• 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.

• 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.