public static PaletteType ProcessImage(VisionImage image)
{
// Initialize the IVA_Data structure to pass results and coordinate systems.
IVA_Data ivaData = new IVA_Data(3, 0);
// Extract Color Plane
using (VisionImage plane = new VisionImage(ImageType.U8, 7))
{
// Extract the green color plane and copy it to the main image.
Algorithms.ExtractColorPlanes(image, ColorMode.Rgb, null, plane, null);
Algorithms.Copy(plane, image);
}
// Creates a new, empty region of interest.
Roi roi = new Roi();
// Creates a new RectangleContour using the given values.
RectangleContour vaRect = new RectangleContour(20, 20, 2552, 1904);
roi.Add(vaRect);
// Geometric Matching
string vaTemplateFile = $"{ @"./VisionModel/Polygon_20.5M/Mode.png"}";
CurveOptions vaCurveOptions = new CurveOptions();
vaCurveOptions.ColumnStepSize = 15;
vaCurveOptions.ExtractionMode = ExtractionMode.NormalImage;
vaCurveOptions.FilterSize = EdgeFilterSize.Normal;
vaCurveOptions.MaximumEndPointGap = 10;
vaCurveOptions.MinimumLength = 20;
vaCurveOptions.RowStepSize = 15;
vaCurveOptions.Threshold = 145;
MatchGeometricPatternEdgeBasedOptions matchGPMOptions = new MatchGeometricPatternEdgeBasedOptions();
matchGPMOptions.Advanced.ContrastMode = ContrastMode.Original;
matchGPMOptions.Advanced.MatchStrategy = GeometricMatchingSearchStrategy.Balanced;
matchGPMOptions.MinimumMatchScore = 800;
matchGPMOptions.Mode = GeometricMatchModes.RotationInvariant;
matchGPMOptions.NumberOfMatchesRequested = 1;
double[] vaRangesMin = { -20, 0, 50, 0 };
double[] vaRangesMax = { 20, 0, 200, 50 };
matchGPMOptions.OcclusionRange = new Range(vaRangesMin[3], vaRangesMax[3]);
matchGPMOptions.RotationAngleRanges.Add(new Range(vaRangesMin[0], vaRangesMax[0]));
matchGPMOptions.RotationAngleRanges.Add(new Range(vaRangesMin[1], vaRangesMax[1]));
matchGPMOptions.ScaleRange = new Range(vaRangesMin[2], vaRangesMax[2]);
matchGPMOptions.SubpixelAccuracy = true;
gpm2Results = IVA_MatchGeometricPattern2(image, vaTemplateFile, vaCurveOptions, matchGPMOptions, ivaData, 2, roi);
roi.Dispose();
// Dispose the IVA_Data structure.
ivaData.Dispose();
// Return the palette type of the final image.
return(PaletteType.Gray);
}
public static void Init()
{
if (MatchOptions == null)
{
MatchOptions = new MatchGeometricPatternEdgeBasedOptions();
//[Description("Original:原始 Reversed:反转 Both:全")]
//[DisplayName("对比模式")]
MatchOptions.Advanced.ContrastMode = ContrastMode.Original;
//[Description("Balanced:平衡 Conservative:保守 Aggressive进取")]
//[DisplayName("几何匹配搜索策略")]
MatchOptions.Advanced.MatchStrategy = GeometricMatchingSearchStrategy.Balanced;
MatchOptions.MinimumMatchScore = 500;
/*
* 匹配策略
* 角度范围 0-360
* 缩放范围 90-110
* 遮挡范围 0-25
*/
MatchOptions.Mode = GeometricMatchModes.RotationInvariant | GeometricMatchModes.ScaleInvariant | GeometricMatchModes.OcclusionInvariant;
MatchOptions.NumberOfMatchesRequested = 1;
MatchOptions.RotationAngleRanges.Add(new Range(-20, 20));
MatchOptions.ScaleRange = new Range(90, 110);
MatchOptions.OcclusionRange = new Range(0, 25);
// 压像素精度
MatchOptions.SubpixelAccuracy = true;
}
if (LearnOptions == null)
{
LearnOptions = new LearnGeometricPatternEdgeBasedAdvancedOptions();
LearnOptions.ImageSamplingFactor = 2; // 图像采样因子
LearnOptions.RotationAngleRange = new Range(0, 360); // 设置模板的旋转角度范围
LearnOptions.ScaleRange = new Range(90, 110); // 设置模板的缩放范围
}
}
private static Collection <GeometricEdgeBasedPatternMatch> IVA_MatchGeometricPattern2(VisionImage image,
string templatePath,
CurveOptions curveOptions,
MatchGeometricPatternEdgeBasedOptions matchOptions,
IVA_Data ivaData,
int stepIndex,
Roi roi)
{
// Geometric Matching (Edge Based)
// Creates the image template.
using (VisionImage imageTemplate = new VisionImage(ImageType.U8, 7))
{
// Read the image template.
imageTemplate.ReadVisionFile(templatePath);
Collection <GeometricEdgeBasedPatternMatch> gpmResults = Algorithms.MatchGeometricPatternEdgeBased(image, imageTemplate, curveOptions, matchOptions, roi);
// Store the results in the data structure.
// First, delete all the results of this step (from a previous iteration)
Functions.IVA_DisposeStepResults(ivaData, stepIndex);
ivaData.stepResults[stepIndex].results.Add(new IVA_Result("# Matches", gpmResults.Count));
for (int i = 0; i < gpmResults.Count; ++i)
{
ivaData.stepResults[stepIndex].results.Add(new IVA_Result(String.Format("Match {0}.X Position (Pix.)", i + 1), gpmResults[i].Position.X));
ivaData.stepResults[stepIndex].results.Add(new IVA_Result(String.Format("Match {0}.Y Position (Pix.)", i + 1), gpmResults[i].Position.Y));
// If the image is calibrated, log the calibrated results.
if ((image.InfoTypes & InfoTypes.Calibration) != 0)
{
ivaData.stepResults[stepIndex].results.Add(new IVA_Result(String.Format("Match {0}.X Position (World)", i + 1), gpmResults[i].CalibratedPosition.X));
ivaData.stepResults[stepIndex].results.Add(new IVA_Result(String.Format("Match {0}.Y Position (World)", i + 1), gpmResults[i].CalibratedPosition.Y));
}
ivaData.stepResults[stepIndex].results.Add(new IVA_Result(String.Format("Match {0}.Angle (degrees)", i + 1), gpmResults[i].Rotation));
ivaData.stepResults[stepIndex].results.Add(new IVA_Result(String.Format("Match {0}.Scale", i + 1), gpmResults[i].Scale));
ivaData.stepResults[stepIndex].results.Add(new IVA_Result(String.Format("Match {0}.Score", i + 1), gpmResults[i].Score));
ivaData.stepResults[stepIndex].results.Add(new IVA_Result(String.Format("Match {0}.Occlusion", i + 1), gpmResults[i].Occlusion));
ivaData.stepResults[stepIndex].results.Add(new IVA_Result(String.Format("Match {0}.Template Target Curve Score", i + 1), gpmResults[i].TemplateMatchCurveScore));
ivaData.stepResults[stepIndex].results.Add(new IVA_Result(String.Format("Match {0}.Correlation Score", i + 1), gpmResults[i].CorrelationScore));
}
return(gpmResults);
}
}
