feat: trace-level matching, health watcher/worker status, timezone config

scripts/extract_face_embedding.py

@@ -0,0 +1,84 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Extract face embedding from an image using InsightFace + CoreML FaceNet.
+
+Usage:
+    python3 scripts/extract_face_embedding.py <image_path>
+
+Output: JSON with "embedding" key (512 floats) or "error" key.
+Exit code: 0 on success, 1 on failure.
+"""
+import json
+import os
+import sys
+
+# Prefer venv if it exists (has insightface + coremltools installed)
+VENV_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "venv")
+VENV_SITE = os.path.join(VENV_PATH, "lib", "python3.11", "site-packages")
+if os.path.isdir(VENV_SITE):
+    sys.path.insert(0, VENV_SITE)
+
+import cv2
+import numpy as np
+
+MODELS_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "models")
+FACENET_PATH = os.path.join(MODELS_DIR, "facenet512.mlpackage")
+
+
+def extract_embedding(image_path: str):
+    import io
+    import warnings
+    warnings.filterwarnings("ignore")
+
+    # Suppress InsightFace verbose stdout during model loading
+    old_stdout = sys.stdout
+    sys.stdout = io.StringIO()
+    try:
+        import insightface
+        from insightface.app import FaceAnalysis
+        import coremltools as ct
+
+        app = FaceAnalysis(name="buffalo_l", providers=["CPUExecutionProvider"])
+        app.prepare(ctx_id=0, det_thresh=0.5)
+        coreml_model = ct.models.MLModel(FACENET_PATH)
+    finally:
+        sys.stdout = old_stdout
+
+    img_bytes = open(image_path, "rb").read()
+    nparr = np.frombuffer(img_bytes, np.uint8)
+    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+    if img is None:
+        print(json.dumps({"error": "Failed to decode image"}))
+        sys.exit(1)
+
+    # Detect faces
+    faces = app.get(img)
+    if not faces:
+        print(json.dumps({"error": "No face detected"}))
+        sys.exit(1)
+
+    largest = max(faces, key=lambda f: (f.bbox[2] - f.bbox[0]) * (f.bbox[3] - f.bbox[1]))
+    x1, y1, x2, y2 = [int(v) for v in largest.bbox]
+    x1, y1 = max(0, x1), max(0, y1)
+    x2, y2 = min(img.shape[1], x2), min(img.shape[0], y2)
+    if x2 <= x1 or y2 <= y1:
+        print(json.dumps({"error": "Invalid face bbox"}))
+        sys.exit(1)
+
+    face_img = img[y1:y2, x1:x2]
+    face_img = cv2.resize(face_img, (160, 160))
+    normalized = (face_img.astype(np.float32) / 127.5) - 1.0
+    normalized = np.transpose(normalized, (2, 0, 1))
+    input_array = np.expand_dims(normalized, axis=0)
+
+    result = coreml_model.predict({"input": input_array})
+    emb_key = [k for k in result.keys() if k.startswith("var_")][0]
+    embedding = result[emb_key].flatten().tolist()
+    print(json.dumps({"embedding": embedding}))
+
+
+if __name__ == "__main__":
+    if len(sys.argv) < 2:
+        print(json.dumps({"error": "Usage: extract_face_embedding.py <image_path>"}))
+        sys.exit(1)
+    extract_embedding(sys.argv[1])