public void ComputeMode1()
{
using (var model = BlockMeanHash.Create(BlockMeanHashMode.Mode1))
using (var img = Image("lenna.png"))
using (var hash = new MatOfByte())
{
model.Compute(img, hash);
Assert.Equal(1, hash.Rows);
Assert.Equal(121, hash.Cols);
Assert.Equal(MatType.CV_8UC1, hash.Type());
var hashArray = hash.ToArray();
Assert.Equal(
new byte[]
{
135, 255, 243, 135, 131, 255, 249, 199, 193, 255, 252, 227, 224, 127, 254, 113, 128, 127, 127,
48, 192, 191, 25, 24, 240, 255, 4, 13, 248, 127, 135, 134, 252, 255, 99, 67, 254, 255, 184, 113,
255, 127, 220, 240, 195, 31, 111, 120, 224, 135, 55, 12, 248, 192, 27, 6, 126, 240, 13, 128, 63,
248, 6, 64, 14, 126, 3, 48, 7, 191, 3, 248, 131, 159, 1, 248, 193, 207, 0, 252, 240, 103, 0,
126, 248, 59, 0, 63, 254, 29, 0, 15, 255, 14, 0, 135, 127, 6, 128, 131, 63, 3, 224, 103, 206, 0,
240, 247, 99, 0, 248, 255, 49, 0, 252, 255, 24, 0, 254, 49, 0
},
hashArray);
}
}
public Stream MatToStream(Mat m)
{
using (var vect = new MatOfByte())
{
if (Imgcodecs.Imencode(".png", m, vect))
{
var stream = new MemoryStream(vect.ToArray());
return(stream);
}
return(null);
}
}
static bool RefineMatchesWithHomography(
KeyPoint[] queryKeypoints,
KeyPoint[] trainKeypoints,
float reprojectionThreshold,
DMatch[] matches,
Mat homography
)
{
int minNumberMatchesAllowed = 8;
if (matches.Length < minNumberMatchesAllowed)
{
return(false);
}
List <Point2f> srcPointsList = new List <Point2f>(matches.Length);
List <Point2f> dstPointsList = new List <Point2f>(matches.Length);
for (int i = 0; i < matches.Length; i++)
{
srcPointsList.Add(trainKeypoints[matches[i].TrainIdx].Pt);
dstPointsList.Add(queryKeypoints[matches[i].QueryIdx].Pt);
}
using (MatOfPoint2f srcPoints = MatOfPoint2f.FromArray(srcPointsList))
using (MatOfPoint2f dstPoints = MatOfPoint2f.FromArray(dstPointsList))
using (MatOfByte inliersMask = new MatOfByte())
{
Cv2.FindHomography(srcPoints, dstPoints, HomographyMethods.Ransac, reprojectionThreshold, inliersMask).CopyTo(homography);
if (homography.Rows != 3 || homography.Cols != 3)
{
return(false);
}
byte[] inliersMaskList = inliersMask.ToArray();
List <DMatch> inliers = new List <DMatch>();
for (int i = 0; i < inliersMaskList.Length; i++)
{
if (inliersMaskList[i] == 1)
{
inliers.Add(matches[i]);
}
}
matches = inliers.ToArray();
}
return(matches.Length > minNumberMatchesAllowed);
}
public void ComputeMode0()
{
using (var model = BlockMeanHash.Create(BlockMeanHashMode.Mode0))
using (var img = Image("lenna.png"))
using (var hash = new MatOfByte())
{
model.Compute(img, hash);
Assert.Equal(1, hash.Rows);
Assert.Equal(32, hash.Cols);
Assert.Equal(MatType.CV_8UC1, hash.Type());
var hashArray = hash.ToArray();
Assert.Equal(
new byte[] { 243, 61, 243, 61, 195, 63, 226, 151, 226, 159, 250, 223, 50, 206, 18, 231, 130, 226, 130, 227, 130, 243, 130, 243, 2, 243, 2, 87, 2, 127, 2, 47 },
hashArray);
}
}
public void Compute()
{
using (var model = RadialVarianceHash.Create())
using (var img = Image("lenna.png"))
using (var hash = new MatOfByte())
{
model.Compute(img, hash);
Assert.Equal(1, hash.Rows);
Assert.Equal(40, hash.Cols);
Assert.Equal(MatType.CV_8UC1, hash.Type());
var hashArray = hash.ToArray();
Assert.Equal(
new byte[] { 142, 0, 209, 255, 100, 131, 131, 190, 107, 68, 192, 159, 136, 135, 131, 159, 129, 184, 140, 134,
145, 120, 162, 157, 136, 142, 139, 134, 149, 146, 134, 151, 137, 139, 148, 144, 140, 135, 144, 144 },
hashArray);
}
}
public void Compute()
{
using (var model = MarrHildrethHash.Create())
using (var img = Image("lenna.png"))
using (var hash = new MatOfByte())
{
model.Compute(img, hash);
Assert.Equal(1, hash.Rows);
Assert.Equal(72, hash.Cols);
Assert.Equal(MatType.CV_8UC1, hash.Type());
var hashArray = hash.ToArray();
Assert.Equal(
new byte[] { 208, 54, 63, 31, 143, 199, 227, 241, 192, 124, 126, 39, 3, 205, 192, 124, 126, 63, 228, 150, 199, 194,
242, 254, 208, 143, 201, 105, 118, 57, 5, 191, 3, 99, 177, 224, 15, 137, 153, 58, 5, 129, 63, 193, 216, 228,
112, 100, 129, 176, 248, 255, 110, 7, 230, 16, 193, 231, 207, 135, 0, 150, 120, 5, 191, 60, 18, 114, 119, 131, 150, 31 },
hashArray);
}
}
public void Compute()
{
using (var model = PHash.Create())
using (var img = Image("lenna.png"))
using (var hash = new MatOfByte())
{
model.Compute(img, hash);
Assert.Equal(1, hash.Rows);
Assert.Equal(8, hash.Cols);
Assert.Equal(MatType.CV_8UC1, hash.Type());
var hashArray = hash.ToArray();
Assert.Equal(152, hashArray[0]);
Assert.Equal(99, hashArray[1]);
Assert.Equal(42, hashArray[2]);
Assert.Equal(180, hashArray[3]);
Assert.Equal(206, hashArray[4]);
Assert.Equal(197, hashArray[5]);
Assert.Equal(97, hashArray[6]);
Assert.Equal(25, hashArray[7]);
}
}
public void Compute()
{
using (var model = AverageHash.Create())
using (var img = Image("lenna.png"))
using (var hash = new MatOfByte())
{
model.Compute(img, hash);
Assert.Equal(1, hash.Rows);
Assert.Equal(8, hash.Cols);
Assert.Equal(MatType.CV_8UC1, hash.Type());
var hashArray = hash.ToArray();
Assert.Equal(101, hashArray[0]);
Assert.Equal(121, hashArray[1]);
Assert.Equal(185, hashArray[2]);
Assert.Equal(149, hashArray[3]);
Assert.Equal(205, hashArray[4]);
Assert.Equal(209, hashArray[5]);
Assert.Equal(213, hashArray[6]);
Assert.Equal(112, hashArray[7]);
}
}
private void BeamDetection(string outputfilename, bool isup)
{// beam Detection
int BinarizeThreshold = 60;
int BrightnessThreshold = 4;
int nop = 7;
double dz = 0;
if (isup == true)
{
dz = -0.003;
}
else
{
dz = 0.003;
}
Camera camera = Camera.GetInstance();
MotorControler mc = MotorControler.GetInstance(parameterManager);
Vector3 InitPoint = mc.GetPoint();
Vector3 p = new Vector3();
TracksManager tm = parameterManager.TracksManager;
int mod = parameterManager.ModuleNo;
int pl = parameterManager.PlateNo;
Track myTrack = tm.GetTrack(tm.TrackingIndex);
string[] sp = myTrack.IdString.Split('-');
//string datfileName = string.Format("{0}.dat", System.DateTime.Now.ToString("yyyyMMdd_HHmmss"));
string datfileName = string.Format(@"c:\test\bpm\{0}\{1}-{2}-{3}-{4}-{5}.dat", mod, mod, pl, sp[0], sp[1], System.DateTime.Now.ToString("ddHHmmss"));
BinaryWriter writer = new BinaryWriter(File.Open(datfileName, FileMode.Create));
byte[] bb = new byte[440 * 512 * nop];
string fileName = string.Format("{0}", outputfilename);
StreamWriter twriter = File.CreateText(fileName);
string stlog = "";
List <ImageTaking> LiIT = TakeSequentialImage(0.0, 0.0, dz, nop);
Mat sum = Mat.Zeros(440, 512, MatType.CV_8UC1);
for (int i = 0; i < LiIT.Count; i++)
{
Mat bin = (Mat)DogContrastBinalize(LiIT[i].img, 31, BinarizeThreshold);
Cv2.Add(sum, bin, sum);
//byte[] b = LiIT[i].img.ToBytes();//format is .png
MatOfByte mob = new MatOfByte(LiIT[i].img);
byte[] b = mob.ToArray();
b.CopyTo(bb, 440 * 512 * i);
}
mc.MovePointZ(InitPoint.Z);
mc.Join();
Cv2.Threshold(sum, sum, BrightnessThreshold, 1, ThresholdType.Binary);
//Cv2.FindContoursをつかうとAccessViolationExceptionになる(Release/Debug両方)ので、C-API風に書く
using (CvMemStorage storage = new CvMemStorage())
{
using (CvContourScanner scanner = new CvContourScanner(sum.ToIplImage(), storage, CvContour.SizeOf, ContourRetrieval.Tree, ContourChain.ApproxSimple))
{
//string fileName = string.Format(@"c:\img\{0}.txt",
// System.DateTime.Now.ToString("yyyyMMdd_HHmmss_fff"));
foreach (CvSeq <CvPoint> c in scanner)
{
CvMoments mom = new CvMoments(c, false);
if (c.ElemSize < 2)
{
continue;
}
if (mom.M00 == 0.0)
{
continue;
}
double mx = mom.M10 / mom.M00;
double my = mom.M01 / mom.M00;
stlog += string.Format("{0:F} {1:F}\n", mx, my);
}
}
}
twriter.Write(stlog);
twriter.Close();
writer.Write(bb);
writer.Flush();
writer.Close();
sum *= 255;
sum.ImWrite(String.Format(@"c:\img\{0}_{1}_{2}.bmp",
System.DateTime.Now.ToString("yyyyMMdd_HHmmss"),
(int)(p.X * 1000),
(int)(p.Y * 1000)));
}//BeamDetection