/**
 * 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
}
Пример #2
0
/**
 * 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()
			}
		}
	}
}