diff --git a/.gitignore b/.gitignore index 26e1e18..af33a29 100644 --- a/.gitignore +++ b/.gitignore @@ -7,4 +7,9 @@ __pycache__/ .DS_Store .venv/ venv/ +.serena/ +models/* +!models/.gitkeep +usb_bundle/ + diff --git a/README.md b/README.md index 2a28e97..fff366f 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ - 自动识别信封图片中的文字信息 - 结构化提取：编号、邮编、地址、联系人、电话 - 支持批量处理，结果导出为 Excel -- 提供 Web 界面，操作简单 +- 提供桌面应用，支持摄像头实时拍照识别 ## 系统要求 @@ -41,76 +41,51 @@ python src/main.py # 结果保存在 data/output/result.xlsx ``` -**Web 界面** +**桌面应用** ```bash -streamlit run src/app.py --server.port 8501 +python src/desktop.py -# 浏览器访问 http://localhost:8501 +# 启动 PyQt6 窗口，可选择摄像头实时拍照识别 ``` -## 部署方案 +--- -### 方案一：内网服务器部署（推荐） +## Windows 桌面离线版（zip 目录包） -适合多人使用，有内网环境的工厂。 +本项目桌面版入口为 `src/desktop.py`（PyQt6 + OpenCV），适合现场工位离线使用。 + +### 1. 准备离线模型（在有网机器执行一次） ```bash -# 启动服务（监听所有网卡） -streamlit run src/app.py --server.address 0.0.0.0 --server.port 8501 - -# 工人通过浏览器访问: http://服务器IP:8501 +pip install -r requirements.txt +python scripts/prepare_models.py --models-dir models ``` -### 方案二：Docker 容器化部署 +执行完成后会生成 `models/whl/...` 目录结构；该 `models/` 目录需要与最终的 exe 同级分发。 -适合需要隔离环境或快速部署的场景。 +### 2. Windows 打包（建议使用 PyInstaller 的 onedir） -```bash -# 构建镜像 -docker build -t envelope-ocr . +请在 Windows 机器上构建 Windows 包（不要跨平台交叉打包）。 -# 运行容器 -docker run -d -p 8501:8501 --name envelope-ocr envelope-ocr +```powershell +pip install -r requirements.txt +pip install pyinstaller + +pyinstaller --noconfirm --clean --windowed --onedir ` + --name "post-ocr-desktop" ` + --paths "src" ` + --collect-all "Cython" ` + --collect-all "paddleocr" ` + --collect-all "paddle" ` + --add-data "models;models" ` + "src/desktop.py" ``` -Dockerfile: -```dockerfile -FROM python:3.10-slim -RUN apt-get update && apt-get install -y libgl1-mesa-glx libglib2.0-0 && rm -rf /var/lib/apt/lists/* -WORKDIR /app -COPY . . -RUN pip install --no-cache-dir -r requirements.txt -EXPOSE 8501 -CMD ["streamlit", "run", "src/app.py", "--server.address", "0.0.0.0"] -``` +打包完成后，将 `dist\post-ocr-desktop\` 整个目录压缩为 zip 交付即可。 -### 方案三：系统服务（开机自启） - -适合长期稳定运行的生产环境。 - -创建服务文件 `/etc/systemd/system/envelope-ocr.service`: -```ini -[Unit] -Description=Envelope OCR Service -After=network.target - -[Service] -User=www-data -WorkingDirectory=/opt/post-ocr -ExecStart=/usr/bin/streamlit run src/app.py --server.address 0.0.0.0 --server.port 8501 -Restart=always -RestartSec=5 - -[Install] -WantedBy=multi-user.target -``` - -启用服务: -```bash -sudo systemctl daemon-reload -sudo systemctl enable envelope-ocr -sudo systemctl start envelope-ocr -``` +注意： +- 本项目默认使用 PaddleOCR 2.10.0（PP-OCRv4 中文）离线模型目录结构 +- 若 `models/` 缺失，程序会直接报错提示，避免触发联网下载 ## 目录结构 @@ -121,7 +96,7 @@ post-ocr/ │ └── output/ # 结果 Excel 及处理日志 ├── src/ │ ├── main.py # 命令行入口 -│ ├── app.py # Web 界面 +│ ├── desktop.py # 桌面应用入口 │ └── processor.py # 核心处理逻辑 ├── requirements.txt └── README.md @@ -130,7 +105,7 @@ post-ocr/ ## 技术栈 - OCR 引擎: PaddleOCR 2.10 (PP-OCRv4) -- Web 框架: Streamlit +- 桌面框架: PyQt6 - 数据处理: Pandas ## 常见问题 diff --git a/heartbeat.py b/heartbeat.py deleted file mode 100644 index 1373da2..0000000 --- a/heartbeat.py +++ /dev/null @@ -1,53 +0,0 @@ -#!/usr/bin/env python3 -"""心跳程序 - 保持服务活跃""" -import sys -import time -import subprocess -import requests -from datetime import datetime - -# 禁用输出缓冲 -sys.stdout.reconfigure(line_buffering=True) - - -def log(msg): - print(f"[{datetime.now():%Y-%m-%d %H:%M:%S}] {msg}", flush=True) - - -def check_streamlit(): - """检查 Streamlit 服务""" - try: - r = requests.get("http://localhost:8501", timeout=5) - return r.status_code == 200 - except: - return False - - -def restart_streamlit(): - """重启 Streamlit""" - subprocess.run(["pkill", "-f", "streamlit run"], capture_output=True) - time.sleep(2) - subprocess.Popen( - ["streamlit", "run", "src/app.py", "--server.port", "8501", "--server.address", "0.0.0.0"], - stdout=subprocess.DEVNULL, - stderr=subprocess.DEVNULL, - ) - print(f"[{datetime.now():%H:%M:%S}] Streamlit 已重启") - - -def main(): - log("心跳程序启动") - - while True: - if not check_streamlit(): - log("Streamlit 无响应，正在重启...") - restart_streamlit() - time.sleep(10) - else: - log("✓ 服务正常") - - time.sleep(60) # 每分钟检查一次 - - -if __name__ == "__main__": - main() diff --git a/requirements-desktop.txt b/requirements-desktop.txt deleted file mode 100644 index 4931fca..0000000 --- a/requirements-desktop.txt +++ /dev/null @@ -1,8 +0,0 @@ -# 桌面版依赖（本地电脑安装） -paddleocr>=2.6,<3 -paddlepaddle>=2.5,<3 -pandas -openpyxl -pydantic -PyQt6 -opencv-python diff --git a/requirements.txt b/requirements.txt index feb491a..4931fca 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,8 @@ +# 桌面版依赖（本地电脑安装） paddleocr>=2.6,<3 paddlepaddle>=2.5,<3 pandas openpyxl pydantic -tqdm -streamlit +PyQt6 +opencv-python diff --git a/scripts/camera_probe.py b/scripts/camera_probe.py new file mode 100755 index 0000000..9dd9d32 --- /dev/null +++ b/scripts/camera_probe.py @@ -0,0 +1,67 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +摄像头探测脚本（用于排查 macOS/iPhone 连续互通相机无画面问题） + +用法： + source .venv/bin/activate + python scripts/camera_probe.py + +输出： +- 列出 0~9 号摄像头是否可打开、是否可读到有效帧、帧尺寸与亮度均值 +""" + +from __future__ import annotations + +import sys + + +def open_cap(cv2, cam_id: int): + if sys.platform == "darwin" and hasattr(cv2, "CAP_AVFOUNDATION"): + return cv2.VideoCapture(cam_id, cv2.CAP_AVFOUNDATION) + return cv2.VideoCapture(cam_id) + + +def main() -> int: + import cv2 # pylint: disable=import-error + + print(f"平台: {sys.platform}") + print(f"OpenCV: {cv2.__version__}") + print("") + + found_any = False + for cam_id in range(10): + cap = open_cap(cv2, cam_id) + opened = cap.isOpened() + ok = False + shape = None + mean = None + if opened: + for _ in range(30): + ret, frame = cap.read() + if ret and frame is not None and frame.size > 0: + ok = True + shape = frame.shape + mean = float(frame.mean()) + break + cap.release() + + if opened: + found_any = True + status = "OK" if ok else ("打开但无画面" if opened else "无法打开") + print(f"摄像头 {cam_id}: {status}", end="") + if ok: + print(f" | shape={shape} | mean={mean:.1f}") + else: + print("") + + if not found_any: + print("\n未检测到可打开的摄像头。") + else: + print("\n如果出现“打开但无画面”，优先检查 macOS 相机权限。") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) + diff --git a/scripts/prepare_models.py b/scripts/prepare_models.py new file mode 100755 index 0000000..2ec9cab --- /dev/null +++ b/scripts/prepare_models.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +离线模型准备脚本（建议在“有网机器”执行一次） + +用途： +- 将 PaddleOCR 2.10.0（PP-OCRv4 中文）所需模型下载到指定 models/ 目录 +- 该 models/ 目录可直接随 Windows zip 目录包分发，实现完全离线运行 + +设计说明： +- 脚本只做“下载/补齐”，不做删除或覆盖，避免误删用户已有模型（高风险操作） +""" + +from __future__ import annotations + +import argparse +import os +from pathlib import Path + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="准备 post-ocr 离线模型（PP-OCRv4 中文）") + parser.add_argument( + "--models-dir", + default="models", + help="模型输出目录（默认：models，建议与 exe 同级）", + ) + parser.add_argument( + "--show-log", + action="store_true", + help="显示 PaddleOCR 初始化日志（默认关闭）", + ) + return parser.parse_args() + + +def main() -> int: + args = parse_args() + models_dir = Path(args.models_dir).resolve() + models_dir.mkdir(parents=True, exist_ok=True) + + # 关键：把 PaddleOCR 默认 base_dir 指到我们指定的 models/ + os.environ["PADDLE_PDX_DISABLE_MODEL_SOURCE_CHECK"] = "True" + os.environ["PADDLE_OCR_BASE_DIR"] = str(models_dir) + + # 延迟导入：确保环境变量在模块加载前生效 + from paddleocr import PaddleOCR # pylint: disable=import-error + + print(f"将下载/补齐模型到: {models_dir}") + print("首次执行需要联网下载（约数百 MB），请耐心等待。") + + # 初始化会自动下载 det/rec/cls 模型到 BASE_DIR/whl/... + PaddleOCR(lang="ch", show_log=args.show_log, use_angle_cls=False) + + print("完成。你可以将该 models/ 目录随 zip 目录包一起分发（与 exe 同级）。") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/app.py b/src/app.py deleted file mode 100644 index c6e2dd1..0000000 --- a/src/app.py +++ /dev/null @@ -1,247 +0,0 @@ -import os -import tempfile -import base64 -import pandas as pd -import streamlit as st -import streamlit.components.v1 as components -from paddleocr import PaddleOCR -from processor import extract_info, save_to_excel - -os.environ["PADDLE_PDX_DISABLE_MODEL_SOURCE_CHECK"] = "True" - -st.set_page_config( - page_title="信封信息提取系统", - page_icon="📮", - layout="centered", - initial_sidebar_state="collapsed", -) - -st.markdown(""" - -""", unsafe_allow_html=True) - -st.title("📮 信封信息提取") - - -@st.cache_resource -def load_ocr(): - return PaddleOCR(use_textline_orientation=True, lang="ch", show_log=False) - - -ocr = load_ocr() - - -def process_image(image_data): - """处理图片数据""" - with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp: - tmp.write(image_data) - tmp_path = tmp.name - - try: - result = ocr.ocr(tmp_path, cls=False) - ocr_texts = [] - if result and result[0]: - for line in result[0]: - if line and len(line) >= 2: - ocr_texts.append(line[1][0]) - return extract_info(ocr_texts), ocr_texts - finally: - os.unlink(tmp_path) - - -# 自定义摄像头组件，带叠加扫描框 -CAMERA_COMPONENT = """ -
- - - -
- -
-
-
-
- - -
邮编
-
地址
-
联系人
-
电话
-
- - -
- ↑ 编号在此处 ↑ -
- - - -

- 📌 将信封背面对齐绿色框，编号对准底部 -

- - -
- - -""" - -# 初始化 session state -if "records" not in st.session_state: - st.session_state.records = [] - -# 输入方式选择 -tab_camera, tab_upload = st.tabs(["📷 拍照扫描", "📁 上传图片"]) - -with tab_camera: - # 使用自定义摄像头组件 - photo_data = components.html(CAMERA_COMPONENT, height=550) - - # 检查是否有拍照数据 - if "captured_image" not in st.session_state: - st.session_state.captured_image = None - - # 文件上传作为备用（用于接收JS传来的数据） - uploaded_photo = st.file_uploader( - "或直接上传照片", - type=["jpg", "jpeg", "png"], - key="camera_upload", - label_visibility="collapsed" - ) - - if uploaded_photo: - with st.spinner("识别中..."): - record, raw_texts = process_image(uploaded_photo.getvalue()) - - st.success("✅ 识别完成！") - - col1, col2 = st.columns(2) - with col1: - st.image(uploaded_photo, caption="拍摄图片", use_container_width=True) - with col2: - st.metric("邮编", record.get("邮编", "-")) - st.metric("电话", record.get("电话", "-")) - st.metric("联系人", record.get("联系人/单位名", "-")) - - st.text_area("地址", record.get("地址", ""), disabled=True, height=68) - st.text_input("编号", record.get("编号", ""), disabled=True) - - if st.button("✅ 添加到列表", type="primary", key="add_camera"): - record["来源"] = "拍照" - st.session_state.records.append(record) - st.success(f"已添加！当前共 {len(st.session_state.records)} 条记录") - st.rerun() - -with tab_upload: - uploaded_files = st.file_uploader( - "选择图片文件", - type=["jpg", "jpeg", "png", "bmp"], - accept_multiple_files=True, - label_visibility="collapsed", - ) - - if uploaded_files: - if st.button("🚀 开始识别", type="primary"): - progress = st.progress(0) - - for i, file in enumerate(uploaded_files): - with st.spinner(f"处理 {file.name}..."): - record, _ = process_image(file.getvalue()) - record["来源"] = file.name - st.session_state.records.append(record) - progress.progress((i + 1) / len(uploaded_files)) - - st.success(f"完成！已添加 {len(uploaded_files)} 条记录") - st.rerun() - -# 显示已收集的记录 -st.divider() -st.subheader(f"📋 已收集 {len(st.session_state.records)} 条记录") - -if st.session_state.records: - df = pd.DataFrame(st.session_state.records) - cols = ["来源", "编号", "邮编", "地址", "联系人/单位名", "电话"] - df = df.reindex(columns=[c for c in cols if c in df.columns]) - - st.dataframe(df, use_container_width=True, hide_index=True) - - col1, col2 = st.columns(2) - - with col1: - output_path = tempfile.mktemp(suffix=".xlsx") - df.to_excel(output_path, index=False) - with open(output_path, "rb") as f: - st.download_button( - "📥 下载 Excel", - data=f, - file_name="信封提取结果.xlsx", - mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", - ) - os.unlink(output_path) - - with col2: - if st.button("🗑️ 清空列表"): - st.session_state.records = [] - st.rerun() -else: - st.info("👆 使用上方拍照或上传功能添加记录") diff --git a/src/desktop.py b/src/desktop.py index c1be899..3b69e97 100644 --- a/src/desktop.py +++ b/src/desktop.py @@ -6,8 +6,11 @@ import os import sys import cv2 -import tempfile import pandas as pd +import time +import logging +import threading +import queue from datetime import datetime from pathlib import Path @@ -17,55 +20,232 @@ from PyQt6.QtWidgets import ( QFileDialog, QMessageBox, QGroupBox, QSplitter, QHeaderView, QStatusBar, QProgressBar ) -from PyQt6.QtCore import Qt, QTimer, QThread, pyqtSignal -from PyQt6.QtGui import QImage, QPixmap, QFont, QAction +from PyQt6.QtCore import Qt, QTimer, pyqtSignal, QObject, pyqtSlot +from PyQt6.QtGui import QImage, QPixmap, QFont, QAction, QKeySequence, QShortcut -from paddleocr import PaddleOCR from processor import extract_info +from ocr_offline import create_offline_ocr, get_models_base_dir os.environ["PADDLE_PDX_DISABLE_MODEL_SOURCE_CHECK"] = "True" +logger = logging.getLogger("post_ocr.desktop") -class OCRWorker(QThread): - """OCR 识别线程""" - finished = pyqtSignal(dict, list) - error = pyqtSignal(str) - def __init__(self, ocr, image_path): +def setup_logging() -> Path: + """ + 日志输出： + - 终端实时打印 + - 写入 data/output/desktop.log（便于用户反馈与排查） + """ + + level_name = os.environ.get("POST_OCR_LOG_LEVEL", "INFO").upper().strip() + level = getattr(logging, level_name, logging.INFO) + + log_dir = Path("data/output").resolve() + log_dir.mkdir(parents=True, exist_ok=True) + log_file = log_dir / "desktop.log" + + fmt = "%(asctime)s.%(msecs)03d %(levelname)s [%(threadName)s] %(name)s: %(message)s" + datefmt = "%Y-%m-%d %H:%M:%S" + + root = logging.getLogger() + root.setLevel(level) + + # 清理旧 handler，避免重复输出 + for h in list(root.handlers): + root.removeHandler(h) + + sh = logging.StreamHandler(stream=sys.stdout) + sh.setLevel(level) + sh.setFormatter(logging.Formatter(fmt=fmt, datefmt=datefmt)) + root.addHandler(sh) + + fh = logging.FileHandler(log_file, encoding="utf-8") + fh.setLevel(level) + fh.setFormatter(logging.Formatter(fmt=fmt, datefmt=datefmt)) + root.addHandler(fh) + + logger.info("日志已初始化，level=%s, file=%s", level_name, str(log_file)) + return log_file + + +class OCRService(QObject): + """ + OCR 后台服务（运行在标准 Python 线程内）。 + + 关键点： + - 避免使用 QThread：在 macOS 上，QThread(Dummy-*) 内 import paddleocr 可能卡死 + - PaddleOCR 实例在后台线程内创建并使用，避免跨线程调用导致卡死/死锁 + - 单线程串行处理任务：避免并发推理挤爆内存或引发底层库竞争 + """ + + finished = pyqtSignal(int, dict, list) + error = pyqtSignal(int, str) + ready = pyqtSignal() + init_error = pyqtSignal(str) + busy_changed = pyqtSignal(bool) + + def __init__(self, models_base_dir: Path): super().__init__() - self.ocr = ocr - self.image_path = image_path + self._models_base_dir = models_base_dir + self._ocr = None + self._busy = False + self._stop_event = threading.Event() + self._queue: "queue.Queue[tuple[int, object] | None]" = queue.Queue() + self._thread = threading.Thread(target=self._run, name="OCRThread", daemon=True) + + def _set_busy(self, busy: bool) -> None: + if self._busy != busy: + self._busy = busy + self.busy_changed.emit(busy) + + def start(self) -> None: + """启动后台线程并执行 warmup。""" + + self._thread.start() + + def stop(self, timeout_ms: int = 8000) -> bool: + """请求停止后台线程并等待退出（后台线程为 daemon，退出失败也不阻塞进程）。""" - def run(self): try: - result = self.ocr.ocr(self.image_path, cls=False) - ocr_texts = [] - if result and result[0]: - for line in result[0]: - if line and len(line) >= 2: - ocr_texts.append(line[1][0]) - record = extract_info(ocr_texts) - self.finished.emit(record, ocr_texts) + self._stop_event.set() + # 用 sentinel 唤醒阻塞在 queue.get() 的线程 + try: + self._queue.put_nowait(None) + except Exception: + pass + self._thread.join(timeout=max(0.0, timeout_ms / 1000.0)) + return not self._thread.is_alive() + except Exception: + return False + + def _ensure_ocr(self) -> None: + if self._ocr is None: + logger.info("OCR ensure_ocr: 开始创建 PaddleOCR（线程=%s）", threading.current_thread().name) + self._ocr = create_offline_ocr(models_base_dir=self._models_base_dir, show_log=False) + logger.info("OCR ensure_ocr: PaddleOCR 创建完成") + self.ready.emit() + + def _warmup(self) -> None: + """提前加载 OCR 模型，避免首次识别时才初始化导致“像卡死”""" + + logger.info("OCR 预热开始（线程=%s）", threading.current_thread().name) + self._ensure_ocr() + logger.info("OCR 预热完成") + + def _run(self) -> None: + try: + self._warmup() except Exception as e: - self.error.emit(str(e)) + logger.exception("OCR 预热失败：%s", str(e)) + self.init_error.emit(str(e)) + return + + while not self._stop_event.is_set(): + item = None + try: + item = self._queue.get() + except Exception: + continue + + if item is None: + # sentinel: stop + break + + job_id, images = item + if self._stop_event.is_set(): + break + self._process_job(job_id, images) + + @pyqtSlot(int, object) + def process(self, job_id: int, images: object) -> None: + """接收 UI 请求：把任务放进队列，由后台线程串行处理。""" + + if self._stop_event.is_set(): + self.error.emit(job_id, "OCR 服务正在关闭，请稍后重试。") + return + # 忙碌或已有排队任务时，直接拒绝，避免积压导致“看起来一直在识别” + if self._busy or (not self._queue.empty()): + self.error.emit(job_id, "OCR 正在进行中，请稍后再试。") + return + try: + # 注意：这里不做耗时工作，只入队，避免阻塞 UI + self._queue.put_nowait((job_id, images)) + except Exception as e: + self.error.emit(job_id, f"OCR 入队失败：{str(e)}") + + def _process_job(self, job_id: int, images: object) -> None: + self._set_busy(True) + try: + self._ensure_ocr() + if not isinstance(images, (list, tuple)) or len(images) == 0: + raise ValueError("内部错误：未传入有效图片数据") + + shapes = [] + for img in images: + try: + shapes.append(getattr(img, "shape", None)) + except Exception: + shapes.append(None) + logger.info("OCR job=%s 开始，images=%s", job_id, shapes) + + ocr_texts: list[str] = [] + for img in images: + if img is None: + continue + result = self._ocr.ocr(img, cls=False) + if result and result[0]: + for line in result[0]: + if line and len(line) >= 2: + ocr_texts.append(line[1][0]) + + record = extract_info(ocr_texts) + logger.info( + "OCR job=%s 完成，lines=%s, record_keys=%s", + job_id, + len(ocr_texts), + list(record.keys()), + ) + self.finished.emit(job_id, record, ocr_texts) + except Exception as e: + logger.exception("OCR job=%s 失败：%s", job_id, str(e)) + self.error.emit(job_id, str(e)) + finally: + self._set_busy(False) class MainWindow(QMainWindow): + request_ocr = pyqtSignal(int, object) + def __init__(self): super().__init__() self.setWindowTitle("📮 信封信息提取系统") self.setMinimumSize(1200, 700) - # 初始化 OCR - self.statusBar().showMessage("正在加载 OCR 模型...") - QApplication.processEvents() - self.ocr = PaddleOCR(use_textline_orientation=True, lang="ch", show_log=False) - self.statusBar().showMessage("OCR 模型加载完成") + # OCR 工作线程（避免 UI 卡死） + self._ocr_job_id = 0 + self._ocr_start_time_by_job: dict[int, float] = {} + self._ocr_ready = False + self._ocr_busy = False + self._shutting_down = False + self._ocr_timeout_prompted = False # 摄像头 self.cap = None self.timer = QTimer() self.timer.timeout.connect(self.update_frame) + self._frame_fail_count = 0 + + # 状态栏进度（识别中显示） + self._progress = QProgressBar() + self._progress.setMaximumWidth(160) + self._progress.setVisible(False) + self.statusBar().addPermanentWidget(self._progress) + + # OCR 看门狗：显示耗时、并在疑似卡住时提示重启 + self._ocr_watchdog = QTimer() + self._ocr_watchdog.setInterval(300) + self._ocr_watchdog.timeout.connect(self._tick_ocr_watchdog) # 数据 self.records = [] @@ -73,6 +253,191 @@ class MainWindow(QMainWindow): self.init_ui() self.load_cameras() + # 主线程预加载：在 macOS 上，必须在主线程 import paddleocr，否则后台线程会卡死 + self.statusBar().showMessage("正在加载 OCR 模块...") + QApplication.processEvents() + try: + logger.info("主线程预加载：import paddleocr") + import paddleocr # noqa: F401 + logger.info("主线程预加载：paddleocr 导入完成") + except Exception as e: + logger.error("主线程预加载失败：%s", e, exc_info=True) + QMessageBox.critical(self, "启动失败", f"无法加载 OCR 模块：{e}") + raise + + # OCR 服务放在 UI 初始化之后启动，避免 ready/busy 信号回调时 btn_capture 尚未创建 + self.statusBar().showMessage("正在启动 OCR 服务...") + QApplication.processEvents() + try: + self._init_ocr_service() + except FileNotFoundError as e: + QMessageBox.critical(self, "离线模型缺失", str(e)) + raise + except Exception as e: + QMessageBox.critical(self, "启动失败", str(e)) + raise + + def shutdown(self, force: bool = False) -> None: + """停止摄像头并关闭后台服务，避免退出时后台任务仍在运行。""" + + if self._shutting_down: + return + self._shutting_down = True + + # 先停止摄像头，避免继续读帧 + try: + if self.cap: + self.timer.stop() + self.cap.release() + self.cap = None + except Exception: + pass + + try: + self._stop_ocr_service(force=force) + except Exception: + pass + + def _stop_ocr_service(self, force: bool = False) -> None: + """仅停止 OCR 服务（用于超时重启/退出）。""" + + try: + self._ocr_watchdog.stop() + except Exception: + pass + + self._ocr_ready = False + self._ocr_busy = False + self._ocr_timeout_prompted = False + try: + self._progress.setVisible(False) + except Exception: + pass + + try: + svc = getattr(self, "_ocr_service", None) + if svc is not None: + ok = svc.stop(timeout_ms=8000 if force else 3000) + if (not ok) and force: + # Python 线程无法可靠“强杀”，这里只做提示并继续退出流程。 + logger.warning("OCR 服务停止超时：后台线程可能仍在运行，建议重启应用。") + except Exception: + pass + + try: + self._ocr_service = None + except Exception: + pass + + def _restart_ocr_service(self) -> None: + """重启 OCR 服务（用于超时恢复）。""" + + if self._shutting_down: + return + self.statusBar().showMessage("正在重启 OCR 服务...") + self._stop_ocr_service(force=True) + self._init_ocr_service() + + def _init_ocr_service(self) -> None: + models_dir = get_models_base_dir() + + # 先校验模型路径是否存在（缺失直接抛错给 UI） + # create_offline_ocr 内部会做更完整校验，这里不提前创建模型，避免阻塞 UI + if not models_dir.exists(): + raise FileNotFoundError(f"离线模型目录不存在：{models_dir}") + + self._ocr_service = OCRService(models_base_dir=models_dir) + + # 注意：OCRService 内部使用 Python 线程做 warmup 与推理。 + # 这里强制使用 QueuedConnection，确保 UI 回调始终在主线程执行。 + self.request_ocr.connect(self._ocr_service.process, Qt.ConnectionType.QueuedConnection) + self._ocr_service.ready.connect(self._on_ocr_ready, Qt.ConnectionType.QueuedConnection) + self._ocr_service.init_error.connect(self._on_ocr_init_error, Qt.ConnectionType.QueuedConnection) + self._ocr_service.busy_changed.connect(self._on_ocr_busy_changed, Qt.ConnectionType.QueuedConnection) + self._ocr_service.finished.connect(self._on_ocr_finished_job, Qt.ConnectionType.QueuedConnection) + self._ocr_service.error.connect(self._on_ocr_error_job, Qt.ConnectionType.QueuedConnection) + + self._ocr_service.start() + + def _on_ocr_ready(self) -> None: + try: + self._ocr_ready = True + self.statusBar().showMessage("OCR 模型已加载（离线）") + btn = getattr(self, "btn_capture", None) + if btn is not None: + btn.setEnabled(self.cap is not None and not self._ocr_busy) + logger.info("OCR ready") + except Exception as e: + logger.exception("处理 OCR ready 回调失败：%s", str(e)) + + def _on_ocr_init_error(self, error: str) -> None: + self.statusBar().showMessage("OCR 模型加载失败") + QMessageBox.critical(self, "OCR 初始化失败", error) + logger.error("OCR init error: %s", error) + + def _on_ocr_busy_changed(self, busy: bool) -> None: + try: + self._ocr_busy = busy + if busy: + self._progress.setRange(0, 0) # 不确定进度条 + self._progress.setVisible(True) + self._ocr_timeout_prompted = False + self._ocr_watchdog.start() + else: + self._progress.setVisible(False) + self._ocr_watchdog.stop() + btn = getattr(self, "btn_capture", None) + if btn is not None: + btn.setEnabled(self.cap is not None and self._ocr_ready and not busy) + except Exception as e: + logger.exception("处理 OCR busy 回调失败：%s", str(e)) + + def _tick_ocr_watchdog(self) -> None: + """识别进行中：更新耗时，超时则提示是否重启 OCR 服务。""" + + if not self._ocr_busy: + return + start_t = self._ocr_start_time_by_job.get(self._ocr_job_id) + if start_t is None: + return + cost = time.monotonic() - start_t + self.statusBar().showMessage(f"正在识别...（已用 {cost:.1f}s）") + + # 超时保护：底层推理偶发卡住时，让用户可以自救 + if cost >= 45 and not self._ocr_timeout_prompted: + self._ocr_timeout_prompted = True + reply = QMessageBox.question( + self, + "识别超时", + "识别已超过 45 秒仍未完成，可能卡住。\n\n是否重启 OCR 服务？\n（若仍无响应，建议直接退出并重新打开应用）", + QMessageBox.StandardButton.Yes | QMessageBox.StandardButton.No, + ) + if reply == QMessageBox.StandardButton.Yes: + self._restart_ocr_service() + + def _on_ocr_finished_job(self, job_id: int, record: dict, texts: list) -> None: + start_t = self._ocr_start_time_by_job.pop(job_id, None) + + # 只处理最新一次请求，避免旧结果回写 + if job_id != self._ocr_job_id: + return + + self.records.append(record) + self.update_table() + cost = "" + if start_t is not None: + cost = f"（耗时 {time.monotonic() - start_t:.1f}s）" + self.statusBar().showMessage(f"识别完成: {record.get('联系人/单位名', '未知')}{cost}") + logger.info("OCR job=%s UI 回写完成 %s", job_id, cost) + + def _on_ocr_error_job(self, job_id: int, error: str) -> None: + self._ocr_start_time_by_job.pop(job_id, None) + if job_id != self._ocr_job_id: + return + self.statusBar().showMessage("识别失败") + QMessageBox.warning(self, "识别失败", error) + logger.error("OCR job=%s error: %s", job_id, error) + def init_ui(self): central = QWidget() self.setCentralWidget(central) @@ -111,7 +476,7 @@ class MainWindow(QMainWindow): self.btn_capture.setFont(QFont("", 14)) self.btn_capture.setStyleSheet("background-color: #ff4b4b; color: white; border-radius: 8px;") self.btn_capture.clicked.connect(self.capture_and_recognize) - self.btn_capture.setEnabled(False) + self.btn_capture.setEnabled(False) # 等摄像头连接 + OCR ready 后启用 left_layout.addWidget(self.btn_capture) # 右侧：结果列表 @@ -152,27 +517,81 @@ class MainWindow(QMainWindow): layout.addWidget(splitter) # 快捷键 - self.shortcut_capture = QAction(self) - self.shortcut_capture.setShortcut("Space") - self.shortcut_capture.triggered.connect(self.capture_and_recognize) - self.addAction(self.shortcut_capture) + # macOS/Qt 下 Space 经常被控件吞掉（按钮激活/表格选择等），用 ApplicationShortcut 更稳 + self._shortcut_capture2 = QShortcut(QKeySequence("Space"), self) + self._shortcut_capture2.setContext(Qt.ShortcutContext.ApplicationShortcut) + self._shortcut_capture2.activated.connect(self.capture_and_recognize) def load_cameras(self): """扫描可用摄像头""" self.cam_combo.clear() - for i in range(10): - cap = cv2.VideoCapture(i) - if cap.isOpened(): - ret, _ = cap.read() - if ret: - self.cam_combo.addItem(f"摄像头 {i}", i) - cap.release() + # macOS 上设备编号会变化（尤其“连续互通相机”/虚拟摄像头），这里多扫一些更稳。 + # 若你想减少探测范围，可设置环境变量 POST_OCR_MAX_CAMERAS，例如：POST_OCR_MAX_CAMERAS=3 + try: + max_probe = int(os.environ.get("POST_OCR_MAX_CAMERAS", "").strip() or "10") + except Exception: + max_probe = 10 + logger.info("开始扫描摄像头：max_probe=%s", max_probe) - if self.cam_combo.count() == 0: - self.cam_combo.addItem("未检测到摄像头", -1) - self.statusBar().showMessage("未检测到摄像头，请连接 Droidcam") + found = 0 + for i in range(max_probe): + cap = None + try: + cap = self._open_capture(i) + if cap is None or (not cap.isOpened()): + continue + + # 暖机：有些设备首帧为空或延迟较大（尤其手机/虚拟摄像头） + has_frame = False + for _ in range(25): + ret, frame = cap.read() + if ret and frame is not None and frame.size > 0: + has_frame = True + break + label = f"摄像头 {i}" if has_frame else f"摄像头 {i}（未验证画面）" + self.cam_combo.addItem(label, i) + logger.info("摄像头探测：id=%s opened, has_frame=%s", i, has_frame) + found += 1 + finally: + try: + if cap is not None: + cap.release() + except Exception: + pass + + if found == 0: + # 自动探测可能因权限/占用/设备延迟失败；仍提供手动尝试入口，避免用户被“无设备”卡住 + for i in range(max_probe): + self.cam_combo.addItem(f"摄像头 {i}（手动尝试）", i) + self.statusBar().showMessage( + "未能自动检测到可用摄像头。" + "如为 macOS，请在 系统设置->隐私与安全->相机 中允许当前终端/应用访问；" + "并确保 iPhone 已解锁且未被其他应用占用。" + ) else: - self.statusBar().showMessage(f"检测到 {self.cam_combo.count()} 个摄像头") + self.statusBar().showMessage(f"检测到 {found} 个摄像头") + logger.info("摄像头扫描结束：found=%s", found) + + def _open_capture(self, cam_id: int): + """ + 打开摄像头。 + + macOS 上优先使用 AVFoundation 后端（对“连续互通相机”等更友好）。 + """ + + if sys.platform == "darwin" and hasattr(cv2, "CAP_AVFOUNDATION"): + cap = cv2.VideoCapture(cam_id, cv2.CAP_AVFOUNDATION) + try: + if cap is not None and cap.isOpened(): + return cap + except Exception: + pass + try: + if cap is not None: + cap.release() + except Exception: + pass + return cv2.VideoCapture(cam_id) def toggle_camera(self): """连接/断开摄像头""" @@ -182,19 +601,51 @@ class MainWindow(QMainWindow): QMessageBox.warning(self, "错误", "请先选择有效的摄像头") return - self.cap = cv2.VideoCapture(cam_id) - self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280) - self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720) + self.cap = self._open_capture(cam_id) if self.cap.isOpened(): + # 不强制分辨率：某些设备（尤其虚拟摄像头/连续互通相机）被强设后会输出黑屏 + # self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280) + # self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720) + + # 暖机读取，尽早发现“能打开但无画面”的情况 + ok = False + for _ in range(20): + ret, frame = self.cap.read() + if ret and frame is not None and frame.size > 0: + ok = True + break + if not ok: + self.cap.release() + self.cap = None + QMessageBox.warning( + self, + "摄像头无画面", + "摄像头已打开，但读取不到画面。\n\n" + "排查建议：\n" + "1) macOS：系统设置 -> 隐私与安全 -> 相机，允许当前运行的终端/应用访问\n" + "2) 连续互通相机：保持 iPhone 解锁并靠近 Mac，且未被其他应用占用\n" + "3) 依次切换“摄像头 0/1/2”尝试\n", + ) + return + self.timer.start(30) # ~33 FPS self.btn_connect.setText("⏹ 断开") - self.btn_capture.setEnabled(True) + self.btn_capture.setEnabled(self._ocr_ready and not self._ocr_busy) self.cam_combo.setEnabled(False) self.statusBar().showMessage("摄像头已连接") else: self.cap = None - QMessageBox.warning(self, "错误", "无法打开摄像头") + QMessageBox.warning( + self, + "无法打开摄像头", + "无法打开摄像头。\n\n" + "排查建议：\n" + "1) macOS：系统设置 -> 隐私与安全 -> 相机，允许当前运行的终端/应用访问\n" + "2) 如果有其他应用正在使用摄像头（微信/会议软件/浏览器），请先退出再试\n" + "3) 连续互通相机：保持 iPhone 解锁并靠近 Mac，且未被其他应用占用\n" + "4) 在下拉框中切换不同编号（0/1/2/3...）重试\n", + ) else: self.timer.stop() self.cap.release() @@ -211,7 +662,8 @@ class MainWindow(QMainWindow): return ret, frame = self.cap.read() - if ret: + if ret and frame is not None and frame.size > 0: + self._frame_fail_count = 0 # 绘制扫描框 h, w = frame.shape[:2] # 框的位置：上方 70%，编号在下方 @@ -247,10 +699,21 @@ class MainWindow(QMainWindow): qimg = QImage(rgb.data, w, h, ch * w, QImage.Format.Format_RGB888) scaled = qimg.scaled(self.video_label.size(), Qt.AspectRatioMode.KeepAspectRatio, Qt.TransformationMode.SmoothTransformation) self.video_label.setPixmap(QPixmap.fromImage(scaled)) + else: + self._frame_fail_count += 1 + if self._frame_fail_count == 1: + self.statusBar().showMessage("摄像头无画面：请检查权限/切换摄像头") def capture_and_recognize(self): """拍照并识别""" if self.cap is None: + self.statusBar().showMessage("请先连接摄像头") + return + if not self._ocr_ready: + self.statusBar().showMessage("OCR 模型尚未就绪，请稍等") + return + if self._ocr_busy: + self.statusBar().showMessage("正在识别中，请稍后再按空格") return ret, frame = self.cap.read() @@ -258,35 +721,56 @@ class MainWindow(QMainWindow): self.statusBar().showMessage("拍照失败") return - # 保存临时文件 - tmp_path = tempfile.mktemp(suffix=".jpg") - cv2.imwrite(tmp_path, frame) + # 裁剪两块 ROI（主信息框 + 编号区域），显著减小像素量，提升速度与稳定性 + h, w = frame.shape[:2] + x1, y1 = int(w * 0.06), int(h * 0.08) + x2 = int(w * 0.94) + y2_box = int(h * 0.78) + + roi_images = [] + try: + roi_box = frame[y1:y2_box, x1:x2] + if roi_box is not None and roi_box.size > 0: + roi_images.append(roi_box) + except Exception: + pass + + try: + # 编号一般在底部中间，取较小区域即可 + nx1, nx2 = int(w * 0.30), int(w * 0.70) + ny1, ny2 = int(h * 0.80), int(h * 0.98) + roi_num = frame[ny1:ny2, nx1:nx2] + if roi_num is not None and roi_num.size > 0: + roi_images.append(roi_num) + except Exception: + pass + + if not roi_images: + self.statusBar().showMessage("拍照失败：未截取到有效区域") + return + + # 超大分辨率下适当缩放（提高稳定性与速度） + resized_images = [] + for img in roi_images: + try: + max_w = 1400 + if img.shape[1] > max_w: + scale = max_w / img.shape[1] + img = cv2.resize(img, (int(img.shape[1] * scale), int(img.shape[0] * scale))) + except Exception: + pass + resized_images.append(img) + + logger.info("UI 触发识别：frame=%s, rois=%s", getattr(frame, "shape", None), [getattr(i, "shape", None) for i in resized_images]) self.statusBar().showMessage("正在识别...") self.btn_capture.setEnabled(False) - # 启动 OCR 线程 - self.worker = OCRWorker(self.ocr, tmp_path) - self.worker.finished.connect(lambda r, t: self.on_ocr_finished(r, t, tmp_path)) - self.worker.error.connect(lambda e: self.on_ocr_error(e, tmp_path)) - self.worker.start() - - def on_ocr_finished(self, record, texts, tmp_path): - """OCR 完成""" - os.unlink(tmp_path) - self.btn_capture.setEnabled(True) - - # 添加到记录 - self.records.append(record) - self.update_table() - - self.statusBar().showMessage(f"识别完成: {record.get('联系人/单位名', '未知')}") - - def on_ocr_error(self, error, tmp_path): - """OCR 错误""" - os.unlink(tmp_path) - self.btn_capture.setEnabled(True) - self.statusBar().showMessage(f"识别失败: {error}") + # 派发到 OCR 工作线程 + self._ocr_job_id += 1 + job_id = self._ocr_job_id + self._ocr_start_time_by_job[job_id] = time.monotonic() + self.request_ocr.emit(job_id, resized_images) def update_table(self): """更新表格""" @@ -334,18 +818,33 @@ class MainWindow(QMainWindow): def closeEvent(self, event): """关闭窗口""" - if self.cap: - self.timer.stop() - self.cap.release() + if self._ocr_busy: + reply = QMessageBox.question( + self, + "正在识别", + "当前正在识别，直接关闭可能导致任务中断。\n\n是否强制退出？", + QMessageBox.StandardButton.Yes | QMessageBox.StandardButton.No, + ) + if reply == QMessageBox.StandardButton.No: + event.ignore() + return + self.shutdown(force=True) + event.accept() + return + + self.shutdown(force=False) event.accept() def main(): + log_file = setup_logging() app = QApplication(sys.argv) app.setStyle("Fusion") window = MainWindow() window.show() + app.aboutToQuit.connect(lambda: window.shutdown(force=False)) + logger.info("应用启动完成，PID=%s，日志=%s", os.getpid(), str(log_file)) sys.exit(app.exec()) diff --git a/src/ocr_offline.py b/src/ocr_offline.py new file mode 100644 index 0000000..2af82d0 --- /dev/null +++ b/src/ocr_offline.py @@ -0,0 +1,178 @@ +# -*- coding: utf-8 -*- +""" +离线 OCR 初始化工具 + +目标： +1. Windows 交付 zip 目录包时，模型随包携带，程序完全离线可用 +2. 如果模型缺失，明确报错并阻止 PaddleOCR 自动联网下载 +3. 统一桌面版 / Web 版 / 命令行的 OCR 初始化逻辑，避免参数漂移 +""" + +from __future__ import annotations + +import os +import sys +from dataclasses import dataclass +from pathlib import Path +import logging + + +@dataclass(frozen=True) +class OCRModelPaths: + """PP-OCRv4（中文）模型目录结构（对应 paddleocr==2.10.0 默认下载结构）""" + + base_dir: Path + det_dir: Path + rec_dir: Path + cls_dir: Path + + +def _is_frozen() -> bool: + """判断是否为 PyInstaller 打包后的运行环境""" + + return bool(getattr(sys, "frozen", False)) + + +def get_app_base_dir() -> Path: + """ + 获取“应用根目录”： + - 开发态：项目根目录（src 的上一级） + - 打包态：exe 所在目录 + """ + + if _is_frozen(): + return Path(sys.executable).resolve().parent + return Path(__file__).resolve().parent.parent + + +def get_models_base_dir(app_base_dir: Path | None = None) -> Path: + """默认模型目录：与应用同级的 models/""" + + base = app_base_dir or get_app_base_dir() + return base / "models" + + +def get_ppocr_v4_ch_model_paths(models_base_dir: Path | None = None) -> OCRModelPaths: + """ + 返回 PP-OCRv4（中文）默认模型目录。 + + 注意：这里的目录结构与 PaddleOCR 2.x 默认下载到 ~/.paddleocr 的结构一致， + 只是我们把 BASE_DIR 指向了随包的 models/，从而实现离线。 + """ + + base = models_base_dir or get_models_base_dir() + det_dir = base / "whl" / "det" / "ch" / "ch_PP-OCRv4_det_infer" + rec_dir = base / "whl" / "rec" / "ch" / "ch_PP-OCRv4_rec_infer" + cls_dir = base / "whl" / "cls" / "ch_ppocr_mobile_v2.0_cls_infer" + return OCRModelPaths(base_dir=base, det_dir=det_dir, rec_dir=rec_dir, cls_dir=cls_dir) + + +def _configure_windows_dll_search_path(app_base_dir: Path) -> None: + """ + Windows 下 PaddlePaddle 依赖的 mkml.dll 等动态库，通常位于打包目录的： + - /_internal/paddle/libs + + 某些情况下动态库加载不会自动命中该路径（error code 126），需要显式加入 DLL 搜索路径。 + """ + + if not sys.platform.startswith("win"): + return + + # Python 3.8+ on Windows 支持 os.add_dll_directory + add_dll_dir = getattr(os, "add_dll_directory", None) + internal_dir = app_base_dir / "_internal" + + candidates = [ + internal_dir / "paddle" / "libs", + internal_dir / "paddle", + internal_dir, + app_base_dir, + ] + + # 同时设置 PATH，兼容不走 add_dll_directory 的加载路径 + path_parts = [os.environ.get("PATH", "")] + for p in candidates: + if p.exists(): + if add_dll_dir is not None: + try: + add_dll_dir(str(p)) + except Exception: + # add_dll_directory 在某些权限/路径场景可能失败，PATH 兜底 + pass + path_parts.insert(0, str(p)) + os.environ["PATH"] = ";".join([x for x in path_parts if x]) + + +def _check_infer_dir(dir_path: Path) -> bool: + """判断一个推理模型目录是否完整（至少包含 inference.pdmodel / inference.pdiparams）""" + + return (dir_path / "inference.pdmodel").exists() and (dir_path / "inference.pdiparams").exists() + + +def verify_offline_models_or_raise(model_paths: OCRModelPaths) -> None: + """ + 校验离线模型是否存在。 + + 设计选择： + - 直接抛异常：由上层（桌面/UI/CLI）决定如何展示错误 + - 不允许缺失时继续初始化：避免触发 PaddleOCR 自动联网下载 + """ + + missing = [] + if not _check_infer_dir(model_paths.det_dir): + missing.append(str(model_paths.det_dir)) + if not _check_infer_dir(model_paths.rec_dir): + missing.append(str(model_paths.rec_dir)) + if not _check_infer_dir(model_paths.cls_dir): + missing.append(str(model_paths.cls_dir)) + + if missing: + hint = ( + "离线模型缺失，无法在离线模式启动。\n\n" + "缺失目录：\n- " + + "\n- ".join(missing) + + "\n\n" + "解决方式：\n" + "1) 在有网机器执行：python scripts/prepare_models.py --models-dir models\n" + "2) 将生成的 models/ 目录随 zip 包一起分发（与 exe 同级）" + ) + raise FileNotFoundError(hint) + + +def create_offline_ocr(models_base_dir: Path | None = None, show_log: bool = False): + """ + 创建 PaddleOCR（离线模式）。 + + 关键点： + - 通过环境变量 PADDLE_OCR_BASE_DIR 将默认下载/查找目录指向随包 models/（与 paddleocr==2.10.0 行为匹配） + - 显式传入 det/rec/cls 的模型目录，避免目录不一致导致重复下载 + - 如果模型缺失，提前报错，阻止联网下载 + """ + + log = logging.getLogger("post_ocr.ocr") + model_paths = get_ppocr_v4_ch_model_paths(models_base_dir=models_base_dir) + verify_offline_models_or_raise(model_paths) + + # Windows 打包运行时，先配置 DLL 搜索路径，避免 mkml.dll 等加载失败（error code 126） + _configure_windows_dll_search_path(get_app_base_dir()) + + # 禁用联网检查（加快启动），并把默认 base_dir 指向随包 models/ + os.environ["PADDLE_PDX_DISABLE_MODEL_SOURCE_CHECK"] = "True" + os.environ["PADDLE_OCR_BASE_DIR"] = str(model_paths.base_dir) + + # 延迟导入：确保环境变量在 paddleocr 模块加载前设置生效 + log.info("create_offline_ocr: importing paddleocr (base_dir=%s)", str(model_paths.base_dir)) + from paddleocr import PaddleOCR # pylint: disable=import-error + + # 注意：paddleocr==2.10.0 不支持 use_textline_orientation 这类 3.x pipeline 参数 + log.info("create_offline_ocr: creating PaddleOCR(det=%s, rec=%s)", str(model_paths.det_dir), str(model_paths.rec_dir)) + ocr = PaddleOCR( + lang="ch", + show_log=show_log, + use_angle_cls=False, + det_model_dir=str(model_paths.det_dir), + rec_model_dir=str(model_paths.rec_dir), + cls_model_dir=str(model_paths.cls_dir), + ) + log.info("create_offline_ocr: PaddleOCR created") + return ocr