"""
blackhat
案A: Black-hat 中心型の線検出
Black-hat 変換で背景より暗い構造を直接抽出し，
固定閾値 + 距離変換 + 行ごと中心抽出で検出する
"""

import cv2
import numpy as np

from pc.vision.line_detector import ImageParams, LineDetectResult
from pc.vision.line_detector import fit_row_centers
from pc.vision.morphology import (
    apply_dist_mask,
    apply_iso_closing,
    apply_width_filter,
)


def detect_blackhat(
    frame: np.ndarray, params: ImageParams,
) -> LineDetectResult:
    """案A: Black-hat 中心型"""
    # Black-hat 変換（暗い構造の抽出）
    bh_k = params.blackhat_ksize | 1
    bh_kernel = cv2.getStructuringElement(
        cv2.MORPH_ELLIPSE, (bh_k, bh_k),
    )
    blackhat = cv2.morphologyEx(
        frame, cv2.MORPH_BLACKHAT, bh_kernel,
    )

    # ガウシアンブラー
    blur_k = params.blur_size | 1
    blurred = cv2.GaussianBlur(
        blackhat, (blur_k, blur_k), 0,
    )

    # 固定閾値（Black-hat 後は線が白）
    _, binary = cv2.threshold(
        blurred, params.binary_thresh, 255,
        cv2.THRESH_BINARY,
    )

    # 等方クロージング + 距離変換マスク + 幅フィルタ
    binary = apply_iso_closing(
        binary, params.iso_close_size,
    )
    binary = apply_dist_mask(
        binary, params.dist_thresh,
    )
    if params.width_near > 0 and params.width_far > 0:
        binary = apply_width_filter(
            binary,
            params.width_near,
            params.width_far,
            params.width_tolerance,
        )

    # 行ごと中心抽出 + フィッティング
    return fit_row_centers(
        binary, params.min_line_width,
        median_ksize=params.median_ksize,
        neighbor_thresh=params.neighbor_thresh,
        residual_thresh=params.residual_thresh,
    )