/// <summary>
/// Calculates eigen objects
/// </summary>
public static void CalcEigenObjects(Bitmap[] input, int maxIteration, double eps, FloatImage[] eigVecs, float[] eigVals, FloatImage avg)
{
if (input.Length == 0)
return;
int nObjects = input.Length;
int nEigens = nObjects - 1;
byte[][] objs = new byte[nObjects][];
float[][] eigs = new float[nEigens][];
int obj_step = 0, old_step = 0;
int eig_step = 0, oldeig_step = 0;
Size obj_size = avg.Size, old_size = avg.Size, oldeig_size = avg.Size;
for (var i = 0; i < nObjects; i++)
{
Bitmap obj = input[i];
objs[i] = obj.CopyGrayscaleBitmapToByteArray(out obj_step);
obj_size = obj.Size;
if (obj_size != avg.Size || obj_size != old_size)
throw new ManagedEigenObjectException("Different sizes of objects");
if (i > 0 && obj_step != old_step)
throw new ManagedEigenObjectException("Different steps of objects");
old_step = obj_step;
old_size = obj_size;
}
for (var i = 0; i < nEigens; i++)
{
FloatImage eig = eigVecs[i];
eig_step = eig.Step;
eigs[i] = eig.Data;
if (eig.Size != avg.Size || eig.Size != oldeig_size)
throw new ManagedEigenObjectException("Different sizes of objects");
if (i > 0 && eig_step != oldeig_step)
throw new ManagedEigenObjectException("Different steps of objects");
oldeig_step = eig.Step;
oldeig_size = eig.Size;
}
CalcEigenObjects(nObjects, objs, obj_step,
eigs, eig_step, obj_size,
maxIteration, eps, avg.Data, avg.Step, eigVals);
}
/// <summary>
/// Caculate the eigen images for the specific traning image
/// </summary>
public static void CalcEigenObjects(Bitmap[] trainingImages, int maxIter, double eps, out FloatImage[] eigenImages, out FloatImage avg)
{
int width = trainingImages[0].Width;
int height = trainingImages[0].Height;
if (maxIter <= 0 || maxIter > trainingImages.Length)
maxIter = trainingImages.Length;
int maxEigenObjs = maxIter;
eigenImages = new FloatImage[maxEigenObjs];
for (int i = 0; i < eigenImages.Length; i++)
eigenImages[i] = new FloatImage(width, height);
avg = new FloatImage(width, height);
ManagedEigenObjects.CalcEigenObjects(trainingImages, maxIter, eps, eigenImages, null, avg);
}
/// <summary>
/// Caculate the eigen images for the specific traning image
/// </summary>
public static void CalcEigenObjects(Bitmap[] trainingImages, int maxIter, double eps, out FloatImage[] eigenImages, out FloatImage avg)
{
int width = trainingImages[0].Width;
int height = trainingImages[0].Height;
if (maxIter <= 0 || maxIter > trainingImages.Length)
{
maxIter = trainingImages.Length;
}
int maxEigenObjs = maxIter;
eigenImages = new FloatImage[maxEigenObjs];
for (int i = 0; i < eigenImages.Length; i++)
{
eigenImages[i] = new FloatImage(width, height);
}
avg = new FloatImage(width, height);
ManagedEigenObjects.CalcEigenObjects(trainingImages, maxIter, eps, eigenImages, null, avg);
}
/// <summary>
/// Decompose the image as eigen values, using the specific eigen vectors
/// </summary>
public static float[] EigenDecomposite(Bitmap src, FloatImage[] eigenImages, FloatImage avg)
{
return ManagedEigenObjects.EigenDecomposite(src, eigenImages, avg);
}
/// <summary>
/// Decompose the image as eigen values, using the specific eigen vectors
/// </summary>
public static float[] EigenDecomposite(Bitmap src, FloatImage[] eigenImages, FloatImage avg)
{
return(ManagedEigenObjects.EigenDecomposite(src, eigenImages, avg));
}
/// <summary>
/// Calculates eigen objects
/// </summary>
public static void CalcEigenObjects(Bitmap[] input, int maxIteration, double eps, FloatImage[] eigVecs, float[] eigVals, FloatImage avg)
{
if (input.Length == 0)
{
return;
}
int nObjects = input.Length;
int nEigens = nObjects - 1;
byte[][] objs = new byte[nObjects][];
float[][] eigs = new float[nEigens][];
int obj_step = 0, old_step = 0;
int eig_step = 0, oldeig_step = 0;
Size obj_size = avg.Size, old_size = avg.Size, oldeig_size = avg.Size;
for (var i = 0; i < nObjects; i++)
{
Bitmap obj = input[i];
objs[i] = obj.CopyGrayscaleBitmapToByteArray(out obj_step);
obj_size = obj.Size;
if (obj_size != avg.Size || obj_size != old_size)
{
throw new ManagedEigenObjectException("Different sizes of objects");
}
if (i > 0 && obj_step != old_step)
{
throw new ManagedEigenObjectException("Different steps of objects");
}
old_step = obj_step;
old_size = obj_size;
}
for (var i = 0; i < nEigens; i++)
{
FloatImage eig = eigVecs[i];
eig_step = eig.Step;
eigs[i] = eig.Data;
if (eig.Size != avg.Size || eig.Size != oldeig_size)
{
throw new ManagedEigenObjectException("Different sizes of objects");
}
if (i > 0 && eig_step != oldeig_step)
{
throw new ManagedEigenObjectException("Different steps of objects");
}
oldeig_step = eig.Step;
oldeig_size = eig.Size;
}
CalcEigenObjects(nObjects, objs, obj_step,
eigs, eig_step, obj_size,
maxIteration, eps, avg.Data, avg.Step, eigVals);
}
/// <summary>
/// Calculates eigen decomposite
/// </summary>
public static float[] EigenDecomposite(Bitmap obj, FloatImage[] eigInput, FloatImage avg)
{
var nEigObjs = eigInput.Length;
var coeffs = new float[nEigObjs];
int i;
int obj_step;
byte[] obj_data = obj.CopyGrayscaleBitmapToByteArray(out obj_step);
Size obj_size = obj.Size;
/*cvGetImageRawData( avg, (uchar **) & avg_data, &avg_step, &avg_size );
if( avg->depth != IPL_DEPTH_32F )
CV_ERROR( CV_BadDepth, cvUnsupportedFormat );
if( avg->nChannels != 1 )
CV_ERROR( CV_BadNumChannels, cvUnsupportedFormat );
cvGetImageRawData( obj, &obj_data, &obj_step, &obj_size );
if( obj->depth != IPL_DEPTH_8U )
CV_ERROR( CV_BadDepth, cvUnsupportedFormat );
if( obj->nChannels != 1 )
CV_ERROR( CV_BadNumChannels, cvUnsupportedFormat );*/
if (obj_size != avg.Size)
throw new ManagedEigenObjectException("Different sizes of objects");
float[][] eigs = new float[nEigObjs][];
int eig_step = 0, old_step = 0;
Size eig_size = avg.Size, old_size = avg.Size;
for (i = 0; i < nEigObjs; i++)
{
FloatImage eig = eigInput[i];
eig_step = eig.Step;
eigs[i] = eig.Data;
if (eig_size != avg.Size || eig_size != old_size)
throw new ManagedEigenObjectException("Different sizes of objects");
if (i > 0 && eig_step != old_step)
throw new ManagedEigenObjectException("Different steps of objects");
old_step = eig.Step;
old_size = eig.Size;
}
EigenDecomposite(obj_data, obj_step, nEigObjs, eigs, eig_step, avg.Data, avg.Step, obj_size, coeffs);
return coeffs;
}
