/// <summary>
/// Constructor
/// </summary>
/// <param name="_startMatrix">Symmetric distance matrix</param>
/// <param name="_labels">Dictionary with labels of the distance matrix</param>
public NeighborJoining(decimal[,] _startMatrix, Dictionary <int, string> _labels)
{
//Check for symmetrical distances
if (_startMatrix.GetLength(0) != _startMatrix.GetLength(1))
{
throw new Exception("Matrix needs to be symmetric!");
}
//Check for existing labels
//Check for unique labels
List <string> lstLabels = new List <string>();
for (int count = 0; count < _labels.Count; count++)
{
string strLabel;
if (_labels.TryGetValue(count, out strLabel))
{
// Check for unique
if (lstLabels.Contains(strLabel))
{
throw new Exception($"Label {strLabel} is not unique!");
}
else
{
lstLabels.Add(strLabel);
}
}
else
{
throw new Exception($"Label for index {count} is missing!");
}
}
//Set properties
StartMatrix = _startMatrix;
StartLabels = _labels;
NodesAndEdges = new List <Tuple <string, decimal, string> >();
// Set node levels to 0
Dictionary <int, int> initNodeLevels = new Dictionary <int, int>();
foreach (int nodeIndex in StartLabels.Keys)
{
initNodeLevels.Add(nodeIndex, level);
}
//Create leaf nodes for every label
Nodes = new List <BifiTree>();
foreach (string node in StartLabels.Values)
{
Nodes.Add(new BifiTree(node));
}
//Init neighbor joining with start matrix
NeighborJoiningController(StartMatrix, StartLabels, initNodeLevels);
}
/// <summary>
/// 向量对角化
/// </summary>
/// <param name="vec">向量</param>
/// <returns>返回对角化矩阵</returns>
public static decimal[,] VecDiag(this decimal[,] vec)
{
decimal[,] diagMatrix = new decimal[vec.GetLength(0), vec.GetLength(0)];
for (int i = 0, len = vec.GetLength(0); i < len; i++)
{
diagMatrix[i, i] = vec[i, 0];
}
return(diagMatrix);
}
public void TestUpgma()
{
//decimal[,] distanceMatrix1 =
//{
// { 0, 0, 0, 0, 0 },
// { 0, 0, 1, 1, 1 },
// { 0, 1, 0, 2, 2 },
// { 0, 1, 2, 0, 3 },
// { 0, 1, 2, 3, 0 },
//};
//var result = UpgmaClustering.Upgma(distanceMatrix1);
decimal[,] distanceMatrix2 =
{
{ 0, 19, 27, 8, 33, 18, 13 },
{ 19, 0, 31, 18, 36, 1, 13 },
{ 27, 31, 0, 26, 41, 32, 29 },
{ 8, 18, 26, 0, 31, 17, 14 },
{ 33, 36, 41, 31, 0, 35, 28 },
{ 18, 1, 32, 17, 35, 0, 12 },
{ 13, 13, 29, 14, 28, 12, 0 }
};
var names = new List <string>();
for (var i = 0; i < distanceMatrix2.GetLength(0); i++)
{
names.Add("" + i);
}
List <string> vectorNames = new List <string>();
var maxVectorLength = distanceMatrix2.GetLength(0) > distanceMatrix2.GetLength(1) ? distanceMatrix2.GetLength(0) : distanceMatrix2.GetLength(1);
for (var i = 0; i < maxVectorLength; i++)
{
vectorNames.Add(SpreadsheetFileHandler.AlphabetLetterRollOver(i) + i);
}
List <List <UpgmaNode> > nodeList;
List <List <string> > treeList;
var minimumOutputTreeLeafs = 1;
List <string> finalTreeLeafOrderList;
UpgmaClustering.Upgma(distanceMatrix2, vectorNames, minimumOutputTreeLeafs, out nodeList, out treeList, out finalTreeLeafOrderList);
CheckUpgmaDistanceConsistency(nodeList[nodeList.Count - 1]);
List <string> treeLeafOrderList;
var tree = Newick.NewickTreeFormat(nodeList[nodeList.Count - 1].ToList <GenericNode>(), names, out treeLeafOrderList, minimumOutputTreeLeafs);
Console.WriteLine(tree);
}
/// <summary>Constructs a QR decomposition.</summary>
/// <param name="value">The matrix A to be decomposed.</param>
/// <param name="transpose">True if the decomposition should be performed on
/// the transpose of A rather than A itself, false otherwise. Default is false.</param>
public QrDecompositionD(decimal[,] value, bool transpose)
{
if (value == null)
{
throw new ArgumentNullException("value", "Matrix cannot be null.");
}
if ((!transpose && value.GetLength(0) < value.GetLength(1)) ||
(transpose && value.GetLength(1) < value.GetLength(0)))
{
throw new ArgumentException("Matrix has more columns than rows.", "value");
}
this.qr = transpose ? value.Transpose() : (decimal[,])value.Clone();
int rows = qr.GetLength(0);
int cols = qr.GetLength(1);
this.Rdiag = new decimal[cols];
for (int k = 0; k < cols; k++)
{
// Compute 2-norm of k-th column without under/overflow.
decimal nrm = 0;
for (int i = k; i < rows; i++)
nrm = Tools.Hypotenuse(nrm, qr[i, k]);
if (nrm != 0)
{
// Form k-th Householder vector.
if (qr[k, k] < 0)
nrm = -nrm;
for (int i = k; i < rows; i++)
qr[i, k] /= nrm;
qr[k, k] += 1;
// Apply transformation to remaining columns.
for (int j = k + 1; j < cols; j++)
{
decimal s = 0;
for (int i = k; i < rows; i++)
s += qr[i, k] * qr[i, j];
s = -s / qr[k, k];
for (int i = k; i < rows; i++)
qr[i, j] += s * qr[i, k];
}
}
this.Rdiag[k] = -nrm;
}
}
/// <summary>
/// Solves a set of equation systems of type <c>A * X = B</c>.
/// </summary>
/// <param name="value">Right hand side matrix with as many rows as <c>A</c> and any number of columns.</param>
/// <returns>Matrix <c>X</c> so that <c>L * U * X = B</c>.</returns>
///
public decimal[,] Solve(decimal[,] value)
{
if (value == null)
{
throw new ArgumentNullException("value");
}
if (value.GetLength(0) != rows)
{
throw new DimensionMismatchException("value", "The matrix should have the same number of rows as the decomposition.");
}
if (!Nonsingular)
{
throw new InvalidOperationException("Matrix is singular.");
}
// Copy right hand side with pivoting
int count = value.GetLength(1);
decimal[,] X = value.Submatrix(pivotVector, null);
// Solve L*Y = B(piv,:)
for (int k = 0; k < cols; k++)
{
for (int i = k + 1; i < cols; i++)
{
for (int j = 0; j < count; j++)
{
X[i, j] -= X[k, j] * lu[i, k];
}
}
}
// Solve U*X = Y;
for (int k = cols - 1; k >= 0; k--)
{
for (int j = 0; j < count; j++)
{
X[k, j] /= lu[k, k];
}
for (int i = 0; i < k; i++)
{
for (int j = 0; j < count; j++)
{
X[i, j] -= X[k, j] * lu[i, k];
}
}
}
return(X);
}
static void Output(decimal[,] matrix)
{
for (int i = 0; i < matrix.GetLength(0); i++)
{
for (int j = 0; j < matrix.GetLength(1); j++)
{
Console.Write(matrix[i, j] + " ");
}
Console.WriteLine();
}
}
/// <summary>
/// prints a matrix to console output
/// </summary>
/// <param name="matrix">matrix to be printed</param>
/// <param name="round">number of decimal points to round answers, default 2</param>
public static void PrintMatrix(decimal[,] matrix, int round = 2)
{
for (int i = 0; i < matrix.GetLength(0); i++)
{
for (int j = 0; j < matrix.GetLength(1); j++)
{
Console.Write(decimal.Round(matrix[i, j], round) + "\t");
}
Console.WriteLine();
}
}
public static decimal[,] Get(decimal[,] resultArray, int cellsPerUnitSquare, GameComponents.RNG rng, decimal persistence = 1m)
{
int width = resultArray.GetLength(0);
int height = resultArray.GetLength(1);
int vectorWidth = (width / cellsPerUnitSquare) + 2; // make sure we have at least enough vectors for all 4 corners
int vectorHeight = (height / cellsPerUnitSquare) + 2;
Point[,] vectors = new Point[vectorWidth, vectorHeight];
for (int vx = 0; vx < vectorWidth; ++vx)
{
for (int vy = 0; vy < vectorHeight; ++vy)
{
int px = rng.GetNext(201) - 100; // store vectors as hundredths, positive or negative
int py = rng.GetNext(201) - 100;
vectors[vx, vy] = new Point(px, py);
}
}
decimal distBetweenMidpoints = 1m / (decimal)cellsPerUnitSquare;
for (int x = 0; x < width; ++x)
{
for (int y = 0; y < height; ++y)
{
// first find gradient vectors:
int sourceX = x / cellsPerUnitSquare;
int sourceY = y / cellsPerUnitSquare;
// also find where this x,y is within the containing square:
int xInCell = x % cellsPerUnitSquare;
int yInCell = y % cellsPerUnitSquare;
decimal xVector0 = (distBetweenMidpoints * 0.5m) + xInCell * distBetweenMidpoints;
decimal yVector0 = (distBetweenMidpoints * 0.5m) + yInCell * distBetweenMidpoints;
decimal xVector1 = 1m - xVector0;
decimal yVector1 = 1m - yVector0;
decimal xGradientx0y0 = vectors[sourceX, sourceY].X / 100m;
decimal xGradientx1y0 = vectors[sourceX + 1, sourceY].X / 100m;
decimal xGradientx0y1 = vectors[sourceX, sourceY + 1].X / 100m;
decimal xGradientx1y1 = vectors[sourceX + 1, sourceY + 1].X / 100m;
decimal yGradientx0y0 = vectors[sourceX, sourceY].Y / 100m;
decimal yGradientx1y0 = vectors[sourceX + 1, sourceY].Y / 100m;
decimal yGradientx0y1 = vectors[sourceX, sourceY + 1].Y / 100m;
decimal yGradientx1y1 = vectors[sourceX + 1, sourceY + 1].Y / 100m;
decimal x0y0Dot = xGradientx0y0 * xVector0 + yGradientx0y0 * yVector0;
decimal x1y0Dot = xGradientx1y0 * xVector1 + yGradientx1y0 * yVector0;
decimal x0y1Dot = xGradientx0y1 * xVector0 + yGradientx0y1 * yVector1;
decimal x1y1Dot = xGradientx1y1 * xVector1 + yGradientx1y1 * yVector1;
decimal lerpX0 = Lerp(x0y0Dot, x1y0Dot, xVector0);
decimal lerpX1 = Lerp(x0y1Dot, x1y1Dot, xVector0);
decimal lerpFinal = Lerp(lerpX0, lerpX1, yVector0);
resultArray[x, y] += lerpFinal * 256m * persistence;
}
}
return(resultArray);
}
public static void PrintMatrix(decimal[,] matrix)
{
for (int i = 0; i < matrix.GetLength(0); i++)
{
for (int j = 0; j < matrix.GetLength(1); j++)
{
Console.Write(" " + matrix[i, j] + " ");
}
Console.WriteLine();
}
}
public decimal[,] Resta(decimal[,] m)
{
if (matriz.GetLength(0) != m.GetLength(0) || matriz.GetLength(1) != m.GetLength(1))
{
throw new Exception("Las matrices son impoisibles de restar");
}
Matrix matriz1 = new Matrix(m);
return(Suma(matriz1.EscalarMult(-1)));
}
private static void PrintArr(decimal[,] array)
{
for (int i = 0; i < array.GetLength(0); i++)
{
for (int j = 0; j < array.GetLength(1); j++)
{
Console.Write(string.Format("{0}", array[i, j]).PadRight(5));
}
Console.WriteLine();
}
}
public static void DisplayDeterminant(decimal[,] Matrix)
{
for (int i = 0; i < Matrix.GetLength(0); i++)
{
for (int j = 0; j < Matrix.GetLength(1); j++)
{
Console.Write(" " + Matrix[i, j]);
}
Console.WriteLine();
}
}
public static void Print_Matrix(decimal[,] array)
{
for (int i = 0; i < array.GetLength(0); ++i)
{
for (int j = 0; j < array.GetLength(1); ++j)
{
Console.Write(array[i, j] + " ");
}
Console.WriteLine();
}
}
private static void PrintMatrix(decimal[,] matrix)
{
for (int row = 0; row < matrix.GetLength(0); row++)
{
for (int col = 0; col < matrix.GetLength(1); col++)
{
Console.Write(matrix[row, col] + " ");
}
Console.WriteLine();
}
}
public static double[] DoTheAnalysis(double sigma, double bottomThresholdCanny, double upperThresholdCanny,
int waveletLength, double bottomThresholdHaar, double upperThresholdHaar, int widthOfBrightnessDiffer, double snr = -1)
{
decimal[,] origImage = MakeOriginImArr(widthOfBrightnessDiffer);
decimal[,] origImageCopyForHaar = MakeOriginImArr(widthOfBrightnessDiffer);
double[,] perfectEdgeImage = MakePerfectEdgeImArr();
Bitmap bmpToSaveForTest = new Bitmap(origImage.GetLength(1), origImage.GetLength(0));
for (int i = 0; i < origImage.GetLength(0); i++)
{
for (int j = 0; j < origImage.GetLength(1); j++)
{
bmpToSaveForTest.SetPixel(j, i, Color.FromArgb((int)Math.Ceiling(origImage[i, j]), (int)Math.Ceiling(origImage[i, j]), (int)Math.Ceiling(origImage[i, j])));
}
}
if (snr > -1)
{
origImage = AddGausNoise(origImage, snr);// зашумляем изображение
origImageCopyForHaar = AddGausNoise(origImageCopyForHaar, snr);
}
CannyForm = new CannyForm(sigma, bottomThresholdCanny, upperThresholdCanny, bmp: null, imageArray: origImage); //создаем окно для вывода результатов метода Канни, передавая путь к выбранному файлу
double[,] cannyResult = new double[CannyForm.cannyResult.Height, CannyForm.cannyResult.Width];
for (int i = 0; i < cannyResult.GetLength(0); i++)
{
for (int j = 0; j < cannyResult.GetLength(1); j++)
{
cannyResult[i, j] = CannyForm.cannyResult.GetPixel(j, i).R;
}
}
HaarForm = new HaarForm(waveletLength, bottomThresholdHaar, upperThresholdHaar, bmp: null, imageArray: origImageCopyForHaar); //создаем окно для вывода результатов метода Хаара, передавая путь к выбранному файлу
double[,] haarResult = new double[HaarForm.resultBmp.Height, HaarForm.resultBmp.Width];
for (int i = 0; i < haarResult.GetLength(0); i++)
{
for (int j = 0; j < haarResult.GetLength(1); j++)
{
haarResult[i, j] = HaarForm.resultBmp.GetPixel(j, i).R;
}
}
double cannyPrettCrit = CountPrettCriteriaWithoutBorderPixels(perfectEdgeImage, cannyResult, waveletLength);
double haarPrettCrit = CountPrettCriteriaWithoutBorderPixels(perfectEdgeImage, haarResult, waveletLength);
double[] prettCrit = new double[2];
prettCrit[0] = cannyPrettCrit;
prettCrit[1] = haarPrettCrit;
return(prettCrit);
}
public static void ImprimirMatriz(decimal[,] matriz)
{
for (int i = 0; i < matriz.GetLength(0); i++)
{
for (int j = 0; j < matriz.GetLength(1); j++)
{
Console.Write(matriz[i, j] + "\t");
}
Console.WriteLine();
}
}
Point[,] getPoints(double[] x, decimal[,] y) // WORKING
{
Point[,] p = new Point[y.GetLength(0), y.GetLength(1)];
for (int i = 0; i < y.GetLength(0); ++i) // dla kazdej funkcji
{
for (int j = 0; j < x.Length; ++j) // dla kazdego x
{
p[i, j] = conv(x[j], y[i, j]);
}
}
return(p);
}
/// <summary>
/// 对矩阵的激活函数
/// </summary>
/// <param name="martix"></param>
/// <returns></returns>
public decimal[,] Active(decimal[,] martix)
{
decimal[,] ret = new decimal[martix.GetLength(0), martix.GetLength(1)];
for (int i = 0, len_i = martix.GetLength(0); i < len_i; i++)
{
for (int j = 0, len_j = martix.GetLength(1); j < len_j; j++)
{
ret[i, j] = Active(martix[i, j]);
}
}
return(ret);
}
/// <summary>
/// 矩阵向量化
/// </summary>
/// <param name="matrix">传入需要向量化的矩阵</param>
/// <returns>返回向量</returns>
public static decimal[,] MatrixVec(this decimal[,] matrix)
{
decimal[,] ret = new decimal[matrix.GetLength(0) * matrix.GetLength(1), 1];
for (int i = 0, len_i = matrix.GetLength(1); i < len_i; i++)
{
for (int j = 0, len_j = matrix.GetLength(0); j < len_j; j++)
{
ret[i * len_j + j, 0] = matrix[j, i];
}
}
return(ret);
}
/// <summary>
/// 矩阵减矩阵
/// </summary>
/// <param name="Matrix1"></param>
/// <param name="Matrix2"></param>
/// <returns></returns>
public static decimal[,] MatrixReduceMatrix(this decimal[,] Matrix1, decimal[,] Matrix2)
{
decimal[,] Ret = new decimal[Matrix1.GetLength(0), Matrix1.GetLength(1)];
for (int i = 0, len_i = Matrix1.GetLength(0); i < len_i; i++)
{
for (int j = 0, len_j = Matrix1.GetLength(1); j < len_j; j++)
{
Ret[i, j] = Matrix1[i, j] - Matrix2[i, j];
}
}
return(Ret);
}
/// <summary>
/// Constructs a new matrix with height and width of <paramref name="size"/>.
/// </summary>
/// <param name="size">Size of matrix.</param>
/// <param name="values">Values to use to initialize the matrix. These values are not used
/// directly, but are rather copied. Addressed as [row, column].</param>
protected TransformationMatrixBase(int size, decimal[,] values)
{
Size = size;
if (values.GetLength(0) != size || values.GetLength(1) != size)
{
throw new ArgumentException("Matrix initialization values are not of the correct size!", "values");
}
M = new decimal[size, size];
Array.Copy(values, M, values.Length);
}
decimal[,] Transpuesta(decimal[,] m)
{
decimal[,] result = new decimal[m.GetLength(0), m.GetLength(1)];
for (int i = 0; i < result.GetLength(0); i++)
{
for (int j = 0; j < result.GetLength(1); j++)
{
result[i, j] = m[j, i];
}
}
return(result);
}
/*public static void OutputUpgmaLog(string outputFilename, List<UpgmaNode> nodeList, List<decimal[,]> distanceMatrixCache, List<List<List<int>>> distanceMatrixMapCache)
* {
* var sb = new StringBuilder();
*
* for (int index = 0; index < nodeList.Count; index++)
* {
* var node = nodeList[index];
*
* var nodeId = string.Join("_", node.VectorIndexes);
* var childNodeId = node.ChildNode != null ? string.Join("_", node.ChildNode.VectorIndexes) : "null";
* var parentNodeIdA = node.ParentNodeA != null ? string.Join("_", node.ParentNodeA.VectorIndexes) : "null";
* var parentNodeIdB = node.ParentNodeB != null ? string.Join("_", node.ParentNodeB.VectorIndexes) : "null";
*
* sb.AppendLine("Node #" + index + ": " + nodeId);
* sb.AppendLine("Is leaf node: " + node.IsLeafNode());
* sb.AppendLine("Is root node: " + node.IsRootNode());
* sb.AppendLine("Is branch node: " + node.IsBranchNode());
* sb.AppendLine("Is unclustered node: " + node.IsNodeUnclustered());
* sb.AppendLine("Loop iteration: " + node.DistanceMatrixIterationNumber);
* sb.AppendLine("Child node: " + childNodeId + ". Distance: " + node.DistanceChildNode);
* sb.AppendLine("Parent node 1: " + parentNodeIdA + ". Distance: " + node.DistanceParentNodeA);
* sb.AppendLine("Parent node 2: " + parentNodeIdB + ". Distance: " + node.DistanceParentNodeB);
* sb.AppendLine();
* }
*
* for (int index = 0; index < distanceMatrixCache.Count; index++)
* {
* var distanceMatrix = distanceMatrixCache[index];
*
* var distanceMatrixMap = distanceMatrixMapCache[index];
*
* sb.AppendLine("Distance Matrix Iteration #" + index + ". " + distanceMatrix.GetLength(0) + " x " + distanceMatrix.GetLength(1) + " matrix:");
*
*
* sb.AppendLine();
*
* for (var matrixIndex = 0; matrixIndex < distanceMatrixMap.Count; matrixIndex++)
* {
* var matrixIndexIdList = distanceMatrixMap[matrixIndex];
*
* sb.AppendLine("Matrix index #" + matrixIndex + ": " + string.Join(", ", matrixIndexIdList));
* }
*
* sb.AppendLine();
*
* var width = distanceMatrix.Cast<decimal>().Select(a => a.ToString().Length).Max() + 1;
*
* if (distanceMatrix.GetLength(0).ToString().Length + 1 > width) width = distanceMatrix.GetLength(0).ToString().Length + 1;
* if (distanceMatrix.GetLength(1).ToString().Length + 1 > width) width = distanceMatrix.GetLength(1).ToString().Length + 1;
*
* sb.Append("".PadLeft(width));
* for (var x = 0; x < distanceMatrix.GetLength(0); x++)
* {
* sb.Append(x.ToString().PadLeft(width));
* }
*
* sb.AppendLine();
*
* for (var y = 0; y < distanceMatrix.GetLength(1); y++)
* {
* sb.Append(y.ToString().PadLeft(width));
*
* for (var x = 0; x < distanceMatrix.GetLength(0); x++)
* {
* string s = distanceMatrix[x, y].ToString().PadLeft(width);
* sb.Append(s);
* }
*
* sb.AppendLine();
* }
*
* sb.AppendLine();
* }
*
* File.WriteAllText(outputFilename, sb.ToString());
* }*/
/*public static void CacheUpgmaMatrixes(decimal[,] distanceMatrix, List<decimal[,]> distanceMatrixCache, List<List<int>> distanceMatrixMap, List<List<List<int>>> distanceMatrixMapCache)
* {
* // Copy distance matrix
* var distanceMatrixCacheCopy = new decimal[distanceMatrix.GetLength(0), distanceMatrix.GetLength(1)];
* Array.Copy(distanceMatrix, distanceMatrixCacheCopy, distanceMatrix.Length);
*
* // Cache distance matrix
* distanceMatrixCache.Add(distanceMatrixCacheCopy);
*
*
* // Copy matrix map
* var distanceMatrixMapCacheCopy = new List<List<int>>();
* for (var i = 0; i < distanceMatrixMap.Count; i++)
* {
* distanceMatrixMapCacheCopy.Add(new List<int>());
* distanceMatrixMapCacheCopy[i].AddRange(distanceMatrixMap[i].Select(a => a).ToList());
* }
*
* // Cache matrix map
* distanceMatrixMapCache.Add(distanceMatrixMapCacheCopy);
* }*/
public static void Upgma(decimal[,] distanceMatrix, List <string> vectorNames, int minimumOutputTreeLeafs,
out List <List <UpgmaNode> > nodeListList, out List <List <string> > treeListList,
out List <string> finalTreeLeafOrderList, bool newickTreeEveryIteration = false,
ProgressActionSet progressActionSet = null)
{
if (distanceMatrix == null || distanceMatrix.GetLength(0) == 0 || distanceMatrix.GetLength(1) == 0)
{
throw new ArgumentOutOfRangeException(nameof(distanceMatrix), "distance matrix is null or empty");
}
Upgma(ConvertTypes.Decimal2DArrayToDecimalListList(distanceMatrix), vectorNames, minimumOutputTreeLeafs, out nodeListList, out treeListList, out finalTreeLeafOrderList, newickTreeEveryIteration, progressActionSet);
}
public static decimal[,] RemoveLast(decimal[,] array)
{
decimal[,] newArray = new decimal[array.GetLength(0) - 1, array.GetLength(1)];
for (int i = 0; i < array.GetLength(0) - 1; i++)
{
for (int x = 0; x < array.GetLength(1); x++)
{
newArray[i, x] = array[i, x];
}
}
return(newArray);
}
public static decimal[,] AddOneLength(decimal[,] array)
{
decimal[,] newArray = new decimal[array.GetLength(0) + 1, array.GetLength(1)];
for (int i = 0; i < array.GetLength(0); i++)
{
for (int x = 0; x < array.GetLength(1); x++)
{
newArray[i, x] = array[i, x];
}
}
return(newArray);
}
public static decimal[,] MultiplyAndAdd(this decimal[,] a, decimal b, decimal[,] c, decimal[,] result)
{
for (int i = 0; i < result.GetLength(0); i++)
{
for (int j = 0; j < result.GetLength(1); j++)
{
result[i, j] = (decimal)((decimal)(a[i, j]) * b + (decimal)(c[i, j]));
}
}
return(result);
}
/// <summary>
/// 矩阵转置
/// </summary>
/// <param name="Matrix"></param>
/// <returns></returns>
public static decimal[,] Turn(this decimal[,] matrix)
{
decimal[,] ret = new decimal[matrix.GetLength(1), matrix.GetLength(0)];
for (int i = 0, len_i = matrix.GetLength(1); i < len_i; i++)
{
for (int j = 0, len_j = matrix.GetLength(0); j < len_j; j++)
{
ret[i, j] = matrix[j, i];
}
}
return(ret);
}
private decimal[,] Expand(decimal[,] array, int size1, int size2)
{
decimal[,] newArray = new decimal[size1, size2];
for (int i = array.GetLength(0) - 1; i >= 0; --i)
{
for (int j = array.GetLength(1) - 1; j >= 0; --j)
{
newArray[i, j] = array[i, j];
}
}
return(newArray);
}
/// <summary>
/// 矩阵乘标量
/// </summary>
/// <param name="Matrix"></param>
/// <param name="Scalar"></param>
/// <returns></returns>
public static decimal[,] MatrixMultiScalar(this decimal[,] Matrix, decimal Scalar)
{
decimal[,] Ret = new decimal[Matrix.GetLength(0), Matrix.GetLength(1)];
for (int i = 0, len_i = Matrix.GetLength(0); i < len_i; i++)
{
for (int j = 0, len_j = Matrix.GetLength(1); j < len_j; j++)
{
Ret[i, j] = Matrix[i, j] * Scalar;
}
}
return(Ret);
}
public CannyForm(double sigma, double bottomThreshold, double upperThreshold, Bitmap bmp = null, decimal[,] imageArray = null)// перегружаем метод, чтобы принять на вход изображение, а не ссылку на него
{
InitializeComponent();
this.bmp = bmp;
this.sigma = sigma;
this.bottomThreshold = bottomThreshold;
this.upperThreshold = upperThreshold;
if (imageArray != null)
{
Bitmap bmp1 = new Bitmap(imageArray.GetLength(1), imageArray.GetLength(0));
for (int i = 0; i < imageArray.GetLength(0); i++)
{
for (int j = 0; j < imageArray.GetLength(1); j++)
{
bmp1.SetPixel(j, i, Color.FromArgb((int)imageArray[i, j], (int)imageArray[i, j], (int)imageArray[i, j]));
}
}
pictureBox1.Image = bmp1;
decimal[,] imageArrayNew = CannyStandardVersion.GradUsingFirstDerOfGaus(imageArray, sigma);
Bitmap bmp2 = new Bitmap(imageArrayNew.GetLength(1), imageArrayNew.GetLength(0));
for (int i = 0; i < imageArrayNew.GetLength(0); i++)
{
for (int j = 0; j < imageArrayNew.GetLength(1); j++)
{
bmp2.SetPixel(j, i, Color.FromArgb((int)imageArrayNew[i, j], (int)imageArrayNew[i, j], (int)imageArrayNew[i, j]));
}
}
pictureBox2.Image = bmp2;
Bitmap bmpFromImageArrayNew = CannyStandardVersion.Non_Maximum_Suppression(bmp = null, imageArrayNew);
pictureBox4.Image = CannyStandardVersion.Double_Threshold(bmpFromImageArrayNew, bottomThreshold, upperThreshold);
Bitmap c = new Bitmap(bmpFromImageArrayNew);
cannyResult = CannyStandardVersion.Hysteresis_Thresholding(c, "Canny");
pictureBox3.Image = cannyResult;
}
else
{
pictureBox1.Image = bmp;
Bitmap a = new Bitmap(pictureBox1.Image);
CannyStandardVersion.GrayScale(a);
CannyStandardVersion.GradUsingFirstDerOfGaus(a, sigma);
pictureBox2.Image = a;
Bitmap b = new Bitmap(a);
CannyStandardVersion.Non_Maximum_Suppression(b);
pictureBox4.Image = CannyStandardVersion.Double_Threshold(b, bottomThreshold, upperThreshold);
Bitmap c = new Bitmap(b);
pictureBox3.Image = CannyStandardVersion.Hysteresis_Thresholding(c, "Canny");
}
}
static decimal SecondaryDiagonalMinValue(decimal[,] array)
{
decimal minValue = decimal.MaxValue;
for (int i = 0; i < array.GetLength(0); i++)
{
if (minValue > array[array.GetLength(0) - i - 1, i])
{
minValue = array[array.GetLength(0) - i - 1, i];
}
}
return(minValue);
}
