Trait opencv::objdetect::DetectorParametersTrait

source · 
pub trait DetectorParametersTrait: DetectorParametersTraitConst {

Show 35 methods    fn as_raw_mut_DetectorParameters(&mut self) -> *mut c_void;

    fn set_adaptive_thresh_win_size_min(&mut self, val: i32) { ... }
    fn set_adaptive_thresh_win_size_max(&mut self, val: i32) { ... }
    fn set_adaptive_thresh_win_size_step(&mut self, val: i32) { ... }
    fn set_adaptive_thresh_constant(&mut self, val: f64) { ... }
    fn set_min_marker_perimeter_rate(&mut self, val: f64) { ... }
    fn set_max_marker_perimeter_rate(&mut self, val: f64) { ... }
    fn set_polygonal_approx_accuracy_rate(&mut self, val: f64) { ... }
    fn set_min_corner_distance_rate(&mut self, val: f64) { ... }
    fn set_min_distance_to_border(&mut self, val: i32) { ... }
    fn set_min_marker_distance_rate(&mut self, val: f64) { ... }
    fn set_corner_refinement_method(&mut self, val: CornerRefineMethod) { ... }
    fn set_corner_refinement_win_size(&mut self, val: i32) { ... }
    fn set_corner_refinement_max_iterations(&mut self, val: i32) { ... }
    fn set_corner_refinement_min_accuracy(&mut self, val: f64) { ... }
    fn set_marker_border_bits(&mut self, val: i32) { ... }
    fn set_perspective_remove_pixel_per_cell(&mut self, val: i32) { ... }
    fn set_perspective_remove_ignored_margin_per_cell(&mut self, val: f64) { ... }
    fn set_max_erroneous_bits_in_border_rate(&mut self, val: f64) { ... }
    fn set_min_otsu_std_dev(&mut self, val: f64) { ... }
    fn set_error_correction_rate(&mut self, val: f64) { ... }
    fn set_april_tag_quad_decimate(&mut self, val: f32) { ... }
    fn set_april_tag_quad_sigma(&mut self, val: f32) { ... }
    fn set_april_tag_min_cluster_pixels(&mut self, val: i32) { ... }
    fn set_april_tag_max_nmaxima(&mut self, val: i32) { ... }
    fn set_april_tag_critical_rad(&mut self, val: f32) { ... }
    fn set_april_tag_max_line_fit_mse(&mut self, val: f32) { ... }
    fn set_april_tag_min_white_black_diff(&mut self, val: i32) { ... }
    fn set_april_tag_deglitch(&mut self, val: i32) { ... }
    fn set_detect_inverted_marker(&mut self, val: bool) { ... }
    fn set_use_aruco3_detection(&mut self, val: bool) { ... }
    fn set_min_side_length_canonical_img(&mut self, val: i32) { ... }
    fn set_min_marker_length_ratio_original_img(&mut self, val: f32) { ... }
    fn read_detector_parameters(&mut self, fn_: &FileNode) -> Result<bool> { ... }
    fn write_detector_parameters(
        &mut self,
        fs: &mut FileStorage,
        name: &str
    ) -> Result<bool> { ... }

}

Required Methods§

Provided Methods§

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

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

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

constant for adaptive thresholding before finding contours (default 7)

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).

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 accuracy during the polygonal approximation process to determine which contours are squares. (default 0.03)

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 imilar, so that the smaller one is removed.

The rate is relative to the smaller perimeter of the two markers (default 0.05).

default value CORNER_REFINE_NONE

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

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

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

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

number of bits (per dimension) for each cell of the marker when removing the perspective (default 4).

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)

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).

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)

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

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

what Gaussian blur should be applied to the segmented image (used for quad detection?)

reject quads containing too few pixels (default 5).

how many corner candidates to consider when segmenting a group of pixels into a quad (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) (default 10*PI/180)

when fitting lines to the contours, what is the maximum mean squared error

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)

should the thresholded image be deglitched? Only useful for very noisy images (default 0).

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)

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

minimum side length of a marker in the canonical image. Latter is the binarized image in which contours are searched.

range [0,1], eq (2) from paper. The parameter tau_i has a direct influence on the processing speed.

Read a new set of DetectorParameters from FileNode (use FileStorage.root()).

Write a set of DetectorParameters to FileStorage

C++ default parameters
  • name: String()

Implementors§