public VisGrainDataCollection(Collection <ClassifierReport> reportType, Collection <ClassifierReport> reportSize, List <VisRectangleContour> listRect)
{
Items = new List <VisGrainData>();
int countSize = reportSize.Count;
int countType = reportType.Count;
int count = 0;
if (countSize > countType)
{
count = countSize;
}
else
{
count = countType;
}
for (int i = 0; i < count; i++)
{
ClassifierReport rSize = reportSize[i];
ClassifierReport rType = reportType[i];
VisGrainSize grainSize = new VisGrainSize {
Name = rSize.BestClassName, ScoreClassification = rSize.ClassificationScore, ScoreIdentification = rSize.IdentificationScore
};
VisGrainType grainType = new VisGrainType {
Name = rType.BestClassName, ScoreClassification = rType.ClassificationScore, ScoreIdentification = rType.IdentificationScore
};
VisGrainData grainData = new VisGrainData(grainType, grainSize, listRect[i]);
Items.Add(grainData);
}
Message = "";
}
// classifyButton_Click is called when the Classify button is pressed. The
// classify file is read in and the currently-loaded image is classified. The
// class name and score are then displayed to the user.
private void classifyButton_Click(object sender, EventArgs e)
{
// Classify the image.
try
{
ClassifierReport report = classifier.Classify(imageViewer1.Image, imageViewer1.Roi);
classBox.Text = report.BestClassName;
scoreBox.Text = report.ClassificationScore.ToString();
}
catch (VisionException)
{
// Unable to classify image
classBox.Text = "Unknown";
scoreBox.Text = "0";
}
}
private Collection <ClassifierReport> IVA_Classify(VisionImage image, Roi roi, string classifierFilePath)
{
//retval = new VisGrainTypeCollection();
// Create a binary image that will contain the segmented image.
using (VisionImage binaryImage = new VisionImage(ImageType.U8, 7))
{
ParticleClassifierSession vaClassifier = new ParticleClassifierSession();
bool fileExists = System.IO.File.Exists(classifierFilePath);
vaClassifier.ReadFile(classifierFilePath);
// Segments the image.
Functions.IVA_Classification_Segmentation(image, binaryImage, roi, vaClassifier.PreprocessingOptions);
// Get the ROIs of all individual particles.
Collection <Roi> rois = Functions.IVA_Classification_Extract_Particles(image, binaryImage);
// Allocates the classifier reports for all objects in the image.
Collection <ClassifierReport> classifierReports = new Collection <ClassifierReport>();
List <VisGrainType> listGrainType = new List <VisGrainType>();
ListShape = new List <VisRectangleContour>();
// Classifies the object located in the given ROIs.
for (int i = 0; i < rois.Count; ++i)
{
//RectangleContour rect = binaryImage=new VisionImage()
object obj1 = rois[i][0].Shape;
RectangleContour rc = (RectangleContour)rois[i][0].Shape;
VisRectangleContour rect = new VisRectangleContour(rc);
ClassifierReport report = vaClassifier.Classify(image, rois[i]);
VisGrainType grainType = new VisGrainType {
Name = report.BestClassName, ScoreClassification = report.ClassificationScore, ScoreIdentification = report.IdentificationScore
};
listGrainType.Add(grainType);
classifierReports.Add(report);
ListShape.Add(rect);
}
GrainResultsType = new VisGrainTypeCollection();
GrainResultsType.Items = listGrainType;
return(classifierReports);
}
}
private void timer1_Tick(object sender, EventArgs e)
{
double[] classCount = new double[8];
OverlayTextOptions textOptions = new OverlayTextOptions("Arial", 14);
textOptions.HorizontalAlignment = HorizontalTextAlignment.Center;
// Get the next image
GetNextImage(imageViewer1.Image);
// Process the image
VisionImage binaryImage = new VisionImage();
Algorithms.Threshold(imageViewer1.Image, binaryImage, new Range(0, 200));
Algorithms.RejectBorder(binaryImage, binaryImage);
Collection <ParticleReport> reports = Algorithms.ParticleReport(binaryImage);
foreach (ParticleReport report in reports)
{
RectangleContour contour = report.BoundingRect;
contour.Left -= 10;
contour.Top -= 10;
contour.Height += 20;
contour.Width += 20;
// Classify the part
ClassifierReport classifierReport = classifier.Classify(binaryImage, new Roi(new Shape[] { contour }));
// Display the result
if (classifierReport.ClassificationScore > 500)
{
textOptions.BackgroundColor = Rgb32Value.TransparentColor;
int classIndex = 0;
Rgb32Value textColor = Rgb32Value.BlackColor;
switch (classifierReport.BestClassName)
{
case "Motor":
textColor = Rgb32Value.WhiteColor;
classIndex = 0;
break;
case "Bolt":
textColor = new Rgb32Value(Color.Cyan);
classIndex = 1;
break;
case "Screw":
textColor = Rgb32Value.RedColor;
classIndex = 2;
break;
case "Gear":
textColor = Rgb32Value.BlackColor;
classIndex = 3;
break;
case "Washer":
textColor = new Rgb32Value(Color.Magenta);
classIndex = 4;
break;
case "Worm Gear":
textColor = Rgb32Value.GreenColor;
classIndex = 5;
break;
case "Bracket":
textColor = new Rgb32Value(0x80, 0x80, 0);
classIndex = 6;
break;
}
classCount[classIndex]++;
imageViewer1.Image.Overlays.Default.AddText(classifierReport.BestClassName, report.CenterOfMass, textColor, textOptions);
}
else
{
textOptions.BackgroundColor = Rgb32Value.BlackColor;
imageViewer1.Image.Overlays.Default.AddText("Unknown!", report.CenterOfMass, Rgb32Value.RedColor, textOptions);
}
}
classificationGraph1.PlotClassifier(classCount);
}
