import cv2
import numpy as np
from numpy.linalg import norm

face_cascade = cv2.CascadeClassifier(
    cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
)

def detect_and_extract_face(img):
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)

    if len(faces) == 0:
        raise Exception("No face detected")

    x, y, w, h = faces[0]
    face = gray[y:y+h, x:x+w]

    path = "data/intermediate/id_face.jpg"
    cv2.imwrite(path, face)
    return path

def get_face_embeddings(image_path):
    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
    img = cv2.resize(img, (100, 100))
    return img.flatten().tolist()

def face_comparison(img1_path, img2_path, threshold=0.6):
    emb1 = np.array(get_face_embeddings(img1_path))
    emb2 = np.array(get_face_embeddings(img2_path))
    similarity = np.dot(emb1, emb2) / (norm(emb1) * norm(emb2))
    return similarity > threshold