grid view of input circles; it must be an 8-bit grayscale or color image. If the parameter is greater than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points for which \(|\texttt{points2[i]}^T \cdot \texttt{F} \cdot \texttt{points1[i]}|>\texttt{threshold}\) ) are rejected prior to computing the homographies. Estimates new camera intrinsic matrix for undistortion or rectification. Computes the undistortion and rectification transformation map. least-median algorithm. To find the distortion parameter, we need to use some sample distorted images, and we need to use some specific points present in these images and their position. 3D points which were reconstructed by triangulation. Check this link for more details about the cv2.findChessboardCorners() function. The function getRectSubPix extracts pixels from src: \[patch(x, y) = src(x + \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y + \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\]. The cheirality check means that the triangulated 3D points should have positive depth. OpenCV comes with two methods for doing this. The function actually builds the maps for the inverse mapping algorithm that is used by remap. Parameter used only for RANSAC. for the change of basis from coordinate system 0 to coordinate system 1 becomes: \[P_1 = R P_0 + t \rightarrow P_{h_1} = \begin{bmatrix} R & t \\ 0 & 1 \end{bmatrix} P_{h_0}.\], use QR instead of SVD decomposition for solving. Uses the selected algorithm for robust estimation. flag, fills all of the destination image pixels. The function transforms an image to compensate radial and tangential lens distortion. It computes ( \(R\), \(T\)) such that: 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: \[\begin{bmatrix} X_2 \\ Y_2 \\ Z_2 \\ 1 \end{bmatrix} = \begin{bmatrix} R & T \\ 0 & 1 \end{bmatrix} \begin{bmatrix} X_1 \\ Y_1 \\ Z_1 \\ 1 \end{bmatrix}.\]. Camera matrix \(\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . Optional output mask set by a robust method ( RANSAC or LMeDS ). In the old interface all the per-view vectors are concatenated. Otherwise, if all the parameters are estimated at once, it makes sense to restrict some parameters, for example, pass CALIB_SAME_FOCAL_LENGTH and CALIB_ZERO_TANGENT_DIST flags, which is usually a reasonable assumption. vector. when the flag WARP_INVERSE_MAP is set. The cv2.getOptimalNewCameraMatrix () function will also return the region of interest, which can be used to crop the image. See Rodrigues for details. This function draws the axes of the world/object coordinate system w.r.t. Vector of vectors of the projections of the calibration pattern points, observed by the first camera. OpenCV-Python is a library of Python bindings designed to solve computer vision problems. R can be computed from H as, initializes maps for remap for wide-angle. The methods RANSAC, LMeDS and RHO try many different random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the computed homography (which is the number of inliers for RANSAC or the least median re-projection error for LMeDS). The camera matrix and the distortion parameters can be determined using calibrateCamera. Starting a PhD Program This Fall but Missing a Single Course from My B.S. Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R, T[, R1[, R2[, P1[, P2[, Q[, flags[, alpha[, newImageSize]]]]]]]], R1, R2, P1, P2, Q, validPixROI1, validPixROI2. The distortion parameters are the radial coefficients \(k_1\), \(k_2\), \(k_3\), \(k_4\), \(k_5\), and \(k_6\) , \(p_1\) and \(p_2\) are the tangential distortion coefficients, and \(s_1\), \(s_2\), \(s_3\), and \(s_4\), are the thin prism distortion coefficients. For example, linearPolar or logPolar transforms: Remaps an image to/from semilog-polar space. In case of a stereo-rectified projector-camera pair, this function is called for the projector while initUndistortRectifyMap is called for the camera head. Estimate the relative position and orientation of the stereo camera "heads" and compute the rectification* transformation that makes the camera optical axes parallel. Vector of vectors of the projections of the calibration pattern points, observed by the second camera. This is done using, 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. The representation is used in the global 3D geometry optimization procedures like calibrateCamera, stereoCalibrate, or solvePnP . If the vector is empty, the zero distortion coefficients are assumed. Due to its duality, this tuple is equivalent to the position of the first camera with respect to the second camera coordinate system. cameraMatrix, distCoeffs, R, newCameraMatrix, size, m1type[, map1[, map2]]. 7-point algorithm is used. If. The function attempts to determine whether the input image is a view of the chessboard pattern and locate the internal chessboard corners. Output 3D affine transformation matrix \(3 \times 4\) of the form. returns 3x3 perspective transformation for the corresponding 4 point pairs. The optional output array depth. Combines two rotation-and-shift transformations. The map represents the pixel X,Y location in the source image for every pixel in the destination image. Due to radial distortion, straight lines will appear curved. Note that the order should be k1, k2, p1, p2, k3. See description for cameraMatrix1. Array of the second image points of the same size and format as points1 . [159]. Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. OpenCV Python Documentation, Release 0.1 26 27 cap.release() 28 cv2.destroyAllWindows() 2.3File File Camera . But in case of the 7-point algorithm, the function may return up to 3 solutions ( \(9 \times 3\) matrix that stores all 3 matrices sequentially). for a 3D homogeneous vector one gets its 2D cartesian counterpart by: \[\begin{bmatrix} X \\ Y \\ W \end{bmatrix} \rightarrow \begin{bmatrix} X / W \\ Y / W \end{bmatrix},\]. This function can be used to process the output E and mask from findEssentialMat. Enumeration Type Documentation anonymous enum. Center of the rotation in the source image. Not the answer you're looking for? Complete Solution Classification for the Perspective-Three-Point Problem [88]. 1xN array containing the second set of points. Hello! P1 and P2 look like: \[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\], \[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\], \[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]. \[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} \cdot \texttt{srcPoints} _i) \|_2 > \texttt{ransacReprojThreshold}\]. The functions are used inside stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication. points1, points2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2[, E[, R[, t[, method[, prob[, threshold[, mask]]]]]]], E, points1, points2, cameraMatrix[, R[, t[, mask]]], E, points1, points2[, R[, t[, focal[, pp[, mask]]]]], E, points1, points2, cameraMatrix, distanceThresh[, R[, t[, mask[, triangulatedPoints]]]]. Coordinates of the points in the original plane, a matrix of the type CV_32FC2 or vector . And the function can also compute the fundamental matrix F: \[F = cameraMatrix2^{-T}\cdot E \cdot cameraMatrix1^{-1}\]. See. Array of feature points in the first image. undistort_left_image = cv2.undistort(frame_left, cameraMatrixLeft, distCoeffsLeft, None, new_cameraMatrixLeft) undistort_right_image = cv2.undistort(frame_right, cameraMatrixRight, distCoeffsRight, None, new_cameraMatrixRight) then,I use opencv find chessboard corner and cornerSubPix function to find the two camera's undistort image points. Output vector indicating which points are inliers (1-inlier, 0-outlier). objectPoints, imagePoints, cameraMatrix, distCoeffs[, rvec[, tvec[, useExtrinsicGuess[, flags]]]]. Otherwise, the transformation is first inverted with invert and then put in the formula above instead of M. The function cannot operate in-place. Computes an optimal affine transformation between two 3D point sets. Am I betraying my professors if I leave a research group because of change of interest? Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera. The return value of, image, cameraMatrix, distCoeffs, rvec, tvec, length[, thickness]. Type of the first output map that can be CV_32FC1, CV_32FC2 or CV_16SC2, see, cameraMatrix, distCoeffs, imageSize, destImageWidth, m1type[, map1[, map2[, projType[, alpha]]]], src, map1, map2, interpolation[, dst[, borderMode[, borderValue]]]. I'm looking to undistort an image using the distortion coefficients that I've computed for my camera, without changing the camera matrix. The cv2.getOptimalNewCameraMatrix() function will also return the region of interest, which can be used to crop the image. An intuitive understanding of this property is that under a projective transformation, all multiples of \(P_h\) are mapped to the same point. Balance is in range of [0, 1]. It needs at least 15 points. I then undistorted the image with initUndistortRectifyMap () and remap () (R as itendity matrix). Similarly to the filtering functions described in the previous section, for some \((x,y)\), either one of \(f_x(x,y)\), or \(f_y(x,y)\), or both of them may fall outside of the image. Python: cv.fisheye.CALIB_USE_INTRINSIC_GUESS, Python: cv.fisheye.CALIB_RECOMPUTE_EXTRINSIC, Python: cv.fisheye.CALIB_FIX_PRINCIPAL_POINT, cv::fisheye::estimateNewCameraMatrixForUndistortRectify. In the output mask only inliers which pass the cheirality check. Finds an object pose from 3 3D-2D point correspondences. Output rotation vector of the superposition. What do multiple contact ratings on a relay represent? Are the NEMA 10-30 to 14-30 adapters with the extra ground wire valid/legal to use and still adhere to code? A 3D real-world point contains three values x, y, and z. The function remap transforms the source image using the specified map: \[\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\]. In this scenario, points1 and points2 are the same input for findEssentialMat : This function differs from the one above that it outputs the triangulated 3D point that are used for the chirality check. Else the pointed-to variable will be set to the optimal scale. Check this link for more details about the cv2.drawChessboardCorners() function. This function reconstructs 3-dimensional points (in homogeneous coordinates) by using their observations with a stereo camera. In this case, an extrapolation method needs to be used. ddepth can also be set to CV_16S, CV_32S or CV_32F. First output derivative matrix d(A*B)/dA of size \(\texttt{A.rows*B.cols} \times {A.rows*A.cols}\) . Input camera intrinsic matrix \(\cameramatrix{A}\) . The algorithm is based on [303], [34] and [239]. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. Note that this function assumes that points1 and points2 are feature points from cameras with same focal length and principal point. In case of a stereo camera, this function is called twice: once for each camera head, after stereoRectify, which in its turn is called after stereoCalibrate. and the matrix \(R(\tau_x, \tau_y)\) is defined by two rotations with angular parameter \(\tau_x\) and \(\tau_y\), respectively, \[ R(\tau_x, \tau_y) = \vecthreethree{\cos(\tau_y)}{0}{-\sin(\tau_y)}{0}{1}{0}{\sin(\tau_y)}{0}{\cos(\tau_y)} \vecthreethree{1}{0}{0}{0}{\cos(\tau_x)}{\sin(\tau_x)}{0}{-\sin(\tau_x)}{\cos(\tau_x)} = \vecthreethree{\cos(\tau_y)}{\sin(\tau_y)\sin(\tau_x)}{-\sin(\tau_y)\cos(\tau_x)} {0}{\cos(\tau_x)}{\sin(\tau_x)} {\sin(\tau_y)}{-\cos(\tau_y)\sin(\tau_x)}{\cos(\tau_y)\cos(\tau_x)}. The function is similar to undistort and initUndistortRectifyMap but it operates on a sparse set of points instead of a raster image. 4 coplanar object points must be defined in the following order: SQPnP: A Consistently Fast and Globally OptimalSolution to the Perspective-n-Point Problem [252]. Output vector of the epipolar lines corresponding to the points in the other image. 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. Returns the number of inliers that pass the check. Now, we can use the distortion parameters and the camera matrix to undistort an image using the cv2.undistort () function of OpenCV. Are arguments that Reason is circular themselves circular and/or self refuting? objectPoints, imagePoints, cameraMatrix, distCoeffs[, rvecs[, tvecs[, useExtrinsicGuess[, flags[, rvec[, tvec[, reprojectionError]]]]]]], Rotation vector used to initialize an iterative PnP refinement algorithm, when flag is, Translation vector used to initialize an iterative PnP refinement algorithm, when flag is. cameraMatrix, distCoeffs, imageSize, alpha[, newImgSize[, centerPrincipalPoint]]. For each camera, the function computes homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D space. In the internal implementation, calibrateCamera is a wrapper for this function. This function decomposes the essential matrix E using svd decomposition [107]. points1, points2, cameraMatrix[, method[, prob[, threshold[, maxIters[, mask]]]]], points1, points2[, focal[, pp[, method[, prob[, threshold[, maxIters[, mask]]]]]]], points1, points2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2[, method[, prob[, threshold[, mask]]]], points1, points2, cameraMatrix1, cameraMatrix2, dist_coeff1, dist_coeff2, params[, mask]. In the simplest case, the coordinates can be just rounded to the nearest integer coordinates and the corresponding pixel can be used. \], In the functions below the coefficients are passed or returned as, \[(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\]. Thus, given the representation of the point \(P\) in world coordinates, \(P_w\), we obtain \(P\)'s representation in the camera coordinate system, \(P_c\), by, \[P_c = \begin{bmatrix} R & t \\ 0 & 1 \end{bmatrix} P_w,\]. We used the append() function to add the corner points to the 2D image points. This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point: \[A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\]. This is an implementation of the algorithm by Umeyama [262] . Input vector of distortion coefficients \(\distcoeffs\) . The function converts a pair of maps for remap from one representation to another. By default, it is set to imageSize . If we have access to the sets of points visible in the camera frame before and after the homography transformation is applied, we can determine which are the true potential solutions and which are the opposites by verifying which homographies are consistent with all visible reference points being in front of the camera. Sample Code 1 importcv2 2 3 cap=cv2.VideoCapture('vtest.avi') 4 5 while(cap.isOpened()): 6 ret, frame=cap.read() 7 gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) 8 cv2.imshow('frame',gray) 9 10 if cv2.waitKey(1)&0xFF==ord('q'): 11 break 12 cap.release() 13 cv2.destroyAllWindows() Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera. Output ideal point coordinates (1xN/Nx1 2-channel or vector ) after undistortion and reverse perspective transformation. Source chessboard view. Anything between 0.95 and 0.99 is usually good enough. Depth of the extracted pixels. See, R_gripper2base, t_gripper2base, R_target2cam, t_target2cam[, R_cam2gripper[, t_cam2gripper[, method]]], Rotation part extracted from the homogeneous matrix that transforms a point expressed in the gripper frame to the robot base frame ( \(_{}^{b}\textrm{T}_g\)). Array of N points from the first image. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a planar object, it does, up to a translation vector, if the proper R is specified. Homography matrix is determined up to a scale. The camera matrix will be a 3-by-3 matrix like the matrix shown below. roi1, roi2, minDisparity, numberOfDisparities, blockSize, objectPoints, imagePoints, imageSize[, aspectRatio], Vector of vectors of the calibration pattern points in the calibration pattern coordinate space. Reverse conversion. Calculates an affine matrix of 2D rotation. Method used to compute a homography matrix. objectPoints, imagePoints, imageSize, iFixedPoint, cameraMatrix, distCoeffs[, rvecs[, tvecs[, newObjPoints[, flags[, criteria]]]]], retval, cameraMatrix, distCoeffs, rvecs, tvecs, newObjPoints, objectPoints, imagePoints, imageSize, iFixedPoint, cameraMatrix, distCoeffs[, rvecs[, tvecs[, newObjPoints[, stdDeviationsIntrinsics[, stdDeviationsExtrinsics[, stdDeviationsObjPoints[, perViewErrors[, flags[, criteria]]]]]]]]], retval, cameraMatrix, distCoeffs, rvecs, tvecs, newObjPoints, stdDeviationsIntrinsics, stdDeviationsExtrinsics, stdDeviationsObjPoints, perViewErrors, Vector of vectors of calibration pattern points in the calibration pattern coordinate space. It determines the inverse magnitude scale parameter too. To undistort using KannalaBrandt, we need to access the cv=cv2.fisheye module. The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. image0 is the picture I'm trying to undistort: If the intrinsic parameters can be estimated with high accuracy for each of the cameras individually (for example, using calibrateCamera ), you are recommended to do so and then pass CALIB_FIX_INTRINSIC flag to the function along with the computed intrinsic parameters. objectPoints, imagePoints, cameraMatrix, distCoeffs, rvec, tvec[, criteria]. If not needed, Output vector of standard deviations estimated for intrinsic parameters. 2xN array of feature points in the first image. Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or a vector . The parameter indicates whether this location should be at the image center or not. Transforms an image to compensate for lens distortion. EPnP: Efficient Perspective-n-Point Camera Pose Estimation [145]. So, once estimated, it can be re-used as long as the focal length is fixed (in case of a zoom lens). First input 3D point set containing \((X,Y,Z)\). Two major distortions are radial distortion and tangential distortion. Camera matrix of the distorted image. Passing 0 will disable refining, so the output matrix will be output of robust method. Applies only to RANSAC. see [233] . Optional rectification transformation in the object space (3x3 matrix). OpenCV undistort () OpenCV Returns the number of inliers that pass the check. It's pretty obvious that I can just go copy out and reimplement a slightly tweaked version of undistort() but I am having some trouble understanding what it is doing. Note that, in general, t can not be used for this tuple, see the parameter described below. Then, given an input image or video frame (i.e. Output translation vector, see description above. For now, we will ignore the rectification component. samples/cpp/tutorial_code/features2D/Homography/pose_from_homography.cpp, samples/cpp/tutorial_code/features2D/Homography/homography_from_camera_displacement.cpp, map1, map2, dstmap1type[, dstmap1[, dstmap2[, nninterpolation]]]. if the number of input points is equal to 4, The method used to estimate the camera pose using all the inliers is defined by the flags parameters unless it is equal to. If. Returned tree rotation matrices and corresponding three Euler angles are only one of the possible solutions. 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. points where the disparity was not computed). Otherwise, they are likely to be smaller (see the picture below). triangulatePoints() void cv::triangulatePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. In case when you specify the forward mapping \(\left: \texttt{src} \rightarrow \texttt{dst}\), the OpenCV functions first compute the corresponding inverse mapping \(\left: \texttt{dst} \rightarrow \texttt{src}\) and then use the above formula. Pose refinement using non-linear Levenberg-Marquardt minimization scheme [166] [68] src, cameraMatrix, distCoeffs[, dst[, R[, P]]], src, cameraMatrix, distCoeffs, R, P, criteria[, dst]. K1, D1, K2, D2, imageSize, R, tvec, flags[, R1[, R2[, P1[, P2[, Q[, newImageSize[, balance[, fov_scale]]]]]]]]. This function finds the intrinsic parameters for each of the two cameras and the extrinsic parameters between the two cameras. The epipolar geometry is described by the following equation: where \(F\) is a fundamental matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. 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. Regardless of the method, robust or not, the computed homography matrix is refined further (using inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the re-projection error even more. In the new interface it is a vector of vectors of the projections of calibration pattern points (e.g. If matrix P is identity or omitted, dst will contain normalized point coordinates. The function converts points homogeneous to Euclidean space using perspective projection. I have obtained the camera matrix and distortion coefficients for a GoPro Hero 2 using calibrateCamera () on a list of points obtained with findChessboardCorners (), essentially following this guide. See, objectPoints, rvec, tvec, K, D[, imagePoints[, alpha[, jacobian]]]. The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix), respectively. vector can also be passed here. The distortion-free projective transformation given by a pinhole camera model is shown below. OpenCV comes with two methods, we will see both. That is, for each pixel \((x, y)\) of the destination image, the functions compute coordinates of the corresponding "donor" pixel in the source image and copy the pixel value: \[\texttt{dst} (x,y)= \texttt{src} (f_x(x,y), f_y(x,y))\]. Check this link for more details about the cv2.undistort() function. objectPoints, imagePoints, cameraMatrix, distCoeffs, rvec, tvec[, criteria[, VVSlambda]]. points1, points2, F, imgSize[, H1[, H2[, threshold]]]. The function estimates and returns an initial camera intrinsic matrix for the camera calibration process. That may be achieved by using an object with known geometry and easily detectable feature points. See. The best subset is then used to produce the initial estimate of the homography matrix and the mask of inliers/outliers. cv2.copyMakeBorder () method is used to create a border around the image like a photo frame. Stereo rectification for fisheye camera model. You can rate examples to help us improve the quality of examples. In OpenCV (v3.1 if that matters), I tried to use . Output vector of standard deviations estimated for refined coordinates of calibration pattern points. Destination image. That is, for each pixel \((u, v)\) in the destination (corrected and rectified) image, the function computes the corresponding coordinates in the source image (that is, in the original image from camera). Python24undistort() . In the case of the c++ version, it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1. Higher-order coefficients are not considered in OpenCV. By default, it is the identity matrix but you may additionally scale and shift the result by using a different matrix. In the above image, the red grid shows the output image plane, and the blue grid shows the input image plane. If you want to resize src so that it fits the pre-created dst, you may call the function as follows: If you want to decimate the image by factor of 2 in each direction, you can call the function this way: To shrink an image, it will generally look best with INTER_AREA interpolation, whereas to enlarge an image, it will generally look best with INTER_CUBIC (slow) or INTER_LINEAR (faster but still looks OK). The index of the 3D object point in objectPoints[0] to be fixed. vector of vectors of the projections of calibration pattern points. The function computes the joint undistortion and rectification transformation and represents the result in the form of maps for remap. In case of a monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by getOptimalNewCameraMatrix for a better control over scaling. The function computes the joint projection and inverse rectification transformation and represents the result in the form of maps for remap. By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original image pixels if there is valuable information in the corners alpha=1 , or get something in between. Applies an affine transformation to an image. Currently, the function only supports planar calibration patterns, which are patterns where each object point has z-coordinate =0.

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