public Contour() { // cvContourArea, cvArcLength // 輪郭によって区切られた領域の面積と,輪郭の長さを求める const int SIZE = 500; // (1)画像を確保し初期化する using (CvMemStorage storage = new CvMemStorage()) using (IplImage img = new IplImage(SIZE, SIZE, BitDepth.U8, 3)) { img.Zero(); // (2)点列を生成する CvSeq<CvPoint> points = new CvSeq<CvPoint>(SeqType.PolyLine, storage); CvRNG rng = new CvRNG((ulong)DateTime.Now.Ticks); double scale = rng.RandReal() + 0.5; CvPoint pt0 = new CvPoint { X = (int)(Math.Cos(0) * SIZE / 4 * scale + SIZE / 2), Y = (int)(Math.Sin(0) * SIZE / 4 * scale + SIZE / 2) }; img.Circle(pt0, 2, CvColor.Green); points.Push(pt0); for (int i = 1; i < 20; i++) { scale = rng.RandReal() + 0.5; CvPoint pt1 = new CvPoint { X = (int)(Math.Cos(i * 2 * Math.PI / 20) * SIZE / 4 * scale + SIZE / 2), Y = (int)(Math.Sin(i * 2 * Math.PI / 20) * SIZE / 4 * scale + SIZE / 2) }; img.Line(pt0, pt1, CvColor.Green, 2); pt0.X = pt1.X; pt0.Y = pt1.Y; img.Circle(pt0, 3, CvColor.Green, Cv.FILLED); points.Push(pt0); } img.Line(pt0, points.GetSeqElem(0).Value, CvColor.Green, 2); // (3)包含矩形,面積,長さを求める CvRect rect = points.BoundingRect(false); double area = points.ContourArea(); double length = points.ArcLength(CvSlice.WholeSeq, 1); // (4)結果を画像に書き込む img.Rectangle(new CvPoint(rect.X, rect.Y), new CvPoint(rect.X + rect.Width, rect.Y + rect.Height), CvColor.Red, 2); string text_area = string.Format("Area: wrect={0}, contour={1}", rect.Width * rect.Height, area); string text_length = string.Format("Length: rect={0}, contour={1}", 2 * (rect.Width + rect.Height), length); using (CvFont font = new CvFont(FontFace.HersheySimplex, 0.7, 0.7, 0, 1, LineType.AntiAlias)) { img.PutText(text_area, new CvPoint(10, img.Height - 30), font, CvColor.White); img.PutText(text_length, new CvPoint(10, img.Height - 10), font, CvColor.White); } // (5)画像を表示,キーが押されたときに終了 using (CvWindow window = new CvWindow("BoundingRect", WindowMode.AutoSize)) { window.Image = img; CvWindow.WaitKey(0); } } }
public FitLine() { CvSize imageSize = new CvSize(500, 500); // cvFitLine CvPoint2D32f[] points = GetRandomPoints(20, imageSize); CvLine2D line = Cv.FitLine2D(points, DistanceType.L2, 0, 0.01, 0.01); using (IplImage img = new IplImage(imageSize, BitDepth.U8, 3)) { img.Zero(); // draw line { CvPoint pt1, pt2; line.FitSize(img.Width, img.Height, out pt1, out pt2); img.Line(pt1, pt2, CvColor.Green, 1, LineType.Link8); } // draw points and distances using (CvFont font = new CvFont(FontFace.HersheySimplex, 0.33, 0.33)) { foreach (CvPoint2D32f p in points) { double d = line.Distance(p); img.Circle(p, 2, CvColor.White, -1, LineType.AntiAlias); img.PutText(string.Format("{0:F1}", d), new CvPoint((int) (p.X + 3), (int) (p.Y + 3)), font, CvColor.Green); } } CvWindow.ShowImages(img); } }
public FaceDetect() { CheckMemoryLeak(); // CvHaarClassifierCascade, cvHaarDetectObjects CvColor[] colors = new CvColor[]{ new CvColor(0,0,255), new CvColor(0,128,255), new CvColor(0,255,255), new CvColor(0,255,0), new CvColor(255,128,0), new CvColor(255,255,0), new CvColor(255,0,0), new CvColor(255,0,255), }; const double Scale = 1.14; const double ScaleFactor = 1.0850; const int MinNeighbors = 2; using (IplImage img = new IplImage(FilePath.Image.Yalta, LoadMode.Color)) using (IplImage smallImg = new IplImage(new CvSize(Cv.Round(img.Width / Scale), Cv.Round(img.Height / Scale)), BitDepth.U8, 1)) { using (IplImage gray = new IplImage(img.Size, BitDepth.U8, 1)) { Cv.CvtColor(img, gray, ColorConversion.BgrToGray); Cv.Resize(gray, smallImg, Interpolation.Linear); Cv.EqualizeHist(smallImg, smallImg); } using (var cascade = CvHaarClassifierCascade.FromFile(FilePath.Text.HaarCascade)) using (var storage = new CvMemStorage()) { storage.Clear(); // 顔の検出 Stopwatch watch = Stopwatch.StartNew(); CvSeq<CvAvgComp> faces = Cv.HaarDetectObjects(smallImg, cascade, storage, ScaleFactor, MinNeighbors, 0, new CvSize(30, 30)); watch.Stop(); Console.WriteLine("detection time = {0}ms\n", watch.ElapsedMilliseconds); // 検出した箇所にまるをつける for (int i = 0; i < faces.Total; i++) { CvRect r = faces[i].Value.Rect; CvPoint center = new CvPoint { X = Cv.Round((r.X + r.Width * 0.5) * Scale), Y = Cv.Round((r.Y + r.Height * 0.5) * Scale) }; int radius = Cv.Round((r.Width + r.Height) * 0.25 * Scale); img.Circle(center, radius, colors[i % 8], 3, LineType.AntiAlias, 0); } } // ウィンドウに表示 CvWindow.ShowImages(img); } }
public Watershed() { // cvWatershed // マウスで円形のマーカー(シード領域)の中心を指定し,複数のマーカーを設定する. // このマーカを画像のgradientに沿って広げて行き,gradientの高い部分に出来る境界を元に領域を分割する. // 領域は,最初に指定したマーカーの数に分割される. // (2)画像の読み込み,マーカー画像の初期化,結果表示用画像領域の確保を行なう using (IplImage srcImg = new IplImage(Const.ImageGoryokaku, LoadMode.AnyDepth | LoadMode.AnyColor)) using (IplImage dstImg = srcImg.Clone()) using (IplImage dspImg = srcImg.Clone()) using (IplImage markers = new IplImage(srcImg.Size, BitDepth.S32, 1)) { markers.Zero(); // (3)入力画像を表示しシードコンポーネント指定のためのマウスイベントを登録する using (CvWindow wImage = new CvWindow("image", WindowMode.AutoSize)) { wImage.Image = srcImg; // クリックにより中心を指定し,円形のシード領域を設定する int seedNum = 0; wImage.OnMouseCallback += delegate(MouseEvent ev, int x, int y, MouseEvent flags) { if (ev == MouseEvent.LButtonDown) { seedNum++; CvPoint pt = new CvPoint(x, y); markers.Circle(pt, 20, CvScalar.ScalarAll(seedNum), Cv.FILLED, LineType.Link8, 0); dspImg.Circle(pt, 20, CvColor.White, 3, LineType.Link8, 0); wImage.Image = dspImg; } }; CvWindow.WaitKey(); } // (4)watershed分割を実行する Cv.Watershed(srcImg, markers); // (5)実行結果の画像中のwatershed境界(ピクセル値=-1)を結果表示用画像上に表示する for (int i = 0; i < markers.Height; i++) { for (int j = 0; j < markers.Width; j++) { int idx = (int)(markers.Get2D(i, j).Val0); if (idx == -1) { dstImg.Set2D(i, j, CvColor.Red); } } } using (CvWindow wDst = new CvWindow("watershed transform", WindowMode.AutoSize)) { wDst.Image = dstImg; CvWindow.WaitKey(); } } }
/// <summary> /// Extracts MSER by C++-style code (cv::MSER) /// </summary> /// <param name="imgGray"></param> /// <param name="imgRender"></param> private void CppStyleMSER(IplImage imgGray, IplImage imgDst) { MSER mser = new MSER(); CvPoint[][] contours = mser.Extract(new Mat(imgGray, false), null); // operator() foreach (CvPoint[] p in contours) { CvColor color = CvColor.Random(); for (int i = 0; i < p.Length; i++) { imgDst.Circle(p[i], 1, color); } } }
public Watershed() { using (var srcImg = new IplImage(FilePath.Image.Goryokaku, LoadMode.AnyDepth | LoadMode.AnyColor)) using (var dstImg = srcImg.Clone()) using (var dspImg = srcImg.Clone()) using (var markers = new IplImage(srcImg.Size, BitDepth.S32, 1)) { markers.Zero(); using (var window = new CvWindow("image", WindowMode.AutoSize)) { window.Image = srcImg; // Mouse event int seedNum = 0; window.OnMouseCallback += delegate(MouseEvent ev, int x, int y, MouseEvent flags) { if (ev == MouseEvent.LButtonDown) { seedNum++; CvPoint pt = new CvPoint(x, y); markers.Circle(pt, 20, CvScalar.ScalarAll(seedNum), Cv.FILLED, LineType.Link8, 0); dspImg.Circle(pt, 20, CvColor.White, 3, LineType.Link8, 0); window.Image = dspImg; } }; CvWindow.WaitKey(); } Cv.Watershed(srcImg, markers); // draws watershed for (int i = 0; i < markers.Height; i++) { for (int j = 0; j < markers.Width; j++) { int idx = (int)(markers.Get2D(i, j).Val0); if (idx == -1) { dstImg.Set2D(i, j, CvColor.Red); } } } using (CvWindow wDst = new CvWindow("watershed transform", WindowMode.AutoSize)) { wDst.Image = dstImg; CvWindow.WaitKey(); } } }
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 Blob() { using (var imgSrc = new IplImage(FilePath.Image.Shapes, LoadMode.Color)) using (var imgBinary = new IplImage(imgSrc.Size, BitDepth.U8, 1)) using (var imgRender = new IplImage(imgSrc.Size, BitDepth.U8, 3)) using (var imgContour = new IplImage(imgSrc.Size, BitDepth.U8, 3)) using (var imgPolygon = new IplImage(imgSrc.Size, BitDepth.U8, 3)) { Cv.CvtColor(imgSrc, imgBinary, ColorConversion.BgrToGray); Cv.Threshold(imgBinary, imgBinary, 100, 255, ThresholdType.Binary); CvBlobs blobs = new CvBlobs(); blobs.Label(imgBinary); foreach (KeyValuePair<int, CvBlob> item in blobs) { CvBlob b = item.Value; Console.WriteLine("{0} | Centroid:{1} Area:{2}", item.Key, b.Centroid, b.Area); CvContourChainCode cc = b.Contour; cc.Render(imgContour); CvContourPolygon polygon = cc.ConvertToPolygon(); foreach (CvPoint p in polygon) { imgPolygon.Circle(p, 1, CvColor.Red, -1); } /* CvPoint2D32f circleCenter; float circleRadius; GetEnclosingCircle(polygon, out circleCenter, out circleRadius); imgPolygon.Circle(circleCenter, (int) circleRadius, CvColor.Green, 2); */ } blobs.RenderBlobs(imgSrc, imgRender); using (new CvWindow("render", imgRender)) using (new CvWindow("contour", imgContour)) using (new CvWindow("polygon vertices", imgPolygon)) { Cv.WaitKey(0); } } }
/// <summary> /// Extracts MSER by C-style code (cvExtractMSER) /// </summary> /// <param name="imgGray"></param> /// <param name="imgRender"></param> private void CStyleMSER(IplImage imgGray, IplImage imgDst) { using (CvMemStorage storage = new CvMemStorage()) { CvContour[] contours; CvMSERParams param = new CvMSERParams(); Cv.ExtractMSER(imgGray, null, out contours, storage, param); foreach (CvContour c in contours) { CvColor color = CvColor.Random(); for (int i = 0; i < c.Total; i++) { imgDst.Circle(c[i].Value, 1, color); } } } }
public BlobOld() { using (IplImage imgSrc = new IplImage(Const.ImageShapes, LoadMode.Color)) using (IplImage imgBinary = new IplImage(imgSrc.Size, BitDepth.U8, 1)) using (IplImage imgLabel = new IplImage(imgSrc.Size, BitDepth.F32, 1)) using (IplImage imgRender = new IplImage(imgSrc.Size, BitDepth.U8, 3)) using (IplImage imgContour = new IplImage(imgSrc.Size, BitDepth.U8, 3)) using (IplImage imgPolygon = new IplImage(imgSrc.Size, BitDepth.U8, 3)) { Cv.CvtColor(imgSrc, imgBinary, ColorConversion.BgrToGray); Cv.Threshold(imgBinary, imgBinary, 100, 255, ThresholdType.Binary); using (CvBlobs blobs = new CvBlobs()) { uint result = blobs.Label(imgBinary, imgLabel); foreach (KeyValuePair<uint, CvBlob> item in blobs) { CvBlob b = item.Value; Console.WriteLine("{0} | Centroid:{1} Area:{2}", item.Key, b.Centroid, b.Area); CvContourChainCode cc = b.Contour; cc.RenderContourChainCode(imgContour); CvContourPolygon polygon = cc.ConvertChainCodesToPolygon(); foreach (CvPoint p in polygon) { imgPolygon.Circle(p, 1, CvColor.Red, -1); } } blobs.RenderBlobs(imgLabel, imgSrc, imgRender); using (new CvWindow("render", imgRender)) using (new CvWindow("contour", imgContour)) using (new CvWindow("polygon vertices", imgPolygon)) { Cv.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"); } } }
public void FaceDetect() { // CvHaarClassifierCascade, cvHaarDetectObjects // 顔を検出するためにHaar分類器のカスケードを用いる CvColor[] colors = new CvColor[]{ new CvColor(0,0,255), new CvColor(0,128,255), new CvColor(0,255,255), new CvColor(0,255,0), new CvColor(255,128,0), new CvColor(255,255,0), new CvColor(255,0,0), new CvColor(255,0,255), }; const double Scale = 1.14; const double ScaleFactor = 1.0850; const int MinNeighbors = 2; using (IplImage img = new IplImage(Application.dataPath + TestImageName, LoadMode.Color)) using (IplImage smallImg = new IplImage(new CvSize(Cv.Round(img.Width / Scale), Cv.Round(img.Height / Scale)), BitDepth.U8, 1)) { // 顔検出用の画像の生成 using (IplImage gray = new IplImage(img.Size, BitDepth.U8, 1)) { Cv.CvtColor(img, gray, ColorConversion.BgrToGray); Cv.Resize(gray, smallImg, Interpolation.Linear); Cv.EqualizeHist(smallImg, smallImg); } //using (CvHaarClassifierCascade cascade = Cv.Load<CvHaarClassifierCascade>(Const.XmlHaarcascade)) // どっちでも可 using (CvHaarClassifierCascade cascade = CvHaarClassifierCascade.FromFile(Application.dataPath + TestTextName)) // using (CvMemStorage storage = new CvMemStorage()) { storage.Clear(); // 顔の検出 Stopwatch watch = Stopwatch.StartNew(); CvSeq<CvAvgComp> faces = Cv.HaarDetectObjects(smallImg, cascade, storage, ScaleFactor, MinNeighbors, 0, new CvSize(30, 30)); watch.Stop(); // Console.WriteLine("detection time = {0}ms\n", watch.ElapsedMilliseconds); UnityEngine.Debug.Log("detection time = " + watch.ElapsedMilliseconds + " ms"); int i=0; for (i = 0; i < faces.Total; i++) { CvRect r = faces[i].Value.Rect; CvPoint center = new CvPoint { X = Cv.Round((r.X + r.Width * 0.5) * Scale), Y = Cv.Round((r.Y + r.Height * 0.5) * Scale) }; int radius = Cv.Round((r.Width + r.Height) * 0.25 * Scale); img.Circle(center, radius, colors[i % 8], 3, LineType.AntiAlias, 0); } } // ウィンドウに表示 CvWindow.ShowImages(img); } }
/// <summary> /// /// </summary> /// <param name="img"></param> /// <param name="fp"></param> /// <param name="color"></param> private void DrawSubdivPoint(IplImage img, CvPoint2D32f fp, CvColor color) { img.Circle(fp, 3, color, Cv.FILLED, LineType.AntiAlias, 0); }
/// <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(); } }