pouringPouringPouringpouringpouring🔗

Short description🔗

pouringPouringPouringpouringpouring — Segment an image by “pouring water” over it.

Signature🔗

pouring( image Image, out region Regions, string Mode, integer MinGray, integer MaxGray )

Description🔗

pouringPouring regards the input image as a “mountain range.” Larger gray values correspond to mountain peaks, while smaller gray values correspond to valley bottoms. pouringPouring segments the input image in several steps. First, the local maxima are extracted, i.e., pixels which either alone or in the form of an extended plateau have larger gray values than their immediate neighbors (in 4-neighborhood). In the next step, the maxima thus found are the starting points for an expansion until ``valley bottoms’’ are reached. The expansion is done as long as there are chains of pixels in which the gray value becomes smaller (like water running downhill from the maxima in all directions). Again, the 4-neighborhood is used, but with a weaker condition (smaller or equal). This means that points at valley bottoms may belong to more than one maximum. These areas are at first not assigned to a region, but rather are split among all competing segments in the last step. The split is done by a uniform expansion of all involved segments, until all ambiguous pixels were assigned. The parameter Modemodemode determines which steps are executed. The following values are possible:

'all'"all" This is the normal mode of operation. All steps of the segmentation are performed. The regions are assigned to maxima, and overlapping regions are split.
'maxima'"maxima" The segmentation only extracts the local maxima of the input image. No corresponding regions are extracted.
'regions'"regions" The segmentation extracts the local maxima of the input image and the corresponding regions, which are uniquely determined. Areas that were assigned to more than one maximum are not split.

In order to prevent the algorithm from splitting a uniform background that is different from the rest of the image, the parameters MinGrayminGraymin_gray and MaxGraymaxGraymax_gray determine gray value thresholds for regions in the image that should be regarded as background. All parts of the image having a gray value smaller than MinGrayminGraymin_gray or larger than MaxGraymaxGraymax_gray are disregarded for the extraction of the maxima as well as for the assignment of regions. For a complete segmentation of the image, MinGrayminGraymin_gray = 0 and MaxGraymaxGraymax_gray = 255 should be selected. MinGrayminGraymin_gray \(<\) MaxGraymaxGraymax_gray must be observed.

Execution information🔗

Execution information

Multithreading type: mutually exclusive (runs in parallel with other non-exclusive operators, but not with itself).
Multithreading scope: global (may be called from any thread).
Processed without parallelization.

Parameters🔗

Imageimageimage (input_object) singlechannelimage → object (byte)HObject (byte)HImage (byte)HObject (byte)Hobject (byte)

Input image.

Regionsregionsregions (output_object) region-array → objectHObjectHRegionHObjectHobject *

Segmented regions.

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

Mode of operation.

Default: 'all'"all"
List of values: 'all', 'maxima', 'regions'"all", "maxima", "regions"

MinGrayminGraymin_gray (input_control) integer → (integer)HTuple (Hlong)HTuple (int / long)intHtuple (Hlong)

All gray values smaller than this threshold are disregarded.

Default: 00
Suggested values: 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110
Value range: 0 ≤ MinGray ≤ 254 (lin)
Minimum increment: 1
Recommended increment: 10

MaxGraymaxGraymax_gray (input_control) integer → (integer)HTuple (Hlong)HTuple (int / long)intHtuple (Hlong)

All gray values larger than this threshold are disregarded.

Default: 255255
Suggested values: 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 255
Value range: 1 ≤ MaxGray ≤ 255 (lin)
Minimum increment: 1
Recommended increment: 10
Restriction: MaxGray > MinGray

Example🔗

(HDevelop)

* Segment a filtered image
read_image(Image,'particle')
mean_image(Image,Mean,11,11)
pouring(Mean,Seg,'all',0,255)
dev_display(Mean)
dev_set_colored(12)
dev_display(Seg)

* Segment an image while masking the dark background
read_image(Image,'particle')
mean_image(Image,ImageMean,15,15)
pouring(Mean,Seg,'all',90,255)
dev_display(Mean)
dev_set_colored(12)
dev_display(Seg)

(C)

/* Segmentation of a filtered image */
read_image(&Image,"br2")\;
mean_image(Image,&Mean,11,11)\;
pouring(Mean,&Seg,"all",0,255)\;
disp_image(Mean,WindowHandle)\;
set_colored(WindowHandle,12)\;
disp_region(Seg,WindowHandle)\;

/* Segmentation of an image with masking of a dark backround */
read_image(&Image,"hand")\;
mean_image(Image,&Mean,15,15)\;
pouring(Mean,&Seg,"all",40,255)\;
disp_image(Mean,WindowHandle)\;
set_colored(WindowHandle,12)\;
disp_region(Seg,WindowHandle)\;

(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[])
{
  HImage   img1 ("br2"),
           img2 ("hand"),
           img3 ("monkey"),
           texture,
           testreg\;
 HWindow      w1, w2, w3\;
  HRegion      region\;
  HRegionArray seg\;
 w1.SetColored (12)\;   img1.Display (w1)\;
  w2.SetColored (12)\;   img2.Display (w2)\;
  w3.SetColored (12)\;   img3.Display (w3)\;
 cout << "Smoothing of images 1 and 2 ..." << endl\;
 HImage mean1 = img1.MeanImage (11, 11)\;
  HImage mean2 = img2.MeanImage (15, 15)\;
 cout << "Full segmentation of images ... " << endl\;
  seg = mean1.Pouring ("all", 0, 255)\;
  seg.Display (w1)\;
 cout << "Segmentation with: a) masked background " << endl\;
  cout << "                   b) intersection " << endl\;
  seg = mean2.Pouring ("regions", 40, 255)\;
  seg.Display (w2)\;
 cout << "Segmentation in 2d-histogram" << endl\;
  cout << "Draw region with the mouse: " << endl\;
 region  = w3.DrawRegion ()\;
  texture = img3.TextureLaws ("el", 2, 5)\;
 testreg = texture.ReduceDomain (region)\;
  histo   = testreg.Histo2dim (texture, region)\;
  seg     = histo.Pouring ("all", 0, 255)\;
 seg.Display (w3)\;
  w3.Click ()\;
  return (0)\;
}

Complexity🔗

Let \(N\) be the number of pixels in the input image and \(M\) be the number of found segments, where the enclosing rectangle of the segment \(i\) contains \(m_{i}\) pixels. Furthermore, let \(K_{i}\) be the number of chords in segment \(i\). Then the runtime complexity is

\[\begin{eqnarray*} O( 3*N + sum_{M} (3*m_{i}) + sum_{M} (K_{i}) ) \enspace .\end{eqnarray*}\]

Result🔗

pouringPouring usually returns the value 2 (H_MSG_TRUE). If necessary, an exception is raised.

Combinations with other operators🔗

Combinations

Possible predecessors

binomial_filterBinomialFilter, gauss_filterGaussFilter, smooth_imageSmoothImage, mean_imageMeanImage

Alternatives

watershedsWatersheds, local_maxLocalMax, watersheds_thresholdWatershedsThreshold, watersheds_markerWatershedsMarker

Module🔗

Foundation