select_contours_xldSelectContoursXldSelectContoursXldselect_contours_xldselect_contours_xld

HALCON ScriptCpp++.NETPythonC

Short description

select_contours_xldSelectContoursXldSelectContoursXldselect_contours_xldselect_contours_xld — Select XLD contours according to several features.

Signature

select_contours_xld( xld_cont Contours, out xld_cont SelectedContours, string Feature, real Min1, real Max1, real Min2, real Max2 )void SelectContoursXld( const HObject& Contours, HObject* SelectedContours, const HTuple& Feature, const HTuple& Min1, const HTuple& Max1, const HTuple& Min2, const HTuple& Max2 )static void HOperatorSet.SelectContoursXld( HObject contours, out HObject selectedContours, HTuple feature, HTuple min1, HTuple max1, HTuple min2, HTuple max2 )def select_contours_xld( contours: HObject, feature: str, min_1: float, max_1: float, min_2: float, max_2: float ) -> HObject

Herror select_contours_xld( const Hobject Contours, Hobject* SelectedContours, const char* Feature, double Min1, double Max1, double Min2, double Max2 )

Herror T_select_contours_xld( const Hobject Contours, Hobject* SelectedContours, const Htuple Feature, const Htuple Min1, const Htuple Max1, const Htuple Min2, const Htuple Max2 )

HXLDCont HXLDCont::SelectContoursXld( const HString& Feature, double Min1, double Max1, double Min2, double Max2 ) const

HXLDCont HXLDCont::SelectContoursXld( const char* Feature, double Min1, double Max1, double Min2, double Max2 ) const

HXLDCont HXLDCont::SelectContoursXld( const wchar_t* Feature, double Min1, double Max1, double Min2, double Max2 ) const (Windows only)

HXLDCont HXLDCont.SelectContoursXld( string feature, double min1, double max1, double min2, double max2 )

Description

select_contours_xldSelectContoursXld selects XLD contours from the input contours Contourscontourscontours according to the following features (parameter Featurefeaturefeature):

• 'contour_length'"contour_length": all contours whose length is smaller than Min1min1min_1 or larger than Max1max1max_1 are not returned (Min2min2min_2 and Max2max2max_2 have no influence here).

• 'maximum_extent'"maximum_extent": all contours whose maximum extent (as measured by their eight extremal points in row and column direction, according to Haralick and Shapiro: Computer and Robot Vision, Addison-Wesley 1992, chapter 3.2) is smaller than Min1min1min_1 or larger than Max1max1max_1 are not returned (Min2min2min_2 and Max2max2max_2 have no influence here).

• 'direction'"direction": only contours for which the direction of the regression line is between Min1min1min_1 and Max1max1max_1 (in radians, counter-clockwise) are returned. Min1min1min_1 and Max1max1max_1 are mapped to the range of [0,2*PI[. (Min2min2min_2 and Max2max2max_2 have no influence here).

• 'curvature'"curvature": only contours for which the mean distance from the regression line lies between Min1min1min_1 and Max1max1max_1, and for which the standard deviation of the distances is between Min2min2min_2 and Max2max2max_2 are returned.

• 'closed'"closed": only contours for which the distance between their start point and their end point is less or equal Max1max1max_1 pixels are returned. (Min1min1min_1, Min2min2min_2 and Max2max2max_2 have no influence here.)

• 'open'"open": only contours for which the distance between their start point and their end point is greater than Min1min1min_1 pixels are returned. (Max1max1max_1, Min2min2min_2 and Max2max2max_2 have no influence here).

If Min1min1min_1 = Max1max1max_1 = 0 or Min2min2min_2 = Max2max2max_2 = 0 is used for the selection according to curvature, the respective feature has no influence on the selection.

Attention

Before contour can be filtered by select_contours_xldSelectContoursXld according to 'direction'"direction" or 'curvature'"curvature", the parameters of the regression lines to the contours must be calculated with regress_contours_xldRegressContoursXld. If this has not been done, select_contours_xldSelectContoursXld calls regress_contours_xldRegressContoursXld internally with the parameters Modemodemode \(=\) 'no'"no" and Iterationsiterationsiterations \(=\) 11. If a different mode should be used, regress_contours_xldRegressContoursXld must be called explicitly.

Execution information

Execution information

• Multithreading type: reentrant (runs in parallel with non-exclusive operators).

• Multithreading scope: global (may be called from any thread).

• Processed without parallelization.

Parameters

Contourscontourscontours (input_object) xld_cont-array → objectHObjectHXLDContHObjectHobject

Input XLD contours.

SelectedContoursselectedContoursselected_contours (output_object) xld_cont-array → objectHObjectHXLDContHObjectHobject *

Output XLD contours.

Featurefeaturefeature (input_control) string → (string)HTuple (HString)HTuple (string)strHtuple (char*)

Feature to select contours with.

Default: 'contour_length'"contour_length"
List of values: 'closed', 'contour_length', 'curvature', 'direction', 'maximum_extent', 'open'"closed", "contour_length", "curvature", "direction", "maximum_extent", "open"

Min1min1min_1 (input_control) real → (real)HTuple (double)HTuple (double)floatHtuple (double)

Lower threshold.

Default: 0.50.5

Max1max1max_1 (input_control) real → (real)HTuple (double)HTuple (double)floatHtuple (double)

Upper threshold.

Default: 200.0200.0

Min2min2min_2 (input_control) real → (real)HTuple (double)HTuple (double)floatHtuple (double)

Lower threshold.

Default: -0.5-0.5

Max2max2max_2 (input_control) real → (real)HTuple (double)HTuple (double)floatHtuple (double)

Upper threshold.

Default: 0.50.5

Combinations with other operators

Combinations

Possible predecessors

regress_contours_xldRegressContoursXld

See also

get_contour_xldGetContourXld, get_contour_attrib_xldGetContourAttribXld, gen_contours_skeleton_xldGenContoursSkeletonXld, lines_gaussLinesGauss, lines_facetLinesFacet, edges_sub_pixEdgesSubPix, get_regress_params_xldGetRegressParamsXld, get_contour_global_attrib_xldGetContourGlobalAttribXld, query_contour_global_attribs_xldQueryContourGlobalAttribsXld

References

R. Haralick, L. Shapiro: “Computer and Robot Vision” Vol. 1; Kapitel 3.2, Addison-Wesley 1992

Module

Foundation