refine cpp infer doc

d90bd95c · MissPenguin · a8e530a5 · d90bd95c · d90bd95c
隐藏空白更改
内联并排

Showing with 70 addition and 71 deletion

deploy/cpp_infer/readme.md deploy/cpp_infer/readme.md +29 -27

deploy/cpp_infer/readme_ch.md deploy/cpp_infer/readme_ch.md +41 -44

未找到文件。
--- a/deploy/cpp_infer/readme.md
+++ b/deploy/cpp_infer/readme.md
- [Server-side C++ Inference](#server-side-c-inference)
+English | [简体中文](readme_ch.md)
-  - [1. Prepare the Environment](#1-prepare-the-environment)
-    - [Environment](#environment)
-    - [1.1 Compile OpenCV](#11-compile-opencv)
-    - [1.2 Compile or Download or the Paddle Inference Library](#12-compile-or-download-or-the-paddle-inference-library)
-      - [1.2.1 Direct download and installation](#121-direct-download-and-installation)
-      - [1.2.2 Compile the inference source code](#122-compile-the-inference-source-code)
-  - [2. Compile and Run the Demo](#2-compile-and-run-the-demo)
-    - [2.1 Export the inference model](#21-export-the-inference-model)
-    - [2.2 Compile PaddleOCR C++ inference demo](#22-compile-paddleocr-c-inference-demo)
-    - [Run the demo](#run-the-demo)
-        - [1. det+cls+rec：](#1-detclsrec)
-        - [2. det+rec：](#2-detrec)
-        - [3. det](#3-det)
-        - [4. cls+rec：](#4-clsrec)
-        - [5. rec](#5-rec)
-        - [6. cls](#6-cls)
-  - [3. FAQ](#3-faq)
 # Server-side C++ Inference
-This chapter introduces the C++ deployment steps of the PaddleOCR model. The corresponding Python predictive deployment method refers to [document](../../doc/doc_ch/inference.md).
+- [1. Prepare the Environment](#1)
-C++ is better than python in terms of performance. Therefore, in CPU and GPU deployment scenarios, C++ deployment is mostly used.
+    - [1.1 Environment](#11)
+    - [1.2 Compile OpenCV](#12)
+    - [1.3 Compile or Download or the Paddle Inference Library](#13)
+- [2. Compile and Run the Demo](#2)
+    - [2.1 Export the inference model](#21)
+    - [2.2 Compile PaddleOCR C++ inference demo](#22)
+    - [2.3 Run the demo](#23)
+- [3. FAQ](#3)
+This chapter introduces the C++ deployment steps of the PaddleOCR model. C++ is better than Python in terms of performance. Therefore, in CPU and GPU deployment scenarios, C++ deployment is mostly used.
 This section will introduce how to configure the C++ environment and deploy PaddleOCR in Linux (CPU\GPU) environment. For Windows deployment please refer to [Windows](./docs/windows_vs2019_build.md) compilation guidelines.
+<a name="1"></a>
 ## 1. Prepare the Environment
-### Environment
+<a name="11"></a>
+### 1.1 Environment
 - Linux, docker is recommended.
 - Windows.
-### 1.1 Compile OpenCV
+<a name="12"></a>
+### 1.2 Compile OpenCV
 * First of all, you need to download the source code compiled package in the Linux environment from the OpenCV official website. Taking OpenCV 3.4.7 as an example, the download command is as follows.
@@ -92,11 +88,12 @@ opencv3/
 |-- share
 ```
-### 1.2 Compile or Download or the Paddle Inference Library
+<a name="13"></a>
+### 1.3 Compile or Download or the Paddle Inference Library
 * There are 2 ways to obtain the Paddle inference library, described in detail below.
-#### 1.2.1 Direct download and installation
+#### 1.3.1 Direct download and installation
 [Paddle inference library official website](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html#linux). You can review and select the appropriate version of the inference library on the official website.
@@ -109,7 +106,7 @@ tar -xf paddle_inference.tgz
 Finally you will see the the folder of `paddle_inference/` in the current path.
-#### 1.2.2 Compile the inference source code
+#### 1.3.2 Compile the inference source code
 * If you want to get the latest Paddle inference library features, you can download the latest code from Paddle GitHub repository and compile the inference library from the source code. It is recommended to download the inference library with paddle version greater than or equal to 2.0.1.
 * You can refer to [Paddle inference library] (https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_guide/inference_deployment/inference/build_and_install_lib_en.html) to get the Paddle source code from GitHub, and then compile To generate the latest inference library. The method of using git to access the code is as follows.
@@ -155,8 +152,10 @@ build/paddle_inference_install_dir/
 `paddle` is the Paddle library required for C++ prediction later, and `version.txt` contains the version information of the current inference library.
+<a name="2"></a>
 ## 2. Compile and Run the Demo
+<a name="21"></a>
 ### 2.1 Export the inference model
 * You can refer to [Model inference](../../doc/doc_ch/inference.md) and export the inference model. After the model is exported, assuming it is placed in the `inference` directory, the directory structure is as follows.
@@ -175,9 +174,9 @@ inference/
 ```
+<a name="22"></a>
 ### 2.2 Compile PaddleOCR C++ inference demo
 * The compilation commands are as follows. The addresses of Paddle C++ inference library, opencv and other Dependencies need to be replaced with the actual addresses on your own machines.
 ```shell
@@ -201,7 +200,9 @@ or the generated Paddle inference library path (`build/paddle_inference_install_
 * After the compilation is completed, an executable file named `ppocr` will be generated in the `build` folder.
-### Run the demo
+<a name="23"></a>
+### 2.3 Run the demo
 Execute the built executable file:
 ```shell
 ./build/ppocr [--param1] [--param2] [...]
@@ -342,6 +343,7 @@ The detection visualized image saved in ./output//12.jpg
 ```
+<a name="3"></a>
 ## 3. FAQ
 1.  Encountered the error `unable to access 'https://github.com/LDOUBLEV/AutoLog.git/': gnutls_handshake() failed: The TLS connection was non-properly terminated.`, change the github address in `deploy/cpp_infer/external-cmake/auto-log.cmake` to the https://gitee.com/Double_V/AutoLog address.
--- a/deploy/cpp_infer/readme_ch.md
+++ b/deploy/cpp_infer/readme_ch.md
- [服务器端C++预测](#服务器端c预测)
+[English](readme.md) | 简体中文
-  - [1. 准备环境](#1-准备环境)
-    - [1.0 运行准备](#10-运行准备)
-    - [1.1 编译opencv库](#11-编译opencv库)
-    - [1.2 下载或者编译Paddle预测库](#12-下载或者编译paddle预测库)
-      - [1.2.1 直接下载安装](#121-直接下载安装)
-      - [1.2.2 预测库源码编译](#122-预测库源码编译)
-  - [2 开始运行](#2-开始运行)
-    - [2.1 将模型导出为inference model](#21-将模型导出为inference-model)
-    - [2.2 编译PaddleOCR C++预测demo](#22-编译paddleocr-c预测demo)
-    - [2.3 运行demo](#23-运行demo)
-        - [1. 检测+分类+识别：](#1-检测分类识别)
-        - [2. 检测+识别：](#2-检测识别)
-        - [3. 检测：](#3-检测)
-        - [4. 分类+识别：](#4-分类识别)
-        - [5. 识别：](#5-识别)
-        - [6. 分类：](#6-分类)
-  - [3. FAQ](#3-faq)
 # 服务器端C++预测
-本章节介绍PaddleOCR 模型的的C++部署方法，与之对应的python预测部署方式参考[文档](../../doc/doc_ch/inference.md)。
+- [1. 准备环境](#1)
-C++在性能计算上优于python，因此，在大多数CPU、GPU部署场景，多采用C++的部署方式，本节将介绍如何在Linux\Windows (CPU\GPU)环境下配置C++环境并完成
+    - [1.1 运行准备](#11)
-PaddleOCR模型部署。
+    - [1.2 编译opencv库](#12)
+    - [1.3 下载或者编译Paddle预测库](#13)
+- [2 开始运行](#2)
+    - [2.1 准备模型](#21)
+    - [2.2 编译PaddleOCR C++预测demo](#22)
+    - [2.3 运行demo](#23)
+- [3. FAQ](#3)
+本章节介绍PaddleOCR 模型的的C++部署方法。C++在性能计算上优于Python，因此，在大多数CPU、GPU部署场景，多采用C++的部署方式，本节将介绍如何在Linux\Windows (CPU\GPU)环境下配置C++环境并完成PaddleOCR模型部署。
 <a name="1"></a>
 ## 1. 准备环境
-<a name="10"></a>
+<a name="11"></a>
-### 1.0 运行准备
+### 1.1 运行准备
 - Linux环境，推荐使用docker。
 - Windows环境。
 * 该文档主要介绍基于Linux环境的PaddleOCR C++预测流程，如果需要在Windows下基于预测库进行C++预测，具体编译方法请参考[Windows下编译教程](./docs/windows_vs2019_build.md)
-<a name="11"></a>
+<a name="12"></a>
-### 1.1 编译opencv库
+### 1.2 编译opencv库
 * 首先需要从opencv官网上下载在Linux环境下源码编译的包，以opencv3.4.7为例，下载命令如下。
@@ -103,35 +94,38 @@ opencv3/
 |-- share
 ```
-<a name="12"></a>
+<a name="13"></a>
-### 1.2 下载或者编译Paddle预测库
-* 有2种方式获取Paddle预测库，下面进行详细介绍。
+### 1.3 下载或者编译Paddle预测库
+可以选择直接下载安装或者从源码编译，下文分别进行具体说明。
-#### 1.2.1 直接下载安装
+<a name="131"></a>
+#### 1.3.1 直接下载安装
-* [Paddle预测库官网](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html#linux) 上提供了不同cuda版本的Linux预测库，可以在官网查看并选择合适的预测库版本（*建议选择paddle版本>=2.0.1版本的预测库* ）。
+[Paddle预测库官网](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html#linux) 上提供了不同cuda版本的Linux预测库，可以在官网查看并选择合适的预测库版本（*建议选择paddle版本>=2.0.1版本的预测库* ）。
-* 下载之后使用下面的方法解压。
+下载之后解压:
-```
+```shell
 tar -xf paddle_inference.tgz
 ```
 最终会在当前的文件夹中生成`paddle_inference/`的子文件夹。
-#### 1.2.2 预测库源码编译
+<a name="132"></a>
-* 如果希望获取最新预测库特性，可以从Paddle github上克隆最新代码，源码编译预测库。
+#### 1.3.2 预测库源码编译
-* 可以参考[Paddle预测库安装编译说明](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.0/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#congyuanmabianyi) 的说明，从github上获取Paddle代码，然后进行编译，生成最新的预测库。使用git获取代码方法如下。
+如果希望获取最新预测库特性，可以从github上克隆最新Paddle代码进行编译，生成最新的预测库。
+* 使用git获取代码:
 ```shell
 git clone https://github.com/PaddlePaddle/Paddle.git
 git checkout develop
 ```
-* 进入Paddle目录后，编译方法如下。
+* 进入Paddle目录，进行编译:
 ```shell
 rm -rf build
@@ -151,7 +145,7 @@ make -j
 make inference_lib_dist
 ```
-更多编译参数选项介绍可以参考[文档说明](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.0/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#congyuanmabianyi)。
+更多编译参数选项介绍可以参考[Paddle预测库编译文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.0/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#congyuanmabianyi)。
 * 编译完成之后，可以在`build/paddle_inference_install_dir/`文件下看到生成了以下文件及文件夹。
@@ -168,13 +162,13 @@ build/paddle_inference_install_dir/
 <a name="2"></a>
-## 2 开始运行
+## 2. 开始运行
 <a name="21"></a>
-### 2.1 将模型导出为inference model
+### 2.1 准备模型
-* 可以参考[模型预测章节](../../doc/doc_ch/inference.md)，导出inference model，用于模型预测。模型导出之后，假设放在`inference`目录下，则目录结构如下。
+直接下载PaddleOCR提供的推理模型，或者参考[模型预测章节](../../doc/doc_ch/inference_ppocr.md)，将训练好的模型导出为推理模型。模型导出之后，假设放在`inference`目录下，则目录结构如下。
 ```
 inference/
@@ -193,13 +187,13 @@ inference/
 ### 2.2 编译PaddleOCR C++预测demo
-* 编译命令如下，其中Paddle C++预测库、opencv等其他依赖库的地址需要换成自己机器上的实际地址。
+编译命令如下，其中Paddle C++预测库、opencv等其他依赖库的地址需要换成自己机器上的实际地址。
 ```shell
 sh tools/build.sh
 ```
-* 具体的，需要修改`tools/build.sh`中环境路径，相关内容如下：
+具体的，需要修改`tools/build.sh`中环境路径，相关内容如下：
 ```shell
 OPENCV_DIR=your_opencv_dir
@@ -211,12 +205,14 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
 其中，`OPENCV_DIR`为opencv编译安装的地址；`LIB_DIR`为下载(`paddle_inference`文件夹)或者编译生成的Paddle预测库地址(`build/paddle_inference_install_dir`文件夹)；`CUDA_LIB_DIR`为cuda库文件地址，在docker中为`/usr/local/cuda/lib64`；`CUDNN_LIB_DIR`为cudnn库文件地址，在docker中为`/usr/lib/x86_64-linux-gnu/`。**注意：以上路径都写绝对路径，不要写相对路径。**
-* 编译完成之后，会在`build`文件夹下生成一个名为`ppocr`的可执行文件。
+编译完成之后，会在`build`文件夹下生成一个名为`ppocr`的可执行文件。
 <a name="23"></a>
 ### 2.3 运行demo
+本demo支持系统串联调用，也支持单个功能的调用，如，只使用检测或识别功能。
 运行方式：  
 ```shell
 ./build/ppocr [--param1] [--param2] [...]
@@ -354,6 +350,7 @@ predict img: ../../doc/imgs/12.jpg
 The detection visualized image saved in ./output//12.jpg
 ```
+<a name="3"></a>
 ## 3. FAQ
 1.  遇到报错 `unable to access 'https://github.com/LDOUBLEV/AutoLog.git/': gnutls_handshake() failed: The TLS connection was non-properly terminated.`， 将 `deploy/cpp_infer/external-cmake/auto-log.cmake` 中的github地址改为 https://gitee.com/Double_V/AutoLog 地址即可。