Oleksandr Bezdieniezhnykh
142c6c4de8
- 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.
2.9 KiB
Augmentation Blackbox Tests
Task: AZ-153_test_augmentation Name: Augmentation Blackbox Tests Description: Implement 8 blackbox tests for the augmentation pipeline — output count, naming, bbox validation, edge cases, filesystem integration Complexity: 3 points Dependencies: AZ-152_test_infrastructure Component: Blackbox Tests Jira: AZ-153 Epic: AZ-151
Problem
The augmentation pipeline transforms annotated images into 8 variants each. Tests must verify output count, naming conventions, bounding box validity, edge cases, and filesystem integration without referencing internals.
Outcome
- 8 passing pytest tests in
tests/test_augmentation.py - Covers: single-image augmentation, naming convention, bbox range, bbox clipping, tiny bbox removal, empty labels, full pipeline, skip-already-processed
Scope
Included
- BT-AUG-01: Single image → 8 outputs
- BT-AUG-02: Augmented filenames follow naming convention
- BT-AUG-03: All output bounding boxes in valid range [0,1]
- BT-AUG-04: Bounding box correction clips edge bboxes
- BT-AUG-05: Tiny bounding boxes removed after correction
- BT-AUG-06: Empty label produces 8 outputs with empty labels
- BT-AUG-07: Full augmentation pipeline (filesystem, 5 images → 40 outputs)
- BT-AUG-08: Augmentation skips already-processed images
Excluded
- Performance tests (separate task)
- Resilience tests (separate task)
Acceptance Criteria
AC-1: Output count Given 1 image + 1 valid label When augment_inner() runs Then exactly 8 ImageLabel objects are returned
AC-2: Naming convention Given image with stem "test_image" When augment_inner() runs Then outputs named test_image.jpg, test_image_1.jpg through test_image_7.jpg with matching .txt labels
AC-3: Bbox validity Given 1 image + label with multiple bboxes When augment_inner() runs Then every bbox coordinate in every output is in [0.0, 1.0]
AC-4: Edge bbox clipping Given label with bbox near edge (x=0.99, w=0.2) When correct_bboxes() runs Then width reduced to fit within bounds; no coordinate exceeds [margin, 1-margin]
AC-5: Tiny bbox removal Given label with bbox that becomes < 0.01 area after clipping When correct_bboxes() runs Then bbox is removed from output
AC-6: Empty label Given 1 image + empty label file When augment_inner() runs Then 8 ImageLabel objects returned, all with empty labels lists
AC-7: Full pipeline Given 5 images + labels in data/ directory When augment_annotations() runs with patched paths Then 40 images in processed images dir, 40 matching labels
AC-8: Skip already-processed Given 5 images in data/, 3 already in processed/ When augment_annotations() runs Then only 2 new images processed (16 new outputs), existing 3 untouched
Constraints
- Must patch constants.py paths to use tmp_path
- Fixture images from _docs/00_problem/input_data/dataset/
- Each test operates in isolated tmp_path