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Running on Zero
Running on Zero
| import numpy as np | |
| import pytest | |
| from PIL import Image | |
| import preprocessors | |
| def gradient_image(): | |
| arr = np.linspace(0, 255, 256 * 256, dtype=np.uint8).reshape(256, 256) | |
| return Image.fromarray(arr).convert("RGB") | |
| def test_modes_are_listed(): | |
| assert preprocessors.MODES == ("Canny", "Depth", "Pose", "Pre-processed") | |
| def test_canny_returns_rgb_image_of_same_size(gradient_image): | |
| out = preprocessors.run("Canny", gradient_image) | |
| assert isinstance(out, Image.Image) | |
| assert out.size == gradient_image.size | |
| assert out.mode == "RGB" | |
| def test_passthrough_returns_input_unchanged(gradient_image): | |
| out = preprocessors.run("Pre-processed", gradient_image) | |
| assert out is gradient_image | |
| def test_unknown_mode_raises(): | |
| with pytest.raises(ValueError): | |
| preprocessors.run("Sobel", Image.new("RGB", (32, 32))) | |
| def test_run_with_image_none_raises(): | |
| with pytest.raises(ValueError): | |
| preprocessors.run("Canny", None) | |