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#include <iostream>
#include <string>
#include <vector>
#include<opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
using namespace std;
void readImagesInFolder(const std::string& folderPath, std::vector<cv::Mat>& images)
{
    cv::String path(folderPath + "/*.jpg"); // 这里假设你的图片格式是.jpg，如果是其他格式请相应修改
    std::vector<cv::String> fileNames;
    cv::glob(path, fileNames, true); // 通过glob函数获取文件夹内所有符合格式的文件名
    for (const auto& fileName : fileNames)
    {   // 使用imread函数读取图片
        cv::Mat bgrImage = cv::imread(fileName, cv::IMREAD_COLOR); 
         // 图片格式转化bgr-->rgb
        if (!bgrImage.empty())
        {	
        	cv::Mat rgbImage;
            cv::cvtColor(bgrImage, rgbImage, cv::COLOR_BGR2RGB);
            images.push_back(rgbImage);
        }
    }
}
cv::Mat transformation(const cv::Mat& image, const cv::Size & targetSize, const cv::Scalar& mean, const cv::Scalar& std) {
    cv::Mat resizedImage;
    //图片尺寸缩放
    cv::resize(image, resizedImage, targetSize, 0, 0, cv::INTER_AREA);
    cv::Mat normalized;
    resizedImage.convertTo(normalized, CV_32F);
    cv::subtract(normalized / 255.0, mean, normalized);
    cv::divide(normalized, std, normalized);
    return normalized;
}
cv::dnn::Net loadModel(const string& onnx_path) {
    cv::dnn::Net net = cv::dnn::readNetFromONNX(onnx_path);
    return net;
}
int main()
{   // 图片存放文件路径
    string folderPath = "D:/C++_demo/opencv_onnx_gpu/CAMO/c";
    std::vector<cv::Mat> rgbImages;
    readImagesInFolder(folderPath, rgbImages);
   // string image_path = "./animal-1.jpg";
    // 加载ONNX模型
    string onnx_path = "D:/C++_demo/opencv_onnx_gpu/PFNet.onnx";
    cv::dnn::Net net = loadModel(onnx_path);
    // 设置CUDA为后端
    net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
    net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);
    cv::Mat output_prob;
    std::vector<cv::Mat> output_probs;
    std::vector<cv::String> output_layer_names = net.getUnconnectedOutLayersNames();
    // 定义目标图像大小
    cv::Size targetSize(416, 416);
    // 定义每个通道的归一化参数
    cv::Scalar mean(0.485, 0.456, 0.406); // 均值
    cv::Scalar std(0.229, 0.224, 0.225);  // 标准差
    // 开始计时
    auto start = chrono::high_resolution_clock::now();
    for (const auto& rgbImage : rgbImages) {
        // 获取图像的大小
        cv::Size originalSize(rgbImage.cols, rgbImage.rows);
        //cv::imshow("输入窗口", rgbImage);
        //cv::waitKey(0);
        //cv::destroyAllWindows();
        // 图片归一化
        cv::Mat normalized = transformation(rgbImage, targetSize, mean, std);
        std::cout << normalized.size() << std::endl;
        cv::Mat blob = cv::dnn::blobFromImage(normalized);
        // 将Blob设置为模型的输入
        net.setInput(blob);
        // 运行前向传播
        net.forward(output_probs, output_layer_names);
        // 获取最完整的预测
        cv::Mat prediction = output_probs[3];
        // 预测图变mask
        cv::Mat mask;
        cv::resize(prediction.reshape(1, 416) * 255.0, mask, originalSize, 0, 0, cv::INTER_AREA);
    }
    auto end = std::chrono::high_resolution_clock::now();
    // 计算耗时
    std::chrono::duration<double> elapsed = end - start;
    double elapsedTime = elapsed.count();
    // 打印耗时
    std::cout << "Elapsed time: " << elapsedTime << " seconds" << std::endl;
    return 0;
}