EasySegmentSupervisedMultiThreadInference

Support

Required licenses

EasySegment

Recommended images

In the Deep Learning Additional Resources, the images from the folder EasySegment Supervised/Coffee/Images

Location

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

C++:

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

- In C#, the sample EasyClassifyMultiThreadInference illustrates a multithread inference for EasyClassify.
- In Qt, the sample EasyLocateMultiThreadInference illustrates a multithread inference for EasyLocate.

Purpose

This sample program demonstrates how to:

□ Perform a multithread inference with EasySegment Supervised in console mode.
□ Implement external and internal multithreading.

Code highlights

For external multithreading, we use the data structures ImageQueue and ResultQueue that implement FIFO queues for multithreading applications. It uses mutex and conditional variables to perform the synchronization and the data access control between the threads.

1. Launch one acquisition thread and NumExternalThreads processing threads. 
// Create the acquisition thread
std::thread acquisitionThread(acquireImages, dataset, queue.get());
// Create the processing threads
std::vector<std::thread> threads;
for (int i = 0; i < NumExternalThreads; i++)
{
  threads.emplace_back(processImage, SegmenterPath, queue.get(), resultQueue.get());
}
► The function acquireImages loads the images and pushes them into the image queue.
► The function processImage applies the tool on the images and pushes the results into the result queue.
2. Set the internal threading for each external thread in the function processImage. 
// Set the internal threads
Easy::SetMaxNumberOfProcessingThreads(NumInternalThreads);
3. The main thread takes the results from the result queue and displays them in the standard output. 
// Wait and process results
while (!resultQueue->isComplete())
{
  ImageResult result = resultQueue->takeResult();
  std::cout << "[" << result.ImageId << "] Processing time: " << result.Time << " | Number of blobs: " << result.Result->GetNumBlobs() << std::endl;
}