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//! # Camera Calibration and 3D Reconstruction //! //! The functions in this section use a so-called pinhole camera model. In this model, a scene view is //! formed by projecting 3D points into the image plane using a perspective transformation. //! //!  //! //! or //! //!  //! //! where: //! //! *  are the coordinates of a 3D point in the world coordinate space //! *  are the coordinates of the projection point in pixels //! *  is a camera matrix, or a matrix of intrinsic parameters //! *  is a principal point that is usually at the image center //! *  are the focal lengths expressed in pixel units. //! //! Thus, if an image from the camera is scaled by a factor, all of these parameters should be scaled //! (multiplied/divided, respectively) by the same factor. The matrix of intrinsic parameters does not //! depend on the scene viewed. So, once estimated, it can be re-used as long as the focal length is //! fixed (in case of zoom lens). The joint rotation-translation matrix  is called a matrix of //! extrinsic parameters. It is used to describe the camera motion around a static scene, or vice versa, //! rigid motion of an object in front of a still camera. That is,  translates coordinates of a //! point  to a coordinate system, fixed with respect to the camera. The transformation above //! is equivalent to the following (when  ): //! //!  //! //! The following figure illustrates the pinhole camera model. //! //!  //! //! Real lenses usually have some distortion, mostly radial distortion and slight tangential distortion. //! So, the above model is extended as: //! //!  //! //! , , , , , and  are radial distortion coefficients.  and  are //! tangential distortion coefficients. , , , and , are the thin prism distortion //! coefficients. Higher-order coefficients are not considered in OpenCV. //! //! The next figures show two common types of radial distortion: barrel distortion (typically ) and pincushion distortion (typically ). //! //!  //!  //! //! In some cases the image sensor may be tilted in order to focus an oblique plane in front of the //! camera (Scheimpfug condition). This can be useful for particle image velocimetry (PIV) or //! triangulation with a laser fan. The tilt causes a perspective distortion of  and //! . This distortion can be modelled in the following way, see e.g. [Louhichi07](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Louhichi07). //! //!  //! //! where the matrix  is defined by two rotations with angular parameter  //! and , respectively, //! //!  //! //! In the functions below the coefficients are passed or returned as //! //!  //! //! vector. That is, if the vector contains four elements, it means that  . The distortion //! coefficients do not depend on the scene viewed. Thus, they also belong to the intrinsic camera //! parameters. And they remain the same regardless of the captured image resolution. If, for example, a //! camera has been calibrated on images of 320 x 240 resolution, absolutely the same distortion //! coefficients can be used for 640 x 480 images from the same camera while , , , and //!  need to be scaled appropriately. //! //! The functions below use the above model to do the following: //! //! * Project 3D points to the image plane given intrinsic and extrinsic parameters. //! * Compute extrinsic parameters given intrinsic parameters, a few 3D points, and their //! projections. //! * Estimate intrinsic and extrinsic camera parameters from several views of a known calibration //! pattern (every view is described by several 3D-2D point correspondences). //! * Estimate the relative position and orientation of the stereo camera "heads" and compute the //! *rectification* transformation that makes the camera optical axes parallel. //! //! //! Note: //! * A calibration sample for 3 cameras in horizontal position can be found at //! opencv_source_code/samples/cpp/3calibration.cpp //! * A calibration sample based on a sequence of images can be found at //! opencv_source_code/samples/cpp/calibration.cpp //! * A calibration sample in order to do 3D reconstruction can be found at //! opencv_source_code/samples/cpp/build3dmodel.cpp //! * A calibration example on stereo calibration can be found at //! opencv_source_code/samples/cpp/stereo_calib.cpp //! * A calibration example on stereo matching can be found at //! opencv_source_code/samples/cpp/stereo_match.cpp //! * (Python) A camera calibration sample can be found at //! opencv_source_code/samples/python/calibrate.py //! # Fisheye camera model //! //! Definitions: Let P be a point in 3D of coordinates X in the world reference frame (stored in the //! matrix X) The coordinate vector of P in the camera reference frame is: //! //!  //! //! where R is the rotation matrix corresponding to the rotation vector om: R = rodrigues(om); call x, y //! and z the 3 coordinates of Xc: //! //!  //! //! The pinhole projection coordinates of P is [a; b] where //! //!  //! //! Fisheye distortion: //! //!  //! //! The distorted point coordinates are [x'; y'] where //! //!  //! //! Finally, conversion into pixel coordinates: The final pixel coordinates vector [u; v] where: //! //!  //! //! # C API use std::os::raw::{c_char, c_void}; use libc::{ptrdiff_t, size_t}; use crate::{Error, Result, core, sys, types}; use crate::core::{_InputArray, _OutputArray}; pub const CALIB_CB_ADAPTIVE_THRESH: i32 = 1; pub const CALIB_CB_ASYMMETRIC_GRID: i32 = 2; pub const CALIB_CB_CLUSTERING: i32 = 4; pub const CALIB_CB_FAST_CHECK: i32 = 8; pub const CALIB_CB_FILTER_QUADS: i32 = 4; pub const CALIB_CB_NORMALIZE_IMAGE: i32 = 2; pub const CALIB_CB_SYMMETRIC_GRID: i32 = 1; pub const CALIB_CHECK_COND: i32 = 1 << 2; pub const CALIB_FIX_ASPECT_RATIO: i32 = 0x00002; pub const CALIB_FIX_FOCAL_LENGTH: i32 = 0x00010; pub const CALIB_FIX_INTRINSIC: i32 = 0x00100; pub const CALIB_FIX_K1: i32 = 0x00020; pub const CALIB_FIX_K2: i32 = 0x00040; pub const CALIB_FIX_K3: i32 = 0x00080; pub const CALIB_FIX_K4: i32 = 0x00800; pub const CALIB_FIX_K5: i32 = 0x01000; pub const CALIB_FIX_K6: i32 = 0x02000; pub const CALIB_FIX_PRINCIPAL_POINT: i32 = 0x00004; pub const CALIB_FIX_S1_S2_S3_S4: i32 = 0x10000; pub const CALIB_FIX_SKEW: i32 = 1 << 3; pub const CALIB_FIX_TANGENT_DIST: i32 = 0x200000; pub const CALIB_FIX_TAUX_TAUY: i32 = 0x80000; /// On-line Hand-Eye Calibration [Andreff99](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Andreff99) pub const CALIB_HAND_EYE_ANDREFF: i32 = 3; /// Hand-Eye Calibration Using Dual Quaternions [Daniilidis98](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Daniilidis98) pub const CALIB_HAND_EYE_DANIILIDIS: i32 = 4; /// Hand-eye Calibration [Horaud95](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Horaud95) pub const CALIB_HAND_EYE_HORAUD: i32 = 2; /// Robot Sensor Calibration: Solving AX = XB on the Euclidean Group [Park94](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Park94) pub const CALIB_HAND_EYE_PARK: i32 = 1; /// A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/Eye Calibration [Tsai89](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Tsai89) pub const CALIB_HAND_EYE_TSAI: i32 = 0; pub const CALIB_RATIONAL_MODEL: i32 = 0x04000; pub const CALIB_RECOMPUTE_EXTRINSIC: i32 = 1 << 1; pub const CALIB_SAME_FOCAL_LENGTH: i32 = 0x00200; pub const CALIB_THIN_PRISM_MODEL: i32 = 0x08000; pub const CALIB_TILTED_MODEL: i32 = 0x40000; /// for stereoCalibrate pub const CALIB_USE_EXTRINSIC_GUESS: i32 = (1 << 22); pub const CALIB_USE_INTRINSIC_GUESS: i32 = 0x00001; /// use LU instead of SVD decomposition for solving. much faster but potentially less precise pub const CALIB_USE_LU: i32 = (1 << 17); /// use QR instead of SVD decomposition for solving. Faster but potentially less precise pub const CALIB_USE_QR: i32 = 0x100000; pub const CALIB_ZERO_DISPARITY: i32 = 0x00400; pub const CALIB_ZERO_TANGENT_DIST: i32 = 0x00008; pub const CirclesGridFinderParameters_ASYMMETRIC_GRID: i32 = 1; pub const CirclesGridFinderParameters_SYMMETRIC_GRID: i32 = 0; /// 7-point algorithm pub const FM_7POINT: i32 = 1; /// 8-point algorithm pub const FM_8POINT: i32 = 2; /// least-median algorithm. 7-point algorithm is used. pub const FM_LMEDS: i32 = 4; /// RANSAC algorithm. It needs at least 15 points. 7-point algorithm is used. pub const FM_RANSAC: i32 = 8; /// least-median of squares algorithm pub const LMEDS: i32 = 4; /// RANSAC algorithm pub const RANSAC: i32 = 8; /// RHO algorithm pub const RHO: i32 = 16; /// An Efficient Algebraic Solution to the Perspective-Three-Point Problem [Ke17](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Ke17) pub const SOLVEPNP_AP3P: i32 = 5; /// A Direct Least-Squares (DLS) Method for PnP [hesch2011direct](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_hesch2011direct) pub const SOLVEPNP_DLS: i32 = 3; /// EPnP: Efficient Perspective-n-Point Camera Pose Estimation [lepetit2009epnp](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_lepetit2009epnp) pub const SOLVEPNP_EPNP: i32 = 1; /// Infinitesimal Plane-Based Pose Estimation [Collins14](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Collins14) pub const SOLVEPNP_IPPE: i32 = 6; /// Infinitesimal Plane-Based Pose Estimation [Collins14](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Collins14) pub const SOLVEPNP_IPPE_SQUARE: i32 = 7; pub const SOLVEPNP_ITERATIVE: i32 = 0; /// Used for count pub const SOLVEPNP_MAX_COUNT: i32 = 7+1; /// Complete Solution Classification for the Perspective-Three-Point Problem [gao2003complete](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_gao2003complete) pub const SOLVEPNP_P3P: i32 = 2; /// Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation [penate2013exhaustive](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_penate2013exhaustive) pub const SOLVEPNP_UPNP: i32 = 4; pub const StereoBM_PREFILTER_NORMALIZED_RESPONSE: i32 = 0; pub const StereoBM_PREFILTER_XSOBEL: i32 = 1; pub const StereoMatcher_DISP_SHIFT: i32 = 4; pub const StereoSGBM_MODE_HH: i32 = 1; pub const StereoSGBM_MODE_HH4: i32 = 3; pub const StereoSGBM_MODE_SGBM: i32 = 0; pub const StereoSGBM_MODE_SGBM_3WAY: i32 = 2; #[repr(C)] #[derive(Debug)] pub enum HandEyeCalibrationMethod { /// A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/Eye Calibration [Tsai89](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Tsai89) CALIB_HAND_EYE_TSAI = CALIB_HAND_EYE_TSAI as isize, /// Robot Sensor Calibration: Solving AX = XB on the Euclidean Group [Park94](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Park94) CALIB_HAND_EYE_PARK = CALIB_HAND_EYE_PARK as isize, /// Hand-eye Calibration [Horaud95](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Horaud95) CALIB_HAND_EYE_HORAUD = CALIB_HAND_EYE_HORAUD as isize, /// On-line Hand-Eye Calibration [Andreff99](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Andreff99) CALIB_HAND_EYE_ANDREFF = CALIB_HAND_EYE_ANDREFF as isize, /// Hand-Eye Calibration Using Dual Quaternions [Daniilidis98](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Daniilidis98) CALIB_HAND_EYE_DANIILIDIS = CALIB_HAND_EYE_DANIILIDIS as isize, } #[repr(C)] #[derive(Debug)] pub enum SolvePnPMethod { SOLVEPNP_ITERATIVE = SOLVEPNP_ITERATIVE as isize, /// EPnP: Efficient Perspective-n-Point Camera Pose Estimation [lepetit2009epnp](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_lepetit2009epnp) SOLVEPNP_EPNP = SOLVEPNP_EPNP as isize, /// Complete Solution Classification for the Perspective-Three-Point Problem [gao2003complete](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_gao2003complete) SOLVEPNP_P3P = SOLVEPNP_P3P as isize, /// A Direct Least-Squares (DLS) Method for PnP [hesch2011direct](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_hesch2011direct) SOLVEPNP_DLS = SOLVEPNP_DLS as isize, /// Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation [penate2013exhaustive](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_penate2013exhaustive) SOLVEPNP_UPNP = SOLVEPNP_UPNP as isize, /// An Efficient Algebraic Solution to the Perspective-Three-Point Problem [Ke17](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Ke17) SOLVEPNP_AP3P = SOLVEPNP_AP3P as isize, /// Infinitesimal Plane-Based Pose Estimation [Collins14](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Collins14) SOLVEPNP_IPPE = SOLVEPNP_IPPE as isize, /// Infinitesimal Plane-Based Pose Estimation [Collins14](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Collins14) SOLVEPNP_IPPE_SQUARE = SOLVEPNP_IPPE_SQUARE as isize, /// Used for count SOLVEPNP_MAX_COUNT = SOLVEPNP_MAX_COUNT as isize, } #[repr(C)] #[derive(Copy,Clone,Debug,PartialEq)] pub struct CirclesGridFinderParameters { pub density_neighborhood_size: core::Size2f, pub min_density: f32, pub kmeans_attempts: i32, pub min_distance_to_add_keypoint: i32, pub keypoint_scale: i32, pub min_graph_confidence: f32, pub vertex_gain: f32, pub vertex_penalty: f32, pub existing_vertex_gain: f32, pub edge_gain: f32, pub edge_penalty: f32, pub convex_hull_factor: f32, pub min_rng_edge_switch_dist: f32, } /// Computes an RQ decomposition of 3x3 matrices. /// /// ## Parameters /// * src: 3x3 input matrix. /// * mtxR: Output 3x3 upper-triangular matrix. /// * mtxQ: Output 3x3 orthogonal matrix. /// * Qx: Optional output 3x3 rotation matrix around x-axis. /// * Qy: Optional output 3x3 rotation matrix around y-axis. /// * Qz: Optional output 3x3 rotation matrix around z-axis. /// /// The function computes a RQ decomposition using the given rotations. This function is used in /// decomposeProjectionMatrix to decompose the left 3x3 submatrix of a projection matrix into a camera /// and a rotation matrix. /// /// It optionally returns three rotation matrices, one for each axis, and the three Euler angles in /// degrees (as the return value) that could be used in OpenGL. Note, there is always more than one /// sequence of rotations about the three principal axes that results in the same orientation of an /// object, e.g. see [Slabaugh](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Slabaugh) . Returned tree rotation matrices and corresponding three Euler angles /// are only one of the possible solutions. /// /// ## C++ default parameters /// * qx: noArray() /// * qy: noArray() /// * qz: noArray() pub fn rq_decomp3x3(src: &dyn core::ToInputArray, mtx_r: &mut dyn core::ToOutputArray, mtx_q: &mut dyn core::ToOutputArray, qx: &mut dyn core::ToOutputArray, qy: &mut dyn core::ToOutputArray, qz: &mut dyn core::ToOutputArray) -> Result<core::Vec3d> { input_array_arg!(src); output_array_arg!(mtx_r); output_array_arg!(mtx_q); output_array_arg!(qx); output_array_arg!(qy); output_array_arg!(qz); unsafe { sys::cv_RQDecomp3x3__InputArray__OutputArray__OutputArray__OutputArray__OutputArray__OutputArray(src.as_raw__InputArray(), mtx_r.as_raw__OutputArray(), mtx_q.as_raw__OutputArray(), qx.as_raw__OutputArray(), qy.as_raw__OutputArray(), qz.as_raw__OutputArray()) }.into_result() } /// Converts a rotation matrix to a rotation vector or vice versa. /// /// ## Parameters /// * src: Input rotation vector (3x1 or 1x3) or rotation matrix (3x3). /// * dst: Output rotation matrix (3x3) or rotation vector (3x1 or 1x3), respectively. /// * jacobian: Optional output Jacobian matrix, 3x9 or 9x3, which is a matrix of partial /// derivatives of the output array components with respect to the input array components. /// ///  /// /// Inverse transformation can be also done easily, since /// ///  /// /// A rotation vector is a convenient and most compact representation of a rotation matrix (since any /// rotation matrix has just 3 degrees of freedom). The representation is used in the global 3D geometry /// optimization procedures like calibrateCamera, stereoCalibrate, or solvePnP . /// /// ## C++ default parameters /// * jacobian: noArray() pub fn rodrigues(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray, jacobian: &mut dyn core::ToOutputArray) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); output_array_arg!(jacobian); unsafe { sys::cv_Rodrigues__InputArray__OutputArray__OutputArray(src.as_raw__InputArray(), dst.as_raw__OutputArray(), jacobian.as_raw__OutputArray()) }.into_result() } /// Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern. /// /// ## Parameters /// * objectPoints: In the new interface it is a vector of vectors of calibration pattern points in /// the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer /// vector contains as many elements as the number of the pattern views. If the same calibration pattern /// is shown in each view and it is fully visible, all the vectors will be the same. Although, it is /// possible to use partially occluded patterns, or even different patterns in different views. Then, /// the vectors will be different. The points are 3D, but since they are in a pattern coordinate system, /// then, if the rig is planar, it may make sense to put the model to a XY coordinate plane so that /// Z-coordinate of each input object point is 0. /// In the old interface all the vectors of object points from different views are concatenated /// together. /// * imagePoints: In the new interface it is a vector of vectors of the projections of calibration /// pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and /// objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i. /// In the old interface all the vectors of object points from different views are concatenated /// together. /// * imageSize: Size of the image used only to initialize the intrinsic camera matrix. /// * cameraMatrix: Output 3x3 floating-point camera matrix ///  . If CV\_CALIB\_USE\_INTRINSIC\_GUESS /// and/or CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be /// initialized before calling the function. /// * distCoeffs: Output vector of distortion coefficients ///  of /// 4, 5, 8, 12 or 14 elements. /// * rvecs: Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view /// (e.g. std::vector<cv::Mat>>). That is, each k-th rotation vector together with the corresponding /// k-th translation vector (see the next output parameter description) brings the calibration pattern /// from the model coordinate space (in which object points are specified) to the world coordinate /// space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. *M* -1). /// * tvecs: Output vector of translation vectors estimated for each pattern view. /// * stdDeviationsIntrinsics: Output vector of standard deviations estimated for intrinsic parameters. /// Order of deviations values: ///  If one of parameters is not estimated, it's deviation is equals to zero. /// * stdDeviationsExtrinsics: Output vector of standard deviations estimated for extrinsic parameters. /// Order of deviations values:  where M is number of pattern views, ///  are concatenated 1x3 vectors. /// * perViewErrors: Output vector of the RMS re-projection error estimated for each pattern view. /// * flags: Different flags that may be zero or a combination of the following values: /// * **CALIB_USE_INTRINSIC_GUESS** cameraMatrix contains valid initial values of /// fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image /// center ( imageSize is used), and focal distances are computed in a least-squares fashion. /// Note, that if intrinsic parameters are known, there is no need to use this function just to /// estimate extrinsic parameters. Use solvePnP instead. /// * **CALIB_FIX_PRINCIPAL_POINT** The principal point is not changed during the global /// optimization. It stays at the center or at a different location specified when /// CALIB_USE_INTRINSIC_GUESS is set too. /// * **CALIB_FIX_ASPECT_RATIO** The functions considers only fy as a free parameter. The /// ratio fx/fy stays the same as in the input cameraMatrix . When /// CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are /// ignored, only their ratio is computed and used further. /// * **CALIB_ZERO_TANGENT_DIST** Tangential distortion coefficients  are set /// to zeros and stay zero. /// * **CALIB_FIX_K1,...,CALIB_FIX_K6** The corresponding radial distortion /// coefficient is not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is /// set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. /// * **CALIB_RATIONAL_MODEL** Coefficients k4, k5, and k6 are enabled. To provide the /// backward compatibility, this extra flag should be explicitly specified to make the /// calibration function use the rational model and return 8 coefficients. If the flag is not /// set, the function computes and returns only 5 distortion coefficients. /// * **CALIB_THIN_PRISM_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the /// backward compatibility, this extra flag should be explicitly specified to make the /// calibration function use the thin prism model and return 12 coefficients. If the flag is not /// set, the function computes and returns only 5 distortion coefficients. /// * **CALIB_FIX_S1_S2_S3_S4** The thin prism distortion coefficients are not changed during /// the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the /// supplied distCoeffs matrix is used. Otherwise, it is set to 0. /// * **CALIB_TILTED_MODEL** Coefficients tauX and tauY are enabled. To provide the /// backward compatibility, this extra flag should be explicitly specified to make the /// calibration function use the tilted sensor model and return 14 coefficients. If the flag is not /// set, the function computes and returns only 5 distortion coefficients. /// * **CALIB_FIX_TAUX_TAUY** The coefficients of the tilted sensor model are not changed during /// the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the /// supplied distCoeffs matrix is used. Otherwise, it is set to 0. /// * criteria: Termination criteria for the iterative optimization algorithm. /// /// ## Returns /// the overall RMS re-projection error. /// /// The function estimates the intrinsic camera parameters and extrinsic parameters for each of the /// views. The algorithm is based on [Zhang2000](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Zhang2000) and [BouguetMCT](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_BouguetMCT) . The coordinates of 3D object /// points and their corresponding 2D projections in each view must be specified. That may be achieved /// by using an object with a known geometry and easily detectable feature points. Such an object is /// called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as /// a calibration rig (see findChessboardCorners ). Currently, initialization of intrinsic parameters /// (when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration /// patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also /// be used as long as initial cameraMatrix is provided. /// /// The algorithm performs the following steps: /// /// * Compute the initial intrinsic parameters (the option only available for planar calibration /// patterns) or read them from the input parameters. The distortion coefficients are all set to /// zeros initially unless some of CALIB_FIX_K? are specified. /// /// * Estimate the initial camera pose as if the intrinsic parameters have been already known. This is /// done using solvePnP . /// /// * Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, /// that is, the total sum of squared distances between the observed feature points imagePoints and /// the projected (using the current estimates for camera parameters and the poses) object points /// objectPoints. See projectPoints for details. /// /// /// Note: /// If you use a non-square (=non-NxN) grid and findChessboardCorners for calibration, and /// calibrateCamera returns bad values (zero distortion coefficients, an image center very far from /// (w/2-0.5,h/2-0.5), and/or large differences between  and  (ratios of 10:1 or more)), /// then you have probably used patternSize=cvSize(rows,cols) instead of using /// patternSize=cvSize(cols,rows) in findChessboardCorners . /// /// ## See also /// findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort /// /// ## C++ default parameters /// * flags: 0 /// * criteria: TermCriteria( TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) pub fn calibrate_camera_with_stddev(object_points: &dyn core::ToInputArray, image_points: &dyn core::ToInputArray, image_size: core::Size, camera_matrix: &mut dyn core::ToInputOutputArray, dist_coeffs: &mut dyn core::ToInputOutputArray, rvecs: &mut dyn core::ToOutputArray, tvecs: &mut dyn core::ToOutputArray, std_deviations_intrinsics: &mut dyn core::ToOutputArray, std_deviations_extrinsics: &mut dyn core::ToOutputArray, per_view_errors: &mut dyn core::ToOutputArray, flags: i32, criteria: &core::TermCriteria) -> Result<f64> { input_array_arg!(object_points); input_array_arg!(image_points); input_output_array_arg!(camera_matrix); input_output_array_arg!(dist_coeffs); output_array_arg!(rvecs); output_array_arg!(tvecs); output_array_arg!(std_deviations_intrinsics); output_array_arg!(std_deviations_extrinsics); output_array_arg!(per_view_errors); unsafe { sys::cv_calibrateCamera__InputArray__InputArray_Size__InputOutputArray__InputOutputArray__OutputArray__OutputArray__OutputArray__OutputArray__OutputArray_int_TermCriteria(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), image_size, camera_matrix.as_raw__InputOutputArray(), dist_coeffs.as_raw__InputOutputArray(), rvecs.as_raw__OutputArray(), tvecs.as_raw__OutputArray(), std_deviations_intrinsics.as_raw__OutputArray(), std_deviations_extrinsics.as_raw__OutputArray(), per_view_errors.as_raw__OutputArray(), flags, criteria.as_raw_TermCriteria()) }.into_result() } /// Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern. /// /// ## Parameters /// * objectPoints: In the new interface it is a vector of vectors of calibration pattern points in /// the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer /// vector contains as many elements as the number of the pattern views. If the same calibration pattern /// is shown in each view and it is fully visible, all the vectors will be the same. Although, it is /// possible to use partially occluded patterns, or even different patterns in different views. Then, /// the vectors will be different. The points are 3D, but since they are in a pattern coordinate system, /// then, if the rig is planar, it may make sense to put the model to a XY coordinate plane so that /// Z-coordinate of each input object point is 0. /// In the old interface all the vectors of object points from different views are concatenated /// together. /// * imagePoints: In the new interface it is a vector of vectors of the projections of calibration /// pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and /// objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i. /// In the old interface all the vectors of object points from different views are concatenated /// together. /// * imageSize: Size of the image used only to initialize the intrinsic camera matrix. /// * cameraMatrix: Output 3x3 floating-point camera matrix ///  . If CV\_CALIB\_USE\_INTRINSIC\_GUESS /// and/or CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be /// initialized before calling the function. /// * distCoeffs: Output vector of distortion coefficients ///  of /// 4, 5, 8, 12 or 14 elements. /// * rvecs: Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view /// (e.g. std::vector<cv::Mat>>). That is, each k-th rotation vector together with the corresponding /// k-th translation vector (see the next output parameter description) brings the calibration pattern /// from the model coordinate space (in which object points are specified) to the world coordinate /// space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. *M* -1). /// * tvecs: Output vector of translation vectors estimated for each pattern view. /// * stdDeviationsIntrinsics: Output vector of standard deviations estimated for intrinsic parameters. /// Order of deviations values: ///  If one of parameters is not estimated, it's deviation is equals to zero. /// * stdDeviationsExtrinsics: Output vector of standard deviations estimated for extrinsic parameters. /// Order of deviations values:  where M is number of pattern views, ///  are concatenated 1x3 vectors. /// * perViewErrors: Output vector of the RMS re-projection error estimated for each pattern view. /// * flags: Different flags that may be zero or a combination of the following values: /// * **CALIB_USE_INTRINSIC_GUESS** cameraMatrix contains valid initial values of /// fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image /// center ( imageSize is used), and focal distances are computed in a least-squares fashion. /// Note, that if intrinsic parameters are known, there is no need to use this function just to /// estimate extrinsic parameters. Use solvePnP instead. /// * **CALIB_FIX_PRINCIPAL_POINT** The principal point is not changed during the global /// optimization. It stays at the center or at a different location specified when /// CALIB_USE_INTRINSIC_GUESS is set too. /// * **CALIB_FIX_ASPECT_RATIO** The functions considers only fy as a free parameter. The /// ratio fx/fy stays the same as in the input cameraMatrix . When /// CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are /// ignored, only their ratio is computed and used further. /// * **CALIB_ZERO_TANGENT_DIST** Tangential distortion coefficients  are set /// to zeros and stay zero. /// * **CALIB_FIX_K1,...,CALIB_FIX_K6** The corresponding radial distortion /// coefficient is not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is /// set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. /// * **CALIB_RATIONAL_MODEL** Coefficients k4, k5, and k6 are enabled. To provide the /// backward compatibility, this extra flag should be explicitly specified to make the /// calibration function use the rational model and return 8 coefficients. If the flag is not /// set, the function computes and returns only 5 distortion coefficients. /// * **CALIB_THIN_PRISM_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the /// backward compatibility, this extra flag should be explicitly specified to make the /// calibration function use the thin prism model and return 12 coefficients. If the flag is not /// set, the function computes and returns only 5 distortion coefficients. /// * **CALIB_FIX_S1_S2_S3_S4** The thin prism distortion coefficients are not changed during /// the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the /// supplied distCoeffs matrix is used. Otherwise, it is set to 0. /// * **CALIB_TILTED_MODEL** Coefficients tauX and tauY are enabled. To provide the /// backward compatibility, this extra flag should be explicitly specified to make the /// calibration function use the tilted sensor model and return 14 coefficients. If the flag is not /// set, the function computes and returns only 5 distortion coefficients. /// * **CALIB_FIX_TAUX_TAUY** The coefficients of the tilted sensor model are not changed during /// the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the /// supplied distCoeffs matrix is used. Otherwise, it is set to 0. /// * criteria: Termination criteria for the iterative optimization algorithm. /// /// ## Returns /// the overall RMS re-projection error. /// /// The function estimates the intrinsic camera parameters and extrinsic parameters for each of the /// views. The algorithm is based on [Zhang2000](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Zhang2000) and [BouguetMCT](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_BouguetMCT) . The coordinates of 3D object /// points and their corresponding 2D projections in each view must be specified. That may be achieved /// by using an object with a known geometry and easily detectable feature points. Such an object is /// called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as /// a calibration rig (see findChessboardCorners ). Currently, initialization of intrinsic parameters /// (when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration /// patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also /// be used as long as initial cameraMatrix is provided. /// /// The algorithm performs the following steps: /// /// * Compute the initial intrinsic parameters (the option only available for planar calibration /// patterns) or read them from the input parameters. The distortion coefficients are all set to /// zeros initially unless some of CALIB_FIX_K? are specified. /// /// * Estimate the initial camera pose as if the intrinsic parameters have been already known. This is /// done using solvePnP . /// /// * Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, /// that is, the total sum of squared distances between the observed feature points imagePoints and /// the projected (using the current estimates for camera parameters and the poses) object points /// objectPoints. See projectPoints for details. /// /// /// Note: /// If you use a non-square (=non-NxN) grid and findChessboardCorners for calibration, and /// calibrateCamera returns bad values (zero distortion coefficients, an image center very far from /// (w/2-0.5,h/2-0.5), and/or large differences between  and  (ratios of 10:1 or more)), /// then you have probably used patternSize=cvSize(rows,cols) instead of using /// patternSize=cvSize(cols,rows) in findChessboardCorners . /// /// ## See also /// findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort /// /// ## Overloaded parameters /// double calibrateCamera( InputArrayOfArrays objectPoints, /// InputArrayOfArrays imagePoints, Size imageSize, /// InputOutputArray cameraMatrix, InputOutputArray distCoeffs, /// OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, /// OutputArray stdDeviations, OutputArray perViewErrors, /// int flags = 0, TermCriteria criteria = TermCriteria( /// TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) ) /// /// ## C++ default parameters /// * flags: 0 /// * criteria: TermCriteria( TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) pub fn calibrate_camera(object_points: &dyn core::ToInputArray, image_points: &dyn core::ToInputArray, image_size: core::Size, camera_matrix: &mut dyn core::ToInputOutputArray, dist_coeffs: &mut dyn core::ToInputOutputArray, rvecs: &mut dyn core::ToOutputArray, tvecs: &mut dyn core::ToOutputArray, flags: i32, criteria: &core::TermCriteria) -> Result<f64> { input_array_arg!(object_points); input_array_arg!(image_points); input_output_array_arg!(camera_matrix); input_output_array_arg!(dist_coeffs); output_array_arg!(rvecs); output_array_arg!(tvecs); unsafe { sys::cv_calibrateCamera__InputArray__InputArray_Size__InputOutputArray__InputOutputArray__OutputArray__OutputArray_int_TermCriteria(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), image_size, camera_matrix.as_raw__InputOutputArray(), dist_coeffs.as_raw__InputOutputArray(), rvecs.as_raw__OutputArray(), tvecs.as_raw__OutputArray(), flags, criteria.as_raw_TermCriteria()) }.into_result() } /// Computes Hand-Eye calibration:  /// /// ## Parameters /// * R_gripper2base: Rotation part extracted from the homogeneous matrix that transforms a point /// expressed in the gripper frame to the robot base frame (). /// This is a vector (`vector<Mat>`) that contains the rotation matrices for all the transformations /// from gripper frame to robot base frame. /// * t_gripper2base: Translation part extracted from the homogeneous matrix that transforms a point /// expressed in the gripper frame to the robot base frame (). /// This is a vector (`vector<Mat>`) that contains the translation vectors for all the transformations /// from gripper frame to robot base frame. /// * R_target2cam: Rotation part extracted from the homogeneous matrix that transforms a point /// expressed in the target frame to the camera frame (). /// This is a vector (`vector<Mat>`) that contains the rotation matrices for all the transformations /// from calibration target frame to camera frame. /// * t_target2cam: Rotation part extracted from the homogeneous matrix that transforms a point /// expressed in the target frame to the camera frame (). /// This is a vector (`vector<Mat>`) that contains the translation vectors for all the transformations /// from calibration target frame to camera frame. /// * R_cam2gripper: [out] Estimated rotation part extracted from the homogeneous matrix that transforms a point /// expressed in the camera frame to the gripper frame (). /// * t_cam2gripper: [out] Estimated translation part extracted from the homogeneous matrix that transforms a point /// expressed in the camera frame to the gripper frame (). /// * method: One of the implemented Hand-Eye calibration method, see cv::HandEyeCalibrationMethod /// /// The function performs the Hand-Eye calibration using various methods. One approach consists in estimating the /// rotation then the translation (separable solutions) and the following methods are implemented: /// - R. Tsai, R. Lenz A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/EyeCalibration \cite Tsai89 /// - F. Park, B. Martin Robot Sensor Calibration: Solving AX = XB on the Euclidean Group \cite Park94 /// - R. Horaud, F. Dornaika Hand-Eye Calibration \cite Horaud95 /// /// Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions), /// with the following implemented method: /// - N. Andreff, R. Horaud, B. Espiau On-line Hand-Eye Calibration \cite Andreff99 /// - K. Daniilidis Hand-Eye Calibration Using Dual Quaternions \cite Daniilidis98 /// /// The following picture describes the Hand-Eye calibration problem where the transformation between a camera ("eye") /// mounted on a robot gripper ("hand") has to be estimated. /// ///  /// /// The calibration procedure is the following: /// - a static calibration pattern is used to estimate the transformation between the target frame /// and the camera frame /// - the robot gripper is moved in order to acquire several poses /// - for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for /// instance the robot kinematics ///  /// - for each pose, the homogeneous transformation between the calibration target frame and the camera frame is recorded using /// for instance a pose estimation method (PnP) from 2D-3D point correspondences ///  /// /// The Hand-Eye calibration procedure returns the following homogeneous transformation ///  /// /// This problem is also known as solving the  equation: ///  /// /// \note /// Additional information can be found on this [website](http://campar.in.tum.de/Chair/HandEyeCalibration). /// \note /// A minimum of 2 motions with non parallel rotation axes are necessary to determine the hand-eye transformation. /// So at least 3 different poses are required, but it is strongly recommended to use many more poses. /// /// ## C++ default parameters /// * method: CALIB_HAND_EYE_TSAI pub fn calibrate_hand_eye(r_gripper2base: &dyn core::ToInputArray, t_gripper2base: &dyn core::ToInputArray, r_target2cam: &dyn core::ToInputArray, t_target2cam: &dyn core::ToInputArray, r_cam2gripper: &mut dyn core::ToOutputArray, t_cam2gripper: &mut dyn core::ToOutputArray, method: crate::calib3d::HandEyeCalibrationMethod) -> Result<()> { input_array_arg!(r_gripper2base); input_array_arg!(t_gripper2base); input_array_arg!(r_target2cam); input_array_arg!(t_target2cam); output_array_arg!(r_cam2gripper); output_array_arg!(t_cam2gripper); unsafe { sys::cv_calibrateHandEye__InputArray__InputArray__InputArray__InputArray__OutputArray__OutputArray_HandEyeCalibrationMethod(r_gripper2base.as_raw__InputArray(), t_gripper2base.as_raw__InputArray(), r_target2cam.as_raw__InputArray(), t_target2cam.as_raw__InputArray(), r_cam2gripper.as_raw__OutputArray(), t_cam2gripper.as_raw__OutputArray(), method) }.into_result() } /// Computes useful camera characteristics from the camera matrix. /// /// ## Parameters /// * cameraMatrix: Input camera matrix that can be estimated by calibrateCamera or /// stereoCalibrate . /// * imageSize: Input image size in pixels. /// * apertureWidth: Physical width in mm of the sensor. /// * apertureHeight: Physical height in mm of the sensor. /// * fovx: Output field of view in degrees along the horizontal sensor axis. /// * fovy: Output field of view in degrees along the vertical sensor axis. /// * focalLength: Focal length of the lens in mm. /// * principalPoint: Principal point in mm. /// * aspectRatio:  /// /// The function computes various useful camera characteristics from the previously estimated camera /// matrix. /// /// /// Note: /// Do keep in mind that the unity measure 'mm' stands for whatever unit of measure one chooses for /// the chessboard pitch (it can thus be any value). pub fn calibration_matrix_values(camera_matrix: &dyn core::ToInputArray, image_size: core::Size, aperture_width: f64, aperture_height: f64, fovx: &mut f64, fovy: &mut f64, focal_length: &mut f64, principal_point: &mut core::Point2d, aspect_ratio: &mut f64) -> Result<()> { input_array_arg!(camera_matrix); unsafe { sys::cv_calibrationMatrixValues__InputArray_Size_double_double_double_double_double_Point2d_double(camera_matrix.as_raw__InputArray(), image_size, aperture_width, aperture_height, fovx, fovy, focal_length, principal_point, aspect_ratio) }.into_result() } /// Combines two rotation-and-shift transformations. /// /// ## Parameters /// * rvec1: First rotation vector. /// * tvec1: First translation vector. /// * rvec2: Second rotation vector. /// * tvec2: Second translation vector. /// * rvec3: Output rotation vector of the superposition. /// * tvec3: Output translation vector of the superposition. /// * dr3dr1: Optional output derivative of rvec3 with regard to rvec1 /// * dr3dt1: Optional output derivative of rvec3 with regard to tvec1 /// * dr3dr2: Optional output derivative of rvec3 with regard to rvec2 /// * dr3dt2: Optional output derivative of rvec3 with regard to tvec2 /// * dt3dr1: Optional output derivative of tvec3 with regard to rvec1 /// * dt3dt1: Optional output derivative of tvec3 with regard to tvec1 /// * dt3dr2: Optional output derivative of tvec3 with regard to rvec2 /// * dt3dt2: Optional output derivative of tvec3 with regard to tvec2 /// /// The functions compute: /// ///  /// /// where  denotes a rotation vector to a rotation matrix transformation, and ///  denotes the inverse transformation. See Rodrigues for details. /// /// Also, the functions can compute the derivatives of the output vectors with regards to the input /// vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in /// your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a /// function that contains a matrix multiplication. /// /// ## C++ default parameters /// * dr3dr1: noArray() /// * dr3dt1: noArray() /// * dr3dr2: noArray() /// * dr3dt2: noArray() /// * dt3dr1: noArray() /// * dt3dt1: noArray() /// * dt3dr2: noArray() /// * dt3dt2: noArray() pub fn compose_rt(rvec1: &dyn core::ToInputArray, tvec1: &dyn core::ToInputArray, rvec2: &dyn core::ToInputArray, tvec2: &dyn core::ToInputArray, rvec3: &mut dyn core::ToOutputArray, tvec3: &mut dyn core::ToOutputArray, dr3dr1: &mut dyn core::ToOutputArray, dr3dt1: &mut dyn core::ToOutputArray, dr3dr2: &mut dyn core::ToOutputArray, dr3dt2: &mut dyn core::ToOutputArray, dt3dr1: &mut dyn core::ToOutputArray, dt3dt1: &mut dyn core::ToOutputArray, dt3dr2: &mut dyn core::ToOutputArray, dt3dt2: &mut dyn core::ToOutputArray) -> Result<()> { input_array_arg!(rvec1); input_array_arg!(tvec1); input_array_arg!(rvec2); input_array_arg!(tvec2); output_array_arg!(rvec3); output_array_arg!(tvec3); output_array_arg!(dr3dr1); output_array_arg!(dr3dt1); output_array_arg!(dr3dr2); output_array_arg!(dr3dt2); output_array_arg!(dt3dr1); output_array_arg!(dt3dt1); output_array_arg!(dt3dr2); output_array_arg!(dt3dt2); unsafe { sys::cv_composeRT__InputArray__InputArray__InputArray__InputArray__OutputArray__OutputArray__OutputArray__OutputArray__OutputArray__OutputArray__OutputArray__OutputArray__OutputArray__OutputArray(rvec1.as_raw__InputArray(), tvec1.as_raw__InputArray(), rvec2.as_raw__InputArray(), tvec2.as_raw__InputArray(), rvec3.as_raw__OutputArray(), tvec3.as_raw__OutputArray(), dr3dr1.as_raw__OutputArray(), dr3dt1.as_raw__OutputArray(), dr3dr2.as_raw__OutputArray(), dr3dt2.as_raw__OutputArray(), dt3dr1.as_raw__OutputArray(), dt3dt1.as_raw__OutputArray(), dt3dr2.as_raw__OutputArray(), dt3dt2.as_raw__OutputArray()) }.into_result() } /// For points in an image of a stereo pair, computes the corresponding epilines in the other image. /// /// ## Parameters /// * points: Input points.  or  matrix of type CV_32FC2 or /// vector\<Point2f\> . /// * whichImage: Index of the image (1 or 2) that contains the points . /// * F: Fundamental matrix that can be estimated using findFundamentalMat or stereoRectify . /// * lines: Output vector of the epipolar lines corresponding to the points in the other image. /// Each line  is encoded by 3 numbers  . /// /// For every point in one of the two images of a stereo pair, the function finds the equation of the /// corresponding epipolar line in the other image. /// /// From the fundamental matrix definition (see findFundamentalMat ), line  in the second /// image for the point  in the first image (when whichImage=1 ) is computed as: /// ///  /// /// And vice versa, when whichImage=2,  is computed from  as: /// ///  /// /// Line coefficients are defined up to a scale. They are normalized so that  . pub fn compute_correspond_epilines(points: &dyn core::ToInputArray, which_image: i32, f: &dyn core::ToInputArray, lines: &mut dyn core::ToOutputArray) -> Result<()> { input_array_arg!(points); input_array_arg!(f); output_array_arg!(lines); unsafe { sys::cv_computeCorrespondEpilines__InputArray_int__InputArray__OutputArray(points.as_raw__InputArray(), which_image, f.as_raw__InputArray(), lines.as_raw__OutputArray()) }.into_result() } /// Converts points from homogeneous to Euclidean space. /// /// ## Parameters /// * src: Input vector of N-dimensional points. /// * dst: Output vector of N-1-dimensional points. /// /// The function converts points homogeneous to Euclidean space using perspective projection. That is, /// each point (x1, x2, ... x(n-1), xn) is converted to (x1/xn, x2/xn, ..., x(n-1)/xn). When xn=0, the /// output point coordinates will be (0,0,0,...). pub fn convert_points_from_homogeneous(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_convertPointsFromHomogeneous__InputArray__OutputArray(src.as_raw__InputArray(), dst.as_raw__OutputArray()) }.into_result() } /// Converts points to/from homogeneous coordinates. /// /// ## Parameters /// * src: Input array or vector of 2D, 3D, or 4D points. /// * dst: Output vector of 2D, 3D, or 4D points. /// /// The function converts 2D or 3D points from/to homogeneous coordinates by calling either /// convertPointsToHomogeneous or convertPointsFromHomogeneous. /// /// /// Note: The function is obsolete. Use one of the previous two functions instead. pub fn convert_points_homogeneous(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_convertPointsHomogeneous__InputArray__OutputArray(src.as_raw__InputArray(), dst.as_raw__OutputArray()) }.into_result() } /// Converts points from Euclidean to homogeneous space. /// /// ## Parameters /// * src: Input vector of N-dimensional points. /// * dst: Output vector of N+1-dimensional points. /// /// The function converts points from Euclidean to homogeneous space by appending 1's to the tuple of /// point coordinates. That is, each point (x1, x2, ..., xn) is converted to (x1, x2, ..., xn, 1). pub fn convert_points_to_homogeneous(src: &dyn core::ToInputArray, dst: &mut dyn core::ToOutputArray) -> Result<()> { input_array_arg!(src); output_array_arg!(dst); unsafe { sys::cv_convertPointsToHomogeneous__InputArray__OutputArray(src.as_raw__InputArray(), dst.as_raw__OutputArray()) }.into_result() } /// Refines coordinates of corresponding points. /// /// ## Parameters /// * F: 3x3 fundamental matrix. /// * points1: 1xN array containing the first set of points. /// * points2: 1xN array containing the second set of points. /// * newPoints1: The optimized points1. /// * newPoints2: The optimized points2. /// /// The function implements the Optimal Triangulation Method (see Multiple View Geometry for details). /// For each given point correspondence points1[i] \<-\> points2[i], and a fundamental matrix F, it /// computes the corrected correspondences newPoints1[i] \<-\> newPoints2[i] that minimize the geometric /// error  (where  is the /// geometric distance between points  and  ) subject to the epipolar constraint ///  . pub fn correct_matches(f: &dyn core::ToInputArray, points1: &dyn core::ToInputArray, points2: &dyn core::ToInputArray, new_points1: &mut dyn core::ToOutputArray, new_points2: &mut dyn core::ToOutputArray) -> Result<()> { input_array_arg!(f); input_array_arg!(points1); input_array_arg!(points2); output_array_arg!(new_points1); output_array_arg!(new_points2); unsafe { sys::cv_correctMatches__InputArray__InputArray__InputArray__OutputArray__OutputArray(f.as_raw__InputArray(), points1.as_raw__InputArray(), points2.as_raw__InputArray(), new_points1.as_raw__OutputArray(), new_points2.as_raw__OutputArray()) }.into_result() } /// Decompose an essential matrix to possible rotations and translation. /// /// ## Parameters /// * E: The input essential matrix. /// * R1: One possible rotation matrix. /// * R2: Another possible rotation matrix. /// * t: One possible translation. /// /// This function decompose an essential matrix E using svd decomposition [HartleyZ00](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_HartleyZ00) . Generally 4 /// possible poses exists for a given E. They are , , , . By /// decomposing E, you can only get the direction of the translation, so the function returns unit t. pub fn decompose_essential_mat(e: &dyn core::ToInputArray, r1: &mut dyn core::ToOutputArray, r2: &mut dyn core::ToOutputArray, t: &mut dyn core::ToOutputArray) -> Result<()> { input_array_arg!(e); output_array_arg!(r1); output_array_arg!(r2); output_array_arg!(t); unsafe { sys::cv_decomposeEssentialMat__InputArray__OutputArray__OutputArray__OutputArray(e.as_raw__InputArray(), r1.as_raw__OutputArray(), r2.as_raw__OutputArray(), t.as_raw__OutputArray()) }.into_result() } /// Decompose a homography matrix to rotation(s), translation(s) and plane normal(s). /// /// ## Parameters /// * H: The input homography matrix between two images. /// * K: The input intrinsic camera calibration matrix. /// * rotations: Array of rotation matrices. /// * translations: Array of translation matrices. /// * normals: Array of plane normal matrices. /// /// This function extracts relative camera motion between two views observing a planar object from the /// homography H induced by the plane. The intrinsic camera matrix K must also be provided. The function /// may return up to four mathematical solution sets. At least two of the solutions may further be /// invalidated if point correspondences are available by applying positive depth constraint (all points /// must be in front of the camera). The decomposition method is described in detail in [Malis](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Malis) . pub fn decompose_homography_mat(h: &dyn core::ToInputArray, k: &dyn core::ToInputArray, rotations: &mut dyn core::ToOutputArray, translations: &mut dyn core::ToOutputArray, normals: &mut dyn core::ToOutputArray) -> Result<i32> { input_array_arg!(h); input_array_arg!(k); output_array_arg!(rotations); output_array_arg!(translations); output_array_arg!(normals); unsafe { sys::cv_decomposeHomographyMat__InputArray__InputArray__OutputArray__OutputArray__OutputArray(h.as_raw__InputArray(), k.as_raw__InputArray(), rotations.as_raw__OutputArray(), translations.as_raw__OutputArray(), normals.as_raw__OutputArray()) }.into_result() } /// Decomposes a projection matrix into a rotation matrix and a camera matrix. /// /// ## Parameters /// * projMatrix: 3x4 input projection matrix P. /// * cameraMatrix: Output 3x3 camera matrix K. /// * rotMatrix: Output 3x3 external rotation matrix R. /// * transVect: Output 4x1 translation vector T. /// * rotMatrixX: Optional 3x3 rotation matrix around x-axis. /// * rotMatrixY: Optional 3x3 rotation matrix around y-axis. /// * rotMatrixZ: Optional 3x3 rotation matrix around z-axis. /// * eulerAngles: Optional three-element vector containing three Euler angles of rotation in /// degrees. /// /// The function computes a decomposition of a projection matrix into a calibration and a rotation /// matrix and the position of a camera. /// /// It optionally returns three rotation matrices, one for each axis, and three Euler angles that could /// be used in OpenGL. Note, there is always more than one sequence of rotations about the three /// principal axes that results in the same orientation of an object, e.g. see [Slabaugh](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Slabaugh) . Returned /// tree rotation matrices and corresponding three Euler angles are only one of the possible solutions. /// /// The function is based on RQDecomp3x3 . /// /// ## C++ default parameters /// * rot_matrix_x: noArray() /// * rot_matrix_y: noArray() /// * rot_matrix_z: noArray() /// * euler_angles: noArray() pub fn decompose_projection_matrix(proj_matrix: &dyn core::ToInputArray, camera_matrix: &mut dyn core::ToOutputArray, rot_matrix: &mut dyn core::ToOutputArray, trans_vect: &mut dyn core::ToOutputArray, rot_matrix_x: &mut dyn core::ToOutputArray, rot_matrix_y: &mut dyn core::ToOutputArray, rot_matrix_z: &mut dyn core::ToOutputArray, euler_angles: &mut dyn core::ToOutputArray) -> Result<()> { input_array_arg!(proj_matrix); output_array_arg!(camera_matrix); output_array_arg!(rot_matrix); output_array_arg!(trans_vect); output_array_arg!(rot_matrix_x); output_array_arg!(rot_matrix_y); output_array_arg!(rot_matrix_z); output_array_arg!(euler_angles); unsafe { sys::cv_decomposeProjectionMatrix__InputArray__OutputArray__OutputArray__OutputArray__OutputArray__OutputArray__OutputArray__OutputArray(proj_matrix.as_raw__InputArray(), camera_matrix.as_raw__OutputArray(), rot_matrix.as_raw__OutputArray(), trans_vect.as_raw__OutputArray(), rot_matrix_x.as_raw__OutputArray(), rot_matrix_y.as_raw__OutputArray(), rot_matrix_z.as_raw__OutputArray(), euler_angles.as_raw__OutputArray()) }.into_result() } /// Renders the detected chessboard corners. /// /// ## Parameters /// * image: Destination image. It must be an 8-bit color image. /// * patternSize: Number of inner corners per a chessboard row and column /// (patternSize = cv::Size(points_per_row,points_per_column)). /// * corners: Array of detected corners, the output of findChessboardCorners. /// * patternWasFound: Parameter indicating whether the complete board was found or not. The /// return value of findChessboardCorners should be passed here. /// /// The function draws individual chessboard corners detected either as red circles if the board was not /// found, or as colored corners connected with lines if the board was found. pub fn draw_chessboard_corners(image: &mut dyn core::ToInputOutputArray, pattern_size: core::Size, corners: &dyn core::ToInputArray, pattern_was_found: bool) -> Result<()> { input_output_array_arg!(image); input_array_arg!(corners); unsafe { sys::cv_drawChessboardCorners__InputOutputArray_Size__InputArray_bool(image.as_raw__InputOutputArray(), pattern_size, corners.as_raw__InputArray(), pattern_was_found) }.into_result() } /// Draw axes of the world/object coordinate system from pose estimation. ## See also /// solvePnP /// /// ## Parameters /// * image: Input/output image. It must have 1 or 3 channels. The number of channels is not altered. /// * cameraMatrix: Input 3x3 floating-point matrix of camera intrinsic parameters. ///  /// * distCoeffs: Input vector of distortion coefficients ///  of /// 4, 5, 8, 12 or 14 elements. If the vector is empty, the zero distortion coefficients are assumed. /// * rvec: Rotation vector (see @ref Rodrigues ) that, together with tvec, brings points from /// the model coordinate system to the camera coordinate system. /// * tvec: Translation vector. /// * length: Length of the painted axes in the same unit than tvec (usually in meters). /// * thickness: Line thickness of the painted axes. /// /// This function draws the axes of the world/object coordinate system w.r.t. to the camera frame. /// OX is drawn in red, OY in green and OZ in blue. /// /// ## C++ default parameters /// * thickness: 3 pub fn draw_frame_axes(image: &mut dyn core::ToInputOutputArray, camera_matrix: &dyn core::ToInputArray, dist_coeffs: &dyn core::ToInputArray, rvec: &dyn core::ToInputArray, tvec: &dyn core::ToInputArray, length: f32, thickness: i32) -> Result<()> { input_output_array_arg!(image); input_array_arg!(camera_matrix); input_array_arg!(dist_coeffs); input_array_arg!(rvec); input_array_arg!(tvec); unsafe { sys::cv_drawFrameAxes__InputOutputArray__InputArray__InputArray__InputArray__InputArray_float_int(image.as_raw__InputOutputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvec.as_raw__InputArray(), tvec.as_raw__InputArray(), length, thickness) }.into_result() } /// Computes an optimal affine transformation between two 2D point sets. /// /// It computes ///  /// /// ## Parameters /// * from: First input 2D point set containing . /// * to: Second input 2D point set containing . /// * inliers: Output vector indicating which points are inliers (1-inlier, 0-outlier). /// * method: Robust method used to compute transformation. The following methods are possible: /// * cv::RANSAC - RANSAC-based robust method /// * cv::LMEDS - Least-Median robust method /// RANSAC is the default method. /// * ransacReprojThreshold: Maximum reprojection error in the RANSAC algorithm to consider /// a point as an inlier. Applies only to RANSAC. /// * maxIters: The maximum number of robust method iterations. /// * confidence: Confidence level, between 0 and 1, for the estimated transformation. Anything /// between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation /// significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. /// * refineIters: Maximum number of iterations of refining algorithm (Levenberg-Marquardt). /// Passing 0 will disable refining, so the output matrix will be output of robust method. /// /// ## Returns /// Output 2D affine transformation matrix  or empty matrix if transformation /// could not be estimated. The returned matrix has the following form: ///  /// /// The function estimates an optimal 2D affine transformation between two 2D point sets using the /// selected robust algorithm. /// /// The computed transformation is then refined further (using only inliers) with the /// Levenberg-Marquardt method to reduce the re-projection error even more. /// /// /// Note: /// The RANSAC method can handle practically any ratio of outliers but needs a threshold to /// distinguish inliers from outliers. The method LMeDS does not need any threshold but it works /// correctly only when there are more than 50% of inliers. /// /// ## See also /// estimateAffinePartial2D, getAffineTransform /// /// ## C++ default parameters /// * inliers: noArray() /// * method: RANSAC /// * ransac_reproj_threshold: 3 /// * max_iters: 2000 /// * confidence: 0.99 /// * refine_iters: 10 pub fn estimate_affine_2d(from: &dyn core::ToInputArray, to: &dyn core::ToInputArray, inliers: &mut dyn core::ToOutputArray, method: i32, ransac_reproj_threshold: f64, max_iters: size_t, confidence: f64, refine_iters: size_t) -> Result<core::Mat> { input_array_arg!(from); input_array_arg!(to); output_array_arg!(inliers); unsafe { sys::cv_estimateAffine2D__InputArray__InputArray__OutputArray_int_double_size_t_double_size_t(from.as_raw__InputArray(), to.as_raw__InputArray(), inliers.as_raw__OutputArray(), method, ransac_reproj_threshold, max_iters, confidence, refine_iters) }.into_result().map(|ptr| core::Mat { ptr }) } /// Computes an optimal affine transformation between two 3D point sets. /// /// It computes ///  /// /// ## Parameters /// * src: First input 3D point set containing . /// * dst: Second input 3D point set containing . /// * out: Output 3D affine transformation matrix  of the form ///  /// * inliers: Output vector indicating which points are inliers (1-inlier, 0-outlier). /// * ransacThreshold: Maximum reprojection error in the RANSAC algorithm to consider a point as /// an inlier. /// * confidence: Confidence level, between 0 and 1, for the estimated transformation. Anything /// between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation /// significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. /// /// The function estimates an optimal 3D affine transformation between two 3D point sets using the /// RANSAC algorithm. /// /// ## C++ default parameters /// * ransac_threshold: 3 /// * confidence: 0.99 pub fn estimate_affine_3d(src: &dyn core::ToInputArray, dst: &dyn core::ToInputArray, out: &mut dyn core::ToOutputArray, inliers: &mut dyn core::ToOutputArray, ransac_threshold: f64, confidence: f64) -> Result<i32> { input_array_arg!(src); input_array_arg!(dst); output_array_arg!(out); output_array_arg!(inliers); unsafe { sys::cv_estimateAffine3D__InputArray__InputArray__OutputArray__OutputArray_double_double(src.as_raw__InputArray(), dst.as_raw__InputArray(), out.as_raw__OutputArray(), inliers.as_raw__OutputArray(), ransac_threshold, confidence) }.into_result() } /// Computes an optimal limited affine transformation with 4 degrees of freedom between /// two 2D point sets. /// /// ## Parameters /// * from: First input 2D point set. /// * to: Second input 2D point set. /// * inliers: Output vector indicating which points are inliers. /// * method: Robust method used to compute transformation. The following methods are possible: /// * cv::RANSAC - RANSAC-based robust method /// * cv::LMEDS - Least-Median robust method /// RANSAC is the default method. /// * ransacReprojThreshold: Maximum reprojection error in the RANSAC algorithm to consider /// a point as an inlier. Applies only to RANSAC. /// * maxIters: The maximum number of robust method iterations. /// * confidence: Confidence level, between 0 and 1, for the estimated transformation. Anything /// between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation /// significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. /// * refineIters: Maximum number of iterations of refining algorithm (Levenberg-Marquardt). /// Passing 0 will disable refining, so the output matrix will be output of robust method. /// /// ## Returns /// Output 2D affine transformation (4 degrees of freedom) matrix  or /// empty matrix if transformation could not be estimated. /// /// The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to /// combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust /// estimation. /// /// The computed transformation is then refined further (using only inliers) with the /// Levenberg-Marquardt method to reduce the re-projection error even more. /// /// Estimated transformation matrix is: ///  /// Where  is the rotation angle,  the scaling factor and  are /// translations in  axes respectively. /// /// /// Note: /// The RANSAC method can handle practically any ratio of outliers but need a threshold to /// distinguish inliers from outliers. The method LMeDS does not need any threshold but it works /// correctly only when there are more than 50% of inliers. /// /// ## See also /// estimateAffine2D, getAffineTransform /// /// ## C++ default parameters /// * inliers: noArray() /// * method: RANSAC /// * ransac_reproj_threshold: 3 /// * max_iters: 2000 /// * confidence: 0.99 /// * refine_iters: 10 pub fn estimate_affine_partial_2d(from: &dyn core::ToInputArray, to: &dyn core::ToInputArray, inliers: &mut dyn core::ToOutputArray, method: i32, ransac_reproj_threshold: f64, max_iters: size_t, confidence: f64, refine_iters: size_t) -> Result<core::Mat> { input_array_arg!(from); input_array_arg!(to); output_array_arg!(inliers); unsafe { sys::cv_estimateAffinePartial2D__InputArray__InputArray__OutputArray_int_double_size_t_double_size_t(from.as_raw__InputArray(), to.as_raw__InputArray(), inliers.as_raw__OutputArray(), method, ransac_reproj_threshold, max_iters, confidence, refine_iters) }.into_result().map(|ptr| core::Mat { ptr }) } /// Filters homography decompositions based on additional information. /// /// ## Parameters /// * rotations: Vector of rotation matrices. /// * normals: Vector of plane normal matrices. /// * beforePoints: Vector of (rectified) visible reference points before the homography is applied /// * afterPoints: Vector of (rectified) visible reference points after the homography is applied /// * possibleSolutions: Vector of int indices representing the viable solution set after filtering /// * pointsMask: optional Mat/Vector of 8u type representing the mask for the inliers as given by the findHomography function /// /// This function is intended to filter the output of the decomposeHomographyMat based on additional /// information as described in [Malis](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Malis) . The summary of the method: the decomposeHomographyMat function /// returns 2 unique solutions and their "opposites" for a total of 4 solutions. If we have access to the /// sets of points visible in the camera frame before and after the homography transformation is applied, /// we can determine which are the true potential solutions and which are the opposites by verifying which /// homographies are consistent with all visible reference points being in front of the camera. The inputs /// are left unchanged; the filtered solution set is returned as indices into the existing one. /// /// ## C++ default parameters /// * points_mask: noArray() pub fn filter_homography_decomp_by_visible_refpoints(rotations: &dyn core::ToInputArray, normals: &dyn core::ToInputArray, before_points: &dyn core::ToInputArray, after_points: &dyn core::ToInputArray, possible_solutions: &mut dyn core::ToOutputArray, points_mask: &dyn core::ToInputArray) -> Result<()> { input_array_arg!(rotations); input_array_arg!(normals); input_array_arg!(before_points); input_array_arg!(after_points); output_array_arg!(possible_solutions); input_array_arg!(points_mask); unsafe { sys::cv_filterHomographyDecompByVisibleRefpoints__InputArray__InputArray__InputArray__InputArray__OutputArray__InputArray(rotations.as_raw__InputArray(), normals.as_raw__InputArray(), before_points.as_raw__InputArray(), after_points.as_raw__InputArray(), possible_solutions.as_raw__OutputArray(), points_mask.as_raw__InputArray()) }.into_result() } /// Filters off small noise blobs (speckles) in the disparity map /// /// ## Parameters /// * img: The input 16-bit signed disparity image /// * newVal: The disparity value used to paint-off the speckles /// * maxSpeckleSize: The maximum speckle size to consider it a speckle. Larger blobs are not /// affected by the algorithm /// * maxDiff: Maximum difference between neighbor disparity pixels to put them into the same /// blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point /// disparity map, where disparity values are multiplied by 16, this scale factor should be taken into /// account when specifying this parameter value. /// * buf: The optional temporary buffer to avoid memory allocation within the function. /// /// ## C++ default parameters /// * buf: noArray() pub fn filter_speckles(img: &mut dyn core::ToInputOutputArray, new_val: f64, max_speckle_size: i32, max_diff: f64, buf: &mut dyn core::ToInputOutputArray) -> Result<()> { input_output_array_arg!(img); input_output_array_arg!(buf); unsafe { sys::cv_filterSpeckles__InputOutputArray_double_int_double__InputOutputArray(img.as_raw__InputOutputArray(), new_val, max_speckle_size, max_diff, buf.as_raw__InputOutputArray()) }.into_result() } /// finds subpixel-accurate positions of the chessboard corners pub fn find4_quad_corner_subpix(img: &dyn core::ToInputArray, corners: &mut dyn core::ToInputOutputArray, region_size: core::Size) -> Result<bool> { input_array_arg!(img); input_output_array_arg!(corners); unsafe { sys::cv_find4QuadCornerSubpix__InputArray__InputOutputArray_Size(img.as_raw__InputArray(), corners.as_raw__InputOutputArray(), region_size) }.into_result() } /// Finds the positions of internal corners of the chessboard. /// /// ## Parameters /// * image: Source chessboard view. It must be an 8-bit grayscale or color image. /// * patternSize: Number of inner corners per a chessboard row and column /// ( patternSize = cvSize(points_per_row,points_per_colum) = cvSize(columns,rows) ). /// * corners: Output array of detected corners. /// * flags: Various operation flags that can be zero or a combination of the following values: /// * **CALIB_CB_ADAPTIVE_THRESH** Use adaptive thresholding to convert the image to black /// and white, rather than a fixed threshold level (computed from the average image brightness). /// * **CALIB_CB_NORMALIZE_IMAGE** Normalize the image gamma with equalizeHist before /// applying fixed or adaptive thresholding. /// * **CALIB_CB_FILTER_QUADS** Use additional criteria (like contour area, perimeter, /// square-like shape) to filter out false quads extracted at the contour retrieval stage. /// * **CALIB_CB_FAST_CHECK** Run a fast check on the image that looks for chessboard corners, /// and shortcut the call if none is found. This can drastically speed up the call in the /// degenerate condition when no chessboard is observed. /// /// The function attempts to determine whether the input image is a view of the chessboard pattern and /// locate the internal chessboard corners. The function returns a non-zero value if all of the corners /// are found and they are placed in a certain order (row by row, left to right in every row). /// Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example, /// a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black /// squares touch each other. The detected coordinates are approximate, and to determine their positions /// more accurately, the function calls cornerSubPix. You also may use the function cornerSubPix with /// different parameters if returned coordinates are not accurate enough. /// /// Sample usage of detecting and drawing chessboard corners: : /// ```ignore /// Size patternsize(8,6); //interior number of corners /// Mat gray = ....; //source image /// vector<Point2f> corners; //this will be filled by the detected corners /// /// //CALIB_CB_FAST_CHECK saves a lot of time on images /// //that do not contain any chessboard corners /// bool patternfound = findChessboardCorners(gray, patternsize, corners, /// CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE /// + CALIB_CB_FAST_CHECK); /// /// if(patternfound) /// cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1), /// TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1)); /// /// drawChessboardCorners(img, patternsize, Mat(corners), patternfound); /// ``` /// /// /// Note: The function requires white space (like a square-thick border, the wider the better) around /// the board to make the detection more robust in various environments. Otherwise, if there is no /// border and the background is dark, the outer black squares cannot be segmented properly and so the /// square grouping and ordering algorithm fails. /// /// ## C++ default parameters /// * flags: CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE pub fn find_chessboard_corners(image: &dyn core::ToInputArray, pattern_size: core::Size, corners: &mut dyn core::ToOutputArray, flags: i32) -> Result<bool> { input_array_arg!(image); output_array_arg!(corners); unsafe { sys::cv_findChessboardCorners__InputArray_Size__OutputArray_int(image.as_raw__InputArray(), pattern_size, corners.as_raw__OutputArray(), flags) }.into_result() } /// Finds centers in the grid of circles. /// /// ## Parameters /// * image: grid view of input circles; it must be an 8-bit grayscale or color image. /// * patternSize: number of circles per row and column /// ( patternSize = Size(points_per_row, points_per_colum) ). /// * centers: output array of detected centers. /// * flags: various operation flags that can be one of the following values: /// * **CALIB_CB_SYMMETRIC_GRID** uses symmetric pattern of circles. /// * **CALIB_CB_ASYMMETRIC_GRID** uses asymmetric pattern of circles. /// * **CALIB_CB_CLUSTERING** uses a special algorithm for grid detection. It is more robust to /// perspective distortions but much more sensitive to background clutter. /// * blobDetector: feature detector that finds blobs like dark circles on light background. /// * parameters: struct for finding circles in a grid pattern. /// /// The function attempts to determine whether the input image contains a grid of circles. If it is, the /// function locates centers of the circles. The function returns a non-zero value if all of the centers /// have been found and they have been placed in a certain order (row by row, left to right in every /// row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0. /// /// Sample usage of detecting and drawing the centers of circles: : /// ```ignore /// Size patternsize(7,7); //number of centers /// Mat gray = ....; //source image /// vector<Point2f> centers; //this will be filled by the detected centers /// /// bool patternfound = findCirclesGrid(gray, patternsize, centers); /// /// drawChessboardCorners(img, patternsize, Mat(centers), patternfound); /// ``` /// /// /// Note: The function requires white space (like a square-thick border, the wider the better) around /// the board to make the detection more robust in various environments. /// /// ## Overloaded parameters /// /// ## C++ default parameters /// * flags: CALIB_CB_SYMMETRIC_GRID /// * blob_detector: SimpleBlobDetector::create() pub fn find_circles_grid(image: &dyn core::ToInputArray, pattern_size: core::Size, centers: &mut dyn core::ToOutputArray, flags: i32, blob_detector: &types::PtrOfFeature2D) -> Result<bool> { input_array_arg!(image); output_array_arg!(centers); unsafe { sys::cv_findCirclesGrid__InputArray_Size__OutputArray_int_PtrOfFeature2D(image.as_raw__InputArray(), pattern_size, centers.as_raw__OutputArray(), flags, blob_detector.as_raw_PtrOfFeature2D()) }.into_result() } /// Finds centers in the grid of circles. /// /// ## Parameters /// * image: grid view of input circles; it must be an 8-bit grayscale or color image. /// * patternSize: number of circles per row and column /// ( patternSize = Size(points_per_row, points_per_colum) ). /// * centers: output array of detected centers. /// * flags: various operation flags that can be one of the following values: /// * **CALIB_CB_SYMMETRIC_GRID** uses symmetric pattern of circles. /// * **CALIB_CB_ASYMMETRIC_GRID** uses asymmetric pattern of circles. /// * **CALIB_CB_CLUSTERING** uses a special algorithm for grid detection. It is more robust to /// perspective distortions but much more sensitive to background clutter. /// * blobDetector: feature detector that finds blobs like dark circles on light background. /// * parameters: struct for finding circles in a grid pattern. /// /// The function attempts to determine whether the input image contains a grid of circles. If it is, the /// function locates centers of the circles. The function returns a non-zero value if all of the centers /// have been found and they have been placed in a certain order (row by row, left to right in every /// row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0. /// /// Sample usage of detecting and drawing the centers of circles: : /// ```ignore /// Size patternsize(7,7); //number of centers /// Mat gray = ....; //source image /// vector<Point2f> centers; //this will be filled by the detected centers /// /// bool patternfound = findCirclesGrid(gray, patternsize, centers); /// /// drawChessboardCorners(img, patternsize, Mat(centers), patternfound); /// ``` /// /// /// Note: The function requires white space (like a square-thick border, the wider the better) around /// the board to make the detection more robust in various environments. pub fn find_circles_grid_params(image: &dyn core::ToInputArray, pattern_size: core::Size, centers: &mut dyn core::ToOutputArray, flags: i32, blob_detector: &types::PtrOfFeature2D, parameters: crate::calib3d::CirclesGridFinderParameters) -> Result<bool> { input_array_arg!(image); output_array_arg!(centers); unsafe { sys::cv_findCirclesGrid__InputArray_Size__OutputArray_int_PtrOfFeature2D_CirclesGridFinderParameters(image.as_raw__InputArray(), pattern_size, centers.as_raw__OutputArray(), flags, blob_detector.as_raw_PtrOfFeature2D(), parameters) }.into_result() } /// Calculates an essential matrix from the corresponding points in two images. /// /// ## Parameters /// * points1: Array of N (N \>= 5) 2D points from the first image. The point coordinates should /// be floating-point (single or double precision). /// * points2: Array of the second image points of the same size and format as points1 . /// * cameraMatrix: Camera matrix  . /// Note that this function assumes that points1 and points2 are feature points from cameras with the /// same camera matrix. /// * method: Method for computing an essential matrix. /// * **RANSAC** for the RANSAC algorithm. /// * **LMEDS** for the LMedS algorithm. /// * prob: Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of /// confidence (probability) that the estimated matrix is correct. /// * threshold: Parameter used for RANSAC. It is the maximum distance from a point to an epipolar /// line in pixels, beyond which the point is considered an outlier and is not used for computing the /// final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the /// point localization, image resolution, and the image noise. /// * mask: Output array of N elements, every element of which is set to 0 for outliers and to 1 /// for the other points. The array is computed only in the RANSAC and LMedS methods. /// /// This function estimates essential matrix based on the five-point algorithm solver in [Nister03](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Nister03) . /// [SteweniusCFS](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_SteweniusCFS) is also a related. The epipolar geometry is described by the following equation: /// ///  /// /// where  is an essential matrix,  and  are corresponding points in the first and the /// second images, respectively. The result of this function may be passed further to /// decomposeEssentialMat or recoverPose to recover the relative pose between cameras. /// /// ## C++ default parameters /// * method: RANSAC /// * prob: 0.999 /// * threshold: 1.0 /// * mask: noArray() pub fn find_essential_mat_matrix(points1: &dyn core::ToInputArray, points2: &dyn core::ToInputArray, camera_matrix: &dyn core::ToInputArray, method: i32, prob: f64, threshold: f64, mask: &mut dyn core::ToOutputArray) -> Result<core::Mat> { input_array_arg!(points1); input_array_arg!(points2); input_array_arg!(camera_matrix); output_array_arg!(mask); unsafe { sys::cv_findEssentialMat__InputArray__InputArray__InputArray_int_double_double__OutputArray(points1.as_raw__InputArray(), points2.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), method, prob, threshold, mask.as_raw__OutputArray()) }.into_result().map(|ptr| core::Mat { ptr }) } /// Calculates an essential matrix from the corresponding points in two images. /// /// ## Parameters /// * points1: Array of N (N \>= 5) 2D points from the first image. The point coordinates should /// be floating-point (single or double precision). /// * points2: Array of the second image points of the same size and format as points1 . /// * cameraMatrix: Camera matrix  . /// Note that this function assumes that points1 and points2 are feature points from cameras with the /// same camera matrix. /// * method: Method for computing an essential matrix. /// * **RANSAC** for the RANSAC algorithm. /// * **LMEDS** for the LMedS algorithm. /// * prob: Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of /// confidence (probability) that the estimated matrix is correct. /// * threshold: Parameter used for RANSAC. It is the maximum distance from a point to an epipolar /// line in pixels, beyond which the point is considered an outlier and is not used for computing the /// final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the /// point localization, image resolution, and the image noise. /// * mask: Output array of N elements, every element of which is set to 0 for outliers and to 1 /// for the other points. The array is computed only in the RANSAC and LMedS methods. /// /// This function estimates essential matrix based on the five-point algorithm solver in [Nister03](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Nister03) . /// [SteweniusCFS](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_SteweniusCFS) is also a related. The epipolar geometry is described by the following equation: /// ///  /// /// where  is an essential matrix,  and  are corresponding points in the first and the /// second images, respectively. The result of this function may be passed further to /// decomposeEssentialMat or recoverPose to recover the relative pose between cameras. /// /// ## Overloaded parameters /// /// * points1: Array of N (N \>= 5) 2D points from the first image. The point coordinates should /// be floating-point (single or double precision). /// * points2: Array of the second image points of the same size and format as points1 . /// * focal: focal length of the camera. Note that this function assumes that points1 and points2 /// are feature points from cameras with same focal length and principal point. /// * pp: principal point of the camera. /// * method: Method for computing a fundamental matrix. /// * **RANSAC** for the RANSAC algorithm. /// * **LMEDS** for the LMedS algorithm. /// * threshold: Parameter used for RANSAC. It is the maximum distance from a point to an epipolar /// line in pixels, beyond which the point is considered an outlier and is not used for computing the /// final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the /// point localization, image resolution, and the image noise. /// * prob: Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of /// confidence (probability) that the estimated matrix is correct. /// * mask: Output array of N elements, every element of which is set to 0 for outliers and to 1 /// for the other points. The array is computed only in the RANSAC and LMedS methods. /// /// This function differs from the one above that it computes camera matrix from focal length and /// principal point: /// ///  /// /// ## C++ default parameters /// * focal: 1.0 /// * pp: Point2d(0, 0) /// * method: RANSAC /// * prob: 0.999 /// * threshold: 1.0 /// * mask: noArray() pub fn find_essential_mat(points1: &dyn core::ToInputArray, points2: &dyn core::ToInputArray, focal: f64, pp: core::Point2d, method: i32, prob: f64, threshold: f64, mask: &mut dyn core::ToOutputArray) -> Result<core::Mat> { input_array_arg!(points1); input_array_arg!(points2); output_array_arg!(mask); unsafe { sys::cv_findEssentialMat__InputArray__InputArray_double_Point2d_int_double_double__OutputArray(points1.as_raw__InputArray(), points2.as_raw__InputArray(), focal, pp, method, prob, threshold, mask.as_raw__OutputArray()) }.into_result().map(|ptr| core::Mat { ptr }) } /// Calculates a fundamental matrix from the corresponding points in two images. /// /// ## Parameters /// * points1: Array of N points from the first image. The point coordinates should be /// floating-point (single or double precision). /// * points2: Array of the second image points of the same size and format as points1 . /// * method: Method for computing a fundamental matrix. /// * **CV_FM_7POINT** for a 7-point algorithm.  /// * **CV_FM_8POINT** for an 8-point algorithm.  /// * **CV_FM_RANSAC** for the RANSAC algorithm.  /// * **CV_FM_LMEDS** for the LMedS algorithm.  /// * ransacReprojThreshold: Parameter used only for RANSAC. It is the maximum distance from a point to an epipolar /// line in pixels, beyond which the point is considered an outlier and is not used for computing the /// final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the /// point localization, image resolution, and the image noise. /// * confidence: Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level /// of confidence (probability) that the estimated matrix is correct. /// * mask: /// /// The epipolar geometry is described by the following equation: /// ///  /// /// where  is a fundamental matrix,  and  are corresponding points in the first and the /// second images, respectively. /// /// The function calculates the fundamental matrix using one of four methods listed above and returns /// the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point /// algorithm, the function may return up to 3 solutions (  matrix that stores all 3 /// matrices sequentially). /// /// The calculated fundamental matrix may be passed further to computeCorrespondEpilines that finds the /// epipolar lines corresponding to the specified points. It can also be passed to /// stereoRectifyUncalibrated to compute the rectification transformation. : /// ```ignore /// // Example. Estimation of fundamental matrix using the RANSAC algorithm /// int point_count = 100; /// vector<Point2f> points1(point_count); /// vector<Point2f> points2(point_count); /// /// // initialize the points here ... /// for( int i = 0; i < point_count; i++ ) /// { /// points1[i] = ...; /// points2[i] = ...; /// } /// /// Mat fundamental_matrix = /// findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99); /// ``` /// /// /// ## Overloaded parameters /// /// ## C++ default parameters /// * method: FM_RANSAC /// * ransac_reproj_threshold: 3. /// * confidence: 0.99 pub fn find_fundamental_mat(points1: &dyn core::ToInputArray, points2: &dyn core::ToInputArray, mask: &mut dyn core::ToOutputArray, method: i32, ransac_reproj_threshold: f64, confidence: f64) -> Result<core::Mat> { input_array_arg!(points1); input_array_arg!(points2); output_array_arg!(mask); unsafe { sys::cv_findFundamentalMat__InputArray__InputArray__OutputArray_int_double_double(points1.as_raw__InputArray(), points2.as_raw__InputArray(), mask.as_raw__OutputArray(), method, ransac_reproj_threshold, confidence) }.into_result().map(|ptr| core::Mat { ptr }) } /// Finds a perspective transformation between two planes. /// /// ## Parameters /// * srcPoints: Coordinates of the points in the original plane, a matrix of the type CV_32FC2 /// or vector\<Point2f\> . /// * dstPoints: Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or /// a vector\<Point2f\> . /// * method: Method used to compute a homography matrix. The following methods are possible: /// * **0** - a regular method using all the points, i.e., the least squares method /// * **RANSAC** - RANSAC-based robust method /// * **LMEDS** - Least-Median robust method /// * **RHO** - PROSAC-based robust method /// * ransacReprojThreshold: Maximum allowed reprojection error to treat a point pair as an inlier /// (used in the RANSAC and RHO methods only). That is, if ///  /// then the point  is considered as an outlier. If srcPoints and dstPoints are measured in pixels, /// it usually makes sense to set this parameter somewhere in the range of 1 to 10. /// * mask: Optional output mask set by a robust method ( RANSAC or LMEDS ). Note that the input /// mask values are ignored. /// * maxIters: The maximum number of RANSAC iterations. /// * confidence: Confidence level, between 0 and 1. /// /// The function finds and returns the perspective transformation  between the source and the /// destination planes: /// ///  /// /// so that the back-projection error /// ///  /// /// is minimized. If the parameter method is set to the default value 0, the function uses all the point /// pairs to compute an initial homography estimate with a simple least-squares scheme. /// /// However, if not all of the point pairs ( ,  ) fit the rigid perspective /// transformation (that is, there are some outliers), this initial estimate will be poor. In this case, /// you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different /// random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix /// using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the /// computed homography (which is the number of inliers for RANSAC or the least median re-projection error for /// LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and /// the mask of inliers/outliers. /// /// Regardless of the method, robust or not, the computed homography matrix is refined further (using /// inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the /// re-projection error even more. /// /// The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to /// distinguish inliers from outliers. The method LMeDS does not need any threshold but it works /// correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the /// noise is rather small, use the default method (method=0). /// /// The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is /// determined up to a scale. Thus, it is normalized so that . Note that whenever an  matrix /// cannot be estimated, an empty one will be returned. /// /// ## See also /// getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, /// perspectiveTransform /// /// ## Overloaded parameters /// /// ## C++ default parameters /// * method: 0 /// * ransac_reproj_threshold: 3 pub fn find_homography(src_points: &dyn core::ToInputArray, dst_points: &dyn core::ToInputArray, mask: &mut dyn core::ToOutputArray, method: i32, ransac_reproj_threshold: f64) -> Result<core::Mat> { input_array_arg!(src_points); input_array_arg!(dst_points); output_array_arg!(mask); unsafe { sys::cv_findHomography__InputArray__InputArray__OutputArray_int_double(src_points.as_raw__InputArray(), dst_points.as_raw__InputArray(), mask.as_raw__OutputArray(), method, ransac_reproj_threshold) }.into_result().map(|ptr| core::Mat { ptr }) } /// Finds a perspective transformation between two planes. /// /// ## Parameters /// * srcPoints: Coordinates of the points in the original plane, a matrix of the type CV_32FC2 /// or vector\<Point2f\> . /// * dstPoints: Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or /// a vector\<Point2f\> . /// * method: Method used to compute a homography matrix. The following methods are possible: /// * **0** - a regular method using all the points, i.e., the least squares method /// * **RANSAC** - RANSAC-based robust method /// * **LMEDS** - Least-Median robust method /// * **RHO** - PROSAC-based robust method /// * ransacReprojThreshold: Maximum allowed reprojection error to treat a point pair as an inlier /// (used in the RANSAC and RHO methods only). That is, if ///  /// then the point  is considered as an outlier. If srcPoints and dstPoints are measured in pixels, /// it usually makes sense to set this parameter somewhere in the range of 1 to 10. /// * mask: Optional output mask set by a robust method ( RANSAC or LMEDS ). Note that the input /// mask values are ignored. /// * maxIters: The maximum number of RANSAC iterations. /// * confidence: Confidence level, between 0 and 1. /// /// The function finds and returns the perspective transformation  between the source and the /// destination planes: /// ///  /// /// so that the back-projection error /// ///  /// /// is minimized. If the parameter method is set to the default value 0, the function uses all the point /// pairs to compute an initial homography estimate with a simple least-squares scheme. /// /// However, if not all of the point pairs ( ,  ) fit the rigid perspective /// transformation (that is, there are some outliers), this initial estimate will be poor. In this case, /// you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different /// random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix /// using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the /// computed homography (which is the number of inliers for RANSAC or the least median re-projection error for /// LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and /// the mask of inliers/outliers. /// /// Regardless of the method, robust or not, the computed homography matrix is refined further (using /// inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the /// re-projection error even more. /// /// The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to /// distinguish inliers from outliers. The method LMeDS does not need any threshold but it works /// correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the /// noise is rather small, use the default method (method=0). /// /// The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is /// determined up to a scale. Thus, it is normalized so that . Note that whenever an  matrix /// cannot be estimated, an empty one will be returned. /// /// ## See also /// getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, /// perspectiveTransform /// /// ## C++ default parameters /// * method: 0 /// * ransac_reproj_threshold: 3 /// * mask: noArray() /// * max_iters: 2000 /// * confidence: 0.995 pub fn find_homography_ext(src_points: &dyn core::ToInputArray, dst_points: &dyn core::ToInputArray, method: i32, ransac_reproj_threshold: f64, mask: &mut dyn core::ToOutputArray, max_iters: i32, confidence: f64) -> Result<core::Mat> { input_array_arg!(src_points); input_array_arg!(dst_points); output_array_arg!(mask); unsafe { sys::cv_findHomography__InputArray__InputArray_int_double__OutputArray_int_double(src_points.as_raw__InputArray(), dst_points.as_raw__InputArray(), method, ransac_reproj_threshold, mask.as_raw__OutputArray(), max_iters, confidence) }.into_result().map(|ptr| core::Mat { ptr }) } /// Performs camera calibaration /// /// ## Parameters /// * objectPoints: vector of vectors of calibration pattern points in the calibration pattern /// coordinate space. /// * imagePoints: vector of vectors of the projections of calibration pattern points. /// imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to /// objectPoints[i].size() for each i. /// * image_size: Size of the image used only to initialize the intrinsic camera matrix. /// * K: Output 3x3 floating-point camera matrix ///  . If /// fisheye::CALIB_USE_INTRINSIC_GUESS/ is specified, some or all of fx, fy, cx, cy must be /// initialized before calling the function. /// * D: Output vector of distortion coefficients . /// * rvecs: Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view. /// That is, each k-th rotation vector together with the corresponding k-th translation vector (see /// the next output parameter description) brings the calibration pattern from the model coordinate /// space (in which object points are specified) to the world coordinate space, that is, a real /// position of the calibration pattern in the k-th pattern view (k=0.. *M* -1). /// * tvecs: Output vector of translation vectors estimated for each pattern view. /// * flags: Different flags that may be zero or a combination of the following values: /// * **fisheye::CALIB_USE_INTRINSIC_GUESS** cameraMatrix contains valid initial values of /// fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image /// center ( imageSize is used), and focal distances are computed in a least-squares fashion. /// * **fisheye::CALIB_RECOMPUTE_EXTRINSIC** Extrinsic will be recomputed after each iteration /// of intrinsic optimization. /// * **fisheye::CALIB_CHECK_COND** The functions will check validity of condition number. /// * **fisheye::CALIB_FIX_SKEW** Skew coefficient (alpha) is set to zero and stay zero. /// * **fisheye::CALIB_FIX_K1..fisheye::CALIB_FIX_K4** Selected distortion coefficients /// are set to zeros and stay zero. /// * **fisheye::CALIB_FIX_PRINCIPAL_POINT** The principal point is not changed during the global /// optimization. It stays at the center or at a different location specified when CALIB_USE_INTRINSIC_GUESS is set too. /// * criteria: Termination criteria for the iterative optimization algorithm. /// /// ## C++ default parameters /// * flags: 0 /// * criteria: TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON) pub fn calibrate(object_points: &dyn core::ToInputArray, image_points: &dyn core::ToInputArray, image_size: core::Size, k: &mut dyn core::ToInputOutputArray, d: &mut dyn core::ToInputOutputArray, rvecs: &mut dyn core::ToOutputArray, tvecs: &mut dyn core::ToOutputArray, flags: i32, criteria: &core::TermCriteria) -> Result<f64> { input_array_arg!(object_points); input_array_arg!(image_points); input_output_array_arg!(k); input_output_array_arg!(d); output_array_arg!(rvecs); output_array_arg!(tvecs); unsafe { sys::cv_fisheye_calibrate__InputArray__InputArray_Size__InputOutputArray__InputOutputArray__OutputArray__OutputArray_int_TermCriteria(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), image_size, k.as_raw__InputOutputArray(), d.as_raw__InputOutputArray(), rvecs.as_raw__OutputArray(), tvecs.as_raw__OutputArray(), flags, criteria.as_raw_TermCriteria()) }.into_result() } /// Distorts 2D points using fisheye model. /// /// ## Parameters /// * undistorted: Array of object points, 1xN/Nx1 2-channel (or vector\<Point2f\> ), where N is /// the number of points in the view. /// * K: Camera matrix . /// * D: Input vector of distortion coefficients . /// * alpha: The skew coefficient. /// * distorted: Output array of image points, 1xN/Nx1 2-channel, or vector\<Point2f\> . /// /// Note that the function assumes the camera matrix of the undistorted points to be identity. /// This means if you want to transform back points undistorted with undistortPoints() you have to /// multiply them with . /// /// ## C++ default parameters /// * alpha: 0 pub fn distort_points(undistorted: &dyn core::ToInputArray, distorted: &mut dyn core::ToOutputArray, k: &dyn core::ToInputArray, d: &dyn core::ToInputArray, alpha: f64) -> Result<()> { input_array_arg!(undistorted); output_array_arg!(distorted); input_array_arg!(k); input_array_arg!(d); unsafe { sys::cv_fisheye_distortPoints__InputArray__OutputArray__InputArray__InputArray_double(undistorted.as_raw__InputArray(), distorted.as_raw__OutputArray(), k.as_raw__InputArray(), d.as_raw__InputArray(), alpha) }.into_result() } /// Estimates new camera matrix for undistortion or rectification. /// /// ## Parameters /// * K: Camera matrix . /// * image_size: Size of the image /// * D: Input vector of distortion coefficients . /// * R: Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 /// 1-channel or 1x1 3-channel /// * P: New camera matrix (3x3) or new projection matrix (3x4) /// * balance: Sets the new focal length in range between the min focal length and the max focal /// length. Balance is in range of [0, 1]. /// * new_size: the new size /// * fov_scale: Divisor for new focal length. /// /// ## C++ default parameters /// * balance: 0.0 /// * new_size: Size() /// * fov_scale: 1.0 pub fn estimate_new_camera_matrix_for_undistort_rectify(k: &dyn core::ToInputArray, d: &dyn core::ToInputArray, image_size: core::Size, r: &dyn core::ToInputArray, p: &mut dyn core::ToOutputArray, balance: f64, new_size: core::Size, fov_scale: f64) -> Result<()> { input_array_arg!(k); input_array_arg!(d); input_array_arg!(r); output_array_arg!(p); unsafe { sys::cv_fisheye_estimateNewCameraMatrixForUndistortRectify__InputArray__InputArray_Size__InputArray__OutputArray_double_Size_double(k.as_raw__InputArray(), d.as_raw__InputArray(), image_size, r.as_raw__InputArray(), p.as_raw__OutputArray(), balance, new_size, fov_scale) }.into_result() } /// Computes undistortion and rectification maps for image transform by cv::remap(). If D is empty zero /// distortion is used, if R or P is empty identity matrixes are used. /// /// ## Parameters /// * K: Camera matrix . /// * D: Input vector of distortion coefficients . /// * R: Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 /// 1-channel or 1x1 3-channel /// * P: New camera matrix (3x3) or new projection matrix (3x4) /// * size: Undistorted image size. /// * m1type: Type of the first output map that can be CV_32FC1 or CV_16SC2 . See convertMaps() /// for details. /// * map1: The first output map. /// * map2: The second output map. pub fn fisheye_init_undistort_rectify_map(k: &dyn core::ToInputArray, d: &dyn core::ToInputArray, r: &dyn core::ToInputArray, p: &dyn core::ToInputArray, size: core::Size, m1type: i32, map1: &mut dyn core::ToOutputArray, map2: &mut dyn core::ToOutputArray) -> Result<()> { input_array_arg!(k); input_array_arg!(d); input_array_arg!(r); input_array_arg!(p); output_array_arg!(map1); output_array_arg!(map2); unsafe { sys::cv_fisheye_initUndistortRectifyMap__InputArray__InputArray__InputArray__InputArray_Size_int__OutputArray__OutputArray(k.as_raw__InputArray(), d.as_raw__InputArray(), r.as_raw__InputArray(), p.as_raw__InputArray(), size, m1type, map1.as_raw__OutputArray(), map2.as_raw__OutputArray()) }.into_result() } /// Projects points using fisheye model /// /// ## Parameters /// * objectPoints: Array of object points, 1xN/Nx1 3-channel (or vector\<Point3f\> ), where N is /// the number of points in the view. /// * imagePoints: Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or /// vector\<Point2f\>. /// * affine: /// * K: Camera matrix . /// * D: Input vector of distortion coefficients . /// * alpha: The skew coefficient. /// * jacobian: Optional output 2Nx15 jacobian matrix of derivatives of image points with respect /// to components of the focal lengths, coordinates of the principal point, distortion coefficients, /// rotation vector, translation vector, and the skew. In the old interface different components of /// the jacobian are returned via different output parameters. /// /// The function computes projections of 3D points to the image plane given intrinsic and extrinsic /// camera parameters. Optionally, the function computes Jacobians - matrices of partial derivatives of /// image points coordinates (as functions of all the input parameters) with respect to the particular /// parameters, intrinsic and/or extrinsic. /// /// ## Overloaded parameters /// /// ## C++ default parameters /// * alpha: 0 /// * jacobian: noArray() pub fn fisheye_project_points(object_points: &dyn core::ToInputArray, image_points: &mut dyn core::ToOutputArray, rvec: &dyn core::ToInputArray, tvec: &dyn core::ToInputArray, k: &dyn core::ToInputArray, d: &dyn core::ToInputArray, alpha: f64, jacobian: &mut dyn core::ToOutputArray) -> Result<()> { input_array_arg!(object_points); output_array_arg!(image_points); input_array_arg!(rvec); input_array_arg!(tvec); input_array_arg!(k); input_array_arg!(d); output_array_arg!(jacobian); unsafe { sys::cv_fisheye_projectPoints__InputArray__OutputArray__InputArray__InputArray__InputArray__InputArray_double__OutputArray(object_points.as_raw__InputArray(), image_points.as_raw__OutputArray(), rvec.as_raw__InputArray(), tvec.as_raw__InputArray(), k.as_raw__InputArray(), d.as_raw__InputArray(), alpha, jacobian.as_raw__OutputArray()) }.into_result() } /// Performs stereo calibration /// /// ## Parameters /// * objectPoints: Vector of vectors of the calibration pattern points. /// * imagePoints1: Vector of vectors of the projections of the calibration pattern points, /// observed by the first camera. /// * imagePoints2: Vector of vectors of the projections of the calibration pattern points, /// observed by the second camera. /// * K1: Input/output first camera matrix: ///  ,  . If /// any of fisheye::CALIB_USE_INTRINSIC_GUESS , fisheye::CALIB_FIX_INTRINSIC are specified, /// some or all of the matrix components must be initialized. /// * D1: Input/output vector of distortion coefficients  of 4 elements. /// * K2: Input/output second camera matrix. The parameter is similar to K1 . /// * D2: Input/output lens distortion coefficients for the second camera. The parameter is /// similar to D1 . /// * imageSize: Size of the image used only to initialize intrinsic camera matrix. /// * R: Output rotation matrix between the 1st and the 2nd camera coordinate systems. /// * T: Output translation vector between the coordinate systems of the cameras. /// * flags: Different flags that may be zero or a combination of the following values: /// * **fisheye::CALIB_FIX_INTRINSIC** Fix K1, K2? and D1, D2? so that only R, T matrices /// are estimated. /// * **fisheye::CALIB_USE_INTRINSIC_GUESS** K1, K2 contains valid initial values of /// fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image /// center (imageSize is used), and focal distances are computed in a least-squares fashion. /// * **fisheye::CALIB_RECOMPUTE_EXTRINSIC** Extrinsic will be recomputed after each iteration /// of intrinsic optimization. /// * **fisheye::CALIB_CHECK_COND** The functions will check validity of condition number. /// * **fisheye::CALIB_FIX_SKEW** Skew coefficient (alpha) is set to zero and stay zero. /// * **fisheye::CALIB_FIX_K1..4** Selected distortion coefficients are set to zeros and stay /// zero. /// * criteria: Termination criteria for the iterative optimization algorithm. /// /// ## C++ default parameters /// * flags: fisheye::CALIB_FIX_INTRINSIC /// * criteria: TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON) pub fn fisheye_stereo_calibrate(object_points: &dyn core::ToInputArray, image_points1: &dyn core::ToInputArray, image_points2: &dyn core::ToInputArray, k1: &mut dyn core::ToInputOutputArray, d1: &mut dyn core::ToInputOutputArray, k2: &mut dyn core::ToInputOutputArray, d2: &mut dyn core::ToInputOutputArray, image_size: core::Size, r: &mut dyn core::ToOutputArray, t: &mut dyn core::ToOutputArray, flags: i32, criteria: &core::TermCriteria) -> Result<f64> { input_array_arg!(object_points); input_array_arg!(image_points1); input_array_arg!(image_points2); input_output_array_arg!(k1); input_output_array_arg!(d1); input_output_array_arg!(k2); input_output_array_arg!(d2); output_array_arg!(r); output_array_arg!(t); unsafe { sys::cv_fisheye_stereoCalibrate__InputArray__InputArray__InputArray__InputOutputArray__InputOutputArray__InputOutputArray__InputOutputArray_Size__OutputArray__OutputArray_int_TermCriteria(object_points.as_raw__InputArray(), image_points1.as_raw__InputArray(), image_points2.as_raw__InputArray(), k1.as_raw__InputOutputArray(), d1.as_raw__InputOutputArray(), k2.as_raw__InputOutputArray(), d2.as_raw__InputOutputArray(), image_size, r.as_raw__OutputArray(), t.as_raw__OutputArray(), flags, criteria.as_raw_TermCriteria()) }.into_result() } /// Stereo rectification for fisheye camera model /// /// ## Parameters /// * K1: First camera matrix. /// * D1: First camera distortion parameters. /// * K2: Second camera matrix. /// * D2: Second camera distortion parameters. /// * imageSize: Size of the image used for stereo calibration. /// * R: Rotation matrix between the coordinate systems of the first and the second /// cameras. /// * tvec: Translation vector between coordinate systems of the cameras. /// * R1: Output 3x3 rectification transform (rotation matrix) for the first camera. /// * R2: Output 3x3 rectification transform (rotation matrix) for the second camera. /// * P1: Output 3x4 projection matrix in the new (rectified) coordinate systems for the first /// camera. /// * P2: Output 3x4 projection matrix in the new (rectified) coordinate systems for the second /// camera. /// * Q: Output  disparity-to-depth mapping matrix (see reprojectImageTo3D ). /// * flags: Operation flags that may be zero or CALIB_ZERO_DISPARITY . If the flag is set, /// the function makes the principal points of each camera have the same pixel coordinates in the /// rectified views. And if the flag is not set, the function may still shift the images in the /// horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the /// useful image area. /// * newImageSize: New image resolution after rectification. The same size should be passed to /// initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) /// is passed (default), it is set to the original imageSize . Setting it to larger value can help you /// preserve details in the original image, especially when there is a big radial distortion. /// * balance: Sets the new focal length in range between the min focal length and the max focal /// length. Balance is in range of [0, 1]. /// * fov_scale: Divisor for new focal length. /// /// ## C++ default parameters /// * new_image_size: Size() /// * balance: 0.0 /// * fov_scale: 1.0 pub fn fisheye_stereo_rectify(k1: &dyn core::ToInputArray, d1: &dyn core::ToInputArray, k2: &dyn core::ToInputArray, d2: &dyn core::ToInputArray, image_size: core::Size, r: &dyn core::ToInputArray, tvec: &dyn core::ToInputArray, r1: &mut dyn core::ToOutputArray, r2: &mut dyn core::ToOutputArray, p1: &mut dyn core::ToOutputArray, p2: &mut dyn core::ToOutputArray, q: &mut dyn core::ToOutputArray, flags: i32, new_image_size: core::Size, balance: f64, fov_scale: f64) -> Result<()> { input_array_arg!(k1); input_array_arg!(d1); input_array_arg!(k2); input_array_arg!(d2); input_array_arg!(r); input_array_arg!(tvec); output_array_arg!(r1); output_array_arg!(r2); output_array_arg!(p1); output_array_arg!(p2); output_array_arg!(q); unsafe { sys::cv_fisheye_stereoRectify__InputArray__InputArray__InputArray__InputArray_Size__InputArray__InputArray__OutputArray__OutputArray__OutputArray__OutputArray__OutputArray_int_Size_double_double(k1.as_raw__InputArray(), d1.as_raw__InputArray(), k2.as_raw__InputArray(), d2.as_raw__InputArray(), image_size, r.as_raw__InputArray(), tvec.as_raw__InputArray(), r1.as_raw__OutputArray(), r2.as_raw__OutputArray(), p1.as_raw__OutputArray(), p2.as_raw__OutputArray(), q.as_raw__OutputArray(), flags, new_image_size, balance, fov_scale) }.into_result() } /// Transforms an image to compensate for fisheye lens distortion. /// /// ## Parameters /// * distorted: image with fisheye lens distortion. /// * undistorted: Output image with compensated fisheye lens distortion. /// * K: Camera matrix . /// * D: Input vector of distortion coefficients . /// * Knew: Camera matrix of the distorted image. By default, it is the identity matrix but you /// may additionally scale and shift the result by using a different matrix. /// * new_size: the new size /// /// The function transforms an image to compensate radial and tangential lens distortion. /// /// The function is simply a combination of fisheye::initUndistortRectifyMap (with unity R ) and remap /// (with bilinear interpolation). See the former function for details of the transformation being /// performed. /// /// See below the results of undistortImage. /// * a\) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3, /// k_4, k_5, k_6) of distortion were optimized under calibration) /// * b\) result of fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2, /// k_3, k_4) of fisheye distortion were optimized under calibration) /// * c\) original image was captured with fisheye lens /// /// Pictures a) and b) almost the same. But if we consider points of image located far from the center /// of image, we can notice that on image a) these points are distorted. /// ///  /// /// ## C++ default parameters /// * knew: cv::noArray() /// * new_size: Size() pub fn fisheye_undistort_image(distorted: &dyn core::ToInputArray, undistorted: &mut dyn core::ToOutputArray, k: &dyn core::ToInputArray, d: &dyn core::ToInputArray, knew: &dyn core::ToInputArray, new_size: core::Size) -> Result<()> { input_array_arg!(distorted); output_array_arg!(undistorted); input_array_arg!(k); input_array_arg!(d); input_array_arg!(knew); unsafe { sys::cv_fisheye_undistortImage__InputArray__OutputArray__InputArray__InputArray__InputArray_Size(distorted.as_raw__InputArray(), undistorted.as_raw__OutputArray(), k.as_raw__InputArray(), d.as_raw__InputArray(), knew.as_raw__InputArray(), new_size) }.into_result() } /// Undistorts 2D points using fisheye model /// /// ## Parameters /// * distorted: Array of object points, 1xN/Nx1 2-channel (or vector\<Point2f\> ), where N is the /// number of points in the view. /// * K: Camera matrix . /// * D: Input vector of distortion coefficients . /// * R: Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 /// 1-channel or 1x1 3-channel /// * P: New camera matrix (3x3) or new projection matrix (3x4) /// * undistorted: Output array of image points, 1xN/Nx1 2-channel, or vector\<Point2f\> . /// /// ## C++ default parameters /// * r: noArray() /// * p: noArray() pub fn fisheye_undistort_points(distorted: &dyn core::ToInputArray, undistorted: &mut dyn core::ToOutputArray, k: &dyn core::ToInputArray, d: &dyn core::ToInputArray, r: &dyn core::ToInputArray, p: &dyn core::ToInputArray) -> Result<()> { input_array_arg!(distorted); output_array_arg!(undistorted); input_array_arg!(k); input_array_arg!(d); input_array_arg!(r); input_array_arg!(p); unsafe { sys::cv_fisheye_undistortPoints__InputArray__OutputArray__InputArray__InputArray__InputArray__InputArray(distorted.as_raw__InputArray(), undistorted.as_raw__OutputArray(), k.as_raw__InputArray(), d.as_raw__InputArray(), r.as_raw__InputArray(), p.as_raw__InputArray()) }.into_result() } /// Returns the new camera matrix based on the free scaling parameter. /// /// ## Parameters /// * cameraMatrix: Input camera matrix. /// * distCoeffs: Input vector of distortion coefficients ///  of /// 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are /// assumed. /// * imageSize: Original image size. /// * alpha: Free scaling parameter between 0 (when all the pixels in the undistorted image are /// valid) and 1 (when all the source image pixels are retained in the undistorted image). See /// stereoRectify for details. /// * newImgSize: Image size after rectification. By default, it is set to imageSize . /// * validPixROI: Optional output rectangle that outlines all-good-pixels region in the /// undistorted image. See roi1, roi2 description in stereoRectify . /// * centerPrincipalPoint: Optional flag that indicates whether in the new camera matrix the /// principal point should be at the image center or not. By default, the principal point is chosen to /// best fit a subset of the source image (determined by alpha) to the corrected image. /// ## Returns /// new_camera_matrix Output new camera matrix. /// /// The function computes and returns the optimal new camera matrix based on the free scaling parameter. /// By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original /// image pixels if there is valuable information in the corners alpha=1 , or get something in between. /// When alpha\>0 , the undistorted result is likely to have some black pixels corresponding to /// "virtual" pixels outside of the captured distorted image. The original camera matrix, distortion /// coefficients, the computed new camera matrix, and newImageSize should be passed to /// initUndistortRectifyMap to produce the maps for remap . /// /// ## C++ default parameters /// * new_img_size: Size() /// * valid_pix_roi: 0 /// * center_principal_point: false pub fn get_optimal_new_camera_matrix(camera_matrix: &dyn core::ToInputArray, dist_coeffs: &dyn core::ToInputArray, image_size: core::Size, alpha: f64, new_img_size: core::Size, valid_pix_roi: &mut core::Rect, center_principal_point: bool) -> Result<core::Mat> { input_array_arg!(camera_matrix); input_array_arg!(dist_coeffs); unsafe { sys::cv_getOptimalNewCameraMatrix__InputArray__InputArray_Size_double_Size_Rect_X_bool(camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), image_size, alpha, new_img_size, valid_pix_roi, center_principal_point) }.into_result().map(|ptr| core::Mat { ptr }) } /// computes valid disparity ROI from the valid ROIs of the rectified images (that are returned by cv::stereoRectify()) pub fn get_valid_disparity_roi(roi1: core::Rect, roi2: core::Rect, min_disparity: i32, number_of_disparities: i32, sad_window_size: i32) -> Result<core::Rect> { unsafe { sys::cv_getValidDisparityROI_Rect_Rect_int_int_int(roi1, roi2, min_disparity, number_of_disparities, sad_window_size) }.into_result() } /// Finds an initial camera matrix from 3D-2D point correspondences. /// /// ## Parameters /// * objectPoints: Vector of vectors of the calibration pattern points in the calibration pattern /// coordinate space. In the old interface all the per-view vectors are concatenated. See /// calibrateCamera for details. /// * imagePoints: Vector of vectors of the projections of the calibration pattern points. In the /// old interface all the per-view vectors are concatenated. /// * imageSize: Image size in pixels used to initialize the principal point. /// * aspectRatio: If it is zero or negative, both  and  are estimated independently. /// Otherwise,  . /// /// The function estimates and returns an initial camera matrix for the camera calibration process. /// Currently, the function only supports planar calibration patterns, which are patterns where each /// object point has z-coordinate =0. /// /// ## C++ default parameters /// * aspect_ratio: 1.0 pub fn init_camera_matrix_2d(object_points: &dyn core::ToInputArray, image_points: &dyn core::ToInputArray, image_size: core::Size, aspect_ratio: f64) -> Result<core::Mat> { input_array_arg!(object_points); input_array_arg!(image_points); unsafe { sys::cv_initCameraMatrix2D__InputArray__InputArray_Size_double(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), image_size, aspect_ratio) }.into_result().map(|ptr| core::Mat { ptr }) } /// Computes partial derivatives of the matrix product for each multiplied matrix. /// /// ## Parameters /// * A: First multiplied matrix. /// * B: Second multiplied matrix. /// * dABdA: First output derivative matrix d(A\*B)/dA of size ///  . /// * dABdB: Second output derivative matrix d(A\*B)/dB of size ///  . /// /// The function computes partial derivatives of the elements of the matrix product  with regard to /// the elements of each of the two input matrices. The function is used to compute the Jacobian /// matrices in stereoCalibrate but can also be used in any other similar optimization function. pub fn mat_mul_deriv(a: &dyn core::ToInputArray, b: &dyn core::ToInputArray, d_a_bd_a: &mut dyn core::ToOutputArray, d_a_bd_b: &mut dyn core::ToOutputArray) -> Result<()> { input_array_arg!(a); input_array_arg!(b); output_array_arg!(d_a_bd_a); output_array_arg!(d_a_bd_b); unsafe { sys::cv_matMulDeriv__InputArray__InputArray__OutputArray__OutputArray(a.as_raw__InputArray(), b.as_raw__InputArray(), d_a_bd_a.as_raw__OutputArray(), d_a_bd_b.as_raw__OutputArray()) }.into_result() } /// Projects 3D points to an image plane. /// /// ## Parameters /// * objectPoints: Array of object points, 3xN/Nx3 1-channel or 1xN/Nx1 3-channel (or /// vector\<Point3f\> ), where N is the number of points in the view. /// * rvec: Rotation vector. See Rodrigues for details. /// * tvec: Translation vector. /// * cameraMatrix: Camera matrix  . /// * distCoeffs: Input vector of distortion coefficients ///  of /// 4, 5, 8, 12 or 14 elements. If the vector is empty, the zero distortion coefficients are assumed. /// * imagePoints: Output array of image points, 1xN/Nx1 2-channel, or /// vector\<Point2f\> . /// * jacobian: Optional output 2Nx(10+\<numDistCoeffs\>) jacobian matrix of derivatives of image /// points with respect to components of the rotation vector, translation vector, focal lengths, /// coordinates of the principal point and the distortion coefficients. In the old interface different /// components of the jacobian are returned via different output parameters. /// * aspectRatio: Optional "fixed aspect ratio" parameter. If the parameter is not 0, the /// function assumes that the aspect ratio (*fx/fy*) is fixed and correspondingly adjusts the jacobian /// matrix. /// /// The function computes projections of 3D points to the image plane given intrinsic and extrinsic /// camera parameters. Optionally, the function computes Jacobians - matrices of partial derivatives of /// image points coordinates (as functions of all the input parameters) with respect to the particular /// parameters, intrinsic and/or extrinsic. The Jacobians are used during the global optimization in /// calibrateCamera, solvePnP, and stereoCalibrate . The function itself can also be used to compute a /// re-projection error given the current intrinsic and extrinsic parameters. /// /// /// Note: By setting rvec=tvec=(0,0,0) or by setting cameraMatrix to a 3x3 identity matrix, or by /// passing zero distortion coefficients, you can get various useful partial cases of the function. This /// means that you can compute the distorted coordinates for a sparse set of points or apply a /// perspective transformation (and also compute the derivatives) in the ideal zero-distortion setup. /// /// ## C++ default parameters /// * jacobian: noArray() /// * aspect_ratio: 0 pub fn project_points(object_points: &dyn core::ToInputArray, rvec: &dyn core::ToInputArray, tvec: &dyn core::ToInputArray, camera_matrix: &dyn core::ToInputArray, dist_coeffs: &dyn core::ToInputArray, image_points: &mut dyn core::ToOutputArray, jacobian: &mut dyn core::ToOutputArray, aspect_ratio: f64) -> Result<()> { input_array_arg!(object_points); input_array_arg!(rvec); input_array_arg!(tvec); input_array_arg!(camera_matrix); input_array_arg!(dist_coeffs); output_array_arg!(image_points); output_array_arg!(jacobian); unsafe { sys::cv_projectPoints__InputArray__InputArray__InputArray__InputArray__InputArray__OutputArray__OutputArray_double(object_points.as_raw__InputArray(), rvec.as_raw__InputArray(), tvec.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), image_points.as_raw__OutputArray(), jacobian.as_raw__OutputArray(), aspect_ratio) }.into_result() } /// Recover relative camera rotation and translation from an estimated essential matrix and the /// corresponding points in two images, using cheirality check. Returns the number of inliers which pass /// the check. /// /// ## Parameters /// * E: The input essential matrix. /// * points1: Array of N 2D points from the first image. The point coordinates should be /// floating-point (single or double precision). /// * points2: Array of the second image points of the same size and format as points1 . /// * cameraMatrix: Camera matrix  . /// Note that this function assumes that points1 and points2 are feature points from cameras with the /// same camera matrix. /// * R: Recovered relative rotation. /// * t: Recovered relative translation. /// * mask: Input/output mask for inliers in points1 and points2. /// : If it is not empty, then it marks inliers in points1 and points2 for then given essential /// matrix E. Only these inliers will be used to recover pose. In the output mask only inliers /// which pass the cheirality check. /// This function decomposes an essential matrix using decomposeEssentialMat and then verifies possible /// pose hypotheses by doing cheirality check. The cheirality check basically means that the /// triangulated 3D points should have positive depth. Some details can be found in [Nister03](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Nister03) . /// /// This function can be used to process output E and mask from findEssentialMat. In this scenario, /// points1 and points2 are the same input for findEssentialMat. : /// ```ignore /// // Example. Estimation of fundamental matrix using the RANSAC algorithm /// int point_count = 100; /// vector<Point2f> points1(point_count); /// vector<Point2f> points2(point_count); /// /// // initialize the points here ... /// for( int i = 0; i < point_count; i++ ) /// { /// points1[i] = ...; /// points2[i] = ...; /// } /// /// // cametra matrix with both focal lengths = 1, and principal point = (0, 0) /// Mat cameraMatrix = Mat::eye(3, 3, CV_64F); /// /// Mat E, R, t, mask; /// /// E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask); /// recoverPose(E, points1, points2, cameraMatrix, R, t, mask); /// ``` /// /// ## C++ default parameters /// * mask: noArray() pub fn recover_pose_camera_with_points(e: &dyn core::ToInputArray, points1: &dyn core::ToInputArray, points2: &dyn core::ToInputArray, camera_matrix: &dyn core::ToInputArray, r: &mut dyn core::ToOutputArray, t: &mut dyn core::ToOutputArray, mask: &mut dyn core::ToInputOutputArray) -> Result<i32> { input_array_arg!(e); input_array_arg!(points1); input_array_arg!(points2); input_array_arg!(camera_matrix); output_array_arg!(r); output_array_arg!(t); input_output_array_arg!(mask); unsafe { sys::cv_recoverPose__InputArray__InputArray__InputArray__InputArray__OutputArray__OutputArray__InputOutputArray(e.as_raw__InputArray(), points1.as_raw__InputArray(), points2.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), r.as_raw__OutputArray(), t.as_raw__OutputArray(), mask.as_raw__InputOutputArray()) }.into_result() } /// Recover relative camera rotation and translation from an estimated essential matrix and the /// corresponding points in two images, using cheirality check. Returns the number of inliers which pass /// the check. /// /// ## Parameters /// * E: The input essential matrix. /// * points1: Array of N 2D points from the first image. The point coordinates should be /// floating-point (single or double precision). /// * points2: Array of the second image points of the same size and format as points1 . /// * cameraMatrix: Camera matrix  . /// Note that this function assumes that points1 and points2 are feature points from cameras with the /// same camera matrix. /// * R: Recovered relative rotation. /// * t: Recovered relative translation. /// * mask: Input/output mask for inliers in points1 and points2. /// : If it is not empty, then it marks inliers in points1 and points2 for then given essential /// matrix E. Only these inliers will be used to recover pose. In the output mask only inliers /// which pass the cheirality check. /// This function decomposes an essential matrix using decomposeEssentialMat and then verifies possible /// pose hypotheses by doing cheirality check. The cheirality check basically means that the /// triangulated 3D points should have positive depth. Some details can be found in [Nister03](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Nister03) . /// /// This function can be used to process output E and mask from findEssentialMat. In this scenario, /// points1 and points2 are the same input for findEssentialMat. : /// ```ignore /// // Example. Estimation of fundamental matrix using the RANSAC algorithm /// int point_count = 100; /// vector<Point2f> points1(point_count); /// vector<Point2f> points2(point_count); /// /// // initialize the points here ... /// for( int i = 0; i < point_count; i++ ) /// { /// points1[i] = ...; /// points2[i] = ...; /// } /// /// // cametra matrix with both focal lengths = 1, and principal point = (0, 0) /// Mat cameraMatrix = Mat::eye(3, 3, CV_64F); /// /// Mat E, R, t, mask; /// /// E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask); /// recoverPose(E, points1, points2, cameraMatrix, R, t, mask); /// ``` /// /// /// ## Overloaded parameters /// /// * E: The input essential matrix. /// * points1: Array of N 2D points from the first image. The point coordinates should be /// floating-point (single or double precision). /// * points2: Array of the second image points of the same size and format as points1. /// * cameraMatrix: Camera matrix  . /// Note that this function assumes that points1 and points2 are feature points from cameras with the /// same camera matrix. /// * R: Recovered relative rotation. /// * t: Recovered relative translation. /// * distanceThresh: threshold distance which is used to filter out far away points (i.e. infinite points). /// * mask: Input/output mask for inliers in points1 and points2. /// : If it is not empty, then it marks inliers in points1 and points2 for then given essential /// matrix E. Only these inliers will be used to recover pose. In the output mask only inliers /// which pass the cheirality check. /// * triangulatedPoints: 3d points which were reconstructed by triangulation. /// /// ## C++ default parameters /// * mask: noArray() /// * triangulated_points: noArray() pub fn recover_pose_camera(e: &dyn core::ToInputArray, points1: &dyn core::ToInputArray, points2: &dyn core::ToInputArray, camera_matrix: &dyn core::ToInputArray, r: &mut dyn core::ToOutputArray, t: &mut dyn core::ToOutputArray, distance_thresh: f64, mask: &mut dyn core::ToInputOutputArray, triangulated_points: &mut dyn core::ToOutputArray) -> Result<i32> { input_array_arg!(e); input_array_arg!(points1); input_array_arg!(points2); input_array_arg!(camera_matrix); output_array_arg!(r); output_array_arg!(t); input_output_array_arg!(mask); output_array_arg!(triangulated_points); unsafe { sys::cv_recoverPose__InputArray__InputArray__InputArray__InputArray__OutputArray__OutputArray_double__InputOutputArray__OutputArray(e.as_raw__InputArray(), points1.as_raw__InputArray(), points2.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), r.as_raw__OutputArray(), t.as_raw__OutputArray(), distance_thresh, mask.as_raw__InputOutputArray(), triangulated_points.as_raw__OutputArray()) }.into_result() } /// Recover relative camera rotation and translation from an estimated essential matrix and the /// corresponding points in two images, using cheirality check. Returns the number of inliers which pass /// the check. /// /// ## Parameters /// * E: The input essential matrix. /// * points1: Array of N 2D points from the first image. The point coordinates should be /// floating-point (single or double precision). /// * points2: Array of the second image points of the same size and format as points1 . /// * cameraMatrix: Camera matrix  . /// Note that this function assumes that points1 and points2 are feature points from cameras with the /// same camera matrix. /// * R: Recovered relative rotation. /// * t: Recovered relative translation. /// * mask: Input/output mask for inliers in points1 and points2. /// : If it is not empty, then it marks inliers in points1 and points2 for then given essential /// matrix E. Only these inliers will be used to recover pose. In the output mask only inliers /// which pass the cheirality check. /// This function decomposes an essential matrix using decomposeEssentialMat and then verifies possible /// pose hypotheses by doing cheirality check. The cheirality check basically means that the /// triangulated 3D points should have positive depth. Some details can be found in [Nister03](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Nister03) . /// /// This function can be used to process output E and mask from findEssentialMat. In this scenario, /// points1 and points2 are the same input for findEssentialMat. : /// ```ignore /// // Example. Estimation of fundamental matrix using the RANSAC algorithm /// int point_count = 100; /// vector<Point2f> points1(point_count); /// vector<Point2f> points2(point_count); /// /// // initialize the points here ... /// for( int i = 0; i < point_count; i++ ) /// { /// points1[i] = ...; /// points2[i] = ...; /// } /// /// // cametra matrix with both focal lengths = 1, and principal point = (0, 0) /// Mat cameraMatrix = Mat::eye(3, 3, CV_64F); /// /// Mat E, R, t, mask; /// /// E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask); /// recoverPose(E, points1, points2, cameraMatrix, R, t, mask); /// ``` /// /// /// ## Overloaded parameters /// /// * E: The input essential matrix. /// * points1: Array of N 2D points from the first image. The point coordinates should be /// floating-point (single or double precision). /// * points2: Array of the second image points of the same size and format as points1 . /// * R: Recovered relative rotation. /// * t: Recovered relative translation. /// * focal: Focal length of the camera. Note that this function assumes that points1 and points2 /// are feature points from cameras with same focal length and principal point. /// * pp: principal point of the camera. /// * mask: Input/output mask for inliers in points1 and points2. /// : If it is not empty, then it marks inliers in points1 and points2 for then given essential /// matrix E. Only these inliers will be used to recover pose. In the output mask only inliers /// which pass the cheirality check. /// /// This function differs from the one above that it computes camera matrix from focal length and /// principal point: /// ///  /// /// ## C++ default parameters /// * focal: 1.0 /// * pp: Point2d(0, 0) /// * mask: noArray() pub fn recover_pose(e: &dyn core::ToInputArray, points1: &dyn core::ToInputArray, points2: &dyn core::ToInputArray, r: &mut dyn core::ToOutputArray, t: &mut dyn core::ToOutputArray, focal: f64, pp: core::Point2d, mask: &mut dyn core::ToInputOutputArray) -> Result<i32> { input_array_arg!(e); input_array_arg!(points1); input_array_arg!(points2); output_array_arg!(r); output_array_arg!(t); input_output_array_arg!(mask); unsafe { sys::cv_recoverPose__InputArray__InputArray__InputArray__OutputArray__OutputArray_double_Point2d__InputOutputArray(e.as_raw__InputArray(), points1.as_raw__InputArray(), points2.as_raw__InputArray(), r.as_raw__OutputArray(), t.as_raw__OutputArray(), focal, pp, mask.as_raw__InputOutputArray()) }.into_result() } /// computes the rectification transformations for 3-head camera, where all the heads are on the same line. pub fn rectify3_collinear(camera_matrix1: &dyn core::ToInputArray, dist_coeffs1: &dyn core::ToInputArray, camera_matrix2: &dyn core::ToInputArray, dist_coeffs2: &dyn core::ToInputArray, camera_matrix3: &dyn core::ToInputArray, dist_coeffs3: &dyn core::ToInputArray, imgpt1: &dyn core::ToInputArray, imgpt3: &dyn core::ToInputArray, image_size: core::Size, r12: &dyn core::ToInputArray, t12: &dyn core::ToInputArray, r13: &dyn core::ToInputArray, t13: &dyn core::ToInputArray, r1: &mut dyn core::ToOutputArray, r2: &mut dyn core::ToOutputArray, r3: &mut dyn core::ToOutputArray, p1: &mut dyn core::ToOutputArray, p2: &mut dyn core::ToOutputArray, p3: &mut dyn core::ToOutputArray, q: &mut dyn core::ToOutputArray, alpha: f64, new_img_size: core::Size, roi1: &mut core::Rect, roi2: &mut core::Rect, flags: i32) -> Result<f32> { input_array_arg!(camera_matrix1); input_array_arg!(dist_coeffs1); input_array_arg!(camera_matrix2); input_array_arg!(dist_coeffs2); input_array_arg!(camera_matrix3); input_array_arg!(dist_coeffs3); input_array_arg!(imgpt1); input_array_arg!(imgpt3); input_array_arg!(r12); input_array_arg!(t12); input_array_arg!(r13); input_array_arg!(t13); output_array_arg!(r1); output_array_arg!(r2); output_array_arg!(r3); output_array_arg!(p1); output_array_arg!(p2); output_array_arg!(p3); output_array_arg!(q); unsafe { sys::cv_rectify3Collinear__InputArray__InputArray__InputArray__InputArray__InputArray__InputArray__InputArray__InputArray_Size__InputArray__InputArray__InputArray__InputArray__OutputArray__OutputArray__OutputArray__OutputArray__OutputArray__OutputArray__OutputArray_double_Size_Rect_X_Rect_X_int(camera_matrix1.as_raw__InputArray(), dist_coeffs1.as_raw__InputArray(), camera_matrix2.as_raw__InputArray(), dist_coeffs2.as_raw__InputArray(), camera_matrix3.as_raw__InputArray(), dist_coeffs3.as_raw__InputArray(), imgpt1.as_raw__InputArray(), imgpt3.as_raw__InputArray(), image_size, r12.as_raw__InputArray(), t12.as_raw__InputArray(), r13.as_raw__InputArray(), t13.as_raw__InputArray(), r1.as_raw__OutputArray(), r2.as_raw__OutputArray(), r3.as_raw__OutputArray(), p1.as_raw__OutputArray(), p2.as_raw__OutputArray(), p3.as_raw__OutputArray(), q.as_raw__OutputArray(), alpha, new_img_size, roi1, roi2, flags) }.into_result() } /// Reprojects a disparity image to 3D space. /// /// ## Parameters /// * disparity: Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit /// floating-point disparity image. If 16-bit signed format is used, the values are assumed to have no /// fractional bits. /// * _3dImage: Output 3-channel floating-point image of the same size as disparity . Each /// element of _3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity /// map. /// * Q:  perspective transformation matrix that can be obtained with stereoRectify. /// * handleMissingValues: Indicates, whether the function should handle missing values (i.e. /// points where the disparity was not computed). If handleMissingValues=true, then pixels with the /// minimal disparity that corresponds to the outliers (see StereoMatcher::compute ) are transformed /// to 3D points with a very large Z value (currently set to 10000). /// * ddepth: The optional output array depth. If it is -1, the output image will have CV_32F /// depth. ddepth can also be set to CV_16S, CV_32S or CV_32F. /// /// The function transforms a single-channel disparity map to a 3-channel image representing a 3D /// surface. That is, for each pixel (x,y) and the corresponding disparity d=disparity(x,y) , it /// computes: /// ///  /// /// The matrix Q can be an arbitrary  matrix (for example, the one computed by /// stereoRectify). To reproject a sparse set of points {(x,y,d),...} to 3D space, use /// perspectiveTransform . /// /// ## C++ default parameters /// * handle_missing_values: false /// * ddepth: -1 pub fn reproject_image_to_3d(disparity: &dyn core::ToInputArray, _3d_image: &mut dyn core::ToOutputArray, q: &dyn core::ToInputArray, handle_missing_values: bool, ddepth: i32) -> Result<()> { input_array_arg!(disparity); output_array_arg!(_3d_image); input_array_arg!(q); unsafe { sys::cv_reprojectImageTo3D__InputArray__OutputArray__InputArray_bool_int(disparity.as_raw__InputArray(), _3d_image.as_raw__OutputArray(), q.as_raw__InputArray(), handle_missing_values, ddepth) }.into_result() } /// Calculates the Sampson Distance between two points. /// /// The function cv::sampsonDistance calculates and returns the first order approximation of the geometric error as: ///  /// The fundamental matrix may be calculated using the cv::findFundamentalMat function. See [HartleyZ00](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_HartleyZ00) 11.4.3 for details. /// ## Parameters /// * pt1: first homogeneous 2d point /// * pt2: second homogeneous 2d point /// * F: fundamental matrix /// ## Returns /// The computed Sampson distance. pub fn sampson_distance(pt1: &dyn core::ToInputArray, pt2: &dyn core::ToInputArray, f: &dyn core::ToInputArray) -> Result<f64> { input_array_arg!(pt1); input_array_arg!(pt2); input_array_arg!(f); unsafe { sys::cv_sampsonDistance__InputArray__InputArray__InputArray(pt1.as_raw__InputArray(), pt2.as_raw__InputArray(), f.as_raw__InputArray()) }.into_result() } /// Finds an object pose from 3 3D-2D point correspondences. /// /// ## Parameters /// * objectPoints: Array of object points in the object coordinate space, 3x3 1-channel or /// 1x3/3x1 3-channel. vector\<Point3f\> can be also passed here. /// * imagePoints: Array of corresponding image points, 3x2 1-channel or 1x3/3x1 2-channel. /// vector\<Point2f\> can be also passed here. /// * cameraMatrix: Input camera matrix  . /// * distCoeffs: Input vector of distortion coefficients ///  of /// 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are /// assumed. /// * rvecs: Output rotation vectors (see @ref Rodrigues ) that, together with tvecs, brings points from /// the model coordinate system to the camera coordinate system. A P3P problem has up to 4 solutions. /// * tvecs: Output translation vectors. /// * flags: Method for solving a P3P problem: /// * **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang /// "Complete Solution Classification for the Perspective-Three-Point Problem" ([gao2003complete](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_gao2003complete)). /// * **SOLVEPNP_AP3P** Method is based on the paper of T. Ke and S. Roumeliotis. /// "An Efficient Algebraic Solution to the Perspective-Three-Point Problem" ([Ke17](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Ke17)). /// /// The function estimates the object pose given 3 object points, their corresponding image /// projections, as well as the camera matrix and the distortion coefficients. /// /// /// Note: /// The solutions are sorted by reprojection errors (lowest to highest). pub fn solve_p3p(object_points: &dyn core::ToInputArray, image_points: &dyn core::ToInputArray, camera_matrix: &dyn core::ToInputArray, dist_coeffs: &dyn core::ToInputArray, rvecs: &mut dyn core::ToOutputArray, tvecs: &mut dyn core::ToOutputArray, flags: i32) -> Result<i32> { input_array_arg!(object_points); input_array_arg!(image_points); input_array_arg!(camera_matrix); input_array_arg!(dist_coeffs); output_array_arg!(rvecs); output_array_arg!(tvecs); unsafe { sys::cv_solveP3P__InputArray__InputArray__InputArray__InputArray__OutputArray__OutputArray_int(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvecs.as_raw__OutputArray(), tvecs.as_raw__OutputArray(), flags) }.into_result() } /// Finds an object pose from 3D-2D point correspondences. /// This function returns a list of all the possible solutions (a solution is a <rotation vector, translation vector> /// couple), depending on the number of input points and the chosen method: /// - P3P methods (@ref SOLVEPNP_P3P, @ref SOLVEPNP_AP3P): 3 or 4 input points. Number of returned solutions can be between 0 and 4 with 3 input points. /// - @ref SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. Returns 2 solutions. /// - @ref SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. /// Number of input points must be 4 and 2 solutions are returned. Object points must be defined in the following order: /// - point 0: [-squareLength / 2, squareLength / 2, 0] /// - point 1: [ squareLength / 2, squareLength / 2, 0] /// - point 2: [ squareLength / 2, -squareLength / 2, 0] /// - point 3: [-squareLength / 2, -squareLength / 2, 0] /// - for all the other flags, number of input points must be >= 4 and object points can be in any configuration. /// Only 1 solution is returned. /// /// ## Parameters /// * objectPoints: Array of object points in the object coordinate space, Nx3 1-channel or /// 1xN/Nx1 3-channel, where N is the number of points. vector\<Point3f\> can be also passed here. /// * imagePoints: Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, /// where N is the number of points. vector\<Point2f\> can be also passed here. /// * cameraMatrix: Input camera matrix  . /// * distCoeffs: Input vector of distortion coefficients ///  of /// 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are /// assumed. /// * rvecs: Vector of output rotation vectors (see @ref Rodrigues ) that, together with tvecs, brings points from /// the model coordinate system to the camera coordinate system. /// * tvecs: Vector of output translation vectors. /// * useExtrinsicGuess: Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses /// the provided rvec and tvec values as initial approximations of the rotation and translation /// vectors, respectively, and further optimizes them. /// * flags: Method for solving a PnP problem: /// * **SOLVEPNP_ITERATIVE** Iterative method is based on a Levenberg-Marquardt optimization. In /// this case the function finds such a pose that minimizes reprojection error, that is the sum /// of squared distances between the observed projections imagePoints and the projected (using /// projectPoints ) objectPoints . /// * **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang /// "Complete Solution Classification for the Perspective-Three-Point Problem" ([gao2003complete](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_gao2003complete)). /// In this case the function requires exactly four object and image points. /// * **SOLVEPNP_AP3P** Method is based on the paper of T. Ke, S. Roumeliotis /// "An Efficient Algebraic Solution to the Perspective-Three-Point Problem" ([Ke17](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Ke17)). /// In this case the function requires exactly four object and image points. /// * **SOLVEPNP_EPNP** Method has been introduced by F.Moreno-Noguer, V.Lepetit and P.Fua in the /// paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" ([lepetit2009epnp](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_lepetit2009epnp)). /// * **SOLVEPNP_DLS** Method is based on the paper of Joel A. Hesch and Stergios I. Roumeliotis. /// "A Direct Least-Squares (DLS) Method for PnP" ([hesch2011direct](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_hesch2011direct)). /// * **SOLVEPNP_UPNP** Method is based on the paper of A.Penate-Sanchez, J.Andrade-Cetto, /// F.Moreno-Noguer. "Exhaustive Linearization for Robust Camera Pose and Focal Length /// Estimation" ([penate2013exhaustive](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_penate2013exhaustive)). In this case the function also estimates the parameters  and  /// assuming that both have the same value. Then the cameraMatrix is updated with the estimated /// focal length. /// * **SOLVEPNP_IPPE** Method is based on the paper of T. Collins and A. Bartoli. /// "Infinitesimal Plane-Based Pose Estimation" ([Collins14](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Collins14)). This method requires coplanar object points. /// * **SOLVEPNP_IPPE_SQUARE** Method is based on the paper of Toby Collins and Adrien Bartoli. /// "Infinitesimal Plane-Based Pose Estimation" ([Collins14](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Collins14)). This method is suitable for marker pose estimation. /// It requires 4 coplanar object points defined in the following order: /// - point 0: [-squareLength / 2, squareLength / 2, 0] /// - point 1: [ squareLength / 2, squareLength / 2, 0] /// - point 2: [ squareLength / 2, -squareLength / 2, 0] /// - point 3: [-squareLength / 2, -squareLength / 2, 0] /// * rvec: Rotation vector used to initialize an iterative PnP refinement algorithm, when flag is SOLVEPNP_ITERATIVE /// and useExtrinsicGuess is set to true. /// * tvec: Translation vector used to initialize an iterative PnP refinement algorithm, when flag is SOLVEPNP_ITERATIVE /// and useExtrinsicGuess is set to true. /// * reprojectionError: Optional vector of reprojection error, that is the RMS error /// () between the input image points /// and the 3D object points projected with the estimated pose. /// /// The function estimates the object pose given a set of object points, their corresponding image /// projections, as well as the camera matrix and the distortion coefficients, see the figure below /// (more precisely, the X-axis of the camera frame is pointing to the right, the Y-axis downward /// and the Z-axis forward). /// ///  /// /// Points expressed in the world frame  are projected into the image plane  /// using the perspective projection model  and the camera intrinsic parameters matrix : /// ///  /// /// The estimated pose is thus the rotation (`rvec`) and the translation (`tvec`) vectors that allow transforming /// a 3D point expressed in the world frame into the camera frame: /// ///  /// /// /// Note: /// * An example of how to use solvePnP for planar augmented reality can be found at /// opencv_source_code/samples/python/plane_ar.py /// * If you are using Python: /// - Numpy array slices won't work as input because solvePnP requires contiguous /// arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of /// modules/calib3d/src/solvepnp.cpp version 2.4.9) /// - The P3P algorithm requires image points to be in an array of shape (N,1,2) due /// to its calling of cv::undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9) /// which requires 2-channel information. /// - Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of /// it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints = /// np.ascontiguousarray(D[:,:2]).reshape((N,1,2)) /// * The methods **SOLVEPNP_DLS** and **SOLVEPNP_UPNP** cannot be used as the current implementations are /// unstable and sometimes give completely wrong results. If you pass one of these two /// flags, **SOLVEPNP_EPNP** method will be used instead. /// * The minimum number of points is 4 in the general case. In the case of **SOLVEPNP_P3P** and **SOLVEPNP_AP3P** /// methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions /// of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error). /// * With **SOLVEPNP_ITERATIVE** method and `useExtrinsicGuess=true`, the minimum number of points is 3 (3 points /// are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the /// global solution to converge. /// * With **SOLVEPNP_IPPE** input points must be >= 4 and object points must be coplanar. /// * With **SOLVEPNP_IPPE_SQUARE** this is a special case suitable for marker pose estimation. /// Number of input points must be 4. Object points must be defined in the following order: /// - point 0: [-squareLength / 2, squareLength / 2, 0] /// - point 1: [ squareLength / 2, squareLength / 2, 0] /// - point 2: [ squareLength / 2, -squareLength / 2, 0] /// - point 3: [-squareLength / 2, -squareLength / 2, 0] /// /// ## C++ default parameters /// * use_extrinsic_guess: false /// * flags: SOLVEPNP_ITERATIVE /// * rvec: noArray() /// * tvec: noArray() /// * reprojection_error: noArray() pub fn solve_pnp_generic(object_points: &dyn core::ToInputArray, image_points: &dyn core::ToInputArray, camera_matrix: &dyn core::ToInputArray, dist_coeffs: &dyn core::ToInputArray, rvecs: &mut dyn core::ToOutputArray, tvecs: &mut dyn core::ToOutputArray, use_extrinsic_guess: bool, flags: crate::calib3d::SolvePnPMethod, rvec: &dyn core::ToInputArray, tvec: &dyn core::ToInputArray, reprojection_error: &mut dyn core::ToOutputArray) -> Result<i32> { input_array_arg!(object_points); input_array_arg!(image_points); input_array_arg!(camera_matrix); input_array_arg!(dist_coeffs); output_array_arg!(rvecs); output_array_arg!(tvecs); input_array_arg!(rvec); input_array_arg!(tvec); output_array_arg!(reprojection_error); unsafe { sys::cv_solvePnPGeneric__InputArray__InputArray__InputArray__InputArray__OutputArray__OutputArray_bool_SolvePnPMethod__InputArray__InputArray__OutputArray(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvecs.as_raw__OutputArray(), tvecs.as_raw__OutputArray(), use_extrinsic_guess, flags, rvec.as_raw__InputArray(), tvec.as_raw__InputArray(), reprojection_error.as_raw__OutputArray()) }.into_result() } /// Finds an object pose from 3D-2D point correspondences using the RANSAC scheme. /// /// ## Parameters /// * objectPoints: Array of object points in the object coordinate space, Nx3 1-channel or /// 1xN/Nx1 3-channel, where N is the number of points. vector\<Point3f\> can be also passed here. /// * imagePoints: Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, /// where N is the number of points. vector\<Point2f\> can be also passed here. /// * cameraMatrix: Input camera matrix  . /// * distCoeffs: Input vector of distortion coefficients ///  of /// 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are /// assumed. /// * rvec: Output rotation vector (see @ref Rodrigues ) that, together with tvec, brings points from /// the model coordinate system to the camera coordinate system. /// * tvec: Output translation vector. /// * useExtrinsicGuess: Parameter used for @ref SOLVEPNP_ITERATIVE. If true (1), the function uses /// the provided rvec and tvec values as initial approximations of the rotation and translation /// vectors, respectively, and further optimizes them. /// * iterationsCount: Number of iterations. /// * reprojectionError: Inlier threshold value used by the RANSAC procedure. The parameter value /// is the maximum allowed distance between the observed and computed point projections to consider it /// an inlier. /// * confidence: The probability that the algorithm produces a useful result. /// * inliers: Output vector that contains indices of inliers in objectPoints and imagePoints . /// * flags: Method for solving a PnP problem (see @ref solvePnP ). /// /// The function estimates an object pose given a set of object points, their corresponding image /// projections, as well as the camera matrix and the distortion coefficients. This function finds such /// a pose that minimizes reprojection error, that is, the sum of squared distances between the observed /// projections imagePoints and the projected (using @ref projectPoints ) objectPoints. The use of RANSAC /// makes the function resistant to outliers. /// /// /// Note: /// * An example of how to use solvePNPRansac for object detection can be found at /// opencv_source_code/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/ /// * The default method used to estimate the camera pose for the Minimal Sample Sets step /// is #SOLVEPNP_EPNP. Exceptions are: /// - if you choose #SOLVEPNP_P3P or #SOLVEPNP_AP3P, these methods will be used. /// - if the number of input points is equal to 4, #SOLVEPNP_P3P is used. /// * The method used to estimate the camera pose using all the inliers is defined by the /// flags parameters unless it is equal to #SOLVEPNP_P3P or #SOLVEPNP_AP3P. In this case, /// the method #SOLVEPNP_EPNP will be used instead. /// /// ## C++ default parameters /// * use_extrinsic_guess: false /// * iterations_count: 100 /// * reprojection_error: 8.0 /// * confidence: 0.99 /// * inliers: noArray() /// * flags: SOLVEPNP_ITERATIVE pub fn solve_pnp_ransac(object_points: &dyn core::ToInputArray, image_points: &dyn core::ToInputArray, camera_matrix: &dyn core::ToInputArray, dist_coeffs: &dyn core::ToInputArray, rvec: &mut dyn core::ToOutputArray, tvec: &mut dyn core::ToOutputArray, use_extrinsic_guess: bool, iterations_count: i32, reprojection_error: f32, confidence: f64, inliers: &mut dyn core::ToOutputArray, flags: i32) -> Result<bool> { input_array_arg!(object_points); input_array_arg!(image_points); input_array_arg!(camera_matrix); input_array_arg!(dist_coeffs); output_array_arg!(rvec); output_array_arg!(tvec); output_array_arg!(inliers); unsafe { sys::cv_solvePnPRansac__InputArray__InputArray__InputArray__InputArray__OutputArray__OutputArray_bool_int_float_double__OutputArray_int(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvec.as_raw__OutputArray(), tvec.as_raw__OutputArray(), use_extrinsic_guess, iterations_count, reprojection_error, confidence, inliers.as_raw__OutputArray(), flags) }.into_result() } /// Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame /// to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution. /// /// ## Parameters /// * objectPoints: Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, /// where N is the number of points. vector\<Point3f\> can also be passed here. /// * imagePoints: Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, /// where N is the number of points. vector\<Point2f\> can also be passed here. /// * cameraMatrix: Input camera matrix  . /// * distCoeffs: Input vector of distortion coefficients ///  of /// 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are /// assumed. /// * rvec: Input/Output rotation vector (see @ref Rodrigues ) that, together with tvec, brings points from /// the model coordinate system to the camera coordinate system. Input values are used as an initial solution. /// * tvec: Input/Output translation vector. Input values are used as an initial solution. /// * criteria: Criteria when to stop the Levenberg-Marquard iterative algorithm. /// /// The function refines the object pose given at least 3 object points, their corresponding image /// projections, an initial solution for the rotation and translation vector, /// as well as the camera matrix and the distortion coefficients. /// The function minimizes the projection error with respect to the rotation and the translation vectors, according /// to a Levenberg-Marquardt iterative minimization [Madsen04](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Madsen04) [Eade13](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Eade13) process. /// /// ## C++ default parameters /// * criteria: TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 20, FLT_EPSILON) pub fn solve_pnp_refine_lm(object_points: &dyn core::ToInputArray, image_points: &dyn core::ToInputArray, camera_matrix: &dyn core::ToInputArray, dist_coeffs: &dyn core::ToInputArray, rvec: &mut dyn core::ToInputOutputArray, tvec: &mut dyn core::ToInputOutputArray, criteria: &core::TermCriteria) -> Result<()> { input_array_arg!(object_points); input_array_arg!(image_points); input_array_arg!(camera_matrix); input_array_arg!(dist_coeffs); input_output_array_arg!(rvec); input_output_array_arg!(tvec); unsafe { sys::cv_solvePnPRefineLM__InputArray__InputArray__InputArray__InputArray__InputOutputArray__InputOutputArray_TermCriteria(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvec.as_raw__InputOutputArray(), tvec.as_raw__InputOutputArray(), criteria.as_raw_TermCriteria()) }.into_result() } /// Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame /// to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution. /// /// ## Parameters /// * objectPoints: Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, /// where N is the number of points. vector\<Point3f\> can also be passed here. /// * imagePoints: Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, /// where N is the number of points. vector\<Point2f\> can also be passed here. /// * cameraMatrix: Input camera matrix  . /// * distCoeffs: Input vector of distortion coefficients ///  of /// 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are /// assumed. /// * rvec: Input/Output rotation vector (see @ref Rodrigues ) that, together with tvec, brings points from /// the model coordinate system to the camera coordinate system. Input values are used as an initial solution. /// * tvec: Input/Output translation vector. Input values are used as an initial solution. /// * criteria: Criteria when to stop the Levenberg-Marquard iterative algorithm. /// * VVSlambda: Gain for the virtual visual servoing control law, equivalent to the  /// gain in the Damped Gauss-Newton formulation. /// /// The function refines the object pose given at least 3 object points, their corresponding image /// projections, an initial solution for the rotation and translation vector, /// as well as the camera matrix and the distortion coefficients. /// The function minimizes the projection error with respect to the rotation and the translation vectors, using a /// virtual visual servoing (VVS) [Chaumette06](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Chaumette06) [Marchand16](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Marchand16) scheme. /// /// ## C++ default parameters /// * criteria: TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 20, FLT_EPSILON) /// * vv_slambda: 1 pub fn solve_pnp_refine_vvs(object_points: &dyn core::ToInputArray, image_points: &dyn core::ToInputArray, camera_matrix: &dyn core::ToInputArray, dist_coeffs: &dyn core::ToInputArray, rvec: &mut dyn core::ToInputOutputArray, tvec: &mut dyn core::ToInputOutputArray, criteria: &core::TermCriteria, vv_slambda: f64) -> Result<()> { input_array_arg!(object_points); input_array_arg!(image_points); input_array_arg!(camera_matrix); input_array_arg!(dist_coeffs); input_output_array_arg!(rvec); input_output_array_arg!(tvec); unsafe { sys::cv_solvePnPRefineVVS__InputArray__InputArray__InputArray__InputArray__InputOutputArray__InputOutputArray_TermCriteria_double(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvec.as_raw__InputOutputArray(), tvec.as_raw__InputOutputArray(), criteria.as_raw_TermCriteria(), vv_slambda) }.into_result() } /// Finds an object pose from 3D-2D point correspondences. /// This function returns the rotation and the translation vectors that transform a 3D point expressed in the object /// coordinate frame to the camera coordinate frame, using different methods: /// - P3P methods (@ref SOLVEPNP_P3P, @ref SOLVEPNP_AP3P): need 4 input points to return a unique solution. /// - @ref SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. /// - @ref SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. /// Number of input points must be 4. Object points must be defined in the following order: /// - point 0: [-squareLength / 2, squareLength / 2, 0] /// - point 1: [ squareLength / 2, squareLength / 2, 0] /// - point 2: [ squareLength / 2, -squareLength / 2, 0] /// - point 3: [-squareLength / 2, -squareLength / 2, 0] /// - for all the other flags, number of input points must be >= 4 and object points can be in any configuration. /// /// ## Parameters /// * objectPoints: Array of object points in the object coordinate space, Nx3 1-channel or /// 1xN/Nx1 3-channel, where N is the number of points. vector\<Point3f\> can be also passed here. /// * imagePoints: Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, /// where N is the number of points. vector\<Point2f\> can be also passed here. /// * cameraMatrix: Input camera matrix  . /// * distCoeffs: Input vector of distortion coefficients ///  of /// 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are /// assumed. /// * rvec: Output rotation vector (see @ref Rodrigues ) that, together with tvec, brings points from /// the model coordinate system to the camera coordinate system. /// * tvec: Output translation vector. /// * useExtrinsicGuess: Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses /// the provided rvec and tvec values as initial approximations of the rotation and translation /// vectors, respectively, and further optimizes them. /// * flags: Method for solving a PnP problem: /// * **SOLVEPNP_ITERATIVE** Iterative method is based on a Levenberg-Marquardt optimization. In /// this case the function finds such a pose that minimizes reprojection error, that is the sum /// of squared distances between the observed projections imagePoints and the projected (using /// projectPoints ) objectPoints . /// * **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang /// "Complete Solution Classification for the Perspective-Three-Point Problem" ([gao2003complete](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_gao2003complete)). /// In this case the function requires exactly four object and image points. /// * **SOLVEPNP_AP3P** Method is based on the paper of T. Ke, S. Roumeliotis /// "An Efficient Algebraic Solution to the Perspective-Three-Point Problem" ([Ke17](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Ke17)). /// In this case the function requires exactly four object and image points. /// * **SOLVEPNP_EPNP** Method has been introduced by F. Moreno-Noguer, V. Lepetit and P. Fua in the /// paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" ([lepetit2009epnp](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_lepetit2009epnp)). /// * **SOLVEPNP_DLS** Method is based on the paper of J. Hesch and S. Roumeliotis. /// "A Direct Least-Squares (DLS) Method for PnP" ([hesch2011direct](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_hesch2011direct)). /// * **SOLVEPNP_UPNP** Method is based on the paper of A. Penate-Sanchez, J. Andrade-Cetto, /// F. Moreno-Noguer. "Exhaustive Linearization for Robust Camera Pose and Focal Length /// Estimation" ([penate2013exhaustive](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_penate2013exhaustive)). In this case the function also estimates the parameters  and  /// assuming that both have the same value. Then the cameraMatrix is updated with the estimated /// focal length. /// * **SOLVEPNP_IPPE** Method is based on the paper of T. Collins and A. Bartoli. /// "Infinitesimal Plane-Based Pose Estimation" ([Collins14](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Collins14)). This method requires coplanar object points. /// * **SOLVEPNP_IPPE_SQUARE** Method is based on the paper of Toby Collins and Adrien Bartoli. /// "Infinitesimal Plane-Based Pose Estimation" ([Collins14](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Collins14)). This method is suitable for marker pose estimation. /// It requires 4 coplanar object points defined in the following order: /// - point 0: [-squareLength / 2, squareLength / 2, 0] /// - point 1: [ squareLength / 2, squareLength / 2, 0] /// - point 2: [ squareLength / 2, -squareLength / 2, 0] /// - point 3: [-squareLength / 2, -squareLength / 2, 0] /// /// The function estimates the object pose given a set of object points, their corresponding image /// projections, as well as the camera matrix and the distortion coefficients, see the figure below /// (more precisely, the X-axis of the camera frame is pointing to the right, the Y-axis downward /// and the Z-axis forward). /// ///  /// /// Points expressed in the world frame  are projected into the image plane  /// using the perspective projection model  and the camera intrinsic parameters matrix : /// ///  /// /// The estimated pose is thus the rotation (`rvec`) and the translation (`tvec`) vectors that allow transforming /// a 3D point expressed in the world frame into the camera frame: /// ///  /// /// /// Note: /// * An example of how to use solvePnP for planar augmented reality can be found at /// opencv_source_code/samples/python/plane_ar.py /// * If you are using Python: /// - Numpy array slices won't work as input because solvePnP requires contiguous /// arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of /// modules/calib3d/src/solvepnp.cpp version 2.4.9) /// - The P3P algorithm requires image points to be in an array of shape (N,1,2) due /// to its calling of cv::undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9) /// which requires 2-channel information. /// - Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of /// it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints = /// np.ascontiguousarray(D[:,:2]).reshape((N,1,2)) /// * The methods **SOLVEPNP_DLS** and **SOLVEPNP_UPNP** cannot be used as the current implementations are /// unstable and sometimes give completely wrong results. If you pass one of these two /// flags, **SOLVEPNP_EPNP** method will be used instead. /// * The minimum number of points is 4 in the general case. In the case of **SOLVEPNP_P3P** and **SOLVEPNP_AP3P** /// methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions /// of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error). /// * With **SOLVEPNP_ITERATIVE** method and `useExtrinsicGuess=true`, the minimum number of points is 3 (3 points /// are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the /// global solution to converge. /// * With **SOLVEPNP_IPPE** input points must be >= 4 and object points must be coplanar. /// * With **SOLVEPNP_IPPE_SQUARE** this is a special case suitable for marker pose estimation. /// Number of input points must be 4. Object points must be defined in the following order: /// - point 0: [-squareLength / 2, squareLength / 2, 0] /// - point 1: [ squareLength / 2, squareLength / 2, 0] /// - point 2: [ squareLength / 2, -squareLength / 2, 0] /// - point 3: [-squareLength / 2, -squareLength / 2, 0] /// /// ## C++ default parameters /// * use_extrinsic_guess: false /// * flags: SOLVEPNP_ITERATIVE pub fn solve_pnp(object_points: &dyn core::ToInputArray, image_points: &dyn core::ToInputArray, camera_matrix: &dyn core::ToInputArray, dist_coeffs: &dyn core::ToInputArray, rvec: &mut dyn core::ToOutputArray, tvec: &mut dyn core::ToOutputArray, use_extrinsic_guess: bool, flags: i32) -> Result<bool> { input_array_arg!(object_points); input_array_arg!(image_points); input_array_arg!(camera_matrix); input_array_arg!(dist_coeffs); output_array_arg!(rvec); output_array_arg!(tvec); unsafe { sys::cv_solvePnP__InputArray__InputArray__InputArray__InputArray__OutputArray__OutputArray_bool_int(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvec.as_raw__OutputArray(), tvec.as_raw__OutputArray(), use_extrinsic_guess, flags) }.into_result() } /// Calibrates the stereo camera. /// /// ## Parameters /// * objectPoints: Vector of vectors of the calibration pattern points. /// * imagePoints1: Vector of vectors of the projections of the calibration pattern points, /// observed by the first camera. /// * imagePoints2: Vector of vectors of the projections of the calibration pattern points, /// observed by the second camera. /// * cameraMatrix1: Input/output first camera matrix: ///  ,  . If /// any of CALIB_USE_INTRINSIC_GUESS , CALIB_FIX_ASPECT_RATIO , /// CALIB_FIX_INTRINSIC , or CALIB_FIX_FOCAL_LENGTH are specified, some or all of the /// matrix components must be initialized. See the flags description for details. /// * distCoeffs1: Input/output vector of distortion coefficients ///  of /// 4, 5, 8, 12 or 14 elements. The output vector length depends on the flags. /// * cameraMatrix2: Input/output second camera matrix. The parameter is similar to cameraMatrix1 /// * distCoeffs2: Input/output lens distortion coefficients for the second camera. The parameter /// is similar to distCoeffs1 . /// * imageSize: Size of the image used only to initialize intrinsic camera matrix. /// * R: Output rotation matrix between the 1st and the 2nd camera coordinate systems. /// * T: Output translation vector between the coordinate systems of the cameras. /// * E: Output essential matrix. /// * F: Output fundamental matrix. /// * perViewErrors: Output vector of the RMS re-projection error estimated for each pattern view. /// * flags: Different flags that may be zero or a combination of the following values: /// * **CALIB_FIX_INTRINSIC** Fix cameraMatrix? and distCoeffs? so that only R, T, E , and F /// matrices are estimated. /// * **CALIB_USE_INTRINSIC_GUESS** Optimize some or all of the intrinsic parameters /// according to the specified flags. Initial values are provided by the user. /// * **CALIB_USE_EXTRINSIC_GUESS** R, T contain valid initial values that are optimized further. /// Otherwise R, T are initialized to the median value of the pattern views (each dimension separately). /// * **CALIB_FIX_PRINCIPAL_POINT** Fix the principal points during the optimization. /// * **CALIB_FIX_FOCAL_LENGTH** Fix  and  . /// * **CALIB_FIX_ASPECT_RATIO** Optimize  . Fix the ratio  /// . /// * **CALIB_SAME_FOCAL_LENGTH** Enforce  and  . /// * **CALIB_ZERO_TANGENT_DIST** Set tangential distortion coefficients for each camera to /// zeros and fix there. /// * **CALIB_FIX_K1,...,CALIB_FIX_K6** Do not change the corresponding radial /// distortion coefficient during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, /// the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. /// * **CALIB_RATIONAL_MODEL** Enable coefficients k4, k5, and k6. To provide the backward /// compatibility, this extra flag should be explicitly specified to make the calibration /// function use the rational model and return 8 coefficients. If the flag is not set, the /// function computes and returns only 5 distortion coefficients. /// * **CALIB_THIN_PRISM_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the /// backward compatibility, this extra flag should be explicitly specified to make the /// calibration function use the thin prism model and return 12 coefficients. If the flag is not /// set, the function computes and returns only 5 distortion coefficients. /// * **CALIB_FIX_S1_S2_S3_S4** The thin prism distortion coefficients are not changed during /// the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the /// supplied distCoeffs matrix is used. Otherwise, it is set to 0. /// * **CALIB_TILTED_MODEL** Coefficients tauX and tauY are enabled. To provide the /// backward compatibility, this extra flag should be explicitly specified to make the /// calibration function use the tilted sensor model and return 14 coefficients. If the flag is not /// set, the function computes and returns only 5 distortion coefficients. /// * **CALIB_FIX_TAUX_TAUY** The coefficients of the tilted sensor model are not changed during /// the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the /// supplied distCoeffs matrix is used. Otherwise, it is set to 0. /// * criteria: Termination criteria for the iterative optimization algorithm. /// /// The function estimates transformation between two cameras making a stereo pair. If you have a stereo /// camera where the relative position and orientation of two cameras is fixed, and if you computed /// poses of an object relative to the first camera and to the second camera, (R1, T1) and (R2, T2), /// respectively (this can be done with solvePnP ), then those poses definitely relate to each other. /// This means that, given ( , ), it should be possible to compute ( , ). You only /// need to know the position and orientation of the second camera relative to the first camera. This is /// what the described function does. It computes ( , ) so that: /// ///  ///  /// /// Optionally, it computes the essential matrix E: /// ///  /// /// where  are components of the translation vector  :  . And the function /// can also compute the fundamental matrix F: /// ///  /// /// Besides the stereo-related information, the function can also perform a full calibration of each of /// two cameras. However, due to the high dimensionality of the parameter space and noise in the input /// data, the function can diverge from the correct solution. If the intrinsic parameters can be /// estimated with high accuracy for each of the cameras individually (for example, using /// calibrateCamera ), you are recommended to do so and then pass CALIB_FIX_INTRINSIC flag to the /// function along with the computed intrinsic parameters. Otherwise, if all the parameters are /// estimated at once, it makes sense to restrict some parameters, for example, pass /// CALIB_SAME_FOCAL_LENGTH and CALIB_ZERO_TANGENT_DIST flags, which is usually a /// reasonable assumption. /// /// Similarly to calibrateCamera , the function minimizes the total re-projection error for all the /// points in all the available views from both cameras. The function returns the final value of the /// re-projection error. /// /// ## C++ default parameters /// * flags: CALIB_FIX_INTRINSIC /// * criteria: TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6) pub fn stereo_calibrate_camera_with_errors(object_points: &dyn core::ToInputArray, image_points1: &dyn core::ToInputArray, image_points2: &dyn core::ToInputArray, camera_matrix1: &mut dyn core::ToInputOutputArray, dist_coeffs1: &mut dyn core::ToInputOutputArray, camera_matrix2: &mut dyn core::ToInputOutputArray, dist_coeffs2: &mut dyn core::ToInputOutputArray, image_size: core::Size, r: &mut dyn core::ToInputOutputArray, t: &mut dyn core::ToInputOutputArray, e: &mut dyn core::ToOutputArray, f: &mut dyn core::ToOutputArray, per_view_errors: &mut dyn core::ToOutputArray, flags: i32, criteria: &core::TermCriteria) -> Result<f64> { input_array_arg!(object_points); input_array_arg!(image_points1); input_array_arg!(image_points2); input_output_array_arg!(camera_matrix1); input_output_array_arg!(dist_coeffs1); input_output_array_arg!(camera_matrix2); input_output_array_arg!(dist_coeffs2); input_output_array_arg!(r); input_output_array_arg!(t); output_array_arg!(e); output_array_arg!(f); output_array_arg!(per_view_errors); unsafe { sys::cv_stereoCalibrate__InputArray__InputArray__InputArray__InputOutputArray__InputOutputArray__InputOutputArray__InputOutputArray_Size__InputOutputArray__InputOutputArray__OutputArray__OutputArray__OutputArray_int_TermCriteria(object_points.as_raw__InputArray(), image_points1.as_raw__InputArray(), image_points2.as_raw__InputArray(), camera_matrix1.as_raw__InputOutputArray(), dist_coeffs1.as_raw__InputOutputArray(), camera_matrix2.as_raw__InputOutputArray(), dist_coeffs2.as_raw__InputOutputArray(), image_size, r.as_raw__InputOutputArray(), t.as_raw__InputOutputArray(), e.as_raw__OutputArray(), f.as_raw__OutputArray(), per_view_errors.as_raw__OutputArray(), flags, criteria.as_raw_TermCriteria()) }.into_result() } /// /// ## C++ default parameters /// * flags: CALIB_FIX_INTRINSIC /// * criteria: TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6) pub fn stereo_calibrate_camera(object_points: &dyn core::ToInputArray, image_points1: &dyn core::ToInputArray, image_points2: &dyn core::ToInputArray, camera_matrix1: &mut dyn core::ToInputOutputArray, dist_coeffs1: &mut dyn core::ToInputOutputArray, camera_matrix2: &mut dyn core::ToInputOutputArray, dist_coeffs2: &mut dyn core::ToInputOutputArray, image_size: core::Size, r: &mut dyn core::ToOutputArray, t: &mut dyn core::ToOutputArray, e: &mut dyn core::ToOutputArray, f: &mut dyn core::ToOutputArray, flags: i32, criteria: &core::TermCriteria) -> Result<f64> { input_array_arg!(object_points); input_array_arg!(image_points1); input_array_arg!(image_points2); input_output_array_arg!(camera_matrix1); input_output_array_arg!(dist_coeffs1); input_output_array_arg!(camera_matrix2); input_output_array_arg!(dist_coeffs2); output_array_arg!(r); output_array_arg!(t); output_array_arg!(e); output_array_arg!(f); unsafe { sys::cv_stereoCalibrate__InputArray__InputArray__InputArray__InputOutputArray__InputOutputArray__InputOutputArray__InputOutputArray_Size__OutputArray__OutputArray__OutputArray__OutputArray_int_TermCriteria(object_points.as_raw__InputArray(), image_points1.as_raw__InputArray(), image_points2.as_raw__InputArray(), camera_matrix1.as_raw__InputOutputArray(), dist_coeffs1.as_raw__InputOutputArray(), camera_matrix2.as_raw__InputOutputArray(), dist_coeffs2.as_raw__InputOutputArray(), image_size, r.as_raw__OutputArray(), t.as_raw__OutputArray(), e.as_raw__OutputArray(), f.as_raw__OutputArray(), flags, criteria.as_raw_TermCriteria()) }.into_result() } /// Computes a rectification transform for an uncalibrated stereo camera. /// /// ## Parameters /// * points1: Array of feature points in the first image. /// * points2: The corresponding points in the second image. The same formats as in /// findFundamentalMat are supported. /// * F: Input fundamental matrix. It can be computed from the same set of point pairs using /// findFundamentalMat . /// * imgSize: Size of the image. /// * H1: Output rectification homography matrix for the first image. /// * H2: Output rectification homography matrix for the second image. /// * threshold: Optional threshold used to filter out the outliers. If the parameter is greater /// than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points /// for which  ) are /// rejected prior to computing the homographies. Otherwise, all the points are considered inliers. /// /// The function computes the rectification transformations without knowing intrinsic parameters of the /// cameras and their relative position in the space, which explains the suffix "uncalibrated". Another /// related difference from stereoRectify is that the function outputs not the rectification /// transformations in the object (3D) space, but the planar perspective transformations encoded by the /// homography matrices H1 and H2 . The function implements the algorithm [Hartley99](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_Hartley99) . /// /// /// Note: /// While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily /// depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion, /// it would be better to correct it before computing the fundamental matrix and calling this /// function. For example, distortion coefficients can be estimated for each head of stereo camera /// separately by using calibrateCamera . Then, the images can be corrected using undistort , or /// just the point coordinates can be corrected with undistortPoints . /// /// ## C++ default parameters /// * threshold: 5 pub fn stereo_rectify_uncalibrated(points1: &dyn core::ToInputArray, points2: &dyn core::ToInputArray, f: &dyn core::ToInputArray, img_size: core::Size, h1: &mut dyn core::ToOutputArray, h2: &mut dyn core::ToOutputArray, threshold: f64) -> Result<bool> { input_array_arg!(points1); input_array_arg!(points2); input_array_arg!(f); output_array_arg!(h1); output_array_arg!(h2); unsafe { sys::cv_stereoRectifyUncalibrated__InputArray__InputArray__InputArray_Size__OutputArray__OutputArray_double(points1.as_raw__InputArray(), points2.as_raw__InputArray(), f.as_raw__InputArray(), img_size, h1.as_raw__OutputArray(), h2.as_raw__OutputArray(), threshold) }.into_result() } /// Computes rectification transforms for each head of a calibrated stereo camera. /// /// ## Parameters /// * cameraMatrix1: First camera matrix. /// * distCoeffs1: First camera distortion parameters. /// * cameraMatrix2: Second camera matrix. /// * distCoeffs2: Second camera distortion parameters. /// * imageSize: Size of the image used for stereo calibration. /// * R: Rotation matrix between the coordinate systems of the first and the second cameras. /// * T: Translation vector between coordinate systems of the cameras. /// * R1: Output 3x3 rectification transform (rotation matrix) for the first camera. /// * R2: Output 3x3 rectification transform (rotation matrix) for the second camera. /// * P1: Output 3x4 projection matrix in the new (rectified) coordinate systems for the first /// camera. /// * P2: Output 3x4 projection matrix in the new (rectified) coordinate systems for the second /// camera. /// * Q: Output  disparity-to-depth mapping matrix (see reprojectImageTo3D ). /// * flags: Operation flags that may be zero or CALIB_ZERO_DISPARITY . If the flag is set, /// the function makes the principal points of each camera have the same pixel coordinates in the /// rectified views. And if the flag is not set, the function may still shift the images in the /// horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the /// useful image area. /// * alpha: Free scaling parameter. If it is -1 or absent, the function performs the default /// scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified /// images are zoomed and shifted so that only valid pixels are visible (no black areas after /// rectification). alpha=1 means that the rectified image is decimated and shifted so that all the /// pixels from the original images from the cameras are retained in the rectified images (no source /// image pixels are lost). Obviously, any intermediate value yields an intermediate result between /// those two extreme cases. /// * newImageSize: New image resolution after rectification. The same size should be passed to /// initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) /// is passed (default), it is set to the original imageSize . Setting it to larger value can help you /// preserve details in the original image, especially when there is a big radial distortion. /// * validPixROI1: Optional output rectangles inside the rectified images where all the pixels /// are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller /// (see the picture below). /// * validPixROI2: Optional output rectangles inside the rectified images where all the pixels /// are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller /// (see the picture below). /// /// The function computes the rotation matrices for each camera that (virtually) make both camera image /// planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies /// the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate /// as input. As output, it provides two rotation matrices and also two projection matrices in the new /// coordinates. The function distinguishes the following two cases: /// /// * **Horizontal stereo**: the first and the second camera views are shifted relative to each other /// mainly along the x axis (with possible small vertical shift). In the rectified images, the /// corresponding epipolar lines in the left and right cameras are horizontal and have the same /// y-coordinate. P1 and P2 look like: /// ///  /// ///  /// /// where  is a horizontal shift between the cameras and  if /// CALIB_ZERO_DISPARITY is set. /// /// * **Vertical stereo**: the first and the second camera views are shifted relative to each other /// mainly in vertical direction (and probably a bit in the horizontal direction too). The epipolar /// lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like: /// ///  /// ///  /// /// where  is a vertical shift between the cameras and  if CALIB_ZERO_DISPARITY is /// set. /// /// As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera /// matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to /// initialize the rectification map for each camera. /// /// See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through /// the corresponding image regions. This means that the images are well rectified, which is what most /// stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that /// their interiors are all valid pixels. /// ///  /// /// ## C++ default parameters /// * flags: CALIB_ZERO_DISPARITY /// * alpha: -1 /// * new_image_size: Size() /// * valid_pix_roi1: 0 /// * valid_pix_roi2: 0 pub fn stereo_rectify_camera(camera_matrix1: &dyn core::ToInputArray, dist_coeffs1: &dyn core::ToInputArray, camera_matrix2: &dyn core::ToInputArray, dist_coeffs2: &dyn core::ToInputArray, image_size: core::Size, r: &dyn core::ToInputArray, t: &dyn core::ToInputArray, r1: &mut dyn core::ToOutputArray, r2: &mut dyn core::ToOutputArray, p1: &mut dyn core::ToOutputArray, p2: &mut dyn core::ToOutputArray, q: &mut dyn core::ToOutputArray, flags: i32, alpha: f64, new_image_size: core::Size, valid_pix_roi1: &mut core::Rect, valid_pix_roi2: &mut core::Rect) -> Result<()> { input_array_arg!(camera_matrix1); input_array_arg!(dist_coeffs1); input_array_arg!(camera_matrix2); input_array_arg!(dist_coeffs2); input_array_arg!(r); input_array_arg!(t); output_array_arg!(r1); output_array_arg!(r2); output_array_arg!(p1); output_array_arg!(p2); output_array_arg!(q); unsafe { sys::cv_stereoRectify__InputArray__InputArray__InputArray__InputArray_Size__InputArray__InputArray__OutputArray__OutputArray__OutputArray__OutputArray__OutputArray_int_double_Size_Rect_X_Rect_X(camera_matrix1.as_raw__InputArray(), dist_coeffs1.as_raw__InputArray(), camera_matrix2.as_raw__InputArray(), dist_coeffs2.as_raw__InputArray(), image_size, r.as_raw__InputArray(), t.as_raw__InputArray(), r1.as_raw__OutputArray(), r2.as_raw__OutputArray(), p1.as_raw__OutputArray(), p2.as_raw__OutputArray(), q.as_raw__OutputArray(), flags, alpha, new_image_size, valid_pix_roi1, valid_pix_roi2) }.into_result() } /// Reconstructs points by triangulation. /// /// ## Parameters /// * projMatr1: 3x4 projection matrix of the first camera. /// * projMatr2: 3x4 projection matrix of the second camera. /// * projPoints1: 2xN array of feature points in the first image. In case of c++ version it can /// be also a vector of feature points or two-channel matrix of size 1xN or Nx1. /// * projPoints2: 2xN array of corresponding points in the second image. In case of c++ version /// it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1. /// * points4D: 4xN array of reconstructed points in homogeneous coordinates. /// /// The function reconstructs 3-dimensional points (in homogeneous coordinates) by using their /// observations with a stereo camera. Projections matrices can be obtained from stereoRectify. /// /// /// Note: /// Keep in mind that all input data should be of float type in order for this function to work. /// /// ## See also /// reprojectImageTo3D pub fn triangulate_points(proj_matr1: &dyn core::ToInputArray, proj_matr2: &dyn core::ToInputArray, proj_points1: &dyn core::ToInputArray, proj_points2: &dyn core::ToInputArray, points4_d: &mut dyn core::ToOutputArray) -> Result<()> { input_array_arg!(proj_matr1); input_array_arg!(proj_matr2); input_array_arg!(proj_points1); input_array_arg!(proj_points2); output_array_arg!(points4_d); unsafe { sys::cv_triangulatePoints__InputArray__InputArray__InputArray__InputArray__OutputArray(proj_matr1.as_raw__InputArray(), proj_matr2.as_raw__InputArray(), proj_points1.as_raw__InputArray(), proj_points2.as_raw__InputArray(), points4_d.as_raw__OutputArray()) }.into_result() } /// validates disparity using the left-right check. The matrix "cost" should be computed by the stereo correspondence algorithm /// /// ## C++ default parameters /// * disp12_max_disp: 1 pub fn validate_disparity(disparity: &mut dyn core::ToInputOutputArray, cost: &dyn core::ToInputArray, min_disparity: i32, number_of_disparities: i32, disp12_max_disp: i32) -> Result<()> { input_output_array_arg!(disparity); input_array_arg!(cost); unsafe { sys::cv_validateDisparity__InputOutputArray__InputArray_int_int_int(disparity.as_raw__InputOutputArray(), cost.as_raw__InputArray(), min_disparity, number_of_disparities, disp12_max_disp) }.into_result() } impl CirclesGridFinderParameters { pub fn default() -> Result<crate::calib3d::CirclesGridFinderParameters> { unsafe { sys::cv_CirclesGridFinderParameters_CirclesGridFinderParameters() }.into_result() } } // Generating impl for trait cv::StereoBM (trait) /// Class for computing stereo correspondence using the block matching algorithm, introduced and /// contributed to OpenCV by K. Konolige. pub trait StereoBM: crate::calib3d::StereoMatcher { #[inline(always)] fn as_raw_StereoBM(&self) -> *mut c_void; fn get_pre_filter_type(&self) -> Result<i32> { unsafe { sys::cv_StereoBM_getPreFilterType_const(self.as_raw_StereoBM()) }.into_result() } fn set_pre_filter_type(&mut self, pre_filter_type: i32) -> Result<()> { unsafe { sys::cv_StereoBM_setPreFilterType_int(self.as_raw_StereoBM(), pre_filter_type) }.into_result() } fn get_pre_filter_size(&self) -> Result<i32> { unsafe { sys::cv_StereoBM_getPreFilterSize_const(self.as_raw_StereoBM()) }.into_result() } fn set_pre_filter_size(&mut self, pre_filter_size: i32) -> Result<()> { unsafe { sys::cv_StereoBM_setPreFilterSize_int(self.as_raw_StereoBM(), pre_filter_size) }.into_result() } fn get_pre_filter_cap(&self) -> Result<i32> { unsafe { sys::cv_StereoBM_getPreFilterCap_const(self.as_raw_StereoBM()) }.into_result() } fn set_pre_filter_cap(&mut self, pre_filter_cap: i32) -> Result<()> { unsafe { sys::cv_StereoBM_setPreFilterCap_int(self.as_raw_StereoBM(), pre_filter_cap) }.into_result() } fn get_texture_threshold(&self) -> Result<i32> { unsafe { sys::cv_StereoBM_getTextureThreshold_const(self.as_raw_StereoBM()) }.into_result() } fn set_texture_threshold(&mut self, texture_threshold: i32) -> Result<()> { unsafe { sys::cv_StereoBM_setTextureThreshold_int(self.as_raw_StereoBM(), texture_threshold) }.into_result() } fn get_uniqueness_ratio(&self) -> Result<i32> { unsafe { sys::cv_StereoBM_getUniquenessRatio_const(self.as_raw_StereoBM()) }.into_result() } fn set_uniqueness_ratio(&mut self, uniqueness_ratio: i32) -> Result<()> { unsafe { sys::cv_StereoBM_setUniquenessRatio_int(self.as_raw_StereoBM(), uniqueness_ratio) }.into_result() } fn get_smaller_block_size(&self) -> Result<i32> { unsafe { sys::cv_StereoBM_getSmallerBlockSize_const(self.as_raw_StereoBM()) }.into_result() } fn set_smaller_block_size(&mut self, block_size: i32) -> Result<()> { unsafe { sys::cv_StereoBM_setSmallerBlockSize_int(self.as_raw_StereoBM(), block_size) }.into_result() } fn get_roi1(&self) -> Result<core::Rect> { unsafe { sys::cv_StereoBM_getROI1_const(self.as_raw_StereoBM()) }.into_result() } fn set_roi1(&mut self, roi1: core::Rect) -> Result<()> { unsafe { sys::cv_StereoBM_setROI1_Rect(self.as_raw_StereoBM(), roi1) }.into_result() } fn get_roi2(&self) -> Result<core::Rect> { unsafe { sys::cv_StereoBM_getROI2_const(self.as_raw_StereoBM()) }.into_result() } fn set_roi2(&mut self, roi2: core::Rect) -> Result<()> { unsafe { sys::cv_StereoBM_setROI2_Rect(self.as_raw_StereoBM(), roi2) }.into_result() } } impl dyn StereoBM + '_ { /// Creates StereoBM object /// /// ## Parameters /// * numDisparities: the disparity search range. For each pixel algorithm will find the best /// disparity from 0 (default minimum disparity) to numDisparities. The search range can then be /// shifted by changing the minimum disparity. /// * blockSize: the linear size of the blocks compared by the algorithm. The size should be odd /// (as the block is centered at the current pixel). Larger block size implies smoother, though less /// accurate disparity map. Smaller block size gives more detailed disparity map, but there is higher /// chance for algorithm to find a wrong correspondence. /// /// The function create StereoBM object. You can then call StereoBM::compute() to compute disparity for /// a specific stereo pair. /// /// ## C++ default parameters /// * num_disparities: 0 /// * block_size: 21 pub fn create(num_disparities: i32, block_size: i32) -> Result<types::PtrOfStereoBM> { unsafe { sys::cv_StereoBM_create_int_int(num_disparities, block_size) }.into_result().map(|ptr| types::PtrOfStereoBM { ptr }) } } // Generating impl for trait cv::StereoMatcher (trait) /// The base class for stereo correspondence algorithms. pub trait StereoMatcher: core::Algorithm { #[inline(always)] fn as_raw_StereoMatcher(&self) -> *mut c_void; /// Computes disparity map for the specified stereo pair /// /// ## Parameters /// * left: Left 8-bit single-channel image. /// * right: Right image of the same size and the same type as the left one. /// * disparity: Output disparity map. It has the same size as the input images. Some algorithms, /// like StereoBM or StereoSGBM compute 16-bit fixed-point disparity map (where each disparity value /// has 4 fractional bits), whereas other algorithms output 32-bit floating-point disparity map. fn compute(&mut self, left: &dyn core::ToInputArray, right: &dyn core::ToInputArray, disparity: &mut dyn core::ToOutputArray) -> Result<()> { input_array_arg!(left); input_array_arg!(right); output_array_arg!(disparity); unsafe { sys::cv_StereoMatcher_compute__InputArray__InputArray__OutputArray(self.as_raw_StereoMatcher(), left.as_raw__InputArray(), right.as_raw__InputArray(), disparity.as_raw__OutputArray()) }.into_result() } fn get_min_disparity(&self) -> Result<i32> { unsafe { sys::cv_StereoMatcher_getMinDisparity_const(self.as_raw_StereoMatcher()) }.into_result() } fn set_min_disparity(&mut self, min_disparity: i32) -> Result<()> { unsafe { sys::cv_StereoMatcher_setMinDisparity_int(self.as_raw_StereoMatcher(), min_disparity) }.into_result() } fn get_num_disparities(&self) -> Result<i32> { unsafe { sys::cv_StereoMatcher_getNumDisparities_const(self.as_raw_StereoMatcher()) }.into_result() } fn set_num_disparities(&mut self, num_disparities: i32) -> Result<()> { unsafe { sys::cv_StereoMatcher_setNumDisparities_int(self.as_raw_StereoMatcher(), num_disparities) }.into_result() } fn get_block_size(&self) -> Result<i32> { unsafe { sys::cv_StereoMatcher_getBlockSize_const(self.as_raw_StereoMatcher()) }.into_result() } fn set_block_size(&mut self, block_size: i32) -> Result<()> { unsafe { sys::cv_StereoMatcher_setBlockSize_int(self.as_raw_StereoMatcher(), block_size) }.into_result() } fn get_speckle_window_size(&self) -> Result<i32> { unsafe { sys::cv_StereoMatcher_getSpeckleWindowSize_const(self.as_raw_StereoMatcher()) }.into_result() } fn set_speckle_window_size(&mut self, speckle_window_size: i32) -> Result<()> { unsafe { sys::cv_StereoMatcher_setSpeckleWindowSize_int(self.as_raw_StereoMatcher(), speckle_window_size) }.into_result() } fn get_speckle_range(&self) -> Result<i32> { unsafe { sys::cv_StereoMatcher_getSpeckleRange_const(self.as_raw_StereoMatcher()) }.into_result() } fn set_speckle_range(&mut self, speckle_range: i32) -> Result<()> { unsafe { sys::cv_StereoMatcher_setSpeckleRange_int(self.as_raw_StereoMatcher(), speckle_range) }.into_result() } fn get_disp12_max_diff(&self) -> Result<i32> { unsafe { sys::cv_StereoMatcher_getDisp12MaxDiff_const(self.as_raw_StereoMatcher()) }.into_result() } fn set_disp12_max_diff(&mut self, disp12_max_diff: i32) -> Result<()> { unsafe { sys::cv_StereoMatcher_setDisp12MaxDiff_int(self.as_raw_StereoMatcher(), disp12_max_diff) }.into_result() } } // Generating impl for trait cv::StereoSGBM (trait) /// The class implements the modified H. Hirschmuller algorithm [HH08](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_HH08) that differs from the original /// one as follows: /// /// * By default, the algorithm is single-pass, which means that you consider only 5 directions /// instead of 8. Set mode=StereoSGBM::MODE_HH in createStereoSGBM to run the full variant of the /// algorithm but beware that it may consume a lot of memory. /// * The algorithm matches blocks, not individual pixels. Though, setting blockSize=1 reduces the /// blocks to single pixels. /// * Mutual information cost function is not implemented. Instead, a simpler Birchfield-Tomasi /// sub-pixel metric from [BT98](https://docs.opencv.org/3.4.7/d0/de3/citelist.html#CITEREF_BT98) is used. Though, the color images are supported as well. /// * Some pre- and post- processing steps from K. Konolige algorithm StereoBM are included, for /// example: pre-filtering (StereoBM::PREFILTER_XSOBEL type) and post-filtering (uniqueness /// check, quadratic interpolation and speckle filtering). /// /// /// Note: /// * (Python) An example illustrating the use of the StereoSGBM matching algorithm can be found /// at opencv_source_code/samples/python/stereo_match.py pub trait StereoSGBM: crate::calib3d::StereoMatcher { #[inline(always)] fn as_raw_StereoSGBM(&self) -> *mut c_void; fn get_pre_filter_cap(&self) -> Result<i32> { unsafe { sys::cv_StereoSGBM_getPreFilterCap_const(self.as_raw_StereoSGBM()) }.into_result() } fn set_pre_filter_cap(&mut self, pre_filter_cap: i32) -> Result<()> { unsafe { sys::cv_StereoSGBM_setPreFilterCap_int(self.as_raw_StereoSGBM(), pre_filter_cap) }.into_result() } fn get_uniqueness_ratio(&self) -> Result<i32> { unsafe { sys::cv_StereoSGBM_getUniquenessRatio_const(self.as_raw_StereoSGBM()) }.into_result() } fn set_uniqueness_ratio(&mut self, uniqueness_ratio: i32) -> Result<()> { unsafe { sys::cv_StereoSGBM_setUniquenessRatio_int(self.as_raw_StereoSGBM(), uniqueness_ratio) }.into_result() } fn get_p1(&self) -> Result<i32> { unsafe { sys::cv_StereoSGBM_getP1_const(self.as_raw_StereoSGBM()) }.into_result() } fn set_p1(&mut self, p1: i32) -> Result<()> { unsafe { sys::cv_StereoSGBM_setP1_int(self.as_raw_StereoSGBM(), p1) }.into_result() } fn get_p2(&self) -> Result<i32> { unsafe { sys::cv_StereoSGBM_getP2_const(self.as_raw_StereoSGBM()) }.into_result() } fn set_p2(&mut self, p2: i32) -> Result<()> { unsafe { sys::cv_StereoSGBM_setP2_int(self.as_raw_StereoSGBM(), p2) }.into_result() } fn get_mode(&self) -> Result<i32> { unsafe { sys::cv_StereoSGBM_getMode_const(self.as_raw_StereoSGBM()) }.into_result() } fn set_mode(&mut self, mode: i32) -> Result<()> { unsafe { sys::cv_StereoSGBM_setMode_int(self.as_raw_StereoSGBM(), mode) }.into_result() } } impl dyn StereoSGBM + '_ { /// Creates StereoSGBM object /// /// ## Parameters /// * minDisparity: Minimum possible disparity value. Normally, it is zero but sometimes /// rectification algorithms can shift images, so this parameter needs to be adjusted accordingly. /// * numDisparities: Maximum disparity minus minimum disparity. The value is always greater than /// zero. In the current implementation, this parameter must be divisible by 16. /// * blockSize: Matched block size. It must be an odd number \>=1 . Normally, it should be /// somewhere in the 3..11 range. /// * P1: The first parameter controlling the disparity smoothness. See below. /// * P2: The second parameter controlling the disparity smoothness. The larger the values are, /// the smoother the disparity is. P1 is the penalty on the disparity change by plus or minus 1 /// between neighbor pixels. P2 is the penalty on the disparity change by more than 1 between neighbor /// pixels. The algorithm requires P2 \> P1 . See stereo_match.cpp sample where some reasonably good /// P1 and P2 values are shown (like 8\*number_of_image_channels\*SADWindowSize\*SADWindowSize and /// 32\*number_of_image_channels\*SADWindowSize\*SADWindowSize , respectively). /// * disp12MaxDiff: Maximum allowed difference (in integer pixel units) in the left-right /// disparity check. Set it to a non-positive value to disable the check. /// * preFilterCap: Truncation value for the prefiltered image pixels. The algorithm first /// computes x-derivative at each pixel and clips its value by [-preFilterCap, preFilterCap] interval. /// The result values are passed to the Birchfield-Tomasi pixel cost function. /// * uniquenessRatio: Margin in percentage by which the best (minimum) computed cost function /// value should "win" the second best value to consider the found match correct. Normally, a value /// within the 5-15 range is good enough. /// * speckleWindowSize: Maximum size of smooth disparity regions to consider their noise speckles /// and invalidate. Set it to 0 to disable speckle filtering. Otherwise, set it somewhere in the /// 50-200 range. /// * speckleRange: Maximum disparity variation within each connected component. If you do speckle /// filtering, set the parameter to a positive value, it will be implicitly multiplied by 16. /// Normally, 1 or 2 is good enough. /// * mode: Set it to StereoSGBM::MODE_HH to run the full-scale two-pass dynamic programming /// algorithm. It will consume O(W\*H\*numDisparities) bytes, which is large for 640x480 stereo and /// huge for HD-size pictures. By default, it is set to false . /// /// The first constructor initializes StereoSGBM with all the default parameters. So, you only have to /// set StereoSGBM::numDisparities at minimum. The second constructor enables you to set each parameter /// to a custom value. /// /// ## C++ default parameters /// * min_disparity: 0 /// * num_disparities: 16 /// * block_size: 3 /// * p1: 0 /// * p2: 0 /// * disp12_max_diff: 0 /// * pre_filter_cap: 0 /// * uniqueness_ratio: 0 /// * speckle_window_size: 0 /// * speckle_range: 0 /// * mode: StereoSGBM::MODE_SGBM pub fn create(min_disparity: i32, num_disparities: i32, block_size: i32, p1: i32, p2: i32, disp12_max_diff: i32, pre_filter_cap: i32, uniqueness_ratio: i32, speckle_window_size: i32, speckle_range: i32, mode: i32) -> Result<types::PtrOfStereoSGBM> { unsafe { sys::cv_StereoSGBM_create_int_int_int_int_int_int_int_int_int_int_int(min_disparity, num_disparities, block_size, p1, p2, disp12_max_diff, pre_filter_cap, uniqueness_ratio, speckle_window_size, speckle_range, mode) }.into_result().map(|ptr| types::PtrOfStereoSGBM { ptr }) } } pub const StereoMatcher_DISP_SCALE: i32 = 0x10; // 16