Refactor constants management to use Pydantic BaseModel for configuration

- Replaced module-level path variables in constants.py with a structured Pydantic Config class.
- Updated all relevant modules (train.py, augmentation.py, exports.py, dataset-visualiser.py, manual_run.py) to access paths through the new config structure.
- Fixed bugs related to image processing and model saving.
- Enhanced test infrastructure to accommodate the new configuration approach.

This refactor improves code maintainability and clarity by centralizing configuration management.

exports.py

@@ -11,7 +11,6 @@ from ultralytics import YOLO
 import constants
 from api_client import ApiClient, ApiCredentials
 from cdn_manager import CDNManager, CDNCredentials
-from constants import datasets_dir, processed_images_dir
 from security import Security
 from utils import Dotdict
 
@@ -26,7 +25,9 @@ def export_rknn(model_path):
     pass
 
 
-def export_onnx(model_path, batch_size=4):
+def export_onnx(model_path, batch_size=None):
+    if batch_size is None:
+        batch_size = constants.config.export.onnx_batch
     model = YOLO(model_path)
     onnx_path = Path(model_path).stem + '.onnx'
     if path.exists(onnx_path):
@@ -34,11 +35,18 @@ def export_onnx(model_path, batch_size=4):
 
     model.export(
         format="onnx",
-        imgsz=1280,
+        imgsz=constants.config.export.onnx_imgsz,
         batch=batch_size,
         simplify=True,
         nms=True,
-        device=0
+    )
+
+
+def export_coreml(model_path):
+    model = YOLO(model_path)
+    model.export(
+        format="coreml",
+        imgsz=constants.config.export.onnx_imgsz,
     )
 
 
@@ -54,7 +62,7 @@ def export_tensorrt(model_path):
 
 def form_data_sample(destination_path, size=500, write_txt_log=False):
     images = []
-    with scandir(processed_images_dir) as imd:
+    with scandir(constants.config.processed_images_dir) as imd:
         for image_file in imd:
             if not image_file.is_file():
                 continue