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README.md
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...@@ -4,6 +4,7 @@ English | [简体中文](README_ch.md) ...@@ -4,6 +4,7 @@ English | [简体中文](README_ch.md)
PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools that help users train better models and apply them into practice. PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools that help users train better models and apply them into practice.
**Recent updates** **Recent updates**
- 2020.11.25 Update a new data annotation tool, i.e., [PPOCRLabel](./PPOCRLabel/README_en.md), which is helpful to improve the labeling efficiency. Moreover, the labeling results can be used in training of the PP-OCR system directly.
- 2020.9.22 Update the PP-OCR technical article, https://arxiv.org/abs/2009.09941 - 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) - 2020.9.19 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) - 2020.9.17 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)
...@@ -77,30 +78,32 @@ For a new language request, please refer to [Guideline for new language_requests ...@@ -77,30 +78,32 @@ For a new language request, please refer to [Guideline for new language_requests
- [Installation](./doc/doc_en/installation_en.md) - [Installation](./doc/doc_en/installation_en.md)
- [Quick Start](./doc/doc_en/quickstart_en.md) - [Quick Start](./doc/doc_en/quickstart_en.md)
- [Code Structure](./doc/doc_en/tree_en.md) - [Code Structure](./doc/doc_en/tree_en.md)
- Algorithm introduction - Algorithm Introduction
- [Text Detection Algorithm](./doc/doc_en/algorithm_overview_en.md) - [Text Detection Algorithm](./doc/doc_en/algorithm_overview_en.md)
- [Text Recognition Algorithm](./doc/doc_en/algorithm_overview_en.md) - [Text Recognition Algorithm](./doc/doc_en/algorithm_overview_en.md)
- [PP-OCR Pipeline](#PP-OCR-Pipeline) - [PP-OCR Pipeline](#PP-OCR-Pipeline)
- Model training/evaluation - Model Training/Evaluation
- [Text Detection](./doc/doc_en/detection_en.md) - [Text Detection](./doc/doc_en/detection_en.md)
- [Text Recognition](./doc/doc_en/recognition_en.md) - [Text Recognition](./doc/doc_en/recognition_en.md)
- [Direction Classification](./doc/doc_en/angle_class_en.md) - [Direction Classification](./doc/doc_en/angle_class_en.md)
- [Yml Configuration](./doc/doc_en/config_en.md) - [Yml Configuration](./doc/doc_en/config_en.md)
- Inference and Deployment - Inference and Deployment
- [Quick inference based on pip](./doc/doc_en/whl_en.md) - [Quick Inference Based on PIP](./doc/doc_en/whl_en.md)
- [Python Inference](./doc/doc_en/inference_en.md) - [Python Inference](./doc/doc_en/inference_en.md)
- [C++ Inference](./deploy/cpp_infer/readme_en.md) - [C++ Inference](./deploy/cpp_infer/readme_en.md)
- [Serving](./deploy/hubserving/readme_en.md) - [Serving](./deploy/hubserving/readme_en.md)
- [Mobile](./deploy/lite/readme_en.md) - [Mobile](./deploy/lite/readme_en.md)
- [Model Quantization](./deploy/slim/quantization/README_en.md) - [Model Quantization](./deploy/slim/quantization/README_en.md)
- [Model Compression](./deploy/slim/prune/README_en.md) - [Model Compression](./deploy/slim/prune/README_en.md)
- [Benchmark](./doc/doc_en/benchmark_en.md) - [Benchmark](./doc/doc_en/benchmark_en.md)
- Data Annotation and Synthesis
- [Semi-automatic Annotation Tool](./PPOCRLabel/README_en.md)
- [Data Annotation Tools](./doc/doc_en/data_annotation_en.md)
- [Data Synthesis Tools](./doc/doc_en/data_synthesis_en.md)
- Datasets - Datasets
- [General OCR Datasets(Chinese/English)](./doc/doc_en/datasets_en.md) - [General OCR Datasets(Chinese/English)](./doc/doc_en/datasets_en.md)
- [HandWritten_OCR_Datasets(Chinese)](./doc/doc_en/handwritten_datasets_en.md) - [HandWritten_OCR_Datasets(Chinese)](./doc/doc_en/handwritten_datasets_en.md)
- [Various OCR Datasets(multilingual)](./doc/doc_en/vertical_and_multilingual_datasets_en.md) - [Various OCR Datasets(multilingual)](./doc/doc_en/vertical_and_multilingual_datasets_en.md)
- [Data Annotation Tools](./doc/doc_en/data_annotation_en.md)
- [Data Synthesis Tools](./doc/doc_en/data_synthesis_en.md)
- [Visualization](#Visualization) - [Visualization](#Visualization)
- [New language requests](#language_requests) - [New language requests](#language_requests)
- [FAQ](./doc/doc_en/FAQ_en.md) - [FAQ](./doc/doc_en/FAQ_en.md)
...@@ -177,3 +180,5 @@ We welcome all the contributions to PaddleOCR and appreciate for your feedback v ...@@ -177,3 +180,5 @@ We welcome all the contributions to PaddleOCR and appreciate for your feedback v
- Thanks [tangmq](https://gitee.com/tangmq) for contributing Dockerized deployment services to PaddleOCR and supporting the rapid release of callable Restful API services. - Thanks [tangmq](https://gitee.com/tangmq) for contributing Dockerized deployment services to PaddleOCR and supporting the rapid release of callable Restful API services.
- Thanks [lijinhan](https://github.com/lijinhan) for contributing a new way, i.e., java SpringBoot, to achieve the request for the Hubserving deployment. - Thanks [lijinhan](https://github.com/lijinhan) for contributing a new way, i.e., java SpringBoot, to achieve the request for the Hubserving deployment.
- Thanks [Mejans](https://github.com/Mejans) for contributing the Occitan corpus and character set. - Thanks [Mejans](https://github.com/Mejans) for contributing the Occitan corpus and character set.
- Thanks [LKKlein](https://github.com/LKKlein) for contributing a new deploying package with the Golang program language.
- Thanks [Evezerest](https://github.com/Evezerest), [ninetailskim](https://github.com/ninetailskim), [edencfc](https://github.com/edencfc), [BeyondYourself](https://github.com/BeyondYourself) and [1084667371](https://github.com/1084667371) for contributing a new data annotation tool, i.e., PPOCRLabel。
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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。 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** **近期更新**
- 2020.12.14 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，总数129个，每周一都会更新，欢迎大家持续关注。
- 2020.11.25 更新半自动标注工具[PPOCRLabel](./PPOCRLabel/README.md)，辅助开发者高效完成标注任务，输出格式与PP-OCR训练任务完美衔接。 - 2020.11.25 更新半自动标注工具[PPOCRLabel](./PPOCRLabel/README.md)，辅助开发者高效完成标注任务，输出格式与PP-OCR训练任务完美衔接。
- 2020.11.16 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，共计109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 - 2020.9.22 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) - 2020.9.19 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) - 2020.9.17 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)
...@@ -100,8 +100,8 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力 ...@@ -100,8 +100,8 @@ 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) - [效果展示](#效果展示)
- FAQ - FAQ
- [【精选】OCR精选10个问题](./doc/doc_ch/FAQ.md) - [【精选】OCR精选10个问题](./doc/doc_ch/FAQ.md)
- [【理论篇】OCR通用27个问题](./doc/doc_ch/FAQ.md) - [【理论篇】OCR通用30个问题](./doc/doc_ch/FAQ.md)
- [【实战篇】PaddleOCR实战72个问题](./doc/doc_ch/FAQ.md) - [【实战篇】PaddleOCR实战84个问题](./doc/doc_ch/FAQ.md)
- [技术交流群](#欢迎加入PaddleOCR技术交流群) - [技术交流群](#欢迎加入PaddleOCR技术交流群)
- [参考文献](./doc/doc_ch/reference.md) - [参考文献](./doc/doc_ch/reference.md)
- [许可证书](#许可证书) - [许可证书](#许可证书)
......
deploy/slim/quantization/README.md
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...@@ -58,4 +58,4 @@ python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o ...@@ -58,4 +58,4 @@ python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o
### 5. 量化模型部署 ### 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。 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) 量化模型部署的可参考 [移动端模型部署](../../lite/readme.md)
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...@@ -65,4 +65,4 @@ python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o ...@@ -65,4 +65,4 @@ python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o
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 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. 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) For quantitative model deployment, please refer to [Mobile terminal model deployment](../../lite/readme_en.md)
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...@@ -9,48 +9,42 @@ ...@@ -9,48 +9,42 @@
## PaddleOCR常见问题汇总(持续更新) ## PaddleOCR常见问题汇总(持续更新)
* [近期更新（2020.11.16）](#近期更新) * [近期更新（2020.12.14）](#近期更新)
* [【精选】OCR精选10个问题](#OCR精选10个问题) * [【精选】OCR精选10个问题](#OCR精选10个问题)
* [【理论篇】OCR通用27个问题](#OCR通用问题) * [【理论篇】OCR通用30个问题](#OCR通用问题)
* [基础知识7题](#基础知识) * [基础知识7题](#基础知识)
* [数据集5题](#数据集) * [数据集7题](#数据集2)
* [模型训练调优7题](#模型训练调优) * [模型训练调优7题](#模型训练调优2)
* [预测部署8题](#预测部署) * [预测部署9题](#预测部署2)
* [【实战篇】PaddleOCR实战72个问题](#PaddleOCR实战问题) * [【实战篇】PaddleOCR实战89个问题](#PaddleOCR实战问题)
* [使用咨询20题](#使用咨询) * [使用咨询21题](#使用咨询)
* [数据集14题](#数据集) * [数据集17题](#数据集3)
* [模型训练调优21题](#模型训练调优) * [模型训练调优27题](#模型训练调优3)
* [预测部署21题](#预测部署) * [预测部署24题](#预测部署3)
<a name="近期更新"></a> <a name="近期更新"></a>
## 近期更新（2020.11.23） ## 近期更新（2020.12.14）
#### Q3.2.11：有哪些标注工具可以标注OCR数据集？ #### Q3.1.21：PaddleOCR支持动态图吗？
**A**：您可以参考：https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/data_annotation_en.md。 **A**：动态图版本正在紧锣密鼓开发中，将于2020年12月16日发布，敬请关注。
我们计划推出高效标注OCR数据的标注工具，请您持续关注PaddleOCR的近期更新。
#### Q3.2.12：一些特殊场景的数据识别效果差，但是数据量很少，不够用来finetune怎么办？ #### Q3.3.25：检测模型训练或预测时出现elementwise_add报错
**A**：您可以合成一些接近使用场景的数据用于训练。 **A**：设置的输入尺寸必须是32的倍数，否则在网络多次下采样和上采样后，feature map会产生1个像素的diff，从而导致elementwise_add时报shape不匹配的错误。
我们计划推出基于特定场景的文本数据合成工具，请您持续关注PaddleOCR的近期更新。
#### Q3.2.13：特殊字符（例如一些标点符号）识别效果不好怎么办？ #### Q3.3.26: DB检测训练输入尺寸640，可以改大一些吗？
**A**：首先请您确认要识别的特殊字符是否在字典中。 **A**: 不建议改大。检测模型训练输入尺寸是预处理中random crop后的尺寸，并非直接将原图进行resize，多数场景下这个尺寸并不小了，改大后可能反而并不合适，而且训练会变慢。另外，代码里可能有的地方参数按照预设输入尺寸适配的，改大后可能有隐藏风险。
如果字符在已经字典中但效果依然不好，可能是由于识别数据较少导致的，您可以增加相应数据finetune模型。
#### Q3.2.14：PaddleOCR可以识别灰度图吗？
**A**：PaddleOCR的模型均为三通道输入。如果您想使用灰度图作为输入，建议直接用3通道的模式读入灰度图， #### Q3.3.27: 识别模型训练时，loss能正常下降，但acc一直为0
或者将单通道图像转换为三通道图像再识别。例如，opencv的cvtColor函数就可以将灰度图转换为RGB三通道模式。
#### Q3.1.20：PaddleOCR与百度的其他OCR产品有什么区别？ **A**: 识别模型训练初期acc为0是正常的，多训一段时间指标就上来了。
**A**：PaddleOCR主要聚焦通用ocr，如果有垂类需求，您可以用PaddleOCR+垂类数据自己训练； #### Q3.4.24：DB模型能正确推理预测，但换成EAST或SAST模型时报错或结果不正确
如果缺少带标注的数据，或者不想投入研发成本，建议直接调用开放的API，开放的API覆盖了目前比较常见的一些垂类。
**A**：使用EAST或SAST模型进行推理预测时，需要在命令中指定参数--det_algorithm="EAST" 或 --det_algorithm="SAST"，使用DB时不用指定是因为该参数默认值是"DB"：https://github.com/PaddlePaddle/PaddleOCR/blob/e7a708e9fdaf413ed7a14da8e4a7b4ac0b211e42/tools/infer/utility.py#L43
<a name="OCR精选10个问题"></a> <a name="OCR精选10个问题"></a>
## 【精选】OCR精选10个问题 ## 【精选】OCR精选10个问题
...@@ -141,6 +135,8 @@ ...@@ -141,6 +135,8 @@
<a name="OCR通用问题"></a> <a name="OCR通用问题"></a>
## 【理论篇】OCR通用问题 ## 【理论篇】OCR通用问题
<a name="基础知识"></a>
### 基础知识 ### 基础知识
#### Q2.1.1：CRNN能否识别两行的文字?还是说必须一行？ #### Q2.1.1：CRNN能否识别两行的文字?还是说必须一行？
...@@ -169,6 +165,7 @@ ...@@ -169,6 +165,7 @@
**A**：处理字符的时候，把多字符的当作一个字就行，字典中每行是一个字。 **A**：处理字符的时候，把多字符的当作一个字就行，字典中每行是一个字。
<a name="数据集2"></a>
### 数据集 ### 数据集
#### Q2.2.1：支持空格的模型，标注数据的时候是不是要标注空格？中间几个空格都要标注出来么？ #### Q2.2.1：支持空格的模型，标注数据的时候是不是要标注空格？中间几个空格都要标注出来么？
...@@ -191,7 +188,15 @@ ...@@ -191,7 +188,15 @@
**A**：使用基于分割的方法，如DB，检测密集文本行时，最好收集一批数据进行训练，并且在训练时，并将生成二值图像的shrink_ratio参数调小一些。 **A**：使用基于分割的方法，如DB，检测密集文本行时，最好收集一批数据进行训练，并且在训练时，并将生成二值图像的shrink_ratio参数调小一些。
#### Q2.2.6: 当训练数据量少时，如何获取更多的数据？
**A**: 当训练数据量少时，可以尝试以下三种方式获取更多的数据：（1）人工采集更多的训练数据，最直接也是最有效的方式。（2）基于PIL和opencv基本图像处理或者变换。例如PIL中ImageFont, Image, ImageDraw三个模块将文字写到背景中，opencv的旋转仿射变换，高斯滤波等。（3）利用数据生成算法合成数据，例如pix2pix等算法。
#### Q2.2.7: 论文《Editing Text in the Wild》中文本合成方法SRNet有什么特点？
**A**: SRNet是借鉴GAN中图像到图像转换、风格迁移的想法合成文本数据。不同于通用GAN的方法只选择一个分支，SRNet将文本合成任务分解为三个简单的子模块，提升合成数据的效果。这三个子模块为不带背景的文本风格迁移模块、背景抽取模块和融合模块。PaddleOCR计划将在2020年12月中旬开源基于SRNet的实用模型。
<a name="模型训练调优2"></a>
### 模型训练调优 ### 模型训练调优
#### Q2.3.1：如何更换文本检测/识别的backbone？ #### Q2.3.1：如何更换文本检测/识别的backbone？
...@@ -233,6 +238,7 @@ ...@@ -233,6 +238,7 @@
（2）调大系统的[l2 dcay值](https://github.com/PaddlePaddle/PaddleOCR/blob/a501603d54ff5513fc4fc760319472e59da25424/configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml#L47) （2）调大系统的[l2 dcay值](https://github.com/PaddlePaddle/PaddleOCR/blob/a501603d54ff5513fc4fc760319472e59da25424/configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml#L47)
<a name="预测部署2"></a>
### 预测部署 ### 预测部署
#### Q2.4.1：请问对于图片中的密集文字，有什么好的处理办法吗？ #### Q2.4.1：请问对于图片中的密集文字，有什么好的处理办法吗？
...@@ -275,11 +281,16 @@ ...@@ -275,11 +281,16 @@
**A**：表格目前学术界比较成熟的解决方案不多 ，可以尝试下分割的论文方案。 **A**：表格目前学术界比较成熟的解决方案不多 ，可以尝试下分割的论文方案。
#### Q2.4.9：弯曲文本有试过opencv的TPS进行弯曲校正吗？
**A**：opencv的tps需要标出上下边界对应的点，这个点很难通过传统方法或者深度学习方法获取。PaddleOCR里StarNet网络中的tps模块实现了自动学点，自动校正，可以直接尝试这个。
<a name="PaddleOCR实战问题"></a> <a name="PaddleOCR实战问题"></a>
## 【实战篇】PaddleOCR实战问题 ## 【实战篇】PaddleOCR实战问题
<a name="使用咨询"></a>
### 使用咨询 ### 使用咨询
#### Q3.1.1：OSError： [WinError 126] 找不到指定的模块。mac pro python 3.4 shapely import 问题 #### Q3.1.1：OSError： [WinError 126] 找不到指定的模块。mac pro python 3.4 shapely import 问题
...@@ -377,7 +388,11 @@ ...@@ -377,7 +388,11 @@
**A**：PaddleOCR主要聚焦通用ocr，如果有垂类需求，您可以用PaddleOCR+垂类数据自己训练； **A**：PaddleOCR主要聚焦通用ocr，如果有垂类需求，您可以用PaddleOCR+垂类数据自己训练；
如果缺少带标注的数据，或者不想投入研发成本，建议直接调用开放的API，开放的API覆盖了目前比较常见的一些垂类。 如果缺少带标注的数据，或者不想投入研发成本，建议直接调用开放的API，开放的API覆盖了目前比较常见的一些垂类。
#### Q3.1.21：PaddleOCR支持动态图吗？
**A**：动态图版本正在紧锣密鼓开发中，本周即将发布，敬请关注。
<a name="数据集3"></a>
### 数据集 ### 数据集
#### Q3.2.1：如何制作PaddleOCR支持的数据格式 #### Q3.2.1：如何制作PaddleOCR支持的数据格式
...@@ -456,7 +471,19 @@ ...@@ -456,7 +471,19 @@
**A**：PaddleOCR的模型均为三通道输入。如果您想使用灰度图作为输入，建议直接用3通道的模式读入灰度图， **A**：PaddleOCR的模型均为三通道输入。如果您想使用灰度图作为输入，建议直接用3通道的模式读入灰度图，
或者将单通道图像转换为三通道图像再识别。例如，opencv的cvtColor函数就可以将灰度图转换为RGB三通道模式。 或者将单通道图像转换为三通道图像再识别。例如，opencv的cvtColor函数就可以将灰度图转换为RGB三通道模式。
#### Q3.2.15: 文本标注工具PPOCRLabel有什么特色？
**A**: PPOCRLabel是一个半自动文本标注工具，它使用基于PPOCR的中英文OCR模型，预先预测文本检测和识别结果，然后用户对上述结果进行校验和修正就行，大大提高用户的标注效率。同时导出的标注结果直接适配PPOCR训练所需要的数据格式，
#### Q3.2.16: 文本标注工具PPOCRLabel，可以更换模型吗？
**A**: PPOCRLabel中OCR部署方式采用的基于pip安装whl包快速推理，可以参考相关文档更换模型路径，进行特定任务的标注适配。基于pip安装whl包快速推理的文档如下，https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/whl.md。
#### Q3.2.17: 文本标注工具PPOCRLabel支持的运行环境有哪些？
**A**: PPOCRLabel可运行于Linux、Windows、MacOS等多种系统。操作步骤可以参考文档，https://github.com/PaddlePaddle/PaddleOCR/blob/develop/PPOCRLabel/README.md
<a name="模型训练调优3"></a>
### 模型训练调优 ### 模型训练调优
#### Q3.3.1：文本长度超过25，应该怎么处理？ #### Q3.3.1：文本长度超过25，应该怎么处理？
...@@ -574,7 +601,32 @@ return paddle.reader.multiprocess_reader(readers, False, queue_size=320) ...@@ -574,7 +601,32 @@ return paddle.reader.multiprocess_reader(readers, False, queue_size=320)
（3）在训练的时候，文本长度超过25的训练图像都会被丢弃，因此需要看下真正参与训练的图像有多少，太少的话也容易过拟合。 （3）在训练的时候，文本长度超过25的训练图像都会被丢弃，因此需要看下真正参与训练的图像有多少，太少的话也容易过拟合。
#### Q3.3.22: 文字检测时怎么模糊的数据增强？
**A**: 模糊的数据增强需要修改代码进行添加，以DB为例，在[这一行](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/det/db_process.py#L145) 之前添加模糊的增强就行
#### Q3.3.23: 文字检测时怎么更改图片旋转的角度，实现360度任意旋转？
**A**: 将[这里](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/det/data_augment.py#L22) 的(-10,10) 改为(-180,180)即可
#### Q3.3.24: 训练数据的长宽比过大怎么修改shape
**A**: 识别修改[这里](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/configs/rec/ch_ppocr_v1.1/rec_chinese_common_train_v1.1.yml#L12) ,
检测修改[这里](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/configs/det/det_mv3_db.yml#L13)
#### Q3.3.25：检测模型训练或预测时出现elementwise_add报错
**A**：设置的输入尺寸必须是32的倍数，否则在网络多次下采样和上采样后，feature map会产生1个像素的diff，从而导致elementwise_add时报shape不匹配的错误。
#### Q3.3.26: DB检测训练输入尺寸640，可以改大一些吗？
**A**: 不建议改大。检测模型训练输入尺寸是预处理中random crop后的尺寸，并非直接将原图进行resize，多数场景下这个尺寸并不小了，改大后可能反而并不合适，而且训练会变慢。另外，代码里可能有的地方参数按照预设输入尺寸适配的，改大后可能有隐藏风险。
#### Q3.3.27: 识别模型训练时，loss能正常下降，但acc一直为0
**A**: 识别模型训练初期acc为0是正常的，多训一段时间指标就上来了。
<a name="预测部署3"></a>
### 预测部署 ### 预测部署
#### Q3.4.1：如何pip安装opt模型转换工具？ #### Q3.4.1：如何pip安装opt模型转换工具？
...@@ -676,3 +728,11 @@ return paddle.reader.multiprocess_reader(readers, False, queue_size=320) ...@@ -676,3 +728,11 @@ return paddle.reader.multiprocess_reader(readers, False, queue_size=320)
#### Q3.4.22：训练ccpd车牌数据集，训练集准确率高，测试均是错误的，这是什么原因？ #### Q3.4.22：训练ccpd车牌数据集，训练集准确率高，测试均是错误的，这是什么原因？
**A**：这是因为训练时将shape修改为[3, 70, 220], 预测时对图片resize，会把高度压缩至32，影响测试结果。注释掉[resize代码](https://github.com/PaddlePaddle/PaddleOCR/blob/ed4313d611b7708a7763d4612f00cb7f318a0e1f/tools/infer/predict_rec.py#L54-L55)可以解决问题。 **A**：这是因为训练时将shape修改为[3, 70, 220], 预测时对图片resize，会把高度压缩至32，影响测试结果。注释掉[resize代码](https://github.com/PaddlePaddle/PaddleOCR/blob/ed4313d611b7708a7763d4612f00cb7f318a0e1f/tools/infer/predict_rec.py#L54-L55)可以解决问题。
#### Q3.4.23：安装paddleocr后，提示没有paddle
**A**：这是因为paddlepaddle gpu版本和cpu版本的名称不一致，现在已经在[whl的文档](./whl.md)里做了安装说明。
#### Q3.4.24：DB模型能正确推理预测，但换成EAST或SAST模型时报错或结果不正确
**A**：使用EAST或SAST模型进行推理预测时，需要在命令中指定参数--det_algorithm="EAST" 或 --det_algorithm="SAST"，使用DB时不用指定是因为该参数默认值是"DB"：https://github.com/PaddlePaddle/PaddleOCR/blob/e7a708e9fdaf413ed7a14da8e4a7b4ac0b211e42/tools/infer/utility.py#L43
doc/doc_ch/whl.md
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...@@ -4,6 +4,8 @@ ...@@ -4,6 +4,8 @@
### 安装whl包 ### 安装whl包
首先需要参照[安装文档](installation.md)安装paddlepaddle，然后开始安装paddleocr package
pip安装 pip安装
```bash ```bash
pip install paddleocr pip install paddleocr
...@@ -166,7 +168,7 @@ paddleocr -h ...@@ -166,7 +168,7 @@ paddleocr -h
* 检测+分类+识别全流程 * 检测+分类+识别全流程
```bash ```bash
paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --use_angle_cls true --cls true paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --use_angle_cls true
``` ```
结果是一个list，每个item包含了文本框，文字和识别置信度 结果是一个list，每个item包含了文本框，文字和识别置信度
```bash ```bash
...@@ -190,7 +192,7 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg ...@@ -190,7 +192,7 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg
* 分类+识别 * 分类+识别
```bash ```bash
paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --use_angle_cls true --cls true --det false paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --use_angle_cls true --det false
``` ```
结果是一个list，每个item只包含识别结果和识别置信度 结果是一个list，每个item只包含识别结果和识别置信度
...@@ -222,7 +224,7 @@ paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --det false ...@@ -222,7 +224,7 @@ paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --det false
* 单独执行分类 * 单独执行分类
```bash ```bash
paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --use_angle_cls true --cls true --det false --rec false paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --use_angle_cls true --det false --rec false
``` ```
结果是一个list，每个item只包含分类结果和分类置信度 结果是一个list，每个item只包含分类结果和分类置信度
...@@ -258,7 +260,7 @@ im_show.save('result.jpg') ...@@ -258,7 +260,7 @@ im_show.save('result.jpg')
### 通过命令行使用 ### 通过命令行使用
```bash ```bash
paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_dir} --rec_model_dir {your_rec_model_dir} --rec_char_dict_path {your_rec_char_dict_path} --cls_model_dir {your_cls_model_dir} --use_angle_cls true --cls true paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_dir} --rec_model_dir {your_rec_model_dir} --rec_char_dict_path {your_rec_char_dict_path} --cls_model_dir {your_cls_model_dir} --use_angle_cls true
``` ```
## 参数说明 ## 参数说明
...@@ -295,4 +297,4 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_ ...@@ -295,4 +297,4 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_
| lang | 模型语言类型,目前支持 中文(ch)和英文(en) | ch | | lang | 模型语言类型,目前支持 中文(ch)和英文(en) | ch |
| det | 前向时使用启动检测 | TRUE | | det | 前向时使用启动检测 | TRUE |
| rec | 前向时是否启动识别 | TRUE | | rec | 前向时是否启动识别 | TRUE |
| cls | 前向时是否启动分类 | FALSE | | cls | 前向时是否启动分类, 此参数仅存在于`代码使用`模式 | FALSE |
doc/doc_en/whl_en.md
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...@@ -2,6 +2,9 @@ ...@@ -2,6 +2,9 @@
## Get started quickly ## Get started quickly
### install package ### install package
First, you need to refer to [installation document](installation_en.md) to install paddlepaddle, and then start to install paddleocr package
install by pypi install by pypi
```bash ```bash
pip install paddleocr pip install paddleocr
...@@ -172,7 +175,7 @@ paddleocr -h ...@@ -172,7 +175,7 @@ paddleocr -h
* detection classification and recognition * detection classification and recognition
```bash ```bash
paddleocr --image_dir PaddleOCR/doc/imgs_en/img_12.jpg --use_angle_cls true -cls true --lang en paddleocr --image_dir PaddleOCR/doc/imgs_en/img_12.jpg --use_angle_cls true --lang en
``` ```
Output will be a list, each item contains bounding box, text and recognition confidence Output will be a list, each item contains bounding box, text and recognition confidence
...@@ -198,7 +201,7 @@ Output will be a list, each item contains bounding box, text and recognition con ...@@ -198,7 +201,7 @@ Output will be a list, each item contains bounding box, text and recognition con
* classification and recognition * classification and recognition
```bash ```bash
paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --use_angle_cls true -cls true --det false --lang en paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --use_angle_cls true --det false --lang en
``` ```
Output will be a list, each item contains text and recognition confidence Output will be a list, each item contains text and recognition confidence
...@@ -221,7 +224,7 @@ Output will be a list, each item only contains bounding box ...@@ -221,7 +224,7 @@ Output will be a list, each item only contains bounding box
* only recognition * only recognition
```bash ```bash
paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --det false --cls false --lang en paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --det false --lang en
``` ```
Output will be a list, each item contains text and recognition confidence Output will be a list, each item contains text and recognition confidence
...@@ -231,7 +234,7 @@ Output will be a list, each item contains text and recognition confidence ...@@ -231,7 +234,7 @@ Output will be a list, each item contains text and recognition confidence
* only classification * only classification
```bash ```bash
paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --use_angle_cls true -cls true --det false --rec false paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --use_angle_cls true --det false --rec false
``` ```
Output will be a list, each item contains classification result and confidence Output will be a list, each item contains classification result and confidence
...@@ -268,7 +271,7 @@ im_show.save('result.jpg') ...@@ -268,7 +271,7 @@ im_show.save('result.jpg')
### Use by command line ### Use by command line
```bash ```bash
paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_dir} --rec_model_dir {your_rec_model_dir} --rec_char_dict_path {your_rec_char_dict_path} --cls_model_dir {your_cls_model_dir} --use_angle_cls true --cls true paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_dir} --rec_model_dir {your_rec_model_dir} --rec_char_dict_path {your_rec_char_dict_path} --cls_model_dir {your_cls_model_dir} --use_angle_cls true
``` ```
## Parameter Description ## Parameter Description
...@@ -305,4 +308,4 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_ ...@@ -305,4 +308,4 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_
| lang | The support language, now only Chinese(ch)、English(en)、French(french)、German(german)、Korean(korean)、Japanese(japan) are supported | ch | | lang | The support language, now only Chinese(ch)、English(en)、French(french)、German(german)、Korean(korean)、Japanese(japan) are supported | ch |
| det | Enable detction when `ppocr.ocr` func exec | TRUE | | det | Enable detction when `ppocr.ocr` func exec | TRUE |
| rec | Enable recognition when `ppocr.ocr` func exec | TRUE | | rec | Enable recognition when `ppocr.ocr` func exec | TRUE |
| cls | Enable classification when `ppocr.ocr` func exec | FALSE | | cls | Enable classification when `ppocr.ocr` func exec，this parameter only exists in `code usage` mode | FALSE |
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paddleocr.py
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...@@ -117,61 +117,101 @@ def maybe_download(model_storage_directory, url): ...@@ -117,61 +117,101 @@ def maybe_download(model_storage_directory, url):
os.remove(tmp_path) os.remove(tmp_path)
def parse_args():
def parse_args(mMain=True,add_help=True):
import argparse import argparse
def str2bool(v): def str2bool(v):
return v.lower() in ("true", "t", "1") return v.lower() in ("true", "t", "1")
parser = argparse.ArgumentParser() if mMain:
# params for prediction engine parser = argparse.ArgumentParser(add_help=add_help)
parser.add_argument("--use_gpu", type=str2bool, default=True) # params for prediction engine
parser.add_argument("--ir_optim", type=str2bool, default=True) parser.add_argument("--use_gpu", type=str2bool, default=True)
parser.add_argument("--use_tensorrt", type=str2bool, default=False) parser.add_argument("--ir_optim", type=str2bool, default=True)
parser.add_argument("--gpu_mem", type=int, default=8000) parser.add_argument("--use_tensorrt", type=str2bool, default=False)
parser.add_argument("--gpu_mem", type=int, default=8000)
# params for text detector
parser.add_argument("--image_dir", type=str) # params for text detector
parser.add_argument("--det_algorithm", type=str, default='DB') parser.add_argument("--image_dir", type=str)
parser.add_argument("--det_model_dir", type=str, default=None) parser.add_argument("--det_algorithm", type=str, default='DB')
parser.add_argument("--det_max_side_len", type=float, default=960) parser.add_argument("--det_model_dir", type=str, default=None)
parser.add_argument("--det_max_side_len", type=float, default=960)
# DB parmas
parser.add_argument("--det_db_thresh", type=float, default=0.3) # DB parmas
parser.add_argument("--det_db_box_thresh", type=float, default=0.5) parser.add_argument("--det_db_thresh", type=float, default=0.3)
parser.add_argument("--det_db_unclip_ratio", type=float, default=2.0) parser.add_argument("--det_db_box_thresh", type=float, default=0.5)
parser.add_argument("--det_db_unclip_ratio", type=float, default=2.0)
# EAST parmas
parser.add_argument("--det_east_score_thresh", type=float, default=0.8) # EAST parmas
parser.add_argument("--det_east_cover_thresh", type=float, default=0.1) parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
parser.add_argument("--det_east_nms_thresh", type=float, default=0.2) parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
parser.add_argument("--det_east_nms_thresh", type=float, default=0.2)
# params for text recognizer
parser.add_argument("--rec_algorithm", type=str, default='CRNN') # params for text recognizer
parser.add_argument("--rec_model_dir", type=str, default=None) parser.add_argument("--rec_algorithm", type=str, default='CRNN')
parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320") parser.add_argument("--rec_model_dir", type=str, default=None)
parser.add_argument("--rec_char_type", type=str, default='ch') parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
parser.add_argument("--rec_batch_num", type=int, default=30) parser.add_argument("--rec_char_type", type=str, default='ch')
parser.add_argument("--max_text_length", type=int, default=25) parser.add_argument("--rec_batch_num", type=int, default=30)
parser.add_argument("--rec_char_dict_path", type=str, default=None) parser.add_argument("--max_text_length", type=int, default=25)
parser.add_argument("--use_space_char", type=bool, default=True) parser.add_argument("--rec_char_dict_path", type=str, default=None)
parser.add_argument("--use_space_char", type=bool, default=True)
# params for text classifier
parser.add_argument("--cls_model_dir", type=str, default=None) # params for text classifier
parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192") parser.add_argument("--cls_model_dir", type=str, default=None)
parser.add_argument("--label_list", type=list, default=['0', '180']) parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192")
parser.add_argument("--cls_batch_num", type=int, default=30) parser.add_argument("--label_list", type=list, default=['0', '180'])
parser.add_argument("--cls_thresh", type=float, default=0.9) parser.add_argument("--cls_batch_num", type=int, default=30)
parser.add_argument("--cls_thresh", type=float, default=0.9)
parser.add_argument("--enable_mkldnn", type=bool, default=False)
parser.add_argument("--use_zero_copy_run", type=bool, default=False) parser.add_argument("--enable_mkldnn", type=bool, default=False)
parser.add_argument("--use_pdserving", type=str2bool, default=False) parser.add_argument("--use_zero_copy_run", type=bool, default=False)
parser.add_argument("--use_pdserving", type=str2bool, default=False)
parser.add_argument("--lang", type=str, default='ch')
parser.add_argument("--det", type=str2bool, default=True) parser.add_argument("--lang", type=str, default='ch')
parser.add_argument("--rec", type=str2bool, default=True) parser.add_argument("--det", type=str2bool, default=True)
parser.add_argument("--use_angle_cls", type=str2bool, default=True) parser.add_argument("--rec", type=str2bool, default=True)
return parser.parse_args() parser.add_argument("--use_angle_cls", type=str2bool, default=True)
return parser.parse_args()
else:
return argparse.Namespace( use_gpu=True,
ir_optim=True,
use_tensorrt=False,
gpu_mem=8000,
image_dir='',
det_algorithm='DB',
det_model_dir=None,
det_max_side_len=960,
det_db_thresh=0.3,
det_db_box_thresh=0.5,
det_db_unclip_ratio=2.0,
det_east_score_thresh=0.8,
det_east_cover_thresh=0.1,
det_east_nms_thresh=0.2,
rec_algorithm='CRNN',
rec_model_dir=None,
rec_image_shape="3, 32, 320",
rec_char_type='ch',
rec_batch_num=30,
max_text_length=25,
rec_char_dict_path=None,
use_space_char=True,
cls_model_dir=None,
cls_image_shape="3, 48, 192",
label_list=['0', '180'],
cls_batch_num=30,
cls_thresh=0.9,
enable_mkldnn=False,
use_zero_copy_run=False,
use_pdserving=False,
lang='ch',
det=True,
rec=True,
use_angle_cls=True
)
class PaddleOCR(predict_system.TextSystem): class PaddleOCR(predict_system.TextSystem):
...@@ -181,7 +221,7 @@ class PaddleOCR(predict_system.TextSystem): ...@@ -181,7 +221,7 @@ class PaddleOCR(predict_system.TextSystem):
args: args:
**kwargs: other params show in paddleocr --help **kwargs: other params show in paddleocr --help
""" """
postprocess_params = parse_args() postprocess_params = parse_args(mMain=False, add_help=False)
postprocess_params.__dict__.update(**kwargs) postprocess_params.__dict__.update(**kwargs)
self.use_angle_cls = postprocess_params.use_angle_cls self.use_angle_cls = postprocess_params.use_angle_cls
lang = postprocess_params.lang lang = postprocess_params.lang
...@@ -259,12 +299,13 @@ class PaddleOCR(predict_system.TextSystem): ...@@ -259,12 +299,13 @@ class PaddleOCR(predict_system.TextSystem):
def main(): def main():
# for com # for com
args = parse_args() args = parse_args(mMain=True)
image_file_list = get_image_file_list(args.image_dir) image_file_list = get_image_file_list(args.image_dir)
if len(image_file_list) == 0: if len(image_file_list) == 0:
logger.error('no images find in {}'.format(args.image_dir)) logger.error('no images find in {}'.format(args.image_dir))
return return
ocr_engine = PaddleOCR()
ocr_engine = PaddleOCR(**(args.__dict__))
for img_path in image_file_list: for img_path in image_file_list:
print(img_path) print(img_path)
result = ocr_engine.ocr(img_path, result = ocr_engine.ocr(img_path,
......
ppocr/modeling/architectures/rec_model.py
浏览文件 @ a8122289
...@@ -160,6 +160,7 @@ class RecModel(object): ...@@ -160,6 +160,7 @@ class RecModel(object):
"We set img_shape to be the same , it may affect the inference effect" "We set img_shape to be the same , it may affect the inference effect"
) )
image_shape = deepcopy(self.image_shape) image_shape = deepcopy(self.image_shape)
image_shape.insert(0, -1)
image = fluid.data(name='image', shape=image_shape, dtype='float32') image = fluid.data(name='image', shape=image_shape, dtype='float32')
image.stop_gradient = False image.stop_gradient = False
if self.loss_type == "srn": if self.loss_type == "srn":
......
setup.py
浏览文件 @ a8122289
...@@ -32,7 +32,7 @@ setup( ...@@ -32,7 +32,7 @@ setup(
package_dir={'paddleocr': ''}, package_dir={'paddleocr': ''},
include_package_data=True, include_package_data=True,
entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]}, entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]},
version='1.1.1', version='1.1.2',
install_requires=requirements, install_requires=requirements,
license='Apache License 2.0', license='Apache License 2.0',
description='Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices', description='Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices',
......
thirdparty/paddleocr-go/README.md 0 → 100644
浏览文件 @ a8122289
# PaddleOCR-GO
本服务是[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)的golang部署版本。
## 1. 环境准备
### 运行环境
- go: 1.14
- OpenCV: 4.3.0
- PaddlePaddle: 1.8.4
- 编译环境：cmake 3.15.4 | gcc 4.8.5
- 基于Centos 7.4运行环境编译，Windows请自行解决`OpenCV`和`PaddlePaddle`的编译问题
*另外，以下编译以`.bashrc`个人环境变量配置文件，如果使用`zsh`，请自行更换为`.zshrc`*
### 1.1 安装golang
从官网下载[golang](https://golang.org/dl/)，建议选择1.13版本以上进行安装。下载完成后，直接解压你需要的安装目录，并配置相关环境变量，此处以1.14版本为例。
```shell
# 下载golang
wget https://golang.org/dl/go1.14.10.linux-amd64.tar.gz
# 解压到 /usr/local 目录下
tar -xzvf go1.14.10.linux-amd64.tar.gz -C /usr/local
# 配置GOROOT，即go的安装目录
echo "export GOROOT=/usr/local/go" >> ~/.bashrc
echo "export PATH=$PATH:$GOROOT/bin" >> ~/.bashrc
# 配置GOPATH，即go相关package的安装目录，可自定义一个目录
echo "export GOPATH=$HOME/golang" >> ~/.bashrc
echo "export PATH=$PATH:$GOPATH/bin" >> ~/.bashrc
# 配置GOPROXY，即go mod包管理器的下载代理，同时打开mod模式
echo "export GO111MODULE=on" >> ~/.bashrc
echo "export GOPROXY=https://mirrors.aliyun.com/goproxy/" >> ~/.bashrc
source ~/.bashrc
```
### 1.2 编译OpenCV库
go语言中，OpenCV的使用主要以[gocv](https://github.com/hybridgroup/gocv)包为主，gocv使用cgo调用OpenCV提供接口，因此还是需要编译OpenCV库。
**踩坑指南之一：[gocv官方实现](https://github.com/hybridgroup/gocv)中，部分接口并没有与原版C++的OpenCV的API保持一致，导致图片处理结果会出现一定的数值偏差。为处理这种偏差，[该仓库](https://github.com/LKKlein/gocv)fork了一份gocv官方源码，并对部分这些不一致的API进行了修正，保证结果与其他语言的一致性。**
对于OpenCV的编译，gocv官方提供了[Makefile](https://github.com/LKKlein/gocv/blob/lk/Makefile)，可以一键进行安装，具体安装步骤详见[官方指南](https://github.com/LKKlein/gocv/blob/lk/README_ORIGIN.md#ubuntulinux)。
这里提供逐步安装的方式，方便排查错误。
- 下载并解压OpenCV-4.3.0和OpenCV-Contrib-4.3.0
```shell
# 创建opencv安装目录
mkdir -p ~/opencv
# 下载OpenCV
cd ~/opencv
curl -sL https://github.com/opencv/opencv/archive/4.3.0.zip > opencv.zip
unzip -q opencv.zip
rm -rf opencv.zip
# 下载OpenCV-Contrib
curl -sL https://github.com/opencv/opencv_contrib/archive/4.3.0.zip > opencv-contrib.zip
unzip -q opencv-contrib.zip
rm -rf opencv-contrib.zip
```
- 安装相关依赖
```shell
sudo yum -y install pkgconfig cmake git gtk2-devel libpng-devel libjpeg-devel libtiff-devel tbb tbb-devel libdc1394-devel
```
- 编译安装
```shell
mkdir -p ~/.local/opencv-4.3.0
cd ~/opencv/opencv-4.3.0
mkdir build
cd build
cmake -D WITH_IPP=OFF \
-D WITH_OPENGL=OFF \
-D WITH_QT=OFF \
-D BUILD_EXAMPLES=OFF \
-D BUILD_TESTS=OFF \
-D BUILD_PERF_TESTS=OFF \
-D BUILD_opencv_java=OFF \
-D BUILD_opencv_python=OFF \
-D BUILD_opencv_python2=OFF \
-D BUILD_opencv_python3=OFF \
-D OPENCV_GENERATE_PKGCONFIG=ON \
-D CMAKE_INSTALL_PREFIX=$HOME/.local/opencv-4.3.0 \
-D OPENCV_ENABLE_NONFREE=ON \
-D OPENCV_EXTRA_MODULES_PATH=$HOME/opencv/opencv_contrib-4.3.0/modules ..
make -j8
make install
sudo ldconfig
```
make进行编译时，可能出现因`xfeatures2d`的两个模块下载失败导致的编译失败，这里只需要手动下载这部分文件到`$HOME/opencv/opencv_contrib-4.3.0/modules/xfeatures2d/src`目录下，然后重新执行`make -j8`即可。这部分文件地址可参考[这里](https://github.com/opencv/opencv_contrib/issues/1301#issuecomment-447181426)给出的链接。
- 配置环境变量
```shell
echo "export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:$HOME/.local/opencv-4.3.0/lib64/pkgconfig" >> ~/.bashrc
echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$HOME/.local/opencv-4.3.0/lib64" >> ~/.bashrc
source ~/.bashrc
```
- 验证安装
```shell
# 安装gocv包，先mod init
go mod init opencv
go get -u github.com/LKKlein/gocv
# 验证安装结果
cd $GOPATH/pkg/mod/github.com/!l!k!klein/gocv@v0.28.0
go run ./cmd/version/main.go
# 输出
# gocv version: 0.28.0
# opencv lib version: 4.3.0
```
### 1.3 编译PaddlePaddle的C语言API
go语言只能通过cgo调用C语言API，而不能直接与C++进行交互，因此需要编译PaddlePaddle的C语言API。当然，也可以自己写C语言调用C++的代码和头文件，这样就可以直接使用PaddlePaddle提供的已编译的C++推理库，无需自己手动编译，详见[该仓库](https://github.com/LKKlein/paddleocr-go/tree/dev_cxx)。
- 获取PaddlePaddle源代码
```shell
cd ~
git clone --recurse-submodules https://github.com/paddlepaddle/paddle
# 切换到v1.8.4版本
cd paddle
git checkout v1.8.4
# 目前版本无论单卡还是多卡都需要先安装nccl
git clone https://github.com/NVIDIA/nccl.git
make -j8
make install
```
- 编译Paddle源代码
**踩坑指南之二：PaddlePaddle的C语言API实现有一个bug，即获取输入输出变量名时只能获取到第一个模型的变量名，后续模型都无法获取输入输出变量名，进而无法获取到模型输出，详情见[issue](https://github.com/PaddlePaddle/Paddle/issues/28309)。因此，编译前需要手动将`paddle/fluid/inference/capi/pd_predictor.cc`文件中`210行`与`215行`的`static`删除。**
在处理完该bug之后，才能进行后续编译。相关编译参数见[官方文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html#id12)，注意部分参数需要相关依赖，请确保依赖完整再启用。
```shell
# 创建c++推理库文件夹
mkdir -p ~/paddle_inference
export PADDLE_INFER=`$HOME/paddle_inference`
# 执行编译
export PADDLE_ROOT=`pwd`
mkdir build
cd build
cmake -DFLUID_INFERENCE_INSTALL_DIR=$PADDLE_INFER \
-DWITH_CONTRIB=OFF \
-DCMAKE_BUILD_TYPE=Release \
-DWITH_PYTHON=OFF \
-DWITH_MKL=ON \
-DWITH_GPU=ON \
-DON_INFER=ON \
--WITH_MKLDNN=ON \
--WITH_XBYAK=ON \
--WITH_DSO=OFF ..
make
make inference_lib_dist
```
编译完成后，可以在`build/fluid_inference_c_install_dir`目录下，看到以下生成的文件
```
build/fluid_inference_c_install_dir
├── paddle
├── third_party
└── version.txt
```
其中`paddle`就是Paddle库的C语言预测API，`version.txt`中包含当前预测库的版本信息。最后，将C推理库配置到环境变量。
```shell
echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$PADDLE_ROOT/build/fluid_inference_c_install_dir/paddle/lib" >> ~/.bashrc
echo "export LIBRARY_PATH=$LIBRARY_PATH:$PADDLE_ROOT/build/fluid_inference_c_install_dir/paddle/lib" >> ~/.bashrc
souce ~/.bashrc
```
## 2. paddleocr-go预测库
### 2.1 安装paddleocr-go
确保C推理库已配置到环境变量，然后直接执行安装命令
```shell
go get -u github.com/PaddlePaddle/PaddleOCR/thirdparty/paddleocr-go
```
### 2.2 相关使用API
在go中使用import引入包
```go
import github.com/PaddlePaddle/PaddleOCR/thirdparty/paddleocr-go/ocr
```
- 预测结果结构体
```go
type OCRText struct {
BBox [][]int `json:"bbox"`
Text string `json:"text"`
Score float64 `json:"score"`
}
```
一张图的OCR结果包含多个`OCRText`结果，每个结果包含预测框、预测文本、预测文本得分。
- OCR预测类
```go
func NewOCRSystem(confFile string, a map[string]interface{}) *OCRSystem
```
`OCRSystem`是主要对外提供API的结构；
`confFile`是yaml配置文件的路径，可在配置文件中修改相关预测参数，也可以传空字符串，这时会全部使用默认配置；
`a`是可以在代码中直接定义的配置参数，优先级高于配置文件，会覆盖配置文件和默认配置的参数。
- 单张图预测API
```go 
func (ocr *OCRSystem) PredictOneImage(img gocv.Mat) []OCRText
```
- 图片文件夹预测API
```go
func (ocr *OCRSystem) PredictDirImages(dirname string) map[string][]OCRText
```
`dirname`图片文件夹的目录，默认会预测改目录下所有`jpg`和`png`图片，并返回每张图的预测结果。
- OCR Server
```go
func (ocr *OCRSystem) StartServer(port string)
```
开启OCR预测Server，开启后，使用`post`请求上传需要识别的图片至`http://$ip:$port/ocr`即可直接获取该图片上所有文本的识别结果。其中，`$ip`是开启服务的主机`ip`或`127.0.0.1`的本地ip， `$port`是传入的端口参数。
## 3. 预测demo
### 3.1 生成预测demo
以下两种方式均可生成预测demo文件，任选其一即可
- 通过下载`paddleocr-go`代码并编译
```shell
git clone https://github.com/PaddlePaddle/PaddleOCR
cd PaddleOCR/thirdparty/paddleocr-go
# 确保C动态库路径已在环境变量中，执行以下命令生成ppocr-go文件
go build ppocr-go.go
```
- 通过go package自动安装
```shell
# 执行后会自动在$GOPATH/bin下生成ppocr-go文件，如果配置了PATH=$PATH:$GOPATH/bin，以下预测命令可以去掉`./`，直接执行ppocr-go
go get -u github.com/PaddlePaddle/PaddleOCR/thirdparty/paddleocr-go
```
### 3.2 修改预测配置
当前给定的配置文件`config/conf.yaml`中，包含了默认的OCR预测配置参数，可根据个人需要更改相关参数。
比如，将`use_gpu`改为`false`，使用CPU执行预测；将`det_model_dir`, `rec_model_dir`, `cls_model_dir`都更改为自己的本地模型路径，也或者是更改字典`rec_char_dict_path`的路径，这四个路径如果配置http链接，会自动下载到本地目录。另外，配置参数包含了预测引擎、检测模型、检测阈值、方向分类模型、识别模型及阈值的相关参数，具体参数的意义可参见[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/whl.md#%E5%8F%82%E6%95%B0%E8%AF%B4%E6%98%8E)。
### 3.3 执行预测demo
预测demo提供了三种预测方式，分别是单张图预测、文件夹批量预测、OCR Server预测。三者命令行优先级依次降低。
#### 3.3.1 单张图预测
```shell
./ppocr-go --config config/conf.yaml --image images/test.jpg
```
执行完成，会输出以下内容：
<img src="./images/result/single_img_result.jpg" style="zoom:80%;" />
#### 3.3.2 文件夹批量预测
```shell
./ppocr-go --config config/conf.yaml --image_dir ./images
```
执行完成，会输出以下内容：
<img src="./images/result/img_dir_result.jpg" style="zoom:80%;" />
#### 3.3.3 开启OCR Server
```shell
./ppocr-go --use_servering --port=18600
```
开启服务后，可以在其他客户端中通过`post`请求进行ocr预测。此处以`Python`客户端为例，如下所示
```python
import requests
files = {'image': open('images/test.jpg','rb')}
url = "http://127.0.0.1:18600/ocr"
r = requests.post(url, files=files)
print(r.text)
```
执行完成可以得到以下结果
![](./images/result/python_client_result.jpg)
最后，在Python中将上述结果可视化可以得到以下结果
![](./images/result/python_vis_result.jpg)
thirdparty/paddleocr-go/config/conf.yaml 0 → 100644
浏览文件 @ a8122289
# params for prediction engine
use_gpu: true
ir_optim: true
enable_mkldnn: false
# use_zero_copy_run: true
use_tensorrt: false
num_cpu_threads: 6
gpu_id: 0
gpu_mem: 2000
# params for text detector
det_algorithm: "DB"
det_model_dir: "https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar"
det_max_side_len: 960
# DB parmas
det_db_thresh: 0.3
det_db_box_thresh: 0.5
det_db_unclip_ratio: 2.0
# EAST parmas
det_east_score_thresh: 0.8
det_east_cover_thresh: 0.1
det_east_nms_thresh: 0.2
# params for text recognizer
rec_algorithm: "CRNN"
rec_model_dir: "https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar"
rec_image_shape: [3, 32, 320]
rec_char_type: "ch"
rec_batch_num: 30
max_text_length: 25
rec_char_dict_path: "https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/develop/ppocr/utils/ppocr_keys_v1.txt"
use_space_char: true
# params for text classifier
use_angle_cls: false
cls_model_dir: "https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar"
cls_image_shape: [3, 48, 192]
label_list: ["0", "180"]
cls_batch_num: 30
cls_thresh: 0.9
lang: ch
det: true
rec: true
cls: false
\ No newline at end of file
thirdparty/paddleocr-go/go.mod 0 → 100644
浏览文件 @ a8122289
module github.com/PaddlePaddle/PaddleOCR/thirdparty/paddleocr-go
go 1.14
require (
github.com/LKKlein/gocv v0.28.0
github.com/ctessum/go.clipper v0.0.0-20200522184404-9c744fa3e86c
gopkg.in/yaml.v3 v3.0.0-20200615113413-eeeca48fe776
)
thirdparty/paddleocr-go/go.sum 0 → 100644
浏览文件 @ a8122289
github.com/LKKlein/gocv v0.28.0 h1:1MMvs9uYf+QGPi86it2pUmN8RRoyMnPLUefKB/Jf1Q0=
github.com/LKKlein/gocv v0.28.0/go.mod h1:MP408EL7eakRU3vzjsozzfELSX7HDDGdMpWANV1IOHY=
github.com/PaddlePaddle/PaddleOCR v1.1.0 h1:zmPevInTs5P+ctSokI9sWQLTThmJBUCo/JCLbB5xbps=
github.com/ctessum/go.clipper v0.0.0-20200522184404-9c744fa3e86c h1:VXCsVlam0R2Yl7VET2GxZBPdOa7gFRexyhfWb9v9QtM=
github.com/ctessum/go.clipper v0.0.0-20200522184404-9c744fa3e86c/go.mod h1:KRMo3PCsooJP3LmCwKI76dkd7f3ki3zwYLHR7Iwbi5k=
gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0=
gopkg.in/yaml.v3 v3.0.0-20200615113413-eeeca48fe776 h1:tQIYjPdBoyREyB9XMu+nnTclpTYkz2zFM+lzLJFO4gQ=
gopkg.in/yaml.v3 v3.0.0-20200615113413-eeeca48fe776/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
thirdparty/paddleocr-go/ocr/core.go 0 → 100644
浏览文件 @ a8122289
package ocr
import (
"bytes"
"encoding/json"
"errors"
"image"
"image/color"
"io"
"log"
"math"
"net/http"
"path"
"path/filepath"
"sort"
"strings"
"github.com/LKKlein/gocv"
"github.com/PaddlePaddle/PaddleOCR/thirdparty/paddleocr-go/paddle"
)
type PaddleModel struct {
predictor *paddle.Predictor
input *paddle.ZeroCopyTensor
outputs []*paddle.ZeroCopyTensor
useGPU bool
deviceID int
initGPUMem int
numThreads int
useMKLDNN bool
useTensorRT bool
useIROptim bool
}
func NewPaddleModel(args map[string]interface{}) *PaddleModel {
return &PaddleModel{
useGPU: getBool(args, "use_gpu", false),
deviceID: getInt(args, "gpu_id", 0),
initGPUMem: getInt(args, "gpu_mem", 1000),
numThreads: getInt(args, "num_cpu_threads", 6),
useMKLDNN: getBool(args, "enable_mkldnn", false),
useTensorRT: getBool(args, "use_tensorrt", false),
useIROptim: getBool(args, "ir_optim", true),
}
}
func (model *PaddleModel) LoadModel(modelDir string) {
config := paddle.NewAnalysisConfig()
config.DisableGlogInfo()
config.SetModel(modelDir+"/model", modelDir+"/params")
if model.useGPU {
config.EnableUseGpu(model.initGPUMem, model.deviceID)
} else {
config.DisableGpu()
config.SetCpuMathLibraryNumThreads(model.numThreads)
if model.useMKLDNN {
config.EnableMkldnn()
}
}
// config.EnableMemoryOptim()
if model.useIROptim {
config.SwitchIrOptim(true)
}
// false for zero copy tensor
config.SwitchUseFeedFetchOps(false)
config.SwitchSpecifyInputNames(true)
model.predictor = paddle.NewPredictor(config)
model.input = model.predictor.GetInputTensors()[0]
model.outputs = model.predictor.GetOutputTensors()
}
type OCRText struct {
BBox [][]int `json:"bbox"`
Text string `json:"text"`
Score float64 `json:"score"`
}
type TextPredictSystem struct {
detector *DBDetector
cls *TextClassifier
rec *TextRecognizer
}
func NewTextPredictSystem(args map[string]interface{}) *TextPredictSystem {
sys := &TextPredictSystem{
detector: NewDBDetector(getString(args, "det_model_dir", ""), args),
rec: NewTextRecognizer(getString(args, "rec_model_dir", ""), args),
}
if getBool(args, "use_angle_cls", false) {
sys.cls = NewTextClassifier(getString(args, "cls_model_dir", ""), args)
}
return sys
}
func (sys *TextPredictSystem) sortBoxes(boxes [][][]int) [][][]int {
sort.Slice(boxes, func(i, j int) bool {
if boxes[i][0][1] < boxes[j][0][1] {
return true
}
if boxes[i][0][1] > boxes[j][0][1] {
return false
}
return boxes[i][0][0] < boxes[j][0][0]
})
for i := 0; i < len(boxes)-1; i++ {
if math.Abs(float64(boxes[i+1][0][1]-boxes[i][0][1])) < 10 && boxes[i+1][0][0] < boxes[i][0][0] {
boxes[i], boxes[i+1] = boxes[i+1], boxes[i]
}
}
return boxes
}
func (sys *TextPredictSystem) getRotateCropImage(img gocv.Mat, box [][]int) gocv.Mat {
cropW := int(math.Sqrt(math.Pow(float64(box[0][0]-box[1][0]), 2) + math.Pow(float64(box[0][1]-box[1][1]), 2)))
cropH := int(math.Sqrt(math.Pow(float64(box[0][0]-box[3][0]), 2) + math.Pow(float64(box[0][1]-box[3][1]), 2)))
ptsstd := make([]image.Point, 4)
ptsstd[0] = image.Pt(0, 0)
ptsstd[1] = image.Pt(cropW, 0)
ptsstd[2] = image.Pt(cropW, cropH)
ptsstd[3] = image.Pt(0, cropH)
points := make([]image.Point, 4)
points[0] = image.Pt(box[0][0], box[0][1])
points[1] = image.Pt(box[1][0], box[1][1])
points[2] = image.Pt(box[2][0], box[2][1])
points[3] = image.Pt(box[3][0], box[3][1])
M := gocv.GetPerspectiveTransform(points, ptsstd)
defer M.Close()
dstimg := gocv.NewMat()
gocv.WarpPerspectiveWithParams(img, &dstimg, M, image.Pt(cropW, cropH),
gocv.InterpolationCubic, gocv.BorderReplicate, color.RGBA{0, 0, 0, 0})
if float64(dstimg.Rows()) >= float64(dstimg.Cols())*1.5 {
srcCopy := gocv.NewMat()
gocv.Transpose(dstimg, &srcCopy)
defer dstimg.Close()
gocv.Flip(srcCopy, &srcCopy, 0)
return srcCopy
}
return dstimg
}
func (sys *TextPredictSystem) Run(img gocv.Mat) []OCRText {
srcimg := gocv.NewMat()
defer srcimg.Close()
img.CopyTo(&srcimg)
boxes := sys.detector.Run(img)
if len(boxes) == 0 {
return nil
}
boxes = sys.sortBoxes(boxes)
cropimages := make([]gocv.Mat, len(boxes))
for i := 0; i < len(boxes); i++ {
tmpbox := make([][]int, len(boxes[i]))
for j := 0; j < len(tmpbox); j++ {
tmpbox[j] = make([]int, len(boxes[i][j]))
copy(tmpbox[j], boxes[i][j])
}
cropimg := sys.getRotateCropImage(srcimg, tmpbox)
cropimages[i] = cropimg
}
if sys.cls != nil {
cropimages = sys.cls.Run(cropimages)
}
recResult := sys.rec.Run(cropimages, boxes)
return recResult
}
type OCRSystem struct {
args map[string]interface{}
tps *TextPredictSystem
}
func NewOCRSystem(confFile string, a map[string]interface{}) *OCRSystem {
args, err := ReadYaml(confFile)
if err != nil {
log.Printf("Read config file %v failed! Please check. err: %v\n", confFile, err)
log.Println("Program will use default config.")
args = defaultArgs
}
for k, v := range a {
args[k] = v
}
return &OCRSystem{
args: args,
tps: NewTextPredictSystem(args),
}
}
func (ocr *OCRSystem) StartServer(port string) {
http.HandleFunc("/ocr", ocr.predictHandler)
log.Println("OCR Server has been started on port :", port)
err := http.ListenAndServe(":"+port, nil)
if err != nil {
log.Panicf("http error! error: %v\n", err)
}
}
func (ocr *OCRSystem) predictHandler(w http.ResponseWriter, r *http.Request) {
if r.Method != "POST" {
w.Write([]byte(errors.New("post method only").Error()))
return
}
r.ParseMultipartForm(32 << 20)
var buf bytes.Buffer
file, header, err := r.FormFile("image")
if err != nil {
w.Write([]byte(err.Error()))
return
}
defer file.Close()
ext := strings.ToLower(path.Ext(header.Filename))
if ext != ".jpg" && ext != ".png" {
w.Write([]byte(errors.New("only support image endswith jpg/png").Error()))
return
}
io.Copy(&buf, file)
img, err2 := gocv.IMDecode(buf.Bytes(), gocv.IMReadColor)
defer img.Close()
if err2 != nil {
w.Write([]byte(err2.Error()))
return
}
result := ocr.PredictOneImage(img)
if output, err3 := json.Marshal(result); err3 != nil {
w.Write([]byte(err3.Error()))
} else {
w.Write(output)
}
}
func (ocr *OCRSystem) PredictOneImage(img gocv.Mat) []OCRText {
return ocr.tps.Run(img)
}
func (ocr *OCRSystem) PredictDirImages(dirname string) map[string][]OCRText {
if dirname == "" {
return nil
}
imgs, _ := filepath.Glob(dirname + "/*.jpg")
tmpimgs, _ := filepath.Glob(dirname + "/*.png")
imgs = append(imgs, tmpimgs...)
results := make(map[string][]OCRText, len(imgs))
for i := 0; i < len(imgs); i++ {
imgname := imgs[i]
img := ReadImage(imgname)
defer img.Close()
res := ocr.PredictOneImage(img)
results[imgname] = res
}
return results
}
thirdparty/paddleocr-go/ocr/default_args.go 0 → 100644
浏览文件 @ a8122289
package ocr
var (
defaultArgs = map[string]interface{}{
"use_gpu": true,
"ir_optim": true,
"enable_mkldnn": false,
"use_tensorrt": false,
"num_cpu_threads": 6,
"gpu_id": 0,
"gpu_mem": 2000,
"det_algorithm": "DB",
"det_model_dir": "https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar",
"det_max_side_len": 960,
"det_db_thresh": 0.3,
"det_db_box_thresh": 0.5,
"det_db_unclip_ratio": 2.0,
"det_east_score_thresh": 0.8,
"det_east_cover_thresh": 0.1,
"det_east_nms_thresh": 0.2,
"rec_algorithm": "CRNN",
"rec_model_dir": "https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar",
"rec_image_shape": []interface{}{3, 32, 320},
"rec_char_type": "ch",
"rec_batch_num": 30,
"max_text_length": 25,
"rec_char_dict_path": "https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/develop/ppocr/utils/ppocr_keys_v1.txt",
"use_space_char": true,
"use_angle_cls": false,
"cls_model_dir": "https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar",
"cls_image_shape": []interface{}{3, 48, 192},
"label_list": []interface{}{"0", "180"},
"cls_batch_num": 30,
"cls_thresh": 0.9,
"lang": "ch",
"det": true,
"rec": true,
"cls": false,
}
)
thirdparty/paddleocr-go/ocr/ocr_cls.go 0 → 100644
浏览文件 @ a8122289
package ocr
import (
"log"
"os"
"time"
"github.com/LKKlein/gocv"
)
type TextClassifier struct {
*PaddleModel
batchNum int
thresh float64
shape []int
labels []string
}
type ClsResult struct {
Score float32
Label int64
}
func NewTextClassifier(modelDir string, args map[string]interface{}) *TextClassifier {
shapes := []int{3, 48, 192}
if v, ok := args["cls_image_shape"]; ok {
for i, s := range v.([]interface{}) {
shapes[i] = s.(int)
}
}
cls := &TextClassifier{
PaddleModel: NewPaddleModel(args),
batchNum: getInt(args, "cls_batch_num", 30),
thresh: getFloat64(args, "cls_thresh", 0.9),
shape: shapes,
}
if checkModelExists(modelDir) {
home, _ := os.UserHomeDir()
modelDir, _ = downloadModel(home+"/.paddleocr/cls", modelDir)
} else {
log.Panicf("cls model path: %v not exist! Please check!", modelDir)
}
cls.LoadModel(modelDir)
return cls
}
func (cls *TextClassifier) Run(imgs []gocv.Mat) []gocv.Mat {
batch := cls.batchNum
var clsTime int64 = 0
clsout := make([]ClsResult, len(imgs))
srcimgs := make([]gocv.Mat, len(imgs))
c, h, w := cls.shape[0], cls.shape[1], cls.shape[2]
for i := 0; i < len(imgs); i += batch {
j := i + batch
if len(imgs) < j {
j = len(imgs)
}
normImgs := make([]float32, (j-i)*c*h*w)
for k := i; k < j; k++ {
tmp := gocv.NewMat()
imgs[k].CopyTo(&tmp)
srcimgs[k] = tmp
img := clsResize(imgs[k], cls.shape)
data := normPermute(img, []float32{0.5, 0.5, 0.5}, []float32{0.5, 0.5, 0.5}, 255.0)
copy(normImgs[(k-i)*c*h*w:], data)
}
st := time.Now()
cls.input.SetValue(normImgs)
cls.input.Reshape([]int32{int32(j - i), int32(c), int32(h), int32(w)})
cls.predictor.SetZeroCopyInput(cls.input)
cls.predictor.ZeroCopyRun()
cls.predictor.GetZeroCopyOutput(cls.outputs[0])
cls.predictor.GetZeroCopyOutput(cls.outputs[1])
var probout [][]float32
var labelout []int64
if len(cls.outputs[0].Shape()) == 2 {
probout = cls.outputs[0].Value().([][]float32)
} else {
labelout = cls.outputs[0].Value().([]int64)
}
if len(cls.outputs[1].Shape()) == 2 {
probout = cls.outputs[1].Value().([][]float32)
} else {
labelout = cls.outputs[1].Value().([]int64)
}
clsTime += int64(time.Since(st).Milliseconds())
for no, label := range labelout {
score := probout[no][label]
clsout[i+no] = ClsResult{
Score: score,
Label: label,
}
if label%2 == 1 && float64(score) > cls.thresh {
gocv.Rotate(srcimgs[i+no], &srcimgs[i+no], gocv.Rotate180Clockwise)
}
}
}
log.Println("cls num: ", len(clsout), ", cls time elapse: ", clsTime, "ms")
return srcimgs
}
thirdparty/paddleocr-go/ocr/ocr_det.go 0 → 100644
浏览文件 @ a8122289
package ocr
import (
"log"
"os"
"time"
"github.com/LKKlein/gocv"
)
type DBDetector struct {
*PaddleModel
preProcess DetPreProcess
postProcess DetPostProcess
}
func NewDBDetector(modelDir string, args map[string]interface{}) *DBDetector {
maxSideLen := getInt(args, "det_max_side_len", 960)
thresh := getFloat64(args, "det_db_thresh", 0.3)
boxThresh := getFloat64(args, "det_db_box_thresh", 0.5)
unClipRatio := getFloat64(args, "det_db_unclip_ratio", 2.0)
detector := &DBDetector{
PaddleModel: NewPaddleModel(args),
preProcess: NewDBProcess(make([]int, 0), maxSideLen),
postProcess: NewDBPostProcess(thresh, boxThresh, unClipRatio),
}
if checkModelExists(modelDir) {
home, _ := os.UserHomeDir()
modelDir, _ = downloadModel(home+"/.paddleocr/det", modelDir)
} else {
log.Panicf("det model path: %v not exist! Please check!", modelDir)
}
detector.LoadModel(modelDir)
return detector
}
func (det *DBDetector) Run(img gocv.Mat) [][][]int {
oriH := img.Rows()
oriW := img.Cols()
data, resizeH, resizeW := det.preProcess.Run(img)
st := time.Now()
det.input.SetValue(data)
det.input.Reshape([]int32{1, 3, int32(resizeH), int32(resizeW)})
det.predictor.SetZeroCopyInput(det.input)
det.predictor.ZeroCopyRun()
det.predictor.GetZeroCopyOutput(det.outputs[0])
ratioH, ratioW := float64(resizeH)/float64(oriH), float64(resizeW)/float64(oriW)
boxes := det.postProcess.Run(det.outputs[0], oriH, oriW, ratioH, ratioW)
log.Println("det_box num: ", len(boxes), ", time elapse: ", time.Since(st))
return boxes
}
thirdparty/paddleocr-go/ocr/ocr_rec.go 0 → 100644
浏览文件 @ a8122289
package ocr
import (
"log"
"os"
"time"
"github.com/LKKlein/gocv"
)
type TextRecognizer struct {
*PaddleModel
batchNum int
textLen int
shape []int
charType string
labels []string
}
func NewTextRecognizer(modelDir string, args map[string]interface{}) *TextRecognizer {
shapes := []int{3, 32, 320}
if v, ok := args["rec_image_shape"]; ok {
for i, s := range v.([]interface{}) {
shapes[i] = s.(int)
}
}
home, _ := os.UserHomeDir()
labelpath := getString(args, "rec_char_dict_path", home+"/.paddleocr/rec/ppocr_keys_v1.txt")
labels := readLines2StringSlice(labelpath)
if getBool(args, "use_space_char", true) {
labels = append(labels, " ")
}
rec := &TextRecognizer{
PaddleModel: NewPaddleModel(args),
batchNum: getInt(args, "rec_batch_num", 30),
textLen: getInt(args, "max_text_length", 25),
charType: getString(args, "rec_char_type", "ch"),
shape: shapes,
labels: labels,
}
if checkModelExists(modelDir) {
modelDir, _ = downloadModel(home+"/.paddleocr/rec/ch", modelDir)
} else {
log.Panicf("rec model path: %v not exist! Please check!", modelDir)
}
rec.LoadModel(modelDir)
return rec
}
func (rec *TextRecognizer) Run(imgs []gocv.Mat, bboxes [][][]int) []OCRText {
recResult := make([]OCRText, 0, len(imgs))
batch := rec.batchNum
var recTime int64 = 0
c, h, w := rec.shape[0], rec.shape[1], rec.shape[2]
for i := 0; i < len(imgs); i += batch {
j := i + batch
if len(imgs) < j {
j = len(imgs)
}
maxwhratio := 0.0
for k := i; k < j; k++ {
h, w := imgs[k].Rows(), imgs[k].Cols()
ratio := float64(w) / float64(h)
if ratio > maxwhratio {
maxwhratio = ratio
}
}
if rec.charType == "ch" {
w = int(32 * maxwhratio)
}
normimgs := make([]float32, (j-i)*c*h*w)
for k := i; k < j; k++ {
data := crnnPreprocess(imgs[k], rec.shape, []float32{0.5, 0.5, 0.5},
[]float32{0.5, 0.5, 0.5}, 255.0, maxwhratio, rec.charType)
defer imgs[k].Close()
copy(normimgs[(k-i)*c*h*w:], data)
}
st := time.Now()
rec.input.SetValue(normimgs)
rec.input.Reshape([]int32{int32(j - i), int32(c), int32(h), int32(w)})
rec.predictor.SetZeroCopyInput(rec.input)
rec.predictor.ZeroCopyRun()
rec.predictor.GetZeroCopyOutput(rec.outputs[0])
rec.predictor.GetZeroCopyOutput(rec.outputs[1])
recIdxBatch := rec.outputs[0].Value().([][]int64)
recIdxLod := rec.outputs[0].Lod()
predictBatch := rec.outputs[1].Value().([][]float32)
predictLod := rec.outputs[1].Lod()
recTime += int64(time.Since(st).Milliseconds())
for rno := 0; rno < len(recIdxLod)-1; rno++ {
predIdx := make([]int, 0, 2)
for beg := recIdxLod[rno]; beg < recIdxLod[rno+1]; beg++ {
predIdx = append(predIdx, int(recIdxBatch[beg][0]))
}
if len(predIdx) == 0 {
continue
}
words := ""
for n := 0; n < len(predIdx); n++ {
words += rec.labels[predIdx[n]]
}
score := 0.0
count := 0
blankPosition := int(rec.outputs[1].Shape()[1])
for beg := predictLod[rno]; beg < predictLod[rno+1]; beg++ {
argMaxID, maxVal := argmax(predictBatch[beg])
if blankPosition-1-argMaxID > 0 {
score += float64(maxVal)
count++
}
}
score = score / float64(count)
recResult = append(recResult, OCRText{
BBox: bboxes[i+rno],
Text: words,
Score: score,
})
}
}
log.Println("rec num: ", len(recResult), ", rec time elapse: ", recTime, "ms")
return recResult
}
thirdparty/paddleocr-go/ocr/postprocess.go 0 → 100644
浏览文件 @ a8122289
package ocr
import (
"image"
"image/color"
"math"
"sort"
"github.com/LKKlein/gocv"
"github.com/PaddlePaddle/PaddleOCR/thirdparty/paddleocr-go/paddle"
clipper "github.com/ctessum/go.clipper"
)
type xFloatSortBy [][]float32
func (a xFloatSortBy) Len() int { return len(a) }
func (a xFloatSortBy) Swap(i, j int) { a[i], a[j] = a[j], a[i] }
func (a xFloatSortBy) Less(i, j int) bool { return a[i][0] < a[j][0] }
type xIntSortBy [][]int
func (a xIntSortBy) Len() int { return len(a) }
func (a xIntSortBy) Swap(i, j int) { a[i], a[j] = a[j], a[i] }
func (a xIntSortBy) Less(i, j int) bool { return a[i][0] < a[j][0] }
type DetPostProcess interface {
Run(output *paddle.ZeroCopyTensor, oriH, oriW int, ratioH, ratioW float64) [][][]int
}
type DBPostProcess struct {
thresh float64
boxThresh float64
maxCandidates int
unClipRatio float64
minSize int
}
func NewDBPostProcess(thresh, boxThresh, unClipRatio float64) *DBPostProcess {
return &DBPostProcess{
thresh: thresh,
boxThresh: boxThresh,
unClipRatio: unClipRatio,
maxCandidates: 1000,
minSize: 3,
}
}
func (d *DBPostProcess) getMinBoxes(rect gocv.RotatedRect) [][]float32 {
points := gocv.NewMat()
gocv.BoxPoints(rect, &points)
defer points.Close()
array := d.mat2slice(points)
sort.Sort(xFloatSortBy(array))
point1, point2, point3, point4 := array[0], array[1], array[2], array[3]
if array[3][1] <= array[2][1] {
point2, point3 = array[3], array[2]
} else {
point2, point3 = array[2], array[3]
}
if array[1][1] <= array[0][1] {
point1, point4 = array[1], array[0]
} else {
point1, point4 = array[0], array[1]
}
array = [][]float32{point1, point2, point3, point4}
return array
}
func (d *DBPostProcess) mat2slice(mat gocv.Mat) [][]float32 {
array := make([][]float32, mat.Rows())
for i := 0; i < mat.Rows(); i++ {
tmp := make([]float32, mat.Cols())
for j := 0; j < mat.Cols(); j++ {
tmp[j] = mat.GetFloatAt(i, j)
}
array[i] = tmp
}
return array
}
func (d *DBPostProcess) boxScoreFast(array [][]float32, pred gocv.Mat) float64 {
height, width := pred.Rows(), pred.Cols()
boxX := []float32{array[0][0], array[1][0], array[2][0], array[3][0]}
boxY := []float32{array[0][1], array[1][1], array[2][1], array[3][1]}
xmin := clip(int(math.Floor(float64(minf(boxX)))), 0, width-1)
xmax := clip(int(math.Ceil(float64(maxf(boxX)))), 0, width-1)
ymin := clip(int(math.Floor(float64(minf(boxY)))), 0, height-1)
ymax := clip(int(math.Ceil(float64(maxf(boxY)))), 0, height-1)
mask := gocv.NewMatWithSize(ymax-ymin+1, xmax-xmin+1, gocv.MatTypeCV8UC1)
defer mask.Close()
ppt := make([][]image.Point, 1)
ppt[0] = make([]image.Point, 4)
ppt[0][0] = image.Point{int(array[0][0]) - xmin, int(array[0][1]) - ymin}
ppt[0][1] = image.Point{int(array[1][0]) - xmin, int(array[1][1]) - ymin}
ppt[0][2] = image.Point{int(array[2][0]) - xmin, int(array[2][1]) - ymin}
ppt[0][3] = image.Point{int(array[3][0]) - xmin, int(array[3][1]) - ymin}
gocv.FillPoly(&mask, ppt, color.RGBA{0, 0, 1, 0})
croppedImg := pred.Region(image.Rect(xmin, ymin, xmax+1, ymax+1))
s := croppedImg.MeanWithMask(mask)
return s.Val1
}
func (d *DBPostProcess) unClip(box [][]float32) gocv.RotatedRect {
var area, dist float64
for i := 0; i < 4; i++ {
area += float64(box[i][0]*box[(i+1)%4][1] - box[i][1]*box[(i+1)%4][0])
dist += math.Sqrt(float64(
(box[i][0]-box[(i+1)%4][0])*(box[i][0]-box[(i+1)%4][0]) +
(box[i][1]-box[(i+1)%4][1])*(box[i][1]-box[(i+1)%4][1]),
))
}
area = math.Abs(area / 2.0)
distance := area * d.unClipRatio / dist
offset := clipper.NewClipperOffset()
path := make([]*clipper.IntPoint, 4)
path[0] = &clipper.IntPoint{X: clipper.CInt(box[0][0]), Y: clipper.CInt(box[0][1])}
path[1] = &clipper.IntPoint{X: clipper.CInt(box[1][0]), Y: clipper.CInt(box[1][1])}
path[2] = &clipper.IntPoint{X: clipper.CInt(box[2][0]), Y: clipper.CInt(box[2][1])}
path[3] = &clipper.IntPoint{X: clipper.CInt(box[3][0]), Y: clipper.CInt(box[3][1])}
offset.AddPath(clipper.Path(path), clipper.JtRound, clipper.EtClosedPolygon)
soln := offset.Execute(distance)
points := make([]image.Point, 0, 4)
for i := 0; i < len(soln); i++ {
for j := 0; j < len(soln[i]); j++ {
points = append(points, image.Point{int(soln[i][j].X), int(soln[i][j].Y)})
}
}
var res gocv.RotatedRect
if len(points) <= 0 {
points = make([]image.Point, 4)
points[0] = image.Pt(0, 0)
points[1] = image.Pt(1, 0)
points[2] = image.Pt(1, 1)
points[3] = image.Pt(0, 1)
res = gocv.RotatedRect{
Contour: points,
BoundingRect: image.Rect(0, 0, 1, 1),
Center: gocv.Point2f{X: 0.5, Y: 0.5},
Width: 1,
Height: 1,
Angle: 0,
}
} else {
res = gocv.MinAreaRect(points)
}
return res
}
func (d *DBPostProcess) boxesFromBitmap(pred gocv.Mat, mask gocv.Mat, ratioH float64, ratioW float64) [][][]int {
height, width := mask.Rows(), mask.Cols()
mask.MultiplyUChar(255)
contours := gocv.FindContours(mask, gocv.RetrievalList, gocv.ChainApproxSimple)
numContours := len(contours)
if numContours > d.maxCandidates {
numContours = d.maxCandidates
}
boxes := make([][][]int, 0, numContours)
for i := 0; i < numContours; i++ {
contour := contours[i]
boundingbox := gocv.MinAreaRect(contour)
if boundingbox.Width < float32(d.minSize) || boundingbox.Height < float32(d.minSize) {
continue
}
points := d.getMinBoxes(boundingbox)
score := d.boxScoreFast(points, pred)
if score < d.boxThresh {
continue
}
box := d.unClip(points)
if box.Width < float32(d.minSize+2) || box.Height < float32(d.minSize+2) {
continue
}
cliparray := d.getMinBoxes(box)
dstHeight, dstWidth := pred.Rows(), pred.Cols()
intcliparray := make([][]int, 4)
for i := 0; i < 4; i++ {
p := []int{
int(float64(clip(int(math.Round(
float64(cliparray[i][0]/float32(width)*float32(dstWidth)))), 0, dstWidth)) / ratioW),
int(float64(clip(int(math.Round(
float64(cliparray[i][1]/float32(height)*float32(dstHeight)))), 0, dstHeight)) / ratioH),
}
intcliparray[i] = p
}
boxes = append(boxes, intcliparray)
}
return boxes
}
func (d *DBPostProcess) orderPointsClockwise(box [][]int) [][]int {
sort.Sort(xIntSortBy(box))
leftmost := [][]int{box[0], box[1]}
rightmost := [][]int{box[2], box[3]}
if leftmost[0][1] > leftmost[1][1] {
leftmost[0], leftmost[1] = leftmost[1], leftmost[0]
}
if rightmost[0][1] > rightmost[1][1] {
rightmost[0], rightmost[1] = rightmost[1], rightmost[0]
}
return [][]int{leftmost[0], rightmost[0], rightmost[1], leftmost[1]}
}
func (d *DBPostProcess) filterTagDetRes(boxes [][][]int, oriH, oriW int) [][][]int {
points := make([][][]int, 0, len(boxes))
for i := 0; i < len(boxes); i++ {
boxes[i] = d.orderPointsClockwise(boxes[i])
for j := 0; j < len(boxes[i]); j++ {
boxes[i][j][0] = clip(boxes[i][j][0], 0, oriW-1)
boxes[i][j][1] = clip(boxes[i][j][1], 0, oriH-1)
}
}
for i := 0; i < len(boxes); i++ {
rectW := int(math.Sqrt(math.Pow(float64(boxes[i][0][0]-boxes[i][1][0]), 2.0) +
math.Pow(float64(boxes[i][0][1]-boxes[i][1][1]), 2.0)))
rectH := int(math.Sqrt(math.Pow(float64(boxes[i][0][0]-boxes[i][3][0]), 2.0) +
math.Pow(float64(boxes[i][0][1]-boxes[i][3][1]), 2.0)))
if rectW <= 4 || rectH <= 4 {
continue
}
points = append(points, boxes[i])
}
return points
}
func (d *DBPostProcess) Run(output *paddle.ZeroCopyTensor, oriH, oriW int, ratioH, ratioW float64) [][][]int {
v := output.Value().([][][][]float32)
shape := output.Shape()
height, width := int(shape[2]), int(shape[3])
pred := gocv.NewMatWithSize(height, width, gocv.MatTypeCV32F)
bitmap := gocv.NewMatWithSize(height, width, gocv.MatTypeCV8UC1)
thresh := float32(d.thresh)
for i := 0; i < height; i++ {
for j := 0; j < width; j++ {
pred.SetFloatAt(i, j, v[0][0][i][j])
if v[0][0][i][j] > thresh {
bitmap.SetUCharAt(i, j, 1)
} else {
bitmap.SetUCharAt(i, j, 0)
}
}
}
mask := gocv.NewMat()
kernel := gocv.GetStructuringElement(gocv.MorphRect, image.Point{2, 2})
gocv.Dilate(bitmap, &mask, kernel)
boxes := d.boxesFromBitmap(pred, mask, ratioH, ratioW)
dtboxes := d.filterTagDetRes(boxes, oriH, oriW)
return dtboxes
}
thirdparty/paddleocr-go/ocr/preprocess.go 0 → 100644
浏览文件 @ a8122289
package ocr
import (
"image"
"image/color"
"math"
"github.com/LKKlein/gocv"
)
func resizeByShape(img gocv.Mat, resizeShape []int) (gocv.Mat, int, int) {
resizeH := resizeShape[0]
resizeW := resizeShape[1]
gocv.Resize(img, &img, image.Pt(resizeW, resizeH), 0, 0, gocv.InterpolationLinear)
return img, resizeH, resizeW
}
func resizeByMaxLen(img gocv.Mat, maxLen int) (gocv.Mat, int, int) {
oriH := img.Rows()
oriW := img.Cols()
var resizeH, resizeW int = oriH, oriW
var ratio float64 = 1.0
if resizeH > maxLen || resizeW > maxLen {
if resizeH > resizeW {
ratio = float64(maxLen) / float64(resizeH)
} else {
ratio = float64(maxLen) / float64(resizeW)
}
}
resizeH = int(float64(resizeH) * ratio)
resizeW = int(float64(resizeW) * ratio)
if resizeH%32 == 0 {
resizeH = resizeH
} else if resizeH/32 <= 1 {
resizeH = 32
} else {
resizeH = (resizeH/32 - 1) * 32
}
if resizeW%32 == 0 {
resizeW = resizeW
} else if resizeW/32 <= 1 {
resizeW = 32
} else {
resizeW = (resizeW/32 - 1) * 32
}
if resizeW <= 0 || resizeH <= 0 {
return gocv.NewMat(), 0, 0
}
gocv.Resize(img, &img, image.Pt(resizeW, resizeH), 0, 0, gocv.InterpolationLinear)
return img, resizeH, resizeW
}
func normPermute(img gocv.Mat, mean []float32, std []float32, scaleFactor float32) []float32 {
img.ConvertTo(&img, gocv.MatTypeCV32F)
img.DivideFloat(scaleFactor)
c := gocv.Split(img)
data := make([]float32, img.Rows()*img.Cols()*img.Channels())
for i := 0; i < 3; i++ {
c[i].SubtractFloat(mean[i])
c[i].DivideFloat(std[i])
defer c[i].Close()
x, _ := c[i].DataPtrFloat32()
copy(data[i*img.Rows()*img.Cols():], x)
}
return data
}
type DetPreProcess interface {
Run(gocv.Mat) ([]float32, int, int)
}
type DBPreProcess struct {
resizeType int
imageShape []int
maxSideLen int
mean []float32
std []float32
scaleFactor float32
}
func NewDBProcess(shape []int, sideLen int) *DBPreProcess {
db := &DBPreProcess{
resizeType: 0,
imageShape: shape,
maxSideLen: sideLen,
mean: []float32{0.485, 0.456, 0.406},
std: []float32{0.229, 0.224, 0.225},
scaleFactor: 255.0,
}
if len(shape) > 0 {
db.resizeType = 1
}
if sideLen == 0 {
db.maxSideLen = 2400
}
return db
}
func (d *DBPreProcess) Run(img gocv.Mat) ([]float32, int, int) {
var resizeH, resizeW int
if d.resizeType == 0 {
img, resizeH, resizeW = resizeByMaxLen(img, d.maxSideLen)
} else {
img, resizeH, resizeW = resizeByShape(img, d.imageShape)
}
im := normPermute(img, d.mean, d.std, d.scaleFactor)
return im, resizeH, resizeW
}
func clsResize(img gocv.Mat, resizeShape []int) gocv.Mat {
imgH, imgW := resizeShape[1], resizeShape[2]
h, w := img.Rows(), img.Cols()
ratio := float64(w) / float64(h)
var resizeW int
if math.Ceil(float64(imgH)*ratio) > float64(imgW) {
resizeW = imgW
} else {
resizeW = int(math.Ceil(float64(imgH) * ratio))
}
gocv.Resize(img, &img, image.Pt(resizeW, imgH), 0, 0, gocv.InterpolationLinear)
if resizeW < imgW {
gocv.CopyMakeBorder(img, &img, 0, 0, 0, imgW-resizeW, gocv.BorderConstant, color.RGBA{0, 0, 0, 0})
}
return img
}
func crnnPreprocess(img gocv.Mat, resizeShape []int, mean []float32, std []float32,
scaleFactor float32, whRatio float64, charType string) []float32 {
imgH := resizeShape[1]
imgW := resizeShape[2]
if charType == "ch" {
imgW = int(32 * whRatio)
}
h, w := img.Rows(), img.Cols()
ratio := float64(w) / float64(h)
var resizeW int
if math.Ceil(float64(imgH)*ratio) > float64(imgW) {
resizeW = imgW
} else {
resizeW = int(math.Ceil(float64(imgH) * ratio))
}
gocv.Resize(img, &img, image.Pt(resizeW, imgH), 0, 0, gocv.InterpolationLinear)
img.ConvertTo(&img, gocv.MatTypeCV32F)
img.DivideFloat(scaleFactor)
img.SubtractScalar(gocv.NewScalar(float64(mean[0]), float64(mean[1]), float64(mean[2]), 0))
img.DivideScalar(gocv.NewScalar(float64(std[0]), float64(std[1]), float64(std[2]), 0))
if resizeW < imgW {
gocv.CopyMakeBorder(img, &img, 0, 0, 0, imgW-resizeW, gocv.BorderConstant, color.RGBA{0, 0, 0, 0})
}
c := gocv.Split(img)
data := make([]float32, img.Rows()*img.Cols()*img.Channels())
for i := 0; i < 3; i++ {
defer c[i].Close()
x, _ := c[i].DataPtrFloat32()
copy(data[i*img.Rows()*img.Cols():], x)
}
return data
}
thirdparty/paddleocr-go/ocr/utils.go 0 → 100644
浏览文件 @ a8122289
package ocr
import (
"archive/tar"
"io"
"io/ioutil"
"log"
"net/http"
"os"
"path"
"path/filepath"
"strings"
"github.com/LKKlein/gocv"
"gopkg.in/yaml.v3"
)
func getString(args map[string]interface{}, key string, dv string) string {
if f, ok := args[key]; ok {
return f.(string)
}
return dv
}
func getFloat64(args map[string]interface{}, key string, dv float64) float64 {
if f, ok := args[key]; ok {
return f.(float64)
}
return dv
}
func getInt(args map[string]interface{}, key string, dv int) int {
if i, ok := args[key]; ok {
return i.(int)
}
return dv
}
func getBool(args map[string]interface{}, key string, dv bool) bool {
if b, ok := args[key]; ok {
return b.(bool)
}
return dv
}
func ReadImage(image_path string) gocv.Mat {
img := gocv.IMRead(image_path, gocv.IMReadColor)
if img.Empty() {
log.Printf("Could not read image %s\n", image_path)
os.Exit(1)
}
return img
}
func clip(value, min, max int) int {
if value <= min {
return min
} else if value >= max {
return max
}
return value
}
func minf(data []float32) float32 {
v := data[0]
for _, val := range data {
if val < v {
v = val
}
}
return v
}
func maxf(data []float32) float32 {
v := data[0]
for _, val := range data {
if val > v {
v = val
}
}
return v
}
func mini(data []int) int {
v := data[0]
for _, val := range data {
if val < v {
v = val
}
}
return v
}
func maxi(data []int) int {
v := data[0]
for _, val := range data {
if val > v {
v = val
}
}
return v
}
func argmax(arr []float32) (int, float32) {
max_value, index := arr[0], 0
for i, item := range arr {
if item > max_value {
max_value = item
index = i
}
}
return index, max_value
}
func checkModelExists(modelPath string) bool {
if isPathExist(modelPath+"/model") && isPathExist(modelPath+"/params") {
return true
}
if strings.HasPrefix(modelPath, "http://") ||
strings.HasPrefix(modelPath, "ftp://") || strings.HasPrefix(modelPath, "https://") {
return true
}
return false
}
func downloadFile(filepath, url string) error {
resp, err := http.Get(url)
if err != nil {
return err
}
defer resp.Body.Close()
out, err := os.Create(filepath)
if err != nil {
return err
}
defer out.Close()
_, err = io.Copy(out, resp.Body)
log.Println("[download_file] from:", url, " to:", filepath)
return err
}
func isPathExist(path string) bool {
if _, err := os.Stat(path); err == nil {
return true
} else if os.IsNotExist(err) {
return false
}
return false
}
func downloadModel(modelDir, modelPath string) (string, error) {
if modelPath != "" && (strings.HasPrefix(modelPath, "http://") ||
strings.HasPrefix(modelPath, "ftp://") || strings.HasPrefix(modelPath, "https://")) {
if checkModelExists(modelDir) {
return modelDir, nil
}
_, suffix := path.Split(modelPath)
outPath := filepath.Join(modelDir, suffix)
outDir := filepath.Dir(outPath)
if !isPathExist(outDir) {
os.MkdirAll(outDir, os.ModePerm)
}
if !isPathExist(outPath) {
err := downloadFile(outPath, modelPath)
if err != nil {
return "", err
}
}
if strings.HasSuffix(outPath, ".tar") && !checkModelExists(modelDir) {
unTar(modelDir, outPath)
os.Remove(outPath)
return modelDir, nil
}
return modelDir, nil
}
return modelPath, nil
}
func unTar(dst, src string) (err error) {
fr, err := os.Open(src)
if err != nil {
return err
}
defer fr.Close()
tr := tar.NewReader(fr)
for {
hdr, err := tr.Next()
switch {
case err == io.EOF:
return nil
case err != nil:
return err
case hdr == nil:
continue
}
var dstFileDir string
if strings.Contains(hdr.Name, "model") {
dstFileDir = filepath.Join(dst, "model")
} else if strings.Contains(hdr.Name, "params") {
dstFileDir = filepath.Join(dst, "params")
}
switch hdr.Typeflag {
case tar.TypeDir:
continue
case tar.TypeReg:
file, err := os.OpenFile(dstFileDir, os.O_CREATE|os.O_RDWR, os.FileMode(hdr.Mode))
if err != nil {
return err
}
_, err2 := io.Copy(file, tr)
if err2 != nil {
return err2
}
file.Close()
}
}
return nil
}
func readLines2StringSlice(filepath string) []string {
if strings.HasPrefix(filepath, "http://") || strings.HasPrefix(filepath, "https://") {
home, _ := os.UserHomeDir()
dir := home + "/.paddleocr/rec/"
_, suffix := path.Split(filepath)
f := dir + suffix
if !isPathExist(f) {
err := downloadFile(f, filepath)
if err != nil {
log.Println("download ppocr key file error! You can specify your local dict path by conf.yaml.")
return nil
}
}
filepath = f
}
content, err := ioutil.ReadFile(filepath)
if err != nil {
log.Println("read ppocr key file error!")
return nil
}
lines := strings.Split(string(content), "\n")
return lines
}
func ReadYaml(yamlPath string) (map[string]interface{}, error) {
data, err := ioutil.ReadFile(yamlPath)
if err != nil {
return nil, err
}
var body interface{}
if err := yaml.Unmarshal(data, &body); err != nil {
return nil, err
}
body = convertYaml2Map(body)
return body.(map[string]interface{}), nil
}
func convertYaml2Map(i interface{}) interface{} {
switch x := i.(type) {
case map[interface{}]interface{}:
m2 := map[string]interface{}{}
for k, v := range x {
m2[k.(string)] = convertYaml2Map(v)
}
return m2
case []interface{}:
for i, v := range x {
x[i] = convertYaml2Map(v)
}
}
return i
}
thirdparty/paddleocr-go/paddle/common.go 0 → 100644
浏览文件 @ a8122289
package paddle
// #cgo CFLAGS: -I../paddle_c/include
// #cgo LDFLAGS: -lpaddle_fluid_c
// #include <stdbool.h>
import "C"
import "fmt"
func ConvertCBooleanToGo(b C.bool) bool {
var c_false C.bool
if b != c_false {
return true
}
return false
}
func numel(shape []int32) int32 {
n := int32(1)
for _, d := range shape {
n *= d
}
return n
}
func bug(format string, args ...interface{}) error {
return fmt.Errorf("Bug %v", fmt.Sprintf(format, args...))
}
thirdparty/paddleocr-go/paddle/config.go 0 → 100644
浏览文件 @ a8122289
package paddle
// #include <stdbool.h>
// #include <stdlib.h>
// #include <paddle_c_api.h>
import "C"
import (
"runtime"
"unsafe"
)
type Precision C.Precision
const (
Precision_FLOAT32 Precision = C.kFloat32
Precision_INT8 Precision = C.kInt8
Precision_HALF Precision = C.kHalf
)
type AnalysisConfig struct {
c *C.PD_AnalysisConfig
}
func NewAnalysisConfig() *AnalysisConfig {
c_config := C.PD_NewAnalysisConfig()
config := &AnalysisConfig{c: c_config}
runtime.SetFinalizer(config, (*AnalysisConfig).finalize)
return config
}
func (config *AnalysisConfig) finalize() {
C.PD_DeleteAnalysisConfig(config.c)
}
func (config *AnalysisConfig) SetModel(model, params string) {
c_model := C.CString(model)
defer C.free(unsafe.Pointer(c_model))
var c_params *C.char
if params == "" {
c_params = nil
} else {
c_params = C.CString(params)
defer C.free(unsafe.Pointer(c_params))
}
C.PD_SetModel(config.c, c_model, c_params)
}
func (config *AnalysisConfig) ModelDir() string {
return C.GoString(C.PD_ModelDir(config.c))
}
func (config *AnalysisConfig) ProgFile() string {
return C.GoString(C.PD_ProgFile(config.c))
}
func (config *AnalysisConfig) ParamsFile() string {
return C.GoString(C.PD_ParamsFile(config.c))
}
func (config *AnalysisConfig) EnableUseGpu(memory_pool_init_size_mb int, device_id int) {
C.PD_EnableUseGpu(config.c, C.int(memory_pool_init_size_mb), C.int(device_id))
}
func (config *AnalysisConfig) DisableGpu() {
C.PD_DisableGpu(config.c)
}
func (config *AnalysisConfig) UseGpu() bool {
return ConvertCBooleanToGo(C.PD_UseGpu(config.c))
}
func (config *AnalysisConfig) GpuDeviceId() int {
return int(C.PD_GpuDeviceId(config.c))
}
func (config *AnalysisConfig) MemoryPoolInitSizeMb() int {
return int(C.PD_MemoryPoolInitSizeMb(config.c))
}
func (config *AnalysisConfig) EnableCudnn() {
C.PD_EnableCUDNN(config.c)
}
func (config *AnalysisConfig) CudnnEnabled() bool {
return ConvertCBooleanToGo(C.PD_CudnnEnabled(config.c))
}
func (config *AnalysisConfig) SwitchIrOptim(x bool) {
C.PD_SwitchIrOptim(config.c, C.bool(x))
}
func (config *AnalysisConfig) IrOptim() bool {
return ConvertCBooleanToGo(C.PD_IrOptim(config.c))
}
func (config *AnalysisConfig) SwitchUseFeedFetchOps(x bool) {
C.PD_SwitchUseFeedFetchOps(config.c, C.bool(x))
}
func (config *AnalysisConfig) UseFeedFetchOpsEnabled() bool {
return ConvertCBooleanToGo(C.PD_UseFeedFetchOpsEnabled(config.c))
}
func (config *AnalysisConfig) SwitchSpecifyInputNames(x bool) {
C.PD_SwitchSpecifyInputNames(config.c, C.bool(x))
}
func (config *AnalysisConfig) SpecifyInputName() bool {
return ConvertCBooleanToGo(C.PD_SpecifyInputName(config.c))
}
func (config *AnalysisConfig) EnableTensorRtEngine(workspace_size int, max_batch_size int, min_subgraph_size int, precision Precision, use_static bool, use_calib_mode bool) {
C.PD_EnableTensorRtEngine(config.c, C.int(workspace_size), C.int(max_batch_size), C.int(min_subgraph_size), C.Precision(precision), C.bool(use_static), C.bool(use_calib_mode))
}
func (config *AnalysisConfig) TensorrtEngineEnabled() bool {
return ConvertCBooleanToGo(C.PD_TensorrtEngineEnabled(config.c))
}
func (config *AnalysisConfig) SwitchIrDebug(x bool) {
C.PD_SwitchIrDebug(config.c, C.bool(x))
}
func (config *AnalysisConfig) EnableMkldnn() {
C.PD_EnableMKLDNN(config.c)
}
func (config *AnalysisConfig) SetCpuMathLibraryNumThreads(n int) {
C.PD_SetCpuMathLibraryNumThreads(config.c, C.int(n))
}
func (config *AnalysisConfig) CpuMathLibraryNumThreads() int {
return int(C.PD_CpuMathLibraryNumThreads(config.c))
}
func (config *AnalysisConfig) EnableMkldnnQuantizer() {
C.PD_EnableMkldnnQuantizer(config.c)
}
func (config *AnalysisConfig) MkldnnQuantizerEnabled() bool {
return ConvertCBooleanToGo(C.PD_MkldnnQuantizerEnabled(config.c))
}
// SetModelBuffer
// ModelFromMemory
func (config *AnalysisConfig) EnableMemoryOptim() {
C.PD_EnableMemoryOptim(config.c)
}
func (config *AnalysisConfig) MemoryOptimEnabled() bool {
return ConvertCBooleanToGo(C.PD_MemoryOptimEnabled(config.c))
}
func (config *AnalysisConfig) EnableProfile() {
C.PD_EnableProfile(config.c)
}
func (config *AnalysisConfig) ProfileEnabled() bool {
return ConvertCBooleanToGo(C.PD_ProfileEnabled(config.c))
}
func (config *AnalysisConfig) DisableGlogInfo() {
C.PD_DisableGlogInfo(config.c)
}
func (config *AnalysisConfig) DeletePass(pass string) {
c_pass := C.CString(pass)
defer C.free(unsafe.Pointer(c_pass))
C.PD_DeletePass(config.c, c_pass)
}
func (config *AnalysisConfig) SetInValid() {
C.PD_SetInValid(config.c)
}
func (config *AnalysisConfig) IsValid() bool {
return ConvertCBooleanToGo(C.PD_IsValid(config.c))
}
thirdparty/paddleocr-go/paddle/predictor.go 0 → 100644
浏览文件 @ a8122289
package paddle
// #include <stdbool.h>
// #include "paddle_c_api.h"
import "C"
import (
"reflect"
"runtime"
"unsafe"
)
type Predictor struct {
c *C.PD_Predictor
}
func NewPredictor(config *AnalysisConfig) *Predictor {
c_predictor := C.PD_NewPredictor((*config).c)
predictor := &Predictor{c: c_predictor}
runtime.SetFinalizer(predictor, (*Predictor).finalize)
return predictor
}
func (predictor *Predictor) finalize() {
C.PD_DeletePredictor(predictor.c)
}
func DeletePredictor(predictor *Predictor) {
C.PD_DeletePredictor(predictor.c)
}
func (predictor *Predictor) GetInputNum() int {
return int(C.PD_GetInputNum(predictor.c))
}
func (predictor *Predictor) GetOutputNum() int {
return int(C.PD_GetOutputNum(predictor.c))
}
func (predictor *Predictor) GetInputName(n int) string {
return C.GoString(C.PD_GetInputName(predictor.c, C.int(n)))
}
func (predictor *Predictor) GetOutputName(n int) string {
return C.GoString(C.PD_GetOutputName(predictor.c, C.int(n)))
}
func (predictor *Predictor) GetInputTensors() [](*ZeroCopyTensor) {
var result [](*ZeroCopyTensor)
for i := 0; i < predictor.GetInputNum(); i++ {
tensor := NewZeroCopyTensor()
tensor.c.name = C.PD_GetInputName(predictor.c, C.int(i))
result = append(result, tensor)
}
return result
}
func (predictor *Predictor) GetOutputTensors() [](*ZeroCopyTensor) {
var result [](*ZeroCopyTensor)
for i := 0; i < predictor.GetOutputNum(); i++ {
tensor := NewZeroCopyTensor()
tensor.c.name = C.PD_GetOutputName(predictor.c, C.int(i))
result = append(result, tensor)
}
return result
}
func (predictor *Predictor) GetInputNames() []string {
names := make([]string, predictor.GetInputNum())
for i := 0; i < len(names); i++ {
names[i] = predictor.GetInputName(i)
}
return names
}
func (predictor *Predictor) GetOutputNames() []string {
names := make([]string, predictor.GetOutputNum())
for i := 0; i < len(names); i++ {
names[i] = predictor.GetOutputName(i)
}
return names
}
func (predictor *Predictor) SetZeroCopyInput(tensor *ZeroCopyTensor) {
C.PD_SetZeroCopyInput(predictor.c, tensor.c)
}
func (predictor *Predictor) GetZeroCopyOutput(tensor *ZeroCopyTensor) {
C.PD_GetZeroCopyOutput(predictor.c, tensor.c)
tensor.name = C.GoString(tensor.c.name)
var shape []int32
shape_hdr := (*reflect.SliceHeader)(unsafe.Pointer(&shape))
shape_hdr.Data = uintptr(unsafe.Pointer(tensor.c.shape.data))
shape_hdr.Len = int(tensor.c.shape.length / C.sizeof_int)
shape_hdr.Cap = int(tensor.c.shape.length / C.sizeof_int)
tensor.Reshape(shape)
}
func (predictor *Predictor) ZeroCopyRun() {
C.PD_ZeroCopyRun(predictor.c)
}
thirdparty/paddleocr-go/paddle/tensor.go 0 → 100644
浏览文件 @ a8122289
package paddle
// #include <stdbool.h>
// #include <stdlib.h>
// #include <string.h>
// #include <paddle_c_api.h>
import "C"
import (
"reflect"
"runtime"
"unsafe"
)
type PaddleDType C.PD_DataType
const (
FLOAT32 PaddleDType = C.PD_FLOAT32
INT32 PaddleDType = C.PD_INT32
INT64 PaddleDType = C.PD_INT64
UINT8 PaddleDType = C.PD_UINT8
UNKDTYPE PaddleDType = C.PD_UNKDTYPE
)
var types = []struct {
gotype reflect.Type
dtype PaddleDType
}{
{reflect.TypeOf(float32(0)), FLOAT32},
{reflect.TypeOf(int32(0)), INT32},
{reflect.TypeOf(int64(0)), INT64},
{reflect.TypeOf(uint8(0)), UINT8},
}
func typeOfDataType(dtype PaddleDType) reflect.Type {
var ret reflect.Type
for _, t := range types {
if t.dtype == dtype {
ret = t.gotype
}
}
return ret
}
func sizeofDataType(dtype PaddleDType) int32 {
switch dtype {
case UINT8:
return int32(C.sizeof_uchar)
case INT32:
return int32(C.sizeof_int)
case INT64:
return int32(C.sizeof_longlong)
case FLOAT32:
return int32(C.sizeof_float)
}
return -1
}
func shapeAndTypeOf(val reflect.Value) (shape []int32, dt PaddleDType) {
gotype := val.Type()
for gotype.Kind() == reflect.Array || gotype.Kind() == reflect.Slice {
shape = append(shape, int32(val.Len()))
if val.Len() > 0 {
val = val.Index(0)
}
gotype = gotype.Elem()
}
for _, t := range types {
if gotype.Kind() == t.gotype.Kind() {
return shape, PaddleDType(t.dtype)
}
}
return shape, dt
}
type ZeroCopyTensor struct {
c *C.PD_ZeroCopyTensor
name string
shape []int32
}
func NewZeroCopyTensor() *ZeroCopyTensor {
c_tensor := C.PD_NewZeroCopyTensor()
tensor := &ZeroCopyTensor{c: c_tensor}
runtime.SetFinalizer(tensor, (*ZeroCopyTensor).finalize)
return tensor
}
func (tensor *ZeroCopyTensor) finalize() {
C.PD_DeleteZeroCopyTensor(tensor.c)
}
func (tensor *ZeroCopyTensor) Shape() []int32 {
return tensor.shape
}
func (tensor *ZeroCopyTensor) Name() string {
return C.GoString(tensor.c.name)
}
func (tensor *ZeroCopyTensor) Rename(name string) {
tensor.name = name
tensor.c.name = (*C.char)(unsafe.Pointer(tensor.c.name))
}
func (tensor *ZeroCopyTensor) Reshape(shape []int32) {
tensor.shape = make([]int32, len(shape))
copy(tensor.shape, shape)
length := C.sizeof_int * C.size_t(len(shape))
if tensor.c.shape.capacity < C.size_t(length) {
if tensor.c.shape.capacity != C.size_t(0) {
C.free(tensor.c.shape.data)
}
tensor.c.shape.data = C.malloc(length)
tensor.c.shape.capacity = length
}
tensor.c.shape.length = length
C.memcpy(tensor.c.shape.data, unsafe.Pointer(&shape[0]), length)
}
func (tensor *ZeroCopyTensor) DataType() PaddleDType {
return PaddleDType(tensor.c.dtype)
}
func (tensor *ZeroCopyTensor) SetValue(value interface{}) {
val := reflect.ValueOf(value)
shape, dtype := shapeAndTypeOf(val)
num := numel(shape)
length := C.size_t(sizeofDataType(dtype) * num)
if tensor.c.data.capacity < length {
if tensor.c.data.capacity != C.size_t(0) {
C.free(tensor.c.data.data)
}
tensor.c.data.data = C.malloc(length)
tensor.c.data.capacity = length
}
tensor.c.data.length = length
switch dtype {
case PaddleDType(UINT8):
data := val.Interface().([]uint8)
C.memcpy(tensor.c.data.data, unsafe.Pointer(&data[0]), length)
case PaddleDType(INT32):
data := val.Interface().([]int32)
C.memcpy(tensor.c.data.data, unsafe.Pointer(&data[0]), length)
case PaddleDType(INT64):
data := val.Interface().([]int64)
C.memcpy(tensor.c.data.data, unsafe.Pointer(&data[0]), length)
case PaddleDType(FLOAT32):
data := val.Interface().([]float32)
C.memcpy(tensor.c.data.data, unsafe.Pointer(&data[0]), length)
}
tensor.c.dtype = C.PD_DataType(dtype)
}
func (tensor *ZeroCopyTensor) tensorData() []byte {
cbytes := tensor.c.data.data
length := tensor.c.data.length
var slice []byte
if unsafe.Sizeof(unsafe.Pointer(nil)) == 8 {
slice = (*[1<<50 - 1]byte)(unsafe.Pointer(cbytes))[:length:length]
} else {
slice = (*[1 << 30]byte)(unsafe.Pointer(cbytes))[:length:length]
}
return slice
}
func (tensor *ZeroCopyTensor) Value() interface{} {
t := typeOfDataType(PaddleDType(tensor.c.dtype))
data := tensor.tensorData()
return decodeTensor(data, tensor.Shape(), t).Interface()
}
// It isn't safe to use reflect.SliceHeader as it uses a uintptr for Data and
// this is not inspected by the garbage collector
type sliceHeader struct {
Data unsafe.Pointer
Len int
Cap int
}
func decodeTensor(raw []byte, shape []int32, t reflect.Type) reflect.Value {
// Create a 1-dimensional slice of the base large enough for the data and
// copy the data in.
n := int(numel(shape))
l := n * int(t.Size())
typ := reflect.SliceOf(t)
slice := reflect.MakeSlice(typ, n, n)
baseBytes := *(*[]byte)(unsafe.Pointer(&sliceHeader{
Data: unsafe.Pointer(slice.Pointer()),
Len: l,
Cap: l,
}))
copy(baseBytes, raw)
if len(shape) == 0 {
// for n
return slice.Index(0)
}
if len(shape) == 1 {
// for {}
return slice
}
// for {{} {}} {{} {}} {{} {}}
if n == 0 {
n = int(numel(shape[:len(shape)-1]))
}
for i := len(shape) - 2; i >= 0; i-- {
underlyingSize := typ.Elem().Size()
typ = reflect.SliceOf(typ)
subsliceLen := int(shape[i+1])
if subsliceLen != 0 {
n = n / subsliceLen
}
data := unsafe.Pointer(slice.Pointer())
nextSlice := reflect.MakeSlice(typ, n, n)
for j := 0; j < n; j++ {
// This is equivalent to nSlice[j] = slice[j*subsliceLen: (j+1)*subsliceLen]
setSliceInSlice(nextSlice, j, sliceHeader{
Data: unsafe.Pointer(uintptr(data) + (uintptr(j*subsliceLen) * underlyingSize)),
Len: subsliceLen,
Cap: subsliceLen,
})
}
slice = nextSlice
}
return slice
}
// setSliceInSlice sets slice[index] = content.
func setSliceInSlice(slice reflect.Value, index int, content sliceHeader) {
const sliceSize = unsafe.Sizeof(sliceHeader{})
// We must cast slice.Pointer to uninptr & back again to avoid GC issues.
// See https://github.com/google/go-cmp/issues/167#issuecomment-546093202
*(*sliceHeader)(unsafe.Pointer(uintptr(unsafe.Pointer(slice.Pointer())) + (uintptr(index) * sliceSize))) = content
}
func (tensor *ZeroCopyTensor) Lod() []uint {
var val []uint
valHdr := (*reflect.SliceHeader)(unsafe.Pointer(&val))
valHdr.Data = uintptr(unsafe.Pointer(tensor.c.lod.data))
valHdr.Len = int(tensor.c.lod.length / C.sizeof_size_t)
valHdr.Cap = int(tensor.c.lod.length / C.sizeof_size_t)
return val
}
thirdparty/paddleocr-go/paddle_c/include/paddle_c_api.h 0 → 100644
浏览文件 @ a8122289
// Copyright (c) 2019 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.
#pragma once
#include <stddef.h>
#include <stdint.h>
#include <stdio.h>
#if defined(_WIN32)
#ifdef PADDLE_ON_INFERENCE
#define PADDLE_CAPI_EXPORT __declspec(dllexport)
#else
#define PADDLE_CAPI_EXPORT __declspec(dllimport)
#endif // PADDLE_ON_INFERENCE
#else
#define PADDLE_CAPI_EXPORT __attribute__((visibility("default")))
#endif // _WIN32
#ifdef __cplusplus
extern "C" {
#endif
enum PD_DataType { PD_FLOAT32, PD_INT32, PD_INT64, PD_UINT8, PD_UNKDTYPE };
typedef enum PD_DataType PD_DataType;
typedef struct PD_PaddleBuf PD_PaddleBuf;
typedef struct PD_AnalysisConfig PD_AnalysisConfig;
typedef struct PD_Predictor PD_Predictor;
typedef struct PD_Buffer {
void* data;
size_t length;
size_t capacity;
} PD_Buffer;
typedef struct PD_ZeroCopyTensor {
PD_Buffer data;
PD_Buffer shape;
PD_Buffer lod;
PD_DataType dtype;
char* name;
} PD_ZeroCopyTensor;
PADDLE_CAPI_EXPORT extern PD_ZeroCopyTensor* PD_NewZeroCopyTensor();
PADDLE_CAPI_EXPORT extern void PD_DeleteZeroCopyTensor(PD_ZeroCopyTensor*);
PADDLE_CAPI_EXPORT extern void PD_InitZeroCopyTensor(PD_ZeroCopyTensor*);
PADDLE_CAPI_EXPORT extern void PD_DestroyZeroCopyTensor(PD_ZeroCopyTensor*);
PADDLE_CAPI_EXPORT extern void PD_DeleteZeroCopyTensor(PD_ZeroCopyTensor*);
typedef struct PD_ZeroCopyData {
char* name;
void* data;
PD_DataType dtype;
int* shape;
int shape_size;
} PD_ZeroCopyData;
typedef struct InTensorShape {
char* name;
int* tensor_shape;
int shape_size;
} InTensorShape;
PADDLE_CAPI_EXPORT extern PD_PaddleBuf* PD_NewPaddleBuf();
PADDLE_CAPI_EXPORT extern void PD_DeletePaddleBuf(PD_PaddleBuf* buf);
PADDLE_CAPI_EXPORT extern void PD_PaddleBufResize(PD_PaddleBuf* buf,
size_t length);
PADDLE_CAPI_EXPORT extern void PD_PaddleBufReset(PD_PaddleBuf* buf, void* data,
size_t length);
PADDLE_CAPI_EXPORT extern bool PD_PaddleBufEmpty(PD_PaddleBuf* buf);
PADDLE_CAPI_EXPORT extern void* PD_PaddleBufData(PD_PaddleBuf* buf);
PADDLE_CAPI_EXPORT extern size_t PD_PaddleBufLength(PD_PaddleBuf* buf);
// PaddleTensor
typedef struct PD_Tensor PD_Tensor;
PADDLE_CAPI_EXPORT extern PD_Tensor* PD_NewPaddleTensor();
PADDLE_CAPI_EXPORT extern void PD_DeletePaddleTensor(PD_Tensor* tensor);
PADDLE_CAPI_EXPORT extern void PD_SetPaddleTensorName(PD_Tensor* tensor,
char* name);
PADDLE_CAPI_EXPORT extern void PD_SetPaddleTensorDType(PD_Tensor* tensor,
PD_DataType dtype);
PADDLE_CAPI_EXPORT extern void PD_SetPaddleTensorData(PD_Tensor* tensor,
PD_PaddleBuf* buf);
PADDLE_CAPI_EXPORT extern void PD_SetPaddleTensorShape(PD_Tensor* tensor,
int* shape, int size);
PADDLE_CAPI_EXPORT extern const char* PD_GetPaddleTensorName(
const PD_Tensor* tensor);
PADDLE_CAPI_EXPORT extern PD_DataType PD_GetPaddleTensorDType(
const PD_Tensor* tensor);
PADDLE_CAPI_EXPORT extern PD_PaddleBuf* PD_GetPaddleTensorData(
const PD_Tensor* tensor);
PADDLE_CAPI_EXPORT extern const int* PD_GetPaddleTensorShape(
const PD_Tensor* tensor, int* size);
// AnalysisPredictor
PADDLE_CAPI_EXPORT extern bool PD_PredictorRun(const PD_AnalysisConfig* config,
PD_Tensor* inputs, int in_size,
PD_Tensor** output_data,
int* out_size, int batch_size);
PADDLE_CAPI_EXPORT extern bool PD_PredictorZeroCopyRun(
const PD_AnalysisConfig* config, PD_ZeroCopyData* inputs, int in_size,
PD_ZeroCopyData** output, int* out_size);
// AnalysisConfig
enum Precision { kFloat32 = 0, kInt8, kHalf };
typedef enum Precision Precision;
PADDLE_CAPI_EXPORT extern PD_AnalysisConfig* PD_NewAnalysisConfig();
PADDLE_CAPI_EXPORT extern void PD_DeleteAnalysisConfig(
PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern void PD_SetModel(PD_AnalysisConfig* config,
const char* model_dir,
const char* params_path);
PADDLE_CAPI_EXPORT
extern void PD_SetProgFile(PD_AnalysisConfig* config, const char* x);
PADDLE_CAPI_EXPORT extern void PD_SetParamsFile(PD_AnalysisConfig* config,
const char* x);
PADDLE_CAPI_EXPORT extern void PD_SetOptimCacheDir(PD_AnalysisConfig* config,
const char* opt_cache_dir);
PADDLE_CAPI_EXPORT extern const char* PD_ModelDir(
const PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern const char* PD_ProgFile(
const PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern const char* PD_ParamsFile(
const PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern void PD_EnableUseGpu(PD_AnalysisConfig* config,
int memory_pool_init_size_mb,
int device_id);
PADDLE_CAPI_EXPORT extern void PD_DisableGpu(PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern bool PD_UseGpu(const PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern int PD_GpuDeviceId(const PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern int PD_MemoryPoolInitSizeMb(
const PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern float PD_FractionOfGpuMemoryForPool(
const PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern void PD_EnableCUDNN(PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern bool PD_CudnnEnabled(const PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern void PD_SwitchIrOptim(PD_AnalysisConfig* config,
bool x);
PADDLE_CAPI_EXPORT extern bool PD_IrOptim(const PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern void PD_SwitchUseFeedFetchOps(
PD_AnalysisConfig* config, bool x);
PADDLE_CAPI_EXPORT extern bool PD_UseFeedFetchOpsEnabled(
const PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern void PD_SwitchSpecifyInputNames(
PD_AnalysisConfig* config, bool x);
PADDLE_CAPI_EXPORT extern bool PD_SpecifyInputName(
const PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern void PD_EnableTensorRtEngine(
PD_AnalysisConfig* config, int workspace_size, int max_batch_size,
int min_subgraph_size, Precision precision, bool use_static,
bool use_calib_mode);
PADDLE_CAPI_EXPORT extern bool PD_TensorrtEngineEnabled(
const PD_AnalysisConfig* config);
typedef struct PD_MaxInputShape {
char* name;
int* shape;
int shape_size;
} PD_MaxInputShape;
PADDLE_CAPI_EXPORT extern void PD_SwitchIrDebug(PD_AnalysisConfig* config,
bool x);
PADDLE_CAPI_EXPORT extern void PD_EnableMKLDNN(PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern void PD_SetMkldnnCacheCapacity(
PD_AnalysisConfig* config, int capacity);
PADDLE_CAPI_EXPORT extern bool PD_MkldnnEnabled(
const PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern void PD_SetCpuMathLibraryNumThreads(
PD_AnalysisConfig* config, int cpu_math_library_num_threads);
PADDLE_CAPI_EXPORT extern int PD_CpuMathLibraryNumThreads(
const PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern void PD_EnableMkldnnQuantizer(
PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern bool PD_MkldnnQuantizerEnabled(
const PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern void PD_SetModelBuffer(PD_AnalysisConfig* config,
const char* prog_buffer,
size_t prog_buffer_size,
const char* params_buffer,
size_t params_buffer_size);
PADDLE_CAPI_EXPORT extern bool PD_ModelFromMemory(
const PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern void PD_EnableMemoryOptim(PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern bool PD_MemoryOptimEnabled(
const PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern void PD_EnableProfile(PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern bool PD_ProfileEnabled(
const PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern void PD_SetInValid(PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern bool PD_IsValid(const PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern void PD_DisableGlogInfo(PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern void PD_DeletePass(PD_AnalysisConfig* config,
char* pass_name);
PADDLE_CAPI_EXPORT extern PD_Predictor* PD_NewPredictor(
const PD_AnalysisConfig* config);
PADDLE_CAPI_EXPORT extern void PD_DeletePredictor(PD_Predictor* predictor);
PADDLE_CAPI_EXPORT extern int PD_GetInputNum(const PD_Predictor*);
PADDLE_CAPI_EXPORT extern int PD_GetOutputNum(const PD_Predictor*);
PADDLE_CAPI_EXPORT extern const char* PD_GetInputName(const PD_Predictor*, int);
PADDLE_CAPI_EXPORT extern const char* PD_GetOutputName(const PD_Predictor*,
int);
PADDLE_CAPI_EXPORT extern void PD_SetZeroCopyInput(
PD_Predictor* predictor, const PD_ZeroCopyTensor* tensor);
PADDLE_CAPI_EXPORT extern void PD_GetZeroCopyOutput(PD_Predictor* predictor,
PD_ZeroCopyTensor* tensor);
PADDLE_CAPI_EXPORT extern void PD_ZeroCopyRun(PD_Predictor* predictor);
#ifdef __cplusplus
} // extern "C"
#endif
thirdparty/paddleocr-go/ppocr-go.go 0 → 100644
浏览文件 @ a8122289
package main
import (
"flag"
"log"
"github.com/PaddlePaddle/PaddleOCR/thirdparty/paddleocr-go/ocr"
)
var (
confFile string
image string
imageDir string
useServering bool
port string
)
func init() {
flag.StringVar(&confFile, "config", "config/conf.yaml", "config from ocr system. If not given, will use default config.")
flag.StringVar(&image, "image", "", "image to predict. if not given, will use image_dir")
flag.StringVar(&imageDir, "image_dir", "", "imgs in dir to be predicted. if not given, will check servering")
flag.BoolVar(&useServering, "use_servering", false, "whether to use ocr server. [default: false]")
flag.StringVar(&port, "port", "18600", "which port to serve ocr server. [default: 18600].")
}
func main() {
flag.Parse()
sys := ocr.NewOCRSystem(confFile, nil)
if image != "" {
img := ocr.ReadImage(image)
defer img.Close()
results := sys.PredictOneImage(img)
for _, res := range results {
log.Println(res)
}
return
}
if imageDir != "" {
results := sys.PredictDirImages(imageDir)
for k, vs := range results {
log.Printf("======== image: %v =======\n", k)
for _, res := range vs {
log.Println(res)
}
}
}
if useServering {
sys.StartServer(port)
}
}
tools/export_model.py
浏览文件 @ a8122289
...@@ -16,6 +16,7 @@ from __future__ import absolute_import ...@@ -16,6 +16,7 @@ from __future__ import absolute_import
from __future__ import division from __future__ import division
from __future__ import print_function from __future__ import print_function
import os import os
import sys import sys
__dir__ = os.path.dirname(os.path.abspath(__file__)) __dir__ = os.path.dirname(os.path.abspath(__file__))
...@@ -38,12 +39,12 @@ set_paddle_flags( ...@@ -38,12 +39,12 @@ set_paddle_flags(
import program import program
from paddle import fluid from paddle import fluid
from ppocr.utils.utility import enable_static_mode
from ppocr.utils.utility import initial_logger from ppocr.utils.utility import initial_logger
logger = initial_logger() logger = initial_logger()
from ppocr.utils.save_load import init_model from ppocr.utils.save_load import init_model
def main(): def main():
startup_prog, eval_program, place, config, _ = program.preprocess() startup_prog, eval_program, place, config, _ = program.preprocess()
...@@ -72,4 +73,5 @@ def main(): ...@@ -72,4 +73,5 @@ def main():
if __name__ == '__main__': if __name__ == '__main__':
enable_static_mode()
main() main()
tools/infer/predict_det.py
浏览文件 @ a8122289
...@@ -191,10 +191,13 @@ if __name__ == "__main__": ...@@ -191,10 +191,13 @@ if __name__ == "__main__":
if count > 0: if count > 0:
total_time += elapse total_time += elapse
count += 1 count += 1
print("Predict time of %s:" % image_file, elapse) logger.info("The predicted time of img: {} is {}:".format(image_file,
elapse))
src_im = utility.draw_text_det_res(dt_boxes, image_file) src_im = utility.draw_text_det_res(dt_boxes, image_file)
img_name_pure = os.path.split(image_file)[-1] # image_file.split("/")[-1] img_name_pure = image_file.split("/")[-1]
img_path = os.path.join(draw_img_save, "det_res_%s" % img_name_pure) cv2.imwrite(
cv2.imwrite(img_path, src_im) os.path.join(draw_img_save, "det_res_%s" % img_name_pure), src_im)
logger.info("The visualized img saved in {}".format(
os.path.join(draw_img_save, "det_res_%s" % img_name_pure)))
if count > 1: if count > 1:
print("Avg Time:", total_time / (count - 1)) logger.info("Avg Time:", total_time / (count - 1))
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