from __future__ import annotations

from typing import Tuple

import numpy as np


_DST_5PTS_112 = np.array(
    [
        [38.2946, 51.6963],
        [73.5318, 51.5014],
        [56.0252, 71.7366],
        [41.5493, 92.3655],
        [70.7299, 92.2041],
    ],
    dtype=np.float32,
)


def align_face_5pts(img_rgb: np.ndarray, landmarks5: np.ndarray, out_size: Tuple[int, int] = (112, 112)) -> np.ndarray:
    """Return aligned RGB image (H,W,3) uint8."""

    import cv2

    if img_rgb is None or img_rgb.ndim != 3 or img_rgb.shape[2] != 3:
        raise ValueError("img_rgb must be HxWx3")
    lmk = np.asarray(landmarks5, dtype=np.float32)
    if lmk.shape != (5, 2):
        raise ValueError("landmarks5 must be shape (5,2)")

    dst = _DST_5PTS_112.copy()
    if out_size != (112, 112):
        sx = out_size[0] / 112.0
        sy = out_size[1] / 112.0
        dst[:, 0] *= sx
        dst[:, 1] *= sy

    M, inliers = cv2.estimateAffinePartial2D(lmk, dst, method=cv2.LMEDS)
    if M is None or not np.isfinite(M).all():
        raise ValueError("estimateAffinePartial2D failed")

    w, h = out_size
    aligned = cv2.warpAffine(img_rgb, M, (w, h), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=0)
    return aligned