from __future__ import annotations

import copy
import hashlib
from dataclasses import dataclass
from itertools import count
from pathlib import Path
from typing import Any, Iterable

import cv2
import numpy as np
from PIL import Image


def _clamp(value: float, minimum: float, maximum: float) -> float:
    return max(minimum, min(maximum, value))


def _round(value: float, digits: int = 3) -> float:
    return round(float(value), digits)


def _rect_iou(a: tuple[int, int, int, int], b: tuple[int, int, int, int]) -> float:
    ax, ay, aw, ah = a
    bx, by, bw, bh = b
    x1 = max(ax, bx)
    y1 = max(ay, by)
    x2 = min(ax + aw, bx + bw)
    y2 = min(ay + ah, by + bh)
    if x2 <= x1 or y2 <= y1:
        return 0.0
    inter = float((x2 - x1) * (y2 - y1))
    union = float(aw * ah + bw * bh - inter)
    return inter / union if union > 0 else 0.0


def _contains(inner: tuple[int, int, int, int], outer: tuple[int, int, int, int], tolerance: int = 0) -> bool:
    ix, iy, iw, ih = inner
    ox, oy, ow, oh = outer
    return (
        ix >= ox - tolerance
        and iy >= oy - tolerance
        and ix + iw <= ox + ow + tolerance
        and iy + ih <= oy + oh + tolerance
    )


def _bounds_to_points(box: tuple[int, int, int, int]) -> list[dict[str, float]]:
    x, y, w, h = box
    return [
        {"x": float(x), "y": float(y)},
        {"x": float(x + w), "y": float(y)},
        {"x": float(x + w), "y": float(y + h)},
        {"x": float(x), "y": float(y + h)},
    ]


def _dedupe_by_iou(items: list[dict[str, Any]], threshold: float) -> list[dict[str, Any]]:
    kept: list[dict[str, Any]] = []
    for item in sorted(items, key=lambda candidate: float(candidate.get("score") or 0.0), reverse=True):
        box = tuple(item["box"])
        if any(_rect_iou(box, tuple(existing["box"])) > threshold for existing in kept):
            continue
        kept.append(item)
    return kept


def _prepare_bgr_image(image: Image.Image | np.ndarray) -> np.ndarray:
    if isinstance(image, np.ndarray):
        if image.ndim == 2:
            return cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
        return image.copy()
    rgb = np.array(image.convert("RGB"))
    return cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR)


_UID_TRANSLATION = {
    "O": "0",
    "Q": "0",
    "D": "0",
    "I": "1",
    "L": "1",
    "|": "1",
    "S": "5",
    "Z": "2",
    "B": "8",
}
_DEFAULT_CONFIG_PATH = Path(__file__).resolve().parents[1] / "configs" / "config.yaml"
_COLOR_RANGES_CACHE: dict[str, Any] | None = None
_ANALYZE_LAYOUT_CACHE: dict[str, dict[str, Any]] = {}
_ANALYZE_LAYOUT_CACHE_ORDER: list[str] = []
_ANALYZE_LAYOUT_CACHE_MAX = 8


def _normalize_binding_uid(value: Any) -> str:
    text = str(value or "").strip().upper()
    if not text:
        return ""
    translated = "".join(_UID_TRANSLATION.get(char, char) for char in text)
    digits = "".join(char for char in translated if char.isdigit())
    if not digits:
        return ""
    stripped = digits.lstrip("0")
    return stripped or digits


def _load_color_ranges() -> dict[str, Any]:
    global _COLOR_RANGES_CACHE
    if _COLOR_RANGES_CACHE is not None:
        return _COLOR_RANGES_CACHE

    try:
        from src.config_loader import load_config

        config = load_config(_DEFAULT_CONFIG_PATH)
        colors = config.get("colors") if isinstance(config, dict) else None
        _COLOR_RANGES_CACHE = dict(colors or {})
    except Exception:
        _COLOR_RANGES_CACHE = {}

    return _COLOR_RANGES_CACHE


def _make_analyze_layout_cache_key(
    image_bgr: np.ndarray,
    *,
    sensitivity: int,
    image_correction: int,
    enhance_image: bool,
    ignore_lines: bool,
) -> str:
    digest = hashlib.blake2b(image_bgr.tobytes(), digest_size=16).hexdigest()
    return (
        f"{image_bgr.shape[1]}x{image_bgr.shape[0]}:{sensitivity}:{image_correction}:"
        f"{int(enhance_image)}:{int(ignore_lines)}:{digest}"
    )


def _cache_layout_result(key: str, result: dict[str, Any]) -> None:
    if key in _ANALYZE_LAYOUT_CACHE:
        _ANALYZE_LAYOUT_CACHE_ORDER.remove(key)
    _ANALYZE_LAYOUT_CACHE[key] = copy.deepcopy(result)
    _ANALYZE_LAYOUT_CACHE_ORDER.append(key)
    while len(_ANALYZE_LAYOUT_CACHE_ORDER) > _ANALYZE_LAYOUT_CACHE_MAX:
        oldest = _ANALYZE_LAYOUT_CACHE_ORDER.pop(0)
        _ANALYZE_LAYOUT_CACHE.pop(oldest, None)


def _prepare_detection_mask(
    image_bgr: np.ndarray,
    *,
    sensitivity: int = 60,
    image_correction: int = 55,
    enhance_image: bool = True,
) -> tuple[np.ndarray, np.ndarray]:
    gray = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2GRAY)

    if enhance_image:
        clip_limit = 1.6 + (max(0, min(100, image_correction)) / 45.0)
        clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=(8, 8))
        gray = clahe.apply(gray)
        sharpen = np.array(
            [[0, -1, 0], [-1, 5, -1], [0, -1, 0]],
            dtype=np.float32,
        )
        gray = cv2.filter2D(gray, -1, sharpen)

    blur = cv2.GaussianBlur(gray, (3, 3), 0)
    block_size = 31 + (max(0, min(100, sensitivity)) // 20) * 2
    if block_size % 2 == 0:
        block_size += 1
    constant = -7 - int((max(0, min(100, sensitivity)) - 60) / 12)
    inv = 255 - blur
    mask = cv2.adaptiveThreshold(
        inv,
        255,
        cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
        cv2.THRESH_BINARY,
        block_size,
        constant,
    )
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=1)
    return gray, mask


@dataclass
class _OverlayRecord:
    kind: str
    category: str
    label: str
    box: tuple[int, int, int, int]
    points: list[dict[str, float]]
    score: float
    meta: dict[str, Any]
    binding_uid: str = ""
    note: str = ""
    context_path: str = ""
    widget_name_override: str = ""
    static_shape_name_override: str = ""

    def to_payload(self, index: int) -> dict[str, Any]:
        x, y, width, height = self.box
        return {
            "id": f"{self.kind}-{index:03d}",
            "kind": self.kind,
            "category": self.category,
            "label": self.label,
            "confidence": _round(self.score, 3),
            "bounds": {
                "x": int(x),
                "y": int(y),
                "width": int(width),
                "height": int(height),
            },
            "points": self.points,
            "meta": self.meta,
            "bindingUid": self.binding_uid,
            "note": self.note,
            "contextPath": self.context_path,
            "widgetNameOverride": self.widget_name_override,
            "staticShapeNameOverride": self.static_shape_name_override,
        }


def _collect_contour_metrics(
    gray: np.ndarray,
    mask: np.ndarray,
) -> list[dict[str, Any]]:
    contours, hierarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    if hierarchy is None or not len(contours):
        return []

    nodes = hierarchy[0]
    items: list[dict[str, Any]] = []

    for index, contour in enumerate(contours):
        x, y, width, height = cv2.boundingRect(contour)
        area = width * height
        if area < 180 or width < 10 or height < 10:
            continue

        perimeter = cv2.arcLength(contour, True)
        approx = cv2.approxPolyDP(contour, 0.02 * perimeter, True)
        contour_area = max(1.0, cv2.contourArea(contour))
        fill = contour_area / max(1.0, area)
        aspect = max(width / max(1.0, height), height / max(1.0, width))
        roi = gray[y : y + height, x : x + width]
        mean = float(roi.mean()) if roi.size else 255.0
        std = float(roi.std()) if roi.size else 0.0

        child_count = 0
        child = int(nodes[index][2])
        while child != -1:
            child_count += 1
            child = int(nodes[child][0])

        hull = cv2.convexHull(contour)
        hull_area = max(1.0, cv2.contourArea(hull))
        solidity = contour_area / hull_area
        points = approx.reshape(-1, 2).tolist() if len(approx) >= 3 else []
        contour_points = contour.reshape(-1, 2)
        edge_pad_x = max(3, int(round(width * 0.05)))
        edge_pad_y = max(3, int(round(height * 0.05)))

        def _span_ratio(values: np.ndarray, extent: int) -> float:
            if values.size < 2 or extent <= 0:
                return 0.0
            return float(values.max() - values.min()) / float(extent)

        top_x = contour_points[contour_points[:, 1] <= y + edge_pad_y][:, 0]
        bottom_x = contour_points[contour_points[:, 1] >= y + height - edge_pad_y][:, 0]
        left_y = contour_points[contour_points[:, 0] <= x + edge_pad_x][:, 1]
        right_y = contour_points[contour_points[:, 0] >= x + width - edge_pad_x][:, 1]
        top_span = _span_ratio(top_x, width)
        bottom_span = _span_ratio(bottom_x, width)
        left_span = _span_ratio(left_y, height)
        right_span = _span_ratio(right_y, height)
        closed_frame = (
            top_span >= 0.45
            and bottom_span >= 0.45
            and left_span >= 0.28
            and right_span >= 0.28
        )

        items.append(
            {
                "box": (int(x), int(y), int(width), int(height)),
                "area": int(area),
                "contour_area": contour_area,
                "fill": float(fill),
                "aspect": float(aspect),
                "mean": mean,
                "std": std,
                "children": child_count,
                "verts": len(approx),
                "solidity": float(solidity),
                "closed_frame": bool(closed_frame),
                "edge_spans": {
                    "top": round(top_span, 3),
                    "bottom": round(bottom_span, 3),
                    "left": round(left_span, 3),
                    "right": round(right_span, 3),
                },
                "points": [{"x": float(px), "y": float(py)} for px, py in points],
            }
        )

    return items


def _classify_cells(items: Iterable[dict[str, Any]]) -> list[dict[str, Any]]:
    cells: list[dict[str, Any]] = []
    for item in items:
        x, y, width, height = item["box"]
        fill = float(item["fill"])
        aspect = float(item["aspect"])
        mean = float(item["mean"])
        std = float(item["std"])
        children = int(item["children"])
        verts = int(item["verts"])
        rectish = verts <= 8
        if not rectish:
            continue
        if not (34 <= width <= 220 and 16 <= height <= 90):
            continue
        if not (1.1 <= aspect <= 9.0):
            continue
        if mean <= 120 or std <= 15:
            continue

        score = 0.0
        score += min(width, 220) / 220.0 * 0.2
        score += min(height, 90) / 90.0 * 0.1
        score += min(0.4, children * 0.05)
        score += (1 - min(abs(fill - 0.65), 0.65) / 0.65) * 0.2
        score += (1 - min(abs(aspect - 3.0), 3.0) / 3.0) * 0.1
        score += min(std / 80.0, 1.0) * 0.2
        cells.append(
            {
                **item,
                "kind": "cell",
                "category": "value",
                "label": "Ячейка",
                "score": float(score),
            }
        )
    return _dedupe_by_iou(cells, 0.8)


def _classify_groups(items: Iterable[dict[str, Any]]) -> list[dict[str, Any]]:
    groups: list[dict[str, Any]] = []
    for item in items:
        width = int(item["box"][2])
        height = int(item["box"][3])
        area = int(item["area"])
        fill = float(item["fill"])
        aspect = float(item["aspect"])
        mean = float(item["mean"])
        std = float(item["std"])
        children = int(item["children"])
        verts = int(item["verts"])
        closed_frame = bool(item.get("closed_frame"))
        rectish = verts <= 12
        if not rectish:
            continue
        if width < 140 or height < 38 or area < 12000:
            continue
        if aspect > 12.0 or mean <= 120:
            continue
        if not closed_frame:
            continue

        score = 0.0
        score += min(0.5, children * 0.09)
        score += (1 - min(abs(fill - 0.75), 0.75) / 0.75) * 0.15
        score += min(std / 80.0, 1.0) * 0.1
        score += min(area / 120000.0, 1.0) * 0.25
        groups.append(
            {
                **item,
                "kind": "form",
                "category": "static",
                "label": "Форма",
                "score": float(score),
                "meta": {
                    **dict(item.get("meta") or {}),
                    "staticSubtype": "form",
                    "staticFormType": "frame",
                },
            }
        )

    groups = _dedupe_by_iou(groups, 0.7)
    filtered: list[dict[str, Any]] = []
    for candidate in groups:
        candidate_box = tuple(candidate["box"])
        nested_groups = [
            other
            for other in groups
            if other is not candidate
            and _contains(tuple(other["box"]), candidate_box, tolerance=3)
            and (other["box"][2] * other["box"][3]) < (candidate_box[2] * candidate_box[3]) * 0.82
        ]
        nested_area = sum(int(other["box"][2]) * int(other["box"][3]) for other in nested_groups)
        cover_ratio = nested_area / max(1.0, candidate_box[2] * candidate_box[3])
        if len(nested_groups) >= 2 and cover_ratio >= 0.34:
            continue
        filtered.append(candidate)
    return filtered


def _classify_arrows(items: Iterable[dict[str, Any]], *, ignore_lines: bool) -> list[dict[str, Any]]:
    arrows: list[dict[str, Any]] = []
    for item in items:
        width = int(item["box"][2])
        height = int(item["box"][3])
        area = int(item["area"])
        fill = float(item["fill"])
        aspect = float(item["aspect"])
        std = float(item["std"])
        verts = int(item["verts"])
        solidity = float(item["solidity"])
        if area > 5000 or width < 18 or height < 10:
            continue
        if area < 120 or aspect > 6.0:
            continue
        if verts not in {3, 4, 5, 6, 7, 8}:
            continue
        if not (0.35 <= solidity <= 0.95):
            continue
        if fill > 0.75 or std <= 10:
            continue
        if ignore_lines and aspect > 5.6 and min(width, height) <= 8:
            continue

        score = 0.0
        score += (1 - abs(solidity - 0.6) / 0.6) * 0.3
        score += (1 - min(abs(aspect - 2.2), 2.2) / 2.2) * 0.2
        score += min(std / 80.0, 1.0) * 0.2
        score += (1.0 if verts == 3 else 0.7) * 0.2
        arrows.append(
            {
                **item,
                "kind": "arrow",
                "category": "static",
                "label": "Стрелка",
                "score": float(score),
                "meta": {
                    **dict(item.get("meta") or {}),
                    "staticSubtype": "process_arrow",
                },
            }
        )
    return _dedupe_by_iou(arrows, 0.5)


def _detect_sensor_cells(image_bgr: np.ndarray) -> tuple[str, list[dict[str, Any]]]:
    color_ranges = _load_color_ranges()
    if not color_ranges:
        return "", []

    positions: list[tuple[int, int, int, int]] = []
    title_text = ""

    try:
        from src.pipeline_hf import detect_sensor_regions

        positions = detect_sensor_regions(image_bgr, color_ranges)
    except Exception:
        positions = []

    try:
        from src.ocr_utils_demo import ocr_title

        height, width = image_bgr.shape[:2]
        title_roi = image_bgr[:45, : int(width / 2.4)]
        title_text = ocr_title(title_roi)
    except Exception:
        title_text = ""

    overlays: list[dict[str, Any]] = []
    for x, y, width, height in positions:
        x = int(x or 0)
        y = int(y or 0)
        width = int(width or 0)
        height = int(height or 0)
        if width <= 0 or height <= 0:
            continue

        box = (x, y, width, height)
        overlays.append(
            {
                "box": box,
                "points": _bounds_to_points(box),
                "score": 0.55,
                "kind": "widget",
                "category": "widget",
                "label": "Ячейка",
                "note": "",
                "bindingUid": "",
                "meta": {
                    "source": "src.pipeline_hf.detect_sensor_regions",
                },
            }
        )

    overlays = _dedupe_by_iou(overlays, 0.78)
    normalized_title = str(title_text or "").strip()
    if normalized_title.lower() == "титул не оцифрован":
        normalized_title = ""
    return normalized_title, overlays


def _merge_cells_with_sensor_ocr(
    cells: list[dict[str, Any]],
    sensor_cells: list[dict[str, Any]],
) -> list[dict[str, Any]]:
    if not sensor_cells:
        return cells

    merged: list[dict[str, Any]] = []
    used_sensor_indexes: set[int] = set()

    for cell in cells:
        cell_box = tuple(cell["box"])
        best_index = -1
        best_score = -1.0
        for index, sensor in enumerate(sensor_cells):
            sensor_box = tuple(sensor["box"])
            overlap = _rect_iou(cell_box, sensor_box)
            if overlap < 0.28 and not _contains(sensor_box, cell_box, tolerance=4) and not _contains(cell_box, sensor_box, tolerance=4):
                continue
            candidate_score = overlap + float(sensor.get("score") or 0.0) * 0.15
            if candidate_score > best_score:
                best_score = candidate_score
                best_index = index

        if best_index >= 0:
            sensor = sensor_cells[best_index]
            used_sensor_indexes.add(best_index)
            merged.append(
                {
                    **cell,
                    "box": tuple(sensor["box"]),
                    "points": list(sensor.get("points") or _bounds_to_points(tuple(sensor["box"]))),
                    "score": max(float(cell.get("score") or 0.0), float(sensor.get("score") or 0.0)),
                    "note": str(sensor.get("note") or ""),
                    "bindingUid": str(sensor.get("bindingUid") or ""),
                    "meta": {
                        **dict(cell.get("meta") or {}),
                        **dict(sensor.get("meta") or {}),
                        "matchedBySensorOcr": True,
                    },
                }
            )
            continue

        merged.append(cell)

    for index, sensor in enumerate(sensor_cells):
        if index not in used_sensor_indexes:
            merged.append(sensor)

    return _dedupe_by_iou(merged, 0.78)


def _ocr_layout_cells(image_bgr: np.ndarray, cells: list[dict[str, Any]]) -> list[dict[str, Any]]:
    if not cells:
        return cells

    try:
        from src.ocr_utils_demo import ocr_sensors
    except Exception:
        return cells

    rois: list[np.ndarray] = []
    crop_boxes: list[tuple[int, int, int, int]] = []
    image_height, image_width = image_bgr.shape[:2]

    for cell in cells:
        x, y, width, height = tuple(cell["box"])
        pad_x = max(2, int(round(width * 0.04)))
        pad_y = max(2, int(round(height * 0.08)))
        left = max(0, x - pad_x)
        top = max(0, y - pad_y)
        right = min(image_width, x + width + pad_x)
        bottom = min(image_height, y + height + pad_y)
        if right <= left or bottom <= top:
            rois.append(np.zeros((1, 1, 3), dtype=np.uint8))
            crop_boxes.append((left, top, right, bottom))
            continue
        rois.append(image_bgr[top:bottom, left:right])
        crop_boxes.append((left, top, right, bottom))

    try:
        ocr_results = ocr_sensors(rois)
    except Exception:
        return cells

    enriched: list[dict[str, Any]] = []
    for cell, result, crop_box in zip(cells, ocr_results, crop_boxes):
        text = str((result or {}).get("text") or "").strip()
        if text == "?":
            text = ""
        score = float((result or {}).get("score") or 0.0)
        existing_note = str(cell.get("note") or "").strip()
        binding_uid = str(cell.get("bindingUid") or "").strip()
        if not binding_uid:
            binding_uid = _normalize_binding_uid(text)

        meta = {
            **dict(cell.get("meta") or {}),
            "cellOcrText": text,
            "cellOcrScore": score,
            "cellOcrCrop": {
                "x": crop_box[0],
                "y": crop_box[1],
                "width": max(0, crop_box[2] - crop_box[0]),
                "height": max(0, crop_box[3] - crop_box[1]),
            },
        }
        enriched.append(
            {
                **cell,
                "bindingUid": binding_uid,
                "note": existing_note or text,
                "meta": meta,
            }
        )

    return enriched


def _attach_group_metrics(groups: list[dict[str, Any]], cells: list[dict[str, Any]], arrows: list[dict[str, Any]]) -> None:
    for group in groups:
        group_box = tuple(group["box"])
        cell_count = sum(1 for cell in cells if _contains(tuple(cell["box"]), group_box, tolerance=2))
        arrow_count = sum(1 for arrow in arrows if _contains(tuple(arrow["box"]), group_box, tolerance=2))
        group.setdefault("meta", {})
        group["meta"]["cellCount"] = cell_count
        group["meta"]["arrowCount"] = arrow_count


def _build_overlay_records(groups: list[dict[str, Any]], cells: list[dict[str, Any]], arrows: list[dict[str, Any]]) -> list[_OverlayRecord]:
    overlays: list[_OverlayRecord] = []

    for item in groups:
        box = tuple(item["box"])
        overlays.append(
            _OverlayRecord(
                kind="form",
                category="static",
                label="Форма",
                box=box,
                points=_bounds_to_points(box),
                score=float(item["score"]),
                meta=dict(item.get("meta") or {}),
            )
        )

    for item in cells:
        box = tuple(item["box"])
        overlays.append(
            _OverlayRecord(
                kind="widget",
                category="widget",
                label="Ячейка",
                box=box,
                points=list(item.get("points") or _bounds_to_points(box)),
                score=float(item["score"]),
                meta=dict(item.get("meta") or {}),
                binding_uid=str(item.get("bindingUid") or ""),
                note=str(item.get("note") or ""),
                context_path=str(item.get("contextPath") or ""),
                widget_name_override=str(item.get("widgetNameOverride") or ""),
                static_shape_name_override=str(item.get("staticShapeNameOverride") or ""),
            )
        )

    for item in arrows:
        box = tuple(item["box"])
        points = item.get("points") or _bounds_to_points(box)
        overlays.append(
            _OverlayRecord(
                kind="arrow",
                category="static",
                label="Стрелка",
                box=box,
                points=points,
                score=float(item["score"]),
                meta=dict(item.get("meta") or {}),
                binding_uid=str(item.get("bindingUid") or ""),
                note=str(item.get("note") or ""),
                context_path=str(item.get("contextPath") or ""),
                widget_name_override=str(item.get("widgetNameOverride") or ""),
                static_shape_name_override=str(item.get("staticShapeNameOverride") or ""),
            )
        )

    overlays.sort(key=lambda item: (item.box[1], item.box[0], item.box[2] * item.box[3]))
    return overlays


def analyze_layout(
    image: Image.Image | np.ndarray,
    *,
    sensitivity: int = 60,
    image_correction: int = 55,
    enhance_image: bool = True,
    ignore_lines: bool = True,
) -> dict[str, Any]:
    image_bgr = _prepare_bgr_image(image)
    cache_key = _make_analyze_layout_cache_key(
        image_bgr,
        sensitivity=sensitivity,
        image_correction=image_correction,
        enhance_image=enhance_image,
        ignore_lines=ignore_lines,
    )
    cached = _ANALYZE_LAYOUT_CACHE.get(cache_key)
    if cached is not None:
        return copy.deepcopy(cached)

    gray, mask = _prepare_detection_mask(
        image_bgr,
        sensitivity=sensitivity,
        image_correction=image_correction,
        enhance_image=enhance_image,
    )
    contour_items = _collect_contour_metrics(gray, mask)
    cells = _classify_cells(contour_items)
    groups = _classify_groups(contour_items)
    arrows = _classify_arrows(contour_items, ignore_lines=ignore_lines)
    title_text, sensor_cells = _detect_sensor_cells(image_bgr)
    cells = _merge_cells_with_sensor_ocr(cells, sensor_cells)
    cells = _ocr_layout_cells(image_bgr, cells)
    _attach_group_metrics(groups, cells, arrows)

    overlays = _build_overlay_records(groups, cells, arrows)
    payloads = [overlay.to_payload(index) for index, overlay in zip(count(1), overlays)]
    counts = {
        "widgets": len(cells),
        "statics": len(groups) + len(arrows),
        "forms": len(groups),
        "shapes": 0,
        "groups": len(groups),
        "cells": len(cells),
        "arrows": len(arrows),
        "ocrCells": sum(1 for cell in cells if str(cell.get("note") or "").strip() or str(cell.get("bindingUid") or "").strip()),
        "total": len(payloads),
    }

    result = {
        "engine": "src-hybrid-layout-v2",
        "summary": (
            f"Распознано объектов: {counts['total']} "
            f"(виджеты: {counts['widgets']}, формы: {counts['forms']}, стрелки: {counts['arrows']}, OCR-ячейки: {counts['ocrCells']})."
        ),
        "counts": counts,
        "overlays": payloads,
        "title": title_text,
        "imageWidth": int(image_bgr.shape[1]),
        "imageHeight": int(image_bgr.shape[0]),
    }
    _cache_layout_result(cache_key, result)
    return result