init
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PROJECT(infer_demo CSharp)
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CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
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# Set the C# language version (defaults to 3.0 if not set).
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set(CMAKE_CSharp_FLAGS "/langversion:10")
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set(CMAKE_DOTNET_TARGET_FRAMEWORK "net6.0")
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set(CMAKE_DOTNET_SDK "Microsoft.NET.Sdk")
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# 指定下载解压后的fastdeploy库路径
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option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
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include(${FASTDEPLOY_INSTALL_DIR}/FastDeployCSharp.cmake)
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add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cs)
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set_property(TARGET infer_demo PROPERTY VS_DOTNET_REFERENCES
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${FASTDEPLOY_DOTNET_REFERENCES}
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)
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set_property(TARGET infer_demo
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PROPERTY VS_PACKAGE_REFERENCES ${FASTDEPLOY_PACKAGE_REFERENCES})
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[English](README.md) | 简体中文
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# PaddleOCR CPU-GPU C#部署示例
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本目录下提供`infer.cs`来调用C# API快速完成PPOCRv3模型在CPU/GPU上部署的示例。
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## 1. 说明
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PaddleOCR支持利用FastDeploy在NVIDIA GPU、X86 CPU、飞腾CPU、ARM CPU、Intel GPU(独立显卡/集成显卡)硬件上快速部署OCR模型.
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## 2. 部署环境准备
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在部署前，需确认软硬件环境，同时下载预编译部署库，参考[FastDeploy安装文档](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#FastDeploy预编译库安装)安装FastDeploy预编译库. 在本目录执行如下命令即可在Windows完成编译测试，支持此模型需保证FastDeploy版本1.0.4以上(x.x.x>=1.0.4)
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## 3. 部署模型准备
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在部署前, 请准备好您所需要运行的推理模型, 您可以在[FastDeploy支持的PaddleOCR模型列表](../README.md)中下载所需模型.
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## 4. 部署示例
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### 4.1 下载C#包管理程序nuget客户端
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> https://dist.nuget.org/win-x86-commandline/v6.4.0/nuget.exe
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下载完成后将该程序添加到环境变量**PATH**中
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### 4.2. 下载模型文件和测试图片
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> https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar # (下载后解压缩)
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> https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
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> https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
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> https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg
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> https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt
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### 4.3 编译示例代码
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本文档编译的示例代码的编译工具依赖VS 2019，**Windows打开x64 Native Tools Command Prompt for VS 2019命令工具**，通过如下命令开始编译
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```shell
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## 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
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https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
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# 下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd D:\FastDeploy\examples\vision\ocr\PP-OCR\cpu-gpu\csharp
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# 如果您希望从PaddleOCR下载示例代码，请运行
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git clone https://github.com/PaddlePaddle/PaddleOCR.git
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# 注意：如果当前分支找不到下面的fastdeploy测试代码，请切换到dygraph分支
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git checkout dygraph
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cd D:\PaddleOCR\deploy\fastdeploy\cpu-gpu\csharp
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mkdir build && cd build
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cmake .. -G "Visual Studio 16 2019" -A x64 -DFASTDEPLOY_INSTALL_DIR=D:\fastdeploy-win-x64-gpu-x.x.x -DCUDA_DIRECTORY="C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.2"
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nuget restore
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msbuild infer_demo.sln /m:4 /p:Configuration=Release /p:Platform=x64
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```
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关于使用Visual Studio 2019创建sln工程，或者CMake工程等方式编译的更详细信息，可参考如下文档
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- [在 Windows 使用 FastDeploy C++ SDK](https://github.com/PaddlePaddle/FastDeploy/tree/develop/docs/cn/faq/use_sdk_on_windows.md)
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- [FastDeploy C++库在Windows上的多种使用方式](https://github.com/PaddlePaddle/FastDeploy/tree/develop/docs/cn/faq/use_sdk_on_windows_build.md)
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### 4.4 运行可执行程序
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注意Windows上运行时，需要将FastDeploy依赖的库拷贝至可执行程序所在目录, 或者配置环境变量。FastDeploy提供了工具帮助我们快速将所有依赖库拷贝至可执行程序所在目录,通过如下命令将所有依赖的dll文件拷贝至可执行程序所在的目录(可能生成的可执行文件在Release下还有一层目录，这里假设生成的可执行文件在Release处)
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```shell
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cd D:\fastdeploy-win-x64-gpu-x.x.x
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fastdeploy_init.bat install %cd% D:\PaddleOCR\deploy\fastdeploy\cpu-gpu\csharp\build\Release
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```
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将dll拷贝到当前路径后，准备好模型和图片，使用如下命令运行可执行程序即可
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```shell
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cd Release
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# CPU推理
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infer_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v3.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 0
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# GPU推理
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infer_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v3.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 1
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```
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## 5. PP-OCRv3 C# API接口简介
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下面提供了PP-OCRv3的C# API简介
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- 如果用户想要更换部署后端或进行其他定制化操作, 请查看[C# Runtime API](https://baidu-paddle.github.io/fastdeploy-api/csharp/html/classfastdeploy_1_1RuntimeOption.html).
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- 更多 PP-OCR C# API 请查看 [C# PP-OCR API](https://github.com/PaddlePaddle/FastDeploy/blob/develop/csharp/fastdeploy/vision/ocr/model.cs)
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### 模型
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```c#
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fastdeploy.vision.ocr.DBDetector(
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string model_file,
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string params_file,
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fastdeploy.RuntimeOption runtime_option = null,
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fastdeploy.ModelFormat model_format = ModelFormat.PADDLE)
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```
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> DBDetector模型加载和初始化。
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> **参数**
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>> * **model_file**(str): 模型文件路径
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>> * **params_file**(str): 参数文件路径
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>> * **runtime_option**(RuntimeOption): 后端推理配置，默认为null，即采用默认配置
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>> * **model_format**(ModelFormat): 模型格式，默认为PADDLE格式
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```c#
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fastdeploy.vision.ocr.Classifier(
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string model_file,
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string params_file,
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fastdeploy.RuntimeOption runtime_option = null,
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fastdeploy.ModelFormat model_format = ModelFormat.PADDLE)
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```
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> Classifier模型加载和初始化。
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> **参数**
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>> * **model_file**(str): 模型文件路径
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>> * **params_file**(str): 参数文件路径
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>> * **runtime_option**(RuntimeOption): 后端推理配置，默认为null，即采用默认配置
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>> * **model_format**(ModelFormat): 模型格式，默认为PADDLE格式
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```c#
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fastdeploy.vision.ocr.Recognizer(
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string model_file,
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string params_file,
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string label_path,
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fastdeploy.RuntimeOption runtime_option = null,
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fastdeploy.ModelFormat model_format = ModelFormat.PADDLE)
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```
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> Recognizer模型加载和初始化。
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> **参数**
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>> * **model_file**(str): 模型文件路径
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>> * **params_file**(str): 参数文件路径
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>> * **label_path**(str): 标签文件路径
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>> * **runtime_option**(RuntimeOption): 后端推理配置，默认为null，即采用默认配置
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>> * **model_format**(ModelFormat): 模型格式，默认为PADDLE格式
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```c#
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fastdeploy.pipeline.PPOCRv3Model(
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DBDetector dbdetector,
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Classifier classifier,
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Recognizer recognizer)
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```
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> PP-OCRv3Model模型加载和初始化。
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> **参数**
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>> * **det_model**(FD_C_DBDetectorWrapper*): DBDetector模型
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>> * **cls_model**(FD_C_ClassifierWrapper*): Classifier模型
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>> * **rec_model**(FD_C_RecognizerWrapper*): Recognizer模型文件
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#### Predict函数
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```c#
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fastdeploy.OCRResult Predict(OpenCvSharp.Mat im)
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```
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> 模型预测接口，输入图像直接输出结果。
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>
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> **参数**
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>
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>> * **im**(Mat): 输入图像，注意需为HWC，BGR格式
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>>
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> **返回值**
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>
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>> * **result**: OCR预测结果,包括由检测模型输出的检测框位置,分类模型输出的方向分类,以及识别模型输出的识别结果, OCRResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
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## 6. 其它文档
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- [FastDeploy部署PaddleOCR模型概览](../../)
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- [PP-OCRv3 Python部署](../python)
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- [PP-OCRv3 C++ 部署](../cpp)
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- [PP-OCRv3 C 部署](../c)
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@@ -0,0 +1,79 @@
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// Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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using System;
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using System.IO;
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using System.Runtime.InteropServices;
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using OpenCvSharp;
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using fastdeploy;
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namespace Test
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{
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public class TestPPOCRv3
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{
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public static void Main(string[] args)
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{
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if (args.Length < 6) {
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Console.WriteLine(
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"Usage: infer_demo path/to/det_model path/to/cls_model " +
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"path/to/rec_model path/to/rec_label_file path/to/image " +
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"run_option, " +
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"e.g ./infer_demo ./ch_PP-OCRv2_det_infer " +
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"./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv2_rec_infer " +
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"./ppocr_keys_v1.txt ./12.jpg 0"
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);
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Console.WriteLine( "The data type of run_option is int, 0: run with cpu; 1: run with gpu");
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return;
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}
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string det_model_dir = args[0];
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string cls_model_dir = args[1];
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string rec_model_dir = args[2];
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string rec_label_file = args[3];
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string image_path = args[4];
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RuntimeOption runtimeoption = new RuntimeOption();
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int device_option = Int32.Parse(args[5]);
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if(device_option==0){
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runtimeoption.UseCpu();
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}else{
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runtimeoption.UseGpu();
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}
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string sep = "\\";
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string det_model_file = det_model_dir + sep + "inference.pdmodel";
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string det_params_file = det_model_dir + sep + "inference.pdiparams";
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string cls_model_file = cls_model_dir + sep + "inference.pdmodel";
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string cls_params_file = cls_model_dir + sep + "inference.pdiparams";
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string rec_model_file = rec_model_dir + sep + "inference.pdmodel";
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string rec_params_file = rec_model_dir + sep + "inference.pdiparams";
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fastdeploy.vision.ocr.DBDetector dbdetector = new fastdeploy.vision.ocr.DBDetector(det_model_file, det_params_file, runtimeoption, ModelFormat.PADDLE);
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fastdeploy.vision.ocr.Classifier classifier = new fastdeploy.vision.ocr.Classifier(cls_model_file, cls_params_file, runtimeoption, ModelFormat.PADDLE);
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fastdeploy.vision.ocr.Recognizer recognizer = new fastdeploy.vision.ocr.Recognizer(rec_model_file, rec_params_file, rec_label_file, runtimeoption, ModelFormat.PADDLE);
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fastdeploy.pipeline.PPOCRv3 model = new fastdeploy.pipeline.PPOCRv3(dbdetector, classifier, recognizer);
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if(!model.Initialized()){
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Console.WriteLine("Failed to initialize.\n");
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}
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Mat image = Cv2.ImRead(image_path);
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fastdeploy.vision.OCRResult res = model.Predict(image);
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Console.WriteLine(res.ToString());
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Mat res_img = fastdeploy.vision.Visualize.VisOcr(image, res);
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Cv2.ImShow("result.png", res_img);
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Cv2.ImWrite("result.png", res_img);
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Cv2.WaitKey(0);
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}
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}
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}
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Reference in New Issue
Block a user