ocr_platform/ocr_utils/module_debug_viz.py

"""
模块级 Debug 可视化（Layout / OCR）

用于 ``{output_dir}/debug/{subdir}/`` 下基于 inference_image 的调试图；
用户审计图由 VisualizationUtils + original_image 负责，不在此模块。
"""
from __future__ import annotations

import json
from pathlib import Path
from typing import Any, Dict, List, Optional, Union

import cv2
import numpy as np
from loguru import logger
from PIL import Image

# 各模块 debug_options 默认落盘根目录（相对 pipeline output_dir）
MODULE_DEBUG_ROOT = "debug"


def resolve_module_debug_dir(
    output_dir: Union[str, Path],
    subdir: str,
    *,
    debug_root: str = MODULE_DEBUG_ROOT,
) -> Path:
    """``{output_dir}/{debug_root}/{subdir}/``，目录不存在则创建。"""
    debug_dir = Path(output_dir) / debug_root / subdir
    debug_dir.mkdir(parents=True, exist_ok=True)
    return debug_dir


LAYOUT_CATEGORY_COLORS_BGR = {
    'table_body': (0, 0, 255),
    'table_caption': (0, 0, 200),
    'table_footnote': (0, 0, 150),
    'text': (255, 0, 0),
    'title': (0, 255, 255),
    'header': (255, 0, 255),
    'footer': (0, 165, 255),
    'image_body': (0, 255, 0),
    'image_caption': (0, 200, 0),
    'image_footnote': (0, 150, 0),
    'abandon': (128, 128, 128),
}

# 亮蓝（BGR），在白底/浅灰流水上比黄色更易辨认；与 layout 红色框区分
OCR_BOX_COLOR_BGR = (255, 0, 0)
OCR_BOX_LINE_THICKNESS = 2


def _to_bgr(image: Union[np.ndarray, Image.Image]) -> np.ndarray:
    if isinstance(image, Image.Image):
        arr = np.array(image)
    else:
        arr = image.copy()
    if arr.ndim == 2:
        return cv2.cvtColor(arr, cv2.COLOR_GRAY2BGR)
    if arr.shape[2] == 3:
        return cv2.cvtColor(arr, cv2.COLOR_RGB2BGR)
    return arr


def draw_layout_boxes_cv2(
    image: Union[np.ndarray, Image.Image],
    layout_results: List[Dict[str, Any]],
) -> np.ndarray:
    """在 BGR 图像上绘制 layout 检测框，返回新图像。"""
    vis = _to_bgr(image)
    for result in layout_results:
        bbox = result.get('bbox', [])
        if not bbox or len(bbox) < 4:
            continue
        category = result.get('category', 'unknown')
        color = LAYOUT_CATEGORY_COLORS_BGR.get(category, (128, 128, 128))
        x1, y1, x2, y2 = int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3])
        cv2.rectangle(vis, (x1, y1), (x2, y2), color, 2)
        label = category
        confidence = result.get('confidence', result.get('score', 0))
        if confidence:
            label += f":{float(confidence):.2f}"
        font = cv2.FONT_HERSHEY_SIMPLEX
        font_scale = 0.4
        text_thickness = 1
        (text_width, text_height), baseline = cv2.getTextSize(
            label, font, font_scale, text_thickness
        )
        text_y = max(y1 - baseline - 1, text_height + baseline)
        cv2.rectangle(
            vis,
            (x1, text_y - text_height - baseline - 2),
            (x1 + text_width, text_y),
            color,
            -1,
        )
        cv2.putText(
            vis, label, (x1, text_y - baseline - 1),
            font, font_scale, (255, 255, 255), text_thickness,
        )
    return vis


def draw_ocr_spans_cv2(
    image: Union[np.ndarray, Image.Image],
    spans: List[Dict[str, Any]],
    *,
    max_label_chars: int = 12,
) -> np.ndarray:
    """在 BGR 图像上绘制 OCR span（poly 或 bbox）。"""
    vis = _to_bgr(image)
    for span in spans:
        poly = span.get('poly')
        bbox = span.get('bbox', [])
        pts = None
        if poly and len(poly) >= 4:
            pts = np.array(poly, dtype=np.int32).reshape(-1, 2)
        elif bbox and len(bbox) >= 4:
            x0, y0, x1, y1 = map(int, bbox[:4])
            pts = np.array(
                [[x0, y0], [x1, y0], [x1, y1], [x0, y1]], dtype=np.int32
            )
        if pts is not None:
            cv2.polylines(
                vis, [pts], True, OCR_BOX_COLOR_BGR, OCR_BOX_LINE_THICKNESS
            )
        text = str(span.get('text', ''))[:max_label_chars]
        if text and pts is not None:
            x, y = int(pts[0][0]), int(pts[0][1])
            cv2.putText(
                vis, text, (x, max(y - 2, 10)),
                cv2.FONT_HERSHEY_SIMPLEX, 0.35, OCR_BOX_COLOR_BGR, 1, cv2.LINE_AA,
            )
    return vis


def save_layout_debug(
    image: Union[np.ndarray, Image.Image],
    layout_results: List[Dict[str, Any]],
    output_dir: Union[str, Path],
    page_name: str,
    *,
    suffix: str = 'raw',
    subdir: str = 'layout_detection',
    image_format: str = 'jpg',
    save_json: bool = True,
) -> Optional[Dict[str, str]]:
    """保存 layout 模块 debug 图与 JSON。"""
    if not layout_results or not output_dir:
        return None
    try:
        fmt = (image_format or 'jpg').lstrip('.')
        debug_dir = resolve_module_debug_dir(output_dir, subdir)
        vis = draw_layout_boxes_cv2(image, layout_results)
        img_path = debug_dir / f'{page_name}_layout_{suffix}.{fmt}'
        cv2.imwrite(str(img_path), vis)
        paths: Dict[str, str] = {'image': str(img_path)}
        logger.info(f"Saved layout detection image ({suffix}): {img_path}")
        if save_json:
            json_data = {
                'page_name': page_name,
                'suffix': suffix,
                'count': len(layout_results),
                'results': [
                    {
                        'category': r.get('category'),
                        'bbox': r.get('bbox'),
                        'confidence': r.get('confidence', r.get('score', 0.0)),
                    }
                    for r in layout_results
                ],
            }
            json_path = debug_dir / f'{page_name}_layout_{suffix}.json'
            json_path.write_text(
                json.dumps(json_data, ensure_ascii=False, indent=2),
                encoding='utf-8',
            )
            paths['json'] = str(json_path)
            logger.info(f"Saved layout detection JSON ({suffix}): {json_path}")
        return paths
    except Exception as e:
        logger.warning(f"Failed to save layout debug ({suffix}): {e}")
        return None


def save_ocr_debug(
    image: Union[np.ndarray, Image.Image],
    spans: List[Dict[str, Any]],
    output_dir: Union[str, Path],
    page_name: str,
    *,
    subdir: str = 'ocr_recognition',
    image_format: str = 'png',
    save_json: bool = True,
) -> Optional[Dict[str, str]]:
    """保存 OCR 模块 debug 图与 JSON。"""
    if not output_dir:
        return None
    try:
        fmt = (image_format or 'png').lstrip('.')
        debug_dir = resolve_module_debug_dir(output_dir, subdir)
        vis = draw_ocr_spans_cv2(image, spans or [])
        img_path = debug_dir / f'{page_name}_ocr_spans.{fmt}'
        cv2.imwrite(str(img_path), vis)
        paths: Dict[str, str] = {'image': str(img_path)}
        logger.info(f"Saved OCR debug image: {img_path}")
        if save_json:
            json_data = {
                'page_name': page_name,
                'count': len(spans or []),
                'spans': [
                    {
                        'bbox': s.get('bbox'),
                        'poly': s.get('poly'),
                        'text': s.get('text'),
                        'confidence': s.get('confidence'),
                    }
                    for s in (spans or [])
                ],
            }
            json_path = debug_dir / f'{page_name}_ocr_spans.json'
            json_path.write_text(
                json.dumps(json_data, ensure_ascii=False, indent=2),
                encoding='utf-8',
            )
            paths['json'] = str(json_path)
            logger.info(f"Saved OCR debug JSON: {json_path}")
        return paths
    except Exception as e:
        logger.warning(f"Failed to save OCR debug: {e}")
        return None