feat: imaging tensor loaders + change hash

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

tests/test_imaging.py

@@ -0,0 +1,43 @@
+import numpy as np, torch
+from PIL import Image
+from gates import imaging
+
+
+def _png(tmp_path, name, color, size=(4, 6)):  # size = (w, h)
+    p = tmp_path / name
+    Image.new("RGB", size, color).save(p)
+    return str(p)
+
+
+def test_load_image_tensor_shape_and_range(tmp_path):
+    t = imaging.load_image_tensor(_png(tmp_path, "a.png", (255, 0, 0)))
+    assert t.shape == (1, 6, 4, 3)         # [B,H,W,C]
+    assert t.dtype == torch.float32
+    assert 0.0 <= float(t.min()) and float(t.max()) <= 1.0
+    assert float(t[0, 0, 0, 0]) > 0.99     # red channel
+
+
+def test_load_mask_none_is_zeros():
+    m = imaging.load_mask_tensor(None, h=6, w=4)
+    assert m.shape == (1, 6, 4)
+    assert float(m.max()) == 0.0
+
+
+def test_load_mask_from_file(tmp_path):
+    p = tmp_path / "m.png"
+    Image.new("L", (4, 6), 255).save(p)
+    m = imaging.load_mask_tensor(str(p), h=6, w=4)
+    assert m.shape == (1, 6, 4)
+    assert float(m.min()) > 0.99
+
+
+def test_empty_image_is_1x1_black():
+    img, mask = imaging.empty_outputs()
+    assert img.shape == (1, 1, 1, 3) and float(img.max()) == 0.0
+    assert mask.shape == (1, 1, 1)
+
+
+def test_change_hash_changes_with_mtime():
+    h1 = imaging.change_hash("p", 0, [1000.0])
+    h2 = imaging.change_hash("p", 0, [1001.0])
+    assert h1 != h2