public void FilterTiles(Mat image, Mat modifiedMat)
{
CvInvoke.Imshow("0", image);
Stopwatch sw1 = new Stopwatch();
sw1.Start();
Mat laplaced = new Mat();
CvInvoke.CvtColor(image, laplaced, Emgu.CV.CvEnum.ColorConversion.Bgr2Gray);
Mat greyResult = laplaced.Clone();
Mat greySource = laplaced.Clone();
Mat cannySrc = new Mat();
//if not half inch, do canny and subtract to separate tiles better. Basically "sharpens" the edge
if (scan.TileSettings.CannyEdges)
{
//create canny image, these parameters could be adjusted probably?
CvInvoke.Canny(greySource, greyResult, 50, 150);
//dilate canny
CvInvoke.Dilate(greyResult, greyResult, null, new System.Drawing.Point(1, 1), scan.TileSettings.CannyDilate, BorderType.Default, CvInvoke.MorphologyDefaultBorderValue);
CvInvoke.Erode(greyResult, greyResult, null, new System.Drawing.Point(1, 1), scan.TileSettings.CannyDilate, BorderType.Default, CvInvoke.MorphologyDefaultBorderValue);
CvInvoke.Imshow("1a", greyResult);
//subtract dilated canny from source to get separation
CvInvoke.Subtract(greySource, greyResult, greyResult);
greySource = greyResult.Clone();
CvInvoke.Imshow("1b", greyResult);
}
if (scan.TileSettings.ThresholdEdges)
{
Mat edges = new Mat();
CvInvoke.Threshold(greyResult, edges, (float)thresholdTrackbar.Value, 0, ThresholdType.ToZero);
CvInvoke.Subtract(greySource, edges, greyResult);
CvInvoke.Erode(greyResult, greyResult, null, new System.Drawing.Point(1, 1), 2, BorderType.Default, CvInvoke.MorphologyDefaultBorderValue);
CvInvoke.Imshow("pres-1c", greyResult);
}
//perform distance transform
CvInvoke.DistanceTransform(greyResult, greyResult, null, DistType.L2, 5);
//normalize the image to bring out the peaks
CvInvoke.Normalize(greyResult, greyResult, 0, 1, NormType.MinMax);
CvInvoke.Imshow("2", greyResult);
//threshold the image, different thresholds for different tiles
CvInvoke.Threshold(greyResult, greyResult, scan.TileSettings.ThresholdVal, 1, ThresholdType.Binary);
CvInvoke.Imshow("3", greyResult);
//erode to split the blobs
CvInvoke.Erode(greyResult, greyResult, null, new System.Drawing.Point(-1, -1), scan.TileSettings.ThresholdErode, BorderType.Default, CvInvoke.MorphologyDefaultBorderValue);
//convert to 8 bit unsigned needed for canny
greyResult.ConvertTo(greyResult, DepthType.Cv8U);
VectorOfVectorOfPoint markers = new VectorOfVectorOfPoint();
//create 32bit, single channel image for result of markers
Mat markerImage = new Mat(greyResult.Size, DepthType.Cv32S, 1);
//set image to 0
markerImage.SetTo(new MCvScalar(0, 0, 0));
//find the contours
CvInvoke.FindContours(greyResult, markers, null, RetrType.External, ChainApproxMethod.LinkRuns);
//label the markers from 1 -> n, the rest of the image should remain 0
for (int i = 0; i < markers.Size; i++)
CvInvoke.DrawContours(markerImage, markers, i, new MCvScalar(i + 1, i + 1, i + 1), -1);
ScalarArray mult = new ScalarArray(5000);
Mat markerVisual = new Mat();
CvInvoke.Multiply(markerImage, mult, markerVisual);
CvInvoke.Imshow("4", markerVisual);
//draw the background marker
CvInvoke.Circle(markerImage,
new System.Drawing.Point(5, 5),
3,
new MCvScalar(255, 255, 255),
-1);
//convert to 3 channel
Mat convertedOriginal = new Mat();
//use canny modified if 3/4", or use the gray image for others
CvInvoke.CvtColor(greySource, convertedOriginal, ColorConversion.Gray2Bgr);
//watershed!!
CvInvoke.Watershed(convertedOriginal, markerImage);
//visualize
CvInvoke.Multiply(markerImage, mult, markerVisual);
CvInvoke.Imshow("5", markerVisual);
//get contours to get the actual tiles now that they are separate...
VectorOfVectorOfPoint tilesContours = new VectorOfVectorOfPoint();
markerVisual.ConvertTo(markerVisual, DepthType.Cv8U);
CvInvoke.BitwiseNot(markerVisual, markerVisual);
CvInvoke.Imshow("6", markerVisual);
CvInvoke.Dilate(markerVisual, markerVisual, null, new System.Drawing.Point(1, 1), 2, BorderType.Default, CvInvoke.MorphologyDefaultBorderValue);
CvInvoke.FindContours(markerVisual, tilesContours, null, RetrType.External, ChainApproxMethod.LinkRuns);
List<System.Drawing.Point> tiles = new List<System.Drawing.Point>();
for (int i = 0; i < tilesContours.Size; i++)
{
using(VectorOfPoint c = tilesContours[i])
using (VectorOfPoint approx = new VectorOfPoint())
{
//epsilon = arclength * .05 to get rid of convex areas
CvInvoke.ApproxPolyDP(c, approx, CvInvoke.ArcLength(c, true) * .05, true);
double area = CvInvoke.ContourArea(approx);
//filter out the small contours...
if (area > scan.TileSettings.MinArea && area < scan.TileSettings.MaxArea)
{
//match the shape to the square
double ratio = CvInvoke.MatchShapes(_square, approx, Emgu.CV.CvEnum.ContoursMatchType.I3);
if (ratio < .05)
{
var M = CvInvoke.Moments(c);
int cx = (int)(M.M10 / M.M00);
int cy = (int)(M.M01 / M.M00);
//filter out any that are too close
if (!tiles.Any(x => Math.Abs(x.X - cx) < 50 && Math.Abs(x.Y - cy) < 50))
{
tiles.Add(new System.Drawing.Point(cx, cy));
for (int j = 0; j < approx.Size; j++)
{
int second = j+1 == approx.Size ? 0 : j + 1;
//do some detection for upsidedown/right side up here....
CvInvoke.Line(image,
new System.Drawing.Point(approx[j].X, approx[j].Y),
new System.Drawing.Point(approx[second].X, approx[second].Y),
new MCvScalar(255, 255, 255,255), 4);
}
}
}
}
}
}
sw1.Stop();
dataTextBox.AppendText(String.Format("Took {0} ms to detect {1} tiles{2}", sw1.ElapsedMilliseconds, tiles.Count, Environment.NewLine));
// dataTextBox.AppendText(String.Format("Found {0} tiles{1}", tiles.Count, Environment.NewLine));
this.originalBox.Image = image;
resultBox.Image = markerVisual;
}
public StopSignDetector(IInputArray stopSignModel)
{
_detector = new SURF(500);
using (Mat redMask = new Mat())
{
GetRedPixelMask(stopSignModel, redMask);
_modelKeypoints = new VectorOfKeyPoint();
_modelDescriptors = new Mat();
_detector.DetectAndCompute(redMask, null, _modelKeypoints, _modelDescriptors, false);
if (_modelKeypoints.Size == 0)
throw new Exception("No image feature has been found in the stop sign model");
}
_modelDescriptorMatcher = new BFMatcher(DistanceType.L2);
_modelDescriptorMatcher.Add(_modelDescriptors);
_octagon = new VectorOfPoint(
new Point[]
{
new Point(1, 0),
new Point(2, 0),
new Point(3, 1),
new Point(3, 2),
new Point(2, 3),
new Point(1, 3),
new Point(0, 2),
new Point(0, 1)
});
}
public static VectorOfPoint FindLargestContour(IInputOutputArray cannyEdges, IInputOutputArray result)
{
int largest_contour_index = 0;
double largest_area = 0;
VectorOfPoint largestContour;
using (Mat hierachy = new Mat())
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
IOutputArray hirarchy;
CvInvoke.FindContours(cannyEdges, contours, hierachy, RetrType.Tree, ChainApproxMethod.ChainApproxNone);
for (int i = 0; i < contours.Size; i++)
{
MCvScalar color = new MCvScalar(0, 0, 255);
double a = CvInvoke.ContourArea(contours[i], false); // Find the area of contour
if (a > largest_area)
{
largest_area = a;
largest_contour_index = i; //Store the index of largest contour
}
CvInvoke.DrawContours(result, contours, largest_contour_index, new MCvScalar(255, 0, 0));
}
CvInvoke.DrawContours(result, contours, largest_contour_index, new MCvScalar(0, 0, 255), 3, LineType.EightConnected, hierachy);
largestContour = new VectorOfPoint(contours[largest_contour_index].ToArray());
}
return largestContour;
}
private void ProcessImage(IInputOutputArray image)
{
Stopwatch watch = Stopwatch.StartNew(); // time the detection process
List<IInputOutputArray> licensePlateImagesList = new List<IInputOutputArray>();
List<IInputOutputArray> filteredLicensePlateImagesList = new List<IInputOutputArray>();
List<RotatedRect> licenseBoxList = new List<RotatedRect>();
List<string> words = _licensePlateDetector.DetectLicensePlate(
image,
licensePlateImagesList,
filteredLicensePlateImagesList,
licenseBoxList);
watch.Stop(); //stop the timer
processTimeLabel.Text = String.Format("License Plate Recognition time: {0} milli-seconds", watch.Elapsed.TotalMilliseconds);
panel1.Controls.Clear();
Point startPoint = new Point(10, 10);
for (int i = 0; i < words.Count; i++)
{
Mat dest = new Mat();
CvInvoke.VConcat(licensePlateImagesList[i], filteredLicensePlateImagesList[i], dest);
AddLabelAndImage(
ref startPoint,
String.Format("License: {0}", words[i]),
dest);
PointF[] verticesF = licenseBoxList[i].GetVertices();
Point[] vertices = Array.ConvertAll(verticesF, Point.Round);
using(VectorOfPoint pts = new VectorOfPoint(vertices))
CvInvoke.Polylines(image, pts, true, new Bgr(Color.Red).MCvScalar,2 );
}
}
/// <summary>
/// Draw the planar subdivision
/// </summary>
/// <param name="maxValue">The points contains values between [0, maxValue)</param>
/// <param name="pointCount">The total number of points</param>
/// <returns>An image representing the planar subvidision of the points</returns>
public static Mat Draw(float maxValue, int pointCount)
{
Triangle2DF[] delaunayTriangles;
VoronoiFacet[] voronoiFacets;
Random r = new Random((int)(DateTime.Now.Ticks & 0x0000ffff));
CreateSubdivision(maxValue, pointCount, out delaunayTriangles, out voronoiFacets);
//create an image for display purpose
Mat img = new Mat((int)maxValue, (int)maxValue, DepthType.Cv8U, 3);
//Draw the voronoi Facets
foreach (VoronoiFacet facet in voronoiFacets)
{
#if NETFX_CORE
Point[] polyline = Extensions.ConvertAll<PointF, Point>(facet.Vertices, Point.Round);
#else
Point[] polyline = Array.ConvertAll<PointF, Point>(facet.Vertices, Point.Round);
#endif
using (VectorOfPoint vp = new VectorOfPoint(polyline))
using (VectorOfVectorOfPoint vvp = new VectorOfVectorOfPoint(vp))
{
//Draw the facet in color
CvInvoke.FillPoly(
img, vvp,
new Bgr(r.NextDouble()*120, r.NextDouble()*120, r.NextDouble()*120).MCvScalar);
//highlight the edge of the facet in black
CvInvoke.Polylines(img, vp, true, new Bgr(0, 0, 0).MCvScalar, 2);
}
//draw the points associated with each facet in red
CvInvoke.Circle(img, Point.Round( facet.Point ), 5, new Bgr(0, 0, 255).MCvScalar, -1);
}
//Draw the Delaunay triangulation
foreach (Triangle2DF triangle in delaunayTriangles)
{
#if NETFX_CORE
Point[] vertices = Extensions.ConvertAll<PointF, Point>(triangle.GetVertices(), Point.Round);
#else
Point[] vertices = Array.ConvertAll<PointF, Point>(triangle.GetVertices(), Point.Round);
#endif
using (VectorOfPoint vp = new VectorOfPoint(vertices))
{
CvInvoke.Polylines(img, vp, true, new Bgr(255, 255, 255).MCvScalar);
}
}
return img;
}
public void FindTiles(Mat image, VectorOfVectorOfPoint contours, int[,] hierachy, int hIndex)
{
Mat resultContours = new Mat(image.Size, image.Depth, image.NumberOfChannels);
// originalBox.Image = image;
//for all of the root hierarchies...
for(; hIndex >= 0; hIndex = hierachy[hIndex,0])
{
MCvScalar color = new MCvScalar(0, 0, 255);
using (VectorOfPoint c = contours[hIndex])
using (VectorOfPoint approx = new VectorOfPoint())
{
CvInvoke.ApproxPolyDP(c, approx, CvInvoke.ArcLength(c, true) * .02, true);//CvInvoke.ArcLength(c, true) * .02, true);
double area = CvInvoke.ContourArea(approx);
//filter out the small contours...
//if (area > 20000 && area < 30000 ) //3/4" tiles
if(area > 0 && area < 100000)
{
//match the shape to the square
double ratio = CvInvoke.MatchShapes(_square, approx, Emgu.CV.CvEnum.ContoursMatchType.I3);
if(ratio < .1)
{
CvInvoke.FillConvexPoly(resultContours, c, new MCvScalar(255), LineType.AntiAlias);
var M = CvInvoke.Moments(c);
int cx = (int)(M.M10 / M.M00);
int cy = (int)(M.M01 / M.M00);
for (int i = 0; i < approx.Size; i++)
{
int second = i + 1;
if (second ==approx.Size)
second = 0;
CvInvoke.Line(resultContours,
new System.Drawing.Point(approx[i].X, approx[i].Y),
new System.Drawing.Point(approx[second].X, approx[second].Y),
new MCvScalar(128), 10);
}
CvInvoke.Rectangle(resultContours, new Rectangle(new System.Drawing.Point(cx - 50, cy - 50), new Size(100, 100)), new MCvScalar(128), 2);
}
}
}
color = new MCvScalar(0, 255, 0);
}
resultBox.Image = resultContours;
}
/// <summary>
/// Get the contour that defines the blob
/// </summary>
/// <returns>The contour of the blob</returns>
public Point[] GetContour()
{
using (VectorOfPoint vp = new VectorOfPoint())
{
cvbCvBlobGetContour(_ptr, vp.Ptr);
return vp.ToArray();
}
}
public void TestConvexityDefacts()
{
Image<Bgr, Byte> image = new Image<Bgr, byte>(300, 300);
Point[] polyline = new Point[] {
new Point(10, 10),
new Point(10, 250),
new Point(100, 100),
new Point(250, 250),
new Point(250, 10)};
using (VectorOfPoint vp = new VectorOfPoint(polyline))
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint(vp))
using (VectorOfInt convexHull = new VectorOfInt())
using (Mat convexityDefect = new Mat())
{
//Draw the contour in white thick line
CvInvoke.DrawContours(image, contours, -1, new MCvScalar(255, 255, 255), 3);
CvInvoke.ConvexHull(vp, convexHull);
CvInvoke.ConvexityDefects(vp, convexHull, convexityDefect);
//convexity defect is a four channel mat, when k rows and 1 cols, where k = the number of convexity defects.
if (!convexityDefect.IsEmpty)
{
//Data from Mat are not directly readable so we convert it to Matrix<>
Matrix<int> m = new Matrix<int>(convexityDefect.Rows, convexityDefect.Cols,
convexityDefect.NumberOfChannels);
convexityDefect.CopyTo(m);
for (int i = 0; i < m.Rows; i++)
{
int startIdx = m.Data[i, 0];
int endIdx = m.Data[i, 1];
Point startPoint = polyline[startIdx];
Point endPoint = polyline[endIdx];
//draw a line connecting the convexity defect start point and end point in thin red line
CvInvoke.Line(image, startPoint, endPoint, new MCvScalar(0, 0, 255));
}
}
//Emgu.CV.UI.ImageViewer.Show(image);
}
}
/// <summary>
/// Approximates an elliptic arc with a polyline.
/// The function ellipse2Poly computes the vertices of a polyline that
/// approximates the specified elliptic arc. It is used by cv::ellipse.
/// </summary>
/// <param name="center">Center of the arc.</param>
/// <param name="axes">Half of the size of the ellipse main axes. See the ellipse for details.</param>
/// <param name="angle">Rotation angle of the ellipse in degrees. See the ellipse for details.</param>
/// <param name="arcStart">Starting angle of the elliptic arc in degrees.</param>
/// <param name="arcEnd">Ending angle of the elliptic arc in degrees.</param>
/// <param name="delta">Angle between the subsequent polyline vertices. It defines the approximation</param>
/// <returns>Output vector of polyline vertices.</returns>
public static Point[] Ellipse2Poly(Point center, Size axes, int angle,
int arcStart, int arcEnd, int delta)
{
using (var vec = new VectorOfPoint())
{
NativeMethods.imgproc_ellipse2Poly(center, axes, angle, arcStart, arcEnd, delta, vec.CvPtr);
return vec.ToArray();
}
}
public void getBlueContours(Image<Gray, Byte> src, VectorOfVectorOfPoint blueborders, List<RotatedRect> Blue_boxList, VectorOfVectorOfPoint othercontours_blue)
{
//blueborders = new VectorOfVectorOfPoint();//list of blue borders
//Blue_boxList = new List<RotatedRect>(); //a box is a rotated rectangle
//othercontours_blue = new VectorOfVectorOfPoint();
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
CvInvoke.FindContours(src, contours, null, RetrType.List, ChainApproxMethod.ChainApproxSimple);
for (int i = 0; i < contours.Size; i++)
{
using (VectorOfPoint contour = contours[i])
using (VectorOfPoint approxContour = new VectorOfPoint())
{
CvInvoke.ApproxPolyDP(contour, approxContour, CvInvoke.ArcLength(contour, true) * 0.05, true);
if (CvInvoke.ContourArea(approxContour, false) > 250 && CvInvoke.BoundingRectangle(approxContour).Width * CvInvoke.BoundingRectangle(approxContour).Height > 1000) //only consider contours with area greater than 250
{
if (approxContour.Size == 4)
{
Blue_boxList.Add(CvInvoke.MinAreaRect(approxContour));
blueborders.Push(contour);
}
else
{
othercontours_blue.Push(contour);
//Point[] pts = approxContour.ToArray();
//other.Add(PointCollection.PolyLine(pts, true));
}
}
}
}
}
}
public void getBlackContours(Image<Gray, Byte> src, VectorOfVectorOfPoint blackborders, List<RotatedRect> Black_boxList, VectorOfVectorOfPoint othercontours_black)
{
//blackborders = new VectorOfVectorOfPoint();//list of black borders
//Black_boxList = new List<RotatedRect>(); //a box is a rotated rectangle
//othercontours_black = new VectorOfVectorOfPoint();
Bitmap TMPGood = new Bitmap(src.ToBitmap() , src.Width, src.Height);
Bitmap TMPBad = new Bitmap(src.ToBitmap(), src.Width, src.Height);
Graphics gGood = Graphics.FromImage(TMPGood);
Graphics gBad = Graphics.FromImage(TMPBad);
//Pen RedPen = new Pen(Color.Red);
//Pen GreenPen = new Pen(Color.Green);
Brush RedBrush = new SolidBrush(Color.Red);
Brush GreenBrush = new SolidBrush(Color.Green);
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
CvInvoke.FindContours(src, contours, null, RetrType.List, ChainApproxMethod.ChainApproxSimple);
for (int i = 0; i < contours.Size; i++)
{
using (VectorOfPoint contour = contours[i])
using (VectorOfPoint approxContour = new VectorOfPoint())
{
Point[] ptsContour = contour.ToArray();
for (int k = 0; k < ptsContour.Length; k++)
{
gBad.FillEllipse(RedBrush, ptsContour[k].X, ptsContour[k].Y, 6, 6);
}
CvInvoke.ApproxPolyDP(contour, approxContour, CvInvoke.ArcLength(contour, true) * 0.05, true);
if (CvInvoke.ContourArea(approxContour, false) > 250) //only consider contours with area greater than 250
{
Point[] ptsApprox = approxContour.ToArray();
//TMP.Draw(pts, new Bgr(Color.DarkOrange), 5); //!!!!!!!!!!!!!!!
for (int k = 0; k < ptsApprox.Length; k++)
{
gGood.FillEllipse(GreenBrush, ptsApprox[k].X, ptsApprox[k].Y, 6, 6);
}
if (CvInvoke.ContourArea(approxContour, false) > 250 && approxContour.Size == 4)
{
Black_boxList.Add(CvInvoke.MinAreaRect(approxContour));
blackborders.Push(contour);
}
else
{
othercontours_black.Push(contour);
//Point[] pts = approxContour.ToArray();
//other.Add(PointCollection.PolyLine(pts, true));
}
}
}
}
}
TMPGood.Save("C:\\Emgu\\Dump\\Black contour corners GOOD.png", System.Drawing.Imaging.ImageFormat.Png);
TMPBad.Save("C:\\Emgu\\Dump\\Black contour corners BAD.png", System.Drawing.Imaging.ImageFormat.Png);
}
protected override void DisposeObject()
{
if (_modelKeypoints != null)
{
_modelKeypoints.Dispose();
_modelKeypoints = null;
}
if (_modelDescriptors != null)
{
_modelDescriptors.Dispose();
_modelDescriptors = null;
}
if (_modelDescriptorMatcher != null)
{
_modelDescriptorMatcher.Dispose();
_modelDescriptorMatcher = null;
}
if (_octagon != null)
{
_octagon.Dispose();
_octagon = null;
}
}
private void timer1_Tick(object sender, EventArgs e)
{
if (time == 10)
{
Mat frame = new Mat();
capture.Retrieve(frame, 0);
Mat grayVideo = new Mat();
CvInvoke.CvtColor(frame, grayVideo, ColorConversion.Bgr2Gray);
UMat videoDescriptors = new UMat();
VectorOfKeyPoint videoKeyPoints = new VectorOfKeyPoint();
calculatedescriptors(grayVideo, videoDescriptors, videoKeyPoints);
VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch();
BFMatcher matcher = new BFMatcher(DistanceType.L2);
matcher.Add(originalImageDescriptors);
matcher.KnnMatch(videoDescriptors, matches, 2, null);
Mat mask = mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
Features2DToolbox.VoteForUniqueness(matches, 0.8, mask);
Mat homography = new Mat();
int nonZeroCount = CvInvoke.CountNonZero(mask);
if (nonZeroCount >= 4)
{
nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(originalImageKeyPoints, videoKeyPoints,
matches, mask, 1.5, 20);
if (nonZeroCount >= 4)
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(originalImageKeyPoints, videoKeyPoints, matches, mask, 2);
}
Mat result = new Mat();
Features2DToolbox.DrawMatches(grayImage, originalImageKeyPoints, grayVideo, videoKeyPoints,
matches, result, new MCvScalar(255, 255, 255), new MCvScalar(255, 255, 255), mask);
if (homography != null)
{
//draw a rectangle along the projected model
Rectangle rect = new Rectangle(Point.Empty, grayImage.Size);
PointF[] pts = new PointF[]
{
new PointF(rect.Left, rect.Bottom),
new PointF(rect.Right, rect.Bottom),
new PointF(rect.Right, rect.Top),
new PointF(rect.Left, rect.Top)
};
pts = CvInvoke.PerspectiveTransform(pts, homography);
Point[] points = Array.ConvertAll<PointF, Point>(pts, Point.Round);
using (VectorOfPoint vp = new VectorOfPoint(points))
{
CvInvoke.Polylines(result, vp, true, new MCvScalar(255, 0, 0, 255), 5);
}
viewer.Image = result;
}
time = 0;
}
else
{
time++;
}
}
//**********************************************************************************************************************************************************************************************
/// <summary>
/// Push a single point to a vector of points
/// </summary>
/// <param name="vector">Vector to push the point to</param>
/// <param name="point">Point to push to the vector</param>
public static void Push(this VectorOfPoint vector, Point point)
{
vector.Push(new Point[] { point });
}
private void ExtractContourAndHull(Image <Gray, byte> skin) // kiem vien va lop ngoai
{
using (MemStorage storage = new MemStorage())
{
VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint();
CvInvoke.FindContours(skin, contours, new Mat(), Emgu.CV.CvEnum.RetrType.External, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxSimple);
VectorOfPoint biggestContour = new VectorOfPoint(); // mang point[] chua vien` lon nhat
Double Result1 = 0; // area dang xet
Result = 0;
for (int i = 0; i < contours.Size; i++)
{
VectorOfPoint contour = contours[i]; // chuyen sang Point[][]
double area = CvInvoke.ContourArea(contour, false); // tinh area
Result1 = area;
if (Result1 > Result)
{
Result = Result1;
biggestContour = contour;
}
}
label8.Text = "Size Rect :" + Result.ToString();
if (biggestContour != null)
{
CvInvoke.ApproxPolyDP(biggestContour, biggestContour, 0.00025, false);
points = biggestContour.ToArray();
currentFrame.Draw(points, new Bgr(255, 0, 255), 4);
VectorOfPoint hull = new VectorOfPoint();
VectorOfInt convexHull = new VectorOfInt();
CvInvoke.ConvexHull(biggestContour, hull, false); //~ Hull
box = CvInvoke.MinAreaRect(hull);
currentFrame.Draw(new CircleF(box.Center, 5), new Bgr(Color.Black), 4);
CvInvoke.ConvexHull(biggestContour, convexHull);
//PointF[] Vertices = box.GetVertices();
// handRect = box.MinAreaRect();
currentFrame.Draw(box, new Bgr(200, 0, 0), 1);
// ve khung ban tay khung bao quanh tay
currentFrame.DrawPolyline(hull.ToArray(), true, new Bgr(200, 125, 75), 4);
currentFrame.Draw(new CircleF(new PointF(box.Center.X, box.Center.Y), 3), new Bgr(200, 125, 75));
// tim convex defect
CvInvoke.ConvexityDefects(biggestContour, convexHull, defect);
// chuyen sang Matrix
if (!defect.IsEmpty)
{
mDefect = new Matrix <int>(defect.Rows, defect.Cols, defect.NumberOfChannels);
defect.CopyTo(mDefect);
}
}
}
}
/// <summary>
/// Count number of fingers on skinMask and draw debug information
/// </summary>
/// <param name="skinMask">Skin mask to count fingers on</param>
/// <returns>Mat with detection debug information</returns>
public Mat FindFingersCount(Mat skinMask)
{
Mat contoursImage = Mat.Ones(skinMask.Height, skinMask.Width, DepthType.Cv8U, 3);
if (skinMask.IsEmpty || skinMask.NumberOfChannels != 1)
{
return(contoursImage);
}
VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint();
Mat hierarchy = new Mat();
CvInvoke.FindContours(skinMask, contours, hierarchy, RetrType.External, ChainApproxMethod.ChainApproxNone);
if (contours.Size <= 0)
{
return(contoursImage);
}
int biggestContourIndex = -1;
double biggestArea = 0;
for (int i = 0; i < contours.Size; i++)
{
double area = CvInvoke.ContourArea(contours[i], false);
if (area > biggestArea)
{
biggestArea = area;
biggestContourIndex = i;
}
}
if (biggestContourIndex < 0)
{
return(contoursImage);
}
VectorOfPoint hullPoints = new VectorOfPoint();
VectorOfInt hullInts = new VectorOfInt();
CvInvoke.ConvexHull(contours[biggestContourIndex], hullPoints, true);
CvInvoke.ConvexHull(contours[biggestContourIndex], hullInts, false);
Mat defects = new Mat();
if (hullInts.Size > 3)
{
CvInvoke.ConvexityDefects(contours[biggestContourIndex], hullInts, defects);
}
else
{
return(contoursImage);
}
Rectangle boundingRectangle = CvInvoke.BoundingRectangle(hullPoints);
Point centerBoundingRectangle = new Point((boundingRectangle.X + boundingRectangle.Right) / 2, (boundingRectangle.Y + boundingRectangle.Bottom) / 2);
VectorOfPoint startPoints = new VectorOfPoint();
VectorOfPoint farPoints = new VectorOfPoint();
int[,,] defectsData = (int[, , ])defects.GetData();
for (int i = 0; i < defectsData.Length / 4; i++)
{
Point startPoint = contours[biggestContourIndex][defectsData[i, 0, 0]];
if (!startPoints.ToArray().ToList().Any(p => Math.Abs(p.X - startPoint.X) < 30 && Math.Abs(p.Y - startPoint.Y) < 30))
{
VectorOfPoint startPointVector = new VectorOfPoint(new Point[] { startPoint });
startPoints.Push(startPointVector);
}
Point farPoint = contours[biggestContourIndex][defectsData[i, 0, 2]];
if (findPointsDistance(farPoint, centerBoundingRectangle) < boundingRectangle.Height * BOUNDING_RECT_FINGER_SIZE_SCALING)
{
VectorOfPoint farPointVector = new VectorOfPoint(new Point[] { farPoint });
farPoints.Push(farPointVector);
}
}
VectorOfPoint filteredStartPoints = CompactOnNeighborhoodMedian(startPoints, boundingRectangle.Height * BOUNDING_RECT_NEIGHBOR_DISTANCE_SCALING);
VectorOfPoint filteredFarPoints = CompactOnNeighborhoodMedian(farPoints, boundingRectangle.Height * BOUNDING_RECT_NEIGHBOR_DISTANCE_SCALING);
VectorOfPoint filteredFingerPoints = new VectorOfPoint();
if (filteredFarPoints.Size > 1)
{
VectorOfPoint fingerPoints = new VectorOfPoint();
for (int i = 0; i < filteredStartPoints.Size; i++)
{
VectorOfPoint closestPoints = findClosestOnX(filteredFarPoints, filteredStartPoints[i]);
if (isFinger(closestPoints[0], filteredStartPoints[i], closestPoints[1], LIMIT_ANGLE_INF, LIMIT_ANGLE_SUP, centerBoundingRectangle, boundingRectangle.Height * BOUNDING_RECT_FINGER_SIZE_SCALING))
{
fingerPoints.Push(new Point[] { filteredStartPoints[i] });
}
}
if (fingerPoints.Size > 0)
{
while (fingerPoints.Size > 5)
{
//Remove extra fingers
//Convert to list and remove last item
List <Point> points = new List <Point>(fingerPoints.ToArray());
points.Remove(points.Last());
fingerPoints = new VectorOfPoint(points.ToArray());
}
for (int i = 0; i < fingerPoints.Size - 1; i++)
{
}
filteredFingerPoints = fingerPoints;
this.NumberOfFingersRaised = filteredFingerPoints.Size;
}
}
Bgr colorRed = new Bgr(Color.Red);
Bgr colorGreen = new Bgr(Color.Green);
Bgr colorBlue = new Bgr(Color.Blue);
Bgr colorYellow = new Bgr(Color.Yellow);
Bgr colorPurple = new Bgr(Color.Purple);
Bgr colorWhite = new Bgr(Color.White);
//Debug, draw defects
defectsData = (int[, , ])defects.GetData();
for (int i = 0; i < defectsData.Length / 4; i++)
{
Point start = contours[biggestContourIndex][defectsData[i, 0, 0]];
Point far = contours[biggestContourIndex][defectsData[i, 0, 2]];
Point end = contours[biggestContourIndex][defectsData[i, 0, 1]];
CvInvoke.Polylines(contoursImage, new Point[] { start, far, end }, true, colorPurple.MCvScalar, DRAW_THICKNESS / 2);
CvInvoke.Circle(contoursImage, start, 5, colorWhite.MCvScalar);
CvInvoke.Circle(contoursImage, far, 5, colorRed.MCvScalar, 10);
CvInvoke.Circle(contoursImage, end, 5, colorBlue.MCvScalar);
}
//Draw information about what was detected (Contours, key points, fingers / how many fingers)
CvInvoke.DrawContours(contoursImage, contours, 0, colorGreen.MCvScalar, DRAW_THICKNESS, LineType.AntiAlias);
CvInvoke.Polylines(contoursImage, hullPoints, true, colorBlue.MCvScalar, DRAW_THICKNESS);
CvInvoke.Rectangle(contoursImage, boundingRectangle, colorRed.MCvScalar, DRAW_THICKNESS);
CvInvoke.Circle(contoursImage, centerBoundingRectangle, 5, colorYellow.MCvScalar, DRAW_THICKNESS);
drawVectorPoints(contoursImage, filteredStartPoints, colorRed.MCvScalar, true, 3);
drawVectorPoints(contoursImage, filteredFarPoints, colorWhite.MCvScalar, true, 3);
drawVectorPoints(contoursImage, filteredFingerPoints, colorYellow.MCvScalar, false, 3);
CvInvoke.PutText(contoursImage, filteredFingerPoints.Size.ToString(), centerBoundingRectangle, FontFace.HersheyComplex, 2, colorYellow.MCvScalar);
return(contoursImage);
}
/// <summary>
/// Draw the model image and observed image, the matched features and homography projection.
/// </summary>
/// <param name="modelImage">The model image</param>
/// <param name="observedImage">The observed image</param>
/// <param name="matchTime">The output total time for computing the homography matrix.</param>
/// <returns>The model image and observed image, the matched features and homography projection.</returns>
public static Mat Draw(Mat modelImage, Mat observedImage, out long matchTime)
{
Mat homography;
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
using (VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch())
{
Mat mask;
FindMatch(modelImage, observedImage, out matchTime, out modelKeyPoints, out observedKeyPoints, matches,
out mask, out homography);
//Draw the matched keypoints
Mat result = new Mat();
Features2DToolbox.DrawMatches(modelImage, modelKeyPoints, observedImage, observedKeyPoints,
matches, result, new MCvScalar(255, 255, 255), new MCvScalar(255, 255, 255), mask);
#region draw the projected region on the image
if (homography != null)
{
//draw a rectangle along the projected model
Rectangle rect = new Rectangle(Point.Empty, modelImage.Size);
PointF[] pts = new PointF[]
{
new PointF(rect.Left, rect.Bottom),
new PointF(rect.Right, rect.Bottom),
new PointF(rect.Right, rect.Top),
new PointF(rect.Left, rect.Top)
};
pts = CvInvoke.PerspectiveTransform(pts, homography);
Point[] points = Array.ConvertAll<PointF, Point>(pts, Point.Round);
using (VectorOfPoint vp = new VectorOfPoint(points))
{
CvInvoke.Polylines(result, vp, true, new MCvScalar(255, 0, 0, 255), 5);
}
}
#endregion
return result;
}
}
static void Main(string[] args)
{
var files = Directory.GetFiles(@"C:\Users\lucas\OneDrive\Imagens\receipts");
foreach (var path in files)
{
var fileName = Path.GetFileName(path);
Bitmap bmp = new Bitmap(path);
bmp.SetResolution(50, 50);
Image <Gray, Byte> GrayBmp;
Image <Bgr, Byte> orig = new Image <Bgr, Byte>(bmp);
double ratioX = (double)500 / (double)orig.Width;
double ratioY = (double)500 / (double)orig.Height;
double ratio = ratioX < ratioY ? ratioX : ratioY;
int newHeight = Convert.ToInt32(orig.Height * ratio);
int newWidth = Convert.ToInt32(orig.Width * ratio);
orig = orig.Resize(newWidth, newHeight, Emgu.CV.CvEnum.Inter.Area);
Bitmap output;
GrayBmp = orig.Convert <Gray, byte>();
GrayBmp._SmoothGaussian(3);
GrayBmp.Save(Path.Combine(@"C:\Users\lucas\OneDrive\Imagens\receipts\result\", DateTime.Now.ToString("dd-MM-yy-mm-hh-ss") + "__GRAY__" + fileName));
Gray grayCannyThreshold = new Gray(75);
Gray grayThreshLinking = new Gray(200);
var Cannybmp = GrayBmp.Canny(grayCannyThreshold.Intensity, grayThreshLinking.Intensity);
output = Cannybmp.ToBitmap();
output.Save(Path.Combine(@"C:\Users\lucas\OneDrive\Imagens\receipts\result\", DateTime.Now.ToString("dd-MM-yy-mm-hh-ss") + "__CANNY__" + fileName));
var r = Cannybmp.HoughLinesBinary(2, Math.PI / 180.0, 100, 30, 3)[0];
var biggestLines = r.OrderByDescending(x => x.Length).Take(4).ToList();
var edges = PointCollection.PolyLine(biggestLines.Select(x => (PointF)x.P1).ToArray(), true);
PointF[] f = new PointF[4];
//PointF[] srcs = new PointF[4]; //Trapezoid shape
//srcs[0] = new PointF(1, 1);
//srcs[1] = new PointF(300, 1);
//srcs[2] = new PointF(400, 150);
//srcs[3] = new PointF(100, 150);
//PointF[] dests = new PointF[4]; // Rectangle Shape
//dests[0] = new PointF(3, 3);
//dests[1] = new PointF(150, 3);
//dests[2] = new PointF(180, 200);
//dests[3] = new PointF(150, 200);
//CvInvoke.WarpPerspective
//var wr = CvInvoke.GetPerspectiveTransform(srcs, dests);
//Image<Gray, byte> transformed = Cannybmp.WarpPerspective<Mat>(
// mapMatrix: wr, width: 400, height: 200, interpolationType: Emgu.CV.CvEnum.Inter.Cubic,
//warpType: Emgu.CV.CvEnum.Warp.Default, borderType: Emgu.CV.CvEnum.BorderType.Default, backgroundColor: new Gray(5));
Cannybmp.DrawPolyline(biggestLines.Select(x => x.P1).ToArray(), true, new Gray(100));
//output.Save(Path.Combine(@"C:\Users\lucas\OneDrive\Imagens\receipts\result\", DateTime.Now.ToString("dd-MM-yy-mm-hh-ss") + "__DRAW__" + fileName));
Mat hierarchy = new Mat();
VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint();
VectorOfPoint screenCnt;
CvInvoke.FindContours(Cannybmp.Copy(), contours, null, Emgu.CV.CvEnum.RetrType.List, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxSimple);
//VectorOfVectorOfPoint asa = new VectorOfVectorOfPoint();
//VectorOfVectorOfPoint asfa = new VectorOfVectorOfPoint(1000);
//CvInvoke.ConvertPointsToHomogeneous(contours, asfa);
for (int i = 0; i < contours.Size; i++)
{
var peri = CvInvoke.ArcLength(contours[i], true);
VectorOfPoint approxCurve = new VectorOfPoint();
CvInvoke.ApproxPolyDP(contours[i], approxCurve, 0.02 * peri, true);
if (approxCurve.Size == 4)
{
screenCnt = approxCurve;
break;
}
}
}
}
private void FindLicensePlate(
VectorOfVectorOfPoint contours, int[,] hierachy, int idx, IInputArray gray, IInputArray canny,
List <IInputOutputArray> licensePlateImagesList, List <IInputOutputArray> filteredLicensePlateImagesList, List <RotatedRect> detectedLicensePlateRegionList,
List <String> licenses)
{
for (; idx >= 0; idx = hierachy[idx, 0])
{
int lettersCount = GetNumberOfChildren(hierachy, idx);
//if it does not contains any children (charactor), it is not a license plate region
if (lettersCount == 0)
{
continue;
}
using (VectorOfPoint contour = contours[idx])
{
if (CvInvoke.ContourArea(contour) > 400)
{
if (lettersCount < 2)
{
//If the contour has less than 3 children, it is not a license plate (assuming license plate has at least 3 charactor)
//However we should search the children of this contour to see if any of them is a license plate
FindLicensePlate(contours, hierachy, hierachy[idx, 2], gray, canny, licensePlateImagesList,
filteredLicensePlateImagesList, detectedLicensePlateRegionList, licenses);
continue;
}
RotatedRect box = CvInvoke.MinAreaRect(contour);
if (box.Angle < -45.0)
{
float tmp = box.Size.Width;
box.Size.Width = box.Size.Height;
box.Size.Height = tmp;
box.Angle += 90.0f;
}
else if (box.Angle > 45.0)
{
float tmp = box.Size.Width;
box.Size.Width = box.Size.Height;
box.Size.Height = tmp;
box.Angle -= 90.0f;
}
using (UMat tmp1 = new UMat())
using (UMat tmp2 = new UMat())
{
PointF[] srcCorners = box.GetVertices();
PointF[] destCorners = new PointF[] {
new PointF(0, box.Size.Height - 1),
new PointF(0, 0),
new PointF(box.Size.Width - 1, 0),
new PointF(box.Size.Width - 1, box.Size.Height - 1)
};
using (Mat rot = CvInvoke.GetAffineTransform(srcCorners, destCorners))
{
CvInvoke.WarpAffine(gray, tmp1, rot, Size.Round(box.Size));
}
//resize the license plate such that the front is ~ 10-12. This size of front results in better accuracy from tesseract
Size approxSize = new Size(240, 180);
double scale = Math.Min(approxSize.Width / box.Size.Width, approxSize.Height / box.Size.Height);
Size newSize = new Size((int)Math.Round(box.Size.Width * scale), (int)Math.Round(box.Size.Height * scale));
CvInvoke.Resize(tmp1, tmp2, newSize, 0, 0, Inter.Cubic);
//removes some pixels from the edge
int edgePixelSize = 3;
Rectangle newRoi = new Rectangle(new Point(edgePixelSize, edgePixelSize),
tmp2.Size - new Size(2 * edgePixelSize, 2 * edgePixelSize));
UMat plate = new UMat(tmp2, newRoi);
ocr.SetImage(plate.Clone());
ocr.Recognize();
licenses.Add(ocr.GetUTF8Text());
licensePlateImagesList.Add(plate);
filteredLicensePlateImagesList.Add(plate);
detectedLicensePlateRegionList.Add(box);
}
}
}
}
}
/// <summary>
/// This comparison iterates over every point in "edge1" contour, finds the closest point in "edge2" contour and sums up those distances.
/// The end result is the sum divided by length of the both contours.
/// It also takes the difference of the distances of the contour endpoints into account.
/// </summary>
/// <param name="edge1">First Edge to compare to edge2</param>
/// <param name="edge2">Second Edge to compare to edge1</param>
/// <returns>Similarity factor of edges. Special values are:
/// 300000000: Same piece
/// 200000000: At least one edge is a line edge
/// 150000000: The pieces have the same edge type
/// 100000000: One of the contour sizes is 0</returns>
public override double CompareEdges(Edge edge1, Edge edge2)
{
try
{
//Return large numbers if we know that these shapes simply wont match...
if (edge1.PieceID == edge2.PieceID)
{
return(300000000);
}
if (edge1.EdgeType == EdgeTypes.LINE || edge2.EdgeType == EdgeTypes.LINE)
{
return(200000000);
}
if (edge1.EdgeType == edge2.EdgeType)
{
return(150000000);
}
if (edge1.NormalizedContour.Size == 0 || edge2.ReverseNormalizedContour.Size == 0)
{
return(100000000);
}
double cost = 0;
double total_length = CvInvoke.ArcLength(edge1.NormalizedContour, false) + CvInvoke.ArcLength(edge2.ReverseNormalizedContour, false);
int windowSizePoints = (int)(Math.Max(edge1.NormalizedContour.Size, edge2.ReverseNormalizedContour.Size) * EdgeCompareWindowSizePercent);
if (windowSizePoints < 1)
{
windowSizePoints = 1;
}
double distEndpointsContour1 = Utils.Distance(edge1.NormalizedContour[0], edge1.NormalizedContour[edge1.NormalizedContour.Size - 1]);
double distEndpointsContour2 = Utils.Distance(edge2.ReverseNormalizedContour[0], edge2.ReverseNormalizedContour[edge2.ReverseNormalizedContour.Size - 1]);
double distEndpointContoursDiff = Math.Abs(distEndpointsContour1 - distEndpointsContour2);
if (distEndpointContoursDiff <= EdgeCompareEndpointDiffIgnoreThreshold)
{
distEndpointContoursDiff = 0;
}
for (int i = 0; i < Math.Min(edge1.NormalizedContour.Size, edge2.ReverseNormalizedContour.Size); i++)
{
double min = 10000000;
for (int j = Math.Max(0, i - windowSizePoints); j < Math.Min(edge2.ReverseNormalizedContour.Size, i + windowSizePoints); j++)
{
if (PluginFactory.CancelToken.IsCancellationRequested)
{
PluginFactory.CancelToken.ThrowIfCancellationRequested();
}
double dist = Utils.Distance(edge1.NormalizedContour[i], edge2.ReverseNormalizedContour[j]);
if (dist < min)
{
min = dist;
}
}
cost += min;
}
double matchResult = cost / total_length;
if (PluginFactory.GetGeneralSettingsPlugin().SolverShowDebugResults)
{
Image <Rgb, byte> contourOverlay = new Image <Rgb, byte>(500, 500);
VectorOfPoint contour1 = Utils.TranslateContour(edge1.NormalizedContour, 100, 0);
VectorOfPoint contour2 = Utils.TranslateContour(edge2.ReverseNormalizedContour, 100, 0);
CvInvoke.DrawContours(contourOverlay, new VectorOfVectorOfPoint(contour1), -1, new MCvScalar(0, 255, 0), 2);
CvInvoke.DrawContours(contourOverlay, new VectorOfVectorOfPoint(contour2), -1, new MCvScalar(0, 0, 255), 2);
PluginFactory.LogHandle.Report(new LogEventImage("Compare " + edge1.PieceID + "_Edge" + edge1.EdgeNumber + " <-->" + edge2.PieceID + "_Edge" + edge2.EdgeNumber + " ==> distEndpoint = " + distEndpointContoursDiff.ToString() + ", MatchResult = " + matchResult, contourOverlay.Bitmap));
}
return(distEndpointContoursDiff + matchResult);
}
catch (OperationCanceledException)
{
throw;
}
}
private void Btn_shapFind_Click(object sender, EventArgs e)
{
StringBuilder msgBuilder = new StringBuilder("Performance: ");
//Load the image from file and resize it for display
var bitmap = this.picSrc.GetFirstRegionRect();
Image <Bgr, Byte> img =
new Image <Bgr, byte>(bitmap)
.Resize(400, 400, Emgu.CV.CvEnum.Inter.Linear, true);
//Convert the image to grayscale and filter out the noise
UMat uimage = new UMat();
CvInvoke.CvtColor(img, uimage, ColorConversion.Bgr2Gray);
CvInvoke.Imshow("Image", uimage);
CvInvoke.WaitKey(2);
//二值化
//UMat cannyEdges = new UMat();
CvInvoke.Threshold(uimage, uimage, 230, 255, ThresholdType.Binary);
CvInvoke.Imshow("After Threshold", uimage);
CvInvoke.WaitKey(2);
//use image pyr to remove noise
//UMat pyrDown = new UMat();
//CvInvoke.PyrDown(uimage, pyrDown);
//CvInvoke.PyrUp(pyrDown, uimage);
//CvInvoke.Imshow("pyrDownUp", uimage);
//CvInvoke.WaitKey(2);
//Image<Gray, Byte> gray = img.Convert<Gray, Byte>().PyrDown().PyrUp();
#region circle detection
Stopwatch watch = Stopwatch.StartNew();
double cannyThreshold = 180.0;
double circleAccumulatorThreshold = 100;
CircleF[] circles = CvInvoke.HoughCircles(uimage, HoughType.Gradient, 2.0, 20.0, cannyThreshold, circleAccumulatorThreshold, 5);
watch.Stop();
msgBuilder.Append(String.Format("Hough circles - {0} ms; ", watch.ElapsedMilliseconds));
#endregion
#region Canny and edge detection
watch.Reset(); watch.Start();
double cannyThresholdLinking = 120.0;
UMat cannyEdges = new UMat();
CvInvoke.Canny(uimage, cannyEdges, cannyThreshold, cannyThresholdLinking);
LineSegment2D[] lines = CvInvoke.HoughLinesP(
cannyEdges,
1, //Distance resolution in pixel-related units
Math.PI / 45.0, //Angle resolution measured in radians.
20, //threshold
30, //min Line width
10); //gap between lines
watch.Stop();
msgBuilder.Append(String.Format("Canny & Hough lines - {0} ms; ", watch.ElapsedMilliseconds));
#endregion
#region Find triangles and rectangles
watch.Reset(); watch.Start();
List <Triangle2DF> triangleList = new List <Triangle2DF>();
List <RotatedRect> boxList = new List <RotatedRect>(); //a box is a rotated rectangle
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
CvInvoke.FindContours(cannyEdges, 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);
if (CvInvoke.ContourArea(approxContour, false) > 200) //only consider contours with area greater than 250
{
if (approxContour.Size == 3) //The contour has 3 vertices, it is a triangle
{
Point[] pts = approxContour.ToArray();
triangleList.Add(new Triangle2DF(
pts[0],
pts[1],
pts[2]
));
}
else if (approxContour.Size == 4) //The contour has 4 vertices.
{
#region determine if all the angles in the contour are within [80, 100] degree
bool isRectangle = true;
Point[] pts = approxContour.ToArray();
LineSegment2D[] edges = PointCollection.PolyLine(pts, true);
for (int j = 0; j < edges.Length; j++)
{
double angle = Math.Abs(
edges[(j + 1) % edges.Length].GetExteriorAngleDegree(edges[j]));
if (angle < 80 || angle > 100)
{
isRectangle = false;
break;
}
}
#endregion
if (isRectangle)
{
boxList.Add(CvInvoke.MinAreaRect(approxContour));
}
}
}
}
}
}
watch.Stop();
msgBuilder.Append(String.Format("Triangles & Rectangles - {0} ms; ", watch.ElapsedMilliseconds));
#endregion
//originalImageBox.Image = img;
Console.WriteLine(msgBuilder.ToString());
#region draw triangles and rectangles
Image <Bgr, Byte> triangleRectangleImage = img.CopyBlank();
foreach (Triangle2DF triangle in triangleList)
{
triangleRectangleImage.Draw(triangle, new Bgr(Color.DarkBlue), 2);
}
foreach (RotatedRect box in boxList)
{
triangleRectangleImage.Draw(box, new Bgr(Color.DarkOrange), 2);
}
this.picRect.LoadImage(triangleRectangleImage.ToBitmap());
#endregion
#region draw circles
Image <Bgr, Byte> circleImage = img.CopyBlank();
foreach (CircleF circle in circles)
{
circleImage.Draw(circle, new Bgr(Color.Brown), 2);
}
this.picCircle.LoadImage(circleImage.Bitmap);
#endregion
#region draw lines
Image <Bgr, Byte> lineImage = img.CopyBlank();
foreach (LineSegment2D line in lines)
{
lineImage.Draw(line, new Bgr(Color.Green), 2);
}
this.picTarget.LoadImage(lineImage.Bitmap);
#endregion
}
/// <summary>
/// Approximates contour or a curve using Douglas-Peucker algorithm
/// </summary>
/// <param name="curve">The polygon or curve to approximate.</param>
/// <param name="epsilon">Specifies the approximation accuracy.
/// This is the maximum distance between the original curve and its approximation.</param>
/// <param name="closed">The result of the approximation;
/// The type should match the type of the input curve</param>
/// <returns>The result of the approximation;
/// The type should match the type of the input curve</returns>
public static Point[] ApproxPolyDP(IEnumerable<Point> curve, double epsilon, bool closed)
{
if(curve == null)
throw new ArgumentNullException("curve");
Point[] curveArray = EnumerableEx.ToArray(curve);
IntPtr approxCurvePtr;
NativeMethods.imgproc_approxPolyDP_Point(curveArray, curveArray.Length, out approxCurvePtr, epsilon, closed ? 1 : 0);
using (var approxCurveVec = new VectorOfPoint(approxCurvePtr))
{
return approxCurveVec.ToArray();
}
}
/// <summary>
/// finds intersection of two convex polygons
/// </summary>
/// <param name="p1"></param>
/// <param name="p2"></param>
/// <param name="p12"></param>
/// <param name="handleNested"></param>
/// <returns></returns>
public static float IntersectConvexConvex(IEnumerable<Point> p1, IEnumerable<Point> p2,
out Point[] p12, bool handleNested = true)
{
if (p1 == null)
throw new ArgumentNullException("p1");
if (p2 == null)
throw new ArgumentNullException("p2");
Point[] p1Array = EnumerableEx.ToArray(p1);
Point[] p2Array = EnumerableEx.ToArray(p2);
IntPtr p12Ptr;
float ret = NativeMethods.imgproc_intersectConvexConvex_Point(p1Array, p1Array.Length, p2Array, p2Array.Length,
out p12Ptr, handleNested ? 1 : 0);
using (var p12Vec = new VectorOfPoint(p12Ptr))
{
p12 = p12Vec.ToArray();
}
return ret;
}
//searcher with contour as input
private Point[] searcher(VectorOfPoint contour, Mat input_image1, Mat input_image2, ref Mat[] templ)
{
Mat image1 = input_image1.Clone();
Mat image2 = input_image2.Clone();
//Convert the image to grayscale and filter out the noise
CvInvoke.CvtColor(image1, image1, ColorConversion.Bgr2Gray);
CvInvoke.CvtColor(image2, image2, ColorConversion.Bgr2Gray);
//blur
Mat imageread = new Mat();
Mat pyrDown1 = new Mat();
Mat imageread2 = new Mat();
Mat pyrDown2 = new Mat();
CvInvoke.PyrDown(image1, pyrDown1);
CvInvoke.PyrUp(pyrDown1, imageread);
CvInvoke.PyrDown(image2, pyrDown2);
CvInvoke.PyrUp(pyrDown2, imageread2);
//unsharp
Size ksize = new Size(3, 3);
double aplha = 1.5;
double beta = -0.5;
double gamma = 0;
CvInvoke.AddWeighted(image1, aplha, imageread, beta, gamma, imageread);
CvInvoke.AddWeighted(image2, aplha, imageread2, beta, gamma, imageread2);
//find corners
VectorOfPoint contour2 = new VectorOfPoint();
CvInvoke.ApproxPolyDP(contour, contour2, CvInvoke.ArcLength(contour, true) * 0.05, true); //approximate the contour to each main lines
Point[] pts = contour2.ToArray(); //make it array from vector
LineSegment2D[] edges = PointCollection.PolyLine(pts, true); //create array of LineSegment2D for each of the main lines
Point[] gwnies = new Point[edges.Length]; //where the edges of the contour are stored
for (int i = 0; i < gwnies.Length; i++) //for every 2 consecutive lines calculate the intersection point with gwnia(LineSegment2D, LineSegment2D) function
{
if (gwnies.Length == i + 1)
{
gwnies[i] = gwnia(edges[i], edges[0]);
}
else
{
gwnies[i] = gwnia(edges[i], edges[i + 1]);
}
if (gwnies[i].X < 0)
{
return(null);
}
}
//
int patternWindow = 30; //the template size
int searchWindow = 150; //the search window size
Mat[] recs = new Mat[edges.Length]; //where the templates are stored
Mat[] img = new Mat[edges.Length]; //where the search windows are stored
Point[] points = new Point[edges.Length]; //where the new edges are stored
for (int i = 0; i < gwnies.Length; i++) //for each corner in the starting contour, search for the most similar point in the next frame
{
recs[i] = new Mat();
img[i] = new Mat();
CvInvoke.GetRectSubPix(imageread, new System.Drawing.Size(patternWindow, patternWindow), gwnies[i], recs[i]); //cut the template from image 1
CvInvoke.GetRectSubPix(imageread2, new System.Drawing.Size(searchWindow, searchWindow), gwnies[i], img[i]); //cut the search windows from image 2
Mat outp = new Mat(); //the match template output
CvInvoke.MatchTemplate(img[i], recs[i], outp, TemplateMatchingType.CcoeffNormed); // match the template inside the window
//CvInvoke.MatchTemplate(img[i], recs[i], outp, TemplateMatchingType.SqdiffNormed);// match the template inside the window
double minVal = 0; double maxVal = 0; Point minLoc = new Point();
CvInvoke.MinMaxLoc(outp, ref minVal, ref maxVal, ref minLoc, ref points[i]); //find the locations of min and max similarity and their values
//CvInvoke.MinMaxLoc(outp, ref minVal, ref maxVal, ref points[i], ref minLoc);//find the locations of min and max similarity and their values
int printpointX = gwnies[i].X + points[i].X - searchWindow / 2 + patternWindow / 2; //translation from X coord of search window to X coord of image2
int printpointY = gwnies[i].Y + points[i].Y - searchWindow / 2 + patternWindow / 2; //translation from Y coord of search window to Y coord of image2
points[i] = new Point(printpointX, printpointY); //the final point
CvInvoke.Circle(input_image2, points[i], (int)(patternWindow / 3), new MCvScalar(132, 255, 122)); //print a circle around the search point ////////////////////
}
templ = recs;
//Draw Lines
Image <Bgr, Byte> Draw = input_image2.ToImage <Bgr, Byte>(); //////////
if (points.Length > 0)
{
int i;
for (i = 1; i < points.Length; i++)
{
Draw.Draw(new LineSegment2D(points[i - 1], points[i]), new Bgr(Color.White), 1);
}
Draw.Draw(new LineSegment2D(points[i - 1], points[0]), new Bgr(Color.White), 1);
}
imageBox2.Image = Draw.Mat;//the output image with circles printed around new corners
return(points);
}
//---------------------ExampleMethod-----------------------------------------------//
//Process Frame() below is our user defined function in which we will create an EmguCv
//type image called ImageFrame. capture a frame from camera and allocate it to our
//ImageFrame. then show this image in ourEmguCV imageBox
//------------------------------------------------------------------------------//
private void ProcessFrame(object sender, EventArgs arg)
{
Mat frame = new Mat();
capture.Retrieve(frame, 0);
imageBox1.Image = frame;
if (State == 0)
{
imageBox2.Image = frame;
contours = Shape1(frame); // run the Shape function to find the area
if (contours != null)
{
PreviousFrame = frame.Clone();
State++;
}
}
else if (State == 1)
{
points = searcher(contours, PreviousFrame, frame, ref templ); // template matching to the second frame after the area is found
pointsStart = points;
try
{
//------------Calculate the norm of each of the corner teplates of the second frame-------------//
for (int i = 0; i < points.Length; i++)
{
Original[i] = new Mat();
CvInvoke.GetRectSubPix(frame, new System.Drawing.Size(20, 20), points[i], Original[i]);
normSquared[i] = Original[i].Dot(Original[i]);
}
//---------------------------------------------------------------------------------------------//
}
catch (System.NullReferenceException e)
{
Console.WriteLine("NullReferenceException in points. Line 669");
points = null;
}
PreviousFrame = frame.Clone();
if (points != null)
{
State++;
}
else
{
State = 0;
}
}
else
{
points = searcher(points, PreviousFrame, frame, ref templ); // template matching to each next frame
PreviousFrame = frame.Clone();
if (framecount5 == 0)
{
points5behind = points;
}
else if (framecount5 == 1)
{
for (int i = 0; i < points.Length; i++)
{
Mat CurrentFrame = new Mat();
CvInvoke.GetRectSubPix(frame, new System.Drawing.Size(20, 20), points[i], CurrentFrame);
double ratio = CurrentFrame.Dot(Original[i]) / normSquared[i]; // ratio betwwen the norm of the first template and the Dot product between the first and new template
if (ratio < 0.9 || ratio > 1.1) //error checking
{
State = 0;
Console.WriteLine(ratio);
Console.WriteLine("Refresh cause Norm");
break;
}
if (points.Length == i + 1)
{
//calculate distances
double tmp3 = Math.Sqrt(Math.Pow(points[0].X - points[i].X, 2) + Math.Pow(points[0].Y - points[i].Y, 2));
double tmp1 = Math.Sqrt(Math.Pow(pointsStart[0].X - pointsStart[i].X, 2) + Math.Pow(pointsStart[0].Y - pointsStart[i].Y, 2));
double tmp2 = Math.Sqrt(Math.Pow(points5behind[0].X - points5behind[i].X, 2) + Math.Pow(points5behind[0].Y - points5behind[i].Y, 2));
ratio = tmp1 / tmp2;
double ratio2 = tmp3 / tmp2;
if ((ratio2 < 0.9 || ratio2 > 1.1) || (ratio < 0.9 || ratio > 1.1)) //error checking
{
State = 0;
Console.WriteLine(ratio);
Console.WriteLine("Refresh");
break;
}
}
else
{
//calculate distances
double tmp3 = Math.Sqrt(Math.Pow(points[i + 1].X - points[i].X, 2) + Math.Pow(points[i + 1].Y - points[i].Y, 2));
double tmp1 = Math.Sqrt(Math.Pow(pointsStart[i + 1].X - pointsStart[i].X, 2) + Math.Pow(pointsStart[i + 1].Y - pointsStart[i].Y, 2));
double tmp2 = Math.Sqrt(Math.Pow(points5behind[i + 1].X - points5behind[i].X, 2) + Math.Pow(points5behind[i + 1].Y - points5behind[i].Y, 2));
ratio = tmp1 / tmp2;
double ratio2 = tmp3 / tmp2;
if ((ratio2 < 0.9 || ratio2 > 1.1) || (ratio < 0.9 || ratio > 1.1)) //error checking
{
State = 0;
Console.WriteLine(ratio);
Console.WriteLine("Refresh");
break;
}
}
}
framecount5 = -1;
}
framecount5++;
}
frame.Dispose();
}
/// <summary>
/// Draws a single or multiple polygonal curves
/// </summary>
/// <param name="img">Image</param>
/// <param name="pts">Array points</param>
/// <param name="isClosed">
/// Indicates whether the polylines must be drawn closed.
/// If !=0, the function draws the line from the last vertex of every contour to the first vertex.
/// </param>
/// <param name="color">Polyline color</param>
/// <param name="thickness">Thickness of the polyline edges</param>
/// <param name="lineType">Type of the line segments, see cvLine description</param>
/// <param name="shift">Number of fractional bits in the vertex coordinates</param>
public static void Polylines(IInputOutputArray img, Point[] pts, bool isClosed, MCvScalar color, int thickness = 1, CvEnum.LineType lineType = CvEnum.LineType.EightConnected, int shift = 0)
{
using (VectorOfPoint vps = new VectorOfPoint(pts))
Polylines(img, vps, isClosed, color, thickness, lineType, shift);
}
/// <summary>
///
/// </summary>
/// <param name="img"></param>
/// <param name="hitThreshold"></param>
/// <param name="winStride"></param>
/// <param name="padding"></param>
/// <returns></returns>
public virtual Point[] Detect(GpuMat img, double hitThreshold, Size winStride, Size padding)
{
if (disposed)
throw new ObjectDisposedException("HOGDescriptor");
if (img == null)
throw new ArgumentNullException("img");
using (var flVec = new VectorOfPoint())
{
NativeMethods.HOGDescriptor_detect(ptr, img.CvPtr, flVec.CvPtr, hitThreshold, winStride, padding);
// std::vector<cv::Point>*からCvPoint[]に移し替えて返す
return flVec.ToArray();
}
}
private void ProcessFrame()
{
try
{
#region Background/Foreground
Image<Bgr, byte> difference = BackgroundSubstractionOptions.Substract(_currentFrame, _frameHistoryBuffer);
Rectangle? handArea = ForegoundExtractionOptions.HighlightForeground(difference);
Image<Bgr, byte> skinDetectionFrame = _currentFrame.Copy();
if (handArea.HasValue)
ForegoundExtractionOptions.CutBackground(skinDetectionFrame, handArea.Value);
#endregion
#region Skin filtering / Morphological / Smooth filtering
Image<Gray, byte> skinDetectionFrameGray = SkinFilteringOptions.ActiveItem.FilterFrame(skinDetectionFrame);
MorphologicalFilteringOptions.StackSync.EnterReadLock();
foreach (var operation in MorphologicalFilteringOptions.OperationStack)
{
if (operation.FilterType == Model.Enums.MorphologicalFilterType.Dilatation)
{
CvInvoke.Dilate(skinDetectionFrameGray, skinDetectionFrameGray, operation.GetKernel(),
new Point(operation.KernelAnchorX, operation.KernelAnchorY), operation.Intensity, operation.KernelBorderType,
new MCvScalar(operation.KernelBorderThickness));
}
else
{
CvInvoke.Erode(skinDetectionFrameGray, skinDetectionFrameGray, operation.GetKernel(),
new Point(operation.KernelAnchorX, operation.KernelAnchorY), operation.Intensity, operation.KernelBorderType,
new MCvScalar(operation.KernelBorderThickness));
}
}
MorphologicalFilteringOptions.StackSync.ExitReadLock();
skinDetectionFrameGray = SmoothFilteringOptions.FilterFrame(skinDetectionFrameGray);
#endregion
#region Contours / ConvexHull / ConvexityDefects
Image<Bgr, byte> fingerTrackerFrame = _currentFrame.Copy();
List<Point> fingers = new List<Point>();
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
CvInvoke.FindContours(skinDetectionFrameGray.Copy(), contours, null, RetrType.List, FingerTrackingOptions.ApproxMethod);
if (contours.Size > 0)
{
VectorOfPoint biggestContour = contours[0];
if (contours.Size > 1)
{
for (int i = 1; i < contours.Size; i++)
{
if (CvInvoke.ContourArea(contours[i], false) > CvInvoke.ContourArea(biggestContour, false))
biggestContour = contours[i];
}
}
if (CvInvoke.ContourArea(biggestContour, false) > FingerTrackingOptions.MinContourArea)
{
using (VectorOfPoint contour = biggestContour)
{
using (VectorOfPoint approxContour = new VectorOfPoint())
{
CvInvoke.ApproxPolyDP(contour, approxContour, CvInvoke.ArcLength(contour, true) * FingerTrackingOptions.PerimeterScalingFactor.Value, true);
fingerTrackerFrame.Draw(approxContour.ToArray(), new Bgr(FingerTrackingOptions.ContourHighlightColor), 2);
VectorOfPoint convexHull = new VectorOfPoint();
VectorOfInt intHull = new VectorOfInt();
CvInvoke.ConvexHull(approxContour, convexHull, FingerTrackingOptions.ConvexHullCW);
CvInvoke.ConvexHull(approxContour, intHull, FingerTrackingOptions.ConvexHullCW);
fingerTrackerFrame.DrawPolyline(convexHull.ToArray(), true, new Bgr(FingerTrackingOptions.ConvexHullColor), 2);
var countourRect = CvInvoke.MinAreaRect(approxContour);
fingerTrackerFrame.Draw(new CircleF(new PointF(countourRect.Center.X, countourRect.Center.Y), 3), new Bgr(FingerTrackingOptions.DefectLinesColor), 2);
Mat defects = new Mat();
CvInvoke.ConvexityDefects(approxContour, intHull, defects);
if (!defects.IsEmpty)
{
var contourPoints = approxContour.ToArray();
Matrix<int> m = new Matrix<int>(defects.Rows, defects.Cols, defects.NumberOfChannels);
defects.CopyTo(m);
for (int i = 0; i < m.Rows; i++)
{
int startIdx = m.Data[i, 0];
int endIdx = m.Data[i, 1];
int depthIdx = m.Data[i, 2];
Point startPoint = contourPoints[startIdx];
Point endPoint = contourPoints[endIdx];
Point depthPoint = contourPoints[depthIdx];
LineSegment2D startDepthLine = new LineSegment2D(startPoint, depthPoint);
LineSegment2D depthEndLine = new LineSegment2D(depthPoint, endPoint);
LineSegment2D startCenterLine = new LineSegment2D(startPoint, new Point((int)countourRect.Center.X, (int)countourRect.Center.Y));
LineSegment2D depthCenterLine = new LineSegment2D(depthPoint, new Point((int)countourRect.Center.X, (int)countourRect.Center.Y));
LineSegment2D endCenterLine = new LineSegment2D(endPoint, new Point((int)countourRect.Center.X, (int)countourRect.Center.Y));
CircleF startCircle = new CircleF(startPoint, 5);
CircleF depthCircle = new CircleF(depthPoint, 5);
CircleF endCircle = new CircleF(endPoint, 5);
if (startPoint.Y < countourRect.Center.Y)
fingers.Add(startPoint);
if (!FingerTrackingOptions.TrackOnlyControlPoint)
{
fingerTrackerFrame.Draw(startCircle, new Bgr(FingerTrackingOptions.DefectStartPointHighlightColor), 2);
fingerTrackerFrame.Draw(depthCircle, new Bgr(FingerTrackingOptions.DefectDepthPointHighlightColor), 2);
fingerTrackerFrame.Draw(endCircle, new Bgr(FingerTrackingOptions.DefectEndPointHighlightColor), 2);
fingerTrackerFrame.Draw(startDepthLine, new Bgr(FingerTrackingOptions.DefectLinesColor), 2);
//fingerTrackerFrame.Draw(depthEndLine, new Bgr(FingerTrackingOptions.DefectLinesColor), 2);
fingerTrackerFrame.Draw(startCenterLine, new Bgr(FingerTrackingOptions.DefectLinesColor), 2);
//fingerTrackerFrame.Draw(depthCenterLine, new Bgr(FingerTrackingOptions.DefectLinesColor), 2);
// fingerTrackerFrame.Draw(endCenterLine, new Bgr(FingerTrackingOptions.DefectLinesColor), 2);
}
}
_lastControlPoint = _currentControlPoint;
_currentControlPoint = MouseControlOptions.UseHandCenter ? new Point((int)countourRect.Center.X, (int)countourRect.Center.Y)
: fingers.FirstOrDefault(f => f.Y == fingers.Min(line => line.Y));
fingers.Clear();
if (FingerTrackingOptions.TrackOnlyControlPoint)
{
fingerTrackerFrame = new Image<Bgr, byte>(fingerTrackerFrame.Width, fingerTrackerFrame.Height, new Bgr(Color.Black));
fingerTrackerFrame.Draw(new CircleF(_currentControlPoint, 5), new Bgr(Color.Red), 2);
}
}
}
}
}
}
}
#endregion
#region Mouse control
if (_currentControlPoint.X != -1 && _currentControlPoint.Y != -1 && _lastControlPoint.X != -1 && _lastControlPoint.Y != -1
&& _currentControlPoint.X != _lastControlPoint.X && _currentControlPoint.Y != _lastControlPoint.Y
&& Math.Abs(_currentControlPoint.X - _lastControlPoint.X) < (MouseControlOptions.FrameWidth / 10)
&& Math.Abs(_currentControlPoint.Y - _lastControlPoint.Y) < (MouseControlOptions.FrameHeight / 10))
{
int frameX = _currentControlPoint.X;
int frameY = _currentControlPoint.Y;
int moveX = _currentControlPoint.X - _lastControlPoint.X;
int moveY = _currentControlPoint.Y - _lastControlPoint.Y;
int sensitiveX = 1;
int sensitiveY = 1;
if (MouseControlOptions.MouseSensitive.Value > 0)
{
sensitiveX = (int)(((double)MouseControlOptions.ScreenWidth / MouseControlOptions.FrameWidth) * MouseControlOptions.MouseSensitive.Value);
sensitiveY = (int)(((double)MouseControlOptions.ScreenHeight / MouseControlOptions.FrameHeight) * MouseControlOptions.MouseSensitive.Value);
}
else if (MouseControlOptions.MouseSensitive.Value < 0)
{
sensitiveX = (int)(((double)MouseControlOptions.FrameWidth / MouseControlOptions.ScreenWidth) * MouseControlOptions.MouseSensitive.Value * -1);
sensitiveY = (int)(((double)MouseControlOptions.FrameHeight / MouseControlOptions.ScreenHeight) * MouseControlOptions.MouseSensitive.Value * -1);
}
moveX *= sensitiveX * -1;
moveY *= sensitiveY;
Point currentMousePosition = GetMousePosition();
int destinationX = currentMousePosition.X + moveX;
int destinationY = currentMousePosition.Y + moveY;
Messanger.PublishOnCurrentThread(new FingerMovedMessage(MouseControlOptions.ControlMouse, frameX, frameY, destinationX, destinationY));
if (MouseControlOptions.ControlMouse && MouseControlOptions.MouseSensitive.Value != 0 && destinationX >= 0 && destinationY >= 0)
SetCursorPos(destinationX, destinationY);
}
#endregion
Messanger.PublishOnCurrentThread(new FrameProcessedMessage(_currentFrame, difference, skinDetectionFrameGray, fingerTrackerFrame));
}
catch { }
}
private Image <Bgr, byte> GetVisualRepresentation(VectorOfPoint liquidContour, VectorOfPoint approxLiquidContour,
VectorOfPoint glassTopContour, VectorOfPoint approxGlassTopContour,
Point topLiquidPoint1, Point topLiquidPoint2,
DrawOptions drawOptions = DrawOptions.TopContour | DrawOptions.TopApproxContour |
DrawOptions.LiquidContour | DrawOptions.ApproxLiquidContour)
{
Size imgSize = new Size(_img.Width, _img.Height);
Image <Bgr, byte> img = new Image <Bgr, byte>(imgSize);
Point[] points;
if (_glassTopContour.Size != 0)
{
Point topPoint1 = new Point(topLiquidPoint1.X, glassTopContour.ToArray()[0].Y);
Point topPoint2 = new Point(topLiquidPoint2.X, glassTopContour.ToArray()[1].Y);
points = new Point[] { topPoint2, topLiquidPoint2,
topLiquidPoint1, topPoint1 };
img.DrawPolyline(points, true, new Bgr(Color.DarkMagenta), 1);
}
points = liquidContour.ToArray();
img.DrawPolyline(points, true, new Bgr(Color.Aqua), 2);
points = approxLiquidContour.ToArray();
img.DrawPolyline(points, true, new Bgr(Color.DeepPink), 4);
points = glassTopContour.ToArray();
img.DrawPolyline(points, true, new Bgr(Color.YellowGreen), 5);
points = approxGlassTopContour.ToArray();
img.DrawPolyline(points, true, new Bgr(Color.OrangeRed), 5);
return(img);
}
public static List<VectorOfPoint> GetContours(Image<Gray, Byte> image, ChainApproxMethod apxMethod = ChainApproxMethod.ChainApproxSimple, RetrType retrievalType = RetrType.List, double accuracy = 0.001d, double minimumArea = 10)
{
List<VectorOfPoint> convertedContours = new List<VectorOfPoint>();
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
using (Image<Gray, Byte> tempImage = image.Copy())
{
CvInvoke.FindContours(tempImage, contours, null, retrievalType, apxMethod);
}
int count = contours.Size;
for (int i = 0; i < count; i++)
{
using (VectorOfPoint contour = contours[i])
{
VectorOfPoint approxContour = new VectorOfPoint();
CvInvoke.ApproxPolyDP(contour, approxContour, accuracy, false);
if (CvInvoke.ContourArea(approxContour, false) > minimumArea)
{
convertedContours.Add(approxContour);
}
}
}
}
return convertedContours;
}
private void ProcessImage()
{
while (_capture.IsOpened)
{
// frame maintenance
Mat workingImage = _capture.QueryFrame();
// resize to PictureBox aspect ratio
int newHeight = (workingImage.Size.Height * pictureBox1.Size.Width) / workingImage.Size.Width;
Size newSize = new Size(pictureBox1.Size.Width, newHeight);
CvInvoke.Resize(workingImage, workingImage, newSize);
// as a test for comparison, create a copy of the image with a binary filter:
var binaryImage = workingImage.ToImage <Gray, byte>().ThresholdBinary(new Gray(125), new
Gray(255)).Mat;
// Sample for gaussian blur:
var blurredImage = new Mat();
var cannyImage = new Mat();
var decoratedImage = new Mat();
CvInvoke.GaussianBlur(workingImage, blurredImage, new Size(9, 9), 0);
// convert to B/W
CvInvoke.CvtColor(blurredImage, blurredImage, typeof(Bgr), typeof(Gray));
// apply canny:
// NOTE: Canny function can frequently create duplicate lines on the same shape
// depending on blur amount and threshold values, some tweaking might be needed.
// You might also find that not using Canny and instead using FindContours on
// a binary-threshold image is more accurate.
CvInvoke.Canny(blurredImage, cannyImage, 150, 255);
// make a copy of the canny image, convert it to color for decorating:
CvInvoke.CvtColor(cannyImage, decoratedImage, typeof(Gray), typeof(Bgr));
// find contours:
//Mat sourceFrameWithArt = workingImage.Clone();
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
string shape = " ";
// Build list of contours
CvInvoke.FindContours(cannyImage, contours, null, RetrType.List, ChainApproxMethod.ChainApproxSimple);
for (int i = 0; i < contours.Size; i++)
{
VectorOfPoint contour = contours[i];
CvInvoke.Polylines(decoratedImage, contour, true, new Bgr(Color.Black).MCvScalar); //*****************This Line hides the uneeded contours***************
using (VectorOfPoint approxContour = new VectorOfPoint())
{
CvInvoke.ApproxPolyDP(contour, approxContour, CvInvoke.ArcLength(contour, true) * 0.05, true);
if (CvInvoke.ContourArea(approxContour, false) > 250) //only consider contours with area greater than 250
{
if (approxContour.Size == 3)
{
shape = "Triangle";
Invoke(new Action(() =>
{
label3.Text = "Triangle";
label8.Text = $"0";
CvInvoke.Polylines(decoratedImage, contour, true, new Bgr(Color.Green).MCvScalar);
}));
}
if (approxContour.Size == 4)
{
shape = "Rectangle";
Invoke(new Action(() =>
{
label3.Text = "Rectangle";
label8.Text = $"1";
CvInvoke.Polylines(decoratedImage, contour, true, new Bgr(Color.Red).MCvScalar);
}));
}
Invoke(new Action(() =>
{
label2.Text = $"There are {approxContour.Size} corners detected";
}));
}
Rectangle boundingBox = CvInvoke.BoundingRectangle(contours[i]);
//CvInvoke.Polylines(decoratedImage, contour, true, new Bgr(Color.Green).MCvScalar);
MarkDetectedObject(workingImage, contours[i], boundingBox, CvInvoke.ContourArea(contour), shape);
Point center = new Point(boundingBox.X + boundingBox.Width / 2, boundingBox.Y + boundingBox.Height / 2);
Invoke(new Action(() =>
{
label4.Text = $"Position: {center.X}, {center.Y}"; //coords that get sent to the arduino
textBox1.Text = $"{center.X}";
textBox2.Text = $"{center.Y}";
}));
Thread.Sleep(50);
}
}
Invoke(new Action(() =>
{
label1.Text = $"There are {contours.Size} contours detected"; //# of total contours
}));
}
// output images:
pictureBox1.Image = workingImage.Bitmap;
pictureBox2.Image = decoratedImage.Bitmap;
}
}
public static Image <Bgr, Byte> CropCodeFromImage(Image <Bgr, Byte> source, VectorOfVectorOfPoint Contours)
{
Image <Bgr, Byte> CroptedImage;
int idMain = 0;
int idBlue = 1;
if (CvInvoke.ContourArea(Contours[0]) < CvInvoke.ContourArea(Contours[1]))
{
idMain = 1;
idBlue = 0;
}
Point[] mainPoints = Contours[idMain].ToArray();
Point[] subPoints = Contours[idBlue].ToArray();
Image <Bgr, Byte> RotatedImage;
// 0.Вырежем прямоугольник описанный вокруг основного контура(в целях оптимизации)
Point[] contourRectangle = new Point[4];
int bottom = mainPoints.Min(x => x.Y);
contourRectangle[0] = mainPoints.Where(p => p.Y == bottom).First();
int left = mainPoints.Min(x => x.X);
contourRectangle[1] = mainPoints.Where(p => p.X == left).First();
int top = mainPoints.Max(x => x.Y);
contourRectangle[2] = mainPoints.Where(p => p.Y == top).First();
int right = mainPoints.Max(x => x.X);
contourRectangle[3] = mainPoints.Where(p => p.X == right).First();
Rectangle box = new Rectangle(left, bottom, right - left, top - bottom);
CroptedImage = source;//.GetSubRect(box);
// 1.Ищем угол поворота относительно вертикали для вертикального выравнивания контуров
double Angle = 0;
// 1.1.найдем две точки : самую левую основного контура и самую левую синего контура
Point mainLeft = (from p in mainPoints orderby p.X select p).FirstOrDefault();
Point mainBottom = (from p in mainPoints orderby p.Y descending select p).FirstOrDefault();
Point subLeft = (from p in subPoints orderby p.X select p).FirstOrDefault();
Point center = new Point(CroptedImage.Width / 2, CroptedImage.Height / 2);
// 1.2 Ищем угол поворота, который поставит одну над второй
if (mainLeft.X != subLeft.X)
{
double tg = -(mainLeft.Y - subLeft.Y) / (double)(mainLeft.X - subLeft.X);
Angle = Math.Atan(tg) + Math.PI / 2;
}
#region вертикальное выравнивание
// случай перевернутого изображения
mainLeft.X -= center.X;
mainLeft.Y -= center.Y;
// умножение на матрицу поворота
Point old = new Point(mainLeft.X, mainLeft.Y);
mainLeft.X = (int)(old.X * Math.Cos(Angle) - old.Y * Math.Sin(Angle));
mainLeft.Y = (int)(+old.X * Math.Sin(Angle) + old.Y * Math.Cos(Angle));
// обратный переход в координаты отрезанного изображения
mainLeft.X += center.X;
mainLeft.Y += center.Y;
// случай перевернутого изображения
subLeft.X -= center.X;
subLeft.Y -= center.Y;
// умножение на матрицу поворота
old = new Point(subLeft.X, subLeft.Y);
subLeft.X = (int)(old.X * Math.Cos(Angle) - old.Y * Math.Sin(Angle));
subLeft.Y = (int)(+old.X * Math.Sin(Angle) + old.Y * Math.Cos(Angle));
// обратный переход в координаты отрезанного изображения
subLeft.X += center.X;
subLeft.Y += center.Y;
if (mainLeft.Y > subLeft.Y)
{
Angle -= Math.PI;
}
#endregion
// 1.3 Поворачиваем основное изображение на этот угол
/*DEBUG*/
//source.Draw(contourRectangle, new Bgr(Color.Blue), 3);
RotatedImage = new Image <Bgr, byte>(CroptedImage.Rotate(Angle * 180 / Math.PI, new Bgr(Color.White), false).Bitmap);
// 2 Поворачиваем контур на этот угол
RotateContour(mainPoints, Angle, center,
new Point(center.X + (RotatedImage.Width - CroptedImage.Width) / 2, center.Y + (RotatedImage.Height - CroptedImage.Height) / 2));
// 3.Вырезаем основной контур(повернутый) из обрезанного и повернутого изображения
bottom = mainPoints.Min(x => x.Y);
top = mainPoints.Max(x => x.Y);
left = mainPoints.Min(x => x.X);
right = mainPoints.Max(x => x.X);
box = new Rectangle(left, bottom, right - left, top - bottom);
VectorOfVectorOfPoint contoursRoteated = new VectorOfVectorOfPoint();
contoursRoteated.Push(new VectorOfPoint());
contoursRoteated.Push(new VectorOfPoint());
contoursRoteated[0].Push(mainPoints);
contoursRoteated[1].Push(mainPoints);
// При некорректном контуре падает
if (left < 0 || right < 0 || top > RotatedImage.Height || right > RotatedImage.Width)
{
RotatedImage.DrawPolyline(mainPoints, true, new Bgr(Color.Red), 2);
return(RotatedImage);
}
else
{
return(CropImage(RotatedImage, contoursRoteated));
CroptedImage = RotatedImage.GetSubRect(box);
}
for (int i = 0; i < mainPoints.Length; i++)
{
mainPoints[i].X -= left;
mainPoints[i].Y -= bottom;
if (mainPoints[i].X > box.Width)
{
mainPoints[i].X = box.Width;
}
if (mainPoints[i].Y > box.Height)
{
mainPoints[i].Y = box.Height;
}
}
return(CropImage(CroptedImage, contoursRoteated));
VectorOfPoint ApproxRect = new VectorOfPoint();
Contours[idMain].Clear();
Contours[idMain].Push(mainPoints);
// Approximation
CvInvoke.ApproxPolyDP(Contours[idMain], ApproxRect, CvInvoke.ArcLength(Contours[idMain], true) * 0.15, true);
MCvMoments moments = CvInvoke.Moments(ApproxRect);
Point Code_gravityCenter = new Point((int)(moments.M10 / moments.M00), (int)(moments.M01 / moments.M00));
//corners
Point[] corners_mainBox = ApproxRect.ToArray();
//sort corners
List <Point> leftl = new List <Point>();
List <Point> rightl = new List <Point>();
for (int i = 0; i < corners_mainBox.Length; i++)
{
if (corners_mainBox[i].X < Code_gravityCenter.X)
{
leftl.Add(corners_mainBox[i]);
}
else
{
rightl.Add(corners_mainBox[i]);
}
}
//matrix with starting Points
// top-left top-right bottom-right bottom-left
PointF[] corners = new PointF[4];
corners[0] = leftl[0].Y < leftl[1].Y ? leftl[0] : leftl[1]; //top-left
corners[1] = rightl[0].Y < rightl[1].Y ? rightl[0] : rightl[1]; //top-right
corners[2] = rightl[0].Y > rightl[1].Y ? rightl[0] : rightl[1]; //bottom-right
corners[3] = leftl[0].Y > leftl[1].Y ? leftl[0] : leftl[1]; //bottom-left
//create matrixes
box = CvInvoke.BoundingRectangle(ApproxRect);
PointF[] targetPF =
{
new PointF(0, 0),
new PointF(box.Width, 0),
new PointF(box.Width, box.Width),
new PointF(0, box.Width),
}; //matrix with destination Points
//rotate crop
Image <Bgr, Byte> newcroppimg = CroptedImage.Clone();
//transformation matrix
Mat TransformMat = CvInvoke.GetPerspectiveTransform(corners, targetPF);
Size ROI = box.Size;
//transformation
CvInvoke.WarpPerspective(CroptedImage, newcroppimg, TransformMat, ROI);
return(newcroppimg);
}
public void PutDescriptions(Image <Bgr, byte> imgInput, VectorOfVectorOfPoint contours, int i, VectorOfPoint approx)
{
var moments = CvInvoke.Moments(contours[i]);
int x = (int)(moments.M10 / moments.M00);
int y = (int)(moments.M01 / moments.M00);
if (approx.Size == 3)
{
CvInvoke.PutText(imgInput, "Triangle", new Point(x, y), Emgu.CV.CvEnum.FontFace.HersheySimplex, 0.5, new MCvScalar(0, 0, 255), 2);
}
if (approx.Size == 4)
{
Rectangle rect = CvInvoke.BoundingRectangle(contours[i]);
if (Math.Abs(1 - (rect.Width / rect.Height)) < 0.06)
{
CvInvoke.PutText(imgInput, "Square", new Point(x, y), Emgu.CV.CvEnum.FontFace.HersheySimplex, 0.5, new MCvScalar(0, 0, 255), 2);
}
else
{
CvInvoke.PutText(imgInput, "Rectangle", new Point(x, y), Emgu.CV.CvEnum.FontFace.HersheySimplex, 0.5, new MCvScalar(0, 0, 255), 2);
}
}
if (approx.Size == 5)
{
CvInvoke.PutText(imgInput, "Pentagon", new Point(x, y), Emgu.CV.CvEnum.FontFace.HersheySimplex, 0.5, new MCvScalar(0, 0, 255), 2);
}
}
/// <summary>
/// check whether a contour is a character or not
/// </summary>
/// <param name="plate_after_preprocessing"></param>
/// <param name="suspected_contour">just a single contour</param>
/// <returns>true: if yes, false: if not</returns>
private static bool is_contour_a_character(Image <Gray, byte> plate_after_preprocessing, VectorOfPoint suspected_contour)
{
Rectangle r = CvInvoke.BoundingRectangle(suspected_contour);
double c_W = r.Width;
double c_H = r.Height;
double i_W = plate_after_preprocessing.Width;
double i_H = plate_after_preprocessing.Height;
// ratio_area_contour_over_img
double ratio_1 = (i_W * i_H) / (c_W * c_H);
// ratio_contour_over_img
double ratio_2 = c_H / i_H;
if ((ratio_1 >= 10 && ratio_1 < 43) && (ratio_2 >= 0.4) && ((c_H / c_W) > 1.2))
{
return(true);
}
else
{
return(false);
}
}
private Mat Draw(Mat observedImage)
{
if (button1.Text == "UnLoad")
{
FindMatch(observedImage);
Mat result = new Mat();
if (matches.Size > 2)
{
//Draw the matched keypoints
Features2DToolbox.DrawMatches(objImage, objKeyPoints, observedImage, observedKeyPoints, matches, result, new MCvScalar(255, 255, 255), new MCvScalar(255, 255, 255), mask, Features2DToolbox.KeypointDrawType.NotDrawSinglePoints);
//Bgr rgb5 = new Bgr(250, 250, 250);
//Features2DToolbox.DrawKeypoints(observedImage, observedKeyPoints, observedImage, rgb5);
int i;
float X = 0;
float Y = 0;
int count = 0;
for (i = 0; i < matches.Size; i++)
{
if (mask.GetData(i)[0] == 1)
{
X += observedKeyPoints[matches[i][0].QueryIdx].Point.X - objKeyPoints[matches[i][0].TrainIdx].Point.X;
Y += observedKeyPoints[matches[i][0].QueryIdx].Point.Y - objKeyPoints[matches[i][0].TrainIdx].Point.Y;
count++;
}
}
if (count > 0)
{
X = X / count + (float)(Math.Abs(objwidth / 2.0));
Y = Y / count + (float)(Math.Abs(objheight / 2.0));
}
else
{
X = -1;
Y = -1;
}
if (CvInvoke.CountNonZero(mask) > 10)
{
if (trackings[trackings.Length - 1].Y >= 0)
{
Point[] newtrackings = new Point[2 * trackings.Length + 1];
for (i = 0; i < trackings.Length; i++)
{
newtrackings[i] = trackings[i];
}
newtrackings[i].X = (int)X;
newtrackings[i].Y = (int)Y;
for (i++; i < newtrackings.Length; i++)
{
newtrackings[i].X = -1;
newtrackings[i].Y = -1;
}
trackings = newtrackings;
}
else
{
for (i = 0; trackings[i].Y >= 0; i++)
{
}
trackings[i].X = (int)X;
trackings[i].Y = (int)Y;
}
}
if (homography != null)
{
//draw a rectangle along the projected model
System.Drawing.Rectangle rect = new System.Drawing.Rectangle(Point.Empty, objImage.Size);
PointF[] pts = new PointF[]
{
new PointF(rect.Left, rect.Bottom),
new PointF(rect.Right, rect.Bottom),
new PointF(rect.Right, rect.Top),
new PointF(rect.Left, rect.Top)
};
pts = CvInvoke.PerspectiveTransform(pts, homography);
Point[] points = Array.ConvertAll <PointF, Point>(pts, Point.Round);
using (VectorOfPoint vp = new VectorOfPoint(points))
{
//CvInvoke.Polylines(observedImage, vp, true, new MCvScalar(255, 0, 0, 255), 5);
}
}
float a = 2;
CvInvoke.Ellipse(result, new RotatedRect(new PointF(X, Y), new SizeF(30, 30), a), new MCvScalar(0, 250, 255, 255), 4);
return(result);
}
}
else if (button1.Text == "Load")
{
uObservedImage = observedImage.GetUMat(AccessType.ReadWrite);
// extract features from the observed image
ORBCPU.DetectAndCompute(uObservedImage, null, observedKeyPoints, observedDescriptors, false);
Bgr rgb1 = new Bgr(250, 250, 250);
Features2DToolbox.DrawKeypoints(observedImage, observedKeyPoints, observedImage, rgb1);
}
return(observedImage);
}
private static PadErrorDetail CheckPad(Models.PadItem padItem, PadSegmentInfo[] padSegment, double umPPixel, bool Inflate = false)
{
Rectangle boundPadRef = padItem.BoudingAdjust;
if (Inflate)
{
boundPadRef.Inflate(3, 3);
}
double sPadRef = padItem.Area;
List <int> idPadSegOverlap = new List <int>();
for (int j = 0; j < padSegment.Length; j++)
{
//if (boundPadRef.IntersectsWith(padSegment[j].Bouding))
//{
// idPadSegOverlap.Add(j);
//}
if (IntersectsContour(padItem.ContourAdjust, padSegment[j].Contours, boundPadRef))
{
idPadSegOverlap.Add(j);
}
}
boundPadRef = padItem.Bouding;
PadErrorDetail padEr = new PadErrorDetail();
double scaleArea = 0;
double scaleAreaAddperimeter = 0;
double shiftx = 0;
double shifty = 0;
int inflate = 40;
padEr.AreaStdHight = padItem.AreaThresh.UM_USL;
padEr.AreaStdLow = padItem.AreaThresh.PERCENT_LSL;
padEr.ShiftXStduM = padItem.ShiftXThresh.UM_USL;
padEr.ShiftYStduM = padItem.ShiftYThresh.UM_USL;
padEr.ShiftXStdArea = padItem.ShiftXThresh.PERCENT_LSL;
padEr.ShiftYStdArea = padItem.ShiftYThresh.PERCENT_LSL;
padEr.ROI = Rectangle.Inflate(boundPadRef, inflate, inflate);
padEr.Pad = padItem;
if (padItem.FOVs.Count > 0)
{
padEr.FOVNo = padItem.FOVs[0];
}
if (idPadSegOverlap.Count > 0)
{
double areaAllPadSeg = 0;
double perimeter = 0;
Rectangle boundAllPadSeg = new Rectangle();
for (int j = 0; j < idPadSegOverlap.Count; j++)
{
PadSegmentInfo padSeg = padSegment[idPadSegOverlap[j]];
areaAllPadSeg += padSeg.Area;
using (VectorOfPoint cnt = new VectorOfPoint(padSeg.Contours))
{
perimeter += CvInvoke.ArcLength(cnt, true) / 2;
}
if (j == 0)
{
boundAllPadSeg = padSeg.Bouding;
continue;
}
if (padSeg.Bouding.X < boundAllPadSeg.X)
{
boundAllPadSeg.X = padSeg.Bouding.X;
}
if (padSeg.Bouding.Y < boundAllPadSeg.Y)
{
boundAllPadSeg.Y = padSeg.Bouding.Y;
}
if (padSeg.Bouding.X + padSeg.Bouding.Width > boundAllPadSeg.X + boundAllPadSeg.Width)
{
boundAllPadSeg.Width = padSeg.Bouding.X + padSeg.Bouding.Width - boundAllPadSeg.X;
}
if (padSeg.Bouding.Y + padSeg.Bouding.Height > boundAllPadSeg.Y + boundAllPadSeg.Height)
{
boundAllPadSeg.Height = padSeg.Bouding.Y + padSeg.Bouding.Height - boundAllPadSeg.Y;
}
}
padEr.Center = new Point(boundAllPadSeg.X + boundAllPadSeg.Width / 2, boundAllPadSeg.Y + boundAllPadSeg.Height / 2);
scaleArea = areaAllPadSeg * 100 / sPadRef;
scaleAreaAddperimeter = (areaAllPadSeg + perimeter) * 100 / sPadRef;
shiftx = (Math.Max(Math.Abs(boundPadRef.X - boundAllPadSeg.X), Math.Abs((boundPadRef.X + boundPadRef.Width) - (boundAllPadSeg.X + boundAllPadSeg.Width))) * umPPixel);
shifty = (Math.Max(Math.Abs(boundPadRef.Y - boundAllPadSeg.Y), Math.Abs((boundPadRef.Y + boundPadRef.Height) - (boundAllPadSeg.Y + boundAllPadSeg.Height))) * umPPixel);
bool insert = false;
double deviation = (100 - (sPadRef / umPPixel)) / 2;
deviation = deviation < 0 ? 0 : deviation;
if (scaleAreaAddperimeter < padItem.AreaThresh.PERCENT_LSL - deviation)
{
//if (sPadRef > 100 || (sPadRef < 100 && scaleArea < 5))
{
insert = true;
}
}
if (scaleArea > padItem.AreaThresh.UM_USL)
{
insert = true;
}
if (shiftx > padItem.ShiftXThresh.UM_USL)
{
insert = true;
}
if (shifty > padItem.ShiftXThresh.UM_USL)
{
insert = true;
}
if (insert)
{
padEr.Area = scaleArea;
padEr.ShiftX = shiftx;
padEr.ShiftY = shifty;
return(padEr);
}
else
{
// pad pass
return(null);
}
}
else
{
// not found solder paste
return(padEr);
}
}
/// <summary>
/// Performs object detection without a multi-scale window.
/// </summary>
/// <param name="img">Source image. CV_8UC1 and CV_8UC4 types are supported for now.</param>
/// <param name="weights"></param>
/// <param name="hitThreshold">Threshold for the distance between features and SVM classifying plane.
/// Usually it is 0 and should be specfied in the detector coefficients (as the last free coefficient).
/// But if the free coefficient is omitted (which is allowed), you can specify it manually here.</param>
/// <param name="winStride">Window stride. It must be a multiple of block stride.</param>
/// <param name="padding">Mock parameter to keep the CPU interface compatibility. It must be (0,0).</param>
/// <param name="searchLocations"></param>
/// <returns>Left-top corner points of detected objects boundaries.</returns>
public virtual Point[] Detect(Mat img, out double[] weights,
double hitThreshold = 0, Size? winStride = null, Size? padding = null, Point[] searchLocations = null)
{
if (disposed)
throw new ObjectDisposedException("HOGDescriptor");
if (img == null)
throw new ArgumentNullException("img");
img.ThrowIfDisposed();
Size winStride0 = winStride.GetValueOrDefault(new Size());
Size padding0 = padding.GetValueOrDefault(new Size());
using (var flVec = new VectorOfPoint())
using (var weightsVec = new VectorOfDouble())
{
int slLength = (searchLocations != null) ? searchLocations.Length : 0;
NativeMethods.objdetect_HOGDescriptor_detect(ptr, img.CvPtr, flVec.CvPtr, weightsVec.CvPtr,
hitThreshold, winStride0, padding0, searchLocations, slLength);
weights = weightsVec.ToArray();
return flVec.ToArray();
}
}
private Drawing.Bitmap GetMaskedBitmap(string imagePath, IList<Point> pointCollection)
{
Mat matrix = new Mat(imagePath, LoadImageType.AnyColor);
UMat uMatrix = matrix.ToUMat(AccessType.ReadWrite);
// Scale Polygon
List<Point> scaledPoints = GetScaledPoints(pointCollection, uMatrix.Rows, uMatrix.Cols);
polygonPoints = GetPolygonPoints(scaledPoints, uMatrix.Rows, uMatrix.Cols);
// Apply Polygon
using (VectorOfPoint vPoint = new VectorOfPoint(polygonPoints.ToArray()))
using (VectorOfVectorOfPoint vvPoint = new VectorOfVectorOfPoint(vPoint))
{
CvInvoke.FillPoly(uMatrix, vvPoint, new Bgr(0, 0, 0).MCvScalar);
}
// Crop Bitmap
int left = (int)scaledPoints.Min(p => p.X);
int top = (int)scaledPoints.Min(p => p.Y);
int width = (int)scaledPoints.Max(p => p.X) - left;
int height = (int)scaledPoints.Max(p => p.Y) - top;
Image<Bgr, byte> image = new Image<Bgr, byte>(uMatrix.Bitmap);
image.ROI = new Drawing.Rectangle(left, top, width, height);
return image.Bitmap;
}
/// <summary>
///
/// </summary>
/// <param name="image">The image</param>
/// <param name="winStride">Window stride. Must be a multiple of block stride. Use Size.Empty for default</param>
/// <param name="padding">Padding. Use Size.Empty for default</param>
/// <param name="locations">Locations for the computation. Can be null if not needed</param>
/// <returns>The descriptor vector</returns>
public float[] Compute(IInputArray image, Size winStride = new Size(), Size padding = new Size(),
Point[] locations = null)
{
using (VectorOfFloat desc = new VectorOfFloat())
using (InputArray iaImage = image.GetInputArray())
{
if (locations == null)
{
CvInvoke.cveHOGDescriptorCompute(_ptr, iaImage, desc, ref winStride, ref padding, IntPtr.Zero);
}
else
{
using (VectorOfPoint vp = new VectorOfPoint(locations))
{
CvInvoke.cveHOGDescriptorCompute(_ptr, iaImage, desc, ref winStride, ref padding, vp);
}
}
return desc.ToArray();
}
}
private void button1_Click(object sender, EventArgs e)
{
if (_frame.IsEmpty)
{
return;
}
//try
//{
Mat m = new Mat();
Mat n = new Mat();
Mat o = new Mat();
Mat aux = new Mat();
Mat binaryDiffFrame = new Mat();
Mat denoisedDiffFrame = new Mat();
Mat finalFrame = new Mat();
//
//OBTENER COLOR
//
Image <Bgr, Byte> imge = _frame.ToImage <Bgr, Byte>();
Image <Bgr, byte> ret = imge.Copy();
Image <Bgr, byte> auxImge = imge.Copy();
Image <Bgr, byte> auxImge2 = imge.Copy();
Image <Bgr, byte> auxImge3 = imge.Copy();
Image <Bgr, byte> resultadoFinal = imge.Copy();
//Transformar a espacio de color HSV
Image <Hsv, Byte> hsvimg = auxImge.Convert <Hsv, Byte>();
//extract the hue and value channels
Image <Gray, Byte>[] channels = hsvimg.Split(); //separar en componentes
Image <Gray, Byte> imghue = channels[0]; //hsv, channels[0] es hue.
Image <Gray, Byte> imgval = channels[2]; //hsv, channels[2] es value.
//Filtro AZUL --> 90 a 120
//Verde --> 40 a 70
Image <Gray, byte> huefilter = imghue.InRange(new Gray(90), new Gray(120));
//Filtro colores menos brillantes
Image <Gray, byte> valfilter = imgval.InRange(new Gray(100), new Gray(255));
//Filtro de saturación - quitar blancos
channels[1]._ThresholdBinary(new Gray(10), new Gray(255)); // Saturacion
//Unir los filtros para obtener la imagen
Image <Gray, byte> colordetimg = huefilter.And(valfilter).And(channels[1]);//aqui habia un Not()
//Colorear imagen
var mat = auxImge2.Mat;
mat.SetTo(new MCvScalar(0, 0, 255), colordetimg);
mat.CopyTo(ret);
//Image<Bgr, byte> imgout = ret.CopyBlank();//imagen sin fondo negro
ret._Or(auxImge2);
//Muestra imagen con los rojos destacados
pictureBox2.Image = ret.Bitmap;
Mat SE2 = CvInvoke.GetStructuringElement(Emgu.CV.CvEnum.ElementShape.Rectangle, new Size(3, 2), new Point(-1, -1));
CvInvoke.MorphologyEx(colordetimg, colordetimg, MorphOp.Erode, SE2, new Point(-1, -1), 1, BorderType.Default, new MCvScalar(255));
Mat SE3 = CvInvoke.GetStructuringElement(Emgu.CV.CvEnum.ElementShape.Rectangle, new Size(3, 2), new Point(-1, -1));
CvInvoke.MorphologyEx(colordetimg, colordetimg, MorphOp.Dilate, SE3, new Point(-1, -1), 1, BorderType.Default, new MCvScalar(255));
Mat SE = CvInvoke.GetStructuringElement(Emgu.CV.CvEnum.ElementShape.Rectangle, new Size(3, 3), new Point(-1, -1));
CvInvoke.MorphologyEx(colordetimg, aux, Emgu.CV.CvEnum.MorphOp.Close, SE, new Point(-1, -1), 2, Emgu.CV.CvEnum.BorderType.Reflect, new MCvScalar(255));
pictureBox2.Image = aux.Bitmap;
Image <Bgr, byte> temp = aux.ToImage <Bgr, byte>();
var temp2 = temp.SmoothGaussian(5).Convert <Gray, byte>().ThresholdBinary(new Gray(230), new Gray(255));
VectorOfVectorOfPoint contorno = new VectorOfVectorOfPoint();
Mat matAux = new Mat();
CvInvoke.FindContours(temp2, contorno, matAux, Emgu.CV.CvEnum.RetrType.External, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxSimple);
if (contorno.Size > 0)
{
for (int i = 0; i < contorno.Size; i++)
{
VectorOfPoint approxContour = new VectorOfPoint();
double perimetro = CvInvoke.ArcLength(contorno[i], true);
VectorOfPoint approx = new VectorOfPoint();
double area = CvInvoke.ContourArea(contorno[i]);
if (area > 1000)
{
var moments = CvInvoke.Moments(contorno[i]);
int x = (int)(moments.M10 / moments.M00);
int y = (int)(moments.M01 / moments.M00);
CvInvoke.ApproxPolyDP(contorno[i], approx, 0.04 * perimetro, true);
CvInvoke.DrawContours(resultadoFinal, contorno, i, new MCvScalar(0, 255, 255), 2);
RotatedRect rectangle = CvInvoke.MinAreaRect(approx);
//CvInvoke.DrawContours(resultadoFinal, contorno, i, new MCvScalar(255, 255, 255), 2, LineType.AntiAlias);
MessageBox.Show("Tamano figura " + rectangle.Size.Width * rectangle.Size.Height);
resultadoFinal.Draw(rectangle, new Bgr(Color.Cyan), 1);
CvInvoke.PutText(resultadoFinal, "Marcador Pagina", new Point(x, y),
Emgu.CV.CvEnum.FontFace.HersheySimplex, 0.5, new MCvScalar(0, 255, 255), 2);
pictureBox3.Image = resultadoFinal.ToBitmap();
}
}
}
}
public static double RadiusAve(System.Drawing.Point pt, List <ConvexityDefect> cdList, VectorOfPoint vp)
{
double sumOfDist = 0.0;
double numOfSample = (double)cdList.Count;
foreach (ConvexityDefect cd in cdList)
{
sumOfDist += Geometry.Distance(pt, vp[cd.Point]);
}
return(sumOfDist / numOfSample);
}
private void button6_Click(object sender, EventArgs e)
{
SWReset();
List <Triangle2DF> triangleList = new List <Triangle2DF>();
//一个box 就是一个旋转的长方形
List <RotatedRect> boxList = new List <RotatedRect>();
//contours 轮廓线
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint()) {
CvInvoke.FindContours(cannyEdges, 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);
if (CvInvoke.ContourArea(approxContour, false) > 10) //只用面积大于250的？
{
if (approxContour.Size == 3) //轮廓有三个顶点，那就是一个三角形
{
Point[] pts = approxContour.ToArray();
triangleList.Add(new Triangle2DF(
pts[0],
pts[1],
pts[2]
));
}
else if (approxContour.Size == 4) //轮廓有四个顶点
//检测轮廓内所有的角度是否为在[80,100]度之间
{
bool isRectangle = true;
Point[] pts = approxContour.ToArray(); //将向量转化为数组
LineSegment2D[] edges = PointCollection.PolyLine(pts, true); //将点转化为线
for (int j = 0; j < edges.Length; j++)
{
double angle = Math.Abs(edges[(j + 1) % edges.Length].GetExteriorAngleDegree(edges[j]));//获取线之间的角度
if (angle < 80 || angle > 100)
{
isRectangle = false;
break;
}
}
if (isRectangle)
{
boxList.Add(CvInvoke.MinAreaRect(approxContour));
}
}
}
}
}
}
SWStop("检测长方形和三角形");
//画三角形和长方形
Image <Bgr, byte> triangleRectangleImage = srcImage.CopyBlank();
foreach (Triangle2DF triangle in triangleList)
{
triangleRectangleImage.Draw(triangle, new Bgr(Color.DarkBlue), 2);
}
foreach (RotatedRect box in boxList)
{
triangleRectangleImage.Draw(box, new Bgr(Color.DarkOrange), 2);
}
imageBox2.Image = triangleRectangleImage;
}
public BestBox(RotatedRect box, double value, VectorOfPoint vector)
{
Box = box;
Value = value;
VectorsList = vector;
}
private VectorOfPointF FindTarget(Mat input)
{
var cannyEdges = new Mat();
var uImage = new Mat();
var gray = new Mat();
var blurred = new Mat();
// Convert to greyscale
CvInvoke.CvtColor(input, uImage, ColorConversion.Bgr2Gray);
CvInvoke.BilateralFilter(uImage, gray, 11, 17, 17);
uImage.Dispose();
CvInvoke.MedianBlur(gray, blurred, 11);
gray.Dispose();
// Get edged version
const double cannyThreshold = 0.0;
const double cannyThresholdLinking = 200.0;
CvInvoke.Canny(blurred, cannyEdges, cannyThreshold, cannyThresholdLinking);
blurred.Dispose();
if (_showEdged)
{
CvInvoke.Imshow("Source", cannyEdges);
}
// Get contours
using (var contours = new VectorOfVectorOfPoint()) {
CvInvoke.FindContours(cannyEdges, contours, null, RetrType.List,
ChainApproxMethod.ChainApproxSimple);
var count = contours.Size;
// Looping contours
for (var i = 0; i < count; i++)
{
var approxContour = new VectorOfPoint();
using var contour = contours[i];
CvInvoke.ApproxPolyDP(contour, approxContour, CvInvoke.ArcLength(contour, true) * 0.02,
true);
if (approxContour.Size != 4)
{
continue;
}
var cntArea = CvInvoke.ContourArea(approxContour);
if (!(cntArea / _srcArea > .15))
{
continue;
}
var pointOut = new VectorOfPointF(SortPoints(approxContour));
_targets.Add(VPointFToVPoint(pointOut));
}
if (_showEdged)
{
var color = new MCvScalar(255, 255, 0);
CvInvoke.DrawContours(input, contours, -1, color);
CvInvoke.Imshow("Edged", cannyEdges);
}
}
var output = CountTargets(_targets);
cannyEdges.Dispose();
return(output);
}
//------------------Main Algorithm----------------------//
private VectorOfPoint Shape1(Mat FrameFormCam)
{
StringBuilder msgBuilder = new StringBuilder("Performance: "); //start clock to measure performance
Mat imageread = FrameFormCam;
int original_Height = imageread.Height;
int original_Width = imageread.Width;
CvInvoke.Resize(imageread, imageread, new Size(640, 480)); // Resize/ Interpolation of our image to reduce the complexity
//---------Convert the image to grayscale and filter out the noise-------//
Mat image = new Mat();
CvInvoke.CvtColor(imageread, image, ColorConversion.Bgr2Gray);
//-----------------------------------------------------------------------//
//------use image pyramid to remove noise----------//
Mat lowerImage = new Mat();
Mat pyrDown = new Mat();
CvInvoke.PyrDown(image, pyrDown);
CvInvoke.PyrUp(pyrDown, lowerImage);
//------------------------------------------------//
//----------unsharp image to enhance contrast-------//
Size ksize = new Size(3, 3);
double aplha = 1.5;
double beta = -0.5;
double gamma = 0;
CvInvoke.AddWeighted(image, aplha, lowerImage, beta, gamma, lowerImage);
//-------------------------------------------------//
//-----------Lower Image Specs-------------//
int Height = lowerImage.Height;
int Width = lowerImage.Width;
//----------------------------------------//
//----------Center of the image----------//
double x_image = Width / 2;
double y_image = Height / 2;
#region Canny and edge detection
Stopwatch watch = Stopwatch.StartNew(); //Time elapsed. It is useful for micro-benchmarks in code optimization.
#region Read Canny Thresholds from User or take default
double cannyThreshold, cannyThresholdLinking;
try // read thresholds from user, if empty take default thresholds
{
cannyThreshold = double.Parse(textBox1.Text);
cannyThresholdLinking = double.Parse(textBox2.Text);
}
catch (System.FormatException e)
{
cannyThreshold = 120;
cannyThresholdLinking = 60;
}
#endregion
Mat cannyEdges = new Mat();
CvInvoke.Canny(lowerImage, cannyEdges, cannyThreshold, cannyThresholdLinking); // Canny Edge Detection
watch.Stop();
msgBuilder.Append(String.Format("Canny - {0} ms; ", watch.ElapsedMilliseconds));
#endregion
#region Find Approximate Rectangles
watch.Reset(); watch.Start();
List <RotatedRect> boxList = new List <RotatedRect>(); // list of the Minimum Area Rectangles of contours
List <VectorOfPoint> ListOfBestBoxContours = new List <VectorOfPoint>(); // list of our contours
VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint(); // A vector of vector of points
Random rng = new Random();
Mat Dilated = new Mat();
Mat rect_6 = CvInvoke.GetStructuringElement(Emgu.CV.CvEnum.ElementShape.Rectangle, new Size(5, 5), new Point(3, 3));
CvInvoke.Dilate(cannyEdges, Dilated, rect_6, new Point(-1, -1), 5, BorderType.Constant, CvInvoke.MorphologyDefaultBorderValue); // Dilate the image 5 times
Mat Eroded = new Mat();
CvInvoke.Erode(Dilated, Eroded, rect_6, new Point(-1, -1), 4, BorderType.Constant, CvInvoke.MorphologyDefaultBorderValue); // Erode the image 4 times
Mat cresult = new Mat(cannyEdges.Size, DepthType.Cv8U, 3);
CvInvoke.FindContours(Eroded, contours, null, RetrType.List, ChainApproxMethod.ChainApproxNone); // Finds the contours of the image
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, 5, true);
if (CvInvoke.ContourArea(approxContour, true) > 6500) // only consider contours with area greater than 6500 pixels
{
if (approxContour.Size > 3) //The contour has > 3 vertices.
{
ListOfBestBoxContours.Add(contour); // add candidate contour to the list
boxList.Add(CvInvoke.MinAreaRect(approxContour)); // add the Minimum Area Rectangle of the candidate contour to the list
}
}
}
}
#endregion
try
{
#region find best rectangle and take its contour
#region find the minArea,MaxArea,minDistance,maxDistance for the Rescaling
double maxArea = 0; // maximum area amongst the rectangles representing the contours
double minArea = 10000000000; // minimum area amongst the rectangles representing the contours
double maxDistance = 0; // the distance of the rectangle witch is further away from the center of the frame from it
double minDistance = 10000000000; // the distance of the rectangle witch is close to the center of the frame from it
List <double> value = new List <double>();
//find the minArea,MaxArea,minDistance,maxDistance for the Rescaling
for (int z = 0; z < boxList.Count; z++)
{
maxArea = Math.Max(maxArea, ((boxList[z].Size.Height) * (boxList[z].Size.Width))); //maxArea = max(maxArea,area[z])
minArea = Math.Min(minArea, ((boxList[z].Size.Height) * (boxList[z].Size.Width))); //minArea = min(minArea,area[z])
maxDistance = Math.Max(maxDistance, (Math.Pow(Math.Abs((boxList[z].Center.X - x_image)), 2) + Math.Pow(Math.Abs((boxList[z].Center.Y - y_image)), 2))); //maxDistance = max(maxDistance,DistanceFromTheCenterOfTheImage[z])
minDistance = Math.Min(minDistance, (Math.Pow(Math.Abs((boxList[z].Center.X - x_image)), 2) + Math.Pow(Math.Abs((boxList[z].Center.Y - y_image)), 2))); //minDistance = min(minDistance,DistanceFromTheCenterOfTheImage[z])
}
#endregion
#region Find the Values of each Rectangle
foreach (RotatedRect y in boxList)
{
// Rescaled(Area[z]) = (Area[z] - MinArea )/(MaxArea-MinArea) , Rescaled(Area[z]) e [0,1]
// Rescaled(DistanceFromCenterOfImage[z]) = (DistanceFromCenterOfImage[z] - minDistance )/(maxDistance-minDistance) , Rescaled(DistanceFromCenterOfImage[z]) e [0,1]
// List of value = 0.5 * Rescaled(Area[z]) + 0.5 * (1-Rescaled(DistanceFromCenterOfImage[z]))
value.Add(((((y.Size.Height) * (y.Size.Width)) - minArea) / (maxArea - minArea)) * 0.5 + 0.5 * (1 - (((Math.Pow(Math.Abs(y.Center.X - x_image), 2) + Math.Pow(Math.Abs(y.Center.Y - y_image), 2)) - minDistance) / (maxDistance - minDistance))));
}
#endregion
#region Insert in class BestBox and sort it by descending value
List <BestBox> Best = new List <BestBox>(); //A Structure for finding the best contour. Deffinition on line 32
for (int i = 0; i < value.Count; i++)
{
// add the rectangle, its contour and its value
Best.Add(new BestBox(boxList[i], value[i], ListOfBestBoxContours[i]));
}
Best.Sort(delegate(BestBox x, BestBox y)
{
//Sort Descending by value
return(y.Value.CompareTo(x.Value));
});
#endregion
#endregion
Image <Bgr, Byte> Filling1 = lowerImage.ToImage <Bgr, Byte>().CopyBlank(); // a blank image to draw on and fill the best contour
CvInvoke.FillConvexPoly(Filling1, Best[0].VectorsList, new MCvScalar(0, 0, 255)); // Fill the inside of the Best Contour to make it solid
CvInvoke.Canny(Filling1, cannyEdges, cannyThreshold, cannyThresholdLinking); // Canny Edge Detection
//clear the lists and structs
boxList.Clear();
ListOfBestBoxContours.Clear();
Best.Clear();
value.Clear();
#region Lough Lines
#region Compute Hough Lines on best contour region
//------------------Compute HoughLines----------------//
LineSegment2D[] lines = CvInvoke.HoughLinesP(
cannyEdges,
1, //Distance resolution in pixel-related units
Math.PI / 180, //Angle resolution measured in radians.
40, //threshold
0, //min Line width
15); //gap between lines
//---------------------------------------------------//
#endregion
#region Extend the Hough Lines
Point A = new Point();
Point B = new Point();
Point CB = new Point();
Point CA = new Point();
int length = 500;
// extending the hough lines, allowing them to intesect with each other
for (int i = 0; i < lines.Length; i++)
{
A = lines[i].P1;
B = lines[i].P2;
CB.X = (int)(B.X + (B.X - A.X) / lines[i].Length * length);
CB.Y = (int)(B.Y + (B.Y - A.Y) / lines[i].Length * length);
CA.X = (int)(A.X + (A.X - B.X) / lines[i].Length * length);
CA.Y = (int)(A.Y + (A.Y - B.Y) / lines[i].Length * length);
lines[i].P1 = CA;
lines[i].P2 = CB;
}
#endregion
#region Draw Hough Lines representing our area on a blank image
Image <Bgr, Byte> DrawLines3 = lowerImage.ToImage <Bgr, Byte>().CopyBlank();
foreach (LineSegment2D line in lines)
{
DrawLines3.Draw(line, new Bgr(Color.White), 2); // Draw these lines on a blank image
}
CvInvoke.CvtColor(DrawLines3, DrawLines3, ColorConversion.Bgr2Gray);
#endregion
#endregion
#region Find new Contours and Rectangle areas
VectorOfVectorOfPoint contours1 = new VectorOfVectorOfPoint();
CvInvoke.FindContours(DrawLines3.Mat, contours1, null, RetrType.List, ChainApproxMethod.ChainApproxNone); //find the contours
VectorOfPoint contour;
count = contours1.Size;
for (int i = 0; i < count; i++)
{
contour = contours1[i];
using (VectorOfPoint approxContour = new VectorOfPoint())
{
CvInvoke.ApproxPolyDP(contour, approxContour, 5, true);
if (CvInvoke.ContourArea(approxContour, true) > 6500) //only consider contours with area greater than 6500
{
if (approxContour.Size > 3) //The contour has > 3 vertices.
{
ListOfBestBoxContours.Add(contour); // add candidate contour to the list
boxList.Add(CvInvoke.MinAreaRect(approxContour)); // add the Minimum Area Rectangle of the candidate contour to the list
}
}
}
}
#endregion
#region find best rectangle and take its contour
maxArea = 0;
minArea = 10000000000;
maxDistance = 0;
minDistance = 10000000000;
#region find the minArea,MaxArea,minDistance,maxDistance for the Rescaling
for (int z = 0; z < boxList.Count; z++)
{
maxArea = Math.Max(maxArea, ((boxList[z].Size.Height) * (boxList[z].Size.Width))); //maxArea = max(maxArea,area[z])
minArea = Math.Min(minArea, ((boxList[z].Size.Height) * (boxList[z].Size.Width))); //minArea = min(minArea,area[z])
maxDistance = Math.Max(maxDistance, (Math.Pow(Math.Abs((boxList[z].Center.X - x_image)), 2) + Math.Pow(Math.Abs((boxList[z].Center.Y - y_image)), 2))); //maxDistance = max(maxDistance,DistanceFromTheCenterOfTheImage[z])
minDistance = Math.Min(minDistance, (Math.Pow(Math.Abs((boxList[z].Center.X - x_image)), 2) + Math.Pow(Math.Abs((boxList[z].Center.Y - y_image)), 2))); //minDistance = min(minDistance,DistanceFromTheCenterOfTheImage[z])
}
#endregion
#region Find the Values of each Rectangle
foreach (RotatedRect y in boxList)
{
// Rescaled(Area[z]) = (Area[z] - MinArea )/(MaxArea-MinArea) , Rescaled(Area[z]) e [0,1]
// Rescaled(DistanceFromCenterOfImage[z]) = (DistanceFromCenterOfImage[z] - minDistance )/(maxDistance-minDistance) , Rescaled(DistanceFromCenterOfImage[z]) e [0,1]
// List of value = 0.5 * Rescaled(Area[z]) + 0.5 * (1-Rescaled(DistanceFromCenterOfImage[z]))
value.Add(((((y.Size.Height) * (y.Size.Width)) - minArea) / (maxArea - minArea)) * 0.5 + 0.5 * (1 - (((Math.Pow(Math.Abs(y.Center.X - x_image), 2) + Math.Pow(Math.Abs(y.Center.Y - y_image), 2)) - minDistance) / (maxDistance - minDistance))));
}
#endregion
#region Insert in class BestBox and sort it by descending value
for (int i = 0; i < value.Count; i++)
{
// add the rectangle, its contour and its value
Best.Add(new BestBox(boxList[i], value[i], ListOfBestBoxContours[i]));
}
Best.Sort(delegate(BestBox x, BestBox y)
{
//Sort Descending by value
return(y.Value.CompareTo(x.Value));
});
#endregion
#endregion
contour = Best[0].VectorsList; //the first element is always the best contour region. This is the final desired area in the frame
return(contour);
}
catch (Exception e) when(e is System.InvalidOperationException || e is System.ArgumentOutOfRangeException)
{
Mat result = imageread;
return(null);
}
}
private void ProcessFrame(object sender, EventArgs e)
{
Mat image = new Mat();
Mat diffImage = new Mat();
capture.Retrieve(image);
if (lastImage != null)
{
CvInvoke.AbsDiff(image, lastImage, diffImage);
}
Image<Gray, byte> mask = new Image<Gray, byte>(image.Width, image.Height);
if (lastImage != null)
{
VectorOfPoint vp = new VectorOfPoint(RegionOfInterestPoints.ToArray());
CvInvoke.Polylines(image, vp, true, new Bgr(0, 0, 255).MCvScalar, 2);
if (vp.Size >= 3)
{
CvInvoke.FillConvexPoly(mask, vp, new MCvScalar(255));
overlayImage = new Mat((int)lastImage.Height, (int)lastImage.Width, DepthType.Cv8U, 3);
diffImage.CopyTo(overlayImage, mask);
byte[] data = new byte[overlayImage.Width * overlayImage.Height * 3];
GCHandle handle = GCHandle.Alloc(data, GCHandleType.Pinned);
using (Mat m2 = new Mat(overlayImage.Size, DepthType.Cv8U, 3, handle.AddrOfPinnedObject(), overlayImage.Width * 3))
CvInvoke.BitwiseNot(overlayImage, m2);
handle.Free();
CheckTrigger(data, overlayImage.Width, overlayImage.Height);
}
}
if (FrameCallback != null)
{
FrameCallback.FrameUpdate(
image,
overlayImage
);
}
lastImage = image;
}
/// <summary>
/// Computes convex hull for a set of 2D points.
/// </summary>
/// <param name="points">The input 2D point set, represented by CV_32SC2 or CV_32FC2 matrix</param>
/// <param name="clockwise">If true, the output convex hull will be oriented clockwise,
/// otherwise it will be oriented counter-clockwise. Here, the usual screen coordinate
/// system is assumed - the origin is at the top-left corner, x axis is oriented to the right,
/// and y axis is oriented downwards.</param>
/// <returns>The output convex hull. It is a vector of points that form
/// the hull (must have the same type as the input points).</returns>
public static Point[] ConvexHull(IEnumerable<Point> points, bool clockwise = false)
{
if (points == null)
throw new ArgumentNullException("points");
Point[] pointsArray = EnumerableEx.ToArray(points);
IntPtr hullPtr;
NativeMethods.imgproc_convexHull_Point_ReturnsPoints(pointsArray, pointsArray.Length, out hullPtr, clockwise ? 1 : 0);
using (var hullVec = new VectorOfPoint(hullPtr))
{
return hullVec.ToArray();
}
}
private void ExtractContourAndHull(Image <Bgr, byte> originalImage, Image <Gray, byte> skin)
{
var contours = new VectorOfVectorOfPoint();
CvInvoke.FindContours(skin, contours, new Mat(), RetrType.List, ChainApproxMethod.ChainApproxSimple);
var result2 = 0;
VectorOfPoint biggestContour = null;
if (contours.Size != 0)
{
biggestContour = contours[0];
}
for (var i = 0; i < contours.Size; i++)
{
var result1 = contours[i].Size;
if (result1 <= result2)
{
continue;
}
result2 = result1;
biggestContour = contours[i];
}
if (biggestContour == null)
{
return;
}
currentContour = new VectorOfPoint();
CvInvoke.ApproxPolyDP(biggestContour, currentContour, 0, true);
//TODO Get to know why it gives exception
//ImageFrame.Draw(biggestContour, 3, new Bgr(Color.LimeGreen));
biggestContour = currentContour;
var pointsToFs = new PointF[currentContour.Size];
for (var i = 0; i < currentContour.Size; i++)
{
pointsToFs[i] = new PointF(currentContour[i].X, currentContour[i].Y);
}
var hull = CvInvoke.ConvexHull(pointsToFs, true);
pointsToFs = new PointF[biggestContour.Size];
for (var i = 0; i < biggestContour.Size; i++)
{
pointsToFs[i] = new PointF(biggestContour[i].X, biggestContour[i].Y);
}
box = CvInvoke.MinAreaRect(pointsToFs);
var points = box.GetVertices();
var ps = new Point[points.Length];
for (var i = 0; i < points.Length; i++)
{
ps[i] = new Point((int)points[i].X, (int)points[i].Y);
}
var hullToPoints = new Point[hull.Length];
for (var i = 0; i < hull.Length; i++)
{
hullToPoints[i] = Point.Round(hull[i]);
}
originalImage.DrawPolyline(hullToPoints, true, new Bgr(200, 125, 75), 2);
originalImage.Draw(new CircleF(new PointF(box.Center.X, box.Center.Y), 3), new Bgr(200, 125, 75), 2);
var convexHull = new VectorOfInt();
CvInvoke.ConvexHull(currentContour, convexHull, false, false);
defects = new Mat();
CvInvoke.ConvexityDefects(currentContour, convexHull, defects);
if (!defects.IsEmpty)
{
Matrix <int> m = new Matrix <int>(defects.Rows, defects.Cols,
defects.NumberOfChannels); // copy Mat to a matrix...
defects.CopyTo(m);
Matrix <int>[] channels = m.Split();
if (channels.Length >= 2)
{
startIndex = channels.ElementAt(0).Data;
endIndex = channels.ElementAt(1).Data;
depthIndex = channels.ElementAt(2).Data;
}
}
}
private void ProcessFrame(object sender, EventArgs e)
{
try
{
Mat imageHSV = new Mat();
Mat imageGray = new Mat();
Mat imageBlured = new Mat();
fluxVideo.Retrieve(image, 0);
CvInvoke.CvtColor(image, imageGray, ColorConversion.Bgr2Gray);
CvInvoke.GaussianBlur(image, imageBlured, new Size(3, 3), 10);
CvInvoke.CvtColor(imageBlured, imageHSV, ColorConversion.Bgr2Hsv);
Image <Hsv, byte> imgHSV = imageHSV.ToImage <Hsv, byte>();
imgHSV = imgHSV.Flip(FlipType.Horizontal);
Image <Gray, byte> imgGray = imgHSV.InRange(new Hsv(min.x, min.y, min.z), new Hsv(max.x, max.y, max.z));
Mat structElement = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(5, 5), new Point(-1, -1));
CvInvoke.Erode(imgGray, imgGray, structElement, new Point(-1, -1), 2, BorderType.Constant, new MCvScalar(0));
//détection de contours
VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint();
Mat m = new Mat();
CvInvoke.FindContours(imgGray, contours, m, Emgu.CV.CvEnum.RetrType.External, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxSimple);
for (int i = 0; i < contours.Size; i++)
{
double perimeter = CvInvoke.ArcLength(contours[i], true);
VectorOfPoint approx = new VectorOfPoint();
CvInvoke.ApproxPolyDP(contours[i], approx, 0.04 * perimeter, true);
CvInvoke.DrawContours(image, contours, i, new MCvScalar(0, 0, 255));
if (approx.Size == 4)
{
isRectangle = true;
}
if (approx.Size == 3)
{
if (perimeter > 100)
{
isTriangle = true;
}
else
{
isTriangle = false;
}
}
if (approx.Size > 4)
{
isCircle = true;
}
}
if (!isTriangle)
{
zones[0].GetComponent <Zone>().SetIsValid(true);
zones[0].GetComponent <MeshRenderer>().material = validMat;
}
else
{
zones[0].GetComponent <Zone>().SetIsValid(false);
zones[0].GetComponent <MeshRenderer>().material = notValidMat;
}
if (!isRectangle)
{
zones[1].GetComponent <Zone>().SetIsValid(true);
zones[1].GetComponent <MeshRenderer>().material = validMat;
}
else
{
zones[1].GetComponent <Zone>().SetIsValid(false);
zones[1].GetComponent <MeshRenderer>().material = notValidMat;
}
if (!isCircle)
{
zones[2].GetComponent <Zone>().SetIsValid(true);
zones[2].GetComponent <MeshRenderer>().material = validMat;
}
else
{
zones[2].GetComponent <Zone>().SetIsValid(false);
zones[2].GetComponent <MeshRenderer>().material = notValidMat;
}
if (!isWaiting)
{
StartCoroutine(wait());
}
Image <Bgra, byte> imgToDisplay = new Image <Bgra, byte>(imgGray.Width, imgGray.Height);
CvInvoke.CvtColor(imgGray, imgToDisplay, ColorConversion.Gray2Bgra);
tex.LoadRawTextureData(imgToDisplay.Bytes);
tex.Apply();
imageCameraBinaire.sprite = Sprite.Create(tex, new Rect(0.0f, 0.0f, tex.width, tex.height), new Vector2(0.5f, 0.5f), 1.0f);
Image <Bgra, byte> imgToDisplayReal = new Image <Bgra, byte>(image.Width, image.Height);
CvInvoke.CvtColor(image, imgToDisplayReal, ColorConversion.Bgr2Bgra);
tex2.LoadRawTextureData(imgToDisplayReal.Bytes);
tex2.Apply();
imageCameraReelle.sprite = Sprite.Create(tex2, new Rect(0.0f, 0.0f, tex2.width, tex2.height), new Vector2(0.5f, 0.5f), 1.0f);
//CvInvoke.Imshow("Cam view", imgGray);
}
catch (Exception exception)
{
Debug.Log(exception.Message);
}
}
private void FindStopSign(Mat img, List<Mat> stopSignList, List<Rectangle> boxList, VectorOfVectorOfPoint contours, int[,] hierachy, int idx)
{
for (; idx >= 0; idx = hierachy[idx, 0])
{
using (VectorOfPoint c = contours[idx])
using (VectorOfPoint approx = new VectorOfPoint())
{
CvInvoke.ApproxPolyDP(c, approx, CvInvoke.ArcLength(c, true) * 0.02, true);
double area = CvInvoke.ContourArea(approx);
if (area > 200)
{
double ratio = CvInvoke.MatchShapes(_octagon, approx, Emgu.CV.CvEnum.ContoursMatchType.I3);
if (ratio > 0.1) //not a good match of contour shape
{
//check children
if (hierachy[idx, 2] >= 0)
FindStopSign(img, stopSignList, boxList, contours, hierachy, hierachy[idx, 2]);
continue;
}
Rectangle box = CvInvoke.BoundingRectangle(c);
Mat candidate = new Mat();
using (Mat tmp = new Mat(img, box))
CvInvoke.CvtColor(tmp, candidate, ColorConversion.Bgr2Gray);
//set the value of pixels not in the contour region to zero
using (Mat mask = new Mat(candidate.Size.Height, candidate.Width, DepthType.Cv8U, 1))
{
mask.SetTo(new MCvScalar(0));
CvInvoke.DrawContours(mask, contours, idx, new MCvScalar(255), -1, LineType.EightConnected, null, int.MaxValue, new Point(-box.X, -box.Y));
double mean = CvInvoke.Mean(candidate, mask).V0;
CvInvoke.Threshold(candidate, candidate, mean, 255, ThresholdType.Binary);
CvInvoke.BitwiseNot(candidate, candidate);
CvInvoke.BitwiseNot(mask, mask);
candidate.SetTo(new MCvScalar(0), mask);
}
int minMatchCount = 8;
double uniquenessThreshold = 0.8;
VectorOfKeyPoint _observeredKeypoint = new VectorOfKeyPoint();
Mat _observeredDescriptor = new Mat();
_detector.DetectAndCompute(candidate, null, _observeredKeypoint, _observeredDescriptor, false);
if (_observeredKeypoint.Size >= minMatchCount)
{
int k = 2;
Mat mask;
using (VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch())
{
_modelDescriptorMatcher.KnnMatch(_observeredDescriptor, matches, k, null);
mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
Features2DToolbox.VoteForUniqueness(matches, uniquenessThreshold, mask);
}
int nonZeroCount = CvInvoke.CountNonZero(mask);
if (nonZeroCount >= minMatchCount)
{
boxList.Add(box);
stopSignList.Add(candidate);
}
}
}
}
}
}
//Mat _frame
public int ObtenerFiguras(Mat _frame, string nombrePdf, int page)
{
Console.WriteLine("Entro en Metodo obtener figuras del dll");
int resultado = 0;
//resultados.Clear();
//if (_frame.IsEmpty)
//{
//return;
//}
Mat finalFrame = new Mat();
Mat aux = new Mat();
Mat aux2 = new Mat();
Mat aux3 = new Mat();
Image <Bgr, byte> img = _frame.ToImage <Bgr, byte>();
//Transformar a espacio de color HSV
Image <Hsv, Byte> hsvimg = img.Convert <Hsv, Byte>();
//extract the hue and value channels
Image <Gray, Byte>[] channels = hsvimg.Split(); //separar en componentes
Image <Gray, Byte> imghue = channels[0]; //hsv, channels[0] es hue.
Image <Gray, Byte> imgval = channels[2]; //hsv, channels[2] es value.
//Filtro color
//140 en adelante
//funciona 160
Image <Gray, byte> huefilter = imghue.InRange(new Gray(150), new Gray(255));
//Filtro colores menos brillantes
Image <Gray, byte> valfilter = imgval.InRange(new Gray(100), new Gray(255));
//Filtro de saturación - quitar blancos
channels[1]._ThresholdBinary(new Gray(20), new Gray(255)); // Saturacion
//Unir los filtros para obtener la imagen
Image <Gray, byte> colordetimg = huefilter.And(valfilter).And(channels[1]); //aqui habia un Not()
//pictureBox2.Image = colordetimg.Bitmap;
//colordetimg._Erode(1);
//2 y 4
Mat SE2 = CvInvoke.GetStructuringElement(Emgu.CV.CvEnum.ElementShape.Rectangle, new Size(3, 2), new System.Drawing.Point(1, 1));
CvInvoke.MorphologyEx(colordetimg, aux, MorphOp.Erode, SE2, new System.Drawing.Point(-1, -1), 2, BorderType.Default, new MCvScalar(255));
Mat SE3 = CvInvoke.GetStructuringElement(Emgu.CV.CvEnum.ElementShape.Rectangle, new Size(3, 3), new System.Drawing.Point(1, 1));
CvInvoke.MorphologyEx(aux, aux2, MorphOp.Dilate, SE3, new System.Drawing.Point(-1, -1), 3, BorderType.Replicate, new MCvScalar(255));
Mat SE = CvInvoke.GetStructuringElement(Emgu.CV.CvEnum.ElementShape.Rectangle, new Size(3, 3), new System.Drawing.Point(-1, -1));
CvInvoke.MorphologyEx(aux2, aux3, Emgu.CV.CvEnum.MorphOp.Close, SE, new System.Drawing.Point(-1, -1), 5, Emgu.CV.CvEnum.BorderType.Reflect, new MCvScalar(255));
CvInvoke.MorphologyEx(aux3, aux3, Emgu.CV.CvEnum.MorphOp.Open, SE, new System.Drawing.Point(-1, -1), 1, Emgu.CV.CvEnum.BorderType.Reflect, new MCvScalar(255));
_frame.CopyTo(finalFrame);
// DetectarFiguras(aux3, finalFrame);
// System.Drawing.Rectangle box = new System.Drawing.Rectangle();
Image <Bgr, byte> temp = aux3.ToImage <Bgr, byte>();
//Image<Bgr, Byte> buffer_im = displayFrame.ToImage<Bgr, Byte>();
//float a = buffer_im.Width;
//float b = buffer_im.Height;
//MessageBox.Show("El tamano es "+ a.ToString()+" y " + b.ToString());
rect.Clear();
circleList.Clear();
ellipseList.Clear();
//transforma imagen
//UMat uimage = new UMat();
// CvInvoke.CvtColor(displayFrame, uimage, ColorConversion.Bgr2Gray);
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
Image <Bgr, Byte> resultadoFinal = finalFrame.ToImage <Bgr, byte>();
double maxArea = 1000;
//int chosen = 0;
// VectorOfPoint contour = null;
//Dibuja borde rojo
var temp2 = temp.SmoothGaussian(5).Convert <Gray, byte>().ThresholdBinary(new Gray(20), new Gray(255));
VectorOfVectorOfPoint contorno = new VectorOfVectorOfPoint();
Mat mat = new Mat();
CvInvoke.FindContours(temp2, contorno, mat, Emgu.CV.CvEnum.RetrType.External, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxSimple);
for (int i = 0; i < contorno.Size; i++)
{
//VectorOfPoint approxContour = new VectorOfPoint();
double perimetro = CvInvoke.ArcLength(contorno[i], true);
VectorOfPoint approx = new VectorOfPoint();
// VectorOfPointF approxF = new VectorOfPointF();
double area = CvInvoke.ContourArea(contorno[i]);
if (area > maxArea)
{
CvInvoke.ApproxPolyDP(contorno[i], approx, 0.04 * perimetro, true);
//Obtiene los centros de las figuras
var moments = CvInvoke.Moments(contorno[i]);
int x = (int)(moments.M10 / moments.M00);
int y = (int)(moments.M01 / moments.M00);
resultados.Add(approx);
if (approx.Size == 3) //The contour has 3 vertices, it is a triangle
{
RotatedRect rectangle = CvInvoke.MinAreaRect(approx);
CvInvoke.DrawContours(resultadoFinal, contorno, i, new MCvScalar(255, 255, 255), 1, LineType.AntiAlias);
resultadoFinal.Draw(rectangle, new Bgr(System.Drawing.Color.Cyan), 1);
rect.Add(CvInvoke.BoundingRectangle(approx));
}
if (approx.Size == 4) //The contour has 4 vertices.
{
//RotatedRect tt = new RotatedRect(CvInvoke.MinAreaRect(approx).Center, CvInvoke.MinAreaRect(approx).Size, 270) ;
//boxList.Add(tt);
//Calcular si es cuadrado
System.Drawing.Rectangle rectAux = CvInvoke.BoundingRectangle(contorno[i]);
double ar = (double)rectAux.Width / rectAux.Height;
//Calcular circularidad
double perimetro2 = CvInvoke.ArcLength(contorno[i], true);
double area2 = CvInvoke.ContourArea(contorno[i]);
double circularidad = 4 * Math.PI * area2 / Math.Pow(perimetro2, 2);
//MessageBox.Show("circularidad rect " + circularidad);
if (circularidad > 0.69)
{
//Si la circularidad>0.6 y cumple proporcion es cuadrado
if (ar >= 0.8 && ar <= 1.0)
{
// MessageBox.Show("Cuadrado ");
RotatedRect rectangle = CvInvoke.MinAreaRect(contorno[i]);
CvInvoke.DrawContours(resultadoFinal, contorno, i, new MCvScalar(255, 255, 255), 1, LineType.AntiAlias);
resultadoFinal.Draw(rectangle, new Bgr(System.Drawing.Color.Cyan), 1);
CvInvoke.PutText(resultadoFinal, "Rectangle", new System.Drawing.Point(x, y),
Emgu.CV.CvEnum.FontFace.HersheySimplex, 0.5, new MCvScalar(0, 255, 255), 2);
rect.Add(CvInvoke.BoundingRectangle(approx));
}
//Es elipse
else
{
//MessageBox.Show("parecia rectangulo pero era elipse ");
Ellipse final_ellipse = new Ellipse(CvInvoke.MinAreaRect(contorno[i]).Center, CvInvoke.MinAreaRect(contorno[i]).Size, 0);
Ellipse final_ellipseDibujo = new Ellipse(CvInvoke.MinAreaRect(contorno[i]).Center, CvInvoke.MinAreaRect(contorno[i]).Size, 90);
ellipseList.Add(final_ellipse);
//IConvexPolygonF poligono = CvInvoke.MinAreaRect(approx);
//resultadoFinal.Draw(poligono, new Bgr(Color.Cyan), 1);
resultadoFinal.Draw(final_ellipseDibujo, new Bgr(System.Drawing.Color.Cyan), 1);
CvInvoke.DrawContours(resultadoFinal, contorno, i, new MCvScalar(255, 255, 255), 1, LineType.AntiAlias);
CvInvoke.PutText(resultadoFinal, "Figura circular", new System.Drawing.Point(x, y),
Emgu.CV.CvEnum.FontFace.HersheySimplex, 0.5, new MCvScalar(0, 255, 255), 2);
}
}
//Es rectangulo
else
{
RotatedRect rectangle = CvInvoke.MinAreaRect(contorno[i]);
CvInvoke.DrawContours(resultadoFinal, contorno, i, new MCvScalar(255, 255, 255), 1, LineType.AntiAlias);
resultadoFinal.Draw(rectangle, new Bgr(System.Drawing.Color.Cyan), 1);
CvInvoke.PutText(resultadoFinal, "Rectangle", new System.Drawing.Point(x, y),
Emgu.CV.CvEnum.FontFace.HersheySimplex, 0.5, new MCvScalar(0, 255, 255), 2);
rect.Add(CvInvoke.BoundingRectangle(approx));
}
}
if (approx.Size >= 5)
{
//double perimetro2 = CvInvoke.ArcLength(contorno[i], true);
//double area2 = CvInvoke.ContourArea(contorno[i]);
// double circularidad = 4 * Math.PI * area2 / Math.Pow(perimetro2, 2);
//MessageBox.Show("circularidad elipse " + circularidad);
Ellipse final_ellipse = new Ellipse(CvInvoke.MinAreaRect(contorno[i]).Center, CvInvoke.MinAreaRect(contorno[i]).Size, 0);
Ellipse final_ellipseDibujo = new Ellipse(CvInvoke.MinAreaRect(contorno[i]).Center, CvInvoke.MinAreaRect(contorno[i]).Size, 90);
ellipseList.Add(final_ellipse);
resultadoFinal.Draw(final_ellipseDibujo, new Bgr(System.Drawing.Color.Cyan), 1);
CvInvoke.DrawContours(resultadoFinal, contorno, i, new MCvScalar(255, 255, 255), 1, LineType.AntiAlias);
CvInvoke.PutText(resultadoFinal, "Figura circular", new System.Drawing.Point(x, y),
Emgu.CV.CvEnum.FontFace.HersheySimplex, 0.5, new MCvScalar(0, 255, 255), 2);
}
}
}
//pictureBox2.Image = resultadoFinal.Bitmap;
//button2.Enabled = true;
}
if (rect.Count != 0 || ellipseList.Count != 0)
{
resultado = 1;
Console.WriteLine("Rect: " + rect.Count + " y elipse: " + ellipseList.Count);
Console.WriteLine("llamare a guardar figuras nombrepdf " + nombrePdf);
GuardarFiguras(nombrePdf, page);
}
else
{
resultado = 0;
}
return(resultado);
}
private void FindStopSign(Mat img, List <Mat> stopSignList, List <Rectangle> boxList, VectorOfVectorOfPoint contours, int[,] hierachy, int idx)
{
for (; idx >= 0; idx = hierachy[idx, 0])
{
using (VectorOfPoint c = contours[idx])
using (VectorOfPoint approx = new VectorOfPoint())
{
CvInvoke.ApproxPolyDP(c, approx, CvInvoke.ArcLength(c, true) * 0.02, true);
double area = CvInvoke.ContourArea(approx);
if (area > 200)
{
double ratio = CvInvoke.MatchShapes(_octagon, approx, Emgu.CV.CvEnum.ContoursMatchType.I3);
if (ratio > 0.1) //not a good match of contour shape
{
//check children
if (hierachy[idx, 2] >= 0)
{
FindStopSign(img, stopSignList, boxList, contours, hierachy, hierachy[idx, 2]);
}
continue;
}
Rectangle box = CvInvoke.BoundingRectangle(c);
Mat candidate = new Mat();
using (Mat tmp = new Mat(img, box))
CvInvoke.CvtColor(tmp, candidate, ColorConversion.Bgr2Gray);
//set the value of pixels not in the contour region to zero
using (Mat mask = new Mat(candidate.Size.Height, candidate.Width, DepthType.Cv8U, 1))
{
mask.SetTo(new MCvScalar(0));
CvInvoke.DrawContours(mask, contours, idx, new MCvScalar(255), -1, LineType.EightConnected, null, int.MaxValue, new Point(-box.X, -box.Y));
double mean = CvInvoke.Mean(candidate, mask).V0;
CvInvoke.Threshold(candidate, candidate, mean, 255, ThresholdType.Binary);
CvInvoke.BitwiseNot(candidate, candidate);
CvInvoke.BitwiseNot(mask, mask);
candidate.SetTo(new MCvScalar(0), mask);
}
int minMatchCount = 8;
double uniquenessThreshold = 0.8;
VectorOfKeyPoint _observeredKeypoint = new VectorOfKeyPoint();
Mat _observeredDescriptor = new Mat();
_detector.DetectAndCompute(candidate, null, _observeredKeypoint, _observeredDescriptor, false);
if (_observeredKeypoint.Size >= minMatchCount)
{
int k = 2;
Mat mask;
using (VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch())
{
_modelDescriptorMatcher.KnnMatch(_observeredDescriptor, matches, k, null);
mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
Features2DToolbox.VoteForUniqueness(matches, uniquenessThreshold, mask);
}
int nonZeroCount = CvInvoke.CountNonZero(mask);
if (nonZeroCount >= minMatchCount)
{
boxList.Add(box);
stopSignList.Add(candidate);
}
}
}
}
}
}
public void imageHanding()
{
if (SoureceImage != null)
{
try
{
Mat img = SoureceImage.Mat;
Image <Gray, byte> otsu = SoureceImage.CopyBlank();
// 转灰度图
//CvInvoke.CvtColor(img, img, ColorConversion.Bgr2Gray);
// 中值滤波(去除椒盐噪声)
CvInvoke.MedianBlur(img, otsu, 3);
// Otsu二值化
CvInvoke.Threshold(otsu, img, 0, 255, ThresholdType.Otsu);
// 高斯滤波
CvInvoke.GaussianBlur(img, img, new Size(3, 3), 0);
// 形态学梯度运算
Mat StructingElement = CvInvoke.GetStructuringElement(ElementShape.Ellipse, new Size(5, 5), new Point(-1, -1));
CvInvoke.MorphologyEx(img, img, MorphOp.Gradient, StructingElement, new Point(-1, -1), 6, BorderType.Default, new MCvScalar(0));
// 边缘检测
CvInvoke.Canny(img, img, 100, 200);
List <RotatedRect> boxList = new List <RotatedRect>();
List <int> index = new List <int>();
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
// 寻找轮廓
CvInvoke.FindContours(img, 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);
if (CvInvoke.ContourArea(approxContour, false) > 200)
{
if (approxContour.Size == 4)
{
bool isRectangle = true;
Point[] pts = approxContour.ToArray();
LineSegment2D[] edges = PointCollection.PolyLine(pts, true);
for (int j = 0; j < edges.Length; j++)
{
double angle = Math.Abs(
edges[(j + 1) % edges.Length].GetExteriorAngleDegree(edges[j]));
if (angle < 80 || angle > 100)
{
isRectangle = false;
break;
}
}
if (isRectangle)
{
boxList.Add(CvInvoke.MinAreaRect(approxContour));
}
}
}
}
}
}
Image <Bgr, Byte> lineImage = SoureceImage.Convert <Bgr, byte>().CopyBlank();
Image <Gray, byte> testimg = SoureceImage.CopyBlank();
foreach (RotatedRect box in boxList)
{
lineImage.Draw(box, new Bgr(Color.DarkOrange), 2);
PointF[] vertices = new PointF[4];
vertices = box.GetVertices();
//for (int i = 0; i < 4; i++)
//line(image, vertices[i], vertices[(i + 1) % 4], Scalar(0, 255, 0));
Rectangle brect = box.MinAreaRect();
lineImage.Draw(brect, new Bgr(Color.DarkOrange), 2);
}
otsu.ROI = boxList[1].MinAreaRect();
testimg = otsu.Clone();
otsu.ROI = Rectangle.Empty;
Mat mapMatrix = new Mat();
PointF poi = new PointF(testimg.Size.Width / 2, testimg.Size.Height / 2);
CvInvoke.GetRotationMatrix2D(poi, boxList[1].Angle, 1.2, mapMatrix);
CvInvoke.WarpAffine(testimg, testimg, mapMatrix, testimg.Size);
SoureceImage.Bitmap = testimg.Bitmap;
//ShowFormImage();
}
catch (Exception)
{
SimpleStatus.Image = Resources.SimpleState_False;
mRuntime.Text = "运行时间:" + 0 + "毫秒";
ShowFormImage();
}
}
}
public static VectorOfVectorOfPoint DetectEdges(UIImage myImage, double th1, double th2, int aperture, bool value)
{
//Load the image from file and resize it for display
Image <Bgr, Byte> img =
new Image <Bgr, byte>(myImage.CGImage);
//.Resize(400, 400, Emgu.CV.CvEnum.Inter.Linear, true);
//Convert the image to grayscale and filter out the noise
UMat uimage = new UMat();
CvInvoke.CvtColor(img, uimage, ColorConversion.Bgr2Gray);
//use image pyr to remove noise
UMat pyrDown = new UMat();
CvInvoke.PyrDown(uimage, pyrDown);
CvInvoke.PyrUp(pyrDown, uimage);
//Image<Gray, Byte> gray = img.Convert<Gray, Byte>().PyrDown().PyrUp();
#region circle detection
double cannyThreshold = th1;
//double circleAccumulatorThreshold = 120;
//CircleF[] circles = CvInvoke.HoughCircles(uimage, HoughType.Gradient, 2.0, 20.0, cannyThreshold, circleAccumulatorThreshold, 5);
#endregion
#region Canny and edge detection
double cannyThresholdLinking = th2;
UMat cannyEdges = new UMat();
CvInvoke.Canny(uimage, cannyEdges, cannyThreshold, cannyThresholdLinking, aperture, true);
VectorOfVectorOfPoint contourEdges = new VectorOfVectorOfPoint();
UMat hierarchy = new UMat();
CvInvoke.FindContours(cannyEdges, contourEdges, hierarchy, 0, ChainApproxMethod.ChainApproxNone);
VectorOfVectorOfPoint newContourEdges = new VectorOfVectorOfPoint();
for (int i = 0; i < contourEdges.Size; i++)
{
if (contourEdges [i].Size > 3000)
{
newContourEdges.Push(contourEdges [i]);
}
}
contourEdges.Dispose();
VectorOfPoint test1 = new VectorOfPoint();
VectorOfVectorOfPoint temp = new VectorOfVectorOfPoint();
temp.Push(newContourEdges [0]);
for (int i = 0; i < newContourEdges.Size; i++)
{
Point[] testing = newContourEdges [i].ToArray();
temp[0].Push(newContourEdges [i].ToArray());
}
VectorOfVectorOfPoint hull = new VectorOfVectorOfPoint(1);
CvInvoke.ConvexHull(temp[0], hull[0], true);
/*LineSegment2D[] lines = CvInvoke.HoughLinesP(
* cannyEdges,
* 1, //Distance resolution in pixel-related units
* Math.PI/45.0, //Angle resolution measured in radians.
* 20, //threshold
* 30, //min Line width
* 5); //gap between lines
*
* //VectorOfPoint test1 = new VectorOfPoint();
* //VectorOfVectorOfPoint temp = new VectorOfVectorOfPoint();
* //temp.Push(contourEdges[0]);
* for (int i = 0; i < contourEdges.Size; i++) {
* //temp[0].Push(contourEdges[i].ToArray());
*
* CvInvoke.DrawContours(img, contourEdges, i, new MCvScalar(255,255,0), 4);
* }*/
//VectorOfVectorOfPoint hull = new VectorOfVectorOfPoint(1);
//CvInvoke.ConvexHull(temp[0], hull[0], true);
//VectorOfVectorOfPoint result = new VectorOfVectorOfPoint();
#endregion
#region Find triangles and rectangles
//List<Triangle2DF> triangleList = new List<Triangle2DF>();
//List<RotatedRect> boxList = new List<RotatedRect>(); //a box is a rotated rectangle
/*using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
* {
* CvInvoke.FindContours(cannyEdges, 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);
* if (CvInvoke.ContourArea(approxContour, false) > 250) //only consider contours with area greater than 250
* {
* if (approxContour.Size == 3) //The contour has 3 vertices, it is a triangle
* {
* Point[] pts = approxContour.ToArray();
* triangleList.Add(new Triangle2DF(
* pts[0],
* pts[1],
* pts[2]
* ));
* } else if (approxContour.Size == 4) //The contour has 4 vertices.
* {
#region determine if all the angles in the contour are within [80, 100] degree
* bool isRectangle = true;
* Point[] pts = approxContour.ToArray();
* LineSegment2D[] edges = PointCollection.PolyLine(pts, true);
*
* for (int j = 0; j < edges.Length; j++)
* {
* double angle = Math.Abs(
* edges[(j + 1) % edges.Length].GetExteriorAngleDegree(edges[j]));
* if (angle < 80 || angle > 100)
* {
* isRectangle = false;
* break;
* }
* }
#endregion
*
* if (isRectangle) boxList.Add(CvInvoke.MinAreaRect(approxContour));
* }
* }
* }
* }
* }*/
#endregion
//imageView.Image = img;
#region draw triangles and rectangles
//Image<Bgr, Byte> triangleRectangleImage = img;
//foreach (Triangle2DF triangle in triangleList)
// triangleRectangleImage.Draw(triangle, new Bgr(Color.DarkBlue), 2);
//foreach (RotatedRect box in boxList)
// triangleRectangleImage.Draw(box, new Bgr(Color.DarkOrange), 2);
//imageView.Image = triangleRectangleImage;
#endregion
#region draw circles
//Image<Bgr, Byte> circleImage = img.CopyBlank();
//foreach (CircleF circle in circles)
// triangleRectangleImage.Draw(circle, new Bgr(Color.Brown), 2);
//imageView.Image = circleImage;
#endregion
#region draw lines
//Image<Bgr, Byte> lineImage = img;
//foreach (LineSegment2D line in lines)
// img.Draw(line, new Bgr(Color.Yellow), 2);
//imageView.Image = lineImage;
#endregion
return(value ? hull : newContourEdges); //lineImage.ToUIImage();
}
/// <summary>
/// evaluate specified ROI and return confidence value for each location
/// </summary>
/// <param name="img"></param>
/// <param name="locations"></param>
/// <param name="foundLocations"></param>
/// <param name="confidences"></param>
/// <param name="hitThreshold"></param>
/// <param name="winStride"></param>
/// <param name="padding"></param>
public void DetectROI(
Mat img, Point[] locations, out Point[] foundLocations, out double[] confidences,
double hitThreshold = 0, Size? winStride = null, Size? padding = null)
{
if (disposed)
throw new ObjectDisposedException("HOGDescriptor");
if (img == null)
throw new ArgumentNullException("img");
if (locations == null)
throw new ArgumentNullException("locations");
img.ThrowIfDisposed();
Size winStride0 = winStride.GetValueOrDefault(new Size());
Size padding0 = padding.GetValueOrDefault(new Size());
using (var flVec = new VectorOfPoint())
using (var cVec = new VectorOfDouble())
{
NativeMethods.objdetect_HOGDescriptor_detectROI(ptr, img.CvPtr, locations, locations.Length,
flVec.CvPtr, cVec.CvPtr, hitThreshold, winStride0, padding0);
foundLocations = flVec.ToArray();
confidences = cVec.ToArray();
}
}
public static Tuple <VectorOfPoint, double> MarkDetection(Image <Gray, byte> ImgBinary, VectorOfPoint Template)
{
VectorOfPoint mark = null;
double crScore = 1;
double areaTemplate = CvInvoke.ContourArea(Template);
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
CvInvoke.FindContours(ImgBinary, contours, null, Emgu.CV.CvEnum.RetrType.Ccomp, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxSimple);
for (int i = 0; i < contours.Size; i++)
{
double scoreMatching = CvInvoke.MatchShapes(Template, contours[i], Emgu.CV.CvEnum.ContoursMatchType.I3);
double scoreCurrent = CvInvoke.ContourArea(contours[i]);
double scoreArea = Math.Min(areaTemplate, scoreCurrent) / Math.Max(areaTemplate, scoreCurrent);
scoreArea = 1 - scoreArea;
double score = Math.Max(scoreMatching, scoreArea);
if (score < crScore)
{
if (mark != null)
{
mark.Dispose();
mark = null;
}
crScore = score;
mark = new VectorOfPoint(contours[i].ToArray());
}
}
}
return(new Tuple <VectorOfPoint, double>(mark, crScore));
}
// get all of the valid contour maps, valid means circumfence > 200 px
// this was not in their code, I added this feature, but I used their logic
public static List<ColorfulContourMap> getAllContourMap(Mat input, int index, int mode = 0)
{
// use for all members
List<ColorfulContourMap> result = new List<ColorfulContourMap>();
MatImage m1 = new MatImage(input);
m1.Convert();
Mat gray = m1.Out();
// use for black background
if (mode == 0)
{
MatImage m2 = new MatImage(gray);
m2.SmoothGaussian(3);
m2.ThresholdBinaryInv(245, 255);
gray = m2.Out();
}
// use for white background
else
{
MatImage m2 = new MatImage(gray);
m2.SmoothGaussian(3);
m2.ThresholdBinaryInv(100, 255);
gray = m2.Out();
}
// one time use
List<Point> pointList = new List<Point>();
List<Point> polyPointList = new List<Point>();
List<ColorfulPoint> cps = new List<ColorfulPoint>();
List<ColorfulPoint> pcps = new List<ColorfulPoint>();
// fetch all the contours using Emgu CV
// fetch all the polys using Emgu CV
// extract the points and colors
Mat temp = gray.Clone();
VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint();
CvInvoke.FindContours(gray, contours, new Mat(), RetrType.List, ChainApproxMethod.ChainApproxNone);
double area = Math.Abs(CvInvoke.ContourArea(contours[0]));
VectorOfPoint maxArea = contours[0]; // maxArea is used as the current contour
//contour = contour.HNext;
// use this to loop
for (int i = 0; i < contours.Size; i++)
{
double nextArea = Math.Abs(CvInvoke.ContourArea(contours[i], false)); // Find the area of contour
area = nextArea;
if (area >= Constants.MIN_AREA)
{
maxArea = contours[i];
VectorOfPoint poly = new VectorOfPoint();
CvInvoke.ApproxPolyDP(maxArea, poly, 1.0, true);
pointList = maxArea.ToArray().ToList();
polyPointList = poly.ToArray().ToList();
foreach (Point p in pointList)
{
ColorfulPoint cp = new ColorfulPoint { X = p.X, Y = p.Y, color = extractPointColor(p, input) };
cps.Add(cp);
}
foreach (Point p in polyPointList)
{
ColorfulPoint cp = new ColorfulPoint { X = p.X, Y = p.Y, color = extractPointColor(p, input) };
pcps.Add(cp);
}
result.Add(new ColorfulContourMap(cps, pcps, index));
// clear temporal lists
pointList = new List<Point>();
polyPointList = new List<Point>();
cps = new List<ColorfulPoint>();
pcps = new List<ColorfulPoint>();
}
}
return result;
}
private void FindLicensePlate(
VectorOfVectorOfPoint contours, int[,] hierachy, int idx, IInputArray gray, IInputArray canny,
List <IInputOutputArray> licensePlateImagesList, List <IInputOutputArray> filteredLicensePlateImagesList, List <RotatedRect> detectedLicensePlateRegionList,
List <String> licenses)
{
for (; idx >= 0; idx = hierachy[idx, 0])
{
int numberOfChildren = GetNumberOfChildren(hierachy, idx);
//if it does not contains any children (charactor), it is not a license plate region
if (numberOfChildren == 0)
{
continue;
}
using (VectorOfPoint contour = contours[idx])
{
if (CvInvoke.ContourArea(contour) > 400)
{
if (numberOfChildren < 3)
{
//If the contour has less than 3 children, it is not a license plate (assuming license plate has at least 3 charactor)
//However we should search the children of this contour to see if any of them is a license plate
FindLicensePlate(contours, hierachy, hierachy[idx, 2], gray, canny, licensePlateImagesList,
filteredLicensePlateImagesList, detectedLicensePlateRegionList, licenses);
continue;
}
RotatedRect box = CvInvoke.MinAreaRect(contour);
if (box.Angle < -45.0)
{
float tmp = box.Size.Width;
box.Size.Width = box.Size.Height;
box.Size.Height = tmp;
box.Angle += 90.0f;
}
else if (box.Angle > 45.0)
{
float tmp = box.Size.Width;
box.Size.Width = box.Size.Height;
box.Size.Height = tmp;
box.Angle -= 90.0f;
}
double whRatio = (double)box.Size.Width / box.Size.Height;
if (!(3.0 < whRatio && whRatio < 10.0))
//if (!(1.0 < whRatio && whRatio < 2.0))
{
//if the width height ratio is not in the specific range,it is not a license plate
//However we should search the children of this contour to see if any of them is a license plate
//Contour<Point> child = contours.VNext;
if (hierachy[idx, 2] > 0)
{
FindLicensePlate(contours, hierachy, hierachy[idx, 2], gray, canny, licensePlateImagesList,
filteredLicensePlateImagesList, detectedLicensePlateRegionList, licenses);
}
continue;
}
using (UMat tmp1 = new UMat())
using (UMat tmp2 = new UMat())
{
PointF[] srcCorners = box.GetVertices();
PointF[] destCorners = new PointF[] {
new PointF(0, box.Size.Height - 1),
new PointF(0, 0),
new PointF(box.Size.Width - 1, 0),
new PointF(box.Size.Width - 1, box.Size.Height - 1)
};
using (Mat rot = CvInvoke.GetAffineTransform(srcCorners, destCorners))
{
CvInvoke.WarpAffine(gray, tmp1, rot, Size.Round(box.Size));
}
//resize the license plate such that the front is ~ 10-12. This size of front results in better accuracy from tesseract
Size approxSize = new Size(240, 180);
double scale = Math.Min(approxSize.Width / box.Size.Width, approxSize.Height / box.Size.Height);
Size newSize = new Size((int)Math.Round(box.Size.Width * scale), (int)Math.Round(box.Size.Height * scale));
CvInvoke.Resize(tmp1, tmp2, newSize, 0, 0, Inter.Cubic);
//removes some pixels from the edge
int edgePixelSize = 2;
Rectangle newRoi = new Rectangle(new Point(edgePixelSize, edgePixelSize),
tmp2.Size - new Size(2 * edgePixelSize, 2 * edgePixelSize));
UMat plate = new UMat(tmp2, newRoi);
UMat filteredPlate = FilterPlate(plate);
Tesseract.Character[] words;
StringBuilder strBuilder = new StringBuilder();
using (UMat tmp = filteredPlate.Clone())
{
_ocr.Recognize(tmp);
words = _ocr.GetCharacters();
if (words.Length == 0)
{
continue;
}
for (int i = 0; i < words.Length; i++)
{
strBuilder.Append(words[i].Text);
}
}
licenses.Add(strBuilder.ToString());
licensePlateImagesList.Add(plate);
filteredLicensePlateImagesList.Add(filteredPlate);
detectedLicensePlateRegionList.Add(box);
}
}
}
}
}
public short Calculate(string imageFilePath, Polygon polygon, Media.PointCollection pointCollection)
{
// Maskiertes Bild laden
// Drawing.Bitmap maskedBitmap = GetMaskedBitmap(imageFilePath, pointCollection);
Image<Bgr, byte> cvImage = new Image<Bgr, byte>(imageFilePath);
// Maske generieren aus Polygon
Mat matMask = new Mat(new Drawing.Size(cvImage.Cols, cvImage.Rows), DepthType.Cv8U, 3);
// Polygone skalieren und generieren
List<Point> scaledPoints = GetScaledPoints(pointCollection, cvImage.Rows, cvImage.Cols);
List<Drawing.Point> scaledDrawingPoints = GetPolygonPoints(scaledPoints, cvImage.Rows, cvImage.Cols);
// Polygon weiss zeichnen
using (VectorOfPoint vPoint = new VectorOfPoint(scaledDrawingPoints.ToArray()))
using (VectorOfVectorOfPoint vvPoint = new VectorOfVectorOfPoint(vPoint))
{
CvInvoke.FillPoly(matMask, vvPoint, new Bgr(255, 255, 255).MCvScalar);
}
Image<Gray, byte> imageMask = new Image<Gray, byte>(matMask.Bitmap);
// Durchschnittsfarbe rechnen mit Maske
Bgr result = cvImage.GetAverage(imageMask);
// Vergleichen mit Referenzbildern
Bgr snow = JsonConvert.DeserializeObject<Bgr>(polygon.BgrSnow);
Bgr normal = JsonConvert.DeserializeObject<Bgr>(polygon.BgrNormal);
double resultSnow = Math.Abs(snow.Blue - result.Blue) + Math.Abs(snow.Green - result.Green) + Math.Abs(snow.Red - result.Red);
double resultNormal = Math.Abs(normal.Blue - result.Blue) + Math.Abs(normal.Green - result.Green) + Math.Abs(normal.Red - result.Red);
if (Math.Abs(resultSnow - resultNormal) < 10)
{
return 0;
}
else if (resultSnow < resultNormal)
{
return 1;
}
else
{
return -1;
}
}
public void CalculateAverageBrightessForArea(string reference0, string reference1, StrassenbilderMetaDataContext dataContext)
{
// Image-Meta-Daten laden
string name0 = Path.GetFileNameWithoutExtension(reference0);
string name1 = Path.GetFileNameWithoutExtension(reference1);
Image image0 = dataContext.Images.Where(i => i.Name == name0).FirstOrDefault();
Image image1 = dataContext.Images.Where(i => i.Name == name1).FirstOrDefault();
// Polygone Laden
IEnumerable<Polygon> polygons = dataContext.Polygons.Where(p => p.CameraName == image0.Place);
// Pro Maske anwenden
foreach (var polygon in polygons)
{
IList<Point> polygonPoints = JsonConvert.DeserializeObject<Media.PointCollection>(polygon.PolygonPointCollection);
// Maskiertes Bild laden
Drawing.Bitmap bitmap0 = GetMaskedBitmap(reference0, polygonPoints);
Drawing.Bitmap bitmap1 = GetMaskedBitmap(reference1, polygonPoints);
Image<Bgr, byte> cvImage0 = new Image<Bgr, byte>(bitmap0);
Image<Bgr, byte> cvImage1 = new Image<Bgr, byte>(bitmap1);
// Maske generieren aus Polygon
Mat matMask = new Mat(new Drawing.Size(cvImage0.Cols, cvImage0.Rows), DepthType.Cv8U, 3);
// Polygone skalieren und generieren
List<Point> scaledPoints = GetScaledPoints(polygonPoints, cvImage0.Rows, cvImage0.Cols);
List<Drawing.Point> scaledDrawingPoints = GetPolygonPoints(scaledPoints, cvImage0.Rows, cvImage0.Cols);
// Polygon weiss zeichnen
using (VectorOfPoint vPoint = new VectorOfPoint(scaledDrawingPoints.ToArray()))
using (VectorOfVectorOfPoint vvPoint = new VectorOfVectorOfPoint(vPoint))
{
CvInvoke.FillPoly(matMask, vvPoint, new Bgr(255, 255, 255).MCvScalar);
}
Image<Gray, byte> imageMask = new Image<Gray, byte>(matMask.Bitmap);
// Durchschnittsfarbe rechnen mit Maske
Bgr result0 = cvImage0.GetAverage(imageMask);
Bgr result1 = cvImage1.GetAverage(imageMask);
// Resultat abspeichern
polygon.BgrSnow = JsonConvert.SerializeObject(result0);
polygon.BgrNormal = JsonConvert.SerializeObject(result1);
dataContext.SubmitChanges();
}
}