if the matrix type is CV_16SC3 , /// the method returns 3\*sizeof(short) or 6. pub fn elem_size(&self) -> Result<size_t> { unsafe { sys::cv_Mat_elemSize_const(self.as_raw_Mat()) }.into_result() } /// Returns the size of each matrix element channel in bytes. /// /// The method returns the matrix element channel size in bytes, that is, it ignores the number of /// channels. For example, if the matrix type is CV_16SC3 , the method returns sizeof(short) or 2. pub fn elem_size1(&self) -> Result<size_t> { unsafe { sys::cv_Mat_elemSize1_const(self.as_raw_Mat()) }.into_result() } /// Returns the type of a matrix element. /// /// The method returns a matrix element type. This is an identifier compatible with the CvMat type /// system, like CV_16SC3 or 16-bit signed 3-channel array, and so on. pub fn typ(&self) -> Result<i32> { unsafe { sys::cv_Mat_type_const(self.as_raw_Mat()) }.into_result() } /// Returns the depth of a matrix element. /// /// The method returns the identifier of the matrix element depth (the type of each individual channel). /// For example, for a 16-bit signed element array, the method returns CV_16S . A complete list of /// matrix types contains the following values: /// * CV_8U - 8-bit unsigned integers ( 0..255 ) /// * CV_8S - 8-bit signed integers ( -128..127 ) /// * CV_16U - 16-bit unsigned integers ( 0..65535 ) /// * CV_16S - 16-bit signed integers ( -32768..32767 ) /// * CV_32S - 32-bit signed integers ( -2147483648..2147483647 ) /// * CV_32F - 32-bit floating-point numbers ( -FLT_MAX..FLT_MAX, INF, NAN ) /// * CV_64F - 64-bit floating-point numbers ( -DBL_MAX..DBL_MAX, INF, NAN ) pub fn depth(&self) -> Result<i32> { unsafe { sys::cv_Mat_depth_const(self.as_raw_Mat()) }.into_result() } /// Returns the number of matrix channels. /// /// The method returns the number of matrix channels. pub fn channels(&self) -> Result<i32> { unsafe { sys::cv_Mat_channels_const(self.as_raw_Mat()) }.into_result() } /// Returns a normalized step. /// /// The method returns a matrix step divided by Mat::elemSize1() . It can be useful to quickly access an /// arbitrary matrix element. /// /// ## C++ default parameters /// * i: 0 pub fn step1(&self, i: i32) -> Result<size_t> { unsafe { sys::cv_Mat_step1_const_int(self.as_raw_Mat(), i) }.into_result() } /// Returns true if the array has no elements. /// /// The method returns true if Mat::total() is 0 or if Mat::data is NULL. Because of pop_back() and /// resize() methods `M.total() == 0` does not imply that `M.data == NULL`. pub fn empty(&self) -> Result<bool> { unsafe { sys::cv_Mat_empty_const(self.as_raw_Mat()) }.into_result() } /// Returns the total number of array elements. /// /// The method returns the number of array elements (a number of pixels if the array represents an /// image). pub fn total(&self) -> Result<size_t> { unsafe { sys::cv_Mat_total_const(self.as_raw_Mat()) }.into_result() } /// Returns the total number of array elements. /// /// The method returns the number of elements within a certain sub-array slice with startDim <= dim < endDim /// /// ## C++ default parameters /// * end_dim: INT_MAX pub fn total_slice(&self, start_dim: i32, end_dim: i32) -> Result<size_t> { unsafe { sys::cv_Mat_total_const_int_int(self.as_raw_Mat(), start_dim, end_dim) }.into_result() } /// ## Parameters /// * elemChannels: Number of channels or number of columns the matrix should have. /// For a 2-D matrix, when the matrix has only 1 column, then it should have /// elemChannels channels; When the matrix has only 1 channel, /// then it should have elemChannels columns. /// For a 3-D matrix, it should have only one channel. Furthermore, /// if the number of planes is not one, then the number of rows /// within every plane has to be 1; if the number of rows within /// every plane is not 1, then the number of planes has to be 1. /// * depth: The depth the matrix should have. Set it to -1 when any depth is fine. /// * requireContinuous: Set it to true to require the matrix to be continuous /// ## Returns /// -1 if the requirement is not satisfied. /// Otherwise, it returns the number of elements in the matrix. Note /// that an element may have multiple channels. /// /// The following code demonstrates its usage for a 2-d matrix: /// @snippet snippets/core_mat_checkVector.cpp example-2d /// /// The following code demonstrates its usage for a 3-d matrix: /// @snippet snippets/core_mat_checkVector.cpp example-3d /// /// ## C++ default parameters /// * depth: -1 /// * require_continuous: true pub fn check_vector(&self, elem_channels: i32, depth: i32, require_continuous: bool) -> Result<i32> { unsafe { sys::cv_Mat_checkVector_const_int_int_bool(self.as_raw_Mat(), elem_channels, depth, require_continuous) }.into_result() } /// Returns a pointer to the specified matrix row. /// /// The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in /// Mat::isContinuous to know how to use these methods. /// ## Parameters /// * i0: A 0-based row index. /// /// ## C++ default parameters /// * i0: 0 pub unsafe fn ptr_mut(&mut self, i0: i32) -> Result<&mut u8> { { sys::cv_Mat_ptr_int(self.as_raw_Mat(), i0) }.into_result().and_then(|x| { x.as_mut() }.ok_or_else(|| Error::new(core::StsNullPtr, format!("Function returned Null pointer")))) } /// /// ## C++ default parameters /// * i0: 0 pub unsafe fn ptr(&self, i0: i32) -> Result<&u8> { { sys::cv_Mat_ptr_const_int(self.as_raw_Mat(), i0) }.into_result().and_then(|x| { x.as_ref() }.ok_or_else(|| Error::new(core::StsNullPtr, format!("Function returned Null pointer")))) } /// ## Parameters /// * row: Index along the dimension 0 /// * col: Index along the dimension 1 pub unsafe fn ptr_2d_mut(&mut self, row: i32, col: i32) -> Result<&mut u8> { { sys::cv_Mat_ptr_int_int(self.as_raw_Mat(), row, col) }.into_result().and_then(|x| { x.as_mut() }.ok_or_else(|| Error::new(core::StsNullPtr, format!("Function returned Null pointer")))) } /// ## Parameters /// * row: Index along the dimension 0 /// * col: Index along the dimension 1 pub unsafe fn ptr_2d(&self, row: i32, col: i32) -> Result<&u8> { { sys::cv_Mat_ptr_const_int_int(self.as_raw_Mat(), row, col) }.into_result().and_then(|x| { x.as_ref() }.ok_or_else(|| Error::new(core::StsNullPtr, format!("Function returned Null pointer")))) } pub unsafe fn ptr_3d_mut(&mut self, i0: i32, i1: i32, i2: i32) -> Result<&mut u8> { { sys::cv_Mat_ptr_int_int_int(self.as_raw_Mat(), i0, i1, i2) }.into_result().and_then(|x| { x.as_mut() }.ok_or_else(|| Error::new(core::StsNullPtr, format!("Function returned Null pointer")))) } pub unsafe fn ptr_3d(&self, i0: i32, i1: i32, i2: i32) -> Result<&u8> { { sys::cv_Mat_ptr_const_int_int_int(self.as_raw_Mat(), i0, i1, i2) }.into_result().and_then(|x| { x.as_ref() }.ok_or_else(|| Error::new(core::StsNullPtr, format!("Function returned Null pointer")))) } pub unsafe fn ptr_nd_mut(&mut self, idx: &[i32]) -> Result<&mut u8> { { sys::cv_Mat_ptr_const_int_X(self.as_raw_Mat(), idx.as_ptr()) }.into_result().and_then(|x| { x.as_mut() }.ok_or_else(|| Error::new(core::StsNullPtr, format!("Function returned Null pointer")))) } pub unsafe fn ptr_nd(&self, idx: &[i32]) -> Result<&u8> { { sys::cv_Mat_ptr_const_const_int_X(self.as_raw_Mat(), idx.as_ptr()) }.into_result().and_then(|x| { x.as_ref() }.ok_or_else(|| Error::new(core::StsNullPtr, format!("Function returned Null pointer")))) } /// Returns a reference to the specified array element. /// /// The template methods return a reference to the specified array element. For the sake of higher /// performance, the index range checks are only performed in the Debug configuration. /// /// Note that the variants with a single index (i) can be used to access elements of single-row or /// single-column 2-dimensional arrays. That is, if, for example, A is a 1 x N floating-point matrix and /// B is an M x 1 integer matrix, you can simply write `A.at<float>(k+4)` and `B.at<int>(2*i+1)` /// instead of `A.at<float>(0,k+4)` and `B.at<int>(2*i+1,0)`, respectively. /// /// The example below initializes a Hilbert matrix: /// ```ignore /// Mat H(100, 100, CV_64F); /// for(int i = 0; i < H.rows; i++) /// for(int j = 0; j < H.cols; j++) /// H.at<double>(i,j)=1./(i+j+1); /// ``` /// /// /// Keep in mind that the size identifier used in the at operator cannot be chosen at random. It depends /// on the image from which you are trying to retrieve the data. The table below gives a better insight in this: /// - If matrix is of type `CV_8U` then use `Mat.at<uchar>(y,x)`. /// - If matrix is of type `CV_8S` then use `Mat.at<schar>(y,x)`. /// - If matrix is of type `CV_16U` then use `Mat.at<ushort>(y,x)`. /// - If matrix is of type `CV_16S` then use `Mat.at<short>(y,x)`. /// - If matrix is of type `CV_32S` then use `Mat.at<int>(y,x)`. /// - If matrix is of type `CV_32F` then use `Mat.at<float>(y,x)`. /// - If matrix is of type `CV_64F` then use `Mat.at<double>(y,x)`. /// /// ## Parameters /// * i0: Index along the dimension 0 /// /// ## C++ default parameters /// * i0: 0 pub fn at_mut<T: core::DataType>(&mut self, i0: i32) -> Result<&mut T> { self._at_mut(i0) } /// ## Parameters /// * i0: Index along the dimension 0 /// /// ## C++ default parameters /// * i0: 0 pub fn at<T: core::DataType>(&self, i0: i32) -> Result<&T> { self._at(i0) } /// ## Parameters /// * row: Index along the dimension 0 /// * col: Index along the dimension 1 pub fn at_2d_mut<T: core::DataType>(&mut self, row: i32, col: i32) -> Result<&mut T> { self._at_2d_mut(row, col) } /// ## Parameters /// * row: Index along the dimension 0 /// * col: Index along the dimension 1 pub fn at_2d<T: core::DataType>(&self, row: i32, col: i32) -> Result<&T> { self._at_2d(row, col) } /// ## Parameters /// * i0: Index along the dimension 0 /// * i1: Index along the dimension 1 /// * i2: Index along the dimension 2 pub fn at_3d_mut<T: core::DataType>(&mut self, i0: i32, i1: i32, i2: i32) -> Result<&mut T> { self._at_3d_mut(i0, i1, i2) } /// ## Parameters /// * i0: Index along the dimension 0 /// * i1: Index along the dimension 1 /// * i2: Index along the dimension 2 pub fn at_3d<T: core::DataType>(&self, i0: i32, i1: i32, i2: i32) -> Result<&T> { self._at_3d(i0, i1, i2) } /// ## Parameters /// * idx: Array of Mat::dims indices. pub fn at_nd_mut<T: core::DataType>(&mut self, idx: &[i32]) -> Result<&mut T> { self._at_nd_mut(idx) } /// ## Parameters /// * idx: Array of Mat::dims indices. pub fn at_nd<T: core::DataType>(&self, idx: &[i32]) -> Result<&T> { self._at_nd(idx) } /// internal use method: updates the continuity flag pub fn update_continuity_flag(&mut self) -> Result<()> { unsafe { sys::cv_Mat_updateContinuityFlag(self.as_raw_Mat()) }.into_result() } } // boxed class cv::MatConstIterator pub struct MatConstIterator { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::MatConstIterator { fn drop(&mut self) { unsafe { sys::cv_MatConstIterator_delete(self.ptr) }; } } impl core::MatConstIterator { #[inline(always)] pub fn as_raw_MatConstIterator(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for MatConstIterator {} impl MatConstIterator { /// default constructor pub fn new() -> Result<core::MatConstIterator> { unsafe { sys::cv_MatConstIterator_MatConstIterator() }.into_result().map(|ptr| core::MatConstIterator { ptr }) } /// constructor that sets the iterator to the beginning of the matrix pub fn over(_m: &core::Mat) -> Result<core::MatConstIterator> { unsafe { sys::cv_MatConstIterator_MatConstIterator_const_Mat(_m.as_raw_Mat()) }.into_result().map(|ptr| core::MatConstIterator { ptr }) } /// constructor that sets the iterator to the specified element of the matrix /// /// ## C++ default parameters /// * _col: 0 pub fn with_rows_cols(_m: &core::Mat, _row: i32, _col: i32) -> Result<core::MatConstIterator> { unsafe { sys::cv_MatConstIterator_MatConstIterator_const_Mat_int_int(_m.as_raw_Mat(), _row, _col) }.into_result().map(|ptr| core::MatConstIterator { ptr }) } /// constructor that sets the iterator to the specified element of the matrix pub fn with_start(_m: &core::Mat, _pt: core::Point) -> Result<core::MatConstIterator> { unsafe { sys::cv_MatConstIterator_MatConstIterator_const_Mat_Point(_m.as_raw_Mat(), _pt) }.into_result().map(|ptr| core::MatConstIterator { ptr }) } /// constructor that sets the iterator to the specified element of the matrix pub fn with_idx(_m: &core::Mat, _idx: &i32) -> Result<core::MatConstIterator> { unsafe { sys::cv_MatConstIterator_MatConstIterator_const_Mat_const_int_X(_m.as_raw_Mat(), _idx) }.into_result().map(|ptr| core::MatConstIterator { ptr }) } /// copy constructor pub fn copy(it: &core::MatConstIterator) -> Result<core::MatConstIterator> { unsafe { sys::cv_MatConstIterator_MatConstIterator_MatConstIterator(it.as_raw_MatConstIterator()) }.into_result().map(|ptr| core::MatConstIterator { ptr }) } /// returns the current iterator position pub fn pos(&self) -> Result<core::Point> { unsafe { sys::cv_MatConstIterator_pos_const(self.as_raw_MatConstIterator()) }.into_result() } /// returns the current iterator position pub fn pos_to(&self, _idx: &mut i32) -> Result<()> { unsafe { sys::cv_MatConstIterator_pos_const_int_X(self.as_raw_MatConstIterator(), _idx) }.into_result() } pub fn lpos(&self) -> Result<ptrdiff_t> { unsafe { sys::cv_MatConstIterator_lpos_const(self.as_raw_MatConstIterator()) }.into_result() } /// /// ## C++ default parameters /// * relative: false pub fn seek(&mut self, ofs: ptrdiff_t, relative: bool) -> Result<()> { unsafe { sys::cv_MatConstIterator_seek_ptrdiff_t_bool(self.as_raw_MatConstIterator(), ofs, relative) }.into_result() } /// /// ## C++ default parameters /// * relative: false pub fn seek_idx(&mut self, _idx: &i32, relative: bool) -> Result<()> { unsafe { sys::cv_MatConstIterator_seek_const_int_X_bool(self.as_raw_MatConstIterator(), _idx, relative) }.into_result() } } // boxed class cv::MatExpr /// Matrix expression representation /// @anchor MatrixExpressions /// This is a list of implemented matrix operations that can be combined in arbitrary complex /// expressions (here A, B stand for matrices ( Mat ), s for a scalar ( Scalar ), alpha for a /// real-valued scalar ( double )): /// * Addition, subtraction, negation: `A+B`, `A-B`, `A+s`, `A-s`, `s+A`, `s-A`, `-A` /// * Scaling: `A*alpha` /// * Per-element multiplication and division: `A.mul(B)`, `A/B`, `alpha/A` /// * Matrix multiplication: `A*B` /// * Transposition: `A.t()` (means A<sup>T</sup>) /// * Matrix inversion and pseudo-inversion, solving linear systems and least-squares problems: /// `A.inv([method]) (~ A<sup>-1</sup>)`, `A.inv([method])*B (~ X: AX=B)` /// * Comparison: `A cmpop B`, `A cmpop alpha`, `alpha cmpop A`, where *cmpop* is one of /// `>`, `>=`, `==`, `!=`, `<=`, `<`. The result of comparison is an 8-bit single channel mask whose /// elements are set to 255 (if the particular element or pair of elements satisfy the condition) or /// 0. /// * Bitwise logical operations: `A logicop B`, `A logicop s`, `s logicop A`, `~A`, where *logicop* is one of /// `&`, `|`, `^`. /// * Element-wise minimum and maximum: `min(A, B)`, `min(A, alpha)`, `max(A, B)`, `max(A, alpha)` /// * Element-wise absolute value: `abs(A)` /// * Cross-product, dot-product: `A.cross(B)`, `A.dot(B)` /// * Any function of matrix or matrices and scalars that returns a matrix or a scalar, such as norm, /// mean, sum, countNonZero, trace, determinant, repeat, and others. /// * Matrix initializers ( Mat::eye(), Mat::zeros(), Mat::ones() ), matrix comma-separated /// initializers, matrix constructors and operators that extract sub-matrices (see Mat description). /// * Mat_<destination_type>() constructors to cast the result to the proper type. /// /// Note: Comma-separated initializers and probably some other operations may require additional /// explicit Mat() or Mat_<T>() constructor calls to resolve a possible ambiguity. /// /// Here are examples of matrix expressions: /// ```ignore /// // compute pseudo-inverse of A, equivalent to A.inv(DECOMP_SVD) /// SVD svd(A); /// Mat pinvA = svd.vt.t()*Mat::diag(1./svd.w)*svd.u.t(); /// /// // compute the new vector of parameters in the Levenberg-Marquardt algorithm /// x -= (A.t()*A + lambda*Mat::eye(A.cols,A.cols,A.type())).inv(DECOMP_CHOLESKY)*(A.t()*err); /// /// // sharpen image using "unsharp mask" algorithm /// Mat blurred; double sigma = 1, threshold = 5, amount = 1; /// GaussianBlur(img, blurred, Size(), sigma, sigma); /// Mat lowContrastMask = abs(img - blurred) < threshold; /// Mat sharpened = img*(1+amount) + blurred*(-amount); /// img.copyTo(sharpened, lowContrastMask); /// ``` pub struct MatExpr { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::MatExpr { fn drop(&mut self) { unsafe { sys::cv_MatExpr_delete(self.ptr) }; } } impl core::MatExpr { #[inline(always)] pub fn as_raw_MatExpr(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for MatExpr {} impl MatExpr { pub fn size(&self) -> Result<core::Size> { unsafe { sys::cv_MatExpr_size_const(self.as_raw_MatExpr()) }.into_result() } pub fn typ(&self) -> Result<i32> { unsafe { sys::cv_MatExpr_type_const(self.as_raw_MatExpr()) }.into_result() } pub fn cross(&self, m: &core::Mat) -> Result<core::Mat> { unsafe { sys::cv_MatExpr_cross_const_Mat(self.as_raw_MatExpr(), m.as_raw_Mat()) }.into_result().map(|ptr| core::Mat { ptr }) } pub fn dot(&self, m: &core::Mat) -> Result<f64> { unsafe { sys::cv_MatExpr_dot_const_Mat(self.as_raw_MatExpr(), m.as_raw_Mat()) }.into_result() } } // Generating impl for trait cv::MatOp (trait) pub trait MatOp { #[inline(always)] fn as_raw_MatOp(&self) -> *mut c_void; } // boxed class cv::MatSize pub struct MatSize { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::MatSize { fn drop(&mut self) { unsafe { sys::cv_MatSize_delete(self.ptr) }; } } impl core::MatSize { #[inline(always)] pub fn as_raw_MatSize(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for MatSize {} impl MatSize { pub fn new(_p: &mut i32) -> Result<core::MatSize> { unsafe { sys::cv_MatSize_MatSize_int_X(_p) }.into_result().map(|ptr| core::MatSize { ptr }) } pub fn dims(&self) -> Result<i32> { unsafe { sys::cv_MatSize_dims_const(self.as_raw_MatSize()) }.into_result() } } // boxed class cv::MatStep pub struct MatStep { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::MatStep { fn drop(&mut self) { unsafe { sys::cv_MatStep_delete(self.ptr) }; } } impl core::MatStep { #[inline(always)] pub fn as_raw_MatStep(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for MatStep {} impl MatStep { pub fn default() -> Result<core::MatStep> { unsafe { sys::cv_MatStep_MatStep() }.into_result().map(|ptr| core::MatStep { ptr }) } pub fn new(s: size_t) -> Result<core::MatStep> { unsafe { sys::cv_MatStep_MatStep_size_t(s) }.into_result().map(|ptr| core::MatStep { ptr }) } } // boxed class cv::Matx_AddOp /// @cond IGNORED pub struct Matx_AddOp { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::Matx_AddOp { fn drop(&mut self) { unsafe { sys::cv_Matx_AddOp_delete(self.ptr) }; } } impl core::Matx_AddOp { #[inline(always)] pub fn as_raw_Matx_AddOp(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for Matx_AddOp {} impl Matx_AddOp { pub fn new() -> Result<core::Matx_AddOp> { unsafe { sys::cv_Matx_AddOp_Matx_AddOp() }.into_result().map(|ptr| core::Matx_AddOp { ptr }) } pub fn copy(unnamed_arg: &core::Matx_AddOp) -> Result<core::Matx_AddOp> { unsafe { sys::cv_Matx_AddOp_Matx_AddOp_Matx_AddOp(unnamed_arg.as_raw_Matx_AddOp()) }.into_result().map(|ptr| core::Matx_AddOp { ptr }) } } // boxed class cv::Matx_DivOp pub struct Matx_DivOp { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::Matx_DivOp { fn drop(&mut self) { unsafe { sys::cv_Matx_DivOp_delete(self.ptr) }; } } impl core::Matx_DivOp { #[inline(always)] pub fn as_raw_Matx_DivOp(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for Matx_DivOp {} impl Matx_DivOp { pub fn new() -> Result<core::Matx_DivOp> { unsafe { sys::cv_Matx_DivOp_Matx_DivOp() }.into_result().map(|ptr| core::Matx_DivOp { ptr }) } pub fn copy(unnamed_arg: &core::Matx_DivOp) -> Result<core::Matx_DivOp> { unsafe { sys::cv_Matx_DivOp_Matx_DivOp_Matx_DivOp(unnamed_arg.as_raw_Matx_DivOp()) }.into_result().map(|ptr| core::Matx_DivOp { ptr }) } } // boxed class cv::Matx_MatMulOp pub struct Matx_MatMulOp { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::Matx_MatMulOp { fn drop(&mut self) { unsafe { sys::cv_Matx_MatMulOp_delete(self.ptr) }; } } impl core::Matx_MatMulOp { #[inline(always)] pub fn as_raw_Matx_MatMulOp(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for Matx_MatMulOp {} impl Matx_MatMulOp { pub fn new() -> Result<core::Matx_MatMulOp> { unsafe { sys::cv_Matx_MatMulOp_Matx_MatMulOp() }.into_result().map(|ptr| core::Matx_MatMulOp { ptr }) } pub fn copy(unnamed_arg: &core::Matx_MatMulOp) -> Result<core::Matx_MatMulOp> { unsafe { sys::cv_Matx_MatMulOp_Matx_MatMulOp_Matx_MatMulOp(unnamed_arg.as_raw_Matx_MatMulOp()) }.into_result().map(|ptr| core::Matx_MatMulOp { ptr }) } } // boxed class cv::Matx_MulOp pub struct Matx_MulOp { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::Matx_MulOp { fn drop(&mut self) { unsafe { sys::cv_Matx_MulOp_delete(self.ptr) }; } } impl core::Matx_MulOp { #[inline(always)] pub fn as_raw_Matx_MulOp(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for Matx_MulOp {} impl Matx_MulOp { pub fn new() -> Result<core::Matx_MulOp> { unsafe { sys::cv_Matx_MulOp_Matx_MulOp() }.into_result().map(|ptr| core::Matx_MulOp { ptr }) } pub fn copy(unnamed_arg: &core::Matx_MulOp) -> Result<core::Matx_MulOp> { unsafe { sys::cv_Matx_MulOp_Matx_MulOp_Matx_MulOp(unnamed_arg.as_raw_Matx_MulOp()) }.into_result().map(|ptr| core::Matx_MulOp { ptr }) } } // boxed class cv::Matx_ScaleOp pub struct Matx_ScaleOp { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::Matx_ScaleOp { fn drop(&mut self) { unsafe { sys::cv_Matx_ScaleOp_delete(self.ptr) }; } } impl core::Matx_ScaleOp { #[inline(always)] pub fn as_raw_Matx_ScaleOp(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for Matx_ScaleOp {} impl Matx_ScaleOp { pub fn new() -> Result<core::Matx_ScaleOp> { unsafe { sys::cv_Matx_ScaleOp_Matx_ScaleOp() }.into_result().map(|ptr| core::Matx_ScaleOp { ptr }) } pub fn copy(unnamed_arg: &core::Matx_ScaleOp) -> Result<core::Matx_ScaleOp> { unsafe { sys::cv_Matx_ScaleOp_Matx_ScaleOp_Matx_ScaleOp(unnamed_arg.as_raw_Matx_ScaleOp()) }.into_result().map(|ptr| core::Matx_ScaleOp { ptr }) } } // boxed class cv::Matx_SubOp pub struct Matx_SubOp { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::Matx_SubOp { fn drop(&mut self) { unsafe { sys::cv_Matx_SubOp_delete(self.ptr) }; } } impl core::Matx_SubOp { #[inline(always)] pub fn as_raw_Matx_SubOp(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for Matx_SubOp {} impl Matx_SubOp { pub fn new() -> Result<core::Matx_SubOp> { unsafe { sys::cv_Matx_SubOp_Matx_SubOp() }.into_result().map(|ptr| core::Matx_SubOp { ptr }) } pub fn copy(unnamed_arg: &core::Matx_SubOp) -> Result<core::Matx_SubOp> { unsafe { sys::cv_Matx_SubOp_Matx_SubOp_Matx_SubOp(unnamed_arg.as_raw_Matx_SubOp()) }.into_result().map(|ptr| core::Matx_SubOp { ptr }) } } // boxed class cv::Matx_TOp pub struct Matx_TOp { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::Matx_TOp { fn drop(&mut self) { unsafe { sys::cv_Matx_TOp_delete(self.ptr) }; } } impl core::Matx_TOp { #[inline(always)] pub fn as_raw_Matx_TOp(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for Matx_TOp {} impl Matx_TOp { pub fn new() -> Result<core::Matx_TOp> { unsafe { sys::cv_Matx_TOp_Matx_TOp() }.into_result().map(|ptr| core::Matx_TOp { ptr }) } pub fn copy(unnamed_arg: &core::Matx_TOp) -> Result<core::Matx_TOp> { unsafe { sys::cv_Matx_TOp_Matx_TOp_Matx_TOp(unnamed_arg.as_raw_Matx_TOp()) }.into_result().map(|ptr| core::Matx_TOp { ptr }) } } // Generating impl for trait cv::MinProblemSolver (trait) /// Basic interface for all solvers pub trait MinProblemSolver: core::Algorithm { #[inline(always)] fn as_raw_MinProblemSolver(&self) -> *mut c_void; /// Getter for the optimized function. /// /// The optimized function is represented by Function interface, which requires derivatives to /// implement the calc(double*) and getDim() methods to evaluate the function. /// /// ## Returns /// Smart-pointer to an object that implements Function interface - it represents the /// function that is being optimized. It can be empty, if no function was given so far. fn get_function(&self) -> Result<types::PtrOfFunction> { unsafe { sys::cv_MinProblemSolver_getFunction_const(self.as_raw_MinProblemSolver()) }.into_result().map(|ptr| types::PtrOfFunction { ptr }) } /// Setter for the optimized function. /// /// *It should be called at least once before the call to* minimize(), as default value is not usable. /// /// ## Parameters /// * f: The new function to optimize. fn set_function(&mut self, f: &types::PtrOfFunction) -> Result<()> { unsafe { sys::cv_MinProblemSolver_setFunction_PtrOfFunction(self.as_raw_MinProblemSolver(), f.as_raw_PtrOfFunction()) }.into_result() } /// Getter for the previously set terminal criteria for this algorithm. /// /// ## Returns /// Deep copy of the terminal criteria used at the moment. fn get_term_criteria(&self) -> Result<core::TermCriteria> { unsafe { sys::cv_MinProblemSolver_getTermCriteria_const(self.as_raw_MinProblemSolver()) }.into_result().map(|ptr| core::TermCriteria { ptr }) } /// Set terminal criteria for solver. /// /// This method *is not necessary* to be called before the first call to minimize(), as the default /// value is sensible. /// /// Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when /// the function values at the vertices of simplex are within termcrit.epsilon range or simplex /// becomes so small that it can enclosed in a box with termcrit.epsilon sides, whatever comes /// first. /// ## Parameters /// * termcrit: Terminal criteria to be used, represented as cv::TermCriteria structure. fn set_term_criteria(&mut self, termcrit: &core::TermCriteria) -> Result<()> { unsafe { sys::cv_MinProblemSolver_setTermCriteria_TermCriteria(self.as_raw_MinProblemSolver(), termcrit.as_raw_TermCriteria()) }.into_result() } /// actually runs the algorithm and performs the minimization. /// /// The sole input parameter determines the centroid of the starting simplex (roughly, it tells /// where to start), all the others (terminal criteria, initial step, function to be minimized) are /// supposed to be set via the setters before the call to this method or the default values (not /// always sensible) will be used. /// /// ## Parameters /// * x: The initial point, that will become a centroid of an initial simplex. After the algorithm /// will terminate, it will be set to the point where the algorithm stops, the point of possible /// minimum. /// ## Returns /// The value of a function at the point found. fn minimize(&mut self, x: &mut core::Mat) -> Result<f64> { unsafe { sys::cv_MinProblemSolver_minimize_Mat(self.as_raw_MinProblemSolver(), x.as_raw_Mat()) }.into_result() } } // Generating impl for trait cv::MinProblemSolver::Function (trait) /// Represents function being optimized pub trait MinProblemSolver_Function { #[inline(always)] fn as_raw_MinProblemSolver_Function(&self) -> *mut c_void; fn get_dims(&self) -> Result<i32> { unsafe { sys::cv_MinProblemSolver_Function_getDims_const(self.as_raw_MinProblemSolver_Function()) }.into_result() } fn get_gradient_eps(&self) -> Result<f64> { unsafe { sys::cv_MinProblemSolver_Function_getGradientEps_const(self.as_raw_MinProblemSolver_Function()) }.into_result() } fn calc(&self, x: &f64) -> Result<f64> { unsafe { sys::cv_MinProblemSolver_Function_calc_const_const_double_X(self.as_raw_MinProblemSolver_Function(), x) }.into_result() } fn get_gradient(&mut self, x: &f64, grad: &mut f64) -> Result<()> { unsafe { sys::cv_MinProblemSolver_Function_getGradient_const_double_X_double_X(self.as_raw_MinProblemSolver_Function(), x, grad) }.into_result() } } impl Moments { /// the default constructor pub fn default() -> Result<core::Moments> { unsafe { sys::cv_Moments_Moments() }.into_result() } /// the full constructor pub fn new(m00: f64, m10: f64, m01: f64, m20: f64, m11: f64, m02: f64, m30: f64, m21: f64, m12: f64, m03: f64) -> Result<core::Moments> { unsafe { sys::cv_Moments_Moments_double_double_double_double_double_double_double_double_double_double(m00, m10, m01, m20, m11, m02, m30, m21, m12, m03) }.into_result() } } // boxed class cv::NAryMatIterator /// n-ary multi-dimensional array iterator. /// /// Use the class to implement unary, binary, and, generally, n-ary element-wise operations on /// multi-dimensional arrays. Some of the arguments of an n-ary function may be continuous arrays, some /// may be not. It is possible to use conventional MatIterator 's for each array but incrementing all of /// the iterators after each small operations may be a big overhead. In this case consider using /// NAryMatIterator to iterate through several matrices simultaneously as long as they have the same /// geometry (dimensionality and all the dimension sizes are the same). On each iteration `it.planes[0]`, /// `it.planes[1]`,... will be the slices of the corresponding matrices. /// /// The example below illustrates how you can compute a normalized and threshold 3D color histogram: /// ```ignore /// void computeNormalizedColorHist(const Mat& image, Mat& hist, int N, double minProb) /// { /// const int histSize[] = {N, N, N}; /// /// // make sure that the histogram has a proper size and type /// hist.create(3, histSize, CV_32F); /// /// // and clear it /// hist = Scalar(0); /// /// // the loop below assumes that the image /// // is a 8-bit 3-channel. check it. /// CV_Assert(image.type() == CV_8UC3); /// MatConstIterator_<Vec3b> it = image.begin<Vec3b>(), /// it_end = image.end<Vec3b>(); /// for( ; it != it_end; ++it ) /// { /// const Vec3b& pix = *it; /// hist.at<float>(pix[0]*N/256, pix[1]*N/256, pix[2]*N/256) += 1.f; /// } /// /// minProb *= image.rows*image.cols; /// /// // initialize iterator (the style is different from STL). /// // after initialization the iterator will contain /// // the number of slices or planes the iterator will go through. /// // it simultaneously increments iterators for several matrices /// // supplied as a null terminated list of pointers /// const Mat* arrays[] = {&hist, 0}; /// Mat planes[1]; /// NAryMatIterator itNAry(arrays, planes, 1); /// double s = 0; /// // iterate through the matrix. on each iteration /// // itNAry.planes[i] (of type Mat) will be set to the current plane /// // of the i-th n-dim matrix passed to the iterator constructor. /// for(int p = 0; p < itNAry.nplanes; p++, ++itNAry) /// { /// threshold(itNAry.planes[0], itNAry.planes[0], minProb, 0, THRESH_TOZERO); /// s += sum(itNAry.planes[0])[0]; /// } /// /// s = 1./s; /// itNAry = NAryMatIterator(arrays, planes, 1); /// for(int p = 0; p < itNAry.nplanes; p++, ++itNAry) /// itNAry.planes[0] *= s; /// } /// ``` pub struct NAryMatIterator { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::NAryMatIterator { fn drop(&mut self) { unsafe { sys::cv_NAryMatIterator_delete(self.ptr) }; } } impl core::NAryMatIterator { #[inline(always)] pub fn as_raw_NAryMatIterator(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for NAryMatIterator {} impl NAryMatIterator { /// the default constructor pub fn new() -> Result<core::NAryMatIterator> { unsafe { sys::cv_NAryMatIterator_NAryMatIterator() }.into_result().map(|ptr| core::NAryMatIterator { ptr }) } } // boxed class cv::PCA /// Principal Component Analysis /// /// The class is used to calculate a special basis for a set of vectors. The /// basis will consist of eigenvectors of the covariance matrix calculated /// from the input set of vectors. The class %PCA can also transform /// vectors to/from the new coordinate space defined by the basis. Usually, /// in this new coordinate system, each vector from the original set (and /// any linear combination of such vectors) can be quite accurately /// approximated by taking its first few components, corresponding to the /// eigenvectors of the largest eigenvalues of the covariance matrix. /// Geometrically it means that you calculate a projection of the vector to /// a subspace formed by a few eigenvectors corresponding to the dominant /// eigenvalues of the covariance matrix. And usually such a projection is /// very close to the original vector. So, you can represent the original /// vector from a high-dimensional space with a much shorter vector /// consisting of the projected vector's coordinates in the subspace. Such a /// transformation is also known as Karhunen-Loeve Transform, or KLT. /// See http://en.wikipedia.org/wiki/Principal_component_analysis /// /// The sample below is the function that takes two matrices. The first /// function stores a set of vectors (a row per vector) that is used to /// calculate PCA. The second function stores another "test" set of vectors /// (a row per vector). First, these vectors are compressed with PCA, then /// reconstructed back, and then the reconstruction error norm is computed /// and printed for each vector. : /// /// ```ignore{.cpp} /// using namespace cv; /// /// PCA compressPCA(const Mat& pcaset, int maxComponents, /// const Mat& testset, Mat& compressed) /// { /// PCA pca(pcaset, // pass the data /// Mat(), // we do not have a pre-computed mean vector, /// // so let the PCA engine to compute it /// PCA::DATA_AS_ROW, // indicate that the vectors /// // are stored as matrix rows /// // (use PCA::DATA_AS_COL if the vectors are /// // the matrix columns) /// maxComponents // specify, how many principal components to retain /// ); /// // if there is no test data, just return the computed basis, ready-to-use /// if( !testset.data ) /// return pca; /// CV_Assert( testset.cols == pcaset.cols ); /// /// compressed.create(testset.rows, maxComponents, testset.type()); /// /// Mat reconstructed; /// for( int i = 0; i < testset.rows; i++ ) /// { /// Mat vec = testset.row(i), coeffs = compressed.row(i), reconstructed; /// // compress the vector, the result will be stored /// // in the i-th row of the output matrix /// pca.project(vec, coeffs); /// // and then reconstruct it /// pca.backProject(coeffs, reconstructed); /// // and measure the error /// printf("%d. diff = %g\n", i, norm(vec, reconstructed, NORM_L2)); /// } /// return pca; /// } /// ``` /// /// ## See also /// calcCovarMatrix, mulTransposed, SVD, dft, dct pub struct PCA { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::PCA { fn drop(&mut self) { unsafe { sys::cv_PCA_delete(self.ptr) }; } } impl core::PCA { #[inline(always)] pub fn as_raw_PCA(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for PCA {} impl PCA { /// default constructor /// /// The default constructor initializes an empty %PCA structure. The other /// constructors initialize the structure and call PCA::operator()(). pub fn default() -> Result<core::PCA> { unsafe { sys::cv_PCA_PCA() }.into_result().map(|ptr| core::PCA { ptr }) } /// ## Parameters /// * data: input samples stored as matrix rows or matrix columns. /// * mean: optional mean value; if the matrix is empty (@c noArray()), /// the mean is computed from the data. /// * flags: operation flags; currently the parameter is only used to /// specify the data layout (PCA::Flags) /// * maxComponents: maximum number of components that %PCA should /// retain; by default, all the components are retained. /// /// ## C++ default parameters /// * max_components: 0 pub fn new_mat_max(data: &core::Mat, mean: &core::Mat, flags: i32, max_components: i32) -> Result<core::PCA> { unsafe { sys::cv_PCA_PCA_Mat_Mat_int_int(data.as_raw_Mat(), mean.as_raw_Mat(), flags, max_components) }.into_result().map(|ptr| core::PCA { ptr }) } /// ## Parameters /// * data: input samples stored as matrix rows or matrix columns. /// * mean: optional mean value; if the matrix is empty (noArray()), /// the mean is computed from the data. /// * flags: operation flags; currently the parameter is only used to /// specify the data layout (PCA::Flags) /// * retainedVariance: Percentage of variance that PCA should retain. /// Using this parameter will let the PCA decided how many components to /// retain but it will always keep at least 2. pub fn new_mat_variance(data: &core::Mat, mean: &core::Mat, flags: i32, retained_variance: f64) -> Result<core::PCA> { unsafe { sys::cv_PCA_PCA_Mat_Mat_int_double(data.as_raw_Mat(), mean.as_raw_Mat(), flags, retained_variance) }.into_result().map(|ptr| core::PCA { ptr }) } /// Projects vector(s) to the principal component subspace. /// /// The methods project one or more vectors to the principal component /// subspace, where each vector projection is represented by coefficients in /// the principal component basis. The first form of the method returns the /// matrix that the second form writes to the result. So the first form can /// be used as a part of expression while the second form can be more /// efficient in a processing loop. /// ## Parameters /// * vec: input vector(s); must have the same dimensionality and the /// same layout as the input data used at %PCA phase, that is, if /// DATA_AS_ROW are specified, then `vec.cols==data.cols` /// (vector dimensionality) and `vec.rows` is the number of vectors to /// project, and the same is true for the PCA::DATA_AS_COL case. pub fn project(&self, vec: &core::Mat) -> Result<core::Mat> { unsafe { sys::cv_PCA_project_const_Mat(self.as_raw_PCA(), vec.as_raw_Mat()) }.into_result().map(|ptr| core::Mat { ptr }) } /// ## Parameters /// * vec: input vector(s); must have the same dimensionality and the /// same layout as the input data used at PCA phase, that is, if /// DATA_AS_ROW are specified, then `vec.cols==data.cols` /// (vector dimensionality) and `vec.rows` is the number of vectors to /// project, and the same is true for the PCA::DATA_AS_COL case. /// * result: output vectors; in case of PCA::DATA_AS_COL, the /// output matrix has as many columns as the number of input vectors, this /// means that `result.cols==vec.cols` and the number of rows match the /// number of principal components (for example, `maxComponents` parameter /// passed to the constructor). pub fn project_to(&self, vec: &core::Mat, result: &mut core::Mat) -> Result<()> { unsafe { sys::cv_PCA_project_const_Mat_Mat(self.as_raw_PCA(), vec.as_raw_Mat(), result.as_raw_Mat()) }.into_result() } /// Reconstructs vectors from their PC projections. /// /// The methods are inverse operations to PCA::project. They take PC /// coordinates of projected vectors and reconstruct the original vectors. /// Unless all the principal components have been retained, the /// reconstructed vectors are different from the originals. But typically, /// the difference is small if the number of components is large enough (but /// still much smaller than the original vector dimensionality). As a /// result, PCA is used. /// ## Parameters /// * vec: coordinates of the vectors in the principal component /// subspace, the layout and size are the same as of PCA::project output /// vectors. pub fn back_project(&self, vec: &core::Mat) -> Result<core::Mat> { unsafe { sys::cv_PCA_backProject_const_Mat(self.as_raw_PCA(), vec.as_raw_Mat()) }.into_result().map(|ptr| core::Mat { ptr }) } /// ## Parameters /// * vec: coordinates of the vectors in the principal component /// subspace, the layout and size are the same as of PCA::project output /// vectors. /// * result: reconstructed vectors; the layout and size are the same as /// of PCA::project input vectors. pub fn back_project_to(&self, vec: &core::Mat, result: &mut core::Mat) -> Result<()> { unsafe { sys::cv_PCA_backProject_const_Mat_Mat(self.as_raw_PCA(), vec.as_raw_Mat(), result.as_raw_Mat()) }.into_result() } } // Generating impl for trait cv::ParallelLoopBody (trait) /// Base class for parallel data processors pub trait ParallelLoopBody { #[inline(always)] fn as_raw_ParallelLoopBody(&self) -> *mut c_void; } // boxed class cv::ParallelLoopBodyLambdaWrapper pub struct ParallelLoopBodyLambdaWrapper { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::ParallelLoopBodyLambdaWrapper { fn drop(&mut self) { unsafe { sys::cv_ParallelLoopBodyLambdaWrapper_delete(self.ptr) }; } } impl core::ParallelLoopBodyLambdaWrapper { #[inline(always)] pub fn as_raw_ParallelLoopBodyLambdaWrapper(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for ParallelLoopBodyLambdaWrapper {} impl core::ParallelLoopBody for ParallelLoopBodyLambdaWrapper { #[inline(always)] fn as_raw_ParallelLoopBody(&self) -> *mut c_void { self.ptr } } impl ParallelLoopBodyLambdaWrapper { } // boxed class cv::Param pub struct Param { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::Param { fn drop(&mut self) { unsafe { sys::cv_Param_delete(self.ptr) }; } } impl core::Param { #[inline(always)] pub fn as_raw_Param(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for Param {} // boxed class cv::Range /// Template class specifying a continuous subsequence (slice) of a sequence. /// /// The class is used to specify a row or a column span in a matrix ( Mat ) and for many other purposes. /// Range(a,b) is basically the same as a:b in Matlab or a..b in Python. As in Python, start is an /// inclusive left boundary of the range and end is an exclusive right boundary of the range. Such a /// half-opened interval is usually denoted as <span lang='latex'>[start,end)</span> . /// /// The static method Range::all() returns a special variable that means "the whole sequence" or "the /// whole range", just like " : " in Matlab or " ... " in Python. All the methods and functions in /// OpenCV that take Range support this special Range::all() value. But, of course, in case of your own /// custom processing, you will probably have to check and handle it explicitly: /// ```ignore /// void my_function(..., const Range& r, ....) /// { /// if(r == Range::all()) { /// // process all the data /// } /// else { /// // process [r.start, r.end) /// } /// } /// ``` pub struct Range { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::Range { fn drop(&mut self) { unsafe { sys::cv_Range_delete(self.ptr) }; } } impl core::Range { #[inline(always)] pub fn as_raw_Range(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for Range {} impl Range { pub fn start(&self) -> Result<i32> { unsafe { sys::cv_Range_start_const(self.as_raw_Range()) }.into_result() } pub fn end(&self) -> Result<i32> { unsafe { sys::cv_Range_end_const(self.as_raw_Range()) }.into_result() } pub fn default() -> Result<core::Range> { unsafe { sys::cv_Range_Range() }.into_result().map(|ptr| core::Range { ptr }) } pub fn new(_start: i32, _end: i32) -> Result<core::Range> { unsafe { sys::cv_Range_Range_int_int(_start, _end) }.into_result().map(|ptr| core::Range { ptr }) } pub fn size(&self) -> Result<i32> { unsafe { sys::cv_Range_size_const(self.as_raw_Range()) }.into_result() } pub fn empty(&self) -> Result<bool> { unsafe { sys::cv_Range_empty_const(self.as_raw_Range()) }.into_result() } pub fn all() -> Result<core::Range> { unsafe { sys::cv_Range_all() }.into_result().map(|ptr| core::Range { ptr }) } } // boxed class cv::RotatedRect /// The class represents rotated (i.e. not up-right) rectangles on a plane. /// /// Each rectangle is specified by the center point (mass center), length of each side (represented by /// #Size2f structure) and the rotation angle in degrees. /// /// The sample below demonstrates how to use RotatedRect: /// @snippet snippets/core_various.cpp RotatedRect_demo ///  /// /// ## See also /// CamShift, fitEllipse, minAreaRect, CvBox2D pub struct RotatedRect { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::RotatedRect { fn drop(&mut self) { unsafe { sys::cv_RotatedRect_delete(self.ptr) }; } } impl core::RotatedRect { #[inline(always)] pub fn as_raw_RotatedRect(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for RotatedRect {} impl RotatedRect { /// returns the rectangle mass center pub fn center(&self) -> Result<core::Point2f> { unsafe { sys::cv_RotatedRect_center_const(self.as_raw_RotatedRect()) }.into_result() } /// returns width and height of the rectangle pub fn size(&self) -> Result<core::Size2f> { unsafe { sys::cv_RotatedRect_size_const(self.as_raw_RotatedRect()) }.into_result() } /// returns the rotation angle. When the angle is 0, 90, 180, 270 etc., the rectangle becomes an up-right rectangle. pub fn angle(&self) -> Result<f32> { unsafe { sys::cv_RotatedRect_angle_const(self.as_raw_RotatedRect()) }.into_result() } /// default constructor pub fn default() -> Result<core::RotatedRect> { unsafe { sys::cv_RotatedRect_RotatedRect() }.into_result().map(|ptr| core::RotatedRect { ptr }) } /// full constructor /// ## Parameters /// * center: The rectangle mass center. /// * size: Width and height of the rectangle. /// * angle: The rotation angle in a clockwise direction. When the angle is 0, 90, 180, 270 etc., /// the rectangle becomes an up-right rectangle. pub fn new(center: core::Point2f, size: core::Size2f, angle: f32) -> Result<core::RotatedRect> { unsafe { sys::cv_RotatedRect_RotatedRect_Point2f_Size2f_float(center, size, angle) }.into_result().map(|ptr| core::RotatedRect { ptr }) } /// Any 3 end points of the RotatedRect. They must be given in order (either clockwise or /// anticlockwise). pub fn for_points(point1: core::Point2f, point2: core::Point2f, point3: core::Point2f) -> Result<core::RotatedRect> { unsafe { sys::cv_RotatedRect_RotatedRect_Point2f_Point2f_Point2f(point1, point2, point3) }.into_result().map(|ptr| core::RotatedRect { ptr }) } /// returns 4 vertices of the rectangle /// ## Parameters /// * pts: The points array for storing rectangle vertices. The order is bottomLeft, topLeft, topRight, bottomRight. pub fn points(&self, pts: &mut [core::Point2f]) -> Result<()> { unsafe { sys::cv_RotatedRect_points_const_Point2f_X(self.as_raw_RotatedRect(), pts.as_mut_ptr()) }.into_result() } /// returns the minimal up-right integer rectangle containing the rotated rectangle pub fn bounding_rect(&self) -> Result<core::Rect> { unsafe { sys::cv_RotatedRect_boundingRect_const(self.as_raw_RotatedRect()) }.into_result() } /// returns the minimal (exact) floating point rectangle containing the rotated rectangle, not intended for use with images pub fn bounding_rect2f(&self) -> Result<core::Rect2f> { unsafe { sys::cv_RotatedRect_boundingRect2f_const(self.as_raw_RotatedRect()) }.into_result() } } // boxed class cv::SparseMat /// The class SparseMat represents multi-dimensional sparse numerical arrays. /// /// Such a sparse array can store elements of any type that Mat can store. *Sparse* means that only /// non-zero elements are stored (though, as a result of operations on a sparse matrix, some of its /// stored elements can actually become 0. It is up to you to detect such elements and delete them /// using SparseMat::erase ). The non-zero elements are stored in a hash table that grows when it is /// filled so that the search time is O(1) in average (regardless of whether element is there or not). /// Elements can be accessed using the following methods: /// * Query operations (SparseMat::ptr and the higher-level SparseMat::ref, SparseMat::value and /// SparseMat::find), for example: /// ```ignore /// const int dims = 5; /// int size[5] = {10, 10, 10, 10, 10}; /// SparseMat sparse_mat(dims, size, CV_32F); /// for(int i = 0; i < 1000; i++) /// { /// int idx[dims]; /// for(int k = 0; k < dims; k++) /// idx[k] = rand() % size[k]; /// sparse_mat.ref<float>(idx) += 1.f; /// } /// cout << "nnz = " << sparse_mat.nzcount() << endl; /// ``` /// /// * Sparse matrix iterators. They are similar to MatIterator but different from NAryMatIterator. /// That is, the iteration loop is familiar to STL users: /// ```ignore /// // prints elements of a sparse floating-point matrix /// // and the sum of elements. /// SparseMatConstIterator_<float> /// it = sparse_mat.begin<float>(), /// it_end = sparse_mat.end<float>(); /// double s = 0; /// int dims = sparse_mat.dims(); /// for(; it != it_end; ++it) /// { /// // print element indices and the element value /// const SparseMat::Node* n = it.node(); /// printf("("); /// for(int i = 0; i < dims; i++) /// printf("%d%s", n->idx[i], i < dims-1 ? ", " : ")"); /// printf(": %g\n", it.value<float>()); /// s += *it; /// } /// printf("Element sum is %g\n", s); /// ``` /// /// If you run this loop, you will notice that elements are not enumerated in a logical order /// (lexicographical, and so on). They come in the same order as they are stored in the hash table /// (semi-randomly). You may collect pointers to the nodes and sort them to get the proper ordering. /// Note, however, that pointers to the nodes may become invalid when you add more elements to the /// matrix. This may happen due to possible buffer reallocation. /// * Combination of the above 2 methods when you need to process 2 or more sparse matrices /// simultaneously. For example, this is how you can compute unnormalized cross-correlation of the 2 /// floating-point sparse matrices: /// ```ignore /// double cross_corr(const SparseMat& a, const SparseMat& b) /// { /// const SparseMat *_a = &a, *_b = &b; /// // if b contains less elements than a, /// // it is faster to iterate through b /// if(_a->nzcount() > _b->nzcount()) /// std::swap(_a, _b); /// SparseMatConstIterator_<float> it = _a->begin<float>(), /// it_end = _a->end<float>(); /// double ccorr = 0; /// for(; it != it_end; ++it) /// { /// // take the next element from the first matrix /// float avalue = *it; /// const Node* anode = it.node(); /// // and try to find an element with the same index in the second matrix. /// // since the hash value depends only on the element index, /// // reuse the hash value stored in the node /// float bvalue = _b->value<float>(anode->idx,&anode->hashval); /// ccorr += avalue*bvalue; /// } /// return ccorr; /// } /// ``` pub struct SparseMat { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::SparseMat { fn drop(&mut self) { unsafe { sys::cv_SparseMat_delete(self.ptr) }; } } impl core::SparseMat { #[inline(always)] pub fn as_raw_SparseMat(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for SparseMat {} impl SparseMat { /// Various SparseMat constructors. pub fn new() -> Result<core::SparseMat> { unsafe { sys::cv_SparseMat_SparseMat() }.into_result().map(|ptr| core::SparseMat { ptr }) } /// ## Parameters /// * dims: Array dimensionality. /// * _sizes: Sparce matrix size on all dementions. /// * _type: Sparse matrix data type. pub fn new_1(dims: i32, _sizes: &i32, _type: i32) -> Result<core::SparseMat> { unsafe { sys::cv_SparseMat_SparseMat_int_const_int_X_int(dims, _sizes, _type) }.into_result().map(|ptr| core::SparseMat { ptr }) } /// ## Parameters /// * m: Source matrix for copy constructor. If m is dense matrix (ocvMat) then it will be converted /// to sparse representation. pub fn new_2(m: &core::Mat) -> Result<core::SparseMat> { unsafe { sys::cv_SparseMat_SparseMat_Mat(m.as_raw_Mat()) }.into_result().map(|ptr| core::SparseMat { ptr }) } /// creates full copy of the matrix pub fn clone(&self) -> Result<core::SparseMat> { unsafe { sys::cv_SparseMat_clone_const(self.as_raw_SparseMat()) }.into_result().map(|ptr| core::SparseMat { ptr }) } /// converts sparse matrix to dense matrix. pub fn copy_to(&self, m: &mut core::Mat) -> Result<()> { unsafe { sys::cv_SparseMat_copyTo_const_Mat(self.as_raw_SparseMat(), m.as_raw_Mat()) }.into_result() } /// converts sparse matrix to dense n-dim matrix with optional type conversion and scaling. /// /// ## C++ default parameters /// * alpha: 1 /// * beta: 0 pub fn convert_to(&self, m: &mut core::Mat, rtype: i32, alpha: f64, beta: f64) -> Result<()> { unsafe { sys::cv_SparseMat_convertTo_const_Mat_int_double_double(self.as_raw_SparseMat(), m.as_raw_Mat(), rtype, alpha, beta) }.into_result() } /// reallocates sparse matrix. pub fn create(&mut self, dims: i32, _sizes: &i32, _type: i32) -> Result<()> { unsafe { sys::cv_SparseMat_create_int_const_int_X_int(self.as_raw_SparseMat(), dims, _sizes, _type) }.into_result() } /// sets all the sparse matrix elements to 0, which means clearing the hash table. pub fn clear(&mut self) -> Result<()> { unsafe { sys::cv_SparseMat_clear(self.as_raw_SparseMat()) }.into_result() } /// manually increments the reference counter to the header. pub fn addref(&mut self) -> Result<()> { unsafe { sys::cv_SparseMat_addref(self.as_raw_SparseMat()) }.into_result() } pub fn release(&mut self) -> Result<()> { unsafe { sys::cv_SparseMat_release(self.as_raw_SparseMat()) }.into_result() } /// converts sparse matrix to the old-style representation; all the elements are copied. /// returns the size of each element in bytes (not including the overhead - the space occupied by SparseMat::Node elements) pub fn elem_size(&self) -> Result<size_t> { unsafe { sys::cv_SparseMat_elemSize_const(self.as_raw_SparseMat()) }.into_result() } /// returns elemSize()/channels() pub fn elem_size1(&self) -> Result<size_t> { unsafe { sys::cv_SparseMat_elemSize1_const(self.as_raw_SparseMat()) }.into_result() } /// returns type of sparse matrix elements pub fn _type(&self) -> Result<i32> { unsafe { sys::cv_SparseMat_type_const(self.as_raw_SparseMat()) }.into_result() } /// returns the depth of sparse matrix elements pub fn depth(&self) -> Result<i32> { unsafe { sys::cv_SparseMat_depth_const(self.as_raw_SparseMat()) }.into_result() } /// returns the number of channels pub fn channels(&self) -> Result<i32> { unsafe { sys::cv_SparseMat_channels_const(self.as_raw_SparseMat()) }.into_result() } /// returns the array of sizes, or NULL if the matrix is not allocated pub fn size(&self) -> Result<&i32> { unsafe { sys::cv_SparseMat_size_const(self.as_raw_SparseMat()) }.into_result().and_then(|x| unsafe { x.as_ref() }.ok_or_else(|| Error::new(core::StsNullPtr, format!("Function returned Null pointer")))) } /// returns the size of i-th matrix dimension (or 0) pub fn size_1(&self, i: i32) -> Result<i32> { unsafe { sys::cv_SparseMat_size_const_int(self.as_raw_SparseMat(), i) }.into_result() } /// returns the matrix dimensionality pub fn dims(&self) -> Result<i32> { unsafe { sys::cv_SparseMat_dims_const(self.as_raw_SparseMat()) }.into_result() } /// returns the number of non-zero elements (=the number of hash table nodes) pub fn nzcount(&self) -> Result<size_t> { unsafe { sys::cv_SparseMat_nzcount_const(self.as_raw_SparseMat()) }.into_result() } /// computes the element hash value (1D case) pub fn hash(&self, i0: i32) -> Result<size_t> { unsafe { sys::cv_SparseMat_hash_const_int(self.as_raw_SparseMat(), i0) }.into_result() } /// computes the element hash value (2D case) pub fn hash_1(&self, i0: i32, i1: i32) -> Result<size_t> { unsafe { sys::cv_SparseMat_hash_const_int_int(self.as_raw_SparseMat(), i0, i1) }.into_result() } /// computes the element hash value (3D case) pub fn hash_2(&self, i0: i32, i1: i32, i2: i32) -> Result<size_t> { unsafe { sys::cv_SparseMat_hash_const_int_int_int(self.as_raw_SparseMat(), i0, i1, i2) }.into_result() } /// computes the element hash value (nD case) pub fn hash_3(&self, idx: &i32) -> Result<size_t> { unsafe { sys::cv_SparseMat_hash_const_const_int_X(self.as_raw_SparseMat(), idx) }.into_result() } /// returns pointer to the specified element (1D case) /// /// ## C++ default parameters /// * hashval: 0 pub fn ptr(&mut self, i0: i32, create_missing: bool, hashval: &mut size_t) -> Result<&mut u8> { unsafe { sys::cv_SparseMat_ptr_int_bool_size_t_X(self.as_raw_SparseMat(), i0, create_missing, hashval) }.into_result().and_then(|x| unsafe { x.as_mut() }.ok_or_else(|| Error::new(core::StsNullPtr, format!("Function returned Null pointer")))) } /// returns pointer to the specified element (2D case) /// /// ## C++ default parameters /// * hashval: 0 pub fn ptr_1(&mut self, i0: i32, i1: i32, create_missing: bool, hashval: &mut size_t) -> Result<&mut u8> { unsafe { sys::cv_SparseMat_ptr_int_int_bool_size_t_X(self.as_raw_SparseMat(), i0, i1, create_missing, hashval) }.into_result().and_then(|x| unsafe { x.as_mut() }.ok_or_else(|| Error::new(core::StsNullPtr, format!("Function returned Null pointer")))) } /// returns pointer to the specified element (3D case) /// /// ## C++ default parameters /// * hashval: 0 pub fn ptr_2(&mut self, i0: i32, i1: i32, i2: i32, create_missing: bool, hashval: &mut size_t) -> Result<&mut u8> { unsafe { sys::cv_SparseMat_ptr_int_int_int_bool_size_t_X(self.as_raw_SparseMat(), i0, i1, i2, create_missing, hashval) }.into_result().and_then(|x| unsafe { x.as_mut() }.ok_or_else(|| Error::new(core::StsNullPtr, format!("Function returned Null pointer")))) } /// returns pointer to the specified element (nD case) /// /// ## C++ default parameters /// * hashval: 0 pub fn ptr_3(&mut self, idx: &i32, create_missing: bool, hashval: &mut size_t) -> Result<&mut u8> { unsafe { sys::cv_SparseMat_ptr_const_int_X_bool_size_t_X(self.as_raw_SparseMat(), idx, create_missing, hashval) }.into_result().and_then(|x| unsafe { x.as_mut() }.ok_or_else(|| Error::new(core::StsNullPtr, format!("Function returned Null pointer")))) } /// erases the specified element (2D case) /// /// ## C++ default parameters /// * hashval: 0 pub fn erase(&mut self, i0: i32, i1: i32, hashval: &mut size_t) -> Result<()> { unsafe { sys::cv_SparseMat_erase_int_int_size_t_X(self.as_raw_SparseMat(), i0, i1, hashval) }.into_result() } /// erases the specified element (3D case) /// /// ## C++ default parameters /// * hashval: 0 pub fn erase_1(&mut self, i0: i32, i1: i32, i2: i32, hashval: &mut size_t) -> Result<()> { unsafe { sys::cv_SparseMat_erase_int_int_int_size_t_X(self.as_raw_SparseMat(), i0, i1, i2, hashval) }.into_result() } /// erases the specified element (nD case) /// /// ## C++ default parameters /// * hashval: 0 pub fn erase_2(&mut self, idx: &i32, hashval: &mut size_t) -> Result<()> { unsafe { sys::cv_SparseMat_erase_const_int_X_size_t_X(self.as_raw_SparseMat(), idx, hashval) }.into_result() } pub fn node(&mut self, nidx: size_t) -> Result<core::SparseMat_Node> { unsafe { sys::cv_SparseMat_node_size_t(self.as_raw_SparseMat(), nidx) }.into_result().map(|ptr| core::SparseMat_Node { ptr }) } pub fn node_1(&self, nidx: size_t) -> Result<core::SparseMat_Node> { unsafe { sys::cv_SparseMat_node_const_size_t(self.as_raw_SparseMat(), nidx) }.into_result().map(|ptr| core::SparseMat_Node { ptr }) } pub fn new_node(&mut self, idx: &i32, hashval: size_t) -> Result<&mut u8> { unsafe { sys::cv_SparseMat_newNode_const_int_X_size_t(self.as_raw_SparseMat(), idx, hashval) }.into_result().and_then(|x| unsafe { x.as_mut() }.ok_or_else(|| Error::new(core::StsNullPtr, format!("Function returned Null pointer")))) } pub fn remove_node(&mut self, hidx: size_t, nidx: size_t, previdx: size_t) -> Result<()> { unsafe { sys::cv_SparseMat_removeNode_size_t_size_t_size_t(self.as_raw_SparseMat(), hidx, nidx, previdx) }.into_result() } pub fn resize_hash_tab(&mut self, newsize: size_t) -> Result<()> { unsafe { sys::cv_SparseMat_resizeHashTab_size_t(self.as_raw_SparseMat(), newsize) }.into_result() } } // boxed class cv::SparseMat::Hdr /// the sparse matrix header pub struct SparseMat_Hdr { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::SparseMat_Hdr { fn drop(&mut self) { unsafe { sys::cv_SparseMat_Hdr_delete(self.ptr) }; } } impl core::SparseMat_Hdr { #[inline(always)] pub fn as_raw_SparseMat_Hdr(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for SparseMat_Hdr {} impl SparseMat_Hdr { pub fn new(_dims: i32, _sizes: &i32, _type: i32) -> Result<core::SparseMat_Hdr> { unsafe { sys::cv_SparseMat_Hdr_Hdr_int_const_int_X_int(_dims, _sizes, _type) }.into_result().map(|ptr| core::SparseMat_Hdr { ptr }) } pub fn clear(&mut self) -> Result<()> { unsafe { sys::cv_SparseMat_Hdr_clear(self.as_raw_SparseMat_Hdr()) }.into_result() } } // boxed class cv::SparseMat::Node /// sparse matrix node - element of a hash table pub struct SparseMat_Node { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::SparseMat_Node { fn drop(&mut self) { unsafe { sys::cv_SparseMat_Node_delete(self.ptr) }; } } impl core::SparseMat_Node { #[inline(always)] pub fn as_raw_SparseMat_Node(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for SparseMat_Node {} // Generating impl for trait cv::SparseMatConstIterator (trait) /// Read-Only Sparse Matrix Iterator. /// /// Here is how to use the iterator to compute the sum of floating-point sparse matrix elements: /// /// \code /// SparseMatConstIterator it = m.begin(), it_end = m.end(); /// double s = 0; /// CV_Assert( m.type() == CV_32F ); /// for( ; it != it_end; ++it ) /// s += it.value<float>(); /// \endcode pub trait SparseMatConstIterator { #[inline(always)] fn as_raw_SparseMatConstIterator(&self) -> *mut c_void; /// returns the current node of the sparse matrix. it.node->idx is the current element index fn node(&self) -> Result<core::SparseMat_Node> { unsafe { sys::cv_SparseMatConstIterator_node_const(self.as_raw_SparseMatConstIterator()) }.into_result().map(|ptr| core::SparseMat_Node { ptr }) } /// moves iterator to the element after the last element fn seek_end(&mut self) -> Result<()> { unsafe { sys::cv_SparseMatConstIterator_seekEnd(self.as_raw_SparseMatConstIterator()) }.into_result() } } // boxed class cv::SparseMatIterator /// Read-write Sparse Matrix Iterator /// /// The class is similar to cv::SparseMatConstIterator, /// but can be used for in-place modification of the matrix elements. pub struct SparseMatIterator { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::SparseMatIterator { fn drop(&mut self) { unsafe { sys::cv_SparseMatIterator_delete(self.ptr) }; } } impl core::SparseMatIterator { #[inline(always)] pub fn as_raw_SparseMatIterator(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for SparseMatIterator {} impl core::SparseMatConstIterator for SparseMatIterator { #[inline(always)] fn as_raw_SparseMatConstIterator(&self) -> *mut c_void { self.ptr } } impl SparseMatIterator { /// returns pointer to the current sparse matrix node. it.node->idx is the index of the current element (do not modify it!) pub fn node(&self) -> Result<core::SparseMat_Node> { unsafe { sys::cv_SparseMatIterator_node_const(self.as_raw_SparseMatIterator()) }.into_result().map(|ptr| core::SparseMat_Node { ptr }) } } // boxed class cv::TermCriteria /// The class defining termination criteria for iterative algorithms. /// /// You can initialize it by default constructor and then override any parameters, or the structure may /// be fully initialized using the advanced variant of the constructor. pub struct TermCriteria { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::TermCriteria { fn drop(&mut self) { unsafe { sys::cv_TermCriteria_delete(self.ptr) }; } } impl core::TermCriteria { #[inline(always)] pub fn as_raw_TermCriteria(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for TermCriteria {} impl TermCriteria { /// the type of termination criteria: COUNT, EPS or COUNT + EPS pub fn _type(&self) -> Result<i32> { unsafe { sys::cv_TermCriteria_type_const(self.as_raw_TermCriteria()) }.into_result() } /// the maximum number of iterations/elements pub fn max_count(&self) -> Result<i32> { unsafe { sys::cv_TermCriteria_maxCount_const(self.as_raw_TermCriteria()) }.into_result() } /// the desired accuracy pub fn epsilon(&self) -> Result<f64> { unsafe { sys::cv_TermCriteria_epsilon_const(self.as_raw_TermCriteria()) }.into_result() } /// default constructor pub fn default() -> Result<core::TermCriteria> { unsafe { sys::cv_TermCriteria_TermCriteria() }.into_result().map(|ptr| core::TermCriteria { ptr }) } /// ## Parameters /// * type: The type of termination criteria, one of TermCriteria::Type /// * maxCount: The maximum number of iterations or elements to compute. /// * epsilon: The desired accuracy or change in parameters at which the iterative algorithm stops. pub fn new(_type: i32, max_count: i32, epsilon: f64) -> Result<core::TermCriteria> { unsafe { sys::cv_TermCriteria_TermCriteria_int_int_double(_type, max_count, epsilon) }.into_result().map(|ptr| core::TermCriteria { ptr }) } pub fn is_valid(&self) -> Result<bool> { unsafe { sys::cv_TermCriteria_isValid_const(self.as_raw_TermCriteria()) }.into_result() } } // boxed class cv::TickMeter /// a Class to measure passing time. /// /// The class computes passing time by counting the number of ticks per second. That is, the following code computes the /// execution time in seconds: /// ```ignore /// TickMeter tm; /// tm.start(); /// // do something ... /// tm.stop(); /// std::cout << tm.getTimeSec(); /// ``` /// /// /// It is also possible to compute the average time over multiple runs: /// ```ignore /// TickMeter tm; /// for (int i = 0; i < 100; i++) /// { /// tm.start(); /// // do something ... /// tm.stop(); /// } /// double average_time = tm.getTimeSec() / tm.getCounter(); /// std::cout << "Average time in second per iteration is: " << average_time << std::endl; /// ``` /// /// ## See also /// getTickCount, getTickFrequency pub struct TickMeter { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::TickMeter { fn drop(&mut self) { unsafe { sys::cv_TickMeter_delete(self.ptr) }; } } impl core::TickMeter { #[inline(always)] pub fn as_raw_TickMeter(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for TickMeter {} impl TickMeter { /// the default constructor pub fn new() -> Result<core::TickMeter> { unsafe { sys::cv_TickMeter_TickMeter() }.into_result().map(|ptr| core::TickMeter { ptr }) } /// starts counting ticks. pub fn start(&mut self) -> Result<()> { unsafe { sys::cv_TickMeter_start(self.as_raw_TickMeter()) }.into_result() } /// stops counting ticks. pub fn stop(&mut self) -> Result<()> { unsafe { sys::cv_TickMeter_stop(self.as_raw_TickMeter()) }.into_result() } /// returns counted ticks. pub fn get_time_ticks(&self) -> Result<i64> { unsafe { sys::cv_TickMeter_getTimeTicks_const(self.as_raw_TickMeter()) }.into_result() } /// returns passed time in microseconds. pub fn get_time_micro(&self) -> Result<f64> { unsafe { sys::cv_TickMeter_getTimeMicro_const(self.as_raw_TickMeter()) }.into_result() } /// returns passed time in milliseconds. pub fn get_time_milli(&self) -> Result<f64> { unsafe { sys::cv_TickMeter_getTimeMilli_const(self.as_raw_TickMeter()) }.into_result() } /// returns passed time in seconds. pub fn get_time_sec(&self) -> Result<f64> { unsafe { sys::cv_TickMeter_getTimeSec_const(self.as_raw_TickMeter()) }.into_result() } /// returns internal counter value. pub fn get_counter(&self) -> Result<i64> { unsafe { sys::cv_TickMeter_getCounter_const(self.as_raw_TickMeter()) }.into_result() } /// resets internal values. pub fn reset(&mut self) -> Result<()> { unsafe { sys::cv_TickMeter_reset(self.as_raw_TickMeter()) }.into_result() } } // boxed class cv::UMat /// @todo document pub struct UMat { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::UMat { fn drop(&mut self) { unsafe { sys::cv_UMat_delete(self.ptr) }; } } impl core::UMat { #[inline(always)] pub fn as_raw_UMat(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for UMat {} impl UMat { pub fn flags(&self) -> Result<i32> { unsafe { sys::cv_UMat_flags_const(self.as_raw_UMat()) }.into_result() } /// the matrix dimensionality, >= 2 pub fn dims(&self) -> Result<i32> { unsafe { sys::cv_UMat_dims_const(self.as_raw_UMat()) }.into_result() } /// the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions pub fn rows(&self) -> Result<i32> { unsafe { sys::cv_UMat_rows_const(self.as_raw_UMat()) }.into_result() } /// the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions pub fn cols(&self) -> Result<i32> { unsafe { sys::cv_UMat_cols_const(self.as_raw_UMat()) }.into_result() } pub fn usage_flags(&self) -> Result<core::UMatUsageFlags> { unsafe { sys::cv_UMat_usageFlags_const(self.as_raw_UMat()) }.into_result() } pub fn offset(&self) -> Result<size_t> { unsafe { sys::cv_UMat_offset_const(self.as_raw_UMat()) }.into_result() } pub fn mat_size(&self) -> Result<core::MatSize> { unsafe { sys::cv_UMat_size_const(self.as_raw_UMat()) }.into_result().map(|ptr| core::MatSize { ptr }) } pub fn mat_step(&self) -> Result<core::MatStep> { unsafe { sys::cv_UMat_step_const(self.as_raw_UMat()) }.into_result().map(|ptr| core::MatStep { ptr }) } /// default constructor /// /// ## C++ default parameters /// * usage_flags: USAGE_DEFAULT pub fn new(usage_flags: core::UMatUsageFlags) -> Result<core::UMat> { unsafe { sys::cv_UMat_UMat_UMatUsageFlags(usage_flags) }.into_result().map(|ptr| core::UMat { ptr }) } /// constructs 2D matrix of the specified size and type /// /// ## C++ default parameters /// * usage_flags: USAGE_DEFAULT pub unsafe fn new_rows_cols(rows: i32, cols: i32, _type: i32, usage_flags: core::UMatUsageFlags) -> Result<core::UMat> { { sys::cv_UMat_UMat_int_int_int_UMatUsageFlags(rows, cols, _type, usage_flags) }.into_result().map(|ptr| core::UMat { ptr }) } /// /// ## C++ default parameters /// * usage_flags: USAGE_DEFAULT pub unsafe fn new_size(size: core::Size, _type: i32, usage_flags: core::UMatUsageFlags) -> Result<core::UMat> { { sys::cv_UMat_UMat_Size_int_UMatUsageFlags(size, _type, usage_flags) }.into_result().map(|ptr| core::UMat { ptr }) } /// constucts 2D matrix and fills it with the specified value _s. /// /// ## C++ default parameters /// * usage_flags: USAGE_DEFAULT pub fn new_rows_cols_with_default(rows: i32, cols: i32, _type: i32, s: core::Scalar, usage_flags: core::UMatUsageFlags) -> Result<core::UMat> { unsafe { sys::cv_UMat_UMat_int_int_int_Scalar_UMatUsageFlags(rows, cols, _type, s, usage_flags) }.into_result().map(|ptr| core::UMat { ptr }) } /// /// ## C++ default parameters /// * usage_flags: USAGE_DEFAULT pub fn new_size_with_default(size: core::Size, _type: i32, s: core::Scalar, usage_flags: core::UMatUsageFlags) -> Result<core::UMat> { unsafe { sys::cv_UMat_UMat_Size_int_Scalar_UMatUsageFlags(size, _type, s, usage_flags) }.into_result().map(|ptr| core::UMat { ptr }) } /// constructs n-dimensional matrix /// /// ## C++ default parameters /// * usage_flags: USAGE_DEFAULT pub unsafe fn new_nd(ndims: i32, sizes: &[i32], _type: i32, usage_flags: core::UMatUsageFlags) -> Result<core::UMat> { { sys::cv_UMat_UMat_int_const_int_X_int_UMatUsageFlags(ndims, sizes.as_ptr(), _type, usage_flags) }.into_result().map(|ptr| core::UMat { ptr }) } /// /// ## C++ default parameters /// * usage_flags: USAGE_DEFAULT pub fn new_nd_with_default(ndims: i32, sizes: &[i32], _type: i32, s: core::Scalar, usage_flags: core::UMatUsageFlags) -> Result<core::UMat> { unsafe { sys::cv_UMat_UMat_int_const_int_X_int_Scalar_UMatUsageFlags(ndims, sizes.as_ptr(), _type, s, usage_flags) }.into_result().map(|ptr| core::UMat { ptr }) } /// copy constructor pub fn copy(m: &core::UMat) -> Result<core::UMat> { unsafe { sys::cv_UMat_UMat_UMat(m.as_raw_UMat()) }.into_result().map(|ptr| core::UMat { ptr }) } /// creates a matrix header for a part of the bigger matrix /// /// ## C++ default parameters /// * col_range: Range::all() pub fn rowscols(m: &core::UMat, row_range: &core::Range, col_range: &core::Range) -> Result<core::UMat> { unsafe { sys::cv_UMat_UMat_UMat_Range_Range(m.as_raw_UMat(), row_range.as_raw_Range(), col_range.as_raw_Range()) }.into_result().map(|ptr| core::UMat { ptr }) } pub fn roi(m: &core::UMat, roi: core::Rect) -> Result<core::UMat> { unsafe { sys::cv_UMat_UMat_UMat_Rect(m.as_raw_UMat(), roi) }.into_result().map(|ptr| core::UMat { ptr }) } pub fn ranges(m: &core::UMat, ranges: &types::VectorOfRange) -> Result<core::UMat> { unsafe { sys::cv_UMat_UMat_UMat_VectorOfRange(m.as_raw_UMat(), ranges.as_raw_VectorOfRange()) }.into_result().map(|ptr| core::UMat { ptr }) } pub fn get_mat(&self, flags: i32) -> Result<core::Mat> { unsafe { sys::cv_UMat_getMat_const_int(self.as_raw_UMat(), flags) }.into_result().map(|ptr| core::Mat { ptr }) } /// returns a new matrix header for the specified row pub fn row(&self, y: i32) -> Result<core::UMat> { unsafe { sys::cv_UMat_row_const_int(self.as_raw_UMat(), y) }.into_result().map(|ptr| core::UMat { ptr }) } /// returns a new matrix header for the specified column pub fn col(&self, x: i32) -> Result<core::UMat> { unsafe { sys::cv_UMat_col_const_int(self.as_raw_UMat(), x) }.into_result().map(|ptr| core::UMat { ptr }) } /// ... for the specified row span pub fn row_bounds(&self, startrow: i32, endrow: i32) -> Result<core::UMat> { unsafe { sys::cv_UMat_rowRange_const_int_int(self.as_raw_UMat(), startrow, endrow) }.into_result().map(|ptr| core::UMat { ptr }) } pub fn row_range(&self, r: &core::Range) -> Result<core::UMat> { unsafe { sys::cv_UMat_rowRange_const_Range(self.as_raw_UMat(), r.as_raw_Range()) }.into_result().map(|ptr| core::UMat { ptr }) } /// ... for the specified column span pub fn col_bounds(&self, startcol: i32, endcol: i32) -> Result<core::UMat> { unsafe { sys::cv_UMat_colRange_const_int_int(self.as_raw_UMat(), startcol, endcol) }.into_result().map(|ptr| core::UMat { ptr }) } pub fn col_range(&self, r: &core::Range) -> Result<core::UMat> { unsafe { sys::cv_UMat_colRange_const_Range(self.as_raw_UMat(), r.as_raw_Range()) }.into_result().map(|ptr| core::UMat { ptr }) } /// ... for the specified diagonal /// (d=0 - the main diagonal, /// >0 - a diagonal from the upper half, /// <0 - a diagonal from the lower half) /// /// ## C++ default parameters /// * d: 0 pub fn diag(&self, d: i32) -> Result<core::UMat> { unsafe { sys::cv_UMat_diag_const_int(self.as_raw_UMat(), d) }.into_result().map(|ptr| core::UMat { ptr }) } /// constructs a square diagonal matrix which main diagonal is vector "d" pub fn diag_1(d: &core::UMat) -> Result<core::UMat> { unsafe { sys::cv_UMat_diag_UMat(d.as_raw_UMat()) }.into_result().map(|ptr| core::UMat { ptr }) } /// returns deep copy of the matrix, i.e. the data is copied pub fn clone(&self) -> Result<core::UMat> { unsafe { sys::cv_UMat_clone_const(self.as_raw_UMat()) }.into_result().map(|ptr| core::UMat { ptr }) } /// copies the matrix content to "m". pub fn copy_to(&self, m: &mut core::Mat) -> Result<()> { unsafe { sys::cv_UMat_copyTo_const_Mat(self.as_raw_UMat(), m.as_raw_Mat()) }.into_result() } /// copies those matrix elements to "m" that are marked with non-zero mask elements. pub fn copy_to_masked(&self, m: &mut core::Mat, mask: &core::Mat) -> Result<()> { unsafe { sys::cv_UMat_copyTo_const_Mat_Mat(self.as_raw_UMat(), m.as_raw_Mat(), mask.as_raw_Mat()) }.into_result() } /// converts matrix to another datatype with optional scaling. See cvConvertScale. /// /// ## C++ default parameters /// * alpha: 1 /// * beta: 0 pub fn convert_to(&self, m: &mut core::Mat, rtype: i32, alpha: f64, beta: f64) -> Result<()> { unsafe { sys::cv_UMat_convertTo_const_Mat_int_double_double(self.as_raw_UMat(), m.as_raw_Mat(), rtype, alpha, beta) }.into_result() } /// /// ## C++ default parameters /// * _type: -1 pub fn assign_to(&self, m: &mut core::UMat, _type: i32) -> Result<()> { unsafe { sys::cv_UMat_assignTo_const_UMat_int(self.as_raw_UMat(), m.as_raw_UMat(), _type) }.into_result() } /// sets some of the matrix elements to s, according to the mask /// /// ## C++ default parameters /// * mask: noArray() pub fn set_to(&mut self, value: &core::Mat, mask: &core::Mat) -> Result<core::UMat> { unsafe { sys::cv_UMat_setTo_Mat_Mat(self.as_raw_UMat(), value.as_raw_Mat(), mask.as_raw_Mat()) }.into_result().map(|ptr| core::UMat { ptr }) } /// creates alternative matrix header for the same data, with different /// /// ## C++ default parameters /// * rows: 0 pub fn reshape(&self, cn: i32, rows: i32) -> Result<core::UMat> { unsafe { sys::cv_UMat_reshape_const_int_int(self.as_raw_UMat(), cn, rows) }.into_result().map(|ptr| core::UMat { ptr }) } pub fn reshape_1(&self, cn: i32, newndims: i32, newsz: &i32) -> Result<core::UMat> { unsafe { sys::cv_UMat_reshape_const_int_int_const_int_X(self.as_raw_UMat(), cn, newndims, newsz) }.into_result().map(|ptr| core::UMat { ptr }) } /// matrix transposition by means of matrix expressions pub fn t(&self) -> Result<core::UMat> { unsafe { sys::cv_UMat_t_const(self.as_raw_UMat()) }.into_result().map(|ptr| core::UMat { ptr }) } /// matrix inversion by means of matrix expressions /// /// ## C++ default parameters /// * method: DECOMP_LU pub fn inv(&self, method: i32) -> Result<core::UMat> { unsafe { sys::cv_UMat_inv_const_int(self.as_raw_UMat(), method) }.into_result().map(|ptr| core::UMat { ptr }) } /// per-element matrix multiplication by means of matrix expressions /// /// ## C++ default parameters /// * scale: 1 pub fn mul(&self, m: &core::Mat, scale: f64) -> Result<core::UMat> { unsafe { sys::cv_UMat_mul_const_Mat_double(self.as_raw_UMat(), m.as_raw_Mat(), scale) }.into_result().map(|ptr| core::UMat { ptr }) } /// computes dot-product pub fn dot(&self, m: &core::Mat) -> Result<f64> { unsafe { sys::cv_UMat_dot_const_Mat(self.as_raw_UMat(), m.as_raw_Mat()) }.into_result() } /// Matlab-style matrix initialization pub fn zeros(rows: i32, cols: i32, _type: i32) -> Result<core::UMat> { unsafe { sys::cv_UMat_zeros_int_int_int(rows, cols, _type) }.into_result().map(|ptr| core::UMat { ptr }) } pub fn zeros_1(size: core::Size, _type: i32) -> Result<core::UMat> { unsafe { sys::cv_UMat_zeros_Size_int(size, _type) }.into_result().map(|ptr| core::UMat { ptr }) } pub fn zeros_2(ndims: i32, sz: &i32, _type: i32) -> Result<core::UMat> { unsafe { sys::cv_UMat_zeros_int_const_int_X_int(ndims, sz, _type) }.into_result().map(|ptr| core::UMat { ptr }) } pub fn ones(rows: i32, cols: i32, _type: i32) -> Result<core::UMat> { unsafe { sys::cv_UMat_ones_int_int_int(rows, cols, _type) }.into_result().map(|ptr| core::UMat { ptr }) } pub fn ones_1(size: core::Size, _type: i32) -> Result<core::UMat> { unsafe { sys::cv_UMat_ones_Size_int(size, _type) }.into_result().map(|ptr| core::UMat { ptr }) } pub fn ones_2(ndims: i32, sz: &i32, _type: i32) -> Result<core::UMat> { unsafe { sys::cv_UMat_ones_int_const_int_X_int(ndims, sz, _type) }.into_result().map(|ptr| core::UMat { ptr }) } pub fn eye(rows: i32, cols: i32, _type: i32) -> Result<core::UMat> { unsafe { sys::cv_UMat_eye_int_int_int(rows, cols, _type) }.into_result().map(|ptr| core::UMat { ptr }) } pub fn eye_1(size: core::Size, _type: i32) -> Result<core::UMat> { unsafe { sys::cv_UMat_eye_Size_int(size, _type) }.into_result().map(|ptr| core::UMat { ptr }) } /// allocates new matrix data unless the matrix already has specified size and type. /// /// ## C++ default parameters /// * usage_flags: USAGE_DEFAULT pub unsafe fn create_rows_cols(&mut self, rows: i32, cols: i32, _type: i32, usage_flags: core::UMatUsageFlags) -> Result<()> { { sys::cv_UMat_create_int_int_int_UMatUsageFlags(self.as_raw_UMat(), rows, cols, _type, usage_flags) }.into_result() } /// /// ## C++ default parameters /// * usage_flags: USAGE_DEFAULT pub unsafe fn create_size(&mut self, size: core::Size, _type: i32, usage_flags: core::UMatUsageFlags) -> Result<()> { { sys::cv_UMat_create_Size_int_UMatUsageFlags(self.as_raw_UMat(), size, _type, usage_flags) }.into_result() } /// /// ## C++ default parameters /// * usage_flags: USAGE_DEFAULT pub unsafe fn create_nd(&mut self, sizes: &types::VectorOfint, _type: i32, usage_flags: core::UMatUsageFlags) -> Result<()> { { sys::cv_UMat_create_VectorOfint_int_UMatUsageFlags(self.as_raw_UMat(), sizes.as_raw_VectorOfint(), _type, usage_flags) }.into_result() } /// increases the reference counter; use with care to avoid memleaks pub fn addref(&mut self) -> Result<()> { unsafe { sys::cv_UMat_addref(self.as_raw_UMat()) }.into_result() } /// decreases reference counter; pub fn release(&mut self) -> Result<()> { unsafe { sys::cv_UMat_release(self.as_raw_UMat()) }.into_result() } /// deallocates the matrix data pub fn deallocate(&mut self) -> Result<()> { unsafe { sys::cv_UMat_deallocate(self.as_raw_UMat()) }.into_result() } /// locates matrix header within a parent matrix. See below pub fn locate_roi(&self, whole_size: &mut core::Size, ofs: &mut core::Point) -> Result<()> { unsafe { sys::cv_UMat_locateROI_const_Size_Point(self.as_raw_UMat(), whole_size, ofs) }.into_result() } /// moves/resizes the current matrix ROI inside the parent matrix. pub fn adjust_roi(&mut self, dtop: i32, dbottom: i32, dleft: i32, dright: i32) -> Result<core::UMat> { unsafe { sys::cv_UMat_adjustROI_int_int_int_int(self.as_raw_UMat(), dtop, dbottom, dleft, dright) }.into_result().map(|ptr| core::UMat { ptr }) } /// returns true iff the matrix data is continuous pub fn is_continuous(&self) -> Result<bool> { unsafe { sys::cv_UMat_isContinuous_const(self.as_raw_UMat()) }.into_result() } /// returns true if the matrix is a submatrix of another matrix pub fn is_submatrix(&self) -> Result<bool> { unsafe { sys::cv_UMat_isSubmatrix_const(self.as_raw_UMat()) }.into_result() } /// returns element size in bytes, pub fn elem_size(&self) -> Result<size_t> { unsafe { sys::cv_UMat_elemSize_const(self.as_raw_UMat()) }.into_result() } /// returns the size of element channel in bytes. pub fn elem_size1(&self) -> Result<size_t> { unsafe { sys::cv_UMat_elemSize1_const(self.as_raw_UMat()) }.into_result() } /// returns element type, similar to CV_MAT_TYPE(cvmat->type) pub fn typ(&self) -> Result<i32> { unsafe { sys::cv_UMat_type_const(self.as_raw_UMat()) }.into_result() } /// returns element type, similar to CV_MAT_DEPTH(cvmat->type) pub fn depth(&self) -> Result<i32> { unsafe { sys::cv_UMat_depth_const(self.as_raw_UMat()) }.into_result() } /// returns element type, similar to CV_MAT_CN(cvmat->type) pub fn channels(&self) -> Result<i32> { unsafe { sys::cv_UMat_channels_const(self.as_raw_UMat()) }.into_result() } /// returns step/elemSize1() /// /// ## C++ default parameters /// * i: 0 pub fn step1(&self, i: i32) -> Result<size_t> { unsafe { sys::cv_UMat_step1_const_int(self.as_raw_UMat(), i) }.into_result() } /// returns true if matrix data is NULL pub fn empty(&self) -> Result<bool> { unsafe { sys::cv_UMat_empty_const(self.as_raw_UMat()) }.into_result() } /// returns the total number of matrix elements pub fn total(&self) -> Result<size_t> { unsafe { sys::cv_UMat_total_const(self.as_raw_UMat()) }.into_result() } /// returns N if the matrix is 1-channel (N x ptdim) or ptdim-channel (1 x N) or (N x 1); negative number otherwise /// /// ## C++ default parameters /// * depth: -1 /// * require_continuous: true pub fn check_vector(&self, elem_channels: i32, depth: i32, require_continuous: bool) -> Result<i32> { unsafe { sys::cv_UMat_checkVector_const_int_int_bool(self.as_raw_UMat(), elem_channels, depth, require_continuous) }.into_result() } pub fn handle(&self, access_flags: i32) -> Result<&mut c_void> { unsafe { sys::cv_UMat_handle_const_int(self.as_raw_UMat(), access_flags) }.into_result().and_then(|x| unsafe { x.as_mut() }.ok_or_else(|| Error::new(core::StsNullPtr, format!("Function returned Null pointer")))) } pub fn ndoffset(&self, ofs: &mut size_t) -> Result<()> { unsafe { sys::cv_UMat_ndoffset_const_size_t_X(self.as_raw_UMat(), ofs) }.into_result() } /// internal use method: updates the continuity flag pub fn update_continuity_flag(&mut self) -> Result<()> { unsafe { sys::cv_UMat_updateContinuityFlag(self.as_raw_UMat()) }.into_result() } } // boxed class cv::UMatData pub struct UMatData { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::UMatData { fn drop(&mut self) { unsafe { sys::cv_UMatData_delete(self.ptr) }; } } impl core::UMatData { #[inline(always)] pub fn as_raw_UMatData(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for UMatData {} impl UMatData { pub fn lock(&mut self) -> Result<()> { unsafe { sys::cv_UMatData_lock(self.as_raw_UMatData()) }.into_result() } pub fn unlock(&mut self) -> Result<()> { unsafe { sys::cv_UMatData_unlock(self.as_raw_UMatData()) }.into_result() } pub fn host_copy_obsolete(&self) -> Result<bool> { unsafe { sys::cv_UMatData_hostCopyObsolete_const(self.as_raw_UMatData()) }.into_result() } pub fn device_copy_obsolete(&self) -> Result<bool> { unsafe { sys::cv_UMatData_deviceCopyObsolete_const(self.as_raw_UMatData()) }.into_result() } pub fn device_mem_mapped(&self) -> Result<bool> { unsafe { sys::cv_UMatData_deviceMemMapped_const(self.as_raw_UMatData()) }.into_result() } pub fn copy_on_map(&self) -> Result<bool> { unsafe { sys::cv_UMatData_copyOnMap_const(self.as_raw_UMatData()) }.into_result() } pub fn temp_u_mat(&self) -> Result<bool> { unsafe { sys::cv_UMatData_tempUMat_const(self.as_raw_UMatData()) }.into_result() } pub fn temp_copied_u_mat(&self) -> Result<bool> { unsafe { sys::cv_UMatData_tempCopiedUMat_const(self.as_raw_UMatData()) }.into_result() } pub fn mark_host_copy_obsolete(&mut self, flag: bool) -> Result<()> { unsafe { sys::cv_UMatData_markHostCopyObsolete_bool(self.as_raw_UMatData(), flag) }.into_result() } pub fn mark_device_copy_obsolete(&mut self, flag: bool) -> Result<()> { unsafe { sys::cv_UMatData_markDeviceCopyObsolete_bool(self.as_raw_UMatData(), flag) }.into_result() } pub fn mark_device_mem_mapped(&mut self, flag: bool) -> Result<()> { unsafe { sys::cv_UMatData_markDeviceMemMapped_bool(self.as_raw_UMatData(), flag) }.into_result() } } // boxed class cv::detail::CheckContext pub struct CheckContext { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::CheckContext { fn drop(&mut self) { unsafe { sys::cv_CheckContext_delete(self.ptr) }; } } impl core::CheckContext { #[inline(always)] pub fn as_raw_CheckContext(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for CheckContext {} // boxed class cv::instr::NodeData pub struct NodeData { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::NodeData { fn drop(&mut self) { unsafe { sys::cv_NodeData_delete(self.ptr) }; } } impl core::NodeData { #[inline(always)] pub fn as_raw_NodeData(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for NodeData {} impl NodeData { /// /// ## C++ default parameters /// * fun_name: 0 /// * file_name: NULL /// * line_num: 0 /// * ret_address: NULL /// * always_expand: false /// * instr_type: TYPE_GENERAL /// * impl_type: IMPL_PLAIN pub fn new(fun_name: &str, file_name: &str, line_num: i32, ret_address: &mut c_void, always_expand: bool, instr_type: core::TYPE, impl_type: core::IMPL) -> Result<core::NodeData> { string_arg!(fun_name); string_arg!(file_name); unsafe { sys::cv_instr_NodeData_NodeData_const_char_X_const_char_X_int_void_X_bool_TYPE_IMPL(fun_name.as_ptr(), file_name.as_ptr(), line_num, ret_address, always_expand, instr_type, impl_type) }.into_result().map(|ptr| core::NodeData { ptr }) } pub fn new_1(_ref: &mut core::NodeData) -> Result<core::NodeData> { unsafe { sys::cv_instr_NodeData_NodeData_NodeData(_ref.as_raw_NodeData()) }.into_result().map(|ptr| core::NodeData { ptr }) } pub fn get_total_ms(&self) -> Result<f64> { unsafe { sys::cv_instr_NodeData_getTotalMs_const(self.as_raw_NodeData()) }.into_result() } pub fn get_mean_ms(&self) -> Result<f64> { unsafe { sys::cv_instr_NodeData_getMeanMs_const(self.as_raw_NodeData()) }.into_result() } } // boxed class cv::instr::NodeDataTls pub struct NodeDataTls { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for core::NodeDataTls { fn drop(&mut self) { unsafe { sys::cv_NodeDataTls_delete(self.ptr) }; } } impl core::NodeDataTls { #[inline(always)] pub fn as_raw_NodeDataTls(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for NodeDataTls {} impl NodeDataTls { pub fn new() -> Result<core::NodeDataTls> { unsafe { sys::cv_instr_NodeDataTls_NodeDataTls() }.into_result().map(|ptr| core::NodeDataTls { ptr }) } } pub const CV_16SC1: i32 = 0x3; // 3 pub const CV_16SC2: i32 = 0xb; // 11 pub const CV_16SC3: i32 = 0x13; // 19 pub const CV_16SC4: i32 = 0x1b; // 27 pub const CV_16UC1: i32 = 0x2; // 2 pub const CV_16UC2: i32 = 0xa; // 10 pub const CV_16UC3: i32 = 0x12; // 18 pub const CV_16UC4: i32 = 0x1a; // 26 pub const CV_32FC1: i32 = 0x5; // 5 pub const CV_32FC2: i32 = 0xd; // 13 pub const CV_32FC3: i32 = 0x15; // 21 pub const CV_32FC4: i32 = 0x1d; // 29 pub const CV_32SC1: i32 = 0x4; // 4 pub const CV_32SC2: i32 = 0xc; // 12 pub const CV_32SC3: i32 = 0x14; // 20 pub const CV_32SC4: i32 = 0x1c; // 28 pub const CV_64FC1: i32 = 0x6; // 6 pub const CV_64FC2: i32 = 0xe; // 14 pub const CV_64FC3: i32 = 0x16; // 22 pub const CV_64FC4: i32 = 0x1e; // 30 pub const CV_8SC1: i32 = 0x1; // 1 pub const CV_8SC2: i32 = 0x9; // 9 pub const CV_8SC3: i32 = 0x11; // 17 pub const CV_8SC4: i32 = 0x19; // 25 pub const CV_8UC1: i32 = 0x0; // 0 pub const CV_8UC2: i32 = 0x8; // 8 pub const CV_8UC3: i32 = 0x10; // 16 pub const CV_8UC4: i32 = 0x18; // 24 pub const CV_DEPTH_MAX: i32 = 0x8; // 8 pub const CV_MAT_CN_MASK: i32 = 0xff8; // 4088 pub const CV_MAT_CONT_FLAG: i32 = 0x4000; // 16384 pub const CV_MAT_DEPTH_MASK: i32 = 0x7; // 7 pub const CV_MAT_TYPE_MASK: i32 = 0xfff; // 4095 pub const CV_SUBMAT_FLAG: i32 = 0x8000; // 32768 pub static CV_VERSION: &'static str = "3.4.6"; pub const Mat_CONTINUOUS_FLAG: i32 = 0x4000; // 16384 pub const Mat_SUBMATRIX_FLAG: i32 = 0x8000; // 32768 pub const _InputArray_CUDA_GPU_MAT: i32 = 0x90000; // 589824 pub const _InputArray_CUDA_HOST_MEM: i32 = 0x80000; // 524288 pub const _InputArray_EXPR: i32 = 0x60000; // 393216 pub const _InputArray_FIXED_SIZE: i32 = 0x40000000; // 1073741824 pub const _InputArray_FIXED_TYPE: i32 = 0x80000000; // -2147483648 pub const _InputArray_KIND_MASK: i32 = 0x1f0000; // 2031616 pub const _InputArray_MATX: i32 = 0x20000; // 131072 pub const _InputArray_NONE: i32 = 0x0; // 0 pub const _InputArray_OPENGL_BUFFER: i32 = 0x70000; // 458752 pub const _InputArray_STD_ARRAY: i32 = 0xe0000; // 917504 pub const _InputArray_STD_ARRAY_MAT: i32 = 0xf0000; // 983040 pub const _InputArray_STD_BOOL_VECTOR: i32 = 0xc0000; // 786432 pub const _InputArray_STD_VECTOR: i32 = 0x30000; // 196608 pub const _InputArray_STD_VECTOR_CUDA_GPU_MAT: i32 = 0xd0000; // 851968 pub const _InputArray_STD_VECTOR_MAT: i32 = 0x50000; // 327680 pub const _InputArray_STD_VECTOR_UMAT: i32 = 0xb0000; // 720896 pub const _InputArray_STD_VECTOR_VECTOR: i32 = 0x40000; // 262144 pub const _InputArray_UMAT: i32 = 0xa0000; // 655360 pub const _OutputArray_DEPTH_MASK_16S: i32 = 0x8; // 8 pub const _OutputArray_DEPTH_MASK_16U: i32 = 0x4; // 4 pub const _OutputArray_DEPTH_MASK_32F: i32 = 0x20; // 32 pub const _OutputArray_DEPTH_MASK_32S: i32 = 0x10; // 16 pub const _OutputArray_DEPTH_MASK_64F: i32 = 0x40; // 64 pub const _OutputArray_DEPTH_MASK_8S: i32 = 0x2; // 2 pub const _OutputArray_DEPTH_MASK_8U: i32 = 0x1; // 1 pub const _OutputArray_DEPTH_MASK_ALL: i32 = 0x7f; // 127 pub const _OutputArray_DEPTH_MASK_ALL_BUT_8S: i32 = 0x7d; // 125 pub const _OutputArray_DEPTH_MASK_FLT: i32 = 0x60; // 96 pub use crate::manual::core::*;