44 lines
1.2 KiB
Python
44 lines
1.2 KiB
Python
import numpy as np, torch
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from PIL import Image
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from gates import imaging
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def _png(tmp_path, name, color, size=(4, 6)): # size = (w, h)
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p = tmp_path / name
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Image.new("RGB", size, color).save(p)
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return str(p)
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def test_load_image_tensor_shape_and_range(tmp_path):
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t = imaging.load_image_tensor(_png(tmp_path, "a.png", (255, 0, 0)))
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assert t.shape == (1, 6, 4, 3) # [B,H,W,C]
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assert t.dtype == torch.float32
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assert 0.0 <= float(t.min()) and float(t.max()) <= 1.0
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assert float(t[0, 0, 0, 0]) > 0.99 # red channel
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def test_load_mask_none_is_zeros():
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m = imaging.load_mask_tensor(None, h=6, w=4)
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assert m.shape == (1, 6, 4)
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assert float(m.max()) == 0.0
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def test_load_mask_from_file(tmp_path):
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p = tmp_path / "m.png"
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Image.new("L", (4, 6), 255).save(p)
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m = imaging.load_mask_tensor(str(p), h=6, w=4)
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assert m.shape == (1, 6, 4)
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assert float(m.min()) > 0.99
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def test_empty_image_is_1x1_black():
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img, mask = imaging.empty_outputs()
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assert img.shape == (1, 1, 1, 3) and float(img.max()) == 0.0
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assert mask.shape == (1, 1, 1)
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def test_change_hash_changes_with_mtime():
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h1 = imaging.change_hash("p", 0, [1000.0])
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h2 = imaging.change_hash("p", 0, [1001.0])
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assert h1 != h2
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