public class Aruco extends Object
| Modifier and Type | Field and Description |
|---|---|
static int |
CORNER_REFINE_APRILTAG |
static int |
CORNER_REFINE_CONTOUR |
static int |
CORNER_REFINE_NONE |
static int |
CORNER_REFINE_SUBPIX |
static int |
DICT_4X4_100 |
static int |
DICT_4X4_1000 |
static int |
DICT_4X4_250 |
static int |
DICT_4X4_50 |
static int |
DICT_5X5_100 |
static int |
DICT_5X5_1000 |
static int |
DICT_5X5_250 |
static int |
DICT_5X5_50 |
static int |
DICT_6X6_100 |
static int |
DICT_6X6_1000 |
static int |
DICT_6X6_250 |
static int |
DICT_6X6_50 |
static int |
DICT_7X7_100 |
static int |
DICT_7X7_1000 |
static int |
DICT_7X7_250 |
static int |
DICT_7X7_50 |
static int |
DICT_APRILTAG_16h5 |
static int |
DICT_APRILTAG_25h9 |
static int |
DICT_APRILTAG_36h10 |
static int |
DICT_APRILTAG_36h11 |
static int |
DICT_ARUCO_ORIGINAL |
| Constructor and Description |
|---|
Aruco() |
| Modifier and Type | Method and Description |
|---|---|
static double |
calibrateCameraAruco(List<Mat> corners,
Mat ids,
Mat counter,
Board board,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs)
It's the same function as #calibrateCameraAruco but without calibration error estimation.
|
static double |
calibrateCameraAruco(List<Mat> corners,
Mat ids,
Mat counter,
Board board,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs)
It's the same function as #calibrateCameraAruco but without calibration error estimation.
|
static double |
calibrateCameraAruco(List<Mat> corners,
Mat ids,
Mat counter,
Board board,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs)
It's the same function as #calibrateCameraAruco but without calibration error estimation.
|
static double |
calibrateCameraAruco(List<Mat> corners,
Mat ids,
Mat counter,
Board board,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs,
int flags)
It's the same function as #calibrateCameraAruco but without calibration error estimation.
|
static double |
calibrateCameraAruco(List<Mat> corners,
Mat ids,
Mat counter,
Board board,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs,
int flags,
TermCriteria criteria)
It's the same function as #calibrateCameraAruco but without calibration error estimation.
|
static double |
calibrateCameraArucoExtended(List<Mat> corners,
Mat ids,
Mat counter,
Board board,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs,
Mat stdDeviationsIntrinsics,
Mat stdDeviationsExtrinsics,
Mat perViewErrors)
Calibrate a camera using aruco markers
|
static double |
calibrateCameraArucoExtended(List<Mat> corners,
Mat ids,
Mat counter,
Board board,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs,
Mat stdDeviationsIntrinsics,
Mat stdDeviationsExtrinsics,
Mat perViewErrors,
int flags)
Calibrate a camera using aruco markers
|
static double |
calibrateCameraArucoExtended(List<Mat> corners,
Mat ids,
Mat counter,
Board board,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs,
Mat stdDeviationsIntrinsics,
Mat stdDeviationsExtrinsics,
Mat perViewErrors,
int flags,
TermCriteria criteria)
Calibrate a camera using aruco markers
|
static double |
calibrateCameraCharuco(List<Mat> charucoCorners,
List<Mat> charucoIds,
CharucoBoard board,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs)
It's the same function as #calibrateCameraCharuco but without calibration error estimation.
|
static double |
calibrateCameraCharuco(List<Mat> charucoCorners,
List<Mat> charucoIds,
CharucoBoard board,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs)
It's the same function as #calibrateCameraCharuco but without calibration error estimation.
|
static double |
calibrateCameraCharuco(List<Mat> charucoCorners,
List<Mat> charucoIds,
CharucoBoard board,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs)
It's the same function as #calibrateCameraCharuco but without calibration error estimation.
|
static double |
calibrateCameraCharuco(List<Mat> charucoCorners,
List<Mat> charucoIds,
CharucoBoard board,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs,
int flags)
It's the same function as #calibrateCameraCharuco but without calibration error estimation.
|
static double |
calibrateCameraCharuco(List<Mat> charucoCorners,
List<Mat> charucoIds,
CharucoBoard board,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs,
int flags,
TermCriteria criteria)
It's the same function as #calibrateCameraCharuco but without calibration error estimation.
|
static double |
calibrateCameraCharucoExtended(List<Mat> charucoCorners,
List<Mat> charucoIds,
CharucoBoard board,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs,
Mat stdDeviationsIntrinsics,
Mat stdDeviationsExtrinsics,
Mat perViewErrors)
Calibrate a camera using Charuco corners
|
static double |
calibrateCameraCharucoExtended(List<Mat> charucoCorners,
List<Mat> charucoIds,
CharucoBoard board,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs,
Mat stdDeviationsIntrinsics,
Mat stdDeviationsExtrinsics,
Mat perViewErrors,
int flags)
Calibrate a camera using Charuco corners
|
static double |
calibrateCameraCharucoExtended(List<Mat> charucoCorners,
List<Mat> charucoIds,
CharucoBoard board,
Size imageSize,
Mat cameraMatrix,
Mat distCoeffs,
List<Mat> rvecs,
List<Mat> tvecs,
Mat stdDeviationsIntrinsics,
Mat stdDeviationsExtrinsics,
Mat perViewErrors,
int flags,
TermCriteria criteria)
Calibrate a camera using Charuco corners
|
static Dictionary |
custom_dictionary_from(int nMarkers,
int markerSize,
Dictionary baseDictionary)
Generates a new customizable marker dictionary
|
static Dictionary |
custom_dictionary_from(int nMarkers,
int markerSize,
Dictionary baseDictionary,
int randomSeed)
Generates a new customizable marker dictionary
|
static Dictionary |
custom_dictionary(int nMarkers,
int markerSize)
SEE: generateCustomDictionary
|
static Dictionary |
custom_dictionary(int nMarkers,
int markerSize,
int randomSeed)
SEE: generateCustomDictionary
|
static void |
detectCharucoDiamond(Mat image,
List<Mat> markerCorners,
Mat markerIds,
float squareMarkerLengthRate,
List<Mat> diamondCorners,
Mat diamondIds)
Detect ChArUco Diamond markers
|
static void |
detectCharucoDiamond(Mat image,
List<Mat> markerCorners,
Mat markerIds,
float squareMarkerLengthRate,
List<Mat> diamondCorners,
Mat diamondIds,
Mat cameraMatrix)
Detect ChArUco Diamond markers
|
static void |
detectCharucoDiamond(Mat image,
List<Mat> markerCorners,
Mat markerIds,
float squareMarkerLengthRate,
List<Mat> diamondCorners,
Mat diamondIds,
Mat cameraMatrix,
Mat distCoeffs)
Detect ChArUco Diamond markers
|
static void |
detectMarkers(Mat image,
Dictionary dictionary,
List<Mat> corners,
Mat ids)
Basic marker detection
|
static void |
detectMarkers(Mat image,
Dictionary dictionary,
List<Mat> corners,
Mat ids,
DetectorParameters parameters)
Basic marker detection
|
static void |
detectMarkers(Mat image,
Dictionary dictionary,
List<Mat> corners,
Mat ids,
DetectorParameters parameters,
List<Mat> rejectedImgPoints)
Basic marker detection
|
static void |
detectMarkers(Mat image,
Dictionary dictionary,
List<Mat> corners,
Mat ids,
DetectorParameters parameters,
List<Mat> rejectedImgPoints,
Mat cameraMatrix)
Basic marker detection
|
static void |
detectMarkers(Mat image,
Dictionary dictionary,
List<Mat> corners,
Mat ids,
DetectorParameters parameters,
List<Mat> rejectedImgPoints,
Mat cameraMatrix,
Mat distCoeff)
Basic marker detection
|
static void |
drawAxis(Mat image,
Mat cameraMatrix,
Mat distCoeffs,
Mat rvec,
Mat tvec,
float length)
Deprecated.
use cv::drawFrameAxes
|
static void |
drawDetectedCornersCharuco(Mat image,
Mat charucoCorners)
Draws a set of Charuco corners
|
static void |
drawDetectedCornersCharuco(Mat image,
Mat charucoCorners,
Mat charucoIds)
Draws a set of Charuco corners
|
static void |
drawDetectedCornersCharuco(Mat image,
Mat charucoCorners,
Mat charucoIds,
Scalar cornerColor)
Draws a set of Charuco corners
|
static void |
drawDetectedDiamonds(Mat image,
List<Mat> diamondCorners)
Draw a set of detected ChArUco Diamond markers
|
static void |
drawDetectedDiamonds(Mat image,
List<Mat> diamondCorners,
Mat diamondIds)
Draw a set of detected ChArUco Diamond markers
|
static void |
drawDetectedDiamonds(Mat image,
List<Mat> diamondCorners,
Mat diamondIds,
Scalar borderColor)
Draw a set of detected ChArUco Diamond markers
|
static void |
drawDetectedMarkers(Mat image,
List<Mat> corners)
Draw detected markers in image
|
static void |
drawDetectedMarkers(Mat image,
List<Mat> corners,
Mat ids)
Draw detected markers in image
|
static void |
drawDetectedMarkers(Mat image,
List<Mat> corners,
Mat ids,
Scalar borderColor)
Draw detected markers in image
|
static void |
drawMarker(Dictionary dictionary,
int id,
int sidePixels,
Mat img)
Draw a canonical marker image
|
static void |
drawMarker(Dictionary dictionary,
int id,
int sidePixels,
Mat img,
int borderBits)
Draw a canonical marker image
|
static void |
drawPlanarBoard(Board board,
Size outSize,
Mat img)
Draw a planar board
SEE: _drawPlanarBoardImpl
|
static void |
drawPlanarBoard(Board board,
Size outSize,
Mat img,
int marginSize)
Draw a planar board
SEE: _drawPlanarBoardImpl
|
static void |
drawPlanarBoard(Board board,
Size outSize,
Mat img,
int marginSize,
int borderBits)
Draw a planar board
SEE: _drawPlanarBoardImpl
|
static int |
estimatePoseBoard(List<Mat> corners,
Mat ids,
Board board,
Mat cameraMatrix,
Mat distCoeffs,
Mat rvec,
Mat tvec)
Pose estimation for a board of markers
|
static int |
estimatePoseBoard(List<Mat> corners,
Mat ids,
Board board,
Mat cameraMatrix,
Mat distCoeffs,
Mat rvec,
Mat tvec,
boolean useExtrinsicGuess)
Pose estimation for a board of markers
|
static boolean |
estimatePoseCharucoBoard(Mat charucoCorners,
Mat charucoIds,
CharucoBoard board,
Mat cameraMatrix,
Mat distCoeffs,
Mat rvec,
Mat tvec)
Pose estimation for a ChArUco board given some of their corners
|
static boolean |
estimatePoseCharucoBoard(Mat charucoCorners,
Mat charucoIds,
CharucoBoard board,
Mat cameraMatrix,
Mat distCoeffs,
Mat rvec,
Mat tvec,
boolean useExtrinsicGuess)
Pose estimation for a ChArUco board given some of their corners
|
static void |
estimatePoseSingleMarkers(List<Mat> corners,
float markerLength,
Mat cameraMatrix,
Mat distCoeffs,
Mat rvecs,
Mat tvecs)
Pose estimation for single markers
|
static void |
estimatePoseSingleMarkers(List<Mat> corners,
float markerLength,
Mat cameraMatrix,
Mat distCoeffs,
Mat rvecs,
Mat tvecs,
Mat _objPoints)
Pose estimation for single markers
|
static void |
getBoardObjectAndImagePoints(Board board,
List<Mat> detectedCorners,
Mat detectedIds,
Mat objPoints,
Mat imgPoints)
Given a board configuration and a set of detected markers, returns the corresponding
image points and object points to call solvePnP
|
static Dictionary |
getPredefinedDictionary(int dict)
Returns one of the predefined dictionaries referenced by DICT_*.
|
static int |
interpolateCornersCharuco(List<Mat> markerCorners,
Mat markerIds,
Mat image,
CharucoBoard board,
Mat charucoCorners,
Mat charucoIds)
Interpolate position of ChArUco board corners
|
static int |
interpolateCornersCharuco(List<Mat> markerCorners,
Mat markerIds,
Mat image,
CharucoBoard board,
Mat charucoCorners,
Mat charucoIds,
Mat cameraMatrix)
Interpolate position of ChArUco board corners
|
static int |
interpolateCornersCharuco(List<Mat> markerCorners,
Mat markerIds,
Mat image,
CharucoBoard board,
Mat charucoCorners,
Mat charucoIds,
Mat cameraMatrix,
Mat distCoeffs)
Interpolate position of ChArUco board corners
|
static int |
interpolateCornersCharuco(List<Mat> markerCorners,
Mat markerIds,
Mat image,
CharucoBoard board,
Mat charucoCorners,
Mat charucoIds,
Mat cameraMatrix,
Mat distCoeffs,
int minMarkers)
Interpolate position of ChArUco board corners
|
static void |
refineDetectedMarkers(Mat image,
Board board,
List<Mat> detectedCorners,
Mat detectedIds,
List<Mat> rejectedCorners)
Refind not detected markers based on the already detected and the board layout
|
static void |
refineDetectedMarkers(Mat image,
Board board,
List<Mat> detectedCorners,
Mat detectedIds,
List<Mat> rejectedCorners,
Mat cameraMatrix)
Refind not detected markers based on the already detected and the board layout
|
static void |
refineDetectedMarkers(Mat image,
Board board,
List<Mat> detectedCorners,
Mat detectedIds,
List<Mat> rejectedCorners,
Mat cameraMatrix,
Mat distCoeffs)
Refind not detected markers based on the already detected and the board layout
|
static void |
refineDetectedMarkers(Mat image,
Board board,
List<Mat> detectedCorners,
Mat detectedIds,
List<Mat> rejectedCorners,
Mat cameraMatrix,
Mat distCoeffs,
float minRepDistance)
Refind not detected markers based on the already detected and the board layout
|
static void |
refineDetectedMarkers(Mat image,
Board board,
List<Mat> detectedCorners,
Mat detectedIds,
List<Mat> rejectedCorners,
Mat cameraMatrix,
Mat distCoeffs,
float minRepDistance,
float errorCorrectionRate)
Refind not detected markers based on the already detected and the board layout
|
static void |
refineDetectedMarkers(Mat image,
Board board,
List<Mat> detectedCorners,
Mat detectedIds,
List<Mat> rejectedCorners,
Mat cameraMatrix,
Mat distCoeffs,
float minRepDistance,
float errorCorrectionRate,
boolean checkAllOrders)
Refind not detected markers based on the already detected and the board layout
|
static void |
refineDetectedMarkers(Mat image,
Board board,
List<Mat> detectedCorners,
Mat detectedIds,
List<Mat> rejectedCorners,
Mat cameraMatrix,
Mat distCoeffs,
float minRepDistance,
float errorCorrectionRate,
boolean checkAllOrders,
Mat recoveredIdxs)
Refind not detected markers based on the already detected and the board layout
|
static void |
refineDetectedMarkers(Mat image,
Board board,
List<Mat> detectedCorners,
Mat detectedIds,
List<Mat> rejectedCorners,
Mat cameraMatrix,
Mat distCoeffs,
float minRepDistance,
float errorCorrectionRate,
boolean checkAllOrders,
Mat recoveredIdxs,
DetectorParameters parameters)
Refind not detected markers based on the already detected and the board layout
|
public static final int CORNER_REFINE_NONE
public static final int CORNER_REFINE_SUBPIX
public static final int CORNER_REFINE_CONTOUR
public static final int CORNER_REFINE_APRILTAG
public static final int DICT_4X4_50
public static final int DICT_4X4_100
public static final int DICT_4X4_250
public static final int DICT_4X4_1000
public static final int DICT_5X5_50
public static final int DICT_5X5_100
public static final int DICT_5X5_250
public static final int DICT_5X5_1000
public static final int DICT_6X6_50
public static final int DICT_6X6_100
public static final int DICT_6X6_250
public static final int DICT_6X6_1000
public static final int DICT_7X7_50
public static final int DICT_7X7_100
public static final int DICT_7X7_250
public static final int DICT_7X7_1000
public static final int DICT_ARUCO_ORIGINAL
public static final int DICT_APRILTAG_16h5
public static final int DICT_APRILTAG_25h9
public static final int DICT_APRILTAG_36h10
public static final int DICT_APRILTAG_36h11
public static Dictionary custom_dictionary_from(int nMarkers, int markerSize, Dictionary baseDictionary, int randomSeed)
nMarkers - number of markers in the dictionarymarkerSize - number of bits per dimension of each markersbaseDictionary - Include the markers in this dictionary at the beginning (optional)randomSeed - a user supplied seed for theRNG()
This function creates a new dictionary composed by nMarkers markers and each markers composed
by markerSize x markerSize bits. If baseDictionary is provided, its markers are directly
included and the rest are generated based on them. If the size of baseDictionary is higher
than nMarkers, only the first nMarkers in baseDictionary are taken and no new marker is added.public static Dictionary custom_dictionary_from(int nMarkers, int markerSize, Dictionary baseDictionary)
nMarkers - number of markers in the dictionarymarkerSize - number of bits per dimension of each markersbaseDictionary - Include the markers in this dictionary at the beginning (optional)
This function creates a new dictionary composed by nMarkers markers and each markers composed
by markerSize x markerSize bits. If baseDictionary is provided, its markers are directly
included and the rest are generated based on them. If the size of baseDictionary is higher
than nMarkers, only the first nMarkers in baseDictionary are taken and no new marker is added.public static Dictionary custom_dictionary(int nMarkers, int markerSize, int randomSeed)
nMarkers - automatically generatedmarkerSize - automatically generatedrandomSeed - automatically generatedpublic static Dictionary custom_dictionary(int nMarkers, int markerSize)
nMarkers - automatically generatedmarkerSize - automatically generatedpublic static Dictionary getPredefinedDictionary(int dict)
dict - automatically generatedpublic static boolean estimatePoseCharucoBoard(Mat charucoCorners, Mat charucoIds, CharucoBoard board, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess)
charucoCorners - vector of detected charuco cornerscharucoIds - list of identifiers for each corner in charucoCornersboard - 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 elementsrvec - 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 \b rvec and \b 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.public static boolean estimatePoseCharucoBoard(Mat charucoCorners, Mat charucoIds, CharucoBoard board, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec)
charucoCorners - vector of detected charuco cornerscharucoIds - list of identifiers for each corner in charucoCornersboard - 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 elementsrvec - 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.
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.public static double calibrateCameraArucoExtended(List<Mat> corners, Mat ids, Mat counter, Board board, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors, int flags, TermCriteria criteria)
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 cornerscounter - number of markers in each frame so that corners and ids can be splitboard - Marker Board layoutimageSize - 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 elementsrvecs - 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.public static double calibrateCameraArucoExtended(List<Mat> corners, Mat ids, Mat counter, Board board, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors, int flags)
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 cornerscounter - number of markers in each frame so that corners and ids can be splitboard - Marker Board layoutimageSize - 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 elementsrvecs - 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).
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.public static double calibrateCameraArucoExtended(List<Mat> corners, Mat ids, Mat counter, Board board, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors)
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 cornerscounter - number of markers in each frame so that corners and ids can be splitboard - Marker Board layoutimageSize - 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 elementsrvecs - 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.
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.public static double calibrateCameraAruco(List<Mat> corners, Mat ids, Mat counter, Board board, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, int flags, TermCriteria criteria)
corners - automatically generatedids - automatically generatedcounter - automatically generatedboard - automatically generatedimageSize - automatically generatedcameraMatrix - automatically generateddistCoeffs - automatically generatedrvecs - automatically generatedtvecs - automatically generatedflags - automatically generatedcriteria - automatically generatedpublic static double calibrateCameraAruco(List<Mat> corners, Mat ids, Mat counter, Board board, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, int flags)
corners - automatically generatedids - automatically generatedcounter - automatically generatedboard - automatically generatedimageSize - automatically generatedcameraMatrix - automatically generateddistCoeffs - automatically generatedrvecs - automatically generatedtvecs - automatically generatedflags - automatically generatedpublic static double calibrateCameraAruco(List<Mat> corners, Mat ids, Mat counter, Board board, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs)
corners - automatically generatedids - automatically generatedcounter - automatically generatedboard - automatically generatedimageSize - automatically generatedcameraMatrix - automatically generateddistCoeffs - automatically generatedrvecs - automatically generatedtvecs - automatically generatedpublic static double calibrateCameraAruco(List<Mat> corners, Mat ids, Mat counter, Board board, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs)
corners - automatically generatedids - automatically generatedcounter - automatically generatedboard - automatically generatedimageSize - automatically generatedcameraMatrix - automatically generateddistCoeffs - automatically generatedrvecs - automatically generatedpublic static double calibrateCameraAruco(List<Mat> corners, Mat ids, Mat counter, Board board, Size imageSize, Mat cameraMatrix, Mat distCoeffs)
corners - automatically generatedids - automatically generatedcounter - automatically generatedboard - automatically generatedimageSize - automatically generatedcameraMatrix - automatically generateddistCoeffs - automatically generatedpublic static double calibrateCameraCharucoExtended(List<Mat> charucoCorners, List<Mat> charucoIds, CharucoBoard board, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors, int flags, TermCriteria criteria)
charucoCorners - vector of detected charuco corners per framecharucoIds - list of identifiers for each corner in charucoCorners per frameboard - Marker Board layoutimageSize - input image sizecameraMatrix - 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 elementsrvecs - 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.public static double calibrateCameraCharucoExtended(List<Mat> charucoCorners, List<Mat> charucoIds, CharucoBoard board, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors, int flags)
charucoCorners - vector of detected charuco corners per framecharucoIds - list of identifiers for each corner in charucoCorners per frameboard - Marker Board layoutimageSize - input image sizecameraMatrix - 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 elementsrvecs - 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).
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.public static double calibrateCameraCharucoExtended(List<Mat> charucoCorners, List<Mat> charucoIds, CharucoBoard board, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors)
charucoCorners - vector of detected charuco corners per framecharucoIds - list of identifiers for each corner in charucoCorners per frameboard - Marker Board layoutimageSize - input image sizecameraMatrix - 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 elementsrvecs - 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.
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.public static double calibrateCameraCharuco(List<Mat> charucoCorners, List<Mat> charucoIds, CharucoBoard board, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, int flags, TermCriteria criteria)
charucoCorners - automatically generatedcharucoIds - automatically generatedboard - automatically generatedimageSize - automatically generatedcameraMatrix - automatically generateddistCoeffs - automatically generatedrvecs - automatically generatedtvecs - automatically generatedflags - automatically generatedcriteria - automatically generatedpublic static double calibrateCameraCharuco(List<Mat> charucoCorners, List<Mat> charucoIds, CharucoBoard board, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, int flags)
charucoCorners - automatically generatedcharucoIds - automatically generatedboard - automatically generatedimageSize - automatically generatedcameraMatrix - automatically generateddistCoeffs - automatically generatedrvecs - automatically generatedtvecs - automatically generatedflags - automatically generatedpublic static double calibrateCameraCharuco(List<Mat> charucoCorners, List<Mat> charucoIds, CharucoBoard board, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs)
charucoCorners - automatically generatedcharucoIds - automatically generatedboard - automatically generatedimageSize - automatically generatedcameraMatrix - automatically generateddistCoeffs - automatically generatedrvecs - automatically generatedtvecs - automatically generatedpublic static double calibrateCameraCharuco(List<Mat> charucoCorners, List<Mat> charucoIds, CharucoBoard board, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs)
charucoCorners - automatically generatedcharucoIds - automatically generatedboard - automatically generatedimageSize - automatically generatedcameraMatrix - automatically generateddistCoeffs - automatically generatedrvecs - automatically generatedpublic static double calibrateCameraCharuco(List<Mat> charucoCorners, List<Mat> charucoIds, CharucoBoard board, Size imageSize, Mat cameraMatrix, Mat distCoeffs)
charucoCorners - automatically generatedcharucoIds - automatically generatedboard - automatically generatedimageSize - automatically generatedcameraMatrix - automatically generateddistCoeffs - automatically generatedpublic static int estimatePoseBoard(List<Mat> corners, Mat ids, Board board, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess)
corners - 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.ids - list of identifiers for each marker in cornersboard - layout of markers in the board. The layout is composed by the marker identifiers
and the positions of each marker corner in the board reference system.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 elementsrvec - Output vector (e.g. cv::Mat) corresponding to the rotation vector of the board
(see cv::Rodrigues). Used as initial guess if not empty.tvec - Output vector (e.g. cv::Mat) corresponding to the translation vector of the board.useExtrinsicGuess - defines whether initial guess for \b rvec and \b tvec will be used or not.
Used as initial guess if not empty.
This function receives the detected markers and returns the pose of a marker board composed
by those markers.
A Board of marker has a single world coordinate system which is defined by the board layout.
The returned transformation is the one that transforms points from the board coordinate system
to the camera coordinate system.
Input markers that are not included in the board layout are ignored.
The function returns the number of markers from the input employed for the board pose estimation.
Note that returning a 0 means the pose has not been estimated.public static int estimatePoseBoard(List<Mat> corners, Mat ids, Board board, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec)
corners - 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.ids - list of identifiers for each marker in cornersboard - layout of markers in the board. The layout is composed by the marker identifiers
and the positions of each marker corner in the board reference system.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 elementsrvec - Output vector (e.g. cv::Mat) corresponding to the rotation vector of the board
(see cv::Rodrigues). Used as initial guess if not empty.tvec - Output vector (e.g. cv::Mat) corresponding to the translation vector of the board.
Used as initial guess if not empty.
This function receives the detected markers and returns the pose of a marker board composed
by those markers.
A Board of marker has a single world coordinate system which is defined by the board layout.
The returned transformation is the one that transforms points from the board coordinate system
to the camera coordinate system.
Input markers that are not included in the board layout are ignored.
The function returns the number of markers from the input employed for the board pose estimation.
Note that returning a 0 means the pose has not been estimated.public static int interpolateCornersCharuco(List<Mat> markerCorners, Mat markerIds, Mat image, CharucoBoard board, Mat charucoCorners, Mat charucoIds, Mat cameraMatrix, Mat distCoeffs, int minMarkers)
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 cornersimage - 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 cornerscharucoIds - interpolated chessboard corners identifierscameraMatrix - 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 elementsminMarkers - 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.public static int interpolateCornersCharuco(List<Mat> markerCorners, Mat markerIds, Mat image, CharucoBoard board, Mat charucoCorners, Mat charucoIds, Mat cameraMatrix, Mat distCoeffs)
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 cornersimage - 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 cornerscharucoIds - interpolated chessboard corners identifierscameraMatrix - 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
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.public static int interpolateCornersCharuco(List<Mat> markerCorners, Mat markerIds, Mat image, CharucoBoard board, Mat charucoCorners, Mat charucoIds, Mat cameraMatrix)
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 cornersimage - 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 cornerscharucoIds - interpolated chessboard corners identifierscameraMatrix - optional 3x3 floating-point camera matrix
\(A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\)
\((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\) of 4, 5, 8 or 12 elements
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.public static int interpolateCornersCharuco(List<Mat> markerCorners, Mat markerIds, Mat image, CharucoBoard board, Mat charucoCorners, Mat charucoIds)
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 cornersimage - 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 cornerscharucoIds - interpolated chessboard corners identifiers
\(A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\)
\((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\) of 4, 5, 8 or 12 elements
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.public static void detectCharucoDiamond(Mat image, List<Mat> markerCorners, Mat markerIds, float squareMarkerLengthRate, List<Mat> diamondCorners, Mat diamondIds, Mat cameraMatrix, Mat distCoeffs)
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.
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 can slightly reduce the detection rate.public static void detectCharucoDiamond(Mat image, List<Mat> markerCorners, Mat markerIds, float squareMarkerLengthRate, List<Mat> diamondCorners, Mat diamondIds, Mat cameraMatrix)
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.
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 can slightly reduce the detection rate.public static void detectCharucoDiamond(Mat image, List<Mat> markerCorners, Mat markerIds, float squareMarkerLengthRate, List<Mat> diamondCorners, Mat diamondIds)
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.
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 can slightly reduce the detection rate.public static void detectMarkers(Mat image, Dictionary dictionary, List<Mat> corners, Mat ids, DetectorParameters parameters, List<Mat> rejectedImgPoints, Mat cameraMatrix, Mat distCoeff)
image - input imagedictionary - indicates the type of markers that will be searchedcorners - vector of detected marker 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 is Nx4. The order of the corners is clockwise.ids - vector of identifiers of the detected markers. The identifier is of type int
(e.g. std::vector<int>). For N detected markers, the size of ids is also N.
The identifiers have the same order than the markers in the imgPoints array.parameters - marker detection parametersrejectedImgPoints - contains the imgPoints of those squares whose inner code has not a
correct codification. Useful for debugging purposes.cameraMatrix - optional input 3x3 floating-point camera matrix
\(A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\)distCoeff - 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
Performs marker detection in the input image. Only markers included in the specific dictionary
are searched. For each detected marker, it returns the 2D position of its corner in the image
and its corresponding identifier.
Note that this function does not perform pose estimation.
SEE: estimatePoseSingleMarkers, estimatePoseBoardpublic static void detectMarkers(Mat image, Dictionary dictionary, List<Mat> corners, Mat ids, DetectorParameters parameters, List<Mat> rejectedImgPoints, Mat cameraMatrix)
image - input imagedictionary - indicates the type of markers that will be searchedcorners - vector of detected marker 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 is Nx4. The order of the corners is clockwise.ids - vector of identifiers of the detected markers. The identifier is of type int
(e.g. std::vector<int>). For N detected markers, the size of ids is also N.
The identifiers have the same order than the markers in the imgPoints array.parameters - marker detection parametersrejectedImgPoints - contains the imgPoints of those squares whose inner code has not a
correct codification. Useful for debugging purposes.cameraMatrix - optional input 3x3 floating-point camera matrix
\(A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\)
\((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\) of 4, 5, 8 or 12 elements
Performs marker detection in the input image. Only markers included in the specific dictionary
are searched. For each detected marker, it returns the 2D position of its corner in the image
and its corresponding identifier.
Note that this function does not perform pose estimation.
SEE: estimatePoseSingleMarkers, estimatePoseBoardpublic static void detectMarkers(Mat image, Dictionary dictionary, List<Mat> corners, Mat ids, DetectorParameters parameters, List<Mat> rejectedImgPoints)
image - input imagedictionary - indicates the type of markers that will be searchedcorners - vector of detected marker 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 is Nx4. The order of the corners is clockwise.ids - vector of identifiers of the detected markers. The identifier is of type int
(e.g. std::vector<int>). For N detected markers, the size of ids is also N.
The identifiers have the same order than the markers in the imgPoints array.parameters - marker detection parametersrejectedImgPoints - contains the imgPoints of those squares whose inner code has not a
correct codification. Useful for debugging purposes.
\(A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\)
\((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\) of 4, 5, 8 or 12 elements
Performs marker detection in the input image. Only markers included in the specific dictionary
are searched. For each detected marker, it returns the 2D position of its corner in the image
and its corresponding identifier.
Note that this function does not perform pose estimation.
SEE: estimatePoseSingleMarkers, estimatePoseBoardpublic static void detectMarkers(Mat image, Dictionary dictionary, List<Mat> corners, Mat ids, DetectorParameters parameters)
image - input imagedictionary - indicates the type of markers that will be searchedcorners - vector of detected marker 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 is Nx4. The order of the corners is clockwise.ids - vector of identifiers of the detected markers. The identifier is of type int
(e.g. std::vector<int>). For N detected markers, the size of ids is also N.
The identifiers have the same order than the markers in the imgPoints array.parameters - marker detection parameters
correct codification. Useful for debugging purposes.
\(A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\)
\((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\) of 4, 5, 8 or 12 elements
Performs marker detection in the input image. Only markers included in the specific dictionary
are searched. For each detected marker, it returns the 2D position of its corner in the image
and its corresponding identifier.
Note that this function does not perform pose estimation.
SEE: estimatePoseSingleMarkers, estimatePoseBoardpublic static void detectMarkers(Mat image, Dictionary dictionary, List<Mat> corners, Mat ids)
image - input imagedictionary - indicates the type of markers that will be searchedcorners - vector of detected marker 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 is Nx4. The order of the corners is clockwise.ids - vector of identifiers of the detected markers. The identifier is of type int
(e.g. std::vector<int>). For N detected markers, the size of ids is also N.
The identifiers have the same order than the markers in the imgPoints array.
correct codification. Useful for debugging purposes.
\(A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\)
\((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\) of 4, 5, 8 or 12 elements
Performs marker detection in the input image. Only markers included in the specific dictionary
are searched. For each detected marker, it returns the 2D position of its corner in the image
and its corresponding identifier.
Note that this function does not perform pose estimation.
SEE: estimatePoseSingleMarkers, estimatePoseBoard@Deprecated public static void drawAxis(Mat image, Mat cameraMatrix, Mat distCoeffs, Mat rvec, Mat tvec, float length)
image - input/output image. It must have 1 or 3 channels. The number of channels is not
altered.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 elementsrvec - rotation vector of the coordinate system that will be drawn. (SEE: Rodrigues).tvec - translation vector of the coordinate system that will be drawn.length - length of the painted axis in the same unit than tvec (usually in meters)
Given the pose estimation of a marker or board, this function draws the axis of the world
coordinate system, i.e. the system centered on the marker/board. Useful for debugging purposes.public static void drawDetectedCornersCharuco(Mat image, Mat charucoCorners, Mat charucoIds, Scalar cornerColor)
image - input/output image. It must have 1 or 3 channels. The number of channels is not
altered.charucoCorners - vector of detected charuco cornerscharucoIds - list of identifiers for each corner in charucoCornerscornerColor - color of the square surrounding each corner
This function draws a set of detected Charuco corners. If identifiers vector is provided, it also
draws the id of each corner.public static void drawDetectedCornersCharuco(Mat image, Mat charucoCorners, Mat charucoIds)
image - input/output image. It must have 1 or 3 channels. The number of channels is not
altered.charucoCorners - vector of detected charuco cornerscharucoIds - list of identifiers for each corner in charucoCorners
This function draws a set of detected Charuco corners. If identifiers vector is provided, it also
draws the id of each corner.public static void drawDetectedCornersCharuco(Mat image, Mat charucoCorners)
image - input/output image. It must have 1 or 3 channels. The number of channels is not
altered.charucoCorners - vector of detected charuco corners
This function draws a set of detected Charuco corners. If identifiers vector is provided, it also
draws the id of each corner.public static void drawDetectedDiamonds(Mat image, List<Mat> diamondCorners, Mat diamondIds, Scalar borderColor)
image - input/output image. It must have 1 or 3 channels. The number of channels is not
altered.diamondCorners - positions of diamond corners in the same format returned by
detectCharucoDiamond(). (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.diamondIds - vector of identifiers for diamonds in diamondCorners, in the same format
returned by detectCharucoDiamond() (e.g. std::vector<Vec4i>).
Optional, if not provided, ids are not painted.borderColor - color of marker borders. Rest of colors (text color and first corner color)
are calculated based on this one.
Given an array of detected diamonds, this functions draws them in the image. The marker borders
are painted and the markers identifiers if provided.
Useful for debugging purposes.public static void drawDetectedDiamonds(Mat image, List<Mat> diamondCorners, Mat diamondIds)
image - input/output image. It must have 1 or 3 channels. The number of channels is not
altered.diamondCorners - positions of diamond corners in the same format returned by
detectCharucoDiamond(). (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.diamondIds - vector of identifiers for diamonds in diamondCorners, in the same format
returned by detectCharucoDiamond() (e.g. std::vector<Vec4i>).
Optional, if not provided, ids are not painted.
are calculated based on this one.
Given an array of detected diamonds, this functions draws them in the image. The marker borders
are painted and the markers identifiers if provided.
Useful for debugging purposes.public static void drawDetectedDiamonds(Mat image, List<Mat> diamondCorners)
image - input/output image. It must have 1 or 3 channels. The number of channels is not
altered.diamondCorners - positions of diamond corners in the same format returned by
detectCharucoDiamond(). (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.
returned by detectCharucoDiamond() (e.g. std::vector<Vec4i>).
Optional, if not provided, ids are not painted.
are calculated based on this one.
Given an array of detected diamonds, this functions draws them in the image. The marker borders
are painted and the markers identifiers if provided.
Useful for debugging purposes.public static void drawDetectedMarkers(Mat image, List<Mat> corners, Mat ids, Scalar borderColor)
image - input/output image. It must have 1 or 3 channels. The number of channels is not
altered.corners - positions of marker corners on input image.
(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.ids - vector of identifiers for markers in markersCorners .
Optional, if not provided, ids are not painted.borderColor - color of marker borders. Rest of colors (text color and first corner color)
are calculated based on this one to improve visualization.
Given an array of detected marker corners and its corresponding ids, this functions draws
the markers in the image. The marker borders are painted and the markers identifiers if provided.
Useful for debugging purposes.public static void drawDetectedMarkers(Mat image, List<Mat> corners, Mat ids)
image - input/output image. It must have 1 or 3 channels. The number of channels is not
altered.corners - positions of marker corners on input image.
(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.ids - vector of identifiers for markers in markersCorners .
Optional, if not provided, ids are not painted.
are calculated based on this one to improve visualization.
Given an array of detected marker corners and its corresponding ids, this functions draws
the markers in the image. The marker borders are painted and the markers identifiers if provided.
Useful for debugging purposes.public static void drawDetectedMarkers(Mat image, List<Mat> corners)
image - input/output image. It must have 1 or 3 channels. The number of channels is not
altered.corners - positions of marker corners on input image.
(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.
Optional, if not provided, ids are not painted.
are calculated based on this one to improve visualization.
Given an array of detected marker corners and its corresponding ids, this functions draws
the markers in the image. The marker borders are painted and the markers identifiers if provided.
Useful for debugging purposes.public static void drawMarker(Dictionary dictionary, int id, int sidePixels, Mat img, int borderBits)
dictionary - dictionary of markers indicating the type of markersid - identifier of the marker that will be returned. It has to be a valid id
in the specified dictionary.sidePixels - size of the image in pixelsimg - output image with the markerborderBits - width of the marker border.
This function returns a marker image in its canonical form (i.e. ready to be printed)public static void drawMarker(Dictionary dictionary, int id, int sidePixels, Mat img)
dictionary - dictionary of markers indicating the type of markersid - identifier of the marker that will be returned. It has to be a valid id
in the specified dictionary.sidePixels - size of the image in pixelsimg - output image with the marker
This function returns a marker image in its canonical form (i.e. ready to be printed)public static void drawPlanarBoard(Board board, Size outSize, Mat img, int marginSize, int borderBits)
board - layout of the board that will be drawn. The board should be planar,
z coordinate is ignoredoutSize - size of the output image in pixels.img - output image with the board. The size of this image will be outSize
and the board will be on the center, keeping the board proportions.marginSize - minimum margins (in pixels) of the board in the output imageborderBits - width of the marker borders.
This function return the image of a planar board, ready to be printed. It assumes
the Board layout specified is planar by ignoring the z coordinates of the object points.public static void drawPlanarBoard(Board board, Size outSize, Mat img, int marginSize)
board - layout of the board that will be drawn. The board should be planar,
z coordinate is ignoredoutSize - size of the output image in pixels.img - output image with the board. The size of this image will be outSize
and the board will be on the center, keeping the board proportions.marginSize - minimum margins (in pixels) of the board in the output image
This function return the image of a planar board, ready to be printed. It assumes
the Board layout specified is planar by ignoring the z coordinates of the object points.public static void drawPlanarBoard(Board board, Size outSize, Mat img)
board - layout of the board that will be drawn. The board should be planar,
z coordinate is ignoredoutSize - size of the output image in pixels.img - output image with the board. The size of this image will be outSize
and the board will be on the center, keeping the board proportions.
This function return the image of a planar board, ready to be printed. It assumes
the Board layout specified is planar by ignoring the z coordinates of the object points.public static void estimatePoseSingleMarkers(List<Mat> corners, float markerLength, Mat cameraMatrix, Mat distCoeffs, Mat rvecs, Mat tvecs, Mat _objPoints)
corners - 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.
SEE: detectMarkersmarkerLength - the length of the markers' side. The returning translation vectors will
be in the same unit. Normally, unit is meters.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 elementsrvecs - array of output rotation vectors (SEE: Rodrigues) (e.g. std::vector<cv::Vec3d>).
Each element in rvecs corresponds to the specific marker in imgPoints.tvecs - array of output translation vectors (e.g. std::vector<cv::Vec3d>).
Each element in tvecs corresponds to the specific marker in imgPoints._objPoints - array of object points of all the marker corners
This function receives the detected markers and returns their pose estimation respect to
the camera individually. So for each marker, one rotation and translation vector is returned.
The returned transformation is the one that transforms points from each marker coordinate system
to the camera coordinate system.
The marker corrdinate system is centered on the middle of the marker, with the Z axis
perpendicular to the marker plane.
The coordinates of the four corners 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)public static void estimatePoseSingleMarkers(List<Mat> corners, float markerLength, Mat cameraMatrix, Mat distCoeffs, Mat rvecs, Mat tvecs)
corners - 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.
SEE: detectMarkersmarkerLength - the length of the markers' side. The returning translation vectors will
be in the same unit. Normally, unit is meters.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 elementsrvecs - array of output rotation vectors (SEE: Rodrigues) (e.g. std::vector<cv::Vec3d>).
Each element in rvecs corresponds to the specific marker in imgPoints.tvecs - array of output translation vectors (e.g. std::vector<cv::Vec3d>).
Each element in tvecs corresponds to the specific marker in imgPoints.
This function receives the detected markers and returns their pose estimation respect to
the camera individually. So for each marker, one rotation and translation vector is returned.
The returned transformation is the one that transforms points from each marker coordinate system
to the camera coordinate system.
The marker corrdinate system is centered on the middle of the marker, with the Z axis
perpendicular to the marker plane.
The coordinates of the four corners 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)public static void getBoardObjectAndImagePoints(Board board, List<Mat> detectedCorners, Mat detectedIds, Mat objPoints, Mat imgPoints)
board - Marker board layout.detectedCorners - List of detected marker corners of the board.detectedIds - List of identifiers for each marker.objPoints - Vector of vectors of board marker points in the board coordinate space.imgPoints - Vector of vectors of the projections of board marker corner points.public static void refineDetectedMarkers(Mat image, Board board, List<Mat> detectedCorners, Mat detectedIds, List<Mat> rejectedCorners, Mat cameraMatrix, Mat distCoeffs, float minRepDistance, float errorCorrectionRate, boolean checkAllOrders, Mat recoveredIdxs, DetectorParameters parameters)
image - input imageboard - layout of markers in the board.detectedCorners - vector of already detected marker corners.detectedIds - vector of already detected marker identifiers.rejectedCorners - vector of rejected candidates during the marker detection process.cameraMatrix - optional input 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 elementsminRepDistance - minimum distance between the corners of the rejected candidate and the
reprojected marker in order to consider it as a correspondence.errorCorrectionRate - rate of allowed erroneous bits respect to the error correction
capability of the used dictionary. -1 ignores the error correction step.checkAllOrders - Consider the four posible corner orders in the rejectedCorners array.
If it set to false, only the provided corner order is considered (default true).recoveredIdxs - Optional array to returns the indexes of the recovered candidates in the
original rejectedCorners array.parameters - marker detection parameters
This function tries to find markers that were not detected in the basic detecMarkers function.
First, based on the current detected marker and the board layout, the function interpolates
the position of the missing markers. Then it tries to find correspondence between the reprojected
markers and the rejected candidates based on the minRepDistance and errorCorrectionRate
parameters.
If camera parameters and distortion coefficients are provided, missing markers are reprojected
using projectPoint function. If not, missing marker projections are interpolated using global
homography, and all the marker corners in the board must have the same Z coordinate.public static void refineDetectedMarkers(Mat image, Board board, List<Mat> detectedCorners, Mat detectedIds, List<Mat> rejectedCorners, Mat cameraMatrix, Mat distCoeffs, float minRepDistance, float errorCorrectionRate, boolean checkAllOrders, Mat recoveredIdxs)
image - input imageboard - layout of markers in the board.detectedCorners - vector of already detected marker corners.detectedIds - vector of already detected marker identifiers.rejectedCorners - vector of rejected candidates during the marker detection process.cameraMatrix - optional input 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 elementsminRepDistance - minimum distance between the corners of the rejected candidate and the
reprojected marker in order to consider it as a correspondence.errorCorrectionRate - rate of allowed erroneous bits respect to the error correction
capability of the used dictionary. -1 ignores the error correction step.checkAllOrders - Consider the four posible corner orders in the rejectedCorners array.
If it set to false, only the provided corner order is considered (default true).recoveredIdxs - Optional array to returns the indexes of the recovered candidates in the
original rejectedCorners array.
This function tries to find markers that were not detected in the basic detecMarkers function.
First, based on the current detected marker and the board layout, the function interpolates
the position of the missing markers. Then it tries to find correspondence between the reprojected
markers and the rejected candidates based on the minRepDistance and errorCorrectionRate
parameters.
If camera parameters and distortion coefficients are provided, missing markers are reprojected
using projectPoint function. If not, missing marker projections are interpolated using global
homography, and all the marker corners in the board must have the same Z coordinate.public static void refineDetectedMarkers(Mat image, Board board, List<Mat> detectedCorners, Mat detectedIds, List<Mat> rejectedCorners, Mat cameraMatrix, Mat distCoeffs, float minRepDistance, float errorCorrectionRate, boolean checkAllOrders)
image - input imageboard - layout of markers in the board.detectedCorners - vector of already detected marker corners.detectedIds - vector of already detected marker identifiers.rejectedCorners - vector of rejected candidates during the marker detection process.cameraMatrix - optional input 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 elementsminRepDistance - minimum distance between the corners of the rejected candidate and the
reprojected marker in order to consider it as a correspondence.errorCorrectionRate - rate of allowed erroneous bits respect to the error correction
capability of the used dictionary. -1 ignores the error correction step.checkAllOrders - Consider the four posible corner orders in the rejectedCorners array.
If it set to false, only the provided corner order is considered (default true).
original rejectedCorners array.
This function tries to find markers that were not detected in the basic detecMarkers function.
First, based on the current detected marker and the board layout, the function interpolates
the position of the missing markers. Then it tries to find correspondence between the reprojected
markers and the rejected candidates based on the minRepDistance and errorCorrectionRate
parameters.
If camera parameters and distortion coefficients are provided, missing markers are reprojected
using projectPoint function. If not, missing marker projections are interpolated using global
homography, and all the marker corners in the board must have the same Z coordinate.public static void refineDetectedMarkers(Mat image, Board board, List<Mat> detectedCorners, Mat detectedIds, List<Mat> rejectedCorners, Mat cameraMatrix, Mat distCoeffs, float minRepDistance, float errorCorrectionRate)
image - input imageboard - layout of markers in the board.detectedCorners - vector of already detected marker corners.detectedIds - vector of already detected marker identifiers.rejectedCorners - vector of rejected candidates during the marker detection process.cameraMatrix - optional input 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 elementsminRepDistance - minimum distance between the corners of the rejected candidate and the
reprojected marker in order to consider it as a correspondence.errorCorrectionRate - rate of allowed erroneous bits respect to the error correction
capability of the used dictionary. -1 ignores the error correction step.
If it set to false, only the provided corner order is considered (default true).
original rejectedCorners array.
This function tries to find markers that were not detected in the basic detecMarkers function.
First, based on the current detected marker and the board layout, the function interpolates
the position of the missing markers. Then it tries to find correspondence between the reprojected
markers and the rejected candidates based on the minRepDistance and errorCorrectionRate
parameters.
If camera parameters and distortion coefficients are provided, missing markers are reprojected
using projectPoint function. If not, missing marker projections are interpolated using global
homography, and all the marker corners in the board must have the same Z coordinate.public static void refineDetectedMarkers(Mat image, Board board, List<Mat> detectedCorners, Mat detectedIds, List<Mat> rejectedCorners, Mat cameraMatrix, Mat distCoeffs, float minRepDistance)
image - input imageboard - layout of markers in the board.detectedCorners - vector of already detected marker corners.detectedIds - vector of already detected marker identifiers.rejectedCorners - vector of rejected candidates during the marker detection process.cameraMatrix - optional input 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 elementsminRepDistance - minimum distance between the corners of the rejected candidate and the
reprojected marker in order to consider it as a correspondence.
capability of the used dictionary. -1 ignores the error correction step.
If it set to false, only the provided corner order is considered (default true).
original rejectedCorners array.
This function tries to find markers that were not detected in the basic detecMarkers function.
First, based on the current detected marker and the board layout, the function interpolates
the position of the missing markers. Then it tries to find correspondence between the reprojected
markers and the rejected candidates based on the minRepDistance and errorCorrectionRate
parameters.
If camera parameters and distortion coefficients are provided, missing markers are reprojected
using projectPoint function. If not, missing marker projections are interpolated using global
homography, and all the marker corners in the board must have the same Z coordinate.public static void refineDetectedMarkers(Mat image, Board board, List<Mat> detectedCorners, Mat detectedIds, List<Mat> rejectedCorners, Mat cameraMatrix, Mat distCoeffs)
image - input imageboard - layout of markers in the board.detectedCorners - vector of already detected marker corners.detectedIds - vector of already detected marker identifiers.rejectedCorners - vector of rejected candidates during the marker detection process.cameraMatrix - optional input 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
reprojected marker in order to consider it as a correspondence.
capability of the used dictionary. -1 ignores the error correction step.
If it set to false, only the provided corner order is considered (default true).
original rejectedCorners array.
This function tries to find markers that were not detected in the basic detecMarkers function.
First, based on the current detected marker and the board layout, the function interpolates
the position of the missing markers. Then it tries to find correspondence between the reprojected
markers and the rejected candidates based on the minRepDistance and errorCorrectionRate
parameters.
If camera parameters and distortion coefficients are provided, missing markers are reprojected
using projectPoint function. If not, missing marker projections are interpolated using global
homography, and all the marker corners in the board must have the same Z coordinate.public static void refineDetectedMarkers(Mat image, Board board, List<Mat> detectedCorners, Mat detectedIds, List<Mat> rejectedCorners, Mat cameraMatrix)
image - input imageboard - layout of markers in the board.detectedCorners - vector of already detected marker corners.detectedIds - vector of already detected marker identifiers.rejectedCorners - vector of rejected candidates during the marker detection process.cameraMatrix - optional input 3x3 floating-point camera matrix
\(A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\)
\((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\) of 4, 5, 8 or 12 elements
reprojected marker in order to consider it as a correspondence.
capability of the used dictionary. -1 ignores the error correction step.
If it set to false, only the provided corner order is considered (default true).
original rejectedCorners array.
This function tries to find markers that were not detected in the basic detecMarkers function.
First, based on the current detected marker and the board layout, the function interpolates
the position of the missing markers. Then it tries to find correspondence between the reprojected
markers and the rejected candidates based on the minRepDistance and errorCorrectionRate
parameters.
If camera parameters and distortion coefficients are provided, missing markers are reprojected
using projectPoint function. If not, missing marker projections are interpolated using global
homography, and all the marker corners in the board must have the same Z coordinate.public static void refineDetectedMarkers(Mat image, Board board, List<Mat> detectedCorners, Mat detectedIds, List<Mat> rejectedCorners)
image - input imageboard - layout of markers in the board.detectedCorners - vector of already detected marker corners.detectedIds - vector of already detected marker identifiers.rejectedCorners - vector of rejected candidates during the marker detection process.
\(A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\)
\((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\) of 4, 5, 8 or 12 elements
reprojected marker in order to consider it as a correspondence.
capability of the used dictionary. -1 ignores the error correction step.
If it set to false, only the provided corner order is considered (default true).
original rejectedCorners array.
This function tries to find markers that were not detected in the basic detecMarkers function.
First, based on the current detected marker and the board layout, the function interpolates
the position of the missing markers. Then it tries to find correspondence between the reprojected
markers and the rejected candidates based on the minRepDistance and errorCorrectionRate
parameters.
If camera parameters and distortion coefficients are provided, missing markers are reprojected
using projectPoint function. If not, missing marker projections are interpolated using global
homography, and all the marker corners in the board must have the same Z coordinate.Copyright © 2020. All rights reserved.