private Dictionary<Features, double> CalculateFeatures(PaletteData data, FeatureParams options)
{
String log = dir + "/out/timelog.txt";
Stopwatch watch = new Stopwatch();
//unpack options
SortedSet<Features> included = options.included;
CIELAB[,] imageLAB = options.imageLAB;
double[,] map = options.map;
double[] colorToSaliency = options.colorToSaliency;
int[] colorToCounts = options.colorToCounts;
double totalSaliency = options.totalSaliency;
ColorNames colorNames = options.colorNames;
double factor = options.factor;
double[] colorToCNSaliency = options.colorToCNSaliency;
double minE = options.minE;
double maxE = options.maxE;
Dictionary<CIELAB, int> swatchIndexOf = options.swatchIndexOf;
Dictionary<Tuple<int, int>, double> imageSwatchDist = options.imageSwatchDist;
double afactor = options.afactor;
double[] swatchToPurity = options.swatchToPurity;
Dictionary<int, double> pToScore = options.pToScore;
double maxN = options.maxN;
double avgN = options.avgN;
double avgE = options.avgE;
double[] segToSaliency = options.segmentToSaliency;
Segmentation seg = options.seg;
int[] swatchIdxToBin = options.swatchIdxToBin;
int[,] binAssignments = options.binAssignments;
double totalCapturable = options.totalCapturableSaliency;
double[,] pixelToDists = options.pixelToDists;
double[,] pixelToCNDists = options.pixelToNDists;
double[] segmentToSD = options.segmentToSD;
double totalSD = options.totalSD;
//calculate the palette features
Dictionary<Features, double> features = new Dictionary<Features, double>();
double sdmin2 = 1;
double sdmax2 = 0;
double sdavg2 = 0;
int width = options.imageLAB.GetLength(0);
int height = options.imageLAB.GetLength(1);
int n = data.lab.Count();
double[] elementToSaliency = new double[n];
int[] elementToCount = new int[n];
//Calculate the error of recoloring the image, each pixel weighted the same, and subtract from 1
double error = 0;
//Coverage weighted by saliency of pixel
double errorWeighted = 0;
//error of recoloring the image, distance is the names distance metric
double nerror = 0;
double nerrorWeighted = 0;
double errorSeg = 0;
double errorSegSal = 0;
double colorsPSeg = 0;
double colorsPSegW = 0;
double withinVariance = 0;
double betweenVariance = 0;
double sqerror = 0;
double sqerrorW = 0;
double sqerrorSeg = 0;
double sqerrorSegSal = 0;
double nsqerrorSeg = 0;
double nsqerrorSegSal = 0;
double nerrorSeg = 0;
double nerrorSegSal = 0;
double nsqerror = 0;
double nsqerrorW = 0;
double softerror = 0;
double softwerror = 0;
double softerrorseg = 0;
double softerrorwseg = 0;
double nsofterror = 0;
double nsoftwerror = 0;
double nsofterrorseg = 0;
double nsofterrorwseg = 0;
double nmeansegerror = 0;
double nmeansegwerror = 0;
double softerrorsegsd = 0;
double nsofterrorsegsd = 0;
double errorsegsd = 0;
double nerrorsegsd = 0;
double nsqerrorsegsd = 0;
double sqerrorsegsd = 0;
double meansegsd = 0;
double nmeansegsd = 0;
watch.Start();
int numPixels = width * height;
//compute the memberships
double[,] memberships = new double[numPixels, n];
double[,] nmemberships = new double[numPixels, n];
//LAB, and SAT coverage
double[] Lbounds = new double[] { double.PositiveInfinity, double.NegativeInfinity };
double[] Abounds = new double[] { double.PositiveInfinity, double.NegativeInfinity };
double[] Bbounds = new double[] { double.PositiveInfinity, double.NegativeInfinity };
double[] Satbounds = new double[] { double.PositiveInfinity, double.NegativeInfinity };
for (int i = 0; i < n; i++)
{
CIELAB lab = data.lab[i];
Color c = data.colors[i];
double s = c.GetSaturation();
Lbounds[0] = Math.Min(Lbounds[0], lab.L);
Lbounds[1] = Math.Max(Lbounds[1], lab.L);
Abounds[0] = Math.Min(Abounds[0], lab.A);
Abounds[1] = Math.Max(Abounds[1], lab.A);
Bbounds[0] = Math.Min(Bbounds[0], lab.B);
Bbounds[1] = Math.Max(Bbounds[1], lab.B);
Satbounds[0] = Math.Min(Satbounds[0], s);
Satbounds[1] = Math.Max(Satbounds[1], s);
}
double Lspan = Lbounds[1] - Lbounds[0];
double Aspan = Abounds[1] - Abounds[0];
double Bspan = Bbounds[1] - Bbounds[0];
double Satspan = Satbounds[1] - Satbounds[0];
double Lcov = Math.Min(Lspan, options.Lspan) / Math.Max(Math.Max(Lspan, options.Lspan), 1);
double Acov = Math.Min(Aspan, options.Aspan) / Math.Max(Math.Max(Aspan, options.Aspan), 1);
double Bcov = Math.Min(Bspan, options.Bspan) / Math.Max(Math.Max(Bspan, options.Bspan), 1);
double Scov = Math.Min(Satspan, options.Satspan) / Math.Max(Math.Max(Satspan, options.Satspan), 1);
double meansegError = 0;
double meansegErrorWeighted = 0;
double[,] colorSegDist = new double[n, seg.numSegments];
double[] segToDist = new double[seg.numSegments];
for (int j = 0; j < seg.numSegments; j++)
{
//find the closest color for each segment
double bestDist = Double.PositiveInfinity;
for (int i = 0; i < n; i++)
{
colorSegDist[i, j] = Math.Sqrt(data.lab[i].SqDist(seg.segToMeanColor[j]));
segToDist[j] += colorSegDist[i, j];
bestDist = Math.Min(colorSegDist[i, j], bestDist);
}
double nbestDist = Double.PositiveInfinity;
for (int i = 0; i < n; i++)
{
double ndist = GetCNDist(colorNames.GetBin(data.lab[i]), colorNames.GetBin(seg.segToMeanColor[j]));
nbestDist = Math.Min(ndist, nbestDist);
}
meansegError += bestDist;
meansegErrorWeighted += bestDist * segToSaliency[j];
nmeansegerror += nbestDist;
nmeansegwerror += nbestDist * segToSaliency[j];
meansegsd += bestDist * segmentToSD[j];
nmeansegsd += nbestDist * segmentToSD[j];
}
meansegError /= seg.numSegments * factor;
meansegErrorWeighted /= totalSaliency * factor;
nmeansegerror /= seg.numSegments;
nmeansegwerror /= totalSaliency;
meansegsd /= totalSD * factor;
nmeansegsd /= totalSD;
//now calculate entropy per segment
//probability is 1-(dist/totaldist)
double[] segEntropy = new double[seg.numSegments];
for (int i = 0; i < seg.numSegments; i++)
{
for (int j = 0; j < n; j++)
{
double prob = 1 - colorSegDist[j, i] / segToDist[i];
segEntropy[i] += prob * Math.Log(prob);
}
}
//average and/or weight by saliency of segment
for (int i = 0; i < seg.numSegments; i++)
{
colorsPSeg += segEntropy[i];
colorsPSegW += segEntropy[i] * segToSaliency[i];
}
colorsPSeg /= seg.numSegments;
colorsPSegW /= totalSaliency;
double[,] nsubs = new double[width, height];
double[,] subs = new double[width, height];
if (included.Overlaps(options.NameCovFeatures) ||
included.Overlaps(options.CoverageFeatures) ||
included.Overlaps(options.SaliencyDensFeatures))
{
double[,] errors = new double[width, height];
int[,] assignment = new int[width, height]; //the pixel to the nearest palette color
double[,] nerrors = new double[width, height];
double[,] nerrorsWeighted = new double[width, height];
double[] clusterSqError = new double[n];
int[,] nassignment = new int[width, height];
Parallel.For(0, width, i =>
{
for (int j = 0; j < height; j++)
{
double bestDist = Double.PositiveInfinity;
int bestIdx = -1;
for (int s = 0; s < data.lab.Count(); s++)
{
double dist = data.lab[s].SqDist(options.imageLAB[i, j]);
if (dist < bestDist)
bestIdx = s;
bestDist = Math.Min(dist, bestDist);
}
assignment[i, j] = bestIdx;
double sqrtBestDist = Math.Sqrt(bestDist);
errors[i, j] = sqrtBestDist;
clusterSqError[bestIdx] += bestDist;
}
if (included.Overlaps(options.NameCovFeatures))
{
for (int j = 0; j < height; j++)
{
int bestIdx = -1;
double bestDist = Double.PositiveInfinity;
for (int s = 0; s < data.lab.Count(); s++)
{
int sIdx = swatchIndexOf[data.lab[s]];
double dist = GetCNDist(binAssignments[i, j], swatchIdxToBin[sIdx]);//imageSwatchDist[new Tuple<int, int>(binAssignments[i,j], swatchIdxToBin[sIdx])]; //imageSwatchDist[new Tuple<int, int>(colorNames.GetBin(imageLAB[i, j]), colorNames.GetBin(data.lab[s]))];
if (dist < bestDist)
bestIdx = s;
bestDist = Math.Min(dist, bestDist);
}
double val = map[i, j];
nerrors[i, j] = bestDist;
nerrorsWeighted[i, j] = bestDist * val;
nassignment[i, j] = bestIdx;
}
}
double epsilon = 0.0001;
for (int j = 0; j < height; j++)
{
//calculate the memberships
double[] dists = new double[n];
double[] ndists = new double[n];
double subtotal = 0;
double nsubtotal = 0;
int idx = j * width + i;
for (int k = 0; k < n; k++)
{
int sidx = swatchIndexOf[data.lab[k]];
dists[k] = Math.Max(pixelToDists[idx, sidx], epsilon);
ndists[k] = Math.Max(pixelToCNDists[idx, sidx], epsilon);
subtotal += (1.0 / dists[k]);
nsubtotal += (1.0 / ndists[k]);
}
for (int k = 0; k < n; k++)
{
memberships[idx, k] = 1.0 / (dists[k] * subtotal);
nmemberships[idx, k] = 1.0 / (ndists[k] * nsubtotal);
}
for (int k = 0; k < n; k++)
{
int sidx = swatchIndexOf[data.lab[k]];
double u = memberships[idx, k];
subs[i, j] += u * u * Math.Max(pixelToDists[idx, sidx], epsilon);
double nu = nmemberships[idx, k];
nsubs[i, j] += nu * nu * Math.Max(pixelToCNDists[idx, sidx], epsilon);
}
}
});
//find J
double[] segToJ = new double[seg.numSegments];
double[] nsegToJ = new double[seg.numSegments];
for (int i = 0; i < width; i++)
{
for (int j = 0; j < height; j++)
{
int idx = j * width + i;
double sub = subs[i, j];
double nsub = nsubs[i, j];
softerror += sub;
nsofterror += nsub;
softwerror += sub * map[i, j];
nsoftwerror += nsub * map[i, j];
int segIdx = seg.assignments[i, j];
segToJ[segIdx] += sub;
nsegToJ[segIdx] += nsub;
}
}
softerror /= numPixels * factor * factor;
nsofterror /= numPixels;
softwerror /= totalSaliency * factor * factor;
nsoftwerror /= totalSaliency;
for (int s = 0; s < seg.numSegments; s++)
{
softerrorseg += segToJ[s] / seg.counts[s];
softerrorwseg += segToSaliency[s] * segToJ[s] / seg.counts[s];
nsofterrorseg += nsegToJ[s] / seg.counts[s];
nsofterrorwseg += segToSaliency[s] * nsegToJ[s] / seg.counts[s];
softerrorsegsd += segmentToSD[s] * segToJ[s] / seg.counts[s];
nsofterrorsegsd += segmentToSD[s] * nsegToJ[s] / seg.counts[s];
}
softerrorseg /= seg.numSegments * factor * factor;
softerrorwseg /= totalSaliency * factor * factor;
nsofterrorseg /= seg.numSegments;
nsofterrorwseg /= totalSaliency;
softerrorsegsd /= totalSD * factor * factor;
nsofterrorsegsd /= totalSD;
//calculate within and between palette cluster variance
//between variance
CIELAB betweenMean = new CIELAB();
for (int j = 0; j < n; j++)
{
betweenMean = betweenMean + data.lab[j];
}
betweenMean = betweenMean / (double)n;
for (int j = 0; j < n; j++)
{
betweenVariance += betweenMean.SqDist(data.lab[j]);
}
betweenVariance /= (double)n * factor * factor;
double[] counts = new double[n];
for (int i = 0; i < width; i++)
{
for (int j = 0; j < height; j++)
{
counts[assignment[i, j]]++;
}
}
for (int i = 0; i < n; i++)
{
withinVariance += clusterSqError[i] / Math.Max(1, counts[i]);
}
withinVariance /= n * (factor * factor);
double[] segmentToError = new double[segToSaliency.Count()];
double[] segToSqError = new double[segToSaliency.Count()];
Dictionary<int, SortedSet<int>> segToColors = new Dictionary<int, SortedSet<int>>();
//now aggregate
for (int i = 0; i < width; i++)
{
for (int j = 0; j < height; j++)
{
double sqError = errors[i, j] * errors[i, j];
double val = map[i, j];
errorWeighted += val * errors[i, j];
sqerrorW += val * sqError;
error += errors[i, j];
sqerror += sqError;
int idx = assignment[i, j];
if (idx >= 0)
{
elementToSaliency[idx] += val;
elementToCount[idx]++;
}
int segIdx = seg.assignments[i, j];
segmentToError[segIdx] += errors[i, j] / seg.counts[segIdx];
segToSqError[segIdx] += sqError / seg.counts[segIdx];
}
}
for (int i = 0; i < seg.numSegments; i++)
{
errorSeg += segmentToError[i];
errorSegSal += segmentToError[i] * segToSaliency[i];
sqerrorSeg += segToSqError[i];
sqerrorSegSal += segToSqError[i] * segToSaliency[i]; //TODO: this is weighted by segment percent salient density
errorsegsd += segmentToError[i] * segmentToSD[i];
sqerrorsegsd += segToSqError[i] * segmentToSD[i];
}
errorSeg /= seg.numSegments * factor;
errorSegSal /= totalSaliency * factor;
sqerrorSeg /= seg.numSegments * factor * factor;
sqerrorSegSal /= totalSaliency * factor * factor;
errorWeighted /= totalSaliency * factor;
error /= width * height * factor;
sqerrorW /= totalSaliency * factor * factor;
sqerror /= width * height * factor * factor;
errorsegsd /= totalSD * factor;
sqerrorsegsd /= totalSD * factor * factor;
//now sum it all up
for (int i = 0; i < width; i++)
{
for (int j = 0; j < height; j++)
{
nerrorWeighted += nerrorsWeighted[i, j];
nerror += nerrors[i, j];
}
}
nerrorWeighted /= totalSaliency;
nerror /= width * height;
double[] nsegmentToError = new double[segToSaliency.Count()];
double[] nsegToSqError = new double[segToSaliency.Count()];
//now aggregate
for (int i = 0; i < width; i++)
{
for (int j = 0; j < height; j++)
{
double sqError = nerrors[i, j] * nerrors[i, j];
double val = map[i, j];
nerrorWeighted += val * nerrors[i, j];
nsqerrorW += val * sqError;
nerror += nerrors[i, j];
nsqerror += sqError;
int idx = nassignment[i, j];
if (idx >= 0)
{
elementToSaliency[idx] += val;
elementToCount[idx]++;
}
int segIdx = seg.assignments[i, j];
nsegmentToError[segIdx] += nerrors[i, j] / seg.counts[segIdx];
nsegToSqError[segIdx] += sqError / seg.counts[segIdx];
}
}
for (int i = 0; i < seg.numSegments; i++)
{
nerrorSeg += nsegmentToError[i];
nerrorSegSal += nsegmentToError[i] * segToSaliency[i];
nsqerrorSeg += nsegToSqError[i];
nsqerrorSegSal += nsegToSqError[i] * segToSaliency[i]; //TODO: this is weighted by segment percent salient density
nerrorsegsd += nsegmentToError[i] * segmentToSD[i];
nsqerrorsegsd += nsegToSqError[i] * segmentToSD[i];
}
nerrorSeg /= seg.numSegments;
nerrorSegSal /= totalSaliency;
nsqerrorSeg /= seg.numSegments;
nsqerrorSegSal /= totalSaliency;
nerrorWeighted /= totalSaliency;
nerror /= width * height;
nsqerrorW /= totalSaliency;
nsqerror /= width * height;
nerrorsegsd /= totalSD;
nsqerrorsegsd /= totalSD;
}
Log(log, "Coverage and Name Coverage" + watch.ElapsedMilliseconds);
watch.Restart();
for (int i = 0; i < n; i++)
{
double dens = elementToSaliency[i] / Math.Max(1, elementToCount[i]);
sdmin2 = Math.Min(dens, sdmin2);
sdmax2 = Math.Max(dens, sdmax2);
sdavg2 += dens;
}
sdavg2 /= data.lab.Count();
Log(log, "SaliencyDensClust " + watch.ElapsedMilliseconds);
watch.Restart();
//total captured salience (salience sum/salience of the image)
double stotal = 0;
//average salience density of each swatch (and min/max)
double sdmin = 1;
double sdmax = 0;
double sdavg = 0;
foreach (CIELAB c in data.lab)
{
double val = colorToSaliency[swatchIndexOf[c]] / Math.Max(1, colorToCounts[swatchIndexOf[c]]);
sdmin = Math.Min(val, sdmin);
sdmax = Math.Max(val, sdavg);
sdavg += val;
stotal += colorToSaliency[swatchIndexOf[c]];
}
sdavg /= data.lab.Count();
stotal /= totalCapturable;
Log(log, "SaliencyDens " + watch.ElapsedMilliseconds);
watch.Restart();
double cnmin = 1;
double cnmax = 0;
double cnavg = 0;
for (int i = 0; i < n; i++)
{
for (int j = i + 1; j < n; j++)
{
double dist = 1 - colorNames.CosineDistance(data.lab[i], data.lab[j]);
cnavg += dist;
cnmin = Math.Min(cnmin, dist);
cnmax = Math.Max(cnmax, dist);
}
}
cnavg /= n * (n - 1) / 2;
Log(log, "NamesDiff " + watch.ElapsedMilliseconds);
watch.Restart();
//Calculate the color name diversity, avg color name distance to closest neighbor
double cndiv = 0;
for (int c = 0; c < n; c++)
{
double bestDist = 1;
for (int i = 0; i < n; i++)
{
if (c == i)
continue;
double curDist = 1 - colorNames.CosineDistance(data.lab[i], data.lab[c]);
if (curDist < bestDist)
bestDist = curDist;
}
cndiv += bestDist / n;
}
Log(log, "NamesClosestAvg " + watch.ElapsedMilliseconds);
watch.Restart();
//calculate color name salience (average, min, max)
double csmin = 1;
double csmax = 0;
double csavg = 0;
for (int i = 0; i < n; i++)
{
double salience = (colorToCNSaliency[swatchIndexOf[data.lab[i]]] - minE) / (maxE - minE);//colorNames.NormalizedSaliency(data.lab[i], minE, maxE);
csavg += salience;
csmin = Math.Min(csmin, salience);
csmax = Math.Max(csmax, salience);
}
csavg /= n;
Log(log, "NameSalience " + watch.ElapsedMilliseconds);
watch.Restart();
//Calculate the diversity, avg distance to closest neighbor
double div = 0;
double divr = 0;
for (int c = 0; c < n; c++)
{
double bestDist = Double.PositiveInfinity;
for (int i = 0; i < n; i++)
{
if (c == i)
continue;
double curDist = data.lab[c].SqDist(data.lab[i]);
if (curDist < bestDist)
bestDist = curDist;
}
div += Math.Sqrt(bestDist); // (n * factor);
}
double temp = div;
div = temp / (n * factor);
divr = temp / (n * afactor);
Log(log, "DiffClosestAvg " + watch.ElapsedMilliseconds);
watch.Restart();
//calculate average distance of one color to the rest of the colors
double dmin = 1;
double dmax = 0;
double davg = 0;
double dminr = 1;
double dmaxr = 0;
double davgr = 0;
for (int i = 0; i < n; i++)
{
for (int j = i + 1; j < n; j++)
{
double dist = Math.Sqrt(data.lab[i].SqDist(data.lab[j]));
davg += dist;
dmin = Math.Min(dmin, dist);
dmax = Math.Max(dmax, dist);
}
}
double dtemp = dmin;
dmin = dtemp / factor;
dminr = dtemp / afactor;
dtemp = dmax;
dmax = dtemp / factor;
dmaxr = dtemp / afactor;
davg /= n * (n - 1) / 2;
dtemp = davg;
davg = dtemp / factor;
davgr = dtemp / afactor;
Log(log, "LABDiff " + watch.ElapsedMilliseconds);
watch.Restart();
double puritymin = double.PositiveInfinity;
double puritymax = double.NegativeInfinity;
double purityavg = 0;
double purityminr = puritymin;
double puritymaxr = puritymax;
double purityavgr = 0;
foreach (CIELAB c in data.lab)
{
double val = swatchToPurity[swatchIndexOf[c]];
puritymin = Math.Min(puritymin, val);
puritymax = Math.Max(puritymax, val);
purityavg += val;
}
double ptemp = puritymin;
puritymin = ptemp / factor;
purityminr = ptemp / factor;
ptemp = puritymax;
puritymax = ptemp / factor;
puritymaxr = ptemp / afactor;
ptemp = purityavg / data.lab.Count();
purityavg = ptemp / factor;
purityavgr = ptemp / afactor;
//Record all the features
features.Add(Features.CovUnweighted, 1 - error);
features.Add(Features.CovWeightedSaliency, 1 - errorWeighted);
features.Add(Features.ErrorUnweighted, error);
features.Add(Features.ErrorWeightedSaliency, errorWeighted);
features.Add(Features.NCov, 1 - nerror);
features.Add(Features.NCovWeightedSaliency, 1 - nerrorWeighted);
features.Add(Features.NError, nerror);
features.Add(Features.NErrorWeightedSaliency, nerrorWeighted);
features.Add(Features.DAvg, davg);
features.Add(Features.DClosestAvg, div);
features.Add(Features.DMin, dmin);
features.Add(Features.DMax, dmax);
features.Add(Features.NClosestAvg, cndiv);
features.Add(Features.NDiffAvg, cnavg / maxN);
features.Add(Features.NDiffMin, cnmin / maxN);
features.Add(Features.NDiffMax, cnmax / maxN);
features.Add(Features.NSaliencyMin, csmin);
features.Add(Features.NSaliencyMax, csmax);
features.Add(Features.NSaliencyAvg, csavg);
features.Add(Features.SaliencyDensMin, sdmin);
features.Add(Features.SaliencyDensMax, sdmax);
features.Add(Features.SaliencyDensAvg, sdavg);
features.Add(Features.SaliencyTotal, stotal);
features.Add(Features.SaliencyDensClustMin, sdmin2);
features.Add(Features.SaliencyDensClustMax, sdmax2);
features.Add(Features.SaliencyDensClustAvg, sdavg2);
features.Add(Features.DMinR, dminr);
features.Add(Features.DMaxR, dmaxr);
features.Add(Features.DAvgR, davgr);
features.Add(Features.DClosestAvgR, divr);
features.Add(Features.PurityMin, puritymin);
features.Add(Features.PurityMax, puritymax);
features.Add(Features.PurityAvg, purityavg);
features.Add(Features.PurityMinR, purityminr);
features.Add(Features.PurityMaxR, puritymaxr);
features.Add(Features.PurityAvgR, purityavgr);
features.Add(Features.NDiffMinR, cnmin / avgN);
features.Add(Features.NDiffMaxR, cnmax / avgN);
features.Add(Features.NDiffAvgR, cnavg / avgN);
features.Add(Features.NSaliencyAvgR, csavg / avgE);
features.Add(Features.NSaliencyMaxR, csmax / avgE);
features.Add(Features.NSaliencyMinR, csmin / avgE);
features.Add(Features.NClosestAvgR, cndiv / avgE);
features.Add(Features.ErrorWeightedSegment, errorSegSal);
features.Add(Features.ErrorUnweightedSegment, errorSeg);
features.Add(Features.ColorsPerSeg, colorsPSeg);
features.Add(Features.ColorsPerSegWeighted, colorsPSegW);
features.Add(Features.MeanSegError, meansegError);
features.Add(Features.MeanSegErrorWeighted, meansegErrorWeighted);
features.Add(Features.LCov, Lcov);
features.Add(Features.ACov, Acov);
features.Add(Features.BCov, Bcov);
features.Add(Features.SatCov, Scov);
features.Add(Features.BetweenVar, betweenVariance);
features.Add(Features.WithinVar, withinVariance);
features.Add(Features.SqErrorUnweighted, sqerror);
features.Add(Features.SqErrorWeightedSaliency, sqerrorW);
features.Add(Features.SqErrorWSeg, sqerrorSegSal);
features.Add(Features.SqErrorSeg, sqerrorSeg);
features.Add(Features.NSqError, nsqerror);
features.Add(Features.NSqErrorW, nsqerrorW);
features.Add(Features.NSqErrorSeg, nsqerrorSeg);
features.Add(Features.NSqErrorWSeg, nsqerrorSegSal);
features.Add(Features.NErrorSeg, nerrorSeg);
features.Add(Features.NErrorWSeg, nerrorSegSal);
features.Add(Features.SoftError, softerror);
features.Add(Features.SoftWError, softwerror);
features.Add(Features.SoftErrorSeg, softerrorseg);
features.Add(Features.SoftErrorWSeg, softerrorwseg);
features.Add(Features.NSoftError, nsofterror);
features.Add(Features.NSoftWError, nsoftwerror);
features.Add(Features.NSoftErrorSeg, nsofterrorseg);
features.Add(Features.NSoftErrorWSeg, nsofterrorwseg);
features.Add(Features.NMeanSegError, nmeansegerror);
features.Add(Features.NMeanSegErrorW, nmeansegwerror);
features.Add(Features.MeanSegErrorDensity, meansegsd);
features.Add(Features.NMeanSegErrorDensity, nmeansegsd);
features.Add(Features.ErrorSegSD, errorsegsd);
features.Add(Features.NErrorSegSD, nerrorsegsd);
features.Add(Features.SqErrorSegSD, sqerrorsegsd);
features.Add(Features.NSqErrorSegSD, nsqerrorsegsd);
features.Add(Features.SoftErrorSegSD, softerrorsegsd);
features.Add(Features.NSoftErrorSegSD, nsofterrorsegsd);
return features;
}
//Precompute some parameters
private FeatureParams SetupFeatureParams(SortedSet<Features> included, String key, String saliencyPattern, bool debug = false, int maxIters=50)
{
FeatureParams options = new FeatureParams();
//find the normalization factor for swatches
double factor = 0;
double afactor = 0;
PaletteData swatches = GetPaletteSwatches(key, maxIters);
int scount = swatches.lab.Count();
for (int i = 0; i < swatches.lab.Count(); i++)
{
for (int j = i + 1; j < swatches.lab.Count(); j++)
{
double dist = swatches.lab[i].SqDist(swatches.lab[j]);
factor = Math.Max(factor, dist);
afactor += Math.Sqrt(dist);
}
}
factor = Math.Sqrt(factor);
afactor /= ((scount - 1) * scount / 2);
//open the image
Bitmap orig = new Bitmap(Image.FromFile(dir + "/" + key));
Bitmap image = orig;
if (debug)
image = new Bitmap(orig, orig.Width / 4, orig.Height / 4);
CIELAB[,] imageLAB = new CIELAB[image.Width, image.Height];
//convert the image to LAB
for (int i = 0; i < image.Width; i++)
for (int j = 0; j < image.Height; j++)
imageLAB[i, j] = Util.RGBtoLAB(image.GetPixel(i, j));
double[,] pixelToDists = new double[image.Width * image.Height, scount];
double[,] pixelToNDists = new double[image.Width * image.Height, scount];
//calculate the assignment of a pixel to a swatch
for (int i = 0; i < image.Width; i++)
{
for (int j = 0; j < image.Height; j++)
{
int idx = j * image.Width + i;
for (int s = 0; s < swatches.lab.Count(); s++)
{
pixelToDists[idx, s] = imageLAB[i, j].SqDist(swatches.lab[s]);
int a = colorNames.GetBin(imageLAB[i, j]);
int b = colorNames.GetBin(swatches.lab[s]);
double ndist = GetCNDist(a, b);
pixelToNDists[idx, s] = ndist * ndist;
}
}
}
//calculate the unnormalized purity of each swatch
double[] purityAvg = new double[scount];
int purityCount = (int)Math.Round(image.Width * image.Height / 20.0);
//for each swatch, find the top 5% pixels closest to it (used by Donovan)
for (int s = 0; s < scount; s++)
{
CIELAB swatch = swatches.lab[s];
List<double> dists = new List<double>(image.Width * image.Height);
for (int i = 0; i < image.Width; i++)
{
for (int j = 0; j < image.Height; j++)
{
dists.Add(imageLAB[i, j].SqDist(swatch));
}
}
dists.Sort();
//now pop off the top
for (int i = 0; i < purityCount; i++)
{
purityAvg[s] += Math.Sqrt(dists[i]);
}
purityAvg[s] /= purityCount;
}
//read the saliency map, and calculate saliency for each swatch
String mapPath = Util.ConvertFileName(key, saliencyPattern);//"gbvs_" + key;
Bitmap map = new Bitmap(Image.FromFile(dir + "/saliency/" + mapPath), image.Width, image.Height);
double[,] mapData = new double[image.Width, image.Height];
for (int i = 0; i < image.Width; i++)
{
for (int j = 0; j < image.Height; j++)
{
mapData[i, j] = map.GetPixel(i, j).G / 255.0;
}
}
Dictionary<CIELAB, int> swatchIndexOf = new Dictionary<CIELAB, int>();
for (int i = 0; i < swatches.lab.Count(); i++)
{
swatchIndexOf.Add(swatches.lab[i], i);
}
//for each cluster, figure out it's total/average saliency
double[] colorToSaliency = new double[swatches.lab.Count()];
int[] colorToCounts = new int[swatches.lab.Count()];
double thresh = Double.PositiveInfinity;
double totalSaliency = 0;
for (int i = 0; i < map.Width; i++)
{
for (int j = 0; j < map.Height; j++)
{
//find the image color, and its closest swatch, within some max threshold
CIELAB c = imageLAB[i, j];
double bestDist = thresh;
int bestSwatch = -1;
for (int s = 0; s < swatches.lab.Count(); s++)
{
if (swatches.lab[s].SqDist(c) < bestDist)
{
bestDist = swatches.lab[s].SqDist(c);
bestSwatch = s;
}
}
double value = map.GetPixel(i, j).G / 255.0;
if (bestSwatch >= 0)
{
colorToSaliency[bestSwatch] += value;
colorToCounts[bestSwatch]++;
}
totalSaliency += value;
}
}
List<double> sorted = colorToSaliency.ToList<double>();
sorted.Sort();
sorted.Reverse();
double totalCapturable = 0;
for (int i = 0; i < 5; i++)
{
totalCapturable += sorted[i];
}
double minE = Double.PositiveInfinity;
double maxE = Double.NegativeInfinity;
double[] colorToCNSaliency = new double[swatches.lab.Count()];
double avgE = 0;
for (int i = 0; i < swatches.lab.Count(); i++)
{
CIELAB s = swatches.lab[i];
double sal = colorNames.Saliency(s);
minE = Math.Min(minE, sal);
maxE = Math.Max(maxE, sal);
avgE += sal;
colorToCNSaliency[i] = sal;
}
avgE /= swatches.lab.Count();
avgE = (avgE - minE) / (maxE - minE);
//find the avg and max color name distance between swatches
double maxN = Double.NegativeInfinity;
double avgN = 0;
for (int i = 0; i < scount; i++)
{
for (int j = i + 1; j < scount; j++)
{
double val = 1 - colorNames.CosineDistance(swatches.lab[i], swatches.lab[j]);
avgN += val;
maxN = Math.Max(maxN, val);
}
}
avgN /= ((scount - 1) * scount / 2);
//cache the distance between image colors and swatches
Dictionary<Tuple<int, int>, double> imageSwatchDist = new Dictionary<Tuple<int, int>, double>();
int[,] binAssignments = new int[image.Width, image.Height];
int[] swatchIdxToBin = new int[swatches.colors.Count()];
if (included.Overlaps(options.NameCovFeatures))
{
for (int i = 0; i < image.Width; i++)
{
for (int j = 0; j < image.Height; j++)
{
CIELAB c = imageLAB[i, j];
for (int s = 0; s < swatches.lab.Count(); s++)
{
int a = colorNames.GetBin(imageLAB[i, j]);
int b = colorNames.GetBin(swatches.lab[s]);
binAssignments[i, j] = a;
swatchIdxToBin[s] = b;
GetCNDist(a, b);
}
}
}
}
//segment to total saliency
Segmentation seg = LoadSegAssignments(key, image.Width, image.Height, imageLAB);
double[] segmentToSaliency = new double[seg.numSegments];
double[] segmentToSD = new double[seg.numSegments];
for (int i = 0; i < image.Width; i++)
{
for (int j = 0; j < image.Height; j++)
{
int id = seg.assignments[i, j];
segmentToSaliency[id] += mapData[i, j];
segmentToSD[id] += mapData[i, j];
}
}
double totalSD = 0;
for (int i = 0; i < seg.numSegments; i++)
{
segmentToSD[i] /= seg.counts[i];
totalSD += segmentToSD[i];
}
//find the l,a,b, sat bounds
double[] Lbounds = new double[] { double.PositiveInfinity, double.NegativeInfinity };
double[] Abounds = new double[] { double.PositiveInfinity, double.NegativeInfinity };
double[] Bbounds = new double[] { double.PositiveInfinity, double.NegativeInfinity };
double[] Satbounds = new double[] { double.PositiveInfinity, double.NegativeInfinity };
for (int i = 0; i < swatches.lab.Count(); i++)
{
CIELAB lab = swatches.lab[i];
Color rgb = swatches.colors[i];
Lbounds[0] = Math.Min(Lbounds[0], lab.L);
Lbounds[1] = Math.Max(Lbounds[1], lab.L);
Abounds[0] = Math.Min(Abounds[0], lab.A);
Abounds[1] = Math.Max(Abounds[1], lab.A);
Bbounds[0] = Math.Min(Bbounds[0], lab.B);
Bbounds[1] = Math.Max(Bbounds[1], lab.B);
double s = rgb.GetSaturation();
Satbounds[0] = Math.Min(Lbounds[0], s);
Satbounds[1] = Math.Max(Lbounds[1], s);
}
//pack up
options.included = included;
options.imageLAB = imageLAB;
options.map = mapData;
options.colorToSaliency = colorToSaliency;
options.colorToCounts = colorToCounts;
options.totalSaliency = totalSaliency;
options.colorNames = colorNames;
options.factor = factor;
options.colorToCNSaliency = colorToCNSaliency;
options.minE = minE;
options.maxE = maxE;
options.swatchIndexOf = swatchIndexOf;
options.imageSwatchDist = imageSwatchDist;
options.afactor = afactor;
options.swatchToPurity = purityAvg;
//options.pToScore = pToScore;
options.maxN = maxN;
options.avgN = avgN;
options.avgE = avgE;
options.segmentToSaliency = segmentToSaliency;
options.seg = seg;
options.Lspan = Lbounds[1] - Lbounds[0];
options.Aspan = Abounds[1] - Abounds[0];
options.Bspan = Bbounds[1] - Bbounds[0];
options.Satspan = Satbounds[1] - Satbounds[0];
options.swatchIdxToBin = swatchIdxToBin;
options.binAssignments = binAssignments;
options.totalCapturableSaliency = totalCapturable;
options.pixelToDists = pixelToDists;
options.pixelToNDists = pixelToNDists;
options.totalSD = totalSD;
options.segmentToSD = segmentToSD;
return options;
}
