Initial commit

src/features/extract_texture_lbp.py

@@ -0,0 +1,44 @@
+from __future__ import annotations
+
+import numpy as np
+from skimage.feature import local_binary_pattern
+
+
+def _downsample(gray_image: np.ndarray, binary_mask: np.ndarray | None = None, max_side: int = 96) -> tuple[np.ndarray, np.ndarray | None]:
+    working = gray_image
+    mask = binary_mask
+    max_dim = max(working.shape[:2])
+    if max_dim <= max_side:
+        return working, mask
+    step = int(np.ceil(max_dim / max_side))
+    working = working[::step, ::step]
+    if mask is not None:
+        mask = mask[::step, ::step]
+    return working, mask
+
+
+def texture_lbp_features(gray_image: np.ndarray, binary_mask: np.ndarray | None = None) -> dict[str, float]:
+    gray_image, binary_mask = _downsample(gray_image, binary_mask)
+    working = np.clip(gray_image, 0, 255).astype(np.uint8, copy=False)
+    lbp_r1 = local_binary_pattern(working, P=8, R=1, method="uniform")
+    lbp_r2 = local_binary_pattern(working, P=16, R=2, method="uniform")
+
+    if binary_mask is None:
+        values_r1 = lbp_r1.ravel()
+        values_r2 = lbp_r2.ravel()
+    else:
+        values_r1 = lbp_r1[binary_mask]
+        values_r2 = lbp_r2[binary_mask]
+        if values_r1.size == 0:
+            values_r1 = lbp_r1.ravel()
+            values_r2 = lbp_r2.ravel()
+
+    hist_r1, _ = np.histogram(values_r1, bins=np.arange(0, 8 + 3), density=True)
+    hist_r2, _ = np.histogram(values_r2, bins=np.arange(0, 16 + 3), density=True)
+
+    features: dict[str, float] = {}
+    for idx, value in enumerate(hist_r1):
+        features[f"texture_lbp_r1_bin_{idx:02d}"] = float(value)
+    for idx, value in enumerate(hist_r2):
+        features[f"texture_lbp_r2_bin_{idx:02d}"] = float(value)
+    return features