ArUco markers and boards detection for robust camera pose estimation

Detailed Description

ArUco Marker Detection Square fiducial markers (also known as Augmented Reality Markers) are useful for easy, fast and robust camera pose estimation.

The main functionality of ArucoDetector class is detection of markers in an image. If the markers are grouped as a board, then you can try to recover the missing markers with ArucoDetector::refineDetectedMarkers(). ArUco markers can also be used for advanced chessboard corner finding. To do this, group the markers in the CharucoBoard and find the corners of the chessboard with the CharucoDetector::detectBoard().

The implementation is based on the ArUco Library by R. Muñoz-Salinas and S. Garrido-Jurado [99].

Markers can also be detected based on the AprilTag 2 [292] fiducial detection method.

See also

[99] This code has been originally developed by Sergio Garrido-Jurado as a project for Google Summer of Code 2015 (GSoC 15).

Classes
class	cv::aruco::ArucoDetector
	The main functionality of ArucoDetector class is detection of markers in an image with detectMarkers() method. More...
class	cv::aruco::Board
	Board of ArUco markers. More...
class	cv::aruco::CharucoBoard
	ChArUco board is a planar chessboard where the markers are placed inside the white squares of a chessboard. More...
class	cv::aruco::CharucoDetector
struct	cv::aruco::CharucoParameters
struct	cv::aruco::DetectorParameters
	struct DetectorParameters is used by ArucoDetector More...
class	cv::aruco::Dictionary
	Dictionary is a set of unique ArUco markers of the same size. More...
class	cv::aruco::GridBoard
	Planar board with grid arrangement of markers. More...
struct	cv::aruco::RefineParameters
	struct RefineParameters is used by ArucoDetector More...

Enumerations
enum	cv::aruco::CornerRefineMethod {   cv::aruco::CORNER_REFINE_NONE ,   cv::aruco::CORNER_REFINE_SUBPIX ,   cv::aruco::CORNER_REFINE_CONTOUR ,   cv::aruco::CORNER_REFINE_APRILTAG }
enum	cv::aruco::PredefinedDictionaryType {   cv::aruco::DICT_4X4_50 = 0 ,   cv::aruco::DICT_4X4_100 ,   cv::aruco::DICT_4X4_250 ,   cv::aruco::DICT_4X4_1000 ,   cv::aruco::DICT_5X5_50 ,   cv::aruco::DICT_5X5_100 ,   cv::aruco::DICT_5X5_250 ,   cv::aruco::DICT_5X5_1000 ,   cv::aruco::DICT_6X6_50 ,   cv::aruco::DICT_6X6_100 ,   cv::aruco::DICT_6X6_250 ,   cv::aruco::DICT_6X6_1000 ,   cv::aruco::DICT_7X7_50 ,   cv::aruco::DICT_7X7_100 ,   cv::aruco::DICT_7X7_250 ,   cv::aruco::DICT_7X7_1000 ,   cv::aruco::DICT_ARUCO_ORIGINAL ,   cv::aruco::DICT_APRILTAG_16h5 ,   cv::aruco::DICT_APRILTAG_25h9 ,   cv::aruco::DICT_APRILTAG_36h10 ,   cv::aruco::DICT_APRILTAG_36h11 ,   cv::aruco::DICT_ARUCO_MIP_36h12 }
	Predefined markers dictionaries/sets. More...

Functions
void	cv::aruco::drawDetectedCornersCharuco (InputOutputArray image, InputArray charucoCorners, InputArray charucoIds=noArray(), Scalar cornerColor=Scalar(255, 0, 0))
	Draws a set of Charuco corners.
void	cv::aruco::drawDetectedDiamonds (InputOutputArray image, InputArrayOfArrays diamondCorners, InputArray diamondIds=noArray(), Scalar borderColor=Scalar(0, 0, 255))
	Draw a set of detected ChArUco Diamond markers.
void	cv::aruco::drawDetectedMarkers (InputOutputArray image, InputArrayOfArrays corners, InputArray ids=noArray(), Scalar borderColor=Scalar(0, 255, 0))
	Draw detected markers in image.
Dictionary	cv::aruco::extendDictionary (int nMarkers, int markerSize, const Dictionary &baseDictionary=Dictionary(), int randomSeed=0)
	Extend base dictionary by new nMarkers.
void	cv::aruco::generateImageMarker (const Dictionary &dictionary, int id, int sidePixels, OutputArray img, int borderBits=1)
	Generate a canonical marker image.
Dictionary	cv::aruco::getPredefinedDictionary (int dict)
	Returns one of the predefined dictionaries referenced by DICT_*.
Dictionary	cv::aruco::getPredefinedDictionary (PredefinedDictionaryType name)
	Returns one of the predefined dictionaries defined in PredefinedDictionaryType.

Enumeration Type Documentation

CornerRefineMethod

enum cv::aruco::CornerRefineMethod

#include <opencv2/objdetect/aruco_detector.hpp>

Enumerator
CORNER_REFINE_NONE  Python: cv.aruco.CORNER_REFINE_NONE	Tag and corners detection based on the ArUco approach.
CORNER_REFINE_SUBPIX  Python: cv.aruco.CORNER_REFINE_SUBPIX	ArUco approach and refine the corners locations using corner subpixel accuracy.
CORNER_REFINE_CONTOUR  Python: cv.aruco.CORNER_REFINE_CONTOUR	ArUco approach and refine the corners locations using the contour-points line fitting.
CORNER_REFINE_APRILTAG  Python: cv.aruco.CORNER_REFINE_APRILTAG	Tag and corners detection based on the AprilTag 2 approach [292].

PredefinedDictionaryType

enum cv::aruco::PredefinedDictionaryType

#include <opencv2/objdetect/aruco_dictionary.hpp>

Predefined markers dictionaries/sets.

Each dictionary indicates the number of bits and the number of markers contained

• DICT_ARUCO_ORIGINAL: standard ArUco Library Markers. 1024 markers, 5x5 bits, 0 minimum distance

Enumerator
DICT_4X4_50  Python: cv.aruco.DICT_4X4_50	4x4 bits, minimum hamming distance between any two codes = 4, 50 codes
DICT_4X4_100  Python: cv.aruco.DICT_4X4_100	4x4 bits, minimum hamming distance between any two codes = 3, 100 codes
DICT_4X4_250  Python: cv.aruco.DICT_4X4_250	4x4 bits, minimum hamming distance between any two codes = 3, 250 codes
DICT_4X4_1000  Python: cv.aruco.DICT_4X4_1000	4x4 bits, minimum hamming distance between any two codes = 2, 1000 codes
DICT_5X5_50  Python: cv.aruco.DICT_5X5_50	5x5 bits, minimum hamming distance between any two codes = 8, 50 codes
DICT_5X5_100  Python: cv.aruco.DICT_5X5_100	5x5 bits, minimum hamming distance between any two codes = 7, 100 codes
DICT_5X5_250  Python: cv.aruco.DICT_5X5_250	5x5 bits, minimum hamming distance between any two codes = 6, 250 codes
DICT_5X5_1000  Python: cv.aruco.DICT_5X5_1000	5x5 bits, minimum hamming distance between any two codes = 5, 1000 codes
DICT_6X6_50  Python: cv.aruco.DICT_6X6_50	6x6 bits, minimum hamming distance between any two codes = 13, 50 codes
DICT_6X6_100  Python: cv.aruco.DICT_6X6_100	6x6 bits, minimum hamming distance between any two codes = 12, 100 codes
DICT_6X6_250  Python: cv.aruco.DICT_6X6_250	6x6 bits, minimum hamming distance between any two codes = 11, 250 codes
DICT_6X6_1000  Python: cv.aruco.DICT_6X6_1000	6x6 bits, minimum hamming distance between any two codes = 9, 1000 codes
DICT_7X7_50  Python: cv.aruco.DICT_7X7_50	7x7 bits, minimum hamming distance between any two codes = 19, 50 codes
DICT_7X7_100  Python: cv.aruco.DICT_7X7_100	7x7 bits, minimum hamming distance between any two codes = 18, 100 codes
DICT_7X7_250  Python: cv.aruco.DICT_7X7_250	7x7 bits, minimum hamming distance between any two codes = 17, 250 codes
DICT_7X7_1000  Python: cv.aruco.DICT_7X7_1000	7x7 bits, minimum hamming distance between any two codes = 14, 1000 codes
DICT_ARUCO_ORIGINAL  Python: cv.aruco.DICT_ARUCO_ORIGINAL	6x6 bits, minimum hamming distance between any two codes = 3, 1024 codes
DICT_APRILTAG_16h5  Python: cv.aruco.DICT_APRILTAG_16h5	4x4 bits, minimum hamming distance between any two codes = 5, 30 codes
DICT_APRILTAG_25h9  Python: cv.aruco.DICT_APRILTAG_25h9	5x5 bits, minimum hamming distance between any two codes = 9, 35 codes
DICT_APRILTAG_36h10  Python: cv.aruco.DICT_APRILTAG_36h10	6x6 bits, minimum hamming distance between any two codes = 10, 2320 codes
DICT_APRILTAG_36h11  Python: cv.aruco.DICT_APRILTAG_36h11	6x6 bits, minimum hamming distance between any two codes = 11, 587 codes
DICT_ARUCO_MIP_36h12  Python: cv.aruco.DICT_ARUCO_MIP_36h12	6x6 bits, minimum hamming distance between any two codes = 12, 250 codes

Function Documentation

drawDetectedCornersCharuco()

void cv::aruco::drawDetectedCornersCharuco	(	InputOutputArray	image,
		InputArray	charucoCorners,
		InputArray	charucoIds = noArray(),
		Scalar	cornerColor = Scalar(255, 0, 0) )

Python:
	cv.aruco.drawDetectedCornersCharuco(	image, charucoCorners[, charucoIds[, cornerColor]]	) ->	image

#include <opencv2/objdetect/charuco_detector.hpp>

Draws a set of Charuco corners.

Parameters

image	input/output image. It must have 1 or 3 channels. The number of channels is not altered.
charucoCorners	vector of detected charuco corners
charucoIds	list of identifiers for each corner in charucoCorners
cornerColor	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.

drawDetectedDiamonds()

void cv::aruco::drawDetectedDiamonds	(	InputOutputArray	image,
		InputArrayOfArrays	diamondCorners,
		InputArray	diamondIds = noArray(),
		Scalar	borderColor = Scalar(0, 0, 255) )

Python:
	cv.aruco.drawDetectedDiamonds(	image, diamondCorners[, diamondIds[, borderColor]]	) ->	image

#include <opencv2/objdetect/charuco_detector.hpp>

Draw a set of detected ChArUco Diamond markers.

Parameters

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.

drawDetectedMarkers()

void cv::aruco::drawDetectedMarkers	(	InputOutputArray	image,
		InputArrayOfArrays	corners,
		InputArray	ids = noArray(),
		Scalar	borderColor = Scalar(0, 255, 0) )

Python:
	cv.aruco.drawDetectedMarkers(	image, corners[, ids[, borderColor]]	) ->	image

#include <opencv2/objdetect/aruco_detector.hpp>

Draw detected markers in image.

Parameters

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.

extendDictionary()

Dictionary cv::aruco::extendDictionary	(	int	nMarkers,
		int	markerSize,
		const Dictionary &	baseDictionary = Dictionary(),
		int	randomSeed = 0 )

Python:
	cv.aruco.extendDictionary(	nMarkers, markerSize[, baseDictionary[, randomSeed]]	) ->	retval

#include <opencv2/objdetect/aruco_dictionary.hpp>

Extend base dictionary by new nMarkers.

Parameters

nMarkers	number of markers in the dictionary
markerSize	number of bits per dimension of each markers
baseDictionary	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.

generateImageMarker()

void cv::aruco::generateImageMarker	(	const Dictionary &	dictionary,
		int	id,
		int	sidePixels,
		OutputArray	img,
		int	borderBits = 1 )

Python:
	cv.aruco.generateImageMarker(	dictionary, id, sidePixels[, img[, borderBits]]	) ->	img

#include <opencv2/objdetect/aruco_detector.hpp>

Generate a canonical marker image.

Parameters

dictionary	dictionary of markers indicating the type of markers
id	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 pixels
img	output image with the marker
borderBits	width of the marker border.

This function returns a marker image in its canonical form (i.e. ready to be printed)

getPredefinedDictionary() [1/2]

Dictionary cv::aruco::getPredefinedDictionary

(

int

dict

)

Python:
	cv.aruco.getPredefinedDictionary(	dict	) ->	retval

#include <opencv2/objdetect/aruco_dictionary.hpp>

Returns one of the predefined dictionaries referenced by DICT_*.

getPredefinedDictionary() [2/2]

Dictionary cv::aruco::getPredefinedDictionary

(

PredefinedDictionaryType

name

)

Python:
	cv.aruco.getPredefinedDictionary(	dict	) ->	retval

#include <opencv2/objdetect/aruco_dictionary.hpp>

Returns one of the predefined dictionaries defined in PredefinedDictionaryType.