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1688 lines
92 KiB
C++
1688 lines
92 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html
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#ifndef OPENCV_CALIB_HPP
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#define OPENCV_CALIB_HPP
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#include "opencv2/core.hpp"
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#include "opencv2/core/types.hpp"
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#include "opencv2/features2d.hpp"
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#include "opencv2/core/affine.hpp"
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/**
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@defgroup calib Camera Calibration
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The functions in this section use a so-called pinhole camera model. The view of a scene
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is obtained by projecting a scene's 3D point \f$P_w\f$ into the image plane using a perspective
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transformation which forms the corresponding pixel \f$p\f$. Both \f$P_w\f$ and \f$p\f$ are
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represented in homogeneous coordinates, i.e. as 3D and 2D homogeneous vector respectively. You will
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find a brief introduction to projective geometry, homogeneous vectors and homogeneous
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transformations at the end of this section's introduction. For more succinct notation, we often drop
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the 'homogeneous' and say vector instead of homogeneous vector.
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The distortion-free projective transformation given by a pinhole camera model is shown below.
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\f[s \; p = A \begin{bmatrix} R|t \end{bmatrix} P_w,\f]
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where \f$P_w\f$ is a 3D point expressed with respect to the world coordinate system,
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\f$p\f$ is a 2D pixel in the image plane, \f$A\f$ is the camera intrinsic matrix,
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\f$R\f$ and \f$t\f$ are the rotation and translation that describe the change of coordinates from
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world to camera coordinate systems (or camera frame) and \f$s\f$ is the projective transformation's
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arbitrary scaling and not part of the camera model.
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The camera intrinsic matrix \f$A\f$ (notation used as in @cite Zhang2000 and also generally notated
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as \f$K\f$) projects 3D points given in the camera coordinate system to 2D pixel coordinates, i.e.
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\f[p = A P_c.\f]
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The camera intrinsic matrix \f$A\f$ is composed of the focal lengths \f$f_x\f$ and \f$f_y\f$, which are
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expressed in pixel units, and the principal point \f$(c_x, c_y)\f$, that is usually close to the
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image center:
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\f[A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1},\f]
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and thus
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\f[s \vecthree{u}{v}{1} = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1} \vecthree{X_c}{Y_c}{Z_c}.\f]
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The matrix of intrinsic parameters does not depend on the scene viewed. So, once estimated, it can
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be re-used as long as the focal length is fixed (in case of a zoom lens). Thus, if an image from the
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camera is scaled by a factor, all of these parameters need to be scaled (multiplied/divided,
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respectively) by the same factor.
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The joint rotation-translation matrix \f$[R|t]\f$ is the matrix product of a projective
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transformation and a homogeneous transformation. The 3-by-4 projective transformation maps 3D points
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represented in camera coordinates to 2D points in the image plane and represented in normalized
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camera coordinates \f$x' = X_c / Z_c\f$ and \f$y' = Y_c / Z_c\f$:
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\f[Z_c \begin{bmatrix}
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x' \\
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y' \\
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1
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\end{bmatrix} = \begin{bmatrix}
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1 & 0 & 0 & 0 \\
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0 & 1 & 0 & 0 \\
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0 & 0 & 1 & 0
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\end{bmatrix}
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\begin{bmatrix}
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X_c \\
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Y_c \\
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Z_c \\
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1
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\end{bmatrix}.\f]
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The homogeneous transformation is encoded by the extrinsic parameters \f$R\f$ and \f$t\f$ and
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represents the change of basis from world coordinate system \f$w\f$ to the camera coordinate sytem
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\f$c\f$. Thus, given the representation of the point \f$P\f$ in world coordinates, \f$P_w\f$, we
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obtain \f$P\f$'s representation in the camera coordinate system, \f$P_c\f$, by
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\f[P_c = \begin{bmatrix}
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R & t \\
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0 & 1
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\end{bmatrix} P_w,\f]
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This homogeneous transformation is composed out of \f$R\f$, a 3-by-3 rotation matrix, and \f$t\f$, a
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3-by-1 translation vector:
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\f[\begin{bmatrix}
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R & t \\
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0 & 1
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\end{bmatrix} = \begin{bmatrix}
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r_{11} & r_{12} & r_{13} & t_x \\
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r_{21} & r_{22} & r_{23} & t_y \\
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r_{31} & r_{32} & r_{33} & t_z \\
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0 & 0 & 0 & 1
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\end{bmatrix},
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\f]
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and therefore
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\f[\begin{bmatrix}
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X_c \\
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Y_c \\
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Z_c \\
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1
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\end{bmatrix} = \begin{bmatrix}
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r_{11} & r_{12} & r_{13} & t_x \\
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r_{21} & r_{22} & r_{23} & t_y \\
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r_{31} & r_{32} & r_{33} & t_z \\
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0 & 0 & 0 & 1
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\end{bmatrix}
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\begin{bmatrix}
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X_w \\
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Y_w \\
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Z_w \\
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1
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\end{bmatrix}.\f]
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Combining the projective transformation and the homogeneous transformation, we obtain the projective
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transformation that maps 3D points in world coordinates into 2D points in the image plane and in
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normalized camera coordinates:
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\f[Z_c \begin{bmatrix}
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x' \\
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y' \\
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1
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\end{bmatrix} = \begin{bmatrix} R|t \end{bmatrix} \begin{bmatrix}
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X_w \\
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Y_w \\
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Z_w \\
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1
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\end{bmatrix} = \begin{bmatrix}
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r_{11} & r_{12} & r_{13} & t_x \\
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r_{21} & r_{22} & r_{23} & t_y \\
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r_{31} & r_{32} & r_{33} & t_z
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\end{bmatrix}
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\begin{bmatrix}
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X_w \\
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Y_w \\
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Z_w \\
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1
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\end{bmatrix},\f]
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with \f$x' = X_c / Z_c\f$ and \f$y' = Y_c / Z_c\f$. Putting the equations for instrincs and extrinsics together, we can write out
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\f$s \; p = A \begin{bmatrix} R|t \end{bmatrix} P_w\f$ as
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\f[s \vecthree{u}{v}{1} = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}
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\begin{bmatrix}
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r_{11} & r_{12} & r_{13} & t_x \\
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r_{21} & r_{22} & r_{23} & t_y \\
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r_{31} & r_{32} & r_{33} & t_z
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\end{bmatrix}
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\begin{bmatrix}
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X_w \\
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Y_w \\
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Z_w \\
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1
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\end{bmatrix}.\f]
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If \f$Z_c \ne 0\f$, the transformation above is equivalent to the following,
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\f[\begin{bmatrix}
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u \\
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v
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\end{bmatrix} = \begin{bmatrix}
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f_x X_c/Z_c + c_x \\
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f_y Y_c/Z_c + c_y
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\end{bmatrix}\f]
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with
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\f[\vecthree{X_c}{Y_c}{Z_c} = \begin{bmatrix}
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R|t
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\end{bmatrix} \begin{bmatrix}
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X_w \\
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Y_w \\
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Z_w \\
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1
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\end{bmatrix}.\f]
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The following figure illustrates the pinhole camera model.
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![Pinhole camera model](pics/pinhole_camera_model.png)
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Real lenses usually have some distortion, mostly radial distortion, and slight tangential distortion.
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So, the above model is extended as:
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\f[\begin{bmatrix}
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u \\
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v
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\end{bmatrix} = \begin{bmatrix}
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f_x x'' + c_x \\
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f_y y'' + c_y
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\end{bmatrix}\f]
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where
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\f[\begin{bmatrix}
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x'' \\
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y''
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\end{bmatrix} = \begin{bmatrix}
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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 \\
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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 \\
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\end{bmatrix}\f]
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with
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\f[r^2 = x'^2 + y'^2\f]
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and
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\f[\begin{bmatrix}
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x'\\
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y'
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\end{bmatrix} = \begin{bmatrix}
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X_c/Z_c \\
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Y_c/Z_c
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\end{bmatrix},\f]
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if \f$Z_c \ne 0\f$.
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The distortion parameters are the radial coefficients \f$k_1\f$, \f$k_2\f$, \f$k_3\f$, \f$k_4\f$, \f$k_5\f$, and \f$k_6\f$
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,\f$p_1\f$ and \f$p_2\f$ are the tangential distortion coefficients, and \f$s_1\f$, \f$s_2\f$, \f$s_3\f$, and \f$s_4\f$,
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are the thin prism distortion coefficients. Higher-order coefficients are not considered in OpenCV.
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The next figures show two common types of radial distortion: barrel distortion
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(\f$ 1 + k_1 r^2 + k_2 r^4 + k_3 r^6 \f$ monotonically decreasing)
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and pincushion distortion (\f$ 1 + k_1 r^2 + k_2 r^4 + k_3 r^6 \f$ monotonically increasing).
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Radial distortion is always monotonic for real lenses,
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and if the estimator produces a non-monotonic result,
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this should be considered a calibration failure.
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More generally, radial distortion must be monotonic and the distortion function must be bijective.
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A failed estimation result may look deceptively good near the image center
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but will work poorly in e.g. AR/SFM applications.
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The optimization method used in OpenCV camera calibration does not include these constraints as
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the framework does not support the required integer programming and polynomial inequalities.
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See [issue #15992](https://github.com/opencv/opencv/issues/15992) for additional information.
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![](pics/distortion_examples.png)
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![](pics/distortion_examples2.png)
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In some cases, the image sensor may be tilted in order to focus an oblique plane in front of the
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camera (Scheimpflug principle). This can be useful for particle image velocimetry (PIV) or
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triangulation with a laser fan. The tilt causes a perspective distortion of \f$x''\f$ and
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\f$y''\f$. This distortion can be modeled in the following way, see e.g. @cite Louhichi07.
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\f[\begin{bmatrix}
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u \\
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v
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\end{bmatrix} = \begin{bmatrix}
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f_x x''' + c_x \\
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f_y y''' + c_y
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\end{bmatrix},\f]
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where
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\f[s\vecthree{x'''}{y'''}{1} =
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\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}(\tau_x, \tau_y)}
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{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)}
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{0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\f]
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and the matrix \f$R(\tau_x, \tau_y)\f$ is defined by two rotations with angular parameter
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\f$\tau_x\f$ and \f$\tau_y\f$, respectively,
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\f[
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R(\tau_x, \tau_y) =
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\vecthreethree{\cos(\tau_y)}{0}{-\sin(\tau_y)}{0}{1}{0}{\sin(\tau_y)}{0}{\cos(\tau_y)}
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\vecthreethree{1}{0}{0}{0}{\cos(\tau_x)}{\sin(\tau_x)}{0}{-\sin(\tau_x)}{\cos(\tau_x)} =
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\vecthreethree{\cos(\tau_y)}{\sin(\tau_y)\sin(\tau_x)}{-\sin(\tau_y)\cos(\tau_x)}
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{0}{\cos(\tau_x)}{\sin(\tau_x)}
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{\sin(\tau_y)}{-\cos(\tau_y)\sin(\tau_x)}{\cos(\tau_y)\cos(\tau_x)}.
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\f]
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In the functions below the coefficients are passed or returned as
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\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]
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vector. That is, if the vector contains four elements, it means that \f$k_3=0\f$ . The distortion
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coefficients do not depend on the scene viewed. Thus, they also belong to the intrinsic camera
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parameters. And they remain the same regardless of the captured image resolution. If, for example, a
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camera has been calibrated on images of 320 x 240 resolution, absolutely the same distortion
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coefficients can be used for 640 x 480 images from the same camera while \f$f_x\f$, \f$f_y\f$,
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\f$c_x\f$, and \f$c_y\f$ need to be scaled appropriately.
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The functions below use the above model to do the following:
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- Project 3D points to the image plane given intrinsic and extrinsic parameters.
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- Compute extrinsic parameters given intrinsic parameters, a few 3D points, and their
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projections.
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- Estimate intrinsic and extrinsic camera parameters from several views of a known calibration
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pattern (every view is described by several 3D-2D point correspondences).
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- Estimate the relative position and orientation of the stereo camera "heads" and compute the
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*rectification* transformation that makes the camera optical axes parallel.
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<B> Homogeneous Coordinates </B><br>
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Homogeneous Coordinates are a system of coordinates that are used in projective geometry. Their use
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allows to represent points at infinity by finite coordinates and simplifies formulas when compared
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to the cartesian counterparts, e.g. they have the advantage that affine transformations can be
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expressed as linear homogeneous transformation.
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One obtains the homogeneous vector \f$P_h\f$ by appending a 1 along an n-dimensional cartesian
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vector \f$P\f$ e.g. for a 3D cartesian vector the mapping \f$P \rightarrow P_h\f$ is:
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\f[\begin{bmatrix}
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X \\
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Y \\
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Z
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\end{bmatrix} \rightarrow \begin{bmatrix}
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X \\
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Y \\
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Z \\
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1
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\end{bmatrix}.\f]
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For the inverse mapping \f$P_h \rightarrow P\f$, one divides all elements of the homogeneous vector
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by its last element, e.g. for a 3D homogeneous vector one gets its 2D cartesian counterpart by:
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\f[\begin{bmatrix}
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X \\
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Y \\
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W
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\end{bmatrix} \rightarrow \begin{bmatrix}
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X / W \\
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Y / W
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\end{bmatrix},\f]
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if \f$W \ne 0\f$.
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Due to this mapping, all multiples \f$k P_h\f$, for \f$k \ne 0\f$, of a homogeneous point represent
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the same point \f$P_h\f$. An intuitive understanding of this property is that under a projective
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transformation, all multiples of \f$P_h\f$ are mapped to the same point. This is the physical
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observation one does for pinhole cameras, as all points along a ray through the camera's pinhole are
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projected to the same image point, e.g. all points along the red ray in the image of the pinhole
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camera model above would be mapped to the same image coordinate. This property is also the source
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for the scale ambiguity s in the equation of the pinhole camera model.
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As mentioned, by using homogeneous coordinates we can express any change of basis parameterized by
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\f$R\f$ and \f$t\f$ as a linear transformation, e.g. for the change of basis from coordinate system
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0 to coordinate system 1 becomes:
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\f[P_1 = R P_0 + t \rightarrow P_{h_1} = \begin{bmatrix}
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R & t \\
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0 & 1
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\end{bmatrix} P_{h_0}.\f]
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@note
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- Many functions in this module take a camera intrinsic matrix as an input parameter. Although all
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functions assume the same structure of this parameter, they may name it differently. The
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parameter's description, however, will be clear in that a camera intrinsic matrix with the structure
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shown above is required.
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- A calibration sample for 3 cameras in a horizontal position can be found at
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opencv_source_code/samples/cpp/3calibration.cpp
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- A calibration sample based on a sequence of images can be found at
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opencv_source_code/samples/cpp/calibration.cpp
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- A calibration sample in order to do 3D reconstruction can be found at
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opencv_source_code/samples/cpp/build3dmodel.cpp
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- A calibration example on stereo calibration can be found at
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opencv_source_code/samples/cpp/stereo_calib.cpp
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- A calibration example on stereo matching can be found at
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opencv_source_code/samples/cpp/stereo_match.cpp
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- (Python) A camera calibration sample can be found at
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opencv_source_code/samples/python/calibrate.py
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@{
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@defgroup calib3d_fisheye Fisheye camera model
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Definitions: Let P be a point in 3D of coordinates X in the world reference frame (stored in the
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matrix X) The coordinate vector of P in the camera reference frame is:
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\f[Xc = R X + T\f]
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where R is the rotation matrix corresponding to the rotation vector om: R = rodrigues(om); call x, y
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and z the 3 coordinates of Xc:
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\f[\begin{array}{l} x = Xc_1 \\ y = Xc_2 \\ z = Xc_3 \end{array} \f]
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The pinhole projection coordinates of P is [a; b] where
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\f[\begin{array}{l} a = x / z \ and \ b = y / z \\ r^2 = a^2 + b^2 \\ \theta = atan(r) \end{array} \f]
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Fisheye distortion:
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\f[\theta_d = \theta (1 + k_1 \theta^2 + k_2 \theta^4 + k_3 \theta^6 + k_4 \theta^8)\f]
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The distorted point coordinates are [x'; y'] where
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\f[\begin{array}{l} x' = (\theta_d / r) a \\ y' = (\theta_d / r) b \end{array} \f]
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Finally, conversion into pixel coordinates: The final pixel coordinates vector [u; v] where:
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\f[\begin{array}{l} u = f_x (x' + \alpha y') + c_x \\
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v = f_y y' + c_y \end{array} \f]
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Summary:
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Generic camera model @cite Kannala2006 with perspective projection and without distortion correction
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@}
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*/
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namespace cv {
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//! @addtogroup calib
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//! @{
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enum { CALIB_CB_ADAPTIVE_THRESH = 1,
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CALIB_CB_NORMALIZE_IMAGE = 2,
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CALIB_CB_FILTER_QUADS = 4,
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CALIB_CB_FAST_CHECK = 8,
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CALIB_CB_EXHAUSTIVE = 16,
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CALIB_CB_ACCURACY = 32,
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CALIB_CB_LARGER = 64,
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CALIB_CB_MARKER = 128,
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CALIB_CB_PLAIN = 256
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};
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enum { CALIB_CB_SYMMETRIC_GRID = 1,
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CALIB_CB_ASYMMETRIC_GRID = 2,
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CALIB_CB_CLUSTERING = 4
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};
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#define CALIB_NINTRINSIC 18 //!< Maximal size of camera internal parameters (initrinsics) vector
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enum CameraModel {
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CALIB_MODEL_PINHOLE = 0, //!< Pinhole camera model
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CALIB_MODEL_FISHEYE = 1, //!< Fisheye camera model
|
|
};
|
|
|
|
enum { CALIB_USE_INTRINSIC_GUESS = 0x00001, //!< Use user provided intrinsics as initial point for optimization.
|
|
CALIB_FIX_ASPECT_RATIO = 0x00002, //!< Use with CALIB_USE_INTRINSIC_GUESS. The ratio fx/fy stays the same as in the input cameraMatrix.
|
|
CALIB_FIX_PRINCIPAL_POINT = 0x00004, //!< The principal point (cx, cy) stays the same as in the input camera matrix. Image center is used as principal point, if CALIB_USE_INTRINSIC_GUESS is not set.
|
|
CALIB_ZERO_TANGENT_DIST = 0x00008, //!< For pinhole model only. Tangential distortion coefficients \f$(p_1, p_2)\f$ are set to zeros and stay zero.
|
|
CALIB_FIX_FOCAL_LENGTH = 0x00010, //!< Use with CALIB_USE_INTRINSIC_GUESS. The focal length (fx, fy) stays the same as in the input cameraMatrix.
|
|
CALIB_FIX_K1 = 0x00020, //!< The corresponding distortion coefficient is not changed during the optimization. 0 value is used, if CALIB_USE_INTRINSIC_GUESS is not set.
|
|
CALIB_FIX_K2 = 0x00040, //!< The corresponding distortion coefficient is not changed during the optimization. 0 value is used, if CALIB_USE_INTRINSIC_GUESS is not set.
|
|
CALIB_FIX_K3 = 0x00080, //!< The corresponding distortion coefficient is not changed during the optimization. 0 value is used, if CALIB_USE_INTRINSIC_GUESS is not set.
|
|
CALIB_FIX_K4 = 0x00800, //!< The corresponding distortion coefficient is not changed during the optimization. 0 value is used, if CALIB_USE_INTRINSIC_GUESS is not set.
|
|
CALIB_FIX_K5 = 0x01000, //!< For pinhole model only. The corresponding distortion coefficient is not changed during the optimization. 0 value is used, if CALIB_USE_INTRINSIC_GUESS is not set.
|
|
CALIB_FIX_K6 = 0x02000, //!< For pinhole model only. The corresponding distortion coefficient is not changed during the optimization. 0 value is used, if CALIB_USE_INTRINSIC_GUESS is not set.
|
|
CALIB_RATIONAL_MODEL = 0x04000, //!< For pinhole model only. Use rational distortion model with coefficients k4..k6.
|
|
CALIB_THIN_PRISM_MODEL = 0x08000, //!< For pinhole model only. Use thin prism distortion model with coefficients s1..s4.
|
|
CALIB_FIX_S1_S2_S3_S4 = 0x10000, //!< For pinhole model only. The thin prism distortion coefficients are not changed during the optimization. 0 value is used, if CALIB_USE_INTRINSIC_GUESS is not set.
|
|
CALIB_TILTED_MODEL = 0x40000, //!< For pinhole model only. Coefficients tauX and tauY are enabled in camera matrix.
|
|
CALIB_FIX_TAUX_TAUY = 0x80000, //!< For pinhole model only. The tauX and tauY coefficients are not changed during the optimization. 0 value is used, if CALIB_USE_INTRINSIC_GUESS is not set.
|
|
CALIB_USE_QR = 0x100000, //!< Use QR instead of SVD decomposition for solving. Faster but potentially less precise
|
|
CALIB_FIX_TANGENT_DIST = 0x200000, //!< For pinhole model only. Tangential distortion coefficients (p1,p2) are set to zeros and stay zero.
|
|
// only for stereo
|
|
CALIB_FIX_INTRINSIC = 0x00100, //!< For stereo and milti-camera calibration only. Do not optimize cameras intrinsics
|
|
CALIB_SAME_FOCAL_LENGTH = 0x00200, //!< For stereo calibration only. Use the same focal length for cameras in pair.
|
|
// for stereo rectification
|
|
CALIB_ZERO_DISPARITY = 0x00400, //!< Deprecated synonim of @ref STEREO_ZERO_DISPARITY. See @ref stereoRectify.
|
|
CALIB_USE_LU = (1 << 17), //!< use LU instead of SVD decomposition for solving. much faster but potentially less precise
|
|
CALIB_USE_EXTRINSIC_GUESS = (1 << 22), //!< For stereo calibration only. Use user provided extrinsics (R, T) as initial point for optimization
|
|
// fisheye only flags
|
|
CALIB_RECOMPUTE_EXTRINSIC = (1 << 23), //!< For fisheye model only. Recompute board position on each calibration iteration
|
|
CALIB_CHECK_COND = (1 << 24), //!< For fisheye model only. Check SVD decomposition quality for each frame during extrinsics estimation
|
|
CALIB_FIX_SKEW = (1 << 25) //!< For fisheye model only. Skew coefficient (alpha) is set to zero and stay zero.
|
|
};
|
|
|
|
enum HandEyeCalibrationMethod
|
|
{
|
|
CALIB_HAND_EYE_TSAI = 0, //!< A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/Eye Calibration @cite Tsai89
|
|
CALIB_HAND_EYE_PARK = 1, //!< Robot Sensor Calibration: Solving AX = XB on the Euclidean Group @cite Park94
|
|
CALIB_HAND_EYE_HORAUD = 2, //!< Hand-eye Calibration @cite Horaud95
|
|
CALIB_HAND_EYE_ANDREFF = 3, //!< On-line Hand-Eye Calibration @cite Andreff99
|
|
CALIB_HAND_EYE_DANIILIDIS = 4 //!< Hand-Eye Calibration Using Dual Quaternions @cite Daniilidis98
|
|
};
|
|
|
|
enum RobotWorldHandEyeCalibrationMethod
|
|
{
|
|
CALIB_ROBOT_WORLD_HAND_EYE_SHAH = 0, //!< Solving the robot-world/hand-eye calibration problem using the kronecker product @cite Shah2013SolvingTR
|
|
CALIB_ROBOT_WORLD_HAND_EYE_LI = 1 //!< Simultaneous robot-world and hand-eye calibration using dual-quaternions and kronecker product @cite Li2010SimultaneousRA
|
|
};
|
|
|
|
/** @brief Finds an initial camera intrinsic 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$f_x = f_y \cdot \texttt{aspectRatio}\f$ .
|
|
|
|
The function estimates and returns an initial camera intrinsic 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 = cv::Size(points_per_row,points_per_colum) = cv::Size(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:
|
|
- @ref 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).
|
|
- @ref CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with equalizeHist before
|
|
applying fixed or adaptive thresholding.
|
|
- @ref 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.
|
|
- @ref 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.
|
|
- @ref CALIB_CB_PLAIN All other flags are ignored. The input image is taken as is.
|
|
No image processing is done to improve to find the checkerboard. This has the effect of speeding up the
|
|
execution of the function but could lead to not recognizing the checkerboard if the image
|
|
is not previously binarized in the appropriate manner.
|
|
|
|
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<Point2f> corners; //this will be filled by the detected corners
|
|
|
|
//CALIB_CB_FAST_CHECK saves a lot of time on images
|
|
//that do not contain any chessboard corners
|
|
bool patternfound = findChessboardCorners(gray, patternsize, corners,
|
|
CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE
|
|
+ CALIB_CB_FAST_CHECK);
|
|
|
|
if(patternfound)
|
|
cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1),
|
|
TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
|
|
|
|
drawChessboardCorners(img, patternsize, Mat(corners), patternfound);
|
|
@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.
|
|
|
|
Use gen_pattern.py (@ref tutorial_camera_calibration_pattern) to create checkerboard.
|
|
*/
|
|
CV_EXPORTS_W bool findChessboardCorners( InputArray image, Size patternSize, OutputArray corners,
|
|
int flags = CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE );
|
|
|
|
/*
|
|
Checks whether the image contains chessboard of the specific size or not.
|
|
If yes, nonzero value is returned.
|
|
*/
|
|
CV_EXPORTS_W bool checkChessboard(InputArray img, Size size);
|
|
|
|
/** @brief Finds the positions of internal corners of the chessboard using a sector based approach.
|
|
|
|
@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 = cv::Size(points_per_row,points_per_colum) = cv::Size(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:
|
|
- @ref CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with equalizeHist before detection.
|
|
- @ref CALIB_CB_EXHAUSTIVE Run an exhaustive search to improve detection rate.
|
|
- @ref CALIB_CB_ACCURACY Up sample input image to improve sub-pixel accuracy due to aliasing effects.
|
|
- @ref CALIB_CB_LARGER The detected pattern is allowed to be larger than patternSize (see description).
|
|
- @ref CALIB_CB_MARKER The detected pattern must have a marker (see description).
|
|
This should be used if an accurate camera calibration is required.
|
|
@param meta Optional output arrray of detected corners (CV_8UC1 and size = cv::Size(columns,rows)).
|
|
Each entry stands for one corner of the pattern and can have one of the following values:
|
|
- 0 = no meta data attached
|
|
- 1 = left-top corner of a black cell
|
|
- 2 = left-top corner of a white cell
|
|
- 3 = left-top corner of a black cell with a white marker dot
|
|
- 4 = left-top corner of a white cell with a black marker dot (pattern origin in case of markers otherwise first corner)
|
|
|
|
The function is analog to #findChessboardCorners but uses a localized radon
|
|
transformation approximated by box filters being more robust to all sort of
|
|
noise, faster on larger images and is able to directly return the sub-pixel
|
|
position of the internal chessboard corners. The Method is based on the paper
|
|
@cite duda2018 "Accurate Detection and Localization of Checkerboard Corners for
|
|
Calibration" demonstrating that the returned sub-pixel positions are more
|
|
accurate than the one returned by cornerSubPix allowing a precise camera
|
|
calibration for demanding applications.
|
|
|
|
In the case, the flags @ref CALIB_CB_LARGER or @ref CALIB_CB_MARKER are given,
|
|
the result can be recovered from the optional meta array. Both flags are
|
|
helpful to use calibration patterns exceeding the field of view of the camera.
|
|
These oversized patterns allow more accurate calibrations as corners can be
|
|
utilized, which are as close as possible to the image borders. For a
|
|
consistent coordinate system across all images, the optional marker (see image
|
|
below) can be used to move the origin of the board to the location where the
|
|
black circle is located.
|
|
|
|
@note The function requires a white boarder with roughly the same width as one
|
|
of the checkerboard fields around the whole board to improve the detection in
|
|
various environments. In addition, because of the localized radon
|
|
transformation it is beneficial to use round corners for the field corners
|
|
which are located on the outside of the board. The following figure illustrates
|
|
a sample checkerboard optimized for the detection. However, any other checkerboard
|
|
can be used as well.
|
|
|
|
Use gen_pattern.py (@ref tutorial_camera_calibration_pattern) to create checkerboard.
|
|
![Checkerboard](pics/checkerboard_radon.png)
|
|
*/
|
|
CV_EXPORTS_AS(findChessboardCornersSBWithMeta)
|
|
bool findChessboardCornersSB(InputArray image,Size patternSize, OutputArray corners,
|
|
int flags,OutputArray meta);
|
|
/** @overload */
|
|
CV_EXPORTS_W inline
|
|
bool findChessboardCornersSB(InputArray image, Size patternSize, OutputArray corners,
|
|
int flags = 0)
|
|
{
|
|
return findChessboardCornersSB(image, patternSize, corners, flags, noArray());
|
|
}
|
|
|
|
/** @brief Estimates the sharpness of a detected chessboard.
|
|
|
|
Image sharpness, as well as brightness, are a critical parameter for accuracte
|
|
camera calibration. For accessing these parameters for filtering out
|
|
problematic calibraiton images, this method calculates edge profiles by traveling from
|
|
black to white chessboard cell centers. Based on this, the number of pixels is
|
|
calculated required to transit from black to white. This width of the
|
|
transition area is a good indication of how sharp the chessboard is imaged
|
|
and should be below ~3.0 pixels.
|
|
|
|
@param image Gray image used to find chessboard corners
|
|
@param patternSize Size of a found chessboard pattern
|
|
@param corners Corners found by #findChessboardCornersSB
|
|
@param rise_distance Rise distance 0.8 means 10% ... 90% of the final signal strength
|
|
@param vertical By default edge responses for horizontal lines are calculated
|
|
@param sharpness Optional output array with a sharpness value for calculated edge responses (see description)
|
|
|
|
The optional sharpness array is of type CV_32FC1 and has for each calculated
|
|
profile one row with the following five entries:
|
|
* 0 = x coordinate of the underlying edge in the image
|
|
* 1 = y coordinate of the underlying edge in the image
|
|
* 2 = width of the transition area (sharpness)
|
|
* 3 = signal strength in the black cell (min brightness)
|
|
* 4 = signal strength in the white cell (max brightness)
|
|
|
|
@return Scalar(average sharpness, average min brightness, average max brightness,0)
|
|
*/
|
|
CV_EXPORTS_W Scalar estimateChessboardSharpness(InputArray image, Size patternSize, InputArray corners,
|
|
float rise_distance=0.8F,bool vertical=false,
|
|
OutputArray sharpness=noArray());
|
|
|
|
|
|
//! finds subpixel-accurate positions of the chessboard corners
|
|
CV_EXPORTS_W 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 );
|
|
|
|
struct CV_EXPORTS_W_SIMPLE CirclesGridFinderParameters
|
|
{
|
|
CV_WRAP CirclesGridFinderParameters();
|
|
CV_PROP_RW cv::Size2f densityNeighborhoodSize;
|
|
CV_PROP_RW float minDensity;
|
|
CV_PROP_RW int kmeansAttempts;
|
|
CV_PROP_RW int minDistanceToAddKeypoint;
|
|
CV_PROP_RW int keypointScale;
|
|
CV_PROP_RW float minGraphConfidence;
|
|
CV_PROP_RW float vertexGain;
|
|
CV_PROP_RW float vertexPenalty;
|
|
CV_PROP_RW float existingVertexGain;
|
|
CV_PROP_RW float edgeGain;
|
|
CV_PROP_RW float edgePenalty;
|
|
CV_PROP_RW float convexHullFactor;
|
|
CV_PROP_RW float minRNGEdgeSwitchDist;
|
|
|
|
enum GridType
|
|
{
|
|
SYMMETRIC_GRID, ASYMMETRIC_GRID
|
|
};
|
|
GridType gridType;
|
|
|
|
CV_PROP_RW float squareSize; //!< Distance between two adjacent points. Used by CALIB_CB_CLUSTERING.
|
|
CV_PROP_RW float maxRectifiedDistance; //!< Max deviation from prediction. Used by CALIB_CB_CLUSTERING.
|
|
};
|
|
|
|
#ifndef DISABLE_OPENCV_3_COMPATIBILITY
|
|
typedef CirclesGridFinderParameters CirclesGridFinderParameters2;
|
|
#endif
|
|
|
|
/** @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:
|
|
- @ref CALIB_CB_SYMMETRIC_GRID uses symmetric pattern of circles.
|
|
- @ref CALIB_CB_ASYMMETRIC_GRID uses asymmetric pattern of circles.
|
|
- @ref 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.
|
|
If `blobDetector` is NULL then `image` represents Point2f array of candidates.
|
|
@param parameters struct for finding circles in a grid pattern.
|
|
|
|
The function attempts to determine whether the input image contains a grid of circles. If it is, the
|
|
function locates centers of the circles. The function returns a non-zero value if all of the centers
|
|
have been found and they have been placed in a certain order (row by row, left to right in every
|
|
row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0.
|
|
|
|
Sample usage of detecting and drawing the centers of circles: :
|
|
@code
|
|
Size patternsize(7,7); //number of centers
|
|
Mat gray = ...; //source image
|
|
vector<Point2f> centers; //this will be filled by the detected centers
|
|
|
|
bool patternfound = findCirclesGrid(gray, patternsize, centers);
|
|
|
|
drawChessboardCorners(img, patternsize, Mat(centers), patternfound);
|
|
@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,
|
|
const Ptr<FeatureDetector> &blobDetector,
|
|
const CirclesGridFinderParameters& parameters);
|
|
|
|
/** @overload */
|
|
CV_EXPORTS_W bool findCirclesGrid( InputArray image, Size patternSize,
|
|
OutputArray centers, int flags = CALIB_CB_SYMMETRIC_GRID,
|
|
const Ptr<FeatureDetector> &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<std::vector<cv::Vec3f>>). The outer
|
|
vector contains as many elements as the number of 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. Although the points are 3D, they all lie in the calibration pattern's
|
|
XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig.
|
|
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<std::vector<cv::Vec2f>>). imagePoints.size() and
|
|
objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal,
|
|
respectively. 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 camera intrinsic matrix.
|
|
@param cameraMatrix Input/output 3x3 floating-point camera intrinsic matrix
|
|
\f$\cameramatrix{A}\f$ . If @ref CALIB_USE_INTRINSIC_GUESS
|
|
and/or @ref CALIB_FIX_ASPECT_RATIO, @ref CALIB_FIX_PRINCIPAL_POINT or @ref CALIB_FIX_FOCAL_LENGTH
|
|
are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
|
|
@param distCoeffs Input/output vector of distortion coefficients
|
|
\f$\distcoeffs\f$.
|
|
@param rvecs Output vector of rotation vectors (@ref Rodrigues ) estimated for each pattern view
|
|
(e.g. std::vector<cv::Mat>>). That is, each i-th rotation vector together with the corresponding
|
|
i-th translation vector (see the next output parameter description) brings the calibration pattern
|
|
from the object coordinate space (in which object points are specified) to the camera coordinate
|
|
space. In more technical terms, the tuple of the i-th rotation and translation vector performs
|
|
a change of basis from object coordinate space to camera coordinate space. Due to its duality, this
|
|
tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate
|
|
space.
|
|
@param tvecs Output vector of translation vectors estimated for each pattern view, see parameter
|
|
description above.
|
|
@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$(R_0, T_0, \dotsc , R_{M - 1}, T_{M - 1})\f$ where M is
|
|
the 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:
|
|
- @ref 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 @ref solvePnP instead.
|
|
- @ref 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
|
|
@ref CALIB_USE_INTRINSIC_GUESS is set too.
|
|
- @ref CALIB_FIX_ASPECT_RATIO The functions consider only fy as a free parameter. The
|
|
ratio fx/fy stays the same as in the input cameraMatrix . When
|
|
@ref 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.
|
|
- @ref CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients \f$(p_1, p_2)\f$ are set
|
|
to zeros and stay zero.
|
|
- @ref CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization if
|
|
@ref CALIB_USE_INTRINSIC_GUESS is set.
|
|
- @ref CALIB_FIX_K1,..., @ref CALIB_FIX_K6 The corresponding radial distortion
|
|
coefficient is not changed during the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is
|
|
set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
|
|
- @ref 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 or more.
|
|
- @ref 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 or more.
|
|
- @ref CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during
|
|
the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
|
|
supplied distCoeffs matrix is used. Otherwise, it is set to 0.
|
|
- @ref 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.
|
|
- @ref CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during
|
|
the optimization. If @ref 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 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 @ref findChessboardCorners). Currently, initialization of intrinsic
|
|
parameters (when @ref CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration
|
|
patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also
|
|
be used as long as initial cameraMatrix is provided.
|
|
|
|
The algorithm performs the following steps:
|
|
|
|
- Compute the initial intrinsic parameters (the option only available for planar calibration
|
|
patterns) or read them from the input parameters. The distortion coefficients are all set to
|
|
zeros initially unless some of CALIB_FIX_K? are specified.
|
|
|
|
- Estimate the initial camera pose as if the intrinsic parameters have been already known. This is
|
|
done using @ref 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 @ref projectPoints for details.
|
|
|
|
@note
|
|
If you use a non-square (i.e. non-N-by-N) grid and @ref findChessboardCorners for calibration,
|
|
and @ref calibrateCamera returns bad values (zero distortion coefficients, \f$c_x\f$ and
|
|
\f$c_y\f$ very far from the image center, and/or large differences between \f$f_x\f$ and
|
|
\f$f_y\f$ (ratios of 10:1 or more)), then you are probably using patternSize=cvSize(rows,cols)
|
|
instead of using patternSize=cvSize(cols,rows) in @ref findChessboardCorners.
|
|
|
|
@note
|
|
The function may throw exceptions, if unsupported combination of parameters is provided or
|
|
the system is underconstrained.
|
|
|
|
@sa
|
|
calibrateCameraRO, 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, 100, DBL_EPSILON) );
|
|
|
|
/** @overload */
|
|
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, 100, DBL_EPSILON) );
|
|
|
|
/** @brief Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
|
|
|
|
This function is an extension of #calibrateCamera with the method of releasing object which was
|
|
proposed in @cite strobl2011iccv. In many common cases with inaccurate, unmeasured, roughly planar
|
|
targets (calibration plates), this method can dramatically improve the precision of the estimated
|
|
camera parameters. Both the object-releasing method and standard method are supported by this
|
|
function. Use the parameter **iFixedPoint** for method selection. In the internal implementation,
|
|
#calibrateCamera is a wrapper for this function.
|
|
|
|
@param objectPoints Vector of vectors of calibration pattern points in the calibration pattern
|
|
coordinate space. See #calibrateCamera for details. If the method of releasing object to be used,
|
|
the identical calibration board must be used in each view and it must be fully visible, and all
|
|
objectPoints[i] must be the same and all points should be roughly close to a plane. **The calibration
|
|
target has to be rigid, or at least static if the camera (rather than the calibration target) is
|
|
shifted for grabbing images.**
|
|
@param imagePoints Vector of vectors of the projections of calibration pattern points. See
|
|
#calibrateCamera for details.
|
|
@param imageSize Size of the image used only to initialize the intrinsic camera matrix.
|
|
@param iFixedPoint The index of the 3D object point in objectPoints[0] to be fixed. It also acts as
|
|
a switch for calibration method selection. If object-releasing method to be used, pass in the
|
|
parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will
|
|
make standard calibration method selected. Usually the top-right corner point of the calibration
|
|
board grid is recommended to be fixed when object-releasing method being utilized. According to
|
|
\cite strobl2011iccv, two other points are also fixed. In this implementation, objectPoints[0].front
|
|
and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and
|
|
newObjPoints are only possible if coordinates of these three fixed points are accurate enough.
|
|
@param cameraMatrix Output 3x3 floating-point camera matrix. See #calibrateCamera for details.
|
|
@param distCoeffs Output vector of distortion coefficients. See #calibrateCamera for details.
|
|
@param rvecs Output vector of rotation vectors estimated for each pattern view. See #calibrateCamera
|
|
for details.
|
|
@param tvecs Output vector of translation vectors estimated for each pattern view.
|
|
@param newObjPoints The updated output vector of calibration pattern points. The coordinates might
|
|
be scaled based on three fixed points. The returned coordinates are accurate only if the above
|
|
mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter
|
|
is ignored with standard calibration method.
|
|
@param stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters.
|
|
See #calibrateCamera for details.
|
|
@param stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters.
|
|
See #calibrateCamera for details.
|
|
@param stdDeviationsObjPoints Output vector of standard deviations estimated for refined coordinates
|
|
of calibration pattern points. It has the same size and order as objectPoints[0] vector. This
|
|
parameter is ignored with standard calibration method.
|
|
@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 some predefined values. See
|
|
#calibrateCamera for details. If the method of releasing object is used, the calibration time may
|
|
be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially
|
|
less precise and less stable in some rare cases.
|
|
@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, @cite BouguetMCT and @cite strobl2011iccv. See
|
|
#calibrateCamera for other detailed explanations.
|
|
@sa
|
|
calibrateCamera, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort
|
|
*/
|
|
CV_EXPORTS_AS(calibrateCameraROExtended) double calibrateCameraRO( InputArrayOfArrays objectPoints,
|
|
InputArrayOfArrays imagePoints, Size imageSize, int iFixedPoint,
|
|
InputOutputArray cameraMatrix, InputOutputArray distCoeffs,
|
|
OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
|
|
OutputArray newObjPoints,
|
|
OutputArray stdDeviationsIntrinsics,
|
|
OutputArray stdDeviationsExtrinsics,
|
|
OutputArray stdDeviationsObjPoints,
|
|
OutputArray perViewErrors,
|
|
int flags = 0, TermCriteria criteria = TermCriteria(
|
|
TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON) );
|
|
|
|
/** @overload */
|
|
CV_EXPORTS_W double calibrateCameraRO( InputArrayOfArrays objectPoints,
|
|
InputArrayOfArrays imagePoints, Size imageSize, int iFixedPoint,
|
|
InputOutputArray cameraMatrix, InputOutputArray distCoeffs,
|
|
OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
|
|
OutputArray newObjPoints,
|
|
int flags = 0, TermCriteria criteria = TermCriteria(
|
|
TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON) );
|
|
|
|
/** @brief Computes useful camera characteristics from the camera intrinsic matrix.
|
|
|
|
@param cameraMatrix Input camera intrinsic 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 a stereo camera set up. This function finds the intrinsic parameters
|
|
for each of the two cameras and the extrinsic parameters between the two cameras.
|
|
|
|
@param objectPoints Vector of vectors of the calibration pattern points. The same structure as
|
|
in @ref calibrateCamera. For each pattern view, both cameras need to see the same object
|
|
points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be
|
|
equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to
|
|
be equal for each i.
|
|
@param imagePoints1 Vector of vectors of the projections of the calibration pattern points,
|
|
observed by the first camera. The same structure as in @ref calibrateCamera.
|
|
@param imagePoints2 Vector of vectors of the projections of the calibration pattern points,
|
|
observed by the second camera. The same structure as in @ref calibrateCamera.
|
|
@param cameraMatrix1 Input/output camera intrinsic matrix for the first camera, the same as in
|
|
@ref calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
|
|
@param distCoeffs1 Input/output vector of distortion coefficients, the same as in
|
|
@ref calibrateCamera.
|
|
@param cameraMatrix2 Input/output second camera intrinsic matrix for the second camera. See description for
|
|
cameraMatrix1.
|
|
@param distCoeffs2 Input/output lens distortion coefficients for the second camera. See
|
|
description for distCoeffs1.
|
|
@param imageSize Size of the image used only to initialize the camera intrinsic matrices.
|
|
@param R Output rotation matrix. Together with the translation vector T, this matrix brings
|
|
points given in the first camera's coordinate system to points in the second camera's
|
|
coordinate system. In more technical terms, the tuple of R and T performs a change of basis
|
|
from the first camera's coordinate system to the second camera's coordinate system. Due to its
|
|
duality, this tuple is equivalent to the position of the first camera with respect to the
|
|
second camera coordinate system.
|
|
@param T Output translation vector, see description above.
|
|
@param E Output essential matrix.
|
|
@param F Output fundamental matrix.
|
|
@param rvecs Output vector of rotation vectors ( @ref Rodrigues ) estimated for each pattern view in the
|
|
coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each
|
|
i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter
|
|
description) brings the calibration pattern from the object coordinate space (in which object points are
|
|
specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms,
|
|
the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space
|
|
to camera coordinate space of the first camera of the stereo pair.
|
|
@param tvecs Output vector of translation vectors estimated for each pattern view, see parameter description
|
|
of previous output parameter ( rvecs ).
|
|
@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:
|
|
- @ref CALIB_FIX_INTRINSIC Fix cameraMatrix? and distCoeffs? so that only R, T, E, and F
|
|
matrices are estimated.
|
|
- @ref CALIB_USE_INTRINSIC_GUESS Optimize some or all of the intrinsic parameters
|
|
according to the specified flags. Initial values are provided by the user.
|
|
- @ref CALIB_USE_EXTRINSIC_GUESS R and T contain valid initial values that are optimized further.
|
|
Otherwise R and T are initialized to the median value of the pattern views (each dimension separately).
|
|
- @ref CALIB_FIX_PRINCIPAL_POINT Fix the principal points during the optimization.
|
|
- @ref CALIB_FIX_FOCAL_LENGTH Fix \f$f^{(j)}_x\f$ and \f$f^{(j)}_y\f$ .
|
|
- @ref CALIB_FIX_ASPECT_RATIO Optimize \f$f^{(j)}_y\f$ . Fix the ratio \f$f^{(j)}_x/f^{(j)}_y\f$
|
|
.
|
|
- @ref CALIB_SAME_FOCAL_LENGTH Enforce \f$f^{(0)}_x=f^{(1)}_x\f$ and \f$f^{(0)}_y=f^{(1)}_y\f$ .
|
|
- @ref CALIB_ZERO_TANGENT_DIST Set tangential distortion coefficients for each camera to
|
|
zeros and fix there.
|
|
- @ref CALIB_FIX_K1,..., @ref CALIB_FIX_K6 Do not change the corresponding radial
|
|
distortion coefficient during the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set,
|
|
the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
|
|
- @ref 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.
|
|
- @ref 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.
|
|
- @ref CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during
|
|
the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
|
|
supplied distCoeffs matrix is used. Otherwise, it is set to 0.
|
|
- @ref 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.
|
|
- @ref CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during
|
|
the optimization. If @ref 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 the transformation between two cameras making a stereo pair. If one computes
|
|
the poses of an object relative to the first camera and to the second camera,
|
|
( \f$R_1\f$,\f$T_1\f$ ) and (\f$R_2\f$,\f$T_2\f$), respectively, for a stereo camera where the
|
|
relative position and orientation between the two cameras are fixed, then those poses definitely
|
|
relate to each other. This means, if the relative position and orientation (\f$R\f$,\f$T\f$) of the
|
|
two cameras is known, it is possible to compute (\f$R_2\f$,\f$T_2\f$) when (\f$R_1\f$,\f$T_1\f$) is
|
|
given. This is what the described function does. It computes (\f$R\f$,\f$T\f$) such that:
|
|
|
|
\f[R_2=R R_1\f]
|
|
\f[T_2=R T_1 + T.\f]
|
|
|
|
Therefore, one can compute the coordinate representation of a 3D point for the second camera's
|
|
coordinate system when given the point's coordinate representation in the first camera's coordinate
|
|
system:
|
|
|
|
\f[\begin{bmatrix}
|
|
X_2 \\
|
|
Y_2 \\
|
|
Z_2 \\
|
|
1
|
|
\end{bmatrix} = \begin{bmatrix}
|
|
R & T \\
|
|
0 & 1
|
|
\end{bmatrix} \begin{bmatrix}
|
|
X_1 \\
|
|
Y_1 \\
|
|
Z_1 \\
|
|
1
|
|
\end{bmatrix}.\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}\cdot E \cdot cameraMatrix1^{-1}\f]
|
|
|
|
Besides the stereo-related information, the function can also perform a full calibration of each of
|
|
the 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 @ref 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
|
|
@ref CALIB_SAME_FOCAL_LENGTH and @ref 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_AS(stereoCalibrateExtended) double stereoCalibrate( InputArrayOfArrays objectPoints,
|
|
InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2,
|
|
InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1,
|
|
InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2,
|
|
Size imageSize, InputOutputArray R, InputOutputArray T, OutputArray E, OutputArray F,
|
|
OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
|
|
OutputArray perViewErrors, int flags = CALIB_FIX_INTRINSIC,
|
|
TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, 1e-6) );
|
|
|
|
/// @overload
|
|
CV_EXPORTS_W double stereoCalibrate( InputArrayOfArrays objectPoints,
|
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InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2,
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InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1,
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InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2,
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Size imageSize, OutputArray R,OutputArray T, OutputArray E, OutputArray F,
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int flags = CALIB_FIX_INTRINSIC,
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TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, 1e-6) );
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/// @overload
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CV_EXPORTS_W double stereoCalibrate( InputArrayOfArrays objectPoints,
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InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2,
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InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1,
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InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2,
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Size imageSize, InputOutputArray R, InputOutputArray T, OutputArray E, OutputArray F,
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OutputArray perViewErrors, int flags = CALIB_FIX_INTRINSIC,
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TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6) );
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/** @brief Calibrates a camera pair set up. This function finds the extrinsic parameters between the two cameras.
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@param objectPoints1 Vector of vectors of the calibration pattern points for camera 1.
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A similar structure as objectPoints in @ref calibrateCamera and for each pattern view,
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both cameras do not need to see the same object points. objectPoints1.size(), imagePoints1.size()
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nees to be equal,as well as objectPoints1[i].size(), imagePoints1[i].size() need to be equal for each i.
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@param objectPoints2 Vector of vectors of the calibration pattern points for camera 2.
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A similar structure as objectPoints1. objectPoints2.size(), and imagePoints2.size() nees to be equal,
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as well as objectPoints2[i].size(), imagePoints2[i].size() need to be equal for each i.
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However, objectPoints1[i].size() and objectPoints2[i].size() are not required to be equal.
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@param imagePoints1 Vector of vectors of the projections of the calibration pattern points,
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observed by the first camera. The same structure as in @ref calibrateCamera.
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@param imagePoints2 Vector of vectors of the projections of the calibration pattern points,
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observed by the second camera. The same structure as in @ref calibrateCamera.
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@param cameraMatrix1 Input/output camera intrinsic matrix for the first camera, the same as in
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@ref calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
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@param distCoeffs1 Input/output vector of distortion coefficients, the same as in
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@ref calibrateCamera.
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@param cameraModel1 Flag reflecting the type of model for camera 1 (pinhole / fisheye):
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- @ref CALIB_MODEL_PINHOLE pinhole camera model
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- @ref CALIB_MODEL_FISHEYE fisheye camera model
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@param cameraMatrix2 Input/output second camera intrinsic matrix for the second camera.
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See description for cameraMatrix1.
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@param distCoeffs2 Input/output lens distortion coefficients for the second camera. See
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description for distCoeffs1.
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@param cameraModel2 Flag reflecting the type of model for camera 2 (pinhole / fisheye).
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See description for cameraModel1.
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@param R Output rotation matrix. Together with the translation vector T, this matrix brings
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points given in the first camera's coordinate system to points in the second camera's
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coordinate system. In more technical terms, the tuple of R and T performs a change of basis
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from the first camera's coordinate system to the second camera's coordinate system. Due to its
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duality, this tuple is equivalent to the position of the first camera with respect to the
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second camera coordinate system.
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@param T Output translation vector, see description above.
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@param E Output essential matrix.
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@param F Output fundamental matrix.
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@param rvecs Output vector of rotation vectors ( @ref Rodrigues ) estimated for each pattern view in the
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coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each
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i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter
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description) brings the calibration pattern from the object coordinate space (in which object points are
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specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms,
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the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space
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to the camera coordinate space of the first camera of the stereo pair.
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@param tvecs Output vector of translation vectors estimated for each pattern view, see parameter description
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of previous output parameter ( rvecs ).
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@param perViewErrors Output vector of the RMS re-projection error estimated for each pattern view.
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@param flags Different flags that may be zero or a combination of the following values:
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- @ref CALIB_USE_EXTRINSIC_GUESS R and T contain valid initial values that are optimized further.
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@param criteria Termination criteria for the iterative optimization algorithm.
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The function estimates the transformation between two cameras similar to stereo pair calibration.
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The principle follows closely to @ref stereoCalibrate. To understand the problem of estimating the
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relative pose between a camera pair, please refer to the description there. The difference for
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this function is that, camera intrinsics are not optimized and two cameras are not required
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to have overlapping fields of view as long as they are observing the same calibration target
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and the absolute positions of each object point are known.
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![](pics/register_pair.png)
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The above illustration shows an example where such a case may become relevant.
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Additionally, it supports a camera pair with the mixed model (pinhole / fisheye).
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Similarly to #calibrateCamera, the function minimizes the total re-projection error for all the
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points in all the available views from both cameras.
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@return the final value of the re-projection error.
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@sa calibrateCamera, stereoCalibrate
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*/
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CV_EXPORTS_AS(registerCamerasExtended) double registerCameras( InputArrayOfArrays objectPoints1,
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InputArrayOfArrays objectPoints2,
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InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2,
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InputArray cameraMatrix1, InputArray distCoeffs1,
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CameraModel cameraModel1,
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InputArray cameraMatrix2, InputArray distCoeffs2,
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CameraModel cameraModel2,
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InputOutputArray R, InputOutputArray T, OutputArray E, OutputArray F,
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OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
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OutputArray perViewErrors,
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int flags = 0,
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TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, 1e-6) );
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/// @overload
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CV_EXPORTS_W double registerCameras( InputArrayOfArrays objectPoints1,
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InputArrayOfArrays objectPoints2,
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InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2,
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InputArray cameraMatrix1, InputArray distCoeffs1,
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CameraModel cameraModel1,
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InputArray cameraMatrix2, InputArray distCoeffs2,
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CameraModel cameraModel2,
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InputOutputArray R, InputOutputArray T, OutputArray E, OutputArray F,
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OutputArray perViewErrors,
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int flags = 0,
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TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, 1e-6) );
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/** @brief Estimates intrinsics and extrinsics (camera pose) for multi-camera system a.k.a multiview calibraton.
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@param[in] objPoints Calibration pattern object points. Expected shape: NUM_FRAMES x NUM_POINTS x 3. Supported data type: CV_32F.
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@param[in] imagePoints Detected pattern points on camera images. Expected shape: NUM_CAMERAS x NUM_FRAMES x NUM_POINTS x 2.
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@param[in] imageSize Images resolution.
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@param[in] detectionMask Pattern detection mask. Each value defines if i-camera observes calibration pattern in j moment of time.
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Expected size: NUM_CAMERAS x NUM_FRAMES. Expected type: CV_8U.
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@param[in] isFisheye indicates whether i-th camera is fisheye. In case if the input data contains
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mix of pinhole and fisheye cameras Rational distortion model is used. See @ref CALIB_RATIONAL_MODEL
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for details. Expected type: CV_8U.
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@param[in] useIntrinsicsGuess Use user specified intrinsic parameters (internal camera matrix and distortion).
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If true intrinsics are not estimated during calibration.
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@param[in] flagsForIntrinsics Flags used for each camera intrinsics calibration.
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Use per-camera call and `useIntrinsicsGuess` flag to get custom intrinsics calibration for each camera.
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See @ref CALIB_USE_INTRINSIC_GUESS and other `CALIB_` constants. Expected shape: NUM_CAMERAS x 1. Supported data type: CV_32S.
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@param[out] Rs Rotation vectors relative to camera 0, where Rs[0] = 0. Output size: NUM_CAMERAS x 3 x 1. See @ref Rodrigues.
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@param[out] Ts Estimated translation vectors relative to camera 0, where Ts[0] = 0. Output size: NUM_CAMERAS x 3 x 1.
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@param[out] rvecs0 Estimated rotation vectors for camera 0. Output size: NUM_FRAMES x 3 x 1 (may contain null Mat, if frame is not valid). See @ref Rodrigues.
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@param[out] tvecs0 Translation vectors for camera 0. Output size: NUM_FRAMES x 3 x 1. (may contain null Mat, if frame is not valid).
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@param[out] Ks Estimated floating-point camera intrinsic matrix. Output size: NUM_CAMERAS x 3 x 3.
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@param[out] distortions Distortion coefficients. Output size: NUM_CAMERAS x NUM_PARAMS.
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@param[out] perFrameErrors RMSE value for each visible frame, (-1 for non-visible). Output size: NUM_CAMERAS x NUM_FRAMES.
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@param[out] initializationPairs Pairs with camera indices that were used for initial pairwise stereo calibration.
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Output size: (NUM_CAMERAS-1) x 2.
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Similarly to #calibrateCamera, the function minimizes the total re-projection error for all the
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points in all the available views from all cameras.
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@return Overall RMS re-projection error over detectionMask.
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@sa findChessboardCorners, findCirclesGrid, calibrateCamera, fisheye::calibrate
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*/
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CV_EXPORTS_W double calibrateMultiview (InputArrayOfArrays objPoints, const std::vector<std::vector<Mat>> &imagePoints,
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const std::vector<Size> &imageSize, InputArray detectionMask,
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OutputArrayOfArrays Rs, OutputArrayOfArrays Ts, CV_IN_OUT std::vector<Mat> &Ks, CV_IN_OUT std::vector<Mat> &distortions,
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OutputArrayOfArrays rvecs0, OutputArrayOfArrays tvecs0, InputArray isFisheye,
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OutputArray perFrameErrors, OutputArray initializationPairs,
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bool useIntrinsicsGuess=false, InputArray flagsForIntrinsics=noArray());
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/** @brief Computes Hand-Eye calibration: \f$_{}^{g}\textrm{T}_c\f$
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@param[in] R_gripper2base Rotation part extracted from the homogeneous matrix that transforms a point
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expressed in the gripper frame to the robot base frame (\f$_{}^{b}\textrm{T}_g\f$).
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This is a vector (`vector<Mat>`) that contains the rotation, `(3x3)` rotation matrices or `(3x1)` rotation vectors,
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for all the transformations from gripper frame to robot base frame.
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@param[in] t_gripper2base Translation part extracted from the homogeneous matrix that transforms a point
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expressed in the gripper frame to the robot base frame (\f$_{}^{b}\textrm{T}_g\f$).
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This is a vector (`vector<Mat>`) that contains the `(3x1)` translation vectors for all the transformations
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from gripper frame to robot base frame.
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@param[in] R_target2cam Rotation part extracted from the homogeneous matrix that transforms a point
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expressed in the target frame to the camera frame (\f$_{}^{c}\textrm{T}_t\f$).
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This is a vector (`vector<Mat>`) that contains the rotation, `(3x3)` rotation matrices or `(3x1)` rotation vectors,
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for all the transformations from calibration target frame to camera frame.
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@param[in] t_target2cam Rotation part extracted from the homogeneous matrix that transforms a point
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expressed in the target frame to the camera frame (\f$_{}^{c}\textrm{T}_t\f$).
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This is a vector (`vector<Mat>`) that contains the `(3x1)` translation vectors for all the transformations
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from calibration target frame to camera frame.
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@param[out] R_cam2gripper Estimated `(3x3)` rotation part extracted from the homogeneous matrix that transforms a point
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expressed in the camera frame to the gripper frame (\f$_{}^{g}\textrm{T}_c\f$).
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@param[out] t_cam2gripper Estimated `(3x1)` translation part extracted from the homogeneous matrix that transforms a point
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expressed in the camera frame to the gripper frame (\f$_{}^{g}\textrm{T}_c\f$).
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@param[in] method One of the implemented Hand-Eye calibration method, see cv::HandEyeCalibrationMethod
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The function performs the Hand-Eye calibration using various methods. One approach consists in estimating the
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rotation then the translation (separable solutions) and the following methods are implemented:
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- R. Tsai, R. Lenz A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/EyeCalibration \cite Tsai89
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- F. Park, B. Martin Robot Sensor Calibration: Solving AX = XB on the Euclidean Group \cite Park94
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- R. Horaud, F. Dornaika Hand-Eye Calibration \cite Horaud95
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Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions),
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with the following implemented methods:
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- N. Andreff, R. Horaud, B. Espiau On-line Hand-Eye Calibration \cite Andreff99
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- K. Daniilidis Hand-Eye Calibration Using Dual Quaternions \cite Daniilidis98
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The following picture describes the Hand-Eye calibration problem where the transformation between a camera ("eye")
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mounted on a robot gripper ("hand") has to be estimated. This configuration is called eye-in-hand.
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The eye-to-hand configuration consists in a static camera observing a calibration pattern mounted on the robot
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end-effector. The transformation from the camera to the robot base frame can then be estimated by inputting
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the suitable transformations to the function, see below.
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![](pics/hand-eye_figure.png)
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The calibration procedure is the following:
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- a static calibration pattern is used to estimate the transformation between the target frame
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and the camera frame
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- the robot gripper is moved in order to acquire several poses
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- for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for
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instance the robot kinematics
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\f[
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\begin{bmatrix}
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X_b\\
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Y_b\\
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Z_b\\
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1
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\end{bmatrix}
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=
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\begin{bmatrix}
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_{}^{b}\textrm{R}_g & _{}^{b}\textrm{t}_g \\
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0_{1 \times 3} & 1
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\end{bmatrix}
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\begin{bmatrix}
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X_g\\
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Y_g\\
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Z_g\\
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1
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\end{bmatrix}
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\f]
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- for each pose, the homogeneous transformation between the calibration target frame and the camera frame is recorded using
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for instance a pose estimation method (PnP) from 2D-3D point correspondences
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\f[
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\begin{bmatrix}
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X_c\\
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Y_c\\
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Z_c\\
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1
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\end{bmatrix}
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=
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\begin{bmatrix}
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_{}^{c}\textrm{R}_t & _{}^{c}\textrm{t}_t \\
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0_{1 \times 3} & 1
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\end{bmatrix}
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\begin{bmatrix}
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X_t\\
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Y_t\\
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Z_t\\
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1
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\end{bmatrix}
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\f]
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The Hand-Eye calibration procedure returns the following homogeneous transformation
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\f[
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\begin{bmatrix}
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X_g\\
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Y_g\\
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Z_g\\
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1
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\end{bmatrix}
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=
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\begin{bmatrix}
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_{}^{g}\textrm{R}_c & _{}^{g}\textrm{t}_c \\
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0_{1 \times 3} & 1
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\end{bmatrix}
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\begin{bmatrix}
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X_c\\
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Y_c\\
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Z_c\\
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1
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\end{bmatrix}
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\f]
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This problem is also known as solving the \f$\mathbf{A}\mathbf{X}=\mathbf{X}\mathbf{B}\f$ equation:
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- for an eye-in-hand configuration
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\f[
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\begin{align*}
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^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &=
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\hspace{0.1em} ^{b}{\textrm{T}_g}^{(2)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\
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(^{b}{\textrm{T}_g}^{(2)})^{-1} \hspace{0.2em} ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c &=
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\hspace{0.1em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{-1} \\
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\textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\
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\end{align*}
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\f]
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- for an eye-to-hand configuration
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\f[
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\begin{align*}
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^{g}{\textrm{T}_b}^{(1)} \hspace{0.2em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &=
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\hspace{0.1em} ^{g}{\textrm{T}_b}^{(2)} \hspace{0.2em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\
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(^{g}{\textrm{T}_b}^{(2)})^{-1} \hspace{0.2em} ^{g}{\textrm{T}_b}^{(1)} \hspace{0.2em} ^{b}\textrm{T}_c &=
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\hspace{0.1em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{-1} \\
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\textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\
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\end{align*}
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\f]
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\note
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Additional information can be found on this [website](http://campar.in.tum.de/Chair/HandEyeCalibration).
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\note
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A minimum of 2 motions with non parallel rotation axes are necessary to determine the hand-eye transformation.
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So at least 3 different poses are required, but it is strongly recommended to use many more poses.
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*/
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CV_EXPORTS void calibrateHandEye( InputArrayOfArrays R_gripper2base, InputArrayOfArrays t_gripper2base,
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InputArrayOfArrays R_target2cam, InputArrayOfArrays t_target2cam,
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OutputArray R_cam2gripper, OutputArray t_cam2gripper,
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HandEyeCalibrationMethod method=CALIB_HAND_EYE_TSAI );
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/** @brief Computes Robot-World/Hand-Eye calibration: \f$_{}^{w}\textrm{T}_b\f$ and \f$_{}^{c}\textrm{T}_g\f$
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@param[in] R_world2cam Rotation part extracted from the homogeneous matrix that transforms a point
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expressed in the world frame to the camera frame (\f$_{}^{c}\textrm{T}_w\f$).
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This is a vector (`vector<Mat>`) that contains the rotation, `(3x3)` rotation matrices or `(3x1)` rotation vectors,
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for all the transformations from world frame to the camera frame.
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@param[in] t_world2cam Translation part extracted from the homogeneous matrix that transforms a point
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expressed in the world frame to the camera frame (\f$_{}^{c}\textrm{T}_w\f$).
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This is a vector (`vector<Mat>`) that contains the `(3x1)` translation vectors for all the transformations
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from world frame to the camera frame.
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@param[in] R_base2gripper Rotation part extracted from the homogeneous matrix that transforms a point
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expressed in the robot base frame to the gripper frame (\f$_{}^{g}\textrm{T}_b\f$).
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This is a vector (`vector<Mat>`) that contains the rotation, `(3x3)` rotation matrices or `(3x1)` rotation vectors,
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for all the transformations from robot base frame to the gripper frame.
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|
@param[in] t_base2gripper Rotation part extracted from the homogeneous matrix that transforms a point
|
|
expressed in the robot base frame to the gripper frame (\f$_{}^{g}\textrm{T}_b\f$).
|
|
This is a vector (`vector<Mat>`) that contains the `(3x1)` translation vectors for all the transformations
|
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from robot base frame to the gripper frame.
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@param[out] R_base2world Estimated `(3x3)` rotation part extracted from the homogeneous matrix that transforms a point
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expressed in the robot base frame to the world frame (\f$_{}^{w}\textrm{T}_b\f$).
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@param[out] t_base2world Estimated `(3x1)` translation part extracted from the homogeneous matrix that transforms a point
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expressed in the robot base frame to the world frame (\f$_{}^{w}\textrm{T}_b\f$).
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@param[out] R_gripper2cam Estimated `(3x3)` rotation part extracted from the homogeneous matrix that transforms a point
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expressed in the gripper frame to the camera frame (\f$_{}^{c}\textrm{T}_g\f$).
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@param[out] t_gripper2cam Estimated `(3x1)` translation part extracted from the homogeneous matrix that transforms a point
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expressed in the gripper frame to the camera frame (\f$_{}^{c}\textrm{T}_g\f$).
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@param[in] method One of the implemented Robot-World/Hand-Eye calibration method, see cv::RobotWorldHandEyeCalibrationMethod
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|
|
|
The function performs the Robot-World/Hand-Eye calibration using various methods. One approach consists in estimating the
|
|
rotation then the translation (separable solutions):
|
|
- M. Shah, Solving the robot-world/hand-eye calibration problem using the kronecker product \cite Shah2013SolvingTR
|
|
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|
Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions),
|
|
with the following implemented method:
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|
- A. Li, L. Wang, and D. Wu, Simultaneous robot-world and hand-eye calibration using dual-quaternions and kronecker product \cite Li2010SimultaneousRA
|
|
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|
The following picture describes the Robot-World/Hand-Eye calibration problem where the transformations between a robot and a world frame
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and between a robot gripper ("hand") and a camera ("eye") mounted at the robot end-effector have to be estimated.
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|
![](pics/robot-world_hand-eye_figure.png)
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|
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|
The calibration procedure is the following:
|
|
- a static calibration pattern is used to estimate the transformation between the target frame
|
|
and the camera frame
|
|
- the robot gripper is moved in order to acquire several poses
|
|
- for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for
|
|
instance the robot kinematics
|
|
\f[
|
|
\begin{bmatrix}
|
|
X_g\\
|
|
Y_g\\
|
|
Z_g\\
|
|
1
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|
\end{bmatrix}
|
|
=
|
|
\begin{bmatrix}
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|
_{}^{g}\textrm{R}_b & _{}^{g}\textrm{t}_b \\
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|
0_{1 \times 3} & 1
|
|
\end{bmatrix}
|
|
\begin{bmatrix}
|
|
X_b\\
|
|
Y_b\\
|
|
Z_b\\
|
|
1
|
|
\end{bmatrix}
|
|
\f]
|
|
- for each pose, the homogeneous transformation between the calibration target frame (the world frame) and the camera frame is recorded using
|
|
for instance a pose estimation method (PnP) from 2D-3D point correspondences
|
|
\f[
|
|
\begin{bmatrix}
|
|
X_c\\
|
|
Y_c\\
|
|
Z_c\\
|
|
1
|
|
\end{bmatrix}
|
|
=
|
|
\begin{bmatrix}
|
|
_{}^{c}\textrm{R}_w & _{}^{c}\textrm{t}_w \\
|
|
0_{1 \times 3} & 1
|
|
\end{bmatrix}
|
|
\begin{bmatrix}
|
|
X_w\\
|
|
Y_w\\
|
|
Z_w\\
|
|
1
|
|
\end{bmatrix}
|
|
\f]
|
|
|
|
The Robot-World/Hand-Eye calibration procedure returns the following homogeneous transformations
|
|
\f[
|
|
\begin{bmatrix}
|
|
X_w\\
|
|
Y_w\\
|
|
Z_w\\
|
|
1
|
|
\end{bmatrix}
|
|
=
|
|
\begin{bmatrix}
|
|
_{}^{w}\textrm{R}_b & _{}^{w}\textrm{t}_b \\
|
|
0_{1 \times 3} & 1
|
|
\end{bmatrix}
|
|
\begin{bmatrix}
|
|
X_b\\
|
|
Y_b\\
|
|
Z_b\\
|
|
1
|
|
\end{bmatrix}
|
|
\f]
|
|
\f[
|
|
\begin{bmatrix}
|
|
X_c\\
|
|
Y_c\\
|
|
Z_c\\
|
|
1
|
|
\end{bmatrix}
|
|
=
|
|
\begin{bmatrix}
|
|
_{}^{c}\textrm{R}_g & _{}^{c}\textrm{t}_g \\
|
|
0_{1 \times 3} & 1
|
|
\end{bmatrix}
|
|
\begin{bmatrix}
|
|
X_g\\
|
|
Y_g\\
|
|
Z_g\\
|
|
1
|
|
\end{bmatrix}
|
|
\f]
|
|
|
|
This problem is also known as solving the \f$\mathbf{A}\mathbf{X}=\mathbf{Z}\mathbf{B}\f$ equation, with:
|
|
- \f$\mathbf{A} \Leftrightarrow \hspace{0.1em} _{}^{c}\textrm{T}_w\f$
|
|
- \f$\mathbf{X} \Leftrightarrow \hspace{0.1em} _{}^{w}\textrm{T}_b\f$
|
|
- \f$\mathbf{Z} \Leftrightarrow \hspace{0.1em} _{}^{c}\textrm{T}_g\f$
|
|
- \f$\mathbf{B} \Leftrightarrow \hspace{0.1em} _{}^{g}\textrm{T}_b\f$
|
|
|
|
\note
|
|
At least 3 measurements are required (input vectors size must be greater or equal to 3).
|
|
|
|
*/
|
|
CV_EXPORTS void calibrateRobotWorldHandEye( InputArrayOfArrays R_world2cam, InputArrayOfArrays t_world2cam,
|
|
InputArrayOfArrays R_base2gripper, InputArrayOfArrays t_base2gripper,
|
|
OutputArray R_base2world, OutputArray t_base2world,
|
|
OutputArray R_gripper2cam, OutputArray t_gripper2cam,
|
|
RobotWorldHandEyeCalibrationMethod method=CALIB_ROBOT_WORLD_HAND_EYE_SHAH );
|
|
|
|
/** @brief The methods in this namespace use a so-called fisheye camera model.
|
|
@ingroup calib3d_fisheye
|
|
*/
|
|
namespace fisheye
|
|
{
|
|
//! @addtogroup calib3d_fisheye
|
|
//! @{
|
|
|
|
// For backward compatibility only!
|
|
// Use unified Pinhole and Fisheye model calibration flags
|
|
using cv::CALIB_USE_INTRINSIC_GUESS;
|
|
using cv::CALIB_RECOMPUTE_EXTRINSIC;
|
|
using cv::CALIB_CHECK_COND;
|
|
using cv::CALIB_FIX_SKEW;
|
|
using cv::CALIB_FIX_K1;
|
|
using cv::CALIB_FIX_K2;
|
|
using cv::CALIB_FIX_K3;
|
|
using cv::CALIB_FIX_K4;
|
|
using cv::CALIB_FIX_INTRINSIC;
|
|
using cv::CALIB_FIX_PRINCIPAL_POINT;
|
|
using cv::CALIB_ZERO_DISPARITY;
|
|
using cv::CALIB_FIX_FOCAL_LENGTH;
|
|
|
|
/** @brief Performs camera calibration
|
|
|
|
@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 camera intrinsic matrix.
|
|
@param K Output 3x3 floating-point camera intrinsic matrix
|
|
\f$\cameramatrix{A}\f$ . If
|
|
@ref cv::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$\distcoeffsfisheye\f$.
|
|
@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:
|
|
- @ref 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.
|
|
- @ref cv::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration
|
|
of intrinsic optimization.
|
|
- @ref cv::CALIB_CHECK_COND The functions will check validity of condition number.
|
|
- @ref cv::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero.
|
|
- @ref cv::CALIB_FIX_K1,..., @ref cv::CALIB_FIX_K4 Selected distortion coefficients
|
|
are set to zeros and stay zero.
|
|
- @ref 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 @ref cv::CALIB_USE_INTRINSIC_GUESS is set too.
|
|
- @ref cv::CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global
|
|
optimization. It is the \f$max(width,height)/\pi\f$ or the provided \f$f_x\f$, \f$f_y\f$ when @ref cv::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 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 intrinsic 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 @ref cv::CALIB_USE_INTRINSIC_GUESS , @ref 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$\distcoeffsfisheye\f$ of 4 elements.
|
|
@param K2 Input/output second camera intrinsic 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 camera intrinsic 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 rvecs Output vector of rotation vectors ( @ref Rodrigues ) estimated for each pattern view in the
|
|
coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each
|
|
i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter
|
|
description) brings the calibration pattern from the object coordinate space (in which object points are
|
|
specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms,
|
|
the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space
|
|
to camera coordinate space of the first camera of the stereo pair.
|
|
@param tvecs Output vector of translation vectors estimated for each pattern view, see parameter description
|
|
of previous output parameter ( rvecs ).
|
|
@param flags Different flags that may be zero or a combination of the following values:
|
|
- @ref cv::CALIB_FIX_INTRINSIC Fix K1, K2? and D1, D2? so that only R, T matrices
|
|
are estimated.
|
|
- @ref cv::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.
|
|
- @ref cv::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration
|
|
of intrinsic optimization.
|
|
- @ref cv::CALIB_CHECK_COND The functions will check validity of condition number.
|
|
- @ref cv::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero.
|
|
- @ref cv::CALIB_FIX_K1,..., @ref cv::CALIB_FIX_K4 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, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags = CALIB_FIX_INTRINSIC,
|
|
TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON));
|
|
|
|
/// @overload
|
|
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 = CALIB_FIX_INTRINSIC,
|
|
TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON));
|
|
|
|
//! @} calib3d_fisheye
|
|
} //end namespace fisheye
|
|
} //end namespace cv
|
|
|
|
#endif
|