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| import numpy as np | |
| from edge_aware_denoiser import add_gaussian_noise, denoise, quality_metrics | |
| def test_methods(): | |
| rng = np.random.default_rng(7) | |
| image = rng.random((64, 64, 3), dtype=np.float32) | |
| noisy = add_gaussian_noise(image, 10, seed=42) | |
| for method in ("Perona–Malik", "Total Variation", "Bilateral"): | |
| result, metadata = denoise(noisy, method=method, iterations=12) | |
| assert result.shape == image.shape | |
| assert 0 <= result.min() <= result.max() <= 1 | |
| assert metadata.method == method | |
| def test_metrics(): | |
| image = np.zeros((32, 32), dtype=np.float32) | |
| assert set(quality_metrics(image, image)) == { | |
| "rmse", | |
| "psnr", | |
| "ssim", | |
| "edge_similarity", | |
| } | |
| if __name__ == "__main__": | |
| test_methods() | |
| test_metrics() | |
| print("Tests passed.") | |