@spec arucoDetector() :: t() | {:error, String.t()}

Basic ArucoDetector constructor

Keyword Arguments

• dictionary: Dictionary.

  indicates the type of markers that will be searched

• detectorParams: DetectorParameters.

  marker detection parameters

• refineParams: RefineParameters.

  marker refine detection parameters

Return

• self: Evision.ArUco.ArucoDetector.t()

Python prototype (for reference only):

ArucoDetector([, dictionary[, detectorParams[, refineParams]]]) -> <aruco_ArucoDetector object>

@spec arucoDetector(Keyword.t()) :: any() | {:error, String.t()}

@spec arucoDetector(
  [detectorParams: term(), dictionary: term(), refineParams: term()]
  | nil
) ::
  t() | {:error, String.t()}

@spec arucoDetector([Evision.ArUco.Dictionary.t()]) :: t() | {:error, String.t()}

Variant 1:

ArucoDetector constructor for multiple dictionaries

Positional Arguments

• dictionaries: [Dictionary].

  indicates the type of markers that will be searched. Empty dictionaries will throw an error.

Keyword Arguments

• detectorParams: DetectorParameters.

  marker detection parameters

• refineParams: RefineParameters.

  marker refine detection parameters

Return

• self: Evision.ArUco.ArucoDetector.t()

Python prototype (for reference only):

ArucoDetector(dictionaries[, detectorParams[, refineParams]]) -> <aruco_ArucoDetector object>

Variant 2:

Basic ArucoDetector constructor

Keyword Arguments

• dictionary: Dictionary.

  indicates the type of markers that will be searched

• detectorParams: DetectorParameters.

  marker detection parameters

• refineParams: RefineParameters.

  marker refine detection parameters

Return

• self: Evision.ArUco.ArucoDetector.t()

Python prototype (for reference only):

ArucoDetector([, dictionary[, detectorParams[, refineParams]]]) -> <aruco_ArucoDetector object>

@spec arucoDetector(
  [Evision.ArUco.Dictionary.t()],
  [detectorParams: term(), refineParams: term()] | nil
) :: t() | {:error, String.t()}

ArucoDetector constructor for multiple dictionaries

Positional Arguments

• dictionaries: [Dictionary].

  indicates the type of markers that will be searched. Empty dictionaries will throw an error.

Keyword Arguments

• detectorParams: DetectorParameters.

  marker detection parameters

• refineParams: RefineParameters.

  marker refine detection parameters

Return

• self: Evision.ArUco.ArucoDetector.t()

Python prototype (for reference only):

ArucoDetector(dictionaries[, detectorParams[, refineParams]]) -> <aruco_ArucoDetector object>

@spec clear(Keyword.t()) :: any() | {:error, String.t()}

@spec clear(t()) :: t() | {:error, String.t()}

Clears the algorithm state

Positional Arguments

• self: Evision.ArUco.ArucoDetector.t()

Python prototype (for reference only):

clear() -> None

@spec detectMarkers(t(), Evision.Mat.maybe_mat_in()) ::
  {[Evision.Mat.t()], Evision.Mat.t(), [Evision.Mat.t()]} | {:error, String.t()}

Basic marker detection

Positional Arguments

• self: Evision.ArUco.ArucoDetector.t()

• image: Evision.Mat.

  input image

Return

• corners: [Evision.Mat].

  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: Evision.Mat.t().

  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.

• rejectedImgPoints: [Evision.Mat].

  contains the imgPoints of those squares whose inner code has not a correct codification. Useful for debugging purposes.

Performs marker detection in the input image. Only markers included in the first specified 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. Note: The function does not correct lens distortion or takes it into account. It's recommended to undistort input image with corresponding camera model, if camera parameters are known @sa undistort, estimatePoseSingleMarkers, estimatePoseBoard

Python prototype (for reference only):

detectMarkers(image[, corners[, ids[, rejectedImgPoints]]]) -> corners, ids, rejectedImgPoints

@spec detectMarkers(t(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) ::
  {[Evision.Mat.t()], Evision.Mat.t(), [Evision.Mat.t()]} | {:error, String.t()}

Basic marker detection

Positional Arguments

• self: Evision.ArUco.ArucoDetector.t()

• image: Evision.Mat.

  input image

Return

• corners: [Evision.Mat].

  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: Evision.Mat.t().

  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.

• rejectedImgPoints: [Evision.Mat].

  contains the imgPoints of those squares whose inner code has not a correct codification. Useful for debugging purposes.

Performs marker detection in the input image. Only markers included in the first specified 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. Note: The function does not correct lens distortion or takes it into account. It's recommended to undistort input image with corresponding camera model, if camera parameters are known @sa undistort, estimatePoseSingleMarkers, estimatePoseBoard

Python prototype (for reference only):

detectMarkers(image[, corners[, ids[, rejectedImgPoints]]]) -> corners, ids, rejectedImgPoints

@spec detectMarkersMultiDict(t(), Evision.Mat.maybe_mat_in()) ::
  {[Evision.Mat.t()], Evision.Mat.t(), [Evision.Mat.t()], Evision.Mat.t()}
  | {:error, String.t()}

Basic marker detection

Positional Arguments

• self: Evision.ArUco.ArucoDetector.t()

• image: Evision.Mat.

  input image

Return

• corners: [Evision.Mat].

  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: Evision.Mat.t().

  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.

• rejectedImgPoints: [Evision.Mat].

  contains the imgPoints of those squares whose inner code has not a correct codification. Useful for debugging purposes.

• dictIndices: Evision.Mat.t().

  vector of dictionary indices for each detected marker. Use getDictionaries() to get the list of corresponding dictionaries.

Performs marker detection in the input image. Only markers included in the specific dictionaries 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. Note: The function does not correct lens distortion or takes it into account. It's recommended to undistort input image with corresponding camera model, if camera parameters are known @sa undistort, estimatePoseSingleMarkers, estimatePoseBoard

Python prototype (for reference only):

detectMarkersMultiDict(image[, corners[, ids[, rejectedImgPoints[, dictIndices]]]]) -> corners, ids, rejectedImgPoints, dictIndices

@spec detectMarkersMultiDict(
  t(),
  Evision.Mat.maybe_mat_in(),
  [{atom(), term()}, ...] | nil
) ::
  {[Evision.Mat.t()], Evision.Mat.t(), [Evision.Mat.t()], Evision.Mat.t()}
  | {:error, String.t()}

Basic marker detection

Positional Arguments

• self: Evision.ArUco.ArucoDetector.t()

• image: Evision.Mat.

  input image

Return

• corners: [Evision.Mat].

  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: Evision.Mat.t().

  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.

• rejectedImgPoints: [Evision.Mat].

  contains the imgPoints of those squares whose inner code has not a correct codification. Useful for debugging purposes.

• dictIndices: Evision.Mat.t().

  vector of dictionary indices for each detected marker. Use getDictionaries() to get the list of corresponding dictionaries.

Performs marker detection in the input image. Only markers included in the specific dictionaries 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. Note: The function does not correct lens distortion or takes it into account. It's recommended to undistort input image with corresponding camera model, if camera parameters are known @sa undistort, estimatePoseSingleMarkers, estimatePoseBoard

Python prototype (for reference only):

detectMarkersMultiDict(image[, corners[, ids[, rejectedImgPoints[, dictIndices]]]]) -> corners, ids, rejectedImgPoints, dictIndices

@spec detectMarkersWithConfidence(t(), Evision.Mat.maybe_mat_in()) ::
  {[Evision.Mat.t()], Evision.Mat.t(), Evision.Mat.t(), [Evision.Mat.t()]}
  | {:error, String.t()}

Marker detection with confidence computation

Positional Arguments

• self: Evision.ArUco.ArucoDetector.t()

• image: Evision.Mat.

  input image

Return

• corners: [Evision.Mat].

  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: Evision.Mat.t().

  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.

• markersConfidence: Evision.Mat.t().

  contains the normalized confidence [0;1] of the markers' detection, defined as 1 minus the normalized uncertainty (percentage of incorrect pixel detections), with 1 describing a pixel perfect detection. The confidence values are of type float (e.g. std::vector<float>)

• rejectedImgPoints: [Evision.Mat].

  contains the imgPoints of those squares whose inner code has not a correct codification. Useful for debugging purposes.

Performs marker detection in the input image. Only markers included in the first specified 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. Note: The function does not correct lens distortion or takes it into account. It's recommended to undistort input image with corresponding camera model, if camera parameters are known @sa undistort, estimatePoseSingleMarkers, estimatePoseBoard

Python prototype (for reference only):

detectMarkersWithConfidence(image[, corners[, ids[, markersConfidence[, rejectedImgPoints]]]]) -> corners, ids, markersConfidence, rejectedImgPoints

@spec detectMarkersWithConfidence(
  t(),
  Evision.Mat.maybe_mat_in(),
  [{atom(), term()}, ...] | nil
) ::
  {[Evision.Mat.t()], Evision.Mat.t(), Evision.Mat.t(), [Evision.Mat.t()]}
  | {:error, String.t()}

Marker detection with confidence computation

Positional Arguments

• self: Evision.ArUco.ArucoDetector.t()

• image: Evision.Mat.

  input image

Return

• corners: [Evision.Mat].

  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: Evision.Mat.t().

  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.

• markersConfidence: Evision.Mat.t().

  contains the normalized confidence [0;1] of the markers' detection, defined as 1 minus the normalized uncertainty (percentage of incorrect pixel detections), with 1 describing a pixel perfect detection. The confidence values are of type float (e.g. std::vector<float>)

• rejectedImgPoints: [Evision.Mat].

  contains the imgPoints of those squares whose inner code has not a correct codification. Useful for debugging purposes.

Performs marker detection in the input image. Only markers included in the first specified 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. Note: The function does not correct lens distortion or takes it into account. It's recommended to undistort input image with corresponding camera model, if camera parameters are known @sa undistort, estimatePoseSingleMarkers, estimatePoseBoard

Python prototype (for reference only):

detectMarkersWithConfidence(image[, corners[, ids[, markersConfidence[, rejectedImgPoints]]]]) -> corners, ids, markersConfidence, rejectedImgPoints

@spec empty(Keyword.t()) :: any() | {:error, String.t()}

@spec empty(t()) :: boolean() | {:error, String.t()}

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

Positional Arguments

• self: Evision.ArUco.ArucoDetector.t()

Return

• retval: bool

Python prototype (for reference only):

empty() -> retval

@spec getDefaultName(Keyword.t()) :: any() | {:error, String.t()}

@spec getDefaultName(t()) :: binary() | {:error, String.t()}

getDefaultName

Positional Arguments

• self: Evision.ArUco.ArucoDetector.t()

Return

• retval: String

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

Python prototype (for reference only):

getDefaultName() -> retval

@spec getDetectorParameters(Keyword.t()) :: any() | {:error, String.t()}

@spec getDetectorParameters(t()) ::
  Evision.ArUco.DetectorParameters.t() | {:error, String.t()}

getDetectorParameters

Positional Arguments

• self: Evision.ArUco.ArucoDetector.t()

Return

• retval: DetectorParameters

Python prototype (for reference only):

getDetectorParameters() -> retval

@spec getDictionaries(Keyword.t()) :: any() | {:error, String.t()}

@spec getDictionaries(t()) :: [Evision.ArUco.Dictionary.t()] | {:error, String.t()}

Returns all dictionaries currently used for marker detection as a vector.

Positional Arguments

• self: Evision.ArUco.ArucoDetector.t()

Return

• retval: [Dictionary]

@return A std::vector<Dictionary> containing all dictionaries used by the ArucoDetector.

Python prototype (for reference only):

getDictionaries() -> retval

@spec getDictionary(Keyword.t()) :: any() | {:error, String.t()}

@spec getDictionary(t()) :: Evision.ArUco.Dictionary.t() | {:error, String.t()}

Returns first dictionary from internal list used for marker detection.

Positional Arguments

• self: Evision.ArUco.ArucoDetector.t()

Return

• retval: Dictionary

@return The first dictionary from the configured ArucoDetector.

Python prototype (for reference only):

getDictionary() -> retval

@spec getRefineParameters(Keyword.t()) :: any() | {:error, String.t()}

@spec getRefineParameters(t()) ::
  Evision.ArUco.RefineParameters.t() | {:error, String.t()}

getRefineParameters

Positional Arguments

• self: Evision.ArUco.ArucoDetector.t()

Return

• retval: RefineParameters

Python prototype (for reference only):

getRefineParameters() -> retval

@spec read(t(), Evision.FileNode.t()) :: t() | {:error, String.t()}

Reads algorithm parameters from a file storage

Positional Arguments

• self: Evision.ArUco.ArucoDetector.t()
• func: Evision.FileNode

Python prototype (for reference only):

read(fn) -> None

@spec refineDetectedMarkers(
  t(),
  Evision.Mat.maybe_mat_in(),
  Evision.ArUco.Board.t(),
  [Evision.Mat.maybe_mat_in()],
  Evision.Mat.maybe_mat_in(),
  [Evision.Mat.maybe_mat_in()]
) ::
  {[Evision.Mat.t()], Evision.Mat.t(), [Evision.Mat.t()], Evision.Mat.t()}
  | {:error, String.t()}

Refine not detected markers based on the already detected and the board layout

Positional Arguments

• self: Evision.ArUco.ArucoDetector.t()

• image: Evision.Mat.

  input image

• board: Board.

  layout of markers in the board.

Keyword Arguments

• cameraMatrix: Evision.Mat.

  optional input 3x3 floating-point camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$

• distCoeffs: Evision.Mat.

  optional vector of distortion coefficients \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements

Return

• detectedCorners: [Evision.Mat].

  vector of already detected marker corners.

• detectedIds: Evision.Mat.t().

  vector of already detected marker identifiers.

• rejectedCorners: [Evision.Mat].

  vector of rejected candidates during the marker detection process.

• recoveredIdxs: Evision.Mat.t().

  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. Note: This function assumes that the board only contains markers from one dictionary, so only the first configured dictionary is used. It has to match the dictionary of the board to work properly.

Python prototype (for reference only):

refineDetectedMarkers(image, board, detectedCorners, detectedIds, rejectedCorners[, cameraMatrix[, distCoeffs[, recoveredIdxs]]]) -> detectedCorners, detectedIds, rejectedCorners, recoveredIdxs

@spec refineDetectedMarkers(
  t(),
  Evision.Mat.maybe_mat_in(),
  Evision.ArUco.Board.t(),
  [Evision.Mat.maybe_mat_in()],
  Evision.Mat.maybe_mat_in(),
  [Evision.Mat.maybe_mat_in()],
  [cameraMatrix: term(), distCoeffs: term()] | nil
) ::
  {[Evision.Mat.t()], Evision.Mat.t(), [Evision.Mat.t()], Evision.Mat.t()}
  | {:error, String.t()}

Refine not detected markers based on the already detected and the board layout

Positional Arguments

• self: Evision.ArUco.ArucoDetector.t()

• image: Evision.Mat.

  input image

• board: Board.

  layout of markers in the board.

Keyword Arguments

• cameraMatrix: Evision.Mat.

  optional input 3x3 floating-point camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$

• distCoeffs: Evision.Mat.

  optional vector of distortion coefficients \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements

Return

• detectedCorners: [Evision.Mat].

  vector of already detected marker corners.

• detectedIds: Evision.Mat.t().

  vector of already detected marker identifiers.

• rejectedCorners: [Evision.Mat].

  vector of rejected candidates during the marker detection process.

• recoveredIdxs: Evision.Mat.t().

  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. Note: This function assumes that the board only contains markers from one dictionary, so only the first configured dictionary is used. It has to match the dictionary of the board to work properly.

Python prototype (for reference only):

refineDetectedMarkers(image, board, detectedCorners, detectedIds, rejectedCorners[, cameraMatrix[, distCoeffs[, recoveredIdxs]]]) -> detectedCorners, detectedIds, rejectedCorners, recoveredIdxs

@spec save(t(), binary()) :: t() | {:error, String.t()}

save

Positional Arguments

• self: Evision.ArUco.ArucoDetector.t()
• filename: String

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

Python prototype (for reference only):

save(filename) -> None

@spec setDetectorParameters(t(), Evision.ArUco.DetectorParameters.t()) ::
  t() | {:error, String.t()}

setDetectorParameters

Positional Arguments

• self: Evision.ArUco.ArucoDetector.t()
• detectorParameters: DetectorParameters

Python prototype (for reference only):

setDetectorParameters(detectorParameters) -> None

@spec setDictionaries(t(), [Evision.ArUco.Dictionary.t()]) ::
  t() | {:error, String.t()}

Sets the entire collection of dictionaries to be used for marker detection, replacing any existing dictionaries.

Positional Arguments

• self: Evision.ArUco.ArucoDetector.t()

• dictionaries: [Dictionary].

  A std::vector<Dictionary> containing the new set of dictionaries to be used.

Configures the ArucoDetector to use the provided vector of dictionaries for marker detection. This method replaces any dictionaries that were previously set. Note: Setting an empty vector of dictionaries will throw an error.

Python prototype (for reference only):

setDictionaries(dictionaries) -> None

@spec setDictionary(t(), Evision.ArUco.Dictionary.t()) :: t() | {:error, String.t()}

Sets and replaces the first dictionary in internal list to be used for marker detection.

Positional Arguments

• self: Evision.ArUco.ArucoDetector.t()

• dictionary: Dictionary.

  The new dictionary that will replace the first dictionary in the internal list.

Python prototype (for reference only):

setDictionary(dictionary) -> None

@spec setRefineParameters(t(), Evision.ArUco.RefineParameters.t()) ::
  t() | {:error, String.t()}

setRefineParameters

Positional Arguments

• self: Evision.ArUco.ArucoDetector.t()
• refineParameters: RefineParameters

Python prototype (for reference only):

setRefineParameters(refineParameters) -> None

@spec write(t(), Evision.FileStorage.t(), binary()) :: t() | {:error, String.t()}

simplified API for language bindings

Positional Arguments

• self: Evision.ArUco.ArucoDetector.t()
• fs: Evision.FileStorage
• name: String

Python prototype (for reference only):

write(fs, name) -> None