"""Face detection with YuNet (OpenCV FaceDetectorYN).

Why YuNet: modern CNN detector with 5-point landmarks built into OpenCV —
no compilation, no extra runtime, works on Windows out of the box, and its
landmarks feed ArcFace alignment directly. Accuracy on frontal surveillance
footage is on par with SCRFD-500M at a fraction of the operational cost.
"""
from __future__ import annotations

import logging
from dataclasses import dataclass, field
from pathlib import Path

import cv2
import numpy as np

from .geometry import clip_box

log = logging.getLogger(__name__)

YUNET_FILENAME = "face_detection_yunet_2023mar.onnx"


@dataclass
class Detection:
    box: tuple  # x1, y1, x2, y2 (int, clipped to frame)
    kps: np.ndarray  # (5, 2) float32, full-frame coordinates
    score: float
    quality: float = 0.0
    attributes: dict = field(default_factory=dict)


class FaceDetector:
    def __init__(self, models_dir: Path, score_threshold: float = 0.75,
                 nms_threshold: float = 0.3, max_faces: int = 20,
                 min_face_px: int = 48):
        model_path = Path(models_dir) / YUNET_FILENAME
        if not model_path.exists():
            raise FileNotFoundError(
                f"{model_path} missing - run: python -m behavision setup-models")
        self._det = cv2.FaceDetectorYN_create(
            str(model_path), "", (320, 320), score_threshold, nms_threshold,
            max_faces)
        self._input_size: "tuple[int, int] | None" = None
        self.min_face_px = min_face_px

    def detect(self, frame: np.ndarray) -> "list[Detection]":
        h, w = frame.shape[:2]
        if self._input_size != (w, h):
            self._det.setInputSize((w, h))
            self._input_size = (w, h)
        _, faces = self._det.detect(frame)
        if faces is None:
            return []
        out: list[Detection] = []
        for f in faces:
            x, y, bw, bh = f[:4]
            if min(bw, bh) < self.min_face_px:
                continue
            box = clip_box((x, y, x + bw, y + bh), w, h)
            if box is None:
                continue
            kps = f[4:14].reshape(5, 2).astype(np.float32)
            out.append(Detection(box=box, kps=kps, score=float(f[14])))
        return out