[split-required] Split final 43 files (500-668 LOC) to complete refactoring
klausur-service (11 files): - cv_gutter_repair, ocr_pipeline_regression, upload_api - ocr_pipeline_sessions, smart_spell, nru_worksheet_generator - ocr_pipeline_overlays, mail/aggregator, zeugnis_api - cv_syllable_detect, self_rag backend-lehrer (17 files): - classroom_engine/suggestions, generators/quiz_generator - worksheets_api, llm_gateway/comparison, state_engine_api - classroom/models (→ 4 submodules), services/file_processor - alerts_agent/api/wizard+digests+routes, content_generators/pdf - classroom/routes/sessions, llm_gateway/inference - classroom_engine/analytics, auth/keycloak_auth - alerts_agent/processing/rule_engine, ai_processor/print_versions agent-core (5 files): - brain/memory_store, brain/knowledge_graph, brain/context_manager - orchestrator/supervisor, sessions/session_manager admin-lehrer (5 components): - GridOverlay, StepGridReview, DevOpsPipelineSidebar - DataFlowDiagram, sbom/wizard/page website (2 files): - DependencyMap, lehrer/abitur-archiv Other: nibis_ingestion, grid_detection_service, export-doclayout-onnx Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Benjamin Admin
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klausur-service/backend/ocr_pipeline_overlay_structure.py
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klausur-service/backend/ocr_pipeline_overlay_structure.py
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"""
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Overlay rendering for structure detection (boxes, zones, colors, graphics).
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Extracted from ocr_pipeline_overlays.py for modularity.
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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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from typing import Any, Dict, List
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import cv2
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import numpy as np
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from fastapi import HTTPException
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from fastapi.responses import Response
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from ocr_pipeline_common import _get_base_image_png
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from ocr_pipeline_session_store import get_session_db
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from cv_color_detect import _COLOR_HEX, _COLOR_RANGES
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from cv_box_detect import detect_boxes, split_page_into_zones
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logger = logging.getLogger(__name__)
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async def _get_structure_overlay(session_id: str) -> Response:
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"""Generate overlay image showing detected boxes, zones, and color regions."""
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base_png = await _get_base_image_png(session_id)
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if not base_png:
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raise HTTPException(status_code=404, detail="No base image available")
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arr = np.frombuffer(base_png, dtype=np.uint8)
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img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
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if img is None:
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raise HTTPException(status_code=500, detail="Failed to decode image")
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h, w = img.shape[:2]
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# Get structure result (run detection if not cached)
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session = await get_session_db(session_id)
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structure = (session or {}).get("structure_result")
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if not structure:
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# Run detection on-the-fly
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margin = int(min(w, h) * 0.03)
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content_x, content_y = margin, margin
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content_w_px = w - 2 * margin
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content_h_px = h - 2 * margin
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boxes = detect_boxes(img, content_x, content_w_px, content_y, content_h_px)
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zones = split_page_into_zones(content_x, content_y, content_w_px, content_h_px, boxes)
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structure = {
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"boxes": [
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{"x": b.x, "y": b.y, "w": b.width, "h": b.height,
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"confidence": b.confidence, "border_thickness": b.border_thickness}
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for b in boxes
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],
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"zones": [
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{"index": z.index, "zone_type": z.zone_type,
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"y": z.y, "h": z.height, "x": z.x, "w": z.width}
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for z in zones
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],
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}
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overlay = img.copy()
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# --- Draw zone boundaries ---
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zone_colors = {
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"content": (200, 200, 200), # light gray
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"box": (255, 180, 0), # blue-ish (BGR)
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}
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for zone in structure.get("zones", []):
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zx = zone["x"]
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zy = zone["y"]
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zw = zone["w"]
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zh = zone["h"]
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color = zone_colors.get(zone["zone_type"], (200, 200, 200))
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# Draw zone boundary as dashed line
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dash_len = 12
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for edge_x in range(zx, zx + zw, dash_len * 2):
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end_x = min(edge_x + dash_len, zx + zw)
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cv2.line(img, (edge_x, zy), (end_x, zy), color, 1)
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cv2.line(img, (edge_x, zy + zh), (end_x, zy + zh), color, 1)
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# Zone label
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zone_label = f"Zone {zone['index']} ({zone['zone_type']})"
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cv2.putText(img, zone_label, (zx + 5, zy + 15),
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cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 1)
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# --- Draw detected boxes ---
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# Color map for box backgrounds (BGR)
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bg_hex_to_bgr = {
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"#dc2626": (38, 38, 220), # red
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"#2563eb": (235, 99, 37), # blue
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"#16a34a": (74, 163, 22), # green
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"#ea580c": (12, 88, 234), # orange
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"#9333ea": (234, 51, 147), # purple
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"#ca8a04": (4, 138, 202), # yellow
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"#6b7280": (128, 114, 107), # gray
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}
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for box_data in structure.get("boxes", []):
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bx = box_data["x"]
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by = box_data["y"]
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bw = box_data["w"]
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bh = box_data["h"]
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conf = box_data.get("confidence", 0)
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thickness = box_data.get("border_thickness", 0)
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bg_hex = box_data.get("bg_color_hex", "#6b7280")
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bg_name = box_data.get("bg_color_name", "")
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# Box fill color
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fill_bgr = bg_hex_to_bgr.get(bg_hex, (128, 114, 107))
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# Semi-transparent fill
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cv2.rectangle(overlay, (bx, by), (bx + bw, by + bh), fill_bgr, -1)
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# Solid border
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border_color = fill_bgr
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cv2.rectangle(img, (bx, by), (bx + bw, by + bh), border_color, 3)
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# Label
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label = f"BOX"
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if bg_name and bg_name not in ("unknown", "white"):
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label += f" ({bg_name})"
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if thickness > 0:
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label += f" border={thickness}px"
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label += f" {int(conf * 100)}%"
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cv2.putText(img, label, (bx + 8, by + 22),
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cv2.FONT_HERSHEY_SIMPLEX, 0.55, (255, 255, 255), 2)
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cv2.putText(img, label, (bx + 8, by + 22),
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cv2.FONT_HERSHEY_SIMPLEX, 0.55, border_color, 1)
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# Blend overlay at 15% opacity
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cv2.addWeighted(overlay, 0.15, img, 0.85, 0, img)
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# --- Draw color regions (HSV masks) ---
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hsv = cv2.cvtColor(
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cv2.imdecode(np.frombuffer(base_png, dtype=np.uint8), cv2.IMREAD_COLOR),
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cv2.COLOR_BGR2HSV,
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)
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color_bgr_map = {
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"red": (0, 0, 255),
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"orange": (0, 140, 255),
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"yellow": (0, 200, 255),
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"green": (0, 200, 0),
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"blue": (255, 150, 0),
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"purple": (200, 0, 200),
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}
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for color_name, ranges in _COLOR_RANGES.items():
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mask = np.zeros((h, w), dtype=np.uint8)
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for lower, upper in ranges:
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mask = cv2.bitwise_or(mask, cv2.inRange(hsv, lower, upper))
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# Only draw if there are significant colored pixels
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if np.sum(mask > 0) < 100:
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continue
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# Draw colored contours
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contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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draw_color = color_bgr_map.get(color_name, (200, 200, 200))
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for cnt in contours:
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area = cv2.contourArea(cnt)
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if area < 20:
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continue
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cv2.drawContours(img, [cnt], -1, draw_color, 2)
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# --- Draw graphic elements ---
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graphics_data = structure.get("graphics", [])
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shape_icons = {
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"image": "IMAGE",
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"illustration": "ILLUST",
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}
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for gfx in graphics_data:
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gx, gy = gfx["x"], gfx["y"]
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gw, gh = gfx["w"], gfx["h"]
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shape = gfx.get("shape", "icon")
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color_hex = gfx.get("color_hex", "#6b7280")
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conf = gfx.get("confidence", 0)
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# Pick draw color based on element color (BGR)
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gfx_bgr = bg_hex_to_bgr.get(color_hex, (128, 114, 107))
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# Draw bounding box (dashed style via short segments)
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dash = 6
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for seg_x in range(gx, gx + gw, dash * 2):
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end_x = min(seg_x + dash, gx + gw)
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cv2.line(img, (seg_x, gy), (end_x, gy), gfx_bgr, 2)
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cv2.line(img, (seg_x, gy + gh), (end_x, gy + gh), gfx_bgr, 2)
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for seg_y in range(gy, gy + gh, dash * 2):
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end_y = min(seg_y + dash, gy + gh)
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cv2.line(img, (gx, seg_y), (gx, end_y), gfx_bgr, 2)
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cv2.line(img, (gx + gw, seg_y), (gx + gw, end_y), gfx_bgr, 2)
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# Label
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icon = shape_icons.get(shape, shape.upper()[:5])
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label = f"{icon} {int(conf * 100)}%"
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# White background for readability
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(tw, th), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.4, 1)
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lx = gx + 2
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ly = max(gy - 4, th + 4)
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cv2.rectangle(img, (lx - 1, ly - th - 2), (lx + tw + 2, ly + 3), (255, 255, 255), -1)
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cv2.putText(img, label, (lx, ly), cv2.FONT_HERSHEY_SIMPLEX, 0.4, gfx_bgr, 1)
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# Encode result
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_, png_buf = cv2.imencode(".png", img)
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return Response(content=png_buf.tobytes(), media_type="image/png")
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