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PaddleOCR
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Merge branch 'develop' of https://github.com/PaddlePaddle/PaddleOCR into fix_cls_shape

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README.md
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PaddleOCR aims to create rich, leading, and practical OCR tools that help users train better models and apply them into practice.
**Recent updates**
- 2020.9.19 Update the ultra lightweight compressed ppocr_mobile_slim series models, the overall model size is 3.5M (see [PP-OCR Pipline](#PP-OCR)), suitable for mobile deployment. [Model Downloads](#Supported-Chinese-model-list)
- 2020.9.22 Update the PP-OCR technical article, https://arxiv.org/abs/2009.09941
- 2020.9.19 Update the ultra lightweight compressed ppocr_mobile_slim series models, the overall model size is 3.5M (see [PP-OCR Pipeline](#PP-OCR-Pipeline)), suitable for mobile deployment. [Model Downloads](#Supported-Chinese-model-list)
- 2020.9.17 Update the ultra lightweight ppocr_mobile series and general ppocr_server series Chinese and English ocr models, which are comparable to commercial effects. [Model Downloads](#Supported-Chinese-model-list)
- 2020.8.24 Support the use of PaddleOCR through whl package installation，pelease refer [PaddleOCR Package](./doc/doc_en/whl_en.md)
- 2020.9.17 update [English recognition model](./doc/doc_en/models_list_en.md#english-recognition-model) and [Multilingual recognition model](doc/doc_en/models_list_en.md#english-recognition-model), `German`, `French`, `Japanese` and `Korean` have been supported. Models for more languages will continue to be updated.
- 2020.8.24 Support the use of PaddleOCR through whl package installation，please refer [PaddleOCR Package](./doc/doc_en/whl_en.md)
- 2020.8.21 Update the replay and PPT of the live lesson at Bilibili on August 18, lesson 2, easy to learn and use OCR tool spree. [Get Address](https://aistudio.baidu.com/aistudio/education/group/info/1519)
- [more](./doc/doc_en/update_en.md)
......@@ -29,7 +30,16 @@ PaddleOCR aims to create rich, leading, and practical OCR tools that help users
<img src="doc/imgs_results/1103.jpg" width="800">
</div>
The above picture is the effect display of the general ppocr_server model. For more effect pictures, please see [More visualization](./doc/doc_en/visualization_en.md).
The above pictures are the visualizations of the general ppocr_server model. For more effect pictures, please see [More visualizations](./doc/doc_en/visualization_en.md).
<a name="Community"></a>
## Community
- Scan the QR code below with your Wechat, you can access to official technical exchange group. Look forward to your participation.
<div align="center">
<img src="./doc/joinus.PNG" width = "200" height = "200" />
</div>
## Quick Experience
......@@ -49,11 +59,11 @@ Mobile DEMO experience (based on EasyEdge and Paddle-Lite, supports iOS and Andr
## PP-OCR 1.1 series model list（Update on Sep 17）
| Model introduction | Model name | Recommended scene | Detection model | Direction classifier | Recognition model | |
| ------------------------------------------------------------ | ---------------------------- | ----------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ---- |
| Chinese and English ultra-lightweight OCR model (8.1M) | ch_ppocr_mobile_v1.1_xx | Mobile & server | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_pre.tar) | |
| Chinese and English general OCR model (155.1M) | ch_ppocr_server_v1.1_xx | Server | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_pre.tar) | |
| Chinese and English ultra-lightweight compressed OCR model (3.5M) | ch_ppocr_mobile_slim_v1.1_xx | Mobile | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_infer.tar) / [slim model](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_opt.nb) | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_infer.tar) / [slim model](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_opt.nb) | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_quant_infer.tar) / [slim model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_cls_quant_opt.nb) | |
| Model introduction | Model name | Recommended scene | Detection model | Direction classifier | Recognition model |
| ------------------------------------------------------------ | ---------------------------- | ----------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| Chinese and English ultra-lightweight OCR model (8.1M) | ch_ppocr_mobile_v1.1_xx | Mobile & server | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_pre.tar) |
| Chinese and English general OCR model (155.1M) | ch_ppocr_server_v1.1_xx | Server | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_pre.tar) |
| Chinese and English ultra-lightweight compressed OCR model (3.5M) | ch_ppocr_mobile_slim_v1.1_xx | Mobile | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_infer.tar) / [slim model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_det_prune_opt.nb) | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_quant_infer.tar) / [slim model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_cls_quant_opt.nb) | [inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_infer.tar) / [slim model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_rec_quant_opt.nb) |
For more model downloads (including multiple languages), please refer to [PP-OCR v1.1 series model downloads](./doc/doc_en/models_list_en.md)
......@@ -65,10 +75,11 @@ For more model downloads (including multiple languages), please refer to [PP-OCR
- Algorithm introduction
- [Text Detection Algorithm](./doc/doc_en/algorithm_overview_en.md)
- [Text Recognition Algorithm](./doc/doc_en/algorithm_overview_en.md)
- [PP-OCR Pipline](#PP-OCR-Pipline)
- [PP-OCR Pipeline](#PP-OCR-Pipeline)
- Model training/evaluation
- [Text Detection](./doc/doc_en/detection_en.md)
- [Text Recognition](./doc/doc_en/recognition_en.md)
- [Direction Classification](./doc/doc_en/angle_class_en.md)
- [Yml Configuration](./doc/doc_en/config_en.md)
- Inference and Deployment
- [Quick inference based on pip](./doc/doc_en/whl_en.md)
......@@ -92,35 +103,40 @@ For more model downloads (including multiple languages), please refer to [PP-OCR
- [License](#LICENSE)
- [Contribution](#CONTRIBUTION)
<a name="PP-OCR-Pipline"></a>
<a name="PP-OCR-Pipeline"></a>
## PP-OCR Pipline
## PP-OCR Pipeline
<div align="center">
<img src="./doc/ppocr_framework.png" width="800">
</div>
PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection, detection frame correction and CRNN text recognition. The system adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module. The final results are an ultra-lightweight Chinese and English OCR model with an overall size of 3.5M and a 2.8M English digital OCR model. For more details, please refer to the PP-OCR technical article (Arxiv article link is being generated).
PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection, detection frame correction and CRNN text recognition. The system adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module. The final results are an ultra-lightweight Chinese and English OCR model with an overall size of 3.5M and a 2.8M English digital OCR model. For more details, please refer to the PP-OCR technical article (https://arxiv.org/abs/2009.09941). Besides, The implementation of the FPGM Pruner and PACT quantization is based on [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim).
## Visualization [more](./doc/doc_en/visualization_en.md)
## Visualization [more](./doc/doc_en/visualization_en.md)
- Chinese OCR model
<div align="center">
<img src="./doc/imgs_results/1102.jpg" width="800">
<img src="./doc/imgs_results/1104.jpg" width="800">
<img src="./doc/imgs_results/1106.jpg" width="800">
<img src="./doc/imgs_results/1105.jpg" width="800">
<img src="./doc/imgs_results/1110.jpg" width="800">
<img src="./doc/imgs_results/1112.jpg" width="800">
</div>
<a name="Community"></a>
## Community
Scan the QR code below with your Wechat and completing the questionnaire, you can access to offical technical exchange group.
- English OCR model
<div align="center">
<img src="./doc/imgs_results/img_12.jpg" width="800">
</div>
- Multilingual OCR model
<div align="center">
<img src="./doc/joinus.PNG" width = "200" height = "200" />
<img src="./doc/imgs_results/1110.jpg" width="800">
<img src="./doc/imgs_results/1112.jpg" width="800">
</div>
<a name="LICENSE"></a>
## License
This project is released under <a href="https://github.com/PaddlePaddle/PaddleOCR/blob/master/LICENSE">Apache 2.0 license</a>
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README_ch.md
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PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。
**近期更新**
- 2020.9.19 更新超轻量压缩ppocr_mobile_slim系列模型，整体模型3.5M(详见[PP-OCR Pipline](#PP-OCR))，适合在移动端部署使用。[模型下载](#模型下载)
- 2020.10.26 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，共计94个常见问题及解答，并且计划以后每周一都会更新，欢迎大家持续关注。
- 2020.9.22 更新PP-OCR技术文章，https://arxiv.org/abs/2009.09941
- 2020.9.19 更新超轻量压缩ppocr_mobile_slim系列模型，整体模型3.5M(详见[PP-OCR Pipeline](#PP-OCR))，适合在移动端部署使用。[模型下载](#模型下载)
- 2020.9.17 更新超轻量ppocr_mobile系列和通用ppocr_server系列中英文ocr模型，媲美商业效果。[模型下载](#模型下载)
- 2020.8.26 更新OCR相关的84个常见问题及解答，具体参考[FAQ](./doc/doc_ch/FAQ.md)
- 2020.9.17 更新[英文识别模型](./doc/doc_ch/models_list.md#英文识别模型)和[多语言识别模型](doc/doc_ch/models_list.md#多语言识别模型)，已支持`德语、法语、日语、韩语`，更多语种识别模型将持续更新。
- 2020.8.24 支持通过whl包安装使用PaddleOCR，具体参考[Paddleocr Package使用说明](./doc/doc_ch/whl.md)
- 2020.8.21 更新8月18日B站直播课回放和PPT，课节2，易学易用的OCR工具大礼包，[获取地址](https://aistudio.baidu.com/aistudio/education/group/info/1519)
- [More](./doc/doc_ch/update.md)
......@@ -14,7 +16,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
## 特性
- PPOCR系列高质量预训练模型，媲美商业效果
- PPOCR系列高质量预训练模型，准确的识别效果
- 超轻量ppocr_mobile移动端系列：检测（2.6M）+方向分类器（0.9M）+ 识别（4.6M）= 8.1M
- 通用ppocr_server系列：检测（47.2M）+方向分类器（0.9M）+ 识别（107M）= 155.1M
- 超轻量压缩ppocr_mobile_slim系列：检测（1.4M）+方向分类器（0.5M）+ 识别（1.6M）= 3.5M
......@@ -33,6 +35,15 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
上图是通用ppocr_server模型效果展示，更多效果图请见[效果展示页面](./doc/doc_ch/visualization.md)。
<a name="欢迎加入PaddleOCR技术交流群"></a>
## 欢迎加入PaddleOCR技术交流群
- 微信扫描二维码加入官方交流群，获得更高效的问题答疑，与各行各业开发者充分交流，期待您的加入。
<div align="center">
<img src="./doc/joinus.PNG" width = "200" height = "200" />
</div>
## 快速体验
- PC端：超轻量级中文OCR在线体验地址：https://www.paddlepaddle.org.cn/hub/scene/ocr
......@@ -48,11 +59,11 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
<a name="模型下载"></a>
## PP-OCR 1.1系列模型列表（9月17日更新）
| 模型简介 | 模型名称 |推荐场景 | 检测模型 | 方向分类器 | 识别模型 | |
| ------------ | --------------- | ----------------|---- | ---------- | -------- | ---- |
| 中英文超轻量OCR模型（8.1M） | ch_ppocr_mobile_v1.1_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar)|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_pre.tar) | |
| 中英文通用OCR模型（155.1M） |ch_ppocr_server_v1.1_xx|服务器端 |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_pre.tar) | |
| 中英文超轻量压缩OCR模型（3.5M） | ch_ppocr_mobile_slim_v1.1_xx| 移动端 |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_opt.nb) |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_opt.nb)|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_quant_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_cls_quant_opt.nb)| | ||
| 模型简介 | 模型名称 |推荐场景 | 检测模型 | 方向分类器 | 识别模型 |
| ------------ | --------------- | ----------------|---- | ---------- | -------- |
| 中英文超轻量OCR模型（8.1M） | ch_ppocr_mobile_v1.1_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar)|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_pre.tar) |
| 中英文通用OCR模型（155.1M） |ch_ppocr_server_v1.1_xx|服务器端 |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_pre.tar) |
| 中英文超轻量压缩OCR模型（3.5M） | ch_ppocr_mobile_slim_v1.1_xx| 移动端 |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_det_prune_opt.nb) |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_quant_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_cls_quant_opt.nb)| [推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_rec_quant_opt.nb)|
更多模型下载（包括多语言），可以参考[PP-OCR v1.1 系列模型下载](./doc/doc_ch/models_list.md)
......@@ -63,19 +74,20 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
- 算法介绍
- [文本检测](./doc/doc_ch/algorithm_overview.md)
- [文本识别](./doc/doc_ch/algorithm_overview.md)
- [PP-OCR Pipline](#PP-OCR)
- [PP-OCR Pipeline](#PP-OCR)
- 模型训练/评估
- [文本检测](./doc/doc_ch/detection.md)
- [文本识别](./doc/doc_ch/recognition.md)
- [方向分类器](./doc/doc_ch/angle_class.md)
- [yml参数配置文件介绍](./doc/doc_ch/config.md)
- 预测部署
- [基于pip安装whl包快速推理](./doc/doc_ch/whl.md)
- [基于Python脚本预测引擎推理](./doc/doc_ch/inference.md)
- [基于C++预测引擎推理](./deploy/cpp_infer/readme.md)
- [服务化部署](./deploy/hubserving/readme.md)
- [服务化部署](./doc/doc_ch/serving_inference.md)
- [端侧部署](./deploy/lite/readme.md)
- [模型量化](./deploy/slim/quantization/README.md)
- [模型裁剪](./deploy/slim/prune/README_ch.md)
- [模型裁剪](./deploy/slim/prune/README.md)
- [Benchmark](./doc/doc_ch/benchmark.md)
- 数据集
- [通用中英文OCR数据集](./doc/doc_ch/datasets.md)
......@@ -86,43 +98,44 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
- [效果展示](#效果展示)
- FAQ
- [【精选】OCR精选10个问题](./doc/doc_ch/FAQ.md)
- [【理论篇】OCR通用21个问题](./doc/doc_ch/FAQ.md)
- [【实战篇】PaddleOCR实战53个问题](./doc/doc_ch/FAQ.md)
- [【理论篇】OCR通用23个问题](./doc/doc_ch/FAQ.md)
- [【实战篇】PaddleOCR实战61个问题](./doc/doc_ch/FAQ.md)
- [技术交流群](#欢迎加入PaddleOCR技术交流群)
- [参考文献](./doc/doc_ch/reference.md)
- [许可证书](#许可证书)
- [贡献代码](#贡献代码)
<a name="PP-OCR"></a>
## PP-OCR Pipline
## PP-OCR Pipeline
<div align="center">
<img src="./doc/ppocr_framework.png" width="800">
</div>
PP-OCR是一个实用的超轻量OCR系统。主要由DB文本检测、检测框矫正和CRNN文本识别三部分组成。该系统从骨干网络选择和调整、预测头部的设计、数据增强、学习率变换策略、正则化参数选择、预训练模型使用以及模型自动裁剪量化8个方面，采用19个有效策略，对各个模块的模型进行效果调优和瘦身，最终得到整体大小为3.5M的超轻量中英文OCR和2.8M的英文数字OCR。更多细节请参考PP-OCR技术文章（Arxiv文章链接生成中）。
PP-OCR是一个实用的超轻量OCR系统。主要由DB文本检测、检测框矫正和CRNN文本识别三部分组成。该系统从骨干网络选择和调整、预测头部的设计、数据增强、学习率变换策略、正则化参数选择、预训练模型使用以及模型自动裁剪量化8个方面，采用19个有效策略，对各个模块的模型进行效果调优和瘦身，最终得到整体大小为3.5M的超轻量中英文OCR和2.8M的英文数字OCR。更多细节请参考PP-OCR技术方案 https://arxiv.org/abs/2009.09941 。其中FPGM裁剪器和PACT量化的实现可以参考[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)。
<a name="效果展示"></a>
## 效果展示 [more](./doc/doc_ch/visualization.md)
- 中文模型
<div align="center">
<img src="./doc/imgs_results/1102.jpg" width="800">
<img src="./doc/imgs_results/1104.jpg" width="800">
<img src="./doc/imgs_results/1106.jpg" width="800">
<img src="./doc/imgs_results/1105.jpg" width="800">
<img src="./doc/imgs_results/1110.jpg" width="800">
<img src="./doc/imgs_results/1112.jpg" width="800">
</div>
- 英文模型
<div align="center">
<img src="./doc/imgs_results/img_12.jpg" width="800">
</div>
<a name="欢迎加入PaddleOCR技术交流群"></a>
## 欢迎加入PaddleOCR技术交流群
请扫描下面二维码，完成问卷填写，获取加群二维码和OCR方向的炼丹秘籍
- 其他语言模型
<div align="center">
<img src="./doc/joinus.PNG" width = "200" height = "200" />
<img src="./doc/imgs_results/1110.jpg" width="800">
<img src="./doc/imgs_results/1112.jpg" width="800">
</div>
<a name="许可证书"></a>
## 许可证书
本项目的发布受<a href="https://github.com/PaddlePaddle/PaddleOCR/blob/master/LICENSE">Apache 2.0 license</a>许可认证。
......
configs/det/det_db_icdar15_reader.yml
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......@@ -18,5 +18,4 @@ TestReader:
infer_img:
img_set_dir: ./train_data/icdar2015/text_localization/
label_file_path: ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
test_image_shape: [736, 1280]
do_eval: True
configs/det/det_mv3_db.yml
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......@@ -24,7 +24,6 @@ Backbone:
function: ppocr.modeling.backbones.det_mobilenet_v3,MobileNetV3
scale: 0.5
model_name: large
disable_se: true
Head:
function: ppocr.modeling.heads.det_db_head,DBHead
......
configs/det/det_mv3_db_v1.1.yml 0 → 100755
浏览文件 @ 7e4aee47
Global:
algorithm: DB
use_gpu: true
epoch_num: 1200
log_smooth_window: 20
print_batch_step: 2
save_model_dir: ./output/det_db/
save_epoch_step: 200
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [4000, 5000]
train_batch_size_per_card: 16
test_batch_size_per_card: 16
image_shape: [3, 640, 640]
reader_yml: ./configs/det/det_db_icdar15_reader.yml
pretrain_weights: ./pretrain_models/MobileNetV3_large_x0_5_pretrained/
checkpoints:
save_res_path: ./output/det_db/predicts_db.txt
save_inference_dir:
Architecture:
function: ppocr.modeling.architectures.det_model,DetModel
Backbone:
function: ppocr.modeling.backbones.det_mobilenet_v3,MobileNetV3
scale: 0.5
model_name: large
disable_se: true
Head:
function: ppocr.modeling.heads.det_db_head,DBHead
model_name: large
k: 50
inner_channels: 96
out_channels: 2
Loss:
function: ppocr.modeling.losses.det_db_loss,DBLoss
balance_loss: true
main_loss_type: DiceLoss
alpha: 5
beta: 10
ohem_ratio: 3
Optimizer:
function: ppocr.optimizer,AdamDecay
base_lr: 0.001
beta1: 0.9
beta2: 0.999
decay:
function: cosine_decay_warmup
step_each_epoch: 16
total_epoch: 1200
PostProcess:
function: ppocr.postprocess.db_postprocess,DBPostProcess
thresh: 0.3
box_thresh: 0.6
max_candidates: 1000
unclip_ratio: 1.5
configs/det/det_r18_vd_db_v1.1.yml 0 → 100755
浏览文件 @ 7e4aee47
Global:
algorithm: DB
use_gpu: true
epoch_num: 1200
log_smooth_window: 20
print_batch_step: 2
save_model_dir: ./output/det_r_18_vd_db/
save_epoch_step: 200
eval_batch_step: [3000, 2000]
train_batch_size_per_card: 8
test_batch_size_per_card: 1
image_shape: [3, 640, 640]
reader_yml: ./configs/det/det_db_icdar15_reader.yml
pretrain_weights: ./pretrain_models/ResNet18_vd_pretrained/
save_res_path: ./output/det_r18_vd_db/predicts_db.txt
checkpoints:
save_inference_dir:
Architecture:
function: ppocr.modeling.architectures.det_model,DetModel
Backbone:
function: ppocr.modeling.backbones.det_resnet_vd,ResNet
layers: 18
Head:
function: ppocr.modeling.heads.det_db_head,DBHead
model_name: large
k: 50
inner_channels: 256
out_channels: 2
Loss:
function: ppocr.modeling.losses.det_db_loss,DBLoss
balance_loss: true
main_loss_type: DiceLoss
alpha: 5
beta: 10
ohem_ratio: 3
Optimizer:
function: ppocr.optimizer,AdamDecay
base_lr: 0.001
beta1: 0.9
beta2: 0.999
decay:
function: cosine_decay_warmup
step_each_epoch: 32
total_epoch: 1200
PostProcess:
function: ppocr.postprocess.db_postprocess,DBPostProcess
thresh: 0.3
box_thresh: 0.5
max_candidates: 1000
unclip_ratio: 1.6
configs/rec/ch_ppocr_v1.1/rec_chinese_common_train_v1.1.yml 0 → 100644
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Global:
algorithm: CRNN
use_gpu: true
epoch_num: 500
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec_CRNN
save_epoch_step: 3
eval_batch_step: 2000
train_batch_size_per_card: 128
test_batch_size_per_card: 128
image_shape: [3, 32, 320]
max_text_length: 25
character_type: ch
character_dict_path: ./ppocr/utils/ppocr_keys_v1.txt
loss_type: ctc
distort: true
use_space_char: true
reader_yml: ./configs/rec/rec_chinese_reader.yml
pretrain_weights:
checkpoints:
save_inference_dir:
infer_img:
Architecture:
function: ppocr.modeling.architectures.rec_model,RecModel
Backbone:
function: ppocr.modeling.backbones.rec_resnet_vd,ResNet
layers: 34
Head:
function: ppocr.modeling.heads.rec_ctc_head,CTCPredict
encoder_type: rnn
fc_decay: 0.00004
SeqRNN:
hidden_size: 256
Loss:
function: ppocr.modeling.losses.rec_ctc_loss,CTCLoss
Optimizer:
function: ppocr.optimizer,AdamDecay
base_lr: 0.0005
l2_decay: 0.00004
beta1: 0.9
beta2: 0.999
decay:
function: cosine_decay_warmup
step_each_epoch: 254
total_epoch: 500
warmup_minibatch: 1000
configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml 0 → 100755
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Global:
algorithm: CRNN
use_gpu: true
epoch_num: 500
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec_CRNN
save_epoch_step: 3
eval_batch_step: 2000
train_batch_size_per_card: 256
test_batch_size_per_card: 256
image_shape: [3, 32, 320]
max_text_length: 25
character_type: ch
character_dict_path: ./ppocr/utils/ppocr_keys_v1.txt
loss_type: ctc
distort: true
use_space_char: true
reader_yml: ./configs/rec/rec_chinese_reader.yml
pretrain_weights:
checkpoints:
save_inference_dir:
infer_img:
Architecture:
function: ppocr.modeling.architectures.rec_model,RecModel
Backbone:
function: ppocr.modeling.backbones.rec_mobilenet_v3,MobileNetV3
scale: 0.5
model_name: small
small_stride: [1, 2, 2, 2]
Head:
function: ppocr.modeling.heads.rec_ctc_head,CTCPredict
encoder_type: rnn
fc_decay: 0.00001
SeqRNN:
hidden_size: 48
Loss:
function: ppocr.modeling.losses.rec_ctc_loss,CTCLoss
Optimizer:
function: ppocr.optimizer,AdamDecay
base_lr: 0.0005
l2_decay: 0.00001
beta1: 0.9
beta2: 0.999
decay:
function: cosine_decay_warmup
step_each_epoch: 254
total_epoch: 500
warmup_minibatch: 1000
configs/rec/multi_languages/rec_french_lite_train.yml
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......@@ -12,7 +12,7 @@ Global:
image_shape: [3, 32, 320]
max_text_length: 25
character_type: french
character_dict_path: ./ppocr/utils/french_dict.txt
character_dict_path: ./ppocr/utils/dict/french_dict.txt
loss_type: ctc
distort: true
use_space_char: false
......
configs/rec/multi_languages/rec_ger_lite_train.yml
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......@@ -12,7 +12,7 @@ Global:
image_shape: [3, 32, 320]
max_text_length: 25
character_type: german
character_dict_path: ./ppocr/utils/german_dict.txt
character_dict_path: ./ppocr/utils/dict/german_dict.txt
loss_type: ctc
distort: true
use_space_char: false
......
configs/rec/multi_languages/rec_japan_lite_train.yml
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......@@ -12,7 +12,7 @@ Global:
image_shape: [3, 32, 320]
max_text_length: 25
character_type: japan
character_dict_path: ./ppocr/utils/japan_dict.txt
character_dict_path: ./ppocr/utils/dict/japan_dict.txt
loss_type: ctc
distort: true
use_space_char: false
......
configs/rec/multi_languages/rec_korean_lite_train.yml
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......@@ -12,7 +12,7 @@ Global:
image_shape: [3, 32, 320]
max_text_length: 25
character_type: korean
character_dict_path: ./ppocr/utils/korean_dict.txt
character_dict_path: ./ppocr/utils/dict/korean_dict.txt
loss_type: ctc
distort: true
use_space_char: false
......
deploy/android_demo/README.md
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......@@ -2,7 +2,7 @@
### 1. 安装最新版本的Android Studio
可以从https://developer.android.com/studio 下载。本Demo使用是4.0版本Android Studio编写。
### 2. 按照NDK 20 以上版本
### 2. 按照NDK 20 以上版本
Demo测试的时候使用的是NDK 20b版本，20版本以上均可以支持编译成功。
如果您是初学者，可以用以下方式安装和测试NDK编译环境。
......@@ -17,3 +17,10 @@ Demo测试的时候使用的是NDK 20b版本，20版本以上均可以支持编
- Demo APP：可使用手机扫码安装，方便手机端快速体验文字识别
- SDK：模型被封装为适配不同芯片硬件和操作系统SDK，包括完善的接口，方便进行二次开发
# FAQ：
Q1: 更新1.1版本的模型后，demo报错？
A1. 如果要更换V1.1 版本的模型，请更新模型的同时，更新预测库文件，建议使用[PaddleLite 2.6.3](https://github.com/PaddlePaddle/Paddle-Lite/releases/tag/v2.6.3)版本的预测库文件，OCR移动端部署参考[教程](../lite/readme.md)。
deploy/android_demo/app/src/main/cpp/ocr_ppredictor.h
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......@@ -98,7 +98,7 @@ private:
* @param origin
* @return
*/
cv::Mat infer_cls(const cv::Mat &origin, float thresh = 0.5);
cv::Mat infer_cls(const cv::Mat &origin, float thresh = 0.9);
/**
* Postprocess or sencod model to extract text
......
deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/OCRPredictorNative.java
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......@@ -35,12 +35,6 @@ public class OCRPredictorNative {
}
public void release() {
if (nativePointer != 0) {
nativePointer = 0;
// destory(nativePointer);
}
}
public ArrayList<OcrResultModel> runImage(float[] inputData, int width, int height, int channels, Bitmap originalImage) {
Log.i("OCRPredictorNative", "begin to run image " + inputData.length + " " + width + " " + height);
......@@ -63,7 +57,7 @@ public class OCRPredictorNative {
protected native float[] forward(long pointer, float[] buf, float[] ddims, Bitmap originalImage);
protected native void destory(long pointer);
public native void release(long pointer);
private ArrayList<OcrResultModel> postprocess(float[] raw) {
ArrayList<OcrResultModel> results = new ArrayList<OcrResultModel>();
......
deploy/cpp_infer/CMakeLists.txt
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......@@ -52,20 +52,29 @@ include_directories(${OpenCV_INCLUDE_DIRS})
if (WIN32)
add_definitions("/DGOOGLE_GLOG_DLL_DECL=")
set(CMAKE_C_FLAGS_DEBUG "${CMAKE_C_FLAGS_DEBUG} /bigobj /MTd")
set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} /bigobj /MT")
set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} /bigobj /MTd")
set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /bigobj /MT")
if(WITH_MKL)
set(FLAG_OPENMP "/openmp")
endif()
set(CMAKE_C_FLAGS_DEBUG "${CMAKE_C_FLAGS_DEBUG} /bigobj /MTd ${FLAG_OPENMP}")
set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} /bigobj /MT ${FLAG_OPENMP}")
set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} /bigobj /MTd ${FLAG_OPENMP}")
set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /bigobj /MT ${FLAG_OPENMP}")
if (WITH_STATIC_LIB)
safe_set_static_flag()
add_definitions(-DSTATIC_LIB)
endif()
message("cmake c debug flags " ${CMAKE_C_FLAGS_DEBUG})
message("cmake c release flags " ${CMAKE_C_FLAGS_RELEASE})
message("cmake cxx debug flags " ${CMAKE_CXX_FLAGS_DEBUG})
message("cmake cxx release flags " ${CMAKE_CXX_FLAGS_RELEASE})
else()
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -o3 -std=c++11")
if(WITH_MKL)
set(FLAG_OPENMP "-fopenmp")
endif()
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -o3 ${FLAG_OPENMP} -std=c++11")
set(CMAKE_STATIC_LIBRARY_PREFIX "")
message("cmake cxx flags" ${CMAKE_CXX_FLAGS})
endif()
message("flags" ${CMAKE_CXX_FLAGS})
if (WITH_GPU)
if (NOT DEFINED CUDA_LIB OR ${CUDA_LIB} STREQUAL "")
......@@ -198,4 +207,4 @@ if (WIN32 AND WITH_MKL)
COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5md.dll ./release/libiomp5md.dll
COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mkldnn/lib/mkldnn.dll ./release/mkldnn.dll
)
endif()
\ No newline at end of file
endif()
deploy/cpp_infer/readme.md
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......@@ -194,7 +194,37 @@ sh tools/run.sh
```
* 若需要使用方向分类器，则需要将`tools/config.txt`中的`use_angle_cls`参数修改为1，表示开启方向分类器的预测。
* 更多地，tools/config.txt中的参数及解释如下。
```
use_gpu 0 # 是否使用GPU，1表示使用，0表示不使用
gpu_id 0 # GPU id，使用GPU时有效
gpu_mem 4000 # 申请的GPU内存
cpu_math_library_num_threads 10 # CPU预测时的线程数，在机器核数充足的情况下，该值越大，预测速度越快
use_mkldnn 1 # 是否使用mkldnn库
use_zero_copy_run 1 # 是否使用use_zero_copy_run进行预测
# det config
max_side_len 960 # 输入图像长宽大于960时，等比例缩放图像，使得图像最长边为960
det_db_thresh 0.3 # 用于过滤DB预测的二值化图像，设置为0.-0.3对结果影响不明显
det_db_box_thresh 0.5 # DB后处理过滤box的阈值，如果检测存在漏框情况，可酌情减小
det_db_unclip_ratio 1.6 # 表示文本框的紧致程度，越小则文本框更靠近文本
det_model_dir ./inference/det_db # 检测模型inference model地址
# cls config
use_angle_cls 0 # 是否使用方向分类器，0表示不使用，1表示使用
cls_model_dir ./inference/cls # 方向分类器inference model地址
cls_thresh 0.9 # 方向分类器的得分阈值
# rec config
rec_model_dir ./inference/rec_crnn # 识别模型inference model地址
char_list_file ../../ppocr/utils/ppocr_keys_v1.txt # 字典文件
# show the detection results
visualize 1 # 是否对结果进行可视化，为1时，会在当前文件夹下保存文件名为`ocr_vis.png`的预测结果。
```
* PaddleOCR也支持多语言的预测，更多细节可以参考[识别文档](../../doc/doc_ch/recognition.md)中的多语言字典与模型部分。
最终屏幕上会输出检测结果如下。
......@@ -205,4 +235,4 @@ sh tools/run.sh
### 2.3 注意
* C++预测默认未开启MKLDNN(`tools/config.txt`中的`use_mkldnn`设置为0)，如果需要使用MKLDNN进行预测加速，则需要将`use_mkldnn`修改为1，同时使用最新版本的Paddle源码编译预测库。在使用MKLDNN进行CPU预测时，如果同时预测多张图像，则会出现内存泄露的问题（不打开MKLDNN则没有该问题），目前该问题正在修复中，临时解决方案为：预测多张图片时，每隔30张图片左右对识别(`CRNNRecognizer`)和检测类(`DBDetector`)重新初始化一次。
* 在使用Paddle预测库时，推荐使用2.0.0-beta0版本的预测库。
deploy/cpp_infer/readme_en.md
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......@@ -202,6 +202,38 @@ sh tools/run.sh
```
* If you want to orientation classifier to correct the detected boxes, you can set `use_angle_cls` in the file `tools/config.txt` as 1 to enable the function.
* What's more, Parameters and their meanings in `tools/config.txt` are as follows.
```
use_gpu 0 # Whether to use GPU, 0 means not to use, 1 means to use
gpu_id 0 # GPU id when use_gpu is 1
gpu_mem 4000 # GPU memory requested
cpu_math_library_num_threads 10 # Number of threads when using CPU inference. When machine cores is enough, the large the value, the faster the inference speed
use_mkldnn 1 # Whether to use mkdlnn library
use_zero_copy_run 1 # Whether to use use_zero_copy_run for inference
max_side_len 960 # Limit the maximum image height and width to 960
det_db_thresh 0.3 # Used to filter the binarized image of DB prediction, setting 0.-0.3 has no obvious effect on the result
det_db_box_thresh 0.5 # DDB post-processing filter box threshold, if there is a missing box detected, it can be reduced as appropriate
det_db_unclip_ratio 1.6 # Indicates the compactness of the text box, the smaller the value, the closer the text box to the text
det_model_dir ./inference/det_db # Address of detection inference model
# cls config
use_angle_cls 0 # Whether to use the direction classifier, 0 means not to use, 1 means to use
cls_model_dir ./inference/cls # Address of direction classifier inference model
cls_thresh 0.9 # Score threshold of the direction classifier
# rec config
rec_model_dir ./inference/rec_crnn # Address of recognition inference model
char_list_file ../../ppocr/utils/ppocr_keys_v1.txt # dictionary file
# show the detection results
visualize 1 # Whether to visualize the results，when it is set as 1, The prediction result will be save in the image file `./ocr_vis.png`.
```
* Multi-language inference is also supported in PaddleOCR, for more details, please refer to part of multi-language dictionaries and models in [recognition tutorial](../../doc/doc_en/recognition_en.md).
The detection results will be shown on the screen, which is as follows.
......@@ -210,6 +242,6 @@ The detection results will be shown on the screen, which is as follows.
</div>
### 2.3 Note
### 2.3 Notes
* `MKLDNN` is disabled by default for C++ inference (`use_mkldnn` in `tools/config.txt` is set to 0), if you need to use MKLDNN for inference acceleration, you need to modify `use_mkldnn` to 1, and use the latest version of the Paddle source code to compile the inference library. When using MKLDNN for CPU prediction, if multiple images are predicted at the same time, there will be a memory leak problem (the problem is not present if MKLDNN is disabled). The problem is currently being fixed, and the temporary solution is: when predicting multiple pictures, Re-initialize the recognition (`CRNNRecognizer`) and detection class (`DBDetector`) every 30 pictures or so.
* Paddle2.0.0-beta0 inference model library is recommanded for this tuturial.
deploy/cpp_infer/src/main.cpp
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......@@ -12,6 +12,8 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "glog/logging.h"
#include "omp.h"
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
......@@ -67,6 +69,19 @@ int main(int argc, char **argv) {
config.use_mkldnn, config.use_zero_copy_run,
config.char_list_file);
#ifdef USE_MKL
#pragma omp parallel
for (auto i = 0; i < 10; i++) {
LOG_IF(WARNING,
config.cpu_math_library_num_threads != omp_get_num_threads())
<< "WARNING! MKL is running on " << omp_get_num_threads()
<< " threads while cpu_math_library_num_threads is set to "
<< config.cpu_math_library_num_threads
<< ". Possible reason could be 1. You have set omp_set_num_threads() "
"somewhere; 2. MKL is not linked properly";
}
#endif
auto start = std::chrono::system_clock::now();
std::vector<std::vector<std::vector<int>>> boxes;
det.Run(srcimg, boxes);
......
deploy/cpp_infer/src/preprocess_op.cpp
浏览文件 @ 7e4aee47
......@@ -85,7 +85,7 @@ void ResizeImgType0::Run(const cv::Mat &img, cv::Mat &resize_img,
if (resize_w % 32 == 0)
resize_w = resize_w;
else if (resize_w / 32 < 1)
else if (resize_w / 32 < 1 + 1e-5)
resize_w = 32;
else
resize_w = (resize_w / 32 - 1) * 32;
......@@ -138,4 +138,4 @@ void ClsResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
}
}
} // namespace PaddleOCR
\ No newline at end of file
} // namespace PaddleOCR
deploy/hubserving/ocr_det/module.py
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......@@ -3,11 +3,14 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
sys.path.insert(0, ".")
import argparse
import ast
import copy
import math
import os
import time
from paddle.fluid.core import AnalysisConfig, create_paddle_predictor, PaddleTensor
......@@ -67,9 +70,7 @@ class OCRDet(hub.Module):
images.append(img)
return images
def predict(self,
images=[],
paths=[]):
def predict(self, images=[], paths=[]):
"""
Get the text box in the predicted images.
Args:
......@@ -87,7 +88,7 @@ class OCRDet(hub.Module):
raise TypeError("The input data is inconsistent with expectations.")
assert predicted_data != [], "There is not any image to be predicted. Please check the input data."
all_results = []
for img in predicted_data:
if img is None:
......@@ -99,11 +100,9 @@ class OCRDet(hub.Module):
rec_res_final = []
for dno in range(len(dt_boxes)):
rec_res_final.append(
{
'text_region': dt_boxes[dno].astype(np.int).tolist()
}
)
rec_res_final.append({
'text_region': dt_boxes[dno].astype(np.int).tolist()
})
all_results.append(rec_res_final)
return all_results
......@@ -116,7 +115,7 @@ class OCRDet(hub.Module):
results = self.predict(images_decode, **kwargs)
return results
if __name__ == '__main__':
ocr = OCRDet()
image_path = [
......@@ -124,4 +123,4 @@ if __name__ == '__main__':
'./doc/imgs/12.jpg',
]
res = ocr.predict(paths=image_path)
print(res)
\ No newline at end of file
print(res)
deploy/hubserving/ocr_det/params.py
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......@@ -37,5 +37,6 @@ def read_params():
# cfg.use_space_char = True
cfg.use_zero_copy_run = False
cfg.use_pdserving = False
return cfg
deploy/hubserving/ocr_rec/module.py
浏览文件 @ 7e4aee47
......@@ -3,11 +3,14 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
sys.path.insert(0, ".")
import argparse
import ast
import copy
import math
import os
import time
from paddle.fluid.core import AnalysisConfig, create_paddle_predictor, PaddleTensor
......@@ -67,9 +70,7 @@ class OCRRec(hub.Module):
images.append(img)
return images
def predict(self,
images=[],
paths=[]):
def predict(self, images=[], paths=[]):
"""
Get the text box in the predicted images.
Args:
......@@ -87,31 +88,28 @@ class OCRRec(hub.Module):
raise TypeError("The input data is inconsistent with expectations.")
assert predicted_data != [], "There is not any image to be predicted. Please check the input data."
img_list = []
for img in predicted_data:
if img is None:
continue
img_list.append(img)
rec_res_final = []
try:
rec_res, predict_time = self.text_recognizer(img_list)
for dno in range(len(rec_res)):
text, score = rec_res[dno]
rec_res_final.append(
{
'text': text,
'confidence': float(score),
}
)
rec_res_final.append({
'text': text,
'confidence': float(score),
})
except Exception as e:
print(e)
return [[]]
return [rec_res_final]
@serving
def serving_method(self, images, **kwargs):
"""
......@@ -121,7 +119,7 @@ class OCRRec(hub.Module):
results = self.predict(images_decode, **kwargs)
return results
if __name__ == '__main__':
ocr = OCRRec()
image_path = [
......@@ -130,4 +128,4 @@ if __name__ == '__main__':
'./doc/imgs_words/ch/word_3.jpg',
]
res = ocr.predict(paths=image_path)
print(res)
\ No newline at end of file
print(res)
deploy/hubserving/ocr_rec/params.py
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......@@ -38,6 +38,15 @@ def read_params():
cfg.rec_char_dict_path = "./ppocr/utils/ppocr_keys_v1.txt"
cfg.use_space_char = True
#params for text classifier
cfg.use_angle_cls = True
cfg.cls_model_dir = "./inference/ch_ppocr_mobile_v1.1_cls_infer/"
cfg.cls_image_shape = "3, 48, 192"
cfg.label_list = ['0', '180']
cfg.cls_batch_num = 30
cfg.cls_thresh = 0.9
cfg.use_zero_copy_run = False
cfg.use_pdserving = False
return cfg
deploy/hubserving/ocr_system/module.py
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......@@ -3,11 +3,14 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
sys.path.insert(0, ".")
import argparse
import ast
import copy
import math
import os
import time
from paddle.fluid.core import AnalysisConfig, create_paddle_predictor, PaddleTensor
......@@ -52,7 +55,7 @@ class OCRSystem(hub.Module):
)
cfg.ir_optim = True
cfg.enable_mkldnn = enable_mkldnn
self.text_sys = TextSystem(cfg)
def read_images(self, paths=[]):
......@@ -67,9 +70,7 @@ class OCRSystem(hub.Module):
images.append(img)
return images
def predict(self,
images=[],
paths=[]):
def predict(self, images=[], paths=[]):
"""
Get the chinese texts in the predicted images.
Args:
......@@ -104,13 +105,11 @@ class OCRSystem(hub.Module):
for dno in range(dt_num):
text, score = rec_res[dno]
rec_res_final.append(
{
'text': text,
'confidence': float(score),
'text_region': dt_boxes[dno].astype(np.int).tolist()
}
)
rec_res_final.append({
'text': text,
'confidence': float(score),
'text_region': dt_boxes[dno].astype(np.int).tolist()
})
all_results.append(rec_res_final)
return all_results
......@@ -123,7 +122,7 @@ class OCRSystem(hub.Module):
results = self.predict(images_decode, **kwargs)
return results
if __name__ == '__main__':
ocr = OCRSystem()
image_path = [
......@@ -131,4 +130,4 @@ if __name__ == '__main__':
'./doc/imgs/12.jpg',
]
res = ocr.predict(paths=image_path)
print(res)
\ No newline at end of file
print(res)
deploy/hubserving/ocr_system/params.py
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......@@ -39,12 +39,14 @@ def read_params():
cfg.use_space_char = True
#params for text classifier
cfg.use_angle_cls = False
cfg.cls_model_dir = "./inference/ch_ppocr_mobile-v1.1.cls_infer/"
cfg.use_angle_cls = True
cfg.cls_model_dir = "./inference/ch_ppocr_mobile_v1.1_cls_infer/"
cfg.cls_image_shape = "3, 48, 192"
cfg.label_list = ['0', '180']
cfg.cls_batch_num = 30
cfg.cls_thresh = 0.9
cfg.use_zero_copy_run = False
cfg.use_pdserving = False
return cfg
deploy/hubserving/readme.md
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[English](readme_en.md) | 简体中文
PaddleOCR提供2种服务部署方式：
- 基于PaddleHub Serving的部署：代码路径为"`./deploy/hubserving`"，按照本教程使用；
- 基于PaddleServing的部署：代码路径为"`./deploy/pdserving`"，使用方法参考[文档](../pdserving/readme.md)。
- 基于PaddleHub Serving的部署：代码路径为"`./deploy/hubserving`"，按照本教程使用；
- 基于PaddleServing的部署：代码路径为"`./deploy/pdserving`"，使用方法参考[文档](../../doc/doc_ch/serving_inference.md)。
# 基于PaddleHub Serving的服务部署
......@@ -29,17 +29,15 @@ deploy/hubserving/ocr_system/
```shell
# 安装paddlehub
pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
# 在Linux下设置环境变量
export PYTHONPATH=.
# 或者，在Windows下设置环境变量
SET PYTHONPATH=.
```
### 2. 下载推理模型
安装服务模块前，需要准备推理模型并放到正确路径。默认使用的是v1.1版的超轻量模型，默认检测模型路径为：
`./inference/ch_ppocr_mobile_v1.1_det_infer/`，识别模型路径为：`./inference/ch_ppocr_mobile_v1.1_rec_infer/`。
安装服务模块前，需要准备推理模型并放到正确路径。默认使用的是v1.1版的超轻量模型，默认模型路径为：
```
检测模型：./inference/ch_ppocr_mobile_v1.1_det_infer/
识别模型：./inference/ch_ppocr_mobile_v1.1_rec_infer/
方向分类器：./inference/ch_ppocr_mobile_v1.1_cls_infer/
```
**模型路径可在`params.py`中查看和修改。** 更多模型可以从PaddleOCR提供的[模型库](../../doc/doc_ch/models_list.md)下载，也可以替换成自己训练转换好的模型。
......@@ -173,7 +171,7 @@ hub serving start -c deploy/hubserving/ocr_system/config.json
```hub serving stop --port/-p XXXX```
- 2、 到相应的`module.py`和`params.py`等文件中根据实际需求修改代码。
例如，如果需要替换部署服务所用模型，则需要到`params.py`中修改模型路径参数`det_model_dir`和`rec_model_dir`，当然，同时可能还需要修改其他相关参数，请根据实际情况修改调试。 **强烈建议修改后先直接运行`module.py`调试，能正确运行预测后再启动服务测试。**
例如，如果需要替换部署服务所用模型，则需要到`params.py`中修改模型路径参数`det_model_dir`和`rec_model_dir`，如果需要关闭文本方向分类器，则将参数`use_angle_cls`置为`False`，当然，同时可能还需要修改其他相关参数，请根据实际情况修改调试。 **强烈建议修改后先直接运行`module.py`调试，能正确运行预测后再启动服务测试。**
- 3、 卸载旧服务包
```hub uninstall ocr_system```
......
deploy/hubserving/readme_en.md
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English | [简体中文](readme.md)
PaddleOCR provides 2 service deployment methods:
- Based on **PaddleHub Serving**: Code path is "`./deploy/hubserving`". Please follow this tutorial.
- Based on **PaddleServing**: Code path is "`./deploy/pdserving`". Please refer to the [tutorial](../pdserving/readme_en.md) for usage.
PaddleOCR provides 2 service deployment methods:
- Based on **PaddleHub Serving**: Code path is "`./deploy/hubserving`". Please follow this tutorial.
- Based on **PaddleServing**: Code path is "`./deploy/pdserving`". Please refer to the [tutorial](../../doc/doc_ch/serving_inference.md) for usage.
# Service deployment based on PaddleHub Serving
......@@ -30,16 +30,15 @@ The following steps take the 2-stage series service as an example. If only the d
```shell
# Install paddlehub
pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
# Set environment variables on Linux
export PYTHONPATH=.
# Set environment variables on Windows
SET PYTHONPATH=.
```
### 2. Download inference model
Before installing the service module, you need to prepare the inference model and put it in the correct path. By default, the ultra lightweight model of v1.1 is used, and the default detection model path is: `./inference/ch_ppocr_mobile_v1.1_det_infer/`, the default recognition model path is: `./inference/ch_ppocr_mobile_v1.1_rec_infer/`.
Before installing the service module, you need to prepare the inference model and put it in the correct path. By default, the ultra lightweight model of v1.1 is used, and the default model path is:
```
detection model: ./inference/ch_ppocr_mobile_v1.1_det_infer/
recognition model: ./inference/ch_ppocr_mobile_v1.1_rec_infer/
text direction classifier: ./inference/ch_ppocr_mobile_v1.1_cls_infer/
```
**The model path can be found and modified in `params.py`.** More models provided by PaddleOCR can be obtained from the [model library](../../doc/doc_en/models_list_en.md). You can also use models trained by yourself.
......@@ -180,7 +179,7 @@ If you need to modify the service logic, the following steps are generally requi
hub serving stop --port/-p XXXX
```
- 2. Modify the code in the corresponding files, like `module.py` and `params.py`, according to the actual needs.
For example, if you need to replace the model used by the deployed service, you need to modify model path parameters `det_model_dir` and `rec_model_dir` in `params.py`. Of course, other related parameters may need to be modified at the same time. Please modify and debug according to the actual situation. It is suggested to run `module.py` directly for debugging after modification before starting the service test.
For example, if you need to replace the model used by the deployed service, you need to modify model path parameters `det_model_dir` and `rec_model_dir` in `params.py`. If you want to turn off the text direction classifier, set the parameter `use_angle_cls` to `False`. Of course, other related parameters may need to be modified at the same time. Please modify and debug according to the actual situation. It is suggested to run `module.py` directly for debugging after modification before starting the service test.
- 3. Uninstall old service module
```shell
hub uninstall ocr_system
......
deploy/lite/config.txt
浏览文件 @ 7e4aee47
max_side_len 960
det_db_thresh 0.3
det_db_box_thresh 0.5
det_db_unclip_ratio 1.6
\ No newline at end of file
det_db_unclip_ratio 1.6
use_direction_classify 0
\ No newline at end of file
deploy/lite/db_post_process.cc
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......@@ -112,7 +112,7 @@ OrderPointsClockwise(std::vector<std::vector<int>> pts) {
}
std::vector<std::vector<float>> GetMiniBoxes(cv::RotatedRect box, float &ssid) {
ssid = std::max(box.size.width, box.size.height);
ssid = std::min(box.size.width, box.size.height);
cv::Mat points;
cv::boxPoints(box, points);
......
deploy/lite/ocr_db_crnn.cc
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......@@ -107,13 +107,14 @@ cv::Mat DetResizeImg(const cv::Mat img, int max_size_len,
}
cv::Mat RunClsModel(cv::Mat img, std::shared_ptr<PaddlePredictor> predictor_cls,
const float thresh = 0.5) {
const float thresh = 0.9) {
std::vector<float> mean = {0.5f, 0.5f, 0.5f};
std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
cv::Mat srcimg;
img.copyTo(srcimg);
cv::Mat crop_img;
img.copyTo(crop_img);
cv::Mat resize_img;
int index = 0;
......@@ -154,7 +155,8 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
std::vector<std::string> &rec_text,
std::vector<float> &rec_text_score,
std::vector<std::string> charactor_dict,
std::shared_ptr<PaddlePredictor> predictor_cls) {
std::shared_ptr<PaddlePredictor> predictor_cls,
int use_direction_classify) {
std::vector<float> mean = {0.5f, 0.5f, 0.5f};
std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
......@@ -166,7 +168,9 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
int index = 0;
for (int i = boxes.size() - 1; i >= 0; i--) {
crop_img = GetRotateCropImage(srcimg, boxes[i]);
crop_img = RunClsModel(crop_img, predictor_cls);
if (use_direction_classify >= 1) {
crop_img = RunClsModel(crop_img, predictor_cls);
}
float wh_ratio =
static_cast<float>(crop_img.cols) / static_cast<float>(crop_img.rows);
......@@ -290,7 +294,7 @@ RunDetModel(std::shared_ptr<PaddlePredictor> predictor, cv::Mat img,
cv::Mat bit_map;
cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY);
cv::Mat dilation_map;
cv::Mat dila_ele = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2,2));
cv::Mat dila_ele = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2));
cv::dilate(bit_map, dilation_map, dila_ele);
auto boxes = BoxesFromBitmap(pred_map, dilation_map, Config);
......@@ -366,7 +370,8 @@ std::map<std::string, double> LoadConfigTxt(std::string config_path) {
int main(int argc, char **argv) {
if (argc < 5) {
std::cerr << "[ERROR] usage: " << argv[0]
<< " det_model_file rec_model_file image_path\n";
<< " det_model_file cls_model_file rec_model_file image_path "
"charactor_dict\n";
exit(1);
}
std::string det_model_file = argv[1];
......@@ -377,6 +382,7 @@ int main(int argc, char **argv) {
//// load config from txt file
auto Config = LoadConfigTxt("./config.txt");
int use_direction_classify = int(Config["use_direction_classify"]);
auto start = std::chrono::system_clock::now();
......@@ -392,8 +398,9 @@ int main(int argc, char **argv) {
std::vector<std::string> rec_text;
std::vector<float> rec_text_score;
RunRecModel(boxes, srcimg, rec_predictor, rec_text, rec_text_score,
charactor_dict, cls_predictor);
charactor_dict, cls_predictor, use_direction_classify);
auto end = std::chrono::system_clock::now();
auto duration =
......
deploy/lite/readme.md
浏览文件 @ 7e4aee47
......@@ -15,10 +15,9 @@ Paddle Lite是飞桨轻量化推理引擎，为手机、IOT端提供高效推理
交叉编译环境用于编译 Paddle Lite 和 PaddleOCR 的C++ demo。
支持多种开发环境，不同开发环境的编译流程请参考对应文档。
1. [Docker](https://paddle-lite.readthedocs.io/zh/latest/user_guides/source_compile.html#docker)
2. [Linux](https://paddle-lite.readthedocs.io/zh/latest/user_guides/source_compile.html#android)
3. [MAC OS](https://paddle-lite.readthedocs.io/zh/latest/user_guides/source_compile.html#id13)
4. [Windows](https://paddle-lite.readthedocs.io/zh/latest/demo_guides/x86.html#id4)
1. [Docker](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#docker)
2. [Linux](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#linux)
3. [MAC OS](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#mac-os)
### 1.2 准备预测库
......@@ -26,17 +25,16 @@ Paddle Lite是飞桨轻量化推理引擎，为手机、IOT端提供高效推理
- 1. 直接下载，预测库下载链接如下：
|平台|预测库下载链接|
|-|-|
|Android|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/Android/inference_lite_lib.android.armv7.gcc.c++_static.with_extra.CV_ON.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/Android/inference_lite_lib.android.armv8.gcc.c++_static.with_extra.CV_ON.tar.gz)|
|IOS|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/iOS/inference_lite_lib.ios.armv7.with_extra.CV_ON.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/iOS/inference_lite_lib.ios64.armv8.with_extra.CV_ON.tar.gz)|
|x86(Linux)|[预测库](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/X86/Linux/inference_lite_lib.x86.linux.tar.gz)|
|Android|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.6.3/inference_lite_lib.android.armv7.gcc.c++_shared.with_extra.with_cv.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.6.3/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz)|
|IOS|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.6.3/inference_lite_lib.ios.armv7.with_cv.with_extra.with_log.tiny_publish.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.6.3/inference_lite_lib.ios.armv8.with_cv.with_extra.with_log.tiny_publish.tar.gz)|
注：如果是从下Paddle-Lite[官网文档](https://paddle-lite.readthedocs.io/zh/latest/user_guides/release_lib.html#android-toolchain-gcc)下载的预测库，
注意选择`with_extra=ON，with_cv=ON`的下载链接。
注：1. 上述预测库为PaddleLite 2.6.3分支编译得到，有关PaddleLite 2.6.3 详细信息可参考[链接](https://github.com/PaddlePaddle/Paddle-Lite/releases/tag/v2.6.3)。
- 2. 编译Paddle-Lite得到预测库，Paddle-Lite的编译方式如下：
- 2. [推荐]编译Paddle-Lite得到预测库，Paddle-Lite的编译方式如下：
```
git clone https://github.com/PaddlePaddle/Paddle-Lite.git
cd Paddle-Lite
# 切换到Paddle-Lite develop稳定分支
git checkout develop
./lite/tools/build_android.sh --arch=armv8 --with_cv=ON --with_extra=ON
```
......@@ -80,11 +78,17 @@ inference_lite_lib.android.armv8/
Paddle-Lite 提供了多种策略来自动优化原始的模型，其中包括量化、子图融合、混合调度、Kernel优选等方法，使用Paddle-lite的opt工具可以自动
对inference模型进行优化，优化后的模型更轻量，模型运行速度更快。
下述表格中提供了优化好的超轻量中文模型：
如果已经准备好了 `.nb` 结尾的模型文件，可以跳过此步骤。
|模型简介|检测模型|识别模型|Paddle-Lite版本|
|-|-|-|-|
|超轻量级中文OCR opt优化模型|[下载地址](https://paddleocr.bj.bcebos.com/deploy/lite/ch_det_mv3_db_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/deploy/lite/ch_rec_mv3_crnn_opt.nb)|develop|
下述表格中也提供了一系列中文移动端模型：
|模型版本|模型简介|模型大小|检测模型|文本方向分类模型|识别模型|Paddle-Lite版本|
|-|-|-|-|-|-|-|
|V1.1|超轻量中文OCR 移动端模型|8.1M|[下载地址](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_det_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_cls_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_rec_opt.nb)|develop|
|【slim】V1.1|超轻量中文OCR 移动端模型|3.5M|[下载地址](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_det_prune_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_cls_quant_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_rec_quant_opt.nb)|develop|
|V1.0|轻量级中文OCR 移动端模型|8.6M|[下载地址](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.0_det_opt.nb)|---|[下载地址](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.0_rec_opt.nb)|develop|
注意：V1.1 3.0M 轻量模型是使用PaddleSlim优化后的，需要配合Paddle-Lite最新预测库使用。
如果直接使用上述表格中的模型进行部署，可略过下述步骤，直接阅读 [2.2节](#2.2与手机联调)。
......@@ -96,6 +100,8 @@ Paddle-Lite 提供了多种策略来自动优化原始的模型，其中包括
git clone https://github.com/PaddlePaddle/Paddle-Lite.git
cd Paddle-Lite
git checkout develop
# 切换到固定的commit
git reset --hard 55c53482bcdd2868373d024dd1144e4c5ec0e6b8
# 启动编译
./lite/tools/build.sh build_optimize_tool
```
......@@ -121,20 +127,27 @@ cd build.opt/lite/api/
下面以PaddleOCR的超轻量中文模型为例，介绍使用编译好的opt文件完成inference模型到Paddle-Lite优化模型的转换。
```
# 下载PaddleOCR的超轻量文inference模型，并解压
# 【推荐】 下载PaddleOCR V1.1版本的中英文 inference模型，V1.1比1.0效果更好，模型更小
wget https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_infer.tar && tar xf ch_ppocr_mobile_v1.1_det_prune_infer.tar
wget https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_infer.tar && tar xf ch_ppocr_mobile_v1.1_rec_quant_infer.tar
# 转换V1.1检测模型
./opt --model_file=./ch_ppocr_mobile_v1.1_det_prune_infer/model --param_file=./ch_ppocr_mobile_v1.1_det_prune_infer/params --optimize_out=./ch_ppocr_mobile_v1.1_det_prune_opt --valid_targets=arm
# 转换V1.1识别模型
./opt --model_file=./ch_ppocr_mobile_v1.1_rec_quant_infer/model --param_file=./ch_ppocr_mobile_v1.1_rec_quant_infer/params --optimize_out=./ch_ppocr_mobile_v1.1_rec_quant_opt --valid_targets=arm
# 或下载使用PaddleOCR的V1.0超轻量中英文 inference模型，解压并转换为移动端支持的模型
wget https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar && tar xf ch_det_mv3_db_infer.tar
wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar xf ch_rec_mv3_crnn_infer.tar
# 转换检测模型
# 转换V1.0检测模型
./opt --model_file=./ch_det_mv3_db/model --param_file=./ch_det_mv3_db/params --optimize_out_type=naive_buffer --optimize_out=./ch_det_mv3_db_opt --valid_targets=arm
# 转换识别模型
# 转换V1.0识别模型
./opt --model_file=./ch_rec_mv3_crnn/model --param_file=./ch_rec_mv3_crnn/params --optimize_out_type=naive_buffer --optimize_out=./ch_rec_mv3_crnn_opt --valid_targets=arm
```
转换成功后，当前目录下会多出`ch_det_mv3_db_opt.nb`, `ch_rec_mv3_crnn_opt.nb`结尾的文件，即是转换成功的模型文件。
转换成功后，当前目录下会多出`.nb`结尾的文件，即是转换成功的模型文件。
注意：使用paddle-lite部署时，需要使用opt工具优化后的模型。 opt 转换的输入模型是paddle保存的inference模型
注意：使用paddle-lite部署时，需要使用opt工具优化后的模型。 opt 工具的输入模型是paddle保存的inference模型
<a name="2.2与手机联调"></a>
### 2.2 与手机联调
......@@ -182,17 +195,18 @@ wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar
```
准备测试图像，以`PaddleOCR/doc/imgs/11.jpg`为例，将测试的图像复制到`demo/cxx/ocr/debug/`文件夹下。
准备lite opt工具优化后的模型文件，`ch_det_mv3_db_opt.nb，ch_rec_mv3_crnn_opt.nb`，放置在`demo/cxx/ocr/debug/`文件夹下。
准备lite opt工具优化后的模型文件，比如使用`ch_ppocr_mobile_v1.1_det_prune_opt.nb，ch_ppocr_mobile_v1.1_rec_quant_opt.nb, ch_ppocr_mobile_cls_quant_opt.nb`，模型文件放置在`demo/cxx/ocr/debug/`文件夹下。
执行完成后，ocr文件夹下将有如下文件格式：
```
demo/cxx/ocr/
|-- debug/
| |--ch_det_mv3_db_opt.nb 优化后的检测模型文件
| |--ch_rec_mv3_crnn_opt.nb 优化后的识别模型文件
| |--ch_ppocr_mobile_v1.1_det_prune_opt.nb 优化后的检测模型文件
| |--ch_ppocr_mobile_v1.1_rec_quant_opt.nb 优化后的识别模型文件
| |--ch_ppocr_mobile_cls_quant_opt.nb 优化后的文字方向分类器模型文件
| |--11.jpg 待测试图像
| |--ppocr_keys_v1.txt 字典文件
| |--ppocr_keys_v1.txt 中文字典文件
| |--libpaddle_light_api_shared.so C++预测库文件
| |--config.txt DB-CRNN超参数配置
|-- config.txt DB-CRNN超参数配置
......@@ -202,7 +216,27 @@ demo/cxx/ocr/
|-- db_post_process.h
|-- Makefile 编译文件
|-- ocr_db_crnn.cc C++预测源文件
```
#### 注意：
1. ppocr_keys_v1.txt是中文字典文件，如果使用的 nb 模型是英文数字或其他语言的模型，需要更换为对应语言的字典。
PaddleOCR 在ppocr/utils/下存放了多种字典，包括：
```
dict/french_dict.txt # 法语字典
dict/german_dict.txt # 德语字典
ic15_dict.txt # 英文字典
dict/japan_dict.txt # 日语字典
dict/korean_dict.txt # 韩语字典
ppocr_keys_v1.txt # 中文字典
```
2. `config.txt` 包含了检测器、分类器的超参数，如下：
```
max_side_len 960 # 输入图像长宽大于960时，等比例缩放图像，使得图像最长边为960
det_db_thresh 0.3 # 用于过滤DB预测的二值化图像，设置为0.-0.3对结果影响不明显
det_db_box_thresh 0.5 # DB后处理过滤box的阈值，如果检测存在漏框情况，可酌情减小
det_db_unclip_ratio 1.6 # 表示文本框的紧致程度，越小则文本框更靠近文本
use_direction_classify 0 # 是否使用方向分类器，0表示不使用，1表示使用
```
5. 启动调试
......@@ -212,7 +246,7 @@ demo/cxx/ocr/
```
# 执行编译，得到可执行文件ocr_db_crnn
# ocr_db_crnn可执行文件的使用方式为:
# ./ocr_db_crnn 检测模型文件 识别模型文件 测试图像路径
# ./ocr_db_crnn 检测模型文件 方向分类器模型文件 识别模型文件 测试图像路径 字典文件路径
make -j
# 将编译的可执行文件移动到debug文件夹中
mv ocr_db_crnn ./debug/
......@@ -220,8 +254,8 @@ demo/cxx/ocr/
adb push debug /data/local/tmp/
adb shell
cd /data/local/tmp/debug
export LD_LIBRARY_PATH=/data/local/tmp/debug:$LD_LIBRARY_PATH
./ocr_db_crnn ch_det_mv3_db_opt.nb ch_rec_mv3_crnn_opt.nb ./11.jpg ppocr_keys_v1.txt
export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
./ocr_db_crnn ch_ppocr_mobile_v1.1_det_prune_opt.nb ch_ppocr_mobile_v1.1_rec_quant_opt.nb ch_ppocr_mobile_cls_quant_opt.nb ./11.jpg ppocr_keys_v1.txt
```
如果对代码做了修改，则需要重新编译并push到手机上。
......@@ -231,3 +265,14 @@ demo/cxx/ocr/
<div align="center">
<img src="../imgs/demo.png" width="600">
</div>
## FAQ
Q1：如果想更换模型怎么办，需要重新按照流程走一遍吗？
A1：如果已经走通了上述步骤，更换模型只需要替换 .nb 模型文件即可，同时要注意字典更新
Q2：换一个图测试怎么做？
A2：替换debug下的.jpg测试图像为你想要测试的图像，adb push 到手机上即可
Q3：如何封装到手机APP中？
A3：此demo旨在提供能在手机上运行OCR的核心算法部分，PaddleOCR/deploy/android_demo是将这个demo封装到手机app的示例，供参考
deploy/lite/readme_en.md
浏览文件 @ 7e4aee47
......@@ -14,18 +14,18 @@ deployment solutions for end-side deployment issues.
- Mobile phone (arm7 or arm8)
## 2. Build PaddleLite library
[build for Docker](https://paddle-lite.readthedocs.io/zh/latest/user_guides/source_compile.html#docker)
[build for Linux](https://paddle-lite.readthedocs.io/zh/latest/user_guides/source_compile.html#android)
[build for MAC OS](https://paddle-lite.readthedocs.io/zh/latest/user_guides/source_compile.html#id13)
[build for windows](https://paddle-lite.readthedocs.io/zh/latest/demo_guides/x86.html#id4)
1. [Docker](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#docker)
2. [Linux](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#linux)
3. [MAC OS](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#mac-os)
## 3. Download prebuild library for android and ios
|Platform|Prebuild library Download Link|
|-|-|
|Android|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/Android/inference_lite_lib.android.armv7.gcc.c++_static.with_extra.CV_ON.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/Android/inference_lite_lib.android.armv8.gcc.c++_static.with_extra.CV_ON.tar.gz)|
|IOS|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/iOS/inference_lite_lib.ios.armv7.with_extra.CV_ON.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/iOS/inference_lite_lib.ios64.armv8.with_extra.CV_ON.tar.gz)|
|x86(Linux)|[预测库](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/X86/Linux/inference_lite_lib.x86.linux.tar.gz)|
|Android|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.6.3/inference_lite_lib.android.armv7.gcc.c++_shared.with_extra.with_cv.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.6.3/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz)|
|IOS|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.6.3/inference_lite_lib.ios.armv7.with_cv.with_extra.with_log.tiny_publish.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.6.3/inference_lite_lib.ios.armv8.with_cv.with_extra.with_log.tiny_publish.tar.gz)|
note: The above pre-build inference library is compiled from the PaddleLite `release/2.6.3` branch. For more information about PaddleLite 2.6.3, please refer to [link](https://github.com/PaddlePaddle/Paddle-Lite/releases/tag/v2.6.3).
The structure of the prediction library is as follows:
......@@ -56,17 +56,20 @@ inference_lite_lib.android.armv8/
```
## 4. Inference Model Optimization
Paddle Lite provides a variety of strategies to automatically optimize the original training model, including quantization, sub-graph fusion, hybrid scheduling, Kernel optimization and so on. In order to make the optimization process more convenient and easy to use, Paddle Lite provide opt tools to automatically complete the optimization steps and output a lightweight, optimal executable model.
If you use PaddleOCR 8.6M OCR model to deploy, you can directly download the optimized model.
If you have prepared the model file ending in `.nb`, you can skip this step.
The following table also provides a series of models that can be deployed on mobile phones to recognize Chinese.
You can directly download the optimized model.
|Introduction|Detection model|Recognition model|Paddle Lite branch |
|-|-|-|-|
|lightweight Chinese OCR optimized model|[Download](https://paddleocr.bj.bcebos.com/deploy/lite/ch_det_mv3_db_opt.nb)|[Download](https://paddleocr.bj.bcebos.com/deploy/lite/ch_rec_mv3_crnn_opt.nb)|develop|
| Version | Introduction | Model size | Detection model | Text Direction model | Recognition model | Paddle Lite branch |
| - | - | - | - | - | - | - |
| V1.1 | extra-lightweight chinese OCR optimized model | 8.1M | [Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_det_opt.nb) | [Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_cls_opt.nb) | [Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_rec_opt.nb) | develop |
| [slim] V1.1 | extra-lightweight chinese OCR optimized model | 3.5M | [Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_det_prune_opt.nb) | [Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_cls_quant_opt.nb) | [Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_rec_quant_opt.nb) | develop |
| V1.0 | lightweight Chinese OCR optimized model | 8.6M | [Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.0_det_opt.nb) | - | [Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.0_rec_opt.nb) | develop |
If the model to be deployed is not in the above table, you need to follow the steps below to obtain the optimized model.
......@@ -74,6 +77,8 @@ If the model to be deployed is not in the above table, you need to follow the st
git clone https://github.com/PaddlePaddle/Paddle-Lite.git
cd Paddle-Lite
git checkout develop
# switch to the specified commit
git reset --hard 55c53482bcdd2868373d024dd1144e4c5ec0e6b8
./lite/tools/build.sh build_optimize_tool
```
......@@ -85,6 +90,14 @@ The `opt` can optimize the inference model saved by paddle.io.save_inference_mod
The usage of opt is as follows：
```
# 【Recommend】V1.1 is better than V1.0. steps for convert V1.1 model to nb file are as follows
wget https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_infer.tar && tar xf ch_ppocr_mobile_v1.1_det_prune_infer.tar
wget https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_infer.tar && tar xf ch_ppocr_mobile_v1.1_rec_quant_infer.tar
./opt --model_file=./ch_ppocr_mobile_v1.1_det_prune_infer/model --param_file=./ch_ppocr_mobile_v1.1_det_prune_infer/params --optimize_out=./ch_ppocr_mobile_v1.1_det_prune_opt --valid_targets=arm
./opt --model_file=./ch_ppocr_mobile_v1.1_rec_quant_infer/model --param_file=./ch_ppocr_mobile_v1.1_rec_quant_infer/params --optimize_out=./ch_ppocr_mobile_v1.1_rec_quant_opt --valid_targets=arm
# or use V1.0 model
wget https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar && tar xf ch_det_mv3_db_infer.tar
wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar xf ch_rec_mv3_crnn_infer.tar
......@@ -93,8 +106,7 @@ wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar
```
When the above code command is completed, there will be two more files `ch_det_mv3_db_opt.nb`,
`ch_rec_mv3_crnn_opt.nb` in the current directory, which is the converted model file.
When the above code command is completed, there will be two more files `.nb` in the current directory, which is the converted model file.
## 5. Run optimized model on Phone
......@@ -153,8 +165,9 @@ The structure of the OCR demo is as follows after the above command is executed:
```
demo/cxx/ocr/
|-- debug/
| |--ch_det_mv3_db_opt.nb Detection model
| |--ch_rec_mv3_crnn_opt.nb Recognition model
| |--ch_ppocr_mobile_v1.1_det_prune_opt.nb Detection model
| |--ch_ppocr_mobile_v1.1_rec_quant_opt.nb Recognition model
| |--ch_ppocr_mobile_cls_quant_opt.nb Text direction classification model
| |--11.jpg Image for OCR
| |--ppocr_keys_v1.txt Dictionary file
| |--libpaddle_light_api_shared.so C++ .so file
......@@ -169,6 +182,28 @@ demo/cxx/ocr/
```
#### Note:
1. ppocr_keys_v1.txt is a Chinese dictionary file.
If the nb model is used for English recognition or other language recognition, dictionary file should be replaced with a dictionary of the corresponding language.
PaddleOCR provides a variety of dictionaries under ppocr/utils/, including:
```
dict/french_dict.txt # french
dict/german_dict.txt # german
ic15_dict.txt # english
dict/japan_dict.txt # japan
dict/korean_dict.txt # korean
ppocr_keys_v1.txt # chinese
```
2. `config.txt` of the detector and classifier, as shown below:
```
max_side_len 960 # Limit the maximum image height and width to 960
det_db_thresh 0.3 # Used to filter the binarized image of DB prediction, setting 0.-0.3 has no obvious effect on the result
det_db_box_thresh 0.5 # DDB post-processing filter box threshold, if there is a missing box detected, it can be reduced as appropriate
det_db_unclip_ratio 1.6 # Indicates the compactness of the text box, the smaller the value, the closer the text box to the text
use_direction_classify 0 # Whether to use the direction classifier, 0 means not to use, 1 means to use
```
5. Run Model on phone
```
......@@ -180,7 +215,7 @@ adb shell
cd /data/local/tmp/debug
export LD_LIBRARY_PATH=/data/local/tmp/debug:$LD_LIBRARY_PATH
# run model
./ocr_db_crnn ch_det_mv3_db_opt.nb ch_rec_mv3_crnn_opt.nb ./11.jpg ppocr_keys_v1.txt
./ocr_db_crnn ch_ppocr_mobile_v1.1_det_prune_opt.nb ch_ppocr_mobile_v1.1_rec_quant_opt.nb ch_ppocr_mobile_cls_quant_opt.nb ./11.jpg ppocr_keys_v1.txt
```
The outputs are as follows:
......@@ -188,3 +223,15 @@ The outputs are as follows:
<div align="center">
<img src="../imgs/demo.png" width="600">
</div>
## FAQ
Q1: What if I want to change the model, do I need to run it again according to the process?
A1: If you have performed the above steps, you only need to replace the .nb model file to complete the model replacement.
Q2: How to test with another picture?
A2: Replace the .jpg test image under `./debug` with the image you want to test, and run `adb push` to push new image to the phone.
Q3: How to package it into the mobile APP?
A3: This demo aims to provide the core algorithm part that can run OCR on mobile phones. Further,
PaddleOCR/deploy/android_demo is an example of encapsulating this demo into a mobile app for reference.
deploy/paddleOcrSpringBoot/mvnw 0 → 100644
浏览文件 @ 7e4aee47
#!/bin/sh
# ----------------------------------------------------------------------------
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
# ----------------------------------------------------------------------------
# ----------------------------------------------------------------------------
# Maven Start Up Batch script
#
# Required ENV vars:
# ------------------
# JAVA_HOME - location of a JDK home dir
#
# Optional ENV vars
# -----------------
# M2_HOME - location of maven2's installed home dir
# MAVEN_OPTS - parameters passed to the Java VM when running Maven
# e.g. to debug Maven itself, use
# set MAVEN_OPTS=-Xdebug -Xrunjdwp:transport=dt_socket,server=y,suspend=y,address=8000
# MAVEN_SKIP_RC - flag to disable loading of mavenrc files
# ----------------------------------------------------------------------------
if [ -z "$MAVEN_SKIP_RC" ] ; then
if [ -f /etc/mavenrc ] ; then
. /etc/mavenrc
fi
if [ -f "$HOME/.mavenrc" ] ; then
. "$HOME/.mavenrc"
fi
fi
# OS specific support. $var _must_ be set to either true or false.
cygwin=false;
darwin=false;
mingw=false
case "`uname`" in
CYGWIN*) cygwin=true ;;
MINGW*) mingw=true;;
Darwin*) darwin=true
# Use /usr/libexec/java_home if available, otherwise fall back to /Library/Java/Home
# See https://developer.apple.com/library/mac/qa/qa1170/_index.html
if [ -z "$JAVA_HOME" ]; then
if [ -x "/usr/libexec/java_home" ]; then
export JAVA_HOME="`/usr/libexec/java_home`"
else
export JAVA_HOME="/Library/Java/Home"
fi
fi
;;
esac
if [ -z "$JAVA_HOME" ] ; then
if [ -r /etc/gentoo-release ] ; then
JAVA_HOME=`java-config --jre-home`
fi
fi
if [ -z "$M2_HOME" ] ; then
## resolve links - $0 may be a link to maven's home
PRG="$0"
# need this for relative symlinks
while [ -h "$PRG" ] ; do
ls=`ls -ld "$PRG"`
link=`expr "$ls" : '.*-> \(.*\)$'`
if expr "$link" : '/.*' > /dev/null; then
PRG="$link"
else
PRG="`dirname "$PRG"`/$link"
fi
done
saveddir=`pwd`
M2_HOME=`dirname "$PRG"`/..
# make it fully qualified
M2_HOME=`cd "$M2_HOME" && pwd`
cd "$saveddir"
# echo Using m2 at $M2_HOME
fi
# For Cygwin, ensure paths are in UNIX format before anything is touched
if $cygwin ; then
[ -n "$M2_HOME" ] &&
M2_HOME=`cygpath --unix "$M2_HOME"`
[ -n "$JAVA_HOME" ] &&
JAVA_HOME=`cygpath --unix "$JAVA_HOME"`
[ -n "$CLASSPATH" ] &&
CLASSPATH=`cygpath --path --unix "$CLASSPATH"`
fi
# For Mingw, ensure paths are in UNIX format before anything is touched
if $mingw ; then
[ -n "$M2_HOME" ] &&
M2_HOME="`(cd "$M2_HOME"; pwd)`"
[ -n "$JAVA_HOME" ] &&
JAVA_HOME="`(cd "$JAVA_HOME"; pwd)`"
fi
if [ -z "$JAVA_HOME" ]; then
javaExecutable="`which javac`"
if [ -n "$javaExecutable" ] && ! [ "`expr \"$javaExecutable\" : '\([^ ]*\)'`" = "no" ]; then
# readlink(1) is not available as standard on Solaris 10.
readLink=`which readlink`
if [ ! `expr "$readLink" : '\([^ ]*\)'` = "no" ]; then
if $darwin ; then
javaHome="`dirname \"$javaExecutable\"`"
javaExecutable="`cd \"$javaHome\" && pwd -P`/javac"
else
javaExecutable="`readlink -f \"$javaExecutable\"`"
fi
javaHome="`dirname \"$javaExecutable\"`"
javaHome=`expr "$javaHome" : '\(.*\)/bin'`
JAVA_HOME="$javaHome"
export JAVA_HOME
fi
fi
fi
if [ -z "$JAVACMD" ] ; then
if [ -n "$JAVA_HOME" ] ; then
if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
# IBM's JDK on AIX uses strange locations for the executables
JAVACMD="$JAVA_HOME/jre/sh/java"
else
JAVACMD="$JAVA_HOME/bin/java"
fi
else
JAVACMD="`which java`"
fi
fi
if [ ! -x "$JAVACMD" ] ; then
echo "Error: JAVA_HOME is not defined correctly." >&2
echo " We cannot execute $JAVACMD" >&2
exit 1
fi
if [ -z "$JAVA_HOME" ] ; then
echo "Warning: JAVA_HOME environment variable is not set."
fi
CLASSWORLDS_LAUNCHER=org.codehaus.plexus.classworlds.launcher.Launcher
# traverses directory structure from process work directory to filesystem root
# first directory with .mvn subdirectory is considered project base directory
find_maven_basedir() {
if [ -z "$1" ]
then
echo "Path not specified to find_maven_basedir"
return 1
fi
basedir="$1"
wdir="$1"
while [ "$wdir" != '/' ] ; do
if [ -d "$wdir"/.mvn ] ; then
basedir=$wdir
break
fi
# workaround for JBEAP-8937 (on Solaris 10/Sparc)
if [ -d "${wdir}" ]; then
wdir=`cd "$wdir/.."; pwd`
fi
# end of workaround
done
echo "${basedir}"
}
# concatenates all lines of a file
concat_lines() {
if [ -f "$1" ]; then
echo "$(tr -s '\n' ' ' < "$1")"
fi
}
BASE_DIR=`find_maven_basedir "$(pwd)"`
if [ -z "$BASE_DIR" ]; then
exit 1;
fi
##########################################################################################
# Extension to allow automatically downloading the maven-wrapper.jar from Maven-central
# This allows using the maven wrapper in projects that prohibit checking in binary data.
##########################################################################################
if [ -r "$BASE_DIR/.mvn/wrapper/maven-wrapper.jar" ]; then
if [ "$MVNW_VERBOSE" = true ]; then
echo "Found .mvn/wrapper/maven-wrapper.jar"
fi
else
if [ "$MVNW_VERBOSE" = true ]; then
echo "Couldn't find .mvn/wrapper/maven-wrapper.jar, downloading it ..."
fi
if [ -n "$MVNW_REPOURL" ]; then
jarUrl="$MVNW_REPOURL/io/takari/maven-wrapper/0.5.6/maven-wrapper-0.5.6.jar"
else
jarUrl="https://repo.maven.apache.org/maven2/io/takari/maven-wrapper/0.5.6/maven-wrapper-0.5.6.jar"
fi
while IFS="=" read key value; do
case "$key" in (wrapperUrl) jarUrl="$value"; break ;;
esac
done < "$BASE_DIR/.mvn/wrapper/maven-wrapper.properties"
if [ "$MVNW_VERBOSE" = true ]; then
echo "Downloading from: $jarUrl"
fi
wrapperJarPath="$BASE_DIR/.mvn/wrapper/maven-wrapper.jar"
if $cygwin; then
wrapperJarPath=`cygpath --path --windows "$wrapperJarPath"`
fi
if command -v wget > /dev/null; then
if [ "$MVNW_VERBOSE" = true ]; then
echo "Found wget ... using wget"
fi
if [ -z "$MVNW_USERNAME" ] || [ -z "$MVNW_PASSWORD" ]; then
wget "$jarUrl" -O "$wrapperJarPath"
else
wget --http-user=$MVNW_USERNAME --http-password=$MVNW_PASSWORD "$jarUrl" -O "$wrapperJarPath"
fi
elif command -v curl > /dev/null; then
if [ "$MVNW_VERBOSE" = true ]; then
echo "Found curl ... using curl"
fi
if [ -z "$MVNW_USERNAME" ] || [ -z "$MVNW_PASSWORD" ]; then
curl -o "$wrapperJarPath" "$jarUrl" -f
else
curl --user $MVNW_USERNAME:$MVNW_PASSWORD -o "$wrapperJarPath" "$jarUrl" -f
fi
else
if [ "$MVNW_VERBOSE" = true ]; then
echo "Falling back to using Java to download"
fi
javaClass="$BASE_DIR/.mvn/wrapper/MavenWrapperDownloader.java"
# For Cygwin, switch paths to Windows format before running javac
if $cygwin; then
javaClass=`cygpath --path --windows "$javaClass"`
fi
if [ -e "$javaClass" ]; then
if [ ! -e "$BASE_DIR/.mvn/wrapper/MavenWrapperDownloader.class" ]; then
if [ "$MVNW_VERBOSE" = true ]; then
echo " - Compiling MavenWrapperDownloader.java ..."
fi
# Compiling the Java class
("$JAVA_HOME/bin/javac" "$javaClass")
fi
if [ -e "$BASE_DIR/.mvn/wrapper/MavenWrapperDownloader.class" ]; then
# Running the downloader
if [ "$MVNW_VERBOSE" = true ]; then
echo " - Running MavenWrapperDownloader.java ..."
fi
("$JAVA_HOME/bin/java" -cp .mvn/wrapper MavenWrapperDownloader "$MAVEN_PROJECTBASEDIR")
fi
fi
fi
fi
##########################################################################################
# End of extension
##########################################################################################
export MAVEN_PROJECTBASEDIR=${MAVEN_BASEDIR:-"$BASE_DIR"}
if [ "$MVNW_VERBOSE" = true ]; then
echo $MAVEN_PROJECTBASEDIR
fi
MAVEN_OPTS="$(concat_lines "$MAVEN_PROJECTBASEDIR/.mvn/jvm.config") $MAVEN_OPTS"
# For Cygwin, switch paths to Windows format before running java
if $cygwin; then
[ -n "$M2_HOME" ] &&
M2_HOME=`cygpath --path --windows "$M2_HOME"`
[ -n "$JAVA_HOME" ] &&
JAVA_HOME=`cygpath --path --windows "$JAVA_HOME"`
[ -n "$CLASSPATH" ] &&
CLASSPATH=`cygpath --path --windows "$CLASSPATH"`
[ -n "$MAVEN_PROJECTBASEDIR" ] &&
MAVEN_PROJECTBASEDIR=`cygpath --path --windows "$MAVEN_PROJECTBASEDIR"`
fi
# Provide a "standardized" way to retrieve the CLI args that will
# work with both Windows and non-Windows executions.
MAVEN_CMD_LINE_ARGS="$MAVEN_CONFIG $@"
export MAVEN_CMD_LINE_ARGS
WRAPPER_LAUNCHER=org.apache.maven.wrapper.MavenWrapperMain
exec "$JAVACMD" \
$MAVEN_OPTS \
-classpath "$MAVEN_PROJECTBASEDIR/.mvn/wrapper/maven-wrapper.jar" \
"-Dmaven.home=${M2_HOME}" "-Dmaven.multiModuleProjectDirectory=${MAVEN_PROJECTBASEDIR}" \
${WRAPPER_LAUNCHER} $MAVEN_CONFIG "$@"
deploy/paddleOcrSpringBoot/mvnw.cmd 0 → 100644
浏览文件 @ 7e4aee47
@REM ----------------------------------------------------------------------------
@REM Licensed to the Apache Software Foundation (ASF) under one
@REM or more contributor license agreements. See the NOTICE file
@REM distributed with this work for additional information
@REM regarding copyright ownership. The ASF licenses this file
@REM to you under the Apache License, Version 2.0 (the
@REM "License"); you may not use this file except in compliance
@REM with the License. You may obtain a copy of the License at
@REM
@REM https://www.apache.org/licenses/LICENSE-2.0
@REM
@REM Unless required by applicable law or agreed to in writing,
@REM software distributed under the License is distributed on an
@REM "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
@REM KIND, either express or implied. See the License for the
@REM specific language governing permissions and limitations
@REM under the License.
@REM ----------------------------------------------------------------------------
@REM ----------------------------------------------------------------------------
@REM Maven Start Up Batch script
@REM
@REM Required ENV vars:
@REM JAVA_HOME - location of a JDK home dir
@REM
@REM Optional ENV vars
@REM M2_HOME - location of maven2's installed home dir
@REM MAVEN_BATCH_ECHO - set to 'on' to enable the echoing of the batch commands
@REM MAVEN_BATCH_PAUSE - set to 'on' to wait for a keystroke before ending
@REM MAVEN_OPTS - parameters passed to the Java VM when running Maven
@REM e.g. to debug Maven itself, use
@REM set MAVEN_OPTS=-Xdebug -Xrunjdwp:transport=dt_socket,server=y,suspend=y,address=8000
@REM MAVEN_SKIP_RC - flag to disable loading of mavenrc files
@REM ----------------------------------------------------------------------------
@REM Begin all REM lines with '@' in case MAVEN_BATCH_ECHO is 'on'
@echo off
@REM set title of command window
title %0
@REM enable echoing by setting MAVEN_BATCH_ECHO to 'on'
@if "%MAVEN_BATCH_ECHO%" == "on" echo %MAVEN_BATCH_ECHO%
@REM set %HOME% to equivalent of $HOME
if "%HOME%" == "" (set "HOME=%HOMEDRIVE%%HOMEPATH%")
@REM Execute a user defined script before this one
if not "%MAVEN_SKIP_RC%" == "" goto skipRcPre
@REM check for pre script, once with legacy .bat ending and once with .cmd ending
if exist "%HOME%\mavenrc_pre.bat" call "%HOME%\mavenrc_pre.bat"
if exist "%HOME%\mavenrc_pre.cmd" call "%HOME%\mavenrc_pre.cmd"
:skipRcPre
@setlocal
set ERROR_CODE=0
@REM To isolate internal variables from possible post scripts, we use another setlocal
@setlocal
@REM ==== START VALIDATION ====
if not "%JAVA_HOME%" == "" goto OkJHome
echo.
echo Error: JAVA_HOME not found in your environment. >&2
echo Please set the JAVA_HOME variable in your environment to match the >&2
echo location of your Java installation. >&2
echo.
goto error
:OkJHome
if exist "%JAVA_HOME%\bin\java.exe" goto init
echo.
echo Error: JAVA_HOME is set to an invalid directory. >&2
echo JAVA_HOME = "%JAVA_HOME%" >&2
echo Please set the JAVA_HOME variable in your environment to match the >&2
echo location of your Java installation. >&2
echo.
goto error
@REM ==== END VALIDATION ====
:init
@REM Find the project base dir, i.e. the directory that contains the folder ".mvn".
@REM Fallback to current working directory if not found.
set MAVEN_PROJECTBASEDIR=%MAVEN_BASEDIR%
IF NOT "%MAVEN_PROJECTBASEDIR%"=="" goto endDetectBaseDir
set EXEC_DIR=%CD%
set WDIR=%EXEC_DIR%
:findBaseDir
IF EXIST "%WDIR%"\.mvn goto baseDirFound
cd ..
IF "%WDIR%"=="%CD%" goto baseDirNotFound
set WDIR=%CD%
goto findBaseDir
:baseDirFound
set MAVEN_PROJECTBASEDIR=%WDIR%
cd "%EXEC_DIR%"
goto endDetectBaseDir
:baseDirNotFound
set MAVEN_PROJECTBASEDIR=%EXEC_DIR%
cd "%EXEC_DIR%"
:endDetectBaseDir
IF NOT EXIST "%MAVEN_PROJECTBASEDIR%\.mvn\jvm.config" goto endReadAdditionalConfig
@setlocal EnableExtensions EnableDelayedExpansion
for /F "usebackq delims=" %%a in ("%MAVEN_PROJECTBASEDIR%\.mvn\jvm.config") do set JVM_CONFIG_MAVEN_PROPS=!JVM_CONFIG_MAVEN_PROPS! %%a
@endlocal & set JVM_CONFIG_MAVEN_PROPS=%JVM_CONFIG_MAVEN_PROPS%
:endReadAdditionalConfig
SET MAVEN_JAVA_EXE="%JAVA_HOME%\bin\java.exe"
set WRAPPER_JAR="%MAVEN_PROJECTBASEDIR%\.mvn\wrapper\maven-wrapper.jar"
set WRAPPER_LAUNCHER=org.apache.maven.wrapper.MavenWrapperMain
set DOWNLOAD_URL="https://repo.maven.apache.org/maven2/io/takari/maven-wrapper/0.5.6/maven-wrapper-0.5.6.jar"
FOR /F "tokens=1,2 delims==" %%A IN ("%MAVEN_PROJECTBASEDIR%\.mvn\wrapper\maven-wrapper.properties") DO (
IF "%%A"=="wrapperUrl" SET DOWNLOAD_URL=%%B
)
@REM Extension to allow automatically downloading the maven-wrapper.jar from Maven-central
@REM This allows using the maven wrapper in projects that prohibit checking in binary data.
if exist %WRAPPER_JAR% (
if "%MVNW_VERBOSE%" == "true" (
echo Found %WRAPPER_JAR%
)
) else (
if not "%MVNW_REPOURL%" == "" (
SET DOWNLOAD_URL="%MVNW_REPOURL%/io/takari/maven-wrapper/0.5.6/maven-wrapper-0.5.6.jar"
)
if "%MVNW_VERBOSE%" == "true" (
echo Couldn't find %WRAPPER_JAR%, downloading it ...
echo Downloading from: %DOWNLOAD_URL%
)
powershell -Command "&{"^
"$webclient = new-object System.Net.WebClient;"^
"if (-not ([string]::IsNullOrEmpty('%MVNW_USERNAME%') -and [string]::IsNullOrEmpty('%MVNW_PASSWORD%'))) {"^
"$webclient.Credentials = new-object System.Net.NetworkCredential('%MVNW_USERNAME%', '%MVNW_PASSWORD%');"^
"}"^
"[Net.ServicePointManager]::SecurityProtocol = [Net.SecurityProtocolType]::Tls12; $webclient.DownloadFile('%DOWNLOAD_URL%', '%WRAPPER_JAR%')"^
"}"
if "%MVNW_VERBOSE%" == "true" (
echo Finished downloading %WRAPPER_JAR%
)
)
@REM End of extension
@REM Provide a "standardized" way to retrieve the CLI args that will
@REM work with both Windows and non-Windows executions.
set MAVEN_CMD_LINE_ARGS=%*
%MAVEN_JAVA_EXE% %JVM_CONFIG_MAVEN_PROPS% %MAVEN_OPTS% %MAVEN_DEBUG_OPTS% -classpath %WRAPPER_JAR% "-Dmaven.multiModuleProjectDirectory=%MAVEN_PROJECTBASEDIR%" %WRAPPER_LAUNCHER% %MAVEN_CONFIG% %*
if ERRORLEVEL 1 goto error
goto end
:error
set ERROR_CODE=1
:end
@endlocal & set ERROR_CODE=%ERROR_CODE%
if not "%MAVEN_SKIP_RC%" == "" goto skipRcPost
@REM check for post script, once with legacy .bat ending and once with .cmd ending
if exist "%HOME%\mavenrc_post.bat" call "%HOME%\mavenrc_post.bat"
if exist "%HOME%\mavenrc_post.cmd" call "%HOME%\mavenrc_post.cmd"
:skipRcPost
@REM pause the script if MAVEN_BATCH_PAUSE is set to 'on'
if "%MAVEN_BATCH_PAUSE%" == "on" pause
if "%MAVEN_TERMINATE_CMD%" == "on" exit %ERROR_CODE%
exit /B %ERROR_CODE%
deploy/paddleOcrSpringBoot/paddleOcrSpringBoot.iml 0 → 100644
浏览文件 @ 7e4aee47
<?xml version="1.0" encoding="UTF-8"?>
<module org.jetbrains.idea.maven.project.MavenProjectsManager.isMavenModule="true" type="JAVA_MODULE" version="4">
<component name="FacetManager">
<facet type="Spring" name="Spring">
<configuration />
</facet>
<facet type="web" name="Web">
<configuration>
<webroots />
<sourceRoots>
<root url="file://$MODULE_DIR$/src/main/java" />
<root url="file://$MODULE_DIR$/src/main/resources" />
</sourceRoots>
</configuration>
</facet>
</component>
<component name="NewModuleRootManager" LANGUAGE_LEVEL="JDK_1_8">
<output url="file://$MODULE_DIR$/target/classes" />
<output-test url="file://$MODULE_DIR$/target/test-classes" />
<content url="file://$MODULE_DIR$">
<sourceFolder url="file://$MODULE_DIR$/src/main/java" isTestSource="false" />
<sourceFolder url="file://$MODULE_DIR$/src/main/resources" type="java-resource" />
<sourceFolder url="file://$MODULE_DIR$/src/test/java" isTestSource="true" />
<excludeFolder url="file://$MODULE_DIR$/target" />
</content>
<orderEntry type="jdk" jdkName="1.8" jdkType="JavaSDK" />
<orderEntry type="sourceFolder" forTests="false" />
<orderEntry type="library" name="Maven: org.springframework.boot:spring-boot-starter-web:2.3.4.RELEASE" level="project" />
<orderEntry type="library" name="Maven: org.springframework.boot:spring-boot-starter:2.3.4.RELEASE" level="project" />
<orderEntry type="library" name="Maven: org.springframework.boot:spring-boot:2.3.4.RELEASE" level="project" />
<orderEntry type="library" name="Maven: org.springframework.boot:spring-boot-autoconfigure:2.3.4.RELEASE" level="project" />
<orderEntry type="library" name="Maven: org.springframework.boot:spring-boot-starter-logging:2.3.4.RELEASE" level="project" />
<orderEntry type="library" name="Maven: ch.qos.logback:logback-classic:1.2.3" level="project" />
<orderEntry type="library" name="Maven: ch.qos.logback:logback-core:1.2.3" level="project" />
<orderEntry type="library" name="Maven: org.apache.logging.log4j:log4j-to-slf4j:2.13.3" level="project" />
<orderEntry type="library" name="Maven: org.apache.logging.log4j:log4j-api:2.13.3" level="project" />
<orderEntry type="library" name="Maven: org.slf4j:jul-to-slf4j:1.7.30" level="project" />
<orderEntry type="library" name="Maven: jakarta.annotation:jakarta.annotation-api:1.3.5" level="project" />
<orderEntry type="library" name="Maven: org.yaml:snakeyaml:1.26" level="project" />
<orderEntry type="library" name="Maven: org.springframework.boot:spring-boot-starter-json:2.3.4.RELEASE" level="project" />
<orderEntry type="library" name="Maven: com.fasterxml.jackson.core:jackson-databind:2.11.2" level="project" />
<orderEntry type="library" name="Maven: com.fasterxml.jackson.core:jackson-annotations:2.11.2" level="project" />
<orderEntry type="library" name="Maven: com.fasterxml.jackson.core:jackson-core:2.11.2" level="project" />
<orderEntry type="library" name="Maven: com.fasterxml.jackson.datatype:jackson-datatype-jdk8:2.11.2" level="project" />
<orderEntry type="library" name="Maven: com.fasterxml.jackson.datatype:jackson-datatype-jsr310:2.11.2" level="project" />
<orderEntry type="library" name="Maven: com.fasterxml.jackson.module:jackson-module-parameter-names:2.11.2" level="project" />
<orderEntry type="library" name="Maven: org.springframework.boot:spring-boot-starter-tomcat:2.3.4.RELEASE" level="project" />
<orderEntry type="library" name="Maven: org.apache.tomcat.embed:tomcat-embed-core:9.0.38" level="project" />
<orderEntry type="library" name="Maven: org.glassfish:jakarta.el:3.0.3" level="project" />
<orderEntry type="library" name="Maven: org.apache.tomcat.embed:tomcat-embed-websocket:9.0.38" level="project" />
<orderEntry type="library" name="Maven: org.springframework:spring-web:5.2.9.RELEASE" level="project" />
<orderEntry type="library" name="Maven: org.springframework:spring-beans:5.2.9.RELEASE" level="project" />
<orderEntry type="library" name="Maven: org.springframework:spring-webmvc:5.2.9.RELEASE" level="project" />
<orderEntry type="library" name="Maven: org.springframework:spring-aop:5.2.9.RELEASE" level="project" />
<orderEntry type="library" name="Maven: org.springframework:spring-context:5.2.9.RELEASE" level="project" />
<orderEntry type="library" name="Maven: org.springframework:spring-expression:5.2.9.RELEASE" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: org.springframework.boot:spring-boot-starter-test:2.3.4.RELEASE" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: org.springframework.boot:spring-boot-test:2.3.4.RELEASE" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: org.springframework.boot:spring-boot-test-autoconfigure:2.3.4.RELEASE" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: com.jayway.jsonpath:json-path:2.4.0" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: net.minidev:json-smart:2.3" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: net.minidev:accessors-smart:1.2" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: org.ow2.asm:asm:5.0.4" level="project" />
<orderEntry type="library" name="Maven: org.slf4j:slf4j-api:1.7.30" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: jakarta.xml.bind:jakarta.xml.bind-api:2.3.3" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: jakarta.activation:jakarta.activation-api:1.2.2" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: org.assertj:assertj-core:3.16.1" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: org.hamcrest:hamcrest:2.2" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: org.junit.jupiter:junit-jupiter:5.6.2" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: org.junit.jupiter:junit-jupiter-api:5.6.2" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: org.apiguardian:apiguardian-api:1.1.0" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: org.opentest4j:opentest4j:1.2.0" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: org.junit.platform:junit-platform-commons:1.6.2" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: org.junit.jupiter:junit-jupiter-params:5.6.2" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: org.junit.jupiter:junit-jupiter-engine:5.6.2" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: org.junit.platform:junit-platform-engine:1.6.2" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: org.mockito:mockito-core:3.3.3" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: net.bytebuddy:byte-buddy:1.10.14" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: net.bytebuddy:byte-buddy-agent:1.10.14" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: org.objenesis:objenesis:2.6" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: org.mockito:mockito-junit-jupiter:3.3.3" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: org.skyscreamer:jsonassert:1.5.0" level="project" />
<orderEntry type="library" name="Maven: org.springframework:spring-core:5.2.9.RELEASE" level="project" />
<orderEntry type="library" name="Maven: org.springframework:spring-jcl:5.2.9.RELEASE" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: org.springframework:spring-test:5.2.9.RELEASE" level="project" />
<orderEntry type="library" scope="TEST" name="Maven: org.xmlunit:xmlunit-core:2.7.0" level="project" />
<orderEntry type="library" name="Maven: org.apache.httpcomponents:httpclient:4.5.12" level="project" />
<orderEntry type="library" name="Maven: org.apache.httpcomponents:httpcore:4.4.13" level="project" />
<orderEntry type="library" name="Maven: commons-codec:commons-codec:1.14" level="project" />
<orderEntry type="library" name="Maven: com.vaadin.external.google:android-json:0.0.20131108.vaadin1" level="project" />
<orderEntry type="library" name="Maven: com.google.code.gson:gson:2.8.6" level="project" />
</component>
</module>
\ No newline at end of file
deploy/paddleOcrSpringBoot/pom.xml 0 → 100644
浏览文件 @ 7e4aee47
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<parent>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-parent</artifactId>
<version>2.3.4.RELEASE</version>
<relativePath/> <!-- lookup parent from repository -->
</parent>
<groupId>com.paddelOcr_springBoot</groupId>
<artifactId>demo</artifactId>
<version>0.0.1-SNAPSHOT</version>
<name>demo</name>
<description>Demo project for Spring Boot</description>
<properties>
<java.version>1.8</java.version>
</properties>
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
<scope>test</scope>
<exclusions>
<exclusion>
<groupId>org.junit.vintage</groupId>
<artifactId>junit-vintage-engine</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<!--引入RestTemplate-->
<groupId>org.apache.httpcomponents</groupId>
<artifactId>httpclient</artifactId>
</dependency>
<dependency>
<groupId>com.vaadin.external.google</groupId>
<artifactId>android-json</artifactId>
<version>0.0.20131108.vaadin1</version>
<scope>compile</scope>
</dependency>
<dependency>
<groupId>com.google.code.gson</groupId>
<artifactId>gson</artifactId>
<version>2.8.6</version>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-maven-plugin</artifactId>
</plugin>
</plugins>
</build>
</project>
deploy/paddleOcrSpringBoot/readme.md 0 → 100644
浏览文件 @ 7e4aee47
简体中文
- 使用本教程前请先基于PaddleHub Serving的部署.
# 基于PaddleHub Serving的Java SpringBoot调用
paddleOcrSpringBoot服务部署目录下包括全部SpringBoot代码。目录结构如下：
```
deploy/paddleOcrSpringBoot/
└─ src 代码文件
└─ main 主函数代码
└─ java\com\paddelocr_springboot\demo
└─ DemoApplication.java SpringBoot启动代码
└─ Controller
└─ OCR.java 控制器代码
└─ test 测试代码
```
- Hub Serving启动后的APi端口如下：
`http://[ip_address]:[port]/predict/[module_name]`
## 返回结果格式说明
返回结果为列表（list），列表中的每一项为词典（dict），词典一共可能包含3种字段，信息如下：
|字段名称|数据类型|意义|
|-|-|-|
|text|str|文本内容|
|confidence|float| 文本识别置信度|
|text_region|list|文本位置坐标|
不同模块返回的字段不同，如，文本识别服务模块返回结果不含`text_region`字段，具体信息如下：
|字段名/模块名|ocr_det|ocr_rec|ocr_system|
|-|-|-|-|
|text||✔|✔|
|confidence||✔|✔|
|text_region|✔||✔|
\ No newline at end of file
deploy/paddleOcrSpringBoot/src/main/java/com/paddelocr_springboot/demo/Controller/OCR.java 0 → 100644
浏览文件 @ 7e4aee47
package com.paddelocr_springboot.demo.Controller;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStream;
import com.google.gson.Gson;
import com.google.gson.JsonElement;
import com.google.gson.JsonObject;
import com.google.gson.JsonParser;
import org.json.JSONObject;
import org.springframework.util.LinkedMultiValueMap;
import org.springframework.util.ResourceUtils;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RestController;
import org.springframework.http.*;
import org.springframework.util.MultiValueMap;
import org.springframework.web.client.RestTemplate;
import sun.misc.BASE64Encoder;
import java.util.Objects;
@RestController
public class OCR {
@RequestMapping("/")
public ResponseEntity<String> hi(){
//创建请求头
HttpHeaders headers = new HttpHeaders();
//设置请求头格式
headers.setContentType(MediaType.APPLICATION_JSON);
//读入静态资源文件1.png
InputStream imagePath = this.getClass().getResourceAsStream("/1.png");
//构建请求参数
MultiValueMap<String, String> map= new LinkedMultiValueMap<String, String>();
//添加请求参数images，并将Base64编码的图片传入
map.add("images", ImageToBase64(imagePath));
//构建请求
HttpEntity<MultiValueMap<String, String>> request = new HttpEntity<MultiValueMap<String, String>>(map, headers);
RestTemplate restTemplate = new RestTemplate();
//发送请求
ResponseEntity<String> response = restTemplate.postForEntity("http://127.0.0.1:8866/predict/ocr_system", request, String.class);
//打印请求返回值
return response;
}
private String ImageToBase64(InputStream imgPath) {
byte[] data = null;
// 读取图片字节数组
try {
InputStream in = imgPath;
System.out.println(imgPath);
data = new byte[in.available()];
in.read(data);
in.close();
} catch (IOException e) {
e.printStackTrace();
}
// 对字节数组Base64编码
BASE64Encoder encoder = new BASE64Encoder();
// 返回Base64编码过的字节数组字符串
//System.out.println("图片转换Base64:" + encoder.encode(Objects.requireNonNull(data)));
return encoder.encode(Objects.requireNonNull(data));
}
}
deploy/paddleOcrSpringBoot/src/main/java/com/paddelocr_springboot/demo/DemoApplication.java 0 → 100644
浏览文件 @ 7e4aee47
package com.paddelocr_springboot.demo;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
@SpringBootApplication
public class DemoApplication {
public static void main(String[] args) {
SpringApplication.run(DemoApplication.class, args);
}
}
deploy/paddleOcrSpringBoot/src/main/resources/application.properties 0 → 100644
浏览文件 @ 7e4aee47
server.port=8081
http_pool.max_total: 200
http_pool.default_max_per_route: 100
http_pool.connect_timeout: 5000
http_pool.connection_request_timeout: 1000
http_pool.socket_timeout: 65000
http_pool.validate_after_inactivity: 2000
\ No newline at end of file
deploy/paddleOcrSpringBoot/src/test/java/com/paddelocr_springboot/demo/DemoApplicationTests.java 0 → 100644
浏览文件 @ 7e4aee47
package com.paddelocr_springboot.demo;
import org.junit.jupiter.api.Test;
import org.springframework.boot.test.context.SpringBootTest;
@SpringBootTest
class DemoApplicationTests {
@Test
void contextLoads() {
}
}
deploy/pdserving/rec_web_server.py → deploy/pdserving/clas_local_server.py
浏览文件 @ 7e4aee47
......@@ -13,65 +13,115 @@
# limitations under the License.
from paddle_serving_client import Client
from paddle_serving_app.reader import OCRReader
import cv2
import sys
import numpy as np
import os
from paddle_serving_client import Client
from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
from paddle_serving_app.reader import Div, Normalize, Transpose
from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
if sys.argv[1] == 'gpu':
from paddle_serving_server_gpu.web_service import WebService
elif sys.argv[1] == 'cpu':
from paddle_serving_server.web_service import WebService
import time
import re
import base64
from tools.infer.predict_cls import TextClassifier
from params import read_params
global_args = read_params()
if global_args.use_gpu:
from paddle_serving_server_gpu.web_service import WebService
else:
from paddle_serving_server.web_service import WebService
class TextClassifierHelper(TextClassifier):
def __init__(self, args):
self.cls_image_shape = [int(v) for v in args.cls_image_shape.split(",")]
self.cls_batch_num = args.rec_batch_num
self.label_list = args.label_list
self.cls_thresh = args.cls_thresh
self.fetch = [
"save_infer_model/scale_0.tmp_0", "save_infer_model/scale_1.tmp_0"
]
def preprocess(self, img_list):
args = {}
img_num = len(img_list)
args["img_list"] = img_list
# Calculate the aspect ratio of all text bars
width_list = []
for img in img_list:
width_list.append(img.shape[1] / float(img.shape[0]))
# Sorting can speed up the cls process
indices = np.argsort(np.array(width_list))
args["indices"] = indices
cls_res = [['', 0.0]] * img_num
batch_num = self.cls_batch_num
predict_time = 0
beg_img_no, end_img_no = 0, img_num
norm_img_batch = []
max_wh_ratio = 0
for ino in range(beg_img_no, end_img_no):
h, w = img_list[indices[ino]].shape[0:2]
wh_ratio = w * 1.0 / h
max_wh_ratio = max(max_wh_ratio, wh_ratio)
for ino in range(beg_img_no, end_img_no):
norm_img = self.resize_norm_img(img_list[indices[ino]])
norm_img = norm_img[np.newaxis, :]
norm_img_batch.append(norm_img)
norm_img_batch = np.concatenate(norm_img_batch)
feed = {"image": norm_img_batch.copy()}
return feed, self.fetch, args
def postprocess(self, outputs, args):
prob_out = outputs[0]
label_out = outputs[1]
indices = args["indices"]
img_list = args["img_list"]
cls_res = [['', 0.0]] * len(label_out)
if len(label_out.shape) != 1:
prob_out, label_out = label_out, prob_out
for rno in range(len(label_out)):
label_idx = label_out[rno]
score = prob_out[rno][label_idx]
label = self.label_list[label_idx]
cls_res[indices[rno]] = [label, score]
if '180' in label and score > self.cls_thresh:
img_list[indices[rno]] = cv2.rotate(img_list[indices[rno]], 1)
return img_list, cls_res
class OCRService(WebService):
def init_rec(self):
self.ocr_reader = OCRReader()
self.text_classifier = TextClassifierHelper(global_args)
def preprocess(self, feed=[], fetch=[]):
# TODO: to handle batch rec images
img_list = []
for feed_data in feed:
data = base64.b64decode(feed_data["image"].encode('utf8'))
data = np.fromstring(data, np.uint8)
im = cv2.imdecode(data, cv2.IMREAD_COLOR)
img_list.append(im)
feed_list = []
max_wh_ratio = 0
for i, boximg in enumerate(img_list):
h, w = boximg.shape[0:2]
wh_ratio = w * 1.0 / h
max_wh_ratio = max(max_wh_ratio, wh_ratio)
for img in img_list:
norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
feed = {"image": norm_img}
feed_list.append(feed)
fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
return feed_list, fetch
feed, fetch, self.tmp_args = self.text_classifier.preprocess(img_list)
return feed, fetch
def postprocess(self, feed={}, fetch=[], fetch_map=None):
rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
res_lst = []
for res in rec_res:
res_lst.append(res[0])
res = {"res": res_lst}
outputs = [fetch_map[x] for x in self.text_classifier.fetch]
for x in fetch_map.keys():
if ".lod" in x:
self.tmp_args[x] = fetch_map[x]
_, rec_res = self.text_classifier.postprocess(outputs, self.tmp_args)
res = {
"pred_text": [x[0] for x in rec_res],
"score": [str(x[1]) for x in rec_res]
}
return res
ocr_service = OCRService(name="ocr")
ocr_service.load_model_config("ocr_rec_model")
ocr_service.init_rec()
if sys.argv[1] == 'gpu':
ocr_service.set_gpus("0")
ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
elif sys.argv[1] == 'cpu':
ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
ocr_service.run_rpc_service()
ocr_service.run_web_service()
if __name__ == "__main__":
ocr_service = OCRService(name="ocr")
ocr_service.load_model_config(global_args.cls_model_dir)
ocr_service.init_rec()
if global_args.use_gpu:
ocr_service.prepare_server(
workdir="workdir", port=9292, device="gpu", gpuid=0)
else:
ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
ocr_service.run_debugger_service()
ocr_service.run_web_service()
deploy/pdserving/clas_rpc_server.py 0 → 100644
浏览文件 @ 7e4aee47
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle_serving_client import Client
import cv2
import sys
import numpy as np
import os
import time
import re
import base64
from tools.infer.predict_cls import TextClassifier
from params import read_params
global_args = read_params()
if global_args.use_gpu:
from paddle_serving_server_gpu.web_service import WebService
else:
from paddle_serving_server.web_service import WebService
class TextClassifierHelper(TextClassifier):
def __init__(self, args):
self.cls_image_shape = [int(v) for v in args.cls_image_shape.split(",")]
self.cls_batch_num = args.rec_batch_num
self.label_list = args.label_list
self.cls_thresh = args.cls_thresh
self.fetch = [
"save_infer_model/scale_0.tmp_0", "save_infer_model/scale_1.tmp_0"
]
def preprocess(self, img_list):
args = {}
img_num = len(img_list)
args["img_list"] = img_list
# Calculate the aspect ratio of all text bars
width_list = []
for img in img_list:
width_list.append(img.shape[1] / float(img.shape[0]))
# Sorting can speed up the cls process
indices = np.argsort(np.array(width_list))
args["indices"] = indices
cls_res = [['', 0.0]] * img_num
batch_num = self.cls_batch_num
predict_time = 0
beg_img_no, end_img_no = 0, img_num
norm_img_batch = []
max_wh_ratio = 0
for ino in range(beg_img_no, end_img_no):
h, w = img_list[indices[ino]].shape[0:2]
wh_ratio = w * 1.0 / h
max_wh_ratio = max(max_wh_ratio, wh_ratio)
for ino in range(beg_img_no, end_img_no):
norm_img = self.resize_norm_img(img_list[indices[ino]])
norm_img = norm_img[np.newaxis, :]
norm_img_batch.append(norm_img)
norm_img_batch = np.concatenate(norm_img_batch)
if img_num > 1:
feed = [{
"image": norm_img_batch[x]
} for x in range(norm_img_batch.shape[0])]
else:
feed = {"image": norm_img_batch[0]}
return feed, self.fetch, args
def postprocess(self, outputs, args):
prob_out = outputs[0]
label_out = outputs[1]
indices = args["indices"]
img_list = args["img_list"]
cls_res = [['', 0.0]] * len(label_out)
if len(label_out.shape) != 1:
prob_out, label_out = label_out, prob_out
for rno in range(len(label_out)):
label_idx = label_out[rno]
score = prob_out[rno][label_idx]
label = self.label_list[label_idx]
cls_res[indices[rno]] = [label, score]
if '180' in label and score > self.cls_thresh:
img_list[indices[rno]] = cv2.rotate(img_list[indices[rno]], 1)
return img_list, cls_res
class OCRService(WebService):
def init_rec(self):
self.text_classifier = TextClassifierHelper(global_args)
def preprocess(self, feed=[], fetch=[]):
# TODO: to handle batch rec images
img_list = []
for feed_data in feed:
data = base64.b64decode(feed_data["image"].encode('utf8'))
data = np.fromstring(data, np.uint8)
im = cv2.imdecode(data, cv2.IMREAD_COLOR)
img_list.append(im)
feed, fetch, self.tmp_args = self.text_classifier.preprocess(img_list)
return feed, fetch
def postprocess(self, feed={}, fetch=[], fetch_map=None):
outputs = [fetch_map[x] for x in self.text_classifier.fetch]
for x in fetch_map.keys():
if ".lod" in x:
self.tmp_args[x] = fetch_map[x]
_, rec_res = self.text_classifier.postprocess(outputs, self.tmp_args)
res = {
"direction": [x[0] for x in rec_res],
"score": [str(x[1]) for x in rec_res]
}
return res
if __name__ == "__main__":
ocr_service = OCRService(name="ocr")
ocr_service.load_model_config(global_args.cls_model_dir)
ocr_service.init_rec()
if global_args.use_gpu:
ocr_service.prepare_server(
workdir="workdir", port=9292, device="gpu", gpuid=0)
else:
ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
ocr_service.run_rpc_service()
ocr_service.run_web_service()
deploy/pdserving/clas_web_client.py 0 → 100644
浏览文件 @ 7e4aee47
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# -*- coding: utf-8 -*-
import requests
import json
import cv2
import base64
import os, sys
import time
def cv2_to_base64(image):
#data = cv2.imencode('.jpg', image)[1]
return base64.b64encode(image).decode(
'utf8') #data.tostring()).decode('utf8')
headers = {"Content-type": "application/json"}
url = "http://127.0.0.1:9292/ocr/prediction"
test_img_dir = "../../doc/imgs_words/ch/"
for img_file in os.listdir(test_img_dir):
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
data = {"feed": [{"image": image}], "fetch": ["res"]}
r = requests.post(url=url, headers=headers, data=json.dumps(data))
print(r.json())
deploy/pdserving/det_local_server.py
浏览文件 @ 7e4aee47
......@@ -17,63 +17,91 @@ import cv2
import sys
import numpy as np
import os
from paddle_serving_client import Client
from paddle_serving_app.reader import Sequential, ResizeByFactor
from paddle_serving_app.reader import Div, Normalize, Transpose
from paddle_serving_app.reader import DBPostProcess, FilterBoxes
if sys.argv[1] == 'gpu':
from paddle_serving_server_gpu.web_service import WebService
elif sys.argv[1] == 'cpu':
from paddle_serving_server.web_service import WebService
import time
import re
import base64
from tools.infer.predict_det import TextDetector
from params import read_params
global_args = read_params()
if global_args.use_gpu:
from paddle_serving_server_gpu.web_service import WebService
else:
from paddle_serving_server.web_service import WebService
class TextDetectorHelper(TextDetector):
def __init__(self, args):
super(TextDetectorHelper, self).__init__(args)
if self.det_algorithm == "SAST":
self.fetch = [
"bn_f_border4.output.tmp_2", "bn_f_tco4.output.tmp_2",
"bn_f_tvo4.output.tmp_2", "sigmoid_0.tmp_0"
]
elif self.det_algorithm == "EAST":
self.fetch = ["sigmoid_0.tmp_0", "tmp_2"]
elif self.det_algorithm == "DB":
self.fetch = ["save_infer_model/scale_0.tmp_0"]
def preprocess(self, img):
img = img.copy()
im, ratio_list = self.preprocess_op(img)
if im is None:
return None, 0
return {
"image": im.copy()
}, self.fetch, {
"ratio_list": [ratio_list],
"ori_im": img
}
def postprocess(self, outputs, args):
outs_dict = {}
if self.det_algorithm == "EAST":
outs_dict['f_geo'] = outputs[0]
outs_dict['f_score'] = outputs[1]
elif self.det_algorithm == 'SAST':
outs_dict['f_border'] = outputs[0]
outs_dict['f_score'] = outputs[1]
outs_dict['f_tco'] = outputs[2]
outs_dict['f_tvo'] = outputs[3]
else:
outs_dict['maps'] = outputs[0]
dt_boxes_list = self.postprocess_op(outs_dict, args["ratio_list"])
dt_boxes = dt_boxes_list[0]
if self.det_algorithm == "SAST" and self.det_sast_polygon:
dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes,
args["ori_im"].shape)
else:
dt_boxes = self.filter_tag_det_res(dt_boxes, args["ori_im"].shape)
return dt_boxes
class OCRService(WebService):
class DetService(WebService):
def init_det(self):
self.det_preprocess = Sequential([
ResizeByFactor(32, 960), Div(255),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
(2, 0, 1))
])
self.filter_func = FilterBoxes(10, 10)
self.post_func = DBPostProcess({
"thresh": 0.3,
"box_thresh": 0.5,
"max_candidates": 1000,
"unclip_ratio": 1.5,
"min_size": 3
})
self.text_detector = TextDetectorHelper(global_args)
def preprocess(self, feed=[], fetch=[]):
data = base64.b64decode(feed[0]["image"].encode('utf8'))
data = np.fromstring(data, np.uint8)
im = cv2.imdecode(data, cv2.IMREAD_COLOR)
self.ori_h, self.ori_w, _ = im.shape
det_img = self.det_preprocess(im)
_, self.new_h, self.new_w = det_img.shape
return {"image": det_img[np.newaxis, :].copy()}, ["concat_1.tmp_0"]
feed, fetch, self.tmp_args = self.text_detector.preprocess(im)
return feed, fetch
def postprocess(self, feed={}, fetch=[], fetch_map=None):
det_out = fetch_map["concat_1.tmp_0"]
ratio_list = [
float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w
]
dt_boxes_list = self.post_func(det_out, [ratio_list])
dt_boxes = self.filter_func(dt_boxes_list[0], [self.ori_h, self.ori_w])
return {"dt_boxes": dt_boxes.tolist()}
outputs = [fetch_map[x] for x in fetch]
res = self.text_detector.postprocess(outputs, self.tmp_args)
return {"boxes": res.tolist()}
ocr_service = OCRService(name="ocr")
ocr_service.load_model_config("ocr_det_model")
ocr_service.init_det()
if sys.argv[1] == 'gpu':
ocr_service.set_gpus("0")
ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
ocr_service.run_debugger_service(gpu=True)
elif sys.argv[1] == 'cpu':
ocr_service.prepare_server(workdir="workdir", port=9292)
if __name__ == "__main__":
ocr_service = DetService(name="ocr")
ocr_service.load_model_config(global_args.det_model_dir)
ocr_service.init_det()
if global_args.use_gpu:
ocr_service.prepare_server(
workdir="workdir", port=9292, device="gpu", gpuid=0)
else:
ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
ocr_service.run_debugger_service()
ocr_service.init_det()
ocr_service.run_web_service()
ocr_service.run_web_service()
deploy/pdserving/det_web_server.py → deploy/pdserving/det_rpc_server.py
浏览文件 @ 7e4aee47
......@@ -17,62 +17,90 @@ import cv2
import sys
import numpy as np
import os
from paddle_serving_client import Client
from paddle_serving_app.reader import Sequential, ResizeByFactor
from paddle_serving_app.reader import Div, Normalize, Transpose
from paddle_serving_app.reader import DBPostProcess, FilterBoxes
if sys.argv[1] == 'gpu':
from paddle_serving_server_gpu.web_service import WebService
elif sys.argv[1] == 'cpu':
from paddle_serving_server.web_service import WebService
import time
import re
import base64
from tools.infer.predict_det import TextDetector
from params import read_params
global_args = read_params()
if global_args.use_gpu:
from paddle_serving_server_gpu.web_service import WebService
else:
from paddle_serving_server.web_service import WebService
class TextDetectorHelper(TextDetector):
def __init__(self, args):
super(TextDetectorHelper, self).__init__(args)
if self.det_algorithm == "SAST":
self.fetch = [
"bn_f_border4.output.tmp_2", "bn_f_tco4.output.tmp_2",
"bn_f_tvo4.output.tmp_2", "sigmoid_0.tmp_0"
]
elif self.det_algorithm == "EAST":
self.fetch = ["sigmoid_0.tmp_0", "tmp_2"]
elif self.det_algorithm == "DB":
self.fetch = ["save_infer_model/scale_0.tmp_0"]
def preprocess(self, img):
im, ratio_list = self.preprocess_op(img)
if im is None:
return None, 0
return {
"image": im[0]
}, self.fetch, {
"ratio_list": [ratio_list],
"ori_im": img
}
def postprocess(self, outputs, args):
outs_dict = {}
if self.det_algorithm == "EAST":
outs_dict['f_geo'] = outputs[0]
outs_dict['f_score'] = outputs[1]
elif self.det_algorithm == 'SAST':
outs_dict['f_border'] = outputs[0]
outs_dict['f_score'] = outputs[1]
outs_dict['f_tco'] = outputs[2]
outs_dict['f_tvo'] = outputs[3]
else:
outs_dict['maps'] = outputs[0]
dt_boxes_list = self.postprocess_op(outs_dict, args["ratio_list"])
dt_boxes = dt_boxes_list[0]
if self.det_algorithm == "SAST" and self.det_sast_polygon:
dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes,
args["ori_im"].shape)
else:
dt_boxes = self.filter_tag_det_res(dt_boxes, args["ori_im"].shape)
return dt_boxes
class OCRService(WebService):
class DetService(WebService):
def init_det(self):
self.det_preprocess = Sequential([
ResizeByFactor(32, 960), Div(255),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
(2, 0, 1))
])
self.filter_func = FilterBoxes(10, 10)
self.post_func = DBPostProcess({
"thresh": 0.3,
"box_thresh": 0.5,
"max_candidates": 1000,
"unclip_ratio": 1.5,
"min_size": 3
})
self.text_detector = TextDetectorHelper(global_args)
def preprocess(self, feed=[], fetch=[]):
data = base64.b64decode(feed[0]["image"].encode('utf8'))
data = np.fromstring(data, np.uint8)
im = cv2.imdecode(data, cv2.IMREAD_COLOR)
self.ori_h, self.ori_w, _ = im.shape
det_img = self.det_preprocess(im)
_, self.new_h, self.new_w = det_img.shape
print(det_img)
return {"image": det_img}, ["concat_1.tmp_0"]
feed, fetch, self.tmp_args = self.text_detector.preprocess(im)
return feed, fetch
def postprocess(self, feed={}, fetch=[], fetch_map=None):
det_out = fetch_map["concat_1.tmp_0"]
ratio_list = [
float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w
]
dt_boxes_list = self.post_func(det_out, [ratio_list])
dt_boxes = self.filter_func(dt_boxes_list[0], [self.ori_h, self.ori_w])
return {"dt_boxes": dt_boxes.tolist()}
outputs = [fetch_map[x] for x in fetch]
res = self.text_detector.postprocess(outputs, self.tmp_args)
return {"boxes": res.tolist()}
ocr_service = OCRService(name="ocr")
ocr_service.load_model_config("ocr_det_model")
if sys.argv[1] == 'gpu':
ocr_service.set_gpus("0")
ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
elif sys.argv[1] == 'cpu':
ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
ocr_service.init_det()
ocr_service.run_rpc_service()
ocr_service.run_web_service()
if __name__ == "__main__":
ocr_service = DetService(name="ocr")
ocr_service.load_model_config(global_args.det_model_dir)
ocr_service.init_det()
if global_args.use_gpu:
ocr_service.prepare_server(
workdir="workdir", port=9292, device="gpu", gpuid=0)
else:
ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
ocr_service.run_rpc_service()
ocr_service.run_web_service()
deploy/pdserving/det_web_client.py 0 → 100644
浏览文件 @ 7e4aee47
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# -*- coding: utf-8 -*-
import requests
import json
import cv2
import base64
import os, sys
import time
def cv2_to_base64(image):
#data = cv2.imencode('.jpg', image)[1]
return base64.b64encode(image).decode(
'utf8') #data.tostring()).decode('utf8')
headers = {"Content-type": "application/json"}
url = "http://127.0.0.1:9292/ocr/prediction"
test_img_dir = "../../doc/imgs/"
for img_file in os.listdir(test_img_dir):
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
data = {"feed": [{"image": image}], "fetch": ["res"]}
r = requests.post(url=url, headers=headers, data=json.dumps(data))
rjson = r.json()
print(rjson)
deploy/pdserving/ocr_local_server.py
浏览文件 @ 7e4aee47
......@@ -13,102 +13,108 @@
# limitations under the License.
from paddle_serving_client import Client
from paddle_serving_app.reader import OCRReader
import cv2
import sys
import numpy as np
import os
from paddle_serving_client import Client
from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
from paddle_serving_app.reader import Div, Normalize, Transpose
from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
if sys.argv[1] == 'gpu':
from paddle_serving_server_gpu.web_service import WebService
elif sys.argv[1] == 'cpu':
from paddle_serving_server.web_service import WebService
from paddle_serving_app.local_predict import Debugger
import time
import re
import base64
from clas_local_server import TextClassifierHelper
from det_local_server import TextDetectorHelper
from rec_local_server import TextRecognizerHelper
from tools.infer.predict_system import TextSystem, sorted_boxes
from paddle_serving_app.local_predict import Debugger
import copy
from params import read_params
global_args = read_params()
class OCRService(WebService):
def init_det_debugger(self, det_model_config):
self.det_preprocess = Sequential([
ResizeByFactor(32, 960), Div(255),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
(2, 0, 1))
])
if global_args.use_gpu:
from paddle_serving_server_gpu.web_service import WebService
else:
from paddle_serving_server.web_service import WebService
class TextSystemHelper(TextSystem):
def __init__(self, args):
self.text_detector = TextDetectorHelper(args)
self.text_recognizer = TextRecognizerHelper(args)
self.use_angle_cls = args.use_angle_cls
if self.use_angle_cls:
self.clas_client = Debugger()
self.clas_client.load_model_config(
global_args.cls_model_dir, gpu=True, profile=False)
self.text_classifier = TextClassifierHelper(args)
self.det_client = Debugger()
if sys.argv[1] == 'gpu':
self.det_client.load_model_config(
det_model_config, gpu=True, profile=False)
elif sys.argv[1] == 'cpu':
self.det_client.load_model_config(
det_model_config, gpu=False, profile=False)
self.ocr_reader = OCRReader()
self.det_client.load_model_config(
global_args.det_model_dir, gpu=True, profile=False)
self.fetch = ["save_infer_model/scale_0.tmp_0", "save_infer_model/scale_1.tmp_0"]
def preprocess(self, img):
feed, fetch, self.tmp_args = self.text_detector.preprocess(img)
fetch_map = self.det_client.predict(feed, fetch)
outputs = [fetch_map[x] for x in fetch]
dt_boxes = self.text_detector.postprocess(outputs, self.tmp_args)
if dt_boxes is None:
return None, None
img_crop_list = []
dt_boxes = sorted_boxes(dt_boxes)
for bno in range(len(dt_boxes)):
tmp_box = copy.deepcopy(dt_boxes[bno])
img_crop = self.get_rotate_crop_image(img, tmp_box)
img_crop_list.append(img_crop)
if self.use_angle_cls:
feed, fetch, self.tmp_args = self.text_classifier.preprocess(
img_crop_list)
fetch_map = self.clas_client.predict(feed, fetch)
outputs = [fetch_map[x] for x in self.text_classifier.fetch]
for x in fetch_map.keys():
if ".lod" in x:
self.tmp_args[x] = fetch_map[x]
img_crop_list, _ = self.text_classifier.postprocess(outputs,
self.tmp_args)
feed, fetch, self.tmp_args = self.text_recognizer.preprocess(
img_crop_list)
return feed, self.fetch, self.tmp_args
def postprocess(self, outputs, args):
return self.text_recognizer.postprocess(outputs, args)
class OCRService(WebService):
def init_rec(self):
self.text_system = TextSystemHelper(global_args)
def preprocess(self, feed=[], fetch=[]):
# TODO: to handle batch rec images
data = base64.b64decode(feed[0]["image"].encode('utf8'))
data = np.fromstring(data, np.uint8)
im = cv2.imdecode(data, cv2.IMREAD_COLOR)
ori_h, ori_w, _ = im.shape
det_img = self.det_preprocess(im)
_, new_h, new_w = det_img.shape
det_img = det_img[np.newaxis, :]
det_img = det_img.copy()
det_out = self.det_client.predict(
feed={"image": det_img}, fetch=["concat_1.tmp_0"])
filter_func = FilterBoxes(10, 10)
post_func = DBPostProcess({
"thresh": 0.3,
"box_thresh": 0.5,
"max_candidates": 1000,
"unclip_ratio": 1.5,
"min_size": 3
})
sorted_boxes = SortedBoxes()
ratio_list = [float(new_h) / ori_h, float(new_w) / ori_w]
dt_boxes_list = post_func(det_out["concat_1.tmp_0"], [ratio_list])
dt_boxes = filter_func(dt_boxes_list[0], [ori_h, ori_w])
dt_boxes = sorted_boxes(dt_boxes)
get_rotate_crop_image = GetRotateCropImage()
img_list = []
max_wh_ratio = 0
for i, dtbox in enumerate(dt_boxes):
boximg = get_rotate_crop_image(im, dt_boxes[i])
img_list.append(boximg)
h, w = boximg.shape[0:2]
wh_ratio = w * 1.0 / h
max_wh_ratio = max(max_wh_ratio, wh_ratio)
if len(img_list) == 0:
return [], []
_, w, h = self.ocr_reader.resize_norm_img(img_list[0],
max_wh_ratio).shape
imgs = np.zeros((len(img_list), 3, w, h)).astype('float32')
for id, img in enumerate(img_list):
norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
imgs[id] = norm_img
feed = {"image": imgs.copy()}
fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
feed, fetch, self.tmp_args = self.text_system.preprocess(im)
return feed, fetch
def postprocess(self, feed={}, fetch=[], fetch_map=None):
rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
res_lst = []
for res in rec_res:
res_lst.append(res[0])
res = {"res": res_lst}
outputs = [fetch_map[x] for x in self.text_system.fetch]
for x in fetch_map.keys():
if ".lod" in x:
self.tmp_args[x] = fetch_map[x]
rec_res = self.text_system.postprocess(outputs, self.tmp_args)
res = {
"pred_text": [x[0] for x in rec_res],
"score": [str(x[1]) for x in rec_res]
}
return res
ocr_service = OCRService(name="ocr")
ocr_service.load_model_config("ocr_rec_model")
ocr_service.init_det_debugger(det_model_config="ocr_det_model")
if sys.argv[1] == 'gpu':
ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
ocr_service.run_debugger_service(gpu=True)
elif sys.argv[1] == 'cpu':
ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
if __name__ == "__main__":
ocr_service = OCRService(name="ocr")
ocr_service.load_model_config(global_args.rec_model_dir)
ocr_service.init_rec()
if global_args.use_gpu:
ocr_service.prepare_server(
workdir="workdir", port=9292, device="gpu", gpuid=0)
else:
ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
ocr_service.run_debugger_service()
ocr_service.run_web_service()
ocr_service.run_web_service()
deploy/pdserving/ocr_rpc_server.py 0 → 100644
浏览文件 @ 7e4aee47
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle_serving_client import Client
import cv2
import sys
import numpy as np
import os
import time
import re
import base64
from clas_rpc_server import TextClassifierHelper
from det_rpc_server import TextDetectorHelper
from rec_rpc_server import TextRecognizerHelper
from tools.infer.predict_system import TextSystem, sorted_boxes
import copy
from params import read_params
global_args = read_params()
if global_args.use_gpu:
from paddle_serving_server_gpu.web_service import WebService
else:
from paddle_serving_server.web_service import WebService
class TextSystemHelper(TextSystem):
def __init__(self, args):
self.text_detector = TextDetectorHelper(args)
self.text_recognizer = TextRecognizerHelper(args)
self.use_angle_cls = args.use_angle_cls
if self.use_angle_cls:
self.clas_client = Client()
self.clas_client.load_client_config(
"cls_infer_client/serving_client_conf.prototxt")
self.clas_client.connect(["127.0.0.1:9294"])
self.text_classifier = TextClassifierHelper(args)
self.det_client = Client()
self.det_client.load_client_config(
"det_infer_client/serving_client_conf.prototxt")
self.det_client.connect(["127.0.0.1:9293"])
self.fetch = ["save_infer_model/scale_0.tmp_0", "save_infer_model/scale_1.tmp_0"]
def preprocess(self, img):
feed, fetch, self.tmp_args = self.text_detector.preprocess(img)
fetch_map = self.det_client.predict(feed, fetch)
outputs = [fetch_map[x] for x in fetch]
dt_boxes = self.text_detector.postprocess(outputs, self.tmp_args)
print(dt_boxes)
if dt_boxes is None:
return None, None
img_crop_list = []
dt_boxes = sorted_boxes(dt_boxes)
for bno in range(len(dt_boxes)):
tmp_box = copy.deepcopy(dt_boxes[bno])
img_crop = self.get_rotate_crop_image(img, tmp_box)
img_crop_list.append(img_crop)
if self.use_angle_cls:
feed, fetch, self.tmp_args = self.text_classifier.preprocess(
img_crop_list)
fetch_map = self.clas_client.predict(feed, fetch)
print(fetch_map)
outputs = [fetch_map[x] for x in self.text_classifier.fetch]
for x in fetch_map.keys():
if ".lod" in x:
self.tmp_args[x] = fetch_map[x]
img_crop_list, _ = self.text_classifier.postprocess(outputs,
self.tmp_args)
feed, fetch, self.tmp_args = self.text_recognizer.preprocess(
img_crop_list)
return feed, self.fetch, self.tmp_args
def postprocess(self, outputs, args):
return self.text_recognizer.postprocess(outputs, args)
class OCRService(WebService):
def init_rec(self):
self.text_system = TextSystemHelper(global_args)
def preprocess(self, feed=[], fetch=[]):
# TODO: to handle batch rec images
data = base64.b64decode(feed[0]["image"].encode('utf8'))
data = np.fromstring(data, np.uint8)
im = cv2.imdecode(data, cv2.IMREAD_COLOR)
feed, fetch, self.tmp_args = self.text_system.preprocess(im)
return feed, fetch
def postprocess(self, feed={}, fetch=[], fetch_map=None):
outputs = [fetch_map[x] for x in self.text_system.fetch]
for x in fetch_map.keys():
if ".lod" in x:
self.tmp_args[x] = fetch_map[x]
rec_res = self.text_system.postprocess(outputs, self.tmp_args)
res = {
"pred_text": [x[0] for x in rec_res],
"score": [str(x[1]) for x in rec_res]
}
return res
if __name__ == "__main__":
ocr_service = OCRService(name="ocr")
ocr_service.load_model_config(global_args.rec_model_dir)
ocr_service.init_rec()
if global_args.use_gpu:
ocr_service.prepare_server(
workdir="workdir", port=9292, device="gpu", gpuid=0)
else:
ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
ocr_service.run_rpc_service()
ocr_service.run_web_service()
deploy/pdserving/ocr_web_client.py
浏览文件 @ 7e4aee47
......@@ -20,11 +20,13 @@ import base64
import os, sys
import time
def cv2_to_base64(image):
#data = cv2.imencode('.jpg', image)[1]
return base64.b64encode(image).decode(
'utf8') #data.tostring()).decode('utf8')
headers = {"Content-type": "application/json"}
url = "http://127.0.0.1:9292/ocr/prediction"
test_img_dir = "../../doc/imgs/"
......@@ -34,4 +36,5 @@ for img_file in os.listdir(test_img_dir):
image = cv2_to_base64(image_data1)
data = {"feed": [{"image": image}], "fetch": ["res"]}
r = requests.post(url=url, headers=headers, data=json.dumps(data))
print(r.json())
rjson = r.json()
print(rjson)
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle_serving_client import Client
from paddle_serving_app.reader import OCRReader
import cv2
import sys
import numpy as np
import os
from paddle_serving_client import Client
from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
from paddle_serving_app.reader import Div, Normalize, Transpose
from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
if sys.argv[1] == 'gpu':
from paddle_serving_server_gpu.web_service import WebService
elif sys.argv[1] == 'cpu':
from paddle_serving_server.web_service import WebService
import time
import re
import base64
class OCRService(WebService):
def init_det_client(self, det_port, det_client_config):
self.det_preprocess = Sequential([
ResizeByFactor(32, 960), Div(255),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
(2, 0, 1))
])
self.det_client = Client()
self.det_client.load_client_config(det_client_config)
self.det_client.connect(["127.0.0.1:{}".format(det_port)])
self.ocr_reader = OCRReader()
def preprocess(self, feed=[], fetch=[]):
data = base64.b64decode(feed[0]["image"].encode('utf8'))
data = np.fromstring(data, np.uint8)
im = cv2.imdecode(data, cv2.IMREAD_COLOR)
ori_h, ori_w, _ = im.shape
det_img = self.det_preprocess(im)
det_out = self.det_client.predict(
feed={"image": det_img}, fetch=["concat_1.tmp_0"])
_, new_h, new_w = det_img.shape
filter_func = FilterBoxes(10, 10)
post_func = DBPostProcess({
"thresh": 0.3,
"box_thresh": 0.5,
"max_candidates": 1000,
"unclip_ratio": 1.5,
"min_size": 3
})
sorted_boxes = SortedBoxes()
ratio_list = [float(new_h) / ori_h, float(new_w) / ori_w]
dt_boxes_list = post_func(det_out["concat_1.tmp_0"], [ratio_list])
dt_boxes = filter_func(dt_boxes_list[0], [ori_h, ori_w])
dt_boxes = sorted_boxes(dt_boxes)
get_rotate_crop_image = GetRotateCropImage()
feed_list = []
img_list = []
max_wh_ratio = 0
for i, dtbox in enumerate(dt_boxes):
boximg = get_rotate_crop_image(im, dt_boxes[i])
img_list.append(boximg)
h, w = boximg.shape[0:2]
wh_ratio = w * 1.0 / h
max_wh_ratio = max(max_wh_ratio, wh_ratio)
for img in img_list:
norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
feed = {"image": norm_img}
feed_list.append(feed)
fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
return feed_list, fetch
def postprocess(self, feed={}, fetch=[], fetch_map=None):
rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
res_lst = []
for res in rec_res:
res_lst.append(res[0])
res = {"res": res_lst}
return res
ocr_service = OCRService(name="ocr")
ocr_service.load_model_config("ocr_rec_model")
if sys.argv[1] == 'gpu':
ocr_service.set_gpus("0")
ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
elif sys.argv[1] == 'cpu':
ocr_service.prepare_server(workdir="workdir", port=9292)
ocr_service.init_det_client(
det_port=9293,
det_client_config="ocr_det_client/serving_client_conf.prototxt")
ocr_service.run_rpc_service()
ocr_service.run_web_service()
deploy/pdserving/params.py 0 → 100644
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# -*- coding:utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
class Config(object):
pass
def read_params():
cfg = Config()
#use gpu
cfg.use_gpu = False
cfg.use_pdserving = True
#params for text detector
cfg.det_algorithm = "DB"
cfg.det_model_dir = "./det_infer_server/"
cfg.det_max_side_len = 960
#DB parmas
cfg.det_db_thresh =0.3
cfg.det_db_box_thresh =0.5
cfg.det_db_unclip_ratio =2.0
#EAST parmas
cfg.det_east_score_thresh = 0.8
cfg.det_east_cover_thresh = 0.1
cfg.det_east_nms_thresh = 0.2
#params for text recognizer
cfg.rec_algorithm = "CRNN"
cfg.rec_model_dir = "./rec_infer_server/"
cfg.rec_image_shape = "3, 32, 320"
cfg.rec_char_type = 'ch'
cfg.rec_batch_num = 30
cfg.max_text_length = 25
cfg.rec_char_dict_path = "./ppocr_keys_v1.txt"
cfg.use_space_char = True
#params for text classifier
cfg.use_angle_cls = True
cfg.cls_model_dir = "./cls_infer_server/"
cfg.cls_image_shape = "3, 48, 192"
cfg.label_list = ['0', '180']
cfg.cls_batch_num = 30
cfg.cls_thresh = 0.9
return cfg
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[English](readme_en.md) | 简体中文
PaddleOCR提供2种服务部署方式：
- 基于PaddleHub Serving的部署：代码路径为"`./deploy/hubserving`"，使用方法参考[文档](../hubserving/readme.md)。
- 基于PaddleServing的部署：代码路径为"`./deploy/pdserving`"，按照本教程使用。
# Paddle Serving 服务部署
本教程将介绍基于[Paddle Serving](https://github.com/PaddlePaddle/Serving)部署PaddleOCR在线预测服务的详细步骤。
## 快速启动服务
### 1. 准备环境
我们先安装Paddle Serving相关组件
我们推荐用户使用GPU来做Paddle Serving的OCR服务部署
**CUDA版本：9.0**
**CUDNN版本：7.0**
**操作系统版本：CentOS 6以上**
**Python版本： 2.7/3.6/3.7**
**Python操作指南：**
```
#CPU/GPU版本选择一个
#GPU版本服务端
python -m pip install paddle_serving_server_gpu
#CPU版本服务端
python -m pip install paddle_serving_server
#客户端和App包使用以下链接（CPU，GPU通用）
python -m pip install paddle_serving_app paddle_serving_client
```
### 2. 模型转换
可以使用`paddle_serving_app`提供的模型，执行下列命令
```
python -m paddle_serving_app.package --get_model ocr_rec
tar -xzvf ocr_rec.tar.gz
python -m paddle_serving_app.package --get_model ocr_det
tar -xzvf ocr_det.tar.gz
```
执行上述命令会下载`db_crnn_mobile`的模型，如果想要下载规模更大的`db_crnn_server`模型，可以在下载预测模型并解压之后。参考[如何从Paddle保存的预测模型转为Paddle Serving格式可部署的模型](https://github.com/PaddlePaddle/Serving/blob/develop/doc/INFERENCE_TO_SERVING_CN.md)。
我们以`ch_rec_r34_vd_crnn`模型作为例子，下载链接在：
```
wget --no-check-certificate https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar
tar xf ch_rec_r34_vd_crnn_infer.tar
```
因此我们按照Serving模型转换教程，运行下列python文件。
```
from paddle_serving_client.io import inference_model_to_serving
inference_model_dir = "ch_rec_r34_vd_crnn"
serving_client_dir = "serving_client_dir"
serving_server_dir = "serving_server_dir"
feed_var_names, fetch_var_names = inference_model_to_serving(
inference_model_dir, serving_client_dir, serving_server_dir, model_filename="model", params_filename="params")
```
最终会在`serving_client_dir`和`serving_server_dir`生成客户端和服务端的模型配置。
### 3. 启动服务
启动服务可以根据实际需求选择启动`标准版`或者`快速版`，两种方式的对比如下表：
|版本|特点|适用场景|
|-|-|-|
|标准版|稳定性高，分布式部署|适用于吞吐量大，需要跨机房部署的情况|
|快速版|部署方便，预测速度快|适用于对预测速度要求高，迭代速度快的场景|
#### 方式1. 启动标准版服务
```
# cpu，gpu启动二选一，以下是cpu启动
python -m paddle_serving_server.serve --model ocr_det_model --port 9293
python ocr_web_server.py cpu
# gpu启动
python -m paddle_serving_server_gpu.serve --model ocr_det_model --port 9293 --gpu_id 0
python ocr_web_server.py gpu
```
#### 方式2. 启动快速版服务
```
# cpu，gpu启动二选一，以下是cpu启动
python ocr_local_server.py cpu
# gpu启动
python ocr_local_server.py gpu
```
## 发送预测请求
```
python ocr_web_client.py
```
## 返回结果格式说明
返回结果是json格式
```
{u'result': {u'res': [u'\u571f\u5730\u6574\u6cbb\u4e0e\u571f\u58e4\u4fee\u590d\u7814\u7a76\u4e2d\u5fc3', u'\u534e\u5357\u519c\u4e1a\u5927\u5b661\u7d20\u56fe']}}
```
我们也可以打印结果json串中`res`字段的每一句话
```
土地整治与土壤修复研究中心
华南农业大学1素图
```
## 自定义修改服务逻辑
在`ocr_web_server.py`或是`ocr_local_server.py`当中的`preprocess`函数里面做了检测服务和识别服务的前处理，`postprocess`函数里面做了识别的后处理服务，可以在相应的函数中做修改。调用了`paddle_serving_app`库提供的常见CV模型的前处理/后处理库。
如果想要单独启动Paddle Serving的检测服务和识别服务，参见下列表格, 执行对应的脚本即可，并且在命令行参数注明用的CPU或是GPU来提供服务。
| 模型 | 标准版 | 快速版 |
| ---- | ----------------- | ------------------- |
| 检测 | det_web_server.py | det_local_server.py |
| 识别 | rec_web_server.py | rec_local_server.py |
更多信息参见[Paddle Serving](https://github.com/PaddlePaddle/Serving)
deploy/pdserving/readme_en.md 已删除 100644 → 0
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English | [简体中文](readme.md)
PaddleOCR provides 2 service deployment methods:
- Based on **PaddleHub Serving**: Code path is "`./deploy/hubserving`". Please refer to the [tutorial](../hubserving/readme_en.md) for usage.
- Based on **PaddleServing**: Code path is "`./deploy/pdserving`". Please follow this tutorial.
# Service deployment based on Paddle Serving
This tutorial will introduce the detail steps of deploying PaddleOCR online prediction service based on [Paddle Serving](https://github.com/PaddlePaddle/Serving).
## Quick start service
### 1. Prepare the environment
Let's first install the relevant components of Paddle Serving. GPU is recommended for service deployment with Paddle Serving.
**Requirements:**
- **CUDA version: 9.0**
- **CUDNN version: 7.0**
- **Operating system version: >= CentOS 6**
- **Python version： 2.7/3.6/3.7**
**Installation：**
```
# install GPU server
python -m pip install paddle_serving_server_gpu
# or, install CPU server
python -m pip install paddle_serving_server
# install client and App package (CPU/GPU)
python -m pip install paddle_serving_app paddle_serving_client
```
### 2. Model transformation
You can directly use converted model provided by `paddle_serving_app` for convenience. Execute the following command to obtain:
```
python -m paddle_serving_app.package --get_model ocr_rec
tar -xzvf ocr_rec.tar.gz
python -m paddle_serving_app.package --get_model ocr_det
tar -xzvf ocr_det.tar.gz
```
Executing the above command will download the `db_crnn_mobile` model, which is in different format with inference model. If you want to use other models for deployment, you can refer to the [tutorial](https://github.com/PaddlePaddle/Serving/blob/develop/doc/INFERENCE_TO_SERVING_CN.md) to convert your inference model to a model which is deployable for Paddle Serving.
We take `ch_rec_r34_vd_crnn` model as example. Download the inference model by executing the following command:
```
wget --no-check-certificate https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar
tar xf ch_rec_r34_vd_crnn_infer.tar
```
Convert the downloaded model by executing the following python script:
```
from paddle_serving_client.io import inference_model_to_serving
inference_model_dir = "ch_rec_r34_vd_crnn"
serving_client_dir = "serving_client_dir"
serving_server_dir = "serving_server_dir"
feed_var_names, fetch_var_names = inference_model_to_serving(
inference_model_dir, serving_client_dir, serving_server_dir, model_filename="model", params_filename="params")
```
Finally, model configuration of client and server will be generated in `serving_client_dir` and `serving_server_dir`.
### 3. Start service
Start the standard version or the fast version service according to your actual needs. The comparison of the two versions is shown in the table below:
|version|characteristics|recommended scenarios|
|-|-|-|
|standard version|High stability, suitable for distributed deployment|Large throughput and cross regional deployment|
|fast version|Easy to deploy and fast to predict|Suitable for scenarios which requires high prediction speed and fast iteration speed|
#### Mode 1. Start the standard mode service
```
# start with CPU
python -m paddle_serving_server.serve --model ocr_det_model --port 9293
python ocr_web_server.py cpu
# or, with GPU
python -m paddle_serving_server_gpu.serve --model ocr_det_model --port 9293 --gpu_id 0
python ocr_web_server.py gpu
```
#### Mode 2. Start the fast mode service
```
# start with CPU
python ocr_local_server.py cpu
# or, with GPU
python ocr_local_server.py gpu
```
## Send prediction requests
```
python ocr_web_client.py
```
## Returned result format
The returned result is a JSON string, eg.
```
{u'result': {u'res': [u'\u571f\u5730\u6574\u6cbb\u4e0e\u571f\u58e4\u4fee\u590d\u7814\u7a76\u4e2d\u5fc3', u'\u534e\u5357\u519c\u4e1a\u5927\u5b661\u7d20\u56fe']}}
```
You can also print the readable result in `res`:
```
土地整治与土壤修复研究中心
华南农业大学1素图
```
## User defined service module modification
The pre-processing and post-processing process, can be found in the `preprocess` and `postprocess` function in `ocr_web_server.py` or `ocr_local_server.py`. The pre-processing/post-processing library for common CV models provided by `paddle_serving_app` is called.
You can modify the corresponding code as actual needs.
If you only want to start the detection service or the recognition service, execute the corresponding script reffering to the following table. Indicate the CPU or GPU is used in the start command parameters.
| task | standard | fast |
| ---- | ----------------- | ------------------- |
| detection | det_web_server.py | det_local_server.py |
| recognition | rec_web_server.py | rec_local_server.py |
More info can be found in [Paddle Serving](https://github.com/PaddlePaddle/Serving).
deploy/pdserving/rec_local_server.py
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......@@ -13,67 +13,161 @@
# limitations under the License.
from paddle_serving_client import Client
from paddle_serving_app.reader import OCRReader
import cv2
import sys
import numpy as np
import os
from paddle_serving_client import Client
from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
from paddle_serving_app.reader import Div, Normalize, Transpose
from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
if sys.argv[1] == 'gpu':
from paddle_serving_server_gpu.web_service import WebService
elif sys.argv[1] == 'cpu':
from paddle_serving_server.web_service import WebService
import time
import re
import base64
from tools.infer.predict_rec import TextRecognizer
from params import read_params
global_args = read_params()
if global_args.use_gpu:
from paddle_serving_server_gpu.web_service import WebService
else:
from paddle_serving_server.web_service import WebService
class TextRecognizerHelper(TextRecognizer):
def __init__(self, args):
super(TextRecognizerHelper, self).__init__(args)
if self.loss_type == "ctc":
self.fetch = ["save_infer_model/scale_0.tmp_0", "save_infer_model/scale_1.tmp_0"]
def preprocess(self, img_list):
img_num = len(img_list)
args = {}
# Calculate the aspect ratio of all text bars
width_list = []
for img in img_list:
width_list.append(img.shape[1] / float(img.shape[0]))
indices = np.argsort(np.array(width_list))
args["indices"] = indices
predict_time = 0
beg_img_no = 0
end_img_no = img_num
norm_img_batch = []
max_wh_ratio = 0
for ino in range(beg_img_no, end_img_no):
h, w = img_list[indices[ino]].shape[0:2]
wh_ratio = w * 1.0 / h
max_wh_ratio = max(max_wh_ratio, wh_ratio)
for ino in range(beg_img_no, end_img_no):
if self.loss_type != "srn":
norm_img = self.resize_norm_img(img_list[indices[ino]],
max_wh_ratio)
norm_img = norm_img[np.newaxis, :]
norm_img_batch.append(norm_img)
else:
norm_img = self.process_image_srn(img_list[indices[ino]],
self.rec_image_shape, 8, 25,
self.char_ops)
encoder_word_pos_list = []
gsrm_word_pos_list = []
gsrm_slf_attn_bias1_list = []
gsrm_slf_attn_bias2_list = []
encoder_word_pos_list.append(norm_img[1])
gsrm_word_pos_list.append(norm_img[2])
gsrm_slf_attn_bias1_list.append(norm_img[3])
gsrm_slf_attn_bias2_list.append(norm_img[4])
norm_img_batch.append(norm_img[0])
norm_img_batch = np.concatenate(norm_img_batch, axis=0).copy()
feed = {"image": norm_img_batch.copy()}
return feed, self.fetch, args
def postprocess(self, outputs, args):
if self.loss_type == "ctc":
rec_idx_batch = outputs[0]
predict_batch = outputs[1]
rec_idx_lod = args["save_infer_model/scale_0.tmp_0.lod"]
predict_lod = args["save_infer_model/scale_1.tmp_0.lod"]
indices = args["indices"]
rec_res = [['', 0.0]] * (len(rec_idx_lod) - 1)
for rno in range(len(rec_idx_lod) - 1):
beg = rec_idx_lod[rno]
end = rec_idx_lod[rno + 1]
rec_idx_tmp = rec_idx_batch[beg:end, 0]
preds_text = self.char_ops.decode(rec_idx_tmp)
beg = predict_lod[rno]
end = predict_lod[rno + 1]
probs = predict_batch[beg:end, :]
ind = np.argmax(probs, axis=1)
blank = probs.shape[1]
valid_ind = np.where(ind != (blank - 1))[0]
if len(valid_ind) == 0:
continue
score = np.mean(probs[valid_ind, ind[valid_ind]])
rec_res[indices[rno]] = [preds_text, score]
elif self.loss_type == 'srn':
char_num = self.char_ops.get_char_num()
preds = rec_idx_batch.reshape(-1)
elapse = time.time() - starttime
predict_time += elapse
total_preds = preds.copy()
for ino in range(int(len(rec_idx_batch) / self.text_len)):
preds = total_preds[ino * self.text_len:(ino + 1) *
self.text_len]
ind = np.argmax(probs, axis=1)
valid_ind = np.where(preds != int(char_num - 1))[0]
if len(valid_ind) == 0:
continue
score = np.mean(probs[valid_ind, ind[valid_ind]])
preds = preds[:valid_ind[-1] + 1]
preds_text = self.char_ops.decode(preds)
rec_res[indices[ino]] = [preds_text, score]
else:
for rno in range(len(rec_idx_batch)):
end_pos = np.where(rec_idx_batch[rno, :] == 1)[0]
if len(end_pos) <= 1:
preds = rec_idx_batch[rno, 1:]
score = np.mean(predict_batch[rno, 1:])
else:
preds = rec_idx_batch[rno, 1:end_pos[1]]
score = np.mean(predict_batch[rno, 1:end_pos[1]])
preds_text = self.char_ops.decode(preds)
rec_res[indices[rno]] = [preds_text, score]
return rec_res
class OCRService(WebService):
def init_rec(self):
self.ocr_reader = OCRReader()
self.text_recognizer = TextRecognizerHelper(global_args)
def preprocess(self, feed=[], fetch=[]):
# TODO: to handle batch rec images
img_list = []
for feed_data in feed:
data = base64.b64decode(feed_data["image"].encode('utf8'))
data = np.fromstring(data, np.uint8)
im = cv2.imdecode(data, cv2.IMREAD_COLOR)
img_list.append(im)
max_wh_ratio = 0
for i, boximg in enumerate(img_list):
h, w = boximg.shape[0:2]
wh_ratio = w * 1.0 / h
max_wh_ratio = max(max_wh_ratio, wh_ratio)
_, w, h = self.ocr_reader.resize_norm_img(img_list[0],
max_wh_ratio).shape
imgs = np.zeros((len(img_list), 3, w, h)).astype('float32')
for i, img in enumerate(img_list):
norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
imgs[i] = norm_img
feed = {"image": imgs.copy()}
fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
feed, fetch, self.tmp_args = self.text_recognizer.preprocess(img_list)
return feed, fetch
def postprocess(self, feed={}, fetch=[], fetch_map=None):
rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
res_lst = []
for res in rec_res:
res_lst.append(res[0])
res = {"res": res_lst}
outputs = [fetch_map[x] for x in self.text_recognizer.fetch]
for x in fetch_map.keys():
if ".lod" in x:
self.tmp_args[x] = fetch_map[x]
rec_res = self.text_recognizer.postprocess(outputs, self.tmp_args)
res = {
"pred_text": [x[0] for x in rec_res],
"score": [str(x[1]) for x in rec_res]
}
return res
ocr_service = OCRService(name="ocr")
ocr_service.load_model_config("ocr_rec_model")
ocr_service.init_rec()
if sys.argv[1] == 'gpu':
ocr_service.set_gpus("0")
ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
ocr_service.run_debugger_service(gpu=True)
elif sys.argv[1] == 'cpu':
ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
if __name__ == "__main__":
ocr_service = OCRService(name="ocr")
ocr_service.load_model_config(global_args.rec_model_dir)
ocr_service.init_rec()
if global_args.use_gpu:
ocr_service.prepare_server(
workdir="workdir", port=9292, device="gpu", gpuid=0)
else:
ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
ocr_service.run_debugger_service()
ocr_service.run_web_service()
ocr_service.run_web_service()
deploy/pdserving/rec_rpc_server.py 0 → 100644
浏览文件 @ 7e4aee47
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle_serving_client import Client
import cv2
import sys
import numpy as np
import os
import time
import re
import base64
from tools.infer.predict_rec import TextRecognizer
from params import read_params
global_args = read_params()
if global_args.use_gpu:
from paddle_serving_server_gpu.web_service import WebService
else:
from paddle_serving_server.web_service import WebService
class TextRecognizerHelper(TextRecognizer):
def __init__(self, args):
super(TextRecognizerHelper, self).__init__(args)
if self.loss_type == "ctc":
self.fetch = ["save_infer_model/scale_0.tmp_0", "save_infer_model/scale_1.tmp_0"]
def preprocess(self, img_list):
img_num = len(img_list)
args = {}
# Calculate the aspect ratio of all text bars
width_list = []
for img in img_list:
width_list.append(img.shape[1] / float(img.shape[0]))
indices = np.argsort(np.array(width_list))
args["indices"] = indices
predict_time = 0
beg_img_no = 0
end_img_no = img_num
norm_img_batch = []
max_wh_ratio = 0
for ino in range(beg_img_no, end_img_no):
h, w = img_list[indices[ino]].shape[0:2]
wh_ratio = w * 1.0 / h
max_wh_ratio = max(max_wh_ratio, wh_ratio)
for ino in range(beg_img_no, end_img_no):
if self.loss_type != "srn":
norm_img = self.resize_norm_img(img_list[indices[ino]],
max_wh_ratio)
norm_img = norm_img[np.newaxis, :]
norm_img_batch.append(norm_img)
else:
norm_img = self.process_image_srn(img_list[indices[ino]],
self.rec_image_shape, 8, 25,
self.char_ops)
encoder_word_pos_list = []
gsrm_word_pos_list = []
gsrm_slf_attn_bias1_list = []
gsrm_slf_attn_bias2_list = []
encoder_word_pos_list.append(norm_img[1])
gsrm_word_pos_list.append(norm_img[2])
gsrm_slf_attn_bias1_list.append(norm_img[3])
gsrm_slf_attn_bias2_list.append(norm_img[4])
norm_img_batch.append(norm_img[0])
norm_img_batch = np.concatenate(norm_img_batch, axis=0)
if img_num > 1:
feed = [{
"image": norm_img_batch[x]
} for x in range(norm_img_batch.shape[0])]
else:
feed = {"image": norm_img_batch[0]}
return feed, self.fetch, args
def postprocess(self, outputs, args):
if self.loss_type == "ctc":
rec_idx_batch = outputs[0]
predict_batch = outputs[1]
rec_idx_lod = args["save_infer_model/scale_0.tmp_0.lod"]
predict_lod = args["save_infer_model/scale_1.tmp_0.lod"]
indices = args["indices"]
rec_res = [['', 0.0]] * (len(rec_idx_lod) - 1)
for rno in range(len(rec_idx_lod) - 1):
beg = rec_idx_lod[rno]
end = rec_idx_lod[rno + 1]
rec_idx_tmp = rec_idx_batch[beg:end, 0]
preds_text = self.char_ops.decode(rec_idx_tmp)
beg = predict_lod[rno]
end = predict_lod[rno + 1]
probs = predict_batch[beg:end, :]
ind = np.argmax(probs, axis=1)
blank = probs.shape[1]
valid_ind = np.where(ind != (blank - 1))[0]
if len(valid_ind) == 0:
continue
score = np.mean(probs[valid_ind, ind[valid_ind]])
rec_res[indices[rno]] = [preds_text, score]
elif self.loss_type == 'srn':
char_num = self.char_ops.get_char_num()
preds = rec_idx_batch.reshape(-1)
elapse = time.time() - starttime
predict_time += elapse
total_preds = preds.copy()
for ino in range(int(len(rec_idx_batch) / self.text_len)):
preds = total_preds[ino * self.text_len:(ino + 1) *
self.text_len]
ind = np.argmax(probs, axis=1)
valid_ind = np.where(preds != int(char_num - 1))[0]
if len(valid_ind) == 0:
continue
score = np.mean(probs[valid_ind, ind[valid_ind]])
preds = preds[:valid_ind[-1] + 1]
preds_text = self.char_ops.decode(preds)
rec_res[indices[ino]] = [preds_text, score]
else:
for rno in range(len(rec_idx_batch)):
end_pos = np.where(rec_idx_batch[rno, :] == 1)[0]
if len(end_pos) <= 1:
preds = rec_idx_batch[rno, 1:]
score = np.mean(predict_batch[rno, 1:])
else:
preds = rec_idx_batch[rno, 1:end_pos[1]]
score = np.mean(predict_batch[rno, 1:end_pos[1]])
preds_text = self.char_ops.decode(preds)
rec_res[indices[rno]] = [preds_text, score]
return rec_res
class OCRService(WebService):
def init_rec(self):
self.text_recognizer = TextRecognizerHelper(global_args)
def preprocess(self, feed=[], fetch=[]):
# TODO: to handle batch rec images
img_list = []
for feed_data in feed:
data = base64.b64decode(feed_data["image"].encode('utf8'))
data = np.fromstring(data, np.uint8)
im = cv2.imdecode(data, cv2.IMREAD_COLOR)
img_list.append(im)
feed, fetch, self.tmp_args = self.text_recognizer.preprocess(img_list)
return feed, fetch
def postprocess(self, feed={}, fetch=[], fetch_map=None):
outputs = [fetch_map[x] for x in self.text_recognizer.fetch]
for x in fetch_map.keys():
if ".lod" in x:
self.tmp_args[x] = fetch_map[x]
rec_res = self.text_recognizer.postprocess(outputs, self.tmp_args)
res = {
"pred_text": [x[0] for x in rec_res],
"score": [str(x[1]) for x in rec_res]
}
return res
if __name__ == "__main__":
ocr_service = OCRService(name="ocr")
ocr_service.load_model_config(global_args.rec_model_dir)
ocr_service.init_rec()
if global_args.use_gpu:
ocr_service.prepare_server(
workdir="workdir", port=9292, device="gpu", gpuid=0)
else:
ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
ocr_service.run_rpc_service()
ocr_service.run_web_service()
deploy/pdserving/rec_web_client.py 0 → 100644
浏览文件 @ 7e4aee47
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# -*- coding: utf-8 -*-
import requests
import json
import cv2
import base64
import os, sys
import time
def cv2_to_base64(image):
#data = cv2.imencode('.jpg', image)[1]
return base64.b64encode(image).decode(
'utf8') #data.tostring()).decode('utf8')
headers = {"Content-type": "application/json"}
url = "http://127.0.0.1:9292/ocr/prediction"
test_img_dir = "../../doc/imgs_words/ch/"
for img_file in os.listdir(test_img_dir):
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
image_data1 = file.read()
image = cv2_to_base64(image_data1)
data = {"feed": [{"image": image}], "fetch": ["res"]}
r = requests.post(url=url, headers=headers, data=json.dumps(data))
print(r.json())
deploy/slim/prune/README.md 0 → 100644
浏览文件 @ 7e4aee47
## 介绍
复杂的模型有利于提高模型的性能，但也导致模型中存在一定冗余，模型裁剪通过移出网络模型中的子模型来减少这种冗余，达到减少模型计算复杂度，提高模型推理性能的目的。
本教程将介绍如何使用飞桨模型压缩库PaddleSlim做PaddleOCR模型的压缩。
[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)集成了模型剪枝、量化（包括量化训练和离线量化）、蒸馏和神经网络搜索等多种业界常用且领先的模型压缩功能，如果您感兴趣，可以关注并了解。
在开始本教程之前，建议先了解：
1. [PaddleOCR模型的训练方法](../../../doc/doc_ch/quickstart.md)
2. [分类模型裁剪教程](https://paddlepaddle.github.io/PaddleSlim/tutorials/pruning_tutorial/)
3. [PaddleSlim 裁剪压缩API](https://paddlepaddle.github.io/PaddleSlim/api/prune_api/)
## 快速开始
模型裁剪主要包括五个步骤：
1. 安装 PaddleSlim
2. 准备训练好的模型
3. 敏感度分析、训练
4. 模型裁剪训练
5. 导出模型、预测部署
### 1. 安装PaddleSlim
```bash
git clone https://github.com/PaddlePaddle/PaddleSlim.git
cd Paddleslim
python setup.py install
```
### 2. 获取预训练模型
模型裁剪需要加载事先训练好的模型，PaddleOCR也提供了一系列模型[../../../doc/doc_ch/models_list.md]，开发者可根据需要自行选择模型或使用自己的模型。
### 3. 敏感度分析训练
加载预训练模型后，通过对现有模型的每个网络层进行敏感度分析，得到敏感度文件：sensitivities_0.data，可以通过PaddleSlim提供的[接口](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/prune/sensitive.py#L221)加载文件，获得各网络层在不同裁剪比例下的精度损失。从而了解各网络层冗余度，决定每个网络层的裁剪比例。
敏感度分析的具体细节见：[敏感度分析](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/tutorials/image_classification_sensitivity_analysis_tutorial.md)
敏感度文件内容格式：
sensitivities_0.data(Dict){
'layer_weight_name_0': sens_of_each_ratio(Dict){'pruning_ratio_0': acc_loss, 'pruning_ratio_1': acc_loss}
'layer_weight_name_1': sens_of_each_ratio(Dict){'pruning_ratio_0': acc_loss, 'pruning_ratio_1': acc_loss}
}
例子：
{
'conv10_expand_weights': {0.1: 0.006509952684312718, 0.2: 0.01827734339798862, 0.3: 0.014528405644659832, 0.6: 0.06536008804270439, 0.8: 0.11798612250664964, 0.7: 0.12391408417493704, 0.4: 0.030615754498018757, 0.5: 0.047105205602406594}
'conv10_linear_weights': {0.1: 0.05113190831455035, 0.2: 0.07705573833558801, 0.3: 0.12096721757739311, 0.6: 0.5135061352930738, 0.8: 0.7908166677143281, 0.7: 0.7272187676899062, 0.4: 0.1819252083008504, 0.5: 0.3728054727792405}
}
加载敏感度文件后会返回一个字典，字典中的keys为网络模型参数模型的名字，values为一个字典，里面保存了相应网络层的裁剪敏感度信息。例如在例子中，conv10_expand_weights所对应的网络层在裁掉10%的卷积核后模型性能相较原模型会下降0.65%，详细信息可见[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/algo/algo.md#2-%E5%8D%B7%E7%A7%AF%E6%A0%B8%E5%89%AA%E8%A3%81%E5%8E%9F%E7%90%86)
进入PaddleOCR根目录，通过以下命令对模型进行敏感度分析训练：
```bash
python deploy/slim/prune/sensitivity_anal.py -c configs/det/det_mv3_db_v1.1.yml -o Global.pretrain_weights="your trained model" Global.test_batch_size_per_card=1
```
### 4. 模型裁剪训练
裁剪时通过之前的敏感度分析文件决定每个网络层的裁剪比例。在具体实现时，为了尽可能多的保留从图像中提取的低阶特征，我们跳过了backbone中靠近输入的4个卷积层。同样，为了减少由于裁剪导致的模型性能损失，我们通过之前敏感度分析所获得的敏感度表，人工挑选出了一些冗余较少，对裁剪较为敏感的[网络层](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/slim/prune/pruning_and_finetune.py#L41)（指在较低的裁剪比例下就导致很高性能损失的网络层），并在之后的裁剪过程中选择避开这些网络层。裁剪过后finetune的过程沿用OCR检测模型原始的训练策略。
```bash
python deploy/slim/prune/pruning_and_finetune.py -c configs/det/det_mv3_db_v1.1.yml -o Global.pretrain_weights=./deploy/slim/prune/pretrain_models/det_mv3_db/best_accuracy Global.test_batch_size_per_card=1
```
通过对比可以发现，经过裁剪训练保存的模型更小。
### 5. 导出模型、预测部署
在得到裁剪训练保存的模型后，我们可以将其导出为inference_model：
```bash
python deploy/slim/prune/export_prune_model.py -c configs/det/det_mv3_db_v1.1.yml -o Global.pretrain_weights=./output/det_db/best_accuracy Global.test_batch_size_per_card=1 Global.save_inference_dir=inference_model
```
inference model的预测和部署参考：
1. [inference model python端预测](../../../doc/doc_ch/inference.md)
2. [inference model C++预测](../../cpp_infer/readme.md)
3. [inference model在移动端部署](../../lite/readme.md)
deploy/slim/prune/README_ch.md 已删除 100644 → 0
浏览文件 @ 5e1b7a18
\> 运行示例前请先安装develop版本PaddleSlim
# 模型裁剪压缩教程
压缩结果：
<table>
<thead>
<tr>
<th>序号</th>
<th>任务</th>
<th>模型</th>
<th>压缩策略<sup><a href="#quant">[3]</a><a href="#prune">[4]</a><sup></th>
<th>精度(自建中文数据集)</th>
<th>耗时<sup><a href="#latency">[1]</a></sup>(ms)</th>
<th>整体耗时<sup><a href="#rec">[2]</a></sup>(ms)</th>
<th>加速比</th>
<th>整体模型大小(M)</th>
<th>压缩比例</th>
<th>下载链接</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="2">0</td>
<td>检测</td>
<td>MobileNetV3_DB</td>
<td>无</td>
<td>61.7</td>
<td>224</td>
<td rowspan="2">375</td>
<td rowspan="2">-</td>
<td rowspan="2">8.6</td>
<td rowspan="2">-</td>
<td></td>
</tr>
<tr>
<td>识别</td>
<td>MobileNetV3_CRNN</td>
<td>无</td>
<td>62.0</td>
<td>9.52</td>
<td></td>
</tr>
<tr>
<td rowspan="2">1</td>
<td>检测</td>
<td>SlimTextDet</td>
<td>PACT量化训练</td>
<td>62.1</td>
<td>195</td>
<td rowspan="2">348</td>
<td rowspan="2">8%</td>
<td rowspan="2">2.8</td>
<td rowspan="2">67.82%</td>
<td></td>
</tr>
<tr>
<td>识别</td>
<td>SlimTextRec</td>
<td>PACT量化训练</td>
<td>61.48</td>
<td>8.6</td>
<td></td>
</tr>
<tr>
<td rowspan="2">2</td>
<td>检测</td>
<td>SlimTextDet_quat_pruning</td>
<td>剪裁+PACT量化训练</td>
<td>60.86</td>
<td>142</td>
<td rowspan="2">288</td>
<td rowspan="2">30%</td>
<td rowspan="2">2.8</td>
<td rowspan="2">67.82%</td>
<td></td>
</tr>
<tr>
<td>识别</td>
<td>SlimTextRec</td>
<td>PACT量化训练</td>
<td>61.48</td>
<td>8.6</td>
<td></td>
</tr>
<tr>
<td rowspan="2">3</td>
<td>检测</td>
<td>SlimTextDet_pruning</td>
<td>剪裁</td>
<td>61.57</td>
<td>138</td>
<td rowspan="2">295</td>
<td rowspan="2">27%</td>
<td rowspan="2">2.9</td>
<td rowspan="2">66.28%</td>
<td></td>
</tr>
<tr>
<td>识别</td>
<td>SlimTextRec</td>
<td>PACT量化训练</td>
<td>61.48</td>
<td>8.6</td>
<td></td>
</tr>
</tbody>
</table>
## 概述
复杂的模型有利于提高模型的性能，但也导致模型中存在一定冗余，模型裁剪通过移出网络模型中的子模型来减少这种冗余，达到减少模型计算复杂度，提高模型推理性能的目的。
该示例使用PaddleSlim提供的[裁剪压缩API](https://paddlepaddle.github.io/PaddleSlim/api/prune_api/)对OCR模型进行压缩。
在阅读该示例前，建议您先了解以下内容：
\- [OCR模型的常规训练方法](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/detection.md)
\- [PaddleSlim使用文档](https://paddlepaddle.github.io/PaddleSlim/)
## 安装PaddleSlim
```bash
git clone https://github.com/PaddlePaddle/PaddleSlim.git
cd Paddleslim
python setup.py install
```
## 获取预训练模型
[检测预训练模型下载地址]()
## 敏感度分析训练
加载预训练模型后，通过对现有模型的每个网络层进行敏感度分析，了解各网络层冗余度，从而决定每个网络层的裁剪比例。敏感度分析的具体细节见：[敏感度分析](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/tutorials/image_classification_sensitivity_analysis_tutorial.md)
进入PaddleOCR根目录，通过以下命令对模型进行敏感度分析：
```bash
python deploy/slim/prune/sensitivity_anal.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./deploy/slim/prune/pretrain_models/det_mv3_db/best_accuracy Global.test_batch_size_per_card=1
```
## 裁剪模型与fine-tune
裁剪时通过之前的敏感度分析文件决定每个网络层的裁剪比例。在具体实现时，为了尽可能多的保留从图像中提取的低阶特征，我们跳过了backbone中靠近输入的4个卷积层。同样，为了减少由于裁剪导致的模型性能损失，我们通过之前敏感度分析所获得的敏感度表，挑选出了一些冗余较少，对裁剪较为敏感的[网络层](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/slim/prune/pruning_and_finetune.py#L41)，并在之后的裁剪过程中选择避开这些网络层。裁剪过后finetune的过程沿用OCR检测模型原始的训练策略。
```bash
python deploy/slim/prune/pruning_and_finetune.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./deploy/slim/prune/pretrain_models/det_mv3_db/best_accuracy Global.test_batch_size_per_card=1
```
## 导出模型
在得到裁剪训练保存的模型后，我们可以将其导出为inference_model，用于预测部署：
```bash
python deploy/slim/prune/export_prune_model.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./output/det_db/best_accuracy Global.test_batch_size_per_card=1 Global.save_inference_dir=inference_model
```
deploy/slim/prune/README_en.md
浏览文件 @ 7e4aee47
\> PaddleSlim develop version should be installed before runing this example.
# Model compress tutorial (Pruning)
Compress results：
<table>
<thead>
<tr>
<th>ID</th>
<th>Task</th>
<th>Model</th>
<th>Compress Strategy<sup><a href="#quant">[3]</a><a href="#prune">[4]</a><sup></th>
<th>Criterion(Chinese dataset)</th>
<th>Inference Time<sup><a href="#latency">[1]</a></sup>(ms)</th>
<th>Inference Time(Total model)<sup><a href="#rec">[2]</a></sup>(ms)</th>
<th>Acceleration Ratio</th>
<th>Model Size(MB)</th>
<th>Commpress Ratio</th>
<th>Download Link</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="2">0</td>
<td>Detection</td>
<td>MobileNetV3_DB</td>
<td>None</td>
<td>61.7</td>
<td>224</td>
<td rowspan="2">375</td>
<td rowspan="2">-</td>
<td rowspan="2">8.6</td>
<td rowspan="2">-</td>
<td></td>
</tr>
<tr>
<td>Recognition</td>
<td>MobileNetV3_CRNN</td>
<td>None</td>
<td>62.0</td>
<td>9.52</td>
<td></td>
</tr>
<tr>
<td rowspan="2">1</td>
<td>Detection</td>
<td>SlimTextDet</td>
<td>PACT Quant Aware Training</td>
<td>62.1</td>
<td>195</td>
<td rowspan="2">348</td>
<td rowspan="2">8%</td>
<td rowspan="2">2.8</td>
<td rowspan="2">67.82%</td>
<td></td>
</tr>
<tr>
<td>Recognition</td>
<td>SlimTextRec</td>
<td>PACT Quant Aware Training</td>
<td>61.48</td>
<td>8.6</td>
<td></td>
</tr>
<tr>
<td rowspan="2">2</td>
<td>Detection</td>
<td>SlimTextDet_quat_pruning</td>
<td>Pruning+PACT Quant Aware Training</td>
<td>60.86</td>
<td>142</td>
<td rowspan="2">288</td>
<td rowspan="2">30%</td>
<td rowspan="2">2.8</td>
<td rowspan="2">67.82%</td>
<td></td>
</tr>
<tr>
<td>Recognition</td>
<td>SlimTextRec</td>
<td>PPACT Quant Aware Training</td>
<td>61.48</td>
<td>8.6</td>
<td></td>
</tr>
<tr>
<td rowspan="2">3</td>
<td>Detection</td>
<td>SlimTextDet_pruning</td>
<td>Pruning</td>
<td>61.57</td>
<td>138</td>
<td rowspan="2">295</td>
<td rowspan="2">27%</td>
<td rowspan="2">2.9</td>
<td rowspan="2">66.28%</td>
<td></td>
</tr>
<tr>
<td>Recognition</td>
<td>SlimTextRec</td>
<td>PACT Quant Aware Training</td>
<td>61.48</td>
<td>8.6</td>
<td></td>
</tr>
</tbody>
</table>
## Overview
## Introduction
Generally, a more complex model would achive better performance in the task, but it also leads to some redundancy in the model. Model Pruning is a technique that reduces this redundancy by removing the sub-models in the neural network model, so as to reduce model calculation complexity and improve model inference performance.
This example uses PaddleSlim provided[APIs of Pruning](https://paddlepaddle.github.io/PaddleSlim/api/prune_api/) to compress the OCR model.
[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim), an open source library which integrates model pruning, quantization (including quantization training and offline quantization), distillation, neural network architecture search, and many other commonly used and leading model compression technique in the industry.
It is recommended that you could understand following pages before reading this example,：
\- [The training strategy of OCR model](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/detection.md)
\- [PaddleSlim Document](https://paddlepaddle.github.io/PaddleSlim/)
It is recommended that you could understand following pages before reading this example：
1. [PaddleOCR training methods](../../../doc/doc_ch/quickstart.md)
2. [The demo of prune](https://paddlepaddle.github.io/PaddleSlim/tutorials/pruning_tutorial/)
3. [PaddleSlim prune API](https://paddlepaddle.github.io/PaddleSlim/api/prune_api/)
## Quick start
Five steps for OCR model prune:
1. Install PaddleSlim
2. Prepare the trained model
3. Sensitivity analysis and training
4. Model tailoring training
5. Export model, predict deployment
## Install PaddleSlim
### 1. Install PaddleSlim
```bash
git clone https://github.com/PaddlePaddle/PaddleSlim.git
cd Paddleslim
python setup.py install
```
## Download Pretrain Model
### 2. Download Pretrain Model
Model prune needs to load pre-trained models.
PaddleOCR also provides a series of (models)[../../../doc/doc_en/models_list_en.md]. Developers can choose their own models or use their own models according to their needs.
[Download link of Detection pretrain model]()
### 3. Pruning sensitivity analysis
## Pruning sensitivity analysis
After the pre-training model is loaded, sensitivity analysis is performed on each network layer of the model to understand the redundancy of each network layer, and save a sensitivity file which named: sensitivities_0.data. After that, user could load the sensitivity file via the [methods provided by PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/prune/sensitive.py#L221) and determining the pruning ratio of each network layer automatically. For specific details of sensitivity analysis, see：[Sensitivity analysis](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/tutorials/image_classification_sensitivity_analysis_tutorial.md)
The data format of sensitivity file：
sensitivities_0.data(Dict){
'layer_weight_name_0': sens_of_each_ratio(Dict){'pruning_ratio_0': acc_loss, 'pruning_ratio_1': acc_loss}
'layer_weight_name_1': sens_of_each_ratio(Dict){'pruning_ratio_0': acc_loss, 'pruning_ratio_1': acc_loss}
}
example：
{
'conv10_expand_weights': {0.1: 0.006509952684312718, 0.2: 0.01827734339798862, 0.3: 0.014528405644659832, 0.6: 0.06536008804270439, 0.8: 0.11798612250664964, 0.7: 0.12391408417493704, 0.4: 0.030615754498018757, 0.5: 0.047105205602406594}
'conv10_linear_weights': {0.1: 0.05113190831455035, 0.2: 0.07705573833558801, 0.3: 0.12096721757739311, 0.6: 0.5135061352930738, 0.8: 0.7908166677143281, 0.7: 0.7272187676899062, 0.4: 0.1819252083008504, 0.5: 0.3728054727792405}
}
The function would return a dict after loading the sensitivity file. The keys of the dict are name of parameters in each layer. And the value of key is the information about pruning sensitivity of correspoding layer. In example, pruning 10% filter of the layer corresponding to conv10_expand_weights would lead to 0.65% degradation of model performance. The details could be seen at: [Sensitivity analysis](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/algo/algo.md#2-%E5%8D%B7%E7%A7%AF%E6%A0%B8%E5%89%AA%E8%A3%81%E5%8E%9F%E7%90%86)
After the pre-training model is loaded, sensitivity analysis is performed on each network layer of the model to understand the redundancy of each network layer, thereby determining the pruning ratio of each network layer. For specific details of sensitivity analysis, see：[Sensitivity analysis](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/tutorials/image_classification_sensitivity_analysis_tutorial.md)
Enter the PaddleOCR root directory，perform sensitivity analysis on the model with the following command：
```bash
python deploy/slim/prune/sensitivity_anal.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./deploy/slim/prune/pretrain_models/det_mv3_db/best_accuracy Global.test_batch_size_per_card=1
python deploy/slim/prune/sensitivity_anal.py -c configs/det/det_mv3_db_v1.1.yml -o Global.pretrain_weights=./deploy/slim/prune/pretrain_models/det_mv3_db/best_accuracy Global.test_batch_size_per_card=1
```
## Model pruning and Fine-tune
### 4. Model pruning and Fine-tune
When pruning, the previous sensitivity analysis file would determines the pruning ratio of each network layer. In the specific implementation, in order to retain as many low-level features extracted from the image as possible, we skipped the 4 convolutional layers close to the input in the backbone. Similarly, in order to reduce the model performance loss caused by pruning, we selected some of the less redundant and more sensitive [network layer](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/slim/prune/pruning_and_finetune.py#L41) through the sensitivity table obtained from the previous sensitivity analysis.And choose to skip these network layers in the subsequent pruning process. After pruning, the model need a finetune process to recover the performance and the training strategy of finetune is similar to the strategy of training original OCR detection model.
```bash
python deploy/slim/prune/pruning_and_finetune.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./deploy/slim/prune/pretrain_models/det_mv3_db/best_accuracy Global.test_batch_size_per_card=1
python deploy/slim/prune/pruning_and_finetune.py -c configs/det/det_mv3_db_v1.1.yml -o Global.pretrain_weights=./deploy/slim/prune/pretrain_models/det_mv3_db/best_accuracy Global.test_batch_size_per_card=1
```
### 5. Export inference model and deploy it
## Export inference model
After getting the model after pruning and finetuning we, can export it as inference_model for predictive deployment:
We can export the pruned model as inference_model for deployment:
```bash
python deploy/slim/prune/export_prune_model.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./output/det_db/best_accuracy Global.test_batch_size_per_card=1 Global.save_inference_dir=inference_model
python deploy/slim/prune/export_prune_model.py -c configs/det/det_mv3_db_v1.1.yml -o Global.pretrain_weights=./output/det_db/best_accuracy Global.test_batch_size_per_card=1 Global.save_inference_dir=inference_model
```
Reference for prediction and deployment of inference model:
1. [inference model python prediction](../../../doc/doc_en/inference_en.md)
2. [inference model C++ prediction](../../cpp_infer/readme_en.md)
3. [Deployment of inference model on mobile](../../lite/readme_en.md)
deploy/slim/prune/pruning_and_finetune.py
浏览文件 @ 7e4aee47
......@@ -92,7 +92,8 @@ def main():
sen = load_sensitivities("sensitivities_0.data")
for i in skip_list:
sen.pop(i)
if i in sen.keys():
sen.pop(i)
back_bone_list = ['conv' + str(x) for x in range(1, 5)]
for i in back_bone_list:
for key in list(sen.keys()):
......@@ -134,7 +135,7 @@ def main():
if alg in ['EAST', 'DB']:
program.train_eval_det_run(
config, exe, train_info_dict, eval_info_dict, is_pruning=True)
config, exe, train_info_dict, eval_info_dict, is_slim="prune")
else:
program.train_eval_rec_run(config, exe, train_info_dict, eval_info_dict)
......
deploy/slim/quantization/README.md
浏览文件 @ 7e4aee47
> 运行示例前请先安装1.2.0或更高版本PaddleSlim
# 模型量化压缩教程
压缩结果：
<table>
<thead>
<tr>
<th>序号</th>
<th>任务</th>
<th>模型</th>
<th>压缩策略</th>
<th>精度(自建中文数据集)</th>
<th>耗时(ms)</th>
<th>整体耗时(ms)</th>
<th>加速比</th>
<th>整体模型大小(M)</th>
<th>压缩比例</th>
<th>下载链接</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="2">0</td>
<td>检测</td>
<td>MobileNetV3_DB</td>
<td>无</td>
<td>61.7</td>
<td>224</td>
<td rowspan="2">375</td>
<td rowspan="2">-</td>
<td rowspan="2">8.6</td>
<td rowspan="2">-</td>
<td></td>
</tr>
<tr>
<td>识别</td>
<td>MobileNetV3_CRNN</td>
<td>无</td>
<td>62.0</td>
<td>9.52</td>
<td></td>
</tr>
<tr>
<td rowspan="2">1</td>
<td>检测</td>
<td>SlimTextDet</td>
<td>PACT量化训练</td>
<td>62.1</td>
<td>195</td>
<td rowspan="2">348</td>
<td rowspan="2">8%</td>
<td rowspan="2">2.8</td>
<td rowspan="2">67.82%</td>
<td></td>
</tr>
<tr>
<td>识别</td>
<td>SlimTextRec</td>
<td>PACT量化训练</td>
<td>61.48</td>
<td>8.6</td>
<td></td>
</tr>
<tr>
<td rowspan="2">2</td>
<td>检测</td>
<td>SlimTextDet_quat_pruning</td>
<td>剪裁+PACT量化训练</td>
<td>60.86</td>
<td>142</td>
<td rowspan="2">288</td>
<td rowspan="2">30%</td>
<td rowspan="2">2.8</td>
<td rowspan="2">67.82%</td>
<td></td>
</tr>
<tr>
<td>识别</td>
<td>SlimTextRec</td>
<td>PACT量化训练</td>
<td>61.48</td>
<td>8.6</td>
<td></td>
</tr>
<tr>
<td rowspan="2">3</td>
<td>检测</td>
<td>SlimTextDet_pruning</td>
<td>剪裁</td>
<td>61.57</td>
<td>138</td>
<td rowspan="2">295</td>
<td rowspan="2">27%</td>
<td rowspan="2">2.9</td>
<td rowspan="2">66.28%</td>
<td></td>
</tr>
<tr>
<td>识别</td>
<td>SlimTextRec</td>
<td>PACT量化训练</td>
<td>61.48</td>
<td>8.6</td>
<td></td>
</tr>
</tbody>
</table>
## 概述
## 介绍
复杂的模型有利于提高模型的性能，但也导致模型中存在一定冗余，模型量化将全精度缩减到定点数减少这种冗余，达到减少模型计算复杂度，提高模型推理性能的目的。
模型量化可以在基本不损失模型的精度的情况下，将FP32精度的模型参数转换为Int8精度，减小模型参数大小并加速计算，使用量化后的模型在移动端等部署时更具备速度优势。
该示例使用PaddleSlim提供的[量化压缩API](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/)对OCR模型进行压缩。
在阅读该示例前，建议您先了解以下内容：
本教程将介绍如何使用飞桨模型压缩库PaddleSlim做PaddleOCR模型的压缩。
[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim) 集成了模型剪枝、量化（包括量化训练和离线量化）、蒸馏和神经网络搜索等多种业界常用且领先的模型压缩功能，如果您感兴趣，可以关注并了解。
- [OCR模型的常规训练方法](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/detection.md)
- [PaddleSlim使用文档](https://paddleslim.readthedocs.io/zh_CN/latest/index.html)
在开始本教程之前，建议先了解[PaddleOCR模型的训练方法](../../../doc/doc_ch/quickstart.md)以及[PaddleSlim](https://paddleslim.readthedocs.io/zh_CN/latest/index.html)
## 快速开始
量化多适用于轻量模型在移动端的部署，当训练出一个模型后，如果希望进一步的压缩模型大小并加速预测，可使用量化的方法压缩模型。
## 安装PaddleSlim
模型量化主要包括五个步骤：
1. 安装 PaddleSlim
2. 准备训练好的模型
3. 量化训练
4. 导出量化推理模型
5. 量化模型预测部署
### 1. 安装PaddleSlim
```bash
git clone https://github.com/PaddlePaddle/PaddleSlim.git
cd Paddleslim
python setup.py install
```
### 2. 准备训练好的模型
PaddleOCR提供了一系列训练好的[模型](../../../doc/doc_ch/models_list.md)，如果待量化的模型不在列表中，需要按照[常规训练](../../../doc/doc_ch/quickstart.md)方法得到训练好的模型。
## 获取预训练模型
[识别预训练模型下载地址]()
[检测预训练模型下载地址]()
### 3. 量化训练
量化训练包括离线量化训练和在线量化训练，在线量化训练效果更好，需加载预训练模型，在定义好量化策略后即可对模型进行量化。
## 量化训练
加载预训练模型后，在定义好量化策略后即可对模型进行量化。量化相关功能的使用具体细节见：[模型量化](https://paddleslim.readthedocs.io/zh_CN/latest/api_cn/quantization_api.html)
进入PaddleOCR根目录，通过以下命令对模型进行量化：
量化训练的代码位于slim/quantization/quant/py 中，比如训练检测模型，训练指令如下：
```bash
python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=det_mv3_db/best_accuracy Global.save_model_dir=./output/quant_model
```
python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights='your trained model' Global.save_model_dir=./output/quant_model
# 比如下载提供的训练模型
wget https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar
tar xf ch_ppocr_mobile_v1.1_det_train.tar
python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./ch_ppocr_mobile_v1.1_det_train/best_accuracy Global.save_model_dir=./output/quant_model
```
如果要训练识别模型的量化，修改配置文件和加载的模型参数即可。
## 导出模型
### 4. 导出模型
在得到量化训练保存的模型后，我们可以将其导出为inference_model，用于预测部署：
```bash
python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_model_dir=./output/quant_inference_model
```
### 5. 量化模型部署
上述步骤导出的量化模型，参数精度仍然是FP32，但是参数的数值范围是int8，导出的模型可以通过PaddleLite的opt模型转换工具完成模型转换。
量化模型部署的可参考 [移动端模型部署](../lite/readme.md)
deploy/slim/quantization/README_en.md
浏览文件 @ 7e4aee47
\> PaddleSlim 1.2.0 or higher version should be installed before runing this example.
# Model compress tutorial (Quantization)
Compress results：
<table>
<thead>
<tr>
<th>ID</th>
<th>Task</th>
<th>Model</th>
<th>Compress Strategy</th>
<th>Criterion(Chinese dataset)</th>
<th>Inference Time(ms)</th>
<th>Inference Time(Total model)(ms)</th>
<th>Acceleration Ratio</th>
<th>Model Size(MB)</th>
<th>Commpress Ratio</th>
<th>Download Link</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="2">0</td>
<td>Detection</td>
<td>MobileNetV3_DB</td>
<td>None</td>
<td>61.7</td>
<td>224</td>
<td rowspan="2">375</td>
<td rowspan="2">-</td>
<td rowspan="2">8.6</td>
<td rowspan="2">-</td>
<td></td>
</tr>
<tr>
<td>Recognition</td>
<td>MobileNetV3_CRNN</td>
<td>None</td>
<td>62.0</td>
<td>9.52</td>
<td></td>
</tr>
<tr>
<td rowspan="2">1</td>
<td>Detection</td>
<td>SlimTextDet</td>
<td>PACT Quant Aware Training</td>
<td>62.1</td>
<td>195</td>
<td rowspan="2">348</td>
<td rowspan="2">8%</td>
<td rowspan="2">2.8</td>
<td rowspan="2">67.82%</td>
<td></td>
</tr>
<tr>
<td>Recognition</td>
<td>SlimTextRec</td>
<td>PACT Quant Aware Training</td>
<td>61.48</td>
<td>8.6</td>
<td></td>
</tr>
<tr>
<td rowspan="2">2</td>
<td>Detection</td>
<td>SlimTextDet_quat_pruning</td>
<td>Pruning+PACT Quant Aware Training</td>
<td>60.86</td>
<td>142</td>
<td rowspan="2">288</td>
<td rowspan="2">30%</td>
<td rowspan="2">2.8</td>
<td rowspan="2">67.82%</td>
<td></td>
</tr>
<tr>
<td>Recognition</td>
<td>SlimTextRec</td>
<td>PPACT Quant Aware Training</td>
<td>61.48</td>
<td>8.6</td>
<td></td>
</tr>
<tr>
<td rowspan="2">3</td>
<td>Detection</td>
<td>SlimTextDet_pruning</td>
<td>Pruning</td>
<td>61.57</td>
<td>138</td>
<td rowspan="2">295</td>
<td rowspan="2">27%</td>
<td rowspan="2">2.9</td>
<td rowspan="2">66.28%</td>
<td></td>
</tr>
<tr>
<td>Recognition</td>
<td>SlimTextRec</td>
<td>PACT Quant Aware Training</td>
<td>61.48</td>
<td>8.6</td>
<td></td>
</tr>
</tbody>
</table>
## Overview
Generally, a more complex model would achive better performance in the task, but it also leads to some redundancy in the model. Quantization is a technique that reduces this redundancyby reducing the full precision data to a fixed number, so as to reduce model calculation complexity and improve model inference performance.
This example uses PaddleSlim provided [APIs of Quantization](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/) to compress the OCR model.
It is recommended that you could understand following pages before reading this example,：
## Introduction
Generally, a more complex model would achive better performance in the task, but it also leads to some redundancy in the model.
Quantization is a technique that reduces this redundancy by reducing the full precision data to a fixed number,
so as to reduce model calculation complexity and improve model inference performance.
This example uses PaddleSlim provided [APIs of Quantization](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/) to compress the OCR model.
- [The training strategy of OCR model](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/detection.md)
It is recommended that you could understand following pages before reading this example：
- [The training strategy of OCR model](../../../doc/doc_en/quickstart_en.md)
- [PaddleSlim Document](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/)
## Quick Start
Quantization is mostly suitable for the deployment of lightweight models on mobile terminals.
After training, if you want to further compress the model size and accelerate the prediction, you can use quantization methods to compress the model according to the following steps.
1. Install PaddleSlim
2. Prepare trained model
3. Quantization-Aware Training
4. Export inference model
5. Deploy quantization inference model
## Install PaddleSlim
### 1. Install PaddleSlim
```bash
git clone https://github.com/PaddlePaddle/PaddleSlim.git
cd Paddleslim
python setup.py install
```
## Download Pretrain Model
### 2. Download Pretrain Model
PaddleOCR provides a series of trained [models](../../../doc/doc_en/models_list_en.md).
If the model to be quantified is not in the list, you need to follow the [Regular Training](../../../doc/doc_en/quickstart_en.md) method to get the trained model.
[Download link of Detection pretrain model]()
[Download link of recognization pretrain model]()
### 3. Quant-Aware Training
Quantization training includes offline quantization training and online quantization training.
Online quantization training is more effective. It is necessary to load the pre-training model.
After the quantization strategy is defined, the model can be quantified.
The code for quantization training is located in `slim/quantization/quant/py`. For example, to train a detection model, the training instructions are as follows:
```bash
python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights='your trained model' Global.save_model_dir=./output/quant_model
## Quan-Aware Training
After loading the pre training model, the model can be quantified after defining the quantization strategy. For specific details of quantization method, see：[Model Quantization](https://paddleslim.readthedocs.io/zh_CN/latest/api_cn/quantization_api.html)
Enter the PaddleOCR root directory，perform model quantization with the following command：
# download provided model
wget https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar
tar xf ch_ppocr_mobile_v1.1_det_train.tar
python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./ch_ppocr_mobile_v1.1_det_train/best_accuracy Global.save_model_dir=./output/quant_model
```bash
python deploy/slim/prune/sensitivity_anal.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./deploy/slim/prune/pretrain_models/det_mv3_db/best_accuracy Global.test_batch_size_per_card=1
```
## Export inference model
### 4. Export inference model
After getting the model after pruning and finetuning we, can export it as inference_model for predictive deployment:
```bash
python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_model_dir=./output/quant_inference_model
```
### 5. Deploy
The numerical range of the quantized model parameters derived from the above steps is still FP32, but the numerical range of the parameters is int8.
The derived model can be converted through the `opt tool` of PaddleLite.
For quantitative model deployment, please refer to [Mobile terminal model deployment](../lite/readme_en.md)
deploy/slim/quantization/export_model.py
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......@@ -51,6 +51,7 @@ from paddleslim.quant import quant_aware, convert
from paddle.fluid.layer_helper import LayerHelper
from eval_utils.eval_det_utils import eval_det_run
from eval_utils.eval_rec_utils import eval_rec_run
from eval_utils.eval_cls_utils import eval_cls_run
def main():
......@@ -105,6 +106,8 @@ def main():
if alg_type == 'det':
final_metrics = eval_det_run(exe, config, quant_info_dict, "eval")
elif alg_type == 'cls':
final_metrics = eval_cls_run(exe, quant_info_dict)
else:
final_metrics = eval_rec_run(exe, config, quant_info_dict, "eval")
print(final_metrics)
......
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......@@ -155,14 +155,13 @@ def main():
act_preprocess_func=act_preprocess_func,
optimizer_func=optimizer_func,
executor=executor,
for_test=False,
return_program=True)
for_test=False)
# compile program for multi-devices
train_compile_program = program.create_multi_devices_program(
quant_train_program, train_opt_loss_name, for_quant=True)
init_model(config, quant_train_program, exe)
init_model(config, train_program, exe)
train_info_dict = {'compile_program':train_compile_program,\
'train_program':quant_train_program,\
......@@ -177,9 +176,14 @@ def main():
'fetch_varname_list':eval_fetch_varname_list}
if train_alg_type == 'det':
program.train_eval_det_run(config, exe, train_info_dict, eval_info_dict)
program.train_eval_det_run(
config, exe, train_info_dict, eval_info_dict, is_slim="quant")
elif train_alg_type == 'rec':
program.train_eval_rec_run(
config, exe, train_info_dict, eval_info_dict, is_slim="quant")
else:
program.train_eval_rec_run(config, exe, train_info_dict, eval_info_dict)
program.train_eval_cls_run(
config, exe, train_info_dict, eval_info_dict, is_slim="quant")
if __name__ == '__main__':
......
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<a name="算法介绍"></a>
## 算法介绍
本文给出了PaddleOCR已支持的文本检测算法和文本识别算法列表，以及每个算法在**英文公开数据集**上的模型和指标，主要用于算法简介和算法性能对比，更多包括中文在内的其他数据集上的模型请参考[PP-OCR v1.1 系列模型下载](./models_list.md)。
- [1.文本检测算法](#文本检测算法)
- [2.文本识别算法](#文本识别算法)
......@@ -29,18 +31,9 @@ PaddleOCR开源的文本检测算法列表：
**说明：** SAST模型训练额外加入了icdar2013、icdar2017、COCO-Text、ArT等公开数据集进行调优。PaddleOCR用到的经过整理格式的英文公开数据集下载：[百度云地址](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (提取码: 2bpi)
使用[LSVT](./datasets.md#1icdar2019-lsvt)街景数据集共3w张数据，训练中文检测模型的相关配置和预训练文件如下：
|模型|骨干网络|配置文件|预训练模型|
|-|-|-|-|
|超轻量中文模型|MobileNetV3|det_mv3_db.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|
|通用中文OCR模型|ResNet50_vd|det_r50_vd_db.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|
* 注： 上述DB模型的训练和评估，需设置后处理参数box_thresh=0.6，unclip_ratio=1.5，使用不同数据集、不同模型训练，可调整这两个参数进行优化
PaddleOCR文本检测算法的训练和使用请参考文档教程中[模型训练/评估中的文本检测部分](./detection.md)。
<a name="文本识别算法"></a>
### 2.文本识别算法
......@@ -68,11 +61,4 @@ PaddleOCR开源的文本识别算法列表：
**说明：** SRN模型使用了数据扰动方法对上述提到对两个训练集进行增广，增广后的数据可以在[百度网盘](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA)上下载，提取码: y3ry。
原始论文使用两阶段训练平均精度为89.74%，PaddleOCR中使用one-stage训练，平均精度为88.33%。两种预训练权重均在[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)中。
使用[LSVT](./datasets.md#1icdar2019-lsvt)街景数据集根据真值将图crop出来30w数据，进行位置校准。此外基于LSVT语料生成500w合成数据训练中文模型，相关配置和预训练文件如下：
|模型|骨干网络|配置文件|预训练模型|
|-|-|-|-|
|超轻量中文模型|MobileNetV3|rec_chinese_lite_train.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|
|通用中文OCR模型|Resnet34_vd|rec_chinese_common_train.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|
PaddleOCR文本识别算法的训练和使用请参考文档教程中[模型训练/评估中的文本识别部分](./recognition.md)。
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