public static OpenCvSharp.IplImage GetSub(this OpenCvSharp.IplImage ipl, OpenCvSharp.CvRect subRect)
{
if (ipl == null)
throw new ArgumentNullException("ipl", "ipl is null.");
var boundingRect = new CvRect(0, 0, ipl.Width, ipl.Height);
if (!boundingRect.Contains(subRect))
throw new InvalidOperationException("subRect is outside of ipl");
try
{
ipl.SetROI(subRect);
OpenCvSharp.IplImage sub = new IplImage(
ipl.GetSize(),
ipl.Depth,
ipl.NChannels);
ipl.Copy(sub);
return sub;
}
finally
{
ipl.ResetROI();
}
}
/// <summary>
/// The AKAZE constructor
/// </summary>
/// <param name="descriptorType"></param>
/// <param name="descriptorSize"></param>
/// <param name="descriptorChannels"></param>
/// <param name="threshold"></param>
/// <param name="nOctaves"></param>
/// <param name="nOctaveLayers"></param>
/// <param name="diffusivity"></param>
public static AKAZE Create(
OpenCvSharp.AKAZEDescriptorType descriptorType = OpenCvSharp.AKAZEDescriptorType.MLDB,
int descriptorSize = 0,
int descriptorChannels = 3,
float threshold = 0.001f,
int nOctaves = 4,
int nOctaveLayers = 4,
KAZEDiffusivity diffusivity = KAZEDiffusivity.DiffPmG2)
{
IntPtr ptr = NativeMethods.features2d_AKAZE_create(
(int) descriptorType, descriptorSize, descriptorChannels,
threshold, nOctaves, nOctaveLayers, (int) diffusivity);
return new AKAZE(ptr);
}
public static OpenCvSharp.IplImage SubImage(
this OpenCvSharp.IplImage whole,
OpenCvSharp.CvRect region)
{
whole.SetROI(region);
OpenCvSharp.IplImage part =
new OpenCvSharp.IplImage(new OpenCvSharp.CvSize(region.Width, region.Height),
whole.Depth, whole.NChannels);
whole.Copy(part);
whole.ResetROI();
return part;
}
public CvPoint2D64f convertKinectToProjector(OpenCvSharp.CvPoint3D64f kinectPoint)
{
CvPoint2D64f outp = new CvPoint2D64f();
//Debug.Log("In: " + kinectPoint.X + " " +kinectPoint.Y + " " +kinectPoint.Z);
//xp = (q1*xk + q2*yk + q3*zk + q4)/(q9*xk + q10*yk + q11*zk + 1)
//outp.X = (result.get(0,0)*kinectPoint.x + result.get(0,1)*kinectPoint.y + result.get(0,2)*kinectPoint.z + result.get(0,3))/
// (result.get(0,8)*kinectPoint.x + result.get(0,9)*kinectPoint.y + result.get(0,10)*kinectPoint.z + 1);
outp.X = ((result.Get<double>(0, 0) * kinectPoint.X) + (result.Get<double>(0, 1) * kinectPoint.Y) + (result.Get<double>(0, 2) * kinectPoint.Z) + result.Get<double>(0, 3)) /
((result.Get<double>(0, 8) * kinectPoint.X) + (result.Get<double>(0, 9) * kinectPoint.Y) + (result.Get<double>(0, 10) * kinectPoint.Z) + 1);
//yp = (q5*xk + q6*yk + q7*zk + q8)/(q9*xk + q10*yk + q11*zk + 1)
//outp.y = (result.get(0, 4) * kinectPoint.x + result.get(0, 5) * kinectPoint.y + result.get(0, 6) * kinectPoint.z + result.get(0, 7)) /
// (result.get(0, 8) * kinectPoint.x + result.get(0, 9) * kinectPoint.y + result.get(0, 10) * kinectPoint.z + 1);
outp.Y = ((result.Get<double>(0, 4) * kinectPoint.X) + (result.Get<double>(0, 5) * kinectPoint.Y) + (result.Get<double>(0, 6) * kinectPoint.Z) + result.Get<double>(0, 7)) /
((result.Get<double>(0, 8) * kinectPoint.X) + (result.Get<double>(0, 9) * kinectPoint.Y) + (result.Get<double>(0, 10) * kinectPoint.Z) + 1);
//Debug.Log(outp.X + " " + outp.Y);
//outp.X = ((result.Get<double>(0, 0) * kinectPoint.X) + (result.Get<double>(1,0) * kinectPoint.Y) + (result.Get<double>(2,0) * kinectPoint.Z) + result.Get<double>(3,0)) /
// ((result.Get<double>(8,0) * kinectPoint.X) + (result.Get<double>(9,0) * kinectPoint.Y) + (result.Get<double>(10,0) * kinectPoint.Z) + 1);
//outp.Y = ((result.Get<double>(4,0) * kinectPoint.X) + (result.Get<double>(5,0) * kinectPoint.Y) + (result.Get<double>(6,0) * kinectPoint.Z) + result.Get<double>(7,0)) /
// ((result.Get<double>(8,0) * kinectPoint.X) + (result.Get<double>(9,0) * kinectPoint.Y) + (result.Get<double>(10,0) * kinectPoint.Z) + 1);
return outp;
}
private static bool IsStaticFrame(OpenCvSharp.CvRect rect)
{
return rect.Width == 0 || rect.Height == 0;
}
//3次元位置取得
Microsoft.Kinect.CameraSpacePoint GetCenterPosition(OpenCvSharp.CPlusPlus.Point colorImagePoint, ushort[] depthBuffer, int imgW,int imgH)
{
Microsoft.Kinect.KinectSensor kinect = Microsoft.Kinect.KinectSensor.GetDefault();
Microsoft.Kinect.CameraSpacePoint[] bodyPosition = new Microsoft.Kinect.CameraSpacePoint[imgW * imgH];
kinect.CoordinateMapper.MapColorFrameToCameraSpace(depthBuffer, bodyPosition);
Microsoft.Kinect.CameraSpacePoint centerPoint = bodyPosition[colorImagePoint.X + colorImagePoint.Y * imgW];
//Console.WriteLine(centerPoint.X.ToString());
return centerPoint;
}
// 2値化(グレースケール化 + 2値化)
private OpenCvSharp.CPlusPlus.Mat procBinary(OpenCvSharp.CPlusPlus.Mat matSrc)
{
OpenCvSharp.CPlusPlus.Mat matDst = matSrc.Clone();
OpenCvSharp.CPlusPlus.Mat matGray = new OpenCvSharp.CPlusPlus.Mat(matSrc.Rows, matSrc.Cols, OpenCvSharp.CPlusPlus.MatType.CV_8UC1);
OpenCvSharp.CPlusPlus.Mat matBinary = new OpenCvSharp.CPlusPlus.Mat(matSrc.Rows, matSrc.Cols, OpenCvSharp.CPlusPlus.MatType.CV_8UC1);
OpenCvSharp.CPlusPlus.Cv2.CvtColor(matSrc, matGray, OpenCvSharp.ColorConversion.BgraToGray, 1);
OpenCvSharp.CPlusPlus.Cv2.Threshold(matGray, matBinary, 0, 255, OpenCvSharp.ThresholdType.Binary | OpenCvSharp.ThresholdType.Otsu);
OpenCvSharp.CPlusPlus.Cv2.CvtColor(matBinary, matDst, OpenCvSharp.ColorConversion.GrayToBgra, 3);
return matDst;
}
private void CompareFace(
Damany.Util.DateTimeRange range,
OpenCvSharp.IplImage targetImage, OpenCvSharp.CvRect rect)
{
try
{
IsRunning = true;
targetImage.ROI = rect;
int count = 0;
var gray = targetImage.GetSub(rect).CvtToGray();
var poi = new PersonOfInterest(targetImage, gray);
var repo = new PersonOfInterest[] { poi };
this._comparer.Load(repo.ToList());
//foreach (var p in portraits)
//{
// if (Exit)
// {
// break;
// }
// this.view.CurrentImage = p.GetIpl().ToBitmap();
// var colorImg = p.GetIpl();
// var imgFromRepository = colorImg.GetSub(p.FaceBounds).CvtToGray();
// var result = this._comparer.CompareTo(imgFromRepository);
// foreach (var repositoryCompareResult in result)
// {
// if (repositoryCompareResult.Similarity > Thresholds[ThresholdIndex])
// {
// count++;
// this.view.AddPortrait(p);
// this.view.SetStatusText(string.Format("检索到 {0} 个目标", count));
// }
// }
//}
}
finally
{
IsRunning = false;
}
}
public void AddVideoNode(OpenCvSharp.VideoCapture video, string video_name, int index)
{
XmlNode video_node = _MetaData.CreateNode(XmlNodeType.Element, "video", null);
XmlAttribute attribute = _MetaData.CreateAttribute("seq");
attribute.Value = index.ToString();
video_node.Attributes.Append(attribute);
XmlAttribute attribute_frame = _MetaData.CreateAttribute("frame");
attribute_frame.Value = video.Fps.ToString();
video_node.Attributes.Append(attribute_frame);
XmlAttribute attribute_width = _MetaData.CreateAttribute("width");
attribute_width.Value = video.FrameWidth.ToString();
video_node.Attributes.Append(attribute_width);
XmlAttribute attribute_height = _MetaData.CreateAttribute("height");
attribute_height.Value = video.FrameHeight.ToString();
video_node.Attributes.Append(attribute_height);
video_node.InnerText = video_name;
VideoNode.AppendChild(video_node);
}
// 平均値シフト法による画像のセグメント化
// パラメータ: 空間窓の半径, 色空間窓の半径, セグメンテーションに用いるピラミッドの最大レベル
private OpenCvSharp.CPlusPlus.Mat procPyrMeanShiftFiltering(OpenCvSharp.CPlusPlus.Mat matSrc, double sp, double sr, int level)
{
// 終了パラメータ
OpenCvSharp.CPlusPlus.TermCriteria term = new OpenCvSharp.CPlusPlus.TermCriteria(OpenCvSharp.CriteriaType.Iteration, 5, 1);
//OpenCvSharp.CPlusPlus.TermCriteria term = new OpenCvSharp.CPlusPlus.TermCriteria(OpenCvSharp.CriteriaType.Epsilon, 5, 1);
// Matの準備
OpenCvSharp.CPlusPlus.Mat matDst = matSrc.Clone();
OpenCvSharp.CPlusPlus.Cv2.PyrMeanShiftFiltering(matSrc, matDst, sp, sr, level, term);
return matDst;
}
// 確率的Hough変換(Canny + Hough) + 統計情報
private OpenCvSharp.CPlusPlus.Mat procHoughStat(OpenCvSharp.CPlusPlus.Mat matSrc, int votes, double minLength, double maxGap)
{
OpenCvSharp.CPlusPlus.Mat matDst = matSrc.Clone();
OpenCvSharp.CPlusPlus.Mat matGray = new OpenCvSharp.CPlusPlus.Mat(matSrc.Rows, matSrc.Cols, OpenCvSharp.CPlusPlus.MatType.CV_8UC1);
OpenCvSharp.CPlusPlus.Mat matBinary = new OpenCvSharp.CPlusPlus.Mat(matSrc.Rows, matSrc.Cols, OpenCvSharp.CPlusPlus.MatType.CV_8UC1);
OpenCvSharp.CPlusPlus.Cv2.CvtColor(matSrc, matGray, OpenCvSharp.ColorConversion.BgraToGray, 1);
OpenCvSharp.CPlusPlus.Cv2.Threshold(matGray, matBinary, 0, 255, OpenCvSharp.ThresholdType.Binary | OpenCvSharp.ThresholdType.Otsu);
// Hough変換
double rho = 1.0; // 距離分解能
double theta = Math.PI / 180.0; // 角度分解能
OpenCvSharp.CvLineSegmentPoint[] lines = OpenCvSharp.CPlusPlus.Cv2.HoughLinesP(matBinary, rho, theta, votes, minLength, maxGap);
// 描画
Random rnd = new Random();
foreach (OpenCvSharp.CvLineSegmentPoint it in lines)
{
//matDst.Line(it.P1, it.P2, new OpenCvSharp.CPlusPlus.Scalar(0, 0, 255), 1, OpenCvSharp.LineType.AntiAlias, 0);
matDst.Line(it.P1, it.P2, new OpenCvSharp.CPlusPlus.Scalar(rnd.Next(0, 255), rnd.Next(0, 255), rnd.Next(0, 255)), 1, OpenCvSharp.LineType.AntiAlias, 0);
}
// 本数、平均長、最大長の計算と表示
//int divNumR = 10;
//int divNumC = 10;
int divSizeR = matDst.Rows / divNumR;
int divSizeC = matDst.Cols / divNumC;
//double[,] sum = new double[divNumR, divNumC];
//double[,] max = new double[divNumR, divNumC];
//int[,] num = new int[divNumR, divNumC];
foreach (OpenCvSharp.CvLineSegmentPoint it in lines)
{
double midR = (it.P1.Y + it.P2.Y) / 2;
double midC = (it.P1.X + it.P2.X) / 2;
double dist = it.P1.DistanceTo(it.P2);
for (int r = 0; r < divNumR; r++)
{
for (int c = 0; c < divNumC; c++)
{
if (midR >= divSizeR * r && midR < divSizeR * (r + 1) && midC >= divSizeC * c && midC < divSizeC * (c + 1))
{
sum[r, c] += dist;
num[r, c]++;
if (max[r, c] < dist)
{
max[r, c] = dist;
}
}
}
}
}
for (int r = 0; r < divNumR; r++)
{
matDst.Line(new OpenCvSharp.CPlusPlus.Point(0, divSizeR * r), new OpenCvSharp.CPlusPlus.Point(matDst.Cols, divSizeR * r), new OpenCvSharp.CPlusPlus.Scalar(0, 0, 255), 1, OpenCvSharp.LineType.AntiAlias, 0);
for (int c = 0; c < divNumC; c++)
{
matDst.Line(new OpenCvSharp.CPlusPlus.Point(divSizeC * c, 0), new OpenCvSharp.CPlusPlus.Point(divSizeC * c, matDst.Cols), new OpenCvSharp.CPlusPlus.Scalar(0, 0, 255), 1, OpenCvSharp.LineType.AntiAlias, 0);
if (num[r, c] > 0)
{
OpenCvSharp.CPlusPlus.Cv2.PutText(matDst, num[r, c].ToString(), new OpenCvSharp.CPlusPlus.Point(10 + divSizeC * c, 20 + divSizeR * r), OpenCvSharp.FontFace.HersheySimplex, 0.5, new OpenCvSharp.CPlusPlus.Scalar(0, 0, 255), 2, OpenCvSharp.LineType.AntiAlias);
OpenCvSharp.CPlusPlus.Cv2.PutText(matDst, (sum[r, c] / num[r, c]).ToString("F2"), new OpenCvSharp.CPlusPlus.Point(10 + divSizeC * c, 40 + divSizeR * r), OpenCvSharp.FontFace.HersheySimplex, 0.5, new OpenCvSharp.CPlusPlus.Scalar(0, 0, 255), 2, OpenCvSharp.LineType.AntiAlias);
OpenCvSharp.CPlusPlus.Cv2.PutText(matDst, max[r, c].ToString("F2"), new OpenCvSharp.CPlusPlus.Point(10 + divSizeC * c, 60 + divSizeR * r), OpenCvSharp.FontFace.HersheySimplex, 0.5, new OpenCvSharp.CPlusPlus.Scalar(0, 0, 255), 2, OpenCvSharp.LineType.AntiAlias);
}
}
}
return matDst;
}
// 確率的Hough変換(Canny + Hough)
private OpenCvSharp.CPlusPlus.Mat procHough(OpenCvSharp.CPlusPlus.Mat matSrc, int votes, double minLength, double maxGap)
{
OpenCvSharp.CPlusPlus.Mat matDst = matSrc.Clone();
OpenCvSharp.CPlusPlus.Mat matCanny = new OpenCvSharp.CPlusPlus.Mat(matSrc.Rows, matSrc.Cols, OpenCvSharp.CPlusPlus.MatType.CV_8UC1);
OpenCvSharp.CPlusPlus.Cv2.Canny(matSrc, matCanny, 100, 200, 3);
// Hough変換
double rho = 1.0; // 距離分解能
double theta = Math.PI / 180.0; // 角度分解能
OpenCvSharp.CvLineSegmentPoint[] lines = OpenCvSharp.CPlusPlus.Cv2.HoughLinesP(matCanny, rho, theta, votes, minLength, maxGap);
// 描画
Random rnd = new Random();
foreach (OpenCvSharp.CvLineSegmentPoint it in lines)
{
//matDst.Line(it.P1, it.P2, new OpenCvSharp.CPlusPlus.Scalar(0, 0, 255), 1, OpenCvSharp.LineType.AntiAlias, 0);
matDst.Line(it.P1, it.P2, new OpenCvSharp.CPlusPlus.Scalar(rnd.Next(0, 255), rnd.Next(0, 255), rnd.Next(0, 255)), 1, OpenCvSharp.LineType.AntiAlias, 0);
}
return matDst;
}
// グレースケール化
private OpenCvSharp.CPlusPlus.Mat procGrayScale(OpenCvSharp.CPlusPlus.Mat matSrc)
{
OpenCvSharp.CPlusPlus.Mat matDst = matSrc.Clone();
OpenCvSharp.CPlusPlus.Mat matGray = new OpenCvSharp.CPlusPlus.Mat(matSrc.Rows, matSrc.Cols, OpenCvSharp.CPlusPlus.MatType.CV_8UC1);
OpenCvSharp.CPlusPlus.Cv2.CvtColor(matSrc, matGray, OpenCvSharp.ColorConversion.BgraToGray, 1);
OpenCvSharp.CPlusPlus.Cv2.CvtColor(matGray, matDst, OpenCvSharp.ColorConversion.GrayToBgra, 3);
return matDst;
}
// 輪郭抽出(グレースケール + 2値化 + 輪郭抽出)(輪郭のみの画像を戻す)
private OpenCvSharp.CPlusPlus.Mat procContour(OpenCvSharp.CPlusPlus.Mat matSrc)
{
OpenCvSharp.CPlusPlus.Mat matDst = new OpenCvSharp.CPlusPlus.Mat(matSrc.Rows, matSrc.Cols, OpenCvSharp.CPlusPlus.MatType.CV_8UC3, new OpenCvSharp.CPlusPlus.Scalar(0, 0, 0));
OpenCvSharp.CPlusPlus.Mat matGray = new OpenCvSharp.CPlusPlus.Mat(matSrc.Rows, matSrc.Cols, OpenCvSharp.CPlusPlus.MatType.CV_8UC1);
OpenCvSharp.CPlusPlus.Mat matBinary = new OpenCvSharp.CPlusPlus.Mat(matSrc.Rows, matSrc.Cols, OpenCvSharp.CPlusPlus.MatType.CV_8UC1);
OpenCvSharp.CPlusPlus.Cv2.CvtColor(matSrc, matGray, OpenCvSharp.ColorConversion.BgraToGray, 1);
OpenCvSharp.CPlusPlus.Cv2.Threshold(matGray, matBinary, 0, 255, OpenCvSharp.ThresholdType.Binary | OpenCvSharp.ThresholdType.Otsu);
// 輪郭抽出
OpenCvSharp.CPlusPlus.Mat[] contours;
OpenCvSharp.CPlusPlus.Mat hierarchy = new OpenCvSharp.CPlusPlus.Mat();
OpenCvSharp.CPlusPlus.Cv2.FindContours(matBinary, out contours, hierarchy, OpenCvSharp.ContourRetrieval.Tree, OpenCvSharp.ContourChain.ApproxNone);
//OpenCvSharp.CPlusPlus.Cv2.FindContours(matBinary, out contours, hierarchy, OpenCvSharp.ContourRetrieval.Tree, OpenCvSharp.ContourChain.ApproxSimple);
//OpenCvSharp.CPlusPlus.Cv2.FindContours(matBinary, out contours, hierarchy, OpenCvSharp.ContourRetrieval.Tree, OpenCvSharp.ContourChain.ApproxTC89KCOS);
//OpenCvSharp.CPlusPlus.Cv2.FindContours(matBinary, out contours, hierarchy, OpenCvSharp.ContourRetrieval.Tree, OpenCvSharp.ContourChain.ApproxTC89L1);
// 描画
//OpenCvSharp.CPlusPlus.Cv2.DrawContours(matDst, contours, -1, new OpenCvSharp.CPlusPlus.Scalar(255, 255, 255), OpenCvSharp.Cv.FILLED, OpenCvSharp.LineType.AntiAlias, hierarchy);
OpenCvSharp.CPlusPlus.Cv2.DrawContours(matDst, contours, -1, new OpenCvSharp.CPlusPlus.Scalar(255, 255, 255), 1, OpenCvSharp.LineType.AntiAlias, hierarchy);
return matDst;
}
// エッジ抽出(Canny)
private OpenCvSharp.CPlusPlus.Mat procCanny(OpenCvSharp.CPlusPlus.Mat matSrc, double threshold1, double threshold2, int apertureSize)
{
OpenCvSharp.CPlusPlus.Mat matDst = matSrc.Clone();
OpenCvSharp.CPlusPlus.Mat matCanny = new OpenCvSharp.CPlusPlus.Mat(matSrc.Rows, matSrc.Cols, OpenCvSharp.CPlusPlus.MatType.CV_8UC1);
OpenCvSharp.CPlusPlus.Cv2.Canny(matSrc, matCanny, threshold1, threshold2, apertureSize);
OpenCvSharp.CPlusPlus.Cv2.CvtColor(matCanny, matDst, OpenCvSharp.ColorConversion.GrayToBgra, 3);
return matDst;
}
// 左右側処理本体
private void buttonProcLR_Click(ComboBox cb, ref OpenCvSharp.CPlusPlus.Mat mat, ref Bitmap bm, TextBox tb1, TextBox tb2, TextBox tb3)
{
if (mat == null) return;
OpenCvSharp.CPlusPlus.Mat matDst;
if (cb.Text.Equals("none (reset)"))
{
mat = this.matOrg.Clone();
bm = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);
}
else if (cb.Text.Equals("PyrSegmentation"))
{
// 画像ピラミッドを用いた画像の領域分割
matDst = this.procPyrSegmentation(mat, int.Parse(tb1.Text), double.Parse(tb2.Text), double.Parse(tb3.Text));
mat = matDst;
bm = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);
}
else if (cb.Text.Equals("PyrMeanShiftFiltering"))
{
// 平均値シフト法による画像のセグメント化
matDst = this.procPyrMeanShiftFiltering(mat, double.Parse(tb1.Text), double.Parse(tb2.Text), int.Parse(tb3.Text));
mat = matDst;
bm = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);
}
else if (cb.Text.Equals("Watershed"))
{
// Watershedアルゴリズムによる画像の領域分割
matDst = this.procWatershed(mat, int.Parse(tb1.Text), int.Parse(tb2.Text), int.Parse(tb3.Text));
bm = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(matDst);
}
else if (cb.Text.Equals("GrayScale"))
{
// グレースケール化
matDst = this.procGrayScale(mat);
mat = matDst;
bm = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);
}
else if (cb.Text.Equals("Canny"))
{
// エッジ抽出(Canny)
matDst = this.procCanny(mat, double.Parse(tb1.Text), double.Parse(tb2.Text), int.Parse(tb3.Text));
mat = matDst;
bm = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);
}
else if (cb.Text.Equals("Binary"))
{
// 2値化(グレースケール化 + 2値化)
matDst = this.procBinary(mat);
mat = matDst;
bm = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);
}
else if (cb.Text.Equals("HoughStat"))
{
// 確率的Hough変換(Canny + Hough) + 統計情報
matDst = this.procHoughStat(mat, int.Parse(tb1.Text), double.Parse(tb2.Text), int.Parse(tb3.Text));
mat = matDst;
bm = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);
}
else if (cb.Text.Equals("Hough"))
{
// 確率的Hough変換(Canny + Hough)
matDst = this.procHough(mat, int.Parse(tb1.Text), double.Parse(tb2.Text), int.Parse(tb3.Text));
mat = matDst;
bm = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);
}
else if (cb.Text.Equals("Contour"))
{
// 輪郭抽出(グレースケール + 2値化 + 輪郭抽出)
matDst = this.procContour(mat);
mat = matDst;
bm = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);
}
}
// 画像ピラミッドを用いた画像の領域分割
// パラメータ: ピラミッドレベル, ピクセルを接続する閾値, クラスタリングの範囲の閾値
private OpenCvSharp.CPlusPlus.Mat procPyrSegmentation(OpenCvSharp.CPlusPlus.Mat matSrc, int level, double threshold1, double threshold2)
{
// Matの準備
OpenCvSharp.CPlusPlus.Mat matDst = matSrc.Clone();
// IplImageの準備(C API用)
OpenCvSharp.IplImage iplSrc = matSrc.ToIplImage();
OpenCvSharp.IplImage iplDst = iplSrc.Clone();
// ピラミッド画像作成のためのROI設定(2^levelで割り切れるサイズ)
OpenCvSharp.CvRect roi;
roi.X = 0;
roi.Y = 0;
roi.Width = iplSrc.Width & -(1 << level);
roi.Height = iplSrc.Height & -(1 << level);
iplSrc.SetROI(roi);
iplDst.SetROI(roi);
OpenCvSharp.Cv.PyrSegmentation(iplSrc, iplDst, level, threshold1, threshold2);
// IplImage -> Matに戻す
matDst = new OpenCvSharp.CPlusPlus.Mat(iplDst);
return matDst;
}
private static OpenCvSharp.CvRect FrameToPortrait(OpenCvSharp.CvRect bounds, OpenCvSharp.CvRect faceBounds)
{
faceBounds.X -= bounds.X;
faceBounds.Y -= bounds.Y;
return faceBounds;
}
// Watershedアルゴリズムによる画像の領域分割
// パラメータ: 分割数(横), 分割数(縦), マーカサイズ
private OpenCvSharp.CPlusPlus.Mat procWatershed(OpenCvSharp.CPlusPlus.Mat matSrc, int wdiv, int hdiv, int msize)
{
// Matの準備
OpenCvSharp.CPlusPlus.Mat matDst = matSrc.Clone();
// IplImageの準備(C API用)
OpenCvSharp.IplImage iplSrc = matSrc.ToIplImage();
OpenCvSharp.IplImage iplDst = iplSrc.Clone();
// マーカ画像の準備
OpenCvSharp.IplImage iplMarker = new OpenCvSharp.IplImage(iplSrc.Size, OpenCvSharp.BitDepth.S32, 1);
iplMarker.Zero();
// マーカ設置(等分割)
OpenCvSharp.CvPoint[,] mpt = new OpenCvSharp.CvPoint[wdiv, hdiv];
for (int i = 0; i < wdiv; i++)
{
for (int j = 0; j < hdiv; j++)
{
mpt[i, j] = new OpenCvSharp.CvPoint((int)(iplSrc.Width / wdiv * (i + 0.5)), (int)(iplSrc.Height / hdiv * (j + 0.5)));
iplMarker.Circle(mpt[i, j], msize, OpenCvSharp.CvScalar.ScalarAll(i * wdiv + j), OpenCvSharp.Cv.FILLED, OpenCvSharp.LineType.Link8, 0);
}
}
// 分割実行
OpenCvSharp.Cv.Watershed(iplSrc, iplMarker);
// マーカの描画
for (int i = 0; i < wdiv; i++)
{
for (int j = 0; j < hdiv; j++)
{
iplDst.Circle(mpt[i, j], msize, OpenCvSharp.CvColor.White, 3, OpenCvSharp.LineType.Link8, 0);
}
}
// 領域境界の描画
for (int i = 0; i < iplMarker.Height; i++)
{
for (int j = 0; j < iplMarker.Width; j++)
{
int idx = (int)(iplMarker.Get2D(i, j).Val0);
if (idx == -1)
{
iplDst.Set2D(i, j, OpenCvSharp.CvColor.Red);
}
}
}
// IplImage -> Matに戻す
matDst = new OpenCvSharp.CPlusPlus.Mat(iplDst);
return matDst;
}
private Dictionary<int, List<MarkerStructure>> MarkerTracker(OpenCvSharp.Mat frame, int video_idx, int frame_number)
{
if (frame.Empty())
return null;
using (var detector = new Aruco.Net.MarkerDetector())
{
Dictionary<int, List<MarkerStructure>> MarkerDict = new Dictionary<int, List<MarkerStructure>>();
var cameraMatrix = new OpenCV.Net.Mat(3, 3, OpenCV.Net.Depth.F32, 1);
var distortion = new OpenCV.Net.Mat(1, 4, OpenCV.Net.Depth.F32, 1);
detector.ThresholdMethod = ThresholdMethod.AdaptiveThreshold;
detector.Param1 = 7.0;
detector.Param2 = 7.0;
detector.MinSize = 0.04f;
detector.MaxSize = 0.5f;
detector.CornerRefinement = CornerRefinementMethod.Lines;
// Detect markers in a sequence of camera images.
var markerSize = 10;
var image2 =
new OpenCV.Net.Mat(new OpenCV.Net.Size(frame.Width, frame.Height),
(OpenCV.Net.Depth)frame.Depth(), frame.Channels(), frame.Data);
try {
var detectedMarkers = detector.Detect(image2, cameraMatrix, distortion, markerSize);
foreach (var marker in detectedMarkers)
{
//event trigger
List<MarkerStructure> tmp = new List<MarkerStructure>();
if (!MarkerDict.TryGetValue(marker.Id, out tmp))
{
if (tmp != null)
{
tmp.Add(new MarkerStructure(marker.Id, video_idx, frame_number,
new OpenCV.Net.Point2f(marker.Center.X - (marker.Size / 2), marker.Center.Y - (marker.Size / 2)),
new OpenCV.Net.Size((int)marker.Size, (int)marker.Size)));
MarkerDict[marker.Id] = tmp;
}
else
{
List<MarkerStructure> tmp2 = new List<MarkerStructure>();
tmp2.Add(new MarkerStructure(marker.Id, video_idx, frame_number,
new OpenCV.Net.Point2f(marker.Center.X - (marker.Size / 2), marker.Center.Y - (marker.Size / 2)),
new OpenCV.Net.Size((int)marker.Size, (int)marker.Size)));
MarkerDict[marker.Id] = tmp2;
}
}
else
{
List<MarkerStructure> new_list = new List<MarkerStructure>();
new_list.Add(new MarkerStructure(marker.Id, video_idx, frame_number,
new OpenCV.Net.Point2f(marker.Center.X - (marker.Size / 2), marker.Center.Y - (marker.Size / 2)),
new OpenCV.Net.Size((int)marker.Size, (int)marker.Size)));
MarkerDict.Add(marker.Id, new_list);
}
}
} catch(Exception)
{
}
return MarkerDict;
}
}
public static extern bool IsFace(IntPtr faceImg, IntPtr backImg, OpenCvSharp.CvRect SubRect);
