private void ProcessImageGetContentFromPage(Image <Rgba32> image, QrReaderData qrData)
{
image.Mutate(x =>
x.Crop(new SixLabors.Primitives.Rectangle(0, qrData.QrCodeBottomPositionY,
x.GetCurrentSize().Width - 1, x.GetCurrentSize().Height - qrData.QrCodeBottomPositionY - 1))
.Rotate((float)qrData.PageRotation).BackgroundColor(Rgba32.White));
var contentArea = image.GetContentArea();
image.Mutate(x => x.Crop(contentArea));
}
private DecodedLetters DecodeExtractedLetters(IEnumerable <IEnumerable <CsvRow> > lettersCollections, QrReaderData qrData)
{
string currentResult = null;
var isChecksumCorrect = false;
foreach (var letters in lettersCollections)
{
var csvReader = DataOutput.GetCsvParser(letters);
var targetName = "class";
var featureNames = csvReader.EnumerateRows(c => c != targetName).First().ColumnNameToIndex.Keys.ToArray();
var testObservations = csvReader.EnumerateRows(featureNames).ToF64Matrix();
testObservations.Map(p => p / 255);
var model = ClassificationNeuralNetModel.Load(() => new StreamReader("network.xml"));
var predictions = model.Predict(testObservations);
var stringBuilder = new StringBuilder();
foreach (var prediction in predictions)
{
stringBuilder.Append(Math.Abs(prediction - -1) < 0.01 ? '-' : Alphabet.Base32Alphabet.ToString()[(int)prediction]);
}
currentResult = stringBuilder.ToString();
isChecksumCorrect = Sha1Helper.IsChecksumCorrect(currentResult, qrData.MetaData.Checksum);
if (isChecksumCorrect)
{
break;
}
}
return(new DecodedLetters {
Letters = currentResult, IsChecksumOk = isChecksumCorrect
});
}
private IEnumerable <IEnumerable <CsvRow> > ProcessImageGetLettersFromPage(Image <Rgba32> image, QrReaderData qrData)
{
var rowsPerPage = (int)Math.Ceiling(qrData.MetaData.DataChunkSize / (double)DataOutput.MaxLettersPerRow);
var letters = new List <CsvRow>((int)qrData.MetaData.DataChunkSize);
var lettersFallback = new List <CsvRow>((int)qrData.MetaData.DataChunkSize);
var nextY = 0;
for (var y = 0; y < rowsPerPage; y++)
{
var nextRowContentArea = image.GetNextRowContentArea(nextY);
nextY = nextRowContentArea.Y + nextRowContentArea.Height;
var rowImage = image.Clone(img => img.Crop(nextRowContentArea));
var currentRowLetters = y == rowsPerPage - 1
? qrData.MetaData.DataChunkSize - DataOutput.MaxLettersPerRow * y : DataOutput.MaxLettersPerRow;
var nextX = 0;
for (var x = 0; x < currentRowLetters; x++)
{
var nextLetterContentArea = rowImage.GetNextLetterContentArea(nextX);
nextX = nextLetterContentArea.X + nextLetterContentArea.Width;
var letterImage = rowImage.Clone(img =>
img.Crop(nextLetterContentArea));
using (var memoryStream = new MemoryStream())
{
letterImage.SaveAsBmp(memoryStream);
memoryStream.Seek(0, 0);
letters.Add(new CsvRow(_columnNameToIndex, LoadImageHelper.FileAsData(memoryStream)));
lettersFallback.Add(new CsvRow(_columnNameToIndex, LoadImageHelper.FileAsData(memoryStream, threshold: 90)));
}
}
}
return(new List <IEnumerable <CsvRow> > {
letters, lettersFallback
});
}
