/// <summary>
/// Computes a scale-space pyramid from the source image.
/// </summary>
/// <param name="source">Source image</param>
/// <param name="levels">Number of levels per octave</param>
/// <param name="sigma">Sigma for the initial convolution Gaussian</param>
/// <returns>A scale-space pyramid</returns>
public static ScaleSpacePyramid <T> Compute(T source, int levels, float sigma)
{
List <T> octaves = new List <T>();
T current = Convolution.ConvolveGaussian <T>(source, sigma);
while (current.Width >= MIN_SIZE && current.Height >= MIN_SIZE)
{
octaves.Add(current);
current = Convolution.ConvolveGaussian <T>(current, 2 * sigma, 2);
}
T[,] pyramid = new T[octaves.Count, levels];
float multiplier = (float)Math.Sqrt(Math.Pow(2, 2.0 / levels) - 1);
for (int o = 0; o < octaves.Count; o++)
{
pyramid[o, 0] = octaves[o];
float currentSigma = multiplier * sigma;
for (int l = 1; l < levels; l++)
{
pyramid[o, l] = Convolution.ConvolveGaussian <T>(pyramid[o, l - 1], currentSigma);
currentSigma *= multiplier;
}
}
return(new ScaleSpacePyramid <T>(pyramid, sigma));
}
/// <summary>
/// Computes the eigensystem image from the second moment image provided.
/// </summary>
/// <param name="moments">Contains the second moments used to compute the eigensystem image.</param>
/// <param name="sigma">The sigma to use when convolving the second moment image</param>
/// <returns>the eigensystem image</returns>
public static unsafe EigensystemImage Compute(SecondMomentImage moments, float sigma)
{
moments = Convolution.ConvolveGaussian <SecondMomentImage>(moments, sigma);
int rows = moments.Rows;
int columns = moments.Columns;
float[, ,] data = new float[rows, columns, 6];
fixed(float *src = moments.RawArray, dst = data)
{
float *srcPtr = src;
float *dstPtr = dst;
int length = rows * columns;
while (length-- > 0)
{
float a11 = *srcPtr++;
float a22 = *srcPtr++;
float a12 = *srcPtr++;
float a21 = a12;
float sqrt = (float)Math.Sqrt(4 * a12 * a21 + Math.Pow(a11 - a22, 2));
float sum = a11 + a22;
float eigen1 = sum + sqrt;
float eigen2 = sum - sqrt;
float x1, y1;
findEigenvector(a11, a12, a21, a22, eigen1, out x1, out y1);
float x2, y2;
findEigenvector(a11, a12, a21, a22, eigen2, out x2, out y2);
*dstPtr++ = eigen1;
*dstPtr++ = eigen2;
*dstPtr++ = x1;
*dstPtr++ = y1;
*dstPtr++ = x2;
*dstPtr++ = y2;
}
}
EigensystemImage image = new EigensystemImage();
image.SetData(data);
return(image);
}
/// <summary>
/// Computes a gradient image from the source image.
/// </summary>
/// <param name="sigma">The sigma to use when blurring the source image.</param>
/// <param name="image">Source image</param>
/// <returns>Gradient image</returns>
public static unsafe GradientImage Compute(GrayscaleImage image, float sigma)
{
int rows = image.Rows;
int columns = image.Columns;
if (sigma > 0)
{
image = Convolution.ConvolveGaussian <GrayscaleImage>(image, sigma);
}
float[, ,] data = new float[rows, columns, 4];
fixed(float *src = image.RawArray, dst = data)
{
float *srcPtr = src;
float *srcPtrP = srcPtr + 1;
float *dstPtr = dst;
dstPtr += 2;
// X derivative
for (int r = 0; r < rows; r++)
{
*dstPtr = *srcPtrP - *srcPtr;
dstPtr += 4;
srcPtrP++;
for (int c = 1; c < columns - 1; c++, srcPtr++, srcPtrP++, dstPtr += 4)
{
*dstPtr = *srcPtrP - *srcPtr;
}
srcPtrP--;
*dstPtr = *srcPtrP - *srcPtr;
dstPtr += 4;
srcPtr += 2;
srcPtrP += 2;
}
srcPtr = src;
srcPtrP = srcPtr + columns;
dstPtr = dst;
dstPtr += 3;
int stride = 4 * columns;
for (int c = 0; c < columns; c++, srcPtr++, srcPtrP++, dstPtr += 4)
{
float *srcScan = srcPtr;
float *srcScanP = srcPtrP;
float *dstScan = dstPtr;
* dstScan = *srcScanP - *srcScan;
dstScan += stride;
srcScanP += columns;
for (int r = 1; r < rows - 1; r++, dstScan += stride, srcScan += columns, srcScanP += columns)
{
*dstScan = *srcScanP - *srcScan;
}
srcScanP -= columns;
*dstScan = *srcScanP - *srcScan;
}
dstPtr = dst;
int length = rows * columns;
for (int i = 0; i < length; i++, dstPtr += 4)
{
setData(dstPtr);
}
}
GradientImage result = new GradientImage();
result.SetData(data);
return(result);
}