[Improvement] Increase Unit Test Coverage to ~90%

### 📝 Description As `supervision` matures into an industry-standard utility package for Computer Vision, the reliability and stability of the codebase become paramount. Currently, our total test coverage sits at approximately **50%**, with critical modules like `metrics` and `key_points` falling well below 40%. To ensure this package remains the go-to solution for production pipelines and research alike, I propose an initiative to systematically increase our unit test coverage to a realistic target of **~90%**. ### 🚀 Motivation Why is this high priority? * **Industry Standard Responsibility:** `supervision` is infrastructure. Users rely on it for critical CV workflows. A subtle bug in a metric calculation (like mAP or confusion matrix) can silently invalidate research experiments. * **Refactoring Confidence:** As we optimize performance, a robust test suite acts as a safety net. * **Handling Edge Cases:** Computer Vision data is notoriously messy. High coverage ensures we handle empty detections, `None` values, and malformed shapes gracefully. ### 📊 Current State According to the latest Codecov report, we have significant gaps in core modules. | Module | Coverage | Status | Notes | | :--- | :--- | :--- | :--- | | **`metrics`** | **38.36%** | 🔴 Critical | Over 1,100 lines untested. | | **`key_points`** | **36.68%** | 🔴 Critical | Lowest percentage in the library. | | **`utils`** | **55.06%** | 🟡 Low | | | **`dataset`** | **57.32%** | 🟡 Low | Formats logic needs hardening. | | **`detection`** | **68.49%** | 🟡 Moderate | High volume of missed lines (639) due to file size. | ### 🛠 Proposed Strategy We should tackle this modularly. The immediate priority is to lift the "Red" modules out of the danger zone. **Priority 1: The Criticals** * **`metrics/`**: This is the highest impact area. We need to verify that mathematical calculations for accuracy, recall, and precision are correct across various edge cases. * **`key_points/`**: Needs basic functional tests for skeleton handling and visualization. **Priority 2: Stability Layers** * **`dataset/`**: Ensure saving/loading functions (YOLO, COCO, Pascal VOC) are robust against file system errors and malformed inputs. * **`utils/`**: General utility functions that are used everywhere need to be bulletproof. ### ✅ Guidelines for New Tests For contributors looking to help: * **Test Behavior, Not Implementation:** Focus on inputs and expected outputs. * **Edge Cases:** Explicitly test for empty lists, `None` values, and mismatched array shapes. * **Mocking:** Use mocking for external I/O (downloading files, displaying windows) to keep the suite fast. * **Parametrization:** Use `@pytest.mark.parametrize` to cover multiple scenarios efficiently. ### 📋 Tracking Please comment below if you would like to pick up a specific module so we don't duplicate work. - [ ] `supervision/metrics` (High Priority) - [ ] `supervision/key_points` (High Priority) - [ ] `supervision/dataset` - [ ] `supervision/utils` - [ ] `supervision/detection` --- The actual coverage report can be found at https://app.codecov.io/gh/roboflow/supervision/blob/develop