"""Framed Ft and Zy measurements shared by interface 42 and the ratio preview."""

import math

FT_POINTS = ("hair_line_right_right", "hair_line_left_left")
ZY_POINTS = ("face_edge_234", "face_edge_454")


class PortraitQualityError(ValueError):
    """An input that must be rejected before feature analysis."""

    def __init__(self, code, message):
        super().__init__(message)
        self.code = code


def prepare_feature_portrait(image_bytes):
    """Check the source image before applying interface 52 framing."""
    import cv2
    import numpy as np

    from face_analysis.pose import PITCH_THRESHOLD, estimate_head_pose
    from hairline.face_landmarks import get_landmarker, to_landmark_list
    from hairline.framing import decode_portrait, normalize_portrait

    image = decode_portrait(image_bytes)
    if image is None:
        raise PortraitQualityError(1008, "图片格式不支持（仅 JPG / PNG）")

    landmarks = get_landmarker().detect(
        cv2.cvtColor(image, cv2.COLOR_BGR2RGB), include_iris=True
    )
    if landmarks is None:
        raise PortraitQualityError(1001, "无法识别人像，请上传包含完整人脸的照片")

    h, w = image.shape[:2]
    face_xy = landmarks[:468, :2] * [w, h]
    if not np.isfinite(face_xy).all() or (
        (face_xy <= 0).any() or (face_xy >= [w - 1, h - 1]).any()
    ):
        raise PortraitQualityError(1003, "人脸不完整，请保留完整的脸部轮廓")

    pose = estimate_head_pose(to_landmark_list(landmarks), w, h)
    # In the camera coordinate system, positive pitch points the face down.
    if pose is not None and pose[1] > PITCH_THRESHOLD:
        raise PortraitQualityError(1003, "俯拍角度过大，请将相机调整至与脸部平视")

    try:
        image, landmarks, _ = normalize_portrait(image, landmarks)
    except ValueError as exc:
        raise PortraitQualityError(1003, "人脸过于贴近边缘，无法扶正裁切") from exc
    return image, landmarks


def detect_framed_ratio_points(image):
    """Use the interface 52 crop and return the endpoints in its coordinate space."""
    from hairline.framing import prepare_portrait

    image, landmarks, framing = prepare_portrait(image)
    data = _detect_ratio_points(image, landmarks)
    data.update(framing=framing, coordinate_space="processed_image")
    return image, data


def _detect_ratio_points(image, landmarks):
    from hairline.face_landmarks import to_landmark_list

    from .special_points import detect_special_points

    data = detect_special_points(image, landmarks_override=to_landmark_list(landmarks))
    points = data["points"]
    data["ft_original_points"] = {key: points[key] for key in FT_POINTS}
    a, b = (points[key] for key in FT_POINTS)
    if a is not None and b is not None:
        middle_y = (a["y"] + b["y"]) / 2
        points[FT_POINTS[0]] = {**a, "y": middle_y}
        points[FT_POINTS[1]] = {**b, "y": middle_y}

    data["ft_alignment"] = "horizontal_at_mean_y"
    return data


def analyze_ratio_features(image, landmarks) -> dict[str, bool | None]:
    """Return both labels from the same measurement of the processed image."""
    from .special_points import NoFaceError, NonFrontalError

    matched = None
    try:
        data = _detect_ratio_points(image, landmarks)
    except (NoFaceError, NonFrontalError):
        pass
    else:
        ft_left, ft_right, zy_left, zy_right = (
            data["points"].get(key) for key in (*FT_POINTS, *ZY_POINTS)
        )
        if all(point is not None for point in (ft_left, ft_right, zy_left, zy_right)):
            ft = abs(ft_left["x"] - ft_right["x"])
            zy = math.hypot(zy_left["x"] - zy_right["x"], zy_left["y"] - zy_right["y"])
            if ft > 0 and zy > 0:
                matched = ft / zy < 0.8
    return {"zygoma_expansion": matched, "temple_hollowing": matched}