private Angle GetAngle(Mat src)
{
Angle angle = new Angle();
Mat angleImg = new Mat();
CvInvoke.Resize(src, angleImg, new Size(angleDstWidth, angleDstHeight));
Tensor <float> inputTensors = OcrUtils.SubstractMeanNormalize(angleImg, MeanValues, NormValues);
var inputs = new List <NamedOnnxValue>
{
NamedOnnxValue.CreateFromTensor(inputNames[0], inputTensors)
};
try
{
using (IDisposableReadOnlyCollection <DisposableNamedOnnxValue> results = angleNet.Run(inputs))
{
var resultsArray = results.ToArray();
Console.WriteLine(resultsArray);
float[] outputData = resultsArray[0].AsEnumerable <float>().ToArray();
return(ScoreToAngle(outputData, angleCols));
}
}
catch (Exception ex)
{
Console.WriteLine(ex.Message + ex.StackTrace);
//throw ex;
}
return(angle);
}
private TextLine GetTextLine(Mat src)
{
TextLine textLine = new TextLine();
float scale = (float)crnnDstHeight / (float)src.Rows;
int dstWidth = (int)((float)src.Cols * scale);
Mat srcResize = new Mat();
CvInvoke.Resize(src, srcResize, new Size(dstWidth, crnnDstHeight));
Tensor <float> inputTensors = OcrUtils.SubstractMeanNormalize(srcResize, MeanValues, NormValues);
var inputs = new List <NamedOnnxValue>
{
NamedOnnxValue.CreateFromTensor(inputNames[0], inputTensors)
};
try
{
using (IDisposableReadOnlyCollection <DisposableNamedOnnxValue> results = crnnNet.Run(inputs))
{
var resultsArray = results.ToArray();
var dimensions = resultsArray[0].AsTensor <float>().Dimensions;
float[] outputData = resultsArray[0].AsEnumerable <float>().ToArray();
return(ScoreToTextLine(outputData, dimensions[1], dimensions[2]));
}
}
catch (Exception ex)
{
Console.WriteLine(ex.Message + ex.StackTrace);
//throw ex;
}
return(textLine);
}
public List <TextBox> GetTextBoxes(Mat src, ScaleParam scale, float boxScoreThresh, float boxThresh, float unClipRatio)
{
Mat srcResize = new Mat();
CvInvoke.Resize(src, srcResize, new Size(scale.DstWidth, scale.DstHeight));
Tensor <float> inputTensors = OcrUtils.SubstractMeanNormalize(srcResize, MeanValues, NormValues);
var inputs = new List <NamedOnnxValue>
{
NamedOnnxValue.CreateFromTensor(inputNames[0], inputTensors)
};
try
{
using (IDisposableReadOnlyCollection <DisposableNamedOnnxValue> results = dbNet.Run(inputs))
{
var resultsArray = results.ToArray();
Console.WriteLine(resultsArray);
var textBoxes = GetTextBoxes(resultsArray, srcResize.Rows, srcResize.Cols, scale, boxScoreThresh, boxThresh, unClipRatio);
return(textBoxes);
}
}
catch (Exception ex)
{
Console.WriteLine(ex.Message + ex.StackTrace);
}
return(null);
}
