public IExportedImage Export(IScanImage scanImage, ExporterOptions options)
{
var export = new StringBuilder();
using (var writer = new StringWriter(export))
{
// Group by RowIndex and select the group Left, Right, Top, Bottom
foreach (var row in scanImage.Blobs.GroupBy(bp => bp.RowIndex))
{
foreach (var blob in row)
{
var key = row.Key + 1;
// Get the blob image.
var newImage = blob.CropBlob(scanImage.Image, options.ExtractedBackColor, options.ExportSize);
Write(writer, key, newImage);
// Write a rotated version.
newImage = blob.CropBlob(scanImage.Image, options.ExtractedBackColor, options.ExportSize, 10);
Write(writer, key, newImage);
// Write another rotated version (to the other dir).
newImage = blob.CropBlob(scanImage.Image, options.ExtractedBackColor, options.ExportSize, -10);
Write(writer, key, newImage);
}
}
writer.Flush();
}
return(new ExportedImage(scanImage, export.ToString()));
}
public ExportedImage(IScanImage scanImage, string exportData)
{
ExportData = exportData;
Image = scanImage.Image;
Blobs = new IBlob[scanImage.Blobs.Length];
scanImage.Blobs.CopyTo(Blobs, 0);
}
public IScanImage Scan(IScanner scanner, Action <IScannerConfiguration> configuration)
{
ScannerConfiguration scannerConfiguration;
configuration(scannerConfiguration = new ScannerConfiguration(scanner));
_scanImage = scannerConfiguration.Scan(scannerConfiguration.Source, scannerConfiguration.Options);
return(_scanImage);
}
public IPredictImage PredictModel(IScanImage scanImage, IAlphabet alphabet, PredictOptions options)
{
_options = options;
var blobs = new List <IBlob>();
// Get the model params.
var thetas = GetModelParamsFromFile();
// Loop thru all blobs and predict.
foreach (var blob in scanImage.Blobs)
{
// Get the blob pixels.
Vector xs = blob.GetPixels(scanImage.Image, options.ExtractedBackColor, options.ExportSize);
// Get the model value (this is what to be used in the Sigmoid function).
var v = (thetas * xs);
// This is for finding the maximum value of all letters predictions (1-vs-all), so
// we know what letter to choose.
var max = new double[3];
int[] maxIndex = { -1, -1, -1 };
// Loop thru the values
for (int i = 0; i < v.Count; i++)
{
// Get the final model prediction (Sigmoid).
v[i] = SpecialFunctions.Logistic(v[i].Real);
// Check if this prediction is in the "top 3".
for (int j = 0; j < max.Length; j++)
{
if (v[i].Real > max[j])
{
max[j] = v[i].Real;
maxIndex[j] = i;
// We want to kepp max array sorted, so once we found a value
// it is bigger than we stop.
break;
}
}
}
var b = (IBlob)blob.Clone();
b.Title = alphabet.Data[maxIndex[0]];
blobs.Add(b);
}
var collection = blobs.OrderBy(b => b.RowIndex)
.ThenBy(b => b.Left)
.GroupBy(g => g.RowIndex)
.ToDictionary(g => g.Key, s => s.Select(g => g).ToList());
return(new PredictImage(scanImage.Image, collection));
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="scanImage">Scan Image Service</param>
/// <param name="logger">Logger</param>
public BlobFunction(IScanImage scanImage, ILogger <BlobFunction> logger)
{
_scanImageService = scanImage ?? throw new ArgumentNullException(nameof(scanImage));
_logger = logger ?? throw new ArgumentNullException(nameof(logger));
}
