Detailed Description

ArUco Marker Detection, module functionality was moved to objdetect module

See also: ArucoDetector, CharucoDetector, Board, GridBoard, CharucoBoard

Classes
struct	cv::aruco::EstimateParameters
	Pose estimation parameters. More...

Enumerations
enum	cv::aruco::PatternPositionType { cv::aruco::ARUCO_CCW_CENTER , cv::aruco::ARUCO_CW_TOP_LEFT_CORNER }
	rvec/tvec define the right handed coordinate system of the marker. More...

Functions
double	cv::aruco::calibrateCameraAruco (InputArrayOfArrays corners, InputArray ids, InputArray counter, const Ptr< Board > &board, Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, OutputArray stdDeviationsIntrinsics, OutputArray stdDeviationsExtrinsics, OutputArray perViewErrors, int flags=0, const TermCriteria &criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON))
	Calibrate a camera using aruco markers.

double	cv::aruco::calibrateCameraAruco (InputArrayOfArrays corners, InputArray ids, InputArray counter, const Ptr< Board > &board, Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs=noArray(), OutputArrayOfArrays tvecs=noArray(), int flags=0, const TermCriteria &criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON))
	It's the same function as calibrateCameraAruco but without calibration error estimation.

double	cv::aruco::calibrateCameraCharuco (InputArrayOfArrays charucoCorners, InputArrayOfArrays charucoIds, const Ptr< CharucoBoard > &board, Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, OutputArray stdDeviationsIntrinsics, OutputArray stdDeviationsExtrinsics, OutputArray perViewErrors, int flags=0, const TermCriteria &criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON))
	Calibrate a camera using Charuco corners.

double	cv::aruco::calibrateCameraCharuco (InputArrayOfArrays charucoCorners, InputArrayOfArrays charucoIds, const Ptr< CharucoBoard > &board, Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs=noArray(), OutputArrayOfArrays tvecs=noArray(), int flags=0, const TermCriteria &criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON))
	It's the same function as calibrateCameraCharuco but without calibration error estimation.

void	cv::aruco::detectCharucoDiamond (InputArray image, InputArrayOfArrays markerCorners, InputArray markerIds, float squareMarkerLengthRate, OutputArrayOfArrays diamondCorners, OutputArray diamondIds, InputArray cameraMatrix=noArray(), InputArray distCoeffs=noArray(), Ptr< Dictionary > dictionary=makePtr< Dictionary >(getPredefinedDictionary(PredefinedDictionaryType::DICT_4X4_50)))
	Detect ChArUco Diamond markers.

void	cv::aruco::detectMarkers (InputArray image, const Ptr< Dictionary > &dictionary, OutputArrayOfArrays corners, OutputArray ids, const Ptr< DetectorParameters > &parameters=makePtr< DetectorParameters >(), OutputArrayOfArrays rejectedImgPoints=noArray())
	detect markers

void	cv::aruco::drawCharucoDiamond (const Ptr< Dictionary > &dictionary, Vec4i ids, int squareLength, int markerLength, OutputArray img, int marginSize=0, int borderBits=1)
	Draw a ChArUco Diamond marker.

void	cv::aruco::drawPlanarBoard (const Ptr< Board > &board, Size outSize, OutputArray img, int marginSize, int borderBits)
	draw planar board

int	cv::aruco::estimatePoseBoard (InputArrayOfArrays corners, InputArray ids, const Ptr< Board > &board, InputArray cameraMatrix, InputArray distCoeffs, InputOutputArray rvec, InputOutputArray tvec, bool useExtrinsicGuess=false)

bool	cv::aruco::estimatePoseCharucoBoard (InputArray charucoCorners, InputArray charucoIds, const Ptr< CharucoBoard > &board, InputArray cameraMatrix, InputArray distCoeffs, InputOutputArray rvec, InputOutputArray tvec, bool useExtrinsicGuess=false)
	Pose estimation for a ChArUco board given some of their corners.

void	cv::aruco::estimatePoseSingleMarkers (InputArrayOfArrays corners, float markerLength, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvecs, OutputArray tvecs, OutputArray objPoints=noArray(), const Ptr< EstimateParameters > &estimateParameters=makePtr< EstimateParameters >())

void	cv::aruco::getBoardObjectAndImagePoints (const Ptr< Board > &board, InputArrayOfArrays detectedCorners, InputArray detectedIds, OutputArray objPoints, OutputArray imgPoints)
	get board object and image points

int	cv::aruco::interpolateCornersCharuco (InputArrayOfArrays markerCorners, InputArray markerIds, InputArray image, const Ptr< CharucoBoard > &board, OutputArray charucoCorners, OutputArray charucoIds, InputArray cameraMatrix=noArray(), InputArray distCoeffs=noArray(), int minMarkers=2)
	Interpolate position of ChArUco board corners.

void	cv::aruco::refineDetectedMarkers (InputArray image, const Ptr< Board > &board, InputOutputArrayOfArrays detectedCorners, InputOutputArray detectedIds, InputOutputArrayOfArrays rejectedCorners, InputArray cameraMatrix=noArray(), InputArray distCoeffs=noArray(), float minRepDistance=10.f, float errorCorrectionRate=3.f, bool checkAllOrders=true, OutputArray recoveredIdxs=noArray(), const Ptr< DetectorParameters > &parameters=makePtr< DetectorParameters >())
	refine detected markers

bool	cv::aruco::testCharucoCornersCollinear (const Ptr< CharucoBoard > &board, InputArray charucoIds)

Enumeration Type Documentation

◆ PatternPositionType

enum cv::aruco::PatternPositionType

#include <opencv2/aruco/aruco_calib.hpp>

rvec/tvec define the right handed coordinate system of the marker.

PatternPositionType defines center this system and axes direction. Axis X (red color) - first coordinate, axis Y (green color) - second coordinate, axis Z (blue color) - third coordinate.

Deprecated: Use Board::matchImagePoints and cv::solvePnP

See also: estimatePoseSingleMarkers()

Enumerator

ARUCO_CCW_CENTER

Python: cv.aruco.ARUCO_CCW_CENTER

The marker coordinate system is centered on the middle of the marker.

The coordinates of the four corners (CCW order) of the marker in its own coordinate system are: (-markerLength/2, markerLength/2, 0), (markerLength/2, markerLength/2, 0), (markerLength/2, -markerLength/2, 0), (-markerLength/2, -markerLength/2, 0).

ARUCO_CW_TOP_LEFT_CORNER

Python: cv.aruco.ARUCO_CW_TOP_LEFT_CORNER

The marker coordinate system is centered on the top-left corner of the marker.

The coordinates of the four corners (CW order) of the marker in its own coordinate system are: (0, 0, 0), (markerLength, 0, 0), (markerLength, markerLength, 0), (0, markerLength, 0).

These pattern dots are convenient to use with a chessboard/ChArUco board.

Function Documentation

◆ calibrateCameraAruco() [1/2]

double cv::aruco::calibrateCameraAruco	(	InputArrayOfArrays	corners,
		InputArray	ids,
		InputArray	counter,
		const Ptr< Board > &	board,
		Size	imageSize,
		InputOutputArray	cameraMatrix,
		InputOutputArray	distCoeffs,
		OutputArrayOfArrays	rvecs,
		OutputArrayOfArrays	tvecs,
		OutputArray	stdDeviationsIntrinsics,
		OutputArray	stdDeviationsExtrinsics,
		OutputArray	perViewErrors,
		int	flags = 0,
		const TermCriteria &	criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON) )

Python:
	cv.aruco.calibrateCameraAruco(	corners, ids, counter, board, imageSize, cameraMatrix, distCoeffs[, rvecs[, tvecs[, flags[, criteria]]]]	) ->	retval, cameraMatrix, distCoeffs, rvecs, tvecs
	cv.aruco.calibrateCameraArucoExtended(	corners, ids, counter, board, imageSize, cameraMatrix, distCoeffs[, rvecs[, tvecs[, stdDeviationsIntrinsics[, stdDeviationsExtrinsics[, perViewErrors[, flags[, criteria]]]]]]]	) ->	retval, cameraMatrix, distCoeffs, rvecs, tvecs, stdDeviationsIntrinsics, stdDeviationsExtrinsics, perViewErrors

#include <opencv2/aruco/aruco_calib.hpp>

Calibrate a camera using aruco markers.

Parameters

corners	vector of detected marker corners in all frames. The corners should have the same format returned by detectMarkers (see detectMarkers).
ids	list of identifiers for each marker in corners
counter	number of markers in each frame so that corners and ids can be split
board	Marker Board layout
imageSize	Size of the image used only to initialize the intrinsic camera matrix.
cameraMatrix	Output 3x3 floating-point camera matrix \(A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . If CV_CALIB_USE_INTRINSIC_GUESS and/or CV_CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
distCoeffs	Output vector of distortion coefficients \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\) of 4, 5, 8 or 12 elements
rvecs	Output vector of rotation vectors (see Rodrigues ) estimated for each board view (e.g. std::vector<cv::Mat>>). That is, each k-th rotation vector together with the corresponding k-th translation vector (see the next output parameter description) brings the board pattern from the model coordinate space (in which object points are specified) to the world coordinate space, that is, a real position of the board pattern in the k-th pattern view (k=0.. M -1).
tvecs	Output vector of translation vectors estimated for each pattern view.
stdDeviationsIntrinsics	Output vector of standard deviations estimated for intrinsic parameters. Order of deviations values: \((f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, s_4, \tau_x, \tau_y)\) If one of parameters is not estimated, it's deviation is equals to zero.
stdDeviationsExtrinsics	Output vector of standard deviations estimated for extrinsic parameters. Order of deviations values: \((R_1, T_1, \dotsc , R_M, T_M)\) where M is number of pattern views, \(R_i, T_i\) are concatenated 1x3 vectors.
perViewErrors	Output vector of average re-projection errors estimated for each pattern view.
flags	flags Different flags for the calibration process (see calibrateCamera for details).
criteria	Termination criteria for the iterative optimization algorithm.

This function calibrates a camera using an Aruco Board. The function receives a list of detected markers from several views of the Board. The process is similar to the chessboard calibration in calibrateCamera(). The function returns the final re-projection error.

Deprecated: Use Board::matchImagePoints and cv::solvePnP

◆ calibrateCameraAruco() [2/2]

double cv::aruco::calibrateCameraAruco	(	InputArrayOfArrays	corners,
		InputArray	ids,
		InputArray	counter,
		const Ptr< Board > &	board,
		Size	imageSize,
		InputOutputArray	cameraMatrix,
		InputOutputArray	distCoeffs,
		OutputArrayOfArrays	rvecs = noArray(),
		OutputArrayOfArrays	tvecs = noArray(),
		int	flags = 0,
		const TermCriteria &	criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON) )

Python:
	cv.aruco.calibrateCameraAruco(	corners, ids, counter, board, imageSize, cameraMatrix, distCoeffs[, rvecs[, tvecs[, flags[, criteria]]]]	) ->	retval, cameraMatrix, distCoeffs, rvecs, tvecs
	cv.aruco.calibrateCameraArucoExtended(	corners, ids, counter, board, imageSize, cameraMatrix, distCoeffs[, rvecs[, tvecs[, stdDeviationsIntrinsics[, stdDeviationsExtrinsics[, perViewErrors[, flags[, criteria]]]]]]]	) ->	retval, cameraMatrix, distCoeffs, rvecs, tvecs, stdDeviationsIntrinsics, stdDeviationsExtrinsics, perViewErrors

#include <opencv2/aruco/aruco_calib.hpp>

It's the same function as calibrateCameraAruco but without calibration error estimation.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Deprecated: Use Board::matchImagePoints and cv::solvePnP

◆ calibrateCameraCharuco() [1/2]

double cv::aruco::calibrateCameraCharuco	(	InputArrayOfArrays	charucoCorners,
		InputArrayOfArrays	charucoIds,
		const Ptr< CharucoBoard > &	board,
		Size	imageSize,
		InputOutputArray	cameraMatrix,
		InputOutputArray	distCoeffs,
		OutputArrayOfArrays	rvecs,
		OutputArrayOfArrays	tvecs,
		OutputArray	stdDeviationsIntrinsics,
		OutputArray	stdDeviationsExtrinsics,
		OutputArray	perViewErrors,
		int	flags = 0,
		const TermCriteria &	criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON) )

Python:
	cv.aruco.calibrateCameraCharuco(	charucoCorners, charucoIds, board, imageSize, cameraMatrix, distCoeffs[, rvecs[, tvecs[, flags[, criteria]]]]	) ->	retval, cameraMatrix, distCoeffs, rvecs, tvecs
	cv.aruco.calibrateCameraCharucoExtended(	charucoCorners, charucoIds, board, imageSize, cameraMatrix, distCoeffs[, rvecs[, tvecs[, stdDeviationsIntrinsics[, stdDeviationsExtrinsics[, perViewErrors[, flags[, criteria]]]]]]]	) ->	retval, cameraMatrix, distCoeffs, rvecs, tvecs, stdDeviationsIntrinsics, stdDeviationsExtrinsics, perViewErrors

#include <opencv2/aruco/aruco_calib.hpp>

Calibrate a camera using Charuco corners.

Parameters

charucoCorners	vector of detected charuco corners per frame
charucoIds	list of identifiers for each corner in charucoCorners per frame
board	Marker Board layout
imageSize	input image size
cameraMatrix	Output 3x3 floating-point camera matrix \(A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) . If CV_CALIB_USE_INTRINSIC_GUESS and/or CV_CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
distCoeffs	Output vector of distortion coefficients \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\) of 4, 5, 8 or 12 elements
rvecs	Output vector of rotation vectors (see Rodrigues ) estimated for each board view (e.g. std::vector<cv::Mat>>). That is, each k-th rotation vector together with the corresponding k-th translation vector (see the next output parameter description) brings the board pattern from the model coordinate space (in which object points are specified) to the world coordinate space, that is, a real position of the board pattern in the k-th pattern view (k=0.. M -1).
tvecs	Output vector of translation vectors estimated for each pattern view.
stdDeviationsIntrinsics	Output vector of standard deviations estimated for intrinsic parameters. Order of deviations values: \((f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, s_4, \tau_x, \tau_y)\) If one of parameters is not estimated, it's deviation is equals to zero.
stdDeviationsExtrinsics	Output vector of standard deviations estimated for extrinsic parameters. Order of deviations values: \((R_1, T_1, \dotsc , R_M, T_M)\) where M is number of pattern views, \(R_i, T_i\) are concatenated 1x3 vectors.
perViewErrors	Output vector of average re-projection errors estimated for each pattern view.
flags	flags Different flags for the calibration process (see calibrateCamera for details).
criteria	Termination criteria for the iterative optimization algorithm.

This function calibrates a camera using a set of corners of a Charuco Board. The function receives a list of detected corners and its identifiers from several views of the Board. The function returns the final re-projection error.

Deprecated: Use CharucoBoard::matchImagePoints and cv::solvePnP

◆ calibrateCameraCharuco() [2/2]

double cv::aruco::calibrateCameraCharuco	(	InputArrayOfArrays	charucoCorners,
		InputArrayOfArrays	charucoIds,
		const Ptr< CharucoBoard > &	board,
		Size	imageSize,
		InputOutputArray	cameraMatrix,
		InputOutputArray	distCoeffs,
		OutputArrayOfArrays	rvecs = noArray(),
		OutputArrayOfArrays	tvecs = noArray(),
		int	flags = 0,
		const TermCriteria &	criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON) )

Python:
	cv.aruco.calibrateCameraCharuco(	charucoCorners, charucoIds, board, imageSize, cameraMatrix, distCoeffs[, rvecs[, tvecs[, flags[, criteria]]]]	) ->	retval, cameraMatrix, distCoeffs, rvecs, tvecs
	cv.aruco.calibrateCameraCharucoExtended(	charucoCorners, charucoIds, board, imageSize, cameraMatrix, distCoeffs[, rvecs[, tvecs[, stdDeviationsIntrinsics[, stdDeviationsExtrinsics[, perViewErrors[, flags[, criteria]]]]]]]	) ->	retval, cameraMatrix, distCoeffs, rvecs, tvecs, stdDeviationsIntrinsics, stdDeviationsExtrinsics, perViewErrors

#include <opencv2/aruco/aruco_calib.hpp>

It's the same function as calibrateCameraCharuco but without calibration error estimation.

Deprecated: Use CharucoBoard::matchImagePoints and cv::solvePnP

◆ detectCharucoDiamond()

void cv::aruco::detectCharucoDiamond	(	InputArray	image,
		InputArrayOfArrays	markerCorners,
		InputArray	markerIds,
		float	squareMarkerLengthRate,
		OutputArrayOfArrays	diamondCorners,
		OutputArray	diamondIds,
		InputArray	cameraMatrix = noArray(),
		InputArray	distCoeffs = noArray(),
		Ptr< Dictionary >	dictionary = makePtr< Dictionary >(getPredefinedDictionary(PredefinedDictionaryType::DICT_4X4_50)) )

Python:
	cv.aruco.detectCharucoDiamond(	image, markerCorners, markerIds, squareMarkerLengthRate[, diamondCorners[, diamondIds[, cameraMatrix[, distCoeffs[, dictionary]]]]]	) ->	diamondCorners, diamondIds

#include <opencv2/aruco/charuco.hpp>

Detect ChArUco Diamond markers.

Parameters

image	input image necessary for corner subpixel.
markerCorners	list of detected marker corners from detectMarkers function.
markerIds	list of marker ids in markerCorners.
squareMarkerLengthRate	rate between square and marker length: squareMarkerLengthRate = squareLength/markerLength. The real units are not necessary.
diamondCorners	output list of detected diamond corners (4 corners per diamond). The order is the same than in marker corners: top left, top right, bottom right and bottom left. Similar format than the corners returned by detectMarkers (e.g std::vector<std::vector<cv::Point2f> > ).
diamondIds	ids of the diamonds in diamondCorners. The id of each diamond is in fact of type Vec4i, so each diamond has 4 ids, which are the ids of the aruco markers composing the diamond.
cameraMatrix	Optional camera calibration matrix.
distCoeffs	Optional camera distortion coefficients.
dictionary	dictionary of markers indicating the type of markers.

This function detects Diamond markers from the previous detected ArUco markers. The diamonds are returned in the diamondCorners and diamondIds parameters. If camera calibration parameters are provided, the diamond search is based on reprojection. If not, diamond search is based on homography. Homography is faster than reprojection, but less accurate.

Deprecated: Use CharucoDetector::detectDiamonds

◆ detectMarkers()

void cv::aruco::detectMarkers	(	InputArray	image,
		const Ptr< Dictionary > &	dictionary,
		OutputArrayOfArrays	corners,
		OutputArray	ids,
		const Ptr< DetectorParameters > &	parameters = makePtr< DetectorParameters >(),
		OutputArrayOfArrays	rejectedImgPoints = noArray() )

Python:
	cv.aruco.detectMarkers(	image, dictionary[, corners[, ids[, parameters[, rejectedImgPoints]]]]	) ->	corners, ids, rejectedImgPoints

#include <opencv2/aruco.hpp>

detect markers

Deprecated: Use class ArucoDetector::detectMarkers

◆ drawCharucoDiamond()

void cv::aruco::drawCharucoDiamond	(	const Ptr< Dictionary > &	dictionary,
		Vec4i	ids,
		int	squareLength,
		int	markerLength,
		OutputArray	img,
		int	marginSize = 0,
		int	borderBits = 1 )

Python:
	cv.aruco.drawCharucoDiamond(	dictionary, ids, squareLength, markerLength[, img[, marginSize[, borderBits]]]	) ->	img

#include <opencv2/aruco/charuco.hpp>

Draw a ChArUco Diamond marker.

Parameters

dictionary	dictionary of markers indicating the type of markers.
ids	list of 4 ids for each ArUco marker in the ChArUco marker.
squareLength	size of the chessboard squares in pixels.
markerLength	size of the markers in pixels.
img	output image with the marker. The size of this image will be 3squareLength + 2marginSize,.
marginSize	minimum margins (in pixels) of the marker in the output image
borderBits	width of the marker borders.

This function return the image of a ChArUco marker, ready to be printed.

Deprecated: Use CharucoBoard::generateImage()

◆ drawPlanarBoard()

void cv::aruco::drawPlanarBoard	(	const Ptr< Board > &	board,
		Size	outSize,
		OutputArray	img,
		int	marginSize,
		int	borderBits )

Python:
	cv.aruco.drawPlanarBoard(	board, outSize, marginSize, borderBits[, img]	) ->	img

#include <opencv2/aruco.hpp>

draw planar board

Deprecated: Use Board::generateImage

◆ estimatePoseBoard()

int cv::aruco::estimatePoseBoard	(	InputArrayOfArrays	corners,
		InputArray	ids,
		const Ptr< Board > &	board,
		InputArray	cameraMatrix,
		InputArray	distCoeffs,
		InputOutputArray	rvec,
		InputOutputArray	tvec,
		bool	useExtrinsicGuess = false )

Python:
	cv.aruco.estimatePoseBoard(	corners, ids, board, cameraMatrix, distCoeffs, rvec, tvec[, useExtrinsicGuess]	) ->	retval, rvec, tvec

#include <opencv2/aruco.hpp>

Deprecated: Use Board::matchImagePoints and cv::solvePnP

◆ estimatePoseCharucoBoard()

bool cv::aruco::estimatePoseCharucoBoard	(	InputArray	charucoCorners,
		InputArray	charucoIds,
		const Ptr< CharucoBoard > &	board,
		InputArray	cameraMatrix,
		InputArray	distCoeffs,
		InputOutputArray	rvec,
		InputOutputArray	tvec,
		bool	useExtrinsicGuess = false )

Python:
	cv.aruco.estimatePoseCharucoBoard(	charucoCorners, charucoIds, board, cameraMatrix, distCoeffs, rvec, tvec[, useExtrinsicGuess]	) ->	retval, rvec, tvec

#include <opencv2/aruco.hpp>

Pose estimation for a ChArUco board given some of their corners.

Parameters

charucoCorners	vector of detected charuco corners
charucoIds	list of identifiers for each corner in charucoCorners
board	layout of ChArUco board.
cameraMatrix	input 3x3 floating-point camera matrix \(A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\)
distCoeffs	vector of distortion coefficients \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\) of 4, 5, 8 or 12 elements
rvec	Output vector (e.g. cv::Mat) corresponding to the rotation vector of the board (see cv::Rodrigues).
tvec	Output vector (e.g. cv::Mat) corresponding to the translation vector of the board.
useExtrinsicGuess	defines whether initial guess for rvec and tvec will be used or not.

This function estimates a Charuco board pose from some detected corners. The function checks if the input corners are enough and valid to perform pose estimation. If pose estimation is valid, returns true, else returns false.

Deprecated: Use CharucoBoard::matchImagePoints and cv::solvePnP

See also: use cv::drawFrameAxes to get world coordinate system axis for object points

◆ estimatePoseSingleMarkers()

void cv::aruco::estimatePoseSingleMarkers	(	InputArrayOfArrays	corners,
		float	markerLength,
		InputArray	cameraMatrix,
		InputArray	distCoeffs,
		OutputArray	rvecs,
		OutputArray	tvecs,
		OutputArray	objPoints = noArray(),
		const Ptr< EstimateParameters > &	estimateParameters = makePtr< EstimateParameters >() )

Python:
	cv.aruco.estimatePoseSingleMarkers(	corners, markerLength, cameraMatrix, distCoeffs[, rvecs[, tvecs[, objPoints[, estimateParameters]]]]	) ->	rvecs, tvecs, objPoints

#include <opencv2/aruco.hpp>

Deprecated: Use cv::solvePnP

◆ getBoardObjectAndImagePoints()

void cv::aruco::getBoardObjectAndImagePoints	(	const Ptr< Board > &	board,
		InputArrayOfArrays	detectedCorners,
		InputArray	detectedIds,
		OutputArray	objPoints,
		OutputArray	imgPoints )

Python:
	cv.aruco.getBoardObjectAndImagePoints(	board, detectedCorners, detectedIds[, objPoints[, imgPoints]]	) ->	objPoints, imgPoints

#include <opencv2/aruco.hpp>

get board object and image points

Deprecated: Use Board::matchImagePoints

◆ interpolateCornersCharuco()

int cv::aruco::interpolateCornersCharuco	(	InputArrayOfArrays	markerCorners,
		InputArray	markerIds,
		InputArray	image,
		const Ptr< CharucoBoard > &	board,
		OutputArray	charucoCorners,
		OutputArray	charucoIds,
		InputArray	cameraMatrix = noArray(),
		InputArray	distCoeffs = noArray(),
		int	minMarkers = 2 )

Python:
	cv.aruco.interpolateCornersCharuco(	markerCorners, markerIds, image, board[, charucoCorners[, charucoIds[, cameraMatrix[, distCoeffs[, minMarkers]]]]]	) ->	retval, charucoCorners, charucoIds

#include <opencv2/aruco/charuco.hpp>

Interpolate position of ChArUco board corners.

Parameters

markerCorners	vector of already detected markers corners. For each marker, its four corners are provided, (e.g std::vector<std::vector<cv::Point2f> > ). For N detected markers, the dimensions of this array should be Nx4. The order of the corners should be clockwise.
markerIds	list of identifiers for each marker in corners
image	input image necesary for corner refinement. Note that markers are not detected and should be sent in corners and ids parameters.
board	layout of ChArUco board.
charucoCorners	interpolated chessboard corners
charucoIds	interpolated chessboard corners identifiers
cameraMatrix	optional 3x3 floating-point camera matrix \(A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\)
distCoeffs	optional vector of distortion coefficients \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\) of 4, 5, 8 or 12 elements
minMarkers	number of adjacent markers that must be detected to return a charuco corner

This function receives the detected markers and returns the 2D position of the chessboard corners from a ChArUco board using the detected Aruco markers. If camera parameters are provided, the process is based in an approximated pose estimation, else it is based on local homography. Only visible corners are returned. For each corner, its corresponding identifier is also returned in charucoIds. The function returns the number of interpolated corners.

Deprecated: Use CharucoDetector::detectBoard

◆ refineDetectedMarkers()

void cv::aruco::refineDetectedMarkers	(	InputArray	image,
		const Ptr< Board > &	board,
		InputOutputArrayOfArrays	detectedCorners,
		InputOutputArray	detectedIds,
		InputOutputArrayOfArrays	rejectedCorners,
		InputArray	cameraMatrix = noArray(),
		InputArray	distCoeffs = noArray(),
		float	minRepDistance = 10.f,
		float	errorCorrectionRate = 3.f,
		bool	checkAllOrders = true,
		OutputArray	recoveredIdxs = noArray(),
		const Ptr< DetectorParameters > &	parameters = makePtr< DetectorParameters >() )

Python:
	cv.aruco.refineDetectedMarkers(	image, board, detectedCorners, detectedIds, rejectedCorners[, cameraMatrix[, distCoeffs[, minRepDistance[, errorCorrectionRate[, checkAllOrders[, recoveredIdxs[, parameters]]]]]]]	) ->	detectedCorners, detectedIds, rejectedCorners, recoveredIdxs

#include <opencv2/aruco.hpp>

refine detected markers

Deprecated: Use class ArucoDetector::refineDetectedMarkers

◆ testCharucoCornersCollinear()

bool cv::aruco::testCharucoCornersCollinear	(	const Ptr< CharucoBoard > &	board,
		InputArray	charucoIds )

Python:
	cv.aruco.testCharucoCornersCollinear(	board, charucoIds	) ->	retval

#include <opencv2/aruco.hpp>

Deprecated: Use CharucoBoard::checkCharucoCornersCollinear

Detailed Description

Classes

Enumerations

Functions

Enumeration Type Documentation

◆ PatternPositionType

Function Documentation

◆ calibrateCameraAruco() [1/2]

◆ calibrateCameraAruco() [2/2]

◆ calibrateCameraCharuco() [1/2]

◆ calibrateCameraCharuco() [2/2]

◆ detectCharucoDiamond()

◆ detectMarkers()

◆ drawCharucoDiamond()

◆ drawPlanarBoard()

◆ estimatePoseBoard()

◆ estimatePoseCharucoBoard()

◆ estimatePoseSingleMarkers()

◆ getBoardObjectAndImagePoints()

◆ interpolateCornersCharuco()

◆ refineDetectedMarkers()

◆ testCharucoCornersCollinear()