# opencv/opencv

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 /*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., all rights reserved. // Copyright (C) 2013, OpenCV Foundation, all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #ifndef OPENCV_CALIB3D_HPP #define OPENCV_CALIB3D_HPP #include "opencv2/core.hpp" #include "opencv2/features2d.hpp" #include "opencv2/core/affine.hpp" /** @defgroup calib3d 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. \f[s \; m' = A [R|t] M'\f] or \f[s \vecthree{u}{v}{1} = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1} \begin{bmatrix} r_{11} & r_{12} & r_{13} & t_1 \\ r_{21} & r_{22} & r_{23} & t_2 \\ r_{31} & r_{32} & r_{33} & t_3 \end{bmatrix} \begin{bmatrix} X \\ Y \\ Z \\ 1 \end{bmatrix}\f] where: - \f$(X, Y, Z)\f$ are the coordinates of a 3D point in the world coordinate space - \f$(u, v)\f$ are the coordinates of the projection point in pixels - \f$A\f$ is a camera matrix, or a matrix of intrinsic parameters - \f$(cx, cy)\f$ is a principal point that is usually at the image center - \f$fx, fy\f$ 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 \f$[R|t]\f$ 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, \f$[R|t]\f$ translates coordinates of a point \f$(X, Y, Z)\f$ to a coordinate system, fixed with respect to the camera. The transformation above is equivalent to the following (when \f$z \ne 0\f$ ): \f[\begin{array}{l} \vecthree{x}{y}{z} = R \vecthree{X}{Y}{Z} + t \\ x' = x/z \\ y' = y/z \\ u = f_x*x' + c_x \\ v = f_y*y' + c_y \end{array}\f] The following figure illustrates the pinhole camera model. ![Pinhole camera model](pics/pinhole_camera_model.png) Real lenses usually have some distortion, mostly radial distortion and slight tangential distortion. So, the above model is extended as: \f[\begin{array}{l} \vecthree{x}{y}{z} = R \vecthree{X}{Y}{Z} + t \\ x' = x/z \\ y' = y/z \\ x'' = x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + 2 p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4 \\ y'' = y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\ \text{where} \quad r^2 = x'^2 + y'^2 \\ u = f_x*x'' + c_x \\ v = f_y*y'' + c_y \end{array}\f] \f$k_1\f$, \f$k_2\f$, \f$k_3\f$, \f$k_4\f$, \f$k_5\f$, and \f$k_6\f$ are radial distortion coefficients. \f$p_1\f$ and \f$p_2\f$ are tangential distortion coefficients. \f$s_1\f$, \f$s_2\f$, \f$s_3\f$, and \f$s_4\f$, are the thin prism distortion coefficients. Higher-order coefficients are not considered in OpenCV. The next figure shows two common types of radial distortion: barrel distortion (typically \f$k_1 > 0 \f$ and pincushion distortion (typically \f$k_1 < 0 \f$). ![](pics/distortion_examples.png) 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 \f$x''\f$ and \f$y''\f$. This distortion can be modelled in the following way, see e.g. @cite Louhichi07. \f[\begin{array}{l} s\vecthree{x'''}{y'''}{1} = \vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}(\tau_x, \tau_y)} {0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} {0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\ u = f_x*x''' + c_x \\ v = f_y*y''' + c_y \end{array}\f] where the matrix \f$R(\tau_x, \tau_y)\f$ is defined by two rotations with angular parameter \f$\tau_x\f$ and \f$\tau_y\f$, respectively, \f[ R(\tau_x, \tau_y) = \vecthreethree{\cos(\tau_y)}{0}{-\sin(\tau_y)}{0}{1}{0}{\sin(\tau_y)}{0}{\cos(\tau_y)} \vecthreethree{1}{0}{0}{0}{\cos(\tau_x)}{\sin(\tau_x)}{0}{-\sin(\tau_x)}{\cos(\tau_x)} = \vecthreethree{\cos(\tau_y)}{\sin(\tau_y)\sin(\tau_x)}{-\sin(\tau_y)\cos(\tau_x)} {0}{\cos(\tau_x)}{\sin(\tau_x)} {\sin(\tau_y)}{-\cos(\tau_y)\sin(\tau_x)}{\cos(\tau_y)\cos(\tau_x)}. \f] In the functions below the coefficients are passed or returned as \f[(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f] vector. That is, if the vector contains four elements, it means that \f$k_3=0\f$ . 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 \f$f_x\f$, \f$f_y\f$, \f$c_x\f$, and \f$c_y\f$ 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 sample of an artificially generated camera and chessboard patterns can be found at opencv_source_code/samples/cpp/calibration_artificial.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 @{ @defgroup calib3d_fisheye 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: \f[Xc = R X + T\f] 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: \f[x = Xc_1 \\ y = Xc_2 \\ z = Xc_3\f] The pinhole projection coordinates of P is [a; b] where \f[a = x / z \ and \ b = y / z \\ r^2 = a^2 + b^2 \\ \theta = atan(r)\f] Fisheye distortion: \f[\theta_d = \theta (1 + k_1 \theta^2 + k_2 \theta^4 + k_3 \theta^6 + k_4 \theta^8)\f] The distorted point coordinates are [x'; y'] where \f[x' = (\theta_d / r) a \\ y' = (\theta_d / r) b \f] Finally, conversion into pixel coordinates: The final pixel coordinates vector [u; v] where: \f[u = f_x (x' + \alpha y') + c_x \\ v = f_y y' + c_y\f] @defgroup calib3d_c C API @} */ namespace cv { //! @addtogroup calib3d //! @{ //! type of the robust estimation algorithm enum { LMEDS = 4, //!< least-median algorithm RANSAC = 8, //!< RANSAC algorithm RHO = 16 //!< RHO algorithm }; enum { SOLVEPNP_ITERATIVE = 0, SOLVEPNP_EPNP = 1, //!< EPnP: Efficient Perspective-n-Point Camera Pose Estimation @cite lepetit2009epnp SOLVEPNP_P3P = 2, //!< Complete Solution Classification for the Perspective-Three-Point Problem @cite gao2003complete SOLVEPNP_DLS = 3, //!< A Direct Least-Squares (DLS) Method for PnP @cite hesch2011direct SOLVEPNP_UPNP = 4 //!< Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation @cite penate2013exhaustive }; enum { CALIB_CB_ADAPTIVE_THRESH = 1, CALIB_CB_NORMALIZE_IMAGE = 2, CALIB_CB_FILTER_QUADS = 4, CALIB_CB_FAST_CHECK = 8 }; enum { CALIB_CB_SYMMETRIC_GRID = 1, CALIB_CB_ASYMMETRIC_GRID = 2, CALIB_CB_CLUSTERING = 4 }; enum { CALIB_USE_INTRINSIC_GUESS = 0x00001, CALIB_FIX_ASPECT_RATIO = 0x00002, CALIB_FIX_PRINCIPAL_POINT = 0x00004, CALIB_ZERO_TANGENT_DIST = 0x00008, CALIB_FIX_FOCAL_LENGTH = 0x00010, CALIB_FIX_K1 = 0x00020, CALIB_FIX_K2 = 0x00040, CALIB_FIX_K3 = 0x00080, CALIB_FIX_K4 = 0x00800, CALIB_FIX_K5 = 0x01000, CALIB_FIX_K6 = 0x02000, CALIB_RATIONAL_MODEL = 0x04000, CALIB_THIN_PRISM_MODEL = 0x08000, CALIB_FIX_S1_S2_S3_S4 = 0x10000, CALIB_TILTED_MODEL = 0x40000, CALIB_FIX_TAUX_TAUY = 0x80000, CALIB_USE_QR = 0x100000, //!< use QR instead of SVD decomposition for solving. Faster but potentially less precise // only for stereo CALIB_FIX_INTRINSIC = 0x00100, CALIB_SAME_FOCAL_LENGTH = 0x00200, // for stereo rectification CALIB_ZERO_DISPARITY = 0x00400, CALIB_USE_LU = (1 << 17), //!< use LU instead of SVD decomposition for solving. much faster but potentially less precise }; //! the algorithm for finding fundamental matrix enum { FM_7POINT = 1, //!< 7-point algorithm FM_8POINT = 2, //!< 8-point algorithm FM_LMEDS = 4, //!< least-median algorithm FM_RANSAC = 8 //!< RANSAC algorithm }; /** @brief Converts a rotation matrix to a rotation vector or vice versa. @param src Input rotation vector (3x1 or 1x3) or rotation matrix (3x3). @param dst Output rotation matrix (3x3) or rotation vector (3x1 or 1x3), respectively. @param 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. \f[\begin{array}{l} \theta \leftarrow norm(r) \\ r \leftarrow r/ \theta \\ R = \cos{\theta} I + (1- \cos{\theta} ) r r^T + \sin{\theta} \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} \end{array}\f] Inverse transformation can be also done easily, since \f[\sin ( \theta ) \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} = \frac{R - R^T}{2}\f] 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 . */ CV_EXPORTS_W void Rodrigues( InputArray src, OutputArray dst, OutputArray jacobian = noArray() ); /** @brief Finds a perspective transformation between two planes. @param srcPoints Coordinates of the points in the original plane, a matrix of the type CV_32FC2 or vector\ . @param dstPoints Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or a vector\ . @param method Method used to computed a homography matrix. The following methods are possible: - **0** - a regular method using all the points - **RANSAC** - RANSAC-based robust method - **LMEDS** - Least-Median robust method - **RHO** - PROSAC-based robust method @param ransacReprojThreshold Maximum allowed reprojection error to treat a point pair as an inlier (used in the RANSAC and RHO methods only). That is, if \f[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} * \texttt{srcPoints} _i) \| > \texttt{ransacReprojThreshold}\f] then the point \f$i\f$ is considered 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. @param mask Optional output mask set by a robust method ( RANSAC or LMEDS ). Note that the input mask values are ignored. @param maxIters The maximum number of RANSAC iterations, 2000 is the maximum it can be. @param confidence Confidence level, between 0 and 1. The function finds and returns the perspective transformation \f$H\f$ between the source and the destination planes: \f[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\f] so that the back-projection error \f[\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\f] 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 ( \f$srcPoints_i\f$, \f$dstPoints_i\f$ ) 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), estimate the homography matrix using this subset and a simple least-square algorithm, and then compute the quality/goodness of the computed homography (which is the number of inliers for RANSAC or the 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 \f$h_{33}=1\f$. Note that whenever an H matrix cannot be estimated, an empty one will be returned. @sa getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, perspectiveTransform @note - A example on calculating a homography for image matching can be found at opencv_source_code/samples/cpp/video_homography.cpp */ CV_EXPORTS_W Mat findHomography( InputArray srcPoints, InputArray dstPoints, int method = 0, double ransacReprojThreshold = 3, OutputArray mask=noArray(), const int maxIters = 2000, const double confidence = 0.995); /** @overload */ CV_EXPORTS Mat findHomography( InputArray srcPoints, InputArray dstPoints, OutputArray mask, int method = 0, double ransacReprojThreshold = 3 ); /** @brief Computes an RQ decomposition of 3x3 matrices. @param src 3x3 input matrix. @param mtxR Output 3x3 upper-triangular matrix. @param mtxQ Output 3x3 orthogonal matrix. @param Qx Optional output 3x3 rotation matrix around x-axis. @param Qy Optional output 3x3 rotation matrix around y-axis. @param 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, eg. see @cite Slabaugh . Returned tree rotation matrices and corresponding three Euler angules are only one of the possible solutions. */ CV_EXPORTS_W Vec3d RQDecomp3x3( InputArray src, OutputArray mtxR, OutputArray mtxQ, OutputArray Qx = noArray(), OutputArray Qy = noArray(), OutputArray Qz = noArray()); /** @brief Decomposes a projection matrix into a rotation matrix and a camera matrix. @param projMatrix 3x4 input projection matrix P. @param cameraMatrix Output 3x3 camera matrix K. @param rotMatrix Output 3x3 external rotation matrix R. @param transVect Output 4x1 translation vector T. @param rotMatrixX Optional 3x3 rotation matrix around x-axis. @param rotMatrixY Optional 3x3 rotation matrix around y-axis. @param rotMatrixZ Optional 3x3 rotation matrix around z-axis. @param 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, eg. see @cite Slabaugh . Returned tree rotation matrices and corresponding three Euler angules are only one of the possible solutions. The function is based on RQDecomp3x3 . */ CV_EXPORTS_W void decomposeProjectionMatrix( InputArray projMatrix, OutputArray cameraMatrix, OutputArray rotMatrix, OutputArray transVect, OutputArray rotMatrixX = noArray(), OutputArray rotMatrixY = noArray(), OutputArray rotMatrixZ = noArray(), OutputArray eulerAngles =noArray() ); /** @brief Computes partial derivatives of the matrix product for each multiplied matrix. @param A First multiplied matrix. @param B Second multiplied matrix. @param dABdA First output derivative matrix d(A\*B)/dA of size \f$\texttt{A.rows*B.cols} \times {A.rows*A.cols}\f$ . @param dABdB Second output derivative matrix d(A\*B)/dB of size \f$\texttt{A.rows*B.cols} \times {B.rows*B.cols}\f$ . The function computes partial derivatives of the elements of the matrix product \f$A*B\f$ 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. */ CV_EXPORTS_W void matMulDeriv( InputArray A, InputArray B, OutputArray dABdA, OutputArray dABdB ); /** @brief Combines two rotation-and-shift transformations. @param rvec1 First rotation vector. @param tvec1 First translation vector. @param rvec2 Second rotation vector. @param tvec2 Second translation vector. @param rvec3 Output rotation vector of the superposition. @param tvec3 Output translation vector of the superposition. @param dr3dr1 @param dr3dt1 @param dr3dr2 @param dr3dt2 @param dt3dr1 @param dt3dt1 @param dt3dr2 @param dt3dt2 Optional output derivatives of rvec3 or tvec3 with regard to rvec1, rvec2, tvec1 and tvec2, respectively. The functions compute: \f[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\f] where \f$\mathrm{rodrigues}\f$ denotes a rotation vector to a rotation matrix transformation, and \f$\mathrm{rodrigues}^{-1}\f$ 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. */ CV_EXPORTS_W void composeRT( InputArray rvec1, InputArray tvec1, InputArray rvec2, InputArray tvec2, OutputArray rvec3, OutputArray tvec3, OutputArray dr3dr1 = noArray(), OutputArray dr3dt1 = noArray(), OutputArray dr3dr2 = noArray(), OutputArray dr3dt2 = noArray(), OutputArray dt3dr1 = noArray(), OutputArray dt3dt1 = noArray(), OutputArray dt3dr2 = noArray(), OutputArray dt3dt2 = noArray() ); /** @brief Projects 3D points to an image plane. @param objectPoints Array of object points, 3xN/Nx3 1-channel or 1xN/Nx1 3-channel (or vector\ ), where N is the number of points in the view. @param rvec Rotation vector. See Rodrigues for details. @param tvec Translation vector. @param cameraMatrix Camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$ . @param distCoeffs Input vector of distortion coefficients \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of 4, 5, 8, 12 or 14 elements. If the vector is empty, the zero distortion coefficients are assumed. @param imagePoints Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or vector\ . @param jacobian Optional output 2Nx(10+\) 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. @param 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. */ CV_EXPORTS_W void projectPoints( InputArray objectPoints, InputArray rvec, InputArray tvec, InputArray cameraMatrix, InputArray distCoeffs, OutputArray imagePoints, OutputArray jacobian = noArray(), double aspectRatio = 0 ); /** @brief Finds an object pose from 3D-2D point correspondences. @param 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\ can be also passed here. @param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector\ can be also passed here. @param cameraMatrix Input camera matrix \f$A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}\f$ . @param distCoeffs Input vector of distortion coefficients \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. @param rvec Output rotation vector (see Rodrigues ) that, together with tvec , brings points from the model coordinate system to the camera coordinate system. @param tvec Output translation vector. @param 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. @param flags Method for solving a PnP problem: - **SOLVEPNP_ITERATIVE** Iterative method is based on 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". 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". - **SOLVEPNP_DLS** Method is based on the paper of Joel A. Hesch and Stergios I. Roumeliotis. "A Direct Least-Squares (DLS) Method for PnP". - **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". In this case the function also estimates the parameters \f$f_x\f$ and \f$f_y\f$ assuming that both have the same value. Then the cameraMatrix is updated with the estimated focal length. 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. @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 completly wrong results. If you pass one of these two flags, **SOLVEPNP_EPNP** method will be used instead. */ CV_EXPORTS_W bool solvePnP( InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int flags = SOLVEPNP_ITERATIVE ); /** @brief Finds an object pose from 3D-2D point correspondences using the RANSAC scheme. @param 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\ can be also passed here. @param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector\ can be also passed here. @param cameraMatrix Input camera matrix \f$A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}\f$ . @param distCoeffs Input vector of distortion coefficients \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. @param rvec Output rotation vector (see Rodrigues ) that, together with tvec , brings points from the model coordinate system to the camera coordinate system. @param tvec Output translation vector. @param 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. @param iterationsCount Number of iterations. @param 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. @param confidence The probability that the algorithm produces a useful result. @param inliers Output vector that contains indices of inliers in objectPoints and imagePoints . @param flags Method for solving a PnP problem (see 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 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/ */ CV_EXPORTS_W bool solvePnPRansac( InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int iterationsCount = 100, float reprojectionError = 8.0, double confidence = 0.99, OutputArray inliers = noArray(), int flags = SOLVEPNP_ITERATIVE ); /** @brief Finds an initial camera matrix from 3D-2D point correspondences. @param 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. @param imagePoints Vector of vectors of the projections of the calibration pattern points. In the old interface all the per-view vectors are concatenated. @param imageSize Image size in pixels used to initialize the principal point. @param aspectRatio If it is zero or negative, both \f$f_x\f$ and \f$f_y\f$ are estimated independently. Otherwise, \f$f_x = f_y * \texttt{aspectRatio}\f$ . 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. */ CV_EXPORTS_W Mat initCameraMatrix2D( InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size imageSize, double aspectRatio = 1.0 ); /** @brief Finds the positions of internal corners of the chessboard. @param image Source chessboard view. It must be an 8-bit grayscale or color image. @param patternSize Number of inner corners per a chessboard row and column ( patternSize = cvSize(points_per_row,points_per_colum) = cvSize(columns,rows) ). @param corners Output array of detected corners. @param flags Various operation flags that can be zero or a combination of the following values: - **CV_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). - **CV_CALIB_CB_NORMALIZE_IMAGE** Normalize the image gamma with equalizeHist before applying fixed or adaptive thresholding. - **CV_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: : @code Size patternsize(8,6); //interior number of corners Mat gray = ....; //source image vector 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); @endcode @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. */ CV_EXPORTS_W bool findChessboardCorners( InputArray image, Size patternSize, OutputArray corners, int flags = CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE ); //! finds subpixel-accurate positions of the chessboard corners CV_EXPORTS bool find4QuadCornerSubpix( InputArray img, InputOutputArray corners, Size region_size ); /** @brief Renders the detected chessboard corners. @param image Destination image. It must be an 8-bit color image. @param patternSize Number of inner corners per a chessboard row and column (patternSize = cv::Size(points_per_row,points_per_column)). @param corners Array of detected corners, the output of findChessboardCorners. @param 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. */ CV_EXPORTS_W void drawChessboardCorners( InputOutputArray image, Size patternSize, InputArray corners, bool patternWasFound ); /** @brief Finds centers in the grid of circles. @param image grid view of input circles; it must be an 8-bit grayscale or color image. @param patternSize number of circles per row and column ( patternSize = Size(points_per_row, points_per_colum) ). @param centers output array of detected centers. @param 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. @param blobDetector feature detector that finds blobs like dark circles on light background. 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: : @code Size patternsize(7,7); //number of centers Mat gray = ....; //source image vector centers; //this will be filled by the detected centers bool patternfound = findCirclesGrid(gray, patternsize, centers); drawChessboardCorners(img, patternsize, Mat(centers), patternfound); @endcode @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. */ CV_EXPORTS_W bool findCirclesGrid( InputArray image, Size patternSize, OutputArray centers, int flags = CALIB_CB_SYMMETRIC_GRID, const Ptr &blobDetector = SimpleBlobDetector::create()); /** @brief Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern. @param 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>). 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. @param imagePoints In the new interface it is a vector of vectors of the projections of calibration pattern points (e.g. std::vector>). 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. @param imageSize Size of the image used only to initialize the intrinsic camera matrix. @param cameraMatrix Output 3x3 floating-point camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If CV\_CALIB\_USE\_INTRINSIC\_GUESS and/or CV_CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be initialized before calling the function. @param distCoeffs Output vector of distortion coefficients \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of 4, 5, 8, 12 or 14 elements. @param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view (e.g. std::vector>). 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). @param tvecs Output vector of translation vectors estimated for each pattern view. @param stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters. Order of deviations values: \f$(f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, s_4, \tau_x, \tau_y)\f$ If one of parameters is not estimated, it's deviation is equals to zero. @param stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters. Order of deviations values: \f$(R_1, T_1, \dotsc , R_M, T_M)\f$ where M is number of pattern views, \f$R_i, T_i\f$ are concatenated 1x3 vectors. @param perViewErrors Output vector of the RMS re-projection error estimated for each pattern view. @param flags Different flags that may be zero or a combination of the following values: - **CV_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. - **CV_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 CV_CALIB_USE_INTRINSIC_GUESS is set too. - **CV_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 CV_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. - **CV_CALIB_ZERO_TANGENT_DIST** Tangential distortion coefficients \f$(p_1, p_2)\f$ are set to zeros and stay zero. - **CV_CALIB_FIX_K1,...,CV_CALIB_FIX_K6** The corresponding radial distortion coefficient is not changed during the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. - **CV_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 CV_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 CV_CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. @param criteria Termination criteria for the iterative optimization algorithm. @return 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 @cite Zhang2000 and @cite 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 CV_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 CV_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 \f$f_x\f$ and \f$f_y\f$ (ratios of 10:1 or more)), then you have probably used patternSize=cvSize(rows,cols) instead of using patternSize=cvSize(cols,rows) in findChessboardCorners . @sa findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort */ CV_EXPORTS_AS(calibrateCameraExtended) double calibrateCamera( InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, OutputArray stdDeviationsIntrinsics, OutputArray stdDeviationsExtrinsics, OutputArray perViewErrors, int flags = 0, TermCriteria criteria = TermCriteria( TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) ); /** @overload 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) ) */ CV_EXPORTS_W double calibrateCamera( InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags = 0, TermCriteria criteria = TermCriteria( TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) ); /** @brief Computes useful camera characteristics from the camera matrix. @param cameraMatrix Input camera matrix that can be estimated by calibrateCamera or stereoCalibrate . @param imageSize Input image size in pixels. @param apertureWidth Physical width in mm of the sensor. @param apertureHeight Physical height in mm of the sensor. @param fovx Output field of view in degrees along the horizontal sensor axis. @param fovy Output field of view in degrees along the vertical sensor axis. @param focalLength Focal length of the lens in mm. @param principalPoint Principal point in mm. @param aspectRatio \f$f_y/f_x\f$ 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). */ CV_EXPORTS_W void calibrationMatrixValues( InputArray cameraMatrix, Size imageSize, double apertureWidth, double apertureHeight, CV_OUT double& fovx, CV_OUT double& fovy, CV_OUT double& focalLength, CV_OUT Point2d& principalPoint, CV_OUT double& aspectRatio ); /** @brief Calibrates the stereo camera. @param objectPoints Vector of vectors of the calibration pattern points. @param imagePoints1 Vector of vectors of the projections of the calibration pattern points, observed by the first camera. @param imagePoints2 Vector of vectors of the projections of the calibration pattern points, observed by the second camera. @param cameraMatrix1 Input/output first camera matrix: \f$\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\f$ , \f$j = 0,\, 1\f$ . If any of CV_CALIB_USE_INTRINSIC_GUESS , CV_CALIB_FIX_ASPECT_RATIO , CV_CALIB_FIX_INTRINSIC , or CV_CALIB_FIX_FOCAL_LENGTH are specified, some or all of the matrix components must be initialized. See the flags description for details. @param distCoeffs1 Input/output vector of distortion coefficients \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of 4, 5, 8, 12 or 14 elements. The output vector length depends on the flags. @param cameraMatrix2 Input/output second camera matrix. The parameter is similar to cameraMatrix1 @param distCoeffs2 Input/output lens distortion coefficients for the second camera. The parameter is similar to distCoeffs1 . @param imageSize Size of the image used only to initialize intrinsic camera matrix. @param R Output rotation matrix between the 1st and the 2nd camera coordinate systems. @param T Output translation vector between the coordinate systems of the cameras. @param E Output essential matrix. @param F Output fundamental matrix. @param flags Different flags that may be zero or a combination of the following values: - **CV_CALIB_FIX_INTRINSIC** Fix cameraMatrix? and distCoeffs? so that only R, T, E , and F matrices are estimated. - **CV_CALIB_USE_INTRINSIC_GUESS** Optimize some or all of the intrinsic parameters according to the specified flags. Initial values are provided by the user. - **CV_CALIB_FIX_PRINCIPAL_POINT** Fix the principal points during the optimization. - **CV_CALIB_FIX_FOCAL_LENGTH** Fix \f$f^{(j)}_x\f$ and \f$f^{(j)}_y\f$ . - **CV_CALIB_FIX_ASPECT_RATIO** Optimize \f$f^{(j)}_y\f$ . Fix the ratio \f$f^{(j)}_x/f^{(j)}_y\f$ . - **CV_CALIB_SAME_FOCAL_LENGTH** Enforce \f$f^{(0)}_x=f^{(1)}_x\f$ and \f$f^{(0)}_y=f^{(1)}_y\f$ . - **CV_CALIB_ZERO_TANGENT_DIST** Set tangential distortion coefficients for each camera to zeros and fix there. - **CV_CALIB_FIX_K1,...,CV_CALIB_FIX_K6** Do not change the corresponding radial distortion coefficient during the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. - **CV_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 CV_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 CV_CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. @param 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 ( \f$R_1\f$,\f$T_1\f$ ), it should be possible to compute ( \f$R_2\f$,\f$T_2\f$ ). 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 ( \f$R\f$,\f$T\f$ ) so that: \f[R_2=R*R_1\f] \f[T_2=R*T_1 + T,\f] Optionally, it computes the essential matrix E: \f[E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} *R\f] where \f$T_i\f$ are components of the translation vector \f$T\f$ : \f$T=[T_0, T_1, T_2]^T\f$ . And the function can also compute the fundamental matrix F: \f[F = cameraMatrix2^{-T} E cameraMatrix1^{-1}\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 CV_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 CV_CALIB_SAME_FOCAL_LENGTH and CV_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. */ CV_EXPORTS_W double stereoCalibrate( InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1, InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2, Size imageSize, OutputArray R,OutputArray T, OutputArray E, OutputArray F, int flags = CALIB_FIX_INTRINSIC, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6) ); /** @brief Computes rectification transforms for each head of a calibrated stereo camera. @param cameraMatrix1 First camera matrix. @param distCoeffs1 First camera distortion parameters. @param cameraMatrix2 Second camera matrix. @param distCoeffs2 Second camera distortion parameters. @param imageSize Size of the image used for stereo calibration. @param R Rotation matrix between the coordinate systems of the first and the second cameras. @param T Translation vector between coordinate systems of the cameras. @param R1 Output 3x3 rectification transform (rotation matrix) for the first camera. @param R2 Output 3x3 rectification transform (rotation matrix) for the second camera. @param P1 Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera. @param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera. @param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see reprojectImageTo3D ). @param flags Operation flags that may be zero or CV_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. @param 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. @param 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. @param 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). @param 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: \f[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\f] \f[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x*f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\f] where \f$T_x\f$ is a horizontal shift between the cameras and \f$cx_1=cx_2\f$ if CV_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: \f[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\f] \f[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y*f \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\f] where \f$T_y\f$ is a vertical shift between the cameras and \f$cy_1=cy_2\f$ 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. ![image](pics/stereo_undistort.jpg) */ CV_EXPORTS_W void stereoRectify( InputArray cameraMatrix1, InputArray distCoeffs1, InputArray cameraMatrix2, InputArray distCoeffs2, Size imageSize, InputArray R, InputArray T, OutputArray R1, OutputArray R2, OutputArray P1, OutputArray P2, OutputArray Q, int flags = CALIB_ZERO_DISPARITY, double alpha = -1, Size newImageSize = Size(), CV_OUT Rect* validPixROI1 = 0, CV_OUT Rect* validPixROI2 = 0 ); /** @brief Computes a rectification transform for an uncalibrated stereo camera. @param points1 Array of feature points in the first image. @param points2 The corresponding points in the second image. The same formats as in findFundamentalMat are supported. @param F Input fundamental matrix. It can be computed from the same set of point pairs using findFundamentalMat . @param imgSize Size of the image. @param H1 Output rectification homography matrix for the first image. @param H2 Output rectification homography matrix for the second image. @param 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 \f$|\texttt{points2[i]}^T*\texttt{F}*\texttt{points1[i]}|>\texttt{threshold}\f$ ) 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 @cite 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 . */ CV_EXPORTS_W bool stereoRectifyUncalibrated( InputArray points1, InputArray points2, InputArray F, Size imgSize, OutputArray H1, OutputArray H2, double threshold = 5 ); //! computes the rectification transformations for 3-head camera, where all the heads are on the same line. CV_EXPORTS_W float rectify3Collinear( InputArray cameraMatrix1, InputArray distCoeffs1, InputArray cameraMatrix2, InputArray distCoeffs2, InputArray cameraMatrix3, InputArray distCoeffs3, InputArrayOfArrays imgpt1, InputArrayOfArrays imgpt3, Size imageSize, InputArray R12, InputArray T12, InputArray R13, InputArray T13, OutputArray R1, OutputArray R2, OutputArray R3, OutputArray P1, OutputArray P2, OutputArray P3, OutputArray Q, double alpha, Size newImgSize, CV_OUT Rect* roi1, CV_OUT Rect* roi2, int flags ); /** @brief Returns the new camera matrix based on the free scaling parameter. @param cameraMatrix Input camera matrix. @param distCoeffs Input vector of distortion coefficients \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. @param imageSize Original image size. @param 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. @param newImgSize Image size after rectification. By default,it is set to imageSize . @param validPixROI Optional output rectangle that outlines all-good-pixels region in the undistorted image. See roi1, roi2 description in stereoRectify . @param 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. @return 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 undistortion 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 . */ CV_EXPORTS_W Mat getOptimalNewCameraMatrix( InputArray cameraMatrix, InputArray distCoeffs, Size imageSize, double alpha, Size newImgSize = Size(), CV_OUT Rect* validPixROI = 0, bool centerPrincipalPoint = false); /** @brief Converts points from Euclidean to homogeneous space. @param src Input vector of N-dimensional points. @param 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). */ CV_EXPORTS_W void convertPointsToHomogeneous( InputArray src, OutputArray dst ); /** @brief Converts points from homogeneous to Euclidean space. @param src Input vector of N-dimensional points. @param 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,...). */ CV_EXPORTS_W void convertPointsFromHomogeneous( InputArray src, OutputArray dst ); /** @brief Converts points to/from homogeneous coordinates. @param src Input array or vector of 2D, 3D, or 4D points. @param 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. */ CV_EXPORTS void convertPointsHomogeneous( InputArray src, OutputArray dst ); /** @brief Calculates a fundamental matrix from the corresponding points in two images. @param points1 Array of N points from the first image. The point coordinates should be floating-point (single or double precision). @param points2 Array of the second image points of the same size and format as points1 . @param method Method for computing a fundamental matrix. - **CV_FM_7POINT** for a 7-point algorithm. \f$N = 7\f$ - **CV_FM_8POINT** for an 8-point algorithm. \f$N \ge 8\f$ - **CV_FM_RANSAC** for the RANSAC algorithm. \f$N \ge 8\f$ - **CV_FM_LMEDS** for the LMedS algorithm. \f$N \ge 8\f$ @param param1 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. @param param2 Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. @param mask The epipolar geometry is described by the following equation: \f[[p_2; 1]^T F [p_1; 1] = 0\f] where \f$F\f$ is a fundamental matrix, \f$p_1\f$ and \f$p_2\f$ 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 ( \f$9 \times 3\f$ 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. : @code // Example. Estimation of fundamental matrix using the RANSAC algorithm int point_count = 100; vector points1(point_count); vector 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); @endcode */ CV_EXPORTS_W Mat findFundamentalMat( InputArray points1, InputArray points2, int method = FM_RANSAC, double param1 = 3., double param2 = 0.99, OutputArray mask = noArray() ); /** @overload */ CV_EXPORTS Mat findFundamentalMat( InputArray points1, InputArray points2, OutputArray mask, int method = FM_RANSAC, double param1 = 3., double param2 = 0.99 ); /** @brief Calculates an essential matrix from the corresponding points in two images. @param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision). @param points2 Array of the second image points of the same size and format as points1 . @param cameraMatrix Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera matrix. @param method Method for computing a fundamental matrix. - **RANSAC** for the RANSAC algorithm. - **MEDS** for the LMedS algorithm. @param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. @param 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. @param 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 @cite Nister03 . @cite SteweniusCFS is also a related. The epipolar geometry is described by the following equation: \f[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\f] where \f$E\f$ is an essential matrix, \f$p_1\f$ and \f$p_2\f$ 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. */ CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2, InputArray cameraMatrix, int method = RANSAC, double prob = 0.999, double threshold = 1.0, OutputArray mask = noArray() ); /** @overload @param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should be floating-point (single or double precision). @param points2 Array of the second image points of the same size and format as points1 . @param 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. @param pp principal point of the camera. @param method Method for computing a fundamental matrix. - **RANSAC** for the RANSAC algorithm. - **LMEDS** for the LMedS algorithm. @param 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. @param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. @param 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: \f[K = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\f] */ CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2, double focal = 1.0, Point2d pp = Point2d(0, 0), int method = RANSAC, double prob = 0.999, double threshold = 1.0, OutputArray mask = noArray() ); /** @brief Decompose an essential matrix to possible rotations and translation. @param E The input essential matrix. @param R1 One possible rotation matrix. @param R2 Another possible rotation matrix. @param t One possible translation. This function decompose an essential matrix E using svd decomposition @cite HartleyZ00 . Generally 4 possible poses exists for a given E. They are \f$[R_1, t]\f$, \f$[R_1, -t]\f$, \f$[R_2, t]\f$, \f$[R_2, -t]\f$. By decomposing E, you can only get the direction of the translation, so the function returns unit t. */ CV_EXPORTS_W void decomposeEssentialMat( InputArray E, OutputArray R1, OutputArray R2, OutputArray t ); /** @brief 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. @param E The input essential matrix. @param points1 Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision). @param points2 Array of the second image points of the same size and format as points1 . @param cameraMatrix Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera matrix. @param R Recovered relative rotation. @param t Recoverd relative translation. @param 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 @cite 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. : @code // Example. Estimation of fundamental matrix using the RANSAC algorithm int point_count = 100; vector points1(point_count); vector 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); @endcode */ CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2, InputArray cameraMatrix, OutputArray R, OutputArray t, InputOutputArray mask = noArray() ); /** @overload @param E The input essential matrix. @param points1 Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision). @param points2 Array of the second image points of the same size and format as points1 . @param R Recovered relative rotation. @param t Recoverd relative translation. @param 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. @param pp principal point of the camera. @param 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: \f[K = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\f] */ CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2, OutputArray R, OutputArray t, double focal = 1.0, Point2d pp = Point2d(0, 0), InputOutputArray mask = noArray() ); /** @brief For points in an image of a stereo pair, computes the corresponding epilines in the other image. @param points Input points. \f$N \times 1\f$ or \f$1 \times N\f$ matrix of type CV_32FC2 or vector\ . @param whichImage Index of the image (1 or 2) that contains the points . @param F Fundamental matrix that can be estimated using findFundamentalMat or stereoRectify . @param lines Output vector of the epipolar lines corresponding to the points in the other image. Each line \f$ax + by + c=0\f$ is encoded by 3 numbers \f$(a, b, c)\f$ . 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 \f$l^{(2)}_i\f$ in the second image for the point \f$p^{(1)}_i\f$ in the first image (when whichImage=1 ) is computed as: \f[l^{(2)}_i = F p^{(1)}_i\f] And vice versa, when whichImage=2, \f$l^{(1)}_i\f$ is computed from \f$p^{(2)}_i\f$ as: \f[l^{(1)}_i = F^T p^{(2)}_i\f] Line coefficients are defined up to a scale. They are normalized so that \f$a_i^2+b_i^2=1\f$ . */ CV_EXPORTS_W void computeCorrespondEpilines( InputArray points, int whichImage, InputArray F, OutputArray lines ); /** @brief Reconstructs points by triangulation. @param projMatr1 3x4 projection matrix of the first camera. @param projMatr2 3x4 projection matrix of the second camera. @param 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. @param 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. @param 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. @sa reprojectImageTo3D */ CV_EXPORTS_W void triangulatePoints( InputArray projMatr1, InputArray projMatr2, InputArray projPoints1, InputArray projPoints2, OutputArray points4D ); /** @brief Refines coordinates of corresponding points. @param F 3x3 fundamental matrix. @param points1 1xN array containing the first set of points. @param points2 1xN array containing the second set of points. @param newPoints1 The optimized points1. @param 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 \f$d(points1[i], newPoints1[i])^2 + d(points2[i],newPoints2[i])^2\f$ (where \f$d(a,b)\f$ is the geometric distance between points \f$a\f$ and \f$b\f$ ) subject to the epipolar constraint \f$newPoints2^T * F * newPoints1 = 0\f$ . */ CV_EXPORTS_W void correctMatches( InputArray F, InputArray points1, InputArray points2, OutputArray newPoints1, OutputArray newPoints2 ); /** @brief Filters off small noise blobs (speckles) in the disparity map @param img The input 16-bit signed disparity image @param newVal The disparity value used to paint-off the speckles @param maxSpeckleSize The maximum speckle size to consider it a speckle. Larger blobs are not affected by the algorithm @param 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. @param buf The optional temporary buffer to avoid memory allocation within the function. */ CV_EXPORTS_W void filterSpeckles( InputOutputArray img, double newVal, int maxSpeckleSize, double maxDiff, InputOutputArray buf = noArray() ); //! computes valid disparity ROI from the valid ROIs of the rectified images (that are returned by cv::stereoRectify()) CV_EXPORTS_W Rect getValidDisparityROI( Rect roi1, Rect roi2, int minDisparity, int numberOfDisparities, int SADWindowSize ); //! validates disparity using the left-right check. The matrix "cost" should be computed by the stereo correspondence algorithm CV_EXPORTS_W void validateDisparity( InputOutputArray disparity, InputArray cost, int minDisparity, int numberOfDisparities, int disp12MaxDisp = 1 ); /** @brief Reprojects a disparity image to 3D space. @param 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. @param _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. @param Q \f$4 \times 4\f$ perspective transformation matrix that can be obtained with stereoRectify. @param 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). @param 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) andthe corresponding disparity d=disparity(x,y) , it computes: \f[\begin{array}{l} [X \; Y \; Z \; W]^T = \texttt{Q} *[x \; y \; \texttt{disparity} (x,y) \; 1]^T \\ \texttt{\_3dImage} (x,y) = (X/W, \; Y/W, \; Z/W) \end{array}\f] The matrix Q can be an arbitrary \f$4 \times 4\f$ matrix (for example, the one computed by stereoRectify). To reproject a sparse set of points {(x,y,d),...} to 3D space, use perspectiveTransform . */ CV_EXPORTS_W void reprojectImageTo3D( InputArray disparity, OutputArray _3dImage, InputArray Q, bool handleMissingValues = false, int ddepth = -1 ); /** @brief Calculates the Sampson Distance between two points. The function sampsonDistance calculates and returns the first order approximation of the geometric error as: \f[sd( \texttt{pt1} , \texttt{pt2} )= \frac{(\texttt{pt2}^t \cdot \texttt{F} \cdot \texttt{pt1})^2}{(\texttt{F} \cdot \texttt{pt1})(0) + (\texttt{F} \cdot \texttt{pt1})(1) + (\texttt{F}^t \cdot \texttt{pt2})(0) + (\texttt{F}^t \cdot \texttt{pt2})(1)}\f] The fundamental matrix may be calculated using the cv::findFundamentalMat function. See HZ 11.4.3 for details. @param pt1 first homogeneous 2d point @param pt2 second homogeneous 2d point @param F fundamental matrix */ CV_EXPORTS_W double sampsonDistance(InputArray pt1, InputArray pt2, InputArray F); /** @brief Computes an optimal affine transformation between two 3D point sets. @param src First input 3D point set. @param dst Second input 3D point set. @param out Output 3D affine transformation matrix \f$3 \times 4\f$ . @param inliers Output vector indicating which points are inliers. @param ransacThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. @param 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. */ CV_EXPORTS_W int estimateAffine3D(InputArray src, InputArray dst, OutputArray out, OutputArray inliers, double ransacThreshold = 3, double confidence = 0.99); /** @brief Computes an optimal affine transformation between two 2D point sets. @param from First input 2D point set. @param to Second input 2D point set. @param inliers Output vector indicating which points are inliers. @param method Robust method used to compute tranformation. The following methods are possible: - cv::RANSAC - RANSAC-based robust method - cv::LMEDS - Least-Median robust method RANSAC is the default method. @param ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC. @param maxIters The maximum number of robust method iterations, 2000 is the maximum it can be. @param 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. @param 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. @return Output 2D affine transformation matrix \f$2 \times 3\f$ or empty matrix if transformation could not be estimated. 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 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. @sa estimateAffinePartial2D, getAffineTransform */ CV_EXPORTS_W cv::Mat estimateAffine2D(InputArray from, InputArray to, OutputArray inliers = noArray(), int method = RANSAC, double ransacReprojThreshold = 3, size_t maxIters = 2000, double confidence = 0.99, size_t refineIters = 10); /** @brief Computes an optimal limited affine transformation with 4 degrees of freedom between two 2D point sets. @param from First input 2D point set. @param to Second input 2D point set. @param inliers Output vector indicating which points are inliers. @param method Robust method used to compute tranformation. The following methods are possible: - cv::RANSAC - RANSAC-based robust method - cv::LMEDS - Least-Median robust method RANSAC is the default method. @param ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as an inlier. Applies only to RANSAC. @param maxIters The maximum number of robust method iterations, 2000 is the maximum it can be. @param 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. @param 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. @return Output 2D affine transformation (4 degrees of freedom) matrix \f$2 \times 3\f$ 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: \f[ \begin{bmatrix} \cos(\theta)s & -\sin(\theta)s & tx \\ \sin(\theta)s & \cos(\theta)s & ty \end{bmatrix} \f] Where \f$\theta \f$ is the rotation angle, \f$s \f$ the scaling factor and \f$tx, ty \f$ are translations in \f$x, y \f$ 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. @sa estimateAffine2D, getAffineTransform */ CV_EXPORTS_W cv::Mat estimateAffinePartial2D(InputArray from, InputArray to, OutputArray inliers = noArray(), int method = RANSAC, double ransacReprojThreshold = 3, size_t maxIters = 2000, double confidence = 0.99, size_t refineIters = 10); /** @brief Decompose a homography matrix to rotation(s), translation(s) and plane normal(s). @param H The input homography matrix between two images. @param K The input intrinsic camera calibration matrix. @param rotations Array of rotation matrices. @param translations Array of translation matrices. @param 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 @cite Malis . */ CV_EXPORTS_W int decomposeHomographyMat(InputArray H, InputArray K, OutputArrayOfArrays rotations, OutputArrayOfArrays translations, OutputArrayOfArrays normals); /** @brief The base class for stereo correspondence algorithms. */ class CV_EXPORTS_W StereoMatcher : public Algorithm { public: enum { DISP_SHIFT = 4, DISP_SCALE = (1 << DISP_SHIFT) }; /** @brief Computes disparity map for the specified stereo pair @param left Left 8-bit single-channel image. @param right Right image of the same size and the same type as the left one. @param 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. */ CV_WRAP virtual void compute( InputArray left, InputArray right, OutputArray disparity ) = 0; CV_WRAP virtual int getMinDisparity() const = 0; CV_WRAP virtual void setMinDisparity(int minDisparity) = 0; CV_WRAP virtual int getNumDisparities() const = 0; CV_WRAP virtual void setNumDisparities(int numDisparities) = 0; CV_WRAP virtual int getBlockSize() const = 0; CV_WRAP virtual void setBlockSize(int blockSize) = 0; CV_WRAP virtual int getSpeckleWindowSize() const = 0; CV_WRAP virtual void setSpeckleWindowSize(int speckleWindowSize) = 0; CV_WRAP virtual int getSpeckleRange() const = 0; CV_WRAP virtual void setSpeckleRange(int speckleRange) = 0; CV_WRAP virtual int getDisp12MaxDiff() const = 0; CV_WRAP virtual void setDisp12MaxDiff(int disp12MaxDiff) = 0; }; /** @brief Class for computing stereo correspondence using the block matching algorithm, introduced and contributed to OpenCV by K. Konolige. */ class CV_EXPORTS_W StereoBM : public StereoMatcher { public: enum { PREFILTER_NORMALIZED_RESPONSE = 0, PREFILTER_XSOBEL = 1 }; CV_WRAP virtual int getPreFilterType() const = 0; CV_WRAP virtual void setPreFilterType(int preFilterType) = 0; CV_WRAP virtual int getPreFilterSize() const = 0; CV_WRAP virtual void setPreFilterSize(int preFilterSize) = 0; CV_WRAP virtual int getPreFilterCap() const = 0; CV_WRAP virtual void setPreFilterCap(int preFilterCap) = 0; CV_WRAP virtual int getTextureThreshold() const = 0; CV_WRAP virtual void setTextureThreshold(int textureThreshold) = 0; CV_WRAP virtual int getUniquenessRatio() const = 0; CV_WRAP virtual void setUniquenessRatio(int uniquenessRatio) = 0; CV_WRAP virtual int getSmallerBlockSize() const = 0; CV_WRAP virtual void setSmallerBlockSize(int blockSize) = 0; CV_WRAP virtual Rect getROI1() const = 0; CV_WRAP virtual void setROI1(Rect roi1) = 0; CV_WRAP virtual Rect getROI2() const = 0; CV_WRAP virtual void setROI2(Rect roi2) = 0; /** @brief Creates StereoBM object @param 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. @param 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. */ CV_WRAP static Ptr create(int numDisparities = 0, int blockSize = 21); }; /** @brief The class implements the modified H. Hirschmuller algorithm @cite 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 @cite 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 */ class CV_EXPORTS_W StereoSGBM : public StereoMatcher { public: enum { MODE_SGBM = 0, MODE_HH = 1, MODE_SGBM_3WAY = 2 }; CV_WRAP virtual int getPreFilterCap() const = 0; CV_WRAP virtual void setPreFilterCap(int preFilterCap) = 0; CV_WRAP virtual int getUniquenessRatio() const = 0; CV_WRAP virtual void setUniquenessRatio(int uniquenessRatio) = 0; CV_WRAP virtual int getP1() const = 0; CV_WRAP virtual void setP1(int P1) = 0; CV_WRAP virtual int getP2() const = 0; CV_WRAP virtual void setP2(int P2) = 0; CV_WRAP virtual int getMode() const = 0; CV_WRAP virtual void setMode(int mode) = 0; /** @brief Creates StereoSGBM object @param minDisparity Minimum possible disparity value. Normally, it is zero but sometimes rectification algorithms can shift images, so this parameter needs to be adjusted accordingly. @param numDisparities Maximum disparity minus minimum disparity. The value is always greater than zero. In the current implementation, this parameter must be divisible by 16. @param blockSize Matched block size. It must be an odd number \>=1 . Normally, it should be somewhere in the 3..11 range. @param P1 The first parameter controlling the disparity smoothness. See below. @param 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). @param 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. @param 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. @param 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. @param 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. @param 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. @param 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. */ CV_WRAP static Ptr create(int minDisparity, int numDisparities, int blockSize, int P1 = 0, int P2 = 0, int disp12MaxDiff = 0, int preFilterCap = 0, int uniquenessRatio = 0, int speckleWindowSize = 0, int speckleRange = 0, int mode = StereoSGBM::MODE_SGBM); }; //! @} calib3d /** @brief The methods in this namespace use a so-called fisheye camera model. @ingroup calib3d_fisheye */ namespace fisheye { //! @addtogroup calib3d_fisheye //! @{ enum{ CALIB_USE_INTRINSIC_GUESS = 1 << 0, CALIB_RECOMPUTE_EXTRINSIC = 1 << 1, CALIB_CHECK_COND = 1 << 2, CALIB_FIX_SKEW = 1 << 3, CALIB_FIX_K1 = 1 << 4, CALIB_FIX_K2 = 1 << 5, CALIB_FIX_K3 = 1 << 6, CALIB_FIX_K4 = 1 << 7, CALIB_FIX_INTRINSIC = 1 << 8, CALIB_FIX_PRINCIPAL_POINT = 1 << 9 }; /** @brief Projects points using fisheye model @param objectPoints Array of object points, 1xN/Nx1 3-channel (or vector\ ), where N is the number of points in the view. @param imagePoints Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or vector\. @param affine @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. @param alpha The skew coefficient. @param 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. */ CV_EXPORTS void projectPoints(InputArray objectPoints, OutputArray imagePoints, const Affine3d& affine, InputArray K, InputArray D, double alpha = 0, OutputArray jacobian = noArray()); /** @overload */ CV_EXPORTS_W void projectPoints(InputArray objectPoints, OutputArray imagePoints, InputArray rvec, InputArray tvec, InputArray K, InputArray D, double alpha = 0, OutputArray jacobian = noArray()); /** @brief Distorts 2D points using fisheye model. @param undistorted Array of object points, 1xN/Nx1 2-channel (or vector\ ), where N is the number of points in the view. @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. @param alpha The skew coefficient. @param distorted Output array of image points, 1xN/Nx1 2-channel, or vector\ . Note that the function assumes the camera matrix of the undistorted points to be indentity. This means if you want to transform back points undistorted with undistortPoints() you have to multiply them with \f$P^{-1}\f$. */ CV_EXPORTS_W void distortPoints(InputArray undistorted, OutputArray distorted, InputArray K, InputArray D, double alpha = 0); /** @brief Undistorts 2D points using fisheye model @param distorted Array of object points, 1xN/Nx1 2-channel (or vector\ ), where N is the number of points in the view. @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel @param P New camera matrix (3x3) or new projection matrix (3x4) @param undistorted Output array of image points, 1xN/Nx1 2-channel, or vector\ . */ CV_EXPORTS_W void undistortPoints(InputArray distorted, OutputArray undistorted, InputArray K, InputArray D, InputArray R = noArray(), InputArray P = noArray()); /** @brief 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. @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel @param P New camera matrix (3x3) or new projection matrix (3x4) @param size Undistorted image size. @param m1type Type of the first output map that can be CV_32FC1 or CV_16SC2 . See convertMaps() for details. @param map1 The first output map. @param map2 The second output map. */ CV_EXPORTS_W void initUndistortRectifyMap(InputArray K, InputArray D, InputArray R, InputArray P, const cv::Size& size, int m1type, OutputArray map1, OutputArray map2); /** @brief Transforms an image to compensate for fisheye lens distortion. @param distorted image with fisheye lens distortion. @param undistorted Output image with compensated fisheye lens distortion. @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. @param 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. @param 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. ![image](pics/fisheye_undistorted.jpg) */ CV_EXPORTS_W void undistortImage(InputArray distorted, OutputArray undistorted, InputArray K, InputArray D, InputArray Knew = cv::noArray(), const Size& new_size = Size()); /** @brief Estimates new camera matrix for undistortion or rectification. @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. @param image_size @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel @param P New camera matrix (3x3) or new projection matrix (3x4) @param 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]. @param new_size @param fov_scale Divisor for new focal length. */ CV_EXPORTS_W void estimateNewCameraMatrixForUndistortRectify(InputArray K, InputArray D, const Size &image_size, InputArray R, OutputArray P, double balance = 0.0, const Size& new_size = Size(), double fov_scale = 1.0); /** @brief Performs camera calibaration @param objectPoints vector of vectors of calibration pattern points in the calibration pattern coordinate space. @param 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. @param image_size Size of the image used only to initialize the intrinsic camera matrix. @param K Output 3x3 floating-point camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If fisheye::CALIB_USE_INTRINSIC_GUESS/ is specified, some or all of fx, fy, cx, cy must be initialized before calling the function. @param D Output vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. @param 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). @param tvecs Output vector of translation vectors estimated for each pattern view. @param 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. @param criteria Termination criteria for the iterative optimization algorithm. */ CV_EXPORTS_W double calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, const Size& image_size, InputOutputArray K, InputOutputArray D, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags = 0, TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON)); /** @brief Stereo rectification for fisheye camera model @param K1 First camera matrix. @param D1 First camera distortion parameters. @param K2 Second camera matrix. @param D2 Second camera distortion parameters. @param imageSize Size of the image used for stereo calibration. @param R Rotation matrix between the coordinate systems of the first and the second cameras. @param tvec Translation vector between coordinate systems of the cameras. @param R1 Output 3x3 rectification transform (rotation matrix) for the first camera. @param R2 Output 3x3 rectification transform (rotation matrix) for the second camera. @param P1 Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera. @param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera. @param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see reprojectImageTo3D ). @param flags Operation flags that may be zero or CV_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. @param 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. @param 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]. @param fov_scale Divisor for new focal length. */ CV_EXPORTS_W void stereoRectify(InputArray K1, InputArray D1, InputArray K2, InputArray D2, const Size &imageSize, InputArray R, InputArray tvec, OutputArray R1, OutputArray R2, OutputArray P1, OutputArray P2, OutputArray Q, int flags, const Size &newImageSize = Size(), double balance = 0.0, double fov_scale = 1.0); /** @brief Performs stereo calibration @param objectPoints Vector of vectors of the calibration pattern points. @param imagePoints1 Vector of vectors of the projections of the calibration pattern points, observed by the first camera. @param imagePoints2 Vector of vectors of the projections of the calibration pattern points, observed by the second camera. @param K1 Input/output first camera matrix: \f$\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\f$ , \f$j = 0,\, 1\f$ . If any of fisheye::CALIB_USE_INTRINSIC_GUESS , fisheye::CV_CALIB_FIX_INTRINSIC are specified, some or all of the matrix components must be initialized. @param D1 Input/output vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$ of 4 elements. @param K2 Input/output second camera matrix. The parameter is similar to K1 . @param D2 Input/output lens distortion coefficients for the second camera. The parameter is similar to D1 . @param imageSize Size of the image used only to initialize intrinsic camera matrix. @param R Output rotation matrix between the 1st and the 2nd camera coordinate systems. @param T Output translation vector between the coordinate systems of the cameras. @param flags Different flags that may be zero or a combination of the following values: - **fisheye::CV_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. @param criteria Termination criteria for the iterative optimization algorithm. */ CV_EXPORTS_W double stereoCalibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, InputOutputArray K1, InputOutputArray D1, InputOutputArray K2, InputOutputArray D2, Size imageSize, OutputArray R, OutputArray T, int flags = fisheye::CALIB_FIX_INTRINSIC, TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON)); //! @} calib3d_fisheye } } // cv #ifndef DISABLE_OPENCV_24_COMPATIBILITY #include "opencv2/calib3d/calib3d_c.h" #endif #endif