OpenCV  4.8.0-dev
Open Source Computer Vision
Classes | Enumerations | Functions
ArUco markers and boards detection for robust camera pose estimation

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

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 [89].

Markers can also be detected based on the AprilTag 2 [276] fiducial detection method.

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

Enumeration Type Documentation

◆ 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 [276].

◆ 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
imageinput/output image. It must have 1 or 3 channels. The number of channels is not altered.
charucoCornersvector of detected charuco corners
charucoIdslist of identifiers for each corner in charucoCorners
cornerColorcolor 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
imageinput/output image. It must have 1 or 3 channels. The number of channels is not altered.
diamondCornerspositions 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.
diamondIdsvector 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.
borderColorcolor 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
imageinput/output image. It must have 1 or 3 channels. The number of channels is not altered.
cornerspositions 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.
idsvector of identifiers for markers in markersCorners . Optional, if not provided, ids are not painted.
borderColorcolor 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
nMarkersnumber of markers in the dictionary
markerSizenumber of bits per dimension of each markers
baseDictionaryInclude the markers in this dictionary at the beginning (optional)
randomSeeda 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
dictionarydictionary of markers indicating the type of markers
ididentifier of the marker that will be returned. It has to be a valid id in the specified dictionary.
sidePixelssize of the image in pixels
imgoutput image with the marker
borderBitswidth 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 ( PredefinedDictionaryType  name)
Python:
cv.aruco.getPredefinedDictionary(dict) -> retval

#include <opencv2/objdetect/aruco_dictionary.hpp>

Returns one of the predefined dictionaries defined in PredefinedDictionaryType.

◆ getPredefinedDictionary() [2/2]

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_*.