//Performs MST based segmentation as described by Pedro and Hutt
//k - theshold. larger values make fewer components
//min-size - smallest size of a component
int Segment_Image(double k, int minsize, IReadAsDoubleGrid src,
IReadAsDoubleGrid dst,
EdgeCostFunction f)
{
int width = src.Width;
int height = src.Height;
this.edgeCostFunction = f;
ds.Clear();
clusters.Clear();
for (int i = 0; i < width; i++)
setmap[i] = -1;
//build the graph we are going to segment over...
//this is an 8 connected graph (carindal and subcardinal 1 neighbors)
int numEdges = 0;
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++)
{
if (x < width - 1) //right neighbor
graph[numEdges++] = new Edge(y * width + x, y * width + (x + 1), edgeCostFunction(src, x, y, x + 1, y));
if (y > 0) //up neighbor
graph[numEdges++] = new Edge(y * width + x, (y - 1) * width + x, edgeCostFunction(src, x, y, x, y - 1));
if ((x < width - 1) && (y > 0)) //up and right neibor
graph[numEdges++] = new Edge(y * width + x, (y - 1) * width + (x + 1), edgeCostFunction(src, x, y, x + 1, y - 1));
if ((x < width - 1) && (y < height - 1)) //down and right neighbor
graph[numEdges++] = new Edge(y * width + x, (y + 1) * width + (x + 1), edgeCostFunction(src, x, y, x + 1, y + 1));
}
}
//now sort the edges
graph.Sort();
//ok graph is built - do some segmentation!
//initalize the tresholds to their initial values (see discussion in loop)
for (int i = 0; i < width * height; i++)
thresholds[i] = ThresholdFunction(k, 1);
//traverse the graph in increasing edge weight order...
//the goal here is to try to assemble cohesive sets using the
//union.
for (int i = 0; i < numEdges; i++)
{
//the a node is the current pixel, the b node is the neighbor
int aID = ds.FindSetRoot(graph[i].aNode);
int bID = ds.FindSetRoot(graph[i].bNode);
//if these are two distinct clusters, can we merge them?
if (aID != bID)
{
//merge if both the aID and bID thesholds are bigger than this current elements weight
if ((graph[i].weight <= thresholds[aID]) && (graph[i].weight <= thresholds[bID]))
{
//join the clusters
ds.Union(aID, bID);
//update the theshold of the newly formed cluster
aID = ds.FindSetRoot(aID); //i think this can be optimized...
//normalize the added theshold weight by the size of this new bigger cluster
//from the paper: this threshold function is a function of the size of the component.
// this ends up making small components require stronger evidence for a boundary
thresholds[aID] = graph[i].weight + ThresholdFunction(k, ds.NumElements(aID));
}
}
}
// post process small components
for (int i = 0; i < numEdges; i++)
{
int aID = ds.FindSetRoot(graph[i].aNode);
int bID = ds.FindSetRoot(graph[i].bNode);
if ((aID != bID) && ((ds.NumElements(aID) < minsize) || (ds.NumElements(bID) < minsize)))
ds.Union(aID, bID);
}
int sets = ds.NumSets();
clusters = new List<List<int>>(sets);
int mapIndex = 0;
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++)
{
int point = y * width + x;
int comp = ds.FindSetRoot(point);
//try to get the setmap...
//first check is this a root?
if (setmap[comp] == -1)
{
setmap[comp] = mapIndex;
clusters.Add(new List<int>(ds.NumElements(comp)));
clusters[mapIndex].Add(point);
mapIndex++;
}
else
{
clusters[setmap[comp]].Add(point);
}
}
}
return sets;
}
//Performs MST based segmentation as described by Pedro and Hutt
//k - theshold. larger values make fewer components
//min-size - smallest size of a component
int Segment_Image(double k, int minsize, IReadAsDoubleGrid src,
IReadAsDoubleGrid dst,
EdgeCostFunction f)
{
int width = src.Width;
int height = src.Height;
this.edgeCostFunction = f;
ds.Clear();
clusters.Clear();
for (int i = 0; i < width; i++)
{
setmap[i] = -1;
}
//build the graph we are going to segment over...
//this is an 8 connected graph (carindal and subcardinal 1 neighbors)
int numEdges = 0;
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++)
{
if (x < width - 1) //right neighbor
{
graph[numEdges++] = new Edge(y * width + x, y * width + (x + 1), edgeCostFunction(src, x, y, x + 1, y));
}
if (y > 0) //up neighbor
{
graph[numEdges++] = new Edge(y * width + x, (y - 1) * width + x, edgeCostFunction(src, x, y, x, y - 1));
}
if ((x < width - 1) && (y > 0)) //up and right neibor
{
graph[numEdges++] = new Edge(y * width + x, (y - 1) * width + (x + 1), edgeCostFunction(src, x, y, x + 1, y - 1));
}
if ((x < width - 1) && (y < height - 1)) //down and right neighbor
{
graph[numEdges++] = new Edge(y * width + x, (y + 1) * width + (x + 1), edgeCostFunction(src, x, y, x + 1, y + 1));
}
}
}
//now sort the edges
graph.Sort();
//ok graph is built - do some segmentation!
//initalize the tresholds to their initial values (see discussion in loop)
for (int i = 0; i < width * height; i++)
{
thresholds[i] = ThresholdFunction(k, 1);
}
//traverse the graph in increasing edge weight order...
//the goal here is to try to assemble cohesive sets using the
//union.
for (int i = 0; i < numEdges; i++)
{
//the a node is the current pixel, the b node is the neighbor
int aID = ds.FindSetRoot(graph[i].aNode);
int bID = ds.FindSetRoot(graph[i].bNode);
//if these are two distinct clusters, can we merge them?
if (aID != bID)
{
//merge if both the aID and bID thesholds are bigger than this current elements weight
if ((graph[i].weight <= thresholds[aID]) && (graph[i].weight <= thresholds[bID]))
{
//join the clusters
ds.Union(aID, bID);
//update the theshold of the newly formed cluster
aID = ds.FindSetRoot(aID); //i think this can be optimized...
//normalize the added theshold weight by the size of this new bigger cluster
//from the paper: this threshold function is a function of the size of the component.
// this ends up making small components require stronger evidence for a boundary
thresholds[aID] = graph[i].weight + ThresholdFunction(k, ds.NumElements(aID));
}
}
}
// post process small components
for (int i = 0; i < numEdges; i++)
{
int aID = ds.FindSetRoot(graph[i].aNode);
int bID = ds.FindSetRoot(graph[i].bNode);
if ((aID != bID) && ((ds.NumElements(aID) < minsize) || (ds.NumElements(bID) < minsize)))
{
ds.Union(aID, bID);
}
}
int sets = ds.NumSets();
clusters = new List <List <int> >(sets);
int mapIndex = 0;
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++)
{
int point = y * width + x;
int comp = ds.FindSetRoot(point);
//try to get the setmap...
//first check is this a root?
if (setmap[comp] == -1)
{
setmap[comp] = mapIndex;
clusters.Add(new List <int>(ds.NumElements(comp)));
clusters[mapIndex].Add(point);
mapIndex++;
}
else
{
clusters[setmap[comp]].Add(point);
}
}
}
return(sets);
}
