| """Tests for preprocessing module.""" |
|
|
| import numpy as np |
| import pytest |
| from src.inference.preprocess import preprocess, preprocess_batch, crop |
|
|
|
|
| class TestPreprocess: |
| def test_output_shape(self, dummy_face_crop): |
| """Output shape is (C, H, W).""" |
| result = preprocess(dummy_face_crop, 128) |
| assert result.shape == (3, 128, 128) |
|
|
| def test_output_range(self, dummy_face_crop): |
| """Output values in [0, 1].""" |
| result = preprocess(dummy_face_crop, 128) |
| assert result.min() >= 0.0 |
| assert result.max() <= 1.0 |
|
|
| def test_output_dtype(self, dummy_face_crop): |
| """Output is float32.""" |
| result = preprocess(dummy_face_crop, 128) |
| assert result.dtype == np.float32 |
|
|
| def test_different_input_sizes(self): |
| """Handles various input sizes.""" |
| for size in [64, 100, 200, 256]: |
| img = np.random.randint(0, 255, (size, size, 3), dtype=np.uint8) |
| result = preprocess(img, 128) |
| assert result.shape == (3, 128, 128) |
|
|
|
|
| class TestPreprocessBatch: |
| def test_batch_shape(self, dummy_face_crop): |
| """Batch output shape is (N, C, H, W).""" |
| batch = [dummy_face_crop, dummy_face_crop] |
| result = preprocess_batch(batch, 128) |
| assert result.shape == (2, 3, 128, 128) |
|
|
| def test_empty_batch_raises(self): |
| """Empty batch raises ValueError.""" |
| with pytest.raises(ValueError): |
| preprocess_batch([], 128) |
|
|
|
|
| class TestCrop: |
| def test_crop_square_output(self): |
| """Crop returns square image.""" |
| img = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8) |
| bbox = (100, 100, 200, 200) |
| result = crop(img, bbox, 1.5) |
|
|
| assert result.shape[0] == result.shape[1] |
|
|
| def test_crop_invalid_bbox_raises(self): |
| """Invalid bbox raises ValueError.""" |
| img = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8) |
| bbox = (100, 100, 100, 100) |
|
|
| with pytest.raises(ValueError): |
| crop(img, bbox, 1.5) |
|
|
