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.

_docs/02_document/components/05_data_pipeline/description.md

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+# Component: Data Pipeline
+
+## Overview
+Tools for preparing and managing annotation data: augmentation of training images, format conversion from external annotation systems, and visual inspection of annotated datasets.
+
+**Pattern**: Batch processing tools (standalone scripts + library)
+**Upstream**: Core (constants), Data Models (ImageLabel, AnnotationClass)
+**Downstream**: Training (augmented images feed into dataset formation)
+
+## Modules
+- `augmentation` — image augmentation pipeline (albumentations)
+- `convert-annotations` — Pascal VOC / oriented bbox → YOLO format converter
+- `dataset-visualiser` — interactive annotation visualization tool
+
+## Internal Interfaces
+
+### Augmentator
+```python
+Augmentator()
+Augmentator.augment_annotations(from_scratch: bool = False) -> None
+Augmentator.augment_inner(img_ann: ImageLabel) -> list[ImageLabel]
+Augmentator.correct_bboxes(labels) -> list
+Augmentator.read_labels(labels_path) -> list[list]
+```
+
+### convert-annotations (functions)
+```python
+convert(folder, dest_folder, read_annotations, ann_format) -> None
+minmax2yolo(width, height, xmin, xmax, ymin, ymax) -> tuple
+read_pascal_voc(width, height, s: str) -> list[str]
+read_bbox_oriented(width, height, s: str) -> list[str]
+```
+
+### dataset-visualiser (functions)
+```python
+visualise_dataset() -> None
+visualise_processed_folder() -> None
+```
+
+## Data Access Patterns
+- **Augmentation**: Reads from `/azaion/data/images/` + `/azaion/data/labels/`, writes to `/azaion/data-processed/images/` + `/azaion/data-processed/labels/`
+- **Conversion**: Reads from user-specified source folder, writes to destination folder
+- **Visualiser**: Reads from datasets or processed folder, renders to matplotlib window
+
+## Implementation Details
+- **Augmentation pipeline**: Per image → 1 original copy + 7 augmented variants (8× data expansion)
+  - HorizontalFlip (60%), BrightnessContrast (40%), Affine (80%), MotionBlur (10%), HueSaturation (40%)
+  - Bbox correction clips outside-boundary boxes, removes boxes < 1% of image
+  - Incremental: skips already-processed images
+  - Continuous mode: infinite loop with 5-minute sleep between rounds
+  - Concurrent: ThreadPoolExecutor for parallel image processing
+- **Format conversion**: Pluggable reader pattern — `convert()` accepts any reader function that maps (width, height, text) → YOLO lines
+- **Visualiser**: Interactive (waits for keypress) — developer debugging tool
+
+## Caveats
+- `dataset-visualiser` imports from `preprocessing` module which does not exist — broken import
+- `dataset-visualiser` has hardcoded dataset date (`2024-06-18`) and start index (35247)
+- `convert-annotations` hardcodes class mappings (Truck=1, Car/Taxi=2) — not configurable
+- Augmentation parameters are hardcoded, not configurable via config file
+- Augmentation `total_to_process` attribute referenced in `augment_annotation` but never set (uses `total_images_to_process`)
+
+## Dependency Graph
+```mermaid
+graph TD
+    constants --> augmentation
+    dto_imageLabel[dto/imageLabel] --> augmentation
+    constants --> dataset-visualiser
+    dto_annotationClass[dto/annotationClass] --> dataset-visualiser
+    dto_imageLabel --> dataset-visualiser
+    augmentation --> manual_run
+```
+
+## Logging Strategy
+Print statements for progress tracking (processed count, errors). No structured logging.