private Line2 FitSimilarLine(List <Line2> lines, Line2 target, double angle = 20)
{
NumericalRecipes.RansacLine2d rcl = new NumericalRecipes.RansacLine2d();
List <Vector2> linepoints = new List <Vector2>();
linepoints.AddRange(target.SamplePoints());
//ransac
for (int i = 0; i < lines.Count; i++)
{
if (Line2.IsParallel(lines[i], target, angle))
{
if (target.DistanceToLine(lines[i].start) < 30 && target.DistanceToLine(lines[i].end) < 30)
{
linepoints.AddRange(lines[i].SamplePoints());
}
}
}
Line2 bestline = rcl.Estimate(linepoints);
if (bestline == null)
{
return(target);
}
if (Vector2.Dot(bestline.dir, target.dir) < 0)
{
bestline.Flip();
}
return(bestline);
}
private Line2 FitCloseLine(List <Line2> lines, Line2 target, double dis = 30)
{
List <Vector2> linepoints = new List <Vector2>();
linepoints.AddRange(target.SamplePoints());
//ransac
for (int i = 0; i < lines.Count; i++)
{
if (Line2.IsParallel(lines[i], target, 45))
{
double d = double.MaxValue;
foreach (Vector2 v in linepoints)
{
d = Math.Min(Math.Min(Vector2.Distance(lines[i].start, v), Vector2.Distance(lines[i].end, v)), d);
}
if (d < dis)
{
linepoints.AddRange(lines[i].SamplePoints());
}
}
}
// least square
//Vector2 dir = Utility.GetDirection(linepoints);
//Line2 bestline = new Line2(linepoints[0], dir, true);
//foreach (Vector2 v in linepoints)
// bestline.UpdateEnd(v);
//ransac
NumericalRecipes.RansacLine2d rcl = new NumericalRecipes.RansacLine2d();
Line2 bestline = rcl.Estimate(linepoints);
if (bestline == null)
{
return(target);
}
if (Vector2.Dot(bestline.dir, target.dir) < 0)
{
bestline.Flip();
}
return(bestline);
}
private List <Vector2> ApproximateStraightAxis(List <Line2> skel_lines)
{
// from skeleton lines(aka. thining image lines) (find lines that have the same direction with guessed axis <- when top face is known)
// ransac, fit one line as main axis
// update end points
// set top circle center as start point
// extend end point
Line2 new_main_axis = null;
Image <Rgb, byte> mainaxis_img = body_img.Copy().Convert <Rgb, byte>();
Line2 main_axis = null;
if (iscube)
{
main_axis = ApproximateFromCubeTop();
}
else
{
main_axis = ApproximateFromTop();
}
if (main_axis == null)
{
return(null);
}
if (noface)
{
NumericalRecipes.RansacLine2d rcl = new NumericalRecipes.RansacLine2d();
List <Vector2> thin_points = IExtension.GetMaskPoints(this.ori_thin_img);
List <Vector2> linepoints = new List <Vector2>();
for (int i = 0; i < skel_lines.Count; i++)
{
linepoints.AddRange(skel_lines[i].SamplePoints());
}
//Line2 bestline = rcl.Estimate(linepoints);
Line2 bestline = rcl.Estimate(thin_points);
double diss = Vector2.Distance(bestline.start, face_center[0]);
double dise = Vector2.Distance(bestline.end, face_center[0]);
if (dise < diss)
{
bestline.Flip();
}
new_main_axis = bestline;
}
else
{
// use ori thinning image as guidance
LineSegment2D[] lines = ori_thin_img.HoughLinesBinary(
1, //Distance resolution in pixel-related units
Math.PI / 180.0, //Angle resolution measured in radians.
3, //threshold
4, //min Line width
1 //gap between lines
)[0]; //Get the lines from the first channel
skel_lines.Clear();
foreach (LineSegment2D line in lines)
{
skel_lines.Add(new Line2(new Vector2(line.P1.X, line.P1.Y), new Vector2(line.P2.X, line.P2.Y)));
}
Line2 bestline = FitSimilarLine(skel_lines, main_axis);
if (Line2.IsParallel(bestline, main_axis, 5))
{
new_main_axis = bestline;
}
else
{
new_main_axis = main_axis;
}
}
// update end
List <Vector2> ori_skel_points = IExtension.GetMaskPoints(ori_thin_img);
for (int i = 0; i < ori_skel_points.Count; i++)
{
new_main_axis.UpdateEnd(ori_skel_points[i]);
}
new_main_axis.start = new_main_axis.ProjToLine(face_center.First());
new_main_axis.start = new_main_axis.GetPointwithT(4);
List <Vector2> axis_points = new List <Vector2>();
// axis_points.Add(face_center.First());
axis_points.AddRange(new_main_axis.SamplePoints());
axis_points.Add(new_main_axis.GetPointwithT((float)new_main_axis.Length() * 1.2f));
axis_points = IExtension.ResetPath(axis_points, 3);
#region visualize
Image <Rgb, byte> mainaxis_point_img = body_img.Copy().Convert <Rgb, byte>();
foreach (Vector2 v in axis_points)
{
mainaxis_point_img.Draw(new CircleF(new PointF(v.x, v.y), 2.0f), new Rgb(255, 0, 0), 1);
}
mainaxis_point_img.Draw(new CircleF(new PointF(axis_points.First().x, axis_points.First().y), 3.0f), new Rgb(0, 255, 0), 1);
mainaxis_point_img.Save(index_forname.ToString() + this.label_forname.ToString() + "_axis_straight.png");
#endregion
return(axis_points);
}
private List <Line2> Skeletonize(out bool iscurve)
{
Image <Gray, byte> img2 = body_img.Copy();
Image <Gray, byte> eroded = new Image <Gray, byte>(img2.Size);
Image <Gray, byte> temp = new Image <Gray, byte>(img2.Size);
Image <Gray, byte> skel = new Image <Gray, byte>(img2.Size);
body_img.Save("test.png");
#region with matlab
string argument1 = "\"" + "test.png" + "\"";
System.Diagnostics.Process process = new System.Diagnostics.Process();
process.StartInfo.FileName = System.Environment.CurrentDirectory + "\\Assets\\frommatlab\\skeleton.exe";
process.StartInfo.Arguments = argument1;
process.StartInfo.UseShellExecute = false;
process.StartInfo.CreateNoWindow = true;
process.StartInfo.RedirectStandardOutput = true;
//启动
process.Start();
process.WaitForExit();
#endregion
skel = new Image <Gray, byte>("prune.png");
ori_thin_img = new Image <Gray, byte>("thin.png");
ori_prune_img = skel;
#region thining - comment
//skel.SetValue(0);
//CvInvoke.Threshold(img2, temp, 127, 256, 0);
//var element = CvInvoke.GetStructuringElement(ElementShape.Cross, new Size(3, 3), new Point(-1, -1));
//bool done = false;
////skeleton
//int itr = 0;
//while (!done)
//{
// CvInvoke.Erode(img2, eroded, element, new Point(-1, -1), 1, BorderType.Reflect, default(MCvScalar));
// CvInvoke.Dilate(eroded, temp, element, new Point(-1, -1), 1, BorderType.Reflect, default(MCvScalar));
// CvInvoke.Subtract(img2, temp, temp);
// CvInvoke.BitwiseOr(skel, temp, skel);
// eroded.CopyTo(img2);
// itr++;
// if (CvInvoke.CountNonZero(img2) == 0) done = true;
//}
//Image<Gray, Byte> cannyimg = body_img.Canny(60, 100);
//CvInvoke.Dilate(cannyimg, cannyimg, element, new Point(-1, -1), 3, BorderType.Reflect, default(MCvScalar));
//CvInvoke.Subtract(skel, cannyimg, skel);
//ori_skel_img = skel.Copy();
////thinning
//if (!noface)
//{
// #region thinning
// List<Mat> cs = new List<Mat>();
// List<Mat> ds = new List<Mat>();
// for (int i = 0; i < 8; i++)
// {
// cs.Add(CvInvoke.GetStructuringElement(ElementShape.Cross, new Size(3, 3), new Point(-1, -1)));
// ds.Add(CvInvoke.GetStructuringElement(ElementShape.Cross, new Size(3, 3), new Point(-1, -1)));
// }
// cs[0].SetTo(new int[] { 0, 0, 0, 0, 1, 0, 1, 1, 1 });
// cs[1].SetTo(new int[] { 1, 0, 0, 1, 1, 0, 1, 0, 0 });
// cs[2].SetTo(new int[] { 1, 1, 1, 0, 1, 0, 0, 0, 0 });
// cs[3].SetTo(new int[] { 0, 0, 1, 0, 1, 1, 0, 0, 1 });
// ds[0].SetTo(new int[] { 1, 1, 1, 0, 0, 0, 0, 0, 0 });
// ds[1].SetTo(new int[] { 0, 0, 1, 0, 0, 1, 0, 0, 1 });
// ds[2].SetTo(new int[] { 0, 0, 0, 0, 0, 0, 1, 1, 1 });
// ds[3].SetTo(new int[] { 1, 0, 0, 1, 0, 0, 1, 0, 0 });
// cs[4].SetTo(new int[] { 0, 0, 0, 1, 1, 0, 1, 1, 0 });
// cs[5].SetTo(new int[] { 1, 1, 0, 1, 1, 0, 0, 0, 0 });
// cs[6].SetTo(new int[] { 0, 1, 1, 0, 1, 1, 0, 0, 0 });
// cs[7].SetTo(new int[] { 0, 0, 0, 0, 1, 1, 0, 1, 1 });
// ds[4].SetTo(new int[] { 0, 1, 1, 0, 0, 1, 0, 0, 0 });
// ds[5].SetTo(new int[] { 0, 0, 0, 0, 0, 1, 0, 1, 1 });
// ds[6].SetTo(new int[] { 0, 0, 0, 1, 0, 0, 1, 1, 0 });
// ds[7].SetTo(new int[] { 1, 1, 0, 1, 0, 0, 0, 0, 0 });
// Image<Gray, byte> img3 = skel.Copy();
// Image<Gray, byte> temp2 = skel.CopyBlank();
// Image<Gray, byte> lastimg3 = skel.Copy();
// done = false;
// while (!done)
// {
// for (int i = 0; i < 8; i++)
// {
// temp = this.HitOrMiss(img3, cs[i], ds[i]);
// CvInvoke.Subtract(img3, temp, img3);
// }
// CvInvoke.Subtract(lastimg3, img3, temp2);
// lastimg3 = img3.Copy();
// if (CvInvoke.CountNonZero(temp2) == 0) done = true;
// }
// //img3.Save("thining.png");
// #endregion
// skel = img3.Copy();
// ori_thinning_img = img3.Copy();
//}
////// remove noise
////for (int i = 0; i < img3.Height; i++)
////{
//// for (int j = 0; j < img3.Width; j++)
//// {
//// if (img3[i, j].Equals(new Gray(255)))
//// {
//// bool change = false;
//// for (int pad = 1; pad < 3; pad++)
//// {
//// if (i >= pad && i < img3.Height - pad && j >= pad && j < img3.Width - pad)
//// {
//// if (img3[i - pad, j].Equals(new Gray(0)) &&
//// img3[i - pad, j - pad].Equals(new Gray(0)) &&
//// img3[i - pad, j + pad].Equals(new Gray(0)) &&
//// img3[i + pad, j].Equals(new Gray(0)) &&
//// img3[i + pad, j - pad].Equals(new Gray(0)) &&
//// img3[i + pad, j + pad].Equals(new Gray(0)) &&
//// img3[i, j - pad].Equals(new Gray(0)) &&
//// img3[i, j + pad].Equals(new Gray(0)))
//// change = true;
//// }
//// }
//// if (change)
//// img3[i, j] = new Gray(0);
//// }
//// }
////}
////img3.Save("thiningdenoise.png");
#endregion
// get line
// consider both straight line and curve
LineSegment2D[] lines = skel.HoughLinesBinary(
1, //Distance resolution in pixel-related units
Math.PI / 180.0, //Angle resolution measured in radians.
3, //threshold
4, //min Line width
1 //gap between lines
)[0]; //Get the lines from the first channel
Image <Gray, byte> lineimg = skel.CopyBlank();
List <Line2> skel_lines = new List <Line2>();
foreach (LineSegment2D line in lines)
{
//remove image boundaries
//if (line.P1.X > 10 && line.P1.Y > 10 && line.P1.X < body_img.Height - 10 && line.P1.Y < body_img.Width &&
// line.P2.X > 10 && line.P2.Y > 10 && line.P2.X < body_img.Height - 10 && line.P2.Y < body_img.Width - 10)
//{
skel_lines.Add(new Line2(new Vector2(line.P1.X, line.P1.Y), new Vector2(line.P2.X, line.P2.Y)));
lineimg.Draw(line, new Gray(255), 2);
//}
}
if (debug)
{
lineimg.Save("skel-line.png");
}
// cluster according to direction and relative distance
// too many cluster means curve axis
IMGSIZE = Math.Min(body_img.Width, body_img.Height);
if (skel_lines.Count > 0)
{
double[][] xy = new double[skel_lines.Count][];
for (int i = 0; i < skel_lines.Count; i++)
{
xy[i] = new double[] { skel_lines[i].start.x, skel_lines[i].start.y,
skel_lines[i].end.x, skel_lines[i].end.y };
}
MeanShift clusterMS = new MeanShift(4, new UniformKernel(), 0.02);
clusterMS.Distance = new myDistanceClass();
MeanShiftClusterCollection clustering = clusterMS.Learn(xy);
var lineLabels = clustering.Decide(xy);
int clustercount = lineLabels.DistinctCount();
//Debug.Log("cluster count: " + clustercount);
if (debug)
{
Image <Rgb, byte> lineimg_rgb = lineimg.Convert <Rgb, byte>();
System.Random rnd = new System.Random();
Rgb[] colortable = new Rgb[clustering.Count];
for (int i = 0; i < clustering.Count; i++)
{
colortable[i] = new Rgb(rnd.Next(255), rnd.Next(255), rnd.Next(255));
}
for (int i = 0; i < skel_lines.Count; i++)
{
int label = lineLabels[i];
lineimg_rgb.Draw(skel_lines[i].ToLineSegment2D(), colortable[label], 2);
}
lineimg_rgb.Save("skel-line-cluster.png");
}
if (noface)
{
thred = 2; // 2
}
if (clustercount > thred)
{
iscurve = true;
}
else
{
iscurve = false;
}
}
else
{
iscurve = false;
NumericalRecipes.RansacLine2d rcl = new NumericalRecipes.RansacLine2d();
List <Vector2> linepoints = new List <Vector2>();
linepoints = IExtension.GetMaskPoints(skel);
Line2 bestline = rcl.Estimate(linepoints);
skel_lines.Add(bestline);
}
return(skel_lines);
}
