"""
악보 페이지 단위 전처리: 해상도 상한, 조명 정규화, LAB-CLAHE, 선택적 원근 보정,
가벼운 엣지 보존 스무딩·언샤프, 소각도 데스큐.
검출·인식은 전처리된 이미지에서 수행하고, 응답 bbox는 업로드 원본 픽셀 좌표로 되돌린다.
"""

from __future__ import annotations

import os
from dataclasses import dataclass, field
from typing import Any, Dict, List, Tuple

import cv2
import numpy as np

_MAX_SIDE_DEFAULT = int(os.environ.get("STELLA_PREPROCESS_MAX_SIDE", "4096"))
_SKIP_DESKEW = os.environ.get("STELLA_SKIP_DESKEW", "").lower() in ("1", "true", "yes")
_SKIP_PERSPECTIVE = os.environ.get("STELLA_SKIP_PERSPECTIVE", "").lower() in ("1", "true", "yes")
# 원근 보정은 악보 단독 크롭에서 오탐 시 화질을 망가뜨릴 수 있어 기본 끔. 문서형 촬영만 켠다.
_ENABLE_PERSPECTIVE = os.environ.get("STELLA_ENABLE_PERSPECTIVE", "").lower() in ("1", "true", "yes")
_SKIP_ILLUMINATION = os.environ.get("STELLA_SKIP_ILLUMINATION", "").lower() in ("1", "true", "yes")
_SKIP_ENHANCE = os.environ.get("STELLA_SKIP_ENHANCE", "").lower() in ("1", "true", "yes")
_DESKEW_MIN_DEG = 0.35
_DESKEW_MAX_DEG = 6.0

# 원근: 너무 약하면 효과 없음, 너무 공격적이면 악보를 찌그러뜨림
_PERSP_MIN_AREA_RATIO = 0.22
_PERSP_FULL_FRAME_RATIO = 0.92
_PERSP_MAX_EDGE_RATIO = 0.035


@dataclass
class PageGeometry:
    """최종 work 픽셀 → 업로드 원본(디코드) 픽셀. 내부적으로 3×3 동차 변환."""

    orig_w: int
    orig_h: int
    work_w: int
    work_h: int
    scale_x: float  # work_w / orig_w (역변환에 사용)
    scale_y: float  # work_h / orig_h
    deskew_deg_applied: float
    _work_to_orig: np.ndarray = field(repr=False)

    def work_point_to_original(self, x: float, y: float) -> Tuple[int, int]:
        v = self._work_to_orig @ np.array([float(x), float(y), 1.0], dtype=np.float64)
        wv = float(v[2])
        if abs(wv) < 1e-12:
            xo = float(v[0]) / self.scale_x
            yo = float(v[1]) / self.scale_y
        else:
            xo = float(v[0]) / wv
            yo = float(v[1]) / wv
        return int(round(xo)), int(round(yo))

    def work_rect_to_original_aabb(self, x0: int, y0: int, w: int, h: int) -> List[int]:
        corners = [
            (x0, y0),
            (x0 + w, y0),
            (x0, y0 + h),
            (x0 + w, y0 + h),
        ]
        mapped = [self.work_point_to_original(px, py) for px, py in corners]
        xs = [p[0] for p in mapped]
        ys = [p[1] for p in mapped]
        x_min, x_max = min(xs), max(xs)
        y_min, y_max = min(ys), max(ys)
        return [x_min, y_min, max(1, x_max - x_min), max(1, y_max - y_min)]


def _clahe_lab_bgr(image_bgr: np.ndarray, clip_limit: float = 2.0, grid: int = 8) -> np.ndarray:
    lab = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2LAB)
    l, a, b_ch = cv2.split(lab)
    clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=(grid, grid))
    l2 = clahe.apply(l)
    merged = cv2.merge((l2, a, b_ch))
    return cv2.cvtColor(merged, cv2.COLOR_LAB2BGR)


def _normalize_illumination_lab(image_bgr: np.ndarray) -> np.ndarray:
    """저주파 조명 성분으로 L 채널을 나누어 그림자·비네팅을 완화."""
    lab = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2LAB)
    l, a, b_ch = cv2.split(lab)
    h, w = l.shape[:2]
    k = int(round(min(h, w) * 0.06))
    k = max(31, k | 1)
    bg = cv2.GaussianBlur(l, (k, k), 0)
    bg_f = np.maximum(bg.astype(np.float32), 8.0)
    l_f = l.astype(np.float32)
    l2 = np.clip((l_f / bg_f) * 96.0 + 48.0, 0, 255).astype(np.uint8)
    merged = cv2.merge((l2, a, b_ch))
    return cv2.cvtColor(merged, cv2.COLOR_LAB2BGR)


def _mild_unsharp_lab_bgr(image_bgr: np.ndarray, sigma: float = 1.0, amount: float = 0.35) -> np.ndarray:
    lab = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2LAB)
    l, a, b_ch = cv2.split(lab)
    blur = cv2.GaussianBlur(l, (0, 0), sigma)
    l2 = cv2.addWeighted(l, 1.0 + amount, blur, -amount, 0)
    merged = cv2.merge((l2, a, b_ch))
    return cv2.cvtColor(merged, cv2.COLOR_LAB2BGR)


def _bilateral_or_light_blur_bgr(image_bgr: np.ndarray) -> Tuple[np.ndarray, str]:
    h, w = image_bgr.shape[:2]
    m = max(h, w)
    if m <= 2000:
        out = cv2.bilateralFilter(image_bgr, d=5, sigmaColor=42, sigmaSpace=42)
        return out, "bilateral_d5"
    if m <= 3200:
        out = cv2.bilateralFilter(image_bgr, d=3, sigmaColor=38, sigmaSpace=38)
        return out, "bilateral_d3"
    lab = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2LAB)
    l, a, b_ch = cv2.split(lab)
    l2 = cv2.GaussianBlur(l, (3, 3), 0)
    merged = cv2.merge((l2, a, b_ch))
    return cv2.cvtColor(merged, cv2.COLOR_LAB2BGR), "gaussian_l3_skip_heavy_bilateral"


def _order_quad_points(pts: np.ndarray) -> np.ndarray:
    """pts (4,2) float → [tl, tr, br, bl]."""
    rect = np.zeros((4, 2), dtype=np.float32)
    s = pts.sum(axis=1)
    rect[0] = pts[np.argmin(s)]
    rect[2] = pts[np.argmax(s)]
    diff = np.diff(pts, axis=1).flatten()
    rect[1] = pts[np.argmin(diff)]
    rect[3] = pts[np.argmax(diff)]
    return rect


def _quad_corners_near_frame(ordered: np.ndarray, w: int, h: int, frac: float = 0.16) -> bool:
    """실제 문서/페이지 모서리는 보통 프레임 가까이에 있다. 내부 잡음 사각형을 걸러낸다."""
    lim = frac * float(min(w, h))
    for x, y in ordered:
        d = min(float(x), float(y), float(w - 1) - float(x), float(h - 1) - float(y))
        if d > lim:
            return False
    return True


def _try_perspective_rectify(image_bgr: np.ndarray) -> Tuple[np.ndarray, np.ndarray, str]:
    """
    문서형 사각형을 찾으면 원근 보정. 반환: (warped_bgr, M_src_to_dst, reason).
    M_src_to_dst 는 getPerspectiveTransform 규약: dst_hom ∝ M @ src_hom.
    """
    h, w = image_bgr.shape[:2]
    gray = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray, (5, 5), 0)
    edges = cv2.Canny(blur, 35, 110)
    edges = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, np.ones((5, 5), np.uint8), iterations=1)
    contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    img_area = float(h * w)
    min_area = _PERSP_MIN_AREA_RATIO * img_area
    for cnt in sorted(contours, key=cv2.contourArea, reverse=True)[:8]:
        area = cv2.contourArea(cnt)
        if area < min_area:
            break
        peri = cv2.arcLength(cnt, True)
        if peri < 1e-6:
            continue
        approx = cv2.approxPolyDP(cnt, 0.02 * peri, True)
        if len(approx) != 4:
            continue
        if not cv2.isContourConvex(approx):
            continue
        if area > _PERSP_FULL_FRAME_RATIO * img_area:
            return image_bgr, np.eye(3, dtype=np.float64), "skipped_near_full_frame"
        pts = approx.reshape(4, 2).astype(np.float32)
        ordered = _order_quad_points(pts)
        side = min(
            np.linalg.norm(ordered[0] - ordered[1]),
            np.linalg.norm(ordered[1] - ordered[2]),
            np.linalg.norm(ordered[2] - ordered[3]),
            np.linalg.norm(ordered[3] - ordered[0]),
        )
        if side < _PERSP_MAX_EDGE_RATIO * float(min(h, w)):
            continue
        if not _quad_corners_near_frame(ordered, w, h):
            continue
        width_top = np.linalg.norm(ordered[0] - ordered[1])
        width_bot = np.linalg.norm(ordered[3] - ordered[2])
        height_r = np.linalg.norm(ordered[1] - ordered[2])
        height_l = np.linalg.norm(ordered[0] - ordered[3])
        max_w = int(max(width_top, width_bot))
        max_h = int(max(height_r, height_l))
        max_w = max(max_w, 32)
        max_h = max(max_h, 32)
        dst = np.array(
            [[0, 0], [max_w - 1, 0], [max_w - 1, max_h - 1], [0, max_h - 1]],
            dtype=np.float32,
        )
        m_src_to_dst = cv2.getPerspectiveTransform(ordered, dst)
        warped = cv2.warpPerspective(
            image_bgr,
            m_src_to_dst,
            (max_w, max_h),
            flags=cv2.INTER_LINEAR,
            borderMode=cv2.BORDER_REPLICATE,
        )
        return warped, m_src_to_dst.astype(np.float64), "applied"

    return image_bgr, np.eye(3, dtype=np.float64), "no_quadrilateral_found"


def estimate_deskew_angle_deg(gray: np.ndarray) -> float:
    """수평 오선에 가까운 선분 각도(도). 양(+)은 오른쪽이 아래로 내려간 기울기에 대응."""
    h, w = gray.shape[:2]
    max_side = max(h, w)
    scale = 800.0 / max_side if max_side > 800 else 1.0
    if scale < 1.0:
        small = cv2.resize(gray, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_AREA)
    else:
        small = gray
    blur = cv2.GaussianBlur(small, (3, 3), 0)
    _, bw = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
    kernel_w = max(15, small.shape[1] // 20)
    h_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_w, 1))
    horiz = cv2.morphologyEx(bw, cv2.MORPH_OPEN, h_kernel, iterations=1)
    min_len = max(30, int(small.shape[1] * 0.18))
    lines = cv2.HoughLinesP(
        horiz,
        1,
        np.pi / 180,
        threshold=40,
        minLineLength=min_len,
        maxLineGap=25,
    )
    if lines is None:
        return 0.0
    angles: List[float] = []
    for x1, y1, x2, y2 in lines.reshape(-1, 4):
        dx, dy = float(x2 - x1), float(y2 - y1)
        if abs(dx) < 2.0:
            continue
        ang = float(np.degrees(np.arctan2(dy, dx)))
        if ang > 45.0:
            ang -= 180.0
        if ang < -45.0:
            ang += 180.0
        if abs(ang) < 14.0:
            angles.append(ang)
    if not angles:
        return 0.0
    return float(np.median(angles))


def preprocess_page_bgr(image_bgr: np.ndarray) -> Tuple[np.ndarray, PageGeometry, Dict[str, Any]]:
    """
    1) 긴 변 max_side 상한으로 축소
    2) LAB 기반 조명 정규화(선택)
    3) LAB CLAHE
    4) 원근 보정(선택, 문서형 사각형 검출 시)
    5) bilateral 또는 가벼운 L 블러(선택)
    6) LAB 언샤프(선택)
    7) |각|이 [_DESKEW_MIN_DEG, _DESKEW_MAX_DEG] 안이면 데스큐
    """
    meta: Dict[str, Any] = {
        "illumination_normalized": False,
        "perspective": "off",
        "perspective_detail": None,
        "enhance": None,
        "unsharp": False,
    }

    orig_h, orig_w = image_bgr.shape[:2]
    max_side = max(orig_h, orig_w)
    cap = max(512, _MAX_SIDE_DEFAULT)
    scale_f = min(1.0, float(cap) / float(max_side))

    if scale_f < 1.0:
        work = cv2.resize(
            image_bgr,
            (int(round(orig_w * scale_f)), int(round(orig_h * scale_f))),
            interpolation=cv2.INTER_AREA,
        )
    else:
        work = image_bgr.copy()

    wh, ww = work.shape[:2]
    sx = ww / float(orig_w)
    sy = wh / float(orig_h)
    t_scale = np.diag([sx, sy, 1.0]).astype(np.float64)

    if not _SKIP_ILLUMINATION:
        work = _normalize_illumination_lab(work)
        meta["illumination_normalized"] = True

    work = _clahe_lab_bgr(work)
    wh, ww = work.shape[:2]

    m_src_to_dst = np.eye(3, dtype=np.float64)
    if _SKIP_PERSPECTIVE:
        meta["perspective"] = "skipped"
        meta["perspective_detail"] = "disabled_by_env"
    elif not _ENABLE_PERSPECTIVE:
        meta["perspective"] = "skipped"
        meta["perspective_detail"] = "disabled_by_default_set_STELLA_ENABLE_PERSPECTIVE"
    else:
        work, m_src_to_dst, persp_reason = _try_perspective_rectify(work)
        meta["perspective"] = "applied" if persp_reason == "applied" else "skipped"
        meta["perspective_detail"] = persp_reason

    wh, ww = work.shape[:2]

    if not _SKIP_ENHANCE:
        work, enh_label = _bilateral_or_light_blur_bgr(work)
        meta["enhance"] = enh_label
        work = _mild_unsharp_lab_bgr(work, sigma=1.0, amount=0.32)
        meta["unsharp"] = True
    else:
        meta["enhance"] = "disabled_by_env"
        meta["unsharp"] = False

    deskew_applied = 0.0
    a_desk = np.eye(3, dtype=np.float64)

    if not _SKIP_DESKEW:
        gray = cv2.cvtColor(work, cv2.COLOR_BGR2GRAY)
        skew = estimate_deskew_angle_deg(gray)
        if _DESKEW_MIN_DEG <= abs(skew) <= _DESKEW_MAX_DEG:
            correction = -skew
            deskew_applied = correction
            m23 = cv2.getRotationMatrix2D((ww / 2.0, wh / 2.0), correction, 1.0)
            work = cv2.warpAffine(
                work,
                m23,
                (ww, wh),
                flags=cv2.INTER_LINEAR,
                borderMode=cv2.BORDER_REPLICATE,
            )
            a_desk = np.vstack([m23.astype(np.float64), np.array([[0.0, 0.0, 1.0]], dtype=np.float64)])

    # 최종 work → 원본: inv(T_scale) @ inv(M_src_to_dst) @ A_desk
    # (orig → final: inv(A) @ M @ T)
    m_inv = np.linalg.inv(m_src_to_dst)
    work_to_orig = np.linalg.inv(t_scale) @ m_inv @ a_desk

    geom = PageGeometry(
        orig_w=orig_w,
        orig_h=orig_h,
        work_w=int(work.shape[1]),
        work_h=int(work.shape[0]),
        scale_x=sx,
        scale_y=sy,
        deskew_deg_applied=deskew_applied,
        _work_to_orig=work_to_orig,
    )
    return work, geom, meta