watershedswatershedsWatershedsWatersheds (Operator)

Name

watershedswatershedsWatershedsWatersheds — Extract watersheds and basins from an image.

Signature

watersheds(Image : Basins, Watersheds : : )

Description

watershedswatershedsWatershedsWatershedsWatersheds segments an image based on the topology of the gray values. The image is interpreted as a “mountain range.” Higher gray values correspond to “mountains,” while lower gray values correspond to “valleys.” In the resulting mountain range watersheds are extracted. These correspond to the bright ridges between dark basins. On output, the parameter BasinsBasinsBasinsBasinsbasins contains these basins, while WatershedsWatershedsWatershedsWatershedswatersheds contains the watersheds, which are at most one pixel wide. WatershedsWatershedsWatershedsWatershedswatersheds always is a single region per input image, while BasinsBasinsBasinsBasinsbasins contains a separate region for each basin.

It is advisable to apply a smoothing operator (e.g., binomial_filterbinomial_filterBinomialFilterBinomialFilterBinomialFilter or gauss_filtergauss_filterGaussFilterGaussFilterGaussFilter) to the input image before calling watershedswatershedsWatershedsWatershedsWatersheds in order to reduce the number of output regions. A more sophisticated way to reduce the number of output regions is to merge neighboring basins based on a threshold criterion by using watersheds_thresholdwatersheds_thresholdWatershedsThresholdWatershedsThresholdWatershedsThreshold instead (for more details please refer to the documentation of watersheds_thresholdwatersheds_thresholdWatershedsThresholdWatershedsThresholdWatershedsThreshold).

Attention

If the image contains many fine structures or is noisy, many output regions result, and thus the runtime increases considerably.

Execution Information

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

Parameters

ImageImageImageImageimage (input_object) singlechannelimage(-array) → object (byte / uint2 / real)

Input image.

BasinsBasinsBasinsBasinsbasins (output_object) region-array → object

Segmented basins.

WatershedsWatershedsWatershedsWatershedswatersheds (output_object) region(-array) → object

Watersheds between the basins.

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 (int argc, char *argv[])
{
  HImage   image (argv[1]), gauss;
  HWindow  win;

  cout << "Gauss of original " << endl;
  gauss  = image.GaussImage (9);
  image.Display (win);

  HRegion        watersheds;
  HRegionArray   basins = gauss.Watersheds (&watersheds);

  win.SetColored (12);
  basins.Display (win);
  win.Click ();

  return (0);
}

Example (C)

read_image(&Cells,"meningg5");
gauss_filter(Cells,&CellsGauss,9);
invert_image(CellsGauss,&CellsInvert);
watersheds(CellsInvert,&Bassins,&Watersheds);
set_colored(WindowHandle,12);
disp_region(Bassins,WindowHandle);

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 (int argc, char *argv[])
{
  HImage   image (argv[1]), gauss;
  HWindow  win;

  cout << "Gauss of original " << endl;
  gauss  = image.GaussImage (9);
  image.Display (win);

  HRegion        watersheds;
  HRegionArray   basins = gauss.Watersheds (&watersheds);

  win.SetColored (12);
  basins.Display (win);
  win.Click ();

  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 (int argc, char *argv[])
{
  HImage   image (argv[1]), gauss;
  HWindow  win;

  cout << "Gauss of original " << endl;
  gauss  = image.GaussImage (9);
  image.Display (win);

  HRegion        watersheds;
  HRegionArray   basins = gauss.Watersheds (&watersheds);

  win.SetColored (12);
  basins.Display (win);
  win.Click ();

  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 (int argc, char *argv[])
{
  HImage   image (argv[1]), gauss;
  HWindow  win;

  cout << "Gauss of original " << endl;
  gauss  = image.GaussImage (9);
  image.Display (win);

  HRegion        watersheds;
  HRegionArray   basins = gauss.Watersheds (&watersheds);

  win.SetColored (12);
  basins.Display (win);
  win.Click ();

  return (0);
}

Result

watershedswatershedsWatershedsWatershedsWatersheds always returns 2 (H_MSG_TRUE). 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_systemSetSystemSetSystemSetSystem. If necessary, an exception is raised.

Possible Predecessors

binomial_filterbinomial_filterBinomialFilterBinomialFilterBinomialFilter, gauss_filtergauss_filterGaussFilterGaussFilterGaussFilter, smooth_imagesmooth_imageSmoothImageSmoothImageSmoothImage, invert_imageinvert_imageInvertImageInvertImageInvertImage

Possible Successors

expand_regionexpand_regionExpandRegionExpandRegionExpandRegion, select_shapeselect_shapeSelectShapeSelectShapeSelectShape, reduce_domainreduce_domainReduceDomainReduceDomainReduceDomain, openingopeningOpeningOpeningOpening

Alternatives

watersheds_thresholdwatersheds_thresholdWatershedsThresholdWatershedsThresholdWatershedsThreshold, pouringpouringPouringPouringPouring, watersheds_markerwatersheds_markerWatershedsMarkerWatershedsMarkerWatershedsMarker

References

L. Vincent, P. Soille: “Watersheds in Digital Space: An Efficient Algorithm Based on Immersion Simulations”; IEEE Transactions on Pattern Analysis and Machine Intelligence; vol. 13, no. 6; pp. 583-598; 1991.

Module

Foundation

Operators