import cv2
import numpy as np
from mtcnn import MTCNN
from keras_facenet import FaceNet


class FaceVerification:
    """
    A class for performing face verification using MTCNN for detection 
    and FaceNet for embedding extraction.

    Attributes
    ----------
    detector : MTCNN
        Face detector model.
    embedder : FaceNet
        Face embedding model.

    Methods
    -------
    extract_face(img, target_size=(160, 160)):
        Detects and crops the face from the input image.

    get_embedding(face):
        Extracts L2-normalized FaceNet embedding from the given face image.

    cosine_similarity(a, b):
        Computes cosine similarity between two embedding vectors.

    compare_faces(img1, img2, threshold=0.5):
        Compares two face images and determines if they belong to the same person.
    """

    def __init__(self):
        """Initialize the MTCNN detector and FaceNet embedder."""
        self.detector = MTCNN()
        self.embedder = FaceNet()
        print("✅ MTCNN and FaceNet initialized successfully.")

    def extract_face(self, img, target_size=(160, 160)):
        """
        Detects and crops a face from an image.

        Parameters
        ----------
        img : np.ndarray
            Input image in BGR format.
        target_size : tuple, optional
            Desired size for the cropped face (default is (160, 160)).

        Returns
        -------
        np.ndarray
            Cropped and resized face image in RGB format.

        Raises
        ------
        ValueError
            If no face is detected or if the image is invalid.
        """
        if img is None:
            raise ValueError("Image not found or invalid input.")

        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        detections = self.detector.detect_faces(img_rgb)

        if not detections:
            raise ValueError("❌ No face detected.")

        # Use first detected face
        x, y, w, h = detections[0]['box']
        x, y = abs(x), abs(y)
        face = img_rgb[y:y + h, x:x + w]

        # Resize to FaceNet input size
        face = cv2.resize(face, target_size)
        return face

    def get_embedding(self, face):
        """
        Extracts L2-normalized FaceNet embedding.

        Parameters
        ----------
        face : np.ndarray
            Cropped face image (RGB format, 160x160).

        Returns
        -------
        np.ndarray
            L2-normalized face embedding vector.
        """
        emb = self.embedder.embeddings([face])
        emb = emb / np.linalg.norm(emb, axis=1, keepdims=True)
        return emb[0]

    def cosine_similarity(self, a, b):
        """
        Computes cosine similarity between two embeddings.

        Parameters
        ----------
        a, b : np.ndarray
            Face embeddings.

        Returns
        -------
        float
            Cosine similarity score.
        """
        return np.dot(a, b)

    def compare_faces(self, img1, img2, threshold=0.5):
        """
        Compares two face images and prints similarity result.

        Parameters
        ----------
        img1, img2 : np.ndarray
            Two input images (BGR format).
        threshold : float, optional
            Similarity threshold (default is 0.5).

        Returns
        -------
        json
            of keys STATUS, ERROR (None if no error)
        """
        try: 
            face1 = self.extract_face(img1)
            face2 = self.extract_face(img2)
        except Exception as e:
            return {"STATUS":"Failed, try again","ERROR":str(e)}

        emb1 = self.get_embedding(face1)
        emb2 = self.get_embedding(face2)

        similarity = self.cosine_similarity(emb1, emb2)
        accuracy = ((similarity + 1) / 2) * 100

        print(f"Cosine similarity: {similarity:.4f}")
        if similarity > threshold:
            print("✅ Same person (accept)")
            return {"STATUS":f"✅ Same person (accept), accuracy {accuracy:.2f}%", "ERROR":"None"}
        else:
            return {"STATUS":f"❌ Different persons (reject), accuracy {accuracy:.2f}%", "ERROR":"None"}