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我正在尝试使用实现“智能剪刀”进行交互式图像分割。因此，我必须从每个顶点代表一个像素的图像创建一个有向图。然后每个顶点通过两条边连接到它的每个邻居：一条出边和一条入边。这是因为边缘 (a,b) 的成本可能与 (b,a) 的成本不同。我正在使用大小为 512*512 像素的图像，因此我需要创建一个具有 262144 个顶点和 2091012 个边的图形。目前，我正在使用下图：

typedef property<vertex_index_t, int,
        property<vertex_distance_t, double,
        property<x_t, int, 
        property<y_t, int
        >>>> VertexProperty;

typedef property<edge_weight_t, double> EdgeProperty;

// define MyGraph
typedef adjacency_list<     
    vecS,           // container used for the out-edges (list)
    vecS,           // container used for the vertices (vector)
    directedS,      // directed edges (not sure if this is the right choice for incidenceGraph)
    VertexProperty,
    EdgeProperty
    > MyGraph;

我正在使用一个额外的类Graph（对不起，没有灵感的命名）来处理图形：

class Graph
{
private:
    MyGraph *graph;
    property_map<MyGraph, vertex_index_t>::type indexmap;
    property_map<MyGraph, vertex_distance_t>::type distancemap;
    property_map<MyGraph, edge_weight_t>::type weightmap;
    property_map<MyGraph, x_t>::type xmap;
    property_map<MyGraph, y_t>::type ymap;
    std::vector<MyGraph::vertex_descriptor> predecessors;
public:
    Graph();
    ~Graph();

};

Creating a new graph with 262144 vertices is pretty fast but the insertion of the edges tooks up to 10 seconds which is way too slow for the desired application. Right now, I'm inserting the edges the following way:

tie(vertexIt, vertexEnd) = vertices(*graph);
for(; vertexIt != vertexEnd; vertexIt++){
    vertexID = *vertexIt;
    x = vertexID % 512;
    y = (vertexID - x) / 512;
    xmap[vertexID] = x;
    ymap[vertexID] = y;
    if(y > 0){
        if(x > 0){
            tie(edgeID, ok) = add_edge(vertexID, indexmap[IRES2D*(y-1)+(x-1)], *graph);    // upper left neighbour
        }
        tie(edgeID, ok) = add_edge(vertexID, indexmap[IRES2D*(y-1)+(x)], *graph);    // upper
        if(x < 511){
            tie(edgeID, ok) = add_edge(vertexID, indexmap[IRES2D*(y-1)+(x+1)], *graph);    // upper right
        }
    }
    if(x < 511){    
        tie(edgeID, ok) = add_edge(vertexID, indexmap[IRES2D*(y)+(x+1)], *graph);    // right
    }
    if(y < 511){
        if(x > 0){
            tie(edgeID, ok) = add_edge(vertexID, indexmap[IRES2D*(y+1)+(x-1)], *graph);    // lower left
        }
        tie(edgeID, ok) = add_edge(vertexID, indexmap[IRES2D*(y+1)+(x)], *graph);    // lower
        if(x < 511){
            tie(edgeID, ok) = add_edge(vertexID, indexmap[IRES2D*(y+1)+(x+1)], *graph);    // lower right
        }
    }
    if(x > 0){
        tie(edgeID, ok) = add_edge(vertexID, indexmap[IRES2D*(y)+(x-1)], *graph);    // left
    }
}

Is there anything I can do do improve the speed of the programm? I'm using Microsoft Visual C++ 2010 Express in release mode with optimization (as recommended by Boost). I thought I could use a listS container for the vertices or edges but the vertices are no problem and if I use listS for the edges, it gets even slower.

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1 回答 1

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adjacency_list is very general purpose; unfortunately it's never going to be as efficient as a solution exploiting the regularity of your particular use-case could be. BGL isn't magic.

Your best bet is probably to come up with the efficient graph representation you'd use in the absence of BGL (hint: for a graph of an image's neighbouring pixels, this is not going to explicitly allocate all those node and edge objects) and then fit BGL to it (example), or equivalently just directly implement a counterpart to the existing adjacency_list / adjacency_matrix templates (concept guidelines) tuned to the regularities of your system.

By an optimised representation, I of course mean one in which you don't actually store all the nodes and edges explicitly but just have some way of iterating over enumerations of the implicit nodes and edges arising from the fact that the image is a particular size. The only thing you should really need to store is an array of edge weights.

于 2011-10-25T23:07:12.133 回答