public void Run_ConnectedComponents(Mat Source, OutputArray _Dst)
{
Mat Dst = _Dst.GetMat();
using (Mat Src = new Mat())
{
Source.CopyTo(Src);
int nLabels = Cv2.ConnectedComponentsWithStats(Src, _Label, _Stats, _Centroids, PixelConnectivity.Connectivity8, MatType.CV_32SC1);
unsafe
{
_MaxArea = int.MinValue;
_MinArea = int.MaxValue;
_ObjCount = _Stats.Rows - 1;
for (int H = 1, ObjArea = 0; H < _Stats.Rows; H++)
{
IntPtr ptr = _Stats.Ptr(H);
int * p = (int *)ptr.ToPointer();
ObjArea = p[4];
if (ObjArea > _MaxArea)
{
_MaxArea = ObjArea;
}
if (ObjArea < _MinArea)
{
_MinArea = ObjArea;
}
}
}
}
}
void DetectLaser()
{
Mat[] bgrChannels = new Mat[3];
Cv2.Split(videoSourceImage, out bgrChannels);
Mat rt = new Mat();
Cv2.Threshold(bgrChannels[2], rt, 240.0, 255.0, ThresholdTypes.Binary);
Cv2.GaussianBlur(rt, rt, new Size(5, 5), 1);
rt *= 255;
Mat labels = new Mat();
Mat stats = new Mat();
Mat centroids = new Mat();
Cv2.ConnectedComponentsWithStats(rt, labels, stats, centroids);
if (centroids.Rows > 1)
{
Cv2.Circle(videoSourceImage, (int)centroids.At <double>(1, 0), (int)centroids.At <double>(1, 1), 20, new Scalar(255, 255, 255));
Cv2.Circle(cannyImage, (int)centroids.At <double>(1, 0), (int)centroids.At <double>(1, 1), 20, new Scalar(255, 255, 255));
}
Cv2.Flip(videoSourceImage, videoSourceImage, FlipMode.XY);
Cv2.Flip(cannyImage, cannyImage, FlipMode.XY);
Cv2.ImShow("SuperLaserDetect", videoSourceImage);
Cv2.ImShow("Processed WebCam", cannyImage);
}
//Find outer defect
static void FindContour_and_outer_defect(Mat img, List <Point[]> contours_final, ref int nLabels, out int [,] stats, string mode)
{
// variable
OpenCvSharp.Point[][] temp = new Point[1][];
//0: 內圈 ; 1: 外圈
OpenCvSharp.Point[] contour_now;
if (mode == "inner")
{
contour_now = contours_final[0];
}
else
{
contour_now = contours_final[1];
}
// Convex hull
var ellipsecontour = Cv2.FitEllipse(contour_now);
Mat convex_mask_img = Mat.Zeros(img.Size(), MatType.CV_8UC1);
Cv2.Ellipse(convex_mask_img, ellipsecontour, 255, -1);
// Contour
temp[0] = contour_now;
Mat contour_mask_img = Mat.Zeros(img.Size(), MatType.CV_8UC1);
Cv2.DrawContours(contour_mask_img, temp, -1, 255, -1);
Mat diff_image = contour_mask_img ^ convex_mask_img;
//Opening
Mat kernel = Mat.Ones(4, 4, MatType.CV_8UC1);//改變凹角大小
diff_image = diff_image.MorphologyEx(MorphTypes.Open, kernel);
//=========================吃掉邊界=======================================
//temp[0] = contour_now;
//Cv2.DrawContours(diff_image, temp, -1, 0, 3);
//================================================================
convex_mask_img.SaveImage("./" + mode + "convex" + ".jpg");
contour_mask_img.SaveImage("./" + mode + "contour" + ".jpg");
diff_image.SaveImage("./" + mode + "mask" + ".jpg");
//Connected Component
var labelMat = new MatOfInt();
var statsMat = new MatOfInt();// Row: number of labels Column: 5
var centroidsMat = new MatOfDouble();
nLabels = Cv2.ConnectedComponentsWithStats(diff_image, labelMat, statsMat, centroidsMat);
var labels = labelMat.ToRectangularArray();
stats = statsMat.ToRectangularArray();
var centroids = centroidsMat.ToRectangularArray();
}
private void bn_Blob_Click(object sender, RoutedEventArgs e)
{
if (listImage.Count > 0)
{
string strTitle = listImage[_nSelWin].Title;
Mat matSrc = listImage[_nSelWin].fn_GetImage();
if (matSrc.Channels() == 1)
{
Mat matDst = matSrc.Clone();
Mat matLabel = new Mat();
Mat matStats = new Mat();
Mat matCentroids = new Mat();
int width = matSrc.Cols;
int height = matSrc.Rows;
timeStart = DateTime.Now;
int NumofLabels = Cv2.ConnectedComponentsWithStats(matSrc, matLabel, matStats, matCentroids);
unsafe
{
byte *pDst = matDst.DataPointer;
int * pLabel = (int *)matLabel.DataPointer;
int * pStats = (int *)matStats.DataPointer;
int * pCentroids = (int *)matCentroids.DataPointer;
//---------------------------------------------------------------------------
// Label 색칠하기.
//for (int stepY = 0; stepY < matLabel.Rows; stepY++)
//{
// for (int stepX = 0; stepX < matLabel.Cols; stepX++)
// {
// if (pLabel[stepY * matLabel.Cols + stepX] > 0)
// {
// pDst[stepY * matLabel.Cols + stepX] = 128;
// }
// }
//}
for (int i = 0; i < NumofLabels; i++)
{
//pStats[i];
Cv2.Rectangle(matDst, new OpenCvSharp.Rect(pStats[i * 5], pStats[(i * 5) + 1], pStats[(i * 5) + 2], pStats[(i * 5) + 3]), new Scalar(128));
}
}
fn_WriteLog($"[Blob] {strTitle} ({(DateTime.Now - timeStart).TotalMilliseconds} ms)");
fn_NewImage(matDst, $"{strTitle}_Blob");
}
else
{
fn_WriteLog($"[Blob] {strTitle} is Color.");
}
}
}
public static void ExecLabeling()
{
string name = $"C:\\Users\\yuuki\\Desktop\\Pre_Data01_t019_page_0011.tif";
Mat origin = Cv2.ImRead(name, ImreadModes.Color);
Mat tmp = Cv2.ImRead(name, 0);
Mat bin = new Mat();
Cv2.Threshold(tmp, bin, 0, 255, ThresholdTypes.Otsu | ThresholdTypes.Binary);
// ラベリング
Mat status = new Mat();
Mat center = new Mat();
Mat labelTmp = new Mat();
int nLabels = Cv2.ConnectedComponentsWithStats(bin, labelTmp, status, center, PixelConnectivity.Connectivity8, MatType.CV_32SC1);
var param = status.GetGenericIndexer <int>();
for (int label = 0; label < nLabels; label++)
{
int x = param[label, 0];
int y = param[label, 1];
int width = param[label, 2];
int height = param[label, 3];
int area = param[label, 4];
double occupancy = (double)area / (height * width);
double aspect = (double)height / width;
if (CheckInRange(width, height, area, occupancy, aspect))
{
origin.Rectangle(new Rect(x, y, width, height), new Scalar(0, 0, 255), 1);
}
}
Cv2.NamedWindow("結果", WindowMode.AutoSize);
Cv2.ImShow("結果", origin);
Cv2.WaitKey(50);
Cv2.WaitKey(0);
Cv2.DestroyAllWindows();
Cv2.ImWrite(@"C:\\Users\\yuuki\\Desktop\\2.tif", origin);
}
public void Detect(Mat bin, ref List <CellState> countList_1, int t, int z)
{
List <CellState> countList = new List <CellState>();
// ラベリング
Mat status = new Mat();
Mat center = new Mat();
Mat labelTmp = new Mat();
int nLabels = Cv2.ConnectedComponentsWithStats(bin, labelTmp, status, center, PixelConnectivity.Connectivity8, MatType.CV_32SC1);
List <Scalar> colors = new List <Scalar>();
var param = status.GetGenericIndexer <int>();
for (int label = 0; label < nLabels; label++)
{
int x = param[label, 0];
int y = param[label, 1];
int width = param[label, 2];
int height = param[label, 3];
int area = param[label, 4];
double occupancy = (double)area / (height * width);
double aspect = (double)height / width;
//ラべリング情報による閾値設定(閾値以内なら分裂候補、以外ならノイズ)
if (CheckInRange(width, height, area, occupancy, aspect))
{
//現在フレームにおける分裂候補の情報を保存
countList.Add(new CellState(x, y, z, width, height, t));
//分裂候補なので白で描画
colors.Add(Scalar.White);
}
else
{
//分裂候補でないので黒で描画（ノイズ）
colors.Add(Scalar.Black);
}
}
int count = countList.Count;
int count_1 = countList_1.Count;
List <CellState> CandidatesTmp = new List <CellState>();
for (int c = 0; c < count - 1; c++)
{
for (int v = 0; v < count_1; v++)
{
//候補座標同士の差が絶対値10px以下ならば分裂候補として再確定
if (Math.Abs(countList[c].x - countList_1[v].x) <= 20 && Math.Abs(countList[c].y - countList_1[v].y) <= 20 && countList[c].y != 0)
{
if (Math.Abs((countList[c].x + countList[c].w) - countList[c + 1].x) <= 10 && Math.Abs((countList[c].y + countList[c].h) - countList[c + 1].y) <= 10)
{
Console.WriteLine("時間：{0}、Z軸：{1}、(x,y)=({2},{3})", countList[count - 1].t, countList[count - 1].z, countList[c].x, countList[c].y);
//分裂候補の情報を保存する
CandidatesTmp.Add(new CellState(countList[c].x, countList[c].y, countList[c].z, countList[c].w, countList[c].h, countList[c].t));
Result[z][t].Rectangle(new Rect(countList[c].x, countList[c].y, countList[c].w, countList[c].h), new Scalar(0, 0, 255), 1);
Result[z][t].Rectangle(new Rect(countList[c + 1].x, countList[c + 1].y, countList[c + 1].w, countList[c + 1].h), new Scalar(0, 0, 255), 1);
}
}
}
}
//1フレームごとに適用させるために配列の初期化を行う
//過去候補配列を初期化
countList_1.Clear();
//過去候補配列に現在フレームの分裂候補結果を保存
countList_1 = countList;
Candidates.Add(CandidatesTmp);
}
private void ImgList_SelectionChanged(object sender, SelectionChangedEventArgs e)
{
int pos;
if (ImgList != null && ImgList.SelectedItem != null)
{
if (DetectList != null)
{
DetectList.Clear();
}
// var OriImageMat = new Mat(ImgList.SelectedItem.ToString());
var ThrImageMat = new Mat();
pos = ImgList.SelectedIndex;
//CurrentFN = ImgList.SelectedItem.ToString();
CurrentFN = fileEntries[pos];
Mat OriImageMat = new Mat(CurrentFN);
Mat GrayImage = OriImageMat.CvtColor(ColorConversionCodes.BGR2GRAY);
//int slideVal;
//int.TryParse(SliderValue, out slideVal);
Cv2.Threshold(GrayImage, ThrImageMat, int.Parse(SliderValue), 255, ThresholdTypes.Binary);
int[] mask = { 1, 1, 1,
1, 1, 1,
1, 1, 1 };
Mat kernel = new Mat(3, 3, MatType.CV_32F, mask);
Cv2.MorphologyEx(ThrImageMat, ThrImageMat, MorphTypes.Open, kernel);
Cv2.MorphologyEx(ThrImageMat, ThrImageMat, MorphTypes.Close, kernel);
Cv2.BitwiseNot(ThrImageMat, ThrImageMat);
var label = new MatOfInt();
var stats = new MatOfInt();
var centroids = new MatOfDouble();
var nLabels = Cv2.ConnectedComponentsWithStats(ThrImageMat, label, stats, centroids, PixelConnectivity.Connectivity8, MatType.CV_32S);
var statsIndexer = stats.GetGenericIndexer <int>();
for (int i = 0; i < nLabels; i++)
{
//Threshold줄때 일정크기 이하의 객체는 노이즈로 간주하고 패스
if (statsIndexer[i, 4] < 90)
{
continue;
}
var rect = new OpenCvSharp.Rect
{
X = statsIndexer[i, 0],
Y = statsIndexer[i, 1],
Width = statsIndexer[i, 2],
Height = statsIndexer[i, 3]
};
Cv2.Rectangle(OriImageMat, rect, new Scalar(33, 113, 243), 3);
obj temp = new obj();
//중심좌표 x,y (0 ~ 1)
temp.X = ((rect.X + rect.Width) / 2.0) / OriImageMat.Width;
temp.Y = ((rect.Y + rect.Height) / 2.0) / OriImageMat.Height;
temp.W = (double)rect.Width / (double)OriImageMat.Width;
temp.H = (double)rect.Height / (double)OriImageMat.Height;
DetectList.Add(temp);
}
OriginalImg.Source = OriImageMat.ToWriteableBitmap();
ThresholdImg.Source = ThrImageMat.ToWriteableBitmap();
}
}
static bool RunOn(string file, OpenCvSharp.Rect boundingBox)
{
int areas = 0;
int[] quadrants = new int[4];
using (OpenCvSharp.Mat m = new OpenCvSharp.Mat(file, OpenCvSharp.ImreadModes.Grayscale))
{
//blur the image a little
using (var blurred = m.GaussianBlur(new Size(3, 3), 0))
{
//make the image binary black or white and make black the background color
using (var g = blurred.Threshold(200, 255, ThresholdTypes.BinaryInv | ThresholdTypes.Otsu))
{
var element = Cv2.GetStructuringElement(
MorphShapes.Rect,
new Size(50, 1));
//remove lines from dark background image by creating a mask
using (var mask = g.MorphologyEx(MorphTypes.Open, element, iterations: 2))
{
using (Mat newMask = new Mat())
{
//mask bits should be 0 to skip copying items
Cv2.BitwiseNot(mask, newMask);
using (Mat newImage = new Mat())
{
//make new image and apply mask so as to not copy the lines
g.CopyTo(newImage, newMask);
//create the box image
using (OpenCvSharp.Mat box = new OpenCvSharp.Mat(new Size(boundingBox.Width, boundingBox.Height), MatType.CV_8U))
{
//copy to the box
newImage[boundingBox].CopyTo(box);
using (Mat labels = new Mat())
{
using (var centroids = new Mat())
{
using (Mat stats = new Mat())
{
//find the white blobs
//populate the quadrants blobs appear in
//create total area of white stuff
int cnt = Cv2.ConnectedComponentsWithStats(box, labels, stats, centroids, PixelConnectivity.Connectivity8);
#if usequadrants
int qh = box.Size().Height / 2;
int qw = box.Size().Width / 2;
var tl = new Rect(0, 0, qw, qh);
var vl = new Rect(0, qh, qw, qh);
var tr = new Rect(qw, 0, qw, qh);
var br = new Rect(qw, qh, qw, qh);
#endif
for (var x = 1; x < stats.Size().Height; x++)
{
#if usequadrants
var left = stats.Get <int>(x, (int)ConnectedComponentsTypes.Left);
var top = stats.Get <int>(x, (int)ConnectedComponentsTypes.Top);
var width = stats.Get <int>(x, (int)ConnectedComponentsTypes.Width);
var height = stats.Get <int>(x, (int)ConnectedComponentsTypes.Height);
var re = new Rect(left, top, width, height);
if (re.IntersectsWith(tl))
{
quadrants[0] = 1;
}
if (re.IntersectsWith(vl))
{
quadrants[1] = 1;
}
if (re.IntersectsWith(tr))
{
quadrants[2] = 1;
}
if (re.IntersectsWith(br))
{
quadrants[3] = 1;
}
#endif
areas += stats.Get <int>(x, (int)ConnectedComponentsTypes.Area);
}
}
}
}
var boxarea = box.Size().Width *box.Size().Height;
double[] areasTest = new double[] { areas };
double[] boxAreas = new double[] { boxarea };
//use infer.net to determine if the mean is good or not
VariableArray <bool> ytest = Variable.Array <bool>(new Range(areasTest.Length));
BayesPointMachine(areasTest, boxAreas, Variable.Random(wPosterior), ytest);
var res = (DistributionStructArray <Bernoulli, bool>)engine.Infer(ytest);
var mean = res[0].GetMean();
Console.WriteLine(boxarea + " " + areas + " " + mean + " "
#if usequadrants
+ quadrants.Sum());
