public BoundingRect()
{
// cvBoundingRect
// 点列を包含する矩形を求める
// (1)画像とメモリストレージを確保し初期化する
// (メモリストレージは、CvSeqを使わないのであれば不要)
using (IplImage img = new IplImage(640, 480, BitDepth.U8, 3))
using (CvMemStorage storage = new CvMemStorage(0))
{
img.Zero();
CvRNG rng = new CvRNG(DateTime.Now);
// (2)点列を生成する
///*
// お手軽な方法 (普通の配列を使う)
CvPoint[] points = new CvPoint[50];
for (int i = 0; i < 50; i++)
{
points[i] = new CvPoint()
{
X = (int)(rng.RandInt() % (img.Width / 2) + img.Width / 4),
Y = (int)(rng.RandInt() % (img.Height / 2) + img.Height / 4)
};
img.Circle(points[i], 3, new CvColor(0, 255, 0), Cv.FILLED);
}
//*/
/*
// サンプルに準拠した方法 (CvSeqを使う)
CvSeq points = new CvSeq(SeqType.EltypePoint, CvSeq.SizeOf, CvPoint.SizeOf, storage);
for (int i = 0; i < 50; i++) {
CvPoint pt = new CvPoint();
pt.X = (int)(rng.RandInt() % (img.Width / 2) + img.Width / 4);
pt.Y = (int)(rng.RandInt() % (img.Height / 2) + img.Height / 4);
points.Push(pt);
img.Circle(pt, 3, new CvColor(0, 255, 0), Cv.FILLED);
}
//*/
// (3)点列を包含する矩形を求めて描画する
CvRect rect = Cv.BoundingRect(points);
img.Rectangle(new CvPoint(rect.X, rect.Y), new CvPoint(rect.X + rect.Width, rect.Y + rect.Height), new CvColor(255, 0, 0), 2);
// (4)画像の表示，キーが押されたときに終了
using (CvWindow w = new CvWindow("BoundingRect", WindowMode.AutoSize, img))
{
CvWindow.WaitKey(0);
}
}
}
public SVM()
{
// CvSVM
// SVMを利用して2次元ベクトルの3クラス分類問題を解く
const int S = 1000;
const int SIZE = 400;
CvRNG rng = new CvRNG((ulong)DateTime.Now.Ticks);
// (1)画像領域の確保と初期化
using (IplImage img = new IplImage(SIZE, SIZE, BitDepth.U8, 3))
{
img.Zero();
// (2)学習データの生成
CvPoint[] pts = new CvPoint[S];
int[] res = new int[S];
for (int i = 0; i < S; i++)
{
pts[i].X = (int)(rng.RandInt() % SIZE);
pts[i].Y = (int)(rng.RandInt() % SIZE);
if (pts[i].Y > 50 * Math.Cos(pts[i].X * Cv.PI / 100) + 200)
{
img.Line(new CvPoint(pts[i].X - 2, pts[i].Y - 2), new CvPoint(pts[i].X + 2, pts[i].Y + 2), new CvColor(255, 0, 0));
img.Line(new CvPoint(pts[i].X + 2, pts[i].Y - 2), new CvPoint(pts[i].X - 2, pts[i].Y + 2), new CvColor(255, 0, 0));
res[i] = 1;
}
else
{
if (pts[i].X > 200)
{
img.Line(new CvPoint(pts[i].X - 2, pts[i].Y - 2), new CvPoint(pts[i].X + 2, pts[i].Y + 2), new CvColor(0, 255, 0));
img.Line(new CvPoint(pts[i].X + 2, pts[i].Y - 2), new CvPoint(pts[i].X - 2, pts[i].Y + 2), new CvColor(0, 255, 0));
res[i] = 2;
}
else
{
img.Line(new CvPoint(pts[i].X - 2, pts[i].Y - 2), new CvPoint(pts[i].X + 2, pts[i].Y + 2), new CvColor(0, 0, 255));
img.Line(new CvPoint(pts[i].X + 2, pts[i].Y - 2), new CvPoint(pts[i].X - 2, pts[i].Y + 2), new CvColor(0, 0, 255));
res[i] = 3;
}
}
}
// (3)学習データの表示
Cv.NamedWindow("SVM", WindowMode.AutoSize);
Cv.ShowImage("SVM", img);
Cv.WaitKey(0);
// (4)学習パラメータの生成
float[] data = new float[S * 2];
for (int i = 0; i < S; i++)
{
data[i * 2] = ((float)pts[i].X) / SIZE;
data[i * 2 + 1] = ((float)pts[i].Y) / SIZE;
}
// (5)SVMの学習
using (CvSVM svm = new CvSVM())
{
CvMat data_mat = new CvMat(S, 2, MatrixType.F32C1, data);
CvMat res_mat = new CvMat(S, 1, MatrixType.S32C1, res);
CvTermCriteria criteria = new CvTermCriteria(1000, float.Epsilon);
CvSVMParams param = new CvSVMParams(SVMType.CSvc, SVMKernelType.Rbf, 10.0, 8.0, 1.0, 10.0, 0.5, 0.1, null, criteria);
svm.Train(data_mat, res_mat, null, null, param);
// (6)学習結果の描画
for (int i = 0; i < SIZE; i++)
{
for (int j = 0; j < SIZE; j++)
{
float[] a = { (float)j / SIZE, (float)i / SIZE };
CvMat m = new CvMat(1, 2, MatrixType.F32C1, a);
float ret = svm.Predict(m);
CvColor color = new CvColor();
switch ((int)ret)
{
case 1:
color = new CvColor(100, 0, 0); break;
case 2:
color = new CvColor(0, 100, 0); break;
case 3:
color = new CvColor(0, 0, 100); break;
}
img[i, j] = color;
}
}
// (7)トレーニングデータの再描画
for (int i = 0; i < S; i++)
{
CvColor color = new CvColor();
switch (res[i])
{
case 1:
color = new CvColor(255, 0, 0); break;
case 2:
color = new CvColor(0, 255, 0); break;
case 3:
color = new CvColor(0, 0, 255); break;
}
img.Line(new CvPoint(pts[i].X - 2, pts[i].Y - 2), new CvPoint(pts[i].X + 2, pts[i].Y + 2), color);
img.Line(new CvPoint(pts[i].X + 2, pts[i].Y - 2), new CvPoint(pts[i].X - 2, pts[i].Y + 2), color);
}
// (8)サポートベクターの描画
int sv_num = svm.GetSupportVectorCount();
for (int i = 0; i < sv_num; i++)
{
var support = svm.GetSupportVector(i);
img.Circle(new CvPoint((int)(support[0] * SIZE), (int)(support[1] * SIZE)), 5, new CvColor(200, 200, 200));
}
// (9)画像の表示
Cv.NamedWindow("SVM", WindowMode.AutoSize);
Cv.ShowImage("SVM", img);
Cv.WaitKey(0);
Cv.DestroyWindow("SVM");
}
}
}
/// <summary>
/// マップのシーケンスのファイルストレージへの書き込み
/// </summary>
/// <param name="fileName">書きこむXML or YAMLファイル</param>
private static void SampleFileStorageWriteSeq(string fileName)
{
// cvStartWriteStruct, cvEndWriteStruct
// 二つのエントリを持つマップのシーケンスをファイルに保存する
const int size = 20;
CvRNG rng = new CvRNG((ulong)DateTime.Now.Ticks);
CvPoint[] pt = new CvPoint[size];
// (1)点列の作成
for (int i = 0; i < pt.Length; i++)
{
pt[i].X = (int)rng.RandInt(100);
pt[i].Y = (int)rng.RandInt(100);
}
// (2)マップのシーケンスとして点列を保存
using (CvFileStorage fs = new CvFileStorage(fileName, null, FileStorageMode.Write))
{
fs.StartWriteStruct("points", NodeType.Seq);
for (int i = 0; i < pt.Length; i++)
{
fs.StartWriteStruct(null, NodeType.Map | NodeType.Flow);
fs.WriteInt("x", pt[i].X);
fs.WriteInt("y", pt[i].Y);
fs.EndWriteStruct();
}
fs.EndWriteStruct();
}
// (3)書きこんだyamlファイルを開く
//using (Process p = Process.Start(fileName)) {
// p.WaitForExit();
//}
}
/// <summary>
///
/// </summary>
/// <param name="fileName">書きこむXML or YAMLファイル</param>
private static void SampleFileStorageWriteSeq(string fileName)
{
// cvStartWriteStruct, cvEndWriteStruct
const int size = 20;
CvRNG rng = new CvRNG((ulong)DateTime.Now.Ticks);
CvPoint[] pt = new CvPoint[size];
for (int i = 0; i < pt.Length; i++)
{
pt[i].X = (int)rng.RandInt(100);
pt[i].Y = (int)rng.RandInt(100);
}
using (CvFileStorage fs = new CvFileStorage(fileName, null, FileStorageMode.Write))
{
fs.StartWriteStruct("points", NodeType.Seq);
for (int i = 0; i < pt.Length; i++)
{
fs.StartWriteStruct(null, NodeType.Map | NodeType.Flow);
fs.WriteInt("x", pt[i].X);
fs.WriteInt("y", pt[i].Y);
fs.EndWriteStruct();
}
fs.EndWriteStruct();
}
}