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77 lines
3.0 KiB
Markdown
77 lines
3.0 KiB
Markdown
# PaddleDetection C++部署示例
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本目录下提供`infer_xxx.cc`快速完成PaddleDetection模型包括PPYOLOE/PicoDet/YOLOX/YOLOv3/PPYOLO/FasterRCNN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/the%20software%20and%20hardware%20requirements.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/quick_start)
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以Linux上推理为例，在本目录执行如下命令即可完成编译测试
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```
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以ppyoloe为例进行推理部署
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#下载SDK，编译模型examples代码（SDK中包含了examples代码）
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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-gpu-0.2.0.tgz
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tar xvf fastdeploy-linux-x64-gpu-0.2.0.tgz
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cd fastdeploy-linux-x64-gpu-0.2.0/examples/vision/detection/paddledetection/cpp
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mkdir build && cd build
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../../../../../fastdeploy-linux-x64-gpu-0.2.0
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make -j
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# 下载PPYOLOE模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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tar xvf ppyoloe_crn_l_300e_coco.tgz
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# CPU推理
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./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 0
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# GPU推理
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./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 1
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# GPU上TensorRT推理
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./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 2
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```
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## PaddleDetection C++接口
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### 模型类
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PaddleDetection目前支持6种模型系列，类名分别为`PPYOLOE`, `PicoDet`, `PaddleYOLOX`, `PPYOLO`, `FasterRCNN`，所有类名的构造函数和预测函数在参数上完全一致，本文档以PPYOLOE为例讲解API
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```
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fastdeploy::vision::detection::PPYOLOE(
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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 Frontend& model_format = Frontend::PADDLE)
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```
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PaddleDetection PPYOLOE模型加载和初始化，其中model_file为导出的ONNX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): 配置文件路径，即PaddleDetection导出的部署yaml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**(Frontend): 模型格式，默认为PADDLE格式
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#### Predict函数
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> ```
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> PPYOLOE::Predict(cv::Mat* im, DetectionResult* 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，各个框的置信度, DetectionResult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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