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Add extensive pycocotools comparison tests with large synthetic datasets (#71)
* Initial plan * Add extensive pycocotools comparison tests for all task types Co-authored-by: Borda <6035284+Borda@users.noreply.github.com> * Add comprehensive test documentation in README and tests/README.md Co-authored-by: Borda <6035284+Borda@users.noreply.github.com> * Address code review feedback - improve documentation and remove unused maxDiff Co-authored-by: Borda <6035284+Borda@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: Borda <6035284+Borda@users.noreply.github.com>
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‎README.md‎

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@@ -144,6 +144,31 @@ Faster-COCO-Eval goes beyond basic evaluation with these advanced capabilities:
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- **Comprehensive API documentation**
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- **Extensive test coverage and reliability**
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## ✅ Testing & Reliability
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Faster-COCO-Eval prioritizes **correctness and reliability** through extensive testing:
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### Comprehensive Test Suite
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- **90+ automated tests** covering all functionality
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- **Exact equality validation** against pycocotools across all metrics
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- **Continuous integration** on Python 3.9-3.13
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- **Edge case coverage** including boundary conditions and error handling
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### Extensive PyCocoTools Comparison
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New comprehensive tests validate **exact numerical equality** with pycocotools:
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- **Object Detection**: Tests with 10-100 images, hundreds to thousands of annotations
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- **Instance Segmentation**: RLE mask encoding and pixel-level IoU validation
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- **Keypoint Detection**: 17-keypoint pose estimation with varied visibility
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- **Multiple Scenarios**: Small/medium/large objects, various confidence distributions
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- **Edge Cases**: Perfect predictions, low-confidence detections, mixed object sizes
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All tests confirm **bit-for-bit identical results** between faster_coco_eval and pycocotools, giving you confidence to use this library as a drop-in replacement while gaining 3-4x performance improvements.
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See [tests/README.md](tests/README.md) for detailed test documentation.
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## 📚 Comprehensive Documentation
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### Usage Examples

‎tests/README.md‎

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# Test Suite Documentation
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This directory contains the test suite for `faster_coco_eval`, which validates that the library produces identical results to `pycocotools` while being significantly faster.
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## Test Organization
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### Core Functionality Tests
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- **test_basic.py** - Basic COCO evaluation functionality
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- **test_coco_metric.py** - COCO metrics with pycocotools comparison (small examples)
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- **test_keypoints.py** - Keypoint evaluation
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- **test_cocoapi_fake_data.py** - Tests with synthetic data
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### Extensive Comparison Tests
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- **test_extensive_pycocotools_comparison.py** - **NEW**: Comprehensive validation against pycocotools with large synthetic datasets
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### Dataset-Specific Tests
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- **test_lvis_metric.py** - LVIS dataset support
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- **test_crowdpose.py** - CrowdPose keypoints dataset
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### API and Integration Tests
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- **test_init_pycocotools.py** - Drop-in replacement compatibility
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- **test_torchmetrics.py** - PyTorch integration (if available)
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- **test_mask_api.py** - Mask utilities
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- **test_boundary.py** - Boundary evaluation
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### Visualization and Utilities
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- **test_extra_draw.py**, **test_extra_utils.py**, **test_simple_extra.py** - Visualization features
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- **test_ranges.py**, **test_dataset.py** - Utility functions
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## Extensive PyCocoTools Comparison Tests
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The `test_extensive_pycocotools_comparison.py` module provides comprehensive validation that `faster_coco_eval` produces **identical results** to `pycocotools` across a wide range of scenarios.
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### Test Coverage
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#### Object Detection (BBox) Tests
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Tests bounding box detection with datasets of varying sizes:
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- **Small dataset**: 10 images, 5 categories, ~50 annotations
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- **Medium dataset**: 50 images, 10 categories, ~500 annotations
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- **Large dataset**: 100 images, 20 categories, ~1500 annotations
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Each test validates that both libraries produce identical mAP, mAP@50, mAP@75, and size-specific metrics (small/medium/large objects).
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#### Instance Segmentation Tests
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Tests segmentation masks with the same dataset size variations as bbox tests. Validates pixel-level mask IoU calculations match exactly between implementations.
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#### Keypoint Detection Tests
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Tests keypoint pose estimation with datasets containing:
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- **Small dataset**: 10 images with 17 keypoints per person
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- **Medium dataset**: 50 images with multiple people per image
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- **Large dataset**: 100 images with varied keypoint visibility
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Validates that OKS (Object Keypoint Similarity) calculations are identical.
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#### Edge Cases
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- **Perfect predictions**: All predictions match ground truth exactly (IoU=1.0)
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- **Low confidence predictions**: Tests with very low-scoring detections
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- **Mixed object sizes**: Validates correct assignment to small/medium/large categories
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### Test Data Generation
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The tests use **synthetic but realistic** COCO-formatted datasets that mimic actual model predictions:
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- **Varied image sizes**: Random dimensions between 400x400 and 800x800 pixels
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- **Realistic bounding boxes**: Objects categorized as small (<32²), medium (32²-96²), or large (>96²)
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- **Segmentation masks**: RLE-encoded binary masks matching bbox regions
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- **Keypoint annotations**: 17 keypoints per instance with realistic visibility flags
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- **Prediction noise**: Simulated detection errors with bbox jitter and confidence scores
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- **False positives**: Includes spurious detections to test precision/recall
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### Running the Tests
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Run all extensive comparison tests:
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```bash
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cd tests/
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pytest test_extensive_pycocotools_comparison.py -v
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```
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Run specific test categories:
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```bash
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# Only bbox tests
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pytest test_extensive_pycocotools_comparison.py -k "bbox" -v
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# Only segmentation tests
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pytest test_extensive_pycocotools_comparison.py -k "segmentation" -v
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# Only keypoint tests
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pytest test_extensive_pycocotools_comparison.py -k "keypoints" -v
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# Only large dataset tests
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pytest test_extensive_pycocotools_comparison.py -k "large" -v
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```
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### Test Success Criteria
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Tests pass if and only if:
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1. All metrics (mAP, mAP@50, mAP@75, mAP_small, mAP_medium, mAP_large, etc.) are **numerically identical** between `faster_coco_eval` and `pycocotools`
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2. Floating-point comparison uses tolerance of `1e-10` (essentially exact)
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3. All intermediate calculations (IoU, OKS) produce identical results
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### Why These Tests Matter
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These extensive tests address the requirement for **confidence in correctness** when using `faster_coco_eval` as a drop-in replacement for `pycocotools`:
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- **Broader coverage**: Tests hundreds to thousands of annotations vs. single-digit examples in original tests
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- **Real-world scenarios**: Synthetic data mimics actual model predictions with realistic error patterns
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- **All task types**: Validates bbox, segmentation, and keypoints independently
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- **Edge cases**: Ensures correct behavior in corner cases that might not appear in small datasets
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- **Continuous validation**: Runs in CI/CD to catch any regression in numerical accuracy
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## Running All Tests
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Run the complete test suite:
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```bash
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cd tests/
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pytest --cov=faster_coco_eval .
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```
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Run tests for a specific Python version (CI/CD runs Python 3.9-3.13):
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```bash
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pytest --cov=faster_coco_eval . -v
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```
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## Test Requirements
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Install test dependencies:
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```bash
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pip install "faster-coco-eval[tests]"
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```
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Or from source:
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```bash
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cd /path/to/faster_coco_eval
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pip install -e ".[tests]"
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```
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Required packages:
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- `pytest` - Test framework
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- `pytest-cov` - Coverage reporting
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- `parameterized` - Parameterized test cases
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- `pycocotools` - Original COCO API for comparison tests
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- `numpy` - Numerical operations
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## Contributing Tests
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When adding new features to `faster_coco_eval`, please:
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1. Add corresponding tests that validate **exact equality** with `pycocotools` behavior
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2. Use parameterized tests to cover multiple scenarios efficiently
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3. Generate synthetic test data programmatically for reproducibility
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4. Set `np.random.seed()` for deterministic test data
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5. Document what each test validates and why it's important

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