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"""Resumable Surya detection on template-subtracted handwriting pages."""
from __future__ import annotations
import argparse
import fcntl
import io
import json
import os
import time
import traceback
from pathlib import Path
from typing import Any
import numpy as np
import pyarrow.parquet as pq
from PIL import Image
from surya.detection import DetectionPredictor
from bina02_template_layers import TemplateCatalog
ROWS = 6_669
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--rank", type=int, required=True)
parser.add_argument("--world-size", type=int, required=True)
parser.add_argument("--data", type=Path, required=True)
parser.add_argument("--background-root", type=Path, required=True)
parser.add_argument("--ocr-root", type=Path, required=True)
parser.add_argument("--output-dir", type=Path, required=True)
parser.add_argument("--variant", required=True)
parser.add_argument("--limit", type=int, default=ROWS)
parser.add_argument("--batch-size", type=int, default=8)
parser.add_argument("--progress-every", type=int, default=20)
return parser.parse_args()
def atomic_json(path: Path, payload: dict[str, Any]) -> None:
temporary = path.with_suffix(path.suffix + f".{os.getpid()}.tmp")
temporary.write_text(
json.dumps(payload, ensure_ascii=False, indent=2) + "\n",
encoding="utf-8",
)
os.replace(temporary, path)
def selected_indices(limit: int) -> list[int]:
if not 1 <= limit <= ROWS:
raise ValueError(f"limit must be in [1, {ROWS}]")
if limit == ROWS:
return list(range(ROWS))
return sorted(
{
int(round(value))
for value in np.linspace(0, ROWS - 1, num=limit)
}
)
def load_rows(data: Path, wanted: set[int]) -> dict[int, dict[str, Any]]:
rows: dict[int, dict[str, Any]] = {}
offset = 0
paths = sorted((data / "data").glob("handwriting-*.parquet"))
if len(paths) != 14:
raise RuntimeError(f"expected 14 handwriting shards, found {len(paths)}")
for path in paths:
count = pq.ParquetFile(path).metadata.num_rows
local = sorted(index - offset for index in wanted if offset <= index < offset + count)
if local:
table = pq.read_table(path, columns=["image", "label"])
for index in local:
rows[offset + index] = table.slice(index, 1).to_pylist()[0]
offset += count
if offset != ROWS or rows.keys() != wanted:
raise RuntimeError(
f"benchmark row contract failed: total={offset}, loaded={len(rows)}, wanted={len(wanted)}"
)
return rows
def decode_image(row: dict[str, Any]) -> Image.Image:
value = row["image"]
encoded = value.get("bytes") if isinstance(value, dict) else value
if encoded is None and isinstance(value, dict) and value.get("path"):
encoded = Path(value["path"]).read_bytes()
if not encoded:
raise ValueError("dataset row has no image bytes")
return Image.open(io.BytesIO(encoded)).convert("RGB")
def line_payload(result: Any) -> list[dict[str, Any]]:
lines = []
for item in getattr(result, "bboxes", []):
bbox = getattr(item, "bbox", None)
if bbox is None or len(bbox) != 4:
continue
polygon = getattr(item, "polygon", None)
lines.append(
{
"bbox": [float(value) for value in bbox],
"confidence": float(getattr(item, "confidence", 0.0)),
"polygon": (
[[float(value) for value in point] for point in polygon]
if polygon is not None
else None
),
}
)
return lines
def load_done(path: Path) -> set[str]:
if not path.is_file():
return set()
with path.open(encoding="utf-8") as handle:
return {
str(row["id"])
for line in handle
if line.strip()
for row in [json.loads(line)]
}
def merge_if_complete(
output_dir: Path,
world_size: int,
selected: list[int],
variant: str,
) -> None:
with (output_dir / "merge.lock").open("a+") as lock:
fcntl.flock(lock, fcntl.LOCK_EX)
statuses = []
for rank in range(world_size):
path = output_dir / f"status-rank{rank:02d}.json"
if not path.is_file():
return
value = json.loads(path.read_text(encoding="utf-8"))
if value.get("state") != "complete":
return
statuses.append(value)
rows: dict[str, dict[str, Any]] = {}
duplicates = []
for rank in range(world_size):
path = output_dir / f"detections-rank{rank:02d}.jsonl"
with path.open(encoding="utf-8") as handle:
for line in handle:
row = json.loads(line)
row_id = str(row["id"])
if row_id in rows:
duplicates.append(row_id)
rows[row_id] = row
expected = [f"handwriting:{index}" for index in selected]
missing = sorted(set(expected) - rows.keys())
extras = sorted(rows.keys() - set(expected))
if duplicates or missing or extras:
raise RuntimeError(
f"detection merge failed: duplicates={duplicates[:3]} missing={missing[:3]} extras={extras[:3]}"
)
temporary = output_dir / "detections.jsonl.tmp"
with temporary.open("w", encoding="utf-8") as handle:
for row_id in expected:
handle.write(json.dumps(rows[row_id], ensure_ascii=False) + "\n")
os.replace(temporary, output_dir / "detections.jsonl")
atomic_json(
output_dir / "run-summary.json",
{
"state": "complete",
"variant": variant,
"selected_indices": selected,
"rows": len(rows),
"failed_pages": sum(row["failures"] for row in statuses),
"template_match_failures": sum(
row["template_match_failures"] for row in statuses
),
"detected_lines": sum(row["detected_lines"] for row in statuses),
"completed_unix": time.time(),
},
)
def main() -> None:
args = parse_args()
selected = selected_indices(args.limit)
assigned = selected[args.rank :: args.world_size]
output_path = args.output_dir / f"detections-rank{args.rank:02d}.jsonl"
status_path = args.output_dir / f"status-rank{args.rank:02d}.json"
args.output_dir.mkdir(parents=True, exist_ok=True)
done = load_done(output_path)
wanted = {
index for index in assigned if f"handwriting:{index}" not in done
}
rows = load_rows(args.data, wanted) if wanted else {}
catalog = TemplateCatalog(args.background_root, args.ocr_root)
predictor = DetectionPredictor.local(device="cuda")
started = time.monotonic()
completed = 0
failures = 0
match_failures = 0
detected_lines = 0
pending: list[tuple[int, dict[str, Any], Image.Image, dict[str, Any]]] = []
with output_path.open("a", encoding="utf-8", buffering=1) as output:
def flush() -> None:
nonlocal completed, failures, detected_lines
if not pending:
return
images = [item[2] for item in pending]
try:
results: list[Any | Exception] = list(predictor(images))
if len(results) != len(images):
raise RuntimeError("detector returned wrong result count")
except Exception:
traceback.print_exc()
results = []
for image in images:
try:
results.append(list(predictor([image]))[0])
except Exception as exc:
results.append(exc)
for (index, row, image, layer), result in zip(pending, results):
lines = []
error = None
if isinstance(result, Exception):
failures += 1
error = f"{type(result).__name__}: {result}"
else:
lines = line_payload(result)
detected_lines += len(lines)
payload = {
"id": f"handwriting:{index}",
"split": "handwriting",
"reference": str(row.get("label") or ""),
"width": image.width,
"height": image.height,
"variant": args.variant,
"lines": lines,
**layer,
}
if error:
payload["error"] = error
output.write(json.dumps(payload, ensure_ascii=False) + "\n")
completed += 1
pending.clear()
for index in assigned:
if index not in wanted:
continue
row = rows[index]
try:
image = decode_image(row)
isolated, match, metrics = catalog.isolate(image, args.variant)
layer = {
"template_id": match.template.id,
"template_file": match.template.file,
"template_text": match.template.template_text,
"template_placeholder_count": match.template.template_text.count(
"{{HANDWRITING}}"
),
"match_median_abs_delta": match.median_abs_delta,
"match_p90_abs_delta": match.p90_abs_delta,
"mask_threshold": metrics["threshold"],
"mask_kept_fraction": metrics["kept_fraction"],
}
pending.append((index, row, isolated, layer))
if len(pending) >= args.batch_size:
flush()
except Exception as exc:
traceback.print_exc()
failures += 1
match_failures += 1
completed += 1
output.write(
json.dumps(
{
"id": f"handwriting:{index}",
"split": "handwriting",
"reference": str(row.get("label") or ""),
"width": 0,
"height": 0,
"variant": args.variant,
"lines": [],
"error": f"{type(exc).__name__}: {exc}",
},
ensure_ascii=False,
)
+ "\n"
)
flush()
elapsed = max(time.monotonic() - started, 1e-9)
atomic_json(
status_path,
{
"state": "complete",
"rank": args.rank,
"assigned": len(assigned),
"completed": completed,
"failures": failures,
"template_match_failures": match_failures,
"detected_lines": detected_lines,
"rate_pages_per_second": completed / elapsed,
"eta_seconds": 0,
"updated_unix": time.time(),
},
)
merge_if_complete(args.output_dir, args.world_size, selected, args.variant)
if __name__ == "__main__":
main()
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