Add opt-in facial recognition: detection and embedding storage

- scripts/faces.py: Python helper using face_recognition (dlib/HOG) to
  detect faces and return 128-D encodings as JSON; called by update.rb
- scripts/update.rb: enrich_faces() stores face boxes and encodings in
  album.json per image (null = not yet processed, [] = processed/none found);
  skips files already processed; gated on faces.enabled in config.yml
- Reads CONFIG_PATH (same env var as app.rb) to check faces.enabled flag
- Feature is off by default; enabled in this install via config.yml
- README.md, DESIGN.md: document installation, opt-in config, data model,
  and planned clustering/people-management pipeline

People management UI and clustering script are the next milestone.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

1 files changed, 51 insertions, 0 deletions

diff --git a/scripts/faces.py b/scripts/faces.py
new file mode 100644
index 0000000..d072376
--- /dev/null
+++ b/scripts/faces.py
@@ -0,0 +1,51 @@
+#!/usr/bin/env python3
+"""
+Detect faces in an image and return their bounding boxes and 128-D encodings.
+
+Usage: python3 faces.py <image_path>
+
+Stdout: JSON array — one object per face:
+  [{"box": [top, right, bottom, left], "encoding": [128 floats]}, ...]
+
+Returns "[]" when no faces are found or the image cannot be opened.
+Errors are written to stderr; stdout is always valid JSON.
+"""
+import sys
+import json
+
+
+def main():
+    if len(sys.argv) < 2:
+        print("[]")
+        return
+
+    path = sys.argv[1]
+    try:
+        import face_recognition
+    except ImportError as e:
+        print(f"face_recognition not available: {e}", file=sys.stderr)
+        print("[]")
+        return
+
+    try:
+        img = face_recognition.load_image_file(path)
+    except Exception as e:
+        print(f"Could not load {path}: {e}", file=sys.stderr)
+        print("[]")
+        return
+
+    try:
+        locations = face_recognition.face_locations(img, model="hog")
+        encodings = face_recognition.face_encodings(img, locations)
+        result = [
+            {"box": list(loc), "encoding": enc.tolist()}
+            for loc, enc in zip(locations, encodings)
+        ]
+        print(json.dumps(result))
+    except Exception as e:
+        print(f"Detection error for {path}: {e}", file=sys.stderr)
+        print("[]")
+
+
+if __name__ == "__main__":
+    main()