/// <inheritdoc />
/// <exception cref="ArgumentNullException">
/// <paramref name="source"/> is <b>null</b>.
/// </exception>
public PointsOfInterestFeatures BuildFeatures(ImageSource source, CancellationToken cancellationToken)
{
if (source == null)
{
throw new ArgumentNullException(nameof(source));
}
Image image = ImagePreprocessing.Process(source.Image, this.ImagePreprocessingOptions, 8);
image = image
////.Scale(null, 100.0 / image.HorizontalResolution, 100.0 / image.VerticalResolution, ScalingOptions.None)
////.Binarize()
.Convert8To1(null, 128)
.CleanOverscan(0.5f, 0.5f)
.Deskew(null)
.Despeckle(null);
ISet <ConnectedComponent> components = image.FindConnectedComponents(8);
image.RemoveConnectedComponents(components.Where(x => x.Power <= 16));
image = image
.CropBlackArea(0, 0)
////.Dilate(null, StructuringElement.Square(3), 1, BorderType.BorderConst, image.WhiteColor)
////.CropBlackArea(0, 0)
.Convert1To8(null)
.Erode(null, StructuringElement.Square(2), 1, BorderType.BorderRepl, 0)
.ScaleByDownsampling2(null)
.FilterLowpass(null, 3, BorderType.BorderRepl, 0);
FeatureDetectors.Features features = this.detector.Detect(image, cancellationToken);
return(new PointsOfInterestFeatures(source.Id, features));
}
// foamliu, 2009/02/04, 开操作.
//
public static int[][] Open(int[][] mat, StructuringElement strel)
{
int[][] temp, output;
Erosion(mat, strel, out temp);
GrayScaleImageLib.MinkowskiAddtion(temp, strel, out output);
return(output);
}
private static void DemoDilation()
{
StructuringElement se = StructuringElement.CreateSquare(3);
Dilation dil = new Dilation(se);
using (StreamReader sr = new StreamReader("img.txt"))
{
String line;
while ((line = sr.ReadLine()) != null)
{
string source = string.Format("grayscale_img/{0}.jpg", line);
Bitmap bmp = Bitmap.FromFile(source) as Bitmap;
Matrix img_matrix = Matrix.FromBitmap(bmp);
Matrix thres_matrix = img_matrix < 100;
Matrix result = dil.Execute(thres_matrix);
string dest = string.Format("dilation/{0}.txt", line);
using (StreamWriter sw = new StreamWriter(dest, false))
{
sw.Write(result.ToString());
}
result.ToBitmap().Save(string.Format("dilation/{0}.png", line));
}
}
}
public Bitmap Process(Bitmap input, ProgressBar progressBar)
{
var w = new BinaryProcessor(input);
var structure = new StructuringElement(StructuringElementShape.Rectangle, 3);
w.Erode(structure);
return(w.GetBitmap());
}
/// <summary>
/// foamliu, 2009/02/04, 腐蚀.
///
/// </summary>
/// <param name="mat">二值图像</param>
/// <param name="b">结构元素</param>
public static void Erosion(int[][] mat, StructuringElement strel, out int[][] output)
{
//int width = mat.Length;
//int height = mat[0].Length;
//output = new int[width][];
//for (int i = 0; i < width; i++)
//{
// output[i] = new int[height];
//}
//int m = b.Width;
//int n = b.height;
//for (int y = 0; y < height; y++)
//{
// for (int x = 0; x < width; x++)
// {
// if (mat[x][y] != 1)
// continue;
// for (int j = -n; j <= n; j++)
// {
// for (int i = -m; i <= m; i++)
// {
// if (b.B(i, j) == 0)
// continue;
// if (Util.GetPixel(mat,x + i,y + j) == 0)
// goto next;
// }
// }
// output[x][y] = 1;
// next: ;
// }
//}
// foamliu, 2009/02/06, 改用二值图像库中的算法.
//
// 先把 strel 求转置
//
StructuringElement trans = StructuringElement.Transposition(strel);
BinaryImageLib.MinkowskiSubtraction(mat, trans, out output);
}
public Bitmap Process(Bitmap input, ProgressBar progressBar)
{
var w = new BinaryProcessor(input);
var structure = new StructuringElement(StructuringElementShape.Rectangle, 3);
var x = (BinaryProcessor)w.Clone();
var y = (BinaryProcessor)w.Clone();
x.Dilate(structure);
y.Erode(structure);
y.Complement();
x.And(y);
return(x.GetBitmap());
}
public virtual void Dilate(StructuringElement structure)
{
// We assume that the hotspot of the structuring element is in
// the middle.
var hotspotX = (int)(structure.Size) / 2;
var hotspotY = (int)(structure.Size) / 2;
var buffer = (int[, ])Image.Clone();
for (int x = 0; x < Width; x++)
{
for (int y = 0; y < Height; y++)
{
var structuredSums = new List <int>();
for (int u = 0; u < structure.Size; u++)
{
for (int v = 0; v < structure.Size; v++)
{
if (structure[u, v] > 0)
{
var p = x + u - hotspotX;
var q = y + v - hotspotY;
if (p >= 0 & q >= 0 & p < Width & q < Height)
{
structuredSums.Add(processStructure(Image[p, q], structure[u, v]));
}
}
}
}
var structuredMax = structuredSums.Max();
if (structuredMax < 0)
{
structuredMax = 0;
}
else if (structuredMax > 255)
{
structuredMax = 255;
}
buffer[x, y] = structuredMax;
}
}
Image = buffer;
}
/// <summary>
/// foamliu, 2009/02/04, 灰度值腐蚀.
///
/// </summary>
/// <param name="mat">灰度图像</param>
/// <param name="b">结构元素</param>
public static void Erosion(int[][] mat, StructuringElement strel, out int[][] output)
{
//int width = mat.Length;
//int height = mat[0].Length;
//output = new int[width][];
//for (int i = 0; i < width; i++)
//{
// output[i] = new int[height];
//}
//int m = b.Length;
//int n = b[0].Length;
//for (int y = 0; y < height; y++)
//{
// for (int x = 0; x < width; x++)
// {
// int min = int.MaxValue;
// for (int j = 0; j < n; j++)
// {
// for (int i = 0; i < m; i++)
// {
// int matxy = Util.GetPixel(mat, x - i, y - j);
// if (matxy + b[i][j] < min)
// {
// min = matxy + b[i][j];
// }
// }
// }
// output[x][y] = min;
// }
//}
// foamliu, 2009/02/06, 改用二值图像库中的算法.
//
// 先把 strel 求转置
//
StructuringElement trans = StructuringElement.Transposition(strel);
GrayScaleImageLib.MinkowskiSubtraction(mat, trans, out output);
}
/// <summary>
/// foamliu, 2009/02/05, 抽取骨骼.
///
/// 主要参照:
///
/// 佳奇发给我 Morphological Image Process 幻灯中倒数第三页: Skeleton Equations.
///
/// </summary>
public static void ExtractSkeleton(int[][] mat, StructuringElement strel, out int[][] output)
{
int width = mat.Length;
int height = mat[0].Length;
int[][] temp = (int[][])mat.Clone();
int K = CalcK(mat, strel);
int[][] Sk;
output = Util.BuildMatInt(width, height);
for (int k = 1; k <= K; k++)
{
temp = BinaryImageLib.MinkowskiSubtraction(temp, strel);
Sk = BinaryImageLib.Difference(temp, BinaryImageLib.Open(temp, strel));
BinaryImageLib.Union(output, Sk);
}
}
// 计算 Skeleton Equations 中的 K.
//
private static int CalcK(int[][] mat, StructuringElement strel)
{
int[][] temp = (int[][])mat.Clone();
int k = 0;
while (true)
{
temp = BinaryImageLib.MinkowskiSubtraction(temp, strel);
if (BinaryImageLib.Empty(temp))
{
break;
}
else
{
k++;
}
}
return(k);
}
public override void Dilate(StructuringElement structure)
{
// We assume that the hotspot of the structuring element is in
// the middle.
var hotspotX = (int)(structure.Size) / 2;
var hotspotY = (int)(structure.Size) / 2;
var buffer = new BinaryProcessor(this.Width, this.Height);
for (int u = 0; u < structure.Size; u++)
{
for (int v = 0; v < structure.Size; v++)
{
if (structure[u, v] > 0)
{
buffer.Max(this, u - hotspotX, v - hotspotY);
}
}
}
this.Image = buffer.Image;
}
public Bitmap Process(Bitmap input, ProgressBar progressBar)
{
var p = new ImageProcessor(input);
var s1 = new StructuringElement(StructuringElementShape.Rectangle, 5);
var s2 = new StructuringElement(StructuringElementShape.Rectangle, 9);
var mask = new BinaryProcessor(_filter.Process(input, progressBar));
mask.Close(s1);
mask.Open(s1);
p.Mask(mask);
mask.Dilate(s2);
var bt = new BoundaryTrace(s1);
var bmp = bt.Process(mask.GetBitmap(), progressBar);
var p2 = new BinaryProcessor(bmp);
//p.Layer(p2);
return(p.GetBitmap());
}
public void ErodeTest1()
{
const int Width = 150;
const int Height = 20;
for (int ix = 0; ix < Width; ix++)
{
for (int iy = 0; iy < Height; iy++)
{
Image image = new Image(Width, Height, 1, 200, 200);
image.SetBlack();
image.SetPixel(ix, iy, 0);
Image dilatedImage = image.Erode(null, StructuringElement.Square(3), 1, BorderType.BorderConst, uint.MaxValue);
if ((ix == 0 || ix == Width - 1) && (iy == 0 || iy == Height - 1))
{
Assert.AreEqual(
(ulong)((Width * Height) - 4),
dilatedImage.Power(),
string.Format(CultureInfo.InvariantCulture, "{0} {1}", ix, iy));
}
else if ((ix == 0 || ix == Width - 1) || (iy == 0 || iy == Height - 1))
{
Assert.AreEqual(
(ulong)((Width * Height) - 6),
dilatedImage.Power(),
string.Format(CultureInfo.InvariantCulture, "{0} {1}", ix, iy));
}
else
{
Assert.AreEqual(
(ulong)((Width * Height) - 9),
dilatedImage.Power(),
string.Format(CultureInfo.InvariantCulture, "{0} {1}", ix, iy));
}
}
}
}
public void DilateTest_3x3()
{
const int Width = 150;
const int Height = 20;
for (int ix = 0; ix < Width; ix++)
{
for (int iy = 0; iy < Height; iy++)
{
Image image = new Image(Width, Height, 1, 200, 200);
image.SetPixel(ix, iy, 1);
Image dilatedImage = image.Dilate(null, StructuringElement.Square(3), 1, BorderType.BorderConst, image.WhiteColor);
if ((ix == 0 || ix == Width - 1) && (iy == 0 || iy == Height - 1))
{
Assert.AreEqual(
4ul,
dilatedImage.Power(),
string.Format(CultureInfo.InvariantCulture, "{0} {1}", ix, iy));
}
else if ((ix == 0 || ix == Width - 1) || (iy == 0 || iy == Height - 1))
{
Assert.AreEqual(
6ul,
dilatedImage.Power(),
string.Format(CultureInfo.InvariantCulture, "{0} {1}", ix, iy));
}
else
{
Assert.AreEqual(
9ul,
dilatedImage.Power(),
string.Format(CultureInfo.InvariantCulture, "{0} {1}", ix, iy));
}
}
}
}
public void CloseTest_3x2()
{
const int Width = 7;
const int Height = 7;
Rectangle bounds = new Rectangle(2, 2, 3, 3);
foreach (int bitsPerPixel in new[] { 1, /*2, 4,*/ 8, 16, 24, 32 })
{
Image image = new Image(Width, Height, bitsPerPixel, 200, 200);
image.SetWhite();
image.SetBlack(bounds);
Image closing = image.MorphClose(null, StructuringElement.Brick(2, 2), 1, BorderType.BorderConst, image.WhiteColor);
for (int ix = 0; ix < Width; ix++)
{
for (int iy = 0; iy < Height; iy++)
{
Assert.AreEqual(bounds.Contains(ix, iy) ? image.BlackColor : image.WhiteColor, closing.GetPixel(ix, iy));
}
}
}
}
/// <summary>
/// Initializes a new instance of the BinaryHitMiss class with
/// the given <see cref="StructuringElement"/> objects.
/// </summary>
/// <param name="se1">
/// The <see cref="StructuringElement"/> to use for binary
/// hit-miss.
/// </param>
/// <param name="se2">
/// The <see cref="StructuringElement"/> to use for binary
/// hit-miss.
/// </param>
public BinaryHitMiss (StructuringElement se1,
StructuringElement se2)
{
this.se1 = se1;
this.se2 = se2;
}
/// <summary>
/// Initializes a new instance of the Closing class with the
/// given <see cref="StructuringElement"/>.
/// </summary>
/// <param name="se">
/// The <see cref="StructuringElement"/> to use for closing.
/// </param>
public Closing(StructuringElement se)
{
this.se = se;
}
public void Erode(StructuringElement structure)
{
this.Complement();
this.Dilate(structure.Reflection);
this.Complement();
}
public void Close(StructuringElement structure)
{
this.Dilate(structure);
this.Erode(structure);
}
public static PaletteType ProcessImage(VisionImage image, string path, out double[] distance)
{
// Initialize the IVA_Data structure to pass results and coordinate systems.
IVA_Data ivaData = new IVA_Data(17, 1);
distance = new double[4] {
0, 0, 0, 0
};
// Creates a new, empty region of interest.
Roi roi = new Roi();
// Creates a new AnnulusContour using the given values.
PointContour vaCenter = new PointContour(1283, 965);
AnnulusContour vaOval = new AnnulusContour(vaCenter, 418, 702, 0, 0);
roi.Add(vaOval);
// Find Circular Edge
EdgeOptions vaOptions = new EdgeOptions();
vaOptions.ColumnProcessingMode = ColumnProcessingMode.Average;
vaOptions.InterpolationType = InterpolationMethod.Bilinear;
vaOptions.KernelSize = 3;
vaOptions.MinimumThreshold = 18;
vaOptions.Polarity = EdgePolaritySearchMode.Rising;
vaOptions.Width = 3;
CircularEdgeFitOptions vaFitOptions = new CircularEdgeFitOptions();
vaFitOptions.ProcessType = RakeProcessType.GetFirstEdges;
vaFitOptions.StepSize = 7;
vaFitOptions.MaxPixelRadius = 3;
vaCircularEdgeReport = IVA_FindCircularEdge(image, roi, SpokeDirection.InsideToOutside, vaOptions, vaFitOptions, ivaData, 1);
roi.Dispose();
// Set Coordinate System
int vaCoordSystemIndex = 0;
int stepIndexOrigin = 1;
int resultIndexOrigin = 0;
int stepIndexAngle = -1;
int resultIndexAngle = 0;
double refSysOriginX = vaCircularEdgeReport.Center.X;
double refSysOriginY = vaCircularEdgeReport.Center.Y;
double refSysAngle = 0;
AxisOrientation refSysAxisOrientation = AxisOrientation.Direct;
int vaCoordSystemType = 0;
IVA_CoordSys(vaCoordSystemIndex, stepIndexOrigin, resultIndexOrigin, stepIndexAngle, resultIndexAngle, refSysOriginX, refSysOriginY, refSysAngle, refSysAxisOrientation, vaCoordSystemType, ivaData);
// Image Buffer: Push
Functions.IVA_PushBuffer(ivaData, image, 0);
// Get Image
string vaFilePath = path;
FileInformation vaFileInfo = Algorithms.GetFileInformation(vaFilePath);
// Set the image size to 0 to speed up the cast.
//image.SetSize(0, 0);
//image.Type = vaFileInfo.ImageType;
//image.BitDepth = 0;
image.ReadFile(vaFilePath);
switch (image.Type)
{
case ImageType.I16:
case ImageType.U16:
if (image.BitDepth == 0 & false)
{
image.BitDepth = 10;
}
break;
default:
break;
}
// Operators: Absolute Difference Image
Algorithms.AbsoluteDifference(image, Functions.IVA_GetBuffer(ivaData, 0), image);
// Creates a new, empty region of interest.
Roi roi2 = new Roi();
// Creates a new AnnulusContour using the given values.
PointContour vaCenter2 = new PointContour(vaCircularEdgeReport.Center.X, vaCircularEdgeReport.Center.Y);
AnnulusContour vaOval2 = new AnnulusContour(vaCenter2, 527, 846, 0, 0);
roi2.Add(vaOval2);
// Reposition the region of interest based on the coordinate system.
int coordSystemIndex = 0;
Algorithms.TransformRoi(roi2, new CoordinateTransform(ivaData.baseCoordinateSystems[coordSystemIndex], ivaData.MeasurementSystems[coordSystemIndex]));
// Mask from ROI
IVA_Mask_From_ROI(image, roi2, false, false);
roi2.Dispose();
// Color Threshold
Range plane1Range = new Range(0, 60);
Range plane2Range = new Range(0, 50);
Range plane3Range = new Range(0, 255);
using (VisionImage thresholdImage = new VisionImage(ImageType.U8, 7))
{
Algorithms.ColorThreshold(image, thresholdImage, ColorMode.Rgb, 1, plane1Range, plane2Range, plane3Range);
Algorithms.Copy(thresholdImage, image);
}
// Truncates the frequencies of an image.
IVA_FFT_Truncate(image, TruncateMode.High, 7);
// Advanced Morphology: Remove Objects
int[] vaCoefficients = { 1, 1, 1, 1, 1, 1, 1, 1, 1 };
StructuringElement vaStructElem = new StructuringElement(3, 3, vaCoefficients);
vaStructElem.Shape = StructuringElementShape.Square;
// Filters particles based on their size.
Algorithms.RemoveParticle(image, image, 30, SizeToKeep.KeepLarge, Connectivity.Connectivity8, vaStructElem);
// Invert Binary Image.
IVA_BinaryInverse(image);
// Advanced Morphology: Remove Objects
int[] vaCoefficients2 = { 1, 1, 1, 1, 1, 1, 1, 1, 1 };
StructuringElement vaStructElem2 = new StructuringElement(3, 3, vaCoefficients2);
vaStructElem.Shape = StructuringElementShape.Square;
// Filters particles based on their size.
Algorithms.RemoveParticle(image, image, 5, SizeToKeep.KeepLarge, Connectivity.Connectivity8, vaStructElem2);
// Basic Morphology - Applies morphological transformations to binary images.
int[] vaCoefficients3 = { 0, 1, 0, 1, 1, 1, 0, 1, 0 };
StructuringElement vaStructElem3 = new StructuringElement(3, 3, vaCoefficients3);
vaStructElem.Shape = StructuringElementShape.Square;
// Applies morphological transformations
for (int i = 0; i < 3; ++i)
{
Algorithms.Morphology(image, image, MorphologyMethod.Erode, vaStructElem3);
}
// Advanced Morphology: Fill Holes
VisionImage image1 = new VisionImage();
Algorithms.FillHoles(image, image1, Connectivity.Connectivity8);
// Particle Analysis - Computes the number of particles detected in a binary image and
// returns the requested measurements about the particles.
Collection <MeasurementType> vaPixelMeasurements = new Collection <MeasurementType>(new MeasurementType[] { MeasurementType.Area });
Collection <MeasurementType> vaCalibratedMeasurements = new Collection <MeasurementType>(new MeasurementType[] { });
IVA_Particle(image1, Connectivity.Connectivity8, vaPixelMeasurements, vaCalibratedMeasurements, ivaData, 16, out vaParticleReport, out vaParticleReportCalibrated);
double[,] area = vaParticleReport.PixelMeasurements;
double Maxarea = 0;
for (int i = 0; i < area.GetLength(0); i++)
{
for (int j = 0; j < area.GetLength(1); j++)
{
if (area[i, j] > Maxarea)
{
Maxarea = area[i, j];
}
}
}
image1.Dispose();
if (Maxarea > 1000000)
{
// Creates a new, empty region of interest.
Roi roi3 = new Roi();
// Creates a new AnnulusContour using the given values.
PointContour vaCenter3 = new PointContour(1295, 963);
AnnulusContour vaOval3 = new AnnulusContour(vaCenter3, 496, 892, 0, 0);
roi3.Add(vaOval3);
// Reposition the region of interest based on the coordinate system.
int coordSystemIndex2 = 0;
Algorithms.TransformRoi(roi3, new CoordinateTransform(ivaData.baseCoordinateSystems[coordSystemIndex2], ivaData.MeasurementSystems[coordSystemIndex2]));
// Extract the contour edges from the image
CurveParameters vaCurveParams = new CurveParameters(ExtractionMode.NormalImage, 1, EdgeFilterSize.ContourTracing, 30, 20, 10, true);
double[] vaConstraintMinArray = { };
double[] vaConstraintMaxArray = { };
ConnectionConstraintType[] vaConstraintTypeArray = { };
ExtractContourReport vaExtractReport = IVA_ExtractContour(image, roi3, ExtractContourDirection.AnnulusOuterInner, vaCurveParams, vaConstraintTypeArray, vaConstraintMinArray, vaConstraintMaxArray, ExtractContourSelection.Closest);
// Fit a circle to the contour
ContourOverlaySettings vaEquationOverlay = new ContourOverlaySettings(true, Rgb32Value.GreenColor, 1, true);
ContourOverlaySettings vaPointsOverlay = new ContourOverlaySettings(true, Rgb32Value.RedColor, 1, true);
PartialCircle vaCircleReport = Algorithms.ContourFitCircle(image, 100, true);
Algorithms.ContourOverlay(image, image, vaPointsOverlay, vaEquationOverlay);
ComputeDistanceReport vaDistanceReport = Algorithms.ContourComputeDistances(image, image, 0);
MaxDistance = 0;
MaxDistanceIndex = 0;
for (int i = 0; i < vaDistanceReport.Distances.Count; i++)
{
if (vaDistanceReport.Distances[i].Distance > MaxDistance)
{
MaxDistance = vaDistanceReport.Distances[i].Distance;
MaxDistanceIndex = i;
}
}
var pos = vaDistanceReport.Distances[MaxDistanceIndex];
distance[0] = MaxDistance;
roi3.Dispose();
// Creates a new, empty region of interest.
Roi roi4 = new Roi();
// Creates a new AnnulusContour using the given values.
PointContour vaCenter4 = new PointContour(1294, 962);
AnnulusContour vaOval4 = new AnnulusContour(vaCenter4, 499, 885, 0, 0);
roi4.Add(vaOval4);
// Reposition the region of interest based on the coordinate system.
int coordSystemIndex3 = 0;
Algorithms.TransformRoi(roi4, new CoordinateTransform(ivaData.baseCoordinateSystems[coordSystemIndex3], ivaData.MeasurementSystems[coordSystemIndex3]));
// Extract the contour edges from the image
CurveParameters vaCurveParams2 = new CurveParameters(ExtractionMode.NormalImage, 1, EdgeFilterSize.ContourTracing, 30, 25, 10, true);
double[] vaConstraintMinArray2 = { };
double[] vaConstraintMaxArray2 = { };
ConnectionConstraintType[] vaConstraintTypeArray2 = { };
ExtractContourReport vaExtractReport2 = IVA_ExtractContour(image, roi4, ExtractContourDirection.AnnulusInnerOuter, vaCurveParams2, vaConstraintTypeArray2, vaConstraintMinArray2, vaConstraintMaxArray2, ExtractContourSelection.Closest);
// Fit a circle to the contour
ContourOverlaySettings vaEquationOverlay2 = new ContourOverlaySettings(true, Rgb32Value.GreenColor, 1, true);
ContourOverlaySettings vaPointsOverlay2 = new ContourOverlaySettings(true, Rgb32Value.RedColor, 1, true);
PartialCircle vaCircleReport2 = Algorithms.ContourFitCircle(image, 100, true);
Algorithms.ContourOverlay(image, image, vaPointsOverlay2, vaEquationOverlay2);
ComputeDistanceReport vaDistanceReport2 = Algorithms.ContourComputeDistances(image, image, 0);
MaxDistance1 = 0;
MaxDistanceIndex1 = 0;
for (int i = 0; i < vaDistanceReport2.Distances.Count; i++)
{
if (vaDistanceReport2.Distances[i].Distance > MaxDistance1)
{
MaxDistance1 = vaDistanceReport2.Distances[i].Distance;
MaxDistanceIndex1 = i;
}
}
var pos1 = vaDistanceReport2.Distances[MaxDistanceIndex1];
distance[1] = MaxDistance1;
distance[2] = (vaCircleReport2.Center.X - vaCircularEdgeReport.Center.X) / 96;
distance[3] = (vaCircleReport2.Center.Y - vaCircularEdgeReport.Center.Y) / 96;
roi4.Dispose();
}
else
{
distance[0] = 9999;
distance[1] = 9999;
distance[2] = 9999;
distance[3] = 9999;
}
// Dispose the IVA_Data structure.
ivaData.Dispose();
if (path == $"{ @"./ImageTemp/temp.jpg"}")
{
image.Dispose();
}
// Return the palette type of the final image.
return(PaletteType.Binary);
}
/// <summary>
/// Initializes a new instance of the Erosion class with the
/// given <see cref="StructuringElement"/>.
/// </summary>
/// <param name="se">
/// The <see cref="StructuringElement"/> to use for erosion.
/// </param>
public Erosion (StructuringElement se)
{
this.se = se;
}
public void XXXTest()
{
#if false
const int Count = 10000000;
Stopwatch stopwatch = new Stopwatch();
RandomGenerator random = new RandomGenerator();
int length = 128;
float[] dense1 = random.Generate(length);
float[] dense2 = new float[length];
for (int i = 0; i < 16; i++)
{
dense2[(int)random.Generate(0, length)] = random.Generate();
}
SparseVectorF sparse = SparseVectorF.FromDense(length, dense2, 0);
stopwatch.Restart();
for (int i = 0; i < Count; i++)
{
Math32f.EuclideanDistance(length, dense1, 0, dense2, 0);
}
stopwatch.Stop();
Console.WriteLine("{0:F4} ms", stopwatch.ElapsedMilliseconds /* / Count*/);
stopwatch.Restart();
for (int i = 0; i < Count; i++)
{
sparse.EuclideanDistance(dense1, 0);
}
stopwatch.Stop();
Console.WriteLine("{0:F4} ms", stopwatch.ElapsedMilliseconds /* / Count*/);
#else
const int Count = 5;
Stopwatch stopwatch = new Stopwatch();
////Image image = new Image(2000, 3000, 8, 200, 200);
////image.Randomize();
////foreach ((Image image, _, _) in Image.FromFile(@"C:\DNN\dnn\test.jpg"))
////foreach ((Image image, _, _) in Image.FromFile(@"C:\DNN\dnn\363978.tif"))
foreach ((Image image, _, _) in Imaging.Image.FromFile(@"L:\FormXtra\Receipts\Concur\Full\jpg\Work\08240EB488FE324E83C7C54042D9813C.jpg"))
{
stopwatch.Restart();
Image xxx = image.ConvertTo(null, 8);
for (int i = 0; i < Count; i++)
{
xxx.Erode(xxx, StructuringElement.Square(2), 1, BorderType.BorderRepl, 0);
xxx.ScaleByDownsampling2(xxx);
////image.Binarize(null, 0, 0, 0, 0, true, 0, 0);
////IntegralImage.FromImage(image);
////long power = image.Power();
////Histogram hyst = image.GrayHistogram();
/*Image workImage = image
* .ConvertTo(null, 8)
* .Scale(null, 100.0 / image.HorizontalResolution, 100.0 / image.VerticalResolution, ScalingOptions.None)
* ////.Binarize(null)
* .Convert8To1(null, 128)
* .CleanOverscan(0.5f, 0.5f)
* .Deskew(null)
* .Despeckle(null);
*
* ISet<ConnectedComponent> components = workImage.FindConnectedComponents(8);
* workImage.RemoveConnectedComponents(components);*/
/*workImage = workImage.Scale(100.0 / image.HorizontalResolution, 100.0 / image.VerticalResolution, Imaging.ScalingOptions.None);
* workImage = workImage.Binarize();
* ////workImage = workImage.Convert8To1(128);
* workImage = workImage.Dilate(StructuringElement.Rectangle(5, 1), 1);
* workImage = workImage.Dilate(StructuringElement.Rectangle(1, 5), 1);
* workImage = workImage.Convert1To8();
*
* DenseVectorPackF vectors = workImage.HOG(8, 2, 1, 9, 0.2f);
*
* vectors = DenseVectorPackF.Pack(vectors.Unpack().Where(x => x.Sum() != 0.0f).ToList());*/
////Image temp = image.Binarize();
}
stopwatch.Stop();
}
Console.WriteLine("{0:F4} ms", stopwatch.ElapsedMilliseconds / Count);
#endif
}
public void DilateTest2()
{
StructuringElement[] ses = new[]
{
StructuringElement.Brick(3, 1),
StructuringElement.Brick(1, 3),
StructuringElement.Brick(1, 4),
StructuringElement.Brick(1, 4, new Point(0, 1)),
StructuringElement.Square(7),
StructuringElement.Brick(8, 6),
StructuringElement.Brick(8, 6, new Point(6, 5)),
StructuringElement.Brick(8, 6, new Point(10, 8)),
StructuringElement.Cross(10, 5),
StructuringElement.Cross(10, 5, new Point(-3, -2)),
};
foreach (int bitsPerPixel in new[] { 1, /*2, 4,*/ 8, 16, 24, 32 })
{
foreach (int width in new[] { 64 * 2, 131 })
{
Image src = new Image(width, 50, bitsPerPixel, 200, 200);
foreach (StructuringElement se in ses)
{
// constant border
foreach (uint borderValue in new[] { src.BlackColor, src.WhiteColor, (uint)(((ulong)src.BlackColor + (ulong)src.WhiteColor) / 2) })
{
src.Randomize();
Image dst = src.Dilate(null, se, 1, BorderType.BorderConst, borderValue);
for (int x = 0; x < src.Width; x++)
{
for (int y = 0; y < src.Height; y++)
{
Assert.AreEqual(ComputePixelBorder(x, y, borderValue), dst.GetPixel(x, y));
}
}
}
// replica border
{
src.Randomize();
Image dst = src.Dilate(null, se, 1, BorderType.BorderRepl, 0);
for (int x = 0; x < src.Width; x++)
{
for (int y = 0; y < src.Height; y++)
{
Assert.AreEqual(ComputePixel(x, y), dst.GetPixel(x, y));
}
}
}
uint ComputePixel(int x, int y)
{
uint maxcolor = uint.MinValue;
foreach (Point point in se.GetElements())
{
Point pt = new Point(x + point.X, y + point.Y);
if (src.Bounds.Contains(pt))
{
maxcolor = Color.Max(maxcolor, src.GetPixel(pt), bitsPerPixel);
}
}
return(maxcolor);
}
uint ComputePixelBorder(int x, int y, uint borderValue)
{
uint maxcolor = uint.MinValue;
foreach (Point point in se.GetElements())
{
Point pt = new Point(x + point.X, y + point.Y);
uint color = src.Bounds.Contains(pt) ? src.GetPixel(pt) : borderValue;
maxcolor = Color.Max(maxcolor, color, bitsPerPixel);
}
return(maxcolor);
}
}
}
}
}
public Open(StructuringElement structure)
{
this._structure = structure;
}
public PaletteType ProcessImage(VisionImage image)
{
//vaParticleReportCalibrated = new ParticleMeasurementsReport();
// Initialize the IVA_Data structure to pass results and coordinate systems.
IVA_Data ivaData = new IVA_Data(15, 0);
// Image Buffer: Push
Functions.IVA_PushBuffer(ivaData, image, 0);
// Operators: NOR Image
Algorithms.Nor(image, Functions.IVA_GetBuffer(ivaData, 0), image);
// Extract Color Plane
using (VisionImage plane = new VisionImage(ImageType.U8, 7))
{
// Extract the red color plane and copy it to the main image.
Algorithms.ExtractColorPlanes(image, ColorMode.Rgb, plane, null, null);
Algorithms.Copy(plane, image);
}
// Thresholds an image into 2 classes by using local thresholding.
LocalThresholdOptions vaLocalThresholdOptions = new LocalThresholdOptions();
vaLocalThresholdOptions.DeviationWeight = 1;
vaLocalThresholdOptions.Method = LocalThresholdMethod.BackgroundCorrection;
vaLocalThresholdOptions.ParticleType = ParticleType.Dark;
vaLocalThresholdOptions.ReplaceValue = 1;
vaLocalThresholdOptions.WindowHeight = 35;
vaLocalThresholdOptions.WindowWidth = 35;
Algorithms.LocalThreshold(image, image, vaLocalThresholdOptions);
// Basic Morphology - Applies morphological transformations to binary images.
int[] vaCoefficients = { 1, 1, 1, 1, 1, 1, 1, 1, 1 };
StructuringElement vaStructElem = new StructuringElement(3, 3, vaCoefficients);
vaStructElem.Shape = StructuringElementShape.Square;
// Applies morphological transformations
Algorithms.Morphology(image, image, MorphologyMethod.GradientOut, vaStructElem);
// Advanced Morphology: Fill Holes
Algorithms.FillHoles(image, image, Connectivity.Connectivity8);
// Advanced Morphology: Remove Objects
int[] vaCoefficients2 = { 1, 1, 1, 1, 1, 1, 1, 1, 1 };
StructuringElement vaStructElem2 = new StructuringElement(3, 3, vaCoefficients2);
vaStructElem.Shape = StructuringElementShape.Hexagon;
// Filters particles based on their size.
Algorithms.RemoveParticle(image, image, 6, SizeToKeep.KeepLarge, Connectivity.Connectivity8, vaStructElem2);
// Advanced Morphology: Remove Border Objects - Eliminates particles touching the border of the image.
Algorithms.RejectBorder(image, image, Connectivity.Connectivity8);
// Lookup Table: Equalize
// Calculates the histogram of the image and redistributes pixel values
// accross the desired range to maintain the same pixel value distribution.
Range equalizeRange = new Range(0, 255);
if (image.Type != ImageType.U8)
{
equalizeRange.Maximum = 0;
}
Algorithms.Equalize(image, image, null, equalizeRange, null);
// Image Buffer: Push
Functions.IVA_PushBuffer(ivaData, image, 1);
// Particle Analysis - Computes the number of particles detected in a binary image and
// returns the requested measurements about the particles.
Collection <MeasurementType> vaPixelMeasurements = new Collection <MeasurementType>(new MeasurementType[] { MeasurementType.BoundingRectLeft, MeasurementType.BoundingRectTop, MeasurementType.BoundingRectRight, MeasurementType.BoundingRectBottom, MeasurementType.MaxFeretDiameter });
Collection <MeasurementType> vaCalibratedMeasurements = new Collection <MeasurementType>(new MeasurementType[] {});
//IVA_Particle(image, Connectivity.Connectivity4, null, null, null, 10, out vaParticleReport, out vaParticleReportCalibrated);
IVA_Particle(image, Connectivity.Connectivity4, vaPixelMeasurements, vaCalibratedMeasurements, ivaData, 10, out vaParticleReport, out vaParticleReportCalibrated);
// Image Buffer: Pop
Algorithms.Copy(Functions.IVA_GetBuffer(ivaData, 1), image);
// Creates a new, empty region of interest.
Roi roi = new Roi();
// Creates a new RectangleContour using the given values.
RectangleContour vaRect = new RectangleContour(0, 0, image.Width, image.Height);
roi.Add(vaRect);
// Classifies all the objects located in the given ROI.
string vaClassifierFilePath = "C:\\DATA\\#hasilscan3\\Particle Classifier.clf";
vaClassifierReports = IVA_Classify(image, roi, vaClassifierFilePath);
VisGrainTypeCollection colType = new VisGrainTypeCollection(vaClassifierReports);
roi.Dispose();
// Image Buffer: Pop
Algorithms.Copy(Functions.IVA_GetBuffer(ivaData, 1), image);
// Creates a new, empty region of interest.
Roi roi2 = new Roi();
// Creates a new RectangleContour using the given values.
RectangleContour vaRect2 = new RectangleContour(0, 0, image.Width, image.Height);
roi2.Add(vaRect2);
// Classifies all the objects located in the given ROI.
string vaClassifierFilePath2 = "C:\\DATA\\#hasilscan3\\Ukuran Classifier.clf";
vaClassifierReports2 = IVA_Classify(image, roi2, vaClassifierFilePath2);
VisGrainSizeCollection colSize = new VisGrainSizeCollection(vaClassifierReports2);
GrainResults = new VisGrainDataCollection(vaClassifierReports, vaClassifierReports2, ListShape);
roi2.Dispose();
// Dispose the IVA_Data structure.
ivaData.Dispose();
// Return the palette type of the final image.
return(PaletteType.Binary);
}
/// <summary>
/// Initializes a new instance of the Opening class with the
/// given <see cref="StructuringElement"/>.
/// </summary>
/// <param name="se">
/// The <see cref="StructuringElement"/> to use for opening.
/// </param>
public Opening (StructuringElement se)
{
this.se = se;
}
public Dilate(StructuringElement s)
{
structure = s;
}
/// <summary>
/// Initializes a new instance of the TopHat class with the
/// given <see cref="StructuringElement"/>.
/// </summary>
/// <param name="se">
/// The <see cref="StructuringElement"/> to use for top hat
/// filtering.
/// </param>
public TopHat(StructuringElement se)
{
this.se = se;
}
public PaletteType ProcessImage(VisionImage image)
{
// Initialize the IVA_Data structure to pass results and coordinate systems.
IVA_Data ivaData = new IVA_Data(7, 0);
// Extract Color Plane
using (VisionImage plane = new VisionImage(ImageType.U8, 7))
{
// Extract the red color plane and copy it to the main image.
Algorithms.ExtractColorPlanes(image, ColorMode.Rgb, plane, null, null);
Algorithms.Copy(plane, image);
}
// Filters: Convolution - Applies a linear filter to an image by convolving the image with a filtering kernel.
double[] vaCoefficients = { 1, 2, 4, 2, 1, 2, 4, 8, 4, 2, 4, 8, 16, 8, 4, 2, 4, 8, 4, 2, 1, 2, 4, 2, 1 };
Algorithms.Convolute(image, image, new Kernel(5, 5, vaCoefficients));
// Filters: Convolution - Applies a linear filter to an image by convolving the image with a filtering kernel.
double[] vaCoefficients2 = { -1, -1, -1, -1, 10, -1, -1, -1, -1 };
Algorithms.Convolute(image, image, new Kernel(3, 3, vaCoefficients2));
// Automatic Threshold
Algorithms.AutoThreshold(image, image, 2, ThresholdMethod.Clustering);
// Basic Morphology - Applies morphological transformations to binary images.
int[] vaCoefficients3 = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 };
StructuringElement vaStructElem = new StructuringElement(7, 7, vaCoefficients3);
vaStructElem.Shape = StructuringElementShape.Hexagon;
// Applies morphological transformations
Algorithms.Morphology(image, image, MorphologyMethod.Open, vaStructElem);
// Lookup Table: Equalize
// Calculates the histogram of the image and redistributes pixel values
// accross the desired range to maintain the same pixel value distribution.
Range equalizeRange = new Range(0, 255);
if (image.Type != ImageType.U8)
{
equalizeRange.Maximum = 0;
}
Algorithms.Equalize(image, image, null, equalizeRange, null);
// Creates a new, empty region of interest.
Roi roi = new Roi();
// Creates a new RectangleContour using the given values.
RectangleContour vaRect = new RectangleContour(0, 0, image.Width, image.Height);
roi.Add(vaRect);
// Classifies all the objects located in the given ROI.
string vaClassifierFilePath = "C:\\DATA\\#hasilscan2\\Ukuran Classifier.clf";
vaClassifierReports = IVA_Classify(image, roi, vaClassifierFilePath);
roi.Dispose();
// Dispose the IVA_Data structure.
ivaData.Dispose();
// Return the palette type of the final image.
return(PaletteType.Binary);
}
/// <summary>
/// Initializes a new instance of the Dilation class with the
/// given <see cref="StructuringElement"/>.
/// </summary>
/// <param name="se">
/// The <see cref="StructuringElement"/> to use for dilation.
/// </param>
public Dilation(StructuringElement se)
{
this.se = se;
}
/// <summary>
/// Initializes a new instance of the TopHat class with the
/// given <see cref="StructuringElement"/>.
/// </summary>
/// <param name="se">
/// The <see cref="StructuringElement"/> to use for top hat
/// filtering.
/// </param>
public TopHat (StructuringElement se)
{
this.se = se;
}
static void Main()
{
var g1 = new StructuringElement(StructuringElementShape.Rectangle, 3);
var g2 = new StructuringElement(StructuringElementShape.Rectangle, 23);
var g3 = new StructuringElement(StructuringElementShape.Rectangle, 43);
var g4 = new StructuringElement(StructuringElementShape.Rectangle, 63);
var g5 = new StructuringElement(StructuringElementShape.Rectangle, 83);
var s_c_3 = new StructuringElement(StructuringElementShape.Cross, 3);
var s_r_3 = new StructuringElement(StructuringElementShape.Rectangle, 3);
var s_c_5 = new StructuringElement(StructuringElementShape.Cross, 5);
var s_r_5 = new StructuringElement(StructuringElementShape.Rectangle, 5);
var s_c_7 = new StructuringElement(StructuringElementShape.Cross, 7);
var s_r_7 = new StructuringElement(StructuringElementShape.Rectangle, 7);
var s_c_9 = new StructuringElement(StructuringElementShape.Cross, 9);
var s_r_9 = new StructuringElement(StructuringElementShape.Rectangle, 9);
var s_r_11 = new StructuringElement(StructuringElementShape.Rectangle, 11);
var ops = new IImageOperation[]
{
new Complement(),
new Threshold(63),
new Threshold(127),
new Threshold(191),
new BandFilter(63, 191),
new NotchFilter(63, 191),
new BandFilter(181, 201),
new NotchFilter(181, 201),
new Mask(new NotchFilter(63, 191)),
new Dilate(s_r_3),
new Dilate(s_r_5),
new Dilate(s_r_7),
new Dilate(s_r_9),
new Dilate(s_r_11),
new Open(g1),
new Open(g2),
new Open(g3),
new Open(g4),
new Open(g5),
new BoundaryTrace(s_c_3),
new BoundaryTrace(s_r_3),
new BoundaryTrace(s_c_5),
new BoundaryTrace(s_r_5),
new BoundaryTrace(s_c_7),
new BoundaryTrace(s_r_7),
new BoundaryTrace(s_c_9),
new BoundaryTrace(s_r_9),
new OpenClose(s_c_3),
new OpenClose(s_r_3),
new OpenClose(s_c_5),
new OpenClose(s_r_5),
new OpenClose(s_c_7),
new OpenClose(s_r_7),
new OpenClose(s_c_9),
new OpenClose(s_r_9),
new CloseOpen(s_c_3),
new CloseOpen(s_r_3),
new CloseOpen(s_c_5),
new CloseOpen(s_r_5),
new CloseOpen(s_c_7),
new CloseOpen(s_r_7),
new CloseOpen(s_c_9),
new CloseOpen(s_r_9),
new ImageX(),
new ImageY(),
new ImageZ(),
};
Application.EnableVisualStyles();
Application.SetCompatibleTextRenderingDefault(false);
Application.Run(new INFOIBV(ops));
}
/// <summary>
/// Initializes a new instance of the Closing class with the
/// given <see cref="StructuringElement"/>.
/// </summary>
/// <param name="se">
/// The <see cref="StructuringElement"/> to use for closing.
/// </param>
public BottomHat (StructuringElement se)
{
this.se = se;
}
/// <summary>
/// Initializes a new instance of the Erosion class with the
/// given <see cref="StructuringElement"/>.
/// </summary>
/// <param name="se">
/// The <see cref="StructuringElement"/> to use for erosion.
/// </param>
public Erosion(StructuringElement se)
{
this.se = se;
}
/// <summary>
/// Initializes a new instance of the Closing class with the
/// given <see cref="StructuringElement"/>.
/// </summary>
/// <param name="se">
/// The <see cref="StructuringElement"/> to use for closing.
/// </param>
public Closing (StructuringElement se)
{
this.se = se;
}
/// <summary>
/// Initializes a new instance of the Opening class with the
/// given <see cref="StructuringElement"/>.
/// </summary>
/// <param name="se">
/// The <see cref="StructuringElement"/> to use for opening.
/// </param>
public Opening(StructuringElement se)
{
this.se = se;
}
