private void OnSaveLatentSvmTrainingSetButtonClick(object sender, RoutedEventArgs e)
{
FolderBrowserDialog dlg = new FolderBrowserDialog();
HwndSource source = PresentationSource.FromVisual(this) as HwndSource;
System.Windows.Forms.IWin32Window win = new OldWindow(source.Handle);
if (dlg.ShowDialog(win) != System.Windows.Forms.DialogResult.OK)
{
return;
}
string directory = dlg.SelectedPath;
using (StreamWriter writer = new StreamWriter(Path.Combine(directory, "all.txt")))
{
string colorModelsPath = Path.Combine(directory, "color_model.clr");
this.colorModels.SaveToFile(colorModelsPath);
writer.WriteLine(colorModelsPath);
int index = 0;
foreach (ImageInfo imageInfo in imageInfos)
{
double scale = this.CalcSegmentedImageScale(imageInfo.Image);
Shape scaledShape = imageInfo.Shape.Scale(scale, Vector.Zero);
BitmapSource scaledImage = ImageHelper.ResizeImage(
imageInfo.Image,
(int)Math.Round(imageInfo.Image.PixelWidth * scale),
(int)Math.Round(imageInfo.Image.PixelHeight * scale));
Image2D <Color> scaledConvertedImage = ImageHelper.BitmapSourceToImage2D(scaledImage);
string shapePath = Path.Combine(directory, string.Format("shape_{0:000}.s", index));
scaledShape.SaveToFile(shapePath);
string imagePath = Path.Combine(directory, string.Format("image_{0:000}.png", index));
Image2D.SaveToFile(scaledConvertedImage, imagePath);
writer.WriteLine("{0}\t{1}", shapePath, imagePath);
++index;
}
}
}
private void SegmentImage(Action <SimpleSegmentationAlgorithm, double> customSetupStep)
{
int selectedIndex = this.backgroundImagesListBox.SelectedIndex;
BitmapSource originalImage = this.imageInfos[selectedIndex].Image;
double scale = CalcSegmentedImageScale(originalImage);
BitmapSource scaledImage = ImageHelper.ResizeImage(
originalImage, (int)(originalImage.PixelWidth * scale), (int)(originalImage.PixelHeight * scale));
Image2D <Color> image = ImageHelper.BitmapSourceToImage2D(scaledImage);
Image2D.SaveToFile(image, "image.png");
// Customize segmentator
SimpleSegmentationAlgorithm segmentator = new SimpleSegmentationAlgorithm();
segmentator.ObjectShapeUnaryTermWeight = this.algorithmProperties.ShapeUnaryTermWeight;
segmentator.BackgroundShapeUnaryTermWeight = this.algorithmProperties.ShapeUnaryTermWeight;
segmentator.ObjectColorUnaryTermWeight = this.algorithmProperties.ColorUnaryTermWeight;
segmentator.BackgroundColorUnaryTermWeight = this.algorithmProperties.ColorUnaryTermWeight;
segmentator.ColorDifferencePairwiseTermWeight = this.algorithmProperties.BinaryTermWeight;
segmentator.ColorDifferencePairwiseTermCutoff = this.algorithmProperties.BrightnessBinaryTermCutoff;
segmentator.ShapeEnergyWeight = this.algorithmProperties.ShapeEnergyWeight;
// Run custom setup step
customSetupStep(segmentator, scale);
// Segment
SegmentationSolution result = segmentator.SegmentImage(image, this.colorModels);
Image2D.SaveToFile(result.Mask, "mask.png");
BitmapSource maskImage = ImageHelper.ResizeImage(
ImageHelper.MaskToBitmapSource(result.Mask), originalImage.PixelWidth, originalImage.PixelHeight);
this.imageInfos[selectedIndex].SegmentationMask = maskImage;
// Show results
this.UpdateControlsAccordingToCurrentState();
this.editorTabControl.SelectedIndex = 1;
}
private static void TestShapeTermsImpl(string testName, ShapeModel shapeModel, IEnumerable<VertexConstraints> vertexConstraints, IEnumerable<EdgeConstraints> edgeConstraints, Size imageSize)
{
ShapeConstraints constraintSet = ShapeConstraints.CreateFromConstraints(shapeModel.Structure, vertexConstraints, edgeConstraints);
// Get CPU results
Image2D<ObjectBackgroundTerm> shapeTermsCpu = new Image2D<ObjectBackgroundTerm>(imageSize.Width, imageSize.Height);
CpuShapeTermsLowerBoundCalculator calculatorCpu = new CpuShapeTermsLowerBoundCalculator();
calculatorCpu.CalculateShapeTerms(shapeModel, constraintSet, shapeTermsCpu);
Image2D.SaveToFile(shapeTermsCpu, -1000, 1000, String.Format("./{0}_cpu.png", testName));
// Get GPU results
Image2D<ObjectBackgroundTerm> shapeTermsGpu = new Image2D<ObjectBackgroundTerm>(imageSize.Width, imageSize.Height);
GpuShapeTermsLowerBoundCalculator calculatorGpu = new GpuShapeTermsLowerBoundCalculator();
calculatorGpu.CalculateShapeTerms(shapeModel, constraintSet, shapeTermsGpu);
Image2D.SaveToFile(shapeTermsGpu, -1000, 1000, String.Format("./{0}_gpu.png", testName));
// Compare with CPU results
for (int x = 0; x < imageSize.Width; ++x)
for (int y = 0; y < imageSize.Height; ++y)
{
Assert.AreEqual(shapeTermsCpu[x, y].ObjectTerm, shapeTermsGpu[x, y].ObjectTerm, 1e-2f);
Assert.AreEqual(shapeTermsCpu[x, y].BackgroundTerm, shapeTermsGpu[x, y].BackgroundTerm, 1e-2f);
}
}
private void OnLearnColorModelButtonClick(object sender, RoutedEventArgs e)
{
if (this.backgroundImagesListBox.SelectedIndex != -1)
{
ImageInfo currentImageInfo = this.imageInfos[this.backgroundImagesListBox.SelectedIndex];
currentImageInfo.ColorModelMask = this.colorMaskEditor.GetMask(currentImageInfo.Image.PixelWidth, currentImageInfo.Image.PixelHeight);
}
List <Color> objectColors = new List <Color>();
List <Color> backgroundColors = new List <Color>();
for (int i = 0; i < this.imageInfos.Count; ++i)
{
if (this.imageInfos[i].ColorModelMask == null)
{
continue;
}
Image2D <Color> image = ImageHelper.BitmapSourceToImage2D(this.imageInfos[i].Image);
ExtractObjectBackgroundColorsByMask(
image,
this.imageInfos[i].ColorModelMask,
objectColors,
backgroundColors);
Image2D.SaveToFile(image, string.Format("image_{0}.png", i));
Image2D.SaveToFile(this.imageInfos[i].ColorModelMask, string.Format("mask_{0}.png", i));
}
if (objectColors.Count == 0)
{
MessageBox.Show("No object pixels specified.");
return;
}
if (backgroundColors.Count == 0)
{
MessageBox.Show("No background pixels specified.");
return;
}
Helper.Subsample(objectColors, this.algorithmProperties.MaxPixelsToLearnFrom);
Helper.Subsample(backgroundColors, this.algorithmProperties.MaxPixelsToLearnFrom);
try
{
GaussianMixtureColorModel objectModel = GaussianMixtureColorModel.Fit(
objectColors.Take(this.algorithmProperties.MaxPixelsToLearnFrom),
this.algorithmProperties.MixtureComponentCount,
this.algorithmProperties.StopTolerance);
GaussianMixtureColorModel backgroundModel = GaussianMixtureColorModel.Fit(
backgroundColors.Take(this.algorithmProperties.MaxPixelsToLearnFrom),
this.algorithmProperties.MixtureComponentCount,
this.algorithmProperties.StopTolerance);
this.colorModels = new ObjectBackgroundColorModels(objectModel, backgroundModel);
this.UpdateControlsAccordingToCurrentState();
}
catch (Exception exception)
{
MessageBox.Show(exception.Message, "Error!", MessageBoxButton.OK, MessageBoxImage.Error);
}
}