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Only cluster left-edges of words that begin a new group within their row (first word or preceded by a large gap). This filters out mid-phrase word positions (IPA transcriptions, second words in multi-word entries) that were causing too many false columns. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
613 lines
21 KiB
Python
613 lines
21 KiB
Python
"""
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Grid Editor API — builds a structured, zone-aware grid from Kombi OCR results.
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Takes the merged word positions from paddle-kombi / rapid-kombi and:
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1. Detects bordered boxes on the image (cv_box_detect)
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2. Splits the page into zones (content + box regions)
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3. Clusters words into columns and rows per zone
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4. Returns a hierarchical StructuredGrid for the frontend Excel-like editor
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Lizenz: Apache 2.0 (kommerziell nutzbar)
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DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
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"""
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import logging
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import time
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from typing import Any, Dict, List, Optional
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import cv2
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import numpy as np
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from fastapi import APIRouter, HTTPException, Request
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from cv_box_detect import detect_boxes, split_page_into_zones
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from cv_words_first import _cluster_rows, _build_cells
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from ocr_pipeline_session_store import (
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get_session_db,
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get_session_image,
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update_session_db,
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)
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/api/v1/ocr-pipeline", tags=["grid-editor"])
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def _cluster_columns_by_alignment(
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words: List[Dict],
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zone_w: int,
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rows: List[Dict],
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) -> List[Dict[str, Any]]:
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"""Detect columns by clustering left-edge alignment across rows.
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Hybrid approach:
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1. Group words by row, find "group start" positions within each row
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(words preceded by a large gap or first word in row)
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2. Cluster group-start left-edges by X-proximity across rows
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3. Filter by row coverage (how many rows have a group start here)
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4. Merge nearby clusters
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5. Build column boundaries
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This filters out mid-phrase word positions (e.g. IPA transcriptions,
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second words in multi-word entries) by only considering positions
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where a new word group begins within a row.
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"""
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if not words or not rows:
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return []
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total_rows = len(rows)
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if total_rows == 0:
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return []
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# --- Group words by row ---
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row_words: Dict[int, List[Dict]] = {}
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for w in words:
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y_center = w["top"] + w["height"] / 2
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best = min(rows, key=lambda r: abs(r["y_center"] - y_center))
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row_words.setdefault(best["index"], []).append(w)
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# --- Compute adaptive gap threshold for group-start detection ---
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all_gaps: List[float] = []
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for ri, rw_list in row_words.items():
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sorted_rw = sorted(rw_list, key=lambda w: w["left"])
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for i in range(len(sorted_rw) - 1):
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right = sorted_rw[i]["left"] + sorted_rw[i]["width"]
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gap = sorted_rw[i + 1]["left"] - right
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if gap > 0:
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all_gaps.append(gap)
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if all_gaps:
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sorted_gaps = sorted(all_gaps)
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median_gap = sorted_gaps[len(sorted_gaps) // 2]
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heights = [w["height"] for w in words if w.get("height", 0) > 0]
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median_h = sorted(heights)[len(heights) // 2] if heights else 25
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# Column boundary: gap > 3× median gap or > 1.5× median word height
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gap_threshold = max(median_gap * 3, median_h * 1.5, 30)
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else:
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gap_threshold = 50
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# --- Find group-start positions (left-edges that begin a new column) ---
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start_positions: List[tuple] = [] # (left_edge, row_index)
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for ri, rw_list in row_words.items():
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sorted_rw = sorted(rw_list, key=lambda w: w["left"])
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# First word in row is always a group start
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start_positions.append((sorted_rw[0]["left"], ri))
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for i in range(1, len(sorted_rw)):
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right_prev = sorted_rw[i - 1]["left"] + sorted_rw[i - 1]["width"]
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gap = sorted_rw[i]["left"] - right_prev
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if gap >= gap_threshold:
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start_positions.append((sorted_rw[i]["left"], ri))
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start_positions.sort(key=lambda x: x[0])
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logger.info(
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"alignment columns: %d group-start positions from %d words "
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"(gap_threshold=%.0f, %d rows)",
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len(start_positions), len(words), gap_threshold, total_rows,
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)
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if not start_positions:
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x_min = min(w["left"] for w in words)
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x_max = max(w["left"] + w["width"] for w in words)
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return [{"index": 0, "type": "column_text", "x_min": x_min, "x_max": x_max}]
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# --- Cluster group-start positions by X-proximity ---
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tolerance = max(10, int(zone_w * 0.01))
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clusters: List[Dict[str, Any]] = []
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cur_edges = [start_positions[0][0]]
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cur_rows = {start_positions[0][1]}
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for left, row_idx in start_positions[1:]:
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if left - cur_edges[-1] <= tolerance:
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cur_edges.append(left)
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cur_rows.add(row_idx)
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else:
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clusters.append({
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"mean_x": int(sum(cur_edges) / len(cur_edges)),
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"min_edge": min(cur_edges),
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"max_edge": max(cur_edges),
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"count": len(cur_edges),
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"distinct_rows": len(cur_rows),
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"row_coverage": len(cur_rows) / total_rows,
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})
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cur_edges = [left]
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cur_rows = {row_idx}
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clusters.append({
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"mean_x": int(sum(cur_edges) / len(cur_edges)),
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"min_edge": min(cur_edges),
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"max_edge": max(cur_edges),
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"count": len(cur_edges),
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"distinct_rows": len(cur_rows),
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"row_coverage": len(cur_rows) / total_rows,
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})
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# --- Filter by row coverage ---
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MIN_COVERAGE_PRIMARY = 0.20
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MIN_COVERAGE_SECONDARY = 0.12
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MIN_WORDS_SECONDARY = 3
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MIN_DISTINCT_ROWS = 2
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primary = [
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c for c in clusters
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if c["row_coverage"] >= MIN_COVERAGE_PRIMARY
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and c["distinct_rows"] >= MIN_DISTINCT_ROWS
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]
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primary_ids = {id(c) for c in primary}
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secondary = [
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c for c in clusters
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if id(c) not in primary_ids
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and c["row_coverage"] >= MIN_COVERAGE_SECONDARY
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and c["count"] >= MIN_WORDS_SECONDARY
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and c["distinct_rows"] >= MIN_DISTINCT_ROWS
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]
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significant = sorted(primary + secondary, key=lambda c: c["mean_x"])
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logger.info(
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"alignment columns: %d clusters, %d primary, %d secondary → %d significant",
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len(clusters), len(primary), len(secondary), len(significant),
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)
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if not significant:
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# Fallback: single column covering all content
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x_min = min(w["left"] for w in words)
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x_max = max(w["left"] + w["width"] for w in words)
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return [{"index": 0, "type": "column_text", "x_min": x_min, "x_max": x_max}]
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# --- Merge nearby clusters ---
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merge_distance = max(25, int(zone_w * 0.03))
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merged = [significant[0].copy()]
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for s in significant[1:]:
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if s["mean_x"] - merged[-1]["mean_x"] < merge_distance:
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prev = merged[-1]
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total = prev["count"] + s["count"]
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prev["mean_x"] = (
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prev["mean_x"] * prev["count"] + s["mean_x"] * s["count"]
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) // total
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prev["count"] = total
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prev["min_edge"] = min(prev["min_edge"], s["min_edge"])
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prev["max_edge"] = max(prev["max_edge"], s["max_edge"])
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prev["distinct_rows"] = max(prev["distinct_rows"], s["distinct_rows"])
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else:
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merged.append(s.copy())
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logger.info(
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"alignment columns: %d after merge (distance=%d)",
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len(merged), merge_distance,
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)
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# --- Build column boundaries ---
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margin = max(5, int(zone_w * 0.005))
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content_x_min = min(w["left"] for w in words)
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content_x_max = max(w["left"] + w["width"] for w in words)
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columns: List[Dict[str, Any]] = []
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for i, cluster in enumerate(merged):
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x_min = max(content_x_min, cluster["min_edge"] - margin)
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if i + 1 < len(merged):
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x_max = merged[i + 1]["min_edge"] - margin
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else:
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x_max = content_x_max
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columns.append({
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"index": i,
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"type": f"column_{i + 1}" if len(merged) > 1 else "column_text",
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"x_min": x_min,
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"x_max": x_max,
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})
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return columns
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def _flatten_word_boxes(cells: List[Dict]) -> List[Dict]:
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"""Extract all word_boxes from cells into a flat list of word dicts."""
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words: List[Dict] = []
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for cell in cells:
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for wb in cell.get("word_boxes") or []:
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if wb.get("text", "").strip():
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words.append({
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"text": wb["text"],
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"left": wb["left"],
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"top": wb["top"],
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"width": wb["width"],
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"height": wb["height"],
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"conf": wb.get("conf", 0),
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})
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return words
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def _words_in_zone(
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words: List[Dict],
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zone_y: int,
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zone_h: int,
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zone_x: int,
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zone_w: int,
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) -> List[Dict]:
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"""Filter words whose Y-center falls within a zone's bounds."""
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zone_y_end = zone_y + zone_h
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zone_x_end = zone_x + zone_w
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result = []
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for w in words:
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cy = w["top"] + w["height"] / 2
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cx = w["left"] + w["width"] / 2
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if zone_y <= cy <= zone_y_end and zone_x <= cx <= zone_x_end:
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result.append(w)
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return result
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def _detect_header_rows(
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rows: List[Dict],
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zone_words: List[Dict],
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zone_y: int,
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) -> List[int]:
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"""Heuristic: the first row is a header if it has bold/large text or
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there's a significant gap after it."""
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if len(rows) < 2:
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return []
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headers = []
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first_row = rows[0]
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second_row = rows[1]
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# Gap between first and second row > 1.5x average row height
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avg_h = sum(r["y_max"] - r["y_min"] for r in rows) / len(rows)
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gap = second_row["y_min"] - first_row["y_max"]
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if gap > avg_h * 0.5:
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headers.append(0)
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# Also check if first row words are taller than average (bold/header text)
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first_row_words = [
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w for w in zone_words
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if first_row["y_min"] <= w["top"] + w["height"] / 2 <= first_row["y_max"]
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]
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if first_row_words:
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first_h = max(w["height"] for w in first_row_words)
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all_heights = [w["height"] for w in zone_words]
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median_h = sorted(all_heights)[len(all_heights) // 2] if all_heights else first_h
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if first_h > median_h * 1.3:
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if 0 not in headers:
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headers.append(0)
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return headers
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def _build_zone_grid(
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zone_words: List[Dict],
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zone_x: int,
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zone_y: int,
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zone_w: int,
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zone_h: int,
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zone_index: int,
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img_w: int,
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img_h: int,
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) -> Dict[str, Any]:
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"""Build columns, rows, cells for a single zone from its words."""
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if not zone_words:
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return {
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"columns": [],
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"rows": [],
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"cells": [],
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"header_rows": [],
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}
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# Cluster rows first (needed for column alignment analysis)
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rows = _cluster_rows(zone_words)
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# Cluster columns by left-edge alignment
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columns = _cluster_columns_by_alignment(zone_words, zone_w, rows)
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if not columns or not rows:
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return {
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"columns": [],
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"rows": [],
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"cells": [],
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"header_rows": [],
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}
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# Build cells
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cells = _build_cells(zone_words, columns, rows, img_w, img_h)
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# Prefix cell IDs with zone index
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for cell in cells:
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cell["cell_id"] = f"Z{zone_index}_{cell['cell_id']}"
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cell["zone_index"] = zone_index
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# Detect header rows
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header_rows = _detect_header_rows(rows, zone_words, zone_y)
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# Convert columns to output format with percentages
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out_columns = []
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for col in columns:
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x_min = col["x_min"]
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x_max = col["x_max"]
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out_columns.append({
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"index": col["index"],
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"label": col["type"],
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"x_min_px": round(x_min),
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"x_max_px": round(x_max),
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"x_min_pct": round(x_min / img_w * 100, 2) if img_w else 0,
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"x_max_pct": round(x_max / img_w * 100, 2) if img_w else 0,
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"bold": False,
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})
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# Convert rows to output format with percentages
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out_rows = []
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for row in rows:
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out_rows.append({
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"index": row["index"],
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"y_min_px": round(row["y_min"]),
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"y_max_px": round(row["y_max"]),
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"y_min_pct": round(row["y_min"] / img_h * 100, 2) if img_h else 0,
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"y_max_pct": round(row["y_max"] / img_h * 100, 2) if img_h else 0,
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"is_header": row["index"] in header_rows,
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})
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return {
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"columns": out_columns,
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"rows": out_rows,
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"cells": cells,
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"header_rows": header_rows,
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}
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def _get_content_bounds(words: List[Dict]) -> tuple:
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"""Get content bounds from word positions."""
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if not words:
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return 0, 0, 0, 0
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x_min = min(w["left"] for w in words)
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y_min = min(w["top"] for w in words)
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x_max = max(w["left"] + w["width"] for w in words)
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y_max = max(w["top"] + w["height"] for w in words)
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return x_min, y_min, x_max - x_min, y_max - y_min
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# ---------------------------------------------------------------------------
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# Endpoints
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# ---------------------------------------------------------------------------
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@router.post("/sessions/{session_id}/build-grid")
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async def build_grid(session_id: str):
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"""Build a structured, zone-aware grid from existing Kombi word results.
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Requires that paddle-kombi or rapid-kombi has already been run on the session.
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Uses the image for box detection and the word positions for grid structuring.
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Returns a StructuredGrid with zones, each containing their own
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columns, rows, and cells — ready for the frontend Excel-like editor.
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"""
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t0 = time.time()
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# 1. Load session and word results
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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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word_result = session.get("word_result")
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if not word_result or not word_result.get("cells"):
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raise HTTPException(
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status_code=400,
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detail="No word results found. Run paddle-kombi or rapid-kombi first.",
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)
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img_w = word_result.get("image_width", 0)
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img_h = word_result.get("image_height", 0)
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if not img_w or not img_h:
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raise HTTPException(status_code=400, detail="Missing image dimensions in word_result")
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# 2. Flatten all word boxes from cells
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all_words = _flatten_word_boxes(word_result["cells"])
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if not all_words:
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raise HTTPException(status_code=400, detail="No word boxes found in cells")
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logger.info("build-grid session %s: %d words from %d cells",
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session_id, len(all_words), len(word_result["cells"]))
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# 3. Load image for box detection
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img_png = await get_session_image(session_id, "cropped")
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if not img_png:
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img_png = await get_session_image(session_id, "dewarped")
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if not img_png:
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img_png = await get_session_image(session_id, "original")
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zones_data: List[Dict[str, Any]] = []
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boxes_detected = 0
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content_x, content_y, content_w, content_h = _get_content_bounds(all_words)
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if img_png:
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# Decode image for box detection
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arr = np.frombuffer(img_png, dtype=np.uint8)
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img_bgr = cv2.imdecode(arr, cv2.IMREAD_COLOR)
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if img_bgr is not None:
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# Detect bordered boxes
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boxes = detect_boxes(
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img_bgr,
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content_x=content_x,
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content_w=content_w,
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content_y=content_y,
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content_h=content_h,
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)
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boxes_detected = len(boxes)
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if boxes:
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# Split page into zones
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page_zones = split_page_into_zones(
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content_x, content_y, content_w, content_h, boxes
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)
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for pz in page_zones:
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zone_words = _words_in_zone(
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all_words, pz.y, pz.height, pz.x, pz.width
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)
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grid = _build_zone_grid(
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zone_words, pz.x, pz.y, pz.width, pz.height,
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pz.index, img_w, img_h,
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)
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zone_entry: Dict[str, Any] = {
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"zone_index": pz.index,
|
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"zone_type": pz.zone_type,
|
||
"bbox_px": {
|
||
"x": pz.x, "y": pz.y,
|
||
"w": pz.width, "h": pz.height,
|
||
},
|
||
"bbox_pct": {
|
||
"x": round(pz.x / img_w * 100, 2) if img_w else 0,
|
||
"y": round(pz.y / img_h * 100, 2) if img_h else 0,
|
||
"w": round(pz.width / img_w * 100, 2) if img_w else 0,
|
||
"h": round(pz.height / img_h * 100, 2) if img_h else 0,
|
||
},
|
||
"border": None,
|
||
"word_count": len(zone_words),
|
||
**grid,
|
||
}
|
||
|
||
if pz.box:
|
||
zone_entry["border"] = {
|
||
"thickness": pz.box.border_thickness,
|
||
"confidence": pz.box.confidence,
|
||
}
|
||
|
||
zones_data.append(zone_entry)
|
||
|
||
# 4. Fallback: no boxes detected → single zone with all words
|
||
if not zones_data:
|
||
grid = _build_zone_grid(
|
||
all_words, content_x, content_y, content_w, content_h,
|
||
0, img_w, img_h,
|
||
)
|
||
zones_data.append({
|
||
"zone_index": 0,
|
||
"zone_type": "content",
|
||
"bbox_px": {
|
||
"x": content_x, "y": content_y,
|
||
"w": content_w, "h": content_h,
|
||
},
|
||
"bbox_pct": {
|
||
"x": round(content_x / img_w * 100, 2) if img_w else 0,
|
||
"y": round(content_y / img_h * 100, 2) if img_h else 0,
|
||
"w": round(content_w / img_w * 100, 2) if img_w else 0,
|
||
"h": round(content_h / img_h * 100, 2) if img_h else 0,
|
||
},
|
||
"border": None,
|
||
"word_count": len(all_words),
|
||
**grid,
|
||
})
|
||
|
||
duration = time.time() - t0
|
||
|
||
# 5. Build result
|
||
total_cells = sum(len(z.get("cells", [])) for z in zones_data)
|
||
total_columns = sum(len(z.get("columns", [])) for z in zones_data)
|
||
total_rows = sum(len(z.get("rows", [])) for z in zones_data)
|
||
|
||
result = {
|
||
"session_id": session_id,
|
||
"image_width": img_w,
|
||
"image_height": img_h,
|
||
"zones": zones_data,
|
||
"boxes_detected": boxes_detected,
|
||
"summary": {
|
||
"total_zones": len(zones_data),
|
||
"total_columns": total_columns,
|
||
"total_rows": total_rows,
|
||
"total_cells": total_cells,
|
||
"total_words": len(all_words),
|
||
},
|
||
"formatting": {
|
||
"bold_columns": [],
|
||
"header_rows": [],
|
||
},
|
||
"duration_seconds": round(duration, 2),
|
||
}
|
||
|
||
# 6. Persist to DB
|
||
await update_session_db(session_id, grid_editor_result=result)
|
||
|
||
logger.info(
|
||
"build-grid session %s: %d zones, %d cols, %d rows, %d cells, "
|
||
"%d boxes in %.2fs",
|
||
session_id, len(zones_data), total_columns, total_rows,
|
||
total_cells, boxes_detected, duration,
|
||
)
|
||
|
||
return result
|
||
|
||
|
||
@router.post("/sessions/{session_id}/save-grid")
|
||
async def save_grid(session_id: str, request: Request):
|
||
"""Save edited grid data from the frontend Excel-like editor.
|
||
|
||
Receives the full StructuredGrid with user edits (text changes,
|
||
formatting changes like bold columns, header rows, etc.) and
|
||
persists it to the session's grid_editor_result.
|
||
"""
|
||
session = await get_session_db(session_id)
|
||
if not session:
|
||
raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
|
||
|
||
body = await request.json()
|
||
|
||
# Validate basic structure
|
||
if "zones" not in body:
|
||
raise HTTPException(status_code=400, detail="Missing 'zones' in request body")
|
||
|
||
# Preserve metadata from the original build
|
||
existing = session.get("grid_editor_result") or {}
|
||
result = {
|
||
"session_id": session_id,
|
||
"image_width": body.get("image_width", existing.get("image_width", 0)),
|
||
"image_height": body.get("image_height", existing.get("image_height", 0)),
|
||
"zones": body["zones"],
|
||
"boxes_detected": body.get("boxes_detected", existing.get("boxes_detected", 0)),
|
||
"summary": body.get("summary", existing.get("summary", {})),
|
||
"formatting": body.get("formatting", existing.get("formatting", {})),
|
||
"duration_seconds": existing.get("duration_seconds", 0),
|
||
"edited": True,
|
||
}
|
||
|
||
await update_session_db(session_id, grid_editor_result=result)
|
||
|
||
logger.info("save-grid session %s: %d zones saved", session_id, len(body["zones"]))
|
||
|
||
return {"session_id": session_id, "saved": True}
|
||
|
||
|
||
@router.get("/sessions/{session_id}/grid-editor")
|
||
async def get_grid(session_id: str):
|
||
"""Retrieve the current grid editor state for a session."""
|
||
session = await get_session_db(session_id)
|
||
if not session:
|
||
raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
|
||
|
||
result = session.get("grid_editor_result")
|
||
if not result:
|
||
raise HTTPException(
|
||
status_code=404,
|
||
detail="No grid editor data. Run build-grid first.",
|
||
)
|
||
|
||
return result
|