#include "TCT.h"

TCT::TCT(QObject* parent)
{


}

TCT::~TCT()
{

}

TCT::TCT(const std::string& onnxModelPath, const cv::Size& modelInputShape, const std::string& classesTxtFile, const bool& runWithCuda)
{
    modelPath = onnxModelPath;
    modelShape = modelInputShape;
    classesPath = classesTxtFile;
    cudaEnabled = runWithCuda;

    loadOnnxNetwork();
    //loadClassesFromFile(); //The classes are hard-coded for this example

}

std::vector<Detection> TCT::runInference(const cv::Mat& input)
{
    cv::Mat modelInput = input;
    if (letterBoxForSquare && modelShape.width == modelShape.height)
        modelInput = formatToSquare(modelInput);

    cv::Mat blob;
    cv::dnn::blobFromImage(modelInput, blob, 1.0 / 255.0, modelShape, cv::Scalar(), true, false);
    net.setInput(blob);

    std::vector<cv::Mat> outputs;
    net.forward(outputs, net.getUnconnectedOutLayersNames());

    int rows = outputs[0].size[1];
    int dimensions = outputs[0].size[2];

    bool yolov8 = false;
    // yolov5 has an output of shape (batchSize, 25200, 85) (Num classes + box[x,y,w,h] + confidence[c])
    // yolov8 has an output of shape (batchSize, 84,  8400) (Num classes + box[x,y,w,h])
    if (dimensions > rows) // Check if the shape[2] is more than shape[1] (yolov8)
    {
        yolov8 = true;
        rows = outputs[0].size[2];
        dimensions = outputs[0].size[1];

        outputs[0] = outputs[0].reshape(1, dimensions);
        cv::transpose(outputs[0], outputs[0]);
    }
    float* data = (float*)outputs[0].data;

    float x_factor = modelInput.cols / modelShape.width;
    float y_factor = modelInput.rows / modelShape.height;

    std::vector<int> class_ids;
    std::vector<float> confidences;
    std::vector<cv::Rect> boxes;

    for (int i = 0; i < rows; ++i)
    {
        if (yolov8)
        {
            float* classes_scores = data + 4;

            cv::Mat scores(1, classes.size(), CV_32FC1, classes_scores);
            cv::Point class_id;
            double maxClassScore;

            minMaxLoc(scores, 0, &maxClassScore, 0, &class_id);

            if (maxClassScore > modelScoreThreshold)
            {
                confidences.push_back(maxClassScore);
                class_ids.push_back(class_id.x);

                float x = data[0];
                float y = data[1];
                float w = data[2];
                float h = data[3];

                int left = int((x - 0.5 * w) * x_factor);
                int top = int((y - 0.5 * h) * y_factor);

                int width = int(w * x_factor);
                int height = int(h * y_factor);

                boxes.push_back(cv::Rect(left, top, width, height));
            }
        }
        else // yolov5
        {
            float confidence = data[4];

            if (confidence >= modelConfidenceThreshold)
            {
                float* classes_scores = data + 5;

                cv::Mat scores(1, classes.size(), CV_32FC1, classes_scores);
                cv::Point class_id;
                double max_class_score;

                minMaxLoc(scores, 0, &max_class_score, 0, &class_id);

                if (max_class_score > modelScoreThreshold)
                {
                    confidences.push_back(confidence);
                    class_ids.push_back(class_id.x);

                    float x = data[0];
                    float y = data[1];
                    float w = data[2];
                    float h = data[3];

                    int left = int((x - 0.5 * w) * x_factor);
                    int top = int((y - 0.5 * h) * y_factor);

                    int width = int(w * x_factor);
                    int height = int(h * y_factor);

                    boxes.push_back(cv::Rect(left, top, width, height));
                }
            }
        }

        data += dimensions;
    }

    std::vector<int> nms_result;
    cv::dnn::NMSBoxes(boxes, confidences, modelScoreThreshold, modelNMSThreshold, nms_result);

    std::vector<Detection> detections{};
    for (unsigned long i = 0; i < nms_result.size(); ++i)
    {
        int idx = nms_result[i];

        Detection result;
        result.class_id = class_ids[idx];
        result.confidence = confidences[idx];

        std::random_device rd;
        std::mt19937 gen(rd());
        std::uniform_int_distribution<int> dis(100, 255);
        result.color = cv::Scalar(dis(gen),
            dis(gen),
            dis(gen));

        result.className = classes[result.class_id];
        result.box = boxes[idx];

        detections.push_back(result);
    }

    return detections;
}

void TCT::loadClassesFromFile()
{
    std::ifstream inputFile(classesPath);
    if (inputFile.is_open())
    {
        std::string classLine;
        while (std::getline(inputFile, classLine))
            classes.push_back(classLine);
        inputFile.close();
    }
}

void TCT::loadOnnxNetwork()
{
    net = cv::dnn::readNetFromONNX(modelPath);
    cudaEnabled = 1;
    if (cudaEnabled)
    {
        std::cout << "\nRunning on CUDA" << std::endl;
        net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
        net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);

    }
    else
    {
        std::cout << "\nRunning on CPU" << std::endl;
        net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
        net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
    }
}

cv::Mat TCT::formatToSquare(const cv::Mat& source)
{
    int col = source.cols;
    int row = source.rows;
    int _max = MAX(col, row);
    cv::Mat result = cv::Mat::zeros(_max, _max, CV_8UC3);
    source.copyTo(result(cv::Rect(0, 0, col, row)));
    return result;
}

//QImageToCvMat函数
cv::Mat convertQImageToCvMat(const QImage& qImage) {
    // 转换为RGB888格式，确保三通道
    QImage swappedImage = qImage.convertToFormat(QImage::Format_RGB888);

    // 创建临时cv::Mat，共享数据
    cv::Mat tmp(swappedImage.height(), swappedImage.width(), CV_8UC3,
        const_cast<uchar*>(swappedImage.bits()),
        static_cast<size_t>(swappedImage.bytesPerLine()));

    // 将RGB转为OpenCV默认的BGR顺序
    cv::Mat cvMat;
    cv::cvtColor(tmp, cvMat, cv::COLOR_RGB2BGR);

    // 克隆数据以确保独立于QImage的生命周期
    return cvMat.clone();
}

// CvMatToQImage函数
QImage CvMatToQImage(const cv::Mat& mat) {
    if (mat.empty())
        return QImage();

    // 处理单通道和三通道情况
    cv::Mat rgbMat;
    switch (mat.channels()) {
    case 1:
        cv::cvtColor(mat, rgbMat, cv::COLOR_GRAY2RGB);
        break;
    case 3:
        cv::cvtColor(mat, rgbMat, cv::COLOR_BGR2RGB);
        break;
    default:
        throw std::runtime_error("Unsupported channel count");
    }

    // 构造QImage并拷贝数据（避免野指针）
    return QImage(
        rgbMat.data,
        rgbMat.cols,
        rgbMat.rows,
        static_cast<int>(rgbMat.step),
        QImage::Format_RGB888
    ).copy(); // 必须.copy()保证独立内存
}

// 总体处理函数
void TCT::main_module(QImage image)
{
    bool runOnGPU = false;
    TCT inf("E:/wjy/QtWidgetsApplication-master/best.onnx", cv::Size(640, 640), "classes.txt", runOnGPU); //处理模型

    //std::vector<std::string> imageNames;
    //imageNames.push_back("D:/4.png");           //读取图片

    cv::Mat frame = convertQImageToCvMat(image);

    std::vector<Detection> output = inf.runInference(frame);

    int detections = output.size();
    std::cout << "Number of detections:" << detections << std::endl;

    for (int i = 0; i < detections; ++i)
    {
        Detection detection = output[i];

        cv::Rect box = detection.box;
        cv::Scalar color = detection.color;

        // Detection box
        cv::rectangle(frame, box, color, 2);

        // Detection box text
        std::string classString = detection.className + ' ' + std::to_string(detection.confidence).substr(0, 4);
        cv::Size textSize = cv::getTextSize(classString, cv::FONT_HERSHEY_DUPLEX, 1, 2, 0);
        cv::Rect textBox(box.x, box.y - 40, textSize.width + 10, textSize.height + 20);

        cv::rectangle(frame, textBox, color, cv::FILLED);
        cv::putText(frame, classString, cv::Point(box.x + 5, box.y - 10), cv::FONT_HERSHEY_DUPLEX, 1, cv::Scalar(0, 0, 0), 2, 0);
    }
    // Inference ends here...

// This is only for preview purposes
    float scale = 0.8;
    QImage img_i = CvMatToQImage(frame);
    cv::Mat myframe = convertQImageToCvMat(img_i);
    emit em_img(img_i);

}