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63c5f12 efeddde 63c5f12 efeddde 63c5f12 efeddde 63c5f12 efeddde | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | import os
import logging
from pathlib import Path
from typing import Union, Optional, Tuple, List
import cv2
import numpy as np
from PIL import Image
logger = logging.getLogger(__name__)
def cosine_similarity(vec1: Union[np.ndarray, List[float]], vec2: Union[np.ndarray, List[float]]) -> float:
"""Compute cosine similarity between two feature vectors."""
a = np.asarray(vec1, dtype=np.float32).flatten()
b = np.asarray(vec2, dtype=np.float32).flatten()
norm_a = np.linalg.norm(a)
norm_b = np.linalg.norm(b)
if norm_a == 0 or norm_b == 0:
return 0.0
return float(np.dot(a, b) / (norm_a * norm_b))
class FaceEncoder:
"""
Local face detection + 512-dimensional ArcFace embedding generation using InsightFace.
Biometric vectors are retained strictly in volatile RAM for similarity ranking
and are never committed to public logs, reports, or the blockchain.
"""
def __init__(self):
self._app = None
self.model_name = os.getenv("FACE_MODEL", "buffalo_s")
def _load(self):
if self._app is not None:
return
try:
from insightface.app import FaceAnalysis
except ImportError as exc:
raise RuntimeError(
"InsightFace is not installed. Run: pip install -r requirements.txt"
) from exc
try:
self._app = FaceAnalysis(
name=self.model_name,
providers=["CPUExecutionProvider"]
)
self._app.prepare(ctx_id=0)
except Exception as exc:
if self.model_name != "buffalo_s":
logger.warning("Could not load %s, falling back to buffalo_s: %s", self.model_name, exc)
self.model_name = "buffalo_s"
self._app = FaceAnalysis(
name="buffalo_s",
providers=["CPUExecutionProvider"]
)
self._app.prepare(ctx_id=0)
else:
raise
def _to_cv2(self, image_input: Union[Path, str, Image.Image, np.ndarray]) -> np.ndarray:
if isinstance(image_input, (str, Path)):
img_path = Path(image_input)
try:
pil_img = Image.open(img_path).convert("RGB")
return cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR)
except Exception:
img = cv2.imread(str(img_path))
if img is None:
raise ValueError(f"Unable to read image from path: {image_input}")
return img
elif isinstance(image_input, Image.Image):
rgb = image_input.convert("RGB")
return cv2.cvtColor(np.array(rgb), cv2.COLOR_RGB2BGR)
elif isinstance(image_input, np.ndarray):
return image_input
else:
raise TypeError(f"Unsupported image input type: {type(image_input)}")
def get_embedding(
self, image_input: Union[Path, str, Image.Image, np.ndarray]
) -> Tuple[Optional[np.ndarray], dict]:
"""
Extract the primary 512-D ArcFace facial embedding and detection telemetry.
"""
self._load()
img = self._to_cv2(image_input)
faces = self._app.get(img)
if not faces:
return None, {"detected": False, "count": 0, "det_score": 0.0, "norm": 0.0}
# Sort faces by detection score descending
faces = sorted(faces, key=lambda f: getattr(f, "det_score", 0.0), reverse=True)
primary = faces[0]
raw_emb = getattr(primary, "embedding", None)
det_score = float(getattr(primary, "det_score", 0.0))
if raw_emb is not None:
embedding = np.asarray(raw_emb, dtype=np.float32).flatten()
norm = float(np.linalg.norm(embedding))
return embedding, {
"detected": True,
"count": len(faces),
"det_score": det_score,
"norm": norm,
}
return None, {"detected": True, "count": len(faces), "det_score": det_score, "norm": 0.0}
def analyze(self, image_path: Path):
"""
Full facial topology analysis for forensic reporting.
"""
self._load()
img = self._to_cv2(image_path)
faces = self._app.get(img)
embedding_generated = False
det_score = 0.0
norm = 0.0
face_boxes = []
landmarks = []
if faces:
faces = sorted(faces, key=lambda f: getattr(f, "det_score", 0.0), reverse=True)
primary = faces[0]
if getattr(primary, "embedding", None) is not None:
embedding_generated = True
emb = np.asarray(primary.embedding, dtype=np.float32).flatten()
norm = float(np.linalg.norm(emb))
det_score = float(getattr(primary, "det_score", 0.0))
for face in faces:
if hasattr(face, "bbox") and face.bbox is not None:
face_boxes.append([float(x) for x in face.bbox])
if hasattr(face, "kps") and face.kps is not None:
landmarks.append([[float(x), float(y)] for x, y in face.kps])
return {
"detected": len(faces) > 0,
"count": len(faces),
"embedding_generated": embedding_generated,
"det_score": det_score,
"norm": norm,
"face_boxes": face_boxes,
"landmarks": landmarks,
}
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