public override void Recycle()
{
base.Recycle();
t = 0;
speed = 4;
recycleTimer.Restart();
}
public void Tick()
{
if (curCount == count)
{
Fire();
count--;
}
timer.Tick();
if (timer.state == MyTimer.STATE.finished && count != 0)
{
Fire();
if (count != -1)
{
count--;
}
timer.Restart();
}
}
private static void fastPruningWithRSC()
{
int s = 100, r = 5;
MyTimer timer = new MyTimer();
string[] templatefiles = Directory.GetFiles(@"D:\Play Data\train_data\sc\", "*.sc")
.Take(1000).ToArray();
#region 打开模板
string templatepath = @"D:\Play Data\template\";
int templatecount = templatefiles.Length;
Debug("打开{0}个模板--------------------------------------------", templatecount);
double[][,] templates = new double[templatecount][,];
int[] templatenums = new int[templatecount];
timer.Restart();
for (int i = 0; i < templatefiles.Length; ++i) {
string file = templatefiles[i];
string filename = Path.GetFileNameWithoutExtension(file);
templatenums[i] = int.Parse(filename.Split('-')[1]);
templates[i] = new double[s, 60];
unsafe {
#region 指针也快不了多少
//byte[] data = File.ReadAllBytes(file);
//fixed (byte* ptr = data) {
// //using (var fs = new FileStream(file, FileMode.Open)) {
// // using (var br = new BinaryReader(fs)) {
// int offset = 0;
// for (int j = 0; j < s; ++j) {
// for (int k = 0; k < 60; ++k) {
// //templates[i][j, k] = br.ReadDouble();
// fixed (double* t = &templates[i][j, k]) {
// for (int p = 0; p < sizeof(double); ++p)
// *((byte*)t + p) = *(ptr + offset++);
// }
// }
// }
//}
#endregion
using (var fs = new FileStream(file, FileMode.Open)) {
using (var br = new BinaryReader(fs)) {
for (int j = 0; j < s; ++j) {
for (int k = 0; k < 60; ++k) {
templates[i][j, k] = br.ReadDouble();
}
}
}
}
}
if (i % 100 == 0)
Debug("已完成{0}个", i);
}
Debug("模板读取完成，用时{0}ms.", timer.Stop());
#endregion
string[] testfiles = Directory.GetFiles(@"D:\Play Data\test\", "*.bmp")
.Take(100).ToArray();
#region 测试
int testcase = testfiles.Length, acc = 0;
Debug("为{0}个对象寻找候选模板------------------------------------------", testcase);
foreach (var file in testfiles) {
timer.Restart();
string filenameext = Path.GetFileName(file);
string filename = Path.GetFileNameWithoutExtension(file);
int thisnum = int.Parse(filename.Split('-')[1]);
Image<Gray, Byte> img = new Image<Gray, byte>(file);
var list = getEdge(img.Resize(2.5, Emgu.CV.CvEnum.INTER.CV_INTER_LINEAR)).Sample(s);
var sc100 = Jim.OCR.ShapeContext2D.ShapeContext.ComputeSC(list);
double[,] scq = new double[r, 60];
for (int i = 0; i < 5; ++i) {
int pos = rand.Next(s);
for (int k = 0; k < 60; ++k) {
scq[i, k] = sc100[pos, k];
}
}
var arr = new ValueIndexPair<double>[templatecount];
for (int i = 0; i < templatecount; ++i) {
double[,] sci = templates[i];
double[,] costmat = Jim.OCR.ShapeContext2D.ShapeContext.HistCost(scq, sci);
double cost = 0;
for (int j = 0; j < 5; ++j) {
double min = double.MaxValue;
for (int u = 0; u < s; ++u) {
double val = costmat[j, u];
if (val < min) min = val;
}
cost += min;
}
arr[i] = new ValueIndexPair<double> { Value = cost, Index = i };
}
Array.Sort(arr, (a, b) => a.Value.CompareTo(b.Value));
int[] matchcount = new int[10];
double[] matchcost = new double[10];
int knn = 10;
foreach (var pair in arr.Take(knn)) {
int num = templatenums[pair.Index];
matchcount[num]++;
matchcost[num] += pair.Value;
}
var match = matchcount.Select((val, i) => new { Count = val, Num = i })
.Where(v => v.Count > 0)
.OrderByDescending(v => v.Count).ToArray();
//var match = matchcost.Select((val, i) => new { Cost = val / matchcount[i], Num = i })
// .Where(v => !double.IsNaN(v.Cost))
// .OrderBy(v => v.Cost).ToArray();
#region 进行精细匹配，效果一般
//double[] matchrate = new double[10];
//foreach (var m in match) {
// if (m.Count == 0) break;
// string template = Path.Combine(templatepath, m.Num + ".bmp");
// Jim.OCR.ShapeContext2D.ShapeContext sc = new Jim.OCR.ShapeContext2D.ShapeContext(file, template);
// sc.debug_flag = false;
// sc.timer_flag = false;
// sc.display_flag = false;
// sc.n_iter = 3;
// sc.matchScale = 2.5;
// sc.maxsamplecount = 100;
// matchrate[m.Num] = sc.MatchFile();
//}
//var bestmatches = matchrate.Select((val, i) => new { Cost = val, Num = i })
// .Where(m => m.Cost > 0)
// .OrderBy(m => m.Cost).ToArray();
//int firstmatch = bestmatches[0].Num;
#endregion
int firstmatch = match[0].Num;
var fc = Console.ForegroundColor;
Console.ForegroundColor = firstmatch == thisnum ? ConsoleColor.Green : ConsoleColor.Red;
string info = String.Format("{0} {1}ms - {2}\t", filename, timer.Stop(), (firstmatch == thisnum ? "Right" : "Wrong"));
//foreach (var m in bestmatches.Take(4)) {
foreach (var m in match) {
info += String.Format("{0}/{1}\t", m.Num, m.Count);
//info += String.Format("{0}/{1:F3}\t", m.Num, m.Cost);
//info += String.Format("{0}/{1:F3}\t", m.Num, m.Cost);
}
Debug(info);
Console.ForegroundColor = fc;
if (firstmatch == thisnum) {
++acc;
}
}
Debug("测试用例：{0}。正确率{1}。", testcase, acc);
#endregion
}
private static ImageShapeContext CreateShapeContext(string path, string fontFamily, int samplecount, char c)
{
MyTimer timer = new MyTimer();
Font font = new Font(fontFamily, 240);
string file = Path.Combine(path, String.Format("{0}-{1}.bmp", fontFamily, c));
string fontpath = Path.Combine(path, fontFamily);
Image<Bgr, Byte> img = new Image<Bgr, byte>(400, 400);
using (Graphics g = Graphics.FromImage(img.Bitmap)) {
g.Clear(Color.Black);
g.DrawString(c.ToString(), font, Brushes.White, 0, 0);
}
timer.Restart();
var canny = img.Convert<Gray, Byte>().Canny(new Gray(100), new Gray(60));
timer.StopAndSay("Canny");
timer.Restart();
List<Point> edge = new List<Point>();
for (int y = 0; y < canny.Height; ++y) {
for (int x = 0; x < canny.Width; ++x) {
if (canny[y, x].Intensity > 0) {
edge.Add(new Point(x, y));
}
}
}
var sample = edge.Sample(samplecount);
timer.StopAndSay("Sample");
using (var sw = new StreamWriter(Path.Combine(path, String.Format("!Sample-{0}-{1}.txt", fontFamily, c)))) {
foreach (var p in sample) {
sw.WriteLine("{0},{1}", p.X, p.Y);
}
}
timer.Restart();
double min, max;
var norm = Jim.OCR.Algorithms.ShapeContext.Normalize(sample, out min, out max);
timer.StopAndSay("Normalize");
timer.Restart();
var scs = new ImageShapeContext(sample);
for (int i = 0; i < norm.Length; ++i) {
scs.ShapeContests[i] = new Jim.OCR.Algorithms.ShapeContext(norm[i]);
//var his = sc.Histogram.ToImage();
var p = sample[i];
//string hisfile = Path.Combine(fontpath, string.Format("[{0},{1}].bmp", p.X, p.Y));
//his.Save(hisfile);
img[p] = new Bgr(0, 0, 255);
}
timer.StopAndSay("ShapeContext");
//foreach (var p in sample) {
// var sc = new ShapeContext(p, sample);
// var his = sc.Histogram.ToImage();
// string hisfile = Path.Combine(fontpath, string.Format("[{0},{1}].bmp", p.X, p.Y));
// his.Save(hisfile);
// img[p] = new Bgr(0, 0, 255);
//}
//var contours = img.Convert<Gray, Byte>().FindContours();
//img.Draw(contours, new Bgr(0, 0, 255), new Bgr(0, 255, 0), 2, 1);
//dfsContour(img, contours, 0, 2);
//foreach (var ii in contours.) {
// //Console.WriteLine
// img[ii] = new Bgr(0, 0, 255);
//}
//img.Save(file);
return scs;
}
private static void computeTemplateSC(int start, int count)
{
int s = 100;
string[] templatefiles = Directory.GetFiles(@"D:\Play Data\my_template\", "*.jpg")
.Skip(start).Take(count).ToArray();
int templatecount = templatefiles.Length;
Debug("解析{0}个模板--------------------------------------------", templatecount);
MyTimer timer = new MyTimer();
timer.Restart();
for (int i = 0; i < templatecount; ++i) {
string file = templatefiles[i];
string filenameext = Path.GetFileName(file);
string filename = Path.GetFileNameWithoutExtension(file);
Image<Gray, Byte> img = new Image<Gray, byte>(file);
var list = getEdge(img.Resize(2.5, Emgu.CV.CvEnum.INTER.CV_INTER_LINEAR)).Sample(s);
if (i % 100 == 0)
Debug("已完成{0}个", i);
var sc = Jim.OCR.ShapeContext2D.ShapeContext.ComputeSC(list);
using (var fs = new FileStream(Path.Combine(@"D:\Play Data\my_template_data\sc\", filename + ".sc"), FileMode.Create))
{
using (var bw = new BinaryWriter(fs)) {
for (int j = 0; j < s; ++j) {
for (int k = 0; k < 60; ++k) {
bw.Write(sc[j, k]);
}
}
}
}
}
Debug("模板计算完成，用时{0}ms.", timer.Stop());
}
private static void clusterSC()
{
int s = 100, K = 100;
string[] templatefiles = Directory.GetFiles(@"D:\Play Data\my_template_data\sc\", "*.sc")
.Take(180).ToArray();
int templatecount = templatefiles.Length;
#region 读取模板
Debug("打开{0}个模板--------------------------------------------", templatecount);
double[][] templates = new double[templatecount * s][];
MyTimer timer = new MyTimer();
timer.Restart();
for (int i = 0; i < templatefiles.Length; ++i) {
string file = templatefiles[i];
string filename = Path.GetFileNameWithoutExtension(file);
using (var fs = new FileStream(file, FileMode.Open)) {
using (var br = new BinaryReader(fs)) {
for (int j = 0; j < s; ++j) {
templates[i * s + j] = new double[60];
for (int k = 0; k < 60; ++k) {
templates[i * s + j][k] = br.ReadDouble();
}
}
}
}
if (i % 100 == 0)
Debug("已完成{0}个", i);
}
Debug("模板读取完成，用时{0}ms.", timer.Stop());
#endregion
#region 聚类
timer.Restart();
KMeans<double[]> kmeans = new KMeans<double[]>(templates, K,
Jim.OCR.ShapeContext2D.ShapeContext.HistCost,
scs => {
double[] scnew = new double[60];
for (int k = 0; k < 60; ++k) {
scnew[k] = scs.Average(sc => sc[k]);
}
return scnew;
}
);
kmeans.MaxIterate = 100;
var cluster = kmeans.Cluster();
Debug("聚类完成，用时{0}ms.", timer.Stop());
#endregion
using (var fs = new FileStream(Path.Combine(@"D:\Play Data\my_template_data\sm-" + K, templatecount + ".sm"), FileMode.Create))
{
using (var bw = new BinaryWriter(fs)) {
for (int i = 0; i < K; ++i) {
for (int k = 0; k < 60; ++k) {
bw.Write(cluster[i].Center[k]);
}
}
}
}
}
private static void testMNIST2()
{
List<string> wrong = new List<string>();
int testcount = 100, pos = 0;
MyTimer timer = new MyTimer();
foreach (var file in Directory.GetFiles(@"D:\Play Data\test", "*.bmp")) {
if (pos == testcount) break;
string filename = Path.GetFileNameWithoutExtension(file);
int ans = int.Parse(filename.Split('-')[1]);
timer.Restart();
double[] results = new double[10];
int scale = 8;
for (int d = 0; d < 10; ++d) {
string template = Path.Combine(@"D:\Play Data\template", d + ".bmp");
var sc = new Jim.OCR.ShapeContext2D.ShapeContext(file, template);
sc.n_iter = 1;
//sc.showScale = 6;
sc.matchScale = 2.5;
sc.maxsamplecount = 100;
sc.display_flag = false;
sc.debug_flag = false;
sc.timer_flag = false;
double matchcost = sc.MatchFile();
results[d] = matchcost;// / sc.nsamp;
}
var sort = results.Select((c, i) => new { Cost = c, Digit = i }).OrderBy(v => v.Cost).ToArray();
if (sort[0].Digit == ans) {
var fc = Console.ForegroundColor;
Console.ForegroundColor = ConsoleColor.Green;
string s = String.Format("File:{0}-{1}ms-Right!\t", filename, timer.Stop());
for (int i = 0; i < 4; ++i) {
s += String.Format("{0}/{1:F4}, ", sort[i].Digit, sort[i].Cost);
}
Debug(s);
Console.ForegroundColor = fc;
} else {
var fc = Console.ForegroundColor;
Console.ForegroundColor = ConsoleColor.Red;
//Debug("File:{0}-{1}ms-Opps!", filename, Timer.Stop());
wrong.Add(filename);
string s = String.Format("File:{0}-{1}ms-Wrong!\t", filename, timer.Stop());
for (int i = 0; i < 4; ++i) {
s += String.Format("{0}/{1:F4}, ", sort[i].Digit, sort[i].Cost);
}
Debug(s);
Console.ForegroundColor = fc;
#region 画对比图
//string dir = Path.Combine(@"D:\Play Data\output", filename);
//if (Directory.Exists(dir)) Directory.Delete(dir, true);
//Directory.CreateDirectory(dir);
//for (int d = 0; d < 10; ++d) {
// var sc = isc1[d];
// var sci = isc2[d];
// var km = kms[d];
// int ax = (int)sc.Points.Average(p => p.X),
// ay = (int)sc.Points.Average(p => p.Y),
// bx = (int)sci.Points.Average(p => p.X),
// by = (int)sci.Points.Average(p => p.Y);
// int dx = bx - ax,
// dy = by - ay;
// Image<Bgr, Byte> img = new Image<Bgr, byte>(28 * scale, 28 * scale);
// Font fs = new Font("Arial", 18);
// using (Graphics g = Graphics.FromImage(img.Bitmap)) {
// g.Clear(Color.Black);
// for (int j = 0; j < sci.Points.Count; ++j) {
// int i = km.MatchPair[j + sc.Points.Count];
// Point pb = sci.Points[j], pa = sc.Points[i];
// pb.X -= dx;
// pb.Y -= dy;
// int r = 2;
// g.DrawEllipse(Pens.Red, pa.X * scale - r, pa.Y * scale - r, r * 2 + 1, r * 2 + 1);
// g.DrawEllipse(Pens.Green, pb.X * scale - r, pb.Y * scale - r, r * 2 + 1, r * 2 + 1);
// g.DrawLine(Pens.Gray, new Point(pa.X * scale, pa.Y * scale), new Point(pb.X * scale, pb.Y * scale));
// }
// g.DrawString(results[d].ToString(), fs, Brushes.White, new PointF(0, 0));
// }
// img.Save(Path.Combine(dir, d + ".bmp"));
//}
#endregion
}
++pos;
}
Debug("Wrong：{0}", wrong.Count);
Debug("Wrong rate:{0:F2}%", 100.0 * wrong.Count / testcount);
}
private static void testMNIST()
{
var timer = new MyTimer();
List<Point>[] Edges = new List<Point>[10];
#region 准备边缘集合
//Font standardFont = new Font("Arial", 20);
//for (int i = 0; i < 10; ++i) {
// Image<Bgr, Byte> img = new Image<Bgr, byte>(28, 28);
// using (Graphics g = Graphics.FromImage(img.Bitmap)) {
// g.Clear(Color.Black);
// g.DrawString(i.ToString(), standardFont, Brushes.White, 0, 0);
// }
// Edges[i] = getEdge(img.Convert<Gray, Byte>());
// //img.Save(Path.Combine(@"D:\Play Data\", i + ".bmp"));
//}
for (int i = 0; i < 10; ++i) {
string template = Path.Combine(@"D:\Play Data\template", i + ".bmp");
Image<Gray, Byte> img = new Image<Gray, byte>(template);
Edges[i] = getEdge(img);
}
#endregion
List<string> wrong = new List<string>();
int testcount = 100, pos = 0;
foreach (var file in Directory.GetFiles(@"D:\Play Data\test", "*.bmp")) {
if (pos == testcount) break;
string filename = Path.GetFileNameWithoutExtension(file);
int ans = int.Parse(filename.Split('-')[1]);
timer.Restart();
List<Point> edge = getEdge(new Image<Gray, byte>(file));
ImageShapeContext[] isc1 = new ImageShapeContext[10];
ImageShapeContext[] isc2 = new ImageShapeContext[10];
KM[] kms = new KM[10];
double[] results = new double[10];
int scale = 8;
for (int d = 0; d < 10; ++d) {
int samplecount = Math.Min(edge.Count, Edges[d].Count);
var sc = createISC(edge, samplecount);
var sci = createISC(Edges[d], samplecount);
var scm = new ShapeContextMatching(sc.ShapeContests, sci.ShapeContests);
scm.BuildCostGraph();
var km = scm.Match();
isc1[d] = sc;
isc2[d] = sci;
kms[d] = km;
results[d] = (double)km.MatchResult / samplecount;
}
var sort = results.Select((c, i) => new { Cost = c, Digit = i }).OrderBy(v => v.Cost).ToArray();
if (sort[0].Digit == ans) {
var fc = Console.ForegroundColor;
Console.ForegroundColor = ConsoleColor.Green;
string s = String.Format("File:{0}-{1}ms-Right!\t", filename, timer.Stop());
for (int i = 0; i < 4; ++i) {
s += String.Format("{0}/{1:F4}, ", sort[i].Digit, sort[i].Cost);
}
Debug(s);
Console.ForegroundColor = fc;
} else {
var fc = Console.ForegroundColor;
Console.ForegroundColor = ConsoleColor.Red;
wrong.Add(filename);
string s = String.Format("File:{0}-{1}ms-Wrong!", filename, timer.Stop());
for (int i = 0; i < 5; ++i) {
s += String.Format("{0}/{1:F2}, ", sort[i].Digit, sort[i].Cost);
}
Debug(s);
Console.ForegroundColor = fc;
string dir = Path.Combine(@"D:\Play Data\output", filename);
if (Directory.Exists(dir)) Directory.Delete(dir, true);
Directory.CreateDirectory(dir);
for (int d = 0; d < 10; ++d) {
var sc = isc1[d];
var sci = isc2[d];
var km = kms[d];
int ax = (int)sc.Points.Average(p => p.X),
ay = (int)sc.Points.Average(p => p.Y),
bx = (int)sci.Points.Average(p => p.X),
by = (int)sci.Points.Average(p => p.Y);
int dx = bx - ax,
dy = by - ay;
Image<Bgr, Byte> img = new Image<Bgr, byte>(28 * scale, 28 * scale);
Font fs = new Font("Arial", 18);
using (Graphics g = Graphics.FromImage(img.Bitmap)) {
g.Clear(Color.Black);
for (int j = 0; j < sci.Points.Count; ++j) {
int i = km.MatchPair[j + sc.Points.Count];
Point pb = sci.Points[j], pa = sc.Points[i];
pb.X -= dx;
pb.Y -= dy;
int r = 2;
g.DrawEllipse(Pens.Red, pa.X * scale - r, pa.Y * scale - r, r * 2 + 1, r * 2 + 1);
g.DrawEllipse(Pens.Green, pb.X * scale - r, pb.Y * scale - r, r * 2 + 1, r * 2 + 1);
g.DrawLine(Pens.Gray, new Point(pa.X * scale, pa.Y * scale), new Point(pb.X * scale, pb.Y * scale));
}
g.DrawString(results[d].ToString(), fs, Brushes.White, new PointF(0, 0));
}
img.Save(Path.Combine(dir, d + ".bmp"));
}
}
++pos;
}
Debug("Wrong：{0}", wrong.Count);
Debug("Wrong rate:{0:F2}%", 100.0 * wrong.Count / testcount);
}
private void openSC(string dir)
{
new Thread(new ThreadStart(() => {
timer.Restart();
const int shapeme_count = 100;
shapemes = new double[shapeme_count][];
showStatus("加载Shapeme");
#region Shapeme
using (var fs = new FileStream(Path.Combine(dir, "data.sm"), FileMode.Open)) {
using (var br = new BinaryReader(fs)) {
for (int i = 0; i < shapeme_count; ++i)
{
shapemes[i] = new double[60];
for (int k = 0; k < 60; ++k)
{
shapemes[i][k] = br.ReadDouble();
}
showProgress((i + 1.0) / shapeme_count);
}
}
}
#endregion
#region SCQ
showStatus("加载量化形状上下文");
var template_files = Directory.GetFiles(Path.Combine(dir, "scq"), "*.scq").ToArray();
int template_count = template_files.Length;
template_histograms = new int[template_count][];
template_chars = new string[template_count];
for (int f = 0; f < template_count; ++f)
{
string file = template_files[f];
string filename = Path.GetFileNameWithoutExtension(file);
template_chars[f] = filename.Split('-')[1];
template_histograms[f] = new int[shapeme_count];
using (var fs = new FileStream(file, FileMode.Open)) {
using (var br = new BinaryReader(fs)) {
for (int i = 0; i < shapeme_count; ++i)
{
template_histograms[f][i] = br.ReadInt32();
}
}
}
showProgress((f + 1.0) / template_count);
}
#endregion
#region SC
showStatus("加载形状上下文");
var sc_files = Directory.GetFiles(Path.Combine(dir, "sc"), "*.sc").ToArray();
template_sc = new double[template_count][, ];
for (int f = 0; f < template_count; ++f)
{
string file = sc_files[f];
string filename = Path.GetFileNameWithoutExtension(file);
template_sc[f] = new double[100, 60];
using (var fs = new FileStream(file, FileMode.Open)) {
using (var br = new BinaryReader(fs)) {
for (int j = 0; j < 100; ++j)
{
for (int k = 0; k < 60; ++k)
{
template_sc[f][j, k] = br.ReadDouble();
}
}
}
}
showProgress((f + 1.0) / template_count);
}
#endregion
showStatus("加载完成，用时{0}ms。", timer.Stop());
})).Start();
}
private static void testCAPTCHA()
{
var timer = new MyTimer();
int s = 100, K = 100, m = 180;
string[] templatefiles = Directory.GetFiles(@"D:\Play Data\my_template_data\scq-" + K + "-" + m, "*.scq")
.Take(10000).ToArray();
#region 打开量化的模板
int templatecount = templatefiles.Length;
Debug("打开{0}个量化模板----------------------------------------", templatecount);
timer.Restart();
int[][] templatehistograms = new int[templatecount][];
string[] templatechars = new string[templatecount];
for (int f = 0; f < templatecount; ++f) {
string file = templatefiles[f];
string filename = Path.GetFileNameWithoutExtension(file);
templatechars[f] = filename.Split('-')[1];
templatehistograms[f] = new int[K];
using (var fs = new FileStream(file, FileMode.Open)) {
using (var br = new BinaryReader(fs)) {
for (int i = 0; i < K; ++i) {
templatehistograms[f][i] = br.ReadInt32();
}
}
}
}
Debug("打开完成，用时{0}ms.", timer.Stop());
#endregion
#region 打开Shapeme
Debug("打开{0}个Shapeme.", K);
timer.Restart();
double[][] shapemes = new double[K][];
using (var fs = new FileStream(Path.Combine(@"D:\Play Data\my_template_data\sm-" + K, m + ".sm"), FileMode.Open)) {
using (var br = new BinaryReader(fs)) {
for (int i = 0; i < K; ++i) {
shapemes[i] = new double[60];
for (int k = 0; k < 60; ++k) {
shapemes[i][k] = br.ReadDouble();
}
}
}
}
Debug("Shapeme读取完成，用时{0}ms.", timer.Stop());
#endregion
#region 识别
foreach (var challengeFile in Directory.GetFiles(@"D:\Play Data\字符\", "*.jpg")) {
timer.Restart();
string filename = Path.GetFileNameWithoutExtension(challengeFile);
Image<Gray, Byte> img = new Image<Gray, byte>(challengeFile);
var mmp = img.Convert<Bgr, Byte>();
Graphics g = Graphics.FromImage(mmp.Bitmap);
double width_height_ratio = (double)img.Width / img.Height;
int sq = (int)(s * width_height_ratio * 1.2);
int randcount = (int)(width_height_ratio * 2);
//int windowcount = (int)(width_height_ratio * 2),
// windowstep = (int)(img.Width / windowcount),
// windowwidth = (int)(img.Width * 1.5 / windowcount);
//int maxrandcount = randcount * 3;
//Console.WriteLine("宽高比{0:F2},采样{1},随机数{2},随机上限{3}.",
// width_height_ratio, sq, randcount, maxrandcount);
int slice = 16,//(int)(width_height_ratio * width_height_ratio),
overlap = 4,
windowcount = 7;//slice - (overlap - 1);
double slice_width = (double)img.Width / slice;
Console.WriteLine("宽高比{0:F2},切片数{1},切片宽度{2:F2},窗口数{3}.",
width_height_ratio, slice, slice_width, windowcount);
var edge = getEdge(img).Sample(sq);
foreach (var p in edge) {
mmp[p] = new Bgr(0, 0, 127);
}
bool[] edge_vi = Utils.InitArray(sq, false);
#region 计算采样点之间的距离
ValueIndexPair<double>[][] edgedists = new ValueIndexPair<double>[sq][];
for (int i = 0; i < sq; ++i) {
edgedists[i] = new ValueIndexPair<double>[sq];
for (int j = 0; j < sq; ++j) {
double xi = edge[i].X, yi = edge[i].Y,
xj = edge[j].X, yj = edge[j].Y;
double d = Math.Sqrt((xi - xj) * (xi - xj) + (yi - yj) * (yi - yj));
edgedists[i][j] = new ValueIndexPair<double> { Value = d, Index = j };
}
Array.Sort(edgedists[i], (a, b) => a.Value.CompareTo(b.Value));
}
#endregion
var charlist = new List<Tuple<string, int, PointF>>(); // <ch, count, center>
//for (int rc = 0, rt = 0; rc < randcount && rt < maxrandcount; ++rt) {
// #region 随机取一个中心点，并且取离他近的点计算形状上下文
//int center = rand.Next(sq);
//if (edge_vi[center]) continue;
//else rc++;
//rc++;
//var nearby = new List<Point>();
//foreach (var pair in edgedists[center].Take((int)(s * 1.5))) {
// nearby.Add(edge[pair.Index]);
// edge_vi[pair.Index] = true;
//}
//nearby = nearby.Sample(s);
for (int wd = 0; wd < windowcount; ++wd) {
#region 滑动窗口位置
//int window_center = wd * windowstep + (windowwidth / 2);
double window_center = (wd * 2 + overlap / 2.0) * slice_width;
g.DrawLine(Pens.Green, (float)window_center, 0, (float)window_center, img.Height);
var nearby = edge.Where(p => Math.Abs(p.X - window_center) < img.Height)
.OrderBy(p => Math.Abs(p.X - window_center))
.Take((int)(s * 1.2))
.ToList().Sample(s);
if (nearby.Average(p => Math.Abs(p.X - window_center)) > img.Height) continue;
var sc = Jim.OCR.ShapeContext2D.ShapeContext.ComputeSC2(nearby);
#endregion
#region 对待测图的形状上下文进行量化
int[] histogram = new int[K];
for (int i = 0; i < s; ++i) {
double[] ds = new double[K];
for (int j = 0; j < K; ++j)
ds[j] = Jim.OCR.ShapeContext2D.ShapeContext.HistCost(sc[i], shapemes[j]);
int id = ds.Select((v, idx) => new ValueIndexPair<double> { Value = v, Index = idx })
.OrderBy(p => p.Value)
.First().Index;
++histogram[id];
}
#endregion
#region 计算量化后的比较距离
double[] dists = new double[templatecount];
for (int i = 0; i < templatecount; ++i) {
//dists[i] = Jim.OCR.ShapeContext2D.ShapeContext.ChiSquareDistance(histogram, templatehistograms[i]);
dists[i] = Jim.OCR.ShapeContext2D.ShapeContext.HistCost(
histogram.Select(d => (double)d).ToArray(),
templatehistograms[i].Select(d => (double)d).ToArray());
}
#endregion
#region 对结果进行排序和统计
var arr = dists.Select((d, i) => new ValueIndexPair<double> { Value = d, Index = i })
.OrderBy(p => p.Value)
.Select(p => new { Distance = p.Value, Char = templatechars[p.Index] })
.Where(p => "0123456789".IndexOf(p.Char) != -1)
.ToArray();
Dictionary<string, int> matchcount = new Dictionary<string, int>();
int knn = 10;
foreach (var pair in arr.Take(knn)) {
string ch = pair.Char;
matchcount[ch] = matchcount.ContainsKey(ch) ? matchcount[ch] + 1 : 1;
}
var match = matchcount.Select(pair => new { Count = pair.Value, Ch = pair.Key })
.Where(v => v.Count > 0)
.OrderByDescending(v => v.Count).ToArray();
var firstmatch = match[0];
PointF newcenter = new PointF(nearby.Average(p => (float)p.X), nearby.Average(p => (float)p.Y));
charlist.Add(new Tuple<string, int, PointF> {
First = firstmatch.Ch,
Second = firstmatch.Count,
Third = newcenter
});
#endregion
}
foreach (var tp in charlist) {
string ch = tp.First;
int acc = tp.Second;
PointF center = tp.Third;
//g.DrawString(num.ToString(), new Font("Arial", 24), new SolidBrush(Color.FromArgb(40 + 20 * acc, 0, 0)),
// new PointF(center.X - 12, center.Y - 12));
g.DrawString(ch, new Font("Arial", 24), (acc >= 5 ? Brushes.Red : Brushes.DarkGreen),
new PointF(center.X - 12, center.Y - 12));
}
mmp.Save(Path.Combine(@"D:\Play Data\测试玩意\", filename + ".jpg"));
Debug("{0}-{1}ms", filename, timer.Stop());
}
#endregion
}
private static void quantizeSC()
{
MyTimer timer = new MyTimer();
int s = 100, K = 100, m = 180;
string[] templatefiles = Directory.GetFiles(@"D:\Play Data\my_template_data\sc\", "*.sc")
.Skip(0).Take(1000).ToArray();
int templatecount = templatefiles.Length;
#region 打开Shapeme
Debug("打开{0}个Shapeme.", K);
timer.Restart();
double[][] shapemes = new double[K][];
using (var fs = new FileStream(Path.Combine(@"D:\Play Data\my_template_data\sm-" + K, m + ".sm"), FileMode.Open)) {
using (var br = new BinaryReader(fs)) {
for (int i = 0; i < K; ++i) {
shapemes[i] = new double[60];
for (int k = 0; k < 60; ++k) {
shapemes[i][k] = br.ReadDouble();
}
}
}
}
Debug("Shapeme读取完成，用时{0}ms.", timer.Stop());
#endregion
#region 读取模板和量化
Debug("对{0}个模板进行量化--------------------------------------------", templatecount);
timer.Restart();
for (int f = 0; f < templatecount; ++f) {
string file = templatefiles[f];
string filename = Path.GetFileNameWithoutExtension(file);
int[] histogram = new int[K];
using (var fs = new FileStream(file, FileMode.Open)) {
using (var br = new BinaryReader(fs)) {
for (int j = 0; j < s; ++j) {
double[] sc = new double[60];
for (int k = 0; k < 60; ++k) {
sc[k] = br.ReadDouble();
}
double[] dists = new double[K];
for (int k = 0; k < K; ++k)
dists[k] = Jim.OCR.ShapeContext2D.ShapeContext.HistCost(sc, shapemes[k]);
int id = dists.Select((v, idx) => new ValueIndexPair<double> { Value = v, Index = idx })
.OrderBy(p => p.Value)
.First().Index;
++histogram[id];
}
}
}
using (var fs = new FileStream(Path.Combine(@"D:\Play Data\my_template_data\scq-" + K + "-" + m, filename + ".scq"), FileMode.Create)) {
using (var bw = new BinaryWriter(fs)) {
for (int i = 0; i < K; ++i) {
bw.Write(histogram[i]);
}
}
}
if (f % 100 == 0)
Debug("已完成{0}个", f);
}
Debug("量化完成，用时{0}ms.", timer.Stop());
#endregion
}
private static void MatchShapeContext(string path, string fa, string fb, char c)
{
MyTimer timer = new MyTimer();
Console.WriteLine("Doing...{0}", c);
var sca = CreateShapeContext(path, fa, 100, c);
var scb = CreateShapeContext(path, fb, 100, c);
var match = new ShapeContextMatching(sca.ShapeContests, scb.ShapeContests);
timer.Restart();
match.BuildCostGraph();
timer.StopAndSay("Build Graph");
timer.Restart();
var km = match.Match();
timer.StopAndSay("Match");
Console.WriteLine("Match Result:{0}", km.MatchResult);
int ax = (int)sca.Points.Average(p => p.X),
ay = (int)sca.Points.Average(p => p.Y),
bx = (int)scb.Points.Average(p => p.X),
by = (int)scb.Points.Average(p => p.Y);
int dx = bx - ax,
dy = by - ay;
int scale = 4;
Image<Bgr, Byte> img = new Image<Bgr, byte>(400 * scale, 400 * scale);
string scdir = Path.Combine(path, c.ToString());
if (Directory.Exists(scdir)) Directory.Delete(scdir, true);
Directory.CreateDirectory(scdir);
Font fs = new Font("Arial", 12);
using (Graphics g = Graphics.FromImage(img.Bitmap)) {
g.Clear(Color.Black);
Font font1 = new Font(fa, 240 * scale);
Font font2 = new Font(fb, 240 * scale);
g.DrawString(c.ToString(), font1, new SolidBrush(Color.FromArgb(30, 255, 0, 0)), new Point(0, 0));
g.DrawString(c.ToString(), font2, new SolidBrush(Color.FromArgb(30, 0, 255, 0)), new Point(-dx * scale, -dy * scale));
for (int j = 0; j < scb.Points.Count; ++j) {
int i = km.MatchPair[j + sca.Points.Count];
Point pb = scb.Points[j], pa = sca.Points[i];
pb.X -= dx;
pb.Y -= dy;
int r = 3;
g.DrawEllipse(Pens.Red, pa.X * scale - r, pa.Y * scale - r, r * 2 + 1, r * 2 + 1);
g.DrawEllipse(Pens.Green, pb.X * scale - r, pb.Y * scale - r, r * 2 + 1, r * 2 + 1);
g.DrawLine(Pens.Gray, new Point(pa.X * scale, pa.Y * scale), new Point(pb.X * scale, pb.Y * scale));
Point ps = new Point(pa.X * scale, pa.Y * scale);
g.DrawString(i.ToString(), fs, Brushes.White, ps);
string hisa = Path.Combine(scdir, String.Format("{0}-{1}.bmp", i, fa));
string hisb = Path.Combine(scdir, String.Format("{0}-{1}.bmp", i, fb));
sca.ShapeContests[i].Histogram.ToImage().Save(hisa);
scb.ShapeContests[j].Histogram.ToImage().Save(hisb);
}
}
img.Save(Path.Combine(path, String.Format("{0}-{1}-{2}.bmp", fa, fb, c)));
}
private Image<Bgr, Byte> kmeans()
{
int trainSampleCount = 1500;
int sigma = 60;
Matrix<float> trainData = new Matrix<float>(trainSampleCount, 2);
Matrix<float> trainData1 = trainData.GetRows(0, trainSampleCount / 3, 1);
trainData1.GetCols(0, 1).SetRandNormal(new MCvScalar(100), new MCvScalar(sigma));
trainData1.GetCols(1, 2).SetRandNormal(new MCvScalar(300), new MCvScalar(sigma));
Matrix<float> trainData2 = trainData.GetRows(trainSampleCount / 3, 2 * trainSampleCount / 3, 1);
trainData2.SetRandNormal(new MCvScalar(400), new MCvScalar(sigma));
Matrix<float> trainData3 = trainData.GetRows(2 * trainSampleCount / 3, trainSampleCount, 1);
trainData3.GetCols(0, 1).SetRandNormal(new MCvScalar(300), new MCvScalar(sigma));
trainData3.GetCols(1, 2).SetRandNormal(new MCvScalar(100), new MCvScalar(sigma));
PointF[] points = new PointF[trainSampleCount];
for (int i = 0; i < points.Length; ++i) {
points[i] = new PointF(trainData[i, 0], trainData[i, 1]);
}
var km = new KMeans<PointF>(points, 3,
(a, b) => ((a.X - b.X) * (a.X - b.X) + (a.Y - b.Y) * (a.Y - b.Y)),
list => new PointF(list.Average(p => p.X), list.Average(p => p.Y))
);
int it = 0;
MyTimer timer = new MyTimer();
timer.Restart();
//var cluster = km.Cluster();
var cluster = km.AnnealCluster(
(a, b) => new PointF(a.X + b.X, a.Y + b.Y),
(a, b) => new PointF(a.X - b.X, a.Y - b.Y),
(p, v) => new PointF((float)(p.X / v), (float)(p.Y / v)),
out it);
var time = timer.Stop();
this.Text = String.Format("n={0}, k={1}, time={2}ms, iter={3}.", trainSampleCount, 3, time, it);
Image<Bgr, Byte> img = new Image<Bgr, byte>(500, 500);
for (int y = 0; y < 500; ++y) {
for (int x = 0; x < 500; ++x) {
double d0 = (x - cluster[0].Center.X) * (x - cluster[0].Center.X)
+ (y - cluster[0].Center.Y) * (y - cluster[0].Center.Y);
double d1 = (x - cluster[1].Center.X) * (x - cluster[1].Center.X)
+ (y - cluster[1].Center.Y) * (y - cluster[1].Center.Y);
double d2 = (x - cluster[2].Center.X) * (x - cluster[2].Center.X)
+ (y - cluster[2].Center.Y) * (y - cluster[2].Center.Y);
Bgr color = new Bgr(0, 0, 0);
if (d0 < d1 && d0 < d2) {
color = new Bgr(20, 0, 0);
}
if (d1 < d0 && d1 < d2) {
color = new Bgr(0, 20, 0);
}
if (d2 < d0 && d2 < d1) {
color = new Bgr(0, 0, 20);
}
img[y, x] = color;
}
}
Bgr[] colors = new[] { new Bgr(128, 0, 0), new Bgr(0, 128, 0), new Bgr(0, 0, 128) };
Bgr[] centers = new[] { new Bgr(255, 0, 0), new Bgr(0, 255, 0), new Bgr(0, 0, 255) };
for (int i = 0; i < 3; ++i) {
foreach (var p in cluster[i]) {
img.Draw(new CircleF(p, 2), colors[i], 1);
}
img.Draw(new CircleF(cluster[i].Center, 5), centers[i], 3);
}
img.Draw(new CircleF(new PointF(100, 300), sigma), new Bgr(128, 128, 128), 2);
img.Draw(new CircleF(new PointF(100, 300), 3), new Bgr(128, 128, 128), 2);
img.Draw(new CircleF(new PointF(300, 100), sigma), new Bgr(128, 128, 128), 2);
img.Draw(new CircleF(new PointF(300, 100), 3), new Bgr(128, 128, 128), 2);
img.Draw(new CircleF(new PointF(400, 400), sigma), new Bgr(128, 128, 128), 2);
img.Draw(new CircleF(new PointF(400, 400), 3), new Bgr(128, 128, 128), 2);
return img;
}
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);
}
private static void fastPruningWithShapeme()
{
var timer = new MyTimer();
int s = 100, K = 100, m = 100;
string[] templatefiles = Directory.GetFiles(@"D:\Play Data\train_data\scq-" + K + "-" + m, "*.scq")
.Take(20000).ToArray();
#region 打开量化的模板
int templatecount = templatefiles.Length;
Debug("打开{0}个量化模板----------------------------------------", templatecount);
timer.Restart();
int[][] templatehistograms = new int[templatecount][];
int[] templatenums = new int[templatecount];
for (int f = 0; f < templatecount; ++f) {
string file = templatefiles[f];
string filename = Path.GetFileNameWithoutExtension(file);
templatenums[f] = int.Parse(filename.Split('-')[1]);
templatehistograms[f] = new int[K];
using (var fs = new FileStream(file, FileMode.Open)) {
using (var br = new BinaryReader(fs)) {
for (int i = 0; i < K; ++i) {
templatehistograms[f][i] = br.ReadInt32();
}
}
}
}
Debug("打开完成，用时{0}ms.", timer.Stop());
#endregion
#region 打开Shapeme
Debug("打开{0}个Shapeme.", K);
timer.Restart();
double[][] shapemes = new double[K][];
using (var fs = new FileStream(Path.Combine(@"D:\Play Data\train_data\sm-" + K, m + ".sm"), FileMode.Open)) {
using (var br = new BinaryReader(fs)) {
for (int i = 0; i < K; ++i) {
shapemes[i] = new double[60];
for (int k = 0; k < 60; ++k) {
shapemes[i][k] = br.ReadDouble();
}
}
}
}
Debug("Shapeme读取完成，用时{0}ms.", timer.Stop());
#endregion
string[] testfiles = Directory.GetFiles(@"D:\Play Data\test\", "*.bmp")
.Take(1000).ToArray();
#region 测试
int testcase = testfiles.Length, acc = 0;
Debug("为{0}个对象寻找候选模板------------------------------------------", testcase);
foreach (var file in testfiles) {
timer.Restart();
#region 计算待测图的形状上下文
string filenameext = Path.GetFileName(file);
string filename = Path.GetFileNameWithoutExtension(file);
int thisnum = int.Parse(filename.Split('-')[1]);
Image<Gray, Byte> img = new Image<Gray, byte>(file);
var list = getEdge(img.Resize(2.5, Emgu.CV.CvEnum.INTER.CV_INTER_LINEAR)).Sample(s);
var sc = Jim.OCR.ShapeContext2D.ShapeContext.ComputeSC2(list);
#endregion
#region 对待测图的形状上下文进行量化
int[] histogram = new int[K];
for (int i = 0; i < s; ++i) {
double[] ds = new double[K];
for (int j = 0; j < K; ++j)
ds[j] = Jim.OCR.ShapeContext2D.ShapeContext.HistCost(sc[i], shapemes[j]);
int id = ds.Select((v, idx) => new ValueIndexPair<double> { Value = v, Index = idx })
.OrderBy(p => p.Value)
.First().Index;
++histogram[id];
}
#endregion
#region 计算量化后的比较距离
double[] dists = new double[templatecount];
for (int i = 0; i < templatecount; ++i) {
dists[i] = Jim.OCR.ShapeContext2D.ShapeContext.ChiSquareDistance(histogram, templatehistograms[i]);
//dists[i] = Jim.OCR.ShapeContext2D.ShapeContext.HistCost(histogram.Cast<double>().ToArray(), templatehistograms[i].Cast<double>().ToArray());
}
#endregion
#region 对结果进行排序和统计
var arr = dists.Select((d, i) => new ValueIndexPair<double> { Value = d, Index = i })
.OrderBy(p => p.Value)
.Select(p => new { Distance = p.Value, Num = templatenums[p.Index] })
.ToArray();
int[] matchcount = new int[10];
int knn = 10;
foreach (var pair in arr.Take(knn)) {
int num = pair.Num;
matchcount[num]++;
}
var match = matchcount.Select((val, i) => new { Count = val, Num = i })
.Where(v => v.Count > 0)
.OrderByDescending(v => v.Count).ToArray();
#endregion
int firstmatch = match[0].Num;
var fc = Console.ForegroundColor;
Console.ForegroundColor = firstmatch == thisnum ? ConsoleColor.Green : ConsoleColor.Red;
string info = String.Format("{0} {1}ms - {2}\t", filename, timer.Stop(), (firstmatch == thisnum ? "Right" : "Wrong"));
foreach (var ma in match.Take(4)) {
info += String.Format("{0}/{1}\t", ma.Num, ma.Count);
}
Debug(info);
Console.ForegroundColor = fc;
if (firstmatch == thisnum) {
++acc;
}
}
Debug("测试用例：{0}。正确率{1}。", testcase, acc);
#endregion
}
private Image <Bgr, Byte> kmeans()
{
int trainSampleCount = 1500;
int sigma = 60;
Matrix <float> trainData = new Matrix <float>(trainSampleCount, 2);
Matrix <float> trainData1 = trainData.GetRows(0, trainSampleCount / 3, 1);
trainData1.GetCols(0, 1).SetRandNormal(new MCvScalar(100), new MCvScalar(sigma));
trainData1.GetCols(1, 2).SetRandNormal(new MCvScalar(300), new MCvScalar(sigma));
Matrix <float> trainData2 = trainData.GetRows(trainSampleCount / 3, 2 * trainSampleCount / 3, 1);
trainData2.SetRandNormal(new MCvScalar(400), new MCvScalar(sigma));
Matrix <float> trainData3 = trainData.GetRows(2 * trainSampleCount / 3, trainSampleCount, 1);
trainData3.GetCols(0, 1).SetRandNormal(new MCvScalar(300), new MCvScalar(sigma));
trainData3.GetCols(1, 2).SetRandNormal(new MCvScalar(100), new MCvScalar(sigma));
PointF[] points = new PointF[trainSampleCount];
for (int i = 0; i < points.Length; ++i)
{
points[i] = new PointF(trainData[i, 0], trainData[i, 1]);
}
var km = new KMeans <PointF>(points, 3,
(a, b) => ((a.X - b.X) * (a.X - b.X) + (a.Y - b.Y) * (a.Y - b.Y)),
list => new PointF(list.Average(p => p.X), list.Average(p => p.Y))
);
int it = 0;
MyTimer timer = new MyTimer();
timer.Restart();
//var cluster = km.Cluster();
var cluster = km.AnnealCluster(
(a, b) => new PointF(a.X + b.X, a.Y + b.Y),
(a, b) => new PointF(a.X - b.X, a.Y - b.Y),
(p, v) => new PointF((float)(p.X / v), (float)(p.Y / v)),
out it);
var time = timer.Stop();
this.Text = String.Format("n={0}, k={1}, time={2}ms, iter={3}.", trainSampleCount, 3, time, it);
Image <Bgr, Byte> img = new Image <Bgr, byte>(500, 500);
for (int y = 0; y < 500; ++y)
{
for (int x = 0; x < 500; ++x)
{
double d0 = (x - cluster[0].Center.X) * (x - cluster[0].Center.X)
+ (y - cluster[0].Center.Y) * (y - cluster[0].Center.Y);
double d1 = (x - cluster[1].Center.X) * (x - cluster[1].Center.X)
+ (y - cluster[1].Center.Y) * (y - cluster[1].Center.Y);
double d2 = (x - cluster[2].Center.X) * (x - cluster[2].Center.X)
+ (y - cluster[2].Center.Y) * (y - cluster[2].Center.Y);
Bgr color = new Bgr(0, 0, 0);
if (d0 < d1 && d0 < d2)
{
color = new Bgr(20, 0, 0);
}
if (d1 < d0 && d1 < d2)
{
color = new Bgr(0, 20, 0);
}
if (d2 < d0 && d2 < d1)
{
color = new Bgr(0, 0, 20);
}
img[y, x] = color;
}
}
Bgr[] colors = new[] { new Bgr(128, 0, 0), new Bgr(0, 128, 0), new Bgr(0, 0, 128) };
Bgr[] centers = new[] { new Bgr(255, 0, 0), new Bgr(0, 255, 0), new Bgr(0, 0, 255) };
for (int i = 0; i < 3; ++i)
{
foreach (var p in cluster[i])
{
img.Draw(new CircleF(p, 2), colors[i], 1);
}
img.Draw(new CircleF(cluster[i].Center, 5), centers[i], 3);
}
img.Draw(new CircleF(new PointF(100, 300), sigma), new Bgr(128, 128, 128), 2);
img.Draw(new CircleF(new PointF(100, 300), 3), new Bgr(128, 128, 128), 2);
img.Draw(new CircleF(new PointF(300, 100), sigma), new Bgr(128, 128, 128), 2);
img.Draw(new CircleF(new PointF(300, 100), 3), new Bgr(128, 128, 128), 2);
img.Draw(new CircleF(new PointF(400, 400), sigma), new Bgr(128, 128, 128), 2);
img.Draw(new CircleF(new PointF(400, 400), 3), new Bgr(128, 128, 128), 2);
return(img);
}
private static void testCluter()
{
int ss = 100, sq = 100, k = 10, m = 10; // 采样数，G取前k，S取前m
int randcount = 5, randtake = 10, randselect = 3;
var timer = new MyTimer();
var templatefiles = Directory.GetFiles(@"D:\Play Data\字符模板");
int templatecount = templatefiles.Length;
var templateChars = new string[templatecount];
var templatePoints = new Vector2[templatecount][];
//var templatesc = new double[templatecount][,];
var templatesc = new double[templatecount][][];
#region 计算模板的SC
for (int f = 0; f < templatecount; ++f) {
string file = templatefiles[f], filename = Path.GetFileNameWithoutExtension(file);
using (Image<Gray, Byte> img = new Image<Gray, byte>(file)) {
var samples = getEdge(img).Sample(ss);
templatesc[f] = Jim.OCR.ShapeContext2D.ShapeContext.ComputeSC2(samples);
templatePoints[f] = samples.Select(p => new Vector2(p.X, p.Y)).ToArray();
}
templateChars[f] = filename;
Console.Write(filename);
}
Console.WriteLine();
Debug("模板读取完成");
#endregion
#region 处理字符
foreach (string file in Directory.GetFiles(@"D:\Play Data\字符")) {
string filename = Path.GetFileNameWithoutExtension(file);
if (filename != "AB") continue;
timer.Restart();
Image<Bgr, Byte> img = new Image<Bgr, Byte>(file);
var samples = getEdge(img.Convert<Gray, Byte>()).Sample(sq);
//double[,] SCQ = Jim.OCR.ShapeContext2D.ShapeContext.ComputeSC(samples);
double[][] SCQ = Jim.OCR.ShapeContext2D.ShapeContext.ComputeSC2(samples);
var Q_Points = samples.Select(p => new Vector2(p.X, p.Y)).ToArray();
var mmg = img.Convert<Bgr, Byte>();
Graphics g = Graphics.FromImage(mmg.Bitmap);
Q_Points.ToList().ForEach(v => { mmg[(int)v.Y, (int)v.X] = new Bgr(0, 0, 255); });
var point_distance = new ValueIndexPair<double>[sq][];
#region 计算采样点之间的距离
for (int i = 0; i < sq; ++i) {
double xi = Q_Points[i].X, yi = Q_Points[i].Y;
point_distance[i] = new ValueIndexPair<double>[sq];
for (int j = 0; j < sq; ++j) {
double xj = Q_Points[j].X, yj = Q_Points[j].Y;
point_distance[i][j] = new ValueIndexPair<double> {
Value = Math.Sqrt((xi - xj) * (xi - xj) + (yi - yj) * (yi - yj)),
Index = j
};
}
Array.Sort(point_distance[i], (a, b) => a.Value.CompareTo(b.Value));
}
#endregion
var randpoints = new int[randcount][];
#region 随机取randcount个点，并在其周围randtake个点中取randselect个
for (int i = 0; i < randcount; ++i) {
int pi = rand.Next(Q_Points.Length);
var p = Q_Points[pi];
mmg.Draw(new CircleF(new PointF((float)p.X, (float)p.Y), 2), new Bgr(255, 0, 0), 1);
randpoints[i] = new int[randselect];
bool[] vi = Utils.InitArray<bool>(randtake, false);
for (int cnt = 0; cnt < randselect; ) {
int rnd = rand.Next(randtake);
if (!vi[rnd]) {
vi[rnd] = true;
randpoints[i][cnt++] = rnd;
}
}
for (int ppp = 0; ppp < randselect; ++ppp) {
var pt = Q_Points[point_distance[pi][randpoints[i][ppp]].Index];
//g.DrawString(i.ToString(), new Font("Arial", 7), new SolidBrush(Color.FromArgb(0, 128, 0)), new PointF((float)pt.X, (float)pt.Y));
}
}
#endregion
#region 为这randcount组RSC分别选最好的模板
var rscmatch = new Tuple<int, double, Vector2>[randcount]; // <Si, d, L>
for (int rc = 0; rc < randcount; ++rc) {
var rsc_matches = new Tuple<double, Vector2>[templatecount]; // <d, L>
for (int i = 0; i < templatecount; ++i) {
#region 拷贝出一个rsc来
var rsc = new double[randselect][];
for (int j = 0; j < randselect; ++j) {
rsc[j] = new double[60];
Array.Copy(SCQ[randpoints[rc][j]], rsc[j], 60);
}
#endregion
var costmat = Jim.OCR.ShapeContext2D.ShapeContext.HistCost2(rsc, templatesc[i]);
var matches = costmat.Select(
row => row.Select((d, c) => new ValueIndexPair<double> { Value = d, Index = c })
.OrderBy(d => d.Value).First()).ToArray();
Vector2 L = Vector2.Zero;
double M = 0;
for (int j = 0; j < randselect; ++j) {
int u = randpoints[rc][j], mu = matches[j].Index;
double d = matches[j].Value;
Vector2 pu = Q_Points[u], pmu = templatePoints[i][mu];
M += (1 - d);
L += (1 - d) * (pu - pmu);
}
L /= M;
rsc_matches[i] = new Tuple<double, Vector2> {
First = matches.Sum(r => r.Value),
Second = L
};
}
var best_template = rsc_matches.Select((mt, i) => new { Match = mt, i })
.OrderBy(t => t.Match.First)
.First();
rscmatch[rc] = new Tuple<int, double, Vector2> {
First = best_template.i,
Second = best_template.Match.First,
Third = best_template.Match.Second
};
string label = templateChars[best_template.i];
g.DrawString(label, new Font("Arial", 48), Brushes.Green,
new PointF((float)best_template.Match.Second.X, (float)best_template.Match.Second.Y));
}
#endregion
//Font f = new Font("Arial", 12);
//var G = new Tuple<int, int, double>[templatecount][]; // <u, m(u), d>
//#region 为每个Si挑选合适的Gi
//{
// var costmats = new double[templatecount][,];
// for (int i = 0; i < templatecount; ++i) {
// double[,] SCi = templatesc[i];
// costmats[i] = Jim.OCR.ShapeContext2D.ShapeContext.HistCost(SCQ, SCi);
// G[i] = new Tuple<int, int, double>[sq * ss];
// for (int u = 0; u < sq; ++u) {
// for (int j = 0; j < ss; ++j) {
// G[i][u * ss + j] = new Tuple<int, int, double> {
// First = u,
// Second = j,
// Third = costmats[i][u, j]
// };
// }
// }
// Array.Sort(G[i], (da, db) => da.Third.CompareTo(db.Third));
// }
//}
//#endregion
//var d_Q_S = new double[templatecount];
//#region 求出每个Si和Q的d(Q, Si)
//{
// for (int i = 0; i < templatecount; ++i) {
// var Gi = G[i].Take(k);
// foreach (var g in Gi) {
// int u = g.First, mu = g.Second;
// double d = g.Third;
// double Nu = G.Average(gi => gi.First(t => t.First == u).Third);
// d_Q_S[i] += d / Nu;
// }
// d_Q_S[i] /= k;
// }
//}
//#endregion
//var firstmG = new Tuple<Tuple<int, int, double>[], double, int>[m]; // <G, d, i> <=> <<u, m(u), d>[], d, i>
//#region 根据d(Q, Si)截取前20个最好的Gi
//{
// var firstmdQS = d_Q_S.Select((d, i) => new ValueIndexPair<double> { Value = d, Index = i })
// .OrderBy(p => p.Value)
// .Take(firstmG.Length).ToArray();
// for (int p = 0; p < firstmG.Length; ++p) {
// double d = firstmdQS[p].Value;
// int i = firstmdQS[p].Index;
// firstmG[p] = new Tuple<Tuple<int, int, double>[], double, int> {
// First = G[i].Take(k).ToArray(),
// Second = d,
// Third = i
// };
// }
//}
//#endregion
//#region 计算每个G的位置
//var L = new Vector2[m];
//{
// for (int i = 0; i < m; ++i) {
// L[i] = Vector2.Zero;
// double Mi = 0;
// var Gi = firstmG[i];
// foreach (var u_mu_d in Gi.First) {
// int u = u_mu_d.First, mu = u_mu_d.Second;
// double d = u_mu_d.Third;
// Vector2 pu = Q_Points[u], pmu = templatePoints[Gi.Third][mu];
// L[i] += (1 - d) * (pu - pmu);
// Mi += (1 - d);
// }
// L[i] /= Mi;
// g.DrawString(templateChars[Gi.Third], new Font("Arial", 12), Brushes.Green,
// new PointF((float)L[i].X, (float)L[i].Y));
// }
//}
//#endregion
mmg.Save(Path.Combine(@"D:\Play Data\测试玩意", filename + ".bmp"));
Debug("{0}\t用时{1}ms.", filename, timer.Stop());
}
#endregion
}
