public void ShowKeyPoints()
{
lstMat.Clear();
lstModelDescriptors.Clear();
var featureDetector = new SIFT();
Emgu.CV.Flann.LinearIndexParams ip = new Emgu.CV.Flann.LinearIndexParams();
Emgu.CV.Flann.SearchParams sp = new SearchParams();
DescriptorMatcher matcher = new FlannBasedMatcher(ip, sp);
Rectangle cropRect = new Rectangle(842, 646, 70, 70);
Mat mask = new Mat(new Size(70, 70), DepthType.Cv8U, 1);
CvInvoke.Rectangle(mask, new Rectangle(0, 0, 70, 70), new MCvScalar(255, 255, 255), -1);
CvInvoke.Circle(mask, new Point(35, 37), 22, new MCvScalar(0, 0, 0), -1);
lstMat.Add(mask);
String[] folders = { @"Linage2\Main\PartyAuto", @"Linage2\Main\PartyManual" };
foreach (String folder in folders)
{
DirectoryInfo imageFolder = new DirectoryInfo(folder);
FileInfo[] files = Utils.GetFilesByExtensions(imageFolder, ".jpg", ".png").ToArray();
foreach (FileInfo finfo in files)
{
Mat img = CvInvoke.Imread(finfo.FullName, ImreadModes.Color);
Mat crop = CVUtil.crop_color_frame(img, cropRect);
//lstMat.Add(crop);
VectorOfKeyPoint modelKeyPoints = new VectorOfKeyPoint();
Mat modelDescriptors = new Mat();
featureDetector.DetectAndCompute(crop, mask, modelKeyPoints, modelDescriptors, false);
lstModelDescriptors.Add(modelDescriptors);
Mat result = new Mat();
Features2DToolbox.DrawKeypoints(crop, modelKeyPoints, result, new Bgr(Color.Red));
lstMat.Add(result);
//BOWImgDescriptorExtractor bow = new BOWImgDescriptorExtractor(featureDetector, matcher);
}
}
/*BOWKMeansTrainer bowtrainer = new BOWKMeansTrainer(1000, new MCvTermCriteria(10, 0.001), 1, Emgu.CV.CvEnum.KMeansInitType.PPCenters);
* foreach (Mat m in lstModelDescriptors) {
* bowtrainer.Add(m);
* }
* Mat dict = new Mat();
* bowtrainer.Cluster();
* StringBuilder sb = new StringBuilder();
* Image<Bgr, Byte> imgsave = dict.ToImage<Bgr, Byte>();
*
* (new XmlSerializer(typeof(Image<Bgr, Byte>))).Serialize(new StringWriter(sb), imgsave);
* Console.WriteLine(sb.ToString());*/
}
public Point[] GetMatchBoundingPoint()
{
Rectangle rect = CVUtil.GetRect(matchFeatureData.keyPoints);
PointF[] src =
{
new PointF(rect.X, rect.Y), new PointF(rect.X, rect.Y + rect.Height - 1),
new PointF(rect.X + rect.Width - 1, rect.Y + rect.Height - 1), new PointF(rect.X + rect.Width - 1, rect.Y)
};
PointF[] points = CvInvoke.PerspectiveTransform(src, homography);
foreach (var p in points)
{
Console.WriteLine(p.ToString());
}
Point[] ap = Array.ConvertAll(points,
new Converter <PointF, Point>(CVUtil.PointFToPoint));
return(ap);
}
public Mat ProcessImage(Mat img)
{
if (colorIndex != -1)
{
img = img.Split()[colorIndex];
}
//CvInvoke.InRange(img, lowerLimit, upperLimit, imgOut);
img = CVUtil.crop_color_frame(img, cropRect);
Mat ret = new Mat();
if (lower != null)
{
CvInvoke.InRange(img, lower, upper, ret);
}
else
{
ret = img;
}
return(ret);
}
private void testToolStripMenuItem_Click(object sender, EventArgs e)
{
lstMat.Clear();
VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch();
Mat testImage = CvInvoke.Imread(@"Linage2\Main\PartyAuto\2e35av2fwbk.png", ImreadModes.Color);
Mat modelImage = CVUtil.crop_color_frame(testImage, new Rectangle(842, 646, 70, 70));
log(modelImage.ToString());
Image <Bgr, Byte> img = modelImage.ToImage <Bgr, Byte>();
CvInvoke.cvSetImageROI(img, new Rectangle(0, 0, 35, 35));
//UMat uModelImage = modelImage.GetUMat(AccessType.Read);
var featureDetector = new SIFT();
Mat modelDescriptors = new Mat();
VectorOfKeyPoint modelKeyPoints = new VectorOfKeyPoint();
VectorOfKeyPoint observedKeyPoints = new VectorOfKeyPoint();
featureDetector.DetectAndCompute(modelImage, null, modelKeyPoints, modelDescriptors, false);
log("model size = " + modelKeyPoints.Size);
Mat observedDescriptors = new Mat();
featureDetector.DetectAndCompute(testImage, null, observedKeyPoints, observedDescriptors, false);
int k = 2;
double uniquenessThreshold = 0.80;
Mat mask;
Mat homography = null;
// Bruteforce, slower but more accurate
// You can use KDTree for faster matching with slight loss in accuracy
using (Emgu.CV.Flann.LinearIndexParams ip = new Emgu.CV.Flann.LinearIndexParams())
using (Emgu.CV.Flann.SearchParams sp = new SearchParams())
using (DescriptorMatcher matcher = new FlannBasedMatcher(ip, sp))
{
matcher.Add(modelDescriptors);
matcher.KnnMatch(observedDescriptors, 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 >= 4)
{
nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints,
matches, mask, 1.5, 20);
if (nonZeroCount >= 4)
{
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, matches, mask, 2);
PointF[] src =
{
new PointF(0, 0), new PointF(0, modelImage.Height - 1), new PointF(modelImage.Width - 1, modelImage.Height - 1), new PointF(modelImage.Width - 1, 0)
};
PointF[] points = CvInvoke.PerspectiveTransform(src, homography);
foreach (var p in points)
{
Console.WriteLine(p.ToString());
}
Point[] ap = Array.ConvertAll(points,
new Converter <PointF, Point>(CVUtil.PointFToPoint));
CvInvoke.Polylines(testImage, ap, true, new MCvScalar(255, 0, 0));
CvInvoke.Rectangle(testImage, new Rectangle(0, 0, 100, 100), new MCvScalar(255, 255, 0));
CvInvoke.Circle(testImage, new Point(100, 100), 50, new MCvScalar(255, 255, 0), -1);
lstMat.Add(testImage);
}
//Mat modelMatches = new Mat();
//Features2DToolbox.DrawKeypoints(modelImage, modelKeyPoints, result, new Bgr(Color.Red));
//Features2DToolbox.DrawKeypoints(testImage, observedKeyPoints, result, new Bgr(Color.Red));
//Features2DToolbox.DrawMatches(modelImage, modelKeyPoints, testImage, observedKeyPoints, matches, modelMatches,
// new MCvScalar(255, 0, 0), new MCvScalar(0, 255, 0));
//lstMat.Add(modelMatches);
//Mat model1 = new Mat();
//Features2DToolbox.DrawKeypoints(modelImage, modelKeyPoints, model1, new Bgr(Color.Red));
//lstMat.Add(model1);
//modelMatches = crop_color_frame(testImage,new Rectangle(842,646,70,70));
}
}
log("Done " + mask.Size);
Refresh();
}
public FeatureResult GetFeature(Bitmap bmpSrc)
{
Mat matTest = CVUtil.BitmapToMat(bmpSrc);
matTest = ProcessImage(matTest);
/*Bitmap bmp = Utils.cropImage(bmpSrc, cropRect);
* Mat matTest = CVUtil.BitmapToMat(bmp);
* if (colorIndex != -1)
* {
* matTest = matTest.Split()[colorIndex];
* }*/
Mat observedDescriptors = new Mat();
VectorOfKeyPoint observedKeyPoints = new VectorOfKeyPoint();
featureDetector.DetectAndCompute(matTest, mask, observedKeyPoints, observedDescriptors, false);
int k = 2;
double uniquenessThreshold = 0.80;
//Mat homography = null;
// Bruteforce, slower but more accurate
// You can use KDTree for faster matching with slight loss in accuracy
using (Emgu.CV.Flann.KdTreeIndexParams ip = new Emgu.CV.Flann.KdTreeIndexParams())
using (Emgu.CV.Flann.SearchParams sp = new SearchParams())
using (DescriptorMatcher matcher = new FlannBasedMatcher(ip, sp))
{
VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch();
foreach (SimpleFeatureData sd in this)
{
matcher.Add(sd.descriptors);
//break;
}
matcher.KnnMatch(observedDescriptors, matches, k, null);
lastMatches = matches;
lastObserved = matTest;
lastObservedKeyPoint = observedKeyPoints;
//Mat mat = new Mat();
//Features2DToolbox.DrawKeypoints(matTest, observedKeyPoints, mat, new Bgr(Color.Blue));
//FormOpenCV.lstMat.Add(mat);
//Console.WriteLine(CVUtil.ToString(observedDescriptors));
//Console.WriteLine(CVUtil.ToString(observedKeyPoints));
//Console.WriteLine(CVUtil.ToString(matches));
//Console.WriteLine(MatchesToString(matches));
Mat uniqueMask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
uniqueMask.SetTo(new MCvScalar(255));
Features2DToolbox.VoteForUniqueness(matches, uniquenessThreshold, uniqueMask);
int nonZeroCount = CvInvoke.CountNonZero(uniqueMask);
if (nonZeroCount > 4)
{
//Console.WriteLine(CVUtil.ToString(uniqueMask));
String retLabel = GetLabelFromMatches(matches, uniqueMask);
SimpleFeatureData mfd = lastMatchFeatureData;
try
{
//int nonZeroCount2 = Features2DToolbox.VoteForSizeAndOrientation(mfd.keyPoints, observedKeyPoints, matches, uniqueMask, 1.5, 20);
//Console.WriteLine("nonZeroCount2=" + nonZeroCount2);
if (nonZeroCount > 4)
{
Mat homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(mfd.keyPoints, observedKeyPoints, matches, uniqueMask, 2);
/*Console.WriteLine(CVUtil.ToString(homography));
* Rectangle rect = CVUtil.GetRect(mfd.keyPoints);
* PointF[] src = {
* new PointF(rect.X,rect.Y),new PointF(rect.X,rect.Y + rect.Height-1),
* new PointF(rect.X + rect.Width-1,rect.Y + rect.Height-1),new PointF(rect.X + rect.Width-1,rect.Y)
* };
* PointF[] points = CvInvoke.PerspectiveTransform(src, homography);
* foreach (var p in points)
* {
* Console.WriteLine(p.ToString());
* }
* Point[] ap = Array.ConvertAll(points,
* new Converter<PointF, Point>(CVUtil.PointFToPoint));
* Mat testImage = matTest.Clone();
* CvInvoke.Polylines(testImage, ap, true, new MCvScalar(255, 0, 0));
*/
//CvInvoke.Rectangle(testImage, new Rectangle(0, 0, 100, 100), new MCvScalar(255, 255, 0));
//CvInvoke.Circle(testImage, new Point(100, 100), 50, new MCvScalar(255, 255, 0), -1);
//lstMat.Add(testImage);
FeatureResult ret = new FeatureResult();
ret.keyPoint = observedKeyPoints;
ret.label = retLabel;
ret.homography = homography;
ret.matchFeatureData = mfd;
return(ret);
}
}catch (Exception ex)
{
}
}
return(null);
}
}