public List<VectorOfKeyPoint> SURF_BruteForceMatcher(Image<Gray, byte> model, Image<Gray, byte> observed, int hessianThreshould, out SURFDetector surfCPU)
{
surfCPU = new SURFDetector(hessianThreshould, false);
List<VectorOfKeyPoint> KeyPointsList = new List<VectorOfKeyPoint>();
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
try
{
modelKeyPoints = surfCPU.DetectKeyPointsRaw(model, null); // Extract features from the object image
observedKeyPoints = surfCPU.DetectKeyPointsRaw(observed, null); // Extract features from the observed image
if (modelKeyPoints.Size <= 0)
throw new System.ArgumentException("Can't find any keypoints in your model image!");
KeyPointsList.Add(modelKeyPoints);
KeyPointsList.Add(observedKeyPoints);
}
catch (Exception e)
{
Log.WriteLine("SURF_BruteForceMatcher: " + e.Message);
Console.WriteLine(e.Message);
throw e;
}
return KeyPointsList;
}
public SURFEngine(Emgu.CV.Image<Gray, byte> roi)
{
surfDetector = new SURFDetector(500, false);
itemImage = roi;
itemKP = surfDetector.DetectKeyPointsRaw(itemImage, null);
itemDescriptors = surfDetector.ComputeDescriptorsRaw(itemImage, null, itemKP);
matcher = new BruteForceMatcher<float>(DistanceType.L2);
matcher.Add(itemDescriptors);
}
public List<Keypoint> usingSurf(Bitmap image)
{
SURFDetector surf = new SURFDetector(750, false);
Image<Gray, Byte> modelImage = new Image<Gray, byte>(new Bitmap(image));
VectorOfKeyPoint modelKeyPoints = surf.DetectKeyPointsRaw(modelImage, null);
MKeyPoint[] keypoints = modelKeyPoints.ToArray();
Keypoint key;
List<Keypoint> keypointsList = new List<Keypoint>();
foreach (MKeyPoint keypoint in keypoints)
{
key = new Keypoint(keypoint.Point.X, keypoint.Point.Y, keypoint.Size);
keypointsList.Add(key);
}
return keypointsList;
}
/// <summary>
/// Draw the model image and observed image, the matched features and homography projection.
/// </summary>
/// <param name="modelImage">The model image</param>
/// <param name="observedImage">The observed image</param>
/// <param name="matchTime">The output total time for computing the homography matrix.</param>
/// <returns>The model image and observed image, the matched features and homography projection.</returns>
public static Image<Bgr, Byte> Draw(Image<Gray, Byte> modelImage, Image<Gray, byte> observedImage, out long matchTime)
{
Stopwatch watch;
HomographyMatrix homography = null;
SURFDetector surfCPU = new SURFDetector (500, false);
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
Matrix<int> indices;
Matrix<byte> mask;
int k = 2;
double uniquenessThreshold = 0.8;
if (GpuInvoke.HasCuda) {
GpuSURFDetector surfGPU = new GpuSURFDetector (surfCPU.SURFParams, 0.01f);
using (GpuImage<Gray, Byte> gpuModelImage = new GpuImage<Gray, byte> (modelImage))
//extract features from the object image
using (GpuMat<float> gpuModelKeyPoints = surfGPU.DetectKeyPointsRaw (gpuModelImage, null))
using (GpuMat<float> gpuModelDescriptors = surfGPU.ComputeDescriptorsRaw (gpuModelImage, null, gpuModelKeyPoints))
using (GpuBruteForceMatcher<float> matcher = new GpuBruteForceMatcher<float> (DistanceType.L2)) {
modelKeyPoints = new VectorOfKeyPoint ();
surfGPU.DownloadKeypoints (gpuModelKeyPoints, modelKeyPoints);
watch = Stopwatch.StartNew ();
// extract features from the observed image
using (GpuImage<Gray, Byte> gpuObservedImage = new GpuImage<Gray, byte> (observedImage))
using (GpuMat<float> gpuObservedKeyPoints = surfGPU.DetectKeyPointsRaw (gpuObservedImage, null))
using (GpuMat<float> gpuObservedDescriptors = surfGPU.ComputeDescriptorsRaw (gpuObservedImage, null, gpuObservedKeyPoints))
using (GpuMat<int> gpuMatchIndices = new GpuMat<int> (gpuObservedDescriptors.Size.Height, k, 1, true))
using (GpuMat<float> gpuMatchDist = new GpuMat<float> (gpuObservedDescriptors.Size.Height, k, 1, true))
using (GpuMat<Byte> gpuMask = new GpuMat<byte> (gpuMatchIndices.Size.Height, 1, 1))
using (Stream stream = new Stream ()) {
matcher.KnnMatchSingle (gpuObservedDescriptors, gpuModelDescriptors, gpuMatchIndices, gpuMatchDist, k, null, stream);
indices = new Matrix<int> (gpuMatchIndices.Size);
mask = new Matrix<byte> (gpuMask.Size);
//gpu implementation of voteForUniquess
using (GpuMat<float> col0 = gpuMatchDist.Col (0))
using (GpuMat<float> col1 = gpuMatchDist.Col (1)) {
GpuInvoke.Multiply (col1, new MCvScalar (uniquenessThreshold), col1, stream);
GpuInvoke.Compare (col0, col1, gpuMask, CMP_TYPE.CV_CMP_LE, stream);
}
observedKeyPoints = new VectorOfKeyPoint ();
surfGPU.DownloadKeypoints (gpuObservedKeyPoints, observedKeyPoints);
//wait for the stream to complete its tasks
//We can perform some other CPU intesive stuffs here while we are waiting for the stream to complete.
stream.WaitForCompletion ();
gpuMask.Download (mask);
gpuMatchIndices.Download (indices);
if (GpuInvoke.CountNonZero (gpuMask) >= 4) {
int nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation (modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
if (nonZeroCount >= 4)
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures (modelKeyPoints, observedKeyPoints, indices, mask, 2);
}
watch.Stop ();
}
}
} else {
//extract features from the object image
modelKeyPoints = surfCPU.DetectKeyPointsRaw (modelImage, null);
Matrix<float> modelDescriptors = surfCPU.ComputeDescriptorsRaw (modelImage, null, modelKeyPoints);
watch = Stopwatch.StartNew ();
// extract features from the observed image
observedKeyPoints = surfCPU.DetectKeyPointsRaw (observedImage, null);
Matrix<float> observedDescriptors = surfCPU.ComputeDescriptorsRaw (observedImage, null, observedKeyPoints);
BruteForceMatcher<float> matcher = new BruteForceMatcher<float> (DistanceType.L2);
matcher.Add (modelDescriptors);
indices = new Matrix<int> (observedDescriptors.Rows, k);
using (Matrix<float> dist = new Matrix<float> (observedDescriptors.Rows, k)) {
matcher.KnnMatch (observedDescriptors, indices, dist, k, null);
mask = new Matrix<byte> (dist.Rows, 1);
mask.SetValue (255);
Features2DToolbox.VoteForUniqueness (dist, uniquenessThreshold, mask);
}
int nonZeroCount = CvInvoke.cvCountNonZero (mask);
if (nonZeroCount >= 4) {
nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation (modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
if (nonZeroCount >= 4)
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures (modelKeyPoints, observedKeyPoints, indices, mask, 2);
}
watch.Stop ();
}
//Draw the matched keypoints
Image<Bgr, Byte> result = Features2DToolbox.DrawMatches (modelImage, modelKeyPoints, observedImage, observedKeyPoints,
indices, new Bgr (255, 255, 255), new Bgr (255, 255, 255), mask, Features2DToolbox.KeypointDrawType.DEFAULT);
#region draw the projected region on the image
if (homography != null) { //draw a rectangle along the projected model
Rectangle rect = modelImage.ROI;
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)
};
homography.ProjectPoints (pts);
result.DrawPolyline (Array.ConvertAll<PointF, Point> (pts, Point.Round), true, new Bgr (Color.Red), 5);
}
#endregion
matchTime = watch.ElapsedMilliseconds;
return result;
}
public Image<Bgr, byte> DrawResult(Image<Gray, byte> modelImage, Image<Gray, byte> observedImage,out double area, int minarea, out Point center)
{
//double estimated_dist =99999;
center = new Point(400, 224);
area = 0;
//modelImage.Save("D:\\temp\\modelimage.jpg");
//observedImage.Save("D:\\temp\\observedimage.jpg");
//单应矩阵
HomographyMatrix homography = null;
//surf算法检测器
var surfCpu = new SURFDetector(500, false);
//原图与实际图中的关键点
Matrix<byte> mask;
//knn匹配的系数
var k = 2;
//滤波系数
var uniquenessThreshold = 0.8;
//从标记图中，提取surf特征点与描述子
var modelKeyPoints = surfCpu.DetectKeyPointsRaw(modelImage, null);
var modelDescriptors = surfCpu.ComputeDescriptorsRaw(modelImage, null, modelKeyPoints);
// 从实际图片提取surf特征点与描述子
var observedKeyPoints = surfCpu.DetectKeyPointsRaw(observedImage, null);
var observedDescriptors = surfCpu.ComputeDescriptorsRaw(observedImage, null, observedKeyPoints);
if (observedDescriptors == null)
{
return null;
}
//使用BF匹配算法，匹配特征向量
//var bfmatcher = new BruteForceMatcher<float>(DistanceType.L2);
//bfmatcher.Add(modelDescriptors);
var indices = new Matrix<int>(observedDescriptors.Rows, k);
var flannMatcher = new Index(modelDescriptors, 4);
//通过特征向量筛选匹配对
using (var dist = new Matrix<float>(observedDescriptors.Rows, k))
{
//最近邻2点特征向量匹配
//bfmatcher.KnnMatch(observedDescriptors, indices, dist, k, null);
flannMatcher.KnnSearch(observedDescriptors, indices, dist, k, 24);
//匹配成功的，将特征点存入mask
mask = new Matrix<byte>(dist.Rows, 1);
mask.SetValue(255);
//通过滤波系数，过滤非特征点，剩余特征点存入mask
Features2DToolbox.VoteForUniqueness(dist, uniquenessThreshold, mask);
}
var nonZeroCount = CvInvoke.cvCountNonZero(mask);
if (nonZeroCount >= 10)
{
//过滤旋转与变形系数异常的特征点，剩余存入mask
nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices,
mask, 1.5, 20);
if (nonZeroCount >= 10)
//使用剩余特征点，构建单应矩阵
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints,
observedKeyPoints, indices, mask, 2);
}
// }
//画出匹配的特征点
//Image<Bgr, Byte> result = Features2DToolbox.DrawMatches(modelImage, modelKeyPoints, observedImage, observedKeyPoints,indices, new Bgr(0, 0, 255), new Bgr(0, 255, 0), mask, Features2DToolbox.KeypointDrawType.DEFAULT);
// result.Save("D:\\temp\\matchedpoints.jpg");
observedImage.ToBitmap();
var result = Features2DToolbox.DrawMatches(modelImage, modelKeyPoints, observedImage, observedKeyPoints,
indices, new Bgr(0, 0, 255), new Bgr(0, 255, 0), mask, Features2DToolbox.KeypointDrawType.DEFAULT);
#region draw the projected region on the Image
//画出单应矩阵
if (homography != null)
{
var rect = modelImage.ROI;
/*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)
};*/
var pts = new[]
{
new PointF(rect.Left + (rect.Right - rect.Left)/5, rect.Bottom - (rect.Bottom - rect.Top)/5),
new PointF(rect.Right - (rect.Right - rect.Left)/5, rect.Bottom - (rect.Bottom - rect.Top)/5),
new PointF(rect.Right - (rect.Right - rect.Left)/5, rect.Top + (rect.Bottom - rect.Top)/5),
new PointF(rect.Left + (rect.Right - rect.Left)/5, rect.Top + (rect.Bottom - rect.Top)/5)
};
//根据整个图片的旋转、变形情况，计算出原图中四个顶点转换后的坐标，并画出四边形
homography.ProjectPoints(pts);
area = Getarea(pts);
double xsum = 0;
double ysum = 0;
foreach (var point in pts)
{
xsum += point.X;
ysum += point.Y;
}
center = new Point(Convert.ToInt32(xsum / 4), Convert.ToInt32(ysum / 4));
if (area > minarea)
{
var temp = new Image<Bgr, byte>(result.Width, result.Height);
temp.DrawPolyline(Array.ConvertAll(pts, Point.Round), true, new Bgr(Color.Red), 5);
//estimated_dist = GetDist(pts);
var a = CountContours(temp.ToBitmap());
if (a == 2)
{
result.DrawPolyline(Array.ConvertAll(pts, Point.Round), true, new Bgr(Color.Red), 5);
//result.Save("D:\\temp\\" + estimated_dist.ToString() + ".jpg");
}
else
{
area = 0; //dst = estimated_dist;
return result;
}
}
}
else area = 0;
#endregion
//dst = estimated_dist;
return result;
}
private void extractData()
{
SURFDetector surfCPU = new SURFDetector(500, false);
_objectKeyPoints = surfCPU.DetectKeyPointsRaw(objectImage, null);
_objectDescriptors = surfCPU.ComputeDescriptorsRaw(objectImage, null, objectKeyPoints);
}
private void PerformSurfDetection(object sender, EventArgs e)
{
this.Text = "working...";
Application.DoEvents();
stopwatch.Restart();
HomographyMatrix homographyMatrix = null;
SURFDetector surfDetector = new SURFDetector(500, false);
Image<Gray, Byte> imgMasterGray;
Image<Gray, Byte> imgToFindGray;
VectorOfKeyPoint vkpMasterKeyPoints;
VectorOfKeyPoint vkpToFindKeyPoints;
Matrix<float> mtxMasterDescriptors;
Matrix<float> mtxToFindDescriptors;
Matrix<int> mtxMatchIndices;
Matrix<float> mtxDistance;
Matrix<Byte> mtxMask;
BruteForceMatcher<float> bruteForceMatcher;
int neighbors = 2;
double ratioUnique = 0.5;
int nonZeroElements;
double scaleIncrement = 1.5;
int rotationBin = 20;
double maxReprojectionError = 2.0;
//PointF[] ptfPointsF;
//Point ptPoints;
imgMasterGray = new Image<Gray, byte>(imgMasterColor.ToBitmap());
imgToFindGray = new Image<Gray, byte>(imgToFindColor.ToBitmap());
vkpMasterKeyPoints = surfDetector.DetectKeyPointsRaw(imgMasterGray, null);
mtxMasterDescriptors = surfDetector.ComputeDescriptorsRaw(imgMasterGray, null, vkpMasterKeyPoints);
vkpToFindKeyPoints = surfDetector.DetectKeyPointsRaw(imgToFindGray, null);
mtxToFindDescriptors = surfDetector.ComputeDescriptorsRaw(imgToFindGray, null, vkpToFindKeyPoints);
bruteForceMatcher = new BruteForceMatcher<float>(DistanceType.L2);
bruteForceMatcher.Add(mtxToFindDescriptors);
mtxMatchIndices = new Matrix<int>(mtxMasterDescriptors.Rows, neighbors);
mtxDistance = new Matrix<float>(mtxMasterDescriptors.Rows, neighbors);
bruteForceMatcher.KnnMatch(mtxMasterDescriptors, mtxMatchIndices, mtxDistance, neighbors, null);
mtxMask = new Matrix<byte>(mtxDistance.Rows, 1);
mtxMask.SetValue(255);
Features2DToolbox.VoteForUniqueness(mtxDistance, ratioUnique, mtxMask);
nonZeroElements = CvInvoke.cvCountNonZero(mtxMask);
if (nonZeroElements >= 4)
{
nonZeroElements = Features2DToolbox.VoteForSizeAndOrientation(vkpToFindKeyPoints, vkpMasterKeyPoints, mtxMatchIndices, mtxMask, scaleIncrement, rotationBin);
if (nonZeroElements >= 4)
{
homographyMatrix = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(vkpToFindKeyPoints, vkpMasterKeyPoints, mtxMatchIndices, mtxMask, maxReprojectionError);
}
}
imgCopyToFind = imgToFindColor.Copy();
imgCopyToFind.Draw(new Rectangle(1, 1, imgCopyToFind.Width - 3, imgCopyToFind.Height - 3), bgrFoundImageColor, 2);
imgResult = imgMasterColor;
imgResult = imgResult.ConcateHorizontal(imgCopyToFind);
if (homographyMatrix != null)
{
// draw a rectangle along the projected model
Rectangle rect = imgCopyToFind.ROI;
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)
};
homographyMatrix.ProjectPoints(pts);
Point[] ptPoints = { Point.Round(pts[0]), Point.Round(pts[1]), Point.Round(pts[2]), Point.Round(pts[3]) };
imgResult.DrawPolyline(ptPoints, true, bgrFoundImageColor, 2);
int X = Convert.ToInt16((pts[0].X + pts[1].X) / 2) + this.Left;
int Y = Convert.ToInt16((pts[1].Y + pts[2].Y) / 2) + this.Top + 30;
LeftClick(X, Y);
}
stopwatch.Stop();
//this.Text = "working time = " + stopwatch.Elapsed.TotalSeconds.ToString() + "sec, done ! ";
}
private static bool IsModelInObserved( Image<Gray, byte> modelImage, Image<Gray, byte> observedImage, double similarityThreshold = 0.075 )
{
var surfCpu = new SURFDetector(500, false);
Matrix<byte> mask;
int k = 2;
double uniquenessThreshold = 0.8;
//extract features from the object image
var modelKeyPoints = surfCpu.DetectKeyPointsRaw( modelImage, null );
Matrix<float> modelDescriptors = surfCpu.ComputeDescriptorsRaw(modelImage, null, modelKeyPoints);
// extract features from the observed image
var observedKeyPoints = surfCpu.DetectKeyPointsRaw( observedImage, null );
Matrix<float> observedDescriptors = surfCpu.ComputeDescriptorsRaw(observedImage, null, observedKeyPoints);
BruteForceMatcher<float> matcher = new BruteForceMatcher<float>(DistanceType.L2);
matcher.Add( modelDescriptors );
var indices = new Matrix<int>( observedDescriptors.Rows, k );
using ( var dist = new Matrix<float>( observedDescriptors.Rows, k ) )
{
matcher.KnnMatch( observedDescriptors, indices, dist, k, null );
mask = new Matrix<byte>( dist.Rows, 1 );
mask.SetValue( 255 );
Features2DToolbox.VoteForUniqueness( dist, uniquenessThreshold, mask );
}
int keypointMatchCount = CvInvoke.cvCountNonZero( mask );
if ( keypointMatchCount >= 4 )
{
keypointMatchCount = Features2DToolbox.VoteForSizeAndOrientation( modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20 );
if ( keypointMatchCount >= 4 )
{
Features2DToolbox.GetHomographyMatrixFromMatchedFeatures( modelKeyPoints, observedKeyPoints, indices, mask, 2 );
}
}
var similarity = (double)keypointMatchCount / observedKeyPoints.Size;
return similarity > similarityThreshold;
}
public static bool FindModelImageInObservedImage( Image<Gray, byte> modelImage, Image<Gray, byte> observedImage )
{
var surfCpu = new SURFDetector(500, false);
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
Matrix<int> indices;
Matrix<byte> mask;
int k = 2;
double uniquenessThreshold = 0.8;
if ( GpuInvoke.HasCuda )
{
GpuSURFDetector surfGpu = new GpuSURFDetector(surfCpu.SURFParams, 0.01f);
using ( GpuImage<Gray, byte> gpuModelImage = new GpuImage<Gray, byte>( modelImage ) )
//extract features from the object image
using ( GpuMat<float> gpuModelKeyPoints = surfGpu.DetectKeyPointsRaw( gpuModelImage, null ) )
using ( GpuMat<float> gpuModelDescriptors = surfGpu.ComputeDescriptorsRaw( gpuModelImage, null, gpuModelKeyPoints ) )
using ( GpuBruteForceMatcher<float> matcher = new GpuBruteForceMatcher<float>( DistanceType.L2 ) )
{
modelKeyPoints = new VectorOfKeyPoint();
surfGpu.DownloadKeypoints( gpuModelKeyPoints, modelKeyPoints );
// extract features from the observed image
using ( GpuImage<Gray, byte> gpuObservedImage = new GpuImage<Gray, byte>( observedImage ) )
using ( GpuMat<float> gpuObservedKeyPoints = surfGpu.DetectKeyPointsRaw( gpuObservedImage, null ) )
using ( GpuMat<float> gpuObservedDescriptors = surfGpu.ComputeDescriptorsRaw( gpuObservedImage, null, gpuObservedKeyPoints ) )
using ( GpuMat<int> gpuMatchIndices = new GpuMat<int>( gpuObservedDescriptors.Size.Height, k, 1, true ) )
using ( GpuMat<float> gpuMatchDist = new GpuMat<float>( gpuObservedDescriptors.Size.Height, k, 1, true ) )
using ( GpuMat<Byte> gpuMask = new GpuMat<byte>( gpuMatchIndices.Size.Height, 1, 1 ) )
using ( var stream = new Emgu.CV.GPU.Stream() )
{
matcher.KnnMatchSingle( gpuObservedDescriptors, gpuModelDescriptors, gpuMatchIndices, gpuMatchDist, k, null, stream );
indices = new Matrix<int>( gpuMatchIndices.Size );
mask = new Matrix<byte>( gpuMask.Size );
//gpu implementation of voteForUniquess
using ( GpuMat<float> col0 = gpuMatchDist.Col( 0 ) )
using ( GpuMat<float> col1 = gpuMatchDist.Col( 1 ) )
{
GpuInvoke.Multiply( col1, new MCvScalar( uniquenessThreshold ), col1, stream );
GpuInvoke.Compare( col0, col1, gpuMask, CMP_TYPE.CV_CMP_LE, stream );
}
observedKeyPoints = new VectorOfKeyPoint();
surfGpu.DownloadKeypoints( gpuObservedKeyPoints, observedKeyPoints );
//wait for the stream to complete its tasks
//We can perform some other CPU intesive stuffs here while we are waiting for the stream to complete.
stream.WaitForCompletion();
gpuMask.Download( mask );
gpuMatchIndices.Download( indices );
if ( GpuInvoke.CountNonZero( gpuMask ) >= 4 )
{
int nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
if ( nonZeroCount >= 4 )
{
Features2DToolbox.GetHomographyMatrixFromMatchedFeatures( modelKeyPoints, observedKeyPoints, indices, mask, 2 );
}
if ( (double)nonZeroCount / mask.Height > 0.02 )
{
return true;
}
}
}
}
}
else
{
//extract features from the object image
modelKeyPoints = surfCpu.DetectKeyPointsRaw( modelImage, null );
Matrix<float> modelDescriptors = surfCpu.ComputeDescriptorsRaw(modelImage, null, modelKeyPoints);
// extract features from the observed image
observedKeyPoints = surfCpu.DetectKeyPointsRaw( observedImage, null );
Matrix<float> observedDescriptors = surfCpu.ComputeDescriptorsRaw(observedImage, null, observedKeyPoints);
BruteForceMatcher<float> matcher = new BruteForceMatcher<float>(DistanceType.L2);
matcher.Add( modelDescriptors );
indices = new Matrix<int>( observedDescriptors.Rows, k );
using ( Matrix<float> dist = new Matrix<float>( observedDescriptors.Rows, k ) )
{
matcher.KnnMatch( observedDescriptors, indices, dist, k, null );
mask = new Matrix<byte>( dist.Rows, 1 );
mask.SetValue( 255 );
Features2DToolbox.VoteForUniqueness( dist, uniquenessThreshold, mask );
}
int nonZeroCount = CvInvoke.cvCountNonZero(mask);
if ( nonZeroCount >= 4 )
{
nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation( modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20 );
if ( nonZeroCount >= 4 )
{
Features2DToolbox.GetHomographyMatrixFromMatchedFeatures( modelKeyPoints, observedKeyPoints, indices, mask, 2 );
}
}
if ( (double)nonZeroCount/mask.Height > 0.02 )
{
return true;
}
}
//Draw the matched keypoints
//var result = Features2DToolbox.DrawMatches(modelImage, modelKeyPoints, observedImage, observedKeyPoints, indices, new Bgr(0, 0, 255), new Bgr(255, 0, 0), mask, Features2DToolbox.KeypointDrawType.DEFAULT);
//result.Save( @"C:\Users\D.Markachev\Desktop\bleh-keypoints.jpg" );
return false;
}
/// <summary>
/// Draw the model image and observed image, the matched features and homography projection.
/// </summary>
/// <param name="modelImageFileName">The model image</param>
/// <param name="observedImageBitmap">The observed image</param>
/// <param name="matchTime">The output total time for computing the homography matrix.</param>
/// <returns>The model image and observed image, the matched features and homography projection.</returns>
private System.Drawing.Point[] DrawBruteForceMatch(String modelImageFileName, Bitmap observedImageBitmap, out long matchTime)
{
try
{
Image<Gray, Byte> modelImage = new Image<Gray, byte>(modelImageFileName);
Image<Gray, Byte> observedImage = new Image<Gray, byte>(observedImageBitmap);
HomographyMatrix homography = null;
Stopwatch watch;
SURFDetector surfCPU = new SURFDetector(500, false);
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
Matrix<int> indices;
Matrix<byte> mask;
int k = 2;
double uniquenessThreshold = 0.8;
//extract features from the object image
modelKeyPoints = surfCPU.DetectKeyPointsRaw(modelImage, null);
Matrix<float> modelDescriptors = surfCPU.ComputeDescriptorsRaw(modelImage, null, modelKeyPoints);
watch = Stopwatch.StartNew();
// extract features from the observed image
observedKeyPoints = surfCPU.DetectKeyPointsRaw(observedImage, null);
Matrix<float> observedDescriptors = surfCPU.ComputeDescriptorsRaw(observedImage, null, observedKeyPoints);
BruteForceMatcher<float> matcher = new BruteForceMatcher<float>(DistanceType.L2);
matcher.Add(modelDescriptors);
indices = new Matrix<int>(observedDescriptors.Rows, k);
Matrix<float> dist = new Matrix<float>(observedDescriptors.Rows, k);
matcher.KnnMatch(observedDescriptors, indices, dist, k, null);
mask = new Matrix<byte>(dist.Rows, 1);
mask.SetValue(255);
Features2DToolbox.VoteForUniqueness(dist, uniquenessThreshold, mask);
int nonZeroCount = CvInvoke.cvCountNonZero(mask);
if (nonZeroCount >= 4)
{
nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
if (nonZeroCount >= 4)
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, indices, mask, 2);
}
watch.Stop();
//Draw the matched keypoints
Image<Bgr, Byte> result = Features2DToolbox.DrawMatches(modelImage, modelKeyPoints, observedImage, observedKeyPoints,
indices, new Bgr(255, 255, 255), new Bgr(255, 255, 255), mask, Features2DToolbox.KeypointDrawType.DEFAULT);
System.Drawing.Point[] newpts = null;
#region draw the projected region on the image
if (homography != null)
{
//draw a rectangle along the projected model
Rectangle rect = modelImage.ROI;
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)};
homography.ProjectPoints(pts);
//result.DrawPolyline(Array.ConvertAll<PointF, System.Drawing.Point>(pts, System.Drawing.Point.Round), true, new Bgr(Color.Red), 2);
//result.Save(@"E:\1.jpg");
newpts = Array.ConvertAll<PointF, System.Drawing.Point>(pts, System.Drawing.Point.Round);
}
#endregion
matchTime = watch.ElapsedMilliseconds;
return newpts;
}
catch (Exception e)
{
Console.WriteLine(e.Message);
matchTime = 0;
return new System.Drawing.Point[] { new System.Drawing.Point(-1, -1), new System.Drawing.Point(-1, -1), new System.Drawing.Point(-1, -1), new System.Drawing.Point(-1, -1) };
}
}