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* 测试 * delete test * add yolov7-face * fit vision.h * add yolov7-face test * fit: yolov7-face infer.cc * fit * fit Yolov7-face Cmakelist * fit yolov7Face.cc * add yolov7-face pybind * add yolov7-face python infer * feat yolov7-face pybind * feat yolov7-face format error * feat yolov7face_pybind error * feat add yolov7face-pybind to facedet-pybind * same as before * same sa before * feat __init__.py * add yolov7face.py * feat yolov7face.h ignore "," * feat .py * fit yolov7face.py * add yolov7face test teadme file * add test file * fit postprocess * delete remain annotation * fit preview * fit yolov7facepreprocessor * fomat code * fomat code * fomat code * fit format error and confthreshold and nmsthres * fit confthreshold and nmsthres * fit test-yolov7-face * fit test_yolov7face * fit review * fit ci error Co-authored-by: kongbohua <kongbh2022@stu.pku.edu.cn> Co-authored-by: CoolCola <49013063+kongbohua@users.noreply.github.com>
91 lines
3.6 KiB
Markdown
91 lines
3.6 KiB
Markdown
# YOLOv7Face C++部署示例
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本目录下提供`infer.cc`快速完成YOLOv7Face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CPU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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```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 # x.x.x > 1.0.2
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tar xvf fastdeploy-linux-x64-x.x.x.tgz # x.x.x > 1.0.2
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x # x.x.x > 1.0.2
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make -j
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#下载官方转换好的YOLOv7Face模型文件和测试图片
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wget https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/lite/resources/test_lite_face_detector_3.jpg
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-lite-e.onnx
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-tiny-face.onnx
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#使用yolov7-tiny-face.onnx模型
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# CPU推理
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./infer_demo yolov7-tiny-face.onnx test_lite_face_detector_3.jpg 0
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# GPU推理
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./infer_demo yolov7-tiny-face.onnx test_lite_face_detector_3.jpg 1
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# GPU上TensorRT推理
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./infer_demo yolov7-tiny-face.onnx test_lite_face_detector_3.jpg 2
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#使用yolov7-lite-e.onnx模型
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# CPU推理
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./infer_demo yolov7-lite-e.onnx test_lite_face_detector_3.jpg 0
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# GPU推理
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./infer_demo yolov7-lite-e.onnx test_lite_face_detector_3.jpg 1
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# GPU上TensorRT推理
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./infer_demo yolov7-lite-e.onnx test_lite_face_detector_3.jpg 2
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```
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运行完成可视化结果如下图所示
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<img width="640" src="https://user-images.githubusercontent.com/49013063/206170111-843febb6-67d6-4c46-a121-d87d003bba21.jpg">
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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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## YOLOv7Face C++接口
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### YOLOv7Face类
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```c++
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fastdeploy::vision::facedet::YOLOv7Face(
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const string& model_file,
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const string& params_file = "",
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const RuntimeOption& runtime_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::ONNX)
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```
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YOLOv7Face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，当模型格式为ONNX时，此参数传入空字符串即可
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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ONNX格式
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#### Predict函数
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> ```c++
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> YOLOv7Face::Predict(cv::Mat* im, FaceDetectionResult* result,
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> float conf_threshold = 0.3,
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> float nms_iou_threshold = 0.5)
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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, FaceDetectionResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
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> > * **conf_threshold**: 检测框置信度过滤阈值
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> > * **nms_iou_threshold**: NMS处理过程中iou阈值
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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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