我需要一些有关优化CCL算法实现的帮助.我用它来检测图像上的黑色区域.在2000×2000上,它需要11秒,这差不多.我需要将运行时间减少到可能达到的最低值.另外,我很高兴知道那里是否有其他算法可以让您执行相同的操作,但比该算法更快.所以这是我的代码：

    //The method returns a dictionary, where the key is the label
    //and the list contains all the pixels with that label
    public Dictionary<short, LinkedList<Point>> ProcessCCL()
    {
        Color backgroundColor = this.image.Palette.Entries[1];
        //Matrix to store pixels' labels
        short[,] labels = new short[this.image.Width, this.image.Height];
        //I particulary don't like how I store the label equality table
        //But I don't know how else can I store it
        //I use LinkedList to add and remove items faster
        Dictionary<short, LinkedList<short>> equalityTable = new Dictionary<short, LinkedList<short>>();
        //Current label
        short currentKey = 1;
        for (int x = 1; x < this.bitmap.Width; x++)
        {
            for (int y = 1; y < this.bitmap.Height; y++)
            {
                if (!GetPixelColor(x, y).Equals(backgroundColor))
                {
                    //Minumum label of the neighbours' labels
                    short label = Math.Min(labels[x - 1, y], labels[x, y - 1]);
                    //If there are no neighbours
                    if (label == 0)
                    {
                        //Create a new unique label
                        labels[x, y] = currentKey;
                        equalityTable.Add(currentKey, new LinkedList<short>());
                        equalityTable[currentKey].AddFirst(currentKey);
                        currentKey++;
                    }
                    else
                    {
                        labels[x, y] = label;
                        short west = labels[x - 1, y], north = labels[x, y - 1];
                        //A little trick:
                        //Because of those "ifs" the lowest label value
                        //will always be the first in the list
                        //but I'm afraid that because of them
                        //the running time also increases
                        if (!equalityTable[label].Contains(west))
                            if (west < equalityTable[label].First.Value)
                                equalityTable[label].AddFirst(west);
                        if (!equalityTable[label].Contains(north))
                            if (north < equalityTable[label].First.Value)
                                equalityTable[label].AddFirst(north);
                    }
                }
            }
        }
        //This dictionary will be returned as the result
        //I'm not proud of using dictionary here too, I guess there 
        //is a better way to store the result
        Dictionary<short, LinkedList<Point>> result = new Dictionary<short, LinkedList<Point>>();
        //I define the variable outside the loops in order 
        //to reuse the memory address
        short cellValue;
        for (int x = 0; x < this.bitmap.Width; x++)
        {
            for (int y = 0; y < this.bitmap.Height; y++)
            {
                cellValue = labels[x, y];
                //If the pixel is not a background
                if (cellValue != 0)
                {
                    //Take the minimum value from the label equality table 
                    short value = equalityTable[cellValue].First.Value;
                    //I'd like to get rid of these lines
                    if (!result.ContainsKey(value))
                        result.Add(value, new LinkedList<Point>());
                    result[value].AddLast(new Point(x, y));
                }
            }
        }
        return result;
    }

提前致谢！

解决方法:

您可以将图片分割为多个子图片,并对其进行并行处理,然后合并结果.
1次通过：4个任务,每个任务处理一个1000×1000的子图片
2次：2个任务,每个通过1次处理2个子图像
第3遍：1个任务,处理第2遍的结果

对于C#,我建议使用Task Parallel Library (TPL),它可以轻松地定义依赖并彼此等待的任务.以下代码项目articel为您提供了有关TPL的基本介绍：The Basics of Task Parallelism via C#.