- 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.
1.6 KiB
NMS Overlap Removal Tests
Task: AZ-162_test_nms Name: NMS Overlap Removal Tests Description: Implement 3 tests for non-maximum suppression — overlapping kept by confidence, non-overlapping preserved, chain overlap resolution Complexity: 1 point Dependencies: AZ-152_test_infrastructure Component: Blackbox Tests Jira: AZ-162 Epic: AZ-151
Problem
The NMS module removes overlapping detections based on IoU threshold (0.3), keeping the higher-confidence detection. Tests verify all overlap scenarios.
Outcome
- 3 passing pytest tests in
tests/test_nms.py
Scope
Included
- BT-NMS-01: Overlapping detections — keep higher confidence (IoU > 0.3 → 1 kept)
- BT-NMS-02: Non-overlapping detections — keep both (IoU < 0.3 → 2 kept)
- BT-NMS-03: Chain overlap resolution (A↔B, B↔C → ≤ 2 kept)
Excluded
- Integration with inference pipeline (separate task)
Acceptance Criteria
AC-1: Overlap removal Given 2 Detections at same position, confidence 0.9 and 0.5, IoU > 0.3 When remove_overlapping_detections() runs Then 1 detection returned (confidence 0.9)
AC-2: Non-overlapping preserved Given 2 Detections at distant positions, IoU < 0.3 When remove_overlapping_detections() runs Then 2 detections returned
AC-3: Chain overlap Given 3 Detections: A overlaps B, B overlaps C, A doesn't overlap C When remove_overlapping_detections() runs Then ≤ 2 detections; highest confidence per overlapping pair kept
Constraints
- Detection objects constructed in-memory (no fixture files)
- IoU threshold is 0.3 (from constants or hardcoded in NMS)