VectorInt ClassSummaryList::LLoydsBinSizes () const
{
VectorInt binSizes;
ClassSummaryList::const_iterator idx;
for (idx = begin (); idx != end (); idx++)
{
const ClassSummaryPtr cs = *idx;
for (kkuint32 lloydsEntryIdx = 0; lloydsEntryIdx < cs->NumOfLLoydsEntries (); lloydsEntryIdx++)
{
LLoydsEntryPtr lloydsEntry = cs->LLoydsEntryByIndex (lloydsEntryIdx);
if (lloydsEntry)
{
kkuint32 zed = 0;
kkint32 binSize = lloydsEntry->LLoydsBinSize ();
// if 'binSize' not in 'binSizes' then add
for (zed = 0; zed < kkuint32 (binSizes.size ()); zed++)
{
if (binSizes[zed] == binSize)
break;
}
if (zed >= binSizes.size ())
binSizes.push_back (binSize);
}
}
}
sort (binSizes.begin (), binSizes.end ());
return binSizes;
} /* LLoydsBinSizes */
float KKB::LLoydsIndexOfPatchiness (const VectorInt& bins)
{
// As defined by Andrew Remsen
// also look at http://www.pmel.noaa.gov/pubs/outstand/stab1646/statistics.shtml
if (bins.size () < 1)
return 0.0f;
kkuint32 x;
double total = 0.0;
double mean = 0.0;
for (x = 0; x < bins.size (); x++)
total += bins[x];
mean = total / double (bins.size ());
double totalDeltaSquared = 0.0;
for (x = 0; x < bins.size (); x++)
{
double delta = double (bins[x]) - mean;
totalDeltaSquared += delta * delta;
}
double var = totalDeltaSquared / bins.size ();
double varDivMean = var / mean;
double oneDivMean = 1.0 / mean;
float p = float ((varDivMean - 1.0 + mean) * oneDivMean);
return p;
} /* LLoydsIndexOfPatchiness */
void ClassSummaryList::SummaryReport (ostream& r) const
{
kkuint32 binSizeIDX = 0;
r << "Statistic Summary by Class" << endl
<< endl;
VectorInt binSizes = LLoydsBinSizes ();
r << "" << "\t" << "" << "\t" << "" << "\t" << "LLoydsIndex's" << endl;
r << "ClassName" << "\t" << "U2Stat" << "\t" << "Z-Score";
for (binSizeIDX = 0; binSizeIDX < binSizes.size (); binSizeIDX++)
r << "\t" << binSizes[binSizeIDX];
r << endl;
const_iterator idx;
for (idx = begin (); idx != end (); idx++)
{
ClassSummaryPtr cs = *idx;
r << cs->ClassName () << "\t"
<< cs->U2Stat () << "\t"
<< cs->ZScore ();
for (binSizeIDX = 0; binSizeIDX < binSizes.size (); binSizeIDX++)
{
kkint32 binSize = binSizes[binSizeIDX];
LLoydsEntryPtr lloydsEntry = cs->LLoydsEntryByBinSize (binSize);
if (lloydsEntry == NULL)
r << "\t" << "-";
else
r << "\t" << lloydsEntry->LLoydsIndex ();
}
r << endl;
}
} /* SummaryReport */
void ClassSummaryList::SpatialOverlapReport (ostream& o) const
{
KKStr classTitle1, classTitle2;
MLClassListPtr classes = GetListOfClasses ();
classes->ExtractTwoTitleLines (classTitle1, classTitle2);
VectorInt binSizes = LLoydsBinSizes ();
for (kkuint32 binSizeIdx = 0; binSizeIdx < kkuint32 (binSizes.size ()); binSizeIdx++)
{
kkint32 binSize = binSizes[binSizeIdx];
o << endl
<< endl
<< endl
<< "Spatial Overlap Indexes for BinSize[" << binSize << "]" << endl
<< endl
<< "Class" << "\t" << classTitle1 << endl
<< "Name" << "\t" << classTitle2 << endl;
ClassSummaryList::const_iterator idx;
for (idx = begin (); idx != end (); idx++)
{
ClassSummaryPtr cs1 = *idx;
o << cs1->ClassName ();
ClassSummaryList::const_iterator idx2;
for (idx2 = begin (); idx2 != end (); idx2++)
{
ClassSummaryPtr cs2 = *idx2;
if (idx == idx2)
o << "\t" << "-";
else
{
double spatialIndex = SpatialOverlapIndex (binSize, cs1, cs2);
o << "\t" << spatialIndex;
}
}
o << endl;
}
}
delete classes;
} /* SpatialOverlapReport */
RasterPtr SegmentorOTSU::SegmentImage (RasterPtr srcImage,
kkint32 numClasses,
double& sep
)
{
/*
function [IDX,sep] = otsu (srcImage, numClasses)
%OTSU Global image thresholding/segmentation using Otsu's method.
% IDX = OTSU(srcImage,N) segments the image srcImage into N classes by means of Otsu's
% N-thresholding method. OTSU returns an array IDX containing the cluster
% indexes (from 1 to N) of each point. Zero values are assigned to
% non-finite (NaN or Inf) pixels.
%
% IDX = OTSU(srcImage) uses two classes (N=2, default value).
%
% [IDX,sep] = OTSU(...) also returns the value (sep) of the separability
% criterion within the range [0 1]. Zero is obtained only with data
% having less than N values, whereas one (optimal value) is obtained only
% with N-valued arrays.
%
% Notes:
% -----
% It should be noticed that the thresholds generally become less credible
% as the number of classes (N) to be separated increases (see Otsu's
% paper for more details).
%
% If srcImage is an RGB image, a Karhunen-Loeve transform is first performed on
% the three R,G,B channels. The segmentation is then carried out on the
% image component that contains most of the energy.
%
% Example:
% -------
% load clown
% subplot(221)
% X = ind2rgb(X,map);
% imshow(X)
% title('Original','FontWeight','bold')
% for numClasses = 2:4
% IDX = otsu(X,numClasses);
% subplot(2,2,numClasses)
% imagesc(IDX), axis image off
% title(['numClasses = ' int2str(numClasses)],'FontWeight','bold')
% end
% colormap(gray)
%
% Reference:
% ---------
% Otsu N, <a href="matlab:web('http://dx.doi.org/doi:10.1109/TSMC.1979.4310076')">A Threshold Selection Method from Gray-Level Histograms</a>,
% IEEE Trans. Syst. Man Cybern. 9:62-66;1979
%
% See also GRAYTHRESH, IM2BW
%
% -- Damien Garcia -- 2007/08, revised 2010/03
% Visit my <a
% href="matlab:web('http://www.biomecardio.com/matlab/otsu.html')">website</a> for more details about OTSU
*/
kkint32 pixelIdx = 0;
kkint32 x = 0;
bool isColorImage = srcImage->Color ();
// Checking numClasses (number of classes)
if (numClasses == 1)
{
//IDX = NaN(size(srcImage));
//sep = 0;
return NULL;
}
else if ((numClasses < 1) || (numClasses > 255))
{
log.Level (-1) << "SegmentorOTSU::SegmentImage ***ERROR*** 'numClasses must be a between 1 and 255 !'" << endl;
sep = 0;
return NULL;
}
if (isColorImage)
{
srcImage = srcImage->CreateGrayScaleKLT ();
}
else
{
srcImage = new Raster (*srcImage);
}
kkint32 totPixels = srcImage->TotPixels ();
kkint32 pixelsCounted = 0;
VectorInt unI;
VectorInt unICounts;
VectorInt32 counts (256, 0);
{
// %% Convert to 256 levels
// srcImage = srcImage-min(srcImage(:));
// srcImage = round(srcImage/max(srcImage(:))*255);
// Re-scale Source Image to utilize range of 0 through 255.
//%% Probability dCistribution
//unI = sort(unique(srcImage));
//nbins = min(length(unI),256);
uchar* greenArea = srcImage->GreenArea ();
uchar pixelMin = 255;
uchar pixelMax = 1;
for (pixelIdx = 1; pixelIdx < totPixels; ++pixelIdx)
{
if (greenArea[pixelIdx] < 1)
continue;
pixelsCounted++;
if (greenArea[pixelIdx] < pixelMin)
pixelMin = greenArea[pixelIdx];
if (greenArea[pixelIdx] > pixelMax)
pixelMax = greenArea[pixelIdx];
}
VectorInt counts (256, 0);
for (pixelIdx = 0; pixelIdx < totPixels; ++pixelIdx)
{
/*
double pixelFraction = (double)((double)(greenArea[pixelIdx]) - pixelMin) / (double)srcRange;
kkuint32 newPixelVal = (uchar)(pixelFraction * 256.0 + 0.5);
if (newPixelVal > 255)
newPixelVal = 255;
greenArea[pixelIdx] = (uchar)newPixelVal;
counts[newPixelVal]++;
*/
counts[greenArea[pixelIdx]]++;
}
for (x = 1; x < (kkint32)counts.size (); ++x)
{
if (counts[x] > 0)
{
unI.push_back (x);
unICounts.push_back (counts[x]);
}
}
}
kkint32 nbins = (kkint32)unI.size ();
if (nbins <= numClasses)
{
// IDX = ones (size(srcImage));
//for i = 1:numClasses, IDX(srcImage==unI(i)) = i; end
//sep = 1;
RasterPtr result = new Raster (srcImage->Height (), srcImage->Width (), false);
for (x = 0; x < nbins; ++x)
{
LabelRaster (result, unI[x], x, srcImage);
}
sep = 1;
delete srcImage;
srcImage = NULL;
return result;
}
//elseif (nbins < 256)
// [histo,pixval] = hist(srcImage(:),unI);
//else
// [histo,pixval] = hist(srcImage(:),256);
VectorInt histo = unICounts;
VectorInt pixval = unI;
//P = histo/sum(histo);
VectorDouble P (histo.size (), 0.0);
for (x = 0; x < (kkint32)histo.size (); ++x)
P[x] = (double)(histo[x]) / (double)pixelsCounted;
//clear unI
unI.clear ();
//%% Zeroth- and first-order cumulative moments
//w = cumsum(P);
VectorDouble w (P.size (), 0.0);
w[0] = P[0];
for (x = 1; x < (kkint32)P.size (); ++x)
w[x] = w[x - 1] + P[x];
//mu = cumsum((1:nbins).*P);
VectorDouble mu (P.size (), 0.0);
mu[0] = 1.0 * P[0];
for (x = 1; x < (kkint32)P.size (); ++x)
mu[x] = mu[x - 1] + ((x + 1) * P[x]);
double muEnd = mu[mu.size () - 1];
//%% Maximal sigmaB^2 and Segmented image
if (numClasses == 2)
{
//sigma2B =...
// (mu(end) * w(2:end-1) - mu(2:end-1)) .^2 ./ w(2:end-1)./(1-w(2:end-1));
// ------------------- P1 ----------------- ----------- P2 -----------
//[maxsig,k] = max(sigma2B);
VectorDouble wSubSet = SubSet (w, 1, (kkint32)w.size () - 2);
VectorDouble muSubSet = SubSet (mu, 1, (kkint32)mu.size () - 2);
VectorDouble P1 = Power (Subt (muEnd * wSubSet, muSubSet), 2.0);
VectorDouble P2 = DotDiv (wSubSet, Subt (1.0, wSubSet));
VectorDouble sigma2B = DotDiv (P1, P2);
double maxSig = sigma2B[0];
kkint32 maxSigIdx = 0;
for (x = 1; x < (kkint32)sigma2B.size (); ++x)
{
if (sigma2B[x] > maxSig)
{
maxSig = sigma2B[x];
maxSigIdx = x;
}
}
//[maxsig,k] = max(sigma2B);
kkint32 k = maxSigIdx;
//% segmented image
//IDX = ones(size(srcImage));
//IDX(srcImage>pixval(k+1)) = 2;
threshold1 = pixval[k + 1];
RasterPtr result = new Raster (srcImage->Height (), srcImage->Width (), false);
uchar* resultArea = result->GreenArea ();
uchar* srcArea = srcImage->GreenArea ();
while (true)
{
kkuint32 numClass2Pixs = 0;
for (x = 0; x < totPixels; ++x)
{
if (srcArea[x] > threshold1)
{
resultArea[x] = 2;
++numClass2Pixs;
}
else
{
resultArea[x] = 1;
}
}
if ((threshold1 < 1) || (numClass2Pixs >100))
break;
--threshold1;
}
//% separability criterion
//sep = maxsig/sum(((1:nbins)-mu(end)).^2.*P);
double sum = 0.0;
kkint32 y = 0;
muEnd = mu[mu.size () - 1];
for (x = 0, y = 1; x < nbins; ++x, ++y)
sum = pow (((double)y - muEnd), 2.0) * P[x];
sep = maxSig / sum;
delete srcImage;
srcImage = NULL;
return result;
}
if (numClasses == 3)
{
//w0 = w;
//w2 = fliplr(cumsum(fliplr(P)));
VectorDouble w0 = w;
VectorDouble w2 = FlipLeftRight (CumSum (FlipLeftRight (P)));
//[w0,w2] = ndgrid(w0,w2);
Matrix w0M ((kkint32)w0.size (), (kkint32)w0.size ());
Matrix w2M ((kkint32)w2.size (), (kkint32)w2.size ());
NdGrid (w0, w2, w0M, w2M);
//mu0 = mu./w;
VectorDouble mu0 = DotDiv (mu, w);
//mu2 = fliplr(cumsum(fliplr((1:nbins).*P)) ./ cumsum(fliplr(P)));
// 1 2 34 4 32 3 4 321
// ---------- P1 ------------- ------ P2 --------
VectorDouble P1 = CumSum (FlipLeftRight (DotMult (BDV (1.0, 1.0, (double)nbins), P)));
VectorDouble P2 = CumSum (FlipLeftRight (P));
VectorDouble mu2 = FlipLeftRight (DotDiv (P1, P2));
// TODO
//[mu0,mu2] = ndgrid(mu0,mu2);
Matrix mu0M ((kkint32)mu0.size (), (kkint32)mu0.size ());
Matrix mu2M ((kkint32)mu2.size (), (kkint32)mu2.size ());
NdGrid (mu0, mu2, mu0M, mu2M);
//w1 = 1-w0-w2;
Matrix w1M = 1.0 - w0M - w2M;
//w1(w1<=0) = NaN;
MakeNanWhenLesOrEqualZero (w1M);
//sigma2B =...
// w0.*(mu0-mu(end)).^2 + w2.*(mu2-mu(end)).^2 +...
// (w0.*(mu0-mu(end)) + w2.*(mu2-mu(end))).^2./w1;
Matrix P1M = (DotMult (w0M, Power ((mu0M - muEnd), 2.0))) + (DotMult (w2M, Power ((mu2M - muEnd), 2.0)));
Matrix P2M = DotDiv (Power ((DotMult (w0M, (mu0M - muEnd)) + DotMult (w2M, (mu2M - muEnd))), 2.0), w1M);
Matrix sigma2B = P1M + P2M;
//sigma2B(isnan(sigma2B)) = 0; % zeroing if k1 >= k2
ZeroOutNaN (sigma2B);
//[maxsig,k] = max(sigma2B(:)); % Turns sigma2B into 1D Array then locates largest value and index.
// [k1,k2] = ind2sub([nbins nbins],k); % Sets k1 and k2 to the indexes for k mapped into a 2D square matrix that is (nbins x nbins)
kkint32 k1, k2;
double maxsig = 0.0;
sigma2B.FindMaxValue (maxsig, k1, k2);
//% segmented image
RasterPtr result = new Raster (srcImage->Height (), srcImage->Width (), false);
{
//IDX = ones(size(srcImage))*3;
//IDX(srcImage<=pixval(k1)) = 1;
//IDX(srcImage>pixval(k1) & srcImage<=pixval(k2)) = 2;
uchar* srcData = srcImage->GreenArea ();
uchar* data = result->GreenArea ();
threshold1 = pixval[k1];
threshold2 = pixval[k2];
for (x = 0; x < totPixels; ++x)
{
if (srcData[x] <= threshold1)
data[x] = 1;
else if (srcData[x] <= threshold2)
data[x] = 2;
else
data[x] = 3;
}
}
//% separability criterion
//sep = maxsig / sum (((1:nbins)-mu(end)).^2.*P);
//VectorDouble xxx = BDV (1.0, 1.0, (double)nbins);
//VectorDouble yyy = Subt (xxx, muEnd);
//VectorDouble zzz = Power (yyy, 2.0);
sep = maxsig / Sum (DotMult (Power (Subt (BDV (1.0, 1.0, (double)nbins), muEnd), 2.0), P));
delete srcImage;
srcImage = NULL;
return result;
}
{
/*
//k0 = linspace(0,1,numClasses+1); % k0 = row vector of linear spaced points between 0 and 1 with (numClasses + 1) points
VectorDouble k0 = LinSpace (0, 1, numClasses + 1);
//k0 = k0(2:numClasses);
[k,y] = fminsearch(@sig_func,k0,optimset('TolX',1));
k = round(k*(nbins-1)+1);
% segmented image
IDX = ones(size(srcImage))*numClasses;
IDX(srcImage<=pixval(k(1))) = 1;
for i = 1:numClasses-2
IDX(srcImage>pixval(k(i)) & srcImage<=pixval(k(i+1))) = i+1;
end
% separability criterion
sep = 1-y;
*/
}
delete srcImage;
srcImage = NULL;
return NULL;
} /* SegmentImage */
/**
*@brief Segments image into 'numClasses' taking into account only pixels
* indicated by 'mask' image.
*@param[in] srcImage Image to segment. If it is a color image will be
* converted to GrayScale using 'CreateGrayScaleKLTOnMaskedArea'
*@param[in] mask Indicates which pixels to consider when thresholding image. Pixels
* that are not part of mask will be assigned label '0'.
*@param[in] numClasses Number of classes to segment image into. Current only '2' and '3' are supported.
*@param[out] sep
*@return Labeled GrayScale image where pixels will be label into their respective class; between '1' and 'numClasses'.
*/
RasterPtr SegmentorOTSU::SegmentMaskedImage (RasterPtr srcImage,
RasterPtr mask,
kkint32 numClasses,
double& sep
)
{
kkint32 pixelIdx = 0;
kkint32 x = 0;
bool isColorImage = srcImage->Color ();
uchar* maskArea = NULL;
uchar maskTh = 0;
if (mask)
{
maskArea = mask->GreenArea ();
maskTh = mask->BackgroundPixelTH ();
}
if (numClasses == 1)
{
return NULL;
}
else if ((numClasses < 1) || (numClasses > 255))
{
log.Level (-1) << endl << endl
<< "SegmentorOTSU::SegmentMaskedImage ***ERROR*** 'numClasses must be a between 1 and 255 !'" << endl
<< endl;
sep = 0;
return NULL;
}
if (isColorImage)
{
if (mask)
srcImage = srcImage->CreateGrayScaleKLTOnMaskedArea (*mask);
else
srcImage = srcImage->CreateGrayScaleKLT ();
}
else
{
srcImage = new Raster (*srcImage);
}
kkint32 totPixels = srcImage->TotPixels ();
kkint32 totMaskPixels = totPixels;
if (mask)
totMaskPixels = mask->TotalBackgroundPixels ();
VectorInt unI;
VectorInt unICounts;
{
uchar* greenArea = srcImage->GreenArea ();
uchar pixelMin = greenArea[0];
uchar pixelMax = greenArea[0];
for (pixelIdx = 1; pixelIdx < totPixels; ++pixelIdx)
{
if ((!mask) || (maskArea[pixelIdx] > maskTh))
{
if (greenArea[pixelIdx] < pixelMin)
pixelMin = greenArea[pixelIdx];
if (greenArea[pixelIdx] > pixelMax)
pixelMax = greenArea[pixelIdx];
}
}
//kkint32 srcRange = pixelMax - pixelMin + 1;
VectorInt counts (256, 0);
for (pixelIdx = 0; pixelIdx < totPixels; ++pixelIdx)
{
if ((!mask) || (maskArea[pixelIdx] > maskTh))
counts[greenArea[pixelIdx]]++;
}
for (x = 0; x < (kkint32)counts.size (); ++x)
{
if (counts[x] > 0)
{
unI.push_back (x);
unICounts.push_back (counts[x]);
}
}
}
kkint32 nbins = (kkint32)unI.size ();
if (nbins <= numClasses)
{
RasterPtr result = new Raster (srcImage->Height (), srcImage->Width (), false);
for (x = 0; x < nbins; ++x)
{
LabelRaster (result, mask, unI[x], x, srcImage);
}
sep = 1;
delete srcImage;
srcImage = NULL;
return result;
}
VectorInt histo = unICounts;
VectorInt pixval = unI;
VectorDouble P (histo.size (), 0.0);
for (x = 0; x < (kkint32)histo.size (); ++x)
P[x] = (double)(histo[x]) / (double)totMaskPixels;
unI.clear ();
//%% Zeroth- and first-order cumulative moments
//w = cumsum(P);
VectorDouble w (P.size (), 0.0);
w[0] = P[0];
for (x = 1; x < (kkint32)P.size (); ++x)
w[x] = w[x - 1] + P[x];
//mu = cumsum((1:nbins).*P);
VectorDouble mu (P.size (), 0.0);
mu[0] = 1.0 * P[0];
for (x = 1; x < (kkint32)P.size (); ++x)
mu[x] = mu[x - 1] + ((x + 1) * P[x]);
double muEnd = mu[mu.size () - 1];
//%% Maximal sigmaB^2 and Segmented image
if (numClasses == 2)
{
//sigma2B =...
// (mu(end) * w(2:end-1) - mu(2:end-1)) .^2 ./ w(2:end-1)./(1-w(2:end-1));
// ------------------- P1 ----------------- ----------- P2 -----------
//[maxsig,k] = max(sigma2B);
VectorDouble wSubSet = SubSet (w, 1, (kkint32)w.size () - 2);
VectorDouble muSubSet = SubSet (mu, 1, (kkint32)mu.size () - 2);
VectorDouble P1 = Power (Subt (muEnd * wSubSet, muSubSet), 2.0);
VectorDouble P2 = DotDiv (wSubSet, Subt (1.0, wSubSet));
VectorDouble sigma2B = DotDiv (P1, P2);
double maxSig = sigma2B[0];
kkint32 maxSigIdx = 0;
for (x = 1; x < (kkint32)sigma2B.size (); ++x)
{
if (sigma2B[x] > maxSig)
{
maxSig = sigma2B[x];
maxSigIdx = x;
}
}
//[maxsig,k] = max(sigma2B);
kkint32 k = maxSigIdx;
//% segmented image
//IDX = ones(size(srcImage));
//IDX(srcImage>pixval(k+1)) = 2;
kkint32 threshold = pixval[k + 1];
RasterPtr result = new Raster (srcImage->Height (), srcImage->Width (), false);
uchar* resultArea = result->GreenArea ();
uchar* srcArea = srcImage->GreenArea ();
for (x = 0; x < totPixels; ++x)
{
if ((!maskArea) || (maskArea[x] > maskTh))
{
if (srcArea[x] > threshold)
resultArea[x] = 2;
else
resultArea[x] = 1;
}
else
{
resultArea[x] = 0;
}
}
//% separability criterion
//sep = maxsig/sum(((1:nbins)-mu(end)).^2.*P);
double sum = 0.0;
kkint32 y = 0;
muEnd = mu[mu.size () - 1];
for (x = 0, y = 1; x < nbins; ++x, ++y)
sum = pow (((double)y - muEnd), 2.0) * P[x];
sep = maxSig / sum;
delete srcImage;
srcImage = NULL;
return result;
}
if (numClasses == 3)
{
VectorDouble w0 = w;
VectorDouble w2 = FlipLeftRight (CumSum (FlipLeftRight (P)));
Matrix w0M ((kkint32)w0.size (), (kkint32)w0.size ());
Matrix w2M ((kkint32)w2.size (), (kkint32)w2.size ());
NdGrid (w0, w2, w0M, w2M);
VectorDouble mu0 = DotDiv (mu, w);
//mu2 = fliplr(cumsum(fliplr((1:nbins).*P)) ./ cumsum(fliplr(P)));
// 1 2 34 4 32 3 4 321
// ---------- P1 ------------- ------ P2 --------
VectorDouble P1 = CumSum (FlipLeftRight (DotMult (BDV (1.0, 1.0, (double)nbins), P)));
VectorDouble P2 = CumSum (FlipLeftRight (P));
VectorDouble mu2 = FlipLeftRight (DotDiv (P1, P2));
//[mu0,mu2] = ndgrid(mu0,mu2);
Matrix mu0M ((kkint32)mu0.size (), (kkint32)mu0.size ());
Matrix mu2M ((kkint32)mu2.size (), (kkint32)mu2.size ());
NdGrid (mu0, mu2, mu0M, mu2M);
//w1 = 1-w0-w2;
Matrix w1M = 1.0 - w0M - w2M;
//w1(w1<=0) = NaN;
MakeNanWhenLesOrEqualZero (w1M);
//sigma2B =...
// w0.*(mu0-mu(end)).^2 + w2.*(mu2-mu(end)).^2 +...
// (w0.*(mu0-mu(end)) + w2.*(mu2-mu(end))).^2./w1;
Matrix P1M = (DotMult (w0M, Power ((mu0M - muEnd), 2.0))) + (DotMult (w2M, Power ((mu2M - muEnd), 2.0)));
Matrix P2M = DotDiv (Power ((DotMult (w0M, (mu0M - muEnd)) + DotMult (w2M, (mu2M - muEnd))), 2.0), w1M);
Matrix sigma2B = P1M + P2M;
//sigma2B(isnan(sigma2B)) = 0; % zeroing if k1 >= k2
ZeroOutNaN (sigma2B);
//[maxsig,k] = max(sigma2B(:)); % Turns sigma2B into 1D Array then locates largest value and index.
// [k1,k2] = ind2sub([nbins nbins],k); % Sets k1 and k2 to the indexes for k mapped into a 2D square matrix that is (nbins x nbins)
kkint32 k1, k2;
double maxsig = 0.0;
sigma2B.FindMaxValue (maxsig, k1, k2);
//% segmented image
RasterPtr result = new Raster (srcImage->Height (), srcImage->Width (), false);
{
//IDX = ones(size(srcImage))*3;
//IDX(srcImage<=pixval(k1)) = 1;
//IDX(srcImage>pixval(k1) & srcImage<=pixval(k2)) = 2;
uchar* srcData = srcImage->GreenArea ();
uchar* data = result->GreenArea ();
double th1 = pixval[k1];
double th2 = pixval[k2];
for (x = 0; x < totPixels; ++x)
{
if ((!maskArea) || (maskArea[x] > maskTh))
{
if (srcData[x] <= th1)
data[x] = 1;
else if (srcData[x] <= th2)
data[x] = 2;
else
data[x] = 3;
}
else
{
data[x] = 0;
}
}
}
sep = maxsig / Sum (DotMult (Power (Subt (BDV (1.0, 1.0, (double)nbins), muEnd), 2.0), P));
delete srcImage;
srcImage = NULL;
return result;
}
delete srcImage;
srcImage = NULL;
return NULL;
} /* SegmentMaskedImage */
void ClassSummaryList::SaveLLoydsBinsData (const KKStr& fileName,
const KKStr& srcDirName,
kkint32 lastScanLine,
kkint32 baseLLoydsBinSize
)
{
ofstream o (fileName.Str (), ios::out);
if (!o.is_open ())
{
log.Level (-1) << endl << endl << endl
<< "ClassSummaryList::SaveLLoydsBinsData *** Invalid File[" << fileName << "]." << endl
<< endl;
return;
}
ClassSummaryList::const_iterator idx;
o << "// LLoyds Bins Data" << endl
<< "//" << endl
<< "// Run Date [" << osGetLocalDateTime () << "]" << endl
<< "// SrcDir [" << srcDirName << "]" << endl
<< "// LastScanLin [" << lastScanLine << "]" << endl
<< "// Base Bin Size [" << baseLLoydsBinSize << "]" << endl
<< "//" << endl;
o << "BinSize" << "\t" << "BinNum" << "\t" << "ScanLines";
kkuint32 numOfBins = NumOfLLoydsBins ();
for (idx = begin (); idx != end (); idx++)
{
ClassSummaryPtr cs = *idx;
o << "\t" << cs->ClassName ();
}
o << endl;
VectorInt binSizes = LLoydsBinSizes ();
size_t binSizeIndex = 0;
for (binSizeIndex = 0; binSizeIndex < binSizes.size (); binSizeIndex++)
{
kkint32 binSize = binSizes[binSizeIndex];
kkint32 startScanLinNum = 0;
kkint32 endScanLineNum = startScanLinNum + binSize - 1;
kkuint32 binNum = 0;
for (binNum = 0; binNum < numOfBins; binNum++)
{
o << binSize << "\t" << binNum <<"\t" << startScanLinNum << "-" << endScanLineNum;
for (idx = begin (); idx != end (); idx++)
{
kkint32 lloydsBin = 0;
ClassSummaryPtr cs = *idx;
LLoydsEntryPtr lloydsEntry = cs->LLoydsEntryByBinSize (binSize);
if (lloydsEntry != NULL)
lloydsBin = lloydsEntry->LLoydsBin (binNum);
o << "\t" << lloydsBin;
}
startScanLinNum += binSize;
endScanLineNum += binSize;
o << endl;
}
}
o.close ();
} /* SaveLLoydsBinsData */