[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
346
klausur-service/backend/ocr_pipeline_dewarp.py
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346
klausur-service/backend/ocr_pipeline_dewarp.py
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"""
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OCR Pipeline Dewarp Endpoints
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Auto dewarp (with VLM/CV ensemble), manual dewarp, combined
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rotation+shear adjustment, 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 json
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import logging
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import os
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import re
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import time
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from datetime import datetime
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from typing import Any, Dict
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import cv2
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from fastapi import APIRouter, HTTPException, Query
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from cv_vocab_pipeline import (
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_apply_shear,
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create_ocr_image,
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dewarp_image,
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dewarp_image_manual,
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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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ManualDewarpRequest,
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CombinedAdjustRequest,
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DewarpGroundTruthRequest,
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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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async def _detect_shear_with_vlm(image_bytes: bytes) -> Dict[str, Any]:
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"""Ask qwen2.5vl:32b to estimate the vertical shear angle of a scanned page.
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The VLM is shown the image and asked: are the column/table borders tilted?
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If yes, by how many degrees? Returns a dict with shear_degrees and confidence.
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Confidence is 0.0 if Ollama is unavailable or parsing fails.
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"""
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import httpx
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import base64
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ollama_base = os.getenv("OLLAMA_BASE_URL", "http://host.docker.internal:11434")
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model = os.getenv("OLLAMA_HTR_MODEL", "qwen2.5vl:32b")
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prompt = (
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"This is a scanned vocabulary worksheet. Look at the vertical borders of the table columns. "
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"Are they perfectly vertical, or do they tilt slightly? "
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"If they tilt, estimate the tilt angle in degrees (positive = top tilts right, negative = top tilts left). "
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"Reply with ONLY a JSON object like: {\"shear_degrees\": 1.2, \"confidence\": 0.8} "
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"Use confidence 0.0-1.0 based on how clearly you can see the tilt. "
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"If the columns look straight, return {\"shear_degrees\": 0.0, \"confidence\": 0.9}"
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)
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img_b64 = base64.b64encode(image_bytes).decode("utf-8")
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payload = {
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"model": model,
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"prompt": prompt,
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"images": [img_b64],
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"stream": False,
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}
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try:
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async with httpx.AsyncClient(timeout=60.0) as client:
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resp = await client.post(f"{ollama_base}/api/generate", json=payload)
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resp.raise_for_status()
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text = resp.json().get("response", "")
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# Parse JSON from response (may have surrounding text)
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match = re.search(r'\{[^}]+\}', text)
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if match:
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data = json.loads(match.group(0))
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shear = float(data.get("shear_degrees", 0.0))
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conf = float(data.get("confidence", 0.0))
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# Clamp to reasonable range
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shear = max(-3.0, min(3.0, shear))
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conf = max(0.0, min(1.0, conf))
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return {"method": "vlm_qwen2.5vl", "shear_degrees": round(shear, 3), "confidence": round(conf, 2)}
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except Exception as e:
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logger.warning(f"VLM dewarp failed: {e}")
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return {"method": "vlm_qwen2.5vl", "shear_degrees": 0.0, "confidence": 0.0}
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@router.post("/sessions/{session_id}/dewarp")
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async def auto_dewarp(
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session_id: str,
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method: str = Query("ensemble", description="Detection method: ensemble | vlm | cv"),
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):
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"""Detect and correct vertical shear on the deskewed image.
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Methods:
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- **ensemble** (default): 3-method CV ensemble (vertical edges + projection + Hough)
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- **cv**: CV ensemble only (same as ensemble)
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- **vlm**: Ask qwen2.5vl:32b to estimate the shear angle visually
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"""
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if method not in ("ensemble", "cv", "vlm"):
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raise HTTPException(status_code=400, detail="method must be one of: ensemble, cv, vlm")
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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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deskewed_bgr = cached.get("deskewed_bgr")
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if deskewed_bgr is None:
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raise HTTPException(status_code=400, detail="Deskew must be completed before dewarp")
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t0 = time.time()
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if method == "vlm":
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# Encode deskewed image to PNG for VLM
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success, png_buf = cv2.imencode(".png", deskewed_bgr)
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img_bytes = png_buf.tobytes() if success 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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duration = time.time() - t0
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# Encode as PNG
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success, png_buf = cv2.imencode(".png", dewarped_bgr)
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dewarped_png = png_buf.tobytes() if success 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(duration, 2),
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"detections": dewarp_info.get("detections", []),
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}
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# Update cache
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cached["dewarped_bgr"] = dewarped_bgr
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cached["dewarp_result"] = dewarp_result
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# Persist to DB
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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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logger.info(f"OCR Pipeline: dewarp session {session_id}: "
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f"method={dewarp_info['method']} shear={dewarp_info['shear_degrees']:.3f} "
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f"conf={dewarp_info['confidence']:.2f} ({duration:.2f}s)")
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await _append_pipeline_log(session_id, "dewarp", {
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"shear_degrees": dewarp_info["shear_degrees"],
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"confidence": dewarp_info["confidence"],
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"method": dewarp_info["method"],
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"ensemble_methods": [d.get("method", "") for d in dewarp_info.get("detections", [])],
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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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**dewarp_result,
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"dewarped_image_url": f"/api/v1/ocr-pipeline/sessions/{session_id}/image/dewarped",
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}
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@router.post("/sessions/{session_id}/dewarp/manual")
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async def manual_dewarp(session_id: str, req: ManualDewarpRequest):
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"""Apply shear correction with a manual angle."""
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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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deskewed_bgr = cached.get("deskewed_bgr")
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if deskewed_bgr is None:
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raise HTTPException(status_code=400, detail="Deskew must be completed before dewarp")
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shear_deg = max(-2.0, min(2.0, req.shear_degrees))
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if abs(shear_deg) < 0.001:
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dewarped_bgr = deskewed_bgr
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else:
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dewarped_bgr = dewarp_image_manual(deskewed_bgr, shear_deg)
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success, png_buf = cv2.imencode(".png", dewarped_bgr)
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dewarped_png = png_buf.tobytes() if success else b""
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dewarp_result = {
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**(cached.get("dewarp_result") or {}),
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"method_used": "manual",
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"shear_degrees": round(shear_deg, 3),
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}
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# Update cache
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cached["dewarped_bgr"] = dewarped_bgr
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cached["dewarp_result"] = dewarp_result
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# Persist to DB
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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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)
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logger.info(f"OCR Pipeline: manual dewarp session {session_id}: shear={shear_deg:.3f}")
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return {
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"session_id": session_id,
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"shear_degrees": round(shear_deg, 3),
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"method_used": "manual",
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"dewarped_image_url": f"/api/v1/ocr-pipeline/sessions/{session_id}/image/dewarped",
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}
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@router.post("/sessions/{session_id}/adjust-combined")
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async def adjust_combined(session_id: str, req: CombinedAdjustRequest):
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"""Apply rotation + shear combined to the original image.
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Used by the fine-tuning sliders to preview arbitrary rotation/shear
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combinations without re-running the full deskew/dewarp pipeline.
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"""
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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("original_bgr")
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if img_bgr is None:
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raise HTTPException(status_code=400, detail="Original image not available")
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rotation = max(-15.0, min(15.0, req.rotation_degrees))
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shear_deg = max(-5.0, min(5.0, req.shear_degrees))
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h, w = img_bgr.shape[:2]
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result_bgr = img_bgr
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# Step 1: Apply rotation
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if abs(rotation) >= 0.001:
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center = (w // 2, h // 2)
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M = cv2.getRotationMatrix2D(center, rotation, 1.0)
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result_bgr = cv2.warpAffine(result_bgr, M, (w, h),
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flags=cv2.INTER_LINEAR,
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borderMode=cv2.BORDER_REPLICATE)
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# Step 2: Apply shear
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if abs(shear_deg) >= 0.001:
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result_bgr = dewarp_image_manual(result_bgr, shear_deg)
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# Encode
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success, png_buf = cv2.imencode(".png", result_bgr)
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dewarped_png = png_buf.tobytes() if success else b""
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# Binarize
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binarized_png = None
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try:
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binarized = create_ocr_image(result_bgr)
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success_bin, bin_buf = cv2.imencode(".png", binarized)
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binarized_png = bin_buf.tobytes() if success_bin else None
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except Exception:
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pass
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# Build combined result dicts
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deskew_result = {
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**(cached.get("deskew_result") or {}),
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"angle_applied": round(rotation, 3),
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"method_used": "manual_combined",
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}
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dewarp_result = {
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**(cached.get("dewarp_result") or {}),
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"method_used": "manual_combined",
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"shear_degrees": round(shear_deg, 3),
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}
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# Update cache
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cached["deskewed_bgr"] = result_bgr
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cached["dewarped_bgr"] = result_bgr
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cached["deskew_result"] = deskew_result
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cached["dewarp_result"] = dewarp_result
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# Persist to DB
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db_update = {
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"dewarped_png": dewarped_png,
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"deskew_result": deskew_result,
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"dewarp_result": dewarp_result,
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}
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if binarized_png:
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db_update["binarized_png"] = binarized_png
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db_update["deskewed_png"] = dewarped_png
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await update_session_db(session_id, **db_update)
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logger.info(f"OCR Pipeline: combined adjust session {session_id}: "
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f"rotation={rotation:.3f} shear={shear_deg:.3f}")
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return {
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"session_id": session_id,
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"rotation_degrees": round(rotation, 3),
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"shear_degrees": round(shear_deg, 3),
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"method_used": "manual_combined",
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"dewarped_image_url": f"/api/v1/ocr-pipeline/sessions/{session_id}/image/dewarped",
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}
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@router.post("/sessions/{session_id}/ground-truth/dewarp")
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async def save_dewarp_ground_truth(session_id: str, req: DewarpGroundTruthRequest):
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"""Save ground truth feedback for the dewarp 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_shear": req.corrected_shear,
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"notes": req.notes,
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"saved_at": datetime.utcnow().isoformat(),
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"dewarp_result": session.get("dewarp_result"),
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}
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ground_truth["dewarp"] = 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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logger.info(f"OCR Pipeline: ground truth dewarp session {session_id}: "
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f"correct={req.is_correct}, corrected_shear={req.corrected_shear}")
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return {"session_id": session_id, "ground_truth": gt}
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