class_2dim_unsup🔗
Short description🔗
class_2dim_unsup — Segment two images by clustering.
Signature🔗
class_2dim_unsup( image Image1, image Image2, out region Classes, integer Threshold, integer NumClasses )
Description🔗
class_2dim_unsup performs a classification with two
single-channel images. First, a two-dimensional histogram of the two
images is computed (histo_2dim). In this histogram,
the first maximum is extracted; it serves as the first cluster
center. The histogram is computed with the intersection of the
domains of both images (see reduce_domain). After this,
all pixels in the images that are at most Threshold
pixels from the cluster center in the maximum norm, are determined.
These pixels form one output region. Next, the pixels thus
classified are deleted from the histogram so that they are not taken
into account for the next class. In this modified histogram, again
the maximum is extracted; it again serves as a cluster center. The
above steps are repeated NumClasses times; thus,
NumClasses output regions result. Only pixels defined in
both images are returned.
Attention🔗
Both input images must have the same size.
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🔗
Image1 (input_object) singlechannelimage → object (byte)
First input image.
Image2 (input_object) singlechannelimage → object (byte)
Second input image.
Classes (output_object) region-array → object
Classification result.
Threshold (input_control) integer → (integer)
Threshold (maximum distance to the cluster’s center).
Default: 15
Suggested values: 0, 2, 5, 8, 12, 17, 20, 30, 50, 70
NumClasses (input_control) integer → (integer)
Number of classes (cluster centers).
Default: 5
Suggested values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, 30, 40, 50
Example🔗
(C)
read_image(&ColorImage,"patras")\;
decompose3(ColorImage,&Red,&Green,&Blue)\;
class_2dim_unsup(Red,Green,&Seg,15,5)\;
set_colored(WindowHandle,12)\;
disp_region(Seg,WindowHandle)\;
#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)\;
}
HImage colimg (argv[1]),
green, blue\;
HWindow w\;
Hlong nc\;
if ((nc = colimg.CountChannels ()) != 3)
{
cout << argv[1] << " is not a rgb-image " << endl\;
return (-2)\;
}
colimg.Display (w)\;
HImage red = colimg.Decompose3 (&green, &blue)\;
HRegionArray seg = red.Class2dimUnsup (green, 15, 5)\;
w.SetDraw ("margin")\;
w.SetColored (12)\;
seg.Display (w)\;
w.Click ()\;
return (0)\;
}
Result🔗
class_2dim_unsup 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', 'empty_region_result', and
'store_empty_region' with set_system.
If necessary, an exception is raised.
Combinations with other operators🔗
Combinations
Possible predecessors
decompose2, decompose3, median_image, anisotropic_diffusion, reduce_domain
Possible successors
select_shape, select_gray, connection
Alternatives
Module🔗
Foundation