无锡网站建设制作,温州网站设计服务,丽水专业网站建设价格,怎样做网络推广给我 你所有地方都上手表情识别模块1.环境部署1.1同样采用fastDeploy库1.2相关模型2.封装成静态库2.1参考[百度Paddle中PP-Mattingv2的部署并将之封装并调用一个C静态库](https://blog.csdn.net/weixin_43564060/article/details/128882099)2.2项目依赖添加2.3生成成功3.test3.1创建emotion_test项目…
表情识别模块1.环境部署1.1同样采用fastDeploy库1.2相关模型2.封装成静态库2.1参考[百度Paddle中PP-Mattingv2的部署并将之封装并调用一个C静态库](https://blog.csdn.net/weixin_43564060/article/details/128882099)2.2项目依赖添加2.3生成成功3.test3.1创建emotion_test项目3.2进行项目配置3.3解决dll文件缺失的问题3.4运行结果1.环境部署
1.1同样采用fastDeploy库
可以参考百度Paddle中PP-Mattingv2的部署并将之封装并调用一个C静态库部署过程大致一样只是核心的代码进行了改动。
1.2相关模型
模型使用的自训练resnet50模型其中输出的标签为
0.angry1.disgust2.fear3.happy4.neutral5.sad6.surprise 模型需要三个文件model.pdmodelmodel.pdiparamsmodel.yml
2.封装成静态库
2.1参考百度Paddle中PP-Mattingv2的部署并将之封装并调用一个C静态库
framework.h代码
#pragma once#define WIN32_LEAN_AND_MEAN // 从 Windows 头文件中排除极少使用的内容
#include fastdeploy/vision.hstd::string emotion_CpuInfer(const std::string model_dir, const cv::Mat image_file);std::string emotion_GpuInfer(const std::string model_dir, const cv::Mat image_file);int emotion_infer_by_camera(const std::string device, const std::string model_dir, const std::string window_name); emotion_StaticLib.cpp代码为
// emotion_StaticLib.cpp : 定义静态库的函数。
//#include pch.h#include framework.h#ifdef WIN32
const char sep \\;
#else
const char sep /;
#endifstd::string emotion_CpuInfer(const std::string model_dir, const cv::Mat image_file) {auto model_file model_dir sep model.pdmodel;auto params_file model_dir sep model.pdiparams;auto config_file model_dir sep inference.yml;auto option fastdeploy::RuntimeOption();option.UseCpu();auto model fastdeploy::vision::classification::PaddleClasModel(model_file, params_file, config_file, option);std::string result;if (!model.Initialized()) {std::cerr Failed to initialize. std::endl;result Failed to initialize.;return result;}auto im image_file;fastdeploy::vision::ClassifyResult res;if (!model.Predict(im, res)) {std::cerr Failed to predict. std::endl;result Failed to initialize.;return result;}// print resstd::cout res.Str() std::endl;return res.Str();
}std::string emotion_GpuInfer(const std::string model_dir, const cv::Mat image_file) {auto model_file model_dir sep model.pdmodel;auto params_file model_dir sep model.pdiparams;auto config_file model_dir sep inference.yml;auto option fastdeploy::RuntimeOption();option.UseGpu();auto model fastdeploy::vision::classification::PaddleClasModel(model_file, params_file, config_file, option);std::string result;if (!model.Initialized()) {std::cerr Failed to initialize. std::endl;result Failed to initialize.;return result;}auto im image_file;fastdeploy::vision::ClassifyResult res;if (!model.Predict(im, res)) {std::cerr Failed to predict. std::endl;result Failed to initialize.;return result;}// print resstd::cout res.Str() std::endl;return res.Str();
}int emotion_infer_by_camera(const std::string device, const std::string model_dir, const std::string window_name video) {cv::VideoCapture cap;cap.open(0);std::string result;if (!cap.isOpened()) {std::cout open camera failed! std::endl;return 0;}cv::namedWindow(window_name, 1);while (1) {time_t t_now time(0);cv::Mat frame;cap frame;if (frame.empty()) {return 0;}cv::imshow(window_name, frame);emotion_CpuInfer(model_dir, frame);if (device gpu) {cv::imshow(window_name, frame);result emotion_GpuInfer(model_dir, frame);}else {cv::imshow(window_name, frame);result emotion_CpuInfer(model_dir, frame);}std::cout emotion此帧共消耗 (time(0) - t_now) 秒 std::endl;if (cv::waitKey(30) 0) break;}cap.release();return 1;
} 所有的环境部署步骤与百度Paddle中PP-Mattingv2的部署并将之封装并调用一个C静态库一致在该部署过程中只进行了cpu环境的部署
2.2项目依赖添加 注意所有的环境必须是Release X64
2.3生成成功 到此为止封装已经超过了在项目里面即可部署使用。
3.test
3.1创建emotion_test项目 emotion_test.cpp
#include vector
#include iostream
#include string
#include C:/Users/44869/Desktop/emotion_StaticLib/emotion_StaticLib/pch.hint main() {emotion_infer_by_camera(cpu, A:/emotion/resnet50, emotion);return 0;
}3.2进行项目配置 3.3解决dll文件缺失的问题
运行C:\Users\44869\Desktop\emotion_StaticLib\fastdeploy-win-x64-1.0.3下的fastdeploy_init.bat 生成的所有dll文件复制到C:\Users\44869\Desktop\emotion_StaticLib\emotion_test\x64\Release下即可
3.4运行结果
