opencv solvePnP

https://docs.opencv.org/4.1.0/d9/d0c/group__calib3d.html#ga549c2075fac14829ff4a58bc931c033d

solvePnP()

bool cv::solvePnP	(	InputArray	objectPoints,
		InputArray	imagePoints,
		InputArray	cameraMatrix,
		InputArray	distCoeffs,
		OutputArray	rvec,
		OutputArray	tvec,
		bool	useExtrinsicGuess = false,
		int	flags = SOLVEPNP_ITERATIVE
	)

Python:
	retval, rvec, tvec	=	cv.solvePnP(	objectPoints, imagePoints, cameraMatrix, distCoeffs[, rvec[, tvec[, useExtrinsicGuess[, flags]]]]	)

#include <opencv2/calib3d.hpp>

Finds an object pose from 3D-2D point correspondences.

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3f> can be also passed here.
imagePoints	Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2f> can be also passed here.
cameraMatrix	Input camera matrix A=⎡⎣⎢⎢fx000fy0cxcy1⎤⎦⎥⎥ .
distCoeffs	Input vector of distortion coefficients (k1,k2,p1,p2[,k3[,k4,k5,k6[,s1,s2,s3,s4[,τx,τy]]]]) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec	Output rotation vector (see Rodrigues ) that, together with tvec , brings points from the model coordinate system to the camera coordinate system.
tvec	Output translation vector.
useExtrinsicGuess	Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
flags	Method for solving a PnP problem: SOLVEPNP_ITERATIVE Iterative method is based on Levenberg-Marquardt optimization. In this case the function finds such a pose that minimizes reprojection error, that is the sum of squared distances between the observed projections imagePoints and the projected (using projectPoints ) objectPoints . SOLVEPNP_P3P Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang "Complete Solution Classification for the Perspective-Three-Point Problem" ([70]). In this case the function requires exactly four object and image points. SOLVEPNP_AP3P Method is based on the paper of T. Ke, S. Roumeliotis "An Efficient Algebraic Solution to the Perspective-Three-Point Problem" ([107]). In this case the function requires exactly four object and image points. SOLVEPNP_EPNP Method has been introduced by F.Moreno-Noguer, V.Lepetit and P.Fua in the paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" ([118]). SOLVEPNP_DLS Method is based on the paper of Joel A. Hesch and Stergios I. Roumeliotis. "A Direct Least-Squares (DLS) Method for PnP" ([91]). SOLVEPNP_UPNP Method is based on the paper of A.Penate-Sanchez, J.Andrade-Cetto, F.Moreno-Noguer. "Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation" ([165]). In this case the function also estimates the parameters fx and fy assuming that both have the same value. Then the cameraMatrix is updated with the estimated focal length. SOLVEPNP_AP3P Method is based on the paper of Tong Ke and Stergios I. Roumeliotis. "An Efficient Algebraic Solution to the Perspective-Three-Point Problem" ([107]). In this case the function requires exactly four object and image points.

 

 

zhangym@zhangym:~/opencv/samples/cpp$ sudo find . -type f | xargs grep solvePnP
[sudo] password for zhangym: 
./select3dobj.cpp:            solvePnP(Mat(boardPoints), Mat(foundBoardCorners), cameraMatrix,
./tutorial_code/calib3d/real_time_pose_estimation/src/main_detection.cpp:        if(good_matches.size() >= 4) // OpenCV requires solvePnPRANSAC to minimally have 4 set of points
./tutorial_code/calib3d/real_time_pose_estimation/src/PnPProblem.cpp:    bool correspondence = cv::solvePnP( list_points3d, list_points2d, A_matrix_, distCoeffs, rvec, tvec,
./tutorial_code/calib3d/real_time_pose_estimation/src/PnPProblem.cpp:    cv::solvePnPRansac( list_points3d, list_points2d, A_matrix_, distCoeffs, rvec, tvec,
./tutorial_code/features2D/Homography/homography_from_camera_displacement.cpp:    solvePnP(objectPoints, corners1, cameraMatrix, distCoeffs, rvec1, tvec1);
./tutorial_code/features2D/Homography/homography_from_camera_displacement.cpp:    solvePnP(objectPoints, corners2, cameraMatrix, distCoeffs, rvec2, tvec2);
./tutorial_code/features2D/Homography/decompose_homography.cpp:    solvePnP(objectPoints, corners1, cameraMatrix, distCoeffs, rvec1, tvec1);
./tutorial_code/features2D/Homography/decompose_homography.cpp:    solvePnP(objectPoints, corners2, cameraMatrix, distCoeffs, rvec2, tvec2);