Add comprehensive test coverage for the image-processing library #4
Description
Description
Introduce a complete automated test suite to ensure the correctness, stability, and long-term maintainability of the image-processing library.
Tests should cover arithmetic operations, filters, decorators, histogram tools, thresholding methods, and utility functions.
The goal is to create a reliable safety net that prevents regressions as the project evolves.
Requirements
1. Testing Framework
- Use pytest as the main testing framework.
- Organize tests in a dedicated directory
2. What Should Be Tested?
Core arithmetic operations (add, multiply, divide, etc.)
- Handle both grayscale and RGB images.
- Clamp results properly.
- Maintain image shape and dtype.
Filters (mean, Gaussian, sharpening, Sobel, median)
- Test correct kernel application.
- Check that invalid parameters are handled gracefully.
- Validate output dtype and value ranges.
Decorators
- Ensure decorators correctly:
- apply per-channel logic,
- perform clamping,
- convert output to
uint8.
Histogram tools
- Histogram stretching correctness.
- Histogram equalization shape & range consistency.
Thresholding methods
- Manual thresholding returns expected binary masks.
- Percentage thresholding uses correct percentile.
- Otsu method selects correct threshold for simple synthetic images.
Utilities
- Image loading/saving wrappers (mocked).
- Validation helpers if they exist.
3. Additional Requirements
- Achieve at least 80% coverage (preferably higher).
- Add a GitHub Actions workflow for:
- Running tests,
- Checking coverage,
- Blocking PRs if tests fail.
Note
Tests should be deterministic — avoid random or non-repeatable inputs unless seeded.