public Image <Bgr, Byte> Drawtwo(Image <Gray, Byte> modelImage, Image <Gray, byte> observedImage)
{
HomographyMatrix homography = null;
FastDetector fastCPU = new FastDetector(10, true);
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
Matrix <int> indices;
BriefDescriptorExtractor descriptor = new BriefDescriptorExtractor();
Matrix <byte> mask;
int k = 2;
double uniquenessThreshold = 0.8;
//extract features from the object image
modelKeyPoints = fastCPU.DetectKeyPointsRaw(modelImage, null);
Matrix <Byte> modelDescriptors = descriptor.ComputeDescriptorsRaw(modelImage, null, modelKeyPoints);
// extract features from the observed image
observedKeyPoints = fastCPU.DetectKeyPointsRaw(observedImage, null);
Matrix <Byte> observedDescriptors = descriptor.ComputeDescriptorsRaw(observedImage, null, observedKeyPoints);
BruteForceMatcher <Byte> matcher = new BruteForceMatcher <Byte>(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);
}
nonZeroCount = CvInvoke.cvCountNonZero(mask);
//print("nonZeroCount is "+nonZeroCount);
if (nonZeroCount >= 4)
{
nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
if (nonZeroCount >= 4)
{
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(
modelKeyPoints, observedKeyPoints, indices, mask, 2);
}
}
//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);
//area = Math.Abs((rect.Top - rect.Bottom) * (rect.Right - rect.Left));
result.DrawPolyline(Array.ConvertAll <PointF, Point>(pts, Point.Round), true, new Bgr(System.Drawing.Color.Red), 5);
}
#endregion
return(result);
}
public Image <Bgr, Byte> ObjectDetector(Image <Bgr, Byte> modelImage, string filepath)
{
Stopwatch watch;
HomographyMatrix homography = null;
SURFDetector surfCPU = new SURFDetector(500, false);
FastDetector fastCPU = new FastDetector(10, true);
VectorOfKeyPoint modelKeyPoints;
BriefDescriptorExtractor descriptor = new BriefDescriptorExtractor();
Image <Gray, byte> grayImage = new Image <Gray, Byte>(filepath);
modelKeyPoints = fastCPU.DetectKeyPointsRaw(grayImage, null);
Matrix <byte> modelDescriptors = descriptor.ComputeDescriptorsRaw(grayImage, null, modelKeyPoints);
Image <Bgr, Byte> result = Features2DToolbox.DrawKeypoints(grayImage, modelKeyPoints, new Bgr(0, 0, 255), Features2DToolbox.KeypointDrawType.DRAW_RICH_KEYPOINTS);
result.Save("C:\\Users\\Sandeep\\Documents\\What_Are_Those\\Assets\\picture645.jpg");
//Image<Bgr, Byte> result = modelImage;
MKeyPoint[] modelpoints = modelKeyPoints.ToArray();
List <PointF> points = new List <PointF>();
//List<PointF> boundarypointsList = new List<PointF>();
Dictionary <float, float> boundaryPoints = new Dictionary <float, float>();
Dictionary <float, float> boundaryPointshorizontal = new Dictionary <float, float>();
Dictionary <float, float> boundaryPointsModified = new Dictionary <float, float>();
Dictionary <float, float> boundaryPointsRed = new Dictionary <float, float>();
for (int i = 0; i < modelpoints.Length; i++)
{
points.Add(modelpoints[i].Point);
//print("X is " + points.ToArray()[i].X + "Y is " + points.ToArray()[i].Y);
}
points.Sort((a, b) => a.X.CompareTo(b.X));
float x = points.ToArray()[0].X;
float y = points.ToArray()[0].Y;
float nextx, nexty;
float miny = grayImage.Height;
float maxx = grayImage.Width;
for (int i = 0; i < points.ToArray().Length - 1; i++)
{
x = points.ToArray()[i].X;
y = points.ToArray()[i].Y;
nextx = points.ToArray()[i + 1].X;
nexty = points.ToArray()[i + 1].Y;
if (x == nextx)
{
miny = Mathf.Min(y, nexty);
}
else
{
boundaryPoints.Add(x, miny);
//boundarypointsList.Add(new PointF(x, miny));
}
//print("X is " + points.ToArray()[i].X + " Y is " + points.ToArray()[i].Y);
}
int lastindex = points.ToArray().Length - 1;
if (x != points.ToArray()[lastindex].X)
{
PointF lastpoint = points.ToArray()[lastindex];
boundaryPoints.Add(lastpoint.X, lastpoint.Y);
}
points.Sort((a, b) => a.Y.CompareTo(b.Y));
for (int i = 0; i < points.ToArray().Length - 1; i++)
{
x = points.ToArray()[i].X;
y = points.ToArray()[i].Y;
nextx = points.ToArray()[i + 1].X;
nexty = points.ToArray()[i + 1].Y;
if (y == nexty)
{
maxx = Mathf.Max(x, nextx);
}
else
{
boundaryPointshorizontal.Add(y, maxx);
//boundarypointsList.Add(new PointF(x, miny));
}
//print("X is " + points.ToArray()[i].X + " Y is " + points.ToArray()[i].Y);
}
lastindex = points.ToArray().Length - 1;
if (y != points.ToArray()[lastindex].Y)
{
PointF lastpoint = points.ToArray()[lastindex];
boundaryPointshorizontal.Add(lastpoint.X, lastpoint.Y);
}
var min = boundaryPoints.ElementAt(0);
var max = boundaryPoints.ElementAt(0);
var hmax = boundaryPoints.ElementAt(0);
for (int i = 0; i < boundaryPoints.Count; i++)
{
var item = boundaryPoints.ElementAt(i);
float itemKey = item.Key;
float itemValue = item.Value;
if (itemValue < min.Value)
{
min = item;
}
if (itemValue > max.Value || max.Value == result.Rows)
{
max = item;
}
//print("X is " + itemKey + " Y is " + itemValue);
}
for (int i = 0; i < boundaryPointshorizontal.Count; i++)
{
var item = boundaryPointshorizontal.ElementAt(i);
float itemKey = item.Key;
float itemValue = item.Value;
if (itemValue < min.Value)
{
min = item;
}
if (itemValue > hmax.Value || hmax.Value == result.Cols)
{
hmax = item;
}
// print("horizontal Y is " + itemKey + " horizontal X is " + itemValue);
}
//print("MIN is " + min.Key + " " + min.Value);
//print("MAX is " + max.Key + " " + max.Value);
//print("HMAX is " + hmax.Key + " " + hmax.Value);
float prev = boundaryPoints.ElementAt(0).Value;
int mid = 0;
for (int i = 0; i < boundaryPoints.ElementAt(0).Key; i++)
{
boundaryPointsModified[(float)i] = boundaryPoints.ElementAt(0).Value;
}
for (int i = 0; i < boundaryPoints.Count && boundaryPoints.ElementAt(i).Key != boundaryPointshorizontal.ElementAt(1).Value; i++)
{
var item = boundaryPoints.ElementAt(i);
float itemKey = item.Key;
float itemValue = item.Value;
//print("itemKey "+itemKey+ " itemValue " + itemValue + " prev " + prev);
if (itemValue > prev)
{
boundaryPointsModified[itemKey] = prev;
}
else if ((prev - itemValue < 80 && prev != result.Rows) || (prev == result.Rows && prev - itemValue > 0))
{
boundaryPointsModified[itemKey] = itemValue;
prev = itemValue;
}
else
{
boundaryPointsModified[itemKey] = prev;
}
mid = i;
}
for (int i = mid + 1; i < boundaryPoints.Count; i++)
{
var item = boundaryPoints.ElementAt(i);
float itemKey = item.Key;
float itemValue = item.Value;
boundaryPointsModified[itemKey] = 0;
}
for (int i = 0; i < boundaryPointsModified.Count - 1; i++)
{
var item = boundaryPointsModified.ElementAt(i);
var itemKey = item.Key;
var itemValue = item.Value;
//print("X modified is " + itemKey + " Y modified is " + itemValue);
}
byte[,,] data = result.Data;
byte[,,] data_model = modelImage.Data;
int xstop = (int)boundaryPointsModified.ElementAt(0).Key;
int ystop = (int)boundaryPointsModified.ElementAt(2).Value;
/* print("xstop is " + xstop + " ystop is "+ystop);
* for (int i = 0; i <= xstop; i++)
* {
* for (int j = 0; j <= ystop; j++)
* {
* data_model[j, i, 0] = 255;
* data_model[j, i, 1] = 255;
* data_model[j, i, 2] = 255;
* }
* }
* modelImage.Data = data_model; */
for (int run = 19; run >= 0; run--)
{
for (int i = 0; i <= modelImage.Cols - 1; i++)
{
for (int j = 0; j <= modelImage.Rows - 1; j++)
{
if (boundaryPoints.ContainsKey((float)i))
{
float stoppingPoint = boundaryPointsModified[(float)i];
//print("Stoppping Point is " + stoppingPoint);
if ((float)j <= stoppingPoint)
{
//print("j is "+j+" i is "+i+" red "+result[j, i].Red);
data_model[j, i, 0] = 246;
data_model[j, i, 1] = 246;
data_model[j, i, 2] = 246;
}
/* else if (i == 600 || i == 612){
* data[j, i, 0] = 255;
* data[j, i, 1] = 0;
* data[j, i, 2] = 0;
* } */
}
else
{
float stoppingPoint = 0;
//print(" i is " + i);
if (i < boundaryPointsModified.Count)
{
stoppingPoint = boundaryPointsModified.ElementAt(i).Value;
}
//print("Stoppping Point is " + stoppingPoint);
if ((float)j <= stoppingPoint)
{
//print("j is "+j+" i is "+i+" red "+result[j, i].Red);
data_model[j, i, 0] = 246;
data_model[j, i, 1] = 246;
data_model[j, i, 2] = 246;
}
}
}
}
modelImage.Data = data_model;
}
// for (int run = 19; run >= 0; run--)
// {
if (min.Key < mid)
{
mid = (int)min.Value;
}
//print("mid is " + mid);
for (int i = result.Cols - 1; i >= mid; i--)
{
for (int j = 0; j <= result.Rows - 1; j++)
{
// if (boundaryPointshorizontal.ContainsKey((float)i))
// {
//float startingPoint = boundaryPointshorizontal[(float)i];
// print("Stoppping Point is " + stoppingPoint);
/*startingPoint <= j */
/* if (data[j, i, 2] < 180)
* {
* data[j, i, 0] = 255;
* data[j, i, 1] = 255;
* data[j, i, 2] = 255;
*
* }
* else
* {
* break;
* } */
if (data[j, i, 2] >= 240)
{
boundaryPointsRed.Add(i, j);
//print("i is " + i + " j is " + j);
break;
}
// }
}
}
//result.Data = data;
// }
int maxredx = 0;
int maxredy = 0;
for (int run = 19; run >= 0; run--)
{
for (int i = result.Cols - 1; i >= mid; i--)
{
for (int j = 0; j <= result.Rows - 1; j++)
{
if (boundaryPointsRed.ContainsKey(i))
{
if (i > maxredx)
{
maxredx = i;
}
if (j > maxredy)
{
maxredy = j;
}
float stoppingPoint = boundaryPointsRed[i];
if ((float)j <= stoppingPoint /* && i != 600 && i != 612 */)
{
//print("j is "+j+" i is "+i+" red "+result[j, i].Red);
data_model[j, i, 0] = 246;
data_model[j, i, 1] = 246;
data_model[j, i, 2] = 246;
}
}
}
}
modelImage.Data = data_model;
}
for (int run = 19; run >= 0; run--)
{
for (int i = maxredy; i >= 0; i--)
{
for (int j = result.Cols - 1; j >= maxredx; j--)
{
data_model[i, j, 0] = 246;
data_model[i, j, 1] = 246;
data_model[i, j, 2] = 246;
}
}
modelImage.Data = data_model;
}
for (int run = 19; run >= 0; run--)
{
for (int i = result.Rows - 1; i >= max.Value; i--)
{
for (int j = 0; j <= result.Cols - 1; j++)
{
data_model[i, j, 0] = 246;
data_model[i, j, 1] = 246;
data_model[i, j, 2] = 246;
}
}
modelImage.Data = data_model;
}
for (int run = 19; run >= 0; run--)
{
for (int i = result.Cols - 1; i >= hmax.Value; i--)
{
for (int j = 0; j <= result.Rows - 1; j++)
{
data_model[j, i, 0] = 246;
data_model[j, i, 1] = 246;
data_model[j, i, 2] = 246;
}
}
modelImage.Data = data_model;
}
return(modelImage);
}
public static Image <Bgr, Byte> FAST(Image <Gray, Byte> modelImage, Image <Gray, byte> observedImage)
{
bool isFound = false;
long matchTime;
Stopwatch watch;
HomographyMatrix homography = null;
FastDetector fastCPU = new FastDetector(10, true);
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
Matrix <int> indices;
BriefDescriptorExtractor descriptor = new BriefDescriptorExtractor();
Matrix <byte> mask;
int k = 2;
double uniquenessThreshold = 0.8;
watch = Stopwatch.StartNew();
//extract features from the object image
modelKeyPoints = fastCPU.DetectKeyPointsRaw(modelImage, null);
Matrix <Byte> modelDescriptors = descriptor.ComputeDescriptorsRaw(modelImage, null, modelKeyPoints);
// extract features from the observed image
observedKeyPoints = fastCPU.DetectKeyPointsRaw(observedImage, null);
Matrix <Byte> observedDescriptors = descriptor.ComputeDescriptorsRaw(observedImage, null, observedKeyPoints);
BruteForceMatcher <Byte> matcher = new BruteForceMatcher <Byte>(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);
if (CvInvoke.cvCountNonZero(mask) >= 10)
{
isFound = true;
}
result.DrawPolyline(Array.ConvertAll <PointF, Point>(pts, Point.Round), true, new Bgr(Color.LightGreen), 5);
}
#endregion
matchTime = watch.ElapsedMilliseconds;
_richTextBox1.Clear();
_richTextBox1.AppendText("objek ditemukan: " + isFound + "\n");
_richTextBox1.AppendText("waktu pendeteksian FAST: " + matchTime + "ms\n");
_richTextBox1.AppendText("fitur model yang terdeteksi: " + modelKeyPoints.Size + "\n");
_richTextBox1.AppendText("match yang ditemukan: " + CvInvoke.cvCountNonZero(mask).ToString());
return(result);
}
