commit 78bd690b7eb8ccaa5277a1222a3f988e65035ed7 Author: kuuhaku Date: Fri Jul 17 10:19:05 2026 +0800 feat:仿照VL1.6实现，并添加了模型选择，默认是medium模型，还有small、tiny可以选 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..d06fa47 --- /dev/null +++ b/.gitignore @@ -0,0 +1,21 @@ +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info/ +.pytest_cache/ + +.venv/ +cpu/.venv/ +gpu/.venv/ +gpu/.gpu-ready + +outputs/* +!outputs/.gitkeep +logs/* +!logs/.gitkeep +benchmarks/cpu/*.json +benchmarks/gpu/*.json +!benchmarks/cpu/.gitkeep +!benchmarks/gpu/.gitkeep diff --git a/README.md b/README.md new file mode 100644 index 0000000..92b20aa --- /dev/null +++ b/README.md @@ -0,0 +1,196 @@ +# PP-OCRv6 本地 OCR + +本项目使用 PP-OCRv6 实现图片、PDF 和目录批量 OCR。工程结构参考 `../ocr-VL1.6`，CPU/GPU 环境隔离，并通过根目录 `ocr.py` 统一调用。 + +输出 Markdown 的定位是**便于阅读的文字结果**。本项目不实现版面分析、表格结构恢复或富文档还原。 + +## 功能 + +- 单图片 PP-OCRv6 识别 +- 图片与 PDF 目录批处理 +- PDF `hybrid` / `text` / `ocr` 模式 +- PDF 文本层质量判断和扫描页自动 OCR +- PDF 页码选择、断点续传与覆盖重做 +- TXT、简单 Markdown、归一化 JSON、Benchmark +- 可选 PaddleOCR 原始 JSON 和 OCR 可视化 +- CPU/GPU 隔离，GPU 失败不回退 CPU +- 模型延迟加载，同一批次复用模型 + +支持三种成对的检测与识别模型规格： + +| 参数 | 检测模型 | 识别模型 | +|---|---|---| +| `tiny` | `PP-OCRv6_tiny_det` | `PP-OCRv6_tiny_rec` | +| `small` | `PP-OCRv6_small_det` | `PP-OCRv6_small_rec` | +| `medium` | `PP-OCRv6_medium_det` | `PP-OCRv6_medium_rec` | + +不传参数时默认使用 `medium`。 + +## 安装 + +### CPU + +```bash +uv sync --project cpu +``` + +### GPU + +```bash +python gpu/setup_env.py --cuda cu118 --dry-run +python gpu/setup_env.py --cuda cu118 +``` + +也可按目标机器使用 `cu126`。CUDA、驱动和 PaddlePaddle Wheel 必须相互兼容。 + +## 使用 + +### 验证环境 + +```bash +python ocr.py verify --device cpu +python ocr.py verify --device gpu +``` + +`verify` 只验证 Paddle 运行环境，不下载和初始化 OCR 模型。 + +### 单图片 + +```bash +# 默认 medium +python ocr.py data/images/手写01.png --device cpu + +# 选择 small +python ocr.py data/images/手写01.png --device cpu --model-size small + +# 选择 tiny；--model 是 --model-size 的别名 +python ocr.py data/images/手写01.png --device cpu --model tiny +``` + +保存可视化和原始结果： + +```bash +python ocr.py data/images/手写01.png \ + --device cpu \ + --save-visualization \ + --save-raw-result +``` + +多轮 Benchmark： + +```bash +python ocr.py data/images/手写01.png --warmup 1 --rounds 3 +``` + +### PDF + +```bash +# 默认混合模式：优先文本层，扫描页才 OCR +python ocr.py data/documents/sample.pdf --device cpu + +# 强制文本层 +python ocr.py sample.pdf --pdf-mode text + +# 强制全部页面 OCR +python ocr.py sample.pdf --pdf-mode ocr + +# 指定页码 +python ocr.py sample.pdf --pages "1-5,8,10-" + +# 中断后恢复 +python ocr.py sample.pdf --resume + +# 删除原任务并重做 +python ocr.py sample.pdf --overwrite +``` + +### 目录 + +```bash +python ocr.py data/ --device cpu +python ocr.py data/ --recursive --device cpu +``` + +目录任务串行处理文件，避免无控制并发造成内存占用和界面卡顿。 + +## 主要 PP-OCRv6 参数 + +```text +--model-size tiny|small|medium +--model tiny|small|medium # --model-size 的别名 +--lang ch +--text-rec-score-thresh 0.0 +--text-det-limit-side-len N +--text-det-limit-type min|max +--text-det-thresh FLOAT +--text-det-box-thresh FLOAT +--text-det-unclip-ratio FLOAT +--text-recognition-batch-size 6 +--return-word-box +--doc-orientation-classify / --no-doc-orientation-classify +--doc-unwarping / --no-doc-unwarping +--textline-orientation / --no-textline-orientation +``` + +默认开启文档方向分类和文本行方向分类，默认关闭文档去畸变。 + +## 输出 + +```text +outputs/ +├── images/ +│ └── <图片名_扩展名>/ +│ ├── result.txt +│ ├── result.md +│ ├── result.json +│ ├── benchmark.json +│ ├── raw-result.json # 可选 +│ └── visualization.jpg # 可选 +├── pdfs/ +│ └── / +│ ├── manifest.json +│ ├── document.md +│ ├── document.json +│ └── pages/ +└── batches/ +``` + +`result.json` 使用项目自有稳定结构，主要包含： + +- 文本行 +- 识别置信度 +- 多边形与矩形坐标 +- 模型和语言信息 +- 图片尺寸 +- 汇总统计 + +## PDF 混合模式 + +每页先提取 PDF 文本层： + +```text +有效文本层 → 直接保存文本 +无效文本层 → 渲染 PNG → PP-OCRv6 +``` + +纯电子 PDF 不加载 PP-OCRv6 模型。Manifest 会记录 PDF 哈希、DPI、文本层阈值和模型配置；关键配置变化时必须使用 `--overwrite`，不能错误续传。 + +## 能力边界 + +本项目只提供通用文字检测和识别： + +- Markdown 是按识别顺序拼接的便读文本； +- 不恢复标题层级； +- 不恢复多栏版面； +- 不恢复表格单元格结构； +- 不提供与 PaddleOCR-VL 等价的富 Markdown。 + +JSON 中保留坐标，调用方可以按具体业务继续处理。 + +## 测试 + +```bash +uv run --project cpu pytest -q +``` + +测试使用假模型覆盖结果适配、输出路由和 PDF 混合流程，不要求每次测试都下载 OCR 模型。 diff --git a/benchmarks/README.md b/benchmarks/README.md new file mode 100644 index 0000000..f6a0f5d --- /dev/null +++ b/benchmarks/README.md @@ -0,0 +1,19 @@ +# Benchmark + +图片 Benchmark 默认保存在： + +```text +outputs/images/<图片名_扩展名>/benchmark.json +``` + +示例： + +```bash +python ocr.py data/images/手写01.png --warmup 1 --rounds 3 +``` + +需要额外复制时： + +```bash +python ocr.py data/images/手写01.png --benchmark-json benchmarks/cpu/手写01.json +``` diff --git a/benchmarks/cpu/.gitkeep b/benchmarks/cpu/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/benchmarks/gpu/.gitkeep b/benchmarks/gpu/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/cpu/.python-version b/cpu/.python-version new file mode 100644 index 0000000..24ee5b1 --- /dev/null +++ b/cpu/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/cpu/README.md b/cpu/README.md new file mode 100644 index 0000000..2350e7a --- /dev/null +++ b/cpu/README.md @@ -0,0 +1,14 @@ +# CPU 子项目 + +安装： + +```bash +uv sync --project cpu +``` + +统一从仓库根目录运行： + +```bash +python ocr.py verify --device cpu +python ocr.py data/images/手写01.png --device cpu +``` diff --git a/cpu/pyproject.toml b/cpu/pyproject.toml new file mode 100644 index 0000000..631eca5 --- /dev/null +++ b/cpu/pyproject.toml @@ -0,0 +1,17 @@ +[project] +name = "pp-ocrv6-cpu" +version = "0.1.0" +description = "CPU runtime for the unified PP-OCRv6 application" +readme = "README.md" +requires-python = ">=3.13" +dependencies = [ + "paddleocr==3.7.0", + "paddlepaddle==3.2.1", + "pypdfium2>=5.11.0", + "setuptools>=83.0.0", +] + +[dependency-groups] +dev = [ + "pytest>=8.4.0", +] diff --git a/cpu/runner.py b/cpu/runner.py new file mode 100644 index 0000000..4b25695 --- /dev/null +++ b/cpu/runner.py @@ -0,0 +1,14 @@ +"""CPU environment runner used by the root unified launcher.""" + +from 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new file mode 100644 index 0000000..71850a0 Binary files /dev/null and b/data/images/手写01.png differ diff --git a/docs/01-可行性分析与实施方案.md b/docs/01-可行性分析与实施方案.md new file mode 100644 index 0000000..d9172f2 --- /dev/null +++ b/docs/01-可行性分析与实施方案.md @@ -0,0 +1,1045 @@ +# PP-OCRv6 可行性分析与实施方案 + +## 1. 文档目的 + +本文评估在当前 `PP-OCRv6` 项目中，参考相邻项目 `../ocr-VL1.6` 的工程结构和功能，建设一套基于 **PP-OCRv6** 的本地 OCR 应用是否可行，并给出推荐架构、改造范围、实施步骤、风险及验收标准。 + +本文所称“仿照 `../ocr-VL1.6`”主要指复用其以下工程能力： + +- 根目录统一入口； +- CPU/GPU 运行环境隔离； +- 图片、PDF、目录自动路由； +- PDF 文本层提取与 OCR 混合处理； +- 模型延迟加载； +- 批量串行处理； +- 统一输出目录； +- Benchmark、日志、断点续传与测试。 + +本项目已确认不实现版面分析和表格结构恢复。Markdown 输出仅定位为“便于阅读的文字结果”，按识别顺序拼接文本，不追求原文档版面或表格结构还原。 + +--- + +## 2. 参考项目现状 + +参考项目 `../ocr-VL1.6` 已具备较完整的本地 OCR 工程框架，其主要结构如下： + +```text +ocr-VL1.6/ +├── ocr.py # 根目录统一入口 +├── ocr_app/ +│ ├── cli.py # 命令行参数 +│ ├── commands.py # 图片/PDF/目录路由 +│ ├── runtime.py # CPU/GPU 校验和模型延迟加载 +│ ├── output.py # 输出归一化 +│ ├── pdf.py # PDF 分页、OCR、断点续传 +│ ├── pdf_text.py # PDF 文本层提取与质量判断 +│ └── logging_utils.py # UTF-8 日志 +├── cpu/ # CPU 独立 uv 项目 +├── gpu/ # GPU 独立 uv 项目 +├── tests/ # 共享逻辑测试 +├── outputs/ +├── benchmarks/ +└── logs/ +``` + +参考项目当前使用： + +- `paddleocr[doc-parser]==3.7.0`； +- CPU：`paddlepaddle==3.2.1`； +- GPU：`paddlepaddle-gpu==3.2.1`； +- PDF 渲染：`pypdfium2>=5.11.0`； +- Python 环境管理：`uv`。 + +经本地已安装包源码核验，PaddleOCR 3.7.0 已原生支持： + +```python +from paddleocr import PaddleOCR + +ocr = PaddleOCR( + ocr_version="PP-OCRv6", + lang="ch", + device="cpu", +) +``` + +当不显式指定模型名称时，PP-OCRv6 默认使用： + +- 默认文本检测：`PP-OCRv6_medium_det`； +- 默认文本识别：`PP-OCRv6_medium_rec`； +- 可选规格：`tiny`、`small`、`medium`，检测与识别模型按相同规格成对选择。 + +因此，参考项目的依赖体系和 CPU/GPU 隔离方案可以继续使用，不需要引入新的 OCR 框架。 + +--- + +## 3. 模型能力差异 + +### 3.1 PaddleOCR-VL-1.6 + +参考项目使用的 PaddleOCR-VL-1.6 属于视觉语言文档解析管线，输出中包含： + +- 版面区域； +- 文档块标签； +- 按版面组织的文本内容； +- 富 Markdown； +- Markdown 图片资源； +- `parsing_res_list`、`layout_det_res` 等结构化信息。 + +它更适合复杂文档解析、版面理解和接近原文档结构的 Markdown 导出。 + +### 3.2 PP-OCRv6 + +PP-OCRv6 是传统检测与识别组合式 OCR 管线，主要输出： + +- `dt_polys`：检测框多边形； +- `rec_texts`：识别文本； +- `rec_scores`：识别置信度； +- `rec_polys`：保留的识别区域； +- `rec_boxes`：矩形坐标； +- 可选文字方向和单词级坐标。 + +其核心优势是文本检测与识别，默认不提供与 PaddleOCR-VL-1.6 等价的： + +- 标题、正文、表格、图片等版面语义分类； +- 复杂阅读顺序恢复； +- 表格结构还原； +- 公式、图表和文档级语义理解； +- 原生富 Markdown。 + +### 3.3 能力对比 + +| 维度 | PaddleOCR-VL-1.6 | PP-OCRv6 | 影响 | +|---|---|---|---| +| 普通文字识别 | 支持 | 支持，通常更轻量 | 可替换 | +| 文本框坐标 | 支持 | 支持 | 可替换 | +| 识别置信度 | 结果形态依管线 | 原生支持 | PP-OCRv6 更直接 | +| 版面分析 | 强 | 非核心能力 | 不可直接等价替换 | +| 表格结构恢复 | 支持文档解析能力 | 默认不支持 | 需要额外模型或降级输出 | +| Markdown | 原生富 Markdown | 需要自行拼接 | 必须重写输出适配器 | +| 推理资源 | 较高 | 通常较低 | PP-OCRv6 更适合通用 OCR | +| 批量吞吐 | 受 VLM 生成影响 | 检测识别管线更适合批量 | 预期更好，需实测 | +| PDF 文本层混合路由 | 工程层实现 | 可复用同一工程层 | 可直接复用 | + +--- + +## 4. 可行性结论 + +### 4.1 总体结论 + +**项目可行，推荐实施。** + +如果目标是实现“图片/PDF/目录的本地文字识别应用”，并保留 `../ocr-VL1.6` 的统一入口、CPU/GPU 隔离、PDF 混合模式、输出管理、Benchmark 和日志能力，则整体可行性为 **高**。 + +本项目不要求与 PaddleOCR-VL-1.6 完全一致的版面结构和富 Markdown，也不实现表格结构恢复，因此上述能力不纳入本次实施范围。 + +### 4.2 分项可行性 + +| 项目 | 可行性 | 说明 | +|---|---|---| +| CPU 本地推理 | 高 | 当前依赖已支持 PP-OCRv6，参考项目 CPU 环境可复用 | +| NVIDIA GPU 推理 | 高但需实机验证 | GPU Wheel 安装逻辑可复用，仍需目标 CUDA 机器验证 | +| 单图片 OCR | 高 | `PaddleOCR.predict()` 可直接处理图片 | +| 批量目录 OCR | 高 | 可复用串行路由和单模型复用机制 | +| 扫描 PDF OCR | 高 | 复用 PDF 渲染后逐页调用 PP-OCRv6 | +| 电子 PDF 文本直取 | 高 | 与 OCR 模型无关，可直接复用 | +| PDF 混合模式 | 高 | 可复用文本层质量判定和按页路由 | +| PDF 断点续传 | 高 | 可复用 manifest 机制，调整结果字段即可 | +| TXT/JSON 输出 | 高 | PP-OCRv6 原生结果适合导出 | +| 简单 Markdown 输出 | 高 | 可按阅读顺序拼接纯文本行 | +| 便读 Markdown | 高 | 按识别顺序拼接文字，不恢复原版面 | +| 版面/表格结构恢复 | 不实施 | 已明确排除在本项目范围之外 | +| 多语言 | 中 | PP-OCRv6 支持范围需按语言确认，不支持的语言应回退 PP-OCRv5 或拒绝启动 | + +### 4.3 推荐适用场景 + +PP-OCRv6 版本适合： + +- 名片、票据、截图、扫描件的文字提取； +- 中文、英文、日文及已支持拉丁语种识别； +- 大量普通图片的批处理； +- 电子 PDF 与扫描 PDF 混合处理； +- 对坐标、置信度和纯文本有明确需求的场景； +- CPU 本地部署或资源受限部署。 + +不建议仅使用 PP-OCRv6 完成： + +- 高保真合同版面还原； +- 表格单元格结构恢复； +- 公式语义识别； +- 图文混排文档的章节层级恢复； +- 与 PaddleOCR-VL 输出完全兼容的富 Markdown 生成。 + +--- + +## 5. 建设目标 + +建议本项目第一阶段实现以下功能： + +1. 根目录统一入口： + + ```bash + python ocr.py <图片|PDF|目录> --device cpu|gpu + ``` + +2. 自动按输入类型路由： + + - 图片：PP-OCRv6 检测与识别； + - PDF：文本层直取或页面 OCR； + - 目录：发现支持的图片和 PDF，串行处理。 + +3. CPU/GPU 环境完全隔离： + + - `cpu/.venv`； + - `gpu/.venv`； + - GPU 不可用时明确失败，不静默回退 CPU。 + +4. 模型延迟加载： + + - 纯电子 PDF 不加载 PP-OCRv6； + - 首张图片或首个扫描 PDF 页面才初始化模型； + - 同一批次复用单个模型实例。 + +5. 输出格式： + + - `result.txt`：纯文本； + - `result.md`：简单 Markdown； + - `result.json`：归一化结构化结果； + - `visualization.jpg`：可选识别可视化； + - `benchmark.json`：耗时、设备、模型和统计信息。 + +6. PDF 能力： + + - `hybrid`、`text`、`ocr` 三种模式； + - 页码选择； + - PDF 密码； + - 断点续传； + - 覆盖重做； + - 每页独立结果和整文档合并结果。 + +7. UTF-8 结构化日志、错误处理和自动化测试。 + +--- + +## 6. 推荐目录结构 + +建议沿用参考项目结构，但将模型相关字段改为 PP-OCRv6： + +```text +PP-OCRv6/ +├── ocr.py +├── ocr_app/ +│ ├── __init__.py +│ ├── cli.py +│ ├── commands.py +│ ├── runtime.py +│ ├── result_adapter.py # PP-OCRv6 结果归一化 +│ ├── output.py +│ ├── pdf.py +│ ├── pdf_text.py +│ └── logging_utils.py +├── cpu/ +│ ├── .python-version +│ ├── pyproject.toml +│ ├── uv.lock +│ ├── README.md +│ └── runner.py +├── gpu/ +│ ├── .python-version +│ ├── pyproject.toml +│ ├── README.md +│ ├── runner.py +│ └── setup_env.py +├── data/ +│ ├── images/ +│ └── documents/ +├── outputs/ +├── benchmarks/ +├── logs/ +├── tests/ +├── docs/ +│ └── 01-可行性分析与实施方案.md +├── .gitignore +└── README.md +``` + +新增 `result_adapter.py` 的原因是 PP-OCRv6 与 PaddleOCR-VL-1.6 的结果对象差异较大。模型原始结果不应直接扩散到 PDF、输出、Benchmark 和日志模块中，应先转换为稳定的项目内部数据结构。 + +--- + +## 7. 核心架构设计 + +### 7.1 分层结构 + +```text +ocr.py + ↓ 选择 CPU/GPU 独立环境 +cpu/runner.py 或 gpu/runner.py + ↓ +ocr_app/cli.py + ↓ +ocr_app/commands.py + ├── 图片路由 + ├── PDF 路由 + └── 目录路由 + ↓ +ocr_app/runtime.py + ↓ 延迟创建 +PaddleOCR(ocr_version="PP-OCRv6") + ↓ +ocr_app/result_adapter.py + ↓ +TXT / Markdown / JSON / 可视化 / Benchmark +``` + +### 7.2 运行时设计 + +`PipelineProvider` 继续负责： + +- 导入 PaddlePaddle； +- CPU 线程配置； +- CUDA 构建检查； +- GPU 数量和设备编号检查； +- 模型延迟初始化； +- GPU 同步； +- 设备和环境元数据收集。 + +模型初始化建议为： + +```python +from paddleocr import PaddleOCR + +self._pipeline = PaddleOCR( + ocr_version="PP-OCRv6", + lang=self.config.lang, + device=self.resolved_device, + use_doc_orientation_classify=self.config.use_doc_orientation_classify, + use_doc_unwarping=self.config.use_doc_unwarping, + use_textline_orientation=self.config.use_textline_orientation, + text_recognition_batch_size=self.config.text_recognition_batch_size, +) +``` + +建议默认配置： + +| 参数 | 建议默认值 | 说明 | +|---|---:|---| +| `ocr_version` | `PP-OCRv6` | 显式锁定版本，避免依赖升级后默认值变化 | +| `lang` | `ch` | 主要面向中英文混合文档 | +| `use_doc_orientation_classify` | `true` | 处理整页旋转 | +| `use_doc_unwarping` | `false` | 默认关闭以降低加载和推理开销，可通过参数开启 | +| `use_textline_orientation` | `true` | 处理倒置文本行 | +| `text_recognition_batch_size` | `6` | 与默认配置保持一致，后续按设备调优 | +| `text_rec_score_thresh` | `0.0` | 保留原始结果，输出层可另设过滤阈值 | + +所有模型选项必须记录到 Benchmark 和 PDF manifest 中，以保证结果可追溯并支持安全续传。 + +### 7.3 统一内部结果模型 + +建议将 PaddleOCR 原始结果转换为以下项目内部结构： + +```json +{ + "schema_version": 1, + "source_type": "image_ocr", + "input_path": "data/images/example.jpg", + "page_index": 0, + "model": { + "ocr_version": "PP-OCRv6", + "detection_model": "PP-OCRv6_medium_det", + "recognition_model": "PP-OCRv6_medium_rec", + "language": "ch" + }, + "image": { + "width": 1920, + "height": 1080 + }, + "lines": [ + { + "index": 1, + "text": "示例文本", + "score": 0.9876, + "polygon": [[10, 20], [200, 20], [200, 50], [10, 50]], + "box": [10, 20, 200, 50], + "orientation": 0 + } + ], + "summary": { + "detected_lines": 1, + "non_empty_lines": 1, + "mean_score": 0.9876, + "min_score": 0.9876 + } +} +``` + +内部结果模型应满足： + +- 不依赖 PaddleOCR 私有类； +- 所有 NumPy 类型转换为标准 Python 类型； +- 字段稳定，可用于测试和后续 API 服务； +- 保留坐标和置信度； +- 原始 PaddleOCR JSON 可选择另存为 `raw-result.json`，但不作为项目主接口。 + +### 7.4 文本阅读顺序 + +PP-OCRv6 已对检测框进行基础排序，但复杂多栏文档的阅读顺序仍可能不符合人类阅读习惯。 + +第一阶段建议： + +1. 使用模型结果顺序； +2. 按顺序写入 TXT 和简单 Markdown； +3. JSON 中完整保留坐标； +4. 不承诺复杂多栏和表格的语义顺序。 + +如后续需要增强，可增加可选的坐标排序策略： + +- 单栏：从上到下、从左到右； +- 多栏：先聚类列，再逐列排序； +- 表格：交由专用表格结构识别模块处理。 + +--- + +## 8. 图片处理方案 + +### 8.1 处理流程 + +```text +校验图片类型 + ↓ +延迟加载 PP-OCRv6 + ↓ +可选预热 + ↓ +执行 N 轮推理 + ↓ +结果适配和统计 + ↓ +输出 TXT/MD/JSON/可视化/Benchmark +``` + +### 8.2 输出目录 + +```text +outputs/images/<图片名_扩展名>/ +├── result.txt +├── result.md +├── result.json +├── raw-result.json # 可选 +├── visualization.jpg # 可选 +└── benchmark.json +``` + +### 8.3 Markdown 输出定义 + +PP-OCRv6 不提供版面语义，因此 `result.md` 应明确定位为“便于阅读的文字结果”，例如： + +```markdown +# example.jpg + +示例文本第一行 + +示例文本第二行 +``` + +不应伪造标题、表格、图片引用等模型未识别出的结构。 + +### 8.4 Benchmark 指标 + +建议记录： + +- Paddle 导入时间； +- 运行时配置时间； +- 模型初始化时间； +- 预热时间； +- 每轮推理时间； +- 结果适配时间； +- 导出时间； +- 文件总耗时； +- 检测文本行数； +- 非空文本行数； +- 平均、最小、最大置信度； +- CPU 线程数； +- GPU 显存信息； +- 模型名称、语言和预处理开关。 + +--- + +## 9. PDF 处理方案 + +### 9.1 复用策略 + +参考项目的 PDF 文本层提取、质量判断、页码解析、渲染、manifest 和断点续传逻辑与具体 OCR 模型耦合较低，建议保留。 + +需要重写的部分主要是： + +- OCR 页结果解析； +- OCR 页 Markdown 生成； +- OCR 页 JSON 结构； +- 页面统计字段； +- manifest 中的模型配置和结果摘要。 + +### 9.2 混合处理流程 + +```text +打开 PDF + ↓ +逐页提取文本层并评估质量 + ├── 文本层有效：直接保存文本 + └── 文本层无效：渲染为 PNG → PP-OCRv6 + ↓ +保存逐页 MD/JSON + ↓ +更新 manifest + ↓ +合并 document.md/document.json +``` + +### 9.3 PDF 模式 + +| 模式 | 行为 | +|---|---| +| `hybrid` | 默认。优先使用有效文本层，必要时才 OCR | +| `text` | 只提取文本层，不加载模型 | +| `ocr` | 所有选中页面均渲染并执行 PP-OCRv6 | + +### 9.4 PDF 输出目录 + +```text +outputs/pdfs// +├── manifest.json +├── document.md +├── document.json +├── pages/ +│ ├── page-0001.md +│ ├── page-0001.json +│ └── ... +├── visualizations/ # 可选 +└── rendered/ # 仅 --keep-rendered 时保留 +``` + +### 9.5 断点续传兼容性 + +建议为 PP-OCRv6 项目设置独立的 manifest 版本，例如： + +```json +{ + "manifest_version": 1, + "pipeline": "PP-OCRv6", + "model_config": { + "language": "ch", + "detection_model": "PP-OCRv6_medium_det", + "recognition_model": "PP-OCRv6_medium_rec", + "use_doc_orientation_classify": true, + "use_doc_unwarping": false, + "use_textline_orientation": true + } +} +``` + +恢复任务时至少校验： + +- PDF SHA-256； +- PDF 模式； +- 页面渲染 DPI； +- 文本层质量阈值； +- PP-OCRv6 模型名称； +- 语言； +- 文档方向、去畸变、文本行方向开关； +- 识别置信度阈值； +- manifest 版本。 + +任一关键配置变化时，应拒绝直接续传并提示使用 `--overwrite`。 + +--- + +## 10. CLI 设计 + +### 10.1 基础命令 + +```bash +python ocr.py data/images/example.jpg --device cpu +python ocr.py data/documents/example.pdf --device cpu +python ocr.py data/ --recursive --device cpu +python ocr.py verify --device gpu +``` + +### 10.2 建议保留的参数 + +```text +--device cpu|gpu +--device-id 0 +--threads N +--recursive +--output PATH +--fail-fast +--warmup N +--rounds N +--benchmark-json PATH +--pdf-mode hybrid|text|ocr +--pages "1-5,8,10-" +--dpi 144 +--password PASSWORD +--resume +--overwrite +--keep-rendered +--log-file PATH +--verbose +``` + +### 10.3 PP-OCRv6 新参数 + +```text +--lang ch +--text-rec-score-thresh 0.0 +--text-det-limit-side-len N +--text-det-limit-type min|max +--text-det-thresh 0.3 +--text-det-box-thresh 0.6 +--text-det-unclip-ratio 1.5 +--text-recognition-batch-size 6 +--return-word-box +--use-doc-orientation-classify / --no-doc-orientation-classify +--use-doc-unwarping / --no-doc-unwarping +--use-textline-orientation / --no-textline-orientation +--save-visualization +--save-raw-result +``` + +### 10.4 应删除的 VLM 专用参数 + +以下参数属于 PaddleOCR-VL 生成式推理，不适用于 PP-OCRv6，应从新项目中移除： + +```text +--max-new-tokens +--min-pixels +--max-pixels +``` + +--- + +## 11. CPU/GPU 环境方案 + +### 11.1 CPU + +建议首先沿用参考项目已验证组合： + +```toml +[project] +requires-python = ">=3.13" +dependencies = [ + "paddleocr==3.7.0", + "paddlepaddle==3.2.1", + "pypdfium2>=5.11.0", + "setuptools>=83.0.0", +] +``` + +如果可视化功能需要 OpenCV，应通过 PaddleOCR/PaddleX 实际依赖验证后补充对应包，避免同时安装冲突的 OpenCV 变体。 + +安装： + +```bash +uv sync --project cpu +``` + +### 11.2 GPU + +GPU 项目继续独立管理： + +```toml +[project] +requires-python = ">=3.11,<3.13" +dependencies = [ + "paddleocr==3.7.0", + "paddlepaddle-gpu==3.2.1", + "pypdfium2>=5.11.0", + "setuptools>=83.0.0", +] +``` + +GPU Wheel 不能仅依赖默认 PyPI 解析，应沿用 `gpu/setup_env.py`，按 PaddlePaddle 官方 CUDA 兼容矩阵选择安装源。 + +安装完成后生成 `gpu/.gpu-ready`，统一入口仅允许调用已完成安装的 GPU 环境。 + +### 11.3 模型缓存 + +首次运行时可能下载多个模型，包括： + +- PP-OCRv6 检测模型； +- PP-OCRv6 识别模型； +- 文档方向分类模型； +- 文本行方向模型； +- 开启去畸变时的去畸变模型。 + +实施时应： + +1. 在 README 中说明首次启动需要网络和下载时间； +2. 记录模型缓存位置； +3. 支持通过模型目录参数加载离线模型； +4. 在正式部署前预下载并执行一次冷启动验证； +5. 不将模型权重提交到 Git。 + +--- + +## 12. 代码复用与改造清单 + +### 12.1 可直接或小幅复用 + +| 参考文件 | 复用程度 | 调整内容 | +|---|---|---| +| `ocr.py` | 高 | 修改项目提示文字 | +| `ocr_app/logging_utils.py` | 高 | 基本可直接复用 | +| `ocr_app/pdf_text.py` | 高 | 基本可直接复用 | +| `ocr_app/cli.py` | 中 | 替换模型参数 | +| `ocr_app/commands.py` | 中 | 替换图片结果统计和推理参数 | +| `ocr_app/pdf.py` | 中 | 替换 OCR 页结果解析和导出 | +| `cpu/runner.py` | 高 | 基本可直接复用 | +| `gpu/runner.py` | 高 | 基本可直接复用 | +| `gpu/setup_env.py` | 高 | 修改项目名称并重新验证 | +| 测试基础设施 | 高 | 保留假模型和临时目录模式 | + +### 12.2 必须重写 + +| 模块 | 原因 | +|---|---| +| `ocr_app/runtime.py` 的模型创建部分 | `PaddleOCRVL` 改为 `PaddleOCR` | +| 图片结果摘要 | 不再存在 `parsing_res_list` 和 `layout_det_res` | +| `ocr_app/output.py` 的 Markdown 导出 | PP-OCRv6 没有 `result.markdown` | +| PDF OCR 页导出 | 需基于 `rec_texts/rec_scores/rec_polys` 生成 | +| Benchmark 统计 | 从版面块统计改为文本行和置信度统计 | +| 模型参数和日志字段 | 删除 VLM 参数，增加检测/识别参数 | + +### 12.3 不建议机械复制 + +不建议直接复制整个 `../ocr-VL1.6` 后仅替换模型类名，原因包括： + +- 输出代码强依赖 `result.markdown`； +- 纯文本生成强依赖 `parsing_res_list`； +- Benchmark 强依赖版面框和解析块； +- PDF 页面统计强依赖 `layout_det_res`； +- VLM 参数会被错误传给 PP-OCRv6； +- 断点续传 manifest 未记录 PP-OCRv6 的关键配置。 + +推荐做法是复用工程骨架，同时新建明确的 PP-OCRv6 结果适配层。 + +--- + +## 13. 实施步骤 + +### 阶段一：工程骨架与环境 + +1. 从参考项目选择性迁移目录结构； +2. 创建根入口、CPU/GPU runner； +3. 建立 CPU/GPU `pyproject.toml`； +4. 迁移日志、PDF 文本层和基础工具； +5. 完成 `verify` 命令； +6. 验证 CPU PaddlePaddle 和 PaddleOCR 版本。 + +交付物： + +- CPU 环境可安装； +- `python ocr.py verify --device cpu` 成功； +- GPU 不可用时有明确提示且不回退 CPU。 + +### 阶段二：单图片 OCR + +1. 实现 PP-OCRv6 延迟加载； +2. 实现单图片推理； +3. 实现 `result_adapter.py`； +4. 输出 TXT、MD、JSON； +5. 实现 Benchmark； +6. 增加可选可视化输出。 + +交付物： + +- 中文、英文、旋转文本样例识别成功； +- 坐标和置信度正确写入 JSON； +- 多轮 Benchmark 可用。 + +### 阶段三：目录批处理 + +1. 迁移后缀路由； +2. 复用单模型实例； +3. 保持串行处理； +4. 处理递归目录和同名文件； +5. 生成批次汇总 JSON； +6. 验证单文件失败不会污染其他结果。 + +交付物： + +- 图片目录可批量处理； +- 输出保留相对目录； +- 批次结果可追溯。 + +### 阶段四：PDF 混合模式 + +1. 接入文本层质量判断； +2. 实现扫描页渲染与 PP-OCRv6 推理； +3. 实现逐页 TXT/MD/JSON； +4. 实现整文档合并； +5. 实现 manifest 和断点续传； +6. 验证电子 PDF 不加载模型。 + +交付物： + +- `hybrid`、`text`、`ocr` 模式可用； +- 可选择页码； +- 中断后可恢复； +- 配置不一致时拒绝错误续传。 + +### 阶段五：GPU、性能与文档 + +1. 在 NVIDIA GPU 机器安装环境； +2. 执行 GPU Smoke Test； +3. 测试冷启动、热启动和批量吞吐； +4. 调整识别 Batch Size； +5. 评估方向分类和去畸变的性能影响； +6. 完善 README、故障排查和部署说明。 + +交付物： + +- CPU/GPU Benchmark 对比； +- GPU 显存使用记录； +- 明确推荐参数。 + +--- + +## 14. 测试方案 + +### 14.1 单元测试 + +建议至少覆盖： + +- 输入类型识别； +- 支持后缀发现； +- 页码范围解析； +- 文本层质量判断； +- PP-OCRv6 原始结果归一化； +- 空识别结果处理； +- NumPy 类型 JSON 序列化； +- TXT/Markdown 生成； +- 输出目录命名； +- 递归目录相对路径； +- manifest 参数一致性校验； +- CPU/GPU 根入口路由。 + +### 14.2 集成测试 + +建议准备以下样例： + +| 样例 | 目的 | +|---|---| +| 中文印刷体图片 | 基础识别 | +| 中英文混合名片 | 多语言和小字号 | +| 90°/180° 旋转图片 | 方向分类 | +| 透视拍摄文档 | 去畸变开关 | +| 手写图片 | 能力边界测试 | +| 纯电子 PDF | 验证不加载 OCR 模型 | +| 纯扫描 PDF | 验证逐页 OCR | +| 混合 PDF | 验证按页路由 | +| 加密 PDF | 密码处理 | +| 多页大 PDF | 断点续传和稳定性 | +| 多栏文档 | 验证阅读顺序限制 | +| 表格文档 | 明确非结构化输出边界 | + +### 14.3 假模型测试 + +与参考项目一致，应使用假模型完成大多数工程测试，避免测试过程下载模型或消耗大量推理时间。 + +假结果至少包含： + +```python +{ + "rec_texts": ["第一行", "第二行"], + "rec_scores": [0.98, 0.95], + "rec_polys": [...], + "rec_boxes": [...], +} +``` + +### 14.4 性能测试 + +分别记录： + +- 首次模型下载时间； +- 冷启动模型初始化时间； +- 单图热推理时间； +- 100 张图片串行总耗时； +- 10 页扫描 PDF 总耗时； +- CPU 不同线程数； +- GPU 不同识别 Batch Size； +- 开启/关闭文档方向分类； +- 开启/关闭去畸变； +- 开启/关闭文本行方向分类。 + +Benchmark 必须标注输入尺寸、DPI、硬件和模型配置，否则不同结果不可直接比较。 + +--- + +## 15. 风险与应对 + +### 风险一：输出能力被误认为与 PaddleOCR-VL 等价 + +**影响：高。** + +应对： + +- README 明确 PP-OCRv6 是文字检测识别管线； +- `result.md` 标注为简单文本 Markdown； +- 不承诺表格、标题层级和复杂阅读顺序； +- 如业务确有结构化需求，另立 PP-Structure/文档解析增强阶段。 + +### 风险二：结果对象格式与参考项目不兼容 + +**影响：高。** + +应对： + +- 新建 `result_adapter.py`； +- 业务层只依赖内部结果模型； +- 使用假模型固定输出契约； +- 原始结果与归一化结果分开保存。 + +### 风险三：PP-OCRv6 语言支持有限 + +**影响：中。** + +本地 PaddleOCR 3.7.0 的 PP-OCRv6 映射适用于中文、繁体中文、英文、日文及多数拉丁语种；韩文、阿拉伯文、部分西里尔文和印度语系等不应默认假设支持 PP-OCRv6。 + +应对： + +- 默认 `lang=ch`； +- 启动时验证 `lang + ocr_version`； +- 不支持时给出明确错误； +- 如业务允许，可设计显式 `--allow-version-fallback` 回退 PP-OCRv5，但默认不应静默回退。 + +### 风险四：首次运行需要下载多个模型 + +**影响：中。** + +应对： + +- 增加部署前模型预热步骤； +- 支持离线模型目录； +- 在日志中记录下载和初始化阶段； +- 在生产环境提前准备缓存。 + +### 风险五：CPU 性能受辅助模型影响 + +**影响：中。** + +默认 OCR 管线可能同时加载方向分类、文本行方向和去畸变模型。 + +应对： + +- 默认关闭去畸变； +- 对方向正常的批量扫描件提供轻量配置； +- 分别 Benchmark 各开关； +- 不以单个样例推断整体性能。 + +### 风险六：GPU 环境未实机验证 + +**影响：中。** + +应对： + +- 保留严格的 GPU 就绪标记； +- 不允许 GPU 失败后自动回退 CPU； +- 在目标机器验证 CUDA、驱动和 Paddle Wheel； +- 记录实际显存和吞吐。 + +### 风险七：复杂 PDF 阅读顺序不准确 + +**影响：中。** + +应对： + +- 第一阶段保留坐标并按模型顺序输出； +- 对多栏和表格文档在验收中标记能力边界； +- 后续按业务需求增加版面排序或结构识别，而非在基础 OCR 中堆叠启发式规则。 + +--- + +## 16. 验收标准 + +### 16.1 功能验收 + +- [ ] `python ocr.py verify --device cpu` 成功； +- [ ] 单张图片可输出 TXT、MD、JSON 和 Benchmark； +- [ ] JSON 包含文本、置信度和坐标； +- [ ] 目录模式可处理图片和 PDF； +- [ ] 递归目录输出不会因同名文件互相覆盖； +- [ ] PDF `hybrid` 模式能区分电子文本页和扫描页； +- [ ] 纯电子 PDF 不初始化 PP-OCRv6； +- [ ] PDF 可按页处理并支持断点续传； +- [ ] `--overwrite` 可安全重做； +- [ ] GPU 环境不可用时明确失败且不回退 CPU； +- [ ] 日志、JSON 和中文路径均使用 UTF-8； +- [ ] 中断或单文件失败后已有结果保持完整。 + +### 16.2 质量验收 + +- [ ] 内部结果模型有固定 `schema_version`； +- [ ] 模型原始结果不直接泄漏到业务层； +- [ ] 关键文件写入采用原子替换； +- [ ] manifest 记录完整模型配置； +- [ ] 配置变化时不能错误续传； +- [ ] 单元测试覆盖核心路由、输出、PDF 和恢复逻辑； +- [ ] 文档明确说明 PP-OCRv6 与 PaddleOCR-VL 的能力差异。 + +### 16.3 性能验收 + +性能目标不应在缺少目标硬件数据时设置绝对秒数。第一版建议以参考基线方式验收： + +- [ ] 同一批次只初始化一次模型； +- [ ] 纯电子 PDF 不产生模型初始化耗时； +- [ ] 批量任务默认串行，不出现无控制的多进程； +- [ ] Benchmark 可分离模型初始化、推理和导出耗时； +- [ ] CPU/GPU 结果均记录完整硬件与参数信息； +- [ ] 连续处理大批量文件时内存无持续明显增长。 + +--- + +## 17. 工作量预估 + +在复用参考项目工程逻辑、且第一阶段不增加表格结构识别和复杂版面分析的前提下，建议工作量如下： + +| 阶段 | 预估 | +|---|---:| +| 工程骨架和 CPU/GPU 环境 | 0.5～1 人日 | +| PP-OCRv6 运行时和单图结果适配 | 1～1.5 人日 | +| 图片输出、Benchmark、批处理 | 0.5～1 人日 | +| PDF 混合模式和断点续传适配 | 1～1.5 人日 | +| 自动化测试 | 1～1.5 人日 | +| GPU 实机验证、性能调优和文档 | 0.5～1 人日 | +| **合计** | **4.5～7.5 人日** | + +如果增加以下能力，需单独评估： + +- 表格结构识别； +- 多栏阅读顺序恢复； +- PP-Structure 集成； +- HTTP API/队列服务； +- 多 GPU 调度； +- TensorRT/HPI 加速； +- 离线模型打包和安装程序。 + +--- + +## 18. 推荐决策 + +推荐采用以下决策： + +1. **确认立项**：PP-OCRv6 可满足通用本地文字识别需求； +2. **复用工程骨架，不机械复制模型输出代码**； +3. **新增统一结果适配层**，隔离 PaddleOCR 原始结果； +4. **第一阶段聚焦 TXT、简单 Markdown、JSON、坐标和置信度**； +5. **保留 PDF 混合模式**，这是参考项目中最有价值且与模型无关的能力； +6. **支持 `tiny`、`small`、`medium` 三种模型规格，默认使用 `PP-OCRv6_medium_det + PP-OCRv6_medium_rec`**； +7. **默认中文模式、开启方向分类、关闭文档去畸变**，后续根据 Benchmark 调整； +8. **GPU 必须实机验收，不声明未经验证的性能结论**； +9. **将版面分析和表格结构识别作为独立增强阶段**，避免基础 OCR 项目范围失控。 + +最终判断： + +> 以 `../ocr-VL1.6` 为工程参考，建设 PP-OCRv6 图片、PDF 和批量本地 OCR 项目，在技术和工程上均可行；主要改造点集中在模型初始化、结果适配、输出生成和统计指标。只要接受 PP-OCRv6 不等价于 PaddleOCR-VL 文档解析能力这一边界，方案风险可控，且预期具有更低资源消耗和更好的通用 OCR 吞吐能力。 diff --git a/gpu/.python-version b/gpu/.python-version new file mode 100644 index 0000000..2c07333 --- /dev/null +++ b/gpu/.python-version @@ -0,0 +1 @@ +3.11 diff --git a/gpu/README.md b/gpu/README.md new file mode 100644 index 0000000..8628431 --- /dev/null +++ b/gpu/README.md @@ -0,0 +1,17 @@ +# GPU 子项目 + +依据目标 CUDA 版本安装： + +```bash +python gpu/setup_env.py --cuda cu118 +# 或 +python gpu/setup_env.py --cuda cu126 +``` + +安装后验证： + +```bash +python ocr.py verify --device gpu +``` + +GPU 失败时不会自动回退 CPU。 diff --git a/gpu/pyproject.toml b/gpu/pyproject.toml new file mode 100644 index 0000000..a028fed --- /dev/null +++ b/gpu/pyproject.toml @@ -0,0 +1,12 @@ +[project] +name = "pp-ocrv6-gpu" +version = "0.1.0" +description = "GPU runtime for the unified PP-OCRv6 application" +readme = "README.md" +requires-python = ">=3.11,<3.13" +dependencies = [ + "paddleocr==3.7.0", + "paddlepaddle-gpu==3.2.1", + "pypdfium2>=5.11.0", + "setuptools>=83.0.0", +] diff --git a/gpu/runner.py b/gpu/runner.py new file mode 100644 index 0000000..c7c0c3e --- /dev/null +++ b/gpu/runner.py @@ -0,0 +1,14 @@ +"""GPU environment runner used by the root unified launcher.""" + +from pathlib import Path +import sys + +PROJECT_ROOT = Path(__file__).resolve().parent.parent +if str(PROJECT_ROOT) not in sys.path: + sys.path.insert(0, str(PROJECT_ROOT)) + +from ocr_app.cli import main + + +if __name__ == "__main__": + raise SystemExit(main(device="gpu")) diff --git a/gpu/setup_env.py b/gpu/setup_env.py new file mode 100644 index 0000000..9873cdf --- /dev/null +++ b/gpu/setup_env.py @@ -0,0 +1,92 @@ +"""根据目标 CUDA 版本创建独立的 GPU uv 环境。""" + +import argparse +import shutil +import subprocess +import sys +from pathlib import Path + +PADDLE_INDEXES = { + "cu118": "https://www.paddlepaddle.org.cn/packages/stable/cu118/", + "cu126": "https://www.paddlepaddle.org.cn/packages/stable/cu126/", +} + + +def main() -> int: + parser = argparse.ArgumentParser( + description="安装 PP-OCRv6 GPU 子项目依赖", + ) + parser.add_argument( + "--cuda", + choices=sorted(PADDLE_INDEXES), + required=True, + help="目标机器的 CUDA Wheel 类型；必须依据 PaddlePaddle 官方兼容表选择", + ) + parser.add_argument( + "--dry-run", + action="store_true", + help="只显示命令，不创建环境", + ) + parser.add_argument( + "--allow-no-gpu", + action="store_true", + help="允许在未检测到 nvidia-smi 时创建环境（仅用于准备/CI，不代表可运行）", + ) + args = parser.parse_args() + + uv = shutil.which("uv") + if not uv: + print("[ERROR] 未找到 uv，请先安装 uv。", file=sys.stderr) + return 1 + + nvidia_smi = shutil.which("nvidia-smi") + if not args.dry_run and not nvidia_smi and not args.allow_no_gpu: + print( + "[ERROR] 未检测到 nvidia-smi，拒绝在无 NVIDIA GPU 的机器安装 CUDA 依赖。\n" + "如仅准备环境，请显式添加 --allow-no-gpu。", + file=sys.stderr, + ) + return 2 + + project_dir = Path(__file__).resolve().parent + index_url = PADDLE_INDEXES[args.cuda] + command = [ + uv, + "sync", + "--project", + str(project_dir), + "--index", + index_url, + ] + + print(f"目标 CUDA Wheel: {args.cuda}") + print(f"PaddlePaddle 索引: {index_url}") + print("执行命令:") + print(" " + " ".join(command)) + + if args.dry_run: + return 0 + + ready_marker = project_dir / ".gpu-ready" + if ready_marker.exists(): + ready_marker.unlink() + + completed = subprocess.run(command, check=False) + if completed.returncode != 0: + print( + "[ERROR] 依赖安装失败。请检查 GPU、驱动、Python 和 PaddlePaddle Wheel 兼容性。", + file=sys.stderr, + ) + return completed.returncode + + ready_marker.write_text( + f"cuda={args.cuda}\nindex={index_url}\n", + encoding="utf-8", + ) + print("\n[OK] GPU 子项目环境已创建。下一步从仓库根目录运行：") + print(" python ocr.py verify --device gpu") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/logs/.gitkeep b/logs/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/ocr.py b/ocr.py new file mode 100644 index 0000000..02a6916 --- /dev/null +++ b/ocr.py @@ -0,0 +1,68 @@ +"""PP-OCRv6 unified launcher using isolated CPU/GPU uv projects.""" + +from __future__ import annotations + +import shutil +import subprocess +import sys +from pathlib import Path + +ROOT = Path(__file__).resolve().parent + + +def _requested_device(argv: list[str]) -> str: + for index, value in enumerate(argv): + if value == "--device": + if index + 1 >= len(argv): + raise SystemExit("--device 需要 cpu 或 gpu") + return argv[index + 1].lower() + if value.startswith("--device="): + return value.split("=", 1)[1].lower() + return "cpu" + + +def main() -> int: + try: + device = _requested_device(sys.argv[1:]) + except SystemExit as exc: + print(exc, file=sys.stderr) + return 2 + if device not in {"cpu", "gpu"}: + print(f"不支持的设备: {device}，可选 cpu/gpu", file=sys.stderr) + return 2 + + project = ROOT / device + runner = project / "runner.py" + if sys.platform == "win32": + environment_python = project / ".venv" / "Scripts" / "python.exe" + else: + environment_python = project / ".venv" / "bin" / "python" + + if device == "gpu": + ready_marker = project / ".gpu-ready" + if not ready_marker.is_file() or not environment_python.is_file(): + print( + "GPU 环境尚未安装完成。请根据目标 CUDA 版本运行：\n" + " python gpu/setup_env.py --cuda cu118\n" + "或：\n" + " python gpu/setup_env.py --cuda cu126", + file=sys.stderr, + ) + return 1 + command = [str(environment_python), str(runner), *sys.argv[1:]] + return subprocess.run(command, cwd=ROOT).returncode + + if environment_python.is_file(): + command = [str(environment_python), str(runner), *sys.argv[1:]] + return subprocess.run(command, cwd=ROOT).returncode + + uv = shutil.which("uv") + if not uv: + print("未找到 CPU 虚拟环境和 uv，请先安装 uv。", file=sys.stderr) + return 1 + command = [uv, "run", "--project", str(project), "python", str(runner), *sys.argv[1:]] + return subprocess.run(command, cwd=ROOT).returncode + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/ocr_app/__init__.py b/ocr_app/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ocr_app/cli.py b/ocr_app/cli.py new file mode 100644 index 0000000..dd5a5a7 --- /dev/null +++ b/ocr_app/cli.py @@ -0,0 +1,140 @@ +"""Path-first unified CLI for PP-OCRv6.""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +from .commands import run_input, run_verify +from .logging_utils import default_log_path, setup_run_logger +from .runtime import PipelineProvider, RuntimeConfig + +PROJECT_ROOT = Path(__file__).resolve().parent.parent +LEGACY_COMMANDS = {"image", "pdf", "batch"} + + +def _add_bool_option(parser: argparse.ArgumentParser, name: str, default: bool, help_text: str) -> None: + parser.add_argument( + f"--{name}", + action=argparse.BooleanOptionalAction, + default=default, + help=help_text, + ) + + +def _add_device_options(parser: argparse.ArgumentParser, default: str | None) -> None: + parser.add_argument("--device", choices=("cpu", "gpu"), default=default or "cpu", help="运行设备") + parser.add_argument("--device-id", type=int, default=0, help="GPU 编号") + parser.add_argument("--threads", type=int, default=None, help="CPU 线程数") + parser.add_argument("--log-file", type=Path, default=None, help="日志文件路径") + parser.add_argument("--verbose", action="store_true", help="输出详细日志") + + +def _add_model_options(parser: argparse.ArgumentParser) -> None: + parser.add_argument( + "--model-size", + "--model", + dest="model_size", + choices=("tiny", "small", "medium"), + default="medium", + help="PP-OCRv6 检测与识别模型规格", + ) + parser.add_argument("--lang", default="ch", help="结果元数据中的语言标识，默认中文/中英混合") + parser.add_argument("--text-rec-score-thresh", type=float, default=0.0, help="识别置信度阈值") + parser.add_argument("--text-det-limit-side-len", type=int, default=None, help="文本检测边长限制") + parser.add_argument("--text-det-limit-type", choices=("min", "max"), default=None, help="检测边长限制方式") + parser.add_argument("--text-det-thresh", type=float, default=None, help="文本检测像素阈值") + parser.add_argument("--text-det-box-thresh", type=float, default=None, help="文本框阈值") + parser.add_argument("--text-det-unclip-ratio", type=float, default=None, help="文本框扩张比例") + parser.add_argument("--text-recognition-batch-size", type=int, default=6, help="文本识别批大小") + parser.add_argument("--return-word-box", action="store_true", help="返回单词级坐标") + _add_bool_option(parser, "doc-orientation-classify", True, "启用文档方向分类") + _add_bool_option(parser, "doc-unwarping", False, "启用文档去畸变") + _add_bool_option(parser, "textline-orientation", True, "启用文本行方向分类") + + +def build_input_parser(device_override: str | None = None) -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + prog="ocr.py", + description="使用 PP-OCRv6 自动处理图片、PDF 或目录", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument("input", type=Path, help="图片、PDF 或目录") + parser.add_argument("--recursive", action="store_true", help="目录模式递归扫描子目录") + parser.add_argument("--output", type=Path, default=PROJECT_ROOT / "outputs", help="输出根目录") + parser.add_argument("--fail-fast", action="store_true", help="单文件失败后立即停止目录任务") + parser.add_argument("--warmup", type=int, default=0, help="首张图片预热轮数") + parser.add_argument("--rounds", type=int, default=1, help="每张图片推理轮数") + parser.add_argument("--benchmark-json", type=Path, default=None, help="额外复制单图 Benchmark JSON") + parser.add_argument("--no-result", action="store_true", help="不在日志中逐行记录识别文字") + parser.add_argument("--save-visualization", action="store_true", help="保存 OCR 可视化图片") + parser.add_argument("--save-raw-result", action="store_true", help="保存 PaddleOCR 原始 JSON") + parser.add_argument("--pdf-mode", "--mode", dest="pdf_mode", choices=("hybrid", "text", "ocr"), default="hybrid", help="PDF 处理模式") + parser.add_argument("--pages", help="PDF 页码范围，例如 1-5,8,10-") + parser.add_argument("--dpi", type=int, default=144, help="PDF OCR 页面渲染 DPI") + parser.add_argument("--password", help="PDF 密码") + parser.add_argument("--resume", action="store_true", help="PDF 断点续传") + parser.add_argument("--overwrite", action="store_true", help="覆盖已有 PDF 输出") + parser.add_argument("--keep-rendered", action="store_true", help="保留 OCR 页面 PNG") + parser.add_argument("--text-min-chars", type=int, default=50, help="有效文本最小字符数") + parser.add_argument("--text-min-printable-ratio", type=float, default=0.85, help="可打印字符比例阈值") + parser.add_argument("--text-min-content-ratio", type=float, default=0.60, help="字母/数字/CJK 比例阈值") + parser.add_argument("--text-max-replacement-ratio", type=float, default=0.02, help="替换字符最大比例") + parser.add_argument("--text-min-density", type=float, default=25.0, help="文本密度阈值") + _add_model_options(parser) + _add_device_options(parser, device_override) + return parser + + +def build_verify_parser(device_override: str | None = None) -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(prog="ocr.py verify", description="验证 CPU/GPU Paddle 环境") + _add_model_options(parser) + _add_device_options(parser, device_override) + return parser + + +def normalize_argv(argv: list[str]) -> tuple[str, list[str]]: + if argv and argv[0] == "verify": + return "verify", argv[1:] + if argv and argv[0] in LEGACY_COMMANDS: + return "input", argv[1:] + return "input", argv + + +def main(device: str | None = None, argv: list[str] | None = None) -> int: + raw_argv = list(sys.argv[1:] if argv is None else argv) + command, normalized = normalize_argv(raw_argv) + parser = build_verify_parser(device) if command == "verify" else build_input_parser(device) + args = parser.parse_args(normalized) + if device is not None: + args.device = device + stem = "verify" if command == "verify" else (args.input.stem or args.input.name or "input") + category = "verify" if command == "verify" else "input" + log_file = args.log_file or default_log_path(PROJECT_ROOT, category, stem, device=args.device) + logger = setup_run_logger(f"ppocrv6.{category}.{args.device}", log_file, verbose=args.verbose) + provider = PipelineProvider( + RuntimeConfig( + device=args.device, + threads=args.threads, + device_id=args.device_id, + lang=args.lang, + model_size=args.model_size, + use_doc_orientation_classify=args.doc_orientation_classify, + use_doc_unwarping=args.doc_unwarping, + use_textline_orientation=args.textline_orientation, + text_recognition_batch_size=args.text_recognition_batch_size, + ), + logger, + ) + try: + if command == "verify": + return run_verify(args, provider, logger, PROJECT_ROOT) + if args.warmup < 0 or args.rounds < 1: + raise ValueError("--warmup 必须 >= 0，--rounds 必须 >= 1") + if args.text_recognition_batch_size < 1: + raise ValueError("--text-recognition-batch-size 必须 >= 1") + return run_input(args, provider, logger, PROJECT_ROOT) + except Exception as exc: + logger.exception("COMMAND_FAILED command=%s error=%s", command, exc) + return 1 diff --git a/ocr_app/commands.py b/ocr_app/commands.py new file mode 100644 index 0000000..8b00bb6 --- /dev/null +++ b/ocr_app/commands.py @@ -0,0 +1,310 @@ +"""Unified suffix-based routing for PP-OCRv6 files and directories.""" + +from __future__ import annotations + +import logging +import statistics +import time +from dataclasses import dataclass +from datetime import datetime +from pathlib import Path +from typing import Any + +from .output import atomic_write_json, image_output_directory, pdf_output_root, safe_stem, save_image_ocr_outputs +from .pdf import preflight_pdf, process_pdf +from .pdf_text import TextLayerPolicy +from .result_adapter import adapt_ocr_result +from .runtime import PipelineProvider + +IMAGE_EXTENSIONS = {".png", ".jpg", ".jpeg", ".bmp", ".tiff", ".tif", ".webp"} +PDF_EXTENSIONS = {".pdf"} +SUPPORTED_EXTENSIONS = IMAGE_EXTENSIONS | PDF_EXTENSIONS + + +@dataclass +class FileProcessResult: + path: Path + kind: str + status: str + seconds: float + details: dict[str, Any] + exit_code: int = 0 + + +def detect_input_kind(path: Path) -> str: + if path.is_dir(): + return "directory" + if path.suffix.lower() in IMAGE_EXTENSIONS: + return "image" + if path.suffix.lower() in PDF_EXTENSIONS: + return "pdf" + return "unsupported" + + +def discover_supported_files(directory: Path, *, recursive: bool, output_root: Path) -> list[Path]: + iterator = directory.rglob("*") if recursive else directory.glob("*") + output_root = output_root.expanduser().resolve() + files: list[Path] = [] + for path in iterator: + if not path.is_file() or path.suffix.lower() not in SUPPORTED_EXTENSIONS: + continue + resolved = path.resolve() + try: + resolved.relative_to(output_root) + except ValueError: + files.append(resolved) + return sorted(files, key=lambda value: str(value).casefold()) + + +def _predict_kwargs(args) -> dict[str, Any]: + return { + key: value + for key, value in { + "text_det_limit_side_len": args.text_det_limit_side_len, + "text_det_limit_type": args.text_det_limit_type, + "text_det_thresh": args.text_det_thresh, + "text_det_box_thresh": args.text_det_box_thresh, + "text_det_unclip_ratio": args.text_det_unclip_ratio, + "text_rec_score_thresh": args.text_rec_score_thresh, + "return_word_box": args.return_word_box, + }.items() + if value is not None + } + + +def process_image_file( + path: Path, + *, + args, + provider: PipelineProvider, + logger: logging.Logger, + project_root: Path, + run_warmup: bool, + batch_root: Path | None, +) -> FileProcessResult: + del project_root + file_started = time.perf_counter() + logger.info("FILE_ROUTED path=%s kind=image", path) + pipeline = provider.get() + predict_kwargs = _predict_kwargs(args) + + warmup_times: list[float] = [] + if run_warmup: + for index in range(args.warmup): + started = time.perf_counter() + pipeline.predict(str(path), **predict_kwargs) + provider.synchronize() + elapsed = time.perf_counter() - started + warmup_times.append(elapsed) + logger.info("WARMUP_COMPLETED path=%s round=%d/%d seconds=%.3f", path, index + 1, args.warmup, elapsed) + + inference_times: list[float] = [] + result = None + for index in range(args.rounds): + provider.synchronize() + started = time.perf_counter() + results = pipeline.predict(str(path), **predict_kwargs) + provider.synchronize() + elapsed = time.perf_counter() - started + inference_times.append(elapsed) + if not results: + raise RuntimeError("PP-OCRv6 未返回图片结果") + result = results[0] + logger.info("INFERENCE_COMPLETED path=%s round=%d/%d seconds=%.3f", path, index + 1, args.rounds, elapsed) + assert result is not None + + adapt_started = time.perf_counter() + normalized = adapt_ocr_result( + result, + input_path=path, + source_type="image_ocr", + language=provider.config.lang, + detection_model=provider.config.text_detection_model_name, + recognition_model=provider.config.text_recognition_model_name, + model_size=provider.config.model_size, + ) + adapt_seconds = time.perf_counter() - adapt_started + summary = normalized["summary"] + benchmark = { + "timestamp": datetime.now().astimezone().isoformat(), + **provider.metadata(), + "image_path": str(path), + "image": normalized["image"], + "ocr_summary": summary, + "warmup_seconds": [round(value, 3) for value in warmup_times], + "inference_seconds": { + "all": [round(value, 3) for value in inference_times], + "min": round(min(inference_times), 3), + "max": round(max(inference_times), 3), + "mean": round(statistics.fmean(inference_times), 3), + "median": round(statistics.median(inference_times), 3), + "stdev": round(statistics.pstdev(inference_times), 3), + }, + "result_adapt_seconds": round(adapt_seconds, 3), + "gpu_memory": provider.gpu_memory(), + "export_seconds": 0.0, + "file_total_seconds": 0.0, + } + output_dir = image_output_directory(args.output, path, batch_root=batch_root, recursive=args.recursive) + export_started = time.perf_counter() + output_paths = save_image_ocr_outputs( + result, + normalized, + output_dir, + input_path=path, + benchmark=benchmark, + save_raw_result=args.save_raw_result, + save_visualization=args.save_visualization, + ) + export_seconds = time.perf_counter() - export_started + total_seconds = time.perf_counter() - file_started + benchmark["export_seconds"] = round(export_seconds, 3) + benchmark["file_total_seconds"] = round(total_seconds, 3) + atomic_write_json(Path(output_paths["benchmark"]), benchmark) + if batch_root is None and args.benchmark_json: + explicit = args.benchmark_json.expanduser().resolve() + atomic_write_json(explicit, benchmark) + output_paths["explicit_benchmark"] = str(explicit) + + logger.info( + "IMAGE_COMPLETED path=%s detected_lines=%d non_empty_lines=%d mean_score=%s inference_mean_seconds=%.3f file_total_seconds=%.3f output=%s", + path, + summary["detected_lines"], + summary["non_empty_lines"], + summary["mean_score"], + statistics.fmean(inference_times), + total_seconds, + output_dir, + ) + if not args.no_result: + for line in normalized["lines"]: + logger.info("OCR_LINE path=%s index=%d score=%s text=%s", path, line["index"], line["score"], line["text"].replace("\n", "\\n")) + return FileProcessResult(path, "image", "completed", total_seconds, {**summary, **output_paths}) + + +def process_pdf_file(path: Path, *, args, provider: PipelineProvider, logger: logging.Logger, batch_root: Path | None) -> FileProcessResult: + started = time.perf_counter() + output_root = pdf_output_root(args.output, path, batch_root=batch_root, recursive=args.recursive) + manifest_exists = (output_root / safe_stem(path.stem) / "manifest.json").is_file() + resume = args.resume if batch_root is None else manifest_exists and not args.overwrite + preflight = preflight_pdf(pdf_path=path, output_root=output_root, pages=args.pages, dpi=args.dpi, password=args.password, resume=resume, overwrite=args.overwrite) + logger.info("PDF_PREFLIGHT_COMPLETED path=%s page_count=%d selected_pages=%d", path, preflight["page_count"], len(preflight["selected_pages"])) + policy = TextLayerPolicy( + min_chars=args.text_min_chars, + min_printable_ratio=args.text_min_printable_ratio, + min_content_ratio=args.text_min_content_ratio, + max_replacement_ratio=args.text_max_replacement_ratio, + min_chars_per_megapixel=args.text_min_density, + ) + if args.pdf_mode == "ocr": + provider.prepare() + summary = process_pdf( + provider=provider, + pdf_path=path, + output_root=output_root, + mode=args.pdf_mode, + text_policy=policy, + pages=args.pages, + dpi=args.dpi, + password=args.password, + resume=resume, + overwrite=args.overwrite, + keep_rendered=args.keep_rendered, + fail_fast=args.fail_fast, + predict_kwargs=_predict_kwargs(args), + logger=logger, + ) + total = time.perf_counter() - started + logger.info("PDF_COMPLETED path=%s status=%s text_pages=%d ocr_pages=%d failed_pages=%s total_seconds=%.3f", path, summary["status"], summary["text_pages"], summary["ocr_pages"], summary["failed_pages"], total) + return FileProcessResult(path, "pdf", summary["status"], total, summary, 0 if not summary["failed_pages"] else 3) + + +def process_single_file(path: Path, *, args, provider: PipelineProvider, logger: logging.Logger, project_root: Path, run_image_warmup: bool, batch_root: Path | None = None) -> FileProcessResult: + path = path.expanduser().resolve() + if not path.is_file(): + raise FileNotFoundError(f"文件不存在: {path}") + kind = detect_input_kind(path) + if kind == "image": + return process_image_file(path, args=args, provider=provider, logger=logger, project_root=project_root, run_warmup=run_image_warmup, batch_root=batch_root) + if kind == "pdf": + return process_pdf_file(path, args=args, provider=provider, logger=logger, batch_root=batch_root) + raise ValueError(f"不支持的文件类型: {path.suffix or '<无后缀>'}") + + +def run_input(args, provider: PipelineProvider, logger: logging.Logger, project_root: Path) -> int: + program_started = time.perf_counter() + input_path = args.input.expanduser().resolve() + if detect_input_kind(input_path) != "directory": + try: + result = process_single_file(input_path, args=args, provider=provider, logger=logger, project_root=project_root, run_image_warmup=True) + except KeyboardInterrupt: + logger.warning("PROGRAM_INTERRUPTED input=%s", input_path) + return 130 + except Exception as exc: + logger.exception("FILE_FAILED path=%s error=%s", input_path, exc) + return 1 + logger.info("PROGRAM_COMPLETED input=%s kind=%s status=%s total_seconds=%.3f", result.path, result.kind, result.status, time.perf_counter() - program_started) + return result.exit_code + + files = discover_supported_files(input_path, recursive=args.recursive, output_root=args.output) + if not files: + logger.error("NO_SUPPORTED_FILES directory=%s", input_path) + return 1 + results: list[FileProcessResult] = [] + failures: list[dict[str, str]] = [] + image_warmup_pending = True + for index, path in enumerate(files, 1): + logger.info("DIRECTORY_PROGRESS_START progress=%d/%d path=%s", index, len(files), path) + try: + result = process_single_file(path, args=args, provider=provider, logger=logger, project_root=project_root, run_image_warmup=image_warmup_pending, batch_root=input_path) + results.append(result) + if result.kind == "image": + image_warmup_pending = False + if result.exit_code: + failures.append({"path": str(path), "error": result.status}) + except KeyboardInterrupt: + return 130 + except Exception as exc: + failures.append({"path": str(path), "error": f"{type(exc).__name__}: {exc}"}) + logger.exception("FILE_FAILED path=%s", path) + if args.fail_fast: + break + program_total = time.perf_counter() - program_started + manifest_path = args.output.expanduser().resolve() / "batches" / f"{safe_stem(input_path.name or 'batch')}-{datetime.now():%Y%m%d-%H%M%S-%f}.json" + atomic_write_json( + manifest_path, + { + "input_directory": str(input_path), + "recursive": args.recursive, + "device": provider.resolved_device, + "model_config": provider.model_config(), + "discovered_files": len(files), + "completed_files": len(results), + "failed_files": len(failures), + "program_total_seconds": round(program_total, 3), + "results": [{"path": str(item.path), "kind": item.kind, "status": item.status, "seconds": round(item.seconds, 3), "outputs": item.details} for item in results], + "failures": failures, + }, + ) + logger.info("DIRECTORY_SUMMARY discovered=%d completed=%d failed=%d manifest=%s", len(files), len(results), len(failures), manifest_path) + return 0 if not failures else 3 + + +def run_verify(args, provider: PipelineProvider, logger: logging.Logger, project_root: Path) -> int: + del args, project_root + started = time.perf_counter() + try: + provider.prepare() + paddle = provider._paddle + if provider.config.device == "gpu": + result = paddle.matmul(paddle.ones([1024, 1024]), paddle.ones([1024, 1024])) + provider.synchronize() + logger.info("GPU_SMOKE_TEST shape=%s", list(result.shape)) + else: + from paddle import core + logger.info("CPU_SMOKE_TEST onednn=%s mkldnn=%s", core.is_compiled_with_onednn(), core.is_compiled_with_mkldnn()) + logger.info("VERIFY_COMPLETED metadata=%s seconds=%.3f", provider.metadata(), time.perf_counter() - started) + return 0 + except Exception: + logger.exception("VERIFY_FAILED") + return 1 diff --git a/ocr_app/logging_utils.py b/ocr_app/logging_utils.py new file mode 100644 index 0000000..0e353cc --- /dev/null +++ b/ocr_app/logging_utils.py @@ -0,0 +1,74 @@ +"""Shared UTF-8 logging helpers for OCR scripts.""" + +from __future__ import annotations + +import logging +import re +import sys +from datetime import datetime +from pathlib import Path + + +def safe_log_stem(value: str) -> str: + cleaned = re.sub(r"[^\w.-]+", "_", value, flags=re.UNICODE).strip("._") + return cleaned or "ocr" + + +def default_log_path( + project_root: Path, + category: str, + stem: str, + *, + device: str | None = None, +) -> Path: + timestamp = datetime.now().strftime("%Y%m%d-%H%M%S") + suffix = f"-{safe_log_stem(device)}" if device else "" + filename = f"{safe_log_stem(stem)}{suffix}-{timestamp}.log" + return project_root / "logs" / safe_log_stem(category) / filename + + +def setup_run_logger( + name: str, + log_file: Path, + *, + verbose: bool = False, + console: bool = True, +) -> logging.Logger: + """Create an isolated logger that writes UTF-8 text and optional console output.""" + log_file = log_file.expanduser().resolve() + log_file.parent.mkdir(parents=True, exist_ok=True) + + logger = logging.getLogger(name) + logger.setLevel(logging.DEBUG if verbose else logging.INFO) + logger.propagate = False + for handler in logger.handlers[:]: + handler.close() + logger.removeHandler(handler) + + formatter = logging.Formatter( + fmt="%(asctime)s | %(levelname)-8s | pid=%(process)d | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + ) + + file_handler = logging.FileHandler(log_file, encoding="utf-8") + file_handler.setLevel(logging.DEBUG) + file_handler.setFormatter(formatter) + logger.addHandler(file_handler) + + if console: + console_handler = logging.StreamHandler(sys.stdout) + console_handler.setLevel(logging.DEBUG if verbose else logging.INFO) + console_handler.setFormatter(formatter) + logger.addHandler(console_handler) + + logger.info("LOG_INITIALIZED file=%s", log_file) + return logger + + +def close_logger(logger: logging.Logger) -> None: + for handler in logger.handlers[:]: + try: + handler.flush() + handler.close() + finally: + logger.removeHandler(handler) diff --git a/ocr_app/output.py b/ocr_app/output.py new file mode 100644 index 0000000..59cec74 --- /dev/null +++ b/ocr_app/output.py @@ -0,0 +1,115 @@ +"""Output helpers for normalized PP-OCRv6 results.""" + +from __future__ import annotations + +import json +import re +from pathlib import Path +from typing import Any + +from PIL import Image + +from .result_adapter import raw_result_json, result_markdown, result_plain_text + + +def safe_stem(value: str) -> str: + cleaned = re.sub(r"[^\w.-]+", "_", value, flags=re.UNICODE).strip("._") + return cleaned or "result" + + +def atomic_write_text(path: Path, content: str) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_name(f".{path.name}.tmp") + temporary.write_text(content, encoding="utf-8") + temporary.replace(path) + + +def atomic_write_json(path: Path, data: Any) -> None: + atomic_write_text(path, json.dumps(data, ensure_ascii=False, indent=2)) + + +def image_output_directory( + output_root: Path, + image_path: Path, + *, + batch_root: Path | None, + recursive: bool, +) -> Path: + base = output_root.expanduser().resolve() / "images" + if batch_root is not None and recursive: + base /= image_path.parent.resolve().relative_to(batch_root.resolve()) + suffix = image_path.suffix.lower().lstrip(".") or "image" + return base / safe_stem(f"{image_path.stem}_{suffix}") + + +def pdf_output_root( + output_root: Path, + pdf_path: Path, + *, + batch_root: Path | None, + recursive: bool, +) -> Path: + base = output_root.expanduser().resolve() / "pdfs" + if batch_root is not None and recursive: + base /= pdf_path.parent.resolve().relative_to(batch_root.resolve()) + return base + + +def _save_visualization(result: Any, path: Path) -> bool: + try: + images = result.img + except Exception: + return False + if isinstance(images, dict): + image = images.get("ocr_res_img") + if image is None: + image = next(iter(images.values()), None) + else: + image = images + if image is None: + return False + if not isinstance(image, Image.Image): + try: + image = Image.fromarray(image) + except Exception: + return False + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_name(f".{path.name}.tmp") + image.convert("RGB").save(temporary, format="JPEG", quality=92) + temporary.replace(path) + return True + + +def save_image_ocr_outputs( + result: Any, + normalized: dict[str, Any], + output_dir: Path, + *, + input_path: Path, + benchmark: dict[str, Any], + save_raw_result: bool, + save_visualization: bool, +) -> dict[str, str]: + output_dir.mkdir(parents=True, exist_ok=True) + paths: dict[str, str] = { + "output_dir": str(output_dir), + "markdown": str(output_dir / "result.md"), + "text": str(output_dir / "result.txt"), + "json": str(output_dir / "result.json"), + "benchmark": str(output_dir / "benchmark.json"), + } + atomic_write_text(Path(paths["markdown"]), result_markdown(normalized, title=input_path.name)) + text = result_plain_text(normalized) + atomic_write_text(Path(paths["text"]), text.rstrip() + ("\n" if text else "")) + atomic_write_json(Path(paths["json"]), normalized) + atomic_write_json(Path(paths["benchmark"]), benchmark) + + if save_raw_result: + raw_path = output_dir / "raw-result.json" + atomic_write_json(raw_path, raw_result_json(result)) + paths["raw_json"] = str(raw_path) + if save_visualization: + visualization = output_dir / "visualization.jpg" + if _save_visualization(result, visualization): + paths["visualization"] = str(visualization) + return paths diff --git a/ocr_app/pdf.py b/ocr_app/pdf.py new file mode 100644 index 0000000..cbb7e10 --- /dev/null +++ b/ocr_app/pdf.py @@ -0,0 +1,397 @@ +"""Hybrid PDF processing with PP-OCRv6 fallback for scanned pages.""" + +from __future__ import annotations + +import hashlib +import json +import logging +import re +import shutil +import time +from datetime import datetime +from pathlib import Path +from typing import Any, Iterable + +import pypdfium2 as pdfium +from PIL import Image + +from .output import atomic_write_json, atomic_write_text, safe_stem +from .pdf_text import TextLayerPolicy, extract_page_text +from .result_adapter import adapt_ocr_result, result_markdown + +MANIFEST_VERSION = 1 +PAGE_SPEC_PATTERN = re.compile(r"^(\d+)(?:-(\d*)?)?$") +PDF_MODES = {"hybrid", "text", "ocr"} + + +def now_iso() -> str: + return datetime.now().astimezone().isoformat() + + +def sha256_file(path: Path, chunk_size: int = 1024 * 1024) -> str: + digest = hashlib.sha256() + with path.open("rb") as file: + while chunk := file.read(chunk_size): + digest.update(chunk) + return digest.hexdigest() + + +def parse_page_spec(spec: str | None, page_count: int) -> list[int]: + if page_count < 1: + return [] + if spec is None or not spec.strip(): + return list(range(page_count)) + selected: set[int] = set() + for raw_part in spec.split(","): + part = raw_part.strip() + match = PAGE_SPEC_PATTERN.fullmatch(part) + if not match: + raise ValueError(f"无效页码范围: {part!r}，示例: 1-5,8,10-") + start = int(match.group(1)) + end_text = match.group(2) + end = start if "-" not in part else int(end_text) if end_text else page_count + if start < 1 or end < 1: + raise ValueError("PDF 页码从 1 开始") + if start > end: + raise ValueError(f"页码起始值不能大于结束值: {part}") + if start > page_count or end > page_count: + raise ValueError(f"页码范围 {part} 超出 PDF 总页数 {page_count}") + selected.update(range(start - 1, end)) + return sorted(selected) + + +def render_page(document: Any, page_index: int, dpi: int) -> Image.Image: + page = document.get_page(page_index) + bitmap = None + try: + bitmap = page.render(scale=dpi / 72.0) + return bitmap.to_pil().convert("RGB").copy() + finally: + if bitmap is not None: + bitmap.close() + page.close() + + +def save_png_atomic(image: Image.Image, path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_name(f".{path.name}.tmp") + image.save(temporary, format="PNG") + temporary.replace(path) + + +def _page_paths(document_dir: Path, page_number: int) -> tuple[Path, Path]: + stem = f"page-{page_number:04d}" + return document_dir / "pages" / f"{stem}.md", document_dir / "pages" / f"{stem}.json" + + +def _page_is_complete(document_dir: Path, manifest: dict[str, Any], page_number: int) -> bool: + record = manifest.get("pages", {}).get(str(page_number), {}) + markdown_path, json_path = _page_paths(document_dir, page_number) + return record.get("status") == "completed" and markdown_path.is_file() and json_path.is_file() + + +def rebuild_combined_outputs(document_dir: Path, manifest: dict[str, Any]) -> None: + markdown_parts = [f"# {manifest['document_name']}"] + page_results = [] + for page_number in manifest.get("selected_pages", []): + record = manifest.get("pages", {}).get(str(page_number), {}) + markdown_path, json_path = _page_paths(document_dir, page_number) + if record.get("status") == "completed" and markdown_path.is_file() and json_path.is_file(): + source = record.get("source_type", "unknown") + text = markdown_path.read_text(encoding="utf-8").strip() + markdown_parts.append(f"\n\n---\n\n## Page {page_number} ({source})\n\n{text}") + page_results.append({"page_number": page_number, "source_type": source, "metrics": record, "result": json.loads(json_path.read_text(encoding="utf-8"))}) + elif record.get("status") == "failed": + markdown_parts.append(f"\n\n---\n\n## Page {page_number}\n\n> Failed: {record.get('error')}") + atomic_write_text(document_dir / "document.md", "".join(markdown_parts).rstrip() + "\n") + atomic_write_json(document_dir / "document.json", {"manifest": manifest, "page_results": page_results}) + + +def validate_pdf_request(pdf_path: Path, output_root: Path, *, resume: bool, overwrite: bool) -> tuple[Path, Path]: + pdf_path = pdf_path.expanduser().resolve() + output_root = output_root.expanduser().resolve() + if not pdf_path.is_file(): + raise FileNotFoundError(f"PDF 不存在: {pdf_path}") + if pdf_path.suffix.lower() != ".pdf": + raise ValueError(f"输入文件不是 PDF: {pdf_path}") + if resume and overwrite: + raise ValueError("--resume 和 --overwrite 不能同时使用") + document_dir = output_root / safe_stem(pdf_path.stem) + if resume and not (document_dir / "manifest.json").is_file(): + raise FileNotFoundError(f"无法续传，缺少 {document_dir / 'manifest.json'}") + if document_dir.exists() and any(document_dir.iterdir()) and not (resume or overwrite): + raise FileExistsError(f"输出目录已存在: {document_dir}；请使用 --resume 或 --overwrite") + return pdf_path, output_root + + +def preflight_pdf(*, pdf_path: Path, output_root: Path, pages: str | None, dpi: int, password: str | None, resume: bool, overwrite: bool) -> dict[str, Any]: + pdf_path, output_root = validate_pdf_request(pdf_path, output_root, resume=resume, overwrite=overwrite) + if dpi < 72 or dpi > 600: + raise ValueError("--dpi 必须在 72 到 600 之间") + document = pdfium.PdfDocument(str(pdf_path), password=password) + try: + page_count = len(document) + selected = parse_page_spec(pages, page_count) + finally: + document.close() + return {"pdf_path": pdf_path, "output_root": output_root, "document_dir": output_root / safe_stem(pdf_path.stem), "page_count": page_count, "selected_pages": [index + 1 for index in selected]} + + +def _prepare_manifest( + *, + pdf_path: Path, + document_dir: Path, + page_count: int, + selected_pages: Iterable[int], + dpi: int, + mode: str, + policy: TextLayerPolicy, + resume: bool, + overwrite: bool, + model_config: dict[str, Any], + run_metadata: dict[str, Any], +) -> dict[str, Any]: + manifest_path = document_dir / "manifest.json" + digest = sha256_file(pdf_path) + selected = [index + 1 for index in selected_pages] + if overwrite and document_dir.exists(): + shutil.rmtree(document_dir) + if resume: + manifest = json.loads(manifest_path.read_text(encoding="utf-8")) + if manifest.get("manifest_version") != MANIFEST_VERSION or manifest.get("pipeline") != "PP-OCRv6": + raise ValueError("manifest 与当前 PP-OCRv6 项目不兼容，请使用 --overwrite") + if manifest.get("input", {}).get("sha256") != digest: + raise ValueError("PDF 内容已变化，请使用 --overwrite") + if manifest.get("render", {}).get("dpi") != dpi or manifest.get("mode") != mode: + raise ValueError("DPI 或模式与原任务不一致，请使用原参数或 --overwrite") + if manifest.get("text_layer_policy") != policy.__dict__: + raise ValueError("文本层阈值与原任务不一致，请使用原参数或 --overwrite") + if manifest.get("model_config") != model_config: + raise ValueError("PP-OCRv6 模型配置与原任务不一致，请使用原参数或 --overwrite") + manifest["selected_pages"] = sorted(set(manifest.get("selected_pages", [])) | set(selected)) + manifest["run_metadata"] = run_metadata + manifest["status"] = "running" + manifest["updated_at"] = now_iso() + else: + document_dir.mkdir(parents=True, exist_ok=True) + manifest = { + "manifest_version": MANIFEST_VERSION, + "pipeline": "PP-OCRv6", + "document_name": pdf_path.stem, + "input": {"path": str(pdf_path), "sha256": digest, "size_bytes": pdf_path.stat().st_size}, + "page_count": page_count, + "selected_pages": selected, + "mode": mode, + "model_config": model_config, + "text_layer_policy": policy.__dict__, + "render": {"dpi": dpi, "format": "png"}, + "run_metadata": run_metadata, + "status": "running", + "created_at": now_iso(), + "updated_at": now_iso(), + "pages": {}, + } + atomic_write_json(manifest_path, manifest) + return manifest + + +def process_pdf( + *, + provider: Any, + pdf_path: Path, + output_root: Path, + mode: str = "hybrid", + text_policy: TextLayerPolicy | None = None, + pages: str | None = None, + dpi: int = 144, + password: str | None = None, + resume: bool = False, + overwrite: bool = False, + keep_rendered: bool = False, + fail_fast: bool = False, + predict_kwargs: dict[str, Any] | None = None, + logger: logging.Logger | None = None, +) -> dict[str, Any]: + if mode not in PDF_MODES: + raise ValueError(f"不支持的 PDF 模式: {mode}") + task_started = time.perf_counter() + model_init_before = provider.model_init_seconds + logger = logger or logging.getLogger(__name__) + text_policy = text_policy or TextLayerPolicy() + predict_kwargs = predict_kwargs or {} + pdf_path, output_root = validate_pdf_request(pdf_path, output_root, resume=resume, overwrite=overwrite) + if dpi < 72 or dpi > 600: + raise ValueError("--dpi 必须在 72 到 600 之间") + + document_dir = output_root / safe_stem(pdf_path.stem) + manifest_path = document_dir / "manifest.json" + cache_dir = document_dir / ".render-cache" + document = pdfium.PdfDocument(str(pdf_path), password=password) + try: + page_count = len(document) + selected_indexes = parse_page_spec(pages, page_count) + manifest = _prepare_manifest( + pdf_path=pdf_path, + document_dir=document_dir, + page_count=page_count, + selected_pages=selected_indexes, + dpi=dpi, + mode=mode, + policy=text_policy, + resume=resume, + overwrite=overwrite, + model_config=provider.model_config(), + run_metadata={"device": provider.resolved_device}, + ) + selected_indexes = [number - 1 for number in manifest["selected_pages"]] + completed_before = sum(_page_is_complete(document_dir, manifest, index + 1) for index in selected_indexes) + pending = [index for index in selected_indexes if not _page_is_complete(document_dir, manifest, index + 1)] + + for position, page_index in enumerate(pending, 1): + page_number = page_index + 1 + started = time.perf_counter() + text_extract_seconds = render_seconds = ocr_seconds = export_seconds = 0.0 + source_type = "unknown" + assessment_dict: dict[str, Any] = {} + render_path = (document_dir / "rendered" if keep_rendered else cache_dir) / f"page-{page_number:04d}.png" + try: + text_started = time.perf_counter() + page = document.get_page(page_index) + try: + extracted_text, assessment = extract_page_text(page, text_policy) + width_points, height_points = page.get_size() + finally: + page.close() + text_extract_seconds = time.perf_counter() - text_started + assessment_dict = assessment.to_dict() + use_text = mode == "text" or (mode == "hybrid" and assessment.usable) + source_type = "text" if use_text else "ocr" + markdown_path, json_path = _page_paths(document_dir, page_number) + + if source_type == "text": + markdown_text = extracted_text.rstrip() + ("\n" if extracted_text else "") + payload = { + "schema_version": 1, + "source_type": "text", + "input_path": str(pdf_path), + "page_index": page_index, + "page_number": page_number, + "page_count": page_count, + "text": extracted_text, + "text_layer": assessment_dict, + "page_size_points": {"width": width_points, "height": height_points}, + } + detected_lines = sum(bool(line.strip()) for line in extracted_text.splitlines()) + mean_score = None + else: + rendered = time.perf_counter() + image = render_page(document, page_index, dpi) + try: + save_png_atomic(image, render_path) + finally: + image.close() + render_seconds = time.perf_counter() - rendered + pipeline = provider.get() + provider.synchronize() + ocr_started = time.perf_counter() + results = pipeline.predict(str(render_path), **predict_kwargs) + provider.synchronize() + ocr_seconds = time.perf_counter() - ocr_started + if not results: + raise RuntimeError("PP-OCRv6 未返回 PDF 页面结果") + payload = adapt_ocr_result( + results[0], + input_path=pdf_path, + source_type="pdf_ocr", + language=provider.config.lang, + detection_model=provider.config.text_detection_model_name, + recognition_model=provider.config.text_recognition_model_name, + model_size=provider.config.model_size, + page_index=page_index, + page_number=page_number, + ) + payload["page_count"] = page_count + payload["ocr_reason"] = assessment.reason if mode == "hybrid" else "forced_ocr_mode" + payload["text_layer"] = assessment_dict + payload["render_dpi"] = dpi + markdown_text = result_markdown(payload) + detected_lines = payload["summary"]["detected_lines"] + mean_score = payload["summary"]["mean_score"] + + export_started = time.perf_counter() + atomic_write_text(markdown_path, markdown_text) + atomic_write_json(json_path, payload) + export_seconds = time.perf_counter() - export_started + total_seconds = time.perf_counter() - started + manifest["pages"][str(page_number)] = { + "status": "completed", + "page_number": page_number, + "source_type": source_type, + "routing_reason": assessment.reason if mode == "hybrid" else f"forced_{source_type}_mode", + "text_layer": assessment_dict, + "text_extract_seconds": round(text_extract_seconds, 3), + "render_seconds": round(render_seconds, 3), + "ocr_seconds": round(ocr_seconds, 3), + "export_seconds": round(export_seconds, 3), + "total_seconds": round(total_seconds, 3), + "detected_lines": detected_lines, + "mean_score": mean_score, + "completed_at": now_iso(), + } + except KeyboardInterrupt: + manifest["status"] = "interrupted" + atomic_write_json(manifest_path, manifest) + rebuild_combined_outputs(document_dir, manifest) + raise + except Exception as exc: + manifest["pages"][str(page_number)] = { + "status": "failed", + "page_number": page_number, + "source_type": source_type, + "text_layer": assessment_dict, + "total_seconds": round(time.perf_counter() - started, 3), + "error": f"{type(exc).__name__}: {exc}", + "failed_at": now_iso(), + } + logger.exception("PAGE_FAILED page=%d source=%s", page_number, source_type) + if fail_fast: + raise + finally: + if not keep_rendered and render_path.is_file(): + render_path.unlink() + + manifest["run_metadata"] = provider.metadata() + manifest["updated_at"] = now_iso() + atomic_write_json(manifest_path, manifest) + rebuild_combined_outputs(document_dir, manifest) + logger.info("PAGE_FINISHED page=%d source=%s progress=%d/%d", page_number, source_type, position, len(pending)) + + if cache_dir.exists(): + shutil.rmtree(cache_dir, ignore_errors=True) + records = [manifest.get("pages", {}).get(str(index + 1), {}) for index in selected_indexes] + failed_pages = [record.get("page_number") for record in records if record.get("status") == "failed"] + completed = [record for record in records if record.get("status") == "completed"] + text_pages = sum(record.get("source_type") == "text" for record in completed) + ocr_pages = sum(record.get("source_type") == "ocr" for record in completed) + manifest["status"] = "completed_with_errors" if failed_pages else "completed" + manifest["run_metadata"] = provider.metadata() + manifest["summary"] = { + "selected_pages": len(selected_indexes), + "completed_pages": len(completed), + "completed_before_resume": completed_before, + "text_pages": text_pages, + "ocr_pages": ocr_pages, + "failed_pages": failed_pages, + "model_used": ocr_pages > 0, + "model_initialized_during_task": model_init_before == 0 and provider.model_init_seconds > 0, + "timing": { + "model_init_seconds": round(provider.model_init_seconds, 3), + "task_total_seconds": round(time.perf_counter() - task_started, 3), + }, + } + manifest["updated_at"] = now_iso() + atomic_write_json(manifest_path, manifest) + rebuild_combined_outputs(document_dir, manifest) + return {"document_dir": str(document_dir), "manifest_path": str(manifest_path), "status": manifest["status"], **manifest["summary"]} + finally: + document.close() diff --git a/ocr_app/pdf_text.py b/ocr_app/pdf_text.py new file mode 100644 index 0000000..0046eef --- /dev/null +++ b/ocr_app/pdf_text.py @@ -0,0 +1,127 @@ +"""PDF text-layer extraction and quality assessment for hybrid OCR.""" + +from __future__ import annotations + +import re +import unicodedata +from dataclasses import asdict, dataclass +from typing import Any + + +@dataclass +class TextLayerPolicy: + min_chars: int = 50 + min_printable_ratio: float = 0.85 + min_content_ratio: float = 0.60 + max_replacement_ratio: float = 0.02 + min_chars_per_megapixel: float = 25.0 + + +@dataclass +class TextLayerAssessment: + usable: bool + reason: str + raw_chars: int + non_whitespace_chars: int + printable_ratio: float + content_ratio: float + replacement_ratio: float + chars_per_megapixel: float + + def to_dict(self) -> dict[str, Any]: + return asdict(self) + + +def normalize_text(text: str) -> str: + text = text.replace("\x00", "") + text = text.replace("\r\n", "\n").replace("\r", "\n") + lines = [re.sub(r"[ \t]+", " ", line).strip() for line in text.splitlines()] + compact_lines: list[str] = [] + previous_blank = False + for line in lines: + if line: + compact_lines.append(line) + previous_blank = False + elif compact_lines and not previous_blank: + compact_lines.append("") + previous_blank = True + return "\n".join(compact_lines).strip() + + +def _is_content_character(character: str) -> bool: + if character.isalnum(): + return True + code = ord(character) + return ( + 0x3400 <= code <= 0x4DBF + or 0x4E00 <= code <= 0x9FFF + or 0xF900 <= code <= 0xFAFF + or 0x3040 <= code <= 0x30FF + or 0xAC00 <= code <= 0xD7AF + ) + + +def assess_text_layer( + text: str, + *, + width_points: float, + height_points: float, + policy: TextLayerPolicy, +) -> TextLayerAssessment: + normalized = normalize_text(text) + compact = [character for character in normalized if not character.isspace()] + count = len(compact) + if count == 0: + return TextLayerAssessment(False, "empty_text_layer", len(text), 0, 0.0, 0.0, 0.0, 0.0) + + printable = sum(character.isprintable() and unicodedata.category(character) != "Cc" for character in compact) + content = sum(_is_content_character(character) for character in compact) + replacements = sum(character in {"\ufffd", "�"} for character in compact) + page_megapixels = max((width_points * height_points) / 1_000_000.0, 0.01) + printable_ratio = printable / count + content_ratio = content / count + replacement_ratio = replacements / count + density = count / page_megapixels + + checks = ( + (count >= policy.min_chars, "too_few_characters"), + (printable_ratio >= policy.min_printable_ratio, "low_printable_ratio"), + (content_ratio >= policy.min_content_ratio, "low_content_ratio"), + (replacement_ratio <= policy.max_replacement_ratio, "high_replacement_ratio"), + (density >= policy.min_chars_per_megapixel, "low_text_density"), + ) + reason = "usable_text_layer" + usable = True + for passed, failure_reason in checks: + if not passed: + usable = False + reason = failure_reason + break + + return TextLayerAssessment( + usable=usable, + reason=reason, + raw_chars=len(text), + non_whitespace_chars=count, + printable_ratio=round(printable_ratio, 4), + content_ratio=round(content_ratio, 4), + replacement_ratio=round(replacement_ratio, 4), + chars_per_megapixel=round(density, 3), + ) + + +def extract_page_text(page: Any, policy: TextLayerPolicy) -> tuple[str, TextLayerAssessment]: + text_page = page.get_textpage() + try: + raw_text = text_page.get_text_bounded() + finally: + text_page.close() + width, height = page.get_size() + normalized = normalize_text(raw_text) + assessment = assess_text_layer( + normalized, + width_points=width, + height_points=height, + policy=policy, + ) + return normalized, assessment diff --git a/ocr_app/result_adapter.py b/ocr_app/result_adapter.py new file mode 100644 index 0000000..6d87309 --- /dev/null +++ b/ocr_app/result_adapter.py @@ -0,0 +1,196 @@ +"""Normalize PP-OCRv6 results into a stable project-owned schema.""" + +from __future__ import annotations + +import math +from pathlib import Path +from typing import Any + +from PIL import Image + +SCHEMA_VERSION = 1 +OCR_VERSION = "PP-OCRv6" +DEFAULT_MODEL_SIZE = "medium" +MODEL_VARIANTS = { + "tiny": ("PP-OCRv6_tiny_det", "PP-OCRv6_tiny_rec"), + "small": ("PP-OCRv6_small_det", "PP-OCRv6_small_rec"), + "medium": ("PP-OCRv6_medium_det", "PP-OCRv6_medium_rec"), +} +DEFAULT_DETECTION_MODEL, DEFAULT_RECOGNITION_MODEL = MODEL_VARIANTS[DEFAULT_MODEL_SIZE] + + +def model_names_for_size(model_size: str) -> tuple[str, str]: + try: + return MODEL_VARIANTS[model_size] + except KeyError as exc: + supported = ", ".join(MODEL_VARIANTS) + raise ValueError(f"不支持的 PP-OCRv6 模型规格: {model_size}；可选: {supported}") from exc + + +def _payload(result: Any) -> dict[str, Any]: + if isinstance(result, dict): + return result + try: + return dict(result) + except (TypeError, ValueError) as exc: + raise TypeError(f"不支持的 PP-OCRv6 结果类型: {type(result).__name__}") from exc + + +def _python_value(value: Any) -> Any: + if hasattr(value, "tolist"): + value = value.tolist() + if isinstance(value, dict): + return {str(key): _python_value(item) for key, item in value.items()} + if isinstance(value, (list, tuple)): + return [_python_value(item) for item in value] + if hasattr(value, "item"): + try: + return value.item() + except (TypeError, ValueError): + pass + return value + + +def _polygon(value: Any) -> list[list[float | int]]: + points = _python_value(value) or [] + normalized: list[list[float | int]] = [] + for point in points: + if isinstance(point, (list, tuple)) and len(point) >= 2: + normalized.append([point[0], point[1]]) + return normalized + + +def _box(value: Any, polygon: list[list[float | int]]) -> list[float | int]: + box = _python_value(value) + if isinstance(box, (list, tuple)) and len(box) >= 4: + return [box[0], box[1], box[2], box[3]] + if not polygon: + return [] + xs = [point[0] for point in polygon] + ys = [point[1] for point in polygon] + return [min(xs), min(ys), max(xs), max(ys)] + + +def _image_size(result: dict[str, Any], input_path: Path | None) -> tuple[int | None, int | None]: + image = result.get("doc_preprocessor_res", {}).get("output_img") + shape = getattr(image, "shape", None) + if shape is not None and len(shape) >= 2: + return int(shape[1]), int(shape[0]) + if input_path is not None and input_path.is_file(): + try: + with Image.open(input_path) as opened: + return opened.size + except OSError: + pass + return None, None + + +def adapt_ocr_result( + result: Any, + *, + input_path: Path | str | None, + source_type: str, + language: str, + detection_model: str = DEFAULT_DETECTION_MODEL, + recognition_model: str = DEFAULT_RECOGNITION_MODEL, + model_size: str | None = None, + page_index: int | None = None, + page_number: int | None = None, +) -> dict[str, Any]: + raw = _payload(result) + path = Path(input_path).expanduser().resolve() if input_path is not None else None + def as_list(value: Any) -> list[Any]: + if value is None: + return [] + if hasattr(value, "tolist"): + value = value.tolist() + return list(value) + + texts = as_list(raw.get("rec_texts")) + scores = as_list(raw.get("rec_scores")) + polygon_values = raw.get("rec_polys") + if polygon_values is None: + polygon_values = raw.get("dt_polys") + polygons = as_list(polygon_values) + boxes = as_list(raw.get("rec_boxes")) + angles = as_list(raw.get("textline_orientation_angles")) + line_count = max(len(texts), len(scores), len(polygons), len(boxes)) + + lines: list[dict[str, Any]] = [] + for index in range(line_count): + text = str(texts[index]) if index < len(texts) else "" + try: + score = float(scores[index]) if index < len(scores) else None + except (TypeError, ValueError): + score = None + polygon = _polygon(polygons[index]) if index < len(polygons) else [] + box = _box(boxes[index] if index < len(boxes) else None, polygon) + orientation = angles[index] if index < len(angles) else None + lines.append( + { + "index": index + 1, + "text": text, + "score": round(score, 6) if score is not None and math.isfinite(score) else None, + "polygon": polygon, + "box": box, + "orientation": _python_value(orientation), + } + ) + + valid_scores = [line["score"] for line in lines if line["score"] is not None] + width, height = _image_size(raw, path) + resolved_page_index = page_index if page_index is not None else raw.get("page_index") + resolved_model_size = model_size + if resolved_model_size is None: + for size, names in MODEL_VARIANTS.items(): + if names == (detection_model, recognition_model): + resolved_model_size = size + break + + payload: dict[str, Any] = { + "schema_version": SCHEMA_VERSION, + "source_type": source_type, + "input_path": str(path) if path is not None else str(raw.get("input_path") or ""), + "page_index": resolved_page_index, + "model": { + "ocr_version": OCR_VERSION, + "model_size": resolved_model_size, + "detection_model": detection_model, + "recognition_model": recognition_model, + "language": language, + }, + "image": {"width": width, "height": height}, + "lines": lines, + "summary": { + "detected_lines": len(lines), + "non_empty_lines": sum(bool(line["text"].strip()) for line in lines), + "mean_score": round(sum(valid_scores) / len(valid_scores), 6) if valid_scores else None, + "min_score": min(valid_scores) if valid_scores else None, + "max_score": max(valid_scores) if valid_scores else None, + }, + } + if page_number is not None: + payload["page_number"] = page_number + return payload + + +def result_plain_text(payload: dict[str, Any]) -> str: + return "\n".join( + str(line.get("text", "")).strip() + for line in payload.get("lines", []) + if str(line.get("text", "")).strip() + ) + + +def result_markdown(payload: dict[str, Any], *, title: str | None = None) -> str: + text = result_plain_text(payload) + if title: + return f"# {title}\n\n{text}".rstrip() + "\n" + return text.rstrip() + ("\n" if text else "") + + +def raw_result_json(result: Any) -> dict[str, Any]: + raw_json = getattr(result, "json", None) + if raw_json is not None: + return _python_value(raw_json) + return _python_value(_payload(result)) diff --git a/ocr_app/runtime.py b/ocr_app/runtime.py new file mode 100644 index 0000000..d6a5f8d --- /dev/null +++ b/ocr_app/runtime.py @@ -0,0 +1,175 @@ +"""Device validation and lazy PP-OCRv6 pipeline creation.""" + +from __future__ import annotations + +import logging +import os +import platform +import time +from dataclasses import dataclass +from typing import Any + +from .result_adapter import DEFAULT_MODEL_SIZE, OCR_VERSION, model_names_for_size + + +@dataclass +class RuntimeConfig: + device: str + threads: int | None = None + device_id: int = 0 + lang: str = "ch" + use_doc_orientation_classify: bool = True + use_doc_unwarping: bool = False + use_textline_orientation: bool = True + text_recognition_batch_size: int = 6 + model_size: str = DEFAULT_MODEL_SIZE + + @property + def text_detection_model_name(self) -> str: + return model_names_for_size(self.model_size)[0] + + @property + def text_recognition_model_name(self) -> str: + return model_names_for_size(self.model_size)[1] + + +class PipelineProvider: + def __init__(self, config: RuntimeConfig, logger: logging.Logger): + self.config = config + self.logger = logger + self._pipeline: Any | None = None + self._paddle: Any | None = None + self._device_name: str | None = None + self.import_seconds = 0.0 + self.setup_seconds = 0.0 + self.model_init_seconds = 0.0 + + @property + def resolved_device(self) -> str: + return "cpu" if self.config.device == "cpu" else f"gpu:{self.config.device_id}" + + def prepare(self) -> None: + if self._paddle is not None: + return + started = time.perf_counter() + try: + import paddle + except ImportError as exc: + package = "paddlepaddle" if self.config.device == "cpu" else "paddlepaddle-gpu" + raise RuntimeError(f"当前子项目未安装 {package}") from exc + self.import_seconds = time.perf_counter() - started + self._paddle = paddle + + setup_started = time.perf_counter() + if self.config.device == "cpu": + from paddle import core + + total_cores = os.cpu_count() or 4 + threads = self.config.threads or max(1, total_cores - 2) + if threads < 1: + raise ValueError("CPU 线程数必须大于等于 1") + self.config.threads = threads + core.set_num_threads(threads) + paddle.set_device("cpu") + self._device_name = platform.processor() or "CPU" + self.logger.info("CPU_CONFIGURED threads=%d total_cores=%d", threads, total_cores) + else: + if not paddle.is_compiled_with_cuda(): + raise RuntimeError("当前 PaddlePaddle 不是 CUDA 构建；不会回退到 CPU") + device_count = paddle.device.cuda.device_count() + if device_count < 1: + raise RuntimeError("未检测到 NVIDIA CUDA GPU；不会回退到 CPU") + if self.config.device_id < 0 or self.config.device_id >= device_count: + raise RuntimeError(f"GPU {self.config.device_id} 不存在，当前检测到 {device_count} 个设备") + paddle.set_device(self.resolved_device) + paddle.device.cuda.synchronize(self.config.device_id) + try: + self._device_name = paddle.device.cuda.get_device_name(self.config.device_id) + except Exception: + self._device_name = "unknown" + self.logger.info("GPU_CONFIGURED device=%s device_name=%s", self.resolved_device, self._device_name) + self.setup_seconds = time.perf_counter() - setup_started + self.logger.info( + "RUNTIME_PREPARED device=%s paddle_version=%s import_seconds=%.3f setup_seconds=%.3f", + self.resolved_device, + paddle.__version__, + self.import_seconds, + self.setup_seconds, + ) + + def get(self): + self.prepare() + if self._pipeline is None: + self.logger.info( + "MODEL_INITIALIZATION_STARTED ocr_version=%s model_size=%s detection_model=%s recognition_model=%s device=%s", + OCR_VERSION, + self.config.model_size, + self.config.text_detection_model_name, + self.config.text_recognition_model_name, + self.resolved_device, + ) + started = time.perf_counter() + from paddleocr import PaddleOCR + + self._pipeline = PaddleOCR( + text_detection_model_name=self.config.text_detection_model_name, + text_recognition_model_name=self.config.text_recognition_model_name, + device=self.resolved_device, + use_doc_orientation_classify=self.config.use_doc_orientation_classify, + use_doc_unwarping=self.config.use_doc_unwarping, + use_textline_orientation=self.config.use_textline_orientation, + text_recognition_batch_size=self.config.text_recognition_batch_size, + ) + self.synchronize() + self.model_init_seconds = time.perf_counter() - started + self.logger.info("MODEL_INITIALIZED seconds=%.3f device=%s", self.model_init_seconds, self.resolved_device) + return self._pipeline + + def synchronize(self) -> None: + if self.config.device == "gpu" and self._paddle is not None: + self._paddle.device.cuda.synchronize(self.config.device_id) + + def gpu_memory(self) -> dict[str, float | None]: + stats = {"allocated_mb": None, "reserved_mb": None, "max_allocated_mb": None, "max_reserved_mb": None} + if self.config.device != "gpu" or self._paddle is None: + return stats + for key, name in { + "allocated_mb": "memory_allocated", + "reserved_mb": "memory_reserved", + "max_allocated_mb": "max_memory_allocated", + "max_reserved_mb": "max_memory_reserved", + }.items(): + function = getattr(self._paddle.device.cuda, name, None) + if function is not None: + try: + stats[key] = round(float(function(self.config.device_id)) / (1024**2), 2) + except Exception: + pass + return stats + + def model_config(self) -> dict[str, Any]: + return { + "ocr_version": OCR_VERSION, + "model_size": self.config.model_size, + "language": self.config.lang, + "detection_model": self.config.text_detection_model_name, + "recognition_model": self.config.text_recognition_model_name, + "use_doc_orientation_classify": self.config.use_doc_orientation_classify, + "use_doc_unwarping": self.config.use_doc_unwarping, + "use_textline_orientation": self.config.use_textline_orientation, + "text_recognition_batch_size": self.config.text_recognition_batch_size, + } + + def metadata(self) -> dict[str, Any]: + return { + "device": self.resolved_device, + "device_name": self._device_name, + "cpu_threads": self.config.threads if self.config.device == "cpu" else None, + "python_version": platform.python_version(), + "platform": platform.platform(), + "paddle_version": self._paddle.__version__ if self._paddle is not None else None, + "runtime_import_seconds": round(self.import_seconds, 3), + "runtime_setup_seconds": round(self.setup_seconds, 3), + "model_init_seconds": round(self.model_init_seconds, 3), + "model_config": self.model_config(), + } diff --git a/outputs/.gitkeep b/outputs/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..7d6fded --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,6 @@ +from pathlib import Path +import sys + +PROJECT_ROOT = Path(__file__).resolve().parent.parent +if str(PROJECT_ROOT) not in sys.path: + sys.path.insert(0, str(PROJECT_ROOT)) diff --git a/tests/test_launcher.py b/tests/test_launcher.py new file mode 100644 index 0000000..7a939b2 --- /dev/null +++ b/tests/test_launcher.py @@ -0,0 +1,13 @@ +from ocr import _requested_device + + +def test_default_device(): + assert _requested_device(["verify"]) == "cpu" + + +def test_requested_gpu(): + assert _requested_device(["verify", "--device", "gpu"]) == "gpu" + + +def test_requested_gpu_equals_syntax(): + assert _requested_device(["verify", "--device=gpu"]) == "gpu" diff --git a/tests/test_model_variants.py b/tests/test_model_variants.py new file mode 100644 index 0000000..1497cdb --- /dev/null +++ b/tests/test_model_variants.py @@ -0,0 +1,35 @@ +import pytest + +from ocr_app.cli import build_input_parser +from ocr_app.result_adapter import model_names_for_size +from ocr_app.runtime import RuntimeConfig + + +@pytest.mark.parametrize( + ("model_size", "detection_model", "recognition_model"), + [ + ("tiny", "PP-OCRv6_tiny_det", "PP-OCRv6_tiny_rec"), + ("small", "PP-OCRv6_small_det", "PP-OCRv6_small_rec"), + ("medium", "PP-OCRv6_medium_det", "PP-OCRv6_medium_rec"), + ], +) +def test_model_size_mapping(model_size, detection_model, recognition_model): + assert model_names_for_size(model_size) == (detection_model, recognition_model) + config = RuntimeConfig(device="cpu", model_size=model_size) + assert config.text_detection_model_name == detection_model + assert config.text_recognition_model_name == recognition_model + + +def test_model_size_defaults_to_medium(): + args = build_input_parser().parse_args(["sample.png"]) + assert args.model_size == "medium" + + +def test_model_alias_selects_tiny(): + args = build_input_parser().parse_args(["sample.png", "--model", "tiny"]) + assert args.model_size == "tiny" + + +def test_invalid_model_size_is_rejected(): + with pytest.raises(ValueError): + model_names_for_size("large") diff --git a/tests/test_output_routing.py b/tests/test_output_routing.py new file mode 100644 index 0000000..cafdaf0 --- /dev/null +++ b/tests/test_output_routing.py @@ -0,0 +1,116 @@ +import json +from argparse import Namespace +from pathlib import Path + +from PIL import Image + +from ocr_app.commands import process_image_file +from ocr_app.logging_utils import setup_run_logger +from ocr_app.output import image_output_directory, pdf_output_root + + +class FakeResult(dict): + @property + def json(self): + return dict(self) + + +class FakePipeline: + def predict(self, path, **kwargs): + return [ + FakeResult( + input_path=path, + page_index=0, + rec_texts=["hello OCR"], + rec_scores=[0.99], + rec_polys=[[[0, 0], [10, 0], [10, 5], [0, 5]]], + rec_boxes=[[0, 0, 10, 5]], + ) + ] + + +class FakeProvider: + class Config: + device = "cpu" + lang = "ch" + model_size = "medium" + text_detection_model_name = "PP-OCRv6_medium_det" + text_recognition_model_name = "PP-OCRv6_medium_rec" + + config = Config() + model_init_seconds = 0.01 + + def get(self): + return FakePipeline() + + def synchronize(self): + pass + + def metadata(self): + return {"device": "cpu", "model_init_seconds": self.model_init_seconds} + + def gpu_memory(self): + return {} + + +def make_args(output): + return Namespace( + warmup=0, + rounds=1, + output=output, + recursive=False, + benchmark_json=None, + no_result=True, + save_raw_result=False, + save_visualization=False, + text_det_limit_side_len=None, + text_det_limit_type=None, + text_det_thresh=None, + text_det_box_thresh=None, + text_det_unclip_ratio=None, + text_rec_score_thresh=0.0, + return_word_box=False, + ) + + +def test_single_image_generates_output_files(tmp_path): + image = tmp_path / "card.jpg" + Image.new("RGB", (20, 10), "white").save(image) + logger = setup_run_logger("test.image.output", tmp_path / "run.log", console=False) + + result = process_image_file( + image, + args=make_args(tmp_path / "outputs"), + provider=FakeProvider(), + logger=logger, + project_root=tmp_path, + run_warmup=True, + batch_root=None, + ) + + output_dir = Path(result.details["output_dir"]) + assert output_dir == tmp_path / "outputs" / "images" / "card_jpg" + assert "hello OCR" in (output_dir / "result.md").read_text("utf-8") + assert (output_dir / "result.txt").read_text("utf-8").strip() == "hello OCR" + data = json.loads((output_dir / "result.json").read_text("utf-8")) + assert data["source_type"] == "image_ocr" + assert data["lines"][0]["score"] == 0.99 + benchmark = json.loads((output_dir / "benchmark.json").read_text("utf-8")) + assert benchmark["file_total_seconds"] >= benchmark["export_seconds"] + + +def test_recursive_output_paths_preserve_relative_directories(tmp_path): + batch_root = tmp_path / "input" + image = batch_root / "sub" / "same.png" + pdf = batch_root / "other" / "same.pdf" + image.parent.mkdir(parents=True) + pdf.parent.mkdir(parents=True) + output = tmp_path / "outputs" + + assert image_output_directory(output, image, batch_root=batch_root, recursive=True) == output / "images" / "sub" / "same_png" + assert pdf_output_root(output, pdf, batch_root=batch_root, recursive=True) == output / "pdfs" / "other" + + +def test_image_extensions_do_not_collide(tmp_path): + output = tmp_path / "outputs" + assert image_output_directory(output, tmp_path / "same.png", batch_root=None, recursive=False) != image_output_directory(output, tmp_path / "same.jpg", batch_root=None, recursive=False) diff --git a/tests/test_page_spec.py b/tests/test_page_spec.py new file mode 100644 index 0000000..8837200 --- /dev/null +++ b/tests/test_page_spec.py @@ -0,0 +1,13 @@ +import pytest + +from ocr_app.pdf import parse_page_spec + + +def test_page_ranges(): + assert parse_page_spec("1-2,4,6-", 7) == [0, 1, 3, 5, 6] + + +@pytest.mark.parametrize("value", ["0", "3-2", "1-a", "8"]) +def test_invalid_page_ranges(value): + with pytest.raises(ValueError): + parse_page_spec(value, 7) diff --git a/tests/test_pdf_hybrid.py b/tests/test_pdf_hybrid.py new file mode 100644 index 0000000..2b273ee --- /dev/null +++ b/tests/test_pdf_hybrid.py @@ -0,0 +1,89 @@ +import json +from pathlib import Path + +from PIL import Image + +from ocr_app.pdf import process_pdf +from ocr_app.pdf_text import TextLayerPolicy + + +class FakeResult(dict): + pass + + +class FakePipeline: + def predict(self, path, **kwargs): + return [ + FakeResult( + input_path=path, + page_index=0, + rec_texts=["mock OCR"], + rec_scores=[0.97], + rec_polys=[[[0, 0], [20, 0], [20, 10], [0, 10]]], + rec_boxes=[[0, 0, 20, 10]], + ) + ] + + +class FakeProvider: + resolved_device = "cpu" + + class Config: + lang = "ch" + model_size = "medium" + text_detection_model_name = "PP-OCRv6_medium_det" + text_recognition_model_name = "PP-OCRv6_medium_rec" + + config = Config() + + def __init__(self): + self.model_init_seconds = 0.0 + self.get_calls = 0 + + def get(self): + self.get_calls += 1 + self.model_init_seconds = 0.01 + return FakePipeline() + + def synchronize(self): + pass + + def model_config(self): + return { + "ocr_version": "PP-OCRv6", + "model_size": "medium", + "language": "ch", + "detection_model": "PP-OCRv6_medium_det", + "recognition_model": "PP-OCRv6_medium_rec", + } + + def metadata(self): + return {"device": "cpu", "model_init_seconds": self.model_init_seconds, "model_config": self.model_config()} + + +def test_electronic_pdf_does_not_load_model(tmp_path): + project_root = Path(__file__).resolve().parent.parent + source = next((project_root / "data" / "documents").glob("*.pdf")) + provider = FakeProvider() + + result = process_pdf(provider=provider, pdf_path=source, output_root=tmp_path, mode="hybrid", pages="1", text_policy=TextLayerPolicy()) + + assert result["text_pages"] == 1 + assert result["ocr_pages"] == 0 + assert not result["model_used"] + assert provider.get_calls == 0 + + +def test_scanned_pdf_falls_back_to_ocr(tmp_path): + source = tmp_path / "scan.pdf" + Image.new("RGB", (72, 72), "white").save(source) + provider = FakeProvider() + + result = process_pdf(provider=provider, pdf_path=source, output_root=tmp_path / "output", mode="hybrid", text_policy=TextLayerPolicy()) + + manifest = json.loads(Path(result["manifest_path"]).read_text(encoding="utf-8")) + page_json = json.loads((Path(result["document_dir"]) / "pages" / "page-0001.json").read_text("utf-8")) + assert result["ocr_pages"] == 1 + assert provider.get_calls == 1 + assert manifest["pages"]["1"]["routing_reason"] == "empty_text_layer" + assert page_json["lines"][0]["text"] == "mock OCR" diff --git a/tests/test_pdf_text.py b/tests/test_pdf_text.py new file mode 100644 index 0000000..a4a9105 --- /dev/null +++ b/tests/test_pdf_text.py @@ -0,0 +1,39 @@ +from ocr_app.pdf_text import TextLayerPolicy, assess_text_layer, normalize_text + + +def test_normalize_text(): + assert normalize_text("a b\r\n\r\n\r\nc") == "a b\n\nc" + + +def test_usable_text_layer(): + text = "有效电子文档内容 123 " * 20 + result = assess_text_layer( + text, + width_points=595, + height_points=842, + policy=TextLayerPolicy(), + ) + assert result.usable + assert result.reason == "usable_text_layer" + + +def test_empty_text_layer_routes_to_ocr(): + result = assess_text_layer( + "", + width_points=595, + height_points=842, + policy=TextLayerPolicy(), + ) + assert not result.usable + assert result.reason == "empty_text_layer" + + +def test_short_text_layer_routes_to_ocr(): + result = assess_text_layer( + "页码 1", + width_points=595, + height_points=842, + policy=TextLayerPolicy(min_chars=50), + ) + assert not result.usable + assert result.reason == "too_few_characters" diff --git a/tests/test_result_adapter.py b/tests/test_result_adapter.py new file mode 100644 index 0000000..b74d3dc --- /dev/null +++ b/tests/test_result_adapter.py @@ -0,0 +1,36 @@ +from pathlib import Path + +from PIL import Image + +from ocr_app.result_adapter import adapt_ocr_result, result_markdown, result_plain_text + + +def test_adapt_ppocr_result(tmp_path): + image = tmp_path / "sample.png" + Image.new("RGB", (100, 50), "white").save(image) + result = { + "page_index": 0, + "rec_texts": ["第一行", "第二行"], + "rec_scores": [0.98, 0.75], + "rec_polys": [ + [[1, 2], [20, 2], [20, 10], [1, 10]], + [[2, 20], [30, 20], [30, 30], [2, 30]], + ], + "rec_boxes": [[1, 2, 20, 10], [2, 20, 30, 30]], + } + + payload = adapt_ocr_result(result, input_path=image, source_type="image_ocr", language="ch") + + assert payload["schema_version"] == 1 + assert payload["image"] == {"width": 100, "height": 50} + assert payload["summary"]["detected_lines"] == 2 + assert payload["summary"]["mean_score"] == 0.865 + assert payload["lines"][0]["text"] == "第一行" + assert result_plain_text(payload) == "第一行\n第二行" + assert result_markdown(payload, title="sample.png").startswith("# sample.png\n\n第一行") + + +def test_empty_result_is_supported(tmp_path): + payload = adapt_ocr_result({}, input_path=Path(tmp_path / "missing.png"), source_type="image_ocr", language="ch") + assert payload["lines"] == [] + assert payload["summary"]["mean_score"] is None