[split-required] Split remaining 500-680 LOC files (final batch)
website (17 pages + 3 components): - multiplayer/wizard, middleware/wizard+test-wizard, communication - builds/wizard, staff-search, voice, sbom/wizard - foerderantrag, mail/tasks, tools/communication, sbom - compliance/evidence, uni-crawler, brandbook (already done) - CollectionsTab, IngestionTab, RiskHeatmap backend-lehrer (5 files): - letters_api (641 → 2), certificates_api (636 → 2) - alerts_agent/db/models (636 → 3) - llm_gateway/communication_service (614 → 2) - game/database already done in prior batch klausur-service (2 files): - hybrid_vocab_extractor (664 → 2) - klausur-service/frontend: api.ts (620 → 3), EHUploadWizard (591 → 2) voice-service (3 files): - bqas/rag_judge (618 → 3), runner (529 → 2) - enhanced_task_orchestrator (519 → 2) studio-v2 (6 files): - korrektur/[klausurId] (578 → 4), fairness (569 → 2) - AlertsWizard (552 → 2), OnboardingWizard (513 → 2) - korrektur/api.ts (506 → 3), geo-lernwelt (501 → 2) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Benjamin Admin
300
klausur-service/backend/hybrid_vocab_ocr.py
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300
klausur-service/backend/hybrid_vocab_ocr.py
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"""
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Hybrid Vocab OCR - PaddleOCR integration and result parsing.
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Handles:
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- PaddleOCR lazy loading and initialization
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- Running OCR on image bytes
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- Parsing PaddleOCR v3 dict and traditional list formats
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- Grouping regions by rows and detecting columns
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"""
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import io
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import logging
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from typing import List, Tuple
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from dataclasses import dataclass
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import numpy as np
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from PIL import Image
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# OpenCV is optional
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try:
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import cv2
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CV2_AVAILABLE = True
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except ImportError:
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cv2 = None
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CV2_AVAILABLE = False
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logger = logging.getLogger(__name__)
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_paddle_ocr = None
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@dataclass
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class OCRRegion:
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"""Ein erkannter Textbereich mit Position."""
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text: str
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confidence: float
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x1: int
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y1: int
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x2: int
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y2: int
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@property
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def center_x(self) -> int:
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return (self.x1 + self.x2) // 2
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@property
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def center_y(self) -> int:
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return (self.y1 + self.y2) // 2
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def get_paddle_ocr():
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"""Lazy load PaddleOCR to avoid startup delay."""
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global _paddle_ocr
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if _paddle_ocr is None:
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try:
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from paddleocr import PaddleOCR
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import logging as std_logging
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for logger_name in ['ppocr', 'paddle', 'paddleocr', 'root']:
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std_logging.getLogger(logger_name).setLevel(std_logging.WARNING)
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try:
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_paddle_ocr = PaddleOCR(lang="de")
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logger.info("PaddleOCR 3.x initialized (lang=de)")
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except Exception as e1:
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logger.warning(f"PaddleOCR lang=de failed: {e1}")
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try:
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_paddle_ocr = PaddleOCR(lang="en")
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logger.info("PaddleOCR 3.x initialized (lang=en)")
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except Exception as e2:
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logger.warning(f"PaddleOCR lang=en failed: {e2}")
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_paddle_ocr = PaddleOCR()
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logger.info("PaddleOCR 3.x initialized (defaults)")
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except Exception as e:
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logger.error(f"PaddleOCR initialization failed: {e}")
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_paddle_ocr = None
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return _paddle_ocr
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def preprocess_image(img: Image.Image) -> np.ndarray:
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"""Bildvorverarbeitung fuer bessere OCR-Ergebnisse."""
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if not CV2_AVAILABLE:
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raise ImportError(
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"OpenCV (cv2) is required for image preprocessing. "
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"Install with: pip install opencv-python-headless"
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)
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img_array = np.array(img)
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if len(img_array.shape) == 2:
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img_array = cv2.cvtColor(img_array, cv2.COLOR_GRAY2RGB)
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elif img_array.shape[2] == 4:
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img_array = cv2.cvtColor(img_array, cv2.COLOR_RGBA2RGB)
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return img_array
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def run_paddle_ocr(image_bytes: bytes) -> Tuple[List[OCRRegion], str]:
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"""Fuehrt PaddleOCR auf einem Bild aus."""
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ocr = get_paddle_ocr()
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if ocr is None:
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logger.error("PaddleOCR not available")
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return [], ""
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try:
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img = Image.open(io.BytesIO(image_bytes))
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img_array = preprocess_image(img)
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try:
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result = ocr.ocr(img_array)
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except TypeError:
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logger.warning("Trying alternative OCR call method")
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result = ocr.ocr(img_array)
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if not result:
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logger.warning("PaddleOCR returned empty result")
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return [], ""
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if isinstance(result, dict):
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logger.info("Processing PaddleOCR 3.x dict format")
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return _parse_paddleocr_v3_dict(result)
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elif isinstance(result, list) and len(result) > 0:
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first_item = result[0]
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if first_item is None:
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logger.warning("PaddleOCR returned None for first page")
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return [], ""
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if hasattr(first_item, 'get') or isinstance(first_item, dict):
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item_dict = dict(first_item) if hasattr(first_item, 'items') else first_item
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if 'rec_texts' in item_dict or 'texts' in item_dict:
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logger.info("Processing PaddleOCR 3.x OCRResult format")
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return _parse_paddleocr_v3_dict(item_dict)
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if isinstance(first_item, list):
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if len(first_item) > 0 and isinstance(first_item[0], (list, tuple)):
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logger.info("Processing PaddleOCR traditional list format")
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return _parse_paddleocr_list(first_item)
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logger.warning(f"Unknown result format. Type: {type(first_item)}")
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try:
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item_dict = dict(first_item)
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if 'rec_texts' in item_dict:
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return _parse_paddleocr_v3_dict(item_dict)
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except Exception as e:
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logger.warning(f"Could not convert to dict: {e}")
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return [], ""
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else:
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logger.warning(f"Unexpected PaddleOCR result type: {type(result)}")
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return [], ""
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except Exception as e:
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logger.error(f"PaddleOCR execution failed: {e}")
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import traceback
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logger.error(traceback.format_exc())
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return [], ""
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def _parse_paddleocr_v3_dict(result: dict) -> Tuple[List[OCRRegion], str]:
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"""Parse PaddleOCR 3.x dict format result."""
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regions = []
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all_text_lines = []
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texts = result.get('rec_texts', result.get('texts', []))
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scores = result.get('rec_scores', result.get('scores', []))
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polys = result.get('dt_polys', result.get('boxes', []))
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rec_boxes = result.get('rec_boxes', [])
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logger.info(f"PaddleOCR 3.x: {len(texts)} texts, {len(scores)} scores, {len(polys)} polys, {len(rec_boxes)} rec_boxes")
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for i, (text, score) in enumerate(zip(texts, scores)):
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if not text or not str(text).strip():
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continue
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x1, y1, x2, y2 = 0, 0, 100, 50
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if i < len(rec_boxes) and rec_boxes[i] is not None:
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box = rec_boxes[i]
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try:
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if hasattr(box, 'flatten'):
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box = box.flatten().tolist()
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if len(box) >= 4:
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x1, y1, x2, y2 = int(box[0]), int(box[1]), int(box[2]), int(box[3])
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except Exception as e:
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logger.debug(f"Could not parse rec_box: {e}")
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elif i < len(polys) and polys[i] is not None:
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poly = polys[i]
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try:
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if hasattr(poly, 'tolist'):
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poly = poly.tolist()
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if len(poly) >= 4:
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x_coords = [p[0] for p in poly]
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y_coords = [p[1] for p in poly]
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x1, y1 = int(min(x_coords)), int(min(y_coords))
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x2, y2 = int(max(x_coords)), int(max(y_coords))
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except Exception as e:
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logger.debug(f"Could not parse polygon: {e}")
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region = OCRRegion(
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text=text.strip(), confidence=float(score) if score else 0.5,
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x1=x1, y1=y1, x2=x2, y2=y2
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)
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regions.append(region)
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all_text_lines.append(text.strip())
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regions.sort(key=lambda r: r.y1)
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raw_text = "\n".join(all_text_lines)
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logger.info(f"PaddleOCR 3.x extracted {len(regions)} text regions")
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return regions, raw_text
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def _parse_paddleocr_list(page_result: list) -> Tuple[List[OCRRegion], str]:
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"""Parse PaddleOCR traditional list format result."""
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regions = []
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all_text_lines = []
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for line in page_result:
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if not line or len(line) < 2:
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continue
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bbox_points = line[0]
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text_info = line[1]
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if isinstance(text_info, tuple) and len(text_info) >= 2:
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text, confidence = text_info[0], text_info[1]
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elif isinstance(text_info, str):
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text, confidence = text_info, 0.5
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else:
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continue
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if not text or not text.strip():
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continue
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x_coords = [p[0] for p in bbox_points]
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y_coords = [p[1] for p in bbox_points]
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region = OCRRegion(
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text=text.strip(), confidence=float(confidence),
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x1=int(min(x_coords)), y1=int(min(y_coords)),
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x2=int(max(x_coords)), y2=int(max(y_coords))
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)
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regions.append(region)
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all_text_lines.append(text.strip())
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regions.sort(key=lambda r: r.y1)
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raw_text = "\n".join(all_text_lines)
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logger.info(f"PaddleOCR extracted {len(regions)} text regions")
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return regions, raw_text
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def group_regions_by_rows(regions: List[OCRRegion], y_tolerance: int = 20) -> List[List[OCRRegion]]:
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"""Gruppiert Textregionen in Zeilen basierend auf Y-Position."""
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if not regions:
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return []
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rows = []
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current_row = [regions[0]]
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current_y = regions[0].center_y
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for region in regions[1:]:
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if abs(region.center_y - current_y) <= y_tolerance:
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current_row.append(region)
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else:
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current_row.sort(key=lambda r: r.x1)
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rows.append(current_row)
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current_row = [region]
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current_y = region.center_y
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if current_row:
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current_row.sort(key=lambda r: r.x1)
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rows.append(current_row)
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return rows
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def detect_columns(rows: List[List[OCRRegion]]) -> int:
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"""Erkennt die Anzahl der Spalten basierend auf den Textpositionen."""
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if not rows:
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return 2
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items_per_row = [len(row) for row in rows if len(row) >= 2]
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if not items_per_row:
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return 2
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avg_items = sum(items_per_row) / len(items_per_row)
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return 3 if avg_items >= 2.5 else 2
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def format_ocr_for_llm(regions: List[OCRRegion]) -> str:
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"""Formatiert OCR-Output fuer LLM-Verarbeitung."""
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rows = group_regions_by_rows(regions)
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num_columns = detect_columns(rows)
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lines = [f"Erkannte Spalten: {num_columns}", "---"]
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for row in rows:
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if len(row) >= 2:
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lines.append("\t".join(r.text for r in row))
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elif len(row) == 1:
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lines.append(row[0].text)
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return "\n".join(lines)
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