/// <summary>
/// Adds a BitmapVector to the list of vectors created by resizing and averaging all input images into one.
/// The dimensions passed are for the output vector, and not the input images.
/// </summary>
/// <param name="images">The list of images to make a vector out of.</param>
/// <param name="width">The width to make the vector.</param>
/// <param name="height">The height to make the vector.</param>
/// <param name="information">Any information that you would like to associate with this BitmapVector.</param>
public void AddBitmapArrayAsOneVector(List<Bitmap> images, int width, int height, string information)
{
BitmapVector v = new BitmapVector(images[0], width, height);
for (int i = 1; i < images.Count; i++)
{
v += new BitmapVector(images[i], width, height);
}
v /= images.Count;
v.Information = information;
Add(v);
}
public static BitmapVector LoadFromFile(BinaryReader r)
{
int width = r.ReadInt32();
int height = r.ReadInt32();
BitmapVector v = new BitmapVector(width, height);
for (int x = 0; x < width; x++)
{
for (int y = 0; y < height; y++)
{
v.matrix[x, y] = r.ReadDouble();
}
}
v.Information = r.ReadString();
return(v);
}
public void AddToGroup(BitmapVector v)
{
bool added = false;
for (int i = 0; i < vectors.Count; i++)
{
if (vectors[i].Information == v.Information)
{
vectors[i] += v;
added = true;
break;
}
}
if (!added)
{
vectors.Add(v);
}
}
public BitmapVector GetClosestMatch(BitmapVector v)
{
if (vectors.Count == 0)
{
throw new CaptchaSolverException("There are no patterns loaded to which a sample pattern may be matched!");
}
int index = 0;
double best = BitmapVector.RootMeanSquareDistance(vectors[0].Scale(), v.Scale());
Parallel.For(1, vectors.Count, i =>
{
double test = BitmapVector.RootMeanSquareDistance(vectors[i].Scale(), v.Scale());
if (test < best)
{
best = test;
index = i;
}
});
return(vectors[index]);
}
public BitmapVector GetClosestMatchNoScale(BitmapVector v)
{
if (vectors.Count == 0)
{
throw new CaptchaSolverException("There are no patterns loaded to match a sample pattern to!");
}
int index = 0;
double best = BitmapVector.RootMeanSquareDistance(vectors[0] / Count, v);
//vectors[0].GetBitmap().Save("getmatch-a--" + index.ToString() + ".bmp");
//v.GetBitmap().Save("getmatch--b-" + index.ToString() + ".bmp");
//(vectors[0].Scale() - v.Scale()).GetBitmap().Save("getmatch-ab-" + index.ToString() + ".bmp");
//for (int i = 1; i < vectors.Count; i++)
Parallel.For(1, vectors.Count, i =>
{
//vectors[i].GetBitmap().Save("getmatch-a--" + i.ToString() + ".bmp");
//v.GetBitmap().Save("getmatch--b-" + i.ToString() + ".bmp");
//(vectors[i].Scale() - v.Scale()).GetBitmap().Save("getmatch-ab-" + i.ToString() + ".bmp");
double test = BitmapVector.RootMeanSquareDistance(vectors[i] / Count, v);
if (test < best)
{
best = test;
index = i;
}
});
return vectors[index];
}
public BitmapVector GetClosestMatch(Bitmap image)
{
BitmapVector v = new BitmapVector(image, vectors[0].Width, vectors[0].Height);
return GetClosestMatch(v);
}
public BitmapVector Subtract(BitmapVector other)
{
BitmapVector sub = this.Clone();
for (int x = 0; x < Width; x++)
{
for (int y = 0; y < Height; y++)
{
sub[x, y] -= other[x, y];
}
}
return sub;
}
public BitmapVector Invert()
{
BitmapVector copy = new BitmapVector(Width, Height);
double max = this.Max();
for (int x = 0; x < Width; x++)
{
for (int y = 0; y < Height; y++)
{
copy[x, y] = max - matrix[x, y];
}
}
copy.Information = Information;
return copy;
}
public BitmapVector Clone()
{
BitmapVector copy = new BitmapVector(Width, Height);
for (int x = 0; x < Width; x++)
{
for (int y = 0; y < Height; y++)
{
copy[x, y] = matrix[x, y];
}
}
copy.Information = Information;
return copy;
}
public BitmapVector GetClosestMatchNoScale(BitmapVector v)
{
if (vectors.Count == 0)
{
throw new CaptchaSolverException("There are no patterns loaded to which a sample pattern may be matched!");
}
int index = 0;
double best = BitmapVector.RootMeanSquareDistance(vectors[0] / Count, v);
Parallel.For(1, vectors.Count, i =>
{
double test = BitmapVector.RootMeanSquareDistance(vectors[i] / Count, v);
if (test < best)
{
best = test;
index = i;
}
});
return vectors[index];
}
public static BitmapVector LoadFromFile(BinaryReader r)
{
int width = r.ReadInt32();
int height = r.ReadInt32();
BitmapVector v = new BitmapVector(width, height);
for (int x = 0; x < width; x++)
{
for (int y = 0; y < height; y++)
{
v.matrix[x, y] = r.ReadDouble();
}
}
v.Information = r.ReadString();
return v;
}
public static BitmapVector ConvertToLinearBitmapVector(DoubleVector v)
{
int width = v.Size;
BitmapVector b = new BitmapVector(width, 1);
for (int x = 0; x < width; x++)
{
b.matrix[x, 0] = v[x];
}
return b;
}
public BitmapVector GetClosestMatch(Bitmap image)
{
BitmapVector v = new BitmapVector(image, vectors[0].Width, vectors[0].Height);
return(GetClosestMatch(v));
}
public void Add(Bitmap image)
{
BitmapVector v = new BitmapVector(image, image.Width, image.Height);
vectors.Add(v);
}
public void Add(BitmapVector v)
{
vectors.Add(v);
}
public void Add(BitmapVector v)
{
vectors.Add(v);
}
public void Add(Bitmap image)
{
BitmapVector v = new BitmapVector(image, image.Width, image.Height);
vectors.Add(v);
}
public static double RootMeanSquareDistance(BitmapVector left, BitmapVector right)
{
double value = 0.0;
for (int x = 0; x < left.Width; x++)
{
for (int y = 0; y < left.Height; y++)
{
value += Math.Pow(left.matrix[x, y] - right.matrix[x, y], 2.0);
}
}
return Math.Sqrt(value / (left.Width * left.Height));
}
public void AddToGroup(BitmapVector v)
{
bool added = false;
for (int i = 0; i < vectors.Count; i++)
{
if (vectors[i].Information == v.Information)
{
vectors[i] += v;
added = true;
break;
}
}
if (!added)
{
vectors.Add(v);
}
}
public BitmapVector Binarize()
{
BitmapVector copy = new BitmapVector(Width, Height);
double max = this.Max();
for (int x = 0; x < Width; x++)
{
for (int y = 0; y < Height; y++)
{
copy[x, y] = matrix[x, y] >= 0.5 ? 1.0 : 0.0;
}
}
copy.Information = Information;
return copy;
}
