Add asynchronous face detection sample for OpenVINO Java API 2.0 (updated from deprecated API 1.0)

[leoheim]

• What was done in this PR?
  Initial project structure created

Set up basic asynchronous structure with threads and queues for frame capturing and inference handling.
Implemented video capture using OpenCV’s VideoCapture class:

Frames continuously read from webcam/video file.
Frames added to a thread-safe queue (BlockingQueue).
Model loading and preprocessing implemented with new API 2.0 methods:

Core.read_model() instead of the deprecated Core.ReadNetwork().
Configured inputs with the modern PrePostProcessor API.
Full asynchronous inference logic implemented:

Created multiple InferRequest instances for parallel inference.
Leveraged start_async() and wait_async() methods clearly and efficiently.
Results processing:

Obtained inference results (Tensor) and parsed detection coordinates.
Results passed to visualization thread through a queue (BlockingQueue<float[]>).
Visualization using OpenCV (HighGui):

Real-time visualization of face detections.
Bounding boxes drawn around detected faces.
Graceful shutdown added for threads and resources:

Proper interruption and termination of threads (captureThread, inferenceThread).
Closing OpenCV windows upon exit.

Feature	Deprecated Java API 1.0	New Java API 2.0 (This PR)
Load Model	CNNNetwork net = core.ReadNetwork(xmlPath)	Model model = core.read_model(xmlPath)
Compile Model	ExecutableNetwork exec = core.LoadNetwork(net, device)	CompiledModel compiledModel = core.compile_model(model, device)
Inference Request	execNetwork.CreateInferRequest()	compiledModel.create_infer_request()
Async Inference	StartAsync() & Wait(WaitMode)	start_async() & wait_async() (simplified)
Input Handling	Old-style Blob	Modern Tensor object
Preprocessing	Verbose manual config	Simplified via PrePostProcessor API
Thread Management	Similar async threads	Improved readability and robustness
Visualization	OpenCV (unchanged)	OpenCV (unchanged)

---

• Key additions and improvements:
  Clean and modern Java API 2.0 usage:
  Introduced modern classes (CompiledModel, Tensor, Model, PrePostProcessor).
  Clear asynchronous logic:
  Simplified inference workflow (start_async(), wait_async()).
  Better readability and maintainability:
  Added extensive inline comments explaining each step clearly.
  Simplified queues management for multi-threading.

• What was removed:
  Deprecated API calls (CNNNetwork, manual Blob management).
  Complex asynchronous management using deprecated WaitMode enums and ID queues.

• JNI implementation status:
  No new JNI methods required.
  All necessary JNI methods (start_async, wait_async, tensor handling) were already implemented previously in JNI files provided in the repository.

[leoheim]

Hey @likholat , in this last commit, I made changes primarily based on the deprecated sample. Let me know if there's anything else I can do to improve it further. This version should be more aligned and likely to work better now. Thanks for your time and review!

[likholat]

@leoheim please keep only source files and remove all the binaries

[leoheim]

Hi @likholat ,

I have updated the repository to keep only the source files

[likholat]

Please remove all the binary files from PR.

[likholat]

please keep only changes related to Java sample and Android sample

[leoheim]

Hey @likholat, I believe it's correct now. I've removed the unnecessary files and kept only the source files as requested. Let me know if there's anything else you'd like me to adjust!