static void Main(string[] args)
{
Console.WriteLine(new string('-', 15));
// IEnumerableExtensions test1 = new IEnumerableExtensions(); << can`t applay instance.
var test1 = new[] { 1, 2, 7, 5, 3 };
var r = test1.Max();
Console.WriteLine(r);
// test SUM
var result1 = test1.Apply(Sum, 0);
Console.WriteLine(result1);
//testing
var result1b = test1.Apply(Sum, test1[0]);
Console.WriteLine(result1b);
// test PRODUCT
var result2 = test1.Apply(Product, 1);
Console.WriteLine(result2);
// test MIN
var result3 = test1.Apply(Min, test1[0]);
Console.WriteLine(result3);
// test MAX
var result4 = test1.Apply(Max, test1[0]);
Console.WriteLine(result4);
// test AVERAGE
Console.WriteLine(test1.Average());
Console.WriteLine(test1.Apply(Count, 0));
}
static void Main()
{
IEnumerable <int> intArr =new List<int>() { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
IEnumerable<double> dblArr =new[] { 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9 };
Console.WriteLine("Sum of: ");
Console.WriteLine("Integer array: {0}", intArr.Sum());
Console.WriteLine("Double array: {0}", dblArr.Sum());
Console.WriteLine();
Console.WriteLine("Product of: ");
Console.WriteLine("Integer array: {0}", intArr.Product());
Console.WriteLine("Double array: {0}", dblArr.Product());
Console.WriteLine();
Console.WriteLine("Average of: ");
Console.WriteLine("Integer array: {0}", intArr.Average());
Console.WriteLine("Double array: {0}", dblArr.Average());
Console.WriteLine();
Console.WriteLine("Min of: ");
Console.WriteLine("Integer array: {0}", intArr.Min());
Console.WriteLine("Double array: {0}", dblArr.Min());
Console.WriteLine();
Console.WriteLine("Max of: ");
Console.WriteLine("Integer array: {0}", intArr.Max());
Console.WriteLine("Double array: {0}", dblArr.Max());
}
public static void Main()
{
Kitten[] kittens = new[]
{
new Kitten("Hello Kitty", 4),
new Kitten("Catwoman", 6),
new Kitten("Thunder cat", 11)
};
Tomcat[] tomcats = new[]
{
new Tomcat("Mr. Bigglesworth", 2),
new Tomcat("Felix", 5),
new Tomcat("Tom", 2)
};
Dog[] dogs = new Dog[]
{
new Dog("Brian Griffin", 2, Genders.Male),
new Dog("Too stupid dog", 5, Genders.Male),
new Dog("Eddie from Frasier", 7, Genders.Male)
};
Frog[] frogs = new[]
{
new Frog("Hypnotoad from Futurama", 13, Genders.Male),
new Frog("Kermit", 50, Genders.Male),
new Frog("Prince Charming", 30, Genders.Male),
};
Console.WriteLine("Average kitten age: {0}", kittens.Average(n => n.Age));
Console.WriteLine("Average tomcat age: {0}", tomcats.Average(n => n.Age));
Console.WriteLine("Average dog age: {0}", dogs.Average(n => n.Age));
Console.WriteLine("Average frog age: {0}", frogs.Average(n => n.Age));
}
static void Main()
{
var elements = new[] { "a", "b" };
Console.WriteLine(elements.Average());
Console.WriteLine(elements.Min());
Console.WriteLine(elements.Max());
Console.WriteLine(elements.Sum());
string a = "a";
string b = "b";
Console.WriteLine(a * b);
}
private static void Main()
{
var strBldr = new StringBuilder("Some test string");
Console.WriteLine(strBldr.Substring(6, 4));
IEnumerable<int> testEnum = new[] { 2, 5, 6, 8 };
Console.WriteLine(testEnum.Average());
Console.WriteLine(testEnum.Max());
Console.WriteLine(testEnum.Min());
}
static void Main()
{
var numbers = new[] { 3, 5, 12, 15, 30, 35 };
Console.WriteLine("Sum: {0}", numbers.Sum());
Console.WriteLine("Product: {0}", numbers.Product());
Console.WriteLine("Min: {0}", numbers.Min());
Console.WriteLine("Max: {0}", numbers.Max());
Console.WriteLine("Average: {0}", numbers.Average());
Console.WriteLine();
}
/*2. Implement a set of extension methods for IEnumerable<T> that implement the following group functions:
* sum, product, min, max, average.*/
static void Main()
{
List<int> listInt = new List<int> { 1, 2, 3, 4, 5 };
double[] testDouble = new[] { 4.5, 6.0, 5.7, 34.7 }; //sum = 50.9
Console.WriteLine("---list <int>---");
Console.WriteLine("Sum: {0}", listInt.Sum());
Console.WriteLine("Average: {0}", listInt.Average());
Console.WriteLine("Product: {0}", listInt.Product());
Console.WriteLine("Minimum: {0}", listInt.Min());
Console.WriteLine("Minimum: {0}", listInt.Max());
Console.WriteLine("---double---");
Console.WriteLine("Sum: {0}", testDouble.Sum());
Console.WriteLine("Average: {0}", testDouble.Average());
Console.WriteLine("Product: {0}", testDouble.Product());
Console.WriteLine("Minimum: {0}", testDouble.Min());
Console.WriteLine("Minimum: {0}", testDouble.Max());
List<int> testEmpty = new List<int>();
Console.WriteLine(testEmpty.Sum()); //return error when enumaration is empty, does not work about array(return 0)
}
static void Main()
{
var items = new[]
{
1.2,
2.3,
3.4
};
Console.Write("Elements: ");
foreach (var item in items)
{
Console.Write("{0} ", item);
}
Console.WriteLine();
Console.WriteLine("Sum: " + items.Sum());
Console.WriteLine("Product: " + items.Product());
Console.WriteLine("Min: " + items.Min());
Console.WriteLine("Max: " + items.Max());
Console.WriteLine("Average: " + items.Average());
}
public void NullableDecimalFromSelector()
{
var source = new[]
{
new{ name = "Tim", num = (decimal?)5.5m},
new{ name = "John", num = (decimal?)15.5m},
new{ name = "Bob", num = (decimal?)null}
};
decimal? expected = 10.5m;
Assert.Equal(expected, source.Average(e => e.num));
}
public void MultipleDecimalFromSelector()
{
var source = new[]
{
new{ name = "Tim", num = 5.5m},
new{ name = "John", num = 15.5m},
new{ name = "Bob", num = 3.0m}
};
decimal expected = 8.0m;
Assert.Equal(expected, source.Average(e => e.num));
}
public void NullableDoubleFromSelector()
{
var source = new[]
{
new{ name = "Tim", num = (double?)5.5 },
new{ name = "John", num = (double?)15.5 },
new{ name = "Bob", num = default(double?) }
};
double? expected = 10.5;
Assert.Equal(expected, source.Average(e => e.num));
}
public void MultipleDoubleFromSelector()
{
var source = new []
{
new { name = "Tim", num = 5.5},
new { name = "John", num = 15.5},
new { name = "Bob", num = 3.0}
};
double expected = 8.0;
Assert.Equal(expected, source.Average(e => e.num));
}
public void NullableLongFromSelector()
{
var source = new []
{
new { name = "Tim", num = (long?)40L },
new { name = "John", num = default(long?) },
new { name = "Bob", num = (long?)30L }
};
double? expected = 35;
Assert.Equal(expected, source.Average(e => e.num));
}
public void MultipleLongFromSelector()
{
var source = new []
{
new { name = "Tim", num = 40L },
new { name = "John", num = 50L },
new { name = "Bob", num = 60L }
};
double expected = 50;
Assert.Equal(expected, source.Average(e => e.num));
}
public void NullableIntFromSelector()
{
var source = new []
{
new { name = "Tim", num = (int?)10 },
new { name = "John", num = default(int?) },
new { name = "Bob", num = (int?)10 }
};
double? expected = 10;
Assert.Equal(expected, source.Average(e => e.num));
}
public void MultipleIntFromSelector()
{
var source = new []
{
new { name="Tim", num = 10 },
new { name="John", num = -10 },
new { name="Bob", num = 15 }
};
double expected = 5;
Assert.Equal(expected, source.Average(e => e.num));
}
public void MultipleFloatFromSelector()
{
var source = new[]
{
new{ name = "Tim", num = 5.5f},
new{ name = "John", num = 15.5f},
new{ name = "Bob", num = 3.0f}
};
float expected = 8.0f;
Assert.Equal(expected, source.Average(e => e.num));
}
static void Main()
{
var myStr = new StringBuilder("Proba");
Console.WriteLine(myStr.Substring(0, 3));
var myCollection = new[] { 1, 2, 3, 4, 5, 13 };
Console.WriteLine("Average -> {0}", myCollection.Average());
Console.WriteLine("Product -> {0}", myCollection.Product());
Console.WriteLine();
var students = new Student[4];
students[0] = new Student
{
FirstName = "Ivan",
LastName = "Georgiev",
Age = 19,
GroupNumber = 1,
Email = "*****@*****.**",
Tel = "0888123456",
Marks = new List<int> { 6, 6 },
FN = 123406789,
};
students[1] = new Student
{
FirstName = "Ana",
LastName = "Georgieva",
Age = 17,
GroupNumber = 1,
Email = "[email protected]",
Tel = "02/8123456",
Marks = new List<int> { 2, 2, 4 },
FN = 123406789,
};
students[2] = new Student
{
FirstName = "Gergana",
LastName = "Hristova",
Age = 21,
GroupNumber = 2,
Email = "*****@*****.**",
Tel = "0888123456",
Marks = new List<int> { 6, 3, 6, 4 },
FN = 123405789,
};
students[3] = new Student
{
FirstName = "Ivan",
LastName = "Antonov",
Age = 25,
GroupNumber = 2,
Email = "*****@*****.**",
Tel = "028123458",
Marks = new List<int> { 4, 3 },
FN = 123404789,
};
var result = Check.ExtMethods.FirstBeforeLast(students);
Print(result);
result = Check.ExtMethods.AgeRange(students, 18, 24);
Print(result);
result = students
.OrderByDescending(x => x.FirstName)
.ThenByDescending(x => x.LastName)
.ToArray();
Print(result);
var linkResult =
from student in students
where student.GroupNumber == 2
orderby student.FirstName
select student;
Print(linkResult);
result = students
.Where(x => x.GroupNumber == 2)
.OrderBy(x => x.FirstName)
.ToArray();
Console.WriteLine("Students in group 2 are (selected by Lambda):");
Print(result);
result = students
.Where(x => x.Email.Substring(x.Email.IndexOf('@') + 1).Equals("abv.bg"))
.ToArray();
Console.WriteLine("Student(s) with email in abv.bg is/are:");
Print(result);
var resultSofiaPhone =
from student in students
where student.Tel.StartsWith("02")
select student;
Console.WriteLine("Student(s) with phone in Sofia is/are:");
Print(resultSofiaPhone);
var excellentStudents =
from student in students
where student.Marks.Contains(6)
select new { FullName = student.FirstName + " " + student.LastName, Marks = string.Join(", ", student.Marks) };
Console.WriteLine(string.Join(", ", excellentStudents));
Console.WriteLine();
result = students
.Where(x => x.Marks.Count == 2)
.ToArray();
Console.WriteLine("Students with exactly two marks:");
Print(result);
var marks = students
.Where(x => x.FN.ToString().Substring(4, 2) == "06")
.Select(x => x.Marks);
Console.WriteLine("All marks in 2006");
foreach (var mark in marks)
{
Console.WriteLine(string.Join(", ", mark));
}
var res =
from x in students
group x by x.GroupNumber;
Console.WriteLine("Students in groups:");
foreach (var group in res)
{
Console.Write("Group ");
Console.WriteLine(group.Key);
foreach (var st in group)
{
Console.WriteLine(st);
}
}
Console.WriteLine();
var resLambda = students
.GroupBy(x => x.GroupNumber);
Console.WriteLine("Students in groups (Lambda):");
foreach (var group in resLambda)
{
Console.Write("Group ");
Console.WriteLine(group.Key);
foreach (var st in group)
{
Console.WriteLine(st);
}
}
}
//Working. this sorts through the list of nearbyentities and calculates a single avoidance vector
public bool AvoidNearbyEntities()
{
//avoiding and avoiding stage two are what I use as "AirBreaks" to bring acceleration down
//most of the time avoiding requires a direction change so this makes it easier to slow down and change direction
//rather than just going full power in an new direction sice dampaners slow down the ship much faster than max opposite power it prevents
//drones from drifting into one another while trying to avoid
if (Avoiding)
Avoiding = false;
try{
if (_shipControls != null && Ship != null)
{
if (Ship.Physics.LinearVelocity.Normalize() > MaxSpeed )
{
_shipControls.MoveAndRotateStopped();
}
else if(Ship!=null)
{
List<Vector3D> avoidanceVectors = new List<Vector3D>();
Vector2 avoidanceRot = Vector2.Zero;
//from the list of my nearby entities select
//only targets that have a mass > 20% my mass (otherwise theres no real need to dodge)
//order them by distance
//select only the top _avoidNumTargets
var topEntities =
_nearbyFloatingObjects
.Where(y => y != null && (y.Physics.Mass > Ship.Physics.Mass*.2)
&& (y != Ship))
//to rule out the chance of the ship avoiding its self... which is annoying
.OrderBy(x => (x.GetPosition() - Ship.GetPosition()).Length())
.Take(_avoidNumTargets).ToList();
//Dodge the _avoidNumTargets Entities found by building a single avoidance direction
foreach (var item in topEntities)
{
if (item != null)
{
var distance = Math.Abs((item.GetPosition() - Ship.GetPosition()).Length());
var enemyBoundingBoxSize = (item.GetPosition() - item.LocalAABB.Max).Length()/2;
if (distance < FollowRange/8)
{
avoidanceRot += NavInfo.CalculateRotation(item.GetPosition(),
_shipControls as IMyEntity);
var temp = (Ship.GetPosition() - item.GetPosition());
var val2 = item.Physics.Mass / Ship.Physics.Mass > 75 ? 75 : item.Physics.Mass / Ship.Physics.Mass;
if (val2 < 30) val2 = 30;
double[] vals = { temp.X, temp.Y, temp.Z };
double min = vals.Min();
if ((int)min == 0)
min = 1;
temp = temp/min*val2;
Util.GetInstance().Log("[DroneNavigation.AvoidNearbyEntities] " + Ship.DisplayName + " avoiding Ship -> Distance: " + distance + " Mass: " + item.Physics.Mass);
Util.GetInstance().Log("^^avoiding power x:" + temp.X + " y:" + temp.Y + " z:" + temp.Z);
avoidanceVectors.Add(temp);
// * (item.Physics.Mass / biggestMass));
}
}
}
foreach (var item in _nearbyAsteroids)
{
if (item != null)
{
var distance = Math.Abs((item.GetPosition() - Ship.GetPosition()).Length());
var enemyBoundingBoxSize = item.LocalAABB.Max.Normalize();
MaxSpeed = MaxSpeed > distance/ApproachSpeedMod ? distance/ApproachSpeedMod : MaxSpeed;
var detectRange = _avoidanceRange + enemyBoundingBoxSize;
if (distance < detectRange)
{
avoidanceRot += NavInfo.CalculateRotation(item.GetPosition(), _shipControls as IMyEntity);
var temp = (Ship.GetPosition() - item.GetPosition());
var rx = (detectRange-Math.Abs(temp.X)) * (temp.X / temp.X);
var y = (detectRange - Math.Abs(temp.Y)) * (temp.Y / temp.Y);
var z = (detectRange - Math.Abs(temp.Z)) * (temp.Z / temp.Z);
double[] vals = new[] { rx, y, z };
double max = vals.Max(x=>Math.Abs(x));
if (max == 0)
max = vals.Average()!=0?vals.Average():1;
var SpeedPowerBoostBasedOnRange = distance/100;
temp = new Vector3D(rx, y, z) / max * SpeedPowerBoostBasedOnRange*20;
Util.GetInstance().Log("[DroneNavigation.AvoidNearbyEntities] " + Ship.DisplayName + " avoiding asteroid -> Distance: " + (distance - detectRange));
Util.GetInstance().Log("^^avoiding power x:"+ temp.X + " y:" + temp.Y + " z:" + temp.Z);
avoidanceVectors.Add(temp);
}
}
}
if (avoidanceVectors.Count > 0)
{
avoidanceVectors = avoidanceVectors.OrderBy(x => x.Length()).ToList();
var avoidanceVector = avoidanceVectors[0];
for (int i = 1; i < avoidanceVectors.Count && i < 5; i++)
{
var temp = avoidanceVectors[i];
avoidanceVector += temp;
}
avoidanceVector = (avoidanceVector/avoidanceVectors.Count);
double[] vals = { avoidanceVector.X, avoidanceVector.Y, avoidanceVector.Z };
double max = vals.Max(x => Math.Abs(x));
if (max == 0)
max = vals.Average() != 0 ? vals.Average() : 1;
avoidanceVector = avoidanceVector / max * 104;
if (_nearbyAsteroids.Count>0)
Util.GetInstance().Log("[DroneNavigation.AvoidNearbyEntities] final avoiding power -> x:" + avoidanceVector.X + " y:" + avoidanceVector.Y + " z:" + avoidanceVector.Z);
if (MaxSpeed < 10)
MaxSpeed = 10;
AvoidTarget(avoidanceVector);
Avoiding = true;
}
}
}
}
catch (Exception e)
{
// i dont care about this error
//Util.GetInstance().LogError(e.ToString());
}
return Avoiding;
}
public void NullableFloatFromSelector()
{
var source = new []
{
new { name = "Tim", num = (float?)5.5f },
new { name = "John", num = (float?)15.5f },
new { name = "Bob", num = default(float?) }
};
float? expected = 10.5f;
Assert.Equal(expected, source.Average(e => e.num));
}
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;
}
