public Bitmap DrawPoints(Color color)
{
Bitmap bmp = new Bitmap(n, n, PixelFormat.Format24bppRgb);
PointsMarker pm = new PointsMarker(Points, color);
return(pm.Apply(bmp));
}
public void FindTest()
{
List <IntPoint> contour = new List <IntPoint>();
int max = 100;
for (int i = 0; i < max; i++)
{
add(contour, i, max);
}
for (int i = 0; i < max; i++)
{
add(contour, max, i);
}
for (int i = 0; i < max; i++)
{
add(contour, 0, i);
}
for (int i = 0; i < max / 2; i++)
{
add(contour, i, i);
}
for (int i = 0; i < max / 2; i++)
{
add(contour, i + max / 2, max / 2 - i);
}
PointsMarker marker = new PointsMarker(contour);
var bitmap = AForge.Imaging.Image.CreateGrayscaleImage(max + 1, max + 1);
bitmap = marker.Apply(bitmap);
// ImageBox.Show(bitmap);
GrahamConvexHull graham = new GrahamConvexHull();
List <IntPoint> hull = graham.FindHull(contour);
ConvexHullDefects hullDefects = new ConvexHullDefects(10);
List <ConvexityDefect> defects = hullDefects.FindDefects(contour, hull);
Assert.AreEqual(1, defects.Count);
Assert.AreEqual(99, defects[0].Depth);
}
public void ApplyTest1()
{
string basePath = Path.Combine(NUnit.Framework.TestContext.CurrentContext.TestDirectory, "watershed");
Directory.CreateDirectory(basePath);
Bitmap shapes = Accord.Imaging.Image.Clone(Resources.water);
shapes.Save(Path.Combine(basePath, "shapes.jpg"));
var bw = new BinaryWatershed();
Bitmap result = bw.Apply(shapes);
Assert.AreEqual(746, result.Width);
Assert.AreEqual(643, result.Height);
Assert.AreEqual(PixelFormat.Format8bppIndexed, result.PixelFormat);
Assert.AreEqual(9, bw.MaxPoints.Count);
string strX = bw.MaxPoints.Select(i => i.X).ToArray().ToCSharp();
string strY = bw.MaxPoints.Select(i => i.Y).ToArray().ToCSharp();
double[] x = new double[] { 310, 546, 136, 254, 429, 612, 398, 345, 498 };
double[] y = new double[] { 436, 153, 392, 201, 336, 339, 242, 183, 319 };
Assert.AreEqual(x, bw.MaxPoints.Select(i => i.X).ToArray());
Assert.AreEqual(y, bw.MaxPoints.Select(i => i.Y).ToArray());
result.Save(Path.Combine(basePath, "watershed.jpg"));
GrayscaleToRGB toRGB = new GrayscaleToRGB();
result = toRGB.Apply(result);
PointsMarker marker = new PointsMarker(Color.Red, 5);
marker.Points = bw.MaxPoints;
Bitmap marked = marker.Apply(result);
marked.Save(Path.Combine(basePath, "watershed-marks.jpg"));
Assert.IsNotNull(result);
Assert.IsNotNull(marked);
}
public static Bitmap MarkPoints(Bitmap bmp, List <IntPoint> points, Color color)
{
PointsMarker pm = new PointsMarker(points, color);
return(pm.Apply(bmp));
}
/// <summary>
/// takes the video and process it two frames at a time to calculate
/// optical flow features and save them on the disk.
/// </summary>
/// <param name="vid">Path of the video on the disk.</param>
/// <param name="save_path">Path to save the features on the disk.</param>
/// <returns></returns>
public void Extract_Featurers2(String vid, String save_path)
{
int mm = 0;
try
{
mag = new Mat();
ang = new Mat();
frame = new Mat();
prev_frame = new Mat();
cap = new VideoCapture(vid);
total_frames = Convert.ToInt32(cap.GetCaptureProperty(CapProp.FrameCount));
F_L = new List <int>();
frame = cap.QueryFrame();
prev_frame = frame;
Console.WriteLine(total_frames);
}
catch (NullReferenceException except)
{
Console.WriteLine(except.Message);
}
//17900
while (mm < total_frames - 2)
{
try
{
prev_frame = frame;
frame = cap.QueryFrame();
Bitmap image = new Bitmap(frame.Bitmap);
// Create a new FAST Corners Detector
FastCornersDetector fast = new FastCornersDetector()
{
Suppress = true, // suppress non-maximum points
Threshold = 70 // less leads to more corners
};
// Process the image looking for corners
List <IntPoint> points = fast.ProcessImage(image);
// Create a filter to mark the corners
PointsMarker marker = new PointsMarker(points);
// Apply the corner-marking filter
Bitmap markers = marker.Apply(image);
// Show on the screen
//Accord.Controls.ImageBox.Show(markers);
// Use it to extract interest points from the Lena image:
List <IntPoint> descriptors = fast.ProcessImage(image);
PointF[] features = new PointF[descriptors.Count];
int c = 0;
foreach (IntPoint p in descriptors)
{
features[c] = new PointF(p.X, p.Y);
c++;
}
ImageViewer viewer = new ImageViewer();
Image <Gray, Byte> prev_grey_img = new Image <Gray, byte>(frame.Width, frame.Height);
Image <Gray, Byte> curr_grey_img = new Image <Gray, byte>(frame.Width, frame.Height);
curr_grey_img = frame.ToImage <Gray, byte>();
prev_grey_img = prev_frame.ToImage <Gray, Byte>();
PointF[] shiftedFeatures;
Byte[] status;
float[] trackErrors;
CvInvoke.CalcOpticalFlowPyrLK(prev_grey_img, curr_grey_img, features, new Size(9, 9), 3, new MCvTermCriteria(20, 0.05),
out shiftedFeatures, out status, out trackErrors);
//Image<Gray, Byte> displayImage = cap.QueryFrame().ToImage<Gray, Byte>();
//for (int i = 0; i < features.Length; i++)
// displayImage.Draw(new LineSegment2DF(features[i], shiftedFeatures[i]), new Gray(), 2);
for (int i = 0; i < features.Length; i++)
{
CvInvoke.Circle(frame, System.Drawing.Point.Round(shiftedFeatures[i]), 4, new MCvScalar(0, 255, 255), 2);
}
int mean_X = 0;
int mean_Y = 0;
foreach (PointF p in shiftedFeatures)
{
mean_X += (int)p.X;
mean_Y += (int)p.Y;
}
mean_X /= shiftedFeatures.Length;
mean_Y /= shiftedFeatures.Length;
F_L.Add(mean_X);
F_L.Add(mean_Y);
//double[] inner = new double[] { mean_X, mean_Y };
//featuers_list[mm] = inner;
//viewer.Image = frame;
//viewer.ShowDialog();
//prev_frame = frame;
//Console.WriteLine("frame:{0} " + mm);
Console.WriteLine("frame:{0} " + mm + " X:{1} " + mean_X + " Y:{2} " + mean_Y);
mm++;
}
catch (Exception e)
{ Console.WriteLine(e.Message); }
}
//int go = 0;
//foreach (double[] arr in featuers_list)
//{
// Console.Write("frame:{0} ", go++);
// foreach (double d in arr)
// Console.Write(d + " ");
// Console.WriteLine();
//}
Serialize.SerializeObject(F_L, save_path);
}
