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_tasks/AZ-154_test_augmentation_nonfunc.md

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+# Augmentation Performance, Resilience & Resource Tests
+
+**Task**: AZ-154_test_augmentation_nonfunc
+**Name**: Augmentation Non-Functional Tests
+**Description**: Implement performance, resilience, and resource limit tests for augmentation — throughput, parallel speedup, error handling, output bounds
+**Complexity**: 2 points
+**Dependencies**: AZ-152_test_infrastructure
+**Component**: Blackbox Tests
+**Jira**: AZ-154
+**Epic**: AZ-151
+
+## Problem
+
+Augmentation must perform within time thresholds, handle corrupted/missing inputs gracefully, and respect output count bounds.
+
+## Outcome
+
+- 6 passing pytest tests across performance and resilience categories
+- Performance tests in `tests/performance/test_augmentation_perf.py`
+- Resilience and resource limit tests in `tests/test_augmentation.py` with markers
+
+## Scope
+
+### Included
+- PT-AUG-01: Augmentation throughput (10 images ≤ 60s)
+- PT-AUG-02: Parallel augmentation speedup (≥ 1.5× faster)
+- RT-AUG-01: Handles corrupted image gracefully
+- RT-AUG-02: Handles missing label file
+- RT-AUG-03: Transform failure produces fewer variants (no crash)
+- RL-AUG-01: Output count bounded to exactly 8
+
+### Excluded
+- Blackbox functional tests (separate task 02)
+
+## Acceptance Criteria
+
+**AC-1: Throughput**
+Given 10 images from fixture dataset
+When augment_annotations() runs
+Then completes within 60 seconds
+
+**AC-2: Parallel speedup**
+Given 10 images from fixture dataset
+When run with ThreadPoolExecutor vs sequential
+Then parallel is ≥ 1.5× faster
+
+**AC-3: Corrupted image**
+Given 1 valid + 1 corrupted image (truncated JPEG)
+When augment_annotations() runs
+Then valid image produces 8 outputs, corrupted skipped, no crash
+
+**AC-4: Missing label**
+Given 1 image with no matching label file
+When augment_annotation() runs on it
+Then exception caught per-thread, pipeline continues
+
+**AC-5: Transform failure**
+Given 1 image + label with extremely narrow bbox
+When augment_inner() runs
+Then 1-8 ImageLabel objects returned, no crash
+
+**AC-6: Output count bounded**
+Given 1 image
+When augment_inner() runs
+Then exactly 8 outputs returned (never more)
+
+## Constraints
+
+- Performance tests require pytest markers: `@pytest.mark.performance`
+- Resilience tests marked: `@pytest.mark.resilience`
+- Resource limit tests marked: `@pytest.mark.resource_limit`
+- Performance thresholds are generous (CPU-bound, no GPU requirement)