• bwlabel函数的c++实现


    实验中需要用到区域联通的算法,就是类似于matlab中bwlabel的函数。网上找了找c++源码未果,bwlabel-python版用python描述了matlab中的实现方法,但是最后对标签的处理部分并未看明白,故自己用c++实现了一个。先直接看bwlabel函数代码:

    cv::Mat bwlabel(const cv::Mat in, int * num, const int mode)
    {
        const int num_runs = number_of_runs(in);
        int * sc = new int[num_runs];
        int * ec = new int[num_runs];
        int * r = new int[num_runs];
        int * labels = new int[num_runs];
        memset(labels, 0, sizeof(int)*num_runs);
        fill_run_vectors(in, sc, ec, r);
        first_pass(sc, ec, r, labels, num_runs, mode);
        cv::Mat result = cv::Mat::zeros(in.size(), CV_8UC1);
    
        int number = 0;
        for(int i = 0; i < num_runs; i++)
        {
            uchar * p_row = result.ptr<uchar>(r[i]);
            for(int j = sc[i]; j <= ec[i]; j++)
                p_row[j] = labels[i];
            if(number < labels[i])
                number = labels[i];
        }
        if(num != NULL)
            *num = number;
        delete [] sc;
        delete [] ec;
        delete [] r;
        delete [] labels;
        return result;
    }

    bwlabel中要用到三个辅助函数:number_of_runs,fill_run_vectors,first_pass。函数number_of_runs计算每一行中非零像素团的个数并累加起来。

    1 1 0 0 0 1 1 1 0 0

    比如,上面这一行就有2个非零像素团,我们称这样的像素团为Run,函数number_of_runs实现如下:

    int number_of_runs(const cv::Mat in)
    {
        const int rows = in.rows;
        const int cols = in.cols;
        int result = 0;
        for(int row = 0; row < rows; row++)
        {
            const uchar * p_row = in.ptr<uchar>(row);
            if(p_row[0] != 0)
                result++;
            for(int col = 1; col < cols; col++)
            {
                if(p_row[col] != 0 && p_row[col-1] == 0)
                    result++;
            }
        }
        return result;
    }

    这个函数算法思想是,扫描每一行,对每一行,如果当前元素非零并且前一元素为零则Run的个数加一。

    函数fill_run_vectors的作用是填充三个数据结构:sc[],ec[],r[],它们分别表示开始列标、结束列标和行标,数组长度为由number_of_runs函数得到的Run的个数。函数fill_run_vectors实现如下:

    void fill_run_vectors(const cv::Mat in, int sc[], int ec[], int r[])
    {
        const int rows = in.rows;
        const int cols = in.cols;
        int idx = 0;
        for(int row = 0; row < rows; row++)
        {
            const uchar * p_row = in.ptr<uchar>(row);
            int prev = 0;
            for(int col = 0; col < cols; col++)
            {
                if(p_row[col] != prev)
                {
                    if(prev == 0)
                    {
                        sc[idx] = col;
                        r[idx] = row;
                        prev = 1;
                    }
                    else
                    {
                        ec[idx++] = col - 1;
                        prev = 0;
                    }
                }
                if(col == cols-1 && prev == 1)
                {
                    ec[idx++] = col;
                }
            }
        }
    }

    算法思想还是遍历每一行,用变量prev保存一行中上一个团是0还是1,如果出现01跳变那么就要记录下新的Run的开始列标和行标,如果出现10跳变(或者这行结束并且prev=1)那么就记录下这个Run的结束列标。

    函数first_pass顾名思义,字面上说第一次扫描。因为函数扫描每一个Run块,给它打标签。当出现如下情况时:

    1 1 0 0 1 1 1 0 
    0 1 1 1 1 0 0 0

    函数给第一行第一个Run打上标签1,第二个Run打上标签2,当遍历到第二行时,发现这一行的一个Run与第一行第一个Run相邻,故打上标签1,但当继续遍历时发现这个Run也与第一行第二个Run相邻,但函数并没有改变第一行第二个Run的标签,而是记录下这两个标签其实该一样。遍历完第二行结果为:

    1 1 0 0 2 2 2 0 
    0 1 1 1 1 0 0 0

    遍历完每一个Run过后就是处理刚才未处理的标签了。函数first_pass实现如下:

    void first_pass(const int sc[], const int ec[], const int r[],int labels[], const int num_runs, const int mode)
    {
        int cur_row = 0;
        int next_label = 1;
        int first_run_on_prev_row = -1;
        int last_run_on_prev_row = -1;
        int first_run_on_this_row = 0;
        int offset = 0;
        int * equal_i = new int[num_runs];
        int * equal_j = new int[num_runs];
        int equal_idx = 0;
        if(mode == 8)
            offset = 1;
        for(int k = 0; k < num_runs; k++)
        {
            if(r[k] == cur_row + 1)
            {
                cur_row += 1;
                first_run_on_prev_row = first_run_on_this_row;
                first_run_on_this_row = k;
                last_run_on_prev_row = k - 1;
            }
            else if(r[k] > cur_row + 1)
            {
                first_run_on_prev_row = -1;
                last_run_on_prev_row = -1;
                first_run_on_this_row = k;
                cur_row = r[k];
            }
            if(first_run_on_prev_row >= 0)
            {
                int p = first_run_on_prev_row;
                while(p <= last_run_on_prev_row && sc[p] <= (ec[k] + offset))
                {
                    if(sc[k] <= ec[p] + offset)
                    {
                        if(labels[k] == 0)
                            labels[k] = labels[p];
                        else if(labels[k] != labels[p])
                        {
                            //labels[p] = labels[k];
                            equal_i[equal_idx] = labels[k];
                            equal_j[equal_idx] = labels[p];
                            equal_idx += 1;
                        }
                    }
                    p += 1;
                }
            }
            if(labels[k] == 0)
            {
                labels[k] = next_label++;
            }
        }
        /////////////////////// process labels
        for(int i = 0; i < equal_idx; i++)
        {
            int max_label = equal_i[i] > equal_j[i] ? equal_i[i] : equal_j[i];
            int min_label = equal_i[i] < equal_j[i] ? equal_i[i] : equal_j[i];
            for(int j = 0; j < num_runs; j++)
            {
                if(labels[j] == max_label)
                    labels[j] = min_label;
            }
        }
        delete [] equal_i;
        delete [] equal_j;
        /////////////////////process ignore labels
        int * hist = new int[next_label];
        int * non_labels = new int[next_label];
        memset(hist, 0, sizeof(int)*next_label);
        int non_num = 0;
        for(int i = 0; i < num_runs; i++)
        {
            hist[labels[i]]++;
        }
        for(int i = 1; i < next_label; i++)
        {
            if(hist[i] == 0)
                non_labels[non_num++] = i;
        }
        for(int j = 0; j < num_runs; j++)
        {
            int k = labels[j];
            for(int i = non_num-1; i >= 0; i--)
            {
                if(k > non_labels[i])
                {
                    labels[j] -= (i+1);
                    break;
                }
            }
        }
        delete [] hist;
        delete [] non_labels;
    }

    前面遍历每一个Run分两种情况,上一行有Run和上一行无Run:当上一行无Run时就分配一个新的标签,当上一行有Run时还要考虑是否与上一行Run相邻,若相邻则打上上一行的标签,当出现上面讲到的情况时就保存这两个标签到数组equal_i,equal_j中。

    接下来就是处理equal_i和equal_j这两个数组了,要将它们当中相同族的不同标签合并到一起(注释process labels下面代码)。

    这样过后还不能完事,有可能出现标签间断的现象(如1,2,4,6),就是还必须把标签(如1,2,4,6)映射到一个连续的空间(1,2,3,4)。参见注释process ignore labels以下代码。

    这样过后就差不多了,最后一步是在bwlabel中给返回的Mat中元素打上对应的标签。

                                                                                                                                                                                                                                                                                                                                       @waring

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  • 原文地址:https://www.cnblogs.com/waring/p/4233705.html
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