[split-required] Split 700-870 LOC files across all services
backend-lehrer (11 files): - llm_gateway/routes/schools.py (867 → 5), recording_api.py (848 → 6) - messenger_api.py (840 → 5), print_generator.py (824 → 5) - unit_analytics_api.py (751 → 5), classroom/routes/context.py (726 → 4) - llm_gateway/routes/edu_search_seeds.py (710 → 4) klausur-service (12 files): - ocr_labeling_api.py (845 → 4), metrics_db.py (833 → 4) - legal_corpus_api.py (790 → 4), page_crop.py (758 → 3) - mail/ai_service.py (747 → 4), github_crawler.py (767 → 3) - trocr_service.py (730 → 4), full_compliance_pipeline.py (723 → 4) - dsfa_rag_api.py (715 → 4), ocr_pipeline_auto.py (705 → 4) website (6 pages): - audit-checklist (867 → 8), content (806 → 6) - screen-flow (790 → 4), scraper (789 → 5) - zeugnisse (776 → 5), modules (745 → 4) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Benjamin Admin
528
klausur-service/backend/ocr_pipeline_auto_steps.py
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528
klausur-service/backend/ocr_pipeline_auto_steps.py
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@@ -0,0 +1,528 @@
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"""
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OCR Pipeline Auto-Mode Orchestrator.
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POST /sessions/{session_id}/run-auto -- full auto-mode with SSE streaming.
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Lizenz: Apache 2.0
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DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
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"""
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import logging
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import time
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from dataclasses import asdict
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from typing import Any, Dict, List, Optional
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import cv2
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import numpy as np
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from fastapi import APIRouter, HTTPException, Request
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from fastapi.responses import StreamingResponse
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from cv_vocab_pipeline import (
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OLLAMA_REVIEW_MODEL,
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PageRegion,
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RowGeometry,
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_cells_to_vocab_entries,
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_detect_header_footer_gaps,
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_detect_sub_columns,
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_fix_character_confusion,
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_fix_phonetic_brackets,
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fix_cell_phonetics,
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analyze_layout,
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build_cell_grid,
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classify_column_types,
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create_layout_image,
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create_ocr_image,
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deskew_image,
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deskew_image_by_word_alignment,
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detect_column_geometry,
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detect_row_geometry,
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_apply_shear,
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dewarp_image,
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llm_review_entries,
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)
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from ocr_pipeline_common import (
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_cache,
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_load_session_to_cache,
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_get_cached,
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)
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from ocr_pipeline_session_store import (
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get_session_db,
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update_session_db,
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)
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from ocr_pipeline_auto_helpers import (
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RunAutoRequest,
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auto_sse_event as _auto_sse_event,
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detect_shear_with_vlm as _detect_shear_with_vlm,
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)
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logger = logging.getLogger(__name__)
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router = APIRouter(tags=["ocr-pipeline"])
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@router.post("/sessions/{session_id}/run-auto")
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async def run_auto(session_id: str, req: RunAutoRequest, request: Request):
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"""Run the full OCR pipeline automatically from a given step, streaming SSE progress.
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Steps:
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1. Deskew -- straighten the scan
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2. Dewarp -- correct vertical shear (ensemble CV or VLM)
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3. Columns -- detect column layout
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4. Rows -- detect row layout
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5. Words -- OCR each cell
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6. LLM review -- correct OCR errors (optional)
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Already-completed steps are skipped unless `from_step` forces a rerun.
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Yields SSE events of the form:
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data: {"step": "deskew", "status": "start"|"done"|"skipped"|"error", ...}
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Final event:
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data: {"step": "complete", "status": "done", "steps_run": [...], "steps_skipped": [...]}
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"""
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if req.from_step < 1 or req.from_step > 6:
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raise HTTPException(status_code=400, detail="from_step must be 1-6")
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if req.dewarp_method not in ("ensemble", "vlm", "cv"):
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raise HTTPException(status_code=400, detail="dewarp_method must be: ensemble, vlm, cv")
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if session_id not in _cache:
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await _load_session_to_cache(session_id)
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async def _generate():
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steps_run: List[str] = []
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steps_skipped: List[str] = []
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error_step: Optional[str] = None
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session = await get_session_db(session_id)
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if not session:
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yield await _auto_sse_event("error", "error", {"message": f"Session {session_id} not found"})
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return
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cached = _get_cached(session_id)
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# Step 1: Deskew
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if req.from_step <= 1:
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yield await _auto_sse_event("deskew", "start", {})
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try:
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t0 = time.time()
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orig_bgr = cached.get("original_bgr")
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if orig_bgr is None:
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raise ValueError("Original image not loaded")
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try:
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deskewed_hough, angle_hough = deskew_image(orig_bgr.copy())
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except Exception:
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deskewed_hough, angle_hough = orig_bgr, 0.0
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success_enc, png_orig = cv2.imencode(".png", orig_bgr)
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orig_bytes = png_orig.tobytes() if success_enc else b""
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try:
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deskewed_wa_bytes, angle_wa = deskew_image_by_word_alignment(orig_bytes)
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except Exception:
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deskewed_wa_bytes, angle_wa = orig_bytes, 0.0
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if abs(angle_wa) >= abs(angle_hough) or abs(angle_hough) < 0.1:
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method_used = "word_alignment"
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angle_applied = angle_wa
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wa_arr = np.frombuffer(deskewed_wa_bytes, dtype=np.uint8)
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deskewed_bgr = cv2.imdecode(wa_arr, cv2.IMREAD_COLOR)
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if deskewed_bgr is None:
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deskewed_bgr = deskewed_hough
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method_used = "hough"
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angle_applied = angle_hough
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else:
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method_used = "hough"
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angle_applied = angle_hough
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deskewed_bgr = deskewed_hough
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success, png_buf = cv2.imencode(".png", deskewed_bgr)
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deskewed_png = png_buf.tobytes() if success else b""
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deskew_result = {
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"method_used": method_used,
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"rotation_degrees": round(float(angle_applied), 3),
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"duration_seconds": round(time.time() - t0, 2),
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}
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cached["deskewed_bgr"] = deskewed_bgr
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cached["deskew_result"] = deskew_result
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await update_session_db(
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session_id,
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deskewed_png=deskewed_png,
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deskew_result=deskew_result,
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auto_rotation_degrees=float(angle_applied),
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current_step=3,
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)
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session = await get_session_db(session_id)
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steps_run.append("deskew")
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yield await _auto_sse_event("deskew", "done", deskew_result)
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except Exception as e:
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logger.error(f"Auto-mode deskew failed for {session_id}: {e}")
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error_step = "deskew"
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yield await _auto_sse_event("deskew", "error", {"message": str(e)})
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yield await _auto_sse_event("complete", "error", {"error_step": error_step})
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return
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else:
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steps_skipped.append("deskew")
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yield await _auto_sse_event("deskew", "skipped", {"reason": "from_step > 1"})
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# Step 2: Dewarp
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if req.from_step <= 2:
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yield await _auto_sse_event("dewarp", "start", {"method": req.dewarp_method})
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try:
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t0 = time.time()
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deskewed_bgr = cached.get("deskewed_bgr")
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if deskewed_bgr is None:
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raise ValueError("Deskewed image not available")
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if req.dewarp_method == "vlm":
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success_enc, png_buf = cv2.imencode(".png", deskewed_bgr)
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img_bytes = png_buf.tobytes() if success_enc else b""
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vlm_det = await _detect_shear_with_vlm(img_bytes)
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shear_deg = vlm_det["shear_degrees"]
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if abs(shear_deg) >= 0.05 and vlm_det["confidence"] >= 0.3:
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dewarped_bgr = _apply_shear(deskewed_bgr, -shear_deg)
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else:
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dewarped_bgr = deskewed_bgr
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dewarp_info = {
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"method": vlm_det["method"],
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"shear_degrees": shear_deg,
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"confidence": vlm_det["confidence"],
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"detections": [vlm_det],
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}
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else:
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dewarped_bgr, dewarp_info = dewarp_image(deskewed_bgr)
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success_enc, png_buf = cv2.imencode(".png", dewarped_bgr)
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dewarped_png = png_buf.tobytes() if success_enc else b""
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dewarp_result = {
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"method_used": dewarp_info["method"],
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"shear_degrees": dewarp_info["shear_degrees"],
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"confidence": dewarp_info["confidence"],
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"duration_seconds": round(time.time() - t0, 2),
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"detections": dewarp_info.get("detections", []),
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}
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cached["dewarped_bgr"] = dewarped_bgr
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cached["dewarp_result"] = dewarp_result
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await update_session_db(
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session_id,
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dewarped_png=dewarped_png,
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dewarp_result=dewarp_result,
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auto_shear_degrees=dewarp_info.get("shear_degrees", 0.0),
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current_step=4,
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)
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session = await get_session_db(session_id)
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steps_run.append("dewarp")
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yield await _auto_sse_event("dewarp", "done", dewarp_result)
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except Exception as e:
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logger.error(f"Auto-mode dewarp failed for {session_id}: {e}")
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error_step = "dewarp"
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yield await _auto_sse_event("dewarp", "error", {"message": str(e)})
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yield await _auto_sse_event("complete", "error", {"error_step": error_step})
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return
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else:
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steps_skipped.append("dewarp")
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yield await _auto_sse_event("dewarp", "skipped", {"reason": "from_step > 2"})
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# Step 3: Columns
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if req.from_step <= 3:
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yield await _auto_sse_event("columns", "start", {})
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try:
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t0 = time.time()
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col_img = cached.get("cropped_bgr") if cached.get("cropped_bgr") is not None else cached.get("dewarped_bgr")
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if col_img is None:
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raise ValueError("Cropped/dewarped image not available")
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ocr_img = create_ocr_image(col_img)
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h, w = ocr_img.shape[:2]
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geo_result = detect_column_geometry(ocr_img, col_img)
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if geo_result is None:
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layout_img = create_layout_image(col_img)
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regions = analyze_layout(layout_img, ocr_img)
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cached["_word_dicts"] = None
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cached["_inv"] = None
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cached["_content_bounds"] = None
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else:
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geometries, left_x, right_x, top_y, bottom_y, word_dicts, inv = geo_result
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content_w = right_x - left_x
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cached["_word_dicts"] = word_dicts
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cached["_inv"] = inv
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cached["_content_bounds"] = (left_x, right_x, top_y, bottom_y)
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header_y, footer_y = _detect_header_footer_gaps(inv, w, h) if inv is not None else (None, None)
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geometries = _detect_sub_columns(geometries, content_w, left_x=left_x,
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top_y=top_y, header_y=header_y, footer_y=footer_y)
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regions = classify_column_types(geometries, content_w, top_y, w, h, bottom_y,
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left_x=left_x, right_x=right_x, inv=inv)
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columns = [asdict(r) for r in regions]
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column_result = {
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"columns": columns,
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"classification_methods": list({c.get("classification_method", "") for c in columns if c.get("classification_method")}),
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"duration_seconds": round(time.time() - t0, 2),
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}
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cached["column_result"] = column_result
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await update_session_db(session_id, column_result=column_result,
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row_result=None, word_result=None, current_step=6)
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session = await get_session_db(session_id)
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steps_run.append("columns")
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yield await _auto_sse_event("columns", "done", {
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"column_count": len(columns),
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"duration_seconds": column_result["duration_seconds"],
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})
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except Exception as e:
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logger.error(f"Auto-mode columns failed for {session_id}: {e}")
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error_step = "columns"
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yield await _auto_sse_event("columns", "error", {"message": str(e)})
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yield await _auto_sse_event("complete", "error", {"error_step": error_step})
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return
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else:
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steps_skipped.append("columns")
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yield await _auto_sse_event("columns", "skipped", {"reason": "from_step > 3"})
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# Step 4: Rows
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if req.from_step <= 4:
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yield await _auto_sse_event("rows", "start", {})
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try:
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t0 = time.time()
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row_img = cached.get("cropped_bgr") if cached.get("cropped_bgr") is not None else cached.get("dewarped_bgr")
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session = await get_session_db(session_id)
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column_result = session.get("column_result") or cached.get("column_result")
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if not column_result or not column_result.get("columns"):
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raise ValueError("Column detection must complete first")
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col_regions = [
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PageRegion(
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type=c["type"], x=c["x"], y=c["y"],
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width=c["width"], height=c["height"],
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classification_confidence=c.get("classification_confidence", 1.0),
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classification_method=c.get("classification_method", ""),
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)
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for c in column_result["columns"]
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]
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word_dicts = cached.get("_word_dicts")
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inv = cached.get("_inv")
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content_bounds = cached.get("_content_bounds")
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if word_dicts is None or inv is None or content_bounds is None:
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ocr_img_tmp = create_ocr_image(row_img)
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geo_result = detect_column_geometry(ocr_img_tmp, row_img)
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if geo_result is None:
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raise ValueError("Column geometry detection failed -- cannot detect rows")
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_g, lx, rx, ty, by, word_dicts, inv = geo_result
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cached["_word_dicts"] = word_dicts
|
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cached["_inv"] = inv
|
||||
cached["_content_bounds"] = (lx, rx, ty, by)
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content_bounds = (lx, rx, ty, by)
|
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left_x, right_x, top_y, bottom_y = content_bounds
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row_geoms = detect_row_geometry(inv, word_dicts, left_x, right_x, top_y, bottom_y)
|
||||
|
||||
row_list = [
|
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{
|
||||
"index": r.index, "x": r.x, "y": r.y,
|
||||
"width": r.width, "height": r.height,
|
||||
"word_count": r.word_count,
|
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"row_type": r.row_type,
|
||||
"gap_before": r.gap_before,
|
||||
}
|
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for r in row_geoms
|
||||
]
|
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row_result = {
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"rows": row_list,
|
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"row_count": len(row_list),
|
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"content_rows": len([r for r in row_geoms if r.row_type == "content"]),
|
||||
"duration_seconds": round(time.time() - t0, 2),
|
||||
}
|
||||
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||||
cached["row_result"] = row_result
|
||||
await update_session_db(session_id, row_result=row_result, current_step=7)
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||||
session = await get_session_db(session_id)
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steps_run.append("rows")
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||||
yield await _auto_sse_event("rows", "done", {
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"row_count": len(row_list),
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||||
"content_rows": row_result["content_rows"],
|
||||
"duration_seconds": row_result["duration_seconds"],
|
||||
})
|
||||
except Exception as e:
|
||||
logger.error(f"Auto-mode rows failed for {session_id}: {e}")
|
||||
error_step = "rows"
|
||||
yield await _auto_sse_event("rows", "error", {"message": str(e)})
|
||||
yield await _auto_sse_event("complete", "error", {"error_step": error_step})
|
||||
return
|
||||
else:
|
||||
steps_skipped.append("rows")
|
||||
yield await _auto_sse_event("rows", "skipped", {"reason": "from_step > 4"})
|
||||
|
||||
# Step 5: Words (OCR)
|
||||
if req.from_step <= 5:
|
||||
yield await _auto_sse_event("words", "start", {"engine": req.ocr_engine})
|
||||
try:
|
||||
t0 = time.time()
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||||
word_img = cached.get("cropped_bgr") if cached.get("cropped_bgr") is not None else cached.get("dewarped_bgr")
|
||||
session = await get_session_db(session_id)
|
||||
|
||||
column_result = session.get("column_result") or cached.get("column_result")
|
||||
row_result = session.get("row_result") or cached.get("row_result")
|
||||
|
||||
col_regions = [
|
||||
PageRegion(
|
||||
type=c["type"], x=c["x"], y=c["y"],
|
||||
width=c["width"], height=c["height"],
|
||||
classification_confidence=c.get("classification_confidence", 1.0),
|
||||
classification_method=c.get("classification_method", ""),
|
||||
)
|
||||
for c in column_result["columns"]
|
||||
]
|
||||
row_geoms = [
|
||||
RowGeometry(
|
||||
index=r["index"], x=r["x"], y=r["y"],
|
||||
width=r["width"], height=r["height"],
|
||||
word_count=r.get("word_count", 0), words=[],
|
||||
row_type=r.get("row_type", "content"),
|
||||
gap_before=r.get("gap_before", 0),
|
||||
)
|
||||
for r in row_result["rows"]
|
||||
]
|
||||
|
||||
word_dicts = cached.get("_word_dicts")
|
||||
if word_dicts is not None:
|
||||
content_bounds = cached.get("_content_bounds")
|
||||
top_y = content_bounds[2] if content_bounds else min(r.y for r in row_geoms)
|
||||
for row in row_geoms:
|
||||
row_y_rel = row.y - top_y
|
||||
row_bottom_rel = row_y_rel + row.height
|
||||
row.words = [
|
||||
w for w in word_dicts
|
||||
if row_y_rel <= w['top'] + w['height'] / 2 < row_bottom_rel
|
||||
]
|
||||
row.word_count = len(row.words)
|
||||
|
||||
ocr_img = create_ocr_image(word_img)
|
||||
img_h, img_w = word_img.shape[:2]
|
||||
|
||||
cells, columns_meta = build_cell_grid(
|
||||
ocr_img, col_regions, row_geoms, img_w, img_h,
|
||||
ocr_engine=req.ocr_engine, img_bgr=word_img,
|
||||
)
|
||||
duration = time.time() - t0
|
||||
|
||||
col_types = {c['type'] for c in columns_meta}
|
||||
is_vocab = bool(col_types & {'column_en', 'column_de'})
|
||||
n_content_rows = len([r for r in row_geoms if r.row_type == 'content'])
|
||||
used_engine = cells[0].get("ocr_engine", "tesseract") if cells else req.ocr_engine
|
||||
|
||||
fix_cell_phonetics(cells, pronunciation=req.pronunciation)
|
||||
|
||||
word_result_data = {
|
||||
"cells": cells,
|
||||
"grid_shape": {
|
||||
"rows": n_content_rows,
|
||||
"cols": len(columns_meta),
|
||||
"total_cells": len(cells),
|
||||
},
|
||||
"columns_used": columns_meta,
|
||||
"layout": "vocab" if is_vocab else "generic",
|
||||
"image_width": img_w,
|
||||
"image_height": img_h,
|
||||
"duration_seconds": round(duration, 2),
|
||||
"ocr_engine": used_engine,
|
||||
"summary": {
|
||||
"total_cells": len(cells),
|
||||
"non_empty_cells": sum(1 for c in cells if c.get("text")),
|
||||
"low_confidence": sum(1 for c in cells if 0 < c.get("confidence", 0) < 50),
|
||||
},
|
||||
}
|
||||
|
||||
has_text_col = 'column_text' in col_types
|
||||
if is_vocab or has_text_col:
|
||||
entries = _cells_to_vocab_entries(cells, columns_meta)
|
||||
entries = _fix_character_confusion(entries)
|
||||
entries = _fix_phonetic_brackets(entries, pronunciation=req.pronunciation)
|
||||
word_result_data["vocab_entries"] = entries
|
||||
word_result_data["entries"] = entries
|
||||
word_result_data["entry_count"] = len(entries)
|
||||
word_result_data["summary"]["total_entries"] = len(entries)
|
||||
|
||||
await update_session_db(session_id, word_result=word_result_data, current_step=8)
|
||||
cached["word_result"] = word_result_data
|
||||
session = await get_session_db(session_id)
|
||||
|
||||
steps_run.append("words")
|
||||
yield await _auto_sse_event("words", "done", {
|
||||
"total_cells": len(cells),
|
||||
"layout": word_result_data["layout"],
|
||||
"duration_seconds": round(duration, 2),
|
||||
"ocr_engine": used_engine,
|
||||
"summary": word_result_data["summary"],
|
||||
})
|
||||
except Exception as e:
|
||||
logger.error(f"Auto-mode words failed for {session_id}: {e}")
|
||||
error_step = "words"
|
||||
yield await _auto_sse_event("words", "error", {"message": str(e)})
|
||||
yield await _auto_sse_event("complete", "error", {"error_step": error_step})
|
||||
return
|
||||
else:
|
||||
steps_skipped.append("words")
|
||||
yield await _auto_sse_event("words", "skipped", {"reason": "from_step > 5"})
|
||||
|
||||
# Step 6: LLM Review (optional)
|
||||
if req.from_step <= 6 and not req.skip_llm_review:
|
||||
yield await _auto_sse_event("llm_review", "start", {"model": OLLAMA_REVIEW_MODEL})
|
||||
try:
|
||||
session = await get_session_db(session_id)
|
||||
word_result = session.get("word_result") or cached.get("word_result")
|
||||
entries = word_result.get("entries") or word_result.get("vocab_entries") or []
|
||||
|
||||
if not entries:
|
||||
yield await _auto_sse_event("llm_review", "skipped", {"reason": "no entries"})
|
||||
steps_skipped.append("llm_review")
|
||||
else:
|
||||
reviewed = await llm_review_entries(entries)
|
||||
|
||||
session = await get_session_db(session_id)
|
||||
word_result_updated = dict(session.get("word_result") or {})
|
||||
word_result_updated["entries"] = reviewed
|
||||
word_result_updated["vocab_entries"] = reviewed
|
||||
word_result_updated["llm_reviewed"] = True
|
||||
word_result_updated["llm_model"] = OLLAMA_REVIEW_MODEL
|
||||
|
||||
await update_session_db(session_id, word_result=word_result_updated, current_step=9)
|
||||
cached["word_result"] = word_result_updated
|
||||
|
||||
steps_run.append("llm_review")
|
||||
yield await _auto_sse_event("llm_review", "done", {
|
||||
"entries_reviewed": len(reviewed),
|
||||
"model": OLLAMA_REVIEW_MODEL,
|
||||
})
|
||||
except Exception as e:
|
||||
logger.warning(f"Auto-mode llm_review failed for {session_id} (non-fatal): {e}")
|
||||
yield await _auto_sse_event("llm_review", "error", {"message": str(e), "fatal": False})
|
||||
steps_skipped.append("llm_review")
|
||||
else:
|
||||
steps_skipped.append("llm_review")
|
||||
reason = "skipped by request" if req.skip_llm_review else "from_step > 6"
|
||||
yield await _auto_sse_event("llm_review", "skipped", {"reason": reason})
|
||||
|
||||
# Final event
|
||||
yield await _auto_sse_event("complete", "done", {
|
||||
"steps_run": steps_run,
|
||||
"steps_skipped": steps_skipped,
|
||||
})
|
||||
|
||||
return StreamingResponse(
|
||||
_generate(),
|
||||
media_type="text/event-stream",
|
||||
headers={
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"X-Accel-Buffering": "no",
|
||||
},
|
||||
)
|
||||
Reference in New Issue
Block a user