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OpenCV 5.0.0-pre
Open Source Computer Vision
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OpenCV 5.0.0-pre
Open Source Computer Vision
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struct DetectorParameters is used by ArucoDetector More...
#include <opencv2/objdetect/aruco_detector.hpp>
Public Member Functions | |
| DetectorParameters () | |
| bool | readDetectorParameters (const FileNode &fn) |
| Read a new set of DetectorParameters from FileNode (use FileStorage.root()). | |
| bool | writeDetectorParameters (FileStorage &fs, const String &name=String()) |
| Write a set of DetectorParameters to FileStorage. | |
Public Attributes | |
| double | adaptiveThreshConstant |
| constant for adaptive thresholding before finding contours (default 7) | |
| int | adaptiveThreshWinSizeMax |
| maximum window size for adaptive thresholding before finding contours (default 23). | |
| int | adaptiveThreshWinSizeMin |
| minimum window size for adaptive thresholding before finding contours (default 3). | |
| int | adaptiveThreshWinSizeStep |
| increments from adaptiveThreshWinSizeMin to adaptiveThreshWinSizeMax during the thresholding (default 10). | |
| float | aprilTagCriticalRad |
| reject quads where pairs of edges have angles that are close to straight or close to 180 degrees. | |
| int | aprilTagDeglitch |
| should the thresholded image be deglitched? Only useful for very noisy images (default 0). | |
| float | aprilTagMaxLineFitMse |
| when fitting lines to the contours, what is the maximum mean squared error | |
| int | aprilTagMaxNmaxima |
| how many corner candidates to consider when segmenting a group of pixels into a quad (default 10). | |
| int | aprilTagMinClusterPixels |
| reject quads containing too few pixels (default 5). | |
| int | aprilTagMinWhiteBlackDiff |
| add an extra check that the white model must be (overall) brighter than the black model. | |
| float | aprilTagQuadDecimate |
| April :: User-configurable parameters. | |
| float | aprilTagQuadSigma |
| what Gaussian blur should be applied to the segmented image (used for quad detection?) | |
| int | cornerRefinementMaxIterations |
| maximum number of iterations for stop criteria of the corner refinement process (default 30). | |
| int | cornerRefinementMethod |
| default value CORNER_REFINE_NONE | |
| double | cornerRefinementMinAccuracy |
| minimum error for the stop cristeria of the corner refinement process (default: 0.1) | |
| int | cornerRefinementWinSize |
| maximum window size for the corner refinement process (in pixels) (default 5). | |
| bool | detectInvertedMarker |
| to check if there is a white marker. | |
| double | errorCorrectionRate |
| error correction rate respect to the maximun error correction capability for each dictionary (default 0.6). | |
| int | markerBorderBits |
| number of bits of the marker border, i.e. marker border width (default 1). | |
| double | maxErroneousBitsInBorderRate |
| maximum number of accepted erroneous bits in the border (i.e. number of allowed white bits in the border). | |
| double | maxMarkerPerimeterRate |
| determine maximum perimeter for marker contour to be detected. | |
| double | minCornerDistanceRate |
| minimum distance between corners for detected markers relative to its perimeter (default 0.05) | |
| int | minDistanceToBorder |
| minimum distance of any corner to the image border for detected markers (in pixels) (default 3) | |
| float | minGroupDistance = 0.21f |
| minimum average distance between the corners of the two markers in group to add them to the list of candidates | |
| double | minMarkerDistanceRate |
| minimum average distance between the corners of the two markers to be grouped (default 0.125). | |
| float | minMarkerLengthRatioOriginalImg |
| range [0,1], eq (2) from paper. The parameter tau_i has a direct influence on the processing speed. | |
| double | minMarkerPerimeterRate |
| determine minimum perimeter for marker contour to be detected. | |
| double | minOtsuStdDev |
| minimun standard deviation in pixels values during the decodification step to apply Otsu thresholding (otherwise, all the bits are set to 0 or 1 depending on mean higher than 128 or not) (default 5.0) | |
| int | minSideLengthCanonicalImg |
| minimum side length of a marker in the canonical image. Latter is the binarized image in which contours are searched. | |
| double | perspectiveRemoveIgnoredMarginPerCell |
| width of the margin of pixels on each cell not considered for the determination of the cell bit. | |
| int | perspectiveRemovePixelPerCell |
| number of bits (per dimension) for each cell of the marker when removing the perspective (default 4). | |
| double | polygonalApproxAccuracyRate |
| minimum accuracy during the polygonal approximation process to determine which contours are squares. (default 0.03) | |
| float | relativeCornerRefinmentWinSize |
| Dynamic window size for corner refinement relative to Aruco module size (default 0.3). | |
| bool | useAruco3Detection |
| enable the new and faster Aruco detection strategy. | |
struct DetectorParameters is used by ArucoDetector
|
inline |
| bool cv::aruco::DetectorParameters::readDetectorParameters | ( | const FileNode & | fn | ) |
Read a new set of DetectorParameters from FileNode (use FileStorage.root()).
| bool cv::aruco::DetectorParameters::writeDetectorParameters | ( | FileStorage & | fs, |
| const String & | name = String() ) |
Write a set of DetectorParameters to FileStorage.
| double cv::aruco::DetectorParameters::adaptiveThreshConstant |
constant for adaptive thresholding before finding contours (default 7)
| int cv::aruco::DetectorParameters::adaptiveThreshWinSizeMax |
maximum window size for adaptive thresholding before finding contours (default 23).
| int cv::aruco::DetectorParameters::adaptiveThreshWinSizeMin |
minimum window size for adaptive thresholding before finding contours (default 3).
| int cv::aruco::DetectorParameters::adaptiveThreshWinSizeStep |
increments from adaptiveThreshWinSizeMin to adaptiveThreshWinSizeMax during the thresholding (default 10).
| float cv::aruco::DetectorParameters::aprilTagCriticalRad |
reject quads where pairs of edges have angles that are close to straight or close to 180 degrees.
Zero means that no quads are rejected. (In radians) (default 10*PI/180)
| int cv::aruco::DetectorParameters::aprilTagDeglitch |
should the thresholded image be deglitched? Only useful for very noisy images (default 0).
| float cv::aruco::DetectorParameters::aprilTagMaxLineFitMse |
when fitting lines to the contours, what is the maximum mean squared error
| int cv::aruco::DetectorParameters::aprilTagMaxNmaxima |
how many corner candidates to consider when segmenting a group of pixels into a quad (default 10).
| int cv::aruco::DetectorParameters::aprilTagMinClusterPixels |
reject quads containing too few pixels (default 5).
| int cv::aruco::DetectorParameters::aprilTagMinWhiteBlackDiff |
add an extra check that the white model must be (overall) brighter than the black model.
When we build our model of black & white pixels, we add an extra check that the white model must be (overall) brighter than the black model. How much brighter? (in pixel values, [0,255]), (default 5)
| float cv::aruco::DetectorParameters::aprilTagQuadDecimate |
April :: User-configurable parameters.
Detection of quads can be done on a lower-resolution image, improving speed at a cost of pose accuracy and a slight decrease in detection rate. Decoding the binary payload is still
| float cv::aruco::DetectorParameters::aprilTagQuadSigma |
what Gaussian blur should be applied to the segmented image (used for quad detection?)
| int cv::aruco::DetectorParameters::cornerRefinementMaxIterations |
maximum number of iterations for stop criteria of the corner refinement process (default 30).
| int cv::aruco::DetectorParameters::cornerRefinementMethod |
default value CORNER_REFINE_NONE
| double cv::aruco::DetectorParameters::cornerRefinementMinAccuracy |
minimum error for the stop cristeria of the corner refinement process (default: 0.1)
| int cv::aruco::DetectorParameters::cornerRefinementWinSize |
maximum window size for the corner refinement process (in pixels) (default 5).
The window size may decrease if the ArUco marker is too small, check relativeCornerRefinmentWinSize. The final window size is calculated as: min(cornerRefinementWinSize, averageArucoModuleSize*relativeCornerRefinmentWinSize), where averageArucoModuleSize is average module size of ArUco marker in pixels. (ArUco marker is composed of black and white modules)
| bool cv::aruco::DetectorParameters::detectInvertedMarker |
to check if there is a white marker.
In order to generate a "white" marker just invert a normal marker by using a tilde, ~markerImage. (default false)
| double cv::aruco::DetectorParameters::errorCorrectionRate |
error correction rate respect to the maximun error correction capability for each dictionary (default 0.6).
| int cv::aruco::DetectorParameters::markerBorderBits |
number of bits of the marker border, i.e. marker border width (default 1).
| double cv::aruco::DetectorParameters::maxErroneousBitsInBorderRate |
maximum number of accepted erroneous bits in the border (i.e. number of allowed white bits in the border).
Represented as a rate respect to the total number of bits per marker (default 0.35).
| double cv::aruco::DetectorParameters::maxMarkerPerimeterRate |
determine maximum perimeter for marker contour to be detected.
This is defined as a rate respect to the maximum dimension of the input image (default 4.0).
| double cv::aruco::DetectorParameters::minCornerDistanceRate |
minimum distance between corners for detected markers relative to its perimeter (default 0.05)
| int cv::aruco::DetectorParameters::minDistanceToBorder |
minimum distance of any corner to the image border for detected markers (in pixels) (default 3)
| float cv::aruco::DetectorParameters::minGroupDistance = 0.21f |
minimum average distance between the corners of the two markers in group to add them to the list of candidates
The average distance between the corners of the two markers is calculated relative to its module size (default 0.21).
| double cv::aruco::DetectorParameters::minMarkerDistanceRate |
minimum average distance between the corners of the two markers to be grouped (default 0.125).
The rate is relative to the smaller perimeter of the two markers. Two markers are grouped if average distance between the corners of the two markers is less than min(MarkerPerimeter1, MarkerPerimeter2)*minMarkerDistanceRate.
default value is 0.125 because 0.125*MarkerPerimeter = (MarkerPerimeter / 4) * 0.5 = half the side of the marker.
| float cv::aruco::DetectorParameters::minMarkerLengthRatioOriginalImg |
range [0,1], eq (2) from paper. The parameter tau_i has a direct influence on the processing speed.
| double cv::aruco::DetectorParameters::minMarkerPerimeterRate |
determine minimum perimeter for marker contour to be detected.
This is defined as a rate respect to the maximum dimension of the input image (default 0.03).
| double cv::aruco::DetectorParameters::minOtsuStdDev |
minimun standard deviation in pixels values during the decodification step to apply Otsu thresholding (otherwise, all the bits are set to 0 or 1 depending on mean higher than 128 or not) (default 5.0)
| int cv::aruco::DetectorParameters::minSideLengthCanonicalImg |
minimum side length of a marker in the canonical image. Latter is the binarized image in which contours are searched.
| double cv::aruco::DetectorParameters::perspectiveRemoveIgnoredMarginPerCell |
width of the margin of pixels on each cell not considered for the determination of the cell bit.
Represents the rate respect to the total size of the cell, i.e. perspectiveRemovePixelPerCell (default 0.13)
| int cv::aruco::DetectorParameters::perspectiveRemovePixelPerCell |
number of bits (per dimension) for each cell of the marker when removing the perspective (default 4).
| double cv::aruco::DetectorParameters::polygonalApproxAccuracyRate |
minimum accuracy during the polygonal approximation process to determine which contours are squares. (default 0.03)
| float cv::aruco::DetectorParameters::relativeCornerRefinmentWinSize |
Dynamic window size for corner refinement relative to Aruco module size (default 0.3).
The final window size is calculated as: min(cornerRefinementWinSize, averageArucoModuleSize*relativeCornerRefinmentWinSize), where averageArucoModuleSize is average module size of ArUco marker in pixels. (ArUco marker is composed of black and white modules) In the case of markers located far from each other, it may be useful to increase the value of the parameter to 0.4-0.5. In the case of markers located close to each other, it may be useful to decrease the parameter value to 0.1-0.2.
| bool cv::aruco::DetectorParameters::useAruco3Detection |
enable the new and faster Aruco detection strategy.
Proposed in the paper: Romero-Ramirez et al: Speeded up detection of squared fiducial markers (2018) https://www.researchgate.net/publication/325787310_Speeded_Up_Detection_of_Squared_Fiducial_Markers
1.12.0