public IplImage ScannerContour(IplImage src)
{
con = new IplImage(src.Size, BitDepth.U8, 3);
Cv.Copy(src, con);
bin = this.Binary(src, 150);
CvMemStorage Storage = new CvMemStorage();
//CvSeq<CvPoint> contours;
CvContourScanner scanner = Cv.StartFindContours(bin, Storage, CvContour.SizeOf, ContourRetrieval.List, ContourChain.ApproxNone);
//while ((contours = Cv.FindNextContour(scanner)) != null)
//{
// if (contours[0].Value == new CvPoint(1, 1)) continue;
// Cv.DrawContours(con, contours, CvColor.Yellow, CvColor.Red, 1, 4, LineType.AntiAlias);
//}
//Cv.EndFindContours(scanner);
foreach (CvSeq <CvPoint> contours in scanner)
{
if (contours[0].Value == new CvPoint(1, 1))
{
continue;
}
con.DrawContours(contours, CvColor.Yellow, CvColor.Red, 1, 4, LineType.AntiAlias);
}
return(con);
}
public ContourScanner()
{
// create IplImages
using (var src = new IplImage(FilePath.Image.Lenna, LoadMode.Color))
using (var gray = new IplImage(src.Size, BitDepth.U8, 1))
using (var canny = new IplImage(src.Size, BitDepth.U8, 1))
using (var result = src.Clone())
{
// detect edges
Cv.CvtColor(src, gray, ColorConversion.BgrToGray);
Cv.Canny(gray, canny, 50, 200);
// find all contours
using (CvMemStorage storage = new CvMemStorage())
{
// find contours by CvContourScanner
// native style
/*
* CvContourScanner scanner = Cv.StartFindContours(canny, storage, CvContour.SizeOf, ContourRetrieval.Tree, ContourChain.ApproxSimple);
* while (true)
* {
* CvSeq<CvPoint> c = Cv.FindNextContour(scanner);
* if (c == null)
* break;
* else
* Cv.DrawContours(result, c, CvColor.Red, CvColor.Green, 0, 3, LineType.AntiAlias);
* }
* Cv.EndFindContours(scanner);
* //*/
// wrapper style
using (CvContourScanner scanner = new CvContourScanner(canny, storage, CvContour.SizeOf, ContourRetrieval.Tree, ContourChain.ApproxSimple))
{
foreach (CvSeq <CvPoint> c in scanner)
{
result.DrawContours(c, CvColor.Red, CvColor.Green, 0, 3, LineType.AntiAlias);
}
}
}
// show canny and result
using (new CvWindow("ContourScanner canny", canny))
using (new CvWindow("ContourScanner result", result))
{
Cv.WaitKey();
}
}
}
public ContourScanner()
{
// create IplImages
using (var src = new IplImage(FilePath.Image.Lenna, LoadMode.Color))
using (var gray = new IplImage(src.Size, BitDepth.U8, 1))
using (var canny = new IplImage(src.Size, BitDepth.U8, 1))
using (var result = src.Clone())
{
// detect edges
Cv.CvtColor(src, gray, ColorConversion.BgrToGray);
Cv.Canny(gray, canny, 50, 200);
// find all contours
using (CvMemStorage storage = new CvMemStorage())
{
// find contours by CvContourScanner
// native style
/*
CvContourScanner scanner = Cv.StartFindContours(canny, storage, CvContour.SizeOf, ContourRetrieval.Tree, ContourChain.ApproxSimple);
while (true)
{
CvSeq<CvPoint> c = Cv.FindNextContour(scanner);
if (c == null)
break;
else
Cv.DrawContours(result, c, CvColor.Red, CvColor.Green, 0, 3, LineType.AntiAlias);
}
Cv.EndFindContours(scanner);
//*/
// wrapper style
using (CvContourScanner scanner = new CvContourScanner(canny, storage, CvContour.SizeOf, ContourRetrieval.Tree, ContourChain.ApproxSimple))
{
foreach (CvSeq<CvPoint> c in scanner)
{
result.DrawContours(c, CvColor.Red, CvColor.Green, 0, 3, LineType.AntiAlias);
}
}
}
// show canny and result
using (new CvWindow("ContourScanner canny", canny))
using (new CvWindow("ContourScanner result", result))
{
Cv.WaitKey();
}
}
}
// The OpenCVSharp is for some reason complicated and needs to be abstracted. So here is the abstraction layer...
// TODO : The CV docs specifically state that the image should be in binary format. Check if it is.
// see usage here: http://stackoverflow.com/questions/35418714/opencvsharps-findcontour-returns-wrong-data
static public ContourData FindContours(CvMat input, ContourRetrieval retrievalMode, ContourChain chainMode, CvPoint offset, double minArea = 0)
{
List <CvPoint[]> pointsArrays = new List <CvPoint[]>();
List <double> areas = new List <double>();
CvSeq <CvPoint> contoursRaw;
using (CvMemStorage storage = new CvMemStorage())
{
Cv.FindContours(input, storage, out contoursRaw, CvContour.SizeOf, retrievalMode, chainMode, offset);
using (CvContourScanner scanner = new CvContourScanner(input, storage, CvContour.SizeOf, retrievalMode, chainMode, offset))
{
foreach (CvSeq <CvPoint> c in scanner)
{
List <CvPoint> points = new List <CvPoint>();
//Some contours have negative area!
double area = c.ContourArea();
if (Math.Abs(area) >= minArea)
{
areas.Add(area);
foreach (CvPoint p in c.ToArray())
{
points.Add(p);
}
pointsArrays.Add(points.ToArray());
}
}
}
}
ContourData data = new ContourData();
data.contours = pointsArrays.ToArray();
data.areas = areas.ToArray();
return(data);
}
/// <summary>
/// 輪郭走査処理を終了する
/// </summary>
/// <param name="scanner">輪郭スキャナへのポインタ</param>
/// <returns></returns>
#else
/// <summary>
/// Finishes scanning process
/// </summary>
/// <param name="scanner">Contour scanner. </param>
/// <returns></returns>
#endif
public static CvSeq<CvPoint> EndFindContours(CvContourScanner scanner)
{
if (scanner == null)
{
throw new ArgumentNullException("scanner");
}
return scanner.EndFindContours();
}
private void task()
{
Camera camera = Camera.GetInstance();
MotorControler mc = MotorControler.GetInstance(parameterManager);
Vector3 CurrentPoint = mc.GetPoint();
Vector3 p = new Vector3();
int BinarizeThreshold = 10;
int BrightnessThreshold = 7;
Mat sum = Mat.Zeros(440, 512, MatType.CV_8UC1);
string datfileName = string.Format(@"c:\img\{0}.dat", System.DateTime.Now.ToString("yyyyMMdd_HHmmss_fff"));
BinaryWriter writer = new BinaryWriter(File.Open(datfileName, FileMode.Create));
for (int i = 0; i < 10; i++)
{
byte[] b = camera.ArrayImage;
writer.Write(b);
p = mc.GetPoint();
Mat mat = new Mat(440, 512, MatType.CV_8U, b);
mat.ImWrite(String.Format(@"c:\img\{0}_{1}_{2}_{3}.bmp",
System.DateTime.Now.ToString("yyyyMMdd_HHmmss_fff"),
(int)(p.X * 1000),
(int)(p.Y * 1000),
(int)(p.Z * 1000)));
Cv2.GaussianBlur(mat, mat, Cv.Size(3, 3), -1);
Mat gau = mat.Clone();
Cv2.GaussianBlur(gau, gau, Cv.Size(31, 31), -1);
Cv2.Subtract(gau, mat, mat);
Cv2.Threshold(mat, mat, BinarizeThreshold, 1, ThresholdType.Binary);
Cv2.Add(sum, mat, sum);
mc.MoveDistance(-0.003, VectorId.Z);
mc.Join();
}
Cv2.Threshold(sum, sum, BrightnessThreshold, 1, ThresholdType.Binary);
//Cv2.FindContoursをつかうとAccessViolationExceptionになる(Release/Debug両方)ので、C-API風に書く
using (CvMemStorage storage = new CvMemStorage())
{
using (CvContourScanner scanner = new CvContourScanner(sum.ToIplImage(), storage, CvContour.SizeOf, ContourRetrieval.Tree, ContourChain.ApproxSimple))
{
//string fileName = string.Format(@"c:\img\{0}.txt",
// System.DateTime.Now.ToString("yyyyMMdd_HHmmss_fff"));
string fileName = string.Format(@"c:\img\u.txt");
foreach (CvSeq <CvPoint> c in scanner)
{
CvMoments mom = new CvMoments(c, false);
if (c.ElemSize < 2)
{
continue;
}
if (mom.M00 == 0.0)
{
continue;
}
double mx = mom.M10 / mom.M00;
double my = mom.M01 / mom.M00;
File.AppendAllText(fileName, string.Format("{0:F} {1:F}\n", mx, my));
}
}
}
sum *= 255;
sum.ImWrite(String.Format(@"c:\img\{0}_{1}_{2}.bmp",
System.DateTime.Now.ToString("yyyyMMdd_HHmmss_fff"),
(int)(p.X * 1000),
(int)(p.Y * 1000)));
Vector2 encoderPoint = new Vector2(-1, -1);
encoderPoint.X = mc.GetPoint().X;
encoderPoint.Y = mc.GetPoint().Y;//おこられたのでしかたなくこうする 吉田20150427
Vector2 viewerPoint = new Vector2(-1, -1);
if (TigerPatternMatch.PatternMatch(ref viewerPoint))
{
encoderPoint = coordManager.TransToEmulsionCoord(viewerPoint);
mc.MovePointXY(encoderPoint);
mc.Join();
}
}
/// <summary>
/// 画像中の次の輪郭を検索する
/// </summary>
/// <param name="scanner">関数cvStartFindContoursで初期化された輪郭スキャナ</param>
/// <returns></returns>
#else
/// <summary>
/// Finds next contour in the image
/// </summary>
/// <param name="scanner">Contour scanner initialized by The function cvStartFindContours </param>
#endif
public static CvSeq<CvPoint> FindNextContour(CvContourScanner scanner)
{
if (scanner == null)
{
throw new ArgumentNullException("scanner");
}
IntPtr result = NativeMethods.cvFindNextContour(scanner.CvPtr);
if (result == IntPtr.Zero)
return null;
else
return new CvSeq<CvPoint>(result);
}
static OpenCvSharp.CPlusPlus.Point TrackDetection(List <Mat> mats, int px, int py, int shiftx = 2, int shifty = 2, int shiftpitch = 4, int windowsize = 40, int phthresh = 5, bool debugflag = false)
{
int x0 = px - 256;
int y0 = py - 220;
List <rawmicrotrack> rms = new List <rawmicrotrack>();
// Point2d pixel_cen = TrackDetection(binimages, 256, 220, 3, 3, 4, 90, 3);
int counter = 0;
for (int ax = -shiftx; ax <= shiftx; ax++)
{
for (int ay = -shifty; ay <= shifty; ay++)
{
using (Mat big = Mat.Zeros(600, 600, MatType.CV_8UC1))
using (Mat imgMask = Mat.Zeros(big.Height, big.Width, MatType.CV_8UC1))
{
//make the size of mask
int ystart = big.Height / 2 + y0 - windowsize / 2;
int yend = big.Height / 2 + y0 + windowsize / 2;
int xstart = big.Width / 2 + x0 - windowsize / 2;
int xend = big.Width / 2 + x0 + windowsize / 2;
//make mask as shape of rectangle. by use of opencv
OpenCvSharp.CPlusPlus.Rect recMask = new OpenCvSharp.CPlusPlus.Rect(xstart, ystart, windowsize, windowsize);
Cv2.Rectangle(imgMask, recMask, 255, -1);//brightness=1, fill
for (int p = 0; p < mats.Count; p++)
{
int startx = big.Width / 2 - mats[p].Width / 2 + (int)(p * ax * shiftpitch / 8.0);
int starty = big.Height / 2 - mats[p].Height / 2 + (int)(p * ay * shiftpitch / 8.0);
Cv2.Add(
big[starty, starty + mats[p].Height, startx, startx + mats[p].Width],
mats[p],
big[starty, starty + mats[p].Height, startx, startx + mats[p].Width]);
}
using (Mat big_c = big.Clone())
{
Cv2.Threshold(big, big, phthresh, 255, ThresholdType.ToZero);
Cv2.BitwiseAnd(big, imgMask, big);
//Mat roi = big[ystart, yend , xstart, xend];//メモリ領域がシーケンシャルにならないから輪郭抽出のときに例外が出る。
if (debugflag == true)
{//
//bigorg.ImWrite(String.Format(@"{0}_{1}_{2}.png",counter,ax,ay));
//Mat roiwrite = roi.Clone() * 30;
//roiwrite.ImWrite(String.Format(@"roi_{0}_{1}_{2}.png", counter, ax, ay));
Cv2.Rectangle(big_c, recMask, 255, 1);//brightness=1, fill
Cv2.ImShow("big_cx30", big_c * 30);
Cv2.ImShow("bigx30", big * 30);
//Cv2.ImShow("imgMask", imgMask);
//Cv2.ImShow("roi", roi * 30);
Cv2.WaitKey(0);
}
}//using big_c
using (CvMemStorage storage = new CvMemStorage())
using (CvContourScanner scanner = new CvContourScanner(big.ToIplImage(), storage, CvContour.SizeOf, ContourRetrieval.Tree, ContourChain.ApproxSimple))
{
foreach (CvSeq <CvPoint> c in scanner)
{
CvMoments mom = new CvMoments(c, false);
if (c.ElemSize < 2)
{
continue;
}
if (mom.M00 < 1.0)
{
continue;
}
double mx = mom.M10 / mom.M00;
double my = mom.M01 / mom.M00;
rawmicrotrack rm = new rawmicrotrack();
rm.ax = ax;
rm.ay = ay;
rm.cx = (int)(mx - big.Width / 2);
rm.cy = (int)(my - big.Height / 2);
rm.pv = (int)(mom.M00);
rms.Add(rm);
//Console.WriteLine(string.Format("{0} {1} {2} {3} {4}", rm.pv, ax, ay, rm.cx, rm.cy ));
}
}//using contour
//big_c.Dispose();
counter++;
}//using Mat
}//ay
}//ax
OpenCvSharp.CPlusPlus.Point trackpos = new OpenCvSharp.CPlusPlus.Point(0, 0);
if (rms.Count > 0)
{
rawmicrotrack rm = new rawmicrotrack();
double meancx = 0;
double meancy = 0;
double meanax = 0;
double meanay = 0;
double meanph = 0;
double meanpv = 0;
double sumpv = 0;
for (int i = 0; i < rms.Count; i++)
{
meanpv += rms[i].pv * rms[i].pv;
meancx += rms[i].cx * rms[i].pv;
meancy += rms[i].cy * rms[i].pv;
meanax += rms[i].ax * rms[i].pv;
meanay += rms[i].ay * rms[i].pv;
sumpv += rms[i].pv;
}
meancx /= sumpv;//重心と傾きを輝度値で重み付き平均
meancy /= sumpv;
meanax /= sumpv;
meanay /= sumpv;
meanpv /= sumpv;
trackpos = new OpenCvSharp.CPlusPlus.Point(
(int)(meancx) + 256 - meanax * shiftpitch,
(int)(meancy) + 220 - meanay * shiftpitch
);
double anglex = (meanax * shiftpitch * 0.267) / (3.0 * 7.0 * 2.2);
double angley = (meanay * shiftpitch * 0.267) / (3.0 * 7.0 * 2.2);
Console.WriteLine(string.Format("{0:f4} {1:f4}", anglex, angley));
}
else
{
trackpos = new OpenCvSharp.CPlusPlus.Point(-1, -1);
}
return(trackpos);
}//track detection
private void task()
{
TracksManager tm = parameterManager.TracksManager;
Track myTrack = tm.GetTrack(tm.TrackingIndex);
MotorControler mc = MotorControler.GetInstance(parameterManager);
Camera camera = Camera.GetInstance();
List <Mat> image_set = new List <Mat>();
List <Mat> image_set_reverse = new List <Mat>();
Surface surface = Surface.GetInstance(parameterManager);//表面認識から境界値を取得
double uptop = surface.UpTop;
double upbottom = surface.UpBottom;
double lowtop = surface.LowTop;
double lowbottom = surface.LowBottom;
double now_x = mc.GetPoint().X;
double now_y = mc.GetPoint().Y;
double now_z = mc.GetPoint().Z;
common_dx = myTrack.MsDX + ((0.265625 * over_dx * 3) / (0.024 * 2.2 * 1000));
common_dy = myTrack.MsDY - ((0.265625 * over_dy * 3) / (0.024 * 2.2 * 1000));
for (int i = 0; i < 8; i++)
{ //myTrack.MsD○はdz1mmあたりのd○の変位mm
double next_x = now_x - i * common_dx * 0.003 * 2.2; //3μm間隔で撮影
double next_y = now_y - i * common_dy * 0.003 * 2.2; //Shrinkage Factor は2.2で計算(仮)
mc.MovePoint(next_x, next_y, now_z - 0.003 * i);
mc.Join();
byte[] b = camera.ArrayImage;
Mat image = new Mat(440, 512, MatType.CV_8U, b);
Mat imagec = image.Clone();
image_set.Add(imagec);
}
for (int i = 7; i >= 0; i--)
{
image_set_reverse.Add(image_set[i]);
}
int n = image_set.Count();//１回分の取得画像の枚数
Mat cont = new Mat(440, 512, MatType.CV_8U);
Mat gau_1 = new Mat(440, 512, MatType.CV_8U);
Mat gau_2 = new Mat(440, 512, MatType.CV_8U);
Mat sub = new Mat(440, 512, MatType.CV_8U);
Mat bin = new Mat(440, 512, MatType.CV_8U);
double Max_kido;
double Min_kido;
OpenCvSharp.CPlusPlus.Point maxloc;
OpenCvSharp.CPlusPlus.Point minloc;
List <Mat> two_set = new List <Mat>();
List <Mat> Part_img = new List <Mat>();
for (int i = 0; i < image_set.Count(); i++)
{
Cv2.GaussianBlur((Mat)image_set_reverse[i], gau_1, Cv.Size(3, 3), -1); //パラメータ見ないといけない。
Cv2.GaussianBlur(gau_1, gau_2, Cv.Size(51, 51), -1); //パラメータ見ないといけない。
Cv2.Subtract(gau_2, gau_1, sub);
Cv2.MinMaxLoc(sub, out Min_kido, out Max_kido, out minloc, out maxloc);
cont = (sub - Min_kido) * 255 / (Max_kido - Min_kido);
cont.ImWrite(string.Format(@"C:\set\cont_{0}.bmp", i));
Cv2.Threshold(cont, bin, 115, 1, ThresholdType.Binary);//パラメータ見ないといけない。
two_set.Add(bin);
}
List <mm> white_area = new List <mm>();
int x0 = 256;
int y0 = 220;//視野の中心
for (int delta_xx = -1; delta_xx <= 1; delta_xx++)//一番下の画像よりどれだけずらすか
{
for (int delta_yy = -1; delta_yy <= 1; delta_yy++)
{
{
// //積層写真の型作り（行列の中身は０行列）
// Mat superimposed = Mat.Zeros(440 + (n - 1) * Math.Abs(delta_yy), 512 + (n - 1) * Math.Abs(delta_xx), MatType.CV_8UC1);
//
// //各写真の型作り
// for (int i = 0; i < two_set.Count; i++) {
// Mat Part = Mat.Zeros(440 + (n - 1) * Math.Abs(delta_yy), 512 + (n - 1) * Math.Abs(delta_xx), MatType.CV_8UC1);
// Part_img.Add(Part);
// }
//積層写真の型作り（行列の中身は０行列）
Mat superimposed = Mat.Zeros(440 + 3 * Math.Abs(delta_yy), 512 + 3 * Math.Abs(delta_xx), MatType.CV_8UC1);
//各写真の型作り
for (int i = 0; i < two_set.Count; i++)
{
Mat Part = Mat.Zeros(440 + 3 * Math.Abs(delta_yy), 512 + 3 * Math.Abs(delta_xx), MatType.CV_8UC1);
Part_img.Add(Part);
}//2枚を１セットにしてずらす場合
if (delta_xx >= 0 && delta_yy >= 0)//画像の右下への移動
{
for (int i = 0; i < two_set.Count; i++)
{
if (i == 0 || i == 1)
{
Part_img[i][
0
, 440
, 0
, 512
] = two_set[i]; //処理済み画像をPartの対応する部分に入れていく
}
else if (i == 2 || i == 3)
{
Part_img[i][
0 + Math.Abs(delta_yy) //yの値のスタート地点
, 440 + Math.Abs(delta_yy) //yの値のゴール地点
, 0 + Math.Abs(delta_xx) //xの値のスタート地点
, 512 + Math.Abs(delta_xx) //xの値のゴール地点
] = two_set[i]; //処理済み画像をPartの対応する部分に入れていく
}
else if (i == 4 || i == 5)
{
Part_img[i][
0 + 2 * Math.Abs(delta_yy) //yの値のスタート地点
, 440 + 2 * Math.Abs(delta_yy) //yの値のゴール地点
, 0 + 2 * Math.Abs(delta_xx) //xの値のスタート地点
, 512 + 2 * Math.Abs(delta_xx) //xの値のゴール地点
] = two_set[i]; //処理済み画像をPartの対応する部分に入れていく
}
else if (i == 6 || i == 7)
{
Part_img[i][
0 + 3 * Math.Abs(delta_yy) //yの値のスタート地点
, 440 + 3 * Math.Abs(delta_yy) //yの値のゴール地点
, 0 + 3 * Math.Abs(delta_xx) //xの値のスタート地点
, 512 + 3 * Math.Abs(delta_xx) //xの値のゴール地点
] = two_set[i]; //処理済み画像をPartの対応する部分に入れていく
}
}
for (int i = 0; i < Part_img.Count(); i++)
{
superimposed += Part_img[i];
}
Cv2.Threshold(superimposed, superimposed, 5, 255, ThresholdType.ToZero);//パラメータ見ないといけない。
superimposed.SubMat(0
, 440
, 0
, 512).CopyTo(superimposed); //１枚目の画像の大きさ、場所で切り取る
}
if (delta_xx >= 0 && delta_yy < 0)//画像の右上への移動
{
for (int i = 0; i < two_set.Count; i++)
{
if (i == 0 || i == 1)
{
Part_img[i][
0 + 3
, 440 + 3
, 0
, 512
] = two_set[i]; //処理済み画像をPartの対応する部分に入れていく
}
else if (i == 2 || i == 3)
{
Part_img[i][
0 + 3 - 1 //yの値のスタート地点
, 440 + 3 - 1 //yの値のゴール地点
, 0 + Math.Abs(delta_xx) //xの値のスタート地点
, 512 + Math.Abs(delta_xx) //xの値のゴール地点
] = two_set[i]; //処理済み画像をPartの対応する部分に入れていく
}
else if (i == 4 || i == 5)
{
Part_img[i][
0 + 3 - 2 //yの値のスタート地点
, 440 + 3 - 2 //yの値のゴール地点
, 0 + 2 * Math.Abs(delta_xx) //xの値のスタート地点
, 512 + 2 * Math.Abs(delta_xx) //xの値のゴール地点
] = two_set[i]; //処理済み画像をPartの対応する部分に入れていく
}
else if (i == 6 || i == 7)
{
Part_img[i][
0 + 3 - 3 //yの値のスタート地点
, 440 + 3 - 3 //yの値のゴール地点
, 0 + 3 * Math.Abs(delta_xx) //xの値のスタート地点
, 512 + 3 * Math.Abs(delta_xx) //xの値のゴール地点
] = two_set[i]; //処理済み画像をPartの対応する部分に入れていく
}
}
for (int i = 0; i < Part_img.Count(); i++)
{
superimposed += Part_img[i];
}
Cv2.Threshold(superimposed, superimposed, 5, 255, ThresholdType.ToZero);//パラメータ見ないといけない。
superimposed.SubMat(0 + 3
, 440 + 3
, 0
, 512).CopyTo(superimposed); //１枚目の画像の大きさ、場所で切り取る
}
if (delta_xx < 0 && delta_yy < 0)//画像の左上への移動
{
for (int i = 0; i < two_set.Count; i++)
{
if (i == 0 || i == 1)
{
Part_img[i][
0 + 3
, 440 + 3
, 0 + 3
, 512 + 3
] = two_set[i]; //処理済み画像をPartの対応する部分に入れていく
}
else if (i == 2 || i == 3)
{
Part_img[i][
0 + 3 - 1 //yの値のスタート地点
, 440 + 3 - 1 //yの値のゴール地点
, 0 + 3 - 1 //xの値のスタート地点
, 512 + 3 - 1 //xの値のゴール地点
] = two_set[i]; //処理済み画像をPartの対応する部分に入れていく
}
else if (i == 4 || i == 5)
{
Part_img[i][
0 + 3 - 2 //yの値のスタート地点
, 440 + 3 - 2 //yの値のゴール地点
, 0 + 3 - 2 //xの値のスタート地点
, 512 + 3 - 2 //xの値のゴール地点
] = two_set[i]; //処理済み画像をPartの対応する部分に入れていく
}
else if (i == 6 || i == 7)
{
Part_img[i][
0 + 3 - 3 //yの値のスタート地点
, 440 + 3 - 3 //yの値のゴール地点
, 0 + 3 - 3 //xの値のスタート地点
, 512 + 3 - 3 //xの値のゴール地点
] = two_set[i]; //処理済み画像をPartの対応する部分に入れていく
}
}
for (int i = 0; i < Part_img.Count(); i++)
{
superimposed += Part_img[i];
}
Cv2.Threshold(superimposed, superimposed, 5, 255, ThresholdType.ToZero);//パラメータ見ないといけない。
superimposed.SubMat(0 + 3
, 440 + 3
, 0 + 3
, 512 + 3).CopyTo(superimposed); //１枚目の画像の大きさ、場所で切り取る
}
if (delta_xx < 0 && delta_yy >= 0)//画像の左下への移動
{
for (int i = 0; i < two_set.Count; i++)
{
if (i == 0 || i == 1)
{
Part_img[i][
0
, 440
, 0 + 3
, 512 + 3
] = two_set[i]; //処理済み画像をPartの対応する部分に入れていく
}
else if (i == 2 || i == 3)
{
Part_img[i][
0 + Math.Abs(delta_yy) //yの値のスタート地点
, 440 + Math.Abs(delta_yy) //yの値のゴール地点
, 0 + 3 - 1 //xの値のスタート地点
, 512 + 3 - 1 //xの値のゴール地点
] = two_set[i]; //処理済み画像をPartの対応する部分に入れていく
}
else if (i == 4 || i == 5)
{
Part_img[i][
0 + 2 * Math.Abs(delta_yy) //yの値のスタート地点
, 440 + 2 * Math.Abs(delta_yy) //yの値のゴール地点
, 0 + 3 - 2 //xの値のスタート地点
, 512 + 3 - 2 //xの値のゴール地点
] = two_set[i]; //処理済み画像をPartの対応する部分に入れていく
}
else if (i == 6 || i == 7)
{
Part_img[i][
0 + 3 * Math.Abs(delta_yy) //yの値のスタート地点
, 440 + 3 * Math.Abs(delta_yy) //yの値のゴール地点
, 0 + 3 - 3 //xの値のスタート地点
, 512 + 3 - 3 //xの値のゴール地点
] = two_set[i]; //処理済み画像をPartの対応する部分に入れていく
}
}
for (int i = 0; i < Part_img.Count(); i++)
{
superimposed += Part_img[i];
}
Cv2.Threshold(superimposed, superimposed, 5, 255, ThresholdType.ToZero);//パラメータ見ないといけない。
superimposed.SubMat(0
, 440
, 0 + 3
, 512 + 3).CopyTo(superimposed); //１枚目の画像の大きさ、場所で切り取る
}
Mat one1 = Mat.Ones(y0 - 20, 512, MatType.CV_8UC1);//視野の中心からどれだけの窓を開けるか
Mat one2 = Mat.Ones(41, x0 - 20, MatType.CV_8UC1);
Mat one3 = Mat.Ones(41, 491 - x0, MatType.CV_8UC1);
Mat one4 = Mat.Ones(419 - y0, 512, MatType.CV_8UC1);
superimposed[0, y0 - 20, 0, 512] = one1 * 0;
superimposed[y0 - 20, y0 + 21, 0, x0 - 20] = one2 * 0;
superimposed[y0 - 20, y0 + 21, x0 + 21, 512] = one3 * 0;
superimposed[y0 + 21, 440, 0, 512] = one4 * 0;//中心から○μｍの正方形以外は黒くする。
superimposed.ImWrite("C:\\set\\superimposed25_1.bmp");
using (CvMemStorage storage = new CvMemStorage())
{
using (CvContourScanner scanner = new CvContourScanner(superimposed.ToIplImage(), storage, CvContour.SizeOf, ContourRetrieval.Tree, ContourChain.ApproxSimple))
{
foreach (CvSeq <CvPoint> c in scanner)
{
CvMoments mom = new CvMoments(c, false);
if (c.ElemSize < 2)
{
continue;
}
if (mom.M00 == 0.0)
{
continue;
}
double mx = mom.M10 / mom.M00;
double my = mom.M01 / mom.M00;
mm koko = new mm();
koko.white_x = mx;
koko.white_y = my;
koko.white_kido = mom.M00;
koko.white_dx = delta_xx;
koko.white_dy = delta_yy;
white_area.Add(koko);
stage.WriteLine(String.Format("mx={0:f2} , my={1:f2} , dx={2:f2} , dy={3:f2} , M={4:f2}", mx, my, delta_xx, delta_yy, mom.M00));
}
}
}
Part_img.Clear();
} //pixel移動x
} //pixel移動y
}
if (white_area.Count > 0)
{
double center_x = 0;
double center_y = 0;
double center_dx = 0;
double center_dy = 0;
double kido_sum = 0;
for (int i = 0; i < white_area.Count; i++)
{
kido_sum += white_area[i].white_kido;
center_x += white_area[i].white_x * white_area[i].white_kido;
center_y += white_area[i].white_y * white_area[i].white_kido;
center_dx += white_area[i].white_dx * white_area[i].white_kido;
center_dy += white_area[i].white_dy * white_area[i].white_kido;
}
center_x = center_x / kido_sum;
center_y = center_y / kido_sum;
center_dx = center_dx / kido_sum;
center_dy = center_dy / kido_sum;
int c_o_g_x;
int c_o_g_y;
if (center_x >= 0)
{
c_o_g_x = (int)(center_x + 0.5);
}
else
{
c_o_g_x = (int)(center_x - 0.5);
}
if (center_x >= 0)
{
c_o_g_y = (int)(center_y + 0.5);
}
else
{
c_o_g_y = (int)(center_y - 0.5);
}
int dx_pixel = c_o_g_x - x0;
int dy_pixel = c_o_g_y - y0;
double dx_micron = dx_pixel * 0.265625 / 1000;
double dy_micron = dy_pixel * 0.265625 / 1000;
double now_x2 = mc.GetPoint().X;
double now_y2 = mc.GetPoint().Y;
mc.MovePointXY(now_x2 - dx_micron, now_y2 + dy_micron);//pixelの軸とstageの軸の関係から
mc.Join();
over_dx = center_dx;
over_dy = center_dy;
}
}
private void BeamDetection(string outputfilename, bool isup)
{// beam Detection
int BinarizeThreshold = 60;
int BrightnessThreshold = 4;
int nop = 7;
double dz = 0;
if (isup == true)
{
dz = -0.003;
}
else
{
dz = 0.003;
}
Camera camera = Camera.GetInstance();
MotorControler mc = MotorControler.GetInstance(parameterManager);
Vector3 InitPoint = mc.GetPoint();
Vector3 p = new Vector3();
TracksManager tm = parameterManager.TracksManager;
int mod = parameterManager.ModuleNo;
int pl = parameterManager.PlateNo;
Track myTrack = tm.GetTrack(tm.TrackingIndex);
string[] sp = myTrack.IdString.Split('-');
//string datfileName = string.Format("{0}.dat", System.DateTime.Now.ToString("yyyyMMdd_HHmmss"));
string datfileName = string.Format(@"c:\test\bpm\{0}\{1}-{2}-{3}-{4}-{5}.dat", mod, mod, pl, sp[0], sp[1], System.DateTime.Now.ToString("ddHHmmss"));
BinaryWriter writer = new BinaryWriter(File.Open(datfileName, FileMode.Create));
byte[] bb = new byte[440 * 512 * nop];
string fileName = string.Format("{0}", outputfilename);
StreamWriter twriter = File.CreateText(fileName);
string stlog = "";
List <ImageTaking> LiIT = TakeSequentialImage(0.0, 0.0, dz, nop);
Mat sum = Mat.Zeros(440, 512, MatType.CV_8UC1);
for (int i = 0; i < LiIT.Count; i++)
{
Mat bin = (Mat)DogContrastBinalize(LiIT[i].img, 31, BinarizeThreshold);
Cv2.Add(sum, bin, sum);
//byte[] b = LiIT[i].img.ToBytes();//format is .png
MatOfByte mob = new MatOfByte(LiIT[i].img);
byte[] b = mob.ToArray();
b.CopyTo(bb, 440 * 512 * i);
}
mc.MovePointZ(InitPoint.Z);
mc.Join();
Cv2.Threshold(sum, sum, BrightnessThreshold, 1, ThresholdType.Binary);
//Cv2.FindContoursをつかうとAccessViolationExceptionになる(Release/Debug両方)ので、C-API風に書く
using (CvMemStorage storage = new CvMemStorage())
{
using (CvContourScanner scanner = new CvContourScanner(sum.ToIplImage(), storage, CvContour.SizeOf, ContourRetrieval.Tree, ContourChain.ApproxSimple))
{
//string fileName = string.Format(@"c:\img\{0}.txt",
// System.DateTime.Now.ToString("yyyyMMdd_HHmmss_fff"));
foreach (CvSeq <CvPoint> c in scanner)
{
CvMoments mom = new CvMoments(c, false);
if (c.ElemSize < 2)
{
continue;
}
if (mom.M00 == 0.0)
{
continue;
}
double mx = mom.M10 / mom.M00;
double my = mom.M01 / mom.M00;
stlog += string.Format("{0:F} {1:F}\n", mx, my);
}
}
}
twriter.Write(stlog);
twriter.Close();
writer.Write(bb);
writer.Flush();
writer.Close();
sum *= 255;
sum.ImWrite(String.Format(@"c:\img\{0}_{1}_{2}.bmp",
System.DateTime.Now.ToString("yyyyMMdd_HHmmss"),
(int)(p.X * 1000),
(int)(p.Y * 1000)));
}//BeamDetection
static public List <microtrack> Select(List <ImageTaking> ITs, tsparams param)
{
List <microtrack> rms = new List <microtrack>();
for (int ax = -param.ax; ax <= param.ax; ax++)
{
for (int ay = -param.ay; ay <= param.ay; ay++)
{
//TODO: 角度におうじてMatのサイズを決定するようにしたい
using (Mat big = Mat.Zeros(800, 800, MatType.CV_8UC1))
{
for (int p = 0; p < ITs.Count; p++)
{
int startx = big.Width / 2 - ITs[p].img.Width / 2 + (int)(p * ax * param.pitchx / 16.0);
int starty = big.Height / 2 - ITs[p].img.Height / 2 + (int)(p * ay * param.pitchy / 16.0);
Cv2.Add(
big[starty, starty + ITs[p].img.Height, startx, startx + ITs[p].img.Width],
ITs[p].img,
big[starty, starty + ITs[p].img.Height, startx, startx + ITs[p].img.Width]);
}
//Cv2.ImWrite(string.Format("{0}_{1}.png", ax, ay), big * 15);
//Cv2.ImShow("big", big * 20);
//Cv2.WaitKey(0);
Cv2.Threshold(big, big, param.phthre, 255, ThresholdType.ToZero);
//Cv2.ImWrite(string.Format("{0}_{1}.png", ax, ay), big * 15);
//Cv2.ImShow("big_thre", big*20);
//Cv2.WaitKey(0);
using (IplImage big_copy = big.Clone().ToIplImage())
using (CvMemStorage storage = new CvMemStorage())
using (CvContourScanner scanner = new CvContourScanner(big_copy, storage, CvContour.SizeOf, ContourRetrieval.External, ContourChain.ApproxNone))
{
CvScalar white = Cv.RGB(255, 255, 255);
foreach (CvSeq <CvPoint> c in scanner)
{
IplImage mask = new IplImage(big.Size(), BitDepth.U8, 1);
Cv.DrawContours(mask, c, white, white, 0, -1);
Cv.And(big.ToIplImage(), mask, mask);
double minval;
double maxval;
CvPoint minLoc;
CvPoint maxLoc;
Cv.MinMaxLoc(mask, out minval, out maxval, out minLoc, out maxLoc);
microtrack rm = new microtrack();
rm.ph = maxval;
rm.ax = ax;
rm.ay = ay;
rm.cx = maxLoc.X - (big.Width / 2 - ITs[0].img.Width / 2);
rm.cy = maxLoc.Y - (big.Height / 2 - ITs[0].img.Height / 2);
rm.pv = mask.Sum();
rms.Add(rm);
//Console.WriteLine(string.Format("{0} {1} {2} {3} {4} {5}", rm.ph, rm.pv, rm.ax, rm.ay, rm.cx, rm.cy));
//Cv.ShowImage("a", mask);
//Cv2.WaitKey(0);
}
}//using contour
}//using Mat
}//for[ay:
}//for[ax]
rms.Sort((x, y) => y.pv.CompareTo(x.pv));
//Console.WriteLine("-----");
//foreach (microtrack rm in rms)
//{
// Console.WriteLine(string.Format("{0} {1} {2} {3} {4} {5}", rm.ph, rm.pv, rm.ax, rm.ay, rm.cx, rm.cy));
//}
List <microtrack> ms = new List <microtrack>();
for (int i = 0; i < rms.Count; i++)
{
microtrack a = rms[i];
if (a.flag == true)
{
continue;//already clusterd
}
rms[i].flag = true;
microtrack mym = new microtrack();
mym.ph = a.ph * a.pv;
mym.pv = a.pv * a.pv;
mym.ax = a.ax * a.pv;
mym.ay = a.ay * a.pv;
mym.cx = a.cx * a.pv;
mym.cy = a.cy * a.pv;
double pvsum = a.pv;
for (int j = i + 1; j < rms.Count; j++)
{
microtrack b = rms[j];
if (b.flag == true)
{
continue;//already clusterd
}
if (Math.Abs(a.cx - b.cx) > param.clpix || Math.Abs(a.cy - b.cy) > param.clpix)
{
continue;
}
if (Math.Abs(a.ax - b.ax) > param.clang || Math.Abs(a.ay - b.ay) > param.clang)
{
continue;
}
mym.ph += b.ph * b.pv;
mym.pv += b.pv * b.pv;
mym.ax += b.ax * b.pv;
mym.ay += b.ay * b.pv;
mym.cx += b.cx * b.pv;
mym.cy += b.cy * b.pv;
mym.n += 1;
pvsum += b.pv;
rms[j].flag = true;
}
mym.ph /= pvsum;
mym.pv /= pvsum;
mym.ax /= pvsum;
mym.ay /= pvsum;
mym.cx /= pvsum;
mym.cy /= pvsum;
ms.Add(mym);
}
return(ms);
}
private void button1_Click(object sender, EventArgs e)
{
getContour();
IplImage imgContour = new IplImage("dataset-dinosaur\\contour\\00.jpg", LoadMode.GrayScale);
IplImage imgContourDraw = new IplImage(new CvSize(imgContour.Width, imgContour.Height), BitDepth.U8, 3);
CvSeq <CvPoint> contourPointSeq;
CvMemStorage memStore = new CvMemStorage(0);
Cv.FindContours(imgContour, memStore, out contourPointSeq);
CvContourScanner cs = Cv.StartFindContours(imgContour, memStore);
CvPoint[] contourPointArray = null;
for (int i = 0; i < 10; i++)
{
contourPointSeq = cs.FindNextContour();
contourPointArray = new CvPoint[contourPointSeq.Total];
Cv.CvtSeqToArray <CvPoint>(contourPointSeq, out contourPointArray);
if (contourPointArray.Length > 100)
{
for (int j = 0; j < contourPointArray.Length; j++)
{
Cv.DrawCircle(imgContourDraw, new CvPoint(contourPointArray[j].X, contourPointArray[j].Y), 10, Cv.RGB(255, 0, 0), 10);
}
StreamWriter sw = new StreamWriter("file.txt", false);
for (int k = 0; k < contourPointArray.Length; k++)
{
sw.Write(contourPointArray[k].X);
sw.Write(" ");
sw.Write(contourPointArray[k].Y);
sw.Write("\n");
}
}
Cv.SaveImage("save.jpg", imgContourDraw);
}
int target_img_contour_num;
int ref_img_contour_num;
double[,] camPara1 = Read_camerpar_txt("Camera parameters.txt", "00.jpg");
double[,] camPara2 = Read_camerpar_txt("Camera parameters.txt", "01.jpg");
double[,] target_img_contour = Read_Contour_txt("目标图轮廓点.txt", "00.jpg", out target_img_contour_num);
double[,] ref_img_contour = Read_Contour_txt("参考图轮廓点.txt", "01.jpg", out ref_img_contour_num);
CvMat right_epiline_point = Computecorrespondepilines(camPara2, camPara1, target_img_contour);
int n = 7;//共分为n个bin,每个bin弧度(max_radius-min_radius)/n
List <double[]> intersection_point_list = Compute_epiline_contour_intersection(right_epiline_point, ref_img_contour, n);
double[][] vertex_3d = new double[intersection_point_list.Count][];
for (int i = 0; i < intersection_point_list.Count; i++)
{
int temp = (int)intersection_point_list[i][0];
//vertex_3d[i] = Cross_intersection_3D(
// intersection_point_list[i],
// camPara2,
// camPara1,
// new CvPoint((int)(target_img_contour[0,temp]), (int)(target_img_contour[1,temp])),
// i, 0, 0, 0, 0, 1, 0);
//vertex_3d[i] = Cross_intersection_3D(
// intersection_point_list[i],
// camPara1,
// camPara2,
// new CvPoint((int)(target_img_contour[0, temp]), (int)(target_img_contour[1, temp])),
// i, 0, 0, 0, 0, 1, 0);
vertex_3d[i] = Cross_intersection_3D_Direct_Solve(
intersection_point_list[i],
camPara1,
camPara2,
new CvPoint((int)(target_img_contour[0, temp]), (int)(target_img_contour[1, temp])),
i, 0, 0, 0, 0, 1, 0);
}
double[] face = new double[0];
save_ply("ply0.ply", vertex_3d, face);
IplImage img1 = Cv.LoadImage("01.jpg");
//Cv.NamedWindow("win1");
//Cv.ShowImage("win1",img1);
//Cv.WaitKey(0);
for (int i = 1; i < ref_img_contour.GetLength(1); i++)
{
Cv.DrawCircle(img1, new CvPoint((int)(ref_img_contour[0, i]), (int)(ref_img_contour[1, i])), 1, Cv.RGB(0, 255, 255));
}
//for (int i = 0; i < right_epiline_point.GetDimSize(1); i++)
//{
// Cv.DrawLine(img1, new CvPoint((int)(right_epiline_point[0,i]), (int)(right_epiline_point[1, i])), new CvPoint((int)(right_epiline_point[2, i]), (int)(right_epiline_point[3, i])),Cv.RGB(0, 255, 0),1);
//}
for (int i = 0; i < intersection_point_list.Count; i++)
{
Cv.DrawCircle(img1, new CvPoint((int)(intersection_point_list[i][1]), (int)(intersection_point_list[i][2])), 2, Cv.RGB(255, 0, 0));
}
Cv.SaveImage("01_save.jpg", img1);
IplImage img2 = Cv.LoadImage("00.jpg");
for (int i = 1; i < target_img_contour.GetLength(1); i++)
{
Cv.DrawCircle(img2, new CvPoint((int)(target_img_contour[0, i]), (int)(target_img_contour[1, i])), 2, Cv.RGB(255, 0, 0));
}
Cv.SaveImage("00_save.jpg", img2);
//for(int i=0;i<right_epiline_point.GetDimSize(1);i++)
//{
// CvPoint epiline_start_point = new CvPoint(right_epiline_point[0,i],
// right_epiline_point[1,i]);
// CvPoint epiline_end_point =new CvPoint(right_epiline_point[2,i],
// right_epiline_point[3,i]);
// CvPoint contour_line_point = new CvPoint
// SL_Cross_Intersection(new CvPoint(,)
//CvMat Epilines_point_mat = new CvMat(4, second_image_contour_point_number, MatrixType.F64C1);
//Epilines_point_mat = Computecorrespondepilines(first_projcet, second_projcet, second_Contour_point_temp);//求第二幅图中的所有轮廓点在第第一副图中的极线（一次性求出）
//c3.X = (int)Epilines_point_mat[0, k5];
//c3.Y = (int)Epilines_point_mat[1, k5];
// c4.X = (int)Epilines_point_mat[2, k5];
//c4.Y = (int)Epilines_point_mat[3, k5];
}
