/// <summary>
/// Use camshift to track the feature
/// </summary>
/// <param name="observedFeatures">The feature found from the observed image</param>
/// <param name="initRegion">The predicted location of the model in the observed image. If not known, use MCvBox2D.Empty as default</param>
/// <param name="priorMask">The mask that should be the same size as the observed image. Contains a priori value of the probability a match can be found. If you are not sure, pass an image fills with 1.0s</param>
/// <returns>If a match is found, the homography projection matrix is returned. Otherwise null is returned</returns>
public HomographyMatrix CamShiftTrack(SURFFeature[] observedFeatures, MCvBox2D initRegion, Image <Gray, Single> priorMask)
{
using (Image <Gray, Single> matchMask = new Image <Gray, Single>(priorMask.Size))
{
#region get the list of matched point on the observed image
Single[, ,] matchMaskData = matchMask.Data;
//Compute the matched features
MatchedSURFFeature[] matchedFeature = _matcher.MatchFeature(observedFeatures, 2, 20);
matchedFeature = VoteForUniqueness(matchedFeature, 0.8);
foreach (MatchedSURFFeature f in matchedFeature)
{
PointF p = f.ObservedFeature.Point.pt;
matchMaskData[(int)p.Y, (int)p.X, 0] = 1.0f / (float)f.SimilarFeatures[0].Distance;
}
#endregion
Rectangle startRegion;
if (initRegion.Equals(MCvBox2D.Empty))
{
startRegion = matchMask.ROI;
}
else
{
startRegion = PointCollection.BoundingRectangle(initRegion.GetVertices());
if (startRegion.IntersectsWith(matchMask.ROI))
{
startRegion.Intersect(matchMask.ROI);
}
}
CvInvoke.cvMul(matchMask.Ptr, priorMask.Ptr, matchMask.Ptr, 1.0);
MCvConnectedComp comp;
MCvBox2D currentRegion;
//Updates the current location
CvInvoke.cvCamShift(matchMask.Ptr, startRegion, new MCvTermCriteria(10, 1.0e-8), out comp, out currentRegion);
#region find the SURF features that belongs to the current Region
MatchedSURFFeature[] featuesInCurrentRegion;
using (MemStorage stor = new MemStorage())
{
Contour <System.Drawing.PointF> contour = new Contour <PointF>(stor);
contour.PushMulti(currentRegion.GetVertices(), Emgu.CV.CvEnum.BACK_OR_FRONT.BACK);
CvInvoke.cvBoundingRect(contour.Ptr, 1); //this is required before calling the InContour function
featuesInCurrentRegion = Array.FindAll(matchedFeature,
delegate(MatchedSURFFeature f)
{ return(contour.InContour(f.ObservedFeature.Point.pt) >= 0); });
}
#endregion
return(GetHomographyMatrixFromMatchedFeatures(VoteForSizeAndOrientation(featuesInCurrentRegion, 1.5, 20)));
}
}
// Create rectangle bounding box with specific aspect ratio starting from a set of landarmarks points.
// Border padding is used for expand the bounding box mantaining same aspect ratio.
public Rectangle GetBoundingBox(PointF[] componentPoints, double ratio, int borderPadding)
{
var rect = PointCollection.BoundingRectangle(componentPoints);
var height = (int)Math.Ceiling(rect.Width * ratio);
var diff = Math.Ceiling((height - rect.Height) / 2d);
rect.Inflate(0, (int)diff);
rect.Inflate(borderPadding, borderPadding);
return(rect);
}
private static void DrawAllRectangles()
{
Image <Gray, Byte> edgeImage = new Image <Gray, byte>(show_img);
#region Find rectangles
boxList = new List <RotatedRect>(); //a box is a rotated rectangle
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
CvInvoke.FindContours(edgeImage, contours, null, RetrType.List, ChainApproxMethod.ChainApproxSimple);
for (int i = 0; i < contours.Size; i++)
{
Image <Rgb, Byte> img = new Image <Rgb, byte>(ori_img);
double rate = img.Width * img.Height / 3864714.0;
using (VectorOfPoint contour = contours[i])
using (VectorOfPoint approxContour = new VectorOfPoint())
{
CvInvoke.ApproxPolyDP(contour, approxContour, CvInvoke.ArcLength(contour, true) * 0.05, true);
double area = CvInvoke.ContourArea(contour, false);
if (area > 40000)
{
Point[] pts = contour.ToArray();
PointF[] ptfs = new PointF[pts.Length];
for (int pi = 0; pi < pts.Length; pi++)
{
ptfs[pi] = new PointF(pts[pi].X, pts[pi].Y);
}
Rectangle rec = PointCollection.BoundingRectangle(ptfs);
img.Draw(rec, new Rgb(Color.DarkOrange), 2);
// Create string to draw.
String drawString = "Area : " + area;
// Draw string to image.
img.Draw(drawString, rec.Location, FontFace.HersheyTriplex, 1, new Rgb(Color.Black), 2);
#region draw rectangle
pre_img = show_img;
show_img = img.Bitmap;
rform.UpdateImage(show_img);
img.Save("rect_" + i + ".png");
#endregion
}
}
}
}
#endregion
}
private void getRectangles()
{
count = 0;
foreach (System.Drawing.Point[] point in Points)
{
if (count % 2 == 0)
{
System.Drawing.PointF[] pointF = Array.ConvertAll(point, new Converter <System.Drawing.Point, System.Drawing.PointF>(PointToPointF));
rect[count] = PointCollection.BoundingRectangle(pointF);
getInfo(count);
}
count++;
}
}
// Hair component is a particular component not identified by landmarks
// This methods extract hair component starting from the bounding box of face and
// from the bounding box of shape identified by landmarks.
public Image <Gray, byte> GetHairComponent(Image <Gray, byte> image, PointF[] face, PointF[] shape, BoundingBoxParam hairParam)
{
var shapeBox = PointCollection.BoundingRectangle(shape);
var faceBox = Rectangle.Ceiling(new RectangleF(face[0].X, face[0].Y, face[1].X - face[0].X, face[1].Y - face[0].Y));
// calculate height from shape box and top border of image
var height = image.Size.Height - (image.Size.Height - shapeBox.Top);
// calculate the normalized height using the desidered aspect ratio
var normHeight = (int)Math.Floor(faceBox.Width * hairParam.AspectRatio);
// calculate if the normalized height go out of image border
var extra = height - normHeight;
// create the right bounding box, adding on bottom of bounding box the extra height
var hairBox = (extra < 0) ?
new Rectangle(faceBox.X, 0, faceBox.Width, height - extra) :
new Rectangle(faceBox.X, extra, faceBox.Width, normHeight);
var cropHeight = (int)Math.Floor(hairParam.CropWidth * hairParam.AspectRatio);
return(image.GetSubRect(hairBox).Resize(hairParam.CropWidth, cropHeight, Inter.Area));
}
/// <summary>
/// Находит объект
/// </summary>
/// <returns>Возвращает координаты объекта. Если оюъект не найден возвращает null.</returns>
public POIData detectObj()
{
var frame = sc.PrintWindow(winDiscript);
var processedFrameSt = ProcessImage(frame);
var processedFrame = processedFrameSt.Canny(180, 120);
List <PointF> pList = new List <PointF>();
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
CvInvoke.FindContours(processedFrame, contours, null, RetrType.External, ChainApproxMethod.ChainApproxSimple);
int count = contours.Size;
for (int i = 0; i < count; i++)
{
using (VectorOfPoint contour = contours[i])
using (VectorOfPoint approxContour = new VectorOfPoint())
{
CvInvoke.ApproxPolyDP(contour, approxContour, CvInvoke.ArcLength(contour, true) * 0.05, true);
double area = CvInvoke.ContourArea(approxContour, false);
for (int k = 0; k < approxContour.Size; k++)
{
pList.Add(approxContour[k]);
}
}
}
}
var rec = PointCollection.BoundingRectangle(pList.ToArray());
if (recWhenBigDist.IsEmpty)
{
recWhenBigDist = rec;
}
if (recWhenBigDist.Width - rec.Width > 3)
{
distLess100 = true;
}
else
{
distLess100 = false;
}
if (rec.Width > 0)
{
if (++distReqest > 20)
{
filtDist = recognizeSpeed(rec, processedFrameSt);
if (filtDist > 1)
{
distReqest = 0;
if (prevDist - filtDist >= 15)
{
situation = 1;
}
if (Math.Abs(prevDist - filtDist) > 1 && Math.Abs(prevDist - filtDist) < 15)
{
situation = 2;
}
prevDist = filtDist;
}
else
{
if (distLess40)
{
situation = 3;
}
}
}
var pd = new POIData(rec.Location, filtDist);
distLess40 = filtDist < 40;
pd.noFiltDist = noFiltDist;
pd.situation = situation;
return(pd);
}
return(null);
}
private Dictionary <int, KeyValuePair <decimal, decimal> > _FindContoursAndCalculate(Emgu.CV.Image <Bgr, Byte> colorImage, out Emgu.CV.Image <Gray, Byte> processedImage)
{
int circles = 0;
VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint();
Mat hierarchy = new Mat();
/// Detect edges using Threshold
Image <Gray, Byte> Img_Source_Gray = colorImage.Convert <Gray, Byte>();
Img_Source_Gray = Img_Source_Gray.SmoothBlur(3, 3);
Image <Gray, Byte> threshold_output = Img_Source_Gray.ThresholdBinary(new Gray(90), new Gray(255));
/// Find contours
CvInvoke.FindContours(threshold_output, contours, hierarchy, RetrType.Tree, ChainApproxMethod.ChainApproxSimple);
/// Approximate contours to polygons + get bounding rects and circles
VectorOfVectorOfPoint contours_poly = new VectorOfVectorOfPoint(contours.Size);
Point[][] con = contours.ToArrayOfArray();
PointF[][] conf = new PointF[con.GetLength(0)][];
for (int i = 0; i < con.GetLength(0); i++)
{
conf[i] = new PointF[con[i].Length];
for (int j = 0; j < con[i].Length; j++)
{
conf[i][j] = new PointF(con[i][j].X, con[i][j].Y);
}
}
for (int i = 0; i < contours.Size; i++)
{
double a = CvInvoke.ContourArea(contours[i], false);
if (a > 30000)
{
CvInvoke.ApproxPolyDP(contours[i], contours_poly[circles], 3, true);
circles++;
}
}
/// Draw polygonal contour + bonding rects + circles
Image <Gray, byte> filteredImage = new Image <Gray, byte>(Img_Source_Gray.Width, Img_Source_Gray.Height);
for (int i = 0; i < circles; i++)
{
CvInvoke.DrawContours(filteredImage, contours_poly, i, new MCvScalar(255, 255, 255), -1);
}
filteredImage = colorImage.Convert <Gray, Byte>().And(filteredImage);
Image <Gray, Byte> whiteAreas = new Image <Gray, byte>(filteredImage.Width, filteredImage.Height);
List <DetectedCircle> detectedCircles = new List <DetectedCircle>();
for (int i = 0; i < circles; i++)
{
PointF[] pointfs = Array.ConvertAll(contours_poly[i].ToArray(), input => new PointF(input.X, input.Y));
Rectangle boundingRect = PointCollection.BoundingRectangle(pointfs);
DetectedCircle detectedCircle = new DetectedCircle()
{
contour = contours_poly[i], boundingRect = boundingRect, center = new Point(boundingRect.X + (boundingRect.Width / 2), boundingRect.Y + (boundingRect.Height / 2))
};
detectedCircles.Add(detectedCircle);
filteredImage.ROI = boundingRect;
int threshold = _ComputeThreshold(filteredImage, detectedCircle);
filteredImage.ROI = Rectangle.Empty;
Image <Gray, Byte> mask = new Image <Gray, byte>(filteredImage.Width, filteredImage.Height);
mask.Draw(boundingRect, new Gray(255), -1);
mask = filteredImage.And(mask);
/* Extract white are solely based on the Blue channel */
mask = mask.ThresholdBinary(new Gray(threshold), new Gray(255));
whiteAreas = whiteAreas.Or(mask.Convert <Gray, Byte>().ThresholdBinary(new Gray(10), new Gray(255)));
CvInvoke.DrawContours(whiteAreas, contours_poly, i, new MCvScalar(255, 255, 255), 3);
}
detectedCircles = detectedCircles.OrderBy(c => c.center.X).ToList();
processedImage = whiteAreas;
return(_ComputeMetrics(whiteAreas, detectedCircles, circles));
}
public void Rotation()
{
//Bitmap src = new Bitmap(@"C:\Users\nhonarva\Documents\strabo-command-line-master\strabo-command-line-master\Strabo.CommandLine\data\intermediate\136_p_4_559_1292_s_0_525_1280_72_24.png");
//Rectangle rect = new Rectangle(0, 0, 100, 50);
//Bitmap cropped = CropRotatedRect(src, rect, -30.5f, true);
//cropped.Save(@"C:\Users\nhonarva\Documents\sample.png");
List <PointF> points = new List <PointF>();
PointF[] pts;
//PointF[] pts=new PointF[4];
//pts[0] = new PointF(40.5f, 60.5f);
//pts[1] = new PointF(34f, 44f);
//pts[2] = new PointF(39f, 70f);
//pts[3] = new PointF(14f, 45f);
Bitmap sourceImage = new Bitmap(@"C:\Users\ronaldiscool\Documents\GitHub\strabo-command-line\Strabo.CommandLine\data\intermediate\138_p_6_1482_1009_s_0_1469_965_28_84.png"); /// this image belongs to map 1920-4 intermediate result
Image <Bgr, byte> source = new Image <Bgr, byte>(@"C:\Users\ronaldiscool\Documents\GitHub\strabo-command-line\Strabo.CommandLine\data\intermediate\138_p_6_1482_1009_s_0_1469_965_28_84.png");
Image <Bgr, byte> source1 = new Image <Bgr, byte>(@"C:\Users\ronaldiscool\Documents\GitHub\strabo-command-line\Strabo.CommandLine\data\intermediate\SourceMapImage_ms.png");
Bitmap sourceImage1 = new Bitmap(@"C:\Users\ronaldiscool\Documents\GitHub\strabo-command-line\Strabo.CommandLine\data\intermediate\BinaryOutput.png");
Image <Bgr, byte> img = new Image <Bgr, byte>(400, 400, new Bgr(Color.White));
for (int i = 0; i < sourceImage.Width; i++)
{
for (int j = 0; j < sourceImage.Height; j++)
{
Color color = sourceImage.GetPixel(i, j);
if (!sourceImage.GetPixel(i, j).Name.Equals("ffffffff"))
{
PointF point = new PointF(i, j);
points.Add(point);
}
}
}
string filename = Path.GetFileNameWithoutExtension((@"C:\Users\ronaldiscool\Documents\GitHub\strabo-command-line\Strabo.CommandLine\data\intermediate\138_p_6_1482_1009_s_0_1469_965_28_84.png"));
// string filename = Path.GetFileNameWithoutExtension(filePaths[i]);
String[] splitTokens = filename.Split('_');
int numberOfLetters = 5;
//if (splitTokens[6].Equals("0"))
//{
int x = Convert.ToInt32(splitTokens[7]);
int y = Convert.ToInt32(splitTokens[8]);
// int width=Convert.ToInt32(splitTokens[9]);
// int height=Convert.ToInt32(splitTokens[10]);
// int estimatedSizeOfOneLetter = width/numberOfLetters;
// double pixelToSizeRatio = (double)points.Count / (double)(width * height);
// Point left = new Point();
// int leftPixelCount = 0;
// double leftPixelToSizeRatio = 0;
// left.X = x - estimatedSizeOfOneLetter;
// left.Y = y;
// Point right = new Point();
// int rightPixelCount = 0;
// double rightPixelToSizeRatio = 0;
// right.X = x + width;
// right.Y = y;
// do
// {
// for (int i = left.X; i < left.X+estimatedSizeOfOneLetter; i++)
// {
// for (int j = left.Y; j < left.Y+height; j++)
// {
// Color color = sourceImage1.GetPixel(i, j);
// if (!sourceImage1.GetPixel(i, j).Name.Equals("ffffffff"))
// {
// leftPixelCount++;
// // sourceImage1.SetPixel(i, j, Color.Green);
// }
// sourceImage1.SetPixel(i, j, Color.Green);
// }
// }
// leftPixelToSizeRatio = (double)leftPixelCount / (double)(estimatedSizeOfOneLetter * height);
// left.X -= estimatedSizeOfOneLetter;
// leftPixelCount = 0;
// } while (leftPixelToSizeRatio > 0.1);
// do
// {
// for (int i = right.X; i < right.X + estimatedSizeOfOneLetter; i++)
// {
// for (int j = right.Y; j < right.Y + height; j++)
// {
// Color color = sourceImage1.GetPixel(i, j);
// if (!sourceImage1.GetPixel(i, j).Name.Equals("ffffffff"))
// {
// rightPixelCount++;
// // sourceImage1.SetPixel(i, j, Color.Green);
// }
// sourceImage1.SetPixel(i, j, Color.Green);
// }
// }
// rightPixelToSizeRatio = (double)(rightPixelCount) / (double)(estimatedSizeOfOneLetter * height);
// right.X += estimatedSizeOfOneLetter;
// rightPixelCount = 0;
// } while (rightPixelToSizeRatio > 0.2);
//}
// else
// {
pts = points.ToArray();
Emgu.CV.Structure.MCvBox2D box = PointCollection.MinAreaRect(pts);
Rectangle boundingbox = PointCollection.BoundingRectangle(pts);
img.Draw(box, new Bgr(Color.Red), 1);
img.Draw(boundingbox, new Bgr(Color.Green), 2);
source.Draw(box, new Bgr(Color.Red), 1);
source.Draw(boundingbox, new Bgr(Color.Green), 2);
Emgu.CV.Structure.MCvBox2D boxinoriginalImage = box;
boxinoriginalImage.center.X += x - boundingbox.X; /// 63=originalboundingbox.x(1069)(I used result of intermediate image)-boundingbox.x(21)
boxinoriginalImage.center.Y += y - boundingbox.Y; //// 175=originalboundingbox.x(1211)(I used result of intermediate image)-boundingbox.x(21)
double angleInRadius = (((-1) * box.angle) * Math.PI) / 180;
// double height = (box.size.Height) / Math.Sin(angleInRadius);
// double width = (box.size.Width) / Math.Sin(angleInRadius);
double addToX = (((box.size.Width) * 3) / 4) * Math.Cos(angleInRadius); // I have multiplied height to 3/4 because we have four letter in recognized image and I want to find the center of added part for boundingbox
double addToY = (((box.size.Width) * 3) / 4) * Math.Sin(angleInRadius);
double OriginalpixelToSizeRatio = points.Count() / (box.size.Height * box.size.Width);
Emgu.CV.Structure.MCvBox2D addedArea = box;
addedArea.center.X += x - boundingbox.X + (float)addToX;
addedArea.center.Y += y - boundingbox.Y - (float)addToY;
addedArea.size.Width = (float)((box.size.Width) / 2);
source1.Draw(boxinoriginalImage, new Bgr(Color.Green), 2);
source1.Draw(addedArea, new Bgr(Color.Red), 2);
// PointF[] vertices = boxinoriginalImage.GetVertices();
PointF[] AddedAreaVertices = addedArea.GetVertices();
int AddedAreaPixelNumber = 0;
double[] slope = new double[4];
double[] b = new double[4];
for (int i = 0; i < AddedAreaVertices.Length; i++)
{
if (i < AddedAreaVertices.Length - 1)
{
slope[i] = (AddedAreaVertices[i].Y - AddedAreaVertices[i + 1].Y) / (AddedAreaVertices[i].X - AddedAreaVertices[i + 1].X);
}
else
{
slope[i] = (AddedAreaVertices[i].Y - AddedAreaVertices[0].Y) / (AddedAreaVertices[i].X - AddedAreaVertices[0].X);
}
b[i] = -(AddedAreaVertices[i].Y - slope[i] * AddedAreaVertices[i].X);
}
for (int i = 0; i < sourceImage1.Width; i++)
{
for (int j = 0; j < sourceImage1.Height; j++)
{
Color color = sourceImage1.GetPixel(i, j);
double[] result = new double[4];
for (int k = 0; k < result.Length; k++)
{
result[k] = j - slope[k] * i + b[k];
}
if (result[0] < 0 && result[1] > 0 && result[2] > 0 && result[3] < 0) /// explore if pixel is in bounding box of Min Area Rectangle
{
// sourceImage1.SetPixel(i, j, Color.Green);
if (!sourceImage1.GetPixel(i, j).Name.Equals("ffffffff"))
{
AddedAreaPixelNumber++;
}
sourceImage1.SetPixel(i, j, Color.Green);
}
}
}
double addedAreaSize = addedArea.size.Height * addedArea.size.Width;
double AddedAreaPixelToSizeRatio = (double)(AddedAreaPixelNumber) / (addedArea.size.Height * addedArea.size.Width);
// PointF[] vertices = boxinoriginalImage.GetVertices();
//// PointF[] otherVertices = addedArea.GetVertices();
// double[] slope = new double[4];
// double[] b=new double[4];
// for (int i = 0; i < vertices.Length;i++)
// {
// if (i < vertices.Length - 1)
// slope[i] = (vertices[i].Y - vertices[i + 1].Y) / (vertices[i].X - vertices[i + 1].X);
// else
// slope[i] = (vertices[i].Y - vertices[0].Y) / (vertices[i].X - vertices[0].X);
// b[i] = -(vertices[i].Y - slope[i] * vertices[i].X);
// }
// for (int i = 0; i < sourceImage.Width; i++)
// {
// for (int j = 0; j < sourceImage.Height; j++)
// {
// Color color = sourceImage.GetPixel(i, j);
// double[] result = new double[4];
// for (int k = 0; k < result.Length;k++)
// result[k] = j - slope[k] * i + b[k];
// if (result[0] < 0 && result[1] > 0 && result[2] > 0 && result[3] < 0) /// explore if pixel is in bounding box of Min Area Rectangle
// {
// sourceImage.SetPixel(i, j, Color.Green);
// }
// }
// }
foreach (PointF p in pts)
{
img.Draw(new CircleF(p, 2), new Bgr(Color.Green), 1);
}
// }
sourceImage.Save(@"C:\Users\ronaldiscool\Documents\separationResult.png");
img.Save(@"C:\Users\ronaldiscool\Documents\sample.png");
source.Save(@"C:\Users\ronaldiscool\Documents\sample1.png");
source1.Save(@"C:\Users\ronaldiscool\Documents\sample2.png");
sourceImage1.Save(@"C:\Users\ronaldiscool\Documents\sample3.png");
}
private static void FindRectangle(int min, int max)
{
Image <Rgb, Byte> img = new Image <Rgb, byte>(ori_img);
Image <Gray, Byte> edgeImage = new Image <Gray, byte>(show_img);
#region Find rectangles
boxList = new List <RotatedRect>(); //a box is a rotated rectangle
double rate = img.Width * img.Height / 3864714.0;
bool isIDPExist = false, isUseExist = false, isAddressExist = false;
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
CvInvoke.FindContours(edgeImage, contours, null, RetrType.List, ChainApproxMethod.ChainApproxSimple);
int count = contours.Size;
for (int i = 0; i < count; i++)
{
using (VectorOfPoint contour = contours[i])
using (VectorOfPoint approxContour = new VectorOfPoint())
{
CvInvoke.ApproxPolyDP(contour, approxContour, CvInvoke.ArcLength(contour, true) * 0.05, true);
double area = CvInvoke.ContourArea(contour, false);
if (area > 100000 * rate && area < 142000 * rate && !isIDPExist)
{
// eid, date, price
Point[] pts = contour.ToArray();
PointF[] ptfs = new PointF[pts.Length];
for (int pi = 0; pi < pts.Length; pi++)
{
ptfs[pi] = new PointF(pts[pi].X, pts[pi].Y);
}
Rectangle rec = PointCollection.BoundingRectangle(ptfs);
img.Draw(rec, new Rgb(Color.DarkOrange), 2);
rects.Add(rec);
isIDPExist = true;
}
else if (area > 350000 * rate && area < 450000 * rate)
{
// use
Point[] pts = contour.ToArray();
PointF[] ptfs = new PointF[4];
int minx = int.MaxValue, miny = int.MaxValue, maxx = 0, maxy = 0;
for (int pi = 0; pi < pts.Length; pi++)
{
if (pts[pi].X < minx)
{
minx = pts[pi].X;
}
if (pts[pi].Y < miny)
{
miny = pts[pi].Y;
}
if (pts[pi].X > maxx)
{
maxx = pts[pi].X;
}
if (pts[pi].Y > maxy)
{
maxy = pts[pi].Y;
}
}
ptfs[0] = new PointF(minx, miny);
ptfs[1] = new PointF(minx, (miny + maxy) / 2);
ptfs[2] = new PointF(maxx, miny);
ptfs[3] = new PointF(maxx, (miny + maxy) / 2);
Rectangle rec = PointCollection.BoundingRectangle(ptfs);
img.Draw(PointCollection.BoundingRectangle(ptfs), new Rgb(Color.DarkOrange), 2);
if (!isUseExist)
{
rects.Add(rec);
isUseExist = true;
}
PointF[] ptfks = new PointF[4];
ptfks[0] = new PointF(maxx * 1.5f, maxy);
ptfks[1] = new PointF(maxx * 1.5f, (float)(maxy + 32 * rate));
ptfks[2] = new PointF(minx, maxy);
ptfks[3] = new PointF(minx, (float)(maxy + 32 * rate));
Rectangle rec2 = PointCollection.BoundingRectangle(ptfks);
img.Draw(PointCollection.BoundingRectangle(ptfks), new Rgb(Color.DarkOrange), 2);
if (!isAddressExist)
{
rects.Add(rec2);
isAddressExist = true;
}
}
else if (area > min * rate && area < max * rate)
{
Point[] pts = contour.ToArray();
PointF[] ptfs = new PointF[pts.Length];
for (int pi = 0; pi < pts.Length; pi++)
{
ptfs[pi] = new PointF(pts[pi].X, pts[pi].Y);
}
Rectangle rec = PointCollection.BoundingRectangle(ptfs);
img.Draw(rec, new Rgb(Color.DarkOrange), 2);
rects.Add(rec);
}
}
}
}
#endregion
#region draw rectangles
foreach (RotatedRect box in boxList)
{
img.Draw(box, new Rgb(Color.DarkOrange), 2);
}
pre_img = show_img;
show_img = img.Bitmap;
rform.UpdateImage(show_img);
//img.Save("rect_" + this.count + ".png");
#endregion
}
public void AddNeighbor(List <TessResult> tessOcrResultList, string inputpath)
{
string file_name = "";
List <PointF> points = new List <PointF>();
Boolean Continue = true;
int Counter = 0;
SVM SVMForClassification = new SVM(@"C:\Users\nhonarva\Documents\MachineLearning\sampleTraintData\");
for (int k = 0; k < tessOcrResultList.Count; k++) /// add neighbors of each bounding box
{
points = new List <PointF>();
string TotalPixelToSizeRatio = "";
file_name = tessOcrResultList[k].fileName;
Bitmap ResultOfTextDetection;
string[] split = file_name.Split('_');
if (split[6].Equals("0"))
{
ResultOfTextDetection = new Bitmap(inputpath + file_name + ".png");
}
else
{
string newFileName = "";
split[6] = "0";
for (int j = 0; j < split.Length; j++)
{
if (j < split.Length - 1)
{
newFileName += split[j] + "_";
}
else
{
newFileName += split[j];
}
}
ResultOfTextDetection = new Bitmap(inputpath + newFileName + ".png");
}
Bitmap OriginalBinaryImage = new Bitmap(inputpath + "BinaryOutput.png");
for (int i = 0; i < ResultOfTextDetection.Width; i++) //// scan image to find foreground pixels
{
for (int j = 0; j < ResultOfTextDetection.Height; j++)
{
Color color = ResultOfTextDetection.GetPixel(i, j);
if (!ResultOfTextDetection.GetPixel(i, j).Name.Equals("ffffffff"))
{
PointF point = new PointF(i, j);
points.Add(point);
}
}
}
String[] splitTokens = file_name.Split('_');
int numberOfRecognizedLetters = tessOcrResultList[k].tess_word3.Length - 2; /// -2 because we have \n\n at the end of word
int x = Convert.ToInt32(tessOcrResultList[k].x);
int y = Convert.ToInt32(tessOcrResultList[k].y);
int width = Convert.ToInt32(tessOcrResultList[k].w);
int height = Convert.ToInt32(tessOcrResultList[k].h);
if (splitTokens[6].Equals("0")) /// bounding box with slope 0 (horizental)
{
int estimatedSizeOfOneLetter = width / numberOfRecognizedLetters;
double pixelToSizeRatio = (double)(points.Count) / (double)(width * height);
TotalPixelToSizeRatio = Convert.ToString(Math.Round((decimal)pixelToSizeRatio, 2));
Point left = new Point(); /// left coordinate for box at left hand side of blob
int leftPixelCount = 0;
double leftPixelToSizeRatio = 0;
left.X = x - estimatedSizeOfOneLetter;
left.Y = y;
Point right = new Point(); //// right coordinate for box at right hand side of blob
int rightPixelCount = 0;
double rightPixelToSizeRatio = 0;
right.X = x + width;
right.Y = y;
Continue = true;
if (char.IsUpper(tessOcrResultList[k].tess_word3.ToCharArray()[0]))
{
Continue = false;
}
if (left.X < 0)
{
Continue = false;
}
// continue;
int AddedAreaWidth = estimatedSizeOfOneLetter;
Counter = 0;
while (Continue)
{
if ((left.X - estimatedSizeOfOneLetter) < 0)
{
AddedAreaWidth = left.X;
}
int[,] AddedAreaPoints = new int[AddedAreaWidth, height];
for (int i = left.X; i < left.X + AddedAreaWidth; i++)
{
for (int j = left.Y; j < left.Y + height; j++)
{
if ((!OriginalBinaryImage.GetPixel(i, j).Name.Equals("ffffffff")))
{
leftPixelCount++;
AddedAreaPoints[i - left.X, j - left.Y] = 1;
}
else
{
AddedAreaPoints[i - left.X, j - left.Y] = 0;
}
}
}
Counter++;
leftPixelToSizeRatio = (double)(leftPixelCount) / (double)(AddedAreaWidth * height);
QGISJsonNeighbothood.AddFeature(left.X, left.Y, left.X + AddedAreaWidth, left.Y, left.X + AddedAreaWidth, left.Y + tessOcrResultList[k].h, left.X, left.Y + tessOcrResultList[k].h, tessOcrResultList[k].id + "-" + Convert.ToString(Counter), Convert.ToString(Math.Round((decimal)leftPixelToSizeRatio, 2)), -1);
left.X -= estimatedSizeOfOneLetter;
leftPixelCount = 0;
if (left.X < 0)
{
Continue = false;
}
if (leftPixelToSizeRatio < 0.2)
{
Continue = false;
}
if (leftPixelToSizeRatio > 0.2)
{
SVMForClassification.Classification(AddedAreaPoints, tessOcrResultList[k].id + "-" + Convert.ToString(Counter));
}
}
Continue = true;
if (right.X > OriginalBinaryImage.Width)
{
Continue = false;
}
AddedAreaWidth = estimatedSizeOfOneLetter;
Counter = 0;
while (Continue)
{
if ((right.X + estimatedSizeOfOneLetter) > OriginalBinaryImage.Width)
{
AddedAreaWidth = OriginalBinaryImage.Width - right.X;
}
int[,] AddedAreaPoints = new int[AddedAreaWidth, height];
for (int i = right.X; i < right.X + AddedAreaWidth; i++)
{
for (int j = right.Y; j < right.Y + height; j++)
{
if (!OriginalBinaryImage.GetPixel(i, j).Name.Equals("ffffffff"))
{
rightPixelCount++;
AddedAreaPoints[i - right.X, j - right.Y] = 1;
}
else
{
AddedAreaPoints[i - right.X, j - right.Y] = 0;
}
}
}
Counter++;
rightPixelToSizeRatio = (double)(rightPixelCount) / (double)(AddedAreaWidth * height);
QGISJsonNeighbothood.AddFeature(right.X, right.Y, right.X + AddedAreaWidth, right.Y, right.X + AddedAreaWidth, right.Y + tessOcrResultList[k].h, right.X, right.Y + tessOcrResultList[k].h, tessOcrResultList[k].id + "-" + Convert.ToString(Counter), Convert.ToString(Math.Round((decimal)rightPixelToSizeRatio, 2)), -1);
right.X += estimatedSizeOfOneLetter;
rightPixelCount = 0;
if (right.X > OriginalBinaryImage.Width)
{
Continue = false;
}
if (rightPixelToSizeRatio < 0.2)
{
Continue = false;
}
if (rightPixelToSizeRatio > 0.2)
{
SVMForClassification.Classification(AddedAreaPoints, tessOcrResultList[k].id + "-" + Convert.ToString(Counter));
}
}
// }
QGISJsonNeighbothood.AddFeature(x, y, x + width, y, x + width, y + height, x, y + height, tessOcrResultList[k].id, TotalPixelToSizeRatio, -1);
}
else
{
PointF[] pts;
pts = points.ToArray();
Emgu.CV.Structure.MCvBox2D box = PointCollection.MinAreaRect(pts);
Rectangle boundingbox = PointCollection.BoundingRectangle(pts);
Emgu.CV.Structure.MCvBox2D boxinoriginalImage = box;
boxinoriginalImage.center.X += x - boundingbox.X; /// 63=originalboundingbox.x(1069)(I used result of intermediate image)-boundingbox.x(21)
boxinoriginalImage.center.Y += y - boundingbox.Y;
PointF[] vertices = boxinoriginalImage.GetVertices();
double pixelToSizeRatio = (double)(points.Count) / (double)(box.size.Width * box.size.Height);
TotalPixelToSizeRatio = Convert.ToString(Math.Round((decimal)pixelToSizeRatio, 2));
QGISJsonNeighbothood.AddFeature(vertices[0].X, vertices[0].Y, vertices[1].X, vertices[1].Y, vertices[2].X, vertices[2].Y, vertices[3].X, vertices[3].Y, tessOcrResultList[k].id, TotalPixelToSizeRatio, -1);
for (int m = 1; m < 4; m++)
{
Emgu.CV.Structure.MCvBox2D addedArea = box;
if (box.size.Height > box.size.Width) ///Non horizental bounding boxes that slope of bounding box is less than zero
{
double angleInRadius = ((90 + box.angle) * Math.PI) / 180;
double addToX = (((box.size.Height) * ((m - 1) * 2 + numberOfRecognizedLetters + 1)) / (numberOfRecognizedLetters * 2)) * Math.Cos(angleInRadius); // I have multiplied height to 3/4 because we have four letter in recognized image and I want to find the center of added part for boundingbox
double addToY = (((box.size.Height) * ((m - 1) * 2 + numberOfRecognizedLetters + 1)) / (numberOfRecognizedLetters * 2)) * Math.Sin(angleInRadius);
addedArea.center.X += (x - boundingbox.X) + (float)addToX;
addedArea.center.Y += (y - boundingbox.Y) + (float)addToY;
addedArea.size.Height = (float)((box.size.Height) / numberOfRecognizedLetters);
}
else ///Non horizental bounding boxes that slope of bounding box is greater than zero
{
double angleInRadius = (((-1) * box.angle) * Math.PI) / 180;
double addToX = (((box.size.Width) * ((m - 1) * 2 + numberOfRecognizedLetters + 1)) / (numberOfRecognizedLetters * 2)) * Math.Cos(angleInRadius); // I have multiplied height to 3/4 because we have four letter in recognized image and I want to find the center of added part for boundingbox
double addToY = (((box.size.Width) * ((m - 1) * 2 + numberOfRecognizedLetters + 1)) / (numberOfRecognizedLetters * 2)) * Math.Sin(angleInRadius);
addedArea.center.X += (x - boundingbox.X) - (float)addToX;
addedArea.center.Y += (y - boundingbox.Y) + (float)addToY;
addedArea.size.Width = (float)((box.size.Width) / numberOfRecognizedLetters);
}
PointF[] AddedAreaVertices = addedArea.GetVertices();
PointF ExploreAddedArea_MinPoint = AddedAreaVertices[0];
PointF ExploreAddedArea_MaxPoint = AddedAreaVertices[0];
for (int i = 1; i < AddedAreaVertices.Length; i++)
{
if (AddedAreaVertices[i].X < ExploreAddedArea_MinPoint.X)
{
ExploreAddedArea_MinPoint.X = AddedAreaVertices[i].X;
}
if (AddedAreaVertices[i].Y < ExploreAddedArea_MinPoint.Y)
{
ExploreAddedArea_MinPoint.Y = AddedAreaVertices[i].Y;
}
if (AddedAreaVertices[i].X > ExploreAddedArea_MaxPoint.X)
{
ExploreAddedArea_MaxPoint.X = AddedAreaVertices[i].X;
}
if (AddedAreaVertices[i].Y > ExploreAddedArea_MaxPoint.Y)
{
ExploreAddedArea_MaxPoint.Y = AddedAreaVertices[i].Y;
}
}
int AddedAreaPixelNumber = 0;
double[] slope = new double[4];
double[] b = new double[4];
for (int i = 0; i < AddedAreaVertices.Length; i++) //// find four lines that create min area rectangle bounding box
{
if (i < AddedAreaVertices.Length - 1)
{
slope[i] = (AddedAreaVertices[i].Y - AddedAreaVertices[i + 1].Y) / (AddedAreaVertices[i].X - AddedAreaVertices[i + 1].X);
}
else
{
slope[i] = (AddedAreaVertices[i].Y - AddedAreaVertices[0].Y) / (AddedAreaVertices[i].X - AddedAreaVertices[0].X);
}
b[i] = -(AddedAreaVertices[i].Y - slope[i] * AddedAreaVertices[i].X);
}
for (int i = Convert.ToInt32(ExploreAddedArea_MinPoint.X); i < Convert.ToInt32(ExploreAddedArea_MaxPoint.X) + 1; i++)
{
for (int j = Convert.ToInt32(ExploreAddedArea_MinPoint.Y); j < Convert.ToInt32(ExploreAddedArea_MaxPoint.Y) + 1; j++)
{
if ((i < OriginalBinaryImage.Width) && (j < OriginalBinaryImage.Width) && (i > 0) && (j > 0))
{
Color color = OriginalBinaryImage.GetPixel(i, j);
double[] result = new double[4];
for (int l = 0; l < result.Length; l++)
{
result[l] = j - slope[l] * i + b[l];
}
if (result[0] < 0 && result[1] > 0 && result[2] > 0 && result[3] < 0) /// explore if pixel is in bounding box of Min Area Rectangle
{
// sourceImage1.SetPixel(i, j, Color.Green);
if (!OriginalBinaryImage.GetPixel(i, j).Name.Equals("ffffffff"))
{
AddedAreaPixelNumber++;
}
}
}
else
{
break;
}
}
}
double addedAreaSize = addedArea.size.Height * addedArea.size.Width;
double AddedAreaPixelToSizeRatio = (double)(AddedAreaPixelNumber) / (addedArea.size.Height * addedArea.size.Width);
QGISJsonNeighbothood.AddFeature(AddedAreaVertices[0].X, AddedAreaVertices[0].Y, AddedAreaVertices[1].X, AddedAreaVertices[1].Y, AddedAreaVertices[2].X, AddedAreaVertices[2].Y, AddedAreaVertices[3].X, AddedAreaVertices[3].Y, tessOcrResultList[k].id, Convert.ToString(Math.Round((decimal)AddedAreaPixelToSizeRatio, 2)), -1);
}
for (int m = 1; m < 4; m++)
{
Emgu.CV.Structure.MCvBox2D addedArea = box;
if (box.size.Height > box.size.Width) ///Non horizental bounding boxes that slope of bounding box is less than zero
{
double angleInRadius = ((90 + box.angle) * Math.PI) / 180;
double addToX = (((box.size.Height) * ((m - 1) * 2 + numberOfRecognizedLetters + 1)) / (numberOfRecognizedLetters * 2)) * Math.Cos(angleInRadius); // I have multiplied height to 3/4 because we have four letter in recognized image and I want to find the center of added part for boundingbox
double addToY = (((box.size.Height) * ((m - 1) * 2 + numberOfRecognizedLetters + 1)) / (numberOfRecognizedLetters * 2)) * Math.Sin(angleInRadius);
addedArea.center.X += (x - boundingbox.X) - (float)addToX;
addedArea.center.Y += (y - boundingbox.Y) - (float)addToY;
addedArea.size.Height = (float)((box.size.Height) / numberOfRecognizedLetters);
}
else ///Non horizental bounding boxes that slope of bounding box is greater than zero
{
double angleInRadius = (((-1) * box.angle) * Math.PI) / 180;
double addToX = (((box.size.Width) * ((m - 1) * 2 + numberOfRecognizedLetters + 1)) / (numberOfRecognizedLetters * 2)) * Math.Cos(angleInRadius); // I have multiplied height to 3/4 because we have four letter in recognized image and I want to find the center of added part for boundingbox
double addToY = (((box.size.Width) * ((m - 1) * 2 + numberOfRecognizedLetters + 1)) / (numberOfRecognizedLetters * 2)) * Math.Sin(angleInRadius);
addedArea.center.X += (x - boundingbox.X) + (float)addToX;
addedArea.center.Y += (y - boundingbox.Y) - (float)addToY;
addedArea.size.Width = (float)((box.size.Width) / numberOfRecognizedLetters);
}
PointF[] AddedAreaVertices = addedArea.GetVertices();
PointF ExploreAddedArea_MinPoint = AddedAreaVertices[0];
PointF ExploreAddedArea_MaxPoint = AddedAreaVertices[0];
for (int i = 1; i < AddedAreaVertices.Length; i++)
{
if (AddedAreaVertices[i].X < ExploreAddedArea_MinPoint.X)
{
ExploreAddedArea_MinPoint.X = AddedAreaVertices[i].X;
}
if (AddedAreaVertices[i].Y < ExploreAddedArea_MinPoint.Y)
{
ExploreAddedArea_MinPoint.Y = AddedAreaVertices[i].Y;
}
if (AddedAreaVertices[i].X > ExploreAddedArea_MaxPoint.X)
{
ExploreAddedArea_MaxPoint.X = AddedAreaVertices[i].X;
}
if (AddedAreaVertices[i].Y > ExploreAddedArea_MaxPoint.Y)
{
ExploreAddedArea_MaxPoint.Y = AddedAreaVertices[i].Y;
}
}
int AddedAreaPixelNumber = 0;
double[] slope = new double[4];
double[] b = new double[4];
for (int i = 0; i < AddedAreaVertices.Length; i++) //// find four lines that create min area rectangle bounding box
{
if (i < AddedAreaVertices.Length - 1)
{
slope[i] = (AddedAreaVertices[i].Y - AddedAreaVertices[i + 1].Y) / (AddedAreaVertices[i].X - AddedAreaVertices[i + 1].X);
}
else
{
slope[i] = (AddedAreaVertices[i].Y - AddedAreaVertices[0].Y) / (AddedAreaVertices[i].X - AddedAreaVertices[0].X);
}
b[i] = -(AddedAreaVertices[i].Y - slope[i] * AddedAreaVertices[i].X);
}
for (int i = Convert.ToInt32(ExploreAddedArea_MinPoint.X); i < Convert.ToInt32(ExploreAddedArea_MaxPoint.X) + 1; i++)
{
for (int j = Convert.ToInt32(ExploreAddedArea_MinPoint.Y); j < Convert.ToInt32(ExploreAddedArea_MaxPoint.Y) + 1; j++)
{
if ((i < OriginalBinaryImage.Width) && (j < OriginalBinaryImage.Width) && (i > 0) && (j > 0))
{
Color color = OriginalBinaryImage.GetPixel(i, j);
double[] result = new double[4];
for (int l = 0; l < result.Length; l++)
{
result[l] = j - slope[l] * i + b[l];
}
if (result[0] < 0 && result[1] > 0 && result[2] > 0 && result[3] < 0) /// explore if pixel is in bounding box of Min Area Rectangle
{
// sourceImage1.SetPixel(i, j, Color.Green);
if (!OriginalBinaryImage.GetPixel(i, j).Name.Equals("ffffffff"))
{
AddedAreaPixelNumber++;
}
}
}
else
{
break;
}
}
}
double addedAreaSize = addedArea.size.Height * addedArea.size.Width;
double AddedAreaPixelToSizeRatio = (double)(AddedAreaPixelNumber) / (addedArea.size.Height * addedArea.size.Width);
QGISJsonNeighbothood.AddFeature(AddedAreaVertices[0].X, AddedAreaVertices[0].Y, AddedAreaVertices[1].X, AddedAreaVertices[1].Y, AddedAreaVertices[2].X, AddedAreaVertices[2].Y, AddedAreaVertices[3].X, AddedAreaVertices[3].Y, tessOcrResultList[k].id, Convert.ToString(Math.Round((decimal)AddedAreaPixelToSizeRatio, 2)), -1);
}
}
}
QGISJsonNeighbothood.WriteGeojsonFileByPixels();
}