/**
* Computes the standard deviations of the intensities of all blocks and
* the minimum standard deviation
*/
func computeStdevs(blocks []image.Image) (float64, []float64) {
stdevs := make([]float64, len(blocks), len(blocks))
minStdev := math.Inf(1)
ch := make(chan indexValuePair)
for i, block := range blocks {
go func(i int, block image.Image) {
variance, mean := 0.0, 0.0
minX, minY := block.Bounds().Min.X, block.Bounds().Min.Y
maxX, maxY := block.Bounds().Max.X, block.Bounds().Max.Y
total := float64((maxX - minX) * (maxY - minY))
for i := minX; i < maxX; i++ {
for j := minY; j < maxY; j++ {
mean += utils.Intensity(block.At(i, j))
}
}
mean /= total
for i := minX; i < maxX; i++ {
for j := minY; j < maxY; j++ {
variance += math.Pow(utils.Intensity(block.At(i, j))-mean, 2)
}
}
ch <- indexValuePair{i: i, value: math.Sqrt(variance / total)}
}(i, block)
}
for i := 0; i < len(blocks); i++ {
pair := <-ch
stdevs[pair.i] = pair.value
minStdev = utils.MinF(pair.value, minStdev)
}
close(ch)
return minStdev, stdevs
}
/**
* Last part of the HMSF algorithm, merge regions if the credit of any of them
* exceeds the weight of the edge connecting them.
*/
func (s *Segmenter) hmsfMergeRegionsByCredit(edges graph.EdgeList, regionCredit []float64) {
for _, edge := range edges {
u := s.resultset.Find(edge.U())
v := s.resultset.Find(edge.V())
if u != v {
credit := utils.MinF(regionCredit[u], regionCredit[v])
if credit > edge.Weight() {
s.resultset.Union(u, v)
survivor := s.resultset.Find(u)
regionCredit[survivor] = credit - edge.Weight()
}
}
}
}