public IEnumerable <Mat> DescribeMany(IEnumerable <Mat> images, out KeyPoint[][] keypoints)
{
List <Mat> descriptors = new List <Mat>();
KeyPoint[][] keypoints2 = _featureDetector.Detect(images);
_featureDetector.Compute(images, ref keypoints2, descriptors);
keypoints = keypoints2;
return(descriptors);
}
public static void ExtractFeatures(Mat image, out VectorOfKeyPoint keypoints, out Mat descriptors)
{
keypoints = new VectorOfKeyPoint();
descriptors = new Mat();
using (var uModelImage = image.ToUMat(AccessType.Read))
{
//_featureDetector.DetectAndCompute(uModelImage, null, keypoints, descriptors, false);
_featureDetector.DetectRaw(uModelImage, keypoints);
_descriptorsComputer.Compute(uModelImage, keypoints, descriptors);
}
}
static object[] DetectAndCompute2(UMat img, Feature2D detector, Feature2D computer) // находит и обрабатывает дескрипторы изображения
{
object[] outp = new object[0];
UMat descriptors = new UMat();
var mkp = new MKeyPoint[0];
VectorOfKeyPoint keypoints;
try
{
mkp = detector.Detect(img);
keypoints = new VectorOfKeyPoint(mkp);
computer.Compute(img, keypoints, descriptors);
outp = new object[] { keypoints, descriptors };
}
finally
{
}
return(outp);
}
private static void BowTest()
{
DescriptorMatcher matcher = new BFMatcher();
Feature2D extractor = AKAZE.Create();
Feature2D detector = AKAZE.Create();
TermCriteria criteria = new TermCriteria(CriteriaType.Count | CriteriaType.Eps, 10, 0.001);
BOWKMeansTrainer bowTrainer = new BOWKMeansTrainer(200, criteria, 1);
BOWImgDescriptorExtractor bowDescriptorExtractor = new BOWImgDescriptorExtractor(extractor, matcher);
Mat img = null;
KeyPoint[] keypoint = detector.Detect(img);
Mat features = new Mat();
extractor.Compute(img, ref keypoint, features);
bowTrainer.Add(features);
throw new NotImplementedException();
}
public static double hessianThresh = 300; // настройка SURF
public static void DetectAndCompute(Mat img, out VectorOfKeyPoint keypoints, out Mat descriptors, Feature2D detector, Feature2D computer) // находит и обрабатывает дескрипторы изображения
{
keypoints = null;
descriptors = new Mat();
try
{
var mkp = detector.Detect(img, null);
keypoints = new VectorOfKeyPoint(mkp);
}
catch (Exception e)
{
throw e;
}
try
{
computer.Compute(img, keypoints, descriptors);
}
catch (Exception e)
{
throw e;
}
}
public static bool TestFeature2DTracker(Feature2D keyPointDetector, Feature2D descriptorGenerator)
{
//for (int k = 0; k < 1; k++)
{
Feature2D feature2D = null;
if (keyPointDetector == descriptorGenerator)
{
feature2D = keyPointDetector as Feature2D;
}
Mat modelImage = EmguAssert.LoadMat("box.png");
//Image<Gray, Byte> modelImage = new Image<Gray, byte>("stop.jpg");
//modelImage = modelImage.Resize(400, 400, true);
//modelImage._EqualizeHist();
#region extract features from the object image
Stopwatch stopwatch = Stopwatch.StartNew();
VectorOfKeyPoint modelKeypoints = new VectorOfKeyPoint();
Mat modelDescriptors = new Mat();
if (feature2D != null)
{
feature2D.DetectAndCompute(modelImage, null, modelKeypoints, modelDescriptors, false);
}
else
{
keyPointDetector.DetectRaw(modelImage, modelKeypoints);
descriptorGenerator.Compute(modelImage, modelKeypoints, modelDescriptors);
}
stopwatch.Stop();
EmguAssert.WriteLine(String.Format("Time to extract feature from model: {0} milli-sec", stopwatch.ElapsedMilliseconds));
#endregion
//Image<Gray, Byte> observedImage = new Image<Gray, byte>("traffic.jpg");
Image <Gray, Byte> observedImage = EmguAssert.LoadImage <Gray, byte>("box_in_scene.png");
//Image<Gray, Byte> observedImage = modelImage.Rotate(45, new Gray(0.0));
//image = image.Resize(400, 400, true);
//observedImage._EqualizeHist();
#region extract features from the observed image
stopwatch.Reset();
stopwatch.Start();
VectorOfKeyPoint observedKeypoints = new VectorOfKeyPoint();
using (Mat observedDescriptors = new Mat())
{
if (feature2D != null)
{
feature2D.DetectAndCompute(observedImage, null, observedKeypoints, observedDescriptors, false);
}
else
{
keyPointDetector.DetectRaw(observedImage, observedKeypoints);
descriptorGenerator.Compute(observedImage, observedKeypoints, observedDescriptors);
}
stopwatch.Stop();
EmguAssert.WriteLine(String.Format("Time to extract feature from image: {0} milli-sec", stopwatch.ElapsedMilliseconds));
#endregion
//Merge the object image and the observed image into one big image for display
Image <Gray, Byte> res = modelImage.ToImage <Gray, Byte>().ConcateVertical(observedImage);
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)
};
Mat homography = null;
stopwatch.Reset();
stopwatch.Start();
int k = 2;
DistanceType dt = modelDescriptors.Depth == CvEnum.DepthType.Cv8U ? DistanceType.Hamming : DistanceType.L2;
//using (Matrix<int> indices = new Matrix<int>(observedDescriptors.Rows, k))
//using (Matrix<float> dist = new Matrix<float>(observedDescriptors.Rows, k))
using (VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch())
using (BFMatcher matcher = new BFMatcher(dt))
{
//ParamDef[] parameterDefs = matcher.GetParams();
matcher.Add(modelDescriptors);
matcher.KnnMatch(observedDescriptors, matches, k, null);
Mat mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
//mask.SetValue(255);
Features2DToolbox.VoteForUniqueness(matches, 0.8, 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);
}
}
}
stopwatch.Stop();
EmguAssert.WriteLine(String.Format("Time for feature matching: {0} milli-sec", stopwatch.ElapsedMilliseconds));
bool success = false;
if (homography != null)
{
PointF[] points = pts.Clone() as PointF[];
points = CvInvoke.PerspectiveTransform(points, homography);
//homography.ProjectPoints(points);
for (int i = 0; i < points.Length; i++)
{
points[i].Y += modelImage.Height;
}
res.DrawPolyline(
#if NETFX_CORE
Extensions.
#else
Array.
#endif
ConvertAll <PointF, Point>(points, Point.Round), true, new Gray(255.0), 5);
success = true;
}
//Emgu.CV.UI.ImageViewer.Show(res);
return(success);
}
/*
* stopwatch.Reset(); stopwatch.Start();
* //set the initial region to be the whole image
* using (Image<Gray, Single> priorMask = new Image<Gray, float>(observedImage.Size))
* {
* priorMask.SetValue(1.0);
* homography = tracker.CamShiftTrack(
* observedFeatures,
* (RectangleF)observedImage.ROI,
* priorMask);
* }
* Trace.WriteLine(String.Format("Time for feature tracking: {0} milli-sec", stopwatch.ElapsedMilliseconds));
*
* if (homography != null) //set the initial tracking window to be the whole image
* {
* PointF[] points = pts.Clone() as PointF[];
* homography.ProjectPoints(points);
*
* for (int i = 0; i < points.Length; i++)
* points[i].Y += modelImage.Height;
* res.DrawPolyline(Array.ConvertAll<PointF, Point>(points, Point.Round), true, new Gray(255.0), 5);
* return true;
* }
* else
* {
* return false;
* }*/
}
}
private void CaptureOnImageGrabbed(object sender, EventArgs eventArgs)
{
var capture = (Capture)sender;
var frame = new Mat();
capture.Retrieve(frame);
// 1. get key points
var keyPoints = new VectorOfKeyPoint(_detector.Detect(frame));
_tempCloudPoints.SetKeyFeatures(_selectedFrameIndex, keyPoints);
// 2. get descripters
var descripters = new Mat();
_descripter.Compute(frame, keyPoints, descripters);
// draw keypoints
var imageFrame = new Mat();
Features2DToolbox.DrawKeypoints(frame, keyPoints, imageFrame, new Bgr(Color.DarkBlue),
Features2DToolbox.KeypointDrawType.NotDrawSinglePoints);
if (_selectedFrameIndex != 0)
{
var previousKeyPoints = _tempCloudPoints.GetKeyFeatures(_selectedFrameIndex - 1);
var previousKeyDescripters = _previousDescripters;
const int k = 2;
const double uniquenessThreshold = 0.8;
// 3. compute all matches with previous frame
var matches = new VectorOfVectorOfDMatch();
var matcher = GetNativeMatcher(SelectedMatcher);
matcher.Add(previousKeyDescripters);
matcher.KnnMatch(descripters, matches, k, null);
var mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
Features2DToolbox.VoteForUniqueness(matches, uniquenessThreshold, mask);
Features2DToolbox.VoteForSizeAndOrientation(previousKeyPoints, keyPoints,
matches, mask, 1.5, 20);
Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(previousKeyPoints,
keyPoints, matches, mask, 2);
var managedMask = mask.GetData();
// 4. separate good matches
var currentKeys = keyPoints;
for (int i = 0; i < matches.Size; i++)
{
var match = matches[i][0];
// filter wrong matches
if (managedMask[i] == 1)
{
var previousIndex = match.TrainIdx;
var currentIndex = match.QueryIdx;
var previousPoint = previousKeyPoints[previousIndex].Point;
var currentPoint = currentKeys[currentIndex].Point;
_tempCloudPoints.Unite(_selectedFrameIndex - 1, previousIndex,
_selectedFrameIndex, currentIndex);
CvInvoke.Line(imageFrame,
Point.Round(previousPoint),
Point.Round(currentPoint),
new Bgr(Color.Red).MCvScalar,
2);
}
}
}
_previousDescripters = descripters;
PreviewImageSource = imageFrame;
_selectedFrameIndex++;
RaisePropertyChanged("Progress");
RaisePropertyChanged("ProgressText");
if (_selectedFrameIndex == _framesCount)
{
GeneratingStates = FeatureGeneratingStates.Finished;
}
}