public void Run()
{
double totaltime = 0;
int s = 100, K = 100, m = 100;
string[] templatefiles = Directory.GetFiles(@"D:\Play Data\train_data\scq-" + K + "-" + m, "*.scq")
.Take(20000).ToArray();
string[] templateSCfiles = Directory.GetFiles(@"D:\Play Data\train_data\sc\", "*.sc")
.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);
var sc2 = new double[s, 60];
for (int i = 0; i < s; ++i)
{
for (int j = 0; j < 60; ++j)
{
sc2[i, j] = sc[i][j];
}
}
#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);
int knn = 10;
#endregion
#region 进行精细匹配
var kmresults = new List <ValueIndexPair <double> >();
foreach (var pair in arr.Take(knn))
{
int idx = pair.Index;
var scfile = templateSCfiles[idx];
// var sci = new double[s][];
var sci = new double[s, 60];
using (var fs = new FileStream(scfile, FileMode.Open)) {
using (var br = new BinaryReader(fs)) {
for (int j = 0; j < s; ++j)
{
//sci[j] = new double[60];
for (int k = 0; k < 60; ++k)
{
//sci[j][k] = br.ReadDouble();
sci[j, k] = br.ReadDouble();
}
}
}
}
var costmat = Jim.OCR.ShapeContext2D.ShapeContext.HistCost(sci, sc2);
var costmat_int = new int[s, s];
for (int ii = 0; ii < s; ++ii)
{
for (int jj = 0; jj < s; ++jj)
{
costmat_int[ii, jj] = (int)(costmat[ii, jj] * 10000);
}
}
var km = new KM(s, costmat_int);
km.Match(false);
kmresults.Add(new ValueIndexPair <double> {
Index = idx, Value = km.MatchResult / 10000.0
});
}
kmresults.Sort((a, b) => a.Value.CompareTo(b.Value));
#endregion
//int firstmatch = match[0].Num;
int firstmatch = templatenums[kmresults[0].Index];
var fc = Console.ForegroundColor;
Console.ForegroundColor = firstmatch == thisnum ? ConsoleColor.Green : ConsoleColor.Red;
var timeused = timer.Stop();
totaltime += timeused;
string info = String.Format("{0} {1}ms\t", filename, timeused);
//foreach (var m in bestmatches.Take(4)) {
foreach (var mmm in kmresults.Take(4))
{
info += String.Format("{0}/{1:F2}\t", templatenums[mmm.Index], mmm.Value);
}
//Debug(info);
if (firstmatch != thisnum)
{
Console.Write(".");
}
Console.ForegroundColor = fc;
if (firstmatch == thisnum)
{
++acc;
}
}
Debug("\nShapeme and KM 测试用例：{0}。正确率{1}。", testcase, acc);
Console.WriteLine("总用时：{0}ms，平均用时：{1}ms。", totaltime, totaltime / testcase);
#endregion
}
public void Run()
{
double totaltime = 0;
int s = 100, K = 100, m = 100;
string[] templatefiles = Directory.GetFiles(@"D:\Play Data\train_data\scq-" + K + "-" + m, "*.scq")
.Take(20000).ToArray();
string[] templateSCfiles = Directory.GetFiles(@"D:\Play Data\train_data\sc\", "*.sc")
.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);
var sc2 = new double[s, 60];
for (int i = 0; i < s; ++i) {
for (int j = 0; j < 60; ++j) {
sc2[i, j] = sc[i][j];
}
}
#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);
int knn = 10;
#endregion
#region 进行精细匹配
var kmresults = new List<ValueIndexPair<double>>();
foreach (var pair in arr.Take(knn)) {
int idx = pair.Index;
var scfile = templateSCfiles[idx];
// var sci = new double[s][];
var sci = new double[s, 60];
using (var fs = new FileStream(scfile, FileMode.Open)) {
using (var br = new BinaryReader(fs)) {
for (int j = 0; j < s; ++j) {
//sci[j] = new double[60];
for (int k = 0; k < 60; ++k) {
//sci[j][k] = br.ReadDouble();
sci[j, k] = br.ReadDouble();
}
}
}
}
var costmat = Jim.OCR.ShapeContext2D.ShapeContext.HistCost(sci, sc2);
var costmat_int = new int[s, s];
for (int ii = 0; ii < s; ++ii) {
for (int jj = 0; jj < s; ++jj) {
costmat_int[ii, jj] = (int)(costmat[ii, jj] * 10000);
}
}
var km = new KM(s, costmat_int);
km.Match(false);
kmresults.Add(new ValueIndexPair<double> { Index = idx, Value = km.MatchResult / 10000.0 });
}
kmresults.Sort((a, b) => a.Value.CompareTo(b.Value));
#endregion
//int firstmatch = match[0].Num;
int firstmatch = templatenums[kmresults[0].Index];
var fc = Console.ForegroundColor;
Console.ForegroundColor = firstmatch == thisnum ? ConsoleColor.Green : ConsoleColor.Red;
var timeused = timer.Stop();
totaltime += timeused;
string info = String.Format("{0} {1}ms\t", filename, timeused);
//foreach (var m in bestmatches.Take(4)) {
foreach (var mmm in kmresults.Take(4)) {
info += String.Format("{0}/{1:F2}\t", templatenums[mmm.Index], mmm.Value);
}
//Debug(info);
if (firstmatch != thisnum) Console.Write(".");
Console.ForegroundColor = fc;
if (firstmatch == thisnum) {
++acc;
}
}
Debug("\nShapeme and KM 测试用例：{0}。正确率{1}。", testcase, acc);
Console.WriteLine("总用时：{0}ms，平均用时：{1}ms。", totaltime, totaltime / testcase);
#endregion
}
private static void km()
{
/* string[] ss = @"3 4 6 4 9
6 4 5 3 8
7 5 3 4 2
6 3 2 2 5
8 4 5 4 7".Split('\n');*/
string[] ss = @"7 6 4 6 1
4 6 5 7 2
3 5 7 6 8
4 7 8 8 5
2 6 5 6 3".Split('\n');
int[,] g = new int[5, 5];
for (int i = 0; i < 5; ++i) {
var l = ss[i].Split(' ');
for (int j = 0; j < 5; ++j) {
g[i, j] = int.Parse(l[j]);
}
}
var km = new KM(5, g);
int val = km.Match(false);
Console.WriteLine(val);
for (int i = 0; i < 5; ++i) {
Console.WriteLine("Y{0} -> X{1} @ {2}", i, km.MatchPair[i], g[km.MatchPair[i], i]);
}
}
public void Run()
{
double totaltime = 0;
int s = 100, r = 5;
string[] templatefiles = Directory.GetFiles(@"D:\Play Data\train_data\sc\", "*.sc")
.Take(100).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];
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(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();
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 knn = 10;
#region 进行精细匹配
var kmresults = new List<ValueIndexPair<double>>();
foreach (var pair in arr.Take(knn)) {
int idx = pair.Index;
var costmat = Jim.OCR.ShapeContext2D.ShapeContext.HistCost(templates[idx], sc100);
var costmat_int = new int[s, s];
for (int ii = 0; ii < s; ++ii) {
for (int jj = 0; jj < s; ++jj) {
costmat_int[ii, jj] = (int)(costmat[ii, jj] * 10000);
}
}
var km = new KM(s, costmat_int);
km.Match(false);
kmresults.Add(new ValueIndexPair<double> { Index = idx, Value = km.MatchResult / 10000.0 });
}
kmresults.Sort((a, b) => a.Value.CompareTo(b.Value));
#endregion
//int firstmatch = match[0].Num;
int firstmatch = templatenums[kmresults[0].Index];
var fc = Console.ForegroundColor;
Console.ForegroundColor = firstmatch == thisnum ? ConsoleColor.Green : ConsoleColor.Red;
var timeused = timer.Stop();
totaltime += timeused;
string info = String.Format("{0} {1}ms\t", filename, timeused);
//foreach (var m in bestmatches.Take(4)) {
foreach (var m in kmresults.Take(4)) {
info += String.Format("{0}/{1:F2}\t", templatenums[m.Index], m.Value);
}
//Debug(info);
if (firstmatch != thisnum) Console.Write(".");
Console.ForegroundColor = fc;
if (firstmatch == thisnum) {
++acc;
}
}
Debug("\nRSC and KM 测试用例：{0}。正确率{1}。", testcase, acc);
Console.WriteLine("总用时：{0}ms，平均用时：{1}ms。", totaltime, totaltime / testcase);
#endregion
}
public void Run()
{
double totaltime = 0;
int s = 100, r = 5;
string[] templatefiles = Directory.GetFiles(@"D:\Play Data\train_data\sc\", "*.sc")
.Take(100).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];
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(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();
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 knn = 10;
#region 进行精细匹配
var kmresults = new List <ValueIndexPair <double> >();
foreach (var pair in arr.Take(knn))
{
int idx = pair.Index;
var costmat = Jim.OCR.ShapeContext2D.ShapeContext.HistCost(templates[idx], sc100);
var costmat_int = new int[s, s];
for (int ii = 0; ii < s; ++ii)
{
for (int jj = 0; jj < s; ++jj)
{
costmat_int[ii, jj] = (int)(costmat[ii, jj] * 10000);
}
}
var km = new KM(s, costmat_int);
km.Match(false);
kmresults.Add(new ValueIndexPair <double> {
Index = idx, Value = km.MatchResult / 10000.0
});
}
kmresults.Sort((a, b) => a.Value.CompareTo(b.Value));
#endregion
//int firstmatch = match[0].Num;
int firstmatch = templatenums[kmresults[0].Index];
var fc = Console.ForegroundColor;
Console.ForegroundColor = firstmatch == thisnum ? ConsoleColor.Green : ConsoleColor.Red;
var timeused = timer.Stop();
totaltime += timeused;
string info = String.Format("{0} {1}ms\t", filename, timeused);
//foreach (var m in bestmatches.Take(4)) {
foreach (var m in kmresults.Take(4))
{
info += String.Format("{0}/{1:F2}\t", templatenums[m.Index], m.Value);
}
//Debug(info);
if (firstmatch != thisnum)
{
Console.Write(".");
}
Console.ForegroundColor = fc;
if (firstmatch == thisnum)
{
++acc;
}
}
Debug("\nRSC and KM 测试用例：{0}。正确率{1}。", testcase, acc);
Console.WriteLine("总用时：{0}ms，平均用时：{1}ms。", totaltime, totaltime / testcase);
#endregion
}
public void Run()
{
double totaltime = 0;
int s = 100, r = 5;
string[] templatefiles = Directory.GetFiles(@"D:\Play Data\train_data\sc\", "*.sc")
.Take(100).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];
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 scq = Jim.OCR.ShapeContext2D.ShapeContext.ComputeSC(list);
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);
var costmat_int = new int[s, s];
for (int ii = 0; ii < s; ++ii)
{
for (int jj = 0; jj < s; ++jj)
{
costmat_int[ii, jj] = (int)(costmat[ii, jj] * 10000);
}
}
var km = new KM(s, costmat_int);
km.Match(false);
arr[i] = new ValueIndexPair <double> {
Index = i, Value = km.MatchResult / 10000.0
};
}
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;
var timeused = timer.Stop();
totaltime += timeused;
string info = String.Format("{0} {1}ms - {2}\t", filename, timeused, (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);
Console.WriteLine("总用时：{0}ms，平均用时：{1}ms。", totaltime, totaltime / testcase);
#endregion
}
public double MatchIteration(Matrix X, Matrix Y, Matrix V1, Matrix V2, Matrix t1, Matrix t2)
{
timer.Clear();
double timeused = 0;
timer.Restart();
var tk = t1.Clone(); // tk=t1;
int w = 4;
int ndum = (int)(nsamp * ndum_frac);
nsamp1 = nsamp2 = nsamp;
#region demo2迭代
Matrix Xk = X.Clone();
int N1 = V1.Rows, N2 = V2.Columns;
if (display_flag)
{
Draw(X, Y, V1, V2, @"D:\Play Data\Iteration\原图.bmp", "原始图像和采样");
//DrawGradient(X, Y, t1, t2, N2, N1, @"D:\Play Data\Iteration\原图梯度.bmp", "原始图像切向量");
}
int k = 0;
var out_vec_1 = Utils.InitArray <bool>(nsamp1, false); //out_vec_1=zeros(1,nsamp1);
var out_vec_2 = Utils.InitArray <bool>(nsamp2, false); //out_vec_2=zeros(1,nsamp2);
double ori_weight = 0.1;
double tan_eps = 1.0;
bool affine_start_flag = true;
bool polarity_flag = true;
double matchcost = double.MaxValue;
double sc_cost = 0, aff_cost = 0, E = 0;
Matrix cx = null, cy = null; // cx, cy是插值线性方程组的解的两列
Matrix axt = null, wxt = null, ayt = null, wyt = null, d2 = null, U = null,
X2 = null, Y2 = null, X2b = null, X3b = null, Y3 = null;
double mean_dist_1 = 0, mean_dist_2 = 0;
var min1 = new[] { new { Val = 0.0, Idx = 0 } };
var min2 = min1;
#region 用于打网格的坐标
Matrix coordX = null, coordY = null;
int coordMargin = (int)(N1 * coordMarginRate);
MatrixUtils.CreateGrid(N1 + coordMargin * 2, N2 + coordMargin * 2, out coordX, out coordY);
coordX = coordX.Each(v => v - coordMargin * 2);
coordY = coordY.Each(v => v - coordMargin * 2);
//int MM = N1 * N2 / 25; // M=length(x);
#endregion
timeused += timer.StopAndSay("初始化");
while (k < n_iter)
{
Debug("Iter={0}", k);
#region 计算两个形状上下文
timer.Restart();
// [BH1,mean_dist_1]=sc_compute(Xk',zeros(1,nsamp),mean_dist_global,nbins_theta,nbins_r,r_inner,r_outer,out_vec_1);
var BH1 = ComputeSC(Xk.Transpose(), Zeros(1, nsamp), mean_dist_global, out mean_dist_1, out_vec_1);
//var BH1 = ComputeSC(Xk.Transpose(), t1.Transpose(), mean_dist_global, out mean_dist_1, out_vec_1);
// [BH2,mean_dist_2]=sc_compute(Y',zeros(1,nsamp),mean_dist_global,nbins_theta,nbins_r,r_inner,r_outer,out_vec_2);
var BH2 = ComputeSC(Y.Transpose(), Zeros(1, nsamp), mean_dist_global, out mean_dist_2, out_vec_2);
//var BH2 = ComputeSC(Y.Transpose(), t2.Transpose(), mean_dist_global, out mean_dist_2, out_vec_2);
timeused += timer.StopAndSay("计算两个形状上下文");
Debug("Mean_dist_1:{0:F4}", mean_dist_1);
Debug("Mean_dist_2:{0:F4}", mean_dist_2);
#endregion
#region 计算lambda_o和beta_k
double lambda_o;
if (affine_start_flag)
{
if (k == 0)
{
lambda_o = 1000;
}
else
{
lambda_o = beta_init * Math.Pow(r, k - 1); // lambda_o=beta_init*r^(k-2);
}
}
else
{
lambda_o = beta_init * Math.Pow(r, k); // lambda_o=beta_init*r^(k-1);
}
double beta_k = mean_dist_2 * mean_dist_2 * lambda_o;
#endregion
#region 计算代价矩阵
timer.Restart();
var costmat_shape = HistCost(BH1, BH2); // costmat_shape = hist_cost_2(BH1, BH2);
// theta_diff=repmat(tk,1,nsamp)-repmat(t2',nsamp,1);
var theta_diff = tk.RepMat(1, nsamp) - t2.Transpose().RepMat(nsamp, 1);
Matrix costmat_theta;
if (polarity_flag)
{
// costmat_theta=0.5*(1-cos(theta_diff));
//costmat_theta = 0.5 * (Ones(costmat_shape.Rows, costmat_shape.Columns) - theta_diff.Each(v => Math.Cos(v)));
costmat_theta = theta_diff.Each(v => 0.5 * (1 - Math.Cos(v)));
}
else
{
// costmat_theta=0.5*(1-cos(2*theta_diff));
//costmat_theta = 0.5 * (Ones(costmat_shape.Rows, costmat_shape.Columns) - theta_diff.Each(v => Math.Cos(2 * v)));
costmat_theta = theta_diff.Each(v => 0.5 * (1 - Math.Cos(2 * v)));
}
// costmat=(1-ori_weight)*costmat_shape+ori_weight*costmat_theta;
var costmat = (1 - ori_weight) * costmat_shape + ori_weight * costmat_theta;
int nptsd = nsamp + ndum; // nptsd=nsamp+ndum;
var costmat2 = new DenseMatrix(nptsd, nptsd, eps_dum); // costmat2=eps_dum*ones(nptsd,nptsd);
costmat2.SetSubMatrix(0, nsamp, 0, nsamp, costmat); // costmat2(1:nsamp,1:nsamp)=costmat;
timeused += timer.StopAndSay("计算代价矩阵");
#endregion
#region 匈牙利算法
timer.Restart();
var costmat_int = new int[nptsd, nptsd];
for (int i = 0; i < nptsd; ++i)
{
for (int j = 0; j < nptsd; ++j)
{
costmat_int[i, j] = (int)(costmat2[i, j] * 10000);
}
}
var km = new KM(nptsd, costmat_int);
km.Match(false);
matchcost = km.MatchResult / 10000.0;
int[] cvec = km.MatchPair; // cvec=hungarian(costmat2);
timeused += timer.StopAndSay("匈牙利算法");
#endregion
#region 计算野点标记向量，重排匹配点
timer.Restart();
int[] cvec2 = cvec.Select((v, i) => new { Val = v, Idx = i })
.OrderBy(v => v.Val)
.Select(v => v.Idx)
.ToArray(); // [a,cvec2]=sort(cvec);
out_vec_1 = cvec2.Take(nsamp1).Select(v => v > nsamp2).ToArray(); // out_vec_1=cvec2(1:nsamp1)>nsamp2;
out_vec_2 = cvec.Take(nsamp2).Select(v => v > nsamp1).ToArray(); // out_vec_2=cvec(1:nsamp2)>nsamp1;
//X2 = NaNs(nptsd, 2); // X2=NaN*ones(nptsd,2);
//X2.SetSubMatrix(0, nsamp1, 0, X2.Columns, Xk); // X2(1:nsamp1,:)=Xk;
//X2 = X2.SortRowsBy(cvec); // X2=X2(cvec,:);
X2b = NaNs(nptsd, 2); // X2b=NaN*ones(nptsd,2);
X2b.SetSubMatrix(0, nsamp1, 0, X2b.Columns, X); // X2b(1:nsamp1,:)=X;
X2b = X2b.SortRowsBy(cvec); // X2b=X2b(cvec,:);
Y2 = NaNs(nptsd, 2); // Y2=NaN*ones(nptsd,2);
Y2.SetSubMatrix(0, nsamp2, 0, Y2.Columns, Y); // Y2(1:nsamp2,:)=Y;
var ind_good = X2b.GetColumn(1).Take(nsamp).FindIdxBy(v => !double.IsNaN(v)); // ind_good=find(~isnan(X2b(1:nsamp,1)));
int n_good = ind_good.Length; // n_good=length(ind_good);
X3b = X2b.FilterRowsBy(ind_good); // X3b=X2b(ind_good,:);
Y3 = Y2.FilterRowsBy(ind_good); // Y3=Y2(ind_good,:);
timeused += timer.StopAndSay("计算野点标记向量，重排匹配点");
#endregion
#region figure 2
if (display_flag)
{
//figure(2)
//plot(X2(:,1),X2(:,2),'b+',Y2(:,1),Y2(:,2),'ro')
//hold on
//h=plot([X2(:,1) Y2(:,1)]',[X2(:,2) Y2(:,2)]','k-');
//if display_flag
//% set(h,'linewidth',1)
//quiver(Xk(:,1),Xk(:,2),cos(tk),sin(tk),0.5,'b') // 画箭头
//quiver(Y(:,1),Y(:,2),cos(t2),sin(t2),0.5,'r')
DrawGradient(Xk, Y, tk, t2, N2, N1,
String.Format(@"D:\Play Data\Iteration\梯度\{0}.bmp", k),
String.Format("Iter={0}梯度方向\n匹配点数{1}", k, n_good));
//end
//hold off
//axis('ij')
//title([int2str(n_good) ' correspondences (warped X)'])
//axis([1 N2 1 N1])
//drawnow
}
#endregion
#region figure 3 显示未变形图的匹配关系
if (display_flag)
{
//% show the correspondences between the untransformed images
//figure(3)
//plot(X(:,1),X(:,2),'b+',Y(:,1),Y(:,2),'ro')
//ind=cvec(ind_good);
//hold on
//plot([X2b(:,1) Y2(:,1)]',[X2b(:,2) Y2(:,2)]','k-')
//hold off
//axis('ij')
//title([int2str(n_good) ' correspondences (unwarped X)'])
//axis([1 N2 1 N1])
//drawnow
Draw(X, Y, cvec, null, N2, N1, String.Format(@"D:\Play Data\Iteration\匹配\{0}.bmp", k),
String.Format("Iter={0}\n匹配代价{1},匹配数{2}", k, matchcost, n_good));
}
#endregion
#region 求解变换矩阵
timer.Restart();
Bookstein(X3b, Y3, beta_k, ref cx, ref cy, ref E); // [cx,cy,E]=bookstein(X3b,Y3,beta_k);
timeused += timer.StopAndSay("求解变换矩阵");
#endregion
#region 通过解出来的变换对点和梯度进行变换
timer.Restart();
// % calculate affine cost
var A = MatrixUtils.RankHorizon(
cx.GetSubMatrix(n_good + 1, 2, 0, 1), cy.GetSubMatrix(n_good + 1, 2, 0, 1)
); //A=[cx(n_good+2:n_good+3,:) cy(n_good+2:n_good+3,:)];
var s = new Svd(A, true).S(); // s=svd(A);
aff_cost = Math.Log(s[0] / s[1]); // aff_cost=log(s(1)/s(2));
// % calculate shape context cost
min1 = costmat.GetColumns().Select(col => {
int minwhere = 0;
for (int i = 1; i < col.Count; ++i)
{
if (col[i] < col[minwhere])
{
minwhere = i;
}
}
return(new { Val = col[minwhere], Idx = minwhere });
}).ToArray();// [a1,b1]=min(costmat,[],1);
min2 = costmat.GetRows().Select(row => {
int minwhere = 0;
for (int i = 1; i < row.Count; ++i)
{
if (row[i] < row[minwhere])
{
minwhere = i;
}
}
return(new { Val = row[minwhere], Idx = minwhere });
}).ToArray(); // [a2,b2]=min(costmat,[],2);}
sc_cost = Math.Max(min1.Average(a => a.Val), min2.Average(a => a.Val)); // sc_cost=max(mean(a1),mean(a2));
// % warp each coordinate
axt = cx.GetSubMatrix(n_good, 3, 0, 1).Transpose(); // axt是cx中的最后三个，即a的x分量
wxt = cx.GetSubMatrix(0, n_good, 0, 1).Transpose(); // wxt是cs中的前n个，即w的x分量
ayt = cy.GetSubMatrix(n_good, 3, 0, 1).Transpose();
wyt = cy.GetSubMatrix(0, n_good, 0, 1).Transpose();
d2 = Dist2(X3b, X).Each(v => v > 0 ? v : 0); // d2=max(dist2(X3b,X),0);
U = d2.PointMultiply(d2.Each(v => Math.Log(v + Epsilon))); // U=d2.*log(d2+eps);
Debug("MatchCost:{0:F4}\taff_cost:{1:F4}\tsc_cost:{2:F4}\tE:{3:F8}", matchcost, aff_cost, sc_cost, E);
var Z = Transformation(X, U, axt, wxt, ayt, wyt);
//% apply the warp to the tangent vectors to get the new angles
var Xtan = X + tan_eps * MatrixUtils.RankHorizon(t1.Each(Math.Cos), t1.Each(Math.Sin)); // Xtan=X+tan_eps*[cos(t1) sin(t1)];
d2 = Dist2(X3b, Xtan).Each(v => v > 0 ? v : 0); // d2=max(dist2(X3b,Xtan),0);
U = d2.PointMultiply(d2.Each(v => Math.Log(v + Epsilon))); // U=d2.*log(d2+eps);
//Transformation(Xtan, U, axt, wxt, ayt, wyt, out fx, out fy);
//var Ztan = MatrixUtils.RankVertical(fx, fy).Transpose(); // Ztan=[fx; fy]';
var Ztan = Transformation(Xtan, U, axt, wxt, ayt, wyt);
for (int i = 0; i < nsamp; ++i)
{
tk[i, 0] = Math.Atan2(Ztan[i, 1] - Z[i, 1], Ztan[i, 0] - Z[i, 0]);
}//tk=atan2(Ztan(:,2)-Z(:,2),Ztan(:,1)-Z(:,1));
timeused += timer.StopAndSay("通过解出来的变换对点和梯度进行变换");
#endregion
#region figure 4 显示变形后的点集
if (display_flag)
{
//figure(4)
//plot(Z(:,1),Z(:,2),'b+',Y(:,1),Y(:,2),'ro');
//axis('ij')
//title(['k=' int2str(k) ', \lambda_o=' num2str(lambda_o) ', I_f=' num2str(E) ', aff.cost=' num2str(aff_cost) ', SC cost=' num2str(sc_cost)])
//axis([1 N2 1 N1])
//% show warped coordinate grid
//fx_aff=cx(n_good+1:n_good+3)'*[ones(1,M); x'; y'];
//d2=dist2(X3b,[x y]);
//fx_wrp=cx(1:n_good)'*(d2.*log(d2+eps));
//fx=fx_aff+fx_wrp;
//fy_aff=cy(n_good+1:n_good+3)'*[ones(1,M); x'; y'];
//fy_wrp=cy(1:n_good)'*(d2.*log(d2+eps));
//fy=fy_aff+fy_wrp;
//hold on
//plot(fx,fy,'k.','markersize',1)
//hold off
//drawnow
d2 = Dist2(X3b, MatrixUtils.RankHorizon(coordX, coordY));//d2=dist2(X3b,[x y]);
U = d2.PointMultiply(d2.Each(v => Math.Log(v + Epsilon)));
//Transformation(MatrixUtils.RankHorizon(coordX, coordY), U, axt, wxt, ayt, wyt, out fx, out fy);
//var coordsT = MatrixUtils.RankVertical(fx, fy).Transpose();
var coordsT = Transformation(MatrixUtils.RankHorizon(coordX, coordY), U, axt, wxt, ayt, wyt);
Draw(Z, Y, null, coordsT, N2, N1, String.Format(@"D:\Play Data\Iteration\变换\{0}.bmp", k),
String.Format("Iter={0}\nλo={1:F4},If={2:F4},aff_cost{3:F4},sc_cost{4:F4}", k, lambda_o, E, aff_cost, sc_cost));
}
#endregion
Xk = Z.Clone();
++k;
}
#endregion
#region 迭代完成后的代价计算
#region 我来尝试计算Dsc
// Xk是Q变换后的结果，而Y是模板图形P
/*
* Dsc = Avg_each_p_in_P(argmin(q in Q, C(p, T(q))) + Avg_each_q_in_Q(argmin(p in P, C(p, T(q)))
* */
double mean_dist_final_1;
var scQ = ComputeSC(Xk.Transpose(), Zeros(1, nsamp), mean_dist_global, out mean_dist_final_1, out_vec_1);
//var scQ = ComputeSC(Xk.Transpose(), Zeros(1, nsamp), mean_dist_1, out mean_dist_final_1, out_vec_1);
//var scQ = ComputeSC(Xk.Transpose(), t1.Transpose(), mean_dist_1, out mean_dist_final_1, out_vec_1);
double mean_dist_final_2;
var scP = ComputeSC(Y.Transpose(), Zeros(1, nsamp), mean_dist_global, out mean_dist_final_2, out_vec_2);
//var scP = ComputeSC(Y.Transpose(), Zeros(1, nsamp), mean_dist_2, out mean_dist_final_2, out_vec_2);
//var scP = ComputeSC(Y.Transpose(), t2.Transpose(), mean_dist_2, out mean_dist_final_2, out_vec_2);
var costmat_final = HistCost(scQ, scP);
double distance_sc = costmat_final.GetRows().Select(row => row.Min()).Average()
+ costmat_final.GetColumns().Select(col => col.Min()).Average();
Debug("distance_sc:{0}", distance_sc);
#endregion
#region 图像变换和插值
timer.Restart();
//[x,y]=meshgrid(1:N2,1:N1);
//x=x(:);y=y(:);
Matrix x = null, y = null;
MatrixUtils.CreateGrid(N1, N2, out x, out y);
//int M = N1 * N2; // M=length(x);
d2 = Dist2(X3b, MatrixUtils.RankHorizon(x, y));//d2=dist2(X3b,[x y]);
U = d2.PointMultiply(d2.Each(v => Math.Log(v + Epsilon)));
//Transformation(MatrixUtils.RankHorizon(x, y), U, axt, wxt, ayt, wyt, out fx, out fy);
var fxy = Transformation(MatrixUtils.RankHorizon(x, y), U, axt, wxt, ayt, wyt);
//disp('computing warped image...')
//V1w=griddata(reshape(fx,N1,N2),reshape(fy,N1,N2),V1,reshape(x,N1,N2),reshape(y,N1,N2));
Matrix V1w = Interpolation(
fxy.GetSubMatrix(0, fxy.Rows, 0, 1).Reshape(N1, N2),
fxy.GetSubMatrix(0, fxy.Rows, 1, 1).Reshape(N1, N2),
V1
);
#region 这个山寨插值方法会造成图像裂缝，用闭运算来尝试修补
Image <Gray, Byte> img = new Image <Gray, byte>(N2, N1);
for (int i = 0; i < N2; ++i)
{
for (int j = 0; j < N1; ++j)
{
img[i, j] = new Gray(V1w[i, j] * 255);
}
}
var see = new StructuringElementEx(new int[, ] {
{ 1, 1, 1 }, { 1, 1, 1 }, { 1, 1, 1 }
}, 1, 1);
//img = img.MorphologyEx(see, Emgu.CV.CvEnum.CV_MORPH_OP.CV_MOP_CLOSE, 1);
img = img.Dilate(1).Erode(1);
for (int i = 0; i < N2; ++i)
{
for (int j = 0; j < N1; ++j)
{
V1w[i, j] = img[i, j].Intensity / 255;
}
}
img.Dispose();
#endregion
timeused += timer.StopAndSay("图像变换和插值");
#endregion
//fz=find(isnan(V1w));
//V1w(fz)=0;
var ssd = (V2 - V1w).Each(v => v * v); //ssd=(V2-V1w).^2; %%%%%%SSD在这里
var ssd_global = ssd.SumAll(); //ssd_global=sum(ssd(:));
Debug("ssd_global:{0}", ssd_global);
#region figure 5
if (display_flag)
{
// figure(5)
// subplot(2,2,1)
// im(V1)
// subplot(2,2,2)
// im(V2)
// subplot(2,2,4)
// im(V1w)
// title('V1 after warping')
// subplot(2,2,3)
// im(V2-V1w)
// h=title(['SSD=' num2str(ssd_global)]);
// colormap(cmap)
}
#endregion
#region 窗口SSD比较
timer.Restart();
//%%%
//%%% windowed SSD comparison
//%%%
var wd = 2 * w + 1; //wd=2*w+1;
var win_fun = MatrixUtils.GaussianKernal(wd); //win_fun=gaussker(wd);
//% extract sets of blocks around each coordinate
//% first do 1st shape; need to use transformed coords.
var win_list_1 = Zeros(nsamp, wd * wd);//win_list_1=zeros(nsamp,wd^2);
for (int qq = 0; qq < nsamp; ++qq)
{
int row_qq = (int)Xk[qq, 1], // row_qq=round(Xk(qq,2));
col_qq = (int)Xk[qq, 0]; // col_qq=round(Xk(qq,1));
row_qq = Math.Max(w + 1, Math.Min(N1 - w - 1, row_qq)); // row_qq=max(w+1,min(N1-w,row_qq));
col_qq = Math.Max(w + 1, Math.Min(N2 - w - 1, col_qq)); // col_qq=max(w+1,min(N2-w,col_qq));
// tmp=V1w(row_qq-w:row_qq+w,col_qq-w:col_qq+w);
var tmp = V1w.GetSubMatrix(row_qq - w, w * 2 + 1, col_qq - w, w * 2 + 1);
tmp = win_fun.PointMultiply(tmp);// tmp=win_fun.*tmp;
// win_list_1(qq,:)=tmp(:)';
win_list_1.SetSubMatrix(qq, 1, 0, win_list_1.Columns, tmp.Reshape(1, tmp.Rows * tmp.Columns));
}
//% now do 2nd shape
var win_list_2 = Zeros(nsamp, wd * wd);//win_list_2=zeros(nsamp,wd^2);
for (int qq = 0; qq < nsamp; ++qq)
{
int row_qq = (int)Y[qq, 1], // row_qq = round(Y(qq, 2));
col_qq = (int)Y[qq, 0]; // col_qq = round(Y(qq, 1));
row_qq = Math.Max(w + 1, Math.Min(N1 - w - 1, row_qq)); // row_qq=max(w+1,min(N1-w,row_qq));
col_qq = Math.Max(w + 1, Math.Min(N2 - w - 1, col_qq)); // col_qq=max(w+1,min(N2-w,col_qq));
// tmp=V2(row_qq-w:row_qq+w,col_qq-w:col_qq+w)
var tmp = V2.GetSubMatrix(row_qq - w, w * 2 + 1, col_qq - w, w * 2 + 1);
tmp = win_fun.PointMultiply(tmp); // tmp=win_fun.*tmp;
win_list_2.SetSubMatrix(qq, 1, 0, win_list_2.Columns, tmp.Reshape(1, tmp.Rows * tmp.Columns)); // win_list_2(qq,:)=tmp(:)';
}
var ssd_all = Dist2(win_list_1, win_list_2).Each(Math.Sqrt);//ssd_all=sqrt(dist2(win_list_1,win_list_2));
timeused += timer.StopAndSay("窗口SSD比较");
#endregion
#region 最后的KNN，不知道干嘛用
timer.Restart();
//%%%%%%% KNN在此
//% loop over nearest neighbors in both directions, project in
//% both directions, take maximum
double cost_1 = 0, cost_2 = 0;
//List<double> cost_1 = new List<double>(), cost_2 = new List<double>();
for (int qq = 0; qq < nsamp; ++qq)
{
cost_1 += ssd_all[qq, min2[qq].Idx]; // cost_1=cost_1+ssd_all(qq,b2(qq));
cost_2 += ssd_all[min1[qq].Idx, qq]; // cost_2=cost_2+ssd_all(b1(qq),qq);
//cost_1.Add(ssd_all[qq, min2[qq].Idx]);
//cost_2.Add(ssd_all[min1[qq].Idx, qq]);
}
var ssd_local = (1.0 / nsamp) * Math.Max(cost_1, cost_2);
var ssd_local_avg = (1.0 / nsamp) * 0.5 * (cost_1 + cost_2);
//var ssd_local = (1.0 / nsamp) * Math.Max(cost_1.Average(), cost_2.Average());//ssd_local=(1/nsamp)*max(mean(cost_1),mean(cost_2));
//var ssd_local_avg = (1.0 / nsamp) * 0.5 * (cost_1.Average() + cost_2.Average());//ssd_local_avg=(1/nsamp)*0.5*(mean(cost_1)+mean(cost_2));
Debug("ssd_local:{0}", ssd_local);
Debug("ssd_local_avg:{0}", ssd_local_avg);
timeused += timer.StopAndSay("计算ssd_local");
#region 最后的组合图
//if display_flag
//% set(h,'string',['local SSD=' num2str(ssd_local) ', avg. local SSD=' num2str(ssd_local_avg)])
// set(h,'string',['local SSD=' num2str(ssd_local)])
//end
if (display_flag)
{
Draw(V1, @"D:\Play Data\Iteration\结果\0-V1.bmp", "V1");
Draw(V2, @"D:\Play Data\Iteration\结果\1-V2.bmp", "V2");
Draw(V1w, @"D:\Play Data\Iteration\结果\2-V1w.bmp", "V1 after warping");
Draw(V2 - V1w, @"D:\Play Data\Iteration\结果\3-V2-V1w.bmp",
String.Format("local SSD={0:F8}", ssd_local_avg));
}
#endregion
#endregion
#endregion
var distance = 1.6 * ssd_local_avg + distance_sc + 0.3 * E; // 不知道最后的距离公式到底是什么
Debug("distance={0:F8}", distance);
return(distance);
}
public void Run()
{
double totaltime = 0;
int s = 100, r = 5;
string[] templatefiles = Directory.GetFiles(@"D:\Play Data\train_data\sc\", "*.sc")
.Take(100).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];
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 scq = Jim.OCR.ShapeContext2D.ShapeContext.ComputeSC(list);
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);
var costmat_int = new int[s, s];
for (int ii = 0; ii < s; ++ii) {
for (int jj = 0; jj < s; ++jj) {
costmat_int[ii, jj] = (int)(costmat[ii, jj] * 10000);
}
}
var km = new KM(s, costmat_int);
km.Match(false);
arr[i] = new ValueIndexPair<double> { Index = i, Value = km.MatchResult / 10000.0 };
}
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;
var timeused = timer.Stop();
totaltime += timeused;
string info = String.Format("{0} {1}ms - {2}\t", filename, timeused, (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);
Console.WriteLine("总用时：{0}ms，平均用时：{1}ms。", totaltime, totaltime / testcase);
#endregion
}
public double MatchIteration(Matrix X, Matrix Y, Matrix V1, Matrix V2, Matrix t1, Matrix t2)
{
timer.Clear();
double timeused = 0;
timer.Restart();
var tk = t1.Clone(); // tk=t1;
int w = 4;
int ndum = (int)(nsamp * ndum_frac);
nsamp1 = nsamp2 = nsamp;
#region demo2迭代
Matrix Xk = X.Clone();
int N1 = V1.Rows, N2 = V2.Columns;
if (display_flag) {
Draw(X, Y, V1, V2, @"D:\Play Data\Iteration\原图.bmp", "原始图像和采样");
//DrawGradient(X, Y, t1, t2, N2, N1, @"D:\Play Data\Iteration\原图梯度.bmp", "原始图像切向量");
}
int k = 0;
var out_vec_1 = Utils.InitArray<bool>(nsamp1, false);//out_vec_1=zeros(1,nsamp1);
var out_vec_2 = Utils.InitArray<bool>(nsamp2, false);//out_vec_2=zeros(1,nsamp2);
double ori_weight = 0.1;
double tan_eps = 1.0;
bool affine_start_flag = true;
bool polarity_flag = true;
double matchcost = double.MaxValue;
double sc_cost = 0, aff_cost = 0, E = 0;
Matrix cx = null, cy = null; // cx, cy是插值线性方程组的解的两列
Matrix axt = null, wxt = null, ayt = null, wyt = null, d2 = null, U = null,
X2 = null, Y2 = null, X2b = null, X3b = null, Y3 = null;
double mean_dist_1 = 0, mean_dist_2 = 0;
var min1 = new[] { new { Val = 0.0, Idx = 0 } };
var min2 = min1;
#region 用于打网格的坐标
Matrix coordX = null, coordY = null;
int coordMargin = (int)(N1 * coordMarginRate);
MatrixUtils.CreateGrid(N1 + coordMargin * 2, N2 + coordMargin * 2, out coordX, out coordY);
coordX = coordX.Each(v => v - coordMargin * 2);
coordY = coordY.Each(v => v - coordMargin * 2);
//int MM = N1 * N2 / 25; // M=length(x);
#endregion
timeused += timer.StopAndSay("初始化");
while (k < n_iter) {
Debug("Iter={0}", k);
#region 计算两个形状上下文
timer.Restart();
// [BH1,mean_dist_1]=sc_compute(Xk',zeros(1,nsamp),mean_dist_global,nbins_theta,nbins_r,r_inner,r_outer,out_vec_1);
var BH1 = ComputeSC(Xk.Transpose(), Zeros(1, nsamp), mean_dist_global, out mean_dist_1, out_vec_1);
//var BH1 = ComputeSC(Xk.Transpose(), t1.Transpose(), mean_dist_global, out mean_dist_1, out_vec_1);
// [BH2,mean_dist_2]=sc_compute(Y',zeros(1,nsamp),mean_dist_global,nbins_theta,nbins_r,r_inner,r_outer,out_vec_2);
var BH2 = ComputeSC(Y.Transpose(), Zeros(1, nsamp), mean_dist_global, out mean_dist_2, out_vec_2);
//var BH2 = ComputeSC(Y.Transpose(), t2.Transpose(), mean_dist_global, out mean_dist_2, out_vec_2);
timeused += timer.StopAndSay("计算两个形状上下文");
Debug("Mean_dist_1:{0:F4}", mean_dist_1);
Debug("Mean_dist_2:{0:F4}", mean_dist_2);
#endregion
#region 计算lambda_o和beta_k
double lambda_o;
if (affine_start_flag) {
if (k == 0)
lambda_o = 1000;
else
lambda_o = beta_init * Math.Pow(r, k - 1); // lambda_o=beta_init*r^(k-2);
} else {
lambda_o = beta_init * Math.Pow(r, k); // lambda_o=beta_init*r^(k-1);
}
double beta_k = mean_dist_2 * mean_dist_2 * lambda_o;
#endregion
#region 计算代价矩阵
timer.Restart();
var costmat_shape = HistCost(BH1, BH2); // costmat_shape = hist_cost_2(BH1, BH2);
// theta_diff=repmat(tk,1,nsamp)-repmat(t2',nsamp,1);
var theta_diff = tk.RepMat(1, nsamp) - t2.Transpose().RepMat(nsamp, 1);
Matrix costmat_theta;
if (polarity_flag) {
// costmat_theta=0.5*(1-cos(theta_diff));
//costmat_theta = 0.5 * (Ones(costmat_shape.Rows, costmat_shape.Columns) - theta_diff.Each(v => Math.Cos(v)));
costmat_theta = theta_diff.Each(v => 0.5 * (1 - Math.Cos(v)));
} else {
// costmat_theta=0.5*(1-cos(2*theta_diff));
//costmat_theta = 0.5 * (Ones(costmat_shape.Rows, costmat_shape.Columns) - theta_diff.Each(v => Math.Cos(2 * v)));
costmat_theta = theta_diff.Each(v => 0.5 * (1 - Math.Cos(2 * v)));
}
// costmat=(1-ori_weight)*costmat_shape+ori_weight*costmat_theta;
var costmat = (1 - ori_weight) * costmat_shape + ori_weight * costmat_theta;
int nptsd = nsamp + ndum; // nptsd=nsamp+ndum;
var costmat2 = new DenseMatrix(nptsd, nptsd, eps_dum); // costmat2=eps_dum*ones(nptsd,nptsd);
costmat2.SetSubMatrix(0, nsamp, 0, nsamp, costmat); // costmat2(1:nsamp,1:nsamp)=costmat;
timeused += timer.StopAndSay("计算代价矩阵");
#endregion
#region 匈牙利算法
timer.Restart();
var costmat_int = new int[nptsd, nptsd];
for (int i = 0; i < nptsd; ++i) {
for (int j = 0; j < nptsd; ++j) {
costmat_int[i, j] = (int)(costmat2[i, j] * 10000);
}
}
var km = new KM(nptsd, costmat_int);
km.Match(false);
matchcost = km.MatchResult / 10000.0;
int[] cvec = km.MatchPair; // cvec=hungarian(costmat2);
timeused += timer.StopAndSay("匈牙利算法");
#endregion
#region 计算野点标记向量，重排匹配点
timer.Restart();
int[] cvec2 = cvec.Select((v, i) => new { Val = v, Idx = i })
.OrderBy(v => v.Val)
.Select(v => v.Idx)
.ToArray();// [a,cvec2]=sort(cvec);
out_vec_1 = cvec2.Take(nsamp1).Select(v => v > nsamp2).ToArray(); // out_vec_1=cvec2(1:nsamp1)>nsamp2;
out_vec_2 = cvec.Take(nsamp2).Select(v => v > nsamp1).ToArray(); // out_vec_2=cvec(1:nsamp2)>nsamp1;
//X2 = NaNs(nptsd, 2); // X2=NaN*ones(nptsd,2);
//X2.SetSubMatrix(0, nsamp1, 0, X2.Columns, Xk); // X2(1:nsamp1,:)=Xk;
//X2 = X2.SortRowsBy(cvec); // X2=X2(cvec,:);
X2b = NaNs(nptsd, 2); // X2b=NaN*ones(nptsd,2);
X2b.SetSubMatrix(0, nsamp1, 0, X2b.Columns, X); // X2b(1:nsamp1,:)=X;
X2b = X2b.SortRowsBy(cvec); // X2b=X2b(cvec,:);
Y2 = NaNs(nptsd, 2); // Y2=NaN*ones(nptsd,2);
Y2.SetSubMatrix(0, nsamp2, 0, Y2.Columns, Y); // Y2(1:nsamp2,:)=Y;
var ind_good = X2b.GetColumn(1).Take(nsamp).FindIdxBy(v => !double.IsNaN(v)); // ind_good=find(~isnan(X2b(1:nsamp,1)));
int n_good = ind_good.Length; // n_good=length(ind_good);
X3b = X2b.FilterRowsBy(ind_good);// X3b=X2b(ind_good,:);
Y3 = Y2.FilterRowsBy(ind_good); // Y3=Y2(ind_good,:);
timeused += timer.StopAndSay("计算野点标记向量，重排匹配点");
#endregion
#region figure 2
if (display_flag) {
//figure(2)
//plot(X2(:,1),X2(:,2),'b+',Y2(:,1),Y2(:,2),'ro')
//hold on
//h=plot([X2(:,1) Y2(:,1)]',[X2(:,2) Y2(:,2)]','k-');
//if display_flag
//% set(h,'linewidth',1)
//quiver(Xk(:,1),Xk(:,2),cos(tk),sin(tk),0.5,'b') // 画箭头
//quiver(Y(:,1),Y(:,2),cos(t2),sin(t2),0.5,'r')
DrawGradient(Xk, Y, tk, t2, N2, N1,
String.Format(@"D:\Play Data\Iteration\梯度\{0}.bmp", k),
String.Format("Iter={0}梯度方向\n匹配点数{1}", k, n_good));
//end
//hold off
//axis('ij')
//title([int2str(n_good) ' correspondences (warped X)'])
//axis([1 N2 1 N1])
//drawnow
}
#endregion
#region figure 3 显示未变形图的匹配关系
if (display_flag) {
//% show the correspondences between the untransformed images
//figure(3)
//plot(X(:,1),X(:,2),'b+',Y(:,1),Y(:,2),'ro')
//ind=cvec(ind_good);
//hold on
//plot([X2b(:,1) Y2(:,1)]',[X2b(:,2) Y2(:,2)]','k-')
//hold off
//axis('ij')
//title([int2str(n_good) ' correspondences (unwarped X)'])
//axis([1 N2 1 N1])
//drawnow
Draw(X, Y, cvec, null, N2, N1, String.Format(@"D:\Play Data\Iteration\匹配\{0}.bmp", k),
String.Format("Iter={0}\n匹配代价{1},匹配数{2}", k, matchcost, n_good));
}
#endregion
#region 求解变换矩阵
timer.Restart();
Bookstein(X3b, Y3, beta_k, ref cx, ref cy, ref E); // [cx,cy,E]=bookstein(X3b,Y3,beta_k);
timeused += timer.StopAndSay("求解变换矩阵");
#endregion
#region 通过解出来的变换对点和梯度进行变换
timer.Restart();
// % calculate affine cost
var A = MatrixUtils.RankHorizon(
cx.GetSubMatrix(n_good + 1, 2, 0, 1), cy.GetSubMatrix(n_good + 1, 2, 0, 1)
);//A=[cx(n_good+2:n_good+3,:) cy(n_good+2:n_good+3,:)];
var s = new Svd(A, true).S(); // s=svd(A);
aff_cost = Math.Log(s[0] / s[1]); // aff_cost=log(s(1)/s(2));
// % calculate shape context cost
min1 = costmat.GetColumns().Select(col => {
int minwhere = 0;
for (int i = 1; i < col.Count; ++i) {
if (col[i] < col[minwhere]) minwhere = i;
}
return new { Val = col[minwhere], Idx = minwhere };
}).ToArray();// [a1,b1]=min(costmat,[],1);
min2 = costmat.GetRows().Select(row => {
int minwhere = 0;
for (int i = 1; i < row.Count; ++i) {
if (row[i] < row[minwhere]) minwhere = i;
}
return new { Val = row[minwhere], Idx = minwhere };
}).ToArray(); // [a2,b2]=min(costmat,[],2);}
sc_cost = Math.Max(min1.Average(a => a.Val), min2.Average(a => a.Val)); // sc_cost=max(mean(a1),mean(a2));
// % warp each coordinate
axt = cx.GetSubMatrix(n_good, 3, 0, 1).Transpose(); // axt是cx中的最后三个，即a的x分量
wxt = cx.GetSubMatrix(0, n_good, 0, 1).Transpose(); // wxt是cs中的前n个，即w的x分量
ayt = cy.GetSubMatrix(n_good, 3, 0, 1).Transpose();
wyt = cy.GetSubMatrix(0, n_good, 0, 1).Transpose();
d2 = Dist2(X3b, X).Each(v => v > 0 ? v : 0); // d2=max(dist2(X3b,X),0);
U = d2.PointMultiply(d2.Each(v => Math.Log(v + Epsilon))); // U=d2.*log(d2+eps);
Debug("MatchCost:{0:F4}\taff_cost:{1:F4}\tsc_cost:{2:F4}\tE:{3:F8}", matchcost, aff_cost, sc_cost, E);
var Z = Transformation(X, U, axt, wxt, ayt, wyt);
//% apply the warp to the tangent vectors to get the new angles
var Xtan = X + tan_eps * MatrixUtils.RankHorizon(t1.Each(Math.Cos), t1.Each(Math.Sin)); // Xtan=X+tan_eps*[cos(t1) sin(t1)];
d2 = Dist2(X3b, Xtan).Each(v => v > 0 ? v : 0); // d2=max(dist2(X3b,Xtan),0);
U = d2.PointMultiply(d2.Each(v => Math.Log(v + Epsilon))); // U=d2.*log(d2+eps);
//Transformation(Xtan, U, axt, wxt, ayt, wyt, out fx, out fy);
//var Ztan = MatrixUtils.RankVertical(fx, fy).Transpose(); // Ztan=[fx; fy]';
var Ztan = Transformation(Xtan, U, axt, wxt, ayt, wyt);
for (int i = 0; i < nsamp; ++i) {
tk[i, 0] = Math.Atan2(Ztan[i, 1] - Z[i, 1], Ztan[i, 0] - Z[i, 0]);
}//tk=atan2(Ztan(:,2)-Z(:,2),Ztan(:,1)-Z(:,1));
timeused += timer.StopAndSay("通过解出来的变换对点和梯度进行变换");
#endregion
#region figure 4 显示变形后的点集
if (display_flag) {
//figure(4)
//plot(Z(:,1),Z(:,2),'b+',Y(:,1),Y(:,2),'ro');
//axis('ij')
//title(['k=' int2str(k) ', \lambda_o=' num2str(lambda_o) ', I_f=' num2str(E) ', aff.cost=' num2str(aff_cost) ', SC cost=' num2str(sc_cost)])
//axis([1 N2 1 N1])
//% show warped coordinate grid
//fx_aff=cx(n_good+1:n_good+3)'*[ones(1,M); x'; y'];
//d2=dist2(X3b,[x y]);
//fx_wrp=cx(1:n_good)'*(d2.*log(d2+eps));
//fx=fx_aff+fx_wrp;
//fy_aff=cy(n_good+1:n_good+3)'*[ones(1,M); x'; y'];
//fy_wrp=cy(1:n_good)'*(d2.*log(d2+eps));
//fy=fy_aff+fy_wrp;
//hold on
//plot(fx,fy,'k.','markersize',1)
//hold off
//drawnow
d2 = Dist2(X3b, MatrixUtils.RankHorizon(coordX, coordY));//d2=dist2(X3b,[x y]);
U = d2.PointMultiply(d2.Each(v => Math.Log(v + Epsilon)));
//Transformation(MatrixUtils.RankHorizon(coordX, coordY), U, axt, wxt, ayt, wyt, out fx, out fy);
//var coordsT = MatrixUtils.RankVertical(fx, fy).Transpose();
var coordsT = Transformation(MatrixUtils.RankHorizon(coordX, coordY), U, axt, wxt, ayt, wyt);
Draw(Z, Y, null, coordsT, N2, N1, String.Format(@"D:\Play Data\Iteration\变换\{0}.bmp", k),
String.Format("Iter={0}\nλo={1:F4},If={2:F4},aff_cost{3:F4},sc_cost{4:F4}", k, lambda_o, E, aff_cost, sc_cost));
}
#endregion
Xk = Z.Clone();
++k;
}
#endregion
#region 迭代完成后的代价计算
#region 我来尝试计算Dsc
// Xk是Q变换后的结果，而Y是模板图形P
/*
* Dsc = Avg_each_p_in_P(argmin(q in Q, C(p, T(q))) + Avg_each_q_in_Q(argmin(p in P, C(p, T(q)))
* */
double mean_dist_final_1;
var scQ = ComputeSC(Xk.Transpose(), Zeros(1, nsamp), mean_dist_global, out mean_dist_final_1, out_vec_1);
//var scQ = ComputeSC(Xk.Transpose(), Zeros(1, nsamp), mean_dist_1, out mean_dist_final_1, out_vec_1);
//var scQ = ComputeSC(Xk.Transpose(), t1.Transpose(), mean_dist_1, out mean_dist_final_1, out_vec_1);
double mean_dist_final_2;
var scP = ComputeSC(Y.Transpose(), Zeros(1, nsamp), mean_dist_global, out mean_dist_final_2, out_vec_2);
//var scP = ComputeSC(Y.Transpose(), Zeros(1, nsamp), mean_dist_2, out mean_dist_final_2, out_vec_2);
//var scP = ComputeSC(Y.Transpose(), t2.Transpose(), mean_dist_2, out mean_dist_final_2, out_vec_2);
var costmat_final = HistCost(scQ, scP);
double distance_sc = costmat_final.GetRows().Select(row => row.Min()).Average()
+ costmat_final.GetColumns().Select(col => col.Min()).Average();
Debug("distance_sc:{0}", distance_sc);
#endregion
#region 图像变换和插值
timer.Restart();
//[x,y]=meshgrid(1:N2,1:N1);
//x=x(:);y=y(:);
Matrix x = null, y = null;
MatrixUtils.CreateGrid(N1, N2, out x, out y);
//int M = N1 * N2; // M=length(x);
d2 = Dist2(X3b, MatrixUtils.RankHorizon(x, y));//d2=dist2(X3b,[x y]);
U = d2.PointMultiply(d2.Each(v => Math.Log(v + Epsilon)));
//Transformation(MatrixUtils.RankHorizon(x, y), U, axt, wxt, ayt, wyt, out fx, out fy);
var fxy = Transformation(MatrixUtils.RankHorizon(x, y), U, axt, wxt, ayt, wyt);
//disp('computing warped image...')
//V1w=griddata(reshape(fx,N1,N2),reshape(fy,N1,N2),V1,reshape(x,N1,N2),reshape(y,N1,N2));
Matrix V1w = Interpolation(
fxy.GetSubMatrix(0, fxy.Rows, 0, 1).Reshape(N1, N2),
fxy.GetSubMatrix(0, fxy.Rows, 1, 1).Reshape(N1, N2),
V1
);
#region 这个山寨插值方法会造成图像裂缝，用闭运算来尝试修补
Image<Gray, Byte> img = new Image<Gray, byte>(N2, N1);
for (int i = 0; i < N2; ++i) {
for (int j = 0; j < N1; ++j) {
img[i, j] = new Gray(V1w[i, j] * 255);
}
}
var see = new StructuringElementEx(new int[,] { { 1, 1, 1 }, { 1, 1, 1 }, { 1, 1, 1 } }, 1, 1);
//img = img.MorphologyEx(see, Emgu.CV.CvEnum.CV_MORPH_OP.CV_MOP_CLOSE, 1);
img = img.Dilate(1).Erode(1);
for (int i = 0; i < N2; ++i) {
for (int j = 0; j < N1; ++j) {
V1w[i, j] = img[i, j].Intensity / 255;
}
}
img.Dispose();
#endregion
timeused += timer.StopAndSay("图像变换和插值");
#endregion
//fz=find(isnan(V1w));
//V1w(fz)=0;
var ssd = (V2 - V1w).Each(v => v * v);//ssd=(V2-V1w).^2; %%%%%%SSD在这里
var ssd_global = ssd.SumAll();//ssd_global=sum(ssd(:));
Debug("ssd_global:{0}", ssd_global);
#region figure 5
if (display_flag) {
// figure(5)
// subplot(2,2,1)
// im(V1)
// subplot(2,2,2)
// im(V2)
// subplot(2,2,4)
// im(V1w)
// title('V1 after warping')
// subplot(2,2,3)
// im(V2-V1w)
// h=title(['SSD=' num2str(ssd_global)]);
// colormap(cmap)
}
#endregion
#region 窗口SSD比较
timer.Restart();
//%%%
//%%% windowed SSD comparison
//%%%
var wd = 2 * w + 1;//wd=2*w+1;
var win_fun = MatrixUtils.GaussianKernal(wd); //win_fun=gaussker(wd);
//% extract sets of blocks around each coordinate
//% first do 1st shape; need to use transformed coords.
var win_list_1 = Zeros(nsamp, wd * wd);//win_list_1=zeros(nsamp,wd^2);
for (int qq = 0; qq < nsamp; ++qq) {
int row_qq = (int)Xk[qq, 1],// row_qq=round(Xk(qq,2));
col_qq = (int)Xk[qq, 0];// col_qq=round(Xk(qq,1));
row_qq = Math.Max(w + 1, Math.Min(N1 - w - 1, row_qq));// row_qq=max(w+1,min(N1-w,row_qq));
col_qq = Math.Max(w + 1, Math.Min(N2 - w - 1, col_qq));// col_qq=max(w+1,min(N2-w,col_qq));
// tmp=V1w(row_qq-w:row_qq+w,col_qq-w:col_qq+w);
var tmp = V1w.GetSubMatrix(row_qq - w, w * 2 + 1, col_qq - w, w * 2 + 1);
tmp = win_fun.PointMultiply(tmp);// tmp=win_fun.*tmp;
// win_list_1(qq,:)=tmp(:)';
win_list_1.SetSubMatrix(qq, 1, 0, win_list_1.Columns, tmp.Reshape(1, tmp.Rows * tmp.Columns));
}
//% now do 2nd shape
var win_list_2 = Zeros(nsamp, wd * wd);//win_list_2=zeros(nsamp,wd^2);
for (int qq = 0; qq < nsamp; ++qq) {
int row_qq = (int)Y[qq, 1],// row_qq = round(Y(qq, 2));
col_qq = (int)Y[qq, 0];// col_qq = round(Y(qq, 1));
row_qq = Math.Max(w + 1, Math.Min(N1 - w - 1, row_qq));// row_qq=max(w+1,min(N1-w,row_qq));
col_qq = Math.Max(w + 1, Math.Min(N2 - w - 1, col_qq));// col_qq=max(w+1,min(N2-w,col_qq));
// tmp=V2(row_qq-w:row_qq+w,col_qq-w:col_qq+w)
var tmp = V2.GetSubMatrix(row_qq - w, w * 2 + 1, col_qq - w, w * 2 + 1);
tmp = win_fun.PointMultiply(tmp);// tmp=win_fun.*tmp;
win_list_2.SetSubMatrix(qq, 1, 0, win_list_2.Columns, tmp.Reshape(1, tmp.Rows * tmp.Columns));// win_list_2(qq,:)=tmp(:)';
}
var ssd_all = Dist2(win_list_1, win_list_2).Each(Math.Sqrt);//ssd_all=sqrt(dist2(win_list_1,win_list_2));
timeused += timer.StopAndSay("窗口SSD比较");
#endregion
#region 最后的KNN，不知道干嘛用
timer.Restart();
//%%%%%%% KNN在此
//% loop over nearest neighbors in both directions, project in
//% both directions, take maximum
double cost_1 = 0, cost_2 = 0;
//List<double> cost_1 = new List<double>(), cost_2 = new List<double>();
for (int qq = 0; qq < nsamp; ++qq) {
cost_1 += ssd_all[qq, min2[qq].Idx];// cost_1=cost_1+ssd_all(qq,b2(qq));
cost_2 += ssd_all[min1[qq].Idx, qq];// cost_2=cost_2+ssd_all(b1(qq),qq);
//cost_1.Add(ssd_all[qq, min2[qq].Idx]);
//cost_2.Add(ssd_all[min1[qq].Idx, qq]);
}
var ssd_local = (1.0 / nsamp) * Math.Max(cost_1, cost_2);
var ssd_local_avg = (1.0 / nsamp) * 0.5 * (cost_1 + cost_2);
//var ssd_local = (1.0 / nsamp) * Math.Max(cost_1.Average(), cost_2.Average());//ssd_local=(1/nsamp)*max(mean(cost_1),mean(cost_2));
//var ssd_local_avg = (1.0 / nsamp) * 0.5 * (cost_1.Average() + cost_2.Average());//ssd_local_avg=(1/nsamp)*0.5*(mean(cost_1)+mean(cost_2));
Debug("ssd_local:{0}", ssd_local);
Debug("ssd_local_avg:{0}", ssd_local_avg);
timeused += timer.StopAndSay("计算ssd_local");
#region 最后的组合图
//if display_flag
//% set(h,'string',['local SSD=' num2str(ssd_local) ', avg. local SSD=' num2str(ssd_local_avg)])
// set(h,'string',['local SSD=' num2str(ssd_local)])
//end
if (display_flag) {
Draw(V1, @"D:\Play Data\Iteration\结果\0-V1.bmp", "V1");
Draw(V2, @"D:\Play Data\Iteration\结果\1-V2.bmp", "V2");
Draw(V1w, @"D:\Play Data\Iteration\结果\2-V1w.bmp", "V1 after warping");
Draw(V2 - V1w, @"D:\Play Data\Iteration\结果\3-V2-V1w.bmp",
String.Format("local SSD={0:F8}", ssd_local_avg));
}
#endregion
#endregion
#endregion
var distance = 1.6 * ssd_local_avg + distance_sc + 0.3 * E; // 不知道最后的距离公式到底是什么
Debug("distance={0:F8}", distance);
return distance;
}
public KM Match()
{
KM km = new KM(scsa.Length + scsb.Length, cost);
km.Match(false);
return km;
}