[Doc]Add English version of documents in examples (#1070)

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2025-12-24 13:28:13 +08:00 · 2023-01-06 09:34:28 +08:00
parent c9a086ec38
commit bb96a6fe8f
313 changed files with 14807 additions and 4810 deletions
--- a/examples/vision/ocr/PP-OCRv2/cpp/README.md
+++ b/examples/vision/ocr/PP-OCRv2/cpp/README.md
@@ -1,25 +1,26 @@
-# PPOCRv2 C++部署示例
+English | [简体中文](README_CN.md)
+# PPOCRv2 C++ Deployment Example

-本目录下提供`infer.cc`快速完成PPOCRv2在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
+This directory provides examples that `infer.cc` fast finishes the deployment of PPOCRv2 on CPU/GPU and GPU accelerated by TensorRT. 

-在部署前，需确认以下两个步骤
+Two steps before deployment

- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)  
- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
+- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)  
+- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)

-以Linux上CPU推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
+Taking the CPU inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) is required to support this model.

 ```
 mkdir build
 cd build
-# 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
+# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above 
 wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
 tar xvf fastdeploy-linux-x64-x.x.x.tgz
 cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j


-# 下载模型,图片和字典文件
+# Download model, image, and dictionary files
 wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
 tar -xvf ch_PP-OCRv2_det_infer.tar

@@ -33,34 +34,29 @@ wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg

 wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt

-# CPU推理
+# CPU inference
 ./infer_demo ./ch_PP-OCRv2_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv2_rec_infer ./ppocr_keys_v1.txt ./12.jpg 0
-# GPU推理
+# GPU inference
 ./infer_demo ./ch_PP-OCRv2_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv2_rec_infer ./ppocr_keys_v1.txt ./12.jpg 1
-# GPU上TensorRT推理
+# TensorRT inference on GPU
 ./infer_demo ./ch_PP-OCRv2_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv2_rec_infer ./ppocr_keys_v1.txt ./12.jpg 2
-# GPU上Paddle-TRT推理
+# Paddle-TRT inference on GPU
 ./infer_demo ./ch_PP-OCRv2_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv2_rec_infer ./ppocr_keys_v1.txt ./12.jpg 3
-# 昆仑芯XPU推理
+# KunlunXin XPU inference
 ./infer_demo ./ch_PP-OCRv2_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv2_rec_infer ./ppocr_keys_v1.txt ./12.jpg 4
-# 华为昇腾推理, 需要使用静态shape的demo, 若用户需要连续地预测图片, 输入图片尺寸需要准备为统一尺寸
-./infer_static_shape_demo ./ch_PP-OCRv2_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv2_rec_infer ./ppocr_keys_v1.txt ./12.jpg 1
 ```

-以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:  
- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
+The above command works for Linux or MacOS. For SDK in Windows, refer to:
+- [How to use FastDeploy C++ SDK in Windows](../../../../../docs/cn/faq/use_sdk_on_windows.md)

-如果用户使用华为昇腾NPU部署, 请参考以下方式在部署前初始化部署环境:
- [如何使用华为昇腾NPU部署](../../../../../docs/cn/faq/use_sdk_on_ascend.md)
-
-运行完成可视化结果如下图所示
+The visualized result after running is as follows

 <img width="640" src="https://user-images.githubusercontent.com/109218879/185826024-f7593a0c-1bd2-4a60-b76c-15588484fa08.jpg">


-## PPOCRv2 C++接口
+## PPOCRv2 C++ Interface 

-### PPOCRv2类
+### PPOCRv2 Class

 ```
 fastdeploy::pipeline::PPOCRv2(fastdeploy::vision::ocr::DBDetector* det_model,
@@ -68,43 +64,43 @@ fastdeploy::pipeline::PPOCRv2(fastdeploy::vision::ocr::DBDetector* det_model,
                fastdeploy::vision::ocr::Recognizer* rec_model);
 ```

-PPOCRv2 的初始化，由检测，分类和识别模型串联构成
+The initialization of PPOCRv2, consisting of detection, classification and recognition models

-**参数**
+**Parameter**

-> * **DBDetector**(model): OCR中的检测模型
-> * **Classifier**(model): OCR中的分类模型
-> * **Recognizer**(model): OCR中的识别模型
+> * **DBDetector**(model): Detection model in OCR
+> * **Classifier**(model): Classification model in OCR
+> * **Recognizer**(model): Recognition model in OCR

 ```
 fastdeploy::pipeline::PPOCRv2(fastdeploy::vision::ocr::DBDetector* det_model,
                fastdeploy::vision::ocr::Recognizer* rec_model);
 ```
-PPOCRv2 的初始化，由检测，识别模型串联构成(无分类器)
+The initialization of PPOCRv2, consisting of detection and recognition models (No classifier)

-**参数**
+**Parameter**

-> * **DBDetector**(model): OCR中的检测模型
-> * **Recognizer**(model): OCR中的识别模型
+> * **DBDetector**(model): Detection model in OCROCR中的检测模型
+> * **Recognizer**(model): Recognition model in OCR

-#### Predict函数
+#### Predict Function

 > ```  
 > bool Predict(cv::Mat* img, fastdeploy::vision::OCRResult* result);
 > bool Predict(const cv::Mat& img, fastdeploy::vision::OCRResult* result);
 > ```
 >
-> 模型预测接口，输入一张图片，返回OCR预测结果
+> Model prediction interface. Input images and output OCR prediction results
 >
-> **参数**
+> **Parameter**
 >
-> > * **img**: 输入图像，注意需为HWC，BGR格式
-> > * **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/)
+> > * **img**: Input images in HWC or BGR format
+> > * **result**: OCR prediction results, including the position of the detection box from the detection model, the classification of the direction from the classification model, and the recognition result from the recognition model. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for OCRResult


-## DBDetector C++接口
+## DBDetector C++ Interface 

-### DBDetector类
+### DBDetector Class

 ```
 fastdeploy::vision::ocr::DBDetector(const std::string& model_file, const std::string& params_file = "",
@@ -112,18 +108,18 @@ fastdeploy::vision::ocr::DBDetector(const std::string& model_file, const std::st
             const ModelFormat& model_format = ModelFormat::PADDLE);
 ```

-DBDetector模型加载和初始化，其中模型为paddle模型格式。
+DBDetector model loading and initialization. The model is in paddle format.

-**参数**
+**Parameter**

-> * **model_file**(str): 模型文件路径
-> * **params_file**(str): 参数文件路径，当模型格式为ONNX时，此参数传入空字符串即可
-> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
-> * **model_format**(ModelFormat): 模型格式，默认为Paddle格式
+> * **model_file**(str): Model file path 
+> * **params_file**(str): Parameter file path. Merely passing an empty string when the model is in ONNX format
+> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
+> * **model_format**(ModelFormat): Model format. Paddle format by default

-### Classifier类与DBDetector类相同
+### The same applies to Classifier Class

-### Recognizer类
+### Recognizer Class
 ```
  Recognizer(const std::string& model_file,
             const std::string& params_file = "",
@@ -131,31 +127,31 @@ DBDetector模型加载和初始化，其中模型为paddle模型格式。
             const RuntimeOption& custom_option = RuntimeOption(),
             const ModelFormat& model_format = ModelFormat::PADDLE);
 ```
-Recognizer类初始化时,需要在label_path参数中,输入识别模型所需的label文件，其他参数均与DBDetector类相同
+For the initialization of the Recognizer class, users should input the label file required by the recognition model in the label_path parameter. Other parameters are the same as the DBDetector class

-**参数**
-> * **label_path**(str): 识别模型的label文件路径
+**Parameter**
+> * **label_path**(str): The label path of the recognition model


-### 类成员变量
-#### DBDetector预处理参数
-用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
+### Class Member Variable
+#### DBDetector Pre-processing Parameter
+Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results

-> > * **max_side_len**(int): 检测算法前向时图片长边的最大尺寸，当长边超出这个值时会将长边resize到这个大小，短边等比例缩放,默认为960
-> > * **det_db_thresh**(double): DB模型输出预测图的二值化阈值，默认为0.3
-> > * **det_db_box_thresh**(double): DB模型输出框的阈值，低于此值的预测框会被丢弃，默认为0.6
-> > * **det_db_unclip_ratio**(double): DB模型输出框扩大的比例，默认为1.5
-> > * **det_db_score_mode**(string):DB后处理中计算文本框平均得分的方式,默认为slow，即求polygon区域的平均分数的方式
-> > * **use_dilation**(bool):是否对检测输出的feature map做膨胀处理,默认为Fasle
+> > * **max_side_len**(int): The long side’s maximum size of the oriented view before detection. The long side will be resized to this size when exceeding the value. And the short side will be scaled in equal proportion. Default 960
+> > * **det_db_thresh**(double): The binarization threshold of the prediction image from DB models. Default 0.3
+> > * **det_db_box_thresh**(double): The threshold for the output box of DB models, below which the predicted box is discarded. Default 0.6 
+> > * **det_db_unclip_ratio**(double): The expansion ratio of the DB model output box. Default 1.5
+> > * **det_db_score_mode**(string): The way to calculate the average score of the text box in DB post-processing. Default slow, which is identical to the calculation of the polygon area’s average score
+> > * **use_dilation**(bool): Whether to expand the feature map from the detection. Default False

-#### Classifier预处理参数
-用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
+#### Classifier Pre-processing Parameter
+Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results

-> > * **cls_thresh**(double): 当分类模型输出的得分超过此阈值，输入的图片将被翻转，默认为0.9
+> > * **cls_thresh**(double): The input image will be flipped when the score output by the classification model exceeds this threshold. Default 0.9

-## 其它文档
+## Other Documents

- [PPOCR 系列模型介绍](../../)
- [PPOCRv2 Python部署](../python)
- [模型预测结果说明](../../../../../docs/api/vision_results/)
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
+- [PPOCR Model Description](../../)
+- [PPOCRv2 Python Deployment](../python)
+- [Model Prediction Results](../../../../../docs/api/vision_results/)
+- [How to switch the model inference backend engine](../../../../../docs/cn/faq/how_to_change_backend.md)