// 数字認識
internal static int recognizeDigit(CvMat image)
{
int nonzero = 0;
nonzero = image.GetCols( image.Cols-2, image.Cols ).CountNonZero();
if ( image.Rows * 2 == nonzero )
// 1 右端2列がすべて輝点
return 1;
nonzero = image.GetRows( image.Rows-2, image.Rows ).CountNonZero();
if ( image.Cols * 2 == nonzero )
// 2 下端2行がすべて輝点
return 2;
nonzero = image.GetRows ( 0, 2 ).CountNonZero();
if ( image.Cols * 2 - 2 < nonzero )
// 7 上端2行がすべて輝点．ただし1ピクセルまで欠けても良い
return 7;
nonzero = image.GetCols ( image.Cols-3, image.Cols-1 ).CountNonZero();
if ( image.Rows * 2 == nonzero )
// 4 右端の左2列がすべて輝点
return 4;
CvRect rect = new CvRect( 0, 0, 1, image.Rows*2/3 );
CvMat subarr;
nonzero = image.GetSubArr ( out subarr, rect ).CountNonZero();
if ( 0 == nonzero )
// 3 左端の上部3分の2がすべて暗点
return 3;
rect = new CvRect ( 0, image.Rows/2, 3, 2 );
nonzero = image.GetSubArr ( out subarr, rect ).CountNonZero();
if ( 0 == nonzero )
// 5 左端の下半分開始すぐのwidth3 height2 がすべて暗点
return 5;
rect = new CvRect ( image.Cols/2, image.Rows/2-1, 1, 3 );
nonzero = image.GetSubArr( out subarr, rect ).CountNonZero();
if ( 0 == nonzero )
// 0 中央列中央3ピクセルがすべて暗点
return 0;
rect = new CvRect ( image.Cols-1, 0, 1, image.Rows*2/5 );
nonzero = image.GetSubArr( out subarr, rect ).CountNonZero();
if ( 0 == nonzero )
// 6 右端上部5分の2がすべて暗点
return 6;
rect = new CvRect ( image.Cols-1, image.Rows-3, 1, 3 );
nonzero = image.GetSubArr( out subarr, rect ).CountNonZero();
if ( 0 == nonzero )
// 右端下部3ピクセルがすべて暗点
return 9;
// 8 上記条件を満たさない
return 8;
}
/** 所属国認識2
* @param image 所属国アイコン部分カラー画像
*/
static 所属国 recognizeNationality2( CvMat image )
{
// 閾値130.0で2値化した画像を基にする
CvMat bin = new CvMat( image.Rows, image.Cols, MatrixType.U8C1);
image.CvtColor( bin, ColorConversion.BgrToGray );
bin.Threshold( bin, 130.0, 255.0, ThresholdType.Binary );
// iel: 7
// net: 23
// http://twitpic.com/azbzjh
int count = bin.GetRows( 2, bin.Rows ).CountNonZero();
if ( count > 15 )
return 所属国.ネツァワル;
if ( count > 3 )
return 所属国.エルソード;
return 所属国.不明;
}
/// <summary>
/// Classical Multidimensional Scaling
/// </summary>
public MDS()
{
// creates distance matrix
int size = CityDistance.GetLength(0);
CvMat t = new CvMat(size, size, MatrixType.F64C1, CityDistance);
// adds Torgerson's additive constant to t
t += Torgerson(t);
// squares all elements of t
t.Mul(t, t);
// centering matrix G
CvMat g = CenteringMatrix(size);
// calculates inner product matrix B
CvMat b = g * t * g.T() * -0.5;
// calculates eigenvalues and eigenvectors of B
CvMat vectors = new CvMat(size, size, MatrixType.F64C1);
CvMat values = new CvMat(size, 1, MatrixType.F64C1);
Cv.EigenVV(b, vectors, values);
for (int r = 0; r < values.Rows; r++)
{
if (values[r] < 0)
values[r] = 0;
}
// multiplies sqrt(eigenvalue) by eigenvector
CvMat result = vectors.GetRows(0, 2);
for (int r = 0; r < result.Rows; r++)
{
for (int c = 0; c < result.Cols; c++)
{
result[r, c] *= Math.Sqrt(values[r]);
}
}
// scaling
Cv.Normalize(result, result, 0, 800, NormType.MinMax);
//Console.WriteLine(vectors);
//Console.WriteLine(values);
//Console.WriteLine(result);
// opens a window
using (IplImage img = new IplImage(800, 600, BitDepth.U8, 3))
using (CvFont font = new CvFont(FontFace.HersheySimplex, 0.5f, 0.5f))
using (CvWindow window = new CvWindow("City Location Estimation"))
{
img.Zero();
for (int c = 0; c < size; c++)
{
double x = result[0, c];
double y = result[1, c];
x = x * 0.7 + img.Width * 0.1;
y = y * 0.7 + img.Height * 0.1;
img.Circle((int)x, (int)y, 5, CvColor.Red, -1);
img.PutText(CityNames[c], new CvPoint((int)x+5, (int)y+10), font, CvColor.White);
}
window.Image = img;
Cv.WaitKey();
}
}
/// <summary>
/// Classical Multidimensional Scaling
/// </summary>
public MDS()
{
// creates distance matrix
int size = CityDistance.GetLength(0);
CvMat t = new CvMat(size, size, MatrixType.F64C1, CityDistance);
// adds Torgerson's additive constant to t
t += Torgerson(t);
// squares all elements of t
t.Mul(t, t);
// centering matrix G
CvMat g = CenteringMatrix(size);
// calculates inner product matrix B
CvMat b = g * t * g.T() * -0.5;
// calculates eigenvalues and eigenvectors of B
CvMat vectors = new CvMat(size, size, MatrixType.F64C1);
CvMat values = new CvMat(size, 1, MatrixType.F64C1);
Cv.EigenVV(b, vectors, values);
for (int r = 0; r < values.Rows; r++)
{
if (values[r] < 0)
{
values[r] = 0;
}
}
// multiplies sqrt(eigenvalue) by eigenvector
CvMat result = vectors.GetRows(0, 2);
for (int r = 0; r < result.Rows; r++)
{
for (int c = 0; c < result.Cols; c++)
{
result[r, c] *= Math.Sqrt(values[r]);
}
}
// scaling
Cv.Normalize(result, result, 0, 800, NormType.MinMax);
//Console.WriteLine(vectors);
//Console.WriteLine(values);
//Console.WriteLine(result);
// opens a window
using (IplImage img = new IplImage(800, 600, BitDepth.U8, 3))
using (CvFont font = new CvFont(FontFace.HersheySimplex, 0.5f, 0.5f))
using (CvWindow window = new CvWindow("City Location Estimation"))
{
img.Zero();
for (int c = 0; c < size; c++)
{
double x = result[0, c];
double y = result[1, c];
x = x * 0.7 + img.Width * 0.1;
y = y * 0.7 + img.Height * 0.1;
img.Circle((int)x, (int)y, 5, CvColor.Red, -1);
img.PutText(CityNames[c], new CvPoint((int)x + 5, (int)y + 10), font, CvColor.White);
}
window.Image = img;
Cv.WaitKey();
}
}
