HALCON Reference Manual 10.0.2
Table of Contents / Segmentation / Classification ClassesClassesClasses | | | Operators

class_2dim_supclass_2dim_supclass_2dim_supClass2dimSupClass2dimSup (Operator)

Name

class_2dim_supclass_2dim_supclass_2dim_supClass2dimSupClass2dimSup — Segment an image using two-dimensional pixel classification.

Signature

class_2dim_sup(ImageCol, ImageRow, FeatureSpace : RegionClass2Dim : : )

Herror class_2dim_sup(const Hobject ImageCol, const Hobject ImageRow, const Hobject FeatureSpace, Hobject* RegionClass2Dim)

Herror T_class_2dim_sup(const Hobject ImageCol, const Hobject ImageRow, const Hobject FeatureSpace, Hobject* RegionClass2Dim)

Herror class_2dim_sup(Hobject ImageCol, Hobject ImageRow, Hobject FeatureSpace, Hobject* RegionClass2Dim)

HRegion HImage::Class2dimSup(const HImage& ImageRow, const HRegion& FeatureSpace) const

HRegionArray HImageArray::Class2dimSup(const HImageArray& ImageRow, const HRegionArray& FeatureSpace) const

void HOperatorSetX.Class2dimSup(
[in] IHUntypedObjectX* ImageCol, [in] IHUntypedObjectX* ImageRow, [in] IHUntypedObjectX* FeatureSpace, [out] IHUntypedObjectX** RegionClass2Dim)

IHRegionX* HImageX.Class2dimSup(
[in] IHImageX* ImageRow, [in] IHRegionX* FeatureSpace)

static void HOperatorSet.Class2dimSup(HObject imageCol, HObject imageRow, HObject featureSpace, out HObject regionClass2Dim)

HRegion HImage.Class2dimSup(HImage imageRow, HRegion featureSpace)

Description

class_2dim_supclass_2dim_supclass_2dim_supClass2dimSupClass2dimSup classifies the points in two-channel images using a two-dimensional feature space. For each point, two gray values (one from each image) are used as features. The feature space is represented by the input region. The classification is done as follows:

A point from the input region of an image is accepted if the point (g_{r},g_{c}), which is determined by the respective gray values, is contained in the region FeatureSpaceFeatureSpaceFeatureSpaceFeatureSpacefeatureSpace. g_{r} is here a gray value from the image ImageRowImageRowImageRowImageRowimageRow, while g_{c} is the corresponding gray value from ImageColImageColImageColImageColimageCol.

Let P be a point with the coordinates P=(R,C), g_{r} be the gray value at position (R,C) in the image ImageRowImageRowImageRowImageRowimageRow, and g_{c} be the gray value at position (R,C) in the image ImageColImageColImageColImageColimageCol. Then the point P is aggregated into the output region if 

      (g_r,g_c)  is an element of FeatureSpaceFeatureSpaceFeatureSpaceFeatureSpacefeatureSpace

g_{r} is interpreted as row coordinate and g_{c} as column coordinate.

For the generation of FeatureSpaceFeatureSpaceFeatureSpaceFeatureSpacefeatureSpace, see histo_2dimhisto_2dimhisto_2dimHisto2dimHisto2dim. The feature space can be modified by applying region transformation operators, such as rank_regionrank_regionrank_regionRankRegionRankRegion, dilation1dilation1dilation1Dilation1Dilation1, shape_transshape_transshape_transShapeTransShapeTrans, elliptic_axiselliptic_axiselliptic_axisEllipticAxisEllipticAxis, etc., before calling class_2dim_supclass_2dim_supclass_2dim_supClass2dimSupClass2dimSup.

The parameters ImageColImageColImageColImageColimageCol and ImageRowImageRowImageRowImageRowimageRow must contain an equal number of images with the same size. The image points are taken from the intersection of the domains of both images (see reduce_domainreduce_domainreduce_domainReduceDomainReduceDomain).

Parallelization

Parameters

ImageColImageColImageColImageColimageCol (input_object)  image(-array) → objectHImageHImageHImageXHobject (byte / direction / cyclic / int1)

Input image (first channel).

ImageRowImageRowImageRowImageRowimageRow (input_object)  image(-array) → objectHImageHImageHImageXHobject (byte / direction / cyclic / int1)

Input image (second channel).

FeatureSpaceFeatureSpaceFeatureSpaceFeatureSpacefeatureSpace (input_object)  region(-array) → objectHRegionHRegionHRegionXHobject

Region defining the feature space.

RegionClass2DimRegionClass2DimRegionClass2DimRegionClass2DimregionClass2Dim (output_object)  region(-array) → objectHRegionHRegionHRegionXHobject *

Classified regions.

Example (C++)

#include "HIOStream.h"
#if !defined(USE_IOSTREAM_H)
using namespace std;
#endif
#include "HalconCpp.h"
using namespace Halcon;

int main (int argc, char *argv[])
{
  if (argc != 2)
  {
    cout << "Usage : " << argv[0] << " 'image' " << endl;
    return (-1);
  }

  HRegion   feats, cd2reg;
  HImage    image (argv[1]),
            text1, text2,
            mean1, mean2,
            histo;

  HWindow   win;
  long      nc;

  if ((nc = image.CountChannels ()) != 3)
  {
    cout << argv[1] << " is not a rgb-image " << endl;
    return (-2);
  }

  image.Display (win);

  win.SetColor ("green");
  cout << "Draw the region of interrest " << endl;

  HRegion  region = win.DrawRegion ();

  text1 = image.TextureLaws ("el", 2, 5);
  mean1 = text1.MeanImage (21, 21);
  text2 = mean1.TextureLaws ("es", 2, 5);
  mean2 = text2.MeanImage (21, 21);

  histo = region.Histo2dim (mean1, mean2);
  feats = histo.Threshold (1.0, 1000000.0);

  win.SetDraw ("fill");
  win.SetColor ("red");

  feats.Display (win);

  cout << "Charakteristics area in red" << endl;

  cd2reg = mean1.Class2dimSup (mean2, feats);

  win.SetColor ("blue");
  cd2reg.Display (win);

  cout << "Result of classification in blue " << endl;
  win.Click ();
  return (0);
}

Example (C)

read_image(&Image,"combine");
open_window(0,0,-1,-1,0,"visible","",&WindowHandle);
disp_image(Image,WindowHandle);
fwrite_string("draw region of interest with the mouse");
fnew_line();
set_color(WindowHandle,"green");
draw_region(&Testreg,draw_region);
/* Texture transformation for 2-dimensional charachteristic */
texture_laws(Image,&T1,"el",2,5);
mean_image(T1,&M1,21,21);
clear_obj(T1);
texture_laws(M1,&T2,"es,",2,5);
mean_image(T2,&M2,21,21);
clear_obj(T2);
/* 2-dimensinal histogram of the test region */
histo_2dim(Testreg,M1,M2,&Histo);
/* All points occuring at least once */
threshold(Histo,&FeatureSpace,1.0,100000.0);
set_draw(WindowHandle,"fill");
set_color(WindowHandle,"red");
disp_region(FeatureSpace,WindowHandle);
fwrite_string("Characteristics area in red");
fnew_line();
/* Segmentation */
class_2dim_sup(M1,M2,FeatureSpace,&RegionClass2Dim);
set_color(WindowHandle,"blue");
disp_region(RegionClass2Dim,WindowHandle);
fwrite_string("Result of classification in blue");
fnew_line();

Example (C++)

#include "HIOStream.h"
#if !defined(USE_IOSTREAM_H)
using namespace std;
#endif
#include "HalconCpp.h"
using namespace Halcon;

int main (int argc, char *argv[])
{
  if (argc != 2)
  {
    cout << "Usage : " << argv[0] << " 'image' " << endl;
    return (-1);
  }

  HRegion   feats, cd2reg;
  HImage    image (argv[1]),
            text1, text2,
            mean1, mean2,
            histo;

  HWindow   win;
  long      nc;

  if ((nc = image.CountChannels ()) != 3)
  {
    cout << argv[1] << " is not a rgb-image " << endl;
    return (-2);
  }

  image.Display (win);

  win.SetColor ("green");
  cout << "Draw the region of interrest " << endl;

  HRegion  region = win.DrawRegion ();

  text1 = image.TextureLaws ("el", 2, 5);
  mean1 = text1.MeanImage (21, 21);
  text2 = mean1.TextureLaws ("es", 2, 5);
  mean2 = text2.MeanImage (21, 21);

  histo = region.Histo2dim (mean1, mean2);
  feats = histo.Threshold (1.0, 1000000.0);

  win.SetDraw ("fill");
  win.SetColor ("red");

  feats.Display (win);

  cout << "Charakteristics area in red" << endl;

  cd2reg = mean1.Class2dimSup (mean2, feats);

  win.SetColor ("blue");
  cd2reg.Display (win);

  cout << "Result of classification in blue " << endl;
  win.Click ();
  return (0);
}

Example (C++)

#include "HIOStream.h"
#if !defined(USE_IOSTREAM_H)
using namespace std;
#endif
#include "HalconCpp.h"
using namespace Halcon;

int main (int argc, char *argv[])
{
  if (argc != 2)
  {
    cout << "Usage : " << argv[0] << " 'image' " << endl;
    return (-1);
  }

  HRegion   feats, cd2reg;
  HImage    image (argv[1]),
            text1, text2,
            mean1, mean2,
            histo;

  HWindow   win;
  long      nc;

  if ((nc = image.CountChannels ()) != 3)
  {
    cout << argv[1] << " is not a rgb-image " << endl;
    return (-2);
  }

  image.Display (win);

  win.SetColor ("green");
  cout << "Draw the region of interrest " << endl;

  HRegion  region = win.DrawRegion ();

  text1 = image.TextureLaws ("el", 2, 5);
  mean1 = text1.MeanImage (21, 21);
  text2 = mean1.TextureLaws ("es", 2, 5);
  mean2 = text2.MeanImage (21, 21);

  histo = region.Histo2dim (mean1, mean2);
  feats = histo.Threshold (1.0, 1000000.0);

  win.SetDraw ("fill");
  win.SetColor ("red");

  feats.Display (win);

  cout << "Charakteristics area in red" << endl;

  cd2reg = mean1.Class2dimSup (mean2, feats);

  win.SetColor ("blue");
  cd2reg.Display (win);

  cout << "Result of classification in blue " << endl;
  win.Click ();
  return (0);
}

Example (C++)

#include "HIOStream.h"
#if !defined(USE_IOSTREAM_H)
using namespace std;
#endif
#include "HalconCpp.h"
using namespace Halcon;

int main (int argc, char *argv[])
{
  if (argc != 2)
  {
    cout << "Usage : " << argv[0] << " 'image' " << endl;
    return (-1);
  }

  HRegion   feats, cd2reg;
  HImage    image (argv[1]),
            text1, text2,
            mean1, mean2,
            histo;

  HWindow   win;
  long      nc;

  if ((nc = image.CountChannels ()) != 3)
  {
    cout << argv[1] << " is not a rgb-image " << endl;
    return (-2);
  }

  image.Display (win);

  win.SetColor ("green");
  cout << "Draw the region of interrest " << endl;

  HRegion  region = win.DrawRegion ();

  text1 = image.TextureLaws ("el", 2, 5);
  mean1 = text1.MeanImage (21, 21);
  text2 = mean1.TextureLaws ("es", 2, 5);
  mean2 = text2.MeanImage (21, 21);

  histo = region.Histo2dim (mean1, mean2);
  feats = histo.Threshold (1.0, 1000000.0);

  win.SetDraw ("fill");
  win.SetColor ("red");

  feats.Display (win);

  cout << "Charakteristics area in red" << endl;

  cd2reg = mean1.Class2dimSup (mean2, feats);

  win.SetColor ("blue");
  cd2reg.Display (win);

  cout << "Result of classification in blue " << endl;
  win.Click ();
  return (0);
}

Complexity

Let A be the area of the input region. Then the runtime complexity is O(256^2 + A).

Result

class_2dim_supclass_2dim_supclass_2dim_supClass2dimSupClass2dimSup returns 2 (H_MSG_TRUE). If all parameters are correct, the behavior with respect to the input images and output regions can be determined by setting the values of the flags 'no_object_result'"no_object_result""no_object_result""no_object_result""no_object_result", 'empty_region_result'"empty_region_result""empty_region_result""empty_region_result""empty_region_result", and 'store_empty_region'"store_empty_region""store_empty_region""store_empty_region""store_empty_region" with set_systemset_systemset_systemSetSystemSetSystem. If necessary, an exception is raised.

Possible Predecessors

histo_2dimhisto_2dimhisto_2dimHisto2dimHisto2dim, thresholdthresholdthresholdThresholdThreshold, draw_regiondraw_regiondraw_regionDrawRegionDrawRegion, dilation1dilation1dilation1Dilation1Dilation1, openingopeningopeningOpeningOpening, shape_transshape_transshape_transShapeTransShapeTrans

Possible Successors

connectionconnectionconnectionConnectionConnection, select_shapeselect_shapeselect_shapeSelectShapeSelectShape, select_grayselect_grayselect_graySelectGraySelectGray

Alternatives

class_ndim_normclass_ndim_normclass_ndim_normClassNdimNormClassNdimNorm, class_ndim_boxclass_ndim_boxclass_ndim_boxClassNdimBoxClassNdimBox, thresholdthresholdthresholdThresholdThreshold

See also

histo_2dimhisto_2dimhisto_2dimHisto2dimHisto2dim

Module

Foundation

Table of Contents / Segmentation / Classification ClassesClassesClasses | | | Operators
HALCON Reference Manual 10.0.2 Copyright © 1996-2011 MVTec Software GmbH