Improvements and notebooks

src/lib/crop.py

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+"""Resize-to-cover then crop, biased toward Immich-detected face boxes."""
+
+import math
+
+from PIL import Image
+
+# Face boxes end at the hairline; extend each box upward by this fraction of
+# its own height so the fit-check considers the head, not just the face.
+HEAD_EXTENSION = 0.4
+
+
+def face_aware_crop(image: Image.Image, target_w: int, target_h: int,
+                    faces: list[dict]) -> Image.Image:
+    """Resize to cover (target_w, target_h), then crop to keep faces in frame.
+
+    Each face dict has imageWidth/imageHeight (the coord-space dims) and
+    boundingBoxX1/Y1/X2/Y2. Per axis: if every (head-extended) face fits in
+    the crop we centre on the joint span so all faces are included with hair
+    clearance on top. If the span doesn't fit, we fall back to the
+    area-weighted centroid of the unextended boxes — that biases toward the
+    biggest, presumably foreground, face. Plain center crop when no faces.
+    """
+    img_w, img_h = image.size
+    img_aspect = img_w / img_h
+    target_aspect = target_w / target_h
+    if img_aspect < target_aspect:
+        new_w = target_w
+        new_h = math.ceil(target_w / img_aspect)
+    else:
+        new_w = math.ceil(target_h * img_aspect)
+        new_h = target_h
+
+    resized = image.resize((new_w, new_h), Image.LANCZOS)
+
+    cx, cy = new_w / 2, new_h / 2
+    if faces:
+        boxes = []
+        for f in faces:
+            sx = new_w / (f.get("imageWidth") or img_w)
+            sy = new_h / (f.get("imageHeight") or img_h)
+            x1 = f["boundingBoxX1"] * sx
+            y1 = f["boundingBoxY1"] * sy
+            x2 = f["boundingBoxX2"] * sx
+            y2 = f["boundingBoxY2"] * sy
+            area = max(0.0, (x2 - x1) * (y2 - y1))
+            boxes.append((x1, y1, x2, y2, area))
+
+        x_lo = min(b[0] for b in boxes)
+        x_hi = max(b[2] for b in boxes)
+        if x_hi - x_lo <= target_w:
+            cx = (x_lo + x_hi) / 2
+        else:
+            cx = _weighted_center(boxes, 0, 2)
+
+        y_lo_ext = min(b[1] - (b[3] - b[1]) * HEAD_EXTENSION for b in boxes)
+        y_hi = max(b[3] for b in boxes)
+        if y_hi - y_lo_ext <= target_h:
+            cy = (y_lo_ext + y_hi) / 2
+        else:
+            cy = _weighted_center(boxes, 1, 3)
+
+    x_off = max(0, min(int(cx - target_w / 2), new_w - target_w))
+    y_off = max(0, min(int(cy - target_h / 2), new_h - target_h))
+    return resized.crop((x_off, y_off, x_off + target_w, y_off + target_h))
+
+
+def _weighted_center(boxes: list[tuple], lo: int, hi: int) -> float:
+    total = sum(b[4] for b in boxes) or 1.0
+    return sum((b[lo] + b[hi]) / 2 * b[4] for b in boxes) / total