采用ONNX格式的deeplabv3+网络进行语义分割的C++代码

以下是使用ONNX格式的deeplabv3+网络进行语义分割的C++代码示例：

#include <iostream>
#include <opencv2/opencv.hpp>
#include <onnxruntime_cxx_api.h>

using namespace std;
using namespace cv;
using namespace Ort;

int main()
{
    // 加载ONNX模型
    Env env(ORT_LOGGING_LEVEL_WARNING, "test");
    SessionOptions session_options;
    session_options.SetIntraOpNumThreads(1);
    session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
    session_options.SetExecutionMode(ExecutionMode::ORT_SEQUENTIAL);
    session_options.SetLogSeverityLevel(4);
    session_options.SetOptimizationLevel(OptimizationLevel::ORT_ENABLE_EXTENDED);
    session_options.SetInterOpNumThreads(1);
    session_options.SetSessionGraphOptimizationLevel(SessionGraphOptimizationLevel::ORT_ENABLE_ALL);
    session_options.SetSessionLogLevel(ORT_LOGGING_LEVEL_WARNING);
    session_options.SetCPUExecutionProviderOptions(0, {{"openmp_threads", "1"}});
    OrtSession session(env, "deeplabv3plus.onnx", session_options);

    // 获取输入输出信息
    auto input_info = session.GetInputTypeInfo(0);
    auto output_info = session.GetOutputTypeInfo(0);
    auto input_name = input_info.GetOnnxName();
    auto output_name = output_info.GetOnnxName();
    auto input_dims = input_info.GetTensorTypeAndShapeInfo().GetShape();
    auto output_dims = output_info.GetTensorTypeAndShapeInfo().GetShape();
    auto input_type = input_info.GetTensorElementType();

    // 打印输入输出信息
    cout << "input_name: " << input_name << endl;
    cout << "output_name: " << output_name << endl;
    cout << "input_dims: ";
    for (auto dim : input_dims)
        cout << dim << " ";
    cout << endl;
    cout << "output_dims: ";
    for (auto dim : output_dims)
        cout << dim << " ";
    cout << endl;
    cout << "input_type: " << input_type << endl;

    // 加载测试图像
    Mat image = imread("test.png");

    // 图像预处理
    int img_h = input_dims[2];
    int img_w = input_dims[3];
    Mat input_image;
    resize(image, input_image, Size(img_w, img_h));
    input_image.convertTo(input_image, CV_32FC3, 1.0 / 255.0);
    Mat mean = (Mat_<float>(1, 1, 3) << 0.485, 0.456, 0.406);
    Mat std = (Mat_<float>(1, 1, 3) << 0.229, 0.224, 0.225);
    subtract(input_image, mean, input_image);
    divide(input_image, std, input_image);
    input_image = input_image.t();

    // 创建输入tensor
    vector<int64_t> input_shape = {1, 3, img_h, img_w};
    auto memory_info = Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
    OrtValue input_tensor = OrtValue::CreateTensor(memory_info, input_image.data, input_image.total() * input_image.elemSize(), input_shape.data(), input_shape.size(), input_type);

    // 执行推理
    auto output_tensors = session.Run(Ort::RunOptions{nullptr}, input_name.c_str(), &input_tensor, 1, &output_name, 1);

    // 获取输出tensor
    auto output_tensor = output_tensors.front().Get<Tensor>();
    auto output_data = output_tensor.Data<float>();

    // 后处理
    Mat output_image(img_h, img_w, CV_8UC1);
    for (int i = 0; i < img_h; i++)
    {
        for (int j = 0; j < img_w; j++)
        {
            int idx = i * img_w + j;
            output_image.at<uchar>(i, j) = output_data[idx] > 0.5 ? 255 : 0;  // 阈值为0.5，二值化
        }
    }

    // 显示结果
    imshow("input", image);
    imshow("output", output_image);
    waitKey();

    return 0;
}

需要注意的是，该代码仅作为示例，具体实现可能会因模型、输入输出信息等因素而有所不同。在实际使用中，需要根据具体情况进行调整和优化。