在Qt中用c++代码实现调用遥感地图语义分割的深度学习模型来进行图片预测

以下是使用TensorFlow C++ API在Qt中调用遥感地图语义分割深度学习模型进行图片预测的代码示例：

#include <tensorflow/cc/client/client_session.h>
#include <tensorflow/cc/ops/standard_ops.h>
#include <tensorflow/core/framework/tensor.h>
#include <tensorflow/core/framework/tensor_shape.h>
#include <tensorflow/core/graph/default_device.h>
#include <tensorflow/core/platform/env.h>
#include <tensorflow/core/platform/logging.h>
#include <tensorflow/core/public/session.h>

#include <QImage>

using namespace tensorflow;

// 加载模型文件
Status LoadModel(Session* session, const std::string& filename) {
    GraphDef graph_def;
    Status status = ReadBinaryProto(Env::Default(), filename, &graph_def);
    if (!status.ok()) {
        return status;
    }

    // 将模型文件加载到session中
    status = session->Create(graph_def);
    if (!status.ok()) {
        return status;
    }

    return Status::OK();
}

// 图片预处理，将QImage转换为TensorFlow可用的数据格式
void PreprocessImage(const QImage& image, Tensor* input_tensor) {
    const int width = image.width();
    const int height = image.height();
    const int channels = 3;

    auto input_tensor_mapped = input_tensor->tensor<float, 4>();

    for (int y = 0; y < height; ++y) {
        const QRgb* row = reinterpret_cast<const QRgb*>(image.scanLine(y));
        for (int x = 0; x < width; ++x) {
            const QRgb pixel = row[x];
            const float r = qRed(pixel) / 255.0f;
            const float g = qGreen(pixel) / 255.0f;
            const float b = qBlue(pixel) / 255.0f;

            input_tensor_mapped(0, y, x, 0) = r;
            input_tensor_mapped(0, y, x, 1) = g;
            input_tensor_mapped(0, y, x, 2) = b;
        }
    }
}

// 图片后处理，将模型输出的结果转换为QImage
QImage PostprocessOutput(const Tensor& output_tensor) {
    const int width = output_tensor.shape().dim_size(2);
    const int height = output_tensor.shape().dim_size(1);

    QImage result(width, height, QImage::Format_RGB888);

    auto output_tensor_mapped = output_tensor.tensor<float, 4>();

    for (int y = 0; y < height; ++y) {
        QRgb* row = reinterpret_cast<QRgb*>(result.scanLine(y));
        for (int x = 0; x < width; ++x) {
            const float r = output_tensor_mapped(0, y, x, 0);
            const float g = output_tensor_mapped(0, y, x, 1);
            const float b = output_tensor_mapped(0, y, x, 2);

            // 将模型输出的RGB值转换为QColor，并写入QImage中
            const QColor color(r * 255.0f, g * 255.0f, b * 255.0f);
            row[x] = color.rgb();
        }
    }

    return result;
}

// 进行图片预测
QImage PredictImage(const QImage& input_image, Session* session) {
    Tensor input_tensor(DT_FLOAT, TensorShape({1, input_image.height(), input_image.width(), 3}));
    PreprocessImage(input_image, &input_tensor);

    std::vector<std::pair<std::string, Tensor>> inputs = {{"input_tensor:0", input_tensor}};
    std::vector<tensorflow::Tensor> outputs;
    Status status = session->Run(inputs, {"output_tensor:0"}, {}, &outputs);

    if (!status.ok()) {
        LOG(ERROR) << "Failed to run model: " << status;
        return QImage();
    }

    return PostprocessOutput(outputs[0]);
}

int main() {
    // 初始化TensorFlow
    tensorflow::Session* session;
    tensorflow::Status status = tensorflow::NewSession(tensorflow::SessionOptions(), &session);
    if (!status.ok()) {
        LOG(ERROR) << "Failed to create TensorFlow session: " << status;
        return 1;
    }

    // 加载模型文件
    status = LoadModel(session, "model.pb");
    if (!status.ok()) {
        LOG(ERROR) << "Failed to load model: " << status;
        return 1;
    }

    // 加载图片并进行预测
    QImage input_image("input.jpg");
    QImage output_image = PredictImage(input_image, session);

    return 0;
}

需要根据实际情况修改模型文件的名字和路径，以及输入图片的名字和路径。同时，也可以根据实际情况修改图片预处理和后处理的代码，以满足模型的输入和输出要求。