/// <summary>
/// Get a copy of the data values as an array
/// </summary>
/// <param name="jagged">If true, a jagged array will returned. Otherwise it will return a regular array.</param>
/// <returns>a copy of the data values as an array</returns>
public Array GetData(bool jagged = true)
{
using (InputArray iaM = this.GetInputArray())
using (Mat m = iaM.GetMat())
{
return(m.GetData(jagged));
}
}
public static void FindMatch(Mat modelImage, Mat observedImage, out long matchTime, out VectorOfKeyPoint modelKeyPoints, out VectorOfKeyPoint observedKeyPoints, VectorOfVectorOfDMatch matches, out Mat mask, out Mat homography, out long score)
{
int k = 2;
double uniquenessThreshold = 0.80;
Stopwatch watch;
homography = null;
modelKeyPoints = new VectorOfKeyPoint();
observedKeyPoints = new VectorOfKeyPoint();
using (UMat uModelImage = modelImage.GetUMat(AccessType.Read))
using (UMat uObservedImage = observedImage.GetUMat(AccessType.Read))
{
KAZE featureDetector = new KAZE();
Mat modelDescriptors = new Mat();
featureDetector.DetectAndCompute(uModelImage, null, modelKeyPoints, modelDescriptors, false);
watch = Stopwatch.StartNew();
Mat observedDescriptors = new Mat();
featureDetector.DetectAndCompute(uObservedImage, null, observedKeyPoints, observedDescriptors, false);
// KdTree for faster results / less accuracy
using (var ip = new Emgu.CV.Flann.KdTreeIndexParams())
using (var 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);
// Calculate score based on matches size
// ---------------------------------------------->
score = 0;
for (int i = 0; i < matches.Size; i++)
{
if (mask.GetData(i)[0] == 0)
{
continue;
}
foreach (var e in matches[i].ToArray())
{
++score;
}
}
// <----------------------------------------------
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);
}
}
}
watch.Stop();
}
matchTime = watch.ElapsedMilliseconds;
}
public static byte[] Canny(IntPtr buffer, int width, int height, bool smooth = false)
{
unsafe
{
Mat source = new Mat(height, width, DepthType.Cv8U, 1, buffer, width);
Mat blurred = new Mat(height, width, DepthType.Cv8U, 1);
if (smooth) {
CvInvoke.Blur(source, blurred, new Size(3, 3), new Point(-1, -1));
}
Mat cannyEdges = new Mat(height, width, DepthType.Cv8U, 1);
double cannyThreshold = 180.0;
double cannyThresholdLinking = 60.0;
CvInvoke.Canny(smooth ? blurred : source, cannyEdges, cannyThreshold, cannyThresholdLinking);
return cannyEdges.GetData();
}
}
//Transform images according to transform matrix
// From Line 130-239
public static ReturnColorImg transformColor(Mat img1, Mat mask1, Mat img2, Mat mask2,
Mat dst, Mat dst_mask,
Point centroid1, Point centroid2, double angle, Point tweak1, Point tweak2, bool mode = true)
{
Mat E = img2.Clone();
Mat E_mask = mask2.Clone();//Don't ruin original images
double intersections = 0;
double x = centroid2.X;
double y = centroid2.Y;
double _x, _y, _y2;
double y2;
LineSegment2D centerLine = new LineSegment2D(new Point((int)x, (int)y), new Point(img2.Width - (int)x, img2.Height - (int)y));
//Rectangle r=new Rectangle((int)x,(int)y,2*(img2.Width-(int)x),2*(img2.Height-(int)y));
Mat ri = new Mat(2 * (img2.Width - (int)x), 2 * (img2.Height - (int)y),DepthType.Cv8U, 3);
ri.SetTo(new MCvScalar(255, 255, 255));
Point oldc = new Point((int)x, (int)y);
bool success = false; // if the tweaking is not successful, return false
// inverse y axis
// y2 = -y;
// rotation of centeroid
x -= img2.Width / 2;
y -= img2.Height / 2;//shift origin to (w/2,h/2)
_x = x * Math.Cos(angle / (180 / Math.PI)) - y * Math.Sin(angle / (180 / Math.PI));//rotate by theta
_y = x * Math.Sin(angle / (180 / Math.PI)) + y * Math.Cos(angle / (180 / Math.PI));
_x += img2.Width / 2;
_y += img2.Height / 2;//back to origin
//_x = x+img2.Width/2;
//_y = y+img2.Height/2;
// inverse y axis
//_y = -_y2;
centroid2.X = (int)_x;
centroid2.Y = (int)_y;
Point shift = new Point();
shift.X = centroid1.X - centroid2.X;
shift.Y = centroid1.Y - centroid2.Y;
MatImage m1 = new MatImage(E);
m1.Rotate(angle, new Bgr(255, 255, 255));
E = m1.Out();
MatImage m2 = new MatImage(E_mask);
m1.Rotate(angle, new Bgr(255, 255, 255));
E_mask = m2.Out();
//Find optimal size of canvas to hold both images and appropriate transformations
Point t1, t2;//transformation 1 and 2
t1 = new Point();
t2 = new Point();
int optimal_h = 0, optimal_w = 0;//of canvas(IplImage* dst)
switch (quadrant(shift))
{
case 1:
t1.X = 0;
t1.Y = 0;
t2 = shift;
optimal_h = Math.Max(img1.Height, img2.Height + shift.Y);
optimal_w = Math.Max(img1.Width, img2.Width + shift.X);
break;
case 2:
t1.X = -shift.X;
t1.Y = 0;
t2.X = 0;
t2.Y = shift.Y;
optimal_h = Math.Max(img1.Height, img2.Height + shift.Y);
optimal_w = Math.Max(img2.Width, img1.Width - shift.X);
break;
case 3:
t1.X = -shift.X;
t1.Y = -shift.Y;
t2.X = 0;
t2.Y = 0;
optimal_h = Math.Max(img1.Height - shift.Y, img2.Height);
optimal_w = Math.Max(img1.Width - shift.X, img2.Width);
break;
case 4:
t1.X = 0;
t1.Y = -shift.Y;
t2.X = shift.X;
t2.Y = 0;
optimal_h = Math.Max(img1.Height - shift.Y, img2.Height);
optimal_w = Math.Max(img2.Width + shift.X, img1.Width);
break;
}
// add tweak factor
t1.X += tweak1.X;
t1.Y += tweak1.Y;
t2.X += tweak2.X;
t2.Y += tweak2.Y;
//optimal_h = 1000;
//optimal_w = 1000;
dst = new Mat(optimal_h, optimal_w,DepthType.Cv8U,3);
dst_mask = new Mat(optimal_h, optimal_w,DepthType.Cv8U,3);
if (mode)
{
dst.SetTo(new MCvScalar(255, 255, 255)); // white background=255, black background=0
}
else
{
dst.SetTo(new MCvScalar(0, 0, 0)); // white background=255, black background=0
}
dst_mask.SetTo(new MCvScalar(0, 0, 0));
/*if (BKG_WHITE)
cvSet(dst, cvScalar(255));//make it white
else
cvSet(dst, cvScalar(0));//make it black*/
for (int i = 0; i < img1.Height; ++i)
{
for (int j = 0; j < img1.Width; ++j)
{
// if black background
if (mode)
{
if (mask1.GetData(i, j)[0] != 255)
{
int i_new = i + t1.Y;
int j_new = j + t1.X;
try
{
dst.SetValue(i_new, j_new, img1.GetData(i, j));
int[] vals = { 255, 255, 255 };
dst_mask.SetValue(i_new, j_new, vals);
}
catch(IndexOutOfRangeException e)
{
//MessageBox.Show("You cannot tweak in that direction further");
success = false;
goto ret;
}
}
}
// if white background
else
{
if (mask1.GetData(i, j) [0] != 0)
{
int i_new = i + t1.Y;
int j_new = j + t1.X;
try
{
dst.SetValue(i_new, j_new, img1.GetData(i, j));
int[] vals = { 0, 0, 0 };
dst_mask.SetValue(i_new, j_new, vals);
}
catch(IndexOutOfRangeException e)
{
//MessageBox.Show("You cannot tweak in that direction further");
success = false;
goto ret;
}
}
}
}
}
//Apply transformation to image2
for (int i = 0; i < img2.Height; ++i)
{
for (int j = 0; j < img2.Width; ++j)
{
// if black background
if (mode)
{
if (E_mask.GetData(i, j) [0] != 255)
{
int i_new = i + t2.Y;
int j_new = j + t2.X;
try
{
if (dst_mask.GetData(i_new,j_new)[0] != 0)
{
intersections++;
}
else
{
dst.SetValue(i_new, j_new, E.GetData(i, j));
int[] vals = { 255, 255, 255 };
dst_mask.SetValue(i_new, j_new, vals);
}
}
catch (IndexOutOfRangeException e)
{
//MessageBox.Show("You cannot tweak in that direction further");
success = false;
goto ret;
}
}
}
// else if white background
else
{
if (E_mask.GetData(i, j)[0] != 0)
{
int i_new = i + t2.Y;
int j_new = j + t2.X;
try
{
if (dst_mask.GetData(i_new, j_new)[0] != 0)
{
intersections++;
}
else
{
dst.SetValue(i_new, j_new, E.GetData(i, j));
int[] vals = { 0, 0, 0 };
dst_mask.SetValue(i_new, j_new, vals);
}
}
catch (IndexOutOfRangeException e)
{
//MessageBox.Show("You cannot tweak in that direction further");
success = false;
goto ret;
}
}
}
}
}
/*for (int i = 0; i < Adst.Length; i++)
{
for(int j=0; j < Adst[0].Length; j++)
{
dst.SetValue(i, j, Adst[i][j]);
}
}
for (int i = 0; i < Adst_mask.Length; i++)
{
for (int j = 0; j < Adst_mask[0].Length; j++)
{
dst_mask.SetValue(i, j, Adst_mask[i][j]);
}
}*/
// dst.SetTo(Adst);
//dst_mask.SetTo(Adst_mask);
success = true;
/*cvReleaseImage(&E);
cvReleaseImage(&E_mask);*/ // should not need these two lines because of garbage collection
// threshold detection is meaningless for 2-piece case, always success
ret:
if (intersections > Constants.THRESHOLD)
{
/*cvReleaseImage(&dst);//In case of failure in joining
cvReleaseImage(&dst_mask);//release memory*/
ReturnColorImg img = new ReturnColorImg();
img.img = dst;
img.img_mask = dst_mask;
img.source1 = img1;
img.source2 = E_mask;
img.center1 = centroid1;
img.center2old = oldc;
img.center2new = centroid2;
img.centerLinee = centerLine;
img.returnbool = false; // for determining if the image is matched or not
img.translate1 = t1;
img.translate2 = t2;
img.overlap = intersections;
img.success = success; // for tweak only
return img;
}
else
{
ReturnColorImg img = new ReturnColorImg();
img.img = dst;
img.img_mask = dst_mask;
img.source1 = img1;
img.source2 = E_mask;
img.center1 = centroid1;
img.center2old = oldc;
img.center2new = centroid2;
img.centerLinee = centerLine;
img.returnbool = true; // for determining if the image is matched or not
img.translate1 = t1;
img.translate2 = t2;
img.overlap = intersections;
img.success = success; // for tweak only
return img;
}
}
