feat(extract): Pixel-Klassifizierung mit Lab-Palette

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

tools/extract/tests/test_classify.py

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+import numpy as np
+from leonida.classify import CLASSES, PALETTE, class_shares, classify
+
+def block(color, size=8):
+    return np.full((size, size, 3), color, dtype=np.uint8)
+
+def test_pure_colors_hit_their_class():
+    for name, color in PALETTE.items():
+        labels = classify(block(color))
+        assert (labels == CLASSES[name]).all(), name
+
+def test_jpeg_noise_still_classifies():
+    rng = np.random.default_rng(0)
+    noisy = block(PALETTE["strasse"], 32).astype(np.int16)
+    noisy += rng.integers(-10, 11, noisy.shape)          # JPEG-Rauschen
+    labels = classify(np.clip(noisy, 0, 255).astype(np.uint8))
+    assert (labels == CLASSES["strasse"]).mean() > 0.98
+
+def test_unknown_pixels_are_filled_from_neighbours():
+    img = block(PALETTE["stadt"], 16)
+    img[7:9, 7:9] = (255, 0, 255)                         # Beschriftung, in keiner Klasse
+    labels = classify(img)
+    assert (labels == CLASSES["stadt"]).all()
+
+def test_reports_the_share_of_unknown_pixels():
+    img = block(PALETTE["stadt"], 16)
+    img[7:9, 7:9] = (255, 0, 255)              # 4 von 256 Pixeln passen zu keiner Farbe
+    labels, share = classify(img, report_unknown=True)
+    assert (labels == CLASSES["stadt"]).all()
+    assert abs(share - 4 / 256) < 1e-9
+
+def test_shares_sum_to_one():
+    img = np.concatenate([block(PALETTE["wasser"], 8), block(PALETTE["land"], 8)], axis=1)
+    shares = class_shares(classify(img))
+    assert abs(sum(shares.values()) - 1.0) < 1e-9
+    assert abs(shares["wasser"] - 0.5) < 1e-9