using namespace cv::dnn;
TextDetectionModel_DB *textDetector=0;
void getTextDetector()
{
     if(textDetector)return;
     String modelPath = "text_detection_DB_TD500_resnet18_2021sep.onnx";  //模型權(quán)重文件
    textDetector=new TextDetectionModel_DB(modelPath);
    float binThresh = 0.3;                                      //二值圖的置信度閾值
    float polyThresh  = 0.5 ;                                   //文本多邊形閾值
    double unclipRatio = 2.0;      //檢測到的文本區(qū)域的未壓縮比率，gai比率確定輸出大小
    uint maxCandidates = 200;
    textDetector->setBinaryThreshold(binThresh)
        .setPolygonThreshold(polyThresh)
        .setUnclipRatio(unclipRatio)
        .setMaxCandidates(maxCandidates);
    double scale = 1.0 / 255.0;
    int height = 736;                                                   //輸出圖片長寬
    int width = 736;
    Size inputSize = Size(width, height);
    Scalar mean = Scalar(122.67891434, 116.66876762, 104.00698793);
    textDetector->setInputParams(scale, inputSize, mean);
}
void deWaterMarkTextDetection(Mat &input,Mat &output,Mat &src,string)
{
    getTextDetector();
    // 推理
    std::vector<std::vector<Point>> results;
    textDetector->detect(input, results);
    Mat mask = Mat::zeros(input.size(), CV_8U);
    fillPoly(mask, results,Scalar::all(255));
    //將掩模進(jìn)行膨脹，使其能夠覆蓋圖像更大區(qū)域
    Mat kernel = getStructuringElement(MORPH_RECT, Size(5, 5));
    dilate(mask, mask, kernel);
    //使用inpaint進(jìn)行圖像修復(fù)
    Mat result;
    inpaint(src, mask, output, 1, INPAINT_NS);
}