public void CreateHistogram(Image<Bgr, Byte> img)
{
Image<Gray, Byte>[] channels = img.Split();
Image<Gray, Byte> blueChannel = channels[0];
Image<Gray, Byte> greenChannel = channels[1];
Image<Gray, Byte> redChannel = channels[2];
//Red colour channel
DenseHistogram dhRed = new DenseHistogram(BIN_SIZE, new RangeF(0.0f, BIN_DEPTH));
dhRed.Calculate<Byte>(new Image<Gray, Byte>[1] { redChannel }, false, null);
redChart.ClearHistogram();
redChart.AddHistogram("Red Channel", System.Drawing.Color.Red, dhRed);
redChart.Refresh();
//Green colour Channel
DenseHistogram dhGreen = new DenseHistogram(BIN_SIZE, new RangeF(0.0f, BIN_DEPTH));
dhGreen.Calculate<Byte>(new Image<Gray, Byte>[1] { greenChannel }, false, null);
greenChart.ClearHistogram();
greenChart.AddHistogram("Green Channel", System.Drawing.Color.Green, dhGreen);
greenChart.Refresh();
//Blue colour Channel
DenseHistogram dhBlue = new DenseHistogram(BIN_SIZE, new RangeF(0.0f, BIN_DEPTH));
dhBlue.Calculate<Byte>(new Image<Gray, Byte>[1] { blueChannel }, false, null);
blueChart.ClearHistogram();
blueChart.Show();
blueChart.AddHistogram("Blue Channel", System.Drawing.Color.Blue, dhBlue);
blueChart.Refresh();
}
/// <summary>
/// Add a plot of the 1D histogram. You should call the Refresh() function to update the control after all modification is complete.
/// </summary>
/// <param name="name">The name of the histogram</param>
/// <param name="color">The drawing color</param>
/// <param name="histogram">The 1D histogram to be drawn</param>
public void AddHistogram(String name, Color color, DenseHistogram histogram)
{
Debug.Assert(histogram.Dimension == 1, Properties.StringTable.Only1DHistogramSupported);
GraphPane pane = new GraphPane();
// Set the Title
pane.Title.Text = name;
pane.XAxis.Title.Text = Properties.StringTable.Value;
pane.YAxis.Title.Text = Properties.StringTable.Count;
#region draw the histogram
RangeF range = histogram.Ranges[0];
int binSize = histogram.BinDimension[0].Size;
float step = (range.Max - range.Min) / binSize;
float start = range.Min;
double[] bin = new double[binSize];
for (int binIndex = 0; binIndex < binSize; binIndex++)
{
bin[binIndex] = start;
start += step;
}
PointPairList pointList = new PointPairList(
bin,
Array.ConvertAll<float, double>( (float[]) histogram.MatND.ManagedArray, System.Convert.ToDouble));
pane.AddCurve(name, pointList, color);
#endregion
zedGraphControl1.MasterPane.Add(pane);
}
public void CalculateHistogram(Image<Gray, byte> source)
{
histogram = new DenseHistogram(16, new RangeF(0, 180));
mask = source.InRange(VideoParameters.Default.CamshiftMaskLow, VideoParameters.Default.CamshiftMaskHigh);
CvInvoke.cvCalcHist(new[] { source.Ptr }, histogram.Ptr, false, mask.Ptr);
SetTrackWindow(source.ROI);
}
public static float[] CalculateHistogram(Image<Gray, byte> image)
{
var histogram = new DenseHistogram(256, new RangeF(0.0f, 255.0f));
histogram.Calculate(new[] {image}, true, null);
var values = new float[256];
histogram.MatND.ManagedArray.CopyTo(values, 0);
return values;
}
/// <summary>
/// Display the specific histogram
/// </summary>
/// <param name="hist">The 1 dimension histogram to be displayed</param>
/// <param name="title">The name of the histogram</param>
public static void Show(DenseHistogram hist, string title)
{
HistogramViewer viewer = new HistogramViewer();
if (hist.Dimension == 1)
viewer.HistogramCtrl.AddHistogram(title, Color.Black, hist);
viewer.HistogramCtrl.Refresh();
viewer.Show();
}
// Returns a histogram for each bgr channel
private DenseHistogram[] CreateHistogram(Image<Bgr, Byte> img, int buckets)
{
DenseHistogram BlueHisto = new DenseHistogram(buckets, new RangeF(0, buckets - 1));
DenseHistogram GreenHisto = new DenseHistogram(buckets, new RangeF(0, buckets - 1));
DenseHistogram RedHisto = new DenseHistogram(buckets, new RangeF(0, buckets - 1));
Image<Gray, Byte> img2Blue = img[0];
Image<Gray, Byte> img2Green = img[1];
Image<Gray, Byte> img2Red = img[2];
BlueHisto.Calculate(new Image<Gray, Byte>[] { img2Blue }, true, null);
GreenHisto.Calculate(new Image<Gray, Byte>[] { img2Green }, true, null);
RedHisto.Calculate(new Image<Gray, Byte>[] { img2Red }, true, null);
//Histo.MatND.ManagedArray
return new DenseHistogram[] { BlueHisto, GreenHisto, RedHisto };
}
private float[][] ArrayHistogram(DenseHistogram[] histo, int buckets)
{
float[] BlueHisto;
float[] GreenHisto;
float[] RedHisto;
BlueHisto = new float[buckets];
histo[0].MatND.ManagedArray.CopyTo(BlueHisto, 0);
GreenHisto = new float[buckets];
histo[1].MatND.ManagedArray.CopyTo(GreenHisto, 0);
RedHisto = new float[buckets];
histo[2].MatND.ManagedArray.CopyTo(RedHisto, 0);
return new float[][] { BlueHisto, GreenHisto, RedHisto };
}
public ObjectTracking(Image<Bgr, Byte> image, Rectangle ROI)
{
// Initialize parameters
trackbox = new MCvBox2D();
trackcomp = new MCvConnectedComp();
hue = new Image<Gray, byte>(image.Width, image.Height);
hue._EqualizeHist();
mask = new Image<Gray, byte>(image.Width, image.Height);
hist = new DenseHistogram(30, new RangeF(0, 180));
backproject = new Image<Gray, byte>(image.Width, image.Height);
// Assign Object's ROI from source image.
trackingWindow = ROI;
// Producing Object's hist
CalObjectHist(image);
}
public Picture Histogram()
{
var chans = 3;
var bins = 256;
var range = new RangeF(0, 255);
var hist = new DenseHistogram(bins, range);
var split = this.bgra.Split();
var colors = new Bgra[]
{
new Bgra(255, 0, 0, 255),
new Bgra(0, 255, 0, 255),
new Bgra(0, 0, 255, 255),
};
var hip = new Picture(bins * chans, bins + 1); // Todo, plus one Jaap, really? Tssssk... wrote Jaap to himself.
hip.Bgra.SetValue(Color.Black.ToBgra());
for (int chan = 0; chan < chans; ++chan)
{
hist.Calculate<byte>(new Image<Gray, byte>[] { split[chan] }, false, null);
// Todo, Jaap, December 2010, hist.Normalize(bins - 1);
float min, max;
int[] minLoc, maxLoc;
hist.MinMax(out min, out max, out minLoc, out maxLoc);
if (max == min)
continue;
var scale = 255.0f / (max - min);
for (int x = 0; x < bins; ++x)
{
var n = hip.Height - (int)(hist[x] * scale);
for (int y = hip.Height - 1; y > n; --y)
hip.Bgra[y, x + chan * bins] = colors[chan];
}
}
foreach (var c in split)
c.Dispose();
return hip;
}
static void Main(string[] args)
{
Console.WriteLine("test1");
imgTemplate = new Image<Gray, byte>("..\\..\\Include\\IMG\\testDetectionBlanc\\1MZoneT2.tif");
Console.WriteLine("test2");
img = new Image<Gray, byte>("..\\..\\Include\\IMG\\testDetectionBlanc\\1ZoneT2.tif");
Console.WriteLine("test3");
/*HistogramViewer.Show(imgTemplate);
HistogramViewer.Show(img);*/
imgTemplate = new ImageModification().convertionBinaire(imgTemplate);
img = new ImageModification().convertionBinaire(img);
// Create and initialize histogram
DenseHistogram hist1 = new DenseHistogram(256, new RangeF(0.0f, 255.0f));
// Histogram Computing
hist1.Calculate<Byte>(new Image<Gray, byte>[] { img }, true, null);
//hist1.
// Create and initialize histogram
DenseHistogram hist2 = new DenseHistogram(256, new RangeF(0.0f, 255.0f));
// Histogram Computing
hist2.Calculate<Byte>(new Image<Gray, byte>[] { imgTemplate }, true, null);
Double result = Emgu.CV.CvInvoke.CompareHist(hist1, hist2, HistogramCompMethod.Correl);
Console.WriteLine("Correlation : "+result);
result = Emgu.CV.CvInvoke.CompareHist(hist1, hist2, HistogramCompMethod.Chisqr);
Console.WriteLine("Chi-Square : " + result);
result = Emgu.CV.CvInvoke.CompareHist(hist1, hist2, HistogramCompMethod.Intersect);
Console.WriteLine("Intersection : " + result);
result = Emgu.CV.CvInvoke.CompareHist(hist1, hist2, HistogramCompMethod.Bhattacharyya);
Console.WriteLine("Bhattacharyya distance : " + result);
result = Emgu.CV.CvInvoke.CompareHist(hist1, hist2, HistogramCompMethod.Hellinger);
Console.WriteLine("Synonym for Bhattacharyya : " + result);
result = Emgu.CV.CvInvoke.CompareHist(hist1, hist2, HistogramCompMethod.ChisqrAlt);
Console.WriteLine("Alternative Chi-Square : " + result);
Console.WriteLine(img.Rows + " " + img.Cols);
double blackPixelImg = (img.Rows * img.Cols) - img.CountNonzero()[0];
double blackPixelImgTemplate = (imgTemplate.Rows * imgTemplate.Cols) - imgTemplate.CountNonzero()[0];
double resultat = blackPixelImgTemplate / blackPixelImg;
Console.WriteLine(resultat);
Console.ReadKey();
}
public void TestDenseHistogram()
{
Image<Gray, Byte> img = new Image<Gray, byte>(400, 400);
img.SetRandUniform(new MCvScalar(), new MCvScalar(255));
DenseHistogram hist = new DenseHistogram(256, new RangeF(0.0f, 255.0f));
hist.Calculate<Byte>(new Image<Gray, byte>[] { img }, true, null);
float[] binValues = hist.GetBinValues();
/*
using (MemoryStream ms = new MemoryStream())
{
System.Runtime.Serialization.Formatters.Binary.BinaryFormatter
formatter = new System.Runtime.Serialization.Formatters.Binary.BinaryFormatter();
formatter.Serialize(ms, hist);
Byte[] bytes = ms.GetBuffer();
using (MemoryStream ms2 = new MemoryStream(bytes))
{
Object o = formatter.Deserialize(ms2);
DenseHistogram hist2 = (DenseHistogram)o;
EmguAssert.IsTrue(hist.Equals(hist2));
}
}*/
}
// Compares histogram over each possible rectangular patch of the specified size in the input images, and stores the results to the output map dst.
// Destination back projection image of the same type as the source images
public static Image <Gray, Single> BackProjectPatch <TDepth>(Image <Gray, TDepth>[] srcs, Size patchSize, DenseHistogram hist, HISTOGRAM_COMP_METHOD method, double factor) where TDepth : new()
{
Debug.Assert(srcs.Length == hist.Dimension, "The number of the source image and the dimension of the histogram must be the same.");
IntPtr[] imgPtrs =
Array.ConvertAll <Image <Gray, TDepth>, IntPtr>(
srcs,
delegate(Image <Gray, TDepth> img) { return(img.Ptr); });
Size size = srcs[0].Size;
size.Width = size.Width - patchSize.Width + 1;
size.Height = size.Height - patchSize.Height + 1;
Image <Gray, Single> res = new Image <Gray, float>(size);
CvInvoke.cvCalcBackProjectPatch(imgPtrs, res.Ptr, patchSize, hist.Ptr, method, factor);
return(res);
}
private void DrawHist2D(Image<Gray, byte> dst, DenseHistogram hist)
{
int pw = (int)(dst.Width / hist.BinDimension[0].Size);
int ph = (int)(dst.Height / hist.BinDimension[1].Size);
float min, max;
int[] tmp1, tmp2;
hist.MinMax(out min, out max, out tmp1, out tmp2);
for (int ix = 0; ix < hist.BinDimension[0].Size; ix++ )
{
for (int iy = 0; iy < hist.BinDimension[1].Size; iy ++)
{
double intensity = hist[ix, iy] * 255 / max;
dst.Draw(new Rectangle(ix * pw, iy * ph, pw, ph), new Gray(intensity), -1);
}
}
}
public Form1()
{
InitializeComponent();
DenseHistogram h = new DenseHistogram(5, new RangeF(0, 255));
ContourPoints = new Point[SEGMENTS];
}
/// <summary>
/// works out hsv histogram of image and give us back an arrays for Hue, saturation, and value
/// </summary>
/// <param name="src">
/// source image we want to get the historgram of
/// </param>
/// <returns>
/// returns 2d array for hue sat and val
/// </returns>
protected float[][] HsvValueFloatArray(Image<Bgr, Byte> src)
{
var HsvList = new List<float[]>();
float[] HueHist;
float[] SatHist;
float[] ValHist;
HueHist = new float[256];
SatHist = new float[256];
ValHist = new float[256];
DenseHistogram HistoHue = new DenseHistogram(256, new RangeF(0, 256));
DenseHistogram HistoSat = new DenseHistogram(256, new RangeF(0, 256));
DenseHistogram HistoVal = new DenseHistogram(256, new RangeF(0, 256));
Image<Hsv, Byte> hsvColor = src.Convert<Hsv, Byte>();
Image<Gray, Byte> Comparedimg2Hsv = hsvColor[0];
Image<Gray, Byte> Comparedimg2Sat = hsvColor[1];
Image<Gray, Byte> Comparedimg2Val = hsvColor[2];
HistoHue.Calculate(new Image<Gray, Byte>[] { Comparedimg2Hsv }, true, null);
HistoSat.Calculate(new Image<Gray, Byte>[] { Comparedimg2Sat }, true, null);
HistoVal.Calculate(new Image<Gray, Byte>[] { Comparedimg2Val }, true, null);
//HistoVal.Calculate(
HistoHue.MatND.ManagedArray.CopyTo(HueHist, 0);
HistoSat.MatND.ManagedArray.CopyTo(SatHist, 0);
HistoVal.MatND.ManagedArray.CopyTo(ValHist, 0);
HsvList.Add(HueHist);
HsvList.Add(SatHist);
HsvList.Add(ValHist);
return HsvList.ToArray();
}
//histogram match
static bool HistogramMatch(Mat frame1, Mat frame2)
{
//convert frames to HSV color space
Mat frame1_hist = new Mat();
Mat frame2_hist = new Mat();
CvInvoke.CvtColor(frame1, frame1_hist, ColorConversion.Bgr2Gray);
CvInvoke.CvtColor(frame2, frame2_hist, ColorConversion.Bgr2Gray);
//set histogram parameters
int h_bins = 50; int s_bins = 60;
int[] histSize = { h_bins, s_bins }; //int histSize[] = { h_bins, s_bins };
float[] h_ranges = { 0, 180 };
float[] s_ranges = { 0, 256 };
//const float[] ranges = { h_ranges, s_ranges };
float[][] ranges = { h_ranges };
//GrayHist = new float[256];
// Image<Gray, Byte> img_gray = new Image<Gray, byte>(frame1_hist.Rows, frame1_hist.Cols);
// frame1_hist.CopyTo(img_gray, null);
// DenseHistogram hist = new DenseHistogram(256, new RangeF(0, 256));
// hist.Calculate(new Image<Gray, Byte>[] { img_gray }, true, null);
//hist.Calculate(new Image<Gray, Byte>[] { frame1_hist.ToImage<Gray, Byte>() }, true, null);
//try each...
//frame1_hist.ToImage<Gray, Byte>().MIplImage()
//frame1_hist.ToImage<Gray, Byte>
//frame1_hist.ToImage<Gray, Byte>().To
//imencode: ``imdecode`` and ``imencode`` to read and write image from/to memory rather than a file.
Image <Gray, float> img_temp = frame1_hist.ToImage <Gray, float>();
Matrix <float> img = new Matrix <float>(img_temp.Width, img_temp.Height);
//img_temp.CopyTo(img);
CvInvoke.cvCopy(img_temp, img, new IntPtr());
DenseHistogram hist = new DenseHistogram(256, new RangeF(0, 256));
hist.Calculate(new Matrix <float>[] { img }, true, null);
// Create a grayscale image
//Image<Gray, Byte> img = new Image<Gray, byte>(400, 400);
// Fill image with random values
//img.SetRandUniform(new MCvScalar(), new MCvScalar(255));
// Create and initialize histogram
//DenseHistogram hist = new DenseHistogram(256, new RangeF(0.0f, 255.0f));
// Histogram Computing
//hist.Calculate<Byte>(new Image<Gray, byte>[] { img }, true, null);
//CvArray<byte> aaa = null;
//frame1_hist.CopyTo(aaa, null);
//Image<Gray, Byte> temp_img = new Image<Gray, byte>(frame1_hist.Rows, frame1_hist.Cols, 1, frame1_hist);
//frame1_hist.CopyTo(temp_img, null);
//hist.Calculate<Byte>(new Image<Gray, byte>[] { temp_img }, true, null);
//int[] channels = { 0, 1 }; //Use the o-th and 1-st channels
////calculate and normalize histograms
////MatND frame1_norm;
////MatND frame2_norm;
//MatND<double> frame1_norm;
//MatND<double> frame2_norm;
//CvInvoke.CalcHist(frame1_hist, channels, null, frame1_norm, histSize, ranges, false);
//CvInvoke.Normalize(frame1_norm, frame1_norm, 0, 1, NormType.MinMax, DepthType.Default, null);
//CvInvoke.CalcHist(frame2_hist, channels, null, frame2_norm, histSize, ranges, false);
//CvInvoke.Normalize(frame2_norm, frame2_norm, 0, 1, NormType.MinMax, DepthType.Default, null);
//double result = CvInvoke.CompareHist(frame1_norm, frame2_norm, 0); //Correlation comparion
//float thresh_min = 0.2f; //get unique keyframes
//float thresh_max = 0.997f; //good for fast moving objects in video
//if (result < thresh_min)
// return false; //not a match: likely different scenes
return(true);
}
public static bool SaveHistogram(string name, string folderExt, string fileNameExt, DenseHistogram hist, ILogger logger)
{
try
{
float[] Hist = hist.GetBinValues();
Save1DArrayToCsv <float>(name, folderExt, fileNameExt, ".csv", Hist, logger);
return(true);
}
catch (Exception ex)
{
logger?.ErrorLog($"Exception occured: {ex}", ClassName);
return(false);
}
}
public List<Bitmap> histogramSearch(List<Bitmap> images, Bitmap ori_image)
{
float[] BlueHist;
float[] GreenHist;
float[] RedHist;
float[] base_BlueHist;
float[] base_GreenHist;
float[] base_RedHist;
histoImageList.Clear();
Image<Bgr, byte> base_img = new Image<Bgr, byte>(ori_image);
DenseHistogram blue_Histo = new DenseHistogram(255, new RangeF(0, 255));
DenseHistogram green_Histo = new DenseHistogram(255, new RangeF(0, 255));
DenseHistogram red_Histo = new DenseHistogram(255, new RangeF(0, 255));
Image<Gray, Byte> base_img2Blue = base_img[0];
Image<Gray, Byte> base_img2Green = base_img[1];
Image<Gray, Byte> base_img2Red = base_img[2];
blue_Histo.Calculate(new Image<Gray, Byte>[] { base_img2Blue }, true, null);
green_Histo.Calculate(new Image<Gray, Byte>[] { base_img2Green }, true, null);
red_Histo.Calculate(new Image<Gray, Byte>[] { base_img2Red }, true, null);
for (int i = 0; i < images.Count; i++)
{
Image<Bgr, byte> img = new Image<Bgr, byte>(images[i]);
DenseHistogram blue_CompareHisto = new DenseHistogram(255, new RangeF(0, 255));
DenseHistogram green_CompareHisto = new DenseHistogram(255, new RangeF(0, 255));
DenseHistogram red_CompareHisto = new DenseHistogram(255, new RangeF(0, 255));
Image<Gray, Byte> img2Blue = img[0];
Image<Gray, Byte> img2Green = img[1];
Image<Gray, Byte> img2Red = img[2];
blue_CompareHisto.Calculate(new Image<Gray, Byte>[] { img2Blue }, true, null);
BlueHist = new float[256];
blue_CompareHisto.MatND.ManagedArray.CopyTo(BlueHist, 0);
green_CompareHisto.Calculate(new Image<Gray, Byte>[] { img2Green }, true, null);
GreenHist = new float[256];
green_CompareHisto.MatND.ManagedArray.CopyTo(GreenHist, 0);
red_CompareHisto.Calculate(new Image<Gray, Byte>[] { img2Red }, true, null);
RedHist = new float[256];
red_CompareHisto.MatND.ManagedArray.CopyTo(RedHist, 0);
double cBlue = CvInvoke.cvCompareHist(blue_Histo, blue_CompareHisto, Emgu.CV.CvEnum.HISTOGRAM_COMP_METHOD.CV_COMP_CORREL);
double cGreen = CvInvoke.cvCompareHist(green_Histo, green_CompareHisto, Emgu.CV.CvEnum.HISTOGRAM_COMP_METHOD.CV_COMP_CORREL);
double cRed = CvInvoke.cvCompareHist(red_Histo, red_CompareHisto, Emgu.CV.CvEnum.HISTOGRAM_COMP_METHOD.CV_COMP_CORREL);
double cBlue_ratio = (cBlue / 1.00) * 100;
double cRed_ratio = (cRed / 1.00) * 100;
double cGreen_ratio = (cGreen / 1.00) * 100;
if (cBlue_ratio > 55 || cRed_ratio > 55 || cGreen_ratio > 55)
{
Image<Bgr, byte> resizedImage = img.Resize(300, 300, Emgu.CV.CvEnum.INTER.CV_INTER_LINEAR);
histoImageList.Add(resizedImage.ToBitmap());
}
}
Image<Bgr, byte> base_resizedImage = base_img.Resize(300, 300, Emgu.CV.CvEnum.INTER.CV_INTER_LINEAR);
histoImageList.Add(base_resizedImage.ToBitmap());
return histoImageList;
}
public override bool Execute(DenseHistogram inputHistogram, out float minPos)
{
minPos = 0;
if (!IsInitialized)
{
Logger?.InfoLog("It is not initialized yet.", ClassName);
return(false);
}
if (inputHistogram == null)
{
Logger.TraceLog("InputHistogramm is null!", ClassName);
return(false);
}
Hist = inputHistogram.GetBinValues();
try
{
float[,] mean = new float[4096, 2];
float[,] deviation = new float[4096, 2];
float[,] num = new float[256, 2];
float min = 100000;
for (int nt = 5; nt < 256 - 5; nt++)
{
for (int n = 0; n < 256; n++)
{
if (n < nt)
{
num[nt, 0] += Hist[n];
mean[nt, 0] += n * Hist[n];
deviation[nt, 0] += n * n * Hist[n];
}
else
{
num[nt, 1] += Hist[n];
mean[nt, 1] += n * Hist[n];
deviation[nt, 1] += n * n * Hist[n];
}
}
mean[nt, 0] = mean[nt, 0] / num[nt, 0];
deviation[nt, 0] = (float)Math.Sqrt(deviation[nt, 0] / num[nt, 0] - mean[nt, 0] * mean[nt, 0]);
mean[nt, 1] = mean[nt, 1] / num[nt, 1];
deviation[nt, 1] = (float)Math.Sqrt(deviation[nt, 1] / num[nt, 1] - mean[nt, 1] * mean[nt, 1]);
if (min > deviation[nt, 0] + deviation[nt, 1])
{
min = deviation[nt, 0] + deviation[nt, 1];
minPos = nt;
}
}
return(true);
}
catch (Exception ex)
{
Logger?.ErrorLog($"Exception occured: {ex}", ClassName);
return(false);
}
}
//Method to recalculate and redisplay the histogram
private void showHistogram()
{
//create the histogram
histogram = new DenseHistogram(16, new RangeF(0, 255));
//Clear out an old histogram, if one exists
//If this is not done, histogram calculated on ROI
histogramBox1.ClearHistogram();
histogramBox1.Refresh();
//Clear the ROI, if one exists
(imageBox1.Image as Image<Bgr, byte> ).ROI = Rectangle.Empty;
histogramBox1.GenerateHistograms( imageBox1.Image, 256);
histogramBox1.Refresh();
}
public BasicInfo(ref Image <Rgba, byte> input)
{
table = new ImgDB.BasicInfoDataTable();
int nBins = 256;
RangeF range1 = new RangeF(0, 255);
DenseHistogram hist = new DenseHistogram(nBins, range1);
Image <Gray, byte>[] isg = input.Split();
histogramsRgba = new List <float[]>();
averagesRgba = new List <Gray>();
sdsRgba = new List <MCvScalar>();
for (short i = 0; i < isg.Count(); i++)
{
hist.Calculate(new Image <Gray, byte>[] { isg[i] }, false, null);
float[] values = hist.GetBinValues();
histogramsRgba.Add(values);
Gray gr = new Gray();
MCvScalar sd = new MCvScalar();
isg[i].AvgSdv(out gr, out sd);
averagesRgba.Add(gr);
sdsRgba.Add(sd);
ImgDB.BasicInfoRow row = table.NewBasicInfoRow();
row.Avg = gr.Intensity;
row.Histogram = values;
row.SD = sd.V0;
row.Channel = i;
if (i == 0)
{
row.ChannelName = "Red";
}
else if (i == 1)
{
row.ChannelName = "Green";
}
else if (i == 2)
{
row.ChannelName = "Blue";
}
else if (i == 3)
{
row.ChannelName = "Alpha";
}
table.AddBasicInfoRow(row);
}
//factores
float rF = 1;
float gF = 1;
float bF = 1;
float AF = 1;
ImgDB.BasicInfoRow red = table.FirstOrDefault(o => o.Channel == 0);
ImgDB.BasicInfoRow green = table.FirstOrDefault(o => o.Channel == 1);
ImgDB.BasicInfoRow blue = table.FirstOrDefault(o => o.Channel == 2);
ImgDB.BasicInfoRow alpha = table.FirstOrDefault(o => o.Channel == 3);
float sum = (float)(red.Avg + green.Avg + blue.Avg + alpha.Avg);
sum /= 4;
rF = (float)(sum / red.Avg);
gF = (float)(sum / green.Avg);
bF = (float)(sum / blue.Avg);
AF = (float)(sum / alpha.Avg);
red.Factor = rF;
green.Factor = gF;
blue.Factor = bF;
alpha.Factor = AF;
}
void histoG(Image <Gray, byte> img, Image <Bgr, byte> img2, ref float meangray, ref float meanR, ref float meanG, ref float meanB)
{
DenseHistogram Histo = new DenseHistogram(256, new RangeF(0, 255));
DenseHistogram Histo_temp = new DenseHistogram(256, new RangeF(0, 255));
float[,] colorHist = new float[3, 256];
float[] tempHist = new float[256];
float[] grayHist = new float[256];
Histo.Calculate(new Image <Gray, byte>[] { img },
true,
null);
Image <Gray, Byte>[] images = img2.Split();
Histo_temp.Calculate(new Image <Gray, byte>[] { images[0] },
true,
null);
Histo_temp.MatND.ManagedArray.CopyTo(tempHist, 0);
for (int m = 0; m < 256; m++)
{
colorHist[0, m] = tempHist[m];
}
Histo_temp.Calculate(new Image <Gray, byte>[] { images[1] },
true,
null);
Histo_temp.MatND.ManagedArray.CopyTo(tempHist, 0);
for (int m = 0; m < 256; m++)
{
colorHist[1, m] = tempHist[m];
}
Histo_temp.Calculate(new Image <Gray, byte>[] { images[2] },
true,
null);
Histo_temp.MatND.ManagedArray.CopyTo(tempHist, 0);
for (int m = 0; m < 256; m++)
{
colorHist[2, m] = tempHist[m];
}
Histo.MatND.ManagedArray.CopyTo(grayHist, 0);
// HistogramViewer.Show(Histo, "histo");
// HistogramViewer.Show(img2, 256);
meangray = 0;
meanR = 0;
meanG = 0;
meanB = 0;
int totalgray = 0, totalR = 0, totalG = 0, totalB = 0;
for (int m = 0; m < 255; m++)
{
meangray = meangray + grayHist[m] * m;
totalgray = totalgray + (int)grayHist[m];
meanB = meanB + colorHist[0, m] * m;
totalB = totalB + (int)colorHist[0, m];
meanG = meanG + colorHist[1, m] * m;
totalG = totalG + (int)colorHist[1, m];
meanR = meanR + colorHist[2, m] * m;
totalR = totalR + (int)colorHist[2, m];
}
meangray = meangray / totalgray;
meanB = meanB / totalB;
meanG = meanG / totalG;
meanR = meanR / totalR;
richTextBox3.Text = "MGray:" + meangray + "\nMG:" + meanG + "\nMB:" + meanB + "\nMR:" + meanR;
}
public Form1()
{
InitializeComponent();
SetAnimation();
//ConnectToServer();
//gamestate = GameState.GAME;
myTimer.Tick += new EventHandler(GameDraw);
myTimer.Interval = 25;
myTimer.Start();
controlTimer.Tick += new EventHandler(GameControlReset);
controlTimer.Interval = 500;
myplayerTimer.Tick += new EventHandler(PlayerStateReset);
myplayerTimer.Interval = 5000;
FPU = new FrameProcessor();
FPU.Reset();
IFormatter formatter = new BinaryFormatter();
FileStream fs = new FileStream("../../hist.dat", FileMode.Open);
HistSerial hs = (HistSerial)formatter.Deserialize(fs);
hist = hs.hist;
FPU.SetHist(hist);
}
public void ProcessFrame(Image<Bgr, Byte> frame)
{
sw.Reset();
sw.Start();
MCvAvgComp[] faces = FaceDetect(frame);
sw.Stop();
t_facedetect = sw.ElapsedMilliseconds;
sw.Reset();
sw.Start();
Image<Hsv, Byte> hsv = frame.Convert<Hsv, Byte>();
Image<Gray, Byte> hue = new Image<Gray, byte>(frame.Width, frame.Height);
Image<Gray, Byte> mask = new Image<Gray, byte>(frame.Width, frame.Height);
Emgu.CV.CvInvoke.cvInRangeS(hsv, new MCvScalar(0, 30, 30, 0), new MCvScalar(180, 256, 256, 0), mask);
Emgu.CV.CvInvoke.cvSplit(hsv, hue, IntPtr.Zero, IntPtr.Zero, IntPtr.Zero);
if (isTracked == false)
{
if (faces.Length != 0)
{
var ff = faces[0];
Rectangle smallFaceROI = new Rectangle(ff.rect.X + ff.rect.Width / 8, ff.rect.Y + ff.rect.Height / 8, ff.rect.Width / 4, ff.rect.Height / 4);
_hist = GetHist(hue, smallFaceROI, mask);
isTracked=true;
th_check = true;
center = new Point[] { new Point(0, 0), new Point(0, 0) };
}
else
{
have_face=false;
have_left=false;
have_right=false;
return;
}
}
sw.Stop();
t_hue = sw.ElapsedMilliseconds;
if (faces.Length != 0)
{
face_rect = faces[0].rect;
face = face_rect;
have_face = true;
}
else
{
face = face_rect;
have_face = false;
}
sw.Reset();
sw.Start();
backproject = GetBackproject(hue, _hist, mask, face_rect).ThresholdToZero(new Gray(backproj_threshold));
sw.Stop();
t_backproject = sw.ElapsedMilliseconds;
sw.Reset();
sw.Start();
if (isTracked)
{
center = kmeans(center, backproject, face_rect, kmeans_scale);
center = refine_center(center, backproject);
}
sw.Stop();
t_kmeans = sw.ElapsedMilliseconds;
sw.Reset();
sw.Start();
right = new Rectangle(center[0].X - hand_size / 2, center[0].Y - hand_size / 2, hand_size, hand_size);
left = new Rectangle(center[1].X - hand_size / 2, center[1].Y - hand_size / 2, hand_size, hand_size);
backproject.ROI = left;
left_mom=backproject.GetMoments(false);
backproject.ROI = right;
right_mom = backproject.GetMoments(false);
Emgu.CV.CvInvoke.cvResetImageROI(backproject);
sw.Stop();
t_hand = sw.ElapsedMilliseconds;
ProcessInput();
}
private DenseHistogram GetHist(Image<Gray, Byte> hue, Rectangle ROI, Image<Gray, Byte> mask)
{
DenseHistogram hist=new DenseHistogram(16,new RangeF(0,180));
Emgu.CV.CvInvoke.cvSetImageROI(hue, ROI);
Emgu.CV.CvInvoke.cvSetImageROI(mask, ROI);
IntPtr[] imgs = new IntPtr[1] { hue };
Emgu.CV.CvInvoke.cvCalcHist(imgs, hist, false, mask);
Emgu.CV.CvInvoke.cvResetImageROI(hue);
Emgu.CV.CvInvoke.cvResetImageROI(mask);
return hist;
}
public List <Bitmap> histogramSearch(List <Bitmap> images, Bitmap ori_image)
{
float[] BlueHist;
float[] GreenHist;
float[] RedHist;
float[] base_BlueHist;
float[] base_GreenHist;
float[] base_RedHist;
histoImageList.Clear();
Image <Bgr, byte> base_img = new Image <Bgr, byte>(ori_image);
DenseHistogram blue_Histo = new DenseHistogram(255, new RangeF(0, 255));
DenseHistogram green_Histo = new DenseHistogram(255, new RangeF(0, 255));
DenseHistogram red_Histo = new DenseHistogram(255, new RangeF(0, 255));
Image <Gray, Byte> base_img2Blue = base_img[0];
Image <Gray, Byte> base_img2Green = base_img[1];
Image <Gray, Byte> base_img2Red = base_img[2];
blue_Histo.Calculate(new Image <Gray, Byte>[] { base_img2Blue }, true, null);
green_Histo.Calculate(new Image <Gray, Byte>[] { base_img2Green }, true, null);
red_Histo.Calculate(new Image <Gray, Byte>[] { base_img2Red }, true, null);
for (int i = 0; i < images.Count; i++)
{
Image <Bgr, byte> img = new Image <Bgr, byte>(images[i]);
DenseHistogram blue_CompareHisto = new DenseHistogram(255, new RangeF(0, 255));
DenseHistogram green_CompareHisto = new DenseHistogram(255, new RangeF(0, 255));
DenseHistogram red_CompareHisto = new DenseHistogram(255, new RangeF(0, 255));
Image <Gray, Byte> img2Blue = img[0];
Image <Gray, Byte> img2Green = img[1];
Image <Gray, Byte> img2Red = img[2];
blue_CompareHisto.Calculate(new Image <Gray, Byte>[] { img2Blue }, true, null);
BlueHist = new float[256];
blue_CompareHisto.MatND.ManagedArray.CopyTo(BlueHist, 0);
green_CompareHisto.Calculate(new Image <Gray, Byte>[] { img2Green }, true, null);
GreenHist = new float[256];
green_CompareHisto.MatND.ManagedArray.CopyTo(GreenHist, 0);
red_CompareHisto.Calculate(new Image <Gray, Byte>[] { img2Red }, true, null);
RedHist = new float[256];
red_CompareHisto.MatND.ManagedArray.CopyTo(RedHist, 0);
double cBlue = CvInvoke.cvCompareHist(blue_Histo, blue_CompareHisto, Emgu.CV.CvEnum.HISTOGRAM_COMP_METHOD.CV_COMP_CORREL);
double cGreen = CvInvoke.cvCompareHist(green_Histo, green_CompareHisto, Emgu.CV.CvEnum.HISTOGRAM_COMP_METHOD.CV_COMP_CORREL);
double cRed = CvInvoke.cvCompareHist(red_Histo, red_CompareHisto, Emgu.CV.CvEnum.HISTOGRAM_COMP_METHOD.CV_COMP_CORREL);
double cBlue_ratio = (cBlue / 1.00) * 100;
double cRed_ratio = (cRed / 1.00) * 100;
double cGreen_ratio = (cGreen / 1.00) * 100;
if (cBlue_ratio > 55 || cRed_ratio > 55 || cGreen_ratio > 55)
{
Image <Bgr, byte> resizedImage = img.Resize(300, 300, Emgu.CV.CvEnum.INTER.CV_INTER_LINEAR);
histoImageList.Add(resizedImage.ToBitmap());
}
}
Image <Bgr, byte> base_resizedImage = base_img.Resize(300, 300, Emgu.CV.CvEnum.INTER.CV_INTER_LINEAR);
histoImageList.Add(base_resizedImage.ToBitmap());
return(histoImageList);
}
private void Model_Click(object sender, EventArgs e)
{
//### set currImage which has been used for taking model as modelImage
modelImg = currImage[0];
//### Background Subtraction ~ currImage is subtracted with the bgImage and the result is stored in finalBlobImg.
bgSubtraction(currImage[0], bgImage[0], ref finalBlobImg1, ref blobRect1);
/* Allocate buffers */
hsv = new Image <Hsv, Byte>(w, h);
hue = new Image <Bgr, Byte>(w, h);
mask = new Image <Gray, Byte>(w, h);
backproject = new Image <Bgr, Byte>(w, h);
hist = new DenseHistogram(hdims, hranges);//cvCreateHist(1, &hdims, CV_HIST_ARRAY, &hranges, 1);
hsv = modelImg.Convert <Hsv, Byte>();
//extract the hue and value channels
Image <Gray, Byte>[] channels = hsv.Split(); //split into components
Image <Gray, Byte>[] imghue = new Image <Gray, byte> [1]; imghue[0] = channels[0]; //hsv, so channels[0] is hue.
Image <Gray, Byte> imgval = channels[2]; //hsv, so channels[2] is value.
Image <Gray, Byte> imgsat = channels[1]; //hsv, so channels[1] is saturation.
/**
* Check if the pixels in hsv fall within a particular range.
* H: 0 to 180
* S: smin to 256
* V: vmin to vmax
* Store the result in variable: mask
*/
Hsv hsv_lower = new Hsv(0, smin, Math.Min(vmin, vmax));
Hsv hsv_upper = new Hsv(180, 256, Math.Max(vmin, vmax));
mask = hsv.InRange(hsv_lower, hsv_upper);
//setting ROI to images
mask.ROI = selection;
imghue[0] = imghue[0].And(finalBlobImg1.Erode(3));
imghue[0].ROI = selection;
/**
* Calculate the histogram of selected region
* Store the result in variable: hist
*/
hist.Calculate(imghue, false, mask);
/* Scale the histogram */
float hMin, hMax;
int[] minLoc;
int[] maxLoc;
hist.MinMax(out hMin, out hMax, out minLoc, out maxLoc);
/* Reset ROI for hue and mask */
CvInvoke.cvResetImageROI(imghue[0]);
CvInvoke.cvResetImageROI(mask);
/* Set tracking windows */
track_window_mean1 = selection;
//track_window_mean2 = selection;
//### Update the pictureBoxes with mask and hsv images
//pictureBox2.Image = mask.Bitmap;
//pictureBox2.Image = finalBlobImg1.Bitmap;
//pictureBox4.Image = finalBlobImg1.Bitmap;
//pictureBox3.Image = imghue[0].Or(finalBlobImg1).Bitmap;
Console.WriteLine("###Model Captured....");
modelFlag = true;
}
private void pictureList_SelectedIndexChanged(object sender, EventArgs e)
{
string zdjecie = pictureList.SelectedItem.ToString();
string pelnaSciezka = SciezkaFolderZeZdjeciami + zdjecie;
Image image = Image.FromFile(pelnaSciezka);
var histogramImage = new Image <Bgr, Byte>(pelnaSciezka);
//var hsvImage = histogramImage.Convert<Hsv, Byte>();
pictureBox.Image = image;
//pictureBoxHSV.Image = hsvImage.Bitmap;
//histogramBox.ClearHistogram();
//histogramBox.GenerateHistograms(histogramImage, 256);
//histogramBox.Refresh();
//histogramBoxHSV.ClearHistogram();
//histogramBoxHSV.GenerateHistograms(hsvImage, 256);
//histogramBoxHSV.Refresh();
#region oblicz wartosci histogramu
float[] BlueHist;
float[] GreenHist;
float[] RedHist;
var img = new Image <Bgr, byte>(pelnaSciezka);
var Histo = new DenseHistogram(255, new RangeF(0, 255));
Image <Gray, Byte> img2Blue = img[0];
Image <Gray, Byte> img2Green = img[1];
Image <Gray, Byte> img2Red = img[2];
Histo.Calculate(new[] { img2Blue }, true, null);
BlueHist = new float[256];
Histo.MatND.ManagedArray.CopyTo(BlueHist, 0);
Histo.Clear();
Histo.Calculate(new[] { img2Green }, true, null);
GreenHist = new float[256];
Histo.MatND.ManagedArray.CopyTo(GreenHist, 0);
Histo.Clear();
Histo.Calculate(new[] { img2Red }, true, null);
RedHist = new float[256];
Histo.MatND.ManagedArray.CopyTo(RedHist, 0);
#endregion
#region narusyj histogram
GraphPane mPane = zedGraph.GraphPane;
mPane.Title.Text = "RGB";
var czerwony = new PointPairList();
var zielony = new PointPairList();
var niebieski = new PointPairList();
for (int i = 0; i < 255; i++)
{
czerwony.Add(i, RedHist[i]);
zielony.Add(i, GreenHist[i]);
niebieski.Add(i, BlueHist[i]);
}
mPane.CurveList.Clear();
LineItem wykresR = mPane.AddCurve("R", czerwony, Color.Red, SymbolType.Default);
LineItem wykresG = mPane.AddCurve("R", zielony, Color.Green, SymbolType.Default);
LineItem wykresB = mPane.AddCurve("R", niebieski, Color.Blue, SymbolType.Default);
zedGraph.AxisChange();
zedGraph.Refresh();
#endregion
#region (uboga) kwantyzacja
int wielkoscPrzedialu = 4;
int aktualnaPozycjaR = 0;
int aktualnaPozycjaG = 0;
int aktualnaPozycjaB = 0;
float wartoscR = 0;
float wartoscG = 0;
float wartoscB = 0;
var RedHistQ = new float[RedHist.Length / wielkoscPrzedialu];
var GreenHistQ = new float[GreenHist.Length / wielkoscPrzedialu];
var BlueHistQ = new float[BlueHist.Length / wielkoscPrzedialu];
for (int i = 0; i < RedHist.Length / wielkoscPrzedialu; i++)
{
for (int j = 0; j < wielkoscPrzedialu; j++)
{
wartoscR += RedHist[aktualnaPozycjaR + j];
}
RedHistQ[i] = wartoscR;
wartoscR = 0;
aktualnaPozycjaR++;
}
for (int i = 0; i < GreenHist.Length / wielkoscPrzedialu; i++)
{
for (int j = 0; j < wielkoscPrzedialu; j++)
{
wartoscG += GreenHist[aktualnaPozycjaG + j];
}
GreenHistQ[i] = wartoscG;
wartoscG = 0;
aktualnaPozycjaG++;
}
for (int i = 0; i < BlueHist.Length / wielkoscPrzedialu; i++)
{
for (int j = 0; j < wielkoscPrzedialu; j++)
{
wartoscB += BlueHist[aktualnaPozycjaB + j];
}
BlueHistQ[i] = wartoscB;
wartoscB = 0;
aktualnaPozycjaB++;
}
GraphPane mPane2 = zedGraphQ.GraphPane;
mPane2.Title.Text = "RGB kwant.";
var czerwonyQ = new PointPairList();
var zielonyQ = new PointPairList();
var niebieskiQ = new PointPairList();
for (int i = 0; i < RedHistQ.Length; i++)
{
czerwonyQ.Add(i, RedHistQ[i]);
zielonyQ.Add(i, GreenHistQ[i]);
niebieskiQ.Add(i, BlueHistQ[i]);
}
mPane2.CurveList.Clear();
LineItem wykresRQ = mPane2.AddCurve("R", czerwonyQ, Color.Red, SymbolType.Default);
LineItem wykresGQ = mPane2.AddCurve("G", zielonyQ, Color.Green, SymbolType.Default);
LineItem wykresBQ = mPane2.AddCurve("B", niebieskiQ, Color.Blue, SymbolType.Default);
zedGraphQ.AxisChange();
zedGraphQ.Refresh();
#endregion
}
/// <summary>
/// 2D計算值方圖(色調與飽和度) ,使用emgucv提供的cvInvoke去調用opencv的函式
/// </summary>
private static DenseHistogram Cal2DHsvHist(IntPtr srcImage, int h_bins, int s_bins)
{
try
{
DenseHistogram histDense;
int[] hist_size = new int[2] {
h_bins, s_bins
};
IntPtr hsv = CvInvoke.cvCreateImage(CvInvoke.cvGetSize(srcImage), Emgu.CV.CvEnum.IPL_DEPTH.IPL_DEPTH_8U, 3);
IntPtr h_plane = CvInvoke.cvCreateImage(CvInvoke.cvGetSize(srcImage), Emgu.CV.CvEnum.IPL_DEPTH.IPL_DEPTH_8U, 1);
IntPtr s_plane = CvInvoke.cvCreateImage(CvInvoke.cvGetSize(srcImage), Emgu.CV.CvEnum.IPL_DEPTH.IPL_DEPTH_8U, 1);
IntPtr v_plane = CvInvoke.cvCreateImage(CvInvoke.cvGetSize(srcImage), Emgu.CV.CvEnum.IPL_DEPTH.IPL_DEPTH_8U, 1);
IntPtr[] planes = new IntPtr[2] {
h_plane, s_plane
};
/* H 分量的变化范围 */
float[] h_ranges = new float[2] {
0, h_max_range
};
/* S 分量的变化范围*/
float[] s_ranges = new float[2] {
0, s_max_range
};
IntPtr inPtr1 = new IntPtr(0);
IntPtr inPtr2 = new IntPtr(0);
//GCHandle:提供從Unmanaged 記憶體存取Managed 物件的方法。
//配置指定型別的數值記憶體
GCHandle gch1 = GCHandle.Alloc(h_ranges, GCHandleType.Pinned);
GCHandle gch2 = GCHandle.Alloc(s_ranges, GCHandleType.Pinned);
try
{
inPtr1 = gch1.AddrOfPinnedObject();
inPtr2 = gch2.AddrOfPinnedObject();
}
finally
{
gch1.Free();
gch2.Free();
}
//有上述的GCHandle,此行才有作用
IntPtr[] ranges = new IntPtr[2] {
inPtr1, inPtr2
};
/* 输入图像转换到HSV颜色空间 */
CvInvoke.cvCvtColor(srcImage, hsv, Emgu.CV.CvEnum.COLOR_CONVERSION.CV_BGR2HSV);
CvInvoke.cvSplit(hsv, h_plane, s_plane, v_plane, System.IntPtr.Zero); // 分离的单通道数组d
/* 创建直方图，二维, 每个维度上均分 */
//emgucv的DenseHistogram資料格式也可使用cvInvoke的openCV函式
RangeF hRange = new RangeF(0f, h_max_range); //H色調分量的變化範圍
RangeF sRange = new RangeF(0f, s_max_range); //S飽和度分量的變化範圍
histDense = new DenseHistogram(hist_size, new RangeF[] { hRange, sRange });
CvInvoke.cvCalcHist(planes, histDense, false, System.IntPtr.Zero);
return(histDense);
}
catch (Exception ex)
{
throw new InvalidOperationException(ex.Message);
}
}
/// <summary>
/// Generate histograms for the image. One histogram is generated for each color channel.
/// You will need to call the Refresh function to do the painting afterward.
/// </summary>
/// <param name="image">The image to generate histogram from</param>
/// <param name="numberOfBins">The number of bins for each histogram</param>
public void GenerateHistograms(IImage image, int numberOfBins)
{
IImage[] channels = image.Split();
Type imageType = Toolbox.GetBaseType(image.GetType(), "Image`2");
IColor typeOfColor = Activator.CreateInstance(imageType.GetGenericArguments()[0]) as IColor;
String[] channelNames = Reflection.ReflectColorType.GetNamesOfChannels(typeOfColor);
Color[] colors = Reflection.ReflectColorType.GetDisplayColorOfChannels(typeOfColor);
float minVal, maxVal;
#region Get the maximum and minimum color intensity values
Type typeOfDepth = imageType.GetGenericArguments()[1];
if (typeOfDepth == typeof(Byte))
{
minVal = 0.0f;
maxVal = 256.0f;
}
else
{
#region obtain the maximum and minimum color value
double[] minValues, maxValues;
Point[] minLocations, maxLocations;
image.MinMax(out minValues, out maxValues, out minLocations, out maxLocations);
double min = minValues[0], max = maxValues[0];
for (int i = 1; i < minValues.Length; i++)
{
if (minValues[i] < min) min = minValues[i];
if (maxValues[i] > max) max = maxValues[i];
}
#endregion
minVal = (float)min;
maxVal = (float)max;
}
#endregion
for (int i = 0; i < channels.Length; i++)
using (DenseHistogram hist = new DenseHistogram(numberOfBins, new RangeF(minVal, maxVal)))
{
hist.Calculate(new IImage[1] { channels[i] }, true, null);
AddHistogram(channelNames[i], colors[i], hist);
}
}
//////////////////////////////////////////////////////////////////////////////////////////////
/// <summary>
/// drawing hue color still have problem
/// 1D值方圖(色調) 的繪製,使用emgucv提供的cvInvoke去調用opencv的函式
/// 繪製與範例的值方圖一致目前先採用
/// </summary>
/// <param name="histDense"></param>
/// <returns>回傳繪製值方圖的影像,直接顯示即可</returns>
public static Image <Bgr, Byte> Generate1DHistogramImgForDraw(DenseHistogram histDense)
{
try
{
float max_value = 0.0f;
int[] a1 = new int[100];
int[] b1 = new int[100];
float ax = 0;
int h_bins = histDense.BinDimension[0].Size;
//1.使用Intptr
// CvInvoke.cvGetMinMaxHistValue(histPtr, ref ax, ref max_value, a1, b1);
//2.emgucv的DenseHistogram資料格式也可使用cvInvoke的openCV函式
CvInvoke.cvGetMinMaxHistValue(histDense, ref ax, ref max_value, a1, b1);
/* 取最大的顏色的位置 並換成RGB
* foreach (int index in a1)
* {
* Console.WriteLine("location="+index+",H Color = "+ HueToBgr(index * 180.0d / h_bins));
* }
* */
/* 设置直方图显示图像 */
int height = 240;
int width = 800;
IntPtr hist_img = CvInvoke.cvCreateImage(new System.Drawing.Size(width, height), Emgu.CV.CvEnum.IPL_DEPTH.IPL_DEPTH_8U, 3);
CvInvoke.cvZero(hist_img);
/* 用来进行HSV到RGB颜色转换的临时单位图像 */
IntPtr hsv_color = CvInvoke.cvCreateImage(new System.Drawing.Size(1, 1), Emgu.CV.CvEnum.IPL_DEPTH.IPL_DEPTH_8U, 3);
IntPtr rgb_color = CvInvoke.cvCreateImage(new System.Drawing.Size(1, 1), Emgu.CV.CvEnum.IPL_DEPTH.IPL_DEPTH_8U, 3);
int bin_w = width / (h_bins);
for (int h = 0; h < h_bins; h++)
{
/* 获得直方图中的统计次数，计算显示在图像中的高度 */
//
//取得值方圖的數值位置,以便之後存成檔案
//2.DenseHistogram
double bin_val = CvInvoke.cvQueryHistValue_1D(histDense, h);
int intensity = (int)System.Math.Round(bin_val * height / max_value);
/* 获得当前直方图代表的hue颜色，转换成RGB用于绘制 */
CvInvoke.cvRectangle(hist_img, new System.Drawing.Point(h * bin_w, height),
new System.Drawing.Point((h + 1) * bin_w, height - intensity),
HueToBgr(h * 180.0d / h_bins), -1, Emgu.CV.CvEnum.LINE_TYPE.EIGHT_CONNECTED, 0);
}
/*
*使用openCV函式繪製
* CvInvoke.cvNamedWindow("Source");
* CvInvoke.cvShowImage("Source", this.srcImage);
* CvInvoke.cvNamedWindow("H-S Histogram");
* CvInvoke.cvShowImage("H-S Histogram", hist_img);
* CvInvoke.cvWaitKey(0);
* */
return(EmguFormatConvetor.IplImagePointerToEmgucvImage <Bgr, Byte>(hist_img));
}
catch (Exception ex)
{
throw new InvalidOperationException(ex.Message);
}
}
private void MainLoop()
{
CurrentFrame = Cam.QueryFrame().Convert <Hsv, byte>();
Image <Gray, byte>[] channels;
Image <Gray, byte> HistImg1 = new Image <Gray, byte>(500, 500);
Image <Gray, byte> HistImg2 = new Image <Gray, byte>(500, 500);
Image <Gray, byte> ProbImage;
DenseHistogram hist1 = new DenseHistogram(new int[] { 10, 10 }, new RangeF[] { new RangeF(0, 255), new RangeF(0, 255) });
DenseHistogram hist2 = new DenseHistogram(new int[] { 10, 10 }, new RangeF[] { new RangeF(0, 255), new RangeF(0, 255) });
MCvConnectedComp comp;
MCvTermCriteria criteria = new MCvTermCriteria(10, 1);
MCvBox2D box;
while (true)
{
CurrentFrame = Cam.QueryFrame().Convert <Hsv, byte>();
if (OnSettingArea && TrackArea != Rectangle.Empty)
{
CurrentFrame.ROI = TrackArea;
channels = CurrentFrame.Split();
hist1.Calculate(new Image <Gray, byte>[] { channels[0], channels[1] }, false, null);
CurrentFrame.Not().CopyTo(CurrentFrame);
CurrentFrame.ROI = Rectangle.Empty;
CurrentFrame.Draw(TrackArea, new Hsv(100, 100, 100), 2);
imageBox1.Image = CurrentFrame;
}
else
{
if (TrackArea != Rectangle.Empty)
{
channels = CurrentFrame.Split();
ProbImage = hist1.BackProject <byte>(new Image <Gray, byte>[] { channels[0], channels[1] });
imageBox_Hist2.Image = ProbImage.Convert <Gray, byte>();
lock (LockObject)
{
if (TrackArea.Height * TrackArea.Width > 0)
{
CvInvoke.cvCamShift(ProbImage, TrackArea, criteria, out comp, out box);
TrackArea = comp.rect;
CurrentFrame.Draw(box, new Hsv(100, 100, 100), 2);
}
/**
* ResetContourPoints();
* for (int i = 0; i < 60; i++)
* {
* ProbImage.Snake(ContourPoints, (float)1.0, (float)0.5, (float)1.5, new Size(17, 17), criteria, true);
* }
* CurrentFrame.DrawPolyline(ContourPoints, true, new Hsv(100, 100, 100), 2);
*/
}
}
imageBox1.Image = CurrentFrame;
//calculate histogram;
//channels = CurrentFrame.Split();
//hist2.Calculate(new Image<Gray, byte>[] { channels[0], channels[1] }, false, null);
//hist2.Normalize(1);
//HistImg1.SetZero();
//DrawHist2D(HistImg1, hist1);
//imageBox_Hist1.Image = HistImg1;
}
}
}
/// <summary>
/// 2D值方圖(色調與飽和度) 的繪製,使用emgucv提供的cvInvoke去調用opencv的函式
/// 繪製與範例的值方圖一致目前先採用
/// </summary>
/// <param name="histDense"></param>
/// <returns>回傳繪製值方圖的影像,直接顯示即可</returns>
public static Image <Bgr, Byte> Generate2DHistogramImgForDraw(DenseHistogram histDense)
{
try
{
float max_value = 0.0f;
int[] a1 = new int[100];
int[] b1 = new int[100];
float ax = 0;
int h_bins = histDense.BinDimension[0].Size;
int s_bins = histDense.BinDimension[1].Size;
//1.使用Intptr
// CvInvoke.cvGetMinMaxHistValue(histPtr, ref ax, ref max_value, a1, b1);
//2.emgucv的DenseHistogram資料格式也可使用cvInvoke的openCV函式
CvInvoke.cvGetMinMaxHistValue(histDense, ref ax, ref max_value, a1, b1);
/* 设置直方图显示图像 */
int height = 300;
int width;
//如果設定的bins超過視窗設定的顯示範圍,另外給予可以符合用額外的彈出視窗顯示的值
if (h_bins * s_bins > 800)
{
width = h_bins * s_bins * 2;
}
else
{
width = 800;
}
IntPtr hist_img = CvInvoke.cvCreateImage(new System.Drawing.Size(width, height), Emgu.CV.CvEnum.IPL_DEPTH.IPL_DEPTH_8U, 3);
CvInvoke.cvZero(hist_img);
/* 用来进行HSV到RGB颜色转换的临时单位图像 */
IntPtr hsv_color = CvInvoke.cvCreateImage(new System.Drawing.Size(1, 1), Emgu.CV.CvEnum.IPL_DEPTH.IPL_DEPTH_8U, 3);
IntPtr rgb_color = CvInvoke.cvCreateImage(new System.Drawing.Size(1, 1), Emgu.CV.CvEnum.IPL_DEPTH.IPL_DEPTH_8U, 3);
int bin_w = width / (h_bins * s_bins);
for (int h = 0; h < h_bins; h++)
{
for (int s = 0; s < s_bins; s++)
{
int i = h * s_bins + s;
/* 获得直方图中的统计次数，计算显示在图像中的高度 */
//
//取得值方圖的數值位置,以便之後存成檔案
//1.Intptr
//double bin_val = CvInvoke.cvQueryHistValue_2D(histPtr, h, s);
//2.DenseHistogram
double bin_val = CvInvoke.cvQueryHistValue_2D(histDense, h, s);
int intensity = (int)System.Math.Round(bin_val * height / max_value);
/* 获得当前直方图代表的颜色，转换成RGB用于绘制 */
CvInvoke.cvSet2D(hsv_color, 0, 0, new Emgu.CV.Structure.MCvScalar(h * 180.0f / h_bins, s * 255.0f / s_bins, 255, 0));
CvInvoke.cvCvtColor(hsv_color, rgb_color, COLOR_CONVERSION.CV_HSV2BGR);
Emgu.CV.Structure.MCvScalar color = CvInvoke.cvGet2D(rgb_color, 0, 0);
CvInvoke.cvRectangle(hist_img, new System.Drawing.Point(i * bin_w, height), new System.Drawing.Point((i + 1) * bin_w, height - intensity), color, -1, Emgu.CV.CvEnum.LINE_TYPE.EIGHT_CONNECTED, 0);
}
}
/*
*使用openCV函式繪製
* CvInvoke.cvNamedWindow("Source");
* CvInvoke.cvShowImage("Source", this.srcImage);
* CvInvoke.cvNamedWindow("H-S Histogram");
* CvInvoke.cvShowImage("H-S Histogram", hist_img);
* CvInvoke.cvWaitKey(0);
* */
return(EmguFormatConvetor.IplImagePointerToEmgucvImage <Bgr, Byte>(hist_img));
}
catch (Exception ex)
{
throw new InvalidOperationException(ex.Message);
}
}
public abstract bool Execute(DenseHistogram hist, out float minPos);
//////////////////////////////////////////////////////////////////////////////////////////////
#endregion
#region 直方圖匹配
//////////////////////////////////////////////////////////////////////////////////////////////
/// <summary>
/// 匹配值方圖,使用CV_COMP_BHATTACHARYYA
/// </summary>
/// <param name="templateHist"></param>
/// <param name="observedHist"></param>
/// <returns>回傳匹配率,CV_COMP_BHATTACHARYYA方法,數值越低比對越精準,反之相似度低,範圍0-1</returns>
public static double CompareHist(DenseHistogram templateHist, DenseHistogram observedHist)
{
return(CvInvoke.cvCompareHist(templateHist, observedHist, HISTOGRAM_COMP_METHOD.CV_COMP_BHATTACHARYYA));
}
protected void Button1_Click(object sender, EventArgs e)
{
if (FileUploader.HasFile)
{
try
{
FileUploader.SaveAs(Server.MapPath(DefaultFileName) + FileUploader.FileName);
Image <Bgr, Byte> originalImage = new Image <Bgr, byte>(Server.MapPath(DefaultFileName) + FileUploader.FileName);
int width, height, channels = 0;
width = originalImage.Width;
height = originalImage.Height;
channels = originalImage.NumberOfChannels;
Image <Bgr, byte> colorImage = new Image <Bgr, byte>(originalImage.ToBitmap());
Image <Gray, byte> grayImage = colorImage.Convert <Gray, Byte>();
float[] GrayHist;
DenseHistogram Histo = new DenseHistogram(255, new RangeF(0, 255));
Histo.Calculate(new Image <Gray, Byte>[] { grayImage }, true, null);
GrayHist = new float[256];
Histo.MatND.ManagedArray.CopyTo(GrayHist, 0);
float largestHist = GrayHist[0];
int thresholdHist = 0;
for (int i = 0; i < 255; i++)
{
if (GrayHist[i] > largestHist)
{
largestHist = GrayHist[i];
thresholdHist = i;
}
}
grayImage = grayImage.ThresholdAdaptive(new Gray(255), ADAPTIVE_THRESHOLD_TYPE.CV_ADAPTIVE_THRESH_MEAN_C, THRESH.CV_THRESH_BINARY, 85, new Gray(4));
colorImage = colorImage.Copy();
int countRedCells = 0;
using (MemStorage storage = new MemStorage())
{
for (Contour <Point> contours = grayImage.FindContours(Emgu.CV.CvEnum.CHAIN_APPROX_METHOD.CV_CHAIN_APPROX_SIMPLE, Emgu.CV.CvEnum.RETR_TYPE.CV_RETR_LIST, storage); contours != null; contours = contours.HNext)
{
Contour <Point> currentContour = contours.ApproxPoly(contours.Perimeter * 0.015, storage);
if (currentContour.BoundingRectangle.Width > 20)
{
CvInvoke.cvDrawContours(colorImage, contours, new MCvScalar(0, 0, 255), new MCvScalar(0, 0, 255), -1, 2, Emgu.CV.CvEnum.LINE_TYPE.EIGHT_CONNECTED, new Point(0, 0));
colorImage.Draw(currentContour.BoundingRectangle, new Bgr(0, 255, 0), 1);
countRedCells++;
}
}
}
Image <Gray, byte> grayImageCopy2 = originalImage.Convert <Gray, Byte>();
grayImageCopy2 = grayImageCopy2.ThresholdBinary(new Gray(100), new Gray(255));
colorImage = colorImage.Copy();
int countMalaria = 0;
using (MemStorage storage = new MemStorage())
{
for (Contour <Point> contours = grayImageCopy2.FindContours(Emgu.CV.CvEnum.CHAIN_APPROX_METHOD.CV_CHAIN_APPROX_SIMPLE, Emgu.CV.CvEnum.RETR_TYPE.CV_RETR_TREE, storage); contours != null; contours = contours.HNext)
{
Contour <Point> currentContour = contours.ApproxPoly(contours.Perimeter * 0.015, storage);
if (currentContour.BoundingRectangle.Width > 20)
{
CvInvoke.cvDrawContours(colorImage, contours, new MCvScalar(255, 0, 0), new MCvScalar(255, 0, 0), -1, 2, Emgu.CV.CvEnum.LINE_TYPE.EIGHT_CONNECTED, new Point(0, 0));
colorImage.Draw(currentContour.BoundingRectangle, new Bgr(0, 255, 0), 1);
countMalaria++;
}
}
}
colorImage.Save(Server.MapPath(DefaultFileName2) + FileUploader.FileName);
inputDiv.Attributes["style"] = "display: block; margin-left: auto; margin-right: auto";
outputDiv.Attributes["style"] = "display: block; margin-left: auto; margin-right: auto";
Image1.ImageUrl = this.ResolveUrl(DefaultFileName + FileUploader.FileName);
Image2.ImageUrl = this.ResolveUrl(DefaultFileName2 + FileUploader.FileName);
Chart1.DataBindTable(GrayHist);
Label1.Text = "Uploaded Successfully";
Label2.Text = "File name: " +
FileUploader.PostedFile.FileName + "<br>" + "File Size: " +
FileUploader.PostedFile.ContentLength + " kb<br>" + "Content type: " + FileUploader.PostedFile.ContentType + "<br>"
+ "Resolution: " + width.ToString() + "x" + height.ToString() + "<br>"
+ "Number of channels: " + channels.ToString() + "<br>"
+ "Histogram (maximum value): " + largestHist + " @ " + thresholdHist;
LabelRed.Text = countRedCells.ToString();
LabelMalaria.Text = countMalaria.ToString();
}
catch (Exception ex)
{
Label1.Text = "ERROR: " + ex.Message.ToString();
Label2.Text = "";
}
}
else
{
Label1.Text = "You have not specified a file.";
Label2.Text = "";
}
}
public RotatedRect Tracking(Image <Bgr, Byte> image)
{
GetFrameHue(image);
// User changed bins num ,recalculate Hist
if (Main._advancedHsv)
{
if (bins != Main.HsvSetting.Getbins)
{
bins = Main.HsvSetting.Getbins;
hist.Dispose();
hist = new DenseHistogram(bins, new RangeF(0, 180));
CalcHist(image);
}
}
backprojection = hist.BackProject(new Image <Gray, Byte>[] { hue });
// Add mask
backprojection._And(mask);
// FindContours
//CvInvoke.Canny(backprojection, backcopy, 3, 6);
backprojection.CopyTo(backcopy);
CvInvoke.FindContours(backcopy, vvp, null, Emgu.CV.CvEnum.RetrType.External, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxNone);
int trackArea = trackingWindow.Height * trackingWindow.Width;
FindTargetByArea(vvp, trackArea * 0.25, trackArea * 10, ref vvpApprox);
vvpApproxDensity = GetVVPDensity(vvpApprox, out vvpApproxRect);
targetVVPIndex = FindTargetByOverlap(vvpApprox, trackingWindow);
//FindTargetByCenter(vvpApprox, new PointF(trackingWindow.X + trackingWindow.Width / 2, trackingWindow.Y + trackingWindow.Height / 2));
// If lost trackbox
if (trackingWindow.IsEmpty || trackingWindow.Width <= 10 || trackingWindow.Height <= 10 || _lost || targetVVPIndex == -1)
{
if (!timer.IsRunning)
{
timer.Start();
}
if (timer.ElapsedMilliseconds > 1000)
{
_lost = true;
}
if (timer.ElapsedMilliseconds > 3000)
{
//targetVVPIndex = Array.IndexOf(vvpApproxDensity, vvpApproxDensity.Max());
}
for (int i = 0; i < vvpApproxDensity.Length; i++)
{
if (vvpApproxDensity[i] >= targetDensity * 0.8)
{
trackingWindow = vvpApproxRect[i];
_lost = false;
timer.Reset();
}
}
}
else
{
trackbox = CvInvoke.CamShift(backprojection, ref trackingWindow, new MCvTermCriteria(10, 1));
targetDensity += vvpApproxDensity[targetVVPIndex];
targetDensity /= 2;
if (timer.IsRunning)
{
timer.Reset();
}
}
return(trackbox);
}
private void MainLoop()
{
CurrentFrame = Cam.QueryFrame().Convert<Hsv, byte>();
Image<Gray, byte>[] channels;
Image<Gray, byte> HistImg1 = new Image<Gray, byte>(500, 500);
Image<Gray, byte> HistImg2 = new Image<Gray, byte>(500, 500);
Image<Gray, byte> ProbImage;
DenseHistogram hist1 = new DenseHistogram(new int[] { 10, 10 }, new RangeF[] { new RangeF(0, 255), new RangeF(0, 255) });
DenseHistogram hist2 = new DenseHistogram(new int[] { 10, 10 }, new RangeF[] { new RangeF(0, 255), new RangeF(0, 255) });
MCvConnectedComp comp;
MCvTermCriteria criteria = new MCvTermCriteria(10, 1);
MCvBox2D box;
while (true)
{
CurrentFrame = Cam.QueryFrame().Convert<Hsv, byte>();
if (OnSettingArea && TrackArea != Rectangle.Empty)
{
CurrentFrame.ROI = TrackArea;
channels = CurrentFrame.Split();
hist1.Calculate(new Image<Gray, byte>[] { channels[0], channels[1] }, false, null);
CurrentFrame.Not().CopyTo(CurrentFrame);
CurrentFrame.ROI = Rectangle.Empty;
CurrentFrame.Draw(TrackArea, new Hsv(100, 100, 100), 2);
imageBox1.Image = CurrentFrame;
}
else
{
if (TrackArea != Rectangle.Empty)
{
channels = CurrentFrame.Split();
ProbImage = hist1.BackProject<byte>(new Image<Gray, byte>[] { channels[0], channels[1] });
imageBox_Hist2.Image = ProbImage.Convert<Gray, byte>();
lock (LockObject)
{
if (TrackArea.Height * TrackArea.Width > 0)
{
CvInvoke.cvCamShift(ProbImage, TrackArea, criteria, out comp, out box);
TrackArea = comp.rect;
CurrentFrame.Draw(box, new Hsv(100, 100, 100), 2);
}
/**
ResetContourPoints();
for (int i = 0; i < 60; i++)
{
ProbImage.Snake(ContourPoints, (float)1.0, (float)0.5, (float)1.5, new Size(17, 17), criteria, true);
}
CurrentFrame.DrawPolyline(ContourPoints, true, new Hsv(100, 100, 100), 2);
*/
}
}
imageBox1.Image = CurrentFrame;
//calculate histogram;
//channels = CurrentFrame.Split();
//hist2.Calculate(new Image<Gray, byte>[] { channels[0], channels[1] }, false, null);
//hist2.Normalize(1);
//HistImg1.SetZero();
//DrawHist2D(HistImg1, hist1);
//imageBox_Hist1.Image = HistImg1;
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////
#endregion
#region 匹配值方圖
//////////////////////////////////////////////////////////////////////////////////////////////
/// <summary>
/// 直方圖匹配(使用BHATTACHARYYA)
/// </summary>
/// <param name="template">樣板值方圖</param>
/// <param name="observedSrcImg">要比對的圖像</param>
/// <param name="observedHist">觀察影像的直方圖</param>
/// <returns>匹配率越低表示匹配度越高</returns>
public static double CompareHistogram(DenseHistogram template, Image <Bgr, Byte> observedSrcImg, out DenseHistogram observedHist)
{
//計算影像的值方圖
if (template.Dimension == 1)
{
observedHist = HistogramOperation.CalHsvHistogram(observedSrcImg.Ptr, template.Dimension, template.BinDimension[0].Size);
}
else if (template.Dimension == 2)
{
observedHist = HistogramOperation.CalHsvHistogram(observedSrcImg.Ptr, template.Dimension, template.BinDimension[0].Size, template.BinDimension[1].Size);
}
else
{
observedHist = HistogramOperation.CalHsvHistogram(observedSrcImg.Ptr, template.Dimension, template.BinDimension[0].Size, template.BinDimension[1].Size, template.BinDimension[2].Size);
}
//匹配後回傳匹配率
return(HistogramOperation.CompareHist(template, observedHist));
}
public void tracking(Mat hist_roi)
{
using (var nextframe = cap.QueryFrame().ToImage <Bgr, Byte>())
{
if (nextframe != null)
{
float[] range = { 0, 180 };
int[] histsize = { 180 };
int[] channels = { 0, 0 };
int[] Chn = { 0 };
//Rectangle ret = new Rectangle();
//ret = nextframe.Mat;
Rectangle trackwindow = new Rectangle(rectx, recty, rectw, recth);
Mat hsv = new Mat();
Mat hist = new DenseHistogram(16, new RangeF(0, 16));
CvInvoke.CvtColor(nextframe, hsv, ColorConversion.Bgr2Hsv);//hsv
Mat mask = new Mat();
CvInvoke.InRange(hsv, new ScalarArray(new MCvScalar(0, 60, 32)), new ScalarArray(new MCvScalar(180, 256, 255)), mask);
Mat hue = new Mat();
hue.Create(hsv.Rows, hsv.Cols, hsv.Depth, 0);
//CvInvoke.MixChannels()
int[] chn = { 0, 0 };
var vhue = new VectorOfMat(hue);
var vhsv = new VectorOfMat(hsv);
//var vhsv = new VectorOfMat(hsv);
CvInvoke.MixChannels(vhsv, vhue, chn);
Mat dst = new Mat();
float[] Range = { 0, 180, 0, 255 };
CvInvoke.CalcBackProject(vhue, Chn, hist_roi, dst, Range, 1);
Size s = new Size(5, 5);
// CvInvoke.GetStructuringElement(ElementShape.Ellipse,s);
CvInvoke.Threshold(dst, dst, 50, 255, 0);
imageBox1.Image = dst;
// MCvTermCriteria termcriteria = new MCvTermCriteria(TermCritType.Eps | TermCritType.Iter, 10, 1);
MCvTermCriteria termCrit = new MCvTermCriteria(10, 0.1);
RotatedRect result = CvInvoke.CamShift(dst, ref trackerbox, termCrit);
var grayframe = nextframe.Convert <Gray, byte>();
grayframe.ROI = trackerbox;
var grayface = grayframe.Copy().Mat;
var faces = haar.DetectMultiScale(grayface, 1.1, 10, Size.Empty);
int totalface = faces.Length;
RectangleF ret = trackerbox;
// PointF[] PTS = CvInvoke.BoxPoints(ret);
// Point[] pts = new Point[10];
//for (int x = 0; x < PTS.Length; x++)
// {
// pts[x] = Point.Round(PTS[x]);
CvInvoke.CvtColor(dst, nextframe, ColorConversion.Gray2Bgr);
//CvInvoke.Polylines(nextframe, pts, true, new MCvScalar(255, 0));
// if (totalface == 1)
// {
MCvScalar color = new MCvScalar(0, 0, 255);
CvInvoke.Ellipse(nextframe, ret, color, 3, LineType.AntiAlias);
// }
}
imageBox1.Image = nextframe;
} //while loop end*/
}
public override bool Execute(DenseHistogram hist, out float minPos)
{
minPos = Param;
return(true);
}
private float[] GetHistogramData(Image<Gray, byte> imgGray)
{
float[] histoGrammData;
DenseHistogram histoGramm = new DenseHistogram(255, new RangeF(0, 255));
histoGramm.Calculate(new Image<Gray, Byte>[] { imgGray }, true, null);
//The data is here
//Histo.MatND.ManagedArray
histoGrammData = new float[256];
histoGramm.MatND.ManagedArray.CopyTo(histoGrammData, 0);
return histoGrammData;
}
public mHistogram(Image <Bgr, Byte> img)
{
Obraz = img;
#region oblicz wartosci histogramów
var Histo = new DenseHistogram(255, new RangeF(0, 255));
Image <Gray, Byte> img2Blue = img[0];
Image <Gray, Byte> img2Green = img[1];
Image <Gray, Byte> img2Red = img[2];
Histo.Calculate(new[] { img2Blue }, true, null);
BlueHist = new float[256];
Histo.MatND.ManagedArray.CopyTo(BlueHist, 0);
Histo.Clear();
Histo.Calculate(new[] { img2Green }, true, null);
GreenHist = new float[256];
Histo.MatND.ManagedArray.CopyTo(GreenHist, 0);
Histo.Clear();
Histo.Calculate(new[] { img2Red }, true, null);
RedHist = new float[256];
Histo.MatND.ManagedArray.CopyTo(RedHist, 0);
#endregion
#region 'kwantyzacja histogramów''
int wielkoscPrzedialu = 4;
int aktualnaPozycjaR = 0;
int aktualnaPozycjaG = 0;
int aktualnaPozycjaB = 0;
float wartoscR = 0;
float wartoscG = 0;
float wartoscB = 0;
RedHistQ = new float[RedHist.Length / wielkoscPrzedialu];
GreenHistQ = new float[GreenHist.Length / wielkoscPrzedialu];
BlueHistQ = new float[BlueHist.Length / wielkoscPrzedialu];
for (int i = 0; i < RedHist.Length / wielkoscPrzedialu; i++)
{
for (int j = 0; j < wielkoscPrzedialu; j++)
{
wartoscR += RedHist[aktualnaPozycjaR + j];
}
RedHistQ[i] = wartoscR;
wartoscR = 0;
aktualnaPozycjaR++;
}
for (int i = 0; i < GreenHist.Length / wielkoscPrzedialu; i++)
{
for (int j = 0; j < wielkoscPrzedialu; j++)
{
wartoscG += GreenHist[aktualnaPozycjaG + j];
}
GreenHistQ[i] = wartoscG;
wartoscG = 0;
aktualnaPozycjaG++;
}
for (int i = 0; i < BlueHist.Length / wielkoscPrzedialu; i++)
{
for (int j = 0; j < wielkoscPrzedialu; j++)
{
wartoscB += BlueHist[aktualnaPozycjaB + j];
}
BlueHistQ[i] = wartoscB;
wartoscB = 0;
aktualnaPozycjaB++;
}
#endregion
}
/// <summary>
/// Eliminate the matched features whose scale and rotation do not aggree with the majority's scale and rotation.
/// </summary>
/// <param name="rotationBins">The numbers of bins for rotation, a good value might be 20 (which means each bin covers 18 degree)</param>
/// <param name="scaleIncrement">This determins the different in scale for neighbour hood bins, a good value might be 1.5 (which means matched features in bin i+1 is scaled 1.5 times larger than matched features in bin i</param>
/// <param name="matchedFeatures">The matched feature that will be participated in the voting. For each matchedFeatures, only the zero indexed ModelFeature will be considered.</param>
public static MatchedSURFFeature[] VoteForSizeAndOrientation(MatchedSURFFeature[] matchedFeatures, double scaleIncrement, int rotationBins)
{
int elementsCount = matchedFeatures.Length;
float[] scales = new float[elementsCount];
float[] rotations = new float[elementsCount];
float[] flags = new float[elementsCount];
float minScale = float.MaxValue;
float maxScale = float.MinValue;
for (int i = 0; i < matchedFeatures.Length; i++)
{
float scale = (float)matchedFeatures[i].ObservedFeature.Point.size / (float)matchedFeatures[i].SimilarFeatures[0].Feature.Point.size;
scale = (float)Math.Log10(scale);
scales[i] = scale;
if (scale < minScale) minScale = scale;
if (scale > maxScale) maxScale = scale;
float rotation = matchedFeatures[i].ObservedFeature.Point.dir - matchedFeatures[i].SimilarFeatures[0].Feature.Point.dir;
rotations[i] = rotation < 0.0 ? rotation + 360 : rotation;
}
int scaleBinSize = (int)Math.Max(((maxScale - minScale) / Math.Log10(scaleIncrement)), 1);
int count;
using (DenseHistogram h = new DenseHistogram(new int[] { scaleBinSize, rotationBins }, new RangeF[] { new RangeF(minScale, maxScale), new RangeF(0, 360) }))
{
GCHandle scaleHandle = GCHandle.Alloc(scales, GCHandleType.Pinned);
GCHandle rotationHandle = GCHandle.Alloc(rotations, GCHandleType.Pinned);
GCHandle flagsHandle = GCHandle.Alloc(flags, GCHandleType.Pinned);
using (Matrix<float> flagsMat = new Matrix<float>(1, elementsCount, flagsHandle.AddrOfPinnedObject()))
using (Matrix<float> scalesMat = new Matrix<float>(1, elementsCount, scaleHandle.AddrOfPinnedObject()))
using (Matrix<float> rotationsMat = new Matrix<float>(1, elementsCount, rotationHandle.AddrOfPinnedObject()))
{
h.Calculate(new Matrix<float>[] { scalesMat, rotationsMat }, true, null);
float minVal, maxVal;
int[] minLoc, maxLoc;
h.MinMax(out minVal, out maxVal, out minLoc, out maxLoc);
h.Threshold(maxVal * 0.5);
CvInvoke.cvCalcBackProject(new IntPtr[] { scalesMat.Ptr, rotationsMat.Ptr }, flagsMat.Ptr, h.Ptr);
count = CvInvoke.cvCountNonZero(flagsMat);
}
scaleHandle.Free();
rotationHandle.Free();
flagsHandle.Free();
MatchedSURFFeature[] matchedGoodFeatures = new MatchedSURFFeature[count];
int index = 0;
for (int i = 0; i < matchedFeatures.Length; i++)
if (flags[i] != 0)
matchedGoodFeatures[index++] = matchedFeatures[i];
return matchedGoodFeatures;
}
}
public void TestHistogram()
{
using (Image<Bgr, Byte> img = new Image<Bgr, byte>("stuff.jpg"))
using (Image<Hsv, Byte> img2 = img.Convert<Hsv, Byte>())
{
Image<Gray, Byte>[] HSVs = img2.Split();
using (DenseHistogram h = new DenseHistogram(20, new RangeF(0, 180)))
{
h.Calculate(new Image<Gray, Byte>[1] { HSVs[0] }, true, null);
using (Image<Gray, Byte> bpj = h.BackProject(new Image<Gray, Byte>[1] { HSVs[0] }))
{
Size sz = bpj.Size;
}
using (Image<Gray, Single> patchBpj = h.BackProjectPatch(
new Image<Gray, Byte>[1] { HSVs[0] },
new Size(5, 5),
Emgu.CV.CvEnum.HISTOGRAM_COMP_METHOD.CV_COMP_CHISQR,
1.0))
{
Size sz = patchBpj.Size;
}
}
foreach (Image<Gray, Byte> i in HSVs) i.Dispose();
}
}
/// <summary>
/// Eliminate the matched features whose scale and rotation do not aggree with the majority's scale and rotation.
/// </summary>
/// <param name="rotationBins">The numbers of bins for rotation, a good value might be 20 (which means each bin covers 18 degree)</param>
/// <param name="scaleIncrement">This determins the different in scale for neighbour hood bins, a good value might be 1.5 (which means matched features in bin i+1 is scaled 1.5 times larger than matched features in bin i</param>
/// <param name="matchedFeatures">The matched feature that will be participated in the voting. For each matchedFeatures, only the zero indexed ModelFeature will be considered.</param>
public static MatchedSURFFeature[] VoteForSizeAndOrientation(MatchedSURFFeature[] matchedFeatures, double scaleIncrement, int rotationBins)
{
int elementsCount = matchedFeatures.Length;
float[] scales = new float[elementsCount];
float[] rotations = new float[elementsCount];
float[] flags = new float[elementsCount];
float minScale = float.MaxValue;
float maxScale = float.MinValue;
for (int i = 0; i < matchedFeatures.Length; i++)
{
float scale = (float)matchedFeatures[i].ObservedFeature.Point.size / (float)matchedFeatures[i].SimilarFeatures[0].Feature.Point.size;
scale = (float)Math.Log10(scale);
scales[i] = scale;
if (scale < minScale)
{
minScale = scale;
}
if (scale > maxScale)
{
maxScale = scale;
}
float rotation = matchedFeatures[i].ObservedFeature.Point.dir - matchedFeatures[i].SimilarFeatures[0].Feature.Point.dir;
rotations[i] = rotation < 0.0 ? rotation + 360 : rotation;
}
int scaleBinSize = (int)Math.Max(((maxScale - minScale) / Math.Log10(scaleIncrement)), 1);
int count;
using (DenseHistogram h = new DenseHistogram(new int[] { scaleBinSize, rotationBins }, new RangeF[] { new RangeF(minScale, maxScale), new RangeF(0, 360) }))
{
GCHandle scaleHandle = GCHandle.Alloc(scales, GCHandleType.Pinned);
GCHandle rotationHandle = GCHandle.Alloc(rotations, GCHandleType.Pinned);
GCHandle flagsHandle = GCHandle.Alloc(flags, GCHandleType.Pinned);
using (Matrix <float> flagsMat = new Matrix <float>(1, elementsCount, flagsHandle.AddrOfPinnedObject()))
using (Matrix <float> scalesMat = new Matrix <float>(1, elementsCount, scaleHandle.AddrOfPinnedObject()))
using (Matrix <float> rotationsMat = new Matrix <float>(1, elementsCount, rotationHandle.AddrOfPinnedObject()))
{
h.Calculate(new Matrix <float>[] { scalesMat, rotationsMat }, true, null);
float minVal, maxVal;
int[] minLoc, maxLoc;
h.MinMax(out minVal, out maxVal, out minLoc, out maxLoc);
h.Threshold(maxVal * 0.5);
CvInvoke.cvCalcBackProject(new IntPtr[] { scalesMat.Ptr, rotationsMat.Ptr }, flagsMat.Ptr, h.Ptr);
count = CvInvoke.cvCountNonZero(flagsMat);
}
scaleHandle.Free();
rotationHandle.Free();
flagsHandle.Free();
MatchedSURFFeature[] matchedGoodFeatures = new MatchedSURFFeature[count];
int index = 0;
for (int i = 0; i < matchedFeatures.Length; i++)
{
if (flags[i] != 0)
{
matchedGoodFeatures[index++] = matchedFeatures[i];
}
}
return(matchedGoodFeatures);
}
}
public ArrayList bgSubtraction(Image <Bgr, Byte> currImage, Image <Bgr, Byte> bgImage, ref Image <Gray, byte> finalBlobImg, ref ArrayList blobRect)
{
//### inputs are currImage and bgImage. Output is finalBlobImg - 1 for FG and 0 for BG and blobRect to store ROIs of each blob in this frame
//### extracting pixels from currImage and bgImage
byte[, ,] curImgPix = new byte[Main.w, Main.h, 1];
byte[, ,] bw_2d = new byte[Main.h / Main.b, Main.w / Main.b, 1];
ArrayList blobDistanceList = new ArrayList(1);
curImgPix = bitmap22D(currImage);
currImgGy = currImage.Convert <Gray, byte>();
bgImgGy = bgImage.Convert <Gray, byte>();
//### Motion Detection between currImage pixels and bgImage pixels
motionDetection(curImgPix, bitmap22D(bgImgGy), Main.w, Main.h, Main.b, ref bw_2d);
//Component Labeling for localizing objects that are moving. There can be more than one objects that are moving in the scene.
byte m = 1;
ArrayList mv = new ArrayList();
for (int x = 0; x < h / b; x++)
{
for (int y = 0; y < w / b; y++)
{
if (bw_2d[x, y, 0] == 1)
{
ArrayList label = new ArrayList();
compLabel(x, y, ++m, label, ref bw_2d);
mv.Add(label);
}
}
}
//Console.WriteLine("-----No of Blobs=" + mv.Count);
//### outimage - binay image of each blob; finalBlobImg - OR of all blob images to get final binay image with 1 for FG and 0 for BG
finalBlobImg = new Image <Gray, byte>(w, h);
Image <Gray, byte> outimage = new Image <Gray, byte>(w, h);
//### blobRect - to store the ROIs of each blob in the currImage.
blobRect = new ArrayList();
//Calculate xmin ymin xmax ymax for each blob detected
foreach (ArrayList blob in mv)
{
if (blob.Count > 25) //no of blocks in each blob > 3
{
//Console.WriteLine(blob.Count+"\n");
int xmin, xmax, ymin, ymax;
IEnumerator iblob = blob.GetEnumerator();
iblob.MoveNext();
ArrayList pt = (ArrayList)iblob.Current;
IEnumerator ipt = pt.GetEnumerator();
ipt.MoveNext();
//Console.Write("{y"+ipt.Current);
ymin = ymax = (int)ipt.Current;
ipt.MoveNext();
xmin = xmax = (int)ipt.Current;
//Console.Write(" x"+ipt.Current+"}\n");
while (iblob.MoveNext())
{
ArrayList ptt = (ArrayList)iblob.Current;
IEnumerator iptt = ptt.GetEnumerator();
iptt.MoveNext();
int y = (int)iptt.Current;
iptt.MoveNext();
int x = (int)iptt.Current;
// Console.Write(x+","+y+" ; ");
if (xmin > x)
{
xmin = x;
}
if (xmax < x)
{
xmax = x;
}
if (ymin > y)
{
ymin = y;
}
if (ymax < y)
{
ymax = y;
}
}
//Console.WriteLine("****" + xmin + " " + xmax + " " + ymin + " " + ymax+" "+blob.Count);
// g.drawRect((xmin*blk),(ymin*blk),((xmax-xmin)*blk)+blk,((ymax-ymin)*blk)+blk);
subrect = new Rectangle((xmin * b), (ymin * b), ((xmax - xmin) * b) + b, ((ymax - ymin) * b) + b);
blobRect.Add(subrect);
currImage.Draw(subrect, new Bgr(0, 0, 0), 1);
Image <Gray, byte> subImgBg = bgImgGy.GetSubRect(subrect);
Image <Gray, byte> subImgFg = currImgGy.GetSubRect(subrect);
//subImgBg.Save("bg.jpg");
//subImgFg.Save("fg"+k+++".jpg");
Image <Gray, byte> imMask = subImgFg.AbsDiff(subImgBg);
//Console.WriteLine(Thread.CurrentThread.Name + " " + subrect);
for (int i = 0; i < subrect.Height; i++)
{
for (int j = 0; j < subrect.Width; j++)
{
//subImgFg.Data[i, j, 0] = 255;
if (imMask.Data[i, j, 0] < Main.ThSub)
{
imMask.Data[i, j, 0] = 0;
outimage.Data[i + subrect.Y, j + subrect.X, 0] = 0;
}
else
{
imMask.Data[i, j, 0] = 255;
outimage.Data[i + subrect.Y, j + subrect.X, 0] = 255;
}
}
//Console.WriteLine();
}
//imMask._Erode(1);
//imMask._Dilate(2);
outimage._Erode(2);
outimage._Dilate(3);
try
{
Image <Bgr, byte> subimg = currImage.And((outimage.Convert <Bgr, Byte>())).GetSubRect(subrect);
//subimg.GetSubRect(subrect);//.Save(Thread.CurrentThread.Name + "\\" + k++ + ".jpg");
//Calc HISTOGRAM of each blob
DenseHistogram histBlob = new DenseHistogram(hdims, hranges); //cvCreateHist(1, &hdims, CV_HIST_ARRAY, &hranges, 1);
Image <Hsv, byte> hsvBlob = subimg.Convert <Hsv, byte>();
//extract the hue and value channels
Image <Gray, Byte>[] channelsBlob = hsvBlob.Split(); //split into components
Image <Gray, Byte>[] imghueBlob = new Image <Gray, byte> [1]; imghueBlob[0] = channelsBlob[0]; //hsv, so channels[0] is hue.
Hsv hsv_lower = new Hsv(0, smin, Math.Min(vmin, vmax));
Hsv hsv_upper = new Hsv(180, 256, Math.Max(vmin, vmax));
Image <Gray, Byte> maskBlob = hsvBlob.InRange(hsv_lower, hsv_upper);
histBlob.Calculate(imghueBlob, false, maskBlob);
double distance = CvInvoke.cvCompareHist(Main.hist.Ptr, histBlob.Ptr, Emgu.CV.CvEnum.HISTOGRAM_COMP_METHOD.CV_COMP_BHATTACHARYYA);
//Console.WriteLine(Thread.CurrentThread.Name + " = " + distance);
//Add rect and distance of each blob into blobDistanceList
Blob currenBlob;
currenBlob.rect = subrect;
currenBlob.distance = distance;
blobDistanceList.Add(currenBlob);
}
catch (CvException cve)
{
// MessageBox.Show(cve.StackTrace);
}
finalBlobImg = finalBlobImg.Or(outimage);
}
}
return(blobDistanceList);
}
private void CalibrateHSV(ref Image<Hsv, Byte> hsvImage, ref DenseHistogram histogram)
{
float horizontalFactor = 0.2f;
float verticalFactor = 0.2f;
int rectWidth = (int)(hsvImage.Width * horizontalFactor);
int rectHeight = (int)(hsvImage.Height * verticalFactor);
int topLeftX = (int)((((float)hsvImage.Width / 2) - rectWidth) / 2);
int topLeftY = (int)(((float)hsvImage.Height - rectHeight) / 2);
Rectangle rangeOfInterest = new Rectangle(topLeftX, topLeftY, rectWidth, rectHeight);
Image<Gray, Byte> maskedImage = hsvImage.InRange(
new Hsv(hue_min, saturation_min, value_min),
new Hsv(hue_max, saturation_max, value_max));
Image<Hsv, byte> partToCompute = hsvImage.Copy(rangeOfInterest);
int[] h_bins = { 30, 30 };
RangeF[] h_ranges = {
new RangeF(0, 180),
new RangeF(0, 255)
};
Image<Gray, byte>[] channels = partToCompute.Split().Take(2).ToArray();
histogram = new DenseHistogram(h_bins, h_ranges);
histogram.Calculate(channels, true, null);
float minValue, maxValue;
int[] posMinValue, posMaxValue;
histogram.MinMax(out minValue, out maxValue, out posMinValue, out posMaxValue);
histogram.Threshold(
(double)minValue + (maxValue - minValue) * 40 / 100
);
hsvImage = maskedImage.Convert<Hsv, Byte>() //tu powstaje jakiś "First chance of exception..."
.SmoothGaussian(5)
.Dilate(1)
.Convert<Rgb, Byte>()
.ThresholdBinary(new Rgb(127,127,127), new Rgb(255,255,255))
.Convert<Hsv, Byte>();
//hsvImage.Draw(rangeOfInterest, new Hsv(255, 255, 255), 3);
}
void dataStream(object sender, EventArgs e)
{
{
RangeF[] range = new RangeF[2];
range[0] = new RangeF(0, 180);
range[1] = new RangeF(0, 255);
pollColorImageStream();
pollDepthImageStream();
//Color------------------
Bitmap bitmapColor = new Bitmap(colorImage.Width, colorImage.Height, System.Drawing.Imaging.PixelFormat.Format32bppArgb);
BitmapData bmd = bitmapColor.LockBits(new System.Drawing.Rectangle(0, 0, colorImage.Width, colorImage.Height), ImageLockMode.ReadWrite, bitmapColor.PixelFormat);
Marshal.Copy(colorPixelData, 0, bmd.Scan0, colorPixelData.Length);
bitmapColor.UnlockBits(bmd);
Image<Bgr, Byte> colorTemp = new Image<Bgr, Byte>(bitmapColor);
//Color------------------end
//depth------------------
byte[] byteDepth = new byte[640 * 480];
byte[] remap = new byte[640 * 480];
sensor.MapDepthFrameToColorFrame(DepthImageFormat.Resolution640x480Fps30, depthPixelData, ColorImageFormat.RgbResolution640x480Fps30, colorCoordinate);
for (int y = 0; y < 480; y++)
{
for (int x = 0; x < 640; x++)
{
int position = y * 640 + x;
short tempShort = depthPixelData[position];
//depthImage[y, x] = new Gray(tempShort);
byteDepth[position] = (byte)(tempShort >> 8);
//byteDepth[y, x] = new Gray((byte)(tempShort));
int positionRemap = colorCoordinate[position].Y * 640 + colorCoordinate[position].X;
if (positionRemap > 640 * 480)
continue;
depthRemapData[positionRemap] = depthPixelData[position];
remap[positionRemap] = (byte)(tempShort >> 8);
//byteDepth[y, x] = new Gray((byte)(tempShort));
}
}
Bitmap bitmapDepth = new Bitmap(depthImage.Width, depthImage.Height, System.Drawing.Imaging.PixelFormat.Format8bppIndexed);
BitmapData bmd2 = bitmapDepth.LockBits(new System.Drawing.Rectangle(0, 0, depthImage.Width, depthImage.Height), ImageLockMode.ReadWrite, bitmapDepth.PixelFormat);
Marshal.Copy(byteDepth, 0, bmd2.Scan0, byteDepth.Length);
bitmapDepth.UnlockBits(bmd2);
Image<Gray, Byte> depthTemp = new Image<Gray, Byte>(bitmapDepth);
//depth------------------end
Byte[] backFrame = new Byte[640 * 480];
BitmapImage trackingOut = new BitmapImage();
if (trackingFlag != 0)
{
Image<Hsv, Byte> hsv = new Image<Hsv, Byte>(640, 480);
CvInvoke.cvCvtColor(colorTemp, hsv, COLOR_CONVERSION.CV_BGR2HSV);
Image<Gray, Byte> hue = hsv.Split()[0];
//range of hist is 180 or 256? not quite sure
DenseHistogram hist = new DenseHistogram(180, new RangeF(0.0f, 179.0f));
Image<Gray, Byte> mask = new Image<Gray, Byte>(trackWindow.Width, trackWindow.Height);
for (int y = 0; y < 480; y++)
{
for (int x = 0; x < 640; x++)
{
if (x >= trackWindow.X && x < trackWindow.X + trackWindow.Width && y >= trackWindow.Y && y < trackWindow.Y + trackWindow.Height)
mask[y - trackWindow.Y, x - trackWindow.X] = hue[y, x];
}
}
hist.Calculate(new IImage[] { mask }, false, null);
//maybe need to re-scale the hist to 0~255?
//back projection
IntPtr backProject = CvInvoke.cvCreateImage(hsv.Size, IPL_DEPTH.IPL_DEPTH_8U, 1);
CvInvoke.cvCalcBackProject(new IntPtr[1] { hue }, backProject, hist);
CvInvoke.cvErode(backProject, backProject, IntPtr.Zero, 3);
//CAMshift
CvInvoke.cvCamShift(backProject, trackWindow, new MCvTermCriteria(50, 0.1), out trackComp, out trackBox);
trackWindow = trackComp.rect;
if (trackWindow.Width < 5 || trackWindow.Height < 5)
{
if (trackWindow.Width < 5)
{
trackWindow.X = trackWindow.X + trackWindow.Width / 2 - 3;
trackWindow.Width = 6;
}
if (trackWindow.Height < 5)
{
trackWindow.Y = trackWindow.Y + trackWindow.Height / 2 - 3;
trackWindow.Height = 6;
}
}
Image<Bgr, Byte> showFrame = colorTemp;
showFrame.Draw(trackWindow, new Bgr(System.Drawing.Color.Blue), 2);
using (var stream = new MemoryStream())
{
showFrame.Bitmap.Save(stream, ImageFormat.Bmp);
trackingOut.BeginInit();
trackingOut.StreamSource = new MemoryStream(stream.ToArray());
trackingOut.EndInit();
}
//calculate the average depth of tracking object
int min = 65528, max = 0, num = 0;
UInt32 sum = 0;
for (int y = 0; y < trackWindow.Height; y++)
{
for (int x = 0; x < trackWindow.Width; x++)
{
int position = (trackWindow.X + x) + (trackWindow.Y + y) * 640;
ushort temp = (ushort)depthRemapData[position];
if (temp != 65528 && temp != 0)//black
{
if (temp < min)
min = temp;
if (temp > max)
max = temp;
sum += temp;
num++;
}
}
}
ushort average = 0;
if (num != 0)
{
average = (ushort)(sum / num);
}
//Int32 depthInches = (Int32)((average >> DepthImageFrame.PlayerIndexBitmaskWidth) * 0.0393700787);
//Int32 depthFt = depthInches / 12;
//depthInches = depthInches % 12;
Int32 depth = average >> DepthImageFrame.PlayerIndexBitmaskWidth;
textBlock1.Text = String.Format("{0}mm", depth);
Double distanceInMeter = (Double)depth / 1000;
Messenger.Default.Send<Double>(distanceInMeter, "Distance");
}
//if (rbtnColorFrame.IsChecked == true)
//{
if (trackingFlag != 0)
image1.Source = trackingOut;
else
image1.Source = BitmapSource.Create(640, 480, 96, 96, PixelFormats.Bgr32, null, colorPixelData, 640 * 4);
// else
// imageOutputBig.Source = BitmapSource.Create(640, 480, 96, 96, PixelFormats.Bgr32, null, colorPixelData, 640 * 4);
// //imageOutputBig.Source = BitmapSource.Create(640, 480, 96, 96, PixelFormats.Gray8, null, backFrame, 640);
// imageOutputSmall.Source = BitmapSource.Create(640, 480, 96, 96, PixelFormats.Gray16, null, depthRemapData, 640 * 2);
//}
//else
//{
// imageOutputSmall.Source = BitmapSource.Create(640, 480, 96, 96, PixelFormats.Bgr32, null, colorPixelData, 640 * 4);
// imageOutputBig.Source = BitmapSource.Create(640, 480, 96, 96, PixelFormats.Gray16, null, depthRemapData, 640 * 2);
//}
//if(CvInvoke.cvWaitKey(0)=='q')
//{
// trackingFlag = 0;
//}
}
}
public void Compute(Image <Bgr, Byte> image)
{
using (Image <Hsv, Byte> hsvImage = image.Convert <Hsv, byte>())
{
Image <Gray, Byte>[] channels = hsvImage.Split();
Rectangle roi1 = new Rectangle(0, 0, image.Width / 2, image.Height / 2);
Rectangle roi2 = new Rectangle(image.Width / 2, 0, image.Width / 2, image.Height / 2);
Rectangle roi3 = new Rectangle(0, image.Height / 2, image.Width / 2, image.Height / 2);
Rectangle roi4 = new Rectangle(image.Width / 2, image.Height / 2, image.Width / 2, image.Height / 2);
Rectangle roiCenter = new Rectangle(image.Width / 4, image.Height / 4, image.Width / 2, image.Height / 2);
DenseHistogram hist = new DenseHistogram(64, new RangeF(0.0f, 255.0f));
double factor = 100.0;
List <float> totalHist = new List <float>();
for (int i = 0; i < channels.Length; ++i)
{
float[] histRaw1 = new float[64];
float[] histRaw2 = new float[64];
float[] histRaw3 = new float[64];
float[] histRaw4 = new float[64];
float[] histRawCenter = new float[64];
Image <Gray, byte>[] tempImages = new Image <Gray, byte>[] { channels[i] };
// Top Left
channels[i].ROI = roi1;
hist.Calculate(tempImages, false, null);
hist.Normalize(factor);
hist.MatND.ManagedArray.CopyTo(histRaw1, 0);
totalHist.AddRange(histRaw1);
// Top Right
channels[i].ROI = roi2;
hist.Calculate(tempImages, false, null);
hist.Normalize(factor);
hist.MatND.ManagedArray.CopyTo(histRaw2, 0);
totalHist.AddRange(histRaw2);
// Bottom Left
channels[i].ROI = roi3;
hist.Calculate(tempImages, false, null);
hist.Normalize(factor);
hist.MatND.ManagedArray.CopyTo(histRaw3, 0);
totalHist.AddRange(histRaw3);
// Bottom Right
channels[i].ROI = roi4;
hist.Calculate(tempImages, false, null);
hist.Normalize(factor);
hist.MatND.ManagedArray.CopyTo(histRaw4, 0);
totalHist.AddRange(histRaw4);
// Center
channels[i].ROI = roiCenter;
hist.Calculate(tempImages, false, null);
hist.Normalize(factor);
hist.MatND.ManagedArray.CopyTo(histRawCenter, 0);
totalHist.AddRange(histRawCenter);
tempImages = null;
}
_descriptors = new Matrix <float>(totalHist.ToArray());
_descriptors = _descriptors.Transpose();
}
}
public void SetHist(DenseHistogram hist)
{
Reset();
_hist = hist;
isTracked = true;
}
//histogram match
static bool HistogramMatch(Bitmap frame1, Bitmap frame2)
{
//convert bitmap to temp "Image" for color processing by older version of emgucv
Image <Bgr, byte> frame1_Image = new Image <Bgr, byte>(frame1);
Image <Bgr, byte> frame2_Image = new Image <Bgr, byte>(frame2);
//convert frames to HSV color space
Image <Gray, byte> frame1_hist = new Image <Gray, byte>(f_width, f_height);
Image <Gray, byte> frame2_hist = new Image <Gray, byte>(f_width, f_height);
CvInvoke.cvCvtColor(frame1_Image, frame1_hist, COLOR_CONVERSION.BGR2GRAY);
CvInvoke.cvCvtColor(frame2_Image, frame2_hist, COLOR_CONVERSION.BGR2GRAY);
//dispose temp Image after color conversion
frame1_Image.Dispose();
frame2_Image.Dispose();
#region Multi-dimentional hitogram (HSV channels)
////set histogram parameters
/*int h_bins = 50; int s_bins = 60;
* int[] histSize = { h_bins, s_bins }; //int histSize[] = { h_bins, s_bins };
*
* float[] h_ranges = { 0, 180 };
* float[] s_ranges = { 0, 256 };
*
* //const float[] ranges = { h_ranges, s_ranges };
* float[][] ranges = { h_ranges };*/
//set histogram parameters
/*int[] bins = {256, 256, 256};
* RangeF r = new RangeF(0, 256);
* RangeF[] ranges = {r, r, r};*/
//DenseHistogram denseHist_frame1 = new DenseHistogram(bins, ranges);
//DenseHistogram denseHist_frame2 = new DenseHistogram(bins, ranges);*
#endregion
#region Single-dimentional (Grayscale) histogram
DenseHistogram denseHist_frame1 = new DenseHistogram(256, new RangeF(0.0f, 256.0f));
DenseHistogram denseHist_frame2 = new DenseHistogram(256, new RangeF(0.0f, 256.0f));
denseHist_frame1.Calculate(new Image <Gray, byte>[] { frame1_hist }, true, null);
denseHist_frame2.Calculate(new Image <Gray, byte>[] { frame2_hist }, true, null);
denseHist_frame1.Normalize(1);
denseHist_frame2.Normalize(1);
#endregion
double histCompareRst = CvInvoke.cvCompareHist(denseHist_frame1, denseHist_frame2, HISTOGRAM_COMP_METHOD.CV_COMP_CORREL);
float thresh_min = 0.2f; //get unique keyframes
float thresh_max = 0.997f; //good for fast moving objects in video
if (histCompareRst < 0.8f)
{
return(false); //not a match: likely different scenes
}
return(true);
}
private int hack(DenseHistogram[] referenceHistogram, IEnumerable<DenseHistogram[]> testHistograms, HISTOGRAM_COMP_METHOD method, bool printHigh)
{
var comparisons = testHistograms.Select(x => GetHistogramComparison(x, referenceHistogram, method));
var multiplied = comparisons.Select(x => Math.Abs(x[0] * x[1] * x[2]));
var i = 0;
foreach (var x in comparisons)
{
Console.WriteLine("{0,1}: {1:E4} {2:E4} {3:E4} --- {4:E4}", i, x[0], x[1], x[2], multiplied.ToList()[i]);
i++;
}
var minAndMax = findMinAndMax(multiplied);
return printHigh ? minAndMax[1] : minAndMax[0];
}
public void run()
{
if (Thread.CurrentThread.Name.EndsWith("2"))
{
Thread.Sleep(10000);//MessageBox.Show("play " + Thread.CurrentThread.Name);// //
}
if (Thread.CurrentThread.Name.EndsWith("3"))
{
Thread.Sleep(23000); //MessageBox.Show("play " + Thread.CurrentThread.Name);// Thread.Sleep(5000);
}
try
{
//if (Thread.CurrentThread.Name.Equals("camera2"))
// Thread.Sleep(1000);
switch (camno)
{
case 1:
break;
case 2:
break;
case 3:
break;
default:
break;
}
//Thread Processing
while ((currImage = cap.QueryFrame()) != null)
{
//### updating the currImage every time
currImage = currImage.Resize(pictureBox1.Width, pictureBox1.Height, Emgu.CV.CvEnum.INTER.CV_INTER_LINEAR);
//### Background Subtraction ~ currImage is subtracted with the bgImage and the result is stored in finalBlobImg.
switch (this.state)
{
case "BGSUB":
// Console.WriteLine("_________________");
if (!Main.tracking) //&& Main.lasttrack!=camno
{
Console.WriteLine("###########" + Thread.CurrentThread.Name + "starts BGSUB");
blobDistanceList = bgSubtraction(currImage, bgImage, ref finalBlobImg, ref blobRect);
if (blobDistanceList != null)
{
foreach (Blob blob in blobDistanceList)
{
//Console.WriteLine("Blob::" + blob.distance + " " + blob.rect);
if (blob.distance < Main.Dis) //set the tracker to cam with same object //&& Main.lasttrack!=camno
{
track_window_mean = blob.rect;
if (blob.rect.Width < 50)
{
track_window_mean.Width = 60;
}
else
{
track_window_mean.Width = blob.rect.Width;
}
if (blob.rect.Width + blob.rect.X > 319)
{
track_window_mean.X = track_window_mean.X - 10;
track_window_mean.Width = 40;
}
if (blob.rect.Height + blob.rect.Y > 239)
{
track_window_mean.Height = track_window_mean.Height - 20;
}
if (blob.rect.Y == 0)
{
track_window_mean.Y = blob.rect.Y + 15;
}
//Console.WriteLine("Blob::::::::" + blob.distance + " " + blob.rect);
Main.lasttrack = camno;
Main.tracking = true;
state = "TRACK";
Console.WriteLine("###########" + Thread.CurrentThread.Name + "starts TRACK");
break;
}
}
}
}
break;
case "TRACK":
blobDistanceList = bgSubtraction(currImage, bgImage, ref finalBlobImg, ref blobRect);
//--------------
foreach (Blob blob in blobDistanceList)
{
Console.WriteLine("Tracking Blob== " + blob.distance + " " + blob.rect);
}
Console.WriteLine("----");
//-------------
Image <Hsv, Byte> hsv = new Image <Hsv, Byte>(w, h);
hsv = currImage.Convert <Hsv, Byte>();
Console.WriteLine("1");
//extract the hue and value channels
Image <Gray, Byte>[] channels = hsv.Split(); //split into components
Image <Gray, Byte>[] imghue = new Image <Gray, byte> [1]; imghue[0] = channels[0]; //hsv, so channels[0] is hue.
Image <Gray, Byte> imgval = channels[2]; //hsv, so channels[2] is value.
Image <Gray, Byte> imgsat = channels[1]; //hsv, so channels[1] is saturation.
mask = new Image <Gray, Byte>(w, h);
Hsv hsv_lower = new Hsv(0, smin, Math.Min(vmin, vmax));
Hsv hsv_upper = new Hsv(180, 256, Math.Max(vmin, vmax));
mask = hsv.InRange(hsv_lower, hsv_upper);
Image <Gray, Byte> backproject = Main.hist.BackProject(imghue);
mask = mask.And(finalBlobImg.Dilate(2));
backproject = mask.And(backproject);
MCvConnectedComp trac_comp = new MCvConnectedComp();
//Console.WriteLine("2");
MCvTermCriteria criteria_mean = new MCvTermCriteria(100, 0.002);
pictureBox2.Image = mask.Bitmap;
//Console.WriteLine(criteria_mean.GetType);
try
{
Emgu.CV.CvInvoke.cvMeanShift(backproject, track_window_mean, criteria_mean, out trac_comp);
}
catch (CvException e)
{
Console.WriteLine(track_window_mean);
MessageBox.Show(e.ToString());
}
// Console.WriteLine("3");
currImage.Draw(trac_comp.rect, new Bgr(255, 0, 0), 2);
currImage.Draw(new Cross2DF(new PointF((trac_comp.rect.X + trac_comp.rect.Width / 2), (trac_comp.rect.Y + trac_comp.rect.Height / 2)), 20, 20), new Bgr(255, 255, 255), 2);
track_window_mean = trac_comp.rect;
//check person left the view
Image <Gray, byte> subImgBg = bgImgGy.GetSubRect(trac_comp.rect);
Image <Gray, byte> subImgFg = currImgGy.GetSubRect(trac_comp.rect);
Image <Gray, byte> imMask = subImgFg.AbsDiff(subImgBg);
Gray cnt = imMask.GetAverage();
if (cnt.Intensity < 10)
{
Main.lasttrack = camno;
Main.tracking = false;
state = "BGSUB";
Console.WriteLine("###########" + Thread.CurrentThread.Name + "switches to BGSUB");
}
//---------------------------
outimage = new Image <Gray, byte>(w, h);
for (int i = 0; i < trac_comp.rect.Height; i++)
{
for (int j = 0; j < trac_comp.rect.Width; j++)
{
//subImgFg.Data[i, j, 0] = 255;
if (imMask.Data[i, j, 0] < Main.ThSub)
{
imMask.Data[i, j, 0] = 0;
outimage.Data[i + trac_comp.rect.Y, j + trac_comp.rect.X, 0] = 0;
}
else
{
imMask.Data[i, j, 0] = 255;
outimage.Data[i + trac_comp.rect.Y, j + trac_comp.rect.X, 0] = 255;
}
}
//Console.WriteLine();
}
outimage._Erode(2);
outimage._Dilate(3);
try
{
Image <Bgr, byte> subimg = currImage.And((outimage.Convert <Bgr, Byte>())).GetSubRect(trac_comp.rect);
//subimg.GetSubRect(subrect);//.Save(Thread.CurrentThread.Name + "\\" + k++ + ".jpg");
//Calc HISTOGRAM of each blob
DenseHistogram histBlob = new DenseHistogram(hdims, hranges); //cvCreateHist(1, &hdims, CV_HIST_ARRAY, &hranges, 1);
Image <Hsv, byte> hsvBlob = subimg.Convert <Hsv, byte>();
//extract the hue and value channels
Image <Gray, Byte>[] channelsBlob = hsvBlob.Split(); //split into components
Image <Gray, Byte>[] imghueBlob = new Image <Gray, byte> [1]; imghueBlob[0] = channelsBlob[0]; //hsv, so channels[0] is hue.
Image <Gray, Byte> maskBlob = hsvBlob.InRange(hsv_lower, hsv_upper);
histBlob.Calculate(imghueBlob, false, maskBlob);
double distance = CvInvoke.cvCompareHist(Main.hist.Ptr, histBlob.Ptr, Emgu.CV.CvEnum.HISTOGRAM_COMP_METHOD.CV_COMP_BHATTACHARYYA);
//if (distance < 0.15)
//{
// // Main.hist = histBlob;
// Console.WriteLine(Thread.CurrentThread.Name + " ===== " + distance);
//}
}
catch (CvException cve)
{
MessageBox.Show(cve.StackTrace);
}
//---------------------------
//pictureBox2.Image = mask.Bitmap;
//pictureBox3.Image = mask.And(finalBlobImg).Bitmap;
break;
}
pictureBox1.Image = currImage.Bitmap;
Thread.Sleep(20);
}
Console.WriteLine("###########" + Thread.CurrentThread.Name + " exited");
}catch (CvException e)
{}
}
public static Image <Bgr, Byte> Draw2DHisImg(Image <Bgr, Byte> srcImage, int h_bins, int s_bins)
{
DenseHistogram histDense = Cal2DHsvHist(srcImage, h_bins, s_bins);
return(draw2DHistImg(histDense, 466, 72));
}
/// <summary>
/// Recognize gesture.
/// </summary>
/// <param name="contour">Hand contour</param>
/// <param name="fingersCount">Number of fingers</param>
/// <returns>Gesture (if any)</returns>
public Gesture RecognizeGesture(Image <Gray, byte> contour, int fingersCount)
{
List <Gesture> recognizedGestures = new List <Gesture>(Gestures);
Gesture bestFit = new Gesture();
bestFit.RecognizedData.ContourMatch = 999;
bestFit.RecognizedData.HistogramMatch = 999;
foreach (var g in recognizedGestures)
{
if (g.FingersCount != fingersCount)
{
continue;
}
using (MemStorage storage = new MemStorage())
{
Contour <Point> c1 = contour.FindContours(CHAIN_APPROX_METHOD.CV_CHAIN_APPROX_SIMPLE,
RETR_TYPE.CV_RETR_LIST, storage);
Contour <Point> c2 = g.Image.FindContours(CHAIN_APPROX_METHOD.CV_CHAIN_APPROX_SIMPLE,
RETR_TYPE.CV_RETR_LIST, storage);
if (c1 != null && c2 != null)
{
DenseHistogram hist1 = new DenseHistogram(new int[2] {
8, 8
}, new RangeF[2] {
new RangeF(-180, 180), new RangeF(100, 100)
});
DenseHistogram hist2 = new DenseHistogram(new int[2] {
8, 8
}, new RangeF[2] {
new RangeF(-180, 180), new RangeF(100, 100)
});
CvInvoke.cvCalcPGH(c1, hist1.Ptr);
CvInvoke.cvCalcPGH(c2, hist2.Ptr);
CvInvoke.cvNormalizeHist(hist1.Ptr, 100.0);
CvInvoke.cvNormalizeHist(hist2.Ptr, 100.0);
g.RecognizedData.Hand = Hand;
g.RecognizedData.HistogramMatch = CvInvoke.cvCompareHist(hist1, hist2, HISTOGRAM_COMP_METHOD.CV_COMP_BHATTACHARYYA);
g.RecognizedData.ContourMatch = CvInvoke.cvMatchShapes(c1, c2, CONTOURS_MATCH_TYPE.CV_CONTOURS_MATCH_I3, 0);
double rating = g.RecognizedData.ContourMatch * g.RecognizedData.HistogramMatch;
double bestSoFar = bestFit.RecognizedData.ContourMatch * bestFit.RecognizedData.HistogramMatch;
if (rating < bestSoFar)
{
bestFit = g;
}
}
}
}
// Reliable, but strict: 0.01, 0.80, 0.20
if (bestFit.RecognizedData.ContourMatch * bestFit.RecognizedData.HistogramMatch <= 0.0125 &&
bestFit.RecognizedData.ContourMatch <= 0.80 &&
bestFit.RecognizedData.HistogramMatch <= 0.20)
{
return(bestFit);
}
else
{
return(null);
}
}
public void Detection()
{
try
{
check = comp1;
for (int i = 0; i < dt.Rows.Count; i++)
{
capturedImg = new Image <Gray, byte>(pbDetectedFace.Image.Bitmap);
DBImg = new Image <Gray, byte>((dt.Rows[i].ItemArray[0]).ToString());
hist1 = new DenseHistogram(256, new RangeF(0.0f, 255.0f));
hist2 = new DenseHistogram(256, new RangeF(0.0f, 255.0f));
hist1.Calculate(new Image <Gray, byte>[] { capturedImg }, false, null);
hist2.Calculate(new Image <Gray, byte>[] { DBImg }, false, null);
mat1 = new Mat();
hist1.CopyTo(mat1);
mat2 = new Mat();
hist2.CopyTo(mat2);
//float[] histFloat = new float[256];
//hist1.CopyTo(histFloat);
//float[] hist2Float = new float[256];
//hist2.CopyTo(hist2Float);
//double count1 = 0, count2 = 0;
//for (int j = 0; j < 256; j++)
//{
// count1 += histFloat[j];
// count2 += hist2Float[j];
//}
//unsafe
//{
// fixed (char* ch = (dt.Rows[i].ItemArray[0]).ToString().ToCharArray())
// {
// fixed (char* ch2 = (dt.Rows[i].ItemArray[0]).ToString().ToCharArray())
// {
// OpenCVWrapper.OpencvWrapperClass obj = new OpencvWrapperClass();
// sbyte* pic1 = (sbyte*)ch2;
// sbyte* pic2 = (sbyte*)ch;
// comp1=obj.CompareHistogram(pic1, pic2);
// }
// }
//}
comp1 = CvInvoke.CompareHist(mat1, mat2, Emgu.CV.CvEnum.HistogramCompMethod.Correl);
if (comp1 > check && comp1 < 0.40 && comp1 > 0.221)
{
check = comp1;
index = i;
}
}
MessageBox.Show(check + " " + index.ToString());
}
catch (IndexOutOfRangeException index)
{
MessageBox.Show(index.Message);
}
catch (Exception ee)
{
MessageBox.Show(ee.Message);
}
}
public DenseHistogram GetHistogram(Image<Gray, Byte> image)
{
//Create a histogram
DenseHistogram histogram =
new DenseHistogram(
256, //number of bins
new RangeF(0, 255) //pixel value range
);
//Compute histogram
histogram.Calculate(
new Image<Gray, Byte>[] { image }, //input image
true, //If it is true, the histogram is not cleared in the beginning
null //no mask is used
);
float[] grayHist = new float[256]; //the resulting histogram array
histogram.MatND.ManagedArray.CopyTo(grayHist, 0); //copy array
//Loop over each bin
for (int i = 0; i < 256; i++)
{
Console.WriteLine("value " + i + " = " + grayHist[i]);
}
return histogram;
}
/// <summary>
/// Generate histograms for the image. One histogram is generated for each color channel.
/// You will need to call the Refresh function to do the painting afterward.
/// </summary>
/// <param name="image">The image to generate histogram from</param>
/// <param name="numberOfBins">The number of bins for each histogram</param>
public void GenerateHistograms(IImage image, int numberOfBins)
{
IImage[] channels;
Type imageType;
if ((imageType = Toolbox.GetBaseType(image.GetType(), "Image`2")) != null)
{
channels = image.Split();
}
else if ((imageType = Toolbox.GetBaseType(image.GetType(), "GpuImage`2")) != null)
{
IImage img = imageType.GetMethod("ToImage").Invoke(image, null) as IImage;
channels = img.Split();
}
else
{
throw new ArgumentException("The input image type of {0} is not supported", image.GetType().ToString());
}
IColor typeOfColor = Activator.CreateInstance(imageType.GetGenericArguments()[0]) as IColor;
String[] channelNames = Reflection.ReflectColorType.GetNamesOfChannels(typeOfColor);
Color[] colors = Reflection.ReflectColorType.GetDisplayColorOfChannels(typeOfColor);
float minVal, maxVal;
#region Get the maximum and minimum color intensity values
Type typeOfDepth = imageType.GetGenericArguments()[1];
if (typeOfDepth == typeof(Byte))
{
minVal = 0.0f;
maxVal = 256.0f;
}
else
{
#region obtain the maximum and minimum color value
double[] minValues, maxValues;
Point[] minLocations, maxLocations;
image.MinMax(out minValues, out maxValues, out minLocations, out maxLocations);
double min = minValues[0], max = maxValues[0];
for (int i = 1; i < minValues.Length; i++)
{
if (minValues[i] < min)
{
min = minValues[i];
}
if (maxValues[i] > max)
{
max = maxValues[i];
}
}
#endregion
minVal = (float)min;
maxVal = (float)max;
}
#endregion
for (int i = 0; i < channels.Length; i++)
{
using (DenseHistogram hist = new DenseHistogram(numberOfBins, new RangeF(minVal, maxVal)))
{
hist.Calculate(new IImage[1] {
channels[i]
}, true, null);
AddHistogram(channelNames[i], colors[i], hist);
}
}
}
private Image<Gray, Byte> GetBackproject(Image<Gray, Byte> hue, DenseHistogram _hist,Image<Gray,Byte> mask,Rectangle hide)
{
Image<Gray, Byte> backproject = new Image<Gray, byte>(hue.Width, hue.Height);
var imgs = new IntPtr[1] { hue };
Emgu.CV.CvInvoke.cvCalcBackProject(imgs, backproject, _hist);
Emgu.CV.CvInvoke.cvAnd(backproject, mask, backproject, IntPtr.Zero);
if (th_check)
{
backproject.ROI = face_rect;
if (backproject.GetAverage().Intensity < backproj_threshold/2)
{
isTracked = false;
}
th_check = false;
Emgu.CV.CvInvoke.cvResetImageROI(backproject);
}
hide.Height += 50;
Emgu.CV.CvInvoke.cvSetImageROI(backproject, hide);
try
{
Emgu.CV.CvInvoke.cvZero(backproject);
}
catch { }
Emgu.CV.CvInvoke.cvResetImageROI(backproject);
return backproject;
}