opencode/process_any_pdf.py

197 lines
6.5 KiB
Python
Raw Normal View History

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Универсальное распознавание PDF в указанную папку.
Поддерживает:
- RapidOCR (локально, быстро)
- RapidOCR + tiling (для больших чертежей)
- qwen-vl-ocr (API, точнее)
Использование:
python process_any_pdf.py <pdf_file> <output_folder> [--use-qwen] [--use-tiling]
"""
import sys
import json
import re
import fitz
from pathlib import Path
from PIL import Image
from rapidocr_onnxruntime import RapidOCR
# ------------------------------------------------------------------
# Параметры
# ------------------------------------------------------------------
DPI = 300
BATCH_SIZE = 5
TILE_SIZE = 2000
TILE_OVERLAP = 200
engine = RapidOCR()
# qwen-vl-ocr lazy import
try:
from ocr_qwen import run_ocr as qwen_ocr
QWEN_AVAILABLE = True
except ImportError:
QWEN_AVAILABLE = False
# ------------------------------------------------------------------
# Tiling OCR helpers
# ------------------------------------------------------------------
def _make_tiles(img: Image.Image, tile_size: int = 2000, overlap: int = 200):
w, h = img.size
tiles = []
step = tile_size - overlap
for y in range(0, h, step):
for x in range(0, w, step):
x2 = min(x + tile_size, w)
y2 = min(y + tile_size, h)
tiles.append((x, y, img.crop((x, y, x2, y2))))
return tiles
def _bbox_iou(a, b):
def _rect(box):
if isinstance(box[0], list):
xs = [p[0] for p in box]
ys = [p[1] for p in box]
return min(xs), min(ys), max(xs), max(ys)
return box[0], box[1], box[2], box[3]
ax1, ay1, ax2, ay2 = _rect(a)
bx1, by1, bx2, by2 = _rect(b)
ix1, iy1 = max(ax1, bx1), max(ay1, by1)
ix2, iy2 = min(ax2, bx2), min(ay2, by2)
if ix2 <= ix1 or iy2 <= iy1:
return 0.0
inter = (ix2 - ix1) * (iy2 - iy1)
union = (ax2 - ax1) * (ay2 - ay1) + (bx2 - bx1) * (by2 - by1) - inter
return inter / union if union > 0 else 0.0
def run_tiling_ocr(img_path: Path, conf_threshold: float = 0.5):
"""Запускает RapidOCR по кропам и объединяет результаты."""
img = Image.open(img_path)
tiles = _make_tiles(img, TILE_SIZE, TILE_OVERLAP)
all_results = []
for off_x, off_y, crop in tiles:
tmp = f"/tmp/tile_ocr.png"
crop.save(tmp)
res = engine(tmp)
if res and res[0]:
for item in res[0]:
box, txt, score = item
if score < conf_threshold:
continue
shifted = [[pt[0] + off_x, pt[1] + off_y] for pt in box]
all_results.append({"text": txt, "confidence": float(score), "bbox": shifted})
# Дедупликация по IoU
unique = []
for r in sorted(all_results, key=lambda x: -x["confidence"]):
is_dup = any(_bbox_iou(r["bbox"], u["bbox"]) > 0.5 for u in unique)
if not is_dup:
unique.append(r)
return unique
# ------------------------------------------------------------------
def process_pdf(pdf_path: Path, out_dir: Path, use_qwen: bool = False, use_tiling: bool = False):
out_dir.mkdir(parents=True, exist_ok=True)
doc = fitz.open(pdf_path)
total = len(doc)
print(f"=== PDF: {pdf_path.name} | Страниц: {total} -> {out_dir} ===")
if use_qwen:
print(f"[INFO] OCR engine: qwen-vl-ocr (API)")
elif use_tiling:
print(f"[INFO] OCR engine: RapidOCR + tiling ({TILE_SIZE}px tiles)")
else:
print(f"[INFO] OCR engine: RapidOCR (local)")
print()
all_pages = []
for i in range(total):
print(f"[{i+1}/{total}] Рендер + OCR ...", end=" ", flush=True)
page = doc.load_page(i)
raw_text = page.get_text("text").strip()
mat = fitz.Matrix(DPI / 72, DPI / 72)
pix = page.get_pixmap(matrix=mat)
img_path = out_dir / f"page_{i+1:03d}.png"
pix.save(img_path)
# Выбор OCR engine
if use_qwen and QWEN_AVAILABLE:
try:
ocr_lines = qwen_ocr(img_path, verbose=False)
print(f"qwen-ocr строк: {len(ocr_lines)}")
except Exception as e:
print(f"qwen-ocr ERR: {e}, fallback to RapidOCR")
ocr_lines = _run_rapidocr(img_path)
print(f"RapidOCR строк: {len(ocr_lines)}")
elif use_tiling:
ocr_lines = run_tiling_ocr(img_path)
print(f"Tiling OCR строк: {len(ocr_lines)}")
else:
ocr_lines = _run_rapidocr(img_path)
print(f"RapidOCR строк: {len(ocr_lines)}")
all_pages.append({
"page_number": i + 1,
"image": str(img_path.name),
"pdf_text_layer": raw_text,
"ocr_lines": ocr_lines,
"ocr_line_count": len(ocr_lines)
})
if (i + 1) % BATCH_SIZE == 0 or i == total - 1:
with open(out_dir / "full_ocr_results.json", "w", encoding="utf-8") as f:
json.dump({"pages": all_pages}, f, ensure_ascii=False, indent=2)
print(f" -> сохранено ({i+1} страниц)")
doc.close()
print(f"\n=== Готово. Результат в {out_dir} ===")
def _run_rapidocr(img_path: Path):
res = engine(img_path)
ocr_lines = []
if res and res[0] is not None:
for item in res[0]:
box, txt, score = item
ocr_lines.append({
"text": txt,
"confidence": float(score),
"bbox": box
})
return ocr_lines
# ------------------------------------------------------------------
def main():
use_qwen = "--use-qwen" in sys.argv
use_tiling = "--use-tiling" in sys.argv
if use_qwen:
sys.argv.remove("--use-qwen")
if use_tiling:
sys.argv.remove("--use-tiling")
if len(sys.argv) < 2:
pdf_file = "123.pdf"
out_name = "output_123"
else:
pdf_file = sys.argv[1]
out_name = sys.argv[2] if len(sys.argv) > 2 else f"output_{Path(pdf_file).stem}"
pdf_path = Path(pdf_file) if Path(pdf_file).exists() else Path(".") / pdf_file
out_dir = Path(out_name)
if not pdf_path.exists():
print(f"[ERR] Файл не найден: {pdf_path}")
sys.exit(1)
process_pdf(pdf_path, out_dir, use_qwen=use_qwen, use_tiling=use_tiling)
if __name__ == "__main__":
main()