Add core functionality for API client, CDN management, and data augmentation

- Introduced `ApiClient` for handling API interactions, including file uploads and downloads.
- Implemented `CDNManager` for managing CDN operations with AWS S3.
- Added `Augmentator` class for image augmentation, including bounding box corrections and transformations.
- Created utility functions for annotation conversion and dataset visualization.
- Established a new rules file for sound notifications during human input requests.

These additions enhance the system's capabilities for data handling and user interaction, laying the groundwork for future features.

Simplify autopilot state file to minimal current-step pointer; add execution safety rule to cursor-meta; remove Completed Steps/Key Decisions/Retry Log/Blockers from state template and all references.

src/start_inference.py

@@ -0,0 +1,50 @@
+import pycuda.driver as cuda
+import yaml
+import constants
+from api_client import ApiClient, ApiCredentials
+from cdn_manager import CDNManager, CDNCredentials
+from inference.inference import Inference
+from inference.tensorrt_engine import TensorRTEngine
+from security import Security
+from utils import Dotdict
+
+
+def get_engine_filename(device_id=0):
+    try:
+        device = cuda.Device(device_id)
+        sm_count = device.multiprocessor_count
+        cc_major, cc_minor = device.compute_capability()
+        return f"azaion.cc_{cc_major}.{cc_minor}_sm_{sm_count}.engine"
+    except Exception:
+        return None
+
+if __name__ == "__main__":
+    # Inference(OnnxEngine('azaion-2025-03-10.onnx', batch_size=4),
+    #            confidence_threshold=0.5, iou_threshold=0.3).process('ForAI_test.mp4')
+    # detection for the first 200sec of video:
+    # onnxInference: 81 sec, 6.3Gb VRAM
+    # tensorrt: 54 sec, 3.7Gb VRAM
+
+    # Inference(TensorRTEngine('azaion-2025-03-10_int8.engine', batch_size=16),
+    #          confidence_threshold=0.5, iou_threshold=0.3).process('ForAI_test.mp4')
+    # INT8 for 200sec: 54 sec 3.7Gb
+
+    # Inference(TensorRTEngine('azaion-2025-03-10_batch8.engine', batch_size=8),
+    #           confidence_threshold=0.5, iou_threshold=0.3).process('ForAI_test.mp4')
+
+    api_client = ApiClient()
+    key = Security.get_model_encryption_key()
+    engine_filename = TensorRTEngine.get_engine_filename()
+    model_bytes = api_client.load_big_small_resource(engine_filename, 'models', key)
+
+    Inference(TensorRTEngine(model_bytes),
+              confidence_threshold=0.5, iou_threshold=0.3).process('tests/ForAI_test.mp4')
+
+
+    # cdn_manager.download(cdn_c.bucket, constants.AI_TENSOR_MODEL_FILE_BIG)
+    # tensor_model_bytes = api_client.load_resource(constants.AI_TENSOR_MODEL_FILE_BIG, constants.AI_TENSOR_MODEL_FILE_SMALL)
+
+    # Inference(OnnxEngine(onxx_model_bytes, batch_size=4),
+    #           confidence_threshold=0.5, iou_threshold=0.3).process('tests/ForAI_test.mp4')
+
+