EasySegmentUnsupervisedInference

Support
Required licenses	EasySegment
Recommended images	In the Deep Learning Additional Resources, the images from the folder EasySegment Unsupervised/Fabric/Images
Location	Deep Learning Inspection/EasySegmentUnsupervisedInference and Console samples/Deep Learning Inspection with Python

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 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(drawAdapter, 2.f, 2.f, -20, -20);
   }
   

3. Draw the segmentation map.

   pDoc->m_Result.Draw(drawAdapter);
   

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());