DetectorParameters Structure

Indicates whether this instance and a specified object are equal.

Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.

Get the detector parameters with default values

Returns the hash code for this instance.

Gets the type of the current instance.

Creates a shallow copy of the current Object.

Returns the fully qualified type name of this instance.

constant for adaptive thresholding before finding contours (default 7)

maximum window size for adaptive thresholding before finding contours (default 23).

minimum window size for adaptive thresholding before finding contours (default 3)

increments from adaptiveThreshWinSizeMin to adaptiveThreshWinSizeMax during the thresholding (default 10).

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).

should the thresholded image be deglitched? Only useful for very noisy images

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.

how many corner candidates to consider when segmenting a group of pixels into a quad.

reject quads containing too few pixels.

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]).

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.

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).

maximum number of iterations for stop criteria of the corner refinement process (default 30).

Corner refinement method

minimum error for the stop criteria of the corner refinement process (default: 0.1)

window size for the corner refinement process (in pixels) (default 5).

error correction rate respect to the maximun error correction capability for each dictionary. (default 0.6).

number of bits of the marker border, i.e. marker border width (default 1).

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).

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).

minimum distance between corners for detected markers relative to its perimeter (default 0.05)

minimum distance of any corner to the image border for detected markers (in pixels) (default 3)

minimum mean distance beetween 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).

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).

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)

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. perpectiveRemovePixelPerCell (default 0.13)

number of bits (per dimension) for each cell of the marker when removing the perspective (default 8).

minimum accuracy during the polygonal approximation process to determine which contours are squares.