手把手教你用c#制作一个小型桌面程序

#pragma once
#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include <iostream>
#include <string>

#include "pch.h"
#include "ResNetDll.h"

// 全局变量，用于存储模型路径和图像路径
static std::string g_imagePath;
static std::string g_modelPath;

// 图像预处理函数
cv::Mat transformImage(const std::string& imagePath) {
    cv::Mat image = cv::imread(imagePath);
    if (image.empty()) {
        throw std::runtime_error("Failed to load image.");
    }

    cv::Mat resizedImage;
    cv::resize(image, resizedImage, cv::Size(224, 224));

    cv::Mat floatImage;
    resizedImage.convertTo(floatImage, CV_32F, 1.0 / 255.0);

    cv::Mat normalizedImage;
    cv::Scalar mean(0.485, 0.456, 0.406);
    cv::Scalar stdDev(0.229, 0.224, 0.225);
    cv::subtract(floatImage, mean, normalizedImage);
    cv::divide(normalizedImage, stdDev, normalizedImage);

    // 从 BGR 转换到 RGB
    cv::Mat rgbImage;
    cv::cvtColor(normalizedImage, rgbImage, cv::COLOR_BGR2RGB);

    return rgbImage;
}

// 推理函数
const char* run_inference() {
    static std::string result;

    try {
        // 加载 ONNX 模型
        cv::dnn::Net net = cv::dnn::readNetFromONNX(g_modelPath);
        if (net.empty()) {
            result = "Failed to load the model.";
            return result.c_str();
        }

        // 预处理图像
        cv::Mat rgbImage = transformImage(g_imagePath);

        // 创建 blob 并设置为网络输入
        cv::Mat blob = cv::dnn::blobFromImage(rgbImage, 1.0, cv::Size(224, 224), cv::Scalar(), true, false);
        net.setInput(blob);

        // 执行推理
        cv::Mat output = net.forward();

        // 处理输出
        cv::Mat prob = output.reshape(1, 1);  // 变换成 1D 张量
        cv::Point classIdPoint;
        double confidence;
        // 用来找到矩阵或图像中元素的最小值和最大值，以及它们所在的位置
        cv::minMaxLoc(prob, 0, &confidence, 0, &classIdPoint);
        int classId = classIdPoint.x;

        // 根据预测结果返回相应的标签
        result = "Predicted Class ID: " + std::to_string(classId) + " with confidence: " + std::to_string(confidence);
        return result.c_str();
    }
    catch (const std::exception& e) {
        result = "Error occurred during inference: " + std::string(e.what());
        return result.c_str();
    }
}

// DLL 暴露的函数，用于设置图像路径
extern "C" RESNETDLL_API void set_image_path(const char* imagePath) {
    g_imagePath = imagePath;
}

// DLL 暴露的函数，用于设置模型路径
extern "C" RESNETDLL_API void set_model_path(const char* modelPath) {
    g_modelPath = modelPath;
}

// DLL 暴露的函数，运行推理
extern "C" RESNETDLL_API const char* run_resnet() {
    return run_inference();
}

#pragma once

#ifdef RESNETDLL_EXPORTS
#define RESNETDLL_API __declspec(dllexport)
#else
#define RESNETDLL_API __declspec(dllimport)
#endif

extern "C" {
    // 设置图像路径
    RESNETDLL_API void set_image_path(const char* imagePath);

    // 设置模型路径
    RESNETDLL_API void set_model_path(const char* modelPath);

    // 运行推理
    RESNETDLL_API const char* run_resnet();
}

using System;
using System.Runtime.InteropServices;

namespace ResNetApp
{
    public static class NativeMethods
    {
        // 导入 DLL 中的 set_image_path 函数
        [DllImport("ResNetDll.dll", CallingConvention = CallingConvention.Cdecl)]
        public static extern void set_image_path(string imagePath);

        // 导入 DLL 中的 set_model_path 函数
        [DllImport("ResNetDll.dll", CallingConvention = CallingConvention.Cdecl)]
        public static extern void set_model_path(string modelPath);

        // 导入 DLL 中的 run_resnet 函数
        [DllImport("ResNetDll.dll", CallingConvention = CallingConvention.Cdecl)]
        public static extern IntPtr run_resnet();
    }
}

using System;
using System.ComponentModel;
using System.Runtime.InteropServices;
using System.Windows.Forms;

namespace ResNetApp
{
    public partial class Form1 : Form
    {
        public Form1()
        {
            InitializeComponent();
        }

        private void buttonSelectImage_Click(object sender, EventArgs e)
        {
            OpenFileDialog openFileDialog = new OpenFileDialog();
            openFileDialog.Filter = "图像文件|*.bmp;*.jpg;*.jpeg;*.png";
            if (openFileDialog.ShowDialog() == DialogResult.OK)
            {
                textBoxImagePath.Text = openFileDialog.FileName; // 显示选择的图像路径
            }
        }

        private void buttonSelectModel_Click(object sender, EventArgs e)
        {
            OpenFileDialog openFileDialog = new OpenFileDialog();
            openFileDialog.Filter = "ONNX 模型文件|*.onnx";
            if (openFileDialog.ShowDialog() == DialogResult.OK)
            {
                textBoxModelPath.Text = openFileDialog.FileName; // 显示选择的模型路径
            }
        }

        private void button1_Click(object sender, EventArgs e)
        {
            try
            {
                string imagePath = textBoxImagePath.Text;
                string modelPath = textBoxModelPath.Text;

                if (string.IsNullOrEmpty(imagePath) || string.IsNullOrEmpty(modelPath))
                {
                    textBox1.Text = "请选择图像和模型路径。";
                    return;
                }

                textBox1.Text = "开始运行 ResNet ...";

                // 设置图像路径和模型路径
                NativeMethods.set_image_path(imagePath);
                NativeMethods.set_model_path(modelPath);

                // 调用 DLL 执行推理
                IntPtr resultPtr = NativeMethods.run_resnet();

                // 将返回的指针转换为字符串
                string result = Marshal.PtrToStringAnsi(resultPtr);

                // 显示结果
                textBox1.Text = result;
            }
            catch (Exception ex)
            {
                textBox1.Text = "错误: " + ex.Message;
            }
        }

    }
}

using System;
using System.Collections.Generic;
using System.Linq;
using System.Threading.Tasks;
using System.Windows.Forms;

namespace ResNetApp
{
    static class Program
    {
        /// <summary>
        /// 应用程序的主入口点。
        /// </summary>
        [STAThread]
        static void Main()
        {
            Application.EnableVisualStyles();
            Application.SetCompatibleTextRenderingDefault(false);
            Application.Run(new Form1());
        }
    }
}