EasyLocateMultiThreadInference

Support

Required licenses

EasyLocate

Recommended images

In the Deep Learning Additional Resources, the images from the folder EasyLocate

Location

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

Qt:

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

- In C++, the sample EasySegmentSupervisedMultiThreadInference illustrates a multithread inference for EasySegment Supervised.
- In C#, the sample EasyClassifyMultiThreadInference illustrates a multithread inference for EasyClassify.

Purpose

This sample program demonstrates how to:

□ Perform a multithread inference with EasyLocate.
□ Implement external and internal multithreading.

Code highlights

1. Launch one acquisition thread running the AcquisitionThread. 
// Acquisition thread creation
acquisitionThread_.reset(new AcquisitionThread(queue_.get(), images_));
connect(acquisitionThread_.get(), &AcquisitionThread::error, this, &MainWindow::showError);

acquisitionThread_->start();
2. The AcquisitionThread pushes the images into a custom image queue type named ImageQueue. 
ImageInfo imageInfo;
imageInfo.ImageId = i;
EImageC24* img = new EImageC24();
img->Load(images_[i].toStdString());

imageInfo.Image.reset(img);
imageInfo.Timestamp = std::chrono::steady_clock::now();

queue_->addImage(imageInfo);
3. The program launches NumExternalThreads ProcessingThread. 
// Processing threads creation
ProcessingThread::ExecutionParameters parameters;
parameters.NumInternalThreads = ui->internalThreads->value();
parameters.BatchSize = ui->batchSize->value();
parameters.Engine = ui->engines->currentText().toStdString();
parameters.Device = ui->devices->currentText().toStdString();
parameters.InferencePrecision = ui->precision->currentText().toStdString();
parameters.ToolFilename = toolFilename_.toStdString();

processingThreads_.clear();
numResults_ = 0;
numThreadFinished_ = 0;

for (int i = 0; i < ui->externalThreads->value(); i++)
{
  processingThreads_.emplace_back(new ProcessingThread(queue_.get(), parameters));
  ...
  processingThreads_.back()->start();
}
4. The class ProcessingThread uses Qt signals and slots to pass the result to the method processResult of the main window.
□ The connection between the signal resultsReady and the slot processResult is made at the thread creation. 
connect(processingThreads_.back().get(), &ProcessingThread::resultsReady, this, &MainWindow::processResult);
5. When the ProcessingThread completes the processing of an image, it emits a signal with the result. 
std::vector<ELocatorResult> results = locator->Apply(imgs);
for (int i = 0; i < imageInfo.size(); i++)
{
  ImageResult result;
  result.ImageId = imageInfo[i].ImageId;
  result.Time = timing;
  result.Timestamp = imageInfo[i].Timestamp;
  result.Result.reset(new ELocatorResult(results[i]));
  imageResults.push_back(result);
}

// Pass the result to another thread
emit resultsReady(imageResults);
6. The methods processResult update the UI and save the result for display. 
void MainWindow::processResult(const QList<ImageResult>& results)
{
  // Display the results
  for (const auto& result: results)
  {
    int numObjects = result.Result->GetNumDetectedObjects();
    ui->imageList->item(result.ImageId, 1)->setText(QString("%1 objects").arg(numObjects));
    ui->imageList->item(result.ImageId, 2)->setText(QString("%1 ms").arg(result.Time));
    ui->imageList->item(result.ImageId, 3)->setText(QString("%1 ms").arg(result.Time / results.size()));

    results_[result.ImageId] = result;
    numResults_ += 1;
  }
  ...
}