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.test
- 3.1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.angry
1.disgust
2.fear
3.happy
4.neutral
5.sad
6.surprise
模型需要三个文件：model.pdmodel，model.pdiparams，model.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.h"


std::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 = '/';
#endif



std::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 res
    std::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 res
    std::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

在这里插入图片描述

注意：所有的环境必须是Release X64

2.3生成成功

在这里插入图片描述

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

在这里插入图片描述
emotion_test.cpp

#include <vector>
#include <iostream>
#include <string>
#include "C:/Users/44869/Desktop/emotion_StaticLib/emotion_StaticLib/pch.h"

int main() {
    emotion_infer_by_camera("cpu", "A:/emotion/resnet50", "emotion");
    return 0;
}

3.2进行项目配置

在这里插入图片描述

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