refactor: Crop nach Deskew/Dewarp verschieben + content-basierter Buchscan-Crop

Pipeline-Reihenfolge neu: Orientierung → Begradigung → Entzerrung → Zuschneiden → Spalten...
Crop arbeitet jetzt auf dem bereits geraden Bild, was bessere Ergebnisse liefert.

page_crop.py komplett ersetzt: Adaptive Threshold + 4-Kanten-Erkennung
(Buchruecken-Schatten links, Ink-Projektion fuer alle Raender) statt
Otsu + groesste Kontur.

Backend: Step-Nummern, Input-Bilder, Reprocess-Kaskade angepasst.
Frontend: PIPELINE_STEPS umgeordnet, Switch-Cases, Vorher-Bilder aktualisiert.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

admin-lehrer/app/(admin)/ai/ocr-pipeline/page.tsx

@@ -71,8 +71,8 @@ export default function OcrPipelinePage() {
 
       // Determine which step to jump to based on current_step
       const dbStep = data.current_step || 1
-      // Steps: 1=deskew, 2=dewarp, 3=columns, ...
-      // UI steps are 0-indexed: 0=deskew, 1=dewarp, 2=columns, ...
+      // DB steps: 1=start, 2=orientation, 3=deskew, 4=dewarp, 5=crop, 6=columns, ...
+      // UI steps are 0-indexed: 0=orientation, 1=deskew, 2=dewarp, 3=crop, 4=columns, ...
       const uiStep = Math.max(0, dbStep - 1)
       const skipSteps = savedDocType?.skip_steps || []
 
@@ -205,8 +205,8 @@ export default function OcrPipelinePage() {
     handleNext()
   }
 
-  const handleDewarpNext = async () => {
-    // Auto-detect document type after dewarp, then advance
+  const handleCropNext = async () => {
+    // Auto-detect document type after crop (last image-processing step), then advance
     if (sessionId) {
       try {
         const res = await fetch(
@@ -273,9 +273,9 @@ export default function OcrPipelinePage() {
 
   const stepNames: Record<number, string> = {
     1: 'Orientierung',
-    2: 'Zuschneiden',
-    3: 'Begradigung',
-    4: 'Entzerrung',
+    2: 'Begradigung',
+    3: 'Entzerrung',
+    4: 'Zuschneiden',
     5: 'Spalten',
     6: 'Zeilen',
     7: 'Woerter',
@@ -312,11 +312,11 @@ export default function OcrPipelinePage() {
       case 0:
         return <StepOrientation sessionId={sessionId} onNext={handleOrientationComplete} />
       case 1:
-        return <StepCrop sessionId={sessionId} onNext={handleNext} />
-      case 2:
         return <StepDeskew sessionId={sessionId} onNext={handleNext} />
+      case 2:
+        return <StepDewarp sessionId={sessionId} onNext={handleNext} />
       case 3:
-        return <StepDewarp sessionId={sessionId} onNext={handleDewarpNext} />
+        return <StepCrop sessionId={sessionId} onNext={handleCropNext} />
       case 4:
         return <StepColumnDetection sessionId={sessionId} onNext={handleNext} />
       case 5:

admin-lehrer/app/(admin)/ai/ocr-pipeline/types.ts

@@ -310,9 +310,9 @@ export const IMAGE_STYLES: { value: ImageStyle; label: string }[] = [
 
 export const PIPELINE_STEPS: PipelineStep[] = [
   { id: 'orientation', name: 'Orientierung', icon: '🔄', status: 'pending' },
-  { id: 'crop', name: 'Zuschneiden', icon: '✂️', status: 'pending' },
   { id: 'deskew', name: 'Begradigung', icon: '📐', status: 'pending' },
   { id: 'dewarp', name: 'Entzerrung', icon: '🔧', status: 'pending' },
+  { id: 'crop', name: 'Zuschneiden', icon: '✂️', status: 'pending' },
   { id: 'columns', name: 'Spalten', icon: '📊', status: 'pending' },
   { id: 'rows', name: 'Zeilen', icon: '📏', status: 'pending' },
   { id: 'words', name: 'Woerter', icon: '🔤', status: 'pending' },

admin-lehrer/components/ocr-pipeline/StepCrop.tsx

@@ -78,7 +78,7 @@ export function StepCrop({ sessionId, onNext }: StepCropProps) {
     return <div className="text-sm text-gray-400">Keine Session ausgewaehlt.</div>
   }
 
-  const orientedUrl = `${KLAUSUR_API}/api/v1/ocr-pipeline/sessions/${sessionId}/image/oriented`
+  const dewarpedUrl = `${KLAUSUR_API}/api/v1/ocr-pipeline/sessions/${sessionId}/image/dewarped`
   const croppedUrl = cropResult
     ? `${KLAUSUR_API}/api/v1/ocr-pipeline/sessions/${sessionId}/image/cropped`
     : null
@@ -95,12 +95,12 @@ export function StepCrop({ sessionId, onNext }: StepCropProps) {
 
       {/* Image comparison */}
       <ImageCompareView
-        originalUrl={orientedUrl}
+        originalUrl={dewarpedUrl}
         deskewedUrl={croppedUrl}
         showGrid={false}
         showBinarized={false}
         binarizedUrl={null}
-        leftLabel="Orientiert"
+        leftLabel="Entzerrt"
         rightLabel="Zugeschnitten"
       />
 

admin-lehrer/components/ocr-pipeline/StepDeskew.tsx

@@ -37,8 +37,8 @@ export function StepDeskew({ sessionId, onNext }: StepDeskewProps) {
           filename: data.filename,
           image_width: data.image_width,
           image_height: data.image_height,
-          // Use cropped image as "before" view
-          original_image_url: `${KLAUSUR_API}/api/v1/ocr-pipeline/sessions/${sessionId}/image/cropped`,
+          // Use oriented image as "before" view (deskew runs right after orientation)
+          original_image_url: `${KLAUSUR_API}/api/v1/ocr-pipeline/sessions/${sessionId}/image/oriented`,
         }
         setSession(sessionInfo)
 
@@ -155,7 +155,7 @@ export function StepDeskew({ sessionId, onNext }: StepDeskewProps) {
           showGrid={showGrid}
           showBinarized={showBinarized}
           binarizedUrl={deskewResult?.binarized_image_url ?? null}
-          leftLabel="Zugeschnitten"
+          leftLabel="Orientiert"
           rightLabel="Begradigt"
         />
       )}

klausur-service/backend/ocr_pipeline_api.py

@@ -3,9 +3,9 @@ OCR Pipeline API - Schrittweise Seitenrekonstruktion.
 
 Zerlegt den OCR-Prozess in 10 einzelne Schritte:
 1. Orientierung - 90/180/270° Drehungen korrigieren (orientation_crop_api.py)
-2. Zuschneiden - Scannerraender entfernen (orientation_crop_api.py)
-3. Deskewing - Scan begradigen
-4. Dewarping - Buchwoelbung entzerren
+2. Begradigung (Deskew) - Scan begradigen
+3. Entzerrung (Dewarp) - Buchwoelbung entzerren
+4. Zuschneiden - Scannerraender/Buchruecken entfernen (orientation_crop_api.py)
 5. Spaltenerkennung - Unsichtbare Spalten finden
 6. Zeilenerkennung - Horizontale Zeilen + Kopf-/Fusszeilen
 7. Worterkennung - OCR mit Bounding Boxes
@@ -483,8 +483,8 @@ async def auto_deskew(session_id: str):
         await _load_session_to_cache(session_id)
     cached = _get_cached(session_id)
 
-    # Use cropped image as input (from step 2), fall back to oriented, then original
-    img_bgr = next((v for k in ("cropped_bgr", "oriented_bgr", "original_bgr")
+    # Deskew runs right after orientation — use oriented image, fall back to original
+    img_bgr = next((v for k in ("oriented_bgr", "original_bgr")
                      if (v := cached.get(k)) is not None), None)
     if img_bgr is None:
         raise HTTPException(status_code=400, detail="No image available for deskewing")
@@ -554,7 +554,7 @@ async def auto_deskew(session_id: str):
     db_update = {
         "deskewed_png": deskewed_png,
         "deskew_result": deskew_result,
-        "current_step": 4,
+        "current_step": 3,
     }
     if binarized_png:
         db_update["binarized_png"] = binarized_png
@@ -585,12 +585,12 @@ async def auto_deskew(session_id: str):
 
 @router.post("/sessions/{session_id}/deskew/manual")
 async def manual_deskew(session_id: str, req: ManualDeskewRequest):
-    """Apply a manual rotation angle to the cropped image."""
+    """Apply a manual rotation angle to the oriented image."""
     if session_id not in _cache:
         await _load_session_to_cache(session_id)
     cached = _get_cached(session_id)
 
-    img_bgr = next((v for k in ("cropped_bgr", "oriented_bgr", "original_bgr")
+    img_bgr = next((v for k in ("oriented_bgr", "original_bgr")
                      if (v := cached.get(k)) is not None), None)
     if img_bgr is None:
         raise HTTPException(status_code=400, detail="No image available for deskewing")
@@ -801,7 +801,7 @@ async def auto_dewarp(
         dewarped_png=dewarped_png,
         dewarp_result=dewarp_result,
         auto_shear_degrees=dewarp_info.get("shear_degrees", 0.0),
-        current_step=5,
+        current_step=4,
     )
 
     logger.info(f"OCR Pipeline: dewarp session {session_id}: "
@@ -993,20 +993,21 @@ async def save_dewarp_ground_truth(session_id: str, req: DewarpGroundTruthReques
 async def detect_type(session_id: str):
     """Detect document type (vocab_table, full_text, generic_table).
 
-    Should be called after dewarp (clean image available).
+    Should be called after crop (clean image available).
+    Falls back to dewarped if crop was skipped.
     Stores result in session for frontend to decide pipeline flow.
     """
     if session_id not in _cache:
         await _load_session_to_cache(session_id)
     cached = _get_cached(session_id)
 
-    dewarped_bgr = cached.get("dewarped_bgr")
-    if dewarped_bgr is None:
-        raise HTTPException(status_code=400, detail="Dewarp must be completed first")
+    img_bgr = cached.get("cropped_bgr") or cached.get("dewarped_bgr")
+    if img_bgr is None:
+        raise HTTPException(status_code=400, detail="Crop or dewarp must be completed first")
 
     t0 = time.time()
-    ocr_img = create_ocr_image(dewarped_bgr)
-    result = detect_document_type(ocr_img, dewarped_bgr)
+    ocr_img = create_ocr_image(img_bgr)
+    result = detect_document_type(ocr_img, img_bgr)
     duration = time.time() - t0
 
     result_dict = {
@@ -1046,27 +1047,27 @@ async def detect_type(session_id: str):
 
 @router.post("/sessions/{session_id}/columns")
 async def detect_columns(session_id: str):
-    """Run column detection on the dewarped image."""
+    """Run column detection on the cropped (or dewarped) image."""
     if session_id not in _cache:
         await _load_session_to_cache(session_id)
     cached = _get_cached(session_id)
 
-    dewarped_bgr = cached.get("dewarped_bgr")
-    if dewarped_bgr is None:
-        raise HTTPException(status_code=400, detail="Dewarp must be completed before column detection")
+    img_bgr = cached.get("cropped_bgr") or cached.get("dewarped_bgr")
+    if img_bgr is None:
+        raise HTTPException(status_code=400, detail="Crop or dewarp must be completed before column detection")
 
     t0 = time.time()
 
     # Binarized image for layout analysis
-    ocr_img = create_ocr_image(dewarped_bgr)
+    ocr_img = create_ocr_image(img_bgr)
     h, w = ocr_img.shape[:2]
 
     # Phase A: Geometry detection (returns word_dicts + inv for reuse)
-    geo_result = detect_column_geometry(ocr_img, dewarped_bgr)
+    geo_result = detect_column_geometry(ocr_img, img_bgr)
 
     if geo_result is None:
         # Fallback to projection-based layout
-        layout_img = create_layout_image(dewarped_bgr)
+        layout_img = create_layout_image(img_bgr)
         regions = analyze_layout(layout_img, ocr_img)
     else:
         geometries, left_x, right_x, top_y, bottom_y, word_dicts, inv = geo_result
@@ -1113,7 +1114,7 @@ async def detect_columns(session_id: str):
         column_result=column_result,
         row_result=None,
         word_result=None,
-        current_step=5,
+        current_step=6,
     )
 
     # Update cache
@@ -1125,7 +1126,7 @@ async def detect_columns(session_id: str):
     logger.info(f"OCR Pipeline: columns session {session_id}: "
                 f"{col_count} columns detected ({duration:.2f}s)")
 
-    img_w = dewarped_bgr.shape[1]
+    img_w = img_bgr.shape[1]
     await _append_pipeline_log(session_id, "columns", {
         "total_columns": len(columns),
         "column_widths_pct": [round(c["width"] / img_w * 100, 1) for c in columns],
@@ -1276,14 +1277,14 @@ async def _get_columns_overlay(session_id: str) -> Response:
 
 @router.post("/sessions/{session_id}/rows")
 async def detect_rows(session_id: str):
-    """Run row detection on the dewarped image using horizontal gap analysis."""
+    """Run row detection on the cropped (or dewarped) image using horizontal gap analysis."""
     if session_id not in _cache:
         await _load_session_to_cache(session_id)
     cached = _get_cached(session_id)
 
-    dewarped_bgr = cached.get("dewarped_bgr")
+    dewarped_bgr = cached.get("cropped_bgr") or cached.get("dewarped_bgr")
     if dewarped_bgr is None:
-        raise HTTPException(status_code=400, detail="Dewarp must be completed before row detection")
+        raise HTTPException(status_code=400, detail="Crop or dewarp must be completed before row detection")
 
     t0 = time.time()
 
@@ -1339,7 +1340,7 @@ async def detect_rows(session_id: str):
         session_id,
         row_result=row_result,
         word_result=None,
-        current_step=6,
+        current_step=7,
     )
 
     cached["row_result"] = row_result
@@ -1453,11 +1454,11 @@ async def detect_words(
         await _load_session_to_cache(session_id)
     cached = _get_cached(session_id)
 
-    dewarped_bgr = cached.get("dewarped_bgr")
+    dewarped_bgr = cached.get("cropped_bgr") or cached.get("dewarped_bgr")
     if dewarped_bgr is None:
-        logger.warning("detect_words: dewarped_bgr is None for session %s (cache keys: %s)",
+        logger.warning("detect_words: no cropped/dewarped image for session %s (cache keys: %s)",
                        session_id, [k for k in cached.keys() if k.endswith('_bgr')])
-        raise HTTPException(status_code=400, detail="Dewarp must be completed before word detection")
+        raise HTTPException(status_code=400, detail="Crop or dewarp must be completed before word detection")
 
     session = await get_session_db(session_id)
     if not session:
@@ -1605,7 +1606,7 @@ async def detect_words(
     await update_session_db(
         session_id,
         word_result=word_result,
-        current_step=7,
+        current_step=8,
     )
 
     cached["word_result"] = word_result
@@ -1749,7 +1750,7 @@ async def _word_batch_stream_generator(
         word_result["summary"]["with_german"] = sum(1 for e in entries if e.get("german"))
         vocab_entries = entries
 
-    await update_session_db(session_id, word_result=word_result, current_step=7)
+    await update_session_db(session_id, word_result=word_result, current_step=8)
     cached["word_result"] = word_result
 
     logger.info(f"OCR Pipeline SSE batch: words session {session_id}: "
@@ -1896,7 +1897,7 @@ async def _word_stream_generator(
     await update_session_db(
         session_id,
         word_result=word_result,
-        current_step=7,
+        current_step=8,
     )
     cached["word_result"] = word_result
 
@@ -2020,7 +2021,7 @@ async def run_llm_review(session_id: str, request: Request, stream: bool = False
         "duration_ms": result["duration_ms"],
         "entries_corrected": result["entries_corrected"],
     }
-    await update_session_db(session_id, word_result=word_result, current_step=8)
+    await update_session_db(session_id, word_result=word_result, current_step=9)
 
     if session_id in _cache:
         _cache[session_id]["word_result"] = word_result
@@ -2069,7 +2070,7 @@ async def _llm_review_stream_generator(
                     "duration_ms": event["duration_ms"],
                     "entries_corrected": event["entries_corrected"],
                 }
-                await update_session_db(session_id, word_result=word_result, current_step=8)
+                await update_session_db(session_id, word_result=word_result, current_step=9)
                 if session_id in _cache:
                     _cache[session_id]["word_result"] = word_result
 
@@ -2157,7 +2158,7 @@ async def save_reconstruction(session_id: str, request: Request):
     cell_updates = body.get("cells", [])
 
     if not cell_updates:
-        await update_session_db(session_id, current_step=9)
+        await update_session_db(session_id, current_step=10)
         return {"session_id": session_id, "updated": 0}
 
     # Build update map: cell_id -> new text
@@ -2193,7 +2194,7 @@ async def save_reconstruction(session_id: str, request: Request):
         if "entries" in word_result:
             word_result["entries"] = entries
 
-    await update_session_db(session_id, word_result=word_result, current_step=9)
+    await update_session_db(session_id, word_result=word_result, current_step=10)
 
     if session_id in _cache:
         _cache[session_id]["word_result"] = word_result
@@ -2589,7 +2590,7 @@ async def save_validation(session_id: str, req: ValidationRequest):
     validation["score"] = req.score
     ground_truth["validation"] = validation
 
-    await update_session_db(session_id, ground_truth=ground_truth, current_step=10)
+    await update_session_db(session_id, ground_truth=ground_truth, current_step=11)
 
     if session_id in _cache:
         _cache[session_id]["ground_truth"] = ground_truth
@@ -2622,11 +2623,14 @@ async def reprocess_session(session_id: str, request: Request):
 
     Body: {"from_step": 5}  (1-indexed step number)
 
+    Pipeline order: Orientation(1) → Deskew(2) → Dewarp(3) → Crop(4) → Columns(5) →
+                    Rows(6) → Words(7) → LLM-Review(8) → Reconstruction(9) → Validation(10)
+
     Clears downstream results:
-    - from_step <= 1: orientation_result, crop_result, deskew_result, dewarp_result, column_result, row_result, word_result
-    - from_step <= 2: crop_result, deskew_result, dewarp_result, column_result, row_result, word_result
-    - from_step <= 3: deskew_result, dewarp_result, column_result, row_result, word_result
-    - from_step <= 4: dewarp_result, column_result, row_result, word_result
+    - from_step <= 1: orientation_result + all downstream
+    - from_step <= 2: deskew_result + all downstream
+    - from_step <= 3: dewarp_result + all downstream
+    - from_step <= 4: crop_result + all downstream
     - from_step <= 5: column_result, row_result, word_result
     - from_step <= 6: row_result, word_result
     - from_step <= 7: word_result (cells, vocab_entries)
@@ -2638,15 +2642,17 @@ async def reprocess_session(session_id: str, request: Request):
 
     body = await request.json()
     from_step = body.get("from_step", 1)
-    if not isinstance(from_step, int) or from_step < 1 or from_step > 9:
-        raise HTTPException(status_code=400, detail="from_step must be between 1 and 9")
+    if not isinstance(from_step, int) or from_step < 1 or from_step > 10:
+        raise HTTPException(status_code=400, detail="from_step must be between 1 and 10")
 
     update_kwargs: Dict[str, Any] = {"current_step": from_step}
 
     # Clear downstream data based on from_step
-    if from_step <= 7:
+    # New pipeline order: Orient(2) → Deskew(3) → Dewarp(4) → Crop(5) →
+    #   Columns(6) → Rows(7) → Words(8) → LLM(9) → Recon(10) → GT(11)
+    if from_step <= 8:
         update_kwargs["word_result"] = None
-    elif from_step == 8:
+    elif from_step == 9:
         # Only clear LLM review from word_result
         word_result = session.get("word_result")
         if word_result:
@@ -2654,16 +2660,16 @@ async def reprocess_session(session_id: str, request: Request):
             word_result.pop("llm_corrections", None)
             update_kwargs["word_result"] = word_result
 
-    if from_step <= 6:
+    if from_step <= 7:
         update_kwargs["row_result"] = None
-    if from_step <= 5:
+    if from_step <= 6:
         update_kwargs["column_result"] = None
     if from_step <= 4:
-        update_kwargs["dewarp_result"] = None
-    if from_step <= 3:
-        update_kwargs["deskew_result"] = None
-    if from_step <= 2:
         update_kwargs["crop_result"] = None
+    if from_step <= 3:
+        update_kwargs["dewarp_result"] = None
+    if from_step <= 2:
+        update_kwargs["deskew_result"] = None
     if from_step <= 1:
         update_kwargs["orientation_result"] = None
 
@@ -3084,7 +3090,7 @@ async def run_auto(session_id: str, req: RunAutoRequest, request: Request):
                     deskewed_png=deskewed_png,
                     deskew_result=deskew_result,
                     auto_rotation_degrees=float(angle_applied),
-                    current_step=4,
+                    current_step=3,
                 )
                 session = await get_session_db(session_id)
 
@@ -3147,7 +3153,7 @@ async def run_auto(session_id: str, req: RunAutoRequest, request: Request):
                     dewarped_png=dewarped_png,
                     dewarp_result=dewarp_result,
                     auto_shear_degrees=dewarp_info.get("shear_degrees", 0.0),
-                    current_step=5,
+                    current_step=4,
                 )
                 session = await get_session_db(session_id)
 
@@ -3170,16 +3176,16 @@ async def run_auto(session_id: str, req: RunAutoRequest, request: Request):
             yield await _auto_sse_event("columns", "start", {})
             try:
                 t0 = time.time()
-                dewarped_bgr = cached.get("dewarped_bgr")
-                if dewarped_bgr is None:
-                    raise ValueError("Dewarped image not available")
+                col_img = cached.get("cropped_bgr") or cached.get("dewarped_bgr")
+                if col_img is None:
+                    raise ValueError("Cropped/dewarped image not available")
 
-                ocr_img = create_ocr_image(dewarped_bgr)
+                ocr_img = create_ocr_image(col_img)
                 h, w = ocr_img.shape[:2]
 
-                geo_result = detect_column_geometry(ocr_img, dewarped_bgr)
+                geo_result = detect_column_geometry(ocr_img, col_img)
                 if geo_result is None:
-                    layout_img = create_layout_image(dewarped_bgr)
+                    layout_img = create_layout_image(col_img)
                     regions = analyze_layout(layout_img, ocr_img)
                     cached["_word_dicts"] = None
                     cached["_inv"] = None
@@ -3231,7 +3237,7 @@ async def run_auto(session_id: str, req: RunAutoRequest, request: Request):
             yield await _auto_sse_event("rows", "start", {})
             try:
                 t0 = time.time()
-                dewarped_bgr = cached.get("dewarped_bgr")
+                row_img = cached.get("cropped_bgr") or cached.get("dewarped_bgr")
                 session = await get_session_db(session_id)
                 column_result = session.get("column_result") or cached.get("column_result")
                 if not column_result or not column_result.get("columns"):
@@ -3252,8 +3258,8 @@ async def run_auto(session_id: str, req: RunAutoRequest, request: Request):
                 content_bounds = cached.get("_content_bounds")
 
                 if word_dicts is None or inv is None or content_bounds is None:
-                    ocr_img_tmp = create_ocr_image(dewarped_bgr)
-                    geo_result = detect_column_geometry(ocr_img_tmp, dewarped_bgr)
+                    ocr_img_tmp = create_ocr_image(row_img)
+                    geo_result = detect_column_geometry(ocr_img_tmp, row_img)
                     if geo_result is None:
                         raise ValueError("Column geometry detection failed — cannot detect rows")
                     _g, lx, rx, ty, by, word_dicts, inv = geo_result
@@ -3309,7 +3315,7 @@ async def run_auto(session_id: str, req: RunAutoRequest, request: Request):
             yield await _auto_sse_event("words", "start", {"engine": req.ocr_engine})
             try:
                 t0 = time.time()
-                dewarped_bgr = cached.get("dewarped_bgr")
+                word_img = cached.get("cropped_bgr") or cached.get("dewarped_bgr")
                 session = await get_session_db(session_id)
 
                 column_result = session.get("column_result") or cached.get("column_result")
@@ -3348,12 +3354,12 @@ async def run_auto(session_id: str, req: RunAutoRequest, request: Request):
                         ]
                         row.word_count = len(row.words)
 
-                ocr_img = create_ocr_image(dewarped_bgr)
-                img_h, img_w = dewarped_bgr.shape[:2]
+                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=dewarped_bgr,
+                    ocr_engine=req.ocr_engine, img_bgr=word_img,
                 )
                 duration = time.time() - t0
 

klausur-service/backend/orientation_crop_api.py

@@ -1,8 +1,8 @@
 """
-Orientation & Crop API - Steps 1-2 of the OCR Pipeline.
+Orientation & Crop API - Steps 1 and 4 of the OCR Pipeline.
 
 Step 1: Orientation detection (fix 90/180/270 degree rotations)
-Step 2: Page cropping (remove scanner borders, detect paper format)
+Step 4 (UI index 3): Page cropping (after deskew + dewarp, so the image is straight)
 
 These endpoints were extracted from the main pipeline to keep files manageable.
 """
@@ -161,21 +161,24 @@ async def detect_orientation(session_id: str):
 
 
 # ---------------------------------------------------------------------------
-# Step 2: Crop
+# Step 4 (UI index 3): Crop — runs after deskew + dewarp
 # ---------------------------------------------------------------------------
 
 @router.post("/sessions/{session_id}/crop")
 async def auto_crop(session_id: str):
-    """Auto-detect and crop scanner borders.
+    """Auto-detect and crop scanner/book borders.
 
-    Reads the oriented image (or original if no orientation step),
-    detects the page boundary and crops.
+    Reads the dewarped image (post-deskew + dewarp, so the page is straight).
+    Falls back to oriented → original if earlier steps were skipped.
     """
     cached = await _ensure_cached(session_id)
 
-    # Use oriented image if available, else original
-    oriented = cached.get("oriented_bgr")
-    img_bgr = oriented if oriented is not None else cached.get("original_bgr")
+    # Use dewarped (preferred), fall back to oriented, then original
+    img_bgr = next(
+        (v for k in ("dewarped_bgr", "oriented_bgr", "original_bgr")
+         if (v := cached.get(k)) is not None),
+        None,
+    )
     if img_bgr is None:
         raise HTTPException(status_code=400, detail="No image available for cropping")
 
@@ -199,7 +202,7 @@ async def auto_crop(session_id: str):
         session_id,
         cropped_png=cropped_png,
         crop_result=crop_info,
-        current_step=3,
+        current_step=5,
     )
 
     logger.info(
@@ -237,8 +240,11 @@ async def manual_crop(session_id: str, req: ManualCropRequest):
     """Manually crop using percentage coordinates."""
     cached = await _ensure_cached(session_id)
 
-    oriented = cached.get("oriented_bgr")
-    img_bgr = oriented if oriented is not None else cached.get("original_bgr")
+    img_bgr = next(
+        (v for k in ("dewarped_bgr", "oriented_bgr", "original_bgr")
+         if (v := cached.get(k)) is not None),
+        None,
+    )
     if img_bgr is None:
         raise HTTPException(status_code=400, detail="No image available for cropping")
 
@@ -278,7 +284,7 @@ async def manual_crop(session_id: str, req: ManualCropRequest):
         session_id,
         cropped_png=cropped_png,
         crop_result=crop_result,
-        current_step=3,
+        current_step=5,
     )
 
     ch, cw = cropped_bgr.shape[:2]
@@ -293,17 +299,20 @@ async def manual_crop(session_id: str, req: ManualCropRequest):
 
 @router.post("/sessions/{session_id}/crop/skip")
 async def skip_crop(session_id: str):
-    """Skip cropping — use oriented (or original) image as-is."""
+    """Skip cropping — use dewarped (or oriented/original) image as-is."""
     cached = await _ensure_cached(session_id)
 
-    oriented = cached.get("oriented_bgr")
-    img_bgr = oriented if oriented is not None else cached.get("original_bgr")
+    img_bgr = next(
+        (v for k in ("dewarped_bgr", "oriented_bgr", "original_bgr")
+         if (v := cached.get(k)) is not None),
+        None,
+    )
     if img_bgr is None:
         raise HTTPException(status_code=400, detail="No image available")
 
     h, w = img_bgr.shape[:2]
 
-    # Store the oriented image as cropped (identity crop)
+    # Store the dewarped image as cropped (identity crop)
     success, png_buf = cv2.imencode(".png", img_bgr)
     cropped_png = png_buf.tobytes() if success else b""
 
@@ -321,7 +330,7 @@ async def skip_crop(session_id: str):
         session_id,
         cropped_png=cropped_png,
         crop_result=crop_result,
-        current_step=3,
+        current_step=5,
     )
 
     return {

klausur-service/backend/page_crop.py

@@ -1,14 +1,15 @@
 """
-Page Crop - Automatic scanner border removal and page format detection.
+Page Crop - Content-based crop for scanned pages and book scans.
 
-Detects the paper boundary in a scanned image and crops away scanner borders.
-Also identifies the paper format (A4, Letter, etc.) from the aspect ratio.
+Detects the content boundary by analysing ink density projections and
+(for book scans) the spine shadow gradient.  Works with both loose A4
+sheets on dark scanners AND book scans with white backgrounds.
 
 License: Apache 2.0
 """
 
 import logging
-from typing import Dict, Any, Tuple
+from typing import Dict, Any, Tuple, Optional
 
 import cv2
 import numpy as np
@@ -24,25 +25,30 @@ PAPER_FORMATS = {
     "A3": 420.0 / 297.0,       # 1.4141
 }
 
+# Minimum ink density (fraction of pixels) to count a row/column as "content"
+_INK_THRESHOLD = 0.003  # 0.3%
+
+# Minimum run length (fraction of dimension) to keep — shorter runs are noise
+_MIN_RUN_FRAC = 0.005  # 0.5%
+
 
 def detect_and_crop_page(
     img_bgr: np.ndarray,
-    min_border_fraction: float = 0.01,
+    margin_frac: float = 0.01,
 ) -> Tuple[np.ndarray, Dict[str, Any]]:
-    """Detect page boundary and crop scanner borders.
+    """Detect content boundary and crop scanner/book borders.
 
-    Algorithm:
-    1. Grayscale + GaussianBlur to smooth out text
-    2. Otsu threshold (page=bright, scanner border=dark)
-    3. Morphological close to fill gaps
-    4. Find largest contour = page
-    5. If contour covers >95% of image area -> no crop needed
-    6. Get bounding rect, add safety margin
-    7. Match aspect ratio to known paper formats
+    Algorithm (4-edge detection):
+    1. Adaptive threshold → binary (text=255, bg=0)
+    2. Left edge: spine-shadow detection via grayscale column means,
+       fallback to binary vertical projection
+    3. Right edge: binary vertical projection (last ink column)
+    4. Top/bottom edges: binary horizontal projection
+    5. Sanity checks, then crop with configurable margin
 
     Args:
-        img_bgr: Input BGR image
-        min_border_fraction: Minimum border fraction to trigger crop (default 1%)
+        img_bgr: Input BGR image (should already be deskewed/dewarped)
+        margin_frac: Extra margin around content (fraction of dimension, default 1%)
 
     Returns:
         Tuple of (cropped_image, result_dict)
@@ -62,41 +68,28 @@ def detect_and_crop_page(
         "border_fractions": {"top": 0.0, "bottom": 0.0, "left": 0.0, "right": 0.0},
     }
 
-    # 1. Grayscale + blur
     gray = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)
-    blurred = cv2.GaussianBlur(gray, (21, 21), 0)
 
-    # 2. Otsu threshold
-    _, binary = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+    # --- Binarise with adaptive threshold (works for white-on-white) ---
+    binary = cv2.adaptiveThreshold(
+        gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+        cv2.THRESH_BINARY_INV, blockSize=51, C=15,
+    )
 
-    # 3. Morphological close to fill text gaps
-    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (50, 50))
-    closed = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)
+    # --- Left edge: spine-shadow detection ---
+    left_edge = _detect_left_edge_shadow(gray, binary, w, h)
 
-    # 4. Find contours
-    contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-    if not contours:
-        logger.info("No contours found - returning original image")
-        return img_bgr, result
+    # --- Right edge: binary vertical projection ---
+    right_edge = _detect_right_edge(binary, w, h)
 
-    # Get the largest contour
-    largest = max(contours, key=cv2.contourArea)
-    contour_area = cv2.contourArea(largest)
+    # --- Top / bottom edges: binary horizontal projection ---
+    top_edge, bottom_edge = _detect_top_bottom_edges(binary, w, h)
 
-    # 5. If contour covers >95% of image, no crop needed
-    if contour_area > 0.95 * total_area:
-        logger.info("Page covers >95%% of image - no crop needed")
-        result["detected_format"], result["format_confidence"] = _detect_format(w, h)
-        return img_bgr, result
-
-    # 6. Get bounding rect
-    rx, ry, rw, rh = cv2.boundingRect(largest)
-
-    # Calculate border fractions
-    border_top = ry / h
-    border_bottom = (h - (ry + rh)) / h
-    border_left = rx / w
-    border_right = (w - (rx + rw)) / w
+    # Compute border fractions
+    border_top = top_edge / h
+    border_bottom = (h - bottom_edge) / h
+    border_left = left_edge / w
+    border_right = (w - right_edge) / w
 
     result["border_fractions"] = {
         "top": round(border_top, 4),
@@ -105,35 +98,34 @@ def detect_and_crop_page(
         "right": round(border_right, 4),
     }
 
-    # 7. Check if borders are significant enough to crop
-    if all(f < min_border_fraction for f in [border_top, border_bottom, border_left, border_right]):
-        logger.info("All borders < %.1f%% - no crop needed", min_border_fraction * 100)
+    # Sanity: only crop if at least one edge has > 2% border
+    min_border = 0.02
+    if all(f < min_border for f in [border_top, border_bottom, border_left, border_right]):
+        logger.info("All borders < %.0f%% — no crop needed", min_border * 100)
         result["detected_format"], result["format_confidence"] = _detect_format(w, h)
         return img_bgr, result
 
-    # 8. Add safety margin (0.5% of image dimensions)
-    margin_x = int(w * 0.005)
-    margin_y = int(h * 0.005)
+    # Add margin
+    margin_x = int(w * margin_frac)
+    margin_y = int(h * margin_frac)
 
-    crop_x = max(0, rx - margin_x)
-    crop_y = max(0, ry - margin_y)
-    crop_x2 = min(w, rx + rw + margin_x)
-    crop_y2 = min(h, ry + rh + margin_y)
+    crop_x = max(0, left_edge - margin_x)
+    crop_y = max(0, top_edge - margin_y)
+    crop_x2 = min(w, right_edge + margin_x)
+    crop_y2 = min(h, bottom_edge + margin_y)
 
     crop_w = crop_x2 - crop_x
     crop_h = crop_y2 - crop_y
 
-    # Sanity check: cropped area should be at least 50% of original
-    if crop_w * crop_h < 0.5 * total_area:
-        logger.warning("Cropped area too small (%.0f%%) - skipping crop",
+    # Sanity: cropped area must be >= 40% of original
+    if crop_w * crop_h < 0.40 * total_area:
+        logger.warning("Cropped area too small (%.0f%%) — skipping crop",
                        100.0 * crop_w * crop_h / total_area)
         result["detected_format"], result["format_confidence"] = _detect_format(w, h)
         return img_bgr, result
 
-    # 9. Crop
     cropped = img_bgr[crop_y:crop_y2, crop_x:crop_x2].copy()
 
-    # 10. Detect format from cropped dimensions
     detected_format, format_confidence = _detect_format(crop_w, crop_h)
 
     result["crop_applied"] = True
@@ -149,23 +141,140 @@ def detect_and_crop_page(
     result["format_confidence"] = format_confidence
     result["aspect_ratio"] = round(max(crop_w, crop_h) / max(min(crop_w, crop_h), 1), 4)
 
-    logger.info("Page cropped: %dx%d -> %dx%d, format=%s (%.0f%%), borders: T=%.1f%% B=%.1f%% L=%.1f%% R=%.1f%%",
-                w, h, crop_w, crop_h, detected_format, format_confidence * 100,
-                border_top * 100, border_bottom * 100, border_left * 100, border_right * 100)
+    logger.info(
+        "Page cropped: %dx%d -> %dx%d, format=%s (%.0f%%), "
+        "borders: T=%.1f%% B=%.1f%% L=%.1f%% R=%.1f%%",
+        w, h, crop_w, crop_h, detected_format, format_confidence * 100,
+        border_top * 100, border_bottom * 100,
+        border_left * 100, border_right * 100,
+    )
 
     return cropped, result
 
 
-def _detect_format(width: int, height: int) -> Tuple[str, float]:
-    """Detect paper format from dimensions by comparing aspect ratios.
+# ---------------------------------------------------------------------------
+# Edge detection helpers
+# ---------------------------------------------------------------------------
 
-    Returns:
-        (format_name, confidence) where confidence is 0.0-1.0
+def _detect_left_edge_shadow(
+    gray: np.ndarray,
+    binary: np.ndarray,
+    w: int,
+    h: int,
+) -> int:
+    """Detect left content edge, accounting for book-spine shadow.
+
+    Strategy: look at the left 25% of the image.
+    1. Compute column-mean brightness in grayscale.
+    2. Smooth with a boxcar kernel.
+    3. Find the transition from shadow (dark) to page (bright).
+    4. Fallback: use binary vertical projection if no shadow detected.
     """
+    search_w = max(1, w // 4)
+
+    # Column-mean brightness in the left quarter
+    col_means = np.mean(gray[:, :search_w], axis=0).astype(np.float64)
+
+    # Smooth with boxcar kernel (width = 1% of image width, min 5)
+    kernel_size = max(5, w // 100)
+    if kernel_size % 2 == 0:
+        kernel_size += 1
+    kernel = np.ones(kernel_size) / kernel_size
+    smoothed = np.convolve(col_means, kernel, mode="same")
+
+    # Determine brightness threshold: midpoint between darkest and brightest
+    val_min = float(np.min(smoothed))
+    val_max = float(np.max(smoothed))
+    shadow_range = val_max - val_min
+
+    # Only use shadow detection if there is a meaningful brightness gradient (> 20 levels)
+    if shadow_range > 20:
+        threshold = val_min + shadow_range * 0.6
+        # Find first column where brightness exceeds threshold
+        above = np.where(smoothed >= threshold)[0]
+        if len(above) > 0:
+            shadow_edge = int(above[0])
+            logger.debug("Left edge: shadow detected at x=%d (range=%.0f)", shadow_edge, shadow_range)
+            return shadow_edge
+
+    # Fallback: binary vertical projection
+    return _detect_edge_projection(binary, axis=0, from_start=True, dim=w)
+
+
+def _detect_right_edge(binary: np.ndarray, w: int, h: int) -> int:
+    """Detect right content edge via binary vertical projection."""
+    return _detect_edge_projection(binary, axis=0, from_start=False, dim=w)
+
+
+def _detect_top_bottom_edges(binary: np.ndarray, w: int, h: int) -> Tuple[int, int]:
+    """Detect top and bottom content edges via binary horizontal projection."""
+    top = _detect_edge_projection(binary, axis=1, from_start=True, dim=h)
+    bottom = _detect_edge_projection(binary, axis=1, from_start=False, dim=h)
+    return top, bottom
+
+
+def _detect_edge_projection(
+    binary: np.ndarray,
+    axis: int,
+    from_start: bool,
+    dim: int,
+) -> int:
+    """Find the first/last row or column with ink density above threshold.
+
+    axis=0 → project vertically (column densities) → returns x position
+    axis=1 → project horizontally (row densities) → returns y position
+
+    Filters out narrow noise runs shorter than _MIN_RUN_FRAC of the dimension.
+    """
+    # Compute density per row/column (mean of binary pixels / 255)
+    projection = np.mean(binary, axis=axis) / 255.0
+
+    # Create mask of "ink" positions
+    ink_mask = projection >= _INK_THRESHOLD
+
+    # Filter narrow runs (noise)
+    min_run = max(1, int(dim * _MIN_RUN_FRAC))
+    ink_mask = _filter_narrow_runs(ink_mask, min_run)
+
+    ink_positions = np.where(ink_mask)[0]
+    if len(ink_positions) == 0:
+        return 0 if from_start else dim
+
+    if from_start:
+        return int(ink_positions[0])
+    else:
+        return int(ink_positions[-1])
+
+
+def _filter_narrow_runs(mask: np.ndarray, min_run: int) -> np.ndarray:
+    """Remove True-runs shorter than min_run pixels."""
+    if min_run <= 1:
+        return mask
+
+    result = mask.copy()
+    n = len(result)
+    i = 0
+    while i < n:
+        if result[i]:
+            start = i
+            while i < n and result[i]:
+                i += 1
+            if i - start < min_run:
+                result[start:i] = False
+        else:
+            i += 1
+    return result
+
+
+# ---------------------------------------------------------------------------
+# Format detection (kept as optional metadata)
+# ---------------------------------------------------------------------------
+
+def _detect_format(width: int, height: int) -> Tuple[str, float]:
+    """Detect paper format from dimensions by comparing aspect ratios."""
     if width <= 0 or height <= 0:
         return "unknown", 0.0
 
-    # Use portrait aspect ratio (taller / shorter)
     aspect = max(width, height) / min(width, height)
 
     best_format = "unknown"
@@ -177,8 +286,6 @@ def _detect_format(width: int, height: int) -> Tuple[str, float]:
             best_diff = diff
             best_format = fmt
 
-    # Confidence: 1.0 if exact match, decreasing with deviation
-    # Threshold: if diff > 0.1, confidence drops below 0.5
     confidence = max(0.0, 1.0 - best_diff * 5.0)
 
     if confidence < 0.3: