File size: 2,194 Bytes
69def8e | 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 | import numpy as np
from deepface import DeepFace
import os
class FaceEmbedder:
def __init__(self, model_name="Facenet"):
self.model_name = model_name
# Trigger a dummy call to download/load the model if needed
print(f"Initializing FaceEmbedder with model: {self.model_name}")
# Note: DeepFace downloads models on first use.
def compute_embedding(self, image_path: str) -> np.ndarray:
if not os.path.exists(image_path):
raise FileNotFoundError(f"Image not found: {image_path}")
objs = DeepFace.represent(
img_path=image_path,
model_name=self.model_name,
enforce_detection=False,
detector_backend="opencv",
align=True
)
if not objs:
return np.zeros(128)
emb = np.array(objs[0]["embedding"], dtype=np.float32)
return emb
def extract_face_details(self, image_input) -> dict:
"""Extract embedding, facial bounding box, and cropped face image.
image_input can be a file path str or numpy ndarray (BGR/RGB image).
"""
objs = DeepFace.represent(
img_path=image_input,
model_name=self.model_name,
enforce_detection=False,
detector_backend="opencv",
align=True
)
if not objs:
return {
"embedding": np.zeros(128, dtype=np.float32),
"facial_area": {"x": 0, "y": 0, "w": 0, "h": 0},
"confidence": 0.0
}
target = objs[0]
emb = np.array(target["embedding"], dtype=np.float32)
facial_area = target.get("facial_area", {"x": 0, "y": 0, "w": 0, "h": 0})
return {
"embedding": emb,
"facial_area": facial_area,
"confidence": target.get("face_confidence", 1.0)
}
def batch_compute_embeddings(self, image_paths: list, batch_size: int = 16) -> np.ndarray:
embeddings = []
for path in image_paths:
emb = self.compute_embedding(path)
embeddings.append(emb)
return np.vstack(embeddings)
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