|
| 1 | +import numpy as np |
| 2 | + |
| 3 | + |
| 4 | +def test_transform_input_image(): |
| 5 | + from bioimageio.core.image_helper import transform_input_image |
| 6 | + |
| 7 | + ax_list = ["yx", "xy", "cyx", "yxc", "bczyx", "xyz", "xyzc", "bzyxc"] |
| 8 | + im = np.random.rand(256, 256) |
| 9 | + for axes in ax_list: |
| 10 | + inp = transform_input_image(im, axes) |
| 11 | + assert inp.ndim == len(axes) |
| 12 | + |
| 13 | + ax_list = ["zyx", "cyx", "yxc", "bczyx", "xyz", "xyzc", "bzyxc"] |
| 14 | + vol = np.random.rand(64, 64, 64) |
| 15 | + for axes in ax_list: |
| 16 | + inp = transform_input_image(vol, axes) |
| 17 | + assert inp.ndim == len(axes) |
| 18 | + |
| 19 | + |
| 20 | +def test_transform_output_tensor(): |
| 21 | + from bioimageio.core.image_helper import transform_output_tensor |
| 22 | + |
| 23 | + tensor = np.random.rand(1, 3, 64, 64, 64) |
| 24 | + tensor_axes = "bczyx" |
| 25 | + |
| 26 | + out_ax_list = ["bczyx", "cyx", "xyc", "byxc", "zyx", "xyz"] |
| 27 | + for out_axes in out_ax_list: |
| 28 | + out = transform_output_tensor(tensor, tensor_axes, out_axes) |
| 29 | + assert out.ndim == len(out_axes) |
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