public void TestGoodFeature()
{
using (GFTTDetector detector = new GFTTDetector(1000, 0.01, 1, 3, false, 0.04))
using (Mat img = EmguAssert.LoadMat("stuff.jpg"))
{
var keypoints = detector.Detect(img);
int nanCount = 0;
foreach (MKeyPoint p in keypoints)
{
CvInvoke.Circle(img, Point.Round(p.Point), 3, new Bgr(255, 0, 0).MCvScalar, 1);
if (float.IsNaN(p.Point.X) || float.IsNaN(p.Point.Y))
nanCount ++;
}
System.Diagnostics.Debug.WriteLine(String.Format("NanCount: {0}", nanCount));
EmguAssert.IsTrue(nanCount == 0);
//ImageViewer.Show(img);
}
using (GFTTDetector detector = new GFTTDetector())
using (Image<Bgr, Byte> img = EmguAssert.LoadImage<Bgr, Byte>("stuff.jpg"))
{
Stopwatch watch = Stopwatch.StartNew();
int runs = 10;
for (int i = 0; i < runs; i++)
{
var pts = detector.Detect(img);
}
watch.Stop();
EmguAssert.WriteLine(String.Format("Avg time to extract good features from image of {0}: {1}", img.Size, watch.ElapsedMilliseconds / runs));
}
}
public void TestGFTTDetector()
{
GFTTDetector keyPointDetector = new GFTTDetector(1000, 0.01, 1, 3, false, 0.04);
SIFT descriptorGenerator = new SIFT();
//ParamDef[] parameters = keyPointDetector.GetParams();
TestFeature2DTracker(keyPointDetector, descriptorGenerator);
}
public void InitOriginalVideo(string initFile)
{
//Capture Image
OutputPath = _defaultInitVideoPath;
List<string> grayImgList = CatchImages(initFile, 0, OutputPath);
if (grayImgList.Count < 3)
{
return;
}
//Get the Optical flow of L-K feature
Image<Gray, Byte> mask = new Image<Gray, Byte>(grayImgList.First());
Image<Gray, Byte> grayImage1 = null;//new Image<Gray, Byte>(grayImgList[1]);
Image<Gray, Byte> grayImage2 = null;//new Image<Gray, Byte>(grayImgList.Last());
for (int i=1; i< grayImgList.Count-1; i++)
{
grayImage1 = new Image<Gray, Byte>(grayImgList[i]);
grayImage2 = new Image<Gray, Byte>(grayImgList[i + 1]);
EmguType features1 = SURFFeatureDetect(grayImage1, mask);
Utils.WriteJsonFile(features1, grayImgList[i] + ".dat");
//VectorOfPointF vp1 = new VectorOfPointF(features1.KeyPoints.ToArray().Select(x => x.Point).ToArray());
//VectorOfPointF vp2 = new VectorOfPointF(vp1.Size);
//VectorOfByte vstatus = new VectorOfByte(vp1.Size);
//VectorOfFloat verr = new VectorOfFloat(vp1.Size);
Size winsize = new Size(grayImage1.Width, grayImage1.Height);
int maxLevel = 1; // if 0, winsize is not used
MCvTermCriteria criteria = new MCvTermCriteria(10, 1);
try
{
if (i % Constants.DETECTIVE_GROUP_COUNT == 1)
{
GFTTDetector gd = new GFTTDetector();
MKeyPoint[] gdkp = gd.Detect(grayImage1, mask);
VectorOfPointF gdvp1 = new VectorOfPointF(gdkp.Select(x => x.Point).ToArray());
VectorOfPointF gdvp2 = new VectorOfPointF(gdvp1.Size);
VectorOfByte vstatus = new VectorOfByte(gdvp1.Size);
VectorOfFloat verr = new VectorOfFloat(gdvp1.Size);
CvInvoke.CalcOpticalFlowPyrLK(grayImage1, grayImage2, gdvp1, gdvp2, vstatus, verr, winsize, maxLevel, criteria);
Utils.WriteJsonFile(gdvp2, grayImgList[i] + "pp.dat");
}
else
{
VectorOfPointF gdvp1 = Utils.ReadJsonFile<VectorOfPointF>(grayImgList[i - 1] + "pp.dat");
VectorOfPointF gdvp2 = new VectorOfPointF(gdvp1.Size);
VectorOfByte vstatus = new VectorOfByte(gdvp1.Size);
VectorOfFloat verr = new VectorOfFloat(gdvp1.Size);
CvInvoke.CalcOpticalFlowPyrLK(grayImage1, grayImage2, gdvp1, gdvp2, vstatus, verr, winsize, maxLevel, criteria);
Utils.WriteJsonFile(gdvp2, grayImgList[i] + "pp.dat");
}
}
catch (Exception e)
{
_log.Debug("error: " + e.Message);
}
}
/*
//Get SIFT Feature
foreach (string grayImgPath in grayImgList)
{
Image<Gray, Byte> grayImage = new Image<Gray, Byte>(grayImgPath);
//List<SiftFeature> features = SiftFeatureDetect(image: grayImage, showDetail: true);
//Image<Gray, float> grayImage = new Image<Gray, float>(grayImgPath);
//List<Feature> features = SiftFeatureDetect(grayImage);
EmguType features = SURFFeatureDetect(grayImage);
Utils.WriteJsonFile(features, grayImgPath + ".dat");
}
*/
_initSuccess = true;
OutputPath = string.Empty;
}