EasyClassifyMultiThreadInference

Support
Required licenses	EasyClassify
Recommended images	In the Deep Learning Additional Resources, the images from the folder EasyClassify/MiniWaffles/Images
Location	Deep Learning Inspection/EasyClassifyMultiThreadInference

Note

• In C++, the sample EasySegmentSupervisedMultiThreadInference illustrates a multithread inference for EasySegment Supervised.

• In Qt, the sample EasyLocateMultiThreadInference illustrates a multithread inference for EasyLocate.

Purpose

This sample program demonstrates how to:

• Perform a multithread inference with EasyClassify.

• Implement external and internal multithreading.

Code highlights

1. Launch one acquisition thread running the function ImageLoop.

   ThreadParameters param = new ThreadParameters(this, engine.SelectedItem.ToString(), device.SelectedItem.ToString(), precision.SelectedItem.ToString(), (int)internalThreads.Value, (int)batchSize.Value);       
   acquisitionThread = new Thread(ImageLoop);              
   acquisitionThread.Start(param);
   

2. The function ImageLoop pushes the images into an image queue.

   ImageBatch batch = new ImageBatch(numImagesPerInference);
   
   for (int j = 0; j < numImagesPerInference; j++)
   {
     batch.Images[j] = new EImageC24();
     batch.Images[j].Load(mainForm.m_images[i + j]);
     batch.ImagesId[j] = i + j;
   }
   
   // Adds the batch of images to the queue and signals the processing threads about the new data.
   lock (mainForm.locker)
   {
     queue.Enqueue(batch);
     Monitor.PulseAll(mainForm.locker);
   }
   

3. The NumExternalThreads processing threads are created and run the function ClassificationLoop.

   // Create the external threads
   processingThreads = new Thread[((int)externalThreads.Value)];
   for (int i = 0; i < externalThreads.Value; i++)
   {
     processingThreads[i] = new Thread(ClassificationLoop);
     processingThreads[i].Start(param);
   }
   

4. The function ClassificationLoop invokes a delegate to pass the computed result to the main UI thread.

   // Invoke the delegate with the results for updating the user interface.
   mainForm.Invoke(mainForm.m_classificationFinishedDelegate, batch.ImagesId, result, stopWatch.ElapsedMilliseconds);
   

5.The delegate calls the method ClassificationFinished that updates the UI to display the result and the processing time for each image.

```c++
public void ClassificationFinished(int[] imagesId, EClassificationResult[] results, long processingTime)
{
  try
  {
    for (int i = 0; i < imagesId.Length; i++)
    {
      m_detectedClasses[imagesId[i]] = results[i].BestLabel;
      m_processingTime[imagesId[i]] = processingTime;
      m_processed += 1;
      listView1.Items[imagesId[i]].SubItems[1].Text = results[i].BestLabel;
      listView1.Items[imagesId[i]].SubItems[2].Text = String.Format("{0} ms for {1} images", processingTime, imagesId.Length);
      listView1.Items[imagesId[i]].SubItems[3].Text = String.Format("{0} ms", processingTime / imagesId.Length);
    }
    ...
  }
}
```