43b1f8be58
Some checks failed
CI / go-lint (push) Has been skipped
CI / python-lint (push) Has been skipped
CI / nodejs-lint (push) Has been skipped
CI / test-go-school (push) Successful in 27s
CI / test-go-edu-search (push) Successful in 30s
CI / test-python-klausur (push) Failing after 1m48s
CI / test-python-agent-core (push) Successful in 18s
CI / test-nodejs-website (push) Successful in 17s
Box zones have 40-60 words, need to capture their diagnostics. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
807 lines
30 KiB
Python
807 lines
30 KiB
Python
"""
|
||
Grid Editor API — builds a structured, zone-aware grid from Kombi OCR results.
|
||
|
||
Takes the merged word positions from paddle-kombi / rapid-kombi and:
|
||
1. Detects bordered boxes on the image (cv_box_detect)
|
||
2. Splits the page into zones (content + box regions)
|
||
3. Clusters words into columns and rows per zone
|
||
4. Returns a hierarchical StructuredGrid for the frontend Excel-like editor
|
||
|
||
Lizenz: Apache 2.0 (kommerziell nutzbar)
|
||
DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
|
||
"""
|
||
|
||
import logging
|
||
import time
|
||
from typing import Any, Dict, List, Optional
|
||
|
||
import cv2
|
||
import numpy as np
|
||
from fastapi import APIRouter, HTTPException, Request
|
||
|
||
from cv_box_detect import detect_boxes, split_page_into_zones
|
||
from cv_color_detect import detect_word_colors, recover_colored_text
|
||
from cv_words_first import _cluster_rows, _build_cells
|
||
from ocr_pipeline_session_store import (
|
||
get_session_db,
|
||
get_session_image,
|
||
update_session_db,
|
||
)
|
||
|
||
logger = logging.getLogger(__name__)
|
||
|
||
router = APIRouter(prefix="/api/v1/ocr-pipeline", tags=["grid-editor"])
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Helpers
|
||
# ---------------------------------------------------------------------------
|
||
|
||
def _cluster_columns_by_alignment(
|
||
words: List[Dict],
|
||
zone_w: int,
|
||
rows: List[Dict],
|
||
) -> List[Dict[str, Any]]:
|
||
"""Detect columns by clustering left-edge alignment across rows.
|
||
|
||
Hybrid approach:
|
||
1. Group words by row, find "group start" positions within each row
|
||
(words preceded by a large gap or first word in row)
|
||
2. Cluster group-start left-edges by X-proximity across rows
|
||
3. Filter by row coverage (how many rows have a group start here)
|
||
4. Merge nearby clusters
|
||
5. Build column boundaries
|
||
|
||
This filters out mid-phrase word positions (e.g. IPA transcriptions,
|
||
second words in multi-word entries) by only considering positions
|
||
where a new word group begins within a row.
|
||
"""
|
||
if not words or not rows:
|
||
return []
|
||
|
||
total_rows = len(rows)
|
||
if total_rows == 0:
|
||
return []
|
||
|
||
# --- Group words by row ---
|
||
row_words: Dict[int, List[Dict]] = {}
|
||
for w in words:
|
||
y_center = w["top"] + w["height"] / 2
|
||
best = min(rows, key=lambda r: abs(r["y_center"] - y_center))
|
||
row_words.setdefault(best["index"], []).append(w)
|
||
|
||
# --- Compute adaptive gap threshold for group-start detection ---
|
||
all_gaps: List[float] = []
|
||
for ri, rw_list in row_words.items():
|
||
sorted_rw = sorted(rw_list, key=lambda w: w["left"])
|
||
for i in range(len(sorted_rw) - 1):
|
||
right = sorted_rw[i]["left"] + sorted_rw[i]["width"]
|
||
gap = sorted_rw[i + 1]["left"] - right
|
||
if gap > 0:
|
||
all_gaps.append(gap)
|
||
|
||
if all_gaps:
|
||
sorted_gaps = sorted(all_gaps)
|
||
median_gap = sorted_gaps[len(sorted_gaps) // 2]
|
||
heights = [w["height"] for w in words if w.get("height", 0) > 0]
|
||
median_h = sorted(heights)[len(heights) // 2] if heights else 25
|
||
# Column boundary: gap > 3× median gap or > 1.5× median word height
|
||
gap_threshold = max(median_gap * 3, median_h * 1.5, 30)
|
||
else:
|
||
gap_threshold = 50
|
||
|
||
# --- Find group-start positions (left-edges that begin a new column) ---
|
||
start_positions: List[tuple] = [] # (left_edge, row_index)
|
||
for ri, rw_list in row_words.items():
|
||
sorted_rw = sorted(rw_list, key=lambda w: w["left"])
|
||
# First word in row is always a group start
|
||
start_positions.append((sorted_rw[0]["left"], ri))
|
||
for i in range(1, len(sorted_rw)):
|
||
right_prev = sorted_rw[i - 1]["left"] + sorted_rw[i - 1]["width"]
|
||
gap = sorted_rw[i]["left"] - right_prev
|
||
if gap >= gap_threshold:
|
||
start_positions.append((sorted_rw[i]["left"], ri))
|
||
|
||
start_positions.sort(key=lambda x: x[0])
|
||
|
||
logger.info(
|
||
"alignment columns: %d group-start positions from %d words "
|
||
"(gap_threshold=%.0f, %d rows)",
|
||
len(start_positions), len(words), gap_threshold, total_rows,
|
||
)
|
||
|
||
if not start_positions:
|
||
x_min = min(w["left"] for w in words)
|
||
x_max = max(w["left"] + w["width"] for w in words)
|
||
return [{"index": 0, "type": "column_text", "x_min": x_min, "x_max": x_max}]
|
||
|
||
# --- Cluster group-start positions by X-proximity ---
|
||
tolerance = max(10, int(zone_w * 0.01))
|
||
clusters: List[Dict[str, Any]] = []
|
||
cur_edges = [start_positions[0][0]]
|
||
cur_rows = {start_positions[0][1]}
|
||
|
||
for left, row_idx in start_positions[1:]:
|
||
if left - cur_edges[-1] <= tolerance:
|
||
cur_edges.append(left)
|
||
cur_rows.add(row_idx)
|
||
else:
|
||
clusters.append({
|
||
"mean_x": int(sum(cur_edges) / len(cur_edges)),
|
||
"min_edge": min(cur_edges),
|
||
"max_edge": max(cur_edges),
|
||
"count": len(cur_edges),
|
||
"distinct_rows": len(cur_rows),
|
||
"row_coverage": len(cur_rows) / total_rows,
|
||
})
|
||
cur_edges = [left]
|
||
cur_rows = {row_idx}
|
||
clusters.append({
|
||
"mean_x": int(sum(cur_edges) / len(cur_edges)),
|
||
"min_edge": min(cur_edges),
|
||
"max_edge": max(cur_edges),
|
||
"count": len(cur_edges),
|
||
"distinct_rows": len(cur_rows),
|
||
"row_coverage": len(cur_rows) / total_rows,
|
||
})
|
||
|
||
# --- Filter by row coverage ---
|
||
MIN_COVERAGE_PRIMARY = 0.20
|
||
MIN_COVERAGE_SECONDARY = 0.12
|
||
MIN_WORDS_SECONDARY = 3
|
||
MIN_DISTINCT_ROWS = 2
|
||
|
||
# Content boundary for left-margin detection
|
||
content_x_min = min(w["left"] for w in words)
|
||
content_x_max = max(w["left"] + w["width"] for w in words)
|
||
content_span = content_x_max - content_x_min
|
||
|
||
primary = [
|
||
c for c in clusters
|
||
if c["row_coverage"] >= MIN_COVERAGE_PRIMARY
|
||
and c["distinct_rows"] >= MIN_DISTINCT_ROWS
|
||
]
|
||
primary_ids = {id(c) for c in primary}
|
||
secondary = [
|
||
c for c in clusters
|
||
if id(c) not in primary_ids
|
||
and c["row_coverage"] >= MIN_COVERAGE_SECONDARY
|
||
and c["count"] >= MIN_WORDS_SECONDARY
|
||
and c["distinct_rows"] >= MIN_DISTINCT_ROWS
|
||
]
|
||
|
||
# Tertiary: narrow left-margin columns (page refs, markers) that have
|
||
# too few rows for secondary but are clearly left-aligned and separated
|
||
# from the main content. These appear at the far left or far right and
|
||
# have a large gap to the nearest significant cluster.
|
||
used_ids = {id(c) for c in primary} | {id(c) for c in secondary}
|
||
sig_xs = [c["mean_x"] for c in primary + secondary]
|
||
|
||
tertiary = []
|
||
for c in clusters:
|
||
if id(c) in used_ids or c["distinct_rows"] < MIN_DISTINCT_ROWS:
|
||
continue
|
||
# Must be near left or right content margin (within 15%)
|
||
rel_pos = (c["mean_x"] - content_x_min) / content_span if content_span else 0.5
|
||
if not (rel_pos < 0.15 or rel_pos > 0.85):
|
||
continue
|
||
# Must have significant gap to nearest significant cluster
|
||
if sig_xs:
|
||
min_dist = min(abs(c["mean_x"] - sx) for sx in sig_xs)
|
||
if min_dist < max(30, content_span * 0.02):
|
||
continue
|
||
tertiary.append(c)
|
||
|
||
if tertiary:
|
||
for c in tertiary:
|
||
logger.info(
|
||
" tertiary (margin) cluster: x=%d (range %d-%d), %d words, %d rows (%.0f%%)",
|
||
c["mean_x"], c["min_edge"], c["max_edge"],
|
||
c["count"], c["distinct_rows"], c["row_coverage"] * 100,
|
||
)
|
||
|
||
significant = sorted(primary + secondary + tertiary, key=lambda c: c["mean_x"])
|
||
|
||
for c in significant:
|
||
logger.info(
|
||
" significant cluster: x=%d (range %d-%d), %d words, %d rows (%.0f%%)",
|
||
c["mean_x"], c["min_edge"], c["max_edge"],
|
||
c["count"], c["distinct_rows"], c["row_coverage"] * 100,
|
||
)
|
||
logger.info(
|
||
"alignment columns: %d clusters, %d primary, %d secondary → %d significant",
|
||
len(clusters), len(primary), len(secondary), len(significant),
|
||
)
|
||
|
||
if not significant:
|
||
# Fallback: single column covering all content
|
||
x_min = min(w["left"] for w in words)
|
||
x_max = max(w["left"] + w["width"] for w in words)
|
||
return [{"index": 0, "type": "column_text", "x_min": x_min, "x_max": x_max}]
|
||
|
||
# --- Merge nearby clusters ---
|
||
merge_distance = max(25, int(zone_w * 0.03))
|
||
merged = [significant[0].copy()]
|
||
for s in significant[1:]:
|
||
if s["mean_x"] - merged[-1]["mean_x"] < merge_distance:
|
||
prev = merged[-1]
|
||
total = prev["count"] + s["count"]
|
||
prev["mean_x"] = (
|
||
prev["mean_x"] * prev["count"] + s["mean_x"] * s["count"]
|
||
) // total
|
||
prev["count"] = total
|
||
prev["min_edge"] = min(prev["min_edge"], s["min_edge"])
|
||
prev["max_edge"] = max(prev["max_edge"], s["max_edge"])
|
||
prev["distinct_rows"] = max(prev["distinct_rows"], s["distinct_rows"])
|
||
else:
|
||
merged.append(s.copy())
|
||
|
||
logger.info(
|
||
"alignment columns: %d after merge (distance=%d)",
|
||
len(merged), merge_distance,
|
||
)
|
||
|
||
# --- Build column boundaries ---
|
||
margin = max(5, int(zone_w * 0.005))
|
||
content_x_min = min(w["left"] for w in words)
|
||
content_x_max = max(w["left"] + w["width"] for w in words)
|
||
|
||
columns: List[Dict[str, Any]] = []
|
||
for i, cluster in enumerate(merged):
|
||
x_min = max(content_x_min, cluster["min_edge"] - margin)
|
||
if i + 1 < len(merged):
|
||
x_max = merged[i + 1]["min_edge"] - margin
|
||
else:
|
||
x_max = content_x_max
|
||
|
||
columns.append({
|
||
"index": i,
|
||
"type": f"column_{i + 1}" if len(merged) > 1 else "column_text",
|
||
"x_min": x_min,
|
||
"x_max": x_max,
|
||
})
|
||
|
||
return columns
|
||
|
||
|
||
def _flatten_word_boxes(cells: List[Dict]) -> List[Dict]:
|
||
"""Extract all word_boxes from cells into a flat list of word dicts."""
|
||
words: List[Dict] = []
|
||
for cell in cells:
|
||
for wb in cell.get("word_boxes") or []:
|
||
if wb.get("text", "").strip():
|
||
words.append({
|
||
"text": wb["text"],
|
||
"left": wb["left"],
|
||
"top": wb["top"],
|
||
"width": wb["width"],
|
||
"height": wb["height"],
|
||
"conf": wb.get("conf", 0),
|
||
})
|
||
return words
|
||
|
||
|
||
def _words_in_zone(
|
||
words: List[Dict],
|
||
zone_y: int,
|
||
zone_h: int,
|
||
zone_x: int,
|
||
zone_w: int,
|
||
) -> List[Dict]:
|
||
"""Filter words whose Y-center falls within a zone's bounds."""
|
||
zone_y_end = zone_y + zone_h
|
||
zone_x_end = zone_x + zone_w
|
||
result = []
|
||
for w in words:
|
||
cy = w["top"] + w["height"] / 2
|
||
cx = w["left"] + w["width"] / 2
|
||
if zone_y <= cy <= zone_y_end and zone_x <= cx <= zone_x_end:
|
||
result.append(w)
|
||
return result
|
||
|
||
|
||
def _detect_header_rows(
|
||
rows: List[Dict],
|
||
zone_words: List[Dict],
|
||
zone_y: int,
|
||
) -> List[int]:
|
||
"""Heuristic: the first row is a header if it has bold/large text or
|
||
there's a significant gap after it."""
|
||
if len(rows) < 2:
|
||
return []
|
||
|
||
headers = []
|
||
first_row = rows[0]
|
||
second_row = rows[1]
|
||
|
||
# Gap between first and second row > 1.5x average row height
|
||
avg_h = sum(r["y_max"] - r["y_min"] for r in rows) / len(rows)
|
||
gap = second_row["y_min"] - first_row["y_max"]
|
||
if gap > avg_h * 0.5:
|
||
headers.append(0)
|
||
|
||
# Also check if first row words are taller than average (bold/header text)
|
||
first_row_words = [
|
||
w for w in zone_words
|
||
if first_row["y_min"] <= w["top"] + w["height"] / 2 <= first_row["y_max"]
|
||
]
|
||
if first_row_words:
|
||
first_h = max(w["height"] for w in first_row_words)
|
||
all_heights = [w["height"] for w in zone_words]
|
||
median_h = sorted(all_heights)[len(all_heights) // 2] if all_heights else first_h
|
||
if first_h > median_h * 1.3:
|
||
if 0 not in headers:
|
||
headers.append(0)
|
||
|
||
return headers
|
||
|
||
|
||
def _build_zone_grid(
|
||
zone_words: List[Dict],
|
||
zone_x: int,
|
||
zone_y: int,
|
||
zone_w: int,
|
||
zone_h: int,
|
||
zone_index: int,
|
||
img_w: int,
|
||
img_h: int,
|
||
global_columns: Optional[List[Dict]] = None,
|
||
) -> Dict[str, Any]:
|
||
"""Build columns, rows, cells for a single zone from its words.
|
||
|
||
Args:
|
||
global_columns: If provided, use these pre-computed column boundaries
|
||
instead of detecting columns per zone. Used for content zones so
|
||
that all content zones (above/between/below boxes) share the same
|
||
column structure. Box zones always detect columns independently.
|
||
"""
|
||
if not zone_words:
|
||
return {
|
||
"columns": [],
|
||
"rows": [],
|
||
"cells": [],
|
||
"header_rows": [],
|
||
}
|
||
|
||
# Cluster rows first (needed for column alignment analysis)
|
||
rows = _cluster_rows(zone_words)
|
||
|
||
# Diagnostic logging for small/medium zones (box zones typically have 40-60 words)
|
||
if len(zone_words) <= 60:
|
||
import statistics as _st
|
||
_heights = [w['height'] for w in zone_words if w.get('height', 0) > 0]
|
||
_med_h = _st.median(_heights) if _heights else 20
|
||
_y_tol = max(_med_h * 0.5, 5)
|
||
logger.info(
|
||
"zone %d row-clustering: %d words, median_h=%.0f, y_tol=%.1f → %d rows",
|
||
zone_index, len(zone_words), _med_h, _y_tol, len(rows),
|
||
)
|
||
for w in sorted(zone_words, key=lambda ww: (ww['top'], ww['left'])):
|
||
logger.info(
|
||
" zone %d word: y=%d x=%d h=%d w=%d '%s'",
|
||
zone_index, w['top'], w['left'], w['height'], w['width'],
|
||
w.get('text', '')[:40],
|
||
)
|
||
for r in rows:
|
||
logger.info(
|
||
" zone %d row %d: y_min=%d y_max=%d y_center=%.0f",
|
||
zone_index, r['index'], r['y_min'], r['y_max'], r['y_center'],
|
||
)
|
||
|
||
# Use global columns if provided, otherwise detect per zone
|
||
columns = global_columns if global_columns else _cluster_columns_by_alignment(zone_words, zone_w, rows)
|
||
|
||
if not columns or not rows:
|
||
return {
|
||
"columns": [],
|
||
"rows": [],
|
||
"cells": [],
|
||
"header_rows": [],
|
||
}
|
||
|
||
# Build cells
|
||
cells = _build_cells(zone_words, columns, rows, img_w, img_h)
|
||
|
||
# Prefix cell IDs with zone index
|
||
for cell in cells:
|
||
cell["cell_id"] = f"Z{zone_index}_{cell['cell_id']}"
|
||
cell["zone_index"] = zone_index
|
||
|
||
# Detect header rows
|
||
header_rows = _detect_header_rows(rows, zone_words, zone_y)
|
||
|
||
# Convert columns to output format with percentages
|
||
out_columns = []
|
||
for col in columns:
|
||
x_min = col["x_min"]
|
||
x_max = col["x_max"]
|
||
out_columns.append({
|
||
"index": col["index"],
|
||
"label": col["type"],
|
||
"x_min_px": round(x_min),
|
||
"x_max_px": round(x_max),
|
||
"x_min_pct": round(x_min / img_w * 100, 2) if img_w else 0,
|
||
"x_max_pct": round(x_max / img_w * 100, 2) if img_w else 0,
|
||
"bold": False,
|
||
})
|
||
|
||
# Convert rows to output format with percentages
|
||
out_rows = []
|
||
for row in rows:
|
||
out_rows.append({
|
||
"index": row["index"],
|
||
"y_min_px": round(row["y_min"]),
|
||
"y_max_px": round(row["y_max"]),
|
||
"y_min_pct": round(row["y_min"] / img_h * 100, 2) if img_h else 0,
|
||
"y_max_pct": round(row["y_max"] / img_h * 100, 2) if img_h else 0,
|
||
"is_header": row["index"] in header_rows,
|
||
})
|
||
|
||
return {
|
||
"columns": out_columns,
|
||
"rows": out_rows,
|
||
"cells": cells,
|
||
"header_rows": header_rows,
|
||
"_raw_columns": columns, # internal: for propagation to other zones
|
||
}
|
||
|
||
|
||
def _get_content_bounds(words: List[Dict]) -> tuple:
|
||
"""Get content bounds from word positions."""
|
||
if not words:
|
||
return 0, 0, 0, 0
|
||
x_min = min(w["left"] for w in words)
|
||
y_min = min(w["top"] for w in words)
|
||
x_max = max(w["left"] + w["width"] for w in words)
|
||
y_max = max(w["top"] + w["height"] for w in words)
|
||
return x_min, y_min, x_max - x_min, y_max - y_min
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Endpoints
|
||
# ---------------------------------------------------------------------------
|
||
|
||
@router.post("/sessions/{session_id}/build-grid")
|
||
async def build_grid(session_id: str):
|
||
"""Build a structured, zone-aware grid from existing Kombi word results.
|
||
|
||
Requires that paddle-kombi or rapid-kombi has already been run on the session.
|
||
Uses the image for box detection and the word positions for grid structuring.
|
||
|
||
Returns a StructuredGrid with zones, each containing their own
|
||
columns, rows, and cells — ready for the frontend Excel-like editor.
|
||
"""
|
||
t0 = time.time()
|
||
|
||
# 1. Load session and word results
|
||
session = await get_session_db(session_id)
|
||
if not session:
|
||
raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
|
||
|
||
word_result = session.get("word_result")
|
||
if not word_result or not word_result.get("cells"):
|
||
raise HTTPException(
|
||
status_code=400,
|
||
detail="No word results found. Run paddle-kombi or rapid-kombi first.",
|
||
)
|
||
|
||
img_w = word_result.get("image_width", 0)
|
||
img_h = word_result.get("image_height", 0)
|
||
if not img_w or not img_h:
|
||
raise HTTPException(status_code=400, detail="Missing image dimensions in word_result")
|
||
|
||
# 2. Flatten all word boxes from cells
|
||
all_words = _flatten_word_boxes(word_result["cells"])
|
||
if not all_words:
|
||
raise HTTPException(status_code=400, detail="No word boxes found in cells")
|
||
|
||
logger.info("build-grid session %s: %d words from %d cells",
|
||
session_id, len(all_words), len(word_result["cells"]))
|
||
|
||
# 3. Load image for box detection
|
||
img_png = await get_session_image(session_id, "cropped")
|
||
if not img_png:
|
||
img_png = await get_session_image(session_id, "dewarped")
|
||
if not img_png:
|
||
img_png = await get_session_image(session_id, "original")
|
||
|
||
zones_data: List[Dict[str, Any]] = []
|
||
boxes_detected = 0
|
||
recovered_count = 0
|
||
img_bgr = None
|
||
|
||
content_x, content_y, content_w, content_h = _get_content_bounds(all_words)
|
||
|
||
if img_png:
|
||
# Decode image for color detection + box detection
|
||
arr = np.frombuffer(img_png, dtype=np.uint8)
|
||
img_bgr = cv2.imdecode(arr, cv2.IMREAD_COLOR)
|
||
|
||
if img_bgr is not None:
|
||
# --- Recover colored text that OCR missed (before grid building) ---
|
||
recovered = recover_colored_text(img_bgr, all_words)
|
||
if recovered:
|
||
recovered_count = len(recovered)
|
||
all_words.extend(recovered)
|
||
logger.info(
|
||
"build-grid session %s: +%d recovered colored words",
|
||
session_id, recovered_count,
|
||
)
|
||
|
||
# Detect bordered boxes
|
||
boxes = detect_boxes(
|
||
img_bgr,
|
||
content_x=content_x,
|
||
content_w=content_w,
|
||
content_y=content_y,
|
||
content_h=content_h,
|
||
)
|
||
boxes_detected = len(boxes)
|
||
|
||
if boxes:
|
||
# Split page into zones
|
||
page_zones = split_page_into_zones(
|
||
content_x, content_y, content_w, content_h, boxes
|
||
)
|
||
|
||
# --- Union columns from all content zones ---
|
||
# Each content zone detects columns independently. Narrow
|
||
# columns (page refs, markers) may appear in only one zone.
|
||
# Merge column split-points from ALL content zones so every
|
||
# zone shares the full column set.
|
||
|
||
# First pass: build grids per zone independently
|
||
zone_grids: List[Dict] = []
|
||
|
||
for pz in page_zones:
|
||
zone_words = _words_in_zone(
|
||
all_words, pz.y, pz.height, pz.x, pz.width
|
||
)
|
||
grid = _build_zone_grid(
|
||
zone_words, pz.x, pz.y, pz.width, pz.height,
|
||
pz.index, img_w, img_h,
|
||
)
|
||
zone_grids.append({"pz": pz, "words": zone_words, "grid": grid})
|
||
|
||
# Second pass: merge column boundaries from all content zones
|
||
content_zones = [
|
||
zg for zg in zone_grids if zg["pz"].zone_type == "content"
|
||
]
|
||
if len(content_zones) > 1:
|
||
# Collect column split points (x_min of non-first columns)
|
||
all_split_xs: List[float] = []
|
||
for zg in content_zones:
|
||
raw_cols = zg["grid"].get("_raw_columns", [])
|
||
for col in raw_cols[1:]:
|
||
all_split_xs.append(col["x_min"])
|
||
|
||
if all_split_xs:
|
||
all_split_xs.sort()
|
||
merge_distance = max(25, int(content_w * 0.03))
|
||
merged_xs = [all_split_xs[0]]
|
||
for x in all_split_xs[1:]:
|
||
if x - merged_xs[-1] < merge_distance:
|
||
merged_xs[-1] = (merged_xs[-1] + x) / 2
|
||
else:
|
||
merged_xs.append(x)
|
||
|
||
total_cols = len(merged_xs) + 1
|
||
max_zone_cols = max(
|
||
len(zg["grid"].get("_raw_columns", []))
|
||
for zg in content_zones
|
||
)
|
||
|
||
# Apply union whenever it has at least as many
|
||
# columns as the best single zone. Even with the
|
||
# same count the union boundaries are better because
|
||
# they incorporate evidence from all zones.
|
||
if total_cols >= max_zone_cols:
|
||
cx_min = min(w["left"] for w in all_words)
|
||
cx_max = max(
|
||
w["left"] + w["width"] for w in all_words
|
||
)
|
||
merged_columns: List[Dict[str, Any]] = []
|
||
prev_x = cx_min
|
||
for i, sx in enumerate(merged_xs):
|
||
merged_columns.append({
|
||
"index": i,
|
||
"type": f"column_{i + 1}",
|
||
"x_min": prev_x,
|
||
"x_max": sx,
|
||
})
|
||
prev_x = sx
|
||
merged_columns.append({
|
||
"index": len(merged_xs),
|
||
"type": f"column_{len(merged_xs) + 1}",
|
||
"x_min": prev_x,
|
||
"x_max": cx_max,
|
||
})
|
||
|
||
# Re-build ALL content zones with merged columns
|
||
for zg in zone_grids:
|
||
pz = zg["pz"]
|
||
if pz.zone_type == "content":
|
||
grid = _build_zone_grid(
|
||
zg["words"], pz.x, pz.y,
|
||
pz.width, pz.height,
|
||
pz.index, img_w, img_h,
|
||
global_columns=merged_columns,
|
||
)
|
||
zg["grid"] = grid
|
||
logger.info(
|
||
"build-grid session %s: union of %d content "
|
||
"zones → %d merged columns (max single zone: %d)",
|
||
session_id, len(content_zones),
|
||
total_cols, max_zone_cols,
|
||
)
|
||
|
||
for zg in zone_grids:
|
||
pz = zg["pz"]
|
||
grid = zg["grid"]
|
||
# Remove internal _raw_columns before adding to response
|
||
grid.pop("_raw_columns", None)
|
||
|
||
zone_entry: Dict[str, Any] = {
|
||
"zone_index": pz.index,
|
||
"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(zg["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,
|
||
)
|
||
grid.pop("_raw_columns", None)
|
||
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,
|
||
})
|
||
|
||
# 5. Color annotation on final word_boxes in cells
|
||
if img_bgr is not None:
|
||
all_wb: List[Dict] = []
|
||
for z in zones_data:
|
||
for cell in z.get("cells", []):
|
||
all_wb.extend(cell.get("word_boxes", []))
|
||
detect_word_colors(img_bgr, all_wb)
|
||
|
||
duration = time.time() - t0
|
||
|
||
# 6. 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)
|
||
|
||
# Collect color statistics from all word_boxes in cells
|
||
color_stats: Dict[str, int] = {}
|
||
for z in zones_data:
|
||
for cell in z.get("cells", []):
|
||
for wb in cell.get("word_boxes", []):
|
||
cn = wb.get("color_name", "black")
|
||
color_stats[cn] = color_stats.get(cn, 0) + 1
|
||
|
||
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),
|
||
"recovered_colored": recovered_count,
|
||
"color_stats": color_stats,
|
||
},
|
||
"formatting": {
|
||
"bold_columns": [],
|
||
"header_rows": [],
|
||
},
|
||
"duration_seconds": round(duration, 2),
|
||
}
|
||
|
||
# 7. 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
|