private void button1_Click(object sender, EventArgs e)
{
clearText();
string path = @"D:\Play Data\毕业设计\测试数据\CSDN";
string savepath = @"D:\Play Data\毕业设计\测试数据\CSDN-out";
Stopwatch timer = new Stopwatch();
foreach (var filename in Directory.GetFiles(path)) {
string file = Path.GetFileNameWithoutExtension(filename);
timer.Reset();
timer.Start();
Bitmap bmp = new Bitmap(filename);
byte[] rgb = ImageProcessor.Bmp2Bytes(bmp);
byte[] grey = new WeightedAverageGreyscale().Greyscale(rgb);
byte[] b2 = new MaxVarianceBinarization().Binarization(grey, bmp.Width, bmp.Height);
int it = 0;
byte[] dn = new AnnealKMeansClusterDenoise().Denoise(b2, bmp.Width, bmp.Height, 1, out it);
var img = ImageProcessor.Greyscale2Bmp(dn, bmp.Width, bmp.Height);
timer.Stop();
appendLine("文件：{0}，用时：{1}ms， 降噪迭代次数：{2}", file, timer.ElapsedMilliseconds, it);
img.Save(Path.Combine(savepath, file + ".out.bmp"));
}
}
private static void ProcessImage()
{
string dirname = "cnbeta";
string path = @"D:\Play Data\毕业设计\测试数据\" + dirname;
string savepath = @"D:\Play Data\毕业设计\测试数据\" + dirname + "-out";
Stopwatch timer = new Stopwatch();
using (StreamWriter sw = new StreamWriter(@"D:\Play Data\毕业设计\测试数据\" + dirname + ".txt")) {
foreach (var filename in Directory.GetFiles(path)) {
string file = Path.GetFileNameWithoutExtension(filename);
timer.Reset();
timer.Start();
Image<Bgr, Byte> bmp = new Image<Bgr, byte>(filename);
Image<Gray, Byte> gray = bmp.Convert<Gray, Byte>();
double threshold = new MaxVarianceBinarization().CalculateThreshold(gray);
Image<Gray, Byte> bi = gray.ThresholdBinaryInv(new Gray(threshold), new Gray(255));
int it;
//Stopwatch timer = Stopwatch.StartNew();
Image<Gray, Byte> dn = new AnnealKMeansClusterDenoise().Denoise(bi, 1, out it);
timer.Stop();
//appendLine("降噪用时{0}ms，迭代次数{1}。", timer.ElapsedMilliseconds, it);
Console.WriteLine("文件：{0}，用时：{1}ms， 降噪迭代次数：{2}", file, timer.ElapsedMilliseconds, it);
dn.Save(Path.Combine(savepath, file + ".out.bmp"));
}
}
}
private void gao()
{
clearText();
string filename = label1.Text;
string path = Path.GetDirectoryName(filename);
string file = Path.GetFileNameWithoutExtension(filename);
string savepath = Path.Combine(path, "out");
appendLine(filename);
Image<Bgr, Byte> bmp = new Image<Bgr, byte>(filename);
Image<Gray, Byte> gray = bmp.Convert<Gray, Byte>();
double threshold = new MaxVarianceBinarization().CalculateThreshold(gray);
appendLine("阈值：{0}", threshold);
//Image<Gray, Byte> bi = gray.ThresholdBinaryInv(new Gray(threshold), new Gray(255));
// Image<Gray, Byte> dn = bi.SmoothMedian(3);
int it;
//Stopwatch timer = Stopwatch.StartNew();
//Image<Gray, Byte> dn = new AnnealKMeansClusterDenoise().Denoise(bi, 1, out it);
//timer.Stop();
//appendLine("降噪用时{0}ms，迭代次数{1}。", timer.ElapsedMilliseconds, it);
//var con = dn.FindContours();
//bmp.Draw(con, new Bgr(0, 0, 255), new Bgr(255, 0, 0), 1, 1);
//var proj = Projecting.VerticalProject(dn);
//var proj = bi.SmoothMedian(3);
//var proj = dn - dn.Erode(1);//dn.Dilate(1) - bi;
//var proj = gray.MorphologyEx(see, Emgu.CV.CvEnum.CV_MORPH_OP.CV_MOP_OPEN, 1);
//var proj =
//showImage(imageBox1, bi);
//showImage(imageBox2, dn);
//showImage(imageBox1, gray);
//showImage(imageBox1, gray);
timer.Restart();
var mask = gray.SmoothMask();
showImage(imageBox1, mask);
appendLine("掩模：{0}ms", timer.Stop());
timer.Restart();
double thr = new MaxVarianceBinarization().CalculateThreshold(mask);
var thrd = mask.ThresholdBinaryInv(new Gray(thr), new Gray(255));
showImage(imageBox2, thrd);
appendLine("二值：{0}ms", timer.Stop());
timer.Restart();
var smooth = thrd.SmoothMedian(7);
showImage(imageBox3, smooth);
appendLine("中值滤波：{0}ms", timer.Stop());
timer.Restart();
var dm = new AnnealKMeansClusterDenoise().Denoise(thrd, 0, out it).SmoothMedian(5);
showImage(imageBox4, dm);
appendLine("区域联通：{0}ms", timer.Stop());
#region 灰度分级连通
//int level = 16;
//var gray4 = mask.ToGrayN(level);
//showImage(imageBox1, gray4);
//timer.Restart();
//var conns = gray4.Connect(level);
//appendLine("分级：{0}ms", timer.Stop());
//var mins = Utils.FindLocalMins(conns.Select(c => c.Domains.Count).ToArray());
//var list = new List<ConnectLevel>();
//for (int i = 1; i < mins.Length; ++i) {
// var cl = conns[mins[i]];
// if (cl.Domains.Count == 1) continue;
// if (cl.MeanArea < 10) continue;
// if (cl.Variance > 20000) continue;
// //if (mins[i] - mins[i - 1] == 1) {
// // var lastcl = conns[mins[i] - 1];
// // if (lastcl.Domains.Count != cl.Domains.Count)
// // list.Add(cl);
// //} else {
// // list.Add(cl);
// //}
// list.Add(cl);
//}
//list.ForEach(
// c => appendLine("Lv{0},C{1},MA{2:F2},VA{3:F4}", c.Level, c.Domains.Count, c.MeanArea, c.Variance));
//timer.Restart();
//var bestlevel = list.OrderByDescending(c => c.MeanArea).ThenBy(c => c.Domains.Count).First();
//var colored = gray4.ColorLevel(bestlevel);
//var see = new StructuringElementEx(4, 4, 1, 1, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_ELLIPSE);
//var bg = colored.Erode(10).Dilate(10);
//showImage(imageBox2, colored - bg);
//appendLine("染色处理{0}ms", timer.Stop());
#endregion
//double t2=new MaxVarianceBinarization().CalculateThreshold(mask);
//var proj = mask.ThresholdBinaryInv(new Gray(t2), new Gray(255));
//var akmcd = new AnnealKMeansClusterDenoise();
//var t3 = new MaxVarianceBinarization().CalculateThreshold(mask);
//var bi2 = mask.ThresholdBinaryInv(new Gray(t3), new Gray(255));
//showImage(imageBox2, bi2);
//var dn2 = akmcd.Denoise(bi2, 1, out it);
//showImage(imageBox3, dn2);
//var contour = ContourFinder.ColorContours(dn2, new Gray(0), new Gray(255));
//showImage(imageBox5, contour);
//var con = dn2.FindContours();
//var con2 = dn2.Convert<Bgr, Byte>();
//con2.Draw(con, new Bgr(0, 0, 255), new Bgr(0, 255, 0), 1, 1);
//showImage(imageBox6, con2);
//p1 = akmcd.Denoise(p1, 0, out it);
//p2 = akmcd.Denoise(p2, 0, out it);
//var p3 = p1.Xor(p2);
//var p4 = p1 - p3;
//var p5 = p2 - p3;
//var p6 = p1 - p2;
//var p4 = akmcd.Denoise(p1, 0, out it);
//var p5 = akmcd.Denoise(p2, 0, out it);
//var p6 = akmcd.Denoise(p3, 0, out it);
//StructuringElementEx se = new StructuringElementEx(3, 3, 1, 1, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
//dn = dn.MorphologyEx(se, Emgu.CV.CvEnum.CV_MORPH_OP.CV_MOP_OPEN, 1);
//var canny = dn.Canny(new Gray(threshold), new Gray(threshold * 0.6));
//showImage(imageBox1, bmp);
//showImage(imageBox2, gray);
//showImage(imageBox3, bi);
//showImage(imageBox4, dn);
//showImage(imageBox5, p1);
//showImage(imageBox6, p2);
//showImage(imageBox4, p4);
//showImage(imageBox5, p5);
//showImage(imageBox6, p6);
}