//private void decodematrix()
//{
// Image img = Image.FromFile(filepath1);
// Bitmap bmap1;
// try
// {
// bmap1 = new Bitmap(img);
// }
// catch (System.IO.IOException ioe)
// {
// MessageBox.Show(ioe.ToString());
// return;
// }
// if (bmap1 == null)
// {
// MessageBox.Show("Could not decode image");
// return;
// }
// LuminanceSource source = new RGBLuminanceSource(bmap, bmap.Width, bmap.Height);
// BinaryBitmap bitmap = new BinaryBitmap(new HybridBinarizer(source));
// Result result;
// try
// {
// result = new MultiFormatReader().decode(bitmap);
// }
// catch (ReaderException re)
// {
// MessageBox.Show(re.ToString());
// return;
// }
// MessageBox.Show(result.Text);
//}
public void testhist()
{
Mat img = new Mat();
img = Cv2.ImRead("d:/lena1.jpg");
//Console.WriteLine("hist");
//using (new Window("histimg", img)) ;
Cv2.ImShow("hs", img);
Cv2.WaitKey();
Rangef[] rangefs = new Rangef[]
{
new Rangef(0, 256),
};
int[] hsize = { 255 };
Mat histimg = new Mat();
Mat output = new Mat();
Mat[] mats = new Mat[] { img };
int[] channels = new int[] { 1 };
Cv2.CvtColor(img, output, ColorConversionCodes.RGB2GRAY);
Cv2.CalcHist(mats, channels, output, histimg, 1, hsize, rangefs, true, false);
for (int i = 0; i < 256; i++)//画直方图
{
// Cv2.Line(HistImage, new Point(binImage.Width/256 * (i - 1), binImage.Height - Math.Round(binImage.At<float>(i - 1))), new Point(binImage.Width/256 * (i - 1), binImage.Height - Math.Round(binImage.At<float>(i))), new Scalar(255, 0, 0), 1, LineTypes.AntiAlias);
// int len = (int)((binImage.Get<float>(i)) * output.Rows);//单个箱子的长度，
//
//Cv2.Line(histimg, new Point(img.Width/256*(i-1), img.Height - Math.Round(img.At<float>(i - 1))), new Point(img.Width / 256 * (i - 1), img.Height - Math.Round(img.At<float>(i))), Scalar.Black, 2);//把线画出来
}
// Cv2.ImShow("hist", histimg);
//Cv2.WaitKey();
}
public MatrixHistogram(Matrix m, int nBins)
{
m._mat.MinMaxIdx(out _min, out _max);
_nBins = nBins;
_binWidth = (float)((_max + 1 - _min) / _nBins);
_range = new Rangef((float)_min, (float)_max + 1);
}
private void button1_Click(object sender, EventArgs e)
{
try
{
Mat panda = new Mat(textBox6.Text.ToString(), ImreadModes.Grayscale); //读取为灰度图
Mat[] mats = new Mat[] { panda }; //一张图片，初始化为panda
Mat hist = new Mat(); //用来接收直方图
int[] channels = new int[] { 0 }; //一个通道,初始化为通道0
int[] histsize = new int[] { 256 }; //一个通道，初始化为256箱子
Rangef[] range = new Rangef[1]; //一个通道，值范围
range[0].Start = 0.0F; //从0开始（含）
range[0].End = 256.0F; //到256结束（不含）
Mat mask = new Mat(); //不做掩码
Cv2.CalcHist(mats, channels, mask, hist, 1, histsize, range); //计算灰度图，dim为1 1维
Console.WriteLine(hist.Rows + "行" + hist.Cols + "列"); //把输出的行列打印出来
Cv2.ImShow("直方图", panda);
Cv2.ImWrite("ZFT.jpg", panda);
curBitmap = (Bitmap)Image.FromFile("ZFT.jpg");
this.pB.Image = curBitmap;
pB.Refresh();
this.groupBox3.Visible = false;
}
catch (Exception ex) { MessageBox.Show(ex.Message, "信息提示"); }
}
public void RangefParametricValueVerification()
{
Rangef range = new Rangef(44.0f, 300.0f);
Assert.IsWithin(0.0f, range.ParametricValue(44.0f), 0.00001f);
Assert.IsWithin(1.0f, range.ParametricValue(300.0f), 0.00001f);
Assert.IsWithin(0.5f, range.ParametricValue(172.0f), 0.00001f);
}
public GuiRange(string name,
IGuiStyle style,
IGuiSize size)
: base(name, size, style)
{
mBounds = new Rangef(0.0f, 1.0f);
Percent = 0.0f;
}
public DVH(double maxDose, double voxelCC, string doseUnit)
{
var topScale = High10((float)maxDose);
_voxelCC = voxelCC;
_nBins = (int)(topScale * 1000);
_binWidth = topScale / _nBins;
_range = new Rangef(0, topScale);
_doseUnit = doseUnit;
}
public void RangefLerpVerification()
{
Rangef range = new Rangef(0.0f, 2.0f);
Rangef range2 = new Rangef(0.0f, 10.0f);
Assert.IsWithin(1.0f, range.Lerp(0.5f), 0.00001f);
Assert.IsWithin(-1.0f, range.Lerp(-0.5f), 0.00001f);
Assert.IsWithin(10.0f, range2.Lerp(1.0f), 0.00001f);
Assert.IsWithin(20.0f, range2.Lerp(2.0f), 0.00001f);
}
public void RangefParseVerification()
{
string rangeString = "-12098.43 234987.4293";
string rangeString2 = "0";
Rangef range1 = Rangef.Parse(rangeString);
Rangef range2 = Rangef.Parse(rangeString2);
Assert.IsWithin(-12098.43f, range1.Low, 0.01f);
Assert.IsWithin(234987.4293f, range1.High, 0.1f);
Assert.AreEqual(range2.Low, range2.High);
}
public GuiRange(string name,
IGuiStyle style,
IGuiSize size,
Rangef bounds,
float position)
: base(name, size, style)
{
if (bounds == null)
{
throw new ArgumentNullException("position");
}
mBounds = bounds;
Percent = position;
}
public double CompareImageByHist(Mat img, Mat refImg, bool showImage = false)
{
Mat imgHsv = new Mat();
Mat refImgHsv = new Mat();
Cv2.CvtColor(img, imgHsv, ColorConversionCodes.RGB2HSV);
Cv2.CvtColor(refImg, refImgHsv, ColorConversionCodes.RGB2HSV);
Mat[] imgHsvs = Cv2.Split(imgHsv);
Mat[] refImgHsvs = Cv2.Split(refImgHsv);
int bin1 = 50;
int bin2 = 60;
int[] bins = { bin1, bin2 };
int[] channels = { 0, 1 };
Rangef[] ranges = new Rangef[]
{
new Rangef(0, 180),
new Rangef(0, 256)
};
Mat imgHist = new Mat(img.Size(), MatType.CV_32FC2);
Mat refImgHist = new Mat(img.Size(), MatType.CV_32FC2);
Cv2.CalcHist(imgHsvs, channels, new Mat(), imgHist, 2, bins, ranges, true, false);
Cv2.Normalize(imgHist, imgHist, 1, 0, NormTypes.MinMax, -1, null);
Cv2.CalcHist(refImgHsvs, channels, new Mat(), refImgHist, 2, bins, ranges, true, false);
Cv2.Normalize(refImgHist, refImgHist, 1, 0, NormTypes.MinMax, -1, null);
double ratio = Cv2.CompareHist(imgHist, refImgHist, HistCompMethods.KLDiv);
if (showImage == true)
{
Mat img1 = img.Clone();
Cv2.PutText(img1, ratio.ToString(), new Point(50, 50), HersheyFonts.HersheyPlain, 1, new Scalar(0, 255, 0), 2, LineTypes.AntiAlias);
Cv2.ImShow("CompareHistTestVSRef", img1);
Cv2.WaitKey();
Cv2.DestroyWindow("CompareHistTestVSRef");
}
return(ratio);
}
/// <summary>
/// 通过直方图比较
/// </summary>
/// <param name="sourceImage"></param>
/// <param name="targetImage"></param>
/// <param name="method"></param>
/// <returns></returns>
public static double CompareImageByHistogram(string sourceImage, string targetImage, HistCompMethods method = HistCompMethods.KLDiv)
{
using (Mat source = new Mat(sourceImage, ImreadModes.AnyColor | ImreadModes.AnyDepth))
using (Mat target = new Mat(targetImage, ImreadModes.AnyColor | ImreadModes.AnyDepth))
{
//1:从BGR空间转换到HSV色彩空间
Mat src1HSV = new Mat();
Mat src2HSV = new Mat();
Cv2.CvtColor(source, src1HSV, ColorConversionCodes.BGR2HSV);
Cv2.CvtColor(target, src2HSV, ColorConversionCodes.BGR2HSV);
//Mat[] mats = new Mat[] { baseHSV, src1HSV, src2HSV };
Mat[] mats2 = Cv2.Split(src1HSV);
Mat[] mats3 = Cv2.Split(src2HSV);
//计算直方图并归一化数据
int bin1 = 50; //灰度等级
int bin2 = 60; //灰度等级
int[] histSiz = { bin1, bin2 };
int[] channels = { 0, 1 }; //图像通道数
Rangef[] rangefs = new Rangef[] //梯度值范围
{
new Rangef(0, 180),
new Rangef(0, 256)
};
Mat Img2 = new Mat(source.Size(), MatType.CV_32FC2);
Mat Img3 = new Mat(target.Size(), MatType.CV_32FC2);
Cv2.CalcHist(mats2, channels, new Mat(), Img2, 2, histSiz, rangefs, true, false);
Cv2.Normalize(Img2, Img2, 0, 1, NormTypes.MinMax, -1, null);
Cv2.CalcHist(mats3, channels, new Mat(), Img3, 2, histSiz, rangefs, true, false);
Cv2.Normalize(Img3, Img3, 0, 1, NormTypes.MinMax, -1, null);
//比较直方图
double s1coms2 = Cv2.CompareHist(Img2, Img3, method);
return(s1coms2);
}
}
public void matchTestCompareHist()
{
Mat img = new Mat(Images.picTest, ImreadModes.Color);
Mat refImg = new Mat(Images.picSetRef, ImreadModes.Color);
Mat imgHsv = new Mat();
Mat refImgHsv = new Mat();
Cv2.CvtColor(img, imgHsv, ColorConversionCodes.RGB2HSV);
Cv2.CvtColor(refImg, refImgHsv, ColorConversionCodes.RGB2HSV);
Mat[] imgHsvs = Cv2.Split(imgHsv);
Mat[] refImgHsvs = Cv2.Split(refImgHsv);
int bin1 = 50;
int bin2 = 60;
int[] bins = { bin1, bin2 };
int[] channels = { 0, 1 };
Rangef[] ranges = new Rangef[]
{
new Rangef(0, 180),
new Rangef(0, 256)
};
Mat imgHist = new Mat(img.Size(), MatType.CV_32FC2);
Mat refImgHist = new Mat(img.Size(), MatType.CV_32FC2);
Cv2.CalcHist(imgHsvs, channels, new Mat(), imgHist, 2, bins, ranges, true, false);
Cv2.Normalize(imgHist, imgHist, 1, 0, NormTypes.MinMax, -1, null);
Cv2.CalcHist(refImgHsvs, channels, new Mat(), refImgHist, 2, bins, ranges, true, false);
Cv2.Normalize(refImgHist, refImgHist, 1, 0, NormTypes.MinMax, -1, null);
double ratio = Cv2.CompareHist(refImgHist, imgHist, HistCompMethods.Bhattacharyya);
}
public Mat opencv_method_run(Mat input, Mat output)
{
try
{
//opencv_method_index index = new opencv_method_index();
#region switch
Scalar scalargreen = new Scalar(0x00, 0xFF, 0x00); //绿色 same as 255
Scalar scalarred = new Scalar(0x00, 0x00, 0xff); //red
Scalar scalar = new Scalar(0, 255, 255);
Mat binImage = new Mat(output.Width, output.Height, MatType.CV_8UC1);
switch (listBox1.SelectedItem.ToString())
{
case "颜色空间转换":
{
Cv2.CvtColor(input, output, ColorConversionCodes.RGB2GRAY);
break;
}
case "OSTU":
{
Cv2.CvtColor(input, output, ColorConversionCodes.RGB2GRAY);
Cv2.Threshold(output, binImage, 10, 250, ThresholdTypes.Otsu);
return(binImage);
//break;
}
case "Hist":
{
Rangef[] rangefs = new Rangef[]
{
new Rangef(0, 256),
};
int[] hsize = { 255 };
Mat histimg = new Mat();
Mat[] mats = new Mat[] { output };
int[] channels = new int[] { 1 };
Cv2.CvtColor(input, output, ColorConversionCodes.RGB2GRAY);
Cv2.CalcHist(mats, channels, output, histimg, 1, hsize, rangefs, true, false);
using (new Window("histImage", WindowMode.Normal, histimg));
//Console.WritleLine的后面加上
//Mat HistImage = new Mat(MatType.CV_8UC3, new Scalar(0, 0, 0));
Mat HistImage = new Mat(binImage.Width, binImage.Height, MatType.CV_8UC1, new Scalar(0, 0, 0));
for (int i = 0; i < 256; i++) //画直方图
{
// Cv2.Line(HistImage, new Point(binImage.Width/256 * (i - 1), binImage.Height - Math.Round(binImage.At<float>(i - 1))), new Point(binImage.Width/256 * (i - 1), binImage.Height - Math.Round(binImage.At<float>(i))), new Scalar(255, 0, 0), 1, LineTypes.AntiAlias);
// int len = (int)((binImage.Get<float>(i)) * output.Rows);//单个箱子的长度，
//
Cv2.Line(histimg, new Point(i, 0), new Point(0, i), Scalar.Black, 2); //把线画出来
}
//Cv2.ImShow("s",HistImage);
//using (new Window("histImage", WindowMode.Normal, HistImage))
return(histimg);
//break;
}
case "固定阈值化":
{
Cv2.Threshold(input, output, 150, 200, ThresholdTypes.Binary);
break;
}
case "边缘检测CANNY":
{
Cv2.Canny(input, output, 45, 200);
break;
}
case "霍夫圆变换":
#region hough circle
{
//LineSegmentPoint[] lines;
CircleSegment[] circles;
OpenCvSharp.Size size = new OpenCvSharp.Size(output.Width, output.Height);
Mat image_out3 = new Mat(size, MatType.CV_8UC3);
Cv2.CvtColor(input, output, ColorConversionCodes.RGB2GRAY);
//3: dp:累加器分辨率与图像分辨率的反比。默认=1
/*4：minDist: 检测到的圆的中心之间的最小距离。(最短距离 - 可以分辨是两个圆的，否则认为是同心圆 - src_gray.rows / 8)
* 5:param1: 第一个方法特定的参数。[默认值是100] canny边缘检测阈值低
* 6:param2: 第二个方法特定于参数。[默认值是100] 中心点累加器阈值 – 候选圆心
* 7:minRadius: 最小半径
* 8:maxRadius: 最大半径*/
circles = Cv2.HoughCircles(output, HoughMethods.Gradient, 1, 20, 100, 30, 1, 5000);
Mat dst = new Mat();
input.CopyTo(dst);
for (int i = 0; i < circles.Length; i++)
{
//圆
Cv2.Circle(dst, (int)circles[i].Center.X, (int)circles[i].Center.Y, (int)circles[i].Radius, scalargreen, 2, LineTypes.AntiAlias);
//加强圆心显示
Cv2.Circle(dst, (int)circles[i].Center.X, (int)circles[i].Center.Y, 3, scalarred, 2, LineTypes.Link8);
}
//using (new Window("OutputImage", WindowMode.AutoSize, dst)) ;
//Cv2.WaitKey();
//break;
return(dst);
}
#endregion
#region hough linesp
case "霍夫累计概率变换":
{
// Cv2.CvtColor(input, output, ColorConversionCodes.RGB2GRAY);
/*
* 1； image: 输入图像 （只能输入单通道图像）
* 2； rho: 累加器的距离分辨率(以像素为单位) 生成极坐标时候的像素扫描步长
* 3； theta: 累加器的角度分辨率(以弧度为单位)生成极坐标时候的角度步长，一般取值CV_PI/180 ==1度
* 4； threshold: 累加器阈值参数。只有那些足够的行才会返回 投票(>阈值)；设置认为几个像素连载一起才能被看做是直线。
* 5； minLineLength: 最小线长度，设置最小线段是有几个像素组成。
* 6；maxLineGap: 同一条线上的点之间连接它们的最大允许间隙。(默认情况下是0）：设置你认为像素之间隔多少个间隙也能认为是直线
*/
Cv2.Canny(input, output, 45, 200);
LineSegmentPoint[] linePoint;
linePoint = Cv2.HoughLinesP(output, 1, 2, 10, 1);
for (int i = 0; i < linePoint.Length; i++)
{
Point p1 = linePoint[i].P1;
Point p2 = linePoint[i].P2;
Cv2.Line(output, p1, p2, scalar, 4, LineTypes.AntiAlias);
}
using (new Window("DST", WindowMode.AutoSize, output))
return(output);
}
#endregion
case "自适应阈值":
{
Cv2.CvtColor(input, output, ColorConversionCodes.RGB2GRAY);
Cv2.AdaptiveThreshold(~output, binImage, 255, AdaptiveThresholdTypes.GaussianC, ThresholdTypes.Binary, 15, -2);
return(binImage);
}
case "漫水填充":
{
// Mat binImage = new Mat();
Cv2.CvtColor(input, output, ColorConversionCodes.RGB2GRAY);
Cv2.AdaptiveThreshold(~output, binImage, 255, AdaptiveThresholdTypes.GaussianC, ThresholdTypes.Binary, 15, -2);
return(binImage);
}
default:
//Console.WriteLine("default");
//FormMain form = new FormMain();
MessageBox.Show("请选择处理功能");
break;
}
#endregion
return(output);
}
catch (Exception e)
{
MessageBox.Show(e.Message + "未先选择处理方法或者处理异常");
return(input);
}
#endregion
}
public static float Remap(this float value, Rangef from, Rangef to)
{
return((value - from.min) / from.Length * to.Length + to.min);
}
public void RenderGraph()
{
viewportRange = new Rangef(0f - viewportWidth + viewportOffset, 1f + viewportWidth + viewportOffset);
float viewportFactor = (float)GraphContext.ActualHeight / viewportRange.Length;
//Draw Grid_SubX
if (gridLines_subX != null)
{
for (int i = 0; i < gridLines_subX.Length; ++i)
{
GraphContext.Children.Remove(gridLines_subX[i]);
}
}
float gridCountF = (viewportRange.Length / GridSpace);
int gridCount = (int)gridCountF;
float gridCountRemainder = gridCountF - gridCount;
if (gridCount % 2 != 0)
{
gridCountRemainder += 1f;
}
float viewportOffsetRemainder = viewportOffset - (int)viewportOffset;
if (gridCountF != 0f)
{
gridLines_subX = new Line[gridCount + 1];
for (int i = 0; i < gridLines_subX.Length; ++i)
{
Line line = gridLines_subX[i] = new Line();
line.StrokeThickness = GridThickness;
line.Stroke = GridSubColor;
GraphContext.Children.Add(line);
float halfIndex = i - gridCountF * 0.5f;
float y = ((((halfIndex + gridCountRemainder * 0.5f) / gridCountF)) * (float)GraphContext.ActualHeight) + (float)GraphContext.ActualHeight * 0.5f;
line.X1 = 0;
line.X2 = GraphContext.ActualWidth;
line.Y1 = y;
line.Y2 = y;
}
}
//Draw Grid_SubY
for (int i = 0; i < gridLines_subY.Length; ++i)
{
Line line = gridLines_subY[i];
float lineX = ((float)i / gridLines_subY.Length) * (float)GraphContext.ActualWidth;
line.X1 = lineX;
line.X2 = lineX;
line.Y1 = 0;
line.Y2 = GraphContext.ActualHeight;
}
//Draw Grid_Main
float[] guideYPoints = new float[] {
0f,
1f,
};
for (int i = 0; i < gridLines_main.Length; ++i)
{
Line line = gridLines_main[i];
Canvas.SetZIndex(line, 1);
float y = guideYPoints[i];
float lineY = (float)(GraphContext.ActualHeight - (y - viewportRange.min) * viewportFactor);
line.X1 = 0f;
line.X2 = GraphContext.ActualWidth;
line.Y1 = lineY;
line.Y2 = lineY;
}
//Draw Line
Stopwatch watch = new Stopwatch();
watch.Start();
double linePieceWidth = GraphContext.ActualWidth / LineResolution;
float nextY = CalcY(0f);
for (int i = 0; i < LineResolution; ++i)
{
Line line = previewLines[i];
Canvas.SetZIndex(line, 2);
float nextTime = (float)(i + 1) / (LineResolution);
float y = nextY;
nextY = CalcY(nextTime);
Vector2 start = new Vector2(0f, y);
Vector2 end = new Vector2((float)linePieceWidth, nextY);
float offset = i * (float)linePieceWidth;
line.X1 = start.x + offset;
line.X2 = end.x + offset;
line.Y1 = GraphContext.ActualHeight - (start.y - viewportRange.min) * viewportFactor;
line.Y2 = GraphContext.ActualHeight - (end.y - viewportRange.min) * viewportFactor;
}
//Draw AnimGrid
for (int i = 0; i < gridLines_main.Length; ++i)
{
Line line = animLines[i];
float x = i == 0 ? PreviewBallMargin : (float)PreviewAnimContext.ActualWidth - (float)PreviewBallMargin;
line.X1 = x;
line.X2 = x;
line.Y1 = 0f;
line.Y2 = (float)PreviewAnimContext.ActualHeight;
}
watch.Stop();
LogText();
}
public static bool IsUnique(Mat face)
{
if (_faces.Count == 0)
{
var color1 = ColorsExtractor.ExtractMiddleColor(face);
color1 = ColorsExtractor.ClosestColorRgb(color1);
_faces.Add(face);
_facesMiddleColors.Add(color1);
return(true);
}
/// Using 50 bins for hue and 60 for saturation
int h_bins = 50;
int s_bins = 60;
int[] histSize = new int[2] {
h_bins, s_bins
};
// hue varies from 0 to 179, saturation from 0 to 255
var h_ranges = new Rangef(0, 180);
var s_ranges = new Rangef(0, 256);
var ranges = new Rangef[] { h_ranges, s_ranges };
// Use the o-th and 1-st channels
int[] channels = new int[2] {
0, 1
};
var faceHsv = face.CvtColor(ColorConversionCodes.BGR2HSV);
var faceHistogram = new Mat();
Cv2.CalcHist(new Mat[] { faceHsv }, channels, new Mat(), faceHistogram, 2, histSize, ranges, true, false);
Cv2.Normalize(faceHistogram, faceHistogram, 0, 1, NormTypes.MinMax, -1, new Mat());
var color = ColorsExtractor.ExtractMiddleColor(face);
foreach (var f in _faces)
{
var fHsv = f.CvtColor(ColorConversionCodes.BGR2HSV);
var fHistogram = new Mat();
Cv2.CalcHist(new Mat[] { fHsv }, channels, new Mat(), fHistogram, 2, histSize, ranges, true, false);
Cv2.Normalize(fHistogram, fHistogram, 0, 1, NormTypes.MinMax, -1, new Mat());
double correl = Cv2.CompareHist(faceHistogram, fHistogram, HistCompMethods.Correl);
//if (correl > 0.5) return false;
bool contains = _facesMiddleColors.Contains(color);
if (correl > 0.4 && contains)
{
return(false);
}
//if (correl > 0.65) return false;
}
color = ColorsExtractor.ExtractMiddleColor(face);
_faces.Add(face);
_facesMiddleColors.Add(color);
return(true);
}
private static void HistogarmToComparison(string path1, string path2, string path3)
{
using (Mat baseMat = new Mat(path1, ImreadModes.AnyColor | ImreadModes.AnyDepth))
using (Mat src1 = new Mat(path2, ImreadModes.AnyColor | ImreadModes.AnyDepth))
using (Mat src2 = new Mat(path3, ImreadModes.AnyColor | ImreadModes.AnyDepth))
{
//1:从BGR空间转换到HSV色彩空间
Mat baseHSV = new Mat();
Mat src1HSV = new Mat();
Mat src2HSV = new Mat();
Cv2.CvtColor(baseMat, baseHSV, ColorConversionCodes.BGR2HSV);
Cv2.CvtColor(src1, src1HSV, ColorConversionCodes.BGR2HSV);
Cv2.CvtColor(src2, src2HSV, ColorConversionCodes.BGR2HSV);
//Mat[] mats = new Mat[] { baseHSV, src1HSV, src2HSV };
Mat[] mats1 = Cv2.Split(baseHSV);
Mat[] mats2 = Cv2.Split(src1HSV);
Mat[] mats3 = Cv2.Split(src2HSV);
//计算直方图并归一化数据
int bin1 = 50; //灰度等级
int bin2 = 60; //灰度等级
int[] histSiz = { bin1, bin2 };
int[] channels = { 0, 1 }; //图像通道数
Rangef[] rangefs = new Rangef[] //梯度值范围
{
new Rangef(0, 180),
new Rangef(0, 256)
};
Mat Img1 = new Mat(baseMat.Size(), MatType.CV_32FC2);
Mat Img2 = new Mat(src1.Size(), MatType.CV_32FC2);
Mat Img3 = new Mat(src2.Size(), MatType.CV_32FC2);
HistCompMethods method = HistCompMethods.KLDiv;
Cv2.CalcHist(mats1, channels, new Mat(), Img1, 2, histSiz, rangefs, true, false);
Cv2.Normalize(Img1, Img1, 0, 1, NormTypes.MinMax, -1, null);
Cv2.CalcHist(mats2, channels, new Mat(), Img2, 2, histSiz, rangefs, true, false);
Cv2.Normalize(Img2, Img2, 0, 1, NormTypes.MinMax, -1, null);
Cv2.CalcHist(mats3, channels, new Mat(), Img3, 2, histSiz, rangefs, true, false);
Cv2.Normalize(Img3, Img3, 0, 1, NormTypes.MinMax, -1, null);
//比较直方图
double bcomb = Cv2.CompareHist(Img1, Img1, method);
double s1coms2 = Cv2.CompareHist(Img2, Img3, method);
double bcoms1 = Cv2.CompareHist(Img1, Img2, method);
double bcoms2 = Cv2.CompareHist(Img1, Img3, method);
Mat mats1Ands2 = new Mat();
src2.CopyTo(mats1Ands2);
Cv2.PutText(baseMat, bcomb.ToString(), new Point(30, 30), HersheyFonts.HersheyComplex, 1, new Scalar(0, 255, 0), 2, LineTypes.AntiAlias);
Cv2.PutText(src1, bcoms1.ToString(), new Point(30, 30), HersheyFonts.HersheyComplex, 1, new Scalar(0, 255, 0), 2, LineTypes.AntiAlias);
Cv2.PutText(src2, bcoms2.ToString(), new Point(30, 30), HersheyFonts.HersheyComplex, 1, new Scalar(0, 255, 0), 2, LineTypes.AntiAlias);
Cv2.PutText(mats1Ands2, s1coms2.ToString(), new Point(30, 30), HersheyFonts.HersheyComplex, 1, new Scalar(0, 255, 0), 2, LineTypes.AntiAlias);
using (new Window("Img1 :Img1", WindowMode.Normal, baseMat))
using (new Window("Img1 :Img2", WindowMode.Normal, src1))
using (new Window("Img1 :Img3", WindowMode.Normal, src2))
{
Cv2.WaitKey(0);
}
}
}
/// 直方图计算
public void HistogramCalculation()
{
Mat img = new Mat(Images.picSetRef, ImreadModes.Color);
Mat src = new Mat();
Cv2.CvtColor(img, src, ColorConversionCodes.RGB2HSV);
//using (Mat src = new Mat(Images.picSetRef, ImreadModes.Color))
using (Mat histogram = new Mat())
{
//计算直方图
Mat[] mats = Cv2.Split(src); //分割图像(把三通道分割为3个单通道)
Mat hist_B = new Mat();
Mat hist_G = new Mat();
Mat hist_R = new Mat();
int[] channels0 = { 0 };
int[] channels1 = { 1 };
int[] channels2 = { 2 };
int[] histSize = { 256 };
Rangef[] rangefs = new Rangef[]
{
new Rangef(0, 256),
};
// computes the joint dense histogram for a set of images.
// 计算一组图像的联合密集直方图。
Cv2.CalcHist(mats, channels0, new Mat(), hist_B, 1, histSize, rangefs, true, false);
Cv2.CalcHist(mats, channels1, new Mat(), hist_G, 1, histSize, rangefs, true, false);
Cv2.CalcHist(mats, channels2, new Mat(), hist_R, 1, histSize, rangefs, true, false);
int high = 400;
int width = 512;
int bin_w = width / 256; //每个bins的宽度 画布的宽度除以bins的个数
Mat histImage = new Mat(width, high, MatType.CV_8UC3, new Scalar(0, 0, 0)); //定义一个Mat对象，相当于一个画布
//归一化，像素值有可能数据量很大，压缩一下。是范围在定义画布的范围内。
Cv2.Normalize(hist_B, hist_B, 0, histImage.Rows, NormTypes.MinMax, -1, null);
Cv2.Normalize(hist_G, hist_G, 0, histImage.Rows, NormTypes.MinMax, -1, null);
Cv2.Normalize(hist_R, hist_R, 0, histImage.Rows, NormTypes.MinMax, -1, null);
//绘制直方图
for (int i = 1; i < 256; i++)//遍历直方图的级数
{
//B 画线，一条线有两个点组成。首先确定每个点的坐标(x,y) .遍历从1开始。0 ~ 1 两个点组成一条线，依次类推。
Cv2.Line(histImage, new Point(bin_w * (i - 1), high - Math.Round(hist_B.At <float>(i - 1))), new Point(bin_w * (i - 1), high - Math.Round(hist_B.At <float>(i))), new Scalar(255, 0, 0), 1, LineTypes.AntiAlias);
//G
Cv2.Line(histImage, new Point(bin_w * (i - 1), high - Math.Round(hist_G.At <float>(i - 1))), new Point(bin_w * (i - 1), high - Math.Round(hist_G.At <float>(i))), new Scalar(0, 255, 0), 1, LineTypes.AntiAlias);
//R
Cv2.Line(histImage, new Point(bin_w * (i - 1), high - Math.Round(hist_R.At <float>(i - 1))), new Point(bin_w * (i - 1), high - Math.Round(hist_R.At <float>(i))), new Scalar(0, 0, 255), 1, LineTypes.AntiAlias);
}
using (new Window("SRC", WindowMode.Normal, src))
using (new Window("histImage", WindowMode.Normal, histImage))
{
Cv2.WaitKey(0);
}
}
}
public StationInfo(XmlNode stationNode, GameObject assetsPrototypeRoot)
{
// Type (Mandatory)
XmlNode typeNode = stationNode.SelectSingleNode("@type");
if (typeNode != null)
{
mType = typeNode.InnerText;
if (mType != "Hair" &&
mType != "Makeup" &&
mType != "Sewing" &&
mType != "Holding")
{
throw new Exception("Unknown Fashion Game Station Type (" + mType + ")");
}
}
else
{
throw new Exception("No type attribute found on Station node. Cannot create station info.");
}
// Asset (Mandatory)
XmlNode assetNode = stationNode.SelectSingleNode("@asset");
if (assetNode != null)
{
mAssetPrototype = GameObjectUtility.GetNamedChild(assetNode.InnerText, assetsPrototypeRoot);
}
else
{
throw new Exception("No asset attribute found on Station node. Cannot create station info.");
}
// Time (Optional)
XmlNode timeNode = stationNode.SelectSingleNode("@time");
if (timeNode != null)
{
mTime = Rangef.Parse(timeNode.InnerText).RandomValue();
}
else
{
mTime = 0.0f;
}
// Icon (Mandatory)
XmlNode imagePathNode = stationNode.SelectSingleNode("Icon/@path");
if (imagePathNode != null)
{
mImagePath = ProtocolUtility.ResolvePath(imagePathNode.InnerText);
}
else
{
mImagePath = null;
}
// Gui Offset (Optional)
XmlNode guiOffsetNode = stationNode.SelectSingleNode("@guiOffset");
if (guiOffsetNode != null)
{
mGuiOffset = SerializationUtility.ToVector3(guiOffsetNode.InnerText);
}
else
{
mGuiOffset = Vector3.zero;
}
// Sounds (Optional, Multiple Supported)
XmlNodeList soundNodes = stationNode.SelectNodes("ActivationSound/@path");
foreach (XmlNode soundNode in soundNodes)
{
mSoundPaths.Add(soundNode.InnerText);
}
// WorkingAnimation (Optional)
XmlNode workingAnimationNode = stationNode.SelectSingleNode("WorkingAnimation/@path");
if (workingAnimationNode != null)
{
mWorkingAnimationPath = workingAnimationNode.InnerText;
}
else
{
mWorkingAnimationPath = null;
}
// IdleAnimation (Optional)
XmlNode idleAnimationNode = stationNode.SelectSingleNode("IdleAnimation/@path");
if (idleAnimationNode != null)
{
mIdleAnimationPath = idleAnimationNode.InnerText;
}
else
{
mIdleAnimationPath = null;
}
}
public Mat[] HistogramCalculation(Mat img, bool showImage = false)
{
//Mat img = new Mat(Images.picSetRef, ImreadModes.Color);
Mat src = new Mat();
Cv2.CvtColor(img, src, ColorConversionCodes.RGB2HSV);
using (Mat histogram = new Mat())
{
//计算直方图
Mat[] mats = Cv2.Split(src); //分割图像(把三通道分割为3个单通道)
//Mat hist_H = new Mat();
//Mat hist_S = new Mat();
//Mat hist_V = new Mat();
Mat[] hist_HSV = { new Mat(), new Mat(), new Mat() };
int[] channels0 = { 0 };
int[] channels1 = { 1 };
int[] channels2 = { 2 };
int[] histSize = { 16 }; //256
Rangef[] rangefh = new Rangef[]
{
new Rangef(0, 180),
};
Rangef[] rangefsv = new Rangef[]
{
new Rangef(0, 256),
};
// computes the joint dense histogram for a set of images.
// 计算一组图像的联合密集直方图。
Cv2.CalcHist(mats, channels0, new Mat(), hist_HSV[0], 1, histSize, rangefh, true, false);
Cv2.CalcHist(mats, channels1, new Mat(), hist_HSV[1], 1, histSize, rangefsv, true, false);
Cv2.CalcHist(mats, channels2, new Mat(), hist_HSV[2], 1, histSize, rangefsv, true, false);
int high = 400;
int width = 512;
int bin_w = width / 16; //每个bins的宽度 画布的宽度除以bins的个数 //256
Mat histImage = new Mat(width, high, MatType.CV_8UC3, new Scalar(0, 0, 0)); //定义一个Mat对象，相当于一个画布
//using (new Window("SRC", src))
//using (new Window("histImage", histImage))
//{
// Cv2.WaitKey(0);
// Cv2.DestroyWindow("SRC");
// Cv2.DestroyWindow("histImage");
//}
//归一化，像素值有可能数据量很大，压缩一下。是范围在定义画布的范围内。
Cv2.Normalize(hist_HSV[0], hist_HSV[0], 0, histImage.Rows, NormTypes.MinMax, -1, null);
Cv2.Normalize(hist_HSV[1], hist_HSV[1], 0, histImage.Rows, NormTypes.MinMax, -1, null);
Cv2.Normalize(hist_HSV[2], hist_HSV[2], 0, histImage.Rows, NormTypes.MinMax, -1, null);
//绘制直方图
for (int i = 1; i < 16; i++)//遍历直方图的级数 //256
{
//B 画线，一条线有两个点组成。首先确定每个点的坐标(x,y) .遍历从1开始。0 ~ 1 两个点组成一条线，依次类推。
Cv2.Line(histImage, new Point(bin_w * (i - 1), high - Math.Round(hist_HSV[0].At <float>(i - 1))), new Point(bin_w * (i - 1), high - Math.Round(hist_HSV[0].At <float>(i))), new Scalar(255, 0, 0), 1, LineTypes.AntiAlias);
//G
Cv2.Line(histImage, new Point(bin_w * (i - 1), high - Math.Round(hist_HSV[1].At <float>(i - 1))), new Point(bin_w * (i - 1), high - Math.Round(hist_HSV[1].At <float>(i))), new Scalar(0, 255, 0), 1, LineTypes.AntiAlias);
//R
Cv2.Line(histImage, new Point(bin_w * (i - 1), high - Math.Round(hist_HSV[2].At <float>(i - 1))), new Point(bin_w * (i - 1), high - Math.Round(hist_HSV[2].At <float>(i))), new Scalar(0, 0, 255), 1, LineTypes.AntiAlias);
}
if (showImage == true)
{
using (new Window("SRC", src)) //WindowMode.Normal
using (new Window("histImage", histImage))
{
Cv2.WaitKey(0);
Cv2.DestroyWindow("SRC");
Cv2.DestroyWindow("histImage");
}
}
return(hist_HSV);
}
}
public GuiRange(GuiRange copy)
: base(copy.Name, copy.GuiSize, copy.Style)
{
mBounds = copy.mBounds;
mPercent = copy.mPercent;
}
