detector/face.py

from deepface import DeepFace
import cv2
import os
from modal_app.modal_app import verify_faces_remote
from fastapi import UploadFile

def face_verify_tool(img1: UploadFile, img2: UploadFile):
    img1_bytes = img1.file.read()
    img2_bytes = img2.file.read()
    
    results = verify_faces_remote(img1_bytes, img2_bytes)

    return results




def verify_faces(img1_path, img2_path, model_name="Facenet", detector_backend="opencv"):
    """
    Compares two face images and returns whether they belong to the same person.
    """
    try:
        result = DeepFace.verify(
            img1_path, 
            img2_path,
            model_name=model_name,
            detector_backend=detector_backend,
            enforce_detection=True
        )
        
        return {
            "verified": result["verified"],
            "distance": result["distance"],
            "threshold": result["threshold"],
            "model": model_name
        }
        
    except Exception as e:
        return {"error": str(e)}


def analyze_face(img_path, actions=["age", "gender", "emotion"], detector_backend="opencv"):
    """
    Analyze attributes of a face in the image.
    """
    try:
        analysis = DeepFace.analyze(
            img_path=img_path,
            actions=actions,
            detector_backend=detector_backend,
            enforce_detection=True
        )
        
        return analysis[0] if isinstance(analysis, list) else analysis
    
    except Exception as e:
        return {"error": str(e)}
    
def extract_embedding(img_path, model_name="Facenet", detector_backend="opencv"):
    """
    Extract a face embedding from an image.
    """
    try:
        embedding = DeepFace.represent(
            img_path=img_path,
            model_name=model_name,
            detector_backend=detector_backend,
            enforce_detection=True
        )
        
        return embedding[0] if isinstance(embedding, list) else embedding
    
    except Exception as e:
        return {"error": str(e)}