FacialRecognitionPython/face_recognition_logic.py

import os
import tempfile
from deepface import DeepFace

EMPLOYEE_DB_PATH = "data/employees"
DEFAULT_MODEL = "VGG-Face"
DETECTOR_BACKEND = "opencv"

def recognize_face(image_bytes, model_name=None):
    model = model_name if model_name else DEFAULT_MODEL
    temp_file = None
    print("request image" + model_name)
    try:
        # Save incoming image to a temporary file
        with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmp:
            temp_file = tmp.name
            tmp.write(image_bytes)

        # Run DeepFace search
        result = DeepFace.find(
            img_path=temp_file,
            db_path=EMPLOYEE_DB_PATH,
            detector_backend=DETECTOR_BACKEND,
            model_name=model,
            enforce_detection=False
        )

        if isinstance(result, list):
            result = result[0]

        if result is None or len(result) == 0:
            return {"name": "Unknown", "confidence": 0.0, "image": b""}

        # Best match
        best_row = result.iloc[0]
        matched_image_path = best_row["identity"]
        name = os.path.splitext(os.path.basename(matched_image_path))[0]

        # Distance metric key depends on model
        key = f"{model}_cosine"
        distance = best_row.get(key, 0.3)
        confidence = float(max(0.0, 1.0 - distance))

        # Read matched image bytes
        with open(matched_image_path, "rb") as f:
            matched_image_bytes = f.read()

        return {"name": name, "confidence": confidence, "image": matched_image_bytes}

    finally:
        if temp_file and os.path.exists(temp_file):
            os.remove(temp_file)