opencv中solvePnPRansac()和solvePnP()

关于Ransac()的介绍参看博文基础算法

solvePnPRansac()

bool cv::solvePnPRansac	(	InputArray	objectPoints,
		InputArray	imagePoints,
		InputArray	cameraMatrix,
		InputArray	distCoeffs,
		OutputArray	rvec,
		OutputArray	tvec,
		bool	useExtrinsicGuess = false,
		int	iterationsCount = 100,
		float	reprojectionError = 8.0,
		double	confidence = 0.99,
		OutputArray	inliers = noArray(),
		int	flags = SOLVEPNP_ITERATIVE
	)

Python:
	retval, rvec, tvec, inliers	=	cv.solvePnPRansac(	objectPoints, imagePoints, cameraMatrix, distCoeffs[, rvec[, tvec[, useExtrinsicGuess[, iterationsCount[, reprojectionError[, confidence[, inliers[, flags]]]]]]]]	)

Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.

Parameters

objectPoints	Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector 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 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.
iterationsCount	Number of iterations. //Ransa算法的迭代次数
reprojectionError	Inlier threshold value used by the RANSAC procedure. The parameter value is the maximum allowed distance between the observed and computed point projections to consider it an inlier. //局内点的阈值
confidence	The probability that the algorithm produces a useful result. //置信度
inliers	Output vector that contains indices of inliers in objectPoints and imagePoints .//Ransa算法的局内点的索引
flags	Method for solving a PnP problem (see solvePnP ).//默认方法SOLVEPNP_EPNP.

The function estimates an object pose given a set of object points, their corresponding image projections, as well as the camera matrix and the distortion coefficients. This function finds such a pose that minimizes reprojection error, that is, the sum of squared distances between the observed projections imagePoints and the projected (using projectPoints ) objectPoints. The use of RANSAC makes the function resistant to outliers.

Note

• An example of how to use solvePNPRansac for object detection can be found at opencv_source_code/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/
• The default method used to estimate the camera pose for the Minimal Sample Sets step is SOLVEPNP_EPNP. Exceptions are:
  • if you choose SOLVEPNP_P3P or SOLVEPNP_AP3P, these methods will be used.
  • if the number of input points is equal to 4, SOLVEPNP_P3P is used.
• The method used to estimate the camera pose using all the inliers is defined by the flags parameters unless it is equal to SOLVEPNP_P3P or SOLVEPNP_AP3P. In this case, the method SOLVEPNP_EPNP will be used instead.

 

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]]]]	)

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 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 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" ([64]). 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" ([96]). 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" ([107]). SOLVEPNP_DLS Method is based on the paper of Joel A. Hesch and Stergios I. Roumeliotis. "A Direct Least-Squares (DLS) Method for PnP" ([84]). 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" ([148]). 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" ([96]). In this case the function requires exactly four object and image points.