/**
* Performs the image segmentation using the "Heuristic for Minimum Spanning
* Forests" algorithm. It uses the weightfn to compute the weight of the
* graph edges. minWeight is the only parameter.
* For more information on this algorithm refer to either my report which link
* is on the repo's README or to:
* http://algo2.iti.kit.edu/wassenberg/wassenberg09parallelSegmentation.pdf
*/
func (s *Segmenter) SegmentHMSF(sigmaSmooth, minWeight float64) {
start := time.Now()
sigma := imagenoise.EstimateStdev(s.img)
s.smoothImage(sigmaSmooth)
s.buildGraph()
fmt.Printf("segment... ")
start = time.Now()
s.resultset = disjointset.New(s.graph.TotalVertices())
edges := s.graph.Edges()
sort.Sort(edges)
setll := s.hmsfMergeEdgesByWeight(edges, minWeight)
regionCredit := s.hmsfComputeCredit(setll, sigma)
s.hmsfMergeRegionsByCredit(edges, regionCredit)
regionCredit[0] = 1
fmt.Println(time.Since(start))
fmt.Println("Components:", s.resultset.Components())
}
/**
* Performs the image segmentation using the "Graph Based Segmentation"
* algorithm. It uses sigma to apply a gaussian filter with it to the image
* to smooth it before running the algorithm.
* k and minSize are the algorithm parameters. For more information on this
* algorithm refer to either my report which link is on the repo's README or
* to: http://cs.brown.edu/~pff/papers/seg-ijcv.pdf
*/
func (s *Segmenter) SegmentGBS(sigma, k float64, minSize int) {
s.smoothImage(sigma)
s.buildGraph()
fmt.Printf("segment... ")
start := time.Now()
s.resultset = disjointset.New(s.graph.TotalVertices())
threshold_vals := make([]float64, s.graph.TotalVertices(), s.graph.TotalVertices())
for v := 0; v < s.graph.TotalVertices(); v++ {
threshold_vals[v] = k
}
edges := s.graph.Edges()
sort.Sort(edges)
s.gbsMergeFromThreshold(edges, threshold_vals, k)
s.gbsMergeSmallRegions(edges, minSize)
fmt.Println(time.Since(start))
fmt.Println("Components:", s.resultset.Components())
}