EasySegmentUnsupervisedInference

Support

Required licenses

EasySegment

Recommended images

In the Deep Learning Additional Resources, the images from the folder EasySegment Unsupervised/Fabric/Images

Location

[…]C:\Users\Public\Documents\Euresys\Open eVision 24.02\Sample Programs
\[LANGUAGE] samples\Deep Learning Inspection\EasySegmentUnsupervisedInference

C++:

C:\Users\Public\Documents\Euresys\Open eVision 24.02\Sample Programs\MsVc samples\Deep Learning Inspection\EasySegmentUnsupervisedInference
C#:

C:\Users\Public\Documents\Euresys\Open eVision 24.02\Sample Programs\MsVcs samples\Deep Learning Inspection\EasySegmentUnsupervisedInference
Qt:

C:\Users\Public\Documents\Euresys\Open eVision 24.02\Sample Programs\Qt samples\Deep Learning Inspection\EasySegmentUnsupervisedInference

Purpose

This sample program demonstrates how to:

□ Perform an inference with EasySegment Unsupervised.
□ Display the results.

Code highlights

In the Deep Learning Additional Resources, use the tool EasySegment Unsupervised/Fabric/Tool 1/Fabric.edltool.

1. Apply the EasySegment Unsupervised tool on an image. 
EUnsupervisedSegmenterResult m_Result = m_Segmenter.Apply(m_Img);
2. Draw the image. 
if (!pDoc->m_Img.IsVoid())
{
  pDoc->m_Img.Draw(hDC, 2.f, 2.f, -20, -20);
}
3. Draw the segmentation map. 
pDoc->m_Result.Draw(hDC);
4. Check whether the image is defective and construct a label for this image. 
std::wstring label;

if (pDoc->m_Result.IsDefective())
{
  label = L"Not ";
}

label += ToUnicode(pDoc->m_Segmenter.GetGoodLabel().c_str());