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[Doc]Add English version of documents in examples (#1070)
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Hu Chuqi
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examples/vision/tracking/pptracking/cpp/README_CN.md
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examples/vision/tracking/pptracking/cpp/README_CN.md
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[English](README.md) | 简体中文
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# PP-Tracking C++部署示例
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本目录下提供`infer.cc`快速完成PP-Tracking在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
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在部署前，需确认以下两个步骤
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- 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)
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- 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)
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以Linux上 PP-Tracking 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)
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```bash
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mkdir build
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cd build
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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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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
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tar xvf fastdeploy-linux-x64-x.x.x.tgz
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
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make -j
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# 下载PP-Tracking模型文件和测试视频
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wget https://bj.bcebos.com/paddlehub/fastdeploy/fairmot_hrnetv2_w18_dlafpn_30e_576x320.tgz
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tar -xvf fairmot_hrnetv2_w18_dlafpn_30e_576x320.tgz
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wget https://bj.bcebos.com/paddlehub/fastdeploy/person.mp4
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# CPU推理
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./infer_demo fairmot_hrnetv2_w18_dlafpn_30e_576x320 person.mp4 0
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# GPU推理
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./infer_demo fairmot_hrnetv2_w18_dlafpn_30e_576x320 person.mp4 1
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# GPU上TensorRT推理
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./infer_demo fairmot_hrnetv2_w18_dlafpn_30e_576x320 person.mp4 2
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```
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以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
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- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
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## PP-Tracking C++接口
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### PPTracking类
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```c++
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fastdeploy::vision::tracking::PPTracking(
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const string& model_file,
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const string& params_file = "",
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const string& config_file,
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const RuntimeOption& runtime_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::PADDLE)
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```
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PP-Tracking模型加载和初始化，其中model_file为导出的Paddle模型格式。
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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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> * **config_file**(str): 推理部署配置文件
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> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，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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#### Predict函数
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> ```c++
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> PPTracking::Predict(cv::Mat* im, MOTResult* result)
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> ```
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>
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> 模型预测接口，输入图像直接输出检测结果。
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>
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> **参数**
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>
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> > * **im**: 输入图像，注意需为HWC，BGR格式
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> > * **result**: 检测结果，包括检测框，跟踪id，各个框的置信度，对象类别id，MOTResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
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- [模型介绍](../../)
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- [Python部署](../python)
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- [视觉模型预测结果](../../../../../docs/api/vision_results/)
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- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
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