class_ndim_normclass_ndim_normClassNdimNormClassNdimNormclass_ndim_norm (Operator)

名称

class_ndim_normclass_ndim_normClassNdimNormClassNdimNormclass_ndim_norm — 使用超球体或超立方体对像素进行分类。

签名

class_ndim_norm(MultiChannelImage : Regions : Metric, SingleMultiple, Radius, Center : )

描述

class_ndim_normclass_ndim_normClassNdimNormClassNdimNormClassNdimNormclass_ndim_norm classifies the pixels of the multi-channel image given in MultiChannelImageMultiChannelImageMultiChannelImageMultiChannelImagemultiChannelImagemulti_channel_image. The result is returned in RegionsRegionsRegionsRegionsregionsregions as one region per classification object. The metric used ('euclid'"euclid""euclid""euclid""euclid""euclid" or 'maximum'"maximum""maximum""maximum""maximum""maximum") is determined by MetricMetricMetricMetricmetricmetric. This parameter must be set to the same value used in learn_ndim_normlearn_ndim_normLearnNdimNormLearnNdimNormLearnNdimNormlearn_ndim_norm. The parameter SingleMultipleSingleMultipleSingleMultipleSingleMultiplesingleMultiplesingle_multiple determines whether one region ('single') or multiples regions ('multiple') are generated for each cluster. RadiusRadiusRadiusRadiusradiusradius determines the radii or half edge length of the clusters, respectively. CenterCenterCenterCentercentercenter determines their centers.

执行信息

Multithreading type: reentrant (runs in parallel with non-exclusive operators).
Multithreading scope: global (may be called from any thread).
Automatically parallelized on tuple level.

参数

MultiChannelImageMultiChannelImageMultiChannelImageMultiChannelImagemultiChannelImagemulti_channel_image (input_object) (multichannel-)image(-array) → object (byte)

Multi channel input image.

RegionsRegionsRegionsRegionsregionsregions (output_object) region-array → object

Classification result.

MetricMetricMetricMetricmetricmetric (input_control) string → (string)

Metric to be used.

默认值： 'euclid' "euclid" "euclid" "euclid" "euclid" "euclid"

值列表： 'euclid'"euclid""euclid""euclid""euclid""euclid", 'maximum'"maximum""maximum""maximum""maximum""maximum"

SingleMultipleSingleMultipleSingleMultipleSingleMultiplesingleMultiplesingle_multiple (input_control) string → (string)

Return one region or one region for each cluster.

默认值： 'single' "single" "single" "single" "single" "single"

值列表： 'multiple'"multiple""multiple""multiple""multiple""multiple", 'single'"single""single""single""single""single"

RadiusRadiusRadiusRadiusradiusradius (input_control) number(-array) → (real / integer)

Cluster radii or half edge lengths (returned by learn_ndim_normlearn_ndim_normLearnNdimNormLearnNdimNormLearnNdimNormlearn_ndim_norm).

CenterCenterCenterCentercentercenter (input_control) number(-array) → (real / integer)

Coordinates of the cluster centers (returned by learn_ndim_normlearn_ndim_normLearnNdimNormLearnNdimNormLearnNdimNormlearn_ndim_norm).

Example (C++ (HALCON 5.0-10.0))

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

int main ()
{
  HImage   image ("meer"),
           t1, t2, t3,
           m1, m2, m3, m;

  HWindow  w;

  w.SetColor ("green");
  image.Display (w);

  cout << "Draw your region of interest " << endl;

  HRegion testreg = w.DrawRegion ();

  t1 = image.TextureLaws ("el", 2, 5);     m1 = t1.MeanImage (21, 21);
  t2 = image.TextureLaws ("es", 2, 5);     m2 = t2.MeanImage (21, 21);
  t3 = image.TextureLaws ("le", 2, 5);     m3 = t3.MeanImage (21, 21);

  m  = m1.Compose3 (m2, m3);

  Tuple Metric = "euclid";
  Tuple Radius = 20.0;
  Tuple MinNum = 5;
  Tuple NbrCha = 3;

  HRegion empty;
  Tuple cen, t;

  Radius = testreg.LearnNdimNorm (empty, m, Metric, Radius,
                                  MinNum, NbrCha, &cen, &t);
  Tuple RegMod = "multiple";

  HRegionArray reg = m.ClassNdimNorm (Metric, RegMod, Radius, cen, NbrCha);

  w.SetColored (12);
  reg.Display (w);
  cout << "Result of classification" << endl;
  return (0);
}

示例（C）

read_image(&Image,"meer:);
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 3-dimensional charachteristic */
texture_laws(Image,&T1,"el",2,5);
mean_image(T1,&M1,21,21);
texture_laws(Image,&T2,"es",2,5);
mean_image(T2,&M2,21,21);
texture_laws(Image,&T3,"le",2,5);
mean_image(T3,&M3,21,21);
compose3(M1,M2,M3,&M);
/* Cluster for 3-dimensional characteristic area determine training area */
create_tuple(&Metric,1);
set_s(Metric,"euclid",0);
create_tuple(&Radius,1);
set_d(Radius,20.0,0);
create_tuple(&MinNumber,1);
set_i(MinNumber,5,0);
T_learn_ndim_norm(Testobj,EMPTY_REGION,&M,"euclid",Radius,MinNumber,
                  &Radius,&Center,NULL);
/* Segmentation */
create_tuple(&RegionMode,1);
set_s(RegionMode,"multiple",0);
class_ndim_norm(M,&Regions,Metric,RegionMode,Radius,Center);
set_colored(WindowHandle,12);
disp_region(Regions,WindowHandle);
fwrite_string("Result of classification;");
fwrite_string("Each cluster in another color.");
fnew_line();

Example (C++ (HALCON 5.0-10.0))

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

int main ()
{
  HImage   image ("meer"),
           t1, t2, t3,
           m1, m2, m3, m;

  HWindow  w;

  w.SetColor ("green");
  image.Display (w);

  cout << "Draw your region of interest " << endl;

  HRegion testreg = w.DrawRegion ();

  t1 = image.TextureLaws ("el", 2, 5);     m1 = t1.MeanImage (21, 21);
  t2 = image.TextureLaws ("es", 2, 5);     m2 = t2.MeanImage (21, 21);
  t3 = image.TextureLaws ("le", 2, 5);     m3 = t3.MeanImage (21, 21);

  m  = m1.Compose3 (m2, m3);

  Tuple Metric = "euclid";
  Tuple Radius = 20.0;
  Tuple MinNum = 5;
  Tuple NbrCha = 3;

  HRegion empty;
  Tuple cen, t;

  Radius = testreg.LearnNdimNorm (empty, m, Metric, Radius,
                                  MinNum, NbrCha, &cen, &t);
  Tuple RegMod = "multiple";

  HRegionArray reg = m.ClassNdimNorm (Metric, RegMod, Radius, cen, NbrCha);

  w.SetColored (12);
  reg.Display (w);
  cout << "Result of classification" << endl;
  return (0);
}

Example (C++ (HALCON 5.0-10.0))

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

int main ()
{
  HImage   image ("meer"),
           t1, t2, t3,
           m1, m2, m3, m;

  HWindow  w;

  w.SetColor ("green");
  image.Display (w);

  cout << "Draw your region of interest " << endl;

  HRegion testreg = w.DrawRegion ();

  t1 = image.TextureLaws ("el", 2, 5);     m1 = t1.MeanImage (21, 21);
  t2 = image.TextureLaws ("es", 2, 5);     m2 = t2.MeanImage (21, 21);
  t3 = image.TextureLaws ("le", 2, 5);     m3 = t3.MeanImage (21, 21);

  m  = m1.Compose3 (m2, m3);

  Tuple Metric = "euclid";
  Tuple Radius = 20.0;
  Tuple MinNum = 5;
  Tuple NbrCha = 3;

  HRegion empty;
  Tuple cen, t;

  Radius = testreg.LearnNdimNorm (empty, m, Metric, Radius,
                                  MinNum, NbrCha, &cen, &t);
  Tuple RegMod = "multiple";

  HRegionArray reg = m.ClassNdimNorm (Metric, RegMod, Radius, cen, NbrCha);

  w.SetColored (12);
  reg.Display (w);
  cout << "Result of classification" << endl;
  return (0);
}

Example (C++ (HALCON 5.0-10.0))

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

int main ()
{
  HImage   image ("meer"),
           t1, t2, t3,
           m1, m2, m3, m;

  HWindow  w;

  w.SetColor ("green");
  image.Display (w);

  cout << "Draw your region of interest " << endl;

  HRegion testreg = w.DrawRegion ();

  t1 = image.TextureLaws ("el", 2, 5);     m1 = t1.MeanImage (21, 21);
  t2 = image.TextureLaws ("es", 2, 5);     m2 = t2.MeanImage (21, 21);
  t3 = image.TextureLaws ("le", 2, 5);     m3 = t3.MeanImage (21, 21);

  m  = m1.Compose3 (m2, m3);

  Tuple Metric = "euclid";
  Tuple Radius = 20.0;
  Tuple MinNum = 5;
  Tuple NbrCha = 3;

  HRegion empty;
  Tuple cen, t;

  Radius = testreg.LearnNdimNorm (empty, m, Metric, Radius,
                                  MinNum, NbrCha, &cen, &t);
  Tuple RegMod = "multiple";

  HRegionArray reg = m.ClassNdimNorm (Metric, RegMod, Radius, cen, NbrCha);

  w.SetColored (12);
  reg.Display (w);
  cout << "Result of classification" << endl;
  return (0);
}

Complexity

Let N be the number of clusters and A be the area of the input region. Then the runtime complexity is O(N,A).

结果

class_ndim_normclass_ndim_normClassNdimNormClassNdimNormClassNdimNormclass_ndim_norm 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""no_object_result", 'empty_region_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""store_empty_region" with set_systemset_systemSetSystemSetSystemSetSystemset_system. If necessary, an exception is raised.

可能的前置算子

learn_ndim_normlearn_ndim_normLearnNdimNormLearnNdimNormLearnNdimNormlearn_ndim_norm, compose2compose2Compose2Compose2Compose2compose2, compose3compose3Compose3Compose3Compose3compose3, compose4compose4Compose4Compose4Compose4compose4, compose5compose5Compose5Compose5Compose5compose5, compose6compose6Compose6Compose6Compose6compose6, compose7compose7Compose7Compose7Compose7compose7

可能的后继算子

connectionconnectionConnectionConnectionConnectionconnection, select_shapeselect_shapeSelectShapeSelectShapeSelectShapeselect_shape, reduce_domainreduce_domainReduceDomainReduceDomainReduceDomainreduce_domain, select_grayselect_graySelectGraySelectGraySelectGrayselect_gray

替代算子

class_2dim_supclass_2dim_supClass2dimSupClass2dimSupClass2dimSupclass_2dim_sup, class_2dim_unsupclass_2dim_unsupClass2dimUnsupClass2dimUnsupClass2dimUnsupclass_2dim_unsup

模块

Foundation

算子