diff --git a/deploy/cpp_infer/readme.md b/deploy/cpp_infer/readme.md
index 725197ad5cf9c7bf54be445f2bb3698096e7f9fb..60e6b9ec4ed384bf521a6378ee4aae77f0483288 100644
--- a/deploy/cpp_infer/readme.md
+++ b/deploy/cpp_infer/readme.md
@@ -1,9 +1,3 @@
-# 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)。
-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。
-
- [服务器端C++预测](#服务器端c预测)
- [1. 准备环境](#1-准备环境)
- [1.0 运行准备](#10-运行准备)
@@ -18,6 +12,14 @@ PaddleOCR模型部署。
- [1. 只调用检测：](#1-只调用检测)
- [2. 只调用识别：](#2-只调用识别)
- [3. 调用串联：](#3-调用串联)
+ - [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)。
+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。
+
@@ -280,10 +282,10 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
|参数名称|类型|默认参数|意义|
| :---: | :---: | :---: | :---: |
|rec_model_dir|string|-|识别模型inference model地址|
-|char_list_file|string|../../ppocr/utils/ppocr_keys_v1.txt|字典文件|
+|rec_char_dict_path|string|../../ppocr/utils/ppocr_keys_v1.txt|字典文件|
-* PaddleOCR也支持多语言的预测，更多支持的语言和模型可以参考[识别文档](../../doc/doc_ch/recognition.md)中的多语言字典与模型部分，如果希望进行多语言预测，只需将修改`char_list_file`（字典文件路径）以及`rec_model_dir`（inference模型路径）字段即可。
+* PaddleOCR也支持多语言的预测，更多支持的语言和模型可以参考[识别文档](../../doc/doc_ch/recognition.md)中的多语言字典与模型部分，如果希望进行多语言预测，只需将修改`rec_char_dict_path`（字典文件路径）以及`rec_model_dir`（inference模型路径）字段即可。
最终屏幕上会输出检测结果如下。
@@ -291,5 +293,6 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
+## 3. FAQ
-**注意：在使用Paddle预测库时，推荐使用2.0.0版本的预测库。**
+ 1. 遇到报错 `unable to access 'https://github.com/LDOUBLEV/AutoLog.git/': gnutls_handshake() failed: The TLS connection was non-properly terminated.` 首先在gitee导入`https://github.com/LDOUBLEV/AutoLog` 项目，然后将 `deploy/cpp_infer/external-cmake/auto-log.cmake` 中的github地址改为gitee地址即可。
diff --git a/deploy/cpp_infer/readme_en.md b/deploy/cpp_infer/readme_en.md
index f4cfab24350c1a6be3d8ebebf6b47b0baaa4f26e..2f69a51a945d38fbe19451a5c9f556885a123fa9 100644
--- a/deploy/cpp_infer/readme_en.md
+++ b/deploy/cpp_infer/readme_en.md
@@ -1,3 +1,19 @@
+- [Server-side C++ Inference](#server-side-c-inference)
+ - [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. run det demo:](#1-run-det-demo)
+ - [2. run rec demo:](#2-run-rec-demo)
+ - [3. run system demo:](#3-run-system-demo)
+ - [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).
@@ -258,9 +274,9 @@ More parameters are as follows,
|parameter|data type|default|meaning|
| --- | --- | --- | --- |
|rec_model_dir|string|-|Address of recognition inference model|
-|char_list_file|string|../../ppocr/utils/ppocr_keys_v1.txt|dictionary file|
+|rec_char_dict_path|string|../../ppocr/utils/ppocr_keys_v1.txt|dictionary file|
-* Multi-language inference is also supported in PaddleOCR, you can refer to [recognition tutorial](../../doc/doc_en/recognition_en.md) for more supported languages and models in PaddleOCR. Specifically, if you want to infer using multi-language models, you just need to modify values of `char_list_file` and `rec_model_dir`.
+* Multi-language inference is also supported in PaddleOCR, you can refer to [recognition tutorial](../../doc/doc_en/recognition_en.md) for more supported languages and models in PaddleOCR. Specifically, if you want to infer using multi-language models, you just need to modify values of `rec_char_dict_path` and `rec_model_dir`.
The detection results will be shown on the screen, which is as follows.
@@ -270,6 +286,6 @@ The detection results will be shown on the screen, which is as follows.
-### 2.3 Notes
+## 3. FAQ
-* Paddle 2.0.0 inference model library is recommended for this tutorial.
+ 1. Encountered the error `unable to access 'https://github.com/LDOUBLEV/AutoLog.git/': gnutls_handshake() failed: The TLS connection was non-properly terminated.` First import `https://github. com/LDOUBLEV/AutoLog` project on gitee, and then change the github address in `deploy/cpp_infer/external-cmake/auto-log.cmake` to the gitee address.
\ No newline at end of file
diff --git a/deploy/cpp_infer/src/main.cpp b/deploy/cpp_infer/src/main.cpp
index 664b10b2f579fd8681c65dcf1ded5ebe53d0424c..31d0685f543a1441eab8b9d2595d008ff65763f8 100644
--- a/deploy/cpp_infer/src/main.cpp
+++ b/deploy/cpp_infer/src/main.cpp
@@ -63,7 +63,7 @@ DEFINE_double(cls_thresh, 0.9, "Threshold of cls_thresh.");
// recognition related
DEFINE_string(rec_model_dir, "", "Path of rec inference model.");
DEFINE_int32(rec_batch_num, 6, "rec_batch_num.");
-DEFINE_string(char_list_file, "../../ppocr/utils/ppocr_keys_v1.txt",
+DEFINE_string(rec_char_dict_path, "../../ppocr/utils/ppocr_keys_v1.txt",
"Path of dictionary.");
using namespace std;
@@ -130,14 +130,14 @@ int main_det(std::vector cv_all_img_names) {
int main_rec(std::vector cv_all_img_names) {
std::vector time_info = {0, 0, 0};
- std::string char_list_file = FLAGS_char_list_file;
+ std::string rec_char_dict_path = FLAGS_rec_char_dict_path;
if (FLAGS_benchmark)
- char_list_file = FLAGS_char_list_file.substr(6);
- cout << "label file: " << char_list_file << endl;
+ rec_char_dict_path = FLAGS_rec_char_dict_path.substr(6);
+ cout << "label file: " << rec_char_dict_path << endl;
CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
- char_list_file, FLAGS_use_tensorrt, FLAGS_precision,
+ rec_char_dict_path, FLAGS_use_tensorrt, FLAGS_precision,
FLAGS_rec_batch_num);
std::vector img_list;
@@ -186,14 +186,14 @@ int main_system(std::vector cv_all_img_names) {
FLAGS_cls_thresh, FLAGS_use_tensorrt, FLAGS_precision);
}
- std::string char_list_file = FLAGS_char_list_file;
+ std::string rec_char_dict_path = FLAGS_rec_char_dict_path;
if (FLAGS_benchmark)
- char_list_file = FLAGS_char_list_file.substr(6);
- cout << "label file: " << char_list_file << endl;
+ rec_char_dict_path = FLAGS_rec_char_dict_path.substr(6);
+ cout << "label file: " << rec_char_dict_path << endl;
CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
- char_list_file, FLAGS_use_tensorrt, FLAGS_precision,
+ rec_char_dict_path, FLAGS_use_tensorrt, FLAGS_precision,
FLAGS_rec_batch_num);
for (int i = 0; i < cv_all_img_names.size(); ++i) {
diff --git a/deploy/cpp_infer/src/utility.cpp b/deploy/cpp_infer/src/utility.cpp
index c3c7b8485520579e8e2a23ae03543e3a9fc821bf..6952be54eed14d06ddcf3572d9bd2f4153894534 100644
--- a/deploy/cpp_infer/src/utility.cpp
+++ b/deploy/cpp_infer/src/utility.cpp
@@ -67,7 +67,7 @@ void Utility::GetAllFiles(const char *dir_name,
return;
}
struct stat s;
- lstat(dir_name, &s);
+ stat(dir_name, &s);
if (!S_ISDIR(s.st_mode)) {
std::cout << "dir_name is not a valid directory !" << std::endl;
all_inputs.push_back(dir_name);