Created by: Tinagao222

根据 https://zhuanlan.zhihu.com/p/145446681?utm_source=wechat_session&utm_medium=social&utm_oi=77894483181568&from=groupmessage&isappinstalled=0&wechatShare=1&s_r=0 ，知乎这篇文章的讲解，将paddle在c++编译成功后，封装成.dll供C#调用，用单张图片时测试成功。但实际应用中需要实时接收相机的图片，然后经过模型，得到的检测结果要输入给C#写的界面，完成计数、显示的工作，因此希望.dll库的接口是有输入输出的函数，而不是void函数。

因此对文中的main.cpp做了如下修改，修改完再生成就报错了 原文程序：

#include <glog/logging.h>
#include <iostream>
#include <string>
#include <vector>
#include "include/object_detector.h"

extern "C" __declspec(dllexport) void  Loadmodel();
extern "C" __declspec(dllexport) int add(int a, int b);
void PredictImage(const std::string& image_path,
    PaddleDetection::ObjectDetector* det);
int add(int a, int b) {
    return a + b;
}
void Loadmodel() {

    std::string model_dir = "D:\\0524\\test\\model";
    std::string image_path = "D:\\0524\\test\\orange_71.jpg";
    std::string video_path = "";
    std::string run_mode = "fluid";
    bool use_gpu = true;

    // Load model and create a object detector
    PaddleDetection::ObjectDetector det(model_dir, use_gpu, run_mode);
    PredictImage(image_path, &det);

}

void PredictImage(const std::string& image_path,
    PaddleDetection::ObjectDetector* det) {
    // Open input image as an opencv cv::Mat object
    cv::Mat im = cv::imread(image_path, 1);
    // Store all detected result
    std::vector<PaddleDetection::ObjectResult> result;
    det->Predict(im, &result);
    for (const auto& item : result) {
        printf("class=%d confidence=%.2f rect=[%d %d %d %d]\n",
            item.class_id,
            item.confidence,
            item.rect[0],
            item.rect[1],
            item.rect[2],
            item.rect[3]);
    }
    // Visualization result
    auto labels = det->GetLabelList();
    auto colormap = PaddleDetection::GenerateColorMap(labels.size());
    cv::Mat vis_img = PaddleDetection::VisualizeResult(
        im, result, labels, colormap);
    std::vector<int> compression_params;
    compression_params.push_back(CV_IMWRITE_JPEG_QUALITY);
    compression_params.push_back(95);
    cv::imwrite("output.jpeg", vis_img, compression_params);
    printf("Visualized output saved as output.jpeg\n");
}

我修改完的程序：

#include <glog/logging.h>
#include <iostream>
#include <string>
#include <vector>
#include "include/object_detector.h"

extern "C" __declspec(dllexport) std::vector<PaddleDetection::ObjectResult> detect(std::string model_dir, cv::Mat im);

std::string model_dir = "D:\\tina\\NN\\BaiduAI\\model";
std::string image_path = "D:\\tina\\NN\\BaiduAI\\2.jpg";
std::string run_mode = "fluid";
bool use_gpu = true;
int gpu_id = 0;

void PredictImage(cv::Mat im, std::vector<PaddleDetection::ObjectResult> &result, PaddleDetection::ObjectDetector* det) {
      
    // Store all detected result
    det->Predict(im, &result);

}
std::vector<PaddleDetection::ObjectResult> pubResult;

std::vector<PaddleDetection::ObjectResult> detect(std::string model_dir, cv::Mat im) {
    // Load model and create a object detector
    PaddleDetection::ObjectDetector det(model_dir, use_gpu=true, run_mode= "fluid", gpu_id=0);

    std::vector<PaddleDetection::ObjectResult> result;
    PredictImage(im,result,  &det);
    pubResult = result;
    return pubResult;
}