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https://github.com/azaion/ai-training.git
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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.
84 lines
2.9 KiB
Markdown
84 lines
2.9 KiB
Markdown
# Augmentation Blackbox Tests
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**Task**: AZ-153_test_augmentation
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**Name**: Augmentation Blackbox Tests
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**Description**: Implement 8 blackbox tests for the augmentation pipeline — output count, naming, bbox validation, edge cases, filesystem integration
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**Complexity**: 3 points
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**Dependencies**: AZ-152_test_infrastructure
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**Component**: Blackbox Tests
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**Jira**: AZ-153
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**Epic**: AZ-151
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## Problem
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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.
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## Outcome
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- 8 passing pytest tests in `tests/test_augmentation.py`
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- Covers: single-image augmentation, naming convention, bbox range, bbox clipping, tiny bbox removal, empty labels, full pipeline, skip-already-processed
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## Scope
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### Included
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- BT-AUG-01: Single image → 8 outputs
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- BT-AUG-02: Augmented filenames follow naming convention
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- BT-AUG-03: All output bounding boxes in valid range [0,1]
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- BT-AUG-04: Bounding box correction clips edge bboxes
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- BT-AUG-05: Tiny bounding boxes removed after correction
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- BT-AUG-06: Empty label produces 8 outputs with empty labels
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- BT-AUG-07: Full augmentation pipeline (filesystem, 5 images → 40 outputs)
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- BT-AUG-08: Augmentation skips already-processed images
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### Excluded
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- Performance tests (separate task)
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- Resilience tests (separate task)
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## Acceptance Criteria
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**AC-1: Output count**
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Given 1 image + 1 valid label
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When augment_inner() runs
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Then exactly 8 ImageLabel objects are returned
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**AC-2: Naming convention**
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Given image with stem "test_image"
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When augment_inner() runs
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Then outputs named test_image.jpg, test_image_1.jpg through test_image_7.jpg with matching .txt labels
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**AC-3: Bbox validity**
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Given 1 image + label with multiple bboxes
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When augment_inner() runs
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Then every bbox coordinate in every output is in [0.0, 1.0]
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**AC-4: Edge bbox clipping**
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Given label with bbox near edge (x=0.99, w=0.2)
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When correct_bboxes() runs
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Then width reduced to fit within bounds; no coordinate exceeds [margin, 1-margin]
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**AC-5: Tiny bbox removal**
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Given label with bbox that becomes < 0.01 area after clipping
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When correct_bboxes() runs
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Then bbox is removed from output
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**AC-6: Empty label**
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Given 1 image + empty label file
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When augment_inner() runs
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Then 8 ImageLabel objects returned, all with empty labels lists
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**AC-7: Full pipeline**
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Given 5 images + labels in data/ directory
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When augment_annotations() runs with patched paths
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Then 40 images in processed images dir, 40 matching labels
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**AC-8: Skip already-processed**
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Given 5 images in data/, 3 already in processed/
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When augment_annotations() runs
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Then only 2 new images processed (16 new outputs), existing 3 untouched
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## Constraints
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- Must patch constants.py paths to use tmp_path
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- Fixture images from _docs/00_problem/input_data/dataset/
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- Each test operates in isolated tmp_path
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