void Hu(Image <Gray, byte> img, ref Double[] MomentHu, ref Double[] MomentR)
{
double r1 = 0, r2 = 0, r3 = 0, r4 = 0, r5 = 0, r6 = 0, r7 = 0, r8 = 0, r9 = 0, r10 = 0;
Image <Gray, byte> imgA = new Image <Gray, byte>(img.Size);
imgA = img.ThresholdBinary(new Gray(60), new Gray(255));
Contour <Point> contourA = imgA.FindContours();
MCvMoments momentsA = contourA.GetMoments();
MCvHuMoments HuMomentsA = momentsA.GetHuMoment();
MomentHu[0] = HuMomentsA.hu1;
MomentHu[1] = HuMomentsA.hu2;
MomentHu[2] = HuMomentsA.hu3;
MomentHu[3] = HuMomentsA.hu4;
MomentHu[4] = HuMomentsA.hu5;
MomentHu[5] = HuMomentsA.hu6;
Rmoment(HuMomentsA.hu1, HuMomentsA.hu2, HuMomentsA.hu3, HuMomentsA.hu4, HuMomentsA.hu5, HuMomentsA.hu6, ref r1, ref r2, ref r3, ref r4, ref r5, ref r6, ref r7, ref r8, ref r9, ref r10);
MomentR[0] = r1;
MomentR[1] = r2;
MomentR[2] = r3;
MomentR[3] = r4;
MomentR[4] = r5;
MomentR[5] = r6;
MomentR[6] = r7;
MomentR[7] = r8;
MomentR[8] = r9;
MomentR[9] = r10;
}
public static void PrintHuMoments(MCvHuMoments huMoment)
{
double[] m = new double[7];
m[0] = huMoment.hu1;
m[1] = huMoment.hu2;
m[2] = huMoment.hu3;
m[3] = huMoment.hu4;
m[4] = huMoment.hu5;
m[5] = huMoment.hu6;
m[6] = huMoment.hu7;
for (int i = 0; i < 7; i++)
{
int sign;
double result = Math.Abs(m[i]);
if (m[i] > 0)
{
sign = 1;
}
else if (m[i] < 0)
{
sign = -1;
}
else
{
sign = 0;
}
result = sign * Math.Log10(result);
Console.WriteLine("hu{0}: {1}", i + 1, result);
}
}
static void Main(string[] args)
{
string img1 = "image1.bmp";
string img2 = "image2.bmp";
double dist;
MCvHuMoments huMoments = new MCvHuMoments();
/*
* IsGetShapeFeature(img1, ref huMoments);
* PrintHuMoments(huMoments);
*
* IsGetShapeFeature(img2, ref huMoments);
* PrintHuMoments(huMoments);
*
* IsGetShapeFeature(img1, ref huMoments);
* PrintHuMoments(huMoments);
*/
//Console.ReadLine();
if ((dist = MatchShapes(img1, img2)) >= 0)
{
Console.WriteLine("The distance between {0} and {1} is {2}", img1, img2, dist);
}
else
{
Console.WriteLine("Fail to compute the distance");
}
Console.ReadLine();
}
void Hu(Image <Gray, byte> img, ref Double[] MomentHu)
{
Image <Gray, byte> imgA = new Image <Gray, byte>(img.Size);
imgA = img.ThresholdBinary(new Gray(60), new Gray(255));
Contour <Point> contourA = imgA.FindContours();
MCvMoments momentsA = contourA.GetMoments();
MCvHuMoments HuMomentsA = momentsA.GetHuMoment();
MomentHu[0] = HuMomentsA.hu1;
MomentHu[1] = HuMomentsA.hu2;
MomentHu[2] = HuMomentsA.hu3;
MomentHu[3] = HuMomentsA.hu4;
MomentHu[4] = HuMomentsA.hu5;
MomentHu[5] = HuMomentsA.hu6;
}
public void GetHuMoments(Image <Gray, Byte> img, double[] m_hu)
{
MCvMoments moments = new MCvMoments();
MCvHuMoments hu = new MCvHuMoments();
CvInvoke.cvMoments(img, ref moments, 0);
CvInvoke.cvGetHuMoments(ref moments, ref hu);
m_hu[0] = hu.hu1;
m_hu[1] = hu.hu2;
m_hu[2] = hu.hu3;
m_hu[3] = hu.hu4;
m_hu[4] = hu.hu5;
m_hu[5] = hu.hu6;
m_hu[6] = hu.hu7;
}
// get the Hu
public static bool IsGetShapeFeature(string ImgPath, ref MCvHuMoments huMoments)
{
IntPtr img = IntPtr.Zero;
MCvMoments moments = new MCvMoments();
if ((img = CvInvoke.cvLoadImage(ImgPath, LOAD_IMAGE_TYPE.CV_LOAD_IMAGE_GRAYSCALE)) != IntPtr.Zero)
{
CvInvoke.cvMoments(img, ref moments, 0);
CvInvoke.cvGetHuMoments(ref moments, ref huMoments);
CvInvoke.cvReleaseImage(ref img);
return(true);
}
else
{
return(false);
}
}
public void ComputeFeatures(Segment s)
{
//Add the relative size
NamedFeature f = new NamedFeature("RelativeSize");
f.values.Add(s.points.Count() / (double)(imageWidth * imageHeight));
s.features.Add(f);
// Relative centroid
PointF c = NormalizedCentroid(s);
s.features.Add(new NamedFeature("RelativeCentroid", new List <double> {
c.X, c.Y
}));
// One interior point
PointF np = RandomNormalizedInteriorPoint(s);
s.features.Add(new NamedFeature("OneInteriorPoint", new List <double> {
np.X, np.Y
}));
//Radial distance
s.features.Add(new NamedFeature("RadialDistance", new List <double> {
Math.Sqrt(c.X * c.X + c.Y * c.Y)
}));
//Normalized Discrete Compactness http://www.m-hikari.com/imf-password2009/25-28-2009/bribiescaIMF25-28-2009.pdf
//Find the segment id
Point sp = s.points.First();
int sidx = assignments[sp.X, sp.Y];
//count number of perimeter edges
int perimeter = 0;
foreach (Point p in s.points)
{
for (int i = -1; i <= 1; i++)
{
for (int j = -1; j <= 1; j++)
{
if (Math.Abs(i) == Math.Abs(j))
{
continue;
}
if (Util.InBounds(p.X + i, p.Y + j, imageWidth, imageHeight) && assignments[p.X + i, p.Y + j] != sidx)
{
perimeter++;
}
else if (!Util.InBounds(p.X + i, p.Y + j, imageWidth, imageHeight)) //edge pixels should be considered perimeter too
{
perimeter++;
}
}
}
}
int n = s.points.Count();
double CD = (4.0 * n - perimeter) / 2;
double CDmin = n - 1;
double CDmax = (4 * n - 4 * Math.Sqrt(n)) / 2;
double CDN = (CD - CDmin) / Math.Max(1, (CDmax - CDmin));
s.features.Add(new NamedFeature("NormalizedDiscreteCompactness", new List <double> {
CDN
}));
//Add elongation (width/length normalized between 0-square to 1-long http://hal.archives-ouvertes.fr/docs/00/44/60/37/PDF/ARS-Journal-SurveyPatternRecognition.pdf
PointF[] points = s.points.Select <Point, PointF>(p => new PointF(p.X, p.Y)).ToArray <PointF>();
Emgu.CV.Structure.MCvBox2D box = Emgu.CV.PointCollection.MinAreaRect(points);
PointF[] vertices = box.GetVertices();
double elongation = 1 - Math.Min(box.size.Width + 1, box.size.Height + 1) / Math.Max(box.size.Width + 1, box.size.Height + 1);
s.features.Add(new NamedFeature("Elongation", new List <double> {
elongation
}));
//Add Hu shape moments, invariant to translation, scale, and rotation (not sure what each measure refers to intuitively though, or if there is an intuitive analog)
//They may also do badly on noisy data however. See: http://hal.archives-ouvertes.fr/docs/00/44/60/37/PDF/ARS-Journal-SurveyPatternRecognition.pdf (called Invariant Moments)
Bitmap regionBitmap = new Bitmap(imageWidth, imageHeight);
Graphics g = Graphics.FromImage(regionBitmap);
g.FillRectangle(new SolidBrush(Color.Black), 0, 0, imageWidth, imageHeight);
foreach (Point p in s.points)
{
regionBitmap.SetPixel(p.X, p.Y, Color.White);
}
Emgu.CV.Image <Gray, byte> region = new Emgu.CV.Image <Gray, byte>(regionBitmap);
MCvMoments moment = region.GetMoments(true);
MCvHuMoments hu = moment.GetHuMoment();
s.features.Add(new NamedFeature("HuMoments", new List <double> {
hu.hu1, hu.hu2, hu.hu3, hu.hu4, hu.hu5, hu.hu6, hu.hu7
}));
region.Dispose();
regionBitmap.Dispose();
}
//return the distances between two pics. dist >= 0: func works right, else fail
public static double MatchShapes(string ImgPath1, string ImgPath2)
{
const double eps = 0.000000000000000001;
MCvHuMoments huMoment1 = new MCvHuMoments();
MCvHuMoments huMoment2 = new MCvHuMoments();
double[] ma = new double[7];
double[] mb = new double[7];
IsGetShapeFeature(ImgPath1, ref huMoment1);
/* Here is a bug.
* I still cannot figure out why the first time calling IsGetShapeFeautre() it returns wrong anwser
*
* Details:
* CvInvoke.cvMoments(img, ref moments, 0) cannot work right first time, then CVInvoke.cvGetHuMoments() returns wrong hu
* And I find when I use C++ to call this function, it works fine at the first, just for C#, it has this problem
* Actual:
* moments.m00, moments.m01, moments.02 etc returns NaN
* huMoments.hu[i] returns NaN
* Excepted:
* moments.m00 etc returns double value
* huMoments.hu[i] returns double value
*
* So I call the IsGetShapeFeature() once to pass the wrong anwser, it's really an ugly way to fix it. I will back to fix it when I am free.
*
* 2009/8/30
*/
if (IsGetShapeFeature(ImgPath1, ref huMoment1) && IsGetShapeFeature(ImgPath2, ref huMoment2))
{
ma[0] = huMoment1.hu1;
ma[1] = huMoment1.hu2;
ma[2] = huMoment1.hu3;
ma[3] = huMoment1.hu4;
ma[4] = huMoment1.hu5;
ma[5] = huMoment1.hu6;
ma[6] = huMoment1.hu7;
mb[0] = huMoment1.hu1;
mb[1] = huMoment2.hu2;
mb[2] = huMoment2.hu3;
mb[3] = huMoment2.hu4;
mb[4] = huMoment2.hu5;
mb[5] = huMoment2.hu6;
mb[6] = huMoment2.hu7;
double result = 0;
for (int i = 0; i < 7; i++)
{
double ama = Math.Abs(ma[i]);
double amb = Math.Abs(mb[i]);
int sma, smb;
if (ma[i] > 0)
{
sma = 1;
}
else if (ma[i] < 0)
{
sma = -1;
}
else
{
sma = 0;
}
if (mb[i] > 0)
{
smb = 1;
}
else if (mb[i] < 0)
{
smb = -1;
}
else
{
smb = 0;
}
if (ama > eps && amb > eps)
{
ama = sma * Math.Log10(ama);
amb = smb * Math.Log10(amb);
result += (amb - ama) * (amb - ama);
}
}
result = Math.Sqrt(result);
return(result);
}
else
{
return(-1);
}
}