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DetectorParameters Properties

The DetectorParameters type exposes the following members.

Properties
  NameDescription
Public propertyAdaptiveThreshConstant
constant for adaptive thresholding before finding contours (default 7)
Public propertyAdaptiveThreshWinSizeMax
adaptiveThreshWinSizeMax: maximum window size for adaptive thresholding before finding contours(default 23).
Public propertyAdaptiveThreshWinSizeMin
minimum window size for adaptive thresholding before finding contours (default 3).
Public propertyAdaptiveThreshWinSizeStep
increments from adaptiveThreshWinSizeMin to adaptiveThreshWinSizeMax during the thresholding(default 10).
Public propertyAprilTagCriticalRad
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).
Public propertyAprilTagDeglitch
should the thresholded image be deglitched? Only useful for very noisy images
Public propertyAprilTagMaxLineFitMse
When fitting lines to the contours, what is the maximum mean squared error allowed? This is useful in rejecting contours that are far from being quad shaped; rejecting these quads "early" saves expensive decoding processing.
Public propertyAprilTagMaxNmaxima
how many corner candidates to consider when segmenting a group of pixels into a quad.
Public propertyAprilTagMinClusterPixels
reject quads containing too few pixels.
Public propertyAprilTagMinWhiteBlackDiff
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]).
Public propertyAprilTagQuadDecimate
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 done at full resolution.
Public propertyAprilTagQuadSigma
What Gaussian blur should be applied to the segmented image (used for quad detection?) Parameter is the standard deviation in pixels. Very noisy images benefit from non-zero values (e.g. 0.8).
Public propertyCornerRefinementMaxIterations
maximum number of iterations for stop criteria of the corner refinement process(default 30).
Public propertyCornerRefinementMethod
corner refinement method. (CORNER_REFINE_NONE, no refinement. CORNER_REFINE_SUBPIX, do subpixel refinement. CORNER_REFINE_CONTOUR use contour-Points)
Public propertyCornerRefinementMinAccuracy
minimum error for the stop criteria of the corner refinement process(default: 0.1)
Public propertyCornerRefinementWinSize
window size for the corner refinement process (in pixels) (default 5).
Public propertyDetectInvertedMarker
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)
Public propertyErrorCorrectionRate
errorCorrectionRate error correction rate respect to the maximun error correction capability for each dictionary. (default 0.6).
Public propertyMarkerBorderBits
number of bits of the marker border, i.e. marker border width (default 1).
Public propertyMaxErroneousBitsInBorderRate
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).
Public propertyMaxMarkerPerimeterRate
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).
Public propertyMinCornerDistanceRate
minimum distance between corners for detected markers relative to its perimeter(default 0.05)
Public propertyMinDistanceToBorder
minimum distance of any corner to the image border for detected markers (in pixels) (default 3)
Public propertyMinMarkerDistanceRate
minimum mean distance between two marker corners to be considered similar, so that the smaller one is removed.The rate is relative to the smaller perimeter of the two markers(default 0.05).
Public propertyMinMarkerPerimeterRate
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).
Public propertyMinOtsuStdDev
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)
Public propertyPerspectiveRemoveIgnoredMarginPerCell
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)
Public propertyPerspectiveRemovePixelPerCell
number of bits (per dimension) for each cell of the marker when removing the perspective(default 8).
Public propertyPolygonalApproxAccuracyRate
minimum accuracy during the polygonal approximation process to determine which contours are squares.
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