[split-required] Split final batch of monoliths >1000 LOC
Python (6 files in klausur-service): - rbac.py (1,132 → 4), admin_api.py (1,012 → 4) - routes/eh.py (1,111 → 4), ocr_pipeline_geometry.py (1,105 → 5) Python (2 files in backend-lehrer): - unit_api.py (1,226 → 6), game_api.py (1,129 → 5) Website (6 page files): - 4x klausur-korrektur pages (1,249-1,328 LOC each) → shared components in website/components/klausur-korrektur/ (17 shared files) - companion (1,057 → 10), magic-help (1,017 → 8) All re-export barrels preserve backward compatibility. Zero import errors verified. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Benjamin Admin
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klausur-service/backend/ocr_pipeline_columns.py
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293
klausur-service/backend/ocr_pipeline_columns.py
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"""
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OCR Pipeline Column Detection Endpoints (Step 5)
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Detect invisible columns, manual column override, and ground truth.
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Extracted from ocr_pipeline_geometry.py for file-size compliance.
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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 datetime import datetime
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from typing import Dict, List
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import cv2
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from fastapi import APIRouter, HTTPException
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from cv_vocab_pipeline import (
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_detect_header_footer_gaps,
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_detect_sub_columns,
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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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analyze_layout,
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detect_column_geometry_zoned,
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expand_narrow_columns,
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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_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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_append_pipeline_log,
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ManualColumnsRequest,
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ColumnGroundTruthRequest,
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)
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/api/v1/ocr-pipeline", tags=["ocr-pipeline"])
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@router.post("/sessions/{session_id}/columns")
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async def detect_columns(session_id: str):
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"""Run column detection on the cropped (or dewarped) image."""
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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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cached = _get_cached(session_id)
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img_bgr = cached.get("cropped_bgr") if cached.get("cropped_bgr") is not None else cached.get("dewarped_bgr")
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if img_bgr is None:
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raise HTTPException(status_code=400, detail="Crop or dewarp must be completed before column detection")
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# -----------------------------------------------------------------------
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# Sub-sessions (box crops): skip column detection entirely.
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# Instead, create a single pseudo-column spanning the full image width.
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# Also run Tesseract + binarization here so that the row detection step
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# can reuse the cached intermediates (_word_dicts, _inv, _content_bounds)
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# instead of falling back to detect_column_geometry() which may fail
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# on small box images with < 5 words.
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# -----------------------------------------------------------------------
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session = await get_session_db(session_id)
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if session and session.get("parent_session_id"):
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h, w = img_bgr.shape[:2]
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# Binarize + invert for row detection (horizontal projection profile)
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ocr_img = create_ocr_image(img_bgr)
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inv = cv2.bitwise_not(ocr_img)
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# Run Tesseract to get word bounding boxes.
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try:
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from PIL import Image as PILImage
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pil_img = PILImage.fromarray(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB))
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import pytesseract
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data = pytesseract.image_to_data(pil_img, lang='eng+deu', output_type=pytesseract.Output.DICT)
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word_dicts = []
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for i in range(len(data['text'])):
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conf = int(data['conf'][i]) if str(data['conf'][i]).lstrip('-').isdigit() else -1
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text = str(data['text'][i]).strip()
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if conf < 30 or not text:
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continue
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word_dicts.append({
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'text': text, 'conf': conf,
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'left': int(data['left'][i]),
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'top': int(data['top'][i]),
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'width': int(data['width'][i]),
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'height': int(data['height'][i]),
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})
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# Log all words including low-confidence ones for debugging
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all_count = sum(1 for i in range(len(data['text']))
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if str(data['text'][i]).strip())
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low_conf = [(str(data['text'][i]).strip(), int(data['conf'][i]) if str(data['conf'][i]).lstrip('-').isdigit() else -1)
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for i in range(len(data['text']))
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if str(data['text'][i]).strip()
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and (int(data['conf'][i]) if str(data['conf'][i]).lstrip('-').isdigit() else -1) < 30
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and (int(data['conf'][i]) if str(data['conf'][i]).lstrip('-').isdigit() else -1) >= 0]
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if low_conf:
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logger.info(f"OCR Pipeline: sub-session {session_id}: {len(low_conf)} words below conf 30: {low_conf[:20]}")
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logger.info(f"OCR Pipeline: sub-session {session_id}: Tesseract found {len(word_dicts)}/{all_count} words (conf>=30)")
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except Exception as e:
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logger.warning(f"OCR Pipeline: sub-session {session_id}: Tesseract failed: {e}")
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word_dicts = []
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# Cache intermediates for row detection (detect_rows reuses these)
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cached["_word_dicts"] = word_dicts
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cached["_inv"] = inv
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cached["_content_bounds"] = (0, w, 0, h)
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column_result = {
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"columns": [{
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"type": "column_text",
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"x": 0, "y": 0,
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"width": w, "height": h,
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}],
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"zones": None,
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"boxes_detected": 0,
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"duration_seconds": 0,
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"method": "sub_session_pseudo_column",
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}
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await update_session_db(
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session_id,
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column_result=column_result,
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row_result=None,
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word_result=None,
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current_step=6,
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)
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cached["column_result"] = column_result
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cached.pop("row_result", None)
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cached.pop("word_result", None)
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logger.info(f"OCR Pipeline: sub-session {session_id}: pseudo-column {w}x{h}px")
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return {"session_id": session_id, **column_result}
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t0 = time.time()
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# Binarized image for layout analysis
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ocr_img = create_ocr_image(img_bgr)
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h, w = ocr_img.shape[:2]
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# Phase A: Zone-aware geometry detection
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zoned_result = detect_column_geometry_zoned(ocr_img, img_bgr)
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boxes_detected = 0
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if zoned_result is None:
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# Fallback to projection-based layout
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layout_img = create_layout_image(img_bgr)
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regions = analyze_layout(layout_img, ocr_img)
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zones_data = None
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else:
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geometries, left_x, right_x, top_y, bottom_y, word_dicts, inv, zones_data, boxes = zoned_result
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content_w = right_x - left_x
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boxes_detected = len(boxes)
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# Cache intermediates for row detection (avoids second Tesseract run)
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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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cached["_zones_data"] = zones_data
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cached["_boxes_detected"] = boxes_detected
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# Detect header/footer early so sub-column clustering ignores them
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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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# Split sub-columns (e.g. page references) before classification
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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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# Expand narrow columns (sub-columns are often very narrow)
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geometries = expand_narrow_columns(geometries, content_w, left_x, word_dicts)
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# Phase B: Content-based classification
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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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duration = time.time() - t0
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columns = [asdict(r) for r in regions]
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# Determine classification methods used
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methods = list(set(
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c.get("classification_method", "") for c in columns
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if c.get("classification_method")
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))
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column_result = {
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"columns": columns,
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"classification_methods": methods,
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"duration_seconds": round(duration, 2),
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"boxes_detected": boxes_detected,
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}
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# Add zone data when boxes are present
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if zones_data and boxes_detected > 0:
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column_result["zones"] = zones_data
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# Persist to DB -- also invalidate downstream results (rows, words)
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await update_session_db(
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session_id,
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column_result=column_result,
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row_result=None,
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word_result=None,
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current_step=6,
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)
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# Update cache
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cached["column_result"] = column_result
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cached.pop("row_result", None)
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cached.pop("word_result", None)
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col_count = len([c for c in columns if c["type"].startswith("column")])
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logger.info(f"OCR Pipeline: columns session {session_id}: "
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f"{col_count} columns detected, {boxes_detected} box(es) ({duration:.2f}s)")
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img_w = img_bgr.shape[1]
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await _append_pipeline_log(session_id, "columns", {
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"total_columns": len(columns),
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"column_widths_pct": [round(c["width"] / img_w * 100, 1) for c in columns],
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"column_types": [c["type"] for c in columns],
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"boxes_detected": boxes_detected,
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}, duration_ms=int(duration * 1000))
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return {
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"session_id": session_id,
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**column_result,
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}
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@router.post("/sessions/{session_id}/columns/manual")
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async def set_manual_columns(session_id: str, req: ManualColumnsRequest):
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"""Override detected columns with manual definitions."""
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column_result = {
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"columns": req.columns,
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"duration_seconds": 0,
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"method": "manual",
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}
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await update_session_db(session_id, column_result=column_result,
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row_result=None, word_result=None)
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if session_id in _cache:
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_cache[session_id]["column_result"] = column_result
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_cache[session_id].pop("row_result", None)
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_cache[session_id].pop("word_result", None)
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logger.info(f"OCR Pipeline: manual columns session {session_id}: "
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f"{len(req.columns)} columns set")
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return {"session_id": session_id, **column_result}
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@router.post("/sessions/{session_id}/ground-truth/columns")
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async def save_column_ground_truth(session_id: str, req: ColumnGroundTruthRequest):
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"""Save ground truth feedback for the column detection step."""
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session = await get_session_db(session_id)
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if not session:
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raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
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ground_truth = session.get("ground_truth") or {}
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gt = {
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"is_correct": req.is_correct,
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"corrected_columns": req.corrected_columns,
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"notes": req.notes,
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"saved_at": datetime.utcnow().isoformat(),
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"column_result": session.get("column_result"),
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}
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ground_truth["columns"] = gt
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await update_session_db(session_id, ground_truth=ground_truth)
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if session_id in _cache:
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_cache[session_id]["ground_truth"] = ground_truth
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return {"session_id": session_id, "ground_truth": gt}
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@router.get("/sessions/{session_id}/ground-truth/columns")
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async def get_column_ground_truth(session_id: str):
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"""Retrieve saved ground truth for column detection, including auto vs GT diff."""
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session = await get_session_db(session_id)
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if not session:
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raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
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ground_truth = session.get("ground_truth") or {}
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columns_gt = ground_truth.get("columns")
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if not columns_gt:
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raise HTTPException(status_code=404, detail="No column ground truth saved")
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return {
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"session_id": session_id,
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"columns_gt": columns_gt,
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"columns_auto": session.get("column_result"),
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}
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