/// <summary>
/// Gets flesh regions by histogram back projection
/// </summary>
/// <param name="imgSrc"></param>
/// <param name="hsvPlanes"></param>
/// <param name="imgRender"></param>
private void RetrieveFleshRegion(IplImage imgSrc, IplImage[] hsvPlanes, IplImage imgDst)
{
int[] histSize = new int[] {30, 32};
float[] hRanges = {0.0f, 20f};
float[] sRanges = {50f, 255f};
float[][] ranges = {hRanges, sRanges};
imgDst.Zero();
using (CvHistogram hist = new CvHistogram(histSize, HistogramFormat.Array, ranges, true))
{
hist.Calc(hsvPlanes, false, null);
float minValue, maxValue;
hist.GetMinMaxValue(out minValue, out maxValue);
hist.Normalize(imgSrc.Width * imgSrc.Height * 255 / maxValue);
hist.CalcBackProject(hsvPlanes, imgDst);
}
}
// Creates an image from a 2D Histogram (x axis = Hue, y axis = Saturation)
void DrawHSHistogram(CvHistogram hist)
{
// Get the maximum and minimum values from the histogram
float minValue, maxValue;
hist.GetMinMaxValue(out minValue, out maxValue);
int xBins = hist.Bins.GetDimSize(0); // Number of hue bins (x axis)
int yBins = hist.Bins.GetDimSize(1); // Number of saturation bins (y axis)
// Create an image to visualize the histogram
int scaleHeight = 5, scaleWidth = 5;
CvMat hist_img = new CvMat(yBins * scaleHeight, xBins * scaleWidth, TriColorMatrix);
hist_img.Zero(); // Set all the pixels to black
double binVal;
int _intensity;
for (int h = 0; h < xBins; h++)
{
for (int s = 0; s < yBins; s++)
{
binVal = Cv.QueryHistValue_2D(hist, h, s);
_intensity = Cv.Round(binVal / maxValue * 255); // 0 to 255
// Draw a rectangle (h, s) to (h+1, s+1) (scaled by window size)
// The pixel value is the color of the histogram value at bin (h, s)
hist_img.Rectangle(Cv.Point(h * scaleWidth, s * scaleHeight),
Cv.Point((h + 1) * scaleWidth - 1, (s + 1) * scaleHeight - 1),
Cv.RGB(_intensity, _intensity, _intensity),
Cv.FILLED);
}
}
Cv.ShowImage("HS Histogram", hist_img);
}
// Takes an image and calculates its histogram in HSV color space
// Color images have 3 channels (4 if you count alpha?)
// Webcam captures them in (R)ed, (G)reen, (B)lue.
// Convert to (H)ue, (S)aturation (V)alue to get better separation for thresholding
CvHistogram CalculateHSVHistogram(CvMat _image)
{
// Hue, Saturation, Value or HSV is a color model that describes colors (hue or tint)
// in terms of their shade (saturation or amount of gray)
// and their brightness (value or luminance).
// For HSV, Hue range is [0,179], Saturation range is [0,255] and Value range is [0,255]
// hue varies from 0 to 179, see cvtColor
float hueMin = 0, hueMax = 179;
float[] hueRanges = new float[2] { hueMin, hueMax };
// saturation varies from 0 (black-gray-white) to
// 255 (pure spectrum color)
float satMin = 0, satMax = 255;
float[] saturationRanges = new float[2] { satMin, satMax };
float valMin = 0, valMax = 255;
float[] valueRanges = new float[2] { valMin, valMax };
float[][] ranges = { hueRanges, saturationRanges, valueRanges };
// Note: You don't need to use all 3 channels for the histogram.
int hueBins = 32; // Number of bins in the Hue histogram (more bins = narrower bins)
int satBins = 32; // Number of bins in the Saturation histogram (more bins = narrower bins)
int valueBins = 8; // Number of bins in the Value histogram (more bins = narrower bins)
float maxValue = 0, minValue = 0; // Minimum and maximum value of calculated histogram
// Number of bins per histogram channel
// If we use all 3 channels (H, S, V) then the histogram will have 3 dimensions.
int[] hist_size = new int[] { hueBins, satBins, valueBins };
CvHistogram hist = new CvHistogram(hist_size, HistogramFormat.Array, ranges, true);
using (CvMat _imageHSV = ConvertToHSV(_image)) // Convert the image to HSV
// We could keep the image in B, G, R, A if we wanted to.
// Just split the channels into B, G, R planes
using (CvMat imgH = new CvMat(_image.Rows, _image.Cols, MonoColorMatrix))
using (CvMat imgS = new CvMat(_image.Rows, _image.Cols, MonoColorMatrix))
using (CvMat imgV = new CvMat(_image.Rows, _image.Cols, MonoColorMatrix))
{
// Break image into H, S, V planes
// If the image were RGB, then it would split into R, G, B planes respectively
_imageHSV.CvtPixToPlane(imgH, imgS, imgV, null); // Cv.Split also does this
// Store HSV planes as an IplImage array to pass to openCV's hist function
IplImage[] hsvPlanes = { Cv.GetImage(imgH), Cv.GetImage(imgS), Cv.GetImage(imgV) };
hist.Calc(hsvPlanes, false, null); // Call hist function (no accumulatation, no mask)
// Do we need to normalize??
hist.GetMinMaxValue(out minValue, out maxValue);
// Scale the histogram to unity height
hist.Normalize(_imageHSV.Width * _imageHSV.Height * hist.Dim * hueMax / maxValue);
}
return hist; // Return the histogram
}
/// <summary>
/// ヒストグラムの計算
/// </summary>
/// <param name="img"></param>
/// <param name="hist"></param>
private static void CalcHist(IplImage img, CvHistogram hist)
{
hist.Calc(img);
float minValue, maxValue;
hist.GetMinMaxValue(out minValue, out maxValue);
Cv.Scale(hist.Bins, hist.Bins, ((double)img.Height) / maxValue, 0);
}