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.

train.py

@@ -16,11 +16,6 @@ from ultralytics import YOLO
 import constants
 from api_client import ApiCredentials, ApiClient
 from cdn_manager import CDNCredentials, CDNManager
-from constants import (processed_images_dir,
-                       processed_labels_dir,
-                       prefix, date_format,
-                       datasets_dir, models_dir,
-                       corrupted_images_dir, corrupted_labels_dir, sample_dir)
 from dto.annotationClass import AnnotationClass
 from inference.onnx_engine import OnnxEngine
 
@@ -28,8 +23,7 @@ from security import Security
 from utils import Dotdict
 from exports import export_tensorrt, upload_model, export_onnx
 
-today_folder = f'{prefix}{datetime.now():{date_format}}'
-today_dataset = path.join(datasets_dir, today_folder)
+today_folder = f'{constants.prefix}{datetime.now():{constants.date_format}}'
 train_set = 70
 valid_set = 20
 test_set = 10
@@ -40,10 +34,11 @@ total_files_copied = 0
 
 
 def form_dataset():
+    today_dataset = path.join(constants.config.datasets_dir, today_folder)
     shutil.rmtree(today_dataset, ignore_errors=True)
     makedirs(today_dataset)
     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
@@ -69,26 +64,27 @@ def copy_annotations(images, folder):
         global total_files_copied
         total_files_copied += 1
         label_name = f'{Path(image.path).stem}.txt'
-        label_path = path.join(processed_labels_dir, label_name)
+        label_path = path.join(constants.config.processed_labels_dir, label_name)
         if check_label(label_path):
             shutil.copy(image.path, path.join(destination_images, image.name))
             shutil.copy(label_path, path.join(destination_labels, label_name))
         else:
-            shutil.copy(image.path, path.join(corrupted_images_dir, image.name))
-            shutil.copy(label_path, path.join(corrupted_labels_dir, label_name))
-            print(f'Label {label_path} is corrupted! Copy with its image to the corrupted directory ({corrupted_labels_dir})')
+            shutil.copy(image.path, path.join(constants.config.corrupted_images_dir, image.name))
+            shutil.copy(label_path, path.join(constants.config.corrupted_labels_dir, label_name))
+            print(f'Label {label_path} is corrupted! Copy with its image to the corrupted directory ({constants.config.corrupted_labels_dir})')
 
         if total_files_copied % 1000 == 0:
             print(f'{total_files_copied} copied...')
 
+    today_dataset = path.join(constants.config.datasets_dir, today_folder)
     destination_images = path.join(today_dataset, folder, 'images')
     makedirs(destination_images, exist_ok=True)
 
     destination_labels = path.join(today_dataset, folder, 'labels')
     makedirs(destination_labels, exist_ok=True)
 
-    makedirs(corrupted_images_dir, exist_ok=True)
-    makedirs(corrupted_labels_dir, exist_ok=True)
+    makedirs(constants.config.corrupted_images_dir, exist_ok=True)
+    makedirs(constants.config.corrupted_labels_dir, exist_ok=True)
 
     copied = 0
     print(f'Copying annotations to {destination_images} and {destination_labels} folders:')
@@ -127,6 +123,7 @@ def create_yaml():
     lines.append(f'val: valid/images')
     lines.append('')
 
+    today_dataset = path.join(constants.config.datasets_dir, today_folder)
     today_yaml = abspath(path.join(today_dataset, 'data.yaml'))
     with open(today_yaml, 'w', encoding='utf-8') as f:
         f.writelines([f'{line}\n' for line in lines])
@@ -137,38 +134,38 @@ def resume_training(last_pt_path):
     model = YOLO(last_pt_path)
     results = model.train(data=yaml,
                           resume=True,
-                          epochs=120,
-                          batch=11,
-                          imgsz=1280,
-                          save_period=1,
-                          workers=24)
+                          epochs=constants.config.training.epochs,
+                          batch=constants.config.training.batch,
+                          imgsz=constants.config.training.imgsz,
+                          save_period=constants.config.training.save_period,
+                          workers=constants.config.training.workers)
 
 
 def train_dataset():
     form_dataset()
     create_yaml()
-    model = YOLO('yolo11m.yaml')
+    model = YOLO(constants.config.training.model)
 
+    today_dataset = path.join(constants.config.datasets_dir, today_folder)
     results = model.train(data=abspath(path.join(today_dataset, 'data.yaml')),
-                           epochs=120,    # Empirically set for good performance and relatively not so long training
-                                          # (360k of annotations on 1 RTX4090 takes 11.5 days of training :( )
-                           batch=11,      # reflects current GPU memory, 24Gb (batch 11 gets ~22Gb, batch 12 fails on 24.2Gb)
-                           imgsz=1280,    # 1280p is a tradeoff between quality and speed
-                           save_period=1, # for resuming in case of power outages / other issues
-                           workers=24)    # loading data workers. Bound to cpus count
+                           epochs=constants.config.training.epochs,
+                           batch=constants.config.training.batch,
+                           imgsz=constants.config.training.imgsz,
+                           save_period=constants.config.training.save_period,
+                           workers=constants.config.training.workers)
 
-    model_dir = path.join(models_dir, today_folder)
+    model_dir = path.join(constants.config.models_dir, today_folder)
 
     shutil.copytree(results.save_dir, model_dir)
     for file in glob.glob(path.join(model_dir, 'weights', 'epoch*')): # remove unnecessary middle epochs
         os.remove(file)
-    shutil.copy(path.join(model_dir, 'weights', 'best.pt'), constants.CURRENT_PT_MODEL)
+    shutil.copy(path.join(model_dir, 'weights', 'best.pt'), constants.config.current_pt_model)
 
 
 def export_current_model():
-    export_onnx(constants.CURRENT_PT_MODEL)
+    export_onnx(constants.config.current_pt_model)
     api_client = ApiClient()
-    with open(constants.CURRENT_ONNX_MODEL, 'rb') as binary_file:
+    with open(constants.config.current_onnx_model, 'rb') as binary_file:
         onnx_bytes = binary_file.read()
 
     key = Security.get_model_encryption_key()