public void ProcessImageTest()
{
UnmanagedImage image = UnmanagedImage.FromManagedImage(Properties.Resources.sample_black);
FastCornersDetector target = new FastCornersDetector();
target.Suppress = false;
target.Threshold = 20;
List <IntPoint> actual = target.ProcessImage(image);
/*
* PointsMarker marker = new PointsMarker(actual.ToArray());
* marker.Width = 3;
* marker.MarkerColor = Color.FromArgb(255, 0, 0);
* var markers = marker.Apply(image);
* ImageBox.Show(markers.ToManagedImage(), PictureBoxSizeMode.Zoom);
*/
Assert.AreEqual(237, actual.Count);
Assert.AreEqual(404, actual[0].X);
Assert.AreEqual(35, actual[0].Y);
Assert.AreEqual(407, actual[6].X);
Assert.AreEqual(36, actual[6].Y);
Assert.AreEqual(407, actual[11].X);
Assert.AreEqual(38, actual[11].Y);
Assert.AreEqual(55, actual[65].X);
Assert.AreEqual(135, actual[65].Y);
Assert.AreEqual(103, actual[73].X);
Assert.AreEqual(137, actual[73].Y);
}
/// <summary>
/// Features from Accelerated Segment Test (FAST) corners detector.
/// <para>Accord.NET internal call. Please see: <see cref="Accord.Imaging.FastCornersDetector"/> for details.</para>
/// </summary>
/// <param name="im">Image.</param>
/// <param name="threshold">The suppression threshold. Decreasing this value increases the number of points detected by the algorithm.</param>
/// <returns>Interest point locations.</returns>
public static List <IntPoint> CornerFeaturesDetector(this Image <Gray, byte> im, int threshold = 20)
{
FastCornersDetector fast = new FastCornersDetector(threshold);
var points = fast.ProcessImage(im.ToAForgeImage(copyAlways: false, failIfCannotCast: true));
return(points);
}
public void ProcessImageTest2()
{
UnmanagedImage image = UnmanagedImage.FromManagedImage(Properties.Resources.lena512);
FastCornersDetector target = new FastCornersDetector();
target.Suppress = true;
target.Threshold = 40;
List <IntPoint> actual = target.ProcessImage(image);
/*
* PointsMarker marker = new PointsMarker(actual.ToArray());
* marker.Width = 3;
* marker.MarkerColor = Color.FromArgb(255, 0, 0);
* var markers = marker.Apply(image);
* ImageBox.Show(markers.ToManagedImage(), PictureBoxSizeMode.Zoom);
*/
Assert.AreEqual(324, actual.Count);
Assert.AreEqual(506, actual[0].X);
Assert.AreEqual(4, actual[0].Y);
Assert.AreEqual(152, actual[6].X);
Assert.AreEqual(75, actual[6].Y);
Assert.AreEqual(416, actual[11].X);
Assert.AreEqual(115, actual[11].Y);
Assert.AreEqual(140, actual[65].X);
Assert.AreEqual(246, actual[65].Y);
Assert.AreEqual(133, actual[73].X);
Assert.AreEqual(253, actual[73].Y);
}
/// <summary>
/// Features from Accelerated Segment Test (FAST) corners detector.
/// <para>Accord.NET internal call. Please see: <see cref="Accord.Imaging.FastCornersDetector"/> for details.</para>
/// </summary>
/// <param name="im">Image.</param>
/// <param name="threshold">The suppression threshold. Decreasing this value increases the number of points detected by the algorithm.</param>
/// <returns>Interest point locations.</returns>
public static List <IntPoint> CornerFeaturesDetector(this Gray <byte>[,] im, int threshold = 20)
{
FastCornersDetector fast = new FastCornersDetector(threshold);
List <IntPoint> points;
using (var uImg = im.Lock())
{
points = fast.ProcessImage(uImg.AsAForgeImage());
}
return(points);
}
public void GetCorners(int threshold, bool supress)
{
// create corners detector's instance
FastCornersDetector fcd = new FastCornersDetector()
{
Suppress = supress,
Threshold = threshold
};
// Apply the filter and return the points
List <IntPoint> corners = fcd.ProcessImage(AForge.Imaging.Image.FromFile(this.CurrentImage));
if (ImageComplete != null)
{
ImageComplete(corners);
}
}
public void batch_test()
{
Bitmap[] images =
{
Accord.Imaging.Image.Clone(Resources.flower01),
Accord.Imaging.Image.Clone(Resources.flower02),
Accord.Imaging.Image.Clone(Resources.flower03),
Accord.Imaging.Image.Clone(Resources.flower04),
Accord.Imaging.Image.Clone(Resources.flower05),
Accord.Imaging.Image.Clone(Resources.flower06),
};
FastCornersDetector target = new FastCornersDetector();
for (int i = 0; i < images.Length; i++)
{
List <IntPoint> actual = target.ProcessImage(images[i]);
Assert.IsNotNull(actual);
}
}
public void doc_test()
{
string localPath = TestContext.CurrentContext.TestDirectory;
#region doc_apply
// Let's load an example image, such as Lena,
// from a standard dataset of example images:
var images = new TestImages(path: localPath);
Bitmap lena = images["lena.bmp"];
// Create FAST with the default parameter values:
var fast = new FastCornersDetector(threshold: 20);
// Use it to extract interest points from the Lena image:
List <IntPoint> descriptors = fast.ProcessImage(lena);
// Now those descriptors can be used to represent the image itself, such
// as for example, in the Bag-of-Visual-Words approach for classification.
#endregion
Assert.AreEqual(1144, descriptors.Count);
}
public void ProcessImageTest2()
{
UnmanagedImage image = UnmanagedImage.FromManagedImage(Accord.Imaging.Image.Clone(Resources.lena512));
FastCornersDetector target = new FastCornersDetector();
target.Suppress = true;
target.Threshold = 40;
List <IntPoint> actual = target.ProcessImage(image);
Assert.AreEqual(324, actual.Count);
Assert.AreEqual(506, actual[0].X);
Assert.AreEqual(4, actual[0].Y);
Assert.AreEqual(152, actual[6].X);
Assert.AreEqual(75, actual[6].Y);
Assert.AreEqual(416, actual[11].X);
Assert.AreEqual(115, actual[11].Y);
Assert.AreEqual(140, actual[65].X);
Assert.AreEqual(246, actual[65].Y);
Assert.AreEqual(133, actual[73].X);
Assert.AreEqual(253, actual[73].Y);
}
public void ProcessImageTest()
{
UnmanagedImage image = UnmanagedImage.FromManagedImage(Accord.Imaging.Image.Clone(Resources.sample_black));
FastCornersDetector target = new FastCornersDetector();
target.Suppress = false;
target.Threshold = 20;
List <IntPoint> actual = target.ProcessImage(image);
Assert.AreEqual(237, actual.Count);
Assert.AreEqual(404, actual[0].X);
Assert.AreEqual(35, actual[0].Y);
Assert.AreEqual(407, actual[6].X);
Assert.AreEqual(36, actual[6].Y);
Assert.AreEqual(407, actual[11].X);
Assert.AreEqual(38, actual[11].Y);
Assert.AreEqual(55, actual[65].X);
Assert.AreEqual(135, actual[65].Y);
Assert.AreEqual(103, actual[73].X);
Assert.AreEqual(137, actual[73].Y);
}
/// <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);
}
