"""
Blink detection from MediaPipe Face Mesh landmarks.

Uses the Eye Aspect Ratio (EAR) method: when the eye closes, the vertical
landmark distances shrink relative to the horizontal width, driving EAR toward 0.
"""

from __future__ import annotations

from enum import Enum
from typing import Optional

import numpy as np

_LEFT_EYE = [33, 160, 158, 133, 153, 144]
_RIGHT_EYE = [362, 385, 387, 263, 373, 380]


class BlinkEvent(str, Enum):
    LEFT = "left_blink"
    RIGHT = "right_blink"
    BOTH = "both_blink"


def eye_aspect_ratio(landmarks: np.ndarray, indices: list[int]) -> float:
    """
    EAR = (||p2-p6|| + ||p3-p5||) / (2 * ||p1-p4||)

    ~0.3 for open eye, ~0.0 for closed.
    """
    p1, p2, p3, p4, p5, p6 = [landmarks[i] for i in indices]
    vert_a = np.linalg.norm(p2 - p6)
    vert_b = np.linalg.norm(p3 - p5)
    horiz = np.linalg.norm(p1 - p4)
    if horiz < 1e-6:
        return 0.0
    return float((vert_a + vert_b) / (2.0 * horiz))


class BlinkDetector:
    """Detect left, right, or both-eye blinks from face mesh landmarks."""

    def __init__(self, threshold: float = 0.20) -> None:
        self._threshold = threshold

    def detect(self, face_landmarks: np.ndarray) -> Optional[BlinkEvent]:
        """
        Args:
            face_landmarks: (478, 3) float32 — MediaPipe Face Mesh with iris refinement.

        Returns:
            BlinkEvent if a blink is detected, else None.
        """
        left_closed = eye_aspect_ratio(face_landmarks, _LEFT_EYE) < self._threshold
        right_closed = eye_aspect_ratio(face_landmarks, _RIGHT_EYE) < self._threshold
        if left_closed and right_closed:
            return BlinkEvent.BOTH
        if left_closed:
            return BlinkEvent.LEFT
        if right_closed:
            return BlinkEvent.RIGHT
        return None