private static Task<ApplyResult> ApplyExtract_DoIt_Task(Convolution2D imageConv, FeatureRecognizer_Extract extract, FeatureRecognizer_Extract_Sub sub)
{
return Task.Run(() =>
{
VectorInt totalReduce = sub.Extract.GetReduction();
Convolution2D finalImage = imageConv;
if (extract.PreFilter != null)
{
finalImage = Convolutions.Convolute(imageConv, extract.PreFilter); //TODO: The final worker method shouldn't do this if too small. I'm just doing it to show the user something
totalReduce += extract.PreFilter.GetReduction();
}
if (imageConv.Width <= totalReduce.X || imageConv.Height <= totalReduce.Y)
{
// Too small, exit early
return new ApplyResult(finalImage, null, null);
}
// Apply convolutions
Convolution2D filtered = Convolutions.Convolute(finalImage, sub.Extract);
// Look at the brightest spots, and see if they are matches
var matches = AnalyzeBrightSpots(filtered, sub.Results);
return new ApplyResult(finalImage, filtered, matches);
});
}
private void BuildChangedExtract(FeatureRecognizer_Extract origExtract, Convolution2D newConv)
{
var image = _images.FirstOrDefault(o => o.UniqueID == origExtract.ImageID);
if (image == null)
{
throw new ApplicationException("Couldn't find the image that the original extract references");
}
// Image convolution
Convolution2D imageConv = ((BitmapCustomCachedBytes)UtilityWPF.ConvertToColorArray(image.Bitmap, false, Colors.Transparent)).ToConvolution();
if (origExtract.PreFilter != null)
{
imageConv = Convolutions.Convolute(imageConv, origExtract.PreFilter);
}
//NOTE: The original extract contains multiple sizes (reductions of the largest one). But since this convolution is derived from
//the largest extract, it would be difficult to reduce. So only keeping the highest resolution image
FeatureRecognizer_Extract_Sub resultPatch = BuildExtractResult(imageConv, newConv);
FinishBuildingExtract(origExtract.PreFilter, new[] { resultPatch }, origExtract.ImageID);
}
private async static void ApplyExtract_DoIt(Grid grid, FeatureRecognizer_Image image, FeatureRecognizer_Extract extract, FeatureRecognizer_Extract_Sub sub, ConvolutionResultNegPosColoring edgeColor, ContextMenu contextMenu)
{
// Source image
BitmapSource bitmap;
if (sub.InputWidth == image.Bitmap.PixelWidth && sub.InputHeight == image.Bitmap.PixelHeight)
{
bitmap = image.Bitmap;
}
else
{
bitmap = UtilityWPF.ResizeImage(image.Bitmap, sub.InputWidth, sub.InputHeight);
}
Convolution2D imageConv = ((BitmapCustomCachedBytes)UtilityWPF.ConvertToColorArray(bitmap, false, Colors.Transparent)).ToConvolution();
// Convolute, look for matches
var results = await ApplyExtract_DoIt_Task(imageConv, extract, sub);
#region Show results
// Left Image
ApplyExtract_Draw_LeftImage(grid, results.ImageConv, extract.PreFilter, edgeColor);
// Right Image
if (results.Filtered != null)
{
ApplyExtract_Draw_RightImage(grid, results.Filtered, edgeColor);
}
// Matches
if (results.Matches != null && results.Matches.Length > 0)
{
ApplyExtract_Draw_Matches(grid, results.Matches, results.Filtered.Size, sub, contextMenu);
}
#endregion
}
private void ApplyExtract(FeatureRecognizer_Image image, FeatureRecognizer_Extract extract)
{
pnlExtractResults.Children.Clear();
ConvolutionResultNegPosColoring edgeColor = (ConvolutionResultNegPosColoring)cboEdgeColors.SelectedValue;
foreach (var sub in extract.Extracts)
{
// Create a panel that will hold the result
Grid grid = new Grid();
if (pnlExtractResults.Children.Count > 0)
{
grid.Margin = new Thickness(0, 10, 0, 0);
}
grid.ColumnDefinitions.Add(new ColumnDefinition() { Width = new GridLength(1, GridUnitType.Star) });
grid.ColumnDefinitions.Add(new ColumnDefinition() { Width = new GridLength(5, GridUnitType.Pixel) });
grid.ColumnDefinitions.Add(new ColumnDefinition() { Width = new GridLength(1, GridUnitType.Star) });
grid.RowDefinitions.Add(new RowDefinition() { Height = new GridLength(1, GridUnitType.Auto) });
grid.RowDefinitions.Add(new RowDefinition() { Height = new GridLength(5, GridUnitType.Pixel) });
grid.RowDefinitions.Add(new RowDefinition() { Height = new GridLength(1, GridUnitType.Auto) });
pnlExtractResults.Children.Add(grid);
// Do the work in another thread
ApplyExtract_DoIt(grid, image, extract, sub, edgeColor, _extractResultContextMenu);
}
}
private void EditConvolution(FeatureRecognizer_Extract extract)
{
ImageFilterPainter viewer = new ImageFilterPainter();
viewer.Closed += Child_Closed;
viewer.SaveRequested += Painter_SaveRequested;
_childWindows.Add(viewer);
viewer.Tag = extract;
viewer.EditKernel((Convolution2D)extract.Extracts[0].Extract); // give it the highest resolution one
viewer.Show();
}
private void AddExtract(FeatureRecognizer_Extract extract)
{
panelExtracts.Children.Add(extract.Control);
_extracts.Add(extract);
}
private void FinishBuildingExtract(ConvolutionBase2D filter, FeatureRecognizer_Extract_Sub[] subs, string imageID)
{
string uniqueID = Guid.NewGuid().ToString();
// Determine filename
string filename = "extract - " + uniqueID + ".xml";
string fullFilename = System.IO.Path.Combine(_workingFolder, filename);
// Add it
FeatureRecognizer_Extract extract = new FeatureRecognizer_Extract()
{
Extracts = subs,
PreFilter = filter,
Control = Convolutions.GetThumbnail(subs[0].Extract, THUMBSIZE_EXTRACT, _extractContextMenu),
ImageID = imageID,
UniqueID = uniqueID,
Filename = filename,
};
if (extract.PreFilter != null && extract.PreFilter is Convolution2D)
{
extract.PreFilterDNA_Single = ((Convolution2D)extract.PreFilter).ToDNA();
}
else if (extract.PreFilter != null && extract.PreFilter is ConvolutionSet2D)
{
extract.PreFilterDNA_Set = ((ConvolutionSet2D)extract.PreFilter).ToDNA();
}
// Copy to the working folder
UtilityCore.SerializeToFile(fullFilename, extract);
AddExtract(extract);
// Update the session file
SaveSession_SessionFile(_workingFolder);
}
