low pri features

tests/node_tests/pixel_classification.py

@@ -0,0 +1,49 @@
+import numpy as np
+from tests.node_tests._shared import make_field
+
+
+def test_output_shape():
+    from backend.nodes.pixel_classification import PixelClassification
+
+    node = PixelClassification()
+    field = make_field(shape=(64, 64))
+    classified, mask = node.process(field, n_classes=3, feature="height", method="quantile")
+    assert classified.data.shape == field.data.shape
+
+
+def test_correct_number_of_classes():
+    from backend.nodes.pixel_classification import PixelClassification
+
+    node = PixelClassification()
+    field = make_field(shape=(64, 64))
+    for n in (2, 4, 5):
+        classified, _ = node.process(field, n_classes=n, feature="height", method="quantile")
+        unique = np.unique(classified.data)
+        assert len(unique) <= n, f"Expected at most {n} classes, got {len(unique)}"
+
+
+def test_equal_range_method():
+    from backend.nodes.pixel_classification import PixelClassification
+
+    node = PixelClassification()
+    # Linear ramp: equal_range should produce evenly distributed labels
+    ramp = np.linspace(0, 1, 64 * 64).reshape(64, 64)
+    field = make_field(data=ramp)
+    classified, _ = node.process(field, n_classes=4, feature="height", method="equal_range")
+    labels = classified.data.astype(int)
+    unique = np.unique(labels)
+    assert len(unique) == 4
+    # Each class should have roughly 25% of pixels
+    counts = [np.sum(labels == u) for u in unique]
+    for c in counts:
+        assert abs(c - 64 * 64 / 4) < 64 * 64 * 0.05  # within 5%
+
+
+def test_mask_output():
+    from backend.nodes.pixel_classification import PixelClassification
+
+    node = PixelClassification()
+    field = make_field(shape=(32, 32))
+    _, mask = node.process(field, n_classes=3, feature="height", method="otsu")
+    assert mask.dtype == np.uint8
+    assert set(np.unique(mask)).issubset({0, 255})