Trait opencv::prelude::MCC_DetectorParametersTraitConst[−][src]

pub trait MCC_DetectorParametersTraitConst {

Show 19 methods    fn as_raw_MCC_DetectorParameters(&self) -> *const c_void;

    fn adaptive_thresh_win_size_min(&self) -> i32 { ... }

    fn adaptive_thresh_win_size_max(&self) -> i32 { ... }

    fn adaptive_thresh_win_size_step(&self) -> i32 { ... }

    fn adaptive_thresh_constant(&self) -> f64 { ... }

    fn min_contours_area_rate(&self) -> f64 { ... }

    fn min_contours_area(&self) -> f64 { ... }

    fn confidence_threshold(&self) -> f64 { ... }

    fn min_contour_solidity(&self) -> f64 { ... }

    fn find_candidates_approx_poly_dp_eps_multiplier(&self) -> f64 { ... }

    fn border_width(&self) -> i32 { ... }

    fn b0factor(&self) -> f32 { ... }

    fn max_error(&self) -> f32 { ... }

    fn min_contour_points_allowed(&self) -> i32 { ... }

    fn min_contour_length_allowed(&self) -> i32 { ... }

    fn min_inter_contour_distance(&self) -> i32 { ... }

    fn min_inter_checker_distance(&self) -> i32 { ... }

    fn min_image_size(&self) -> i32 { ... }

    fn min_group_size(&self) -> u32 { ... }

}
Expand description

Parameters for the detectMarker process:

  • int adaptiveThreshWinSizeMin : minimum window size for adaptive thresholding before finding contours (default 23).
  • int adaptiveThreshWinSizeMax : maximum window size for adaptive thresholding before finding contours (default 153).
  • int adaptiveThreshWinSizeStep : increments from adaptiveThreshWinSizeMin to adaptiveThreshWinSizeMax during the thresholding (default 16).
  • double adaptiveThreshConstant : constant for adaptive thresholding before finding contours (default 7)
  • double minContoursAreaRate : determine minimum area for marker contour to be detected. This is defined as a rate respect to the area of the input image. Used only if neural network is used (default 0.003).
  • double minContoursArea : determine minimum area for marker contour to be detected. This is defined as the actual area. Used only if neural network is not used (default 100).
  • double confidenceThreshold : minimum confidence for a bounding box detected by neural network to classify as detection.(default 0.5) (0<=confidenceThreshold<=1)
  • double minContourSolidity : minimum solidity of a contour for it be detected as a square in the chart. (default 0.9).
  • double findCandidatesApproxPolyDPEpsMultiplier : multipler to be used in cv::ApproxPolyDP function (default 0.05)
  • int borderWidth : width of the padding used to pass the inital neural network detection in the succeeding system.(default 0)
  • float B0factor : distance between two neighbours squares of the same chart. Defined as the ratio between distance and large dimension of square (default 1.25)
  • float maxError : maximum allowed error in the detection of a chart. default(0.1)
  • int minContourPointsAllowed : minium points in a detected contour. default(4)
  • int minContourLengthAllowed : minimum length of a countour. default(100)
  • int minInterContourDistance : minimum distance between two contours. default(100)
  • int minInterCheckerDistance : minimum distance between two checkers. default(10000)
  • int minImageSize : minimum size of the smaller dimension of the image. default(1000)
  • unsigned minGroupSize : minimum number of a squared of a chart that must be detected. default(4)

Required methods

Provided methods

Implementors