目标分割算法之分水岭算法

1、标分割算法之分岭算法分岭实现原理分岭算法常于标分割研究，其将图像以灰度为参考可视化为种拓扑地貌，灰度等价于海拔低，如果向地貌中的进漫，定程度时丘将被分开成独个体1.1基于降法实现分岭如上图所，假设从给出降落，最终在聚集，不同的被赋予不同颜，然，在不同颜出现汇聚的地，混合颜的位置就可以修建坝，将标分割开1.2基于灌法实现分岭如上图所平图，从各个挖个洞向上灌(灌位置对应局部极值,伴随位上升，不同的相遇时，就可以修建坝具体实现附：opencv官应举例：const int SHED=-1;const int INQUEUE=-2;struct point2D *other position is 0.

2、(32-bit 1 channel img)cvtColor(gray_img, gray_img, COLOR_BGR2GRAY);/res设为输出标记图像for (int i =0; i res.cols; i) p =; /第置为-1p =res.ptrint(Rows);for (int i =1; i Rows; i)/当前res代表标记图像int res_p_pre =res.ptrint (i - 1);int res_p_cur =res.ptrint ();int res_p_next =res.ptrint (i +1);uchar gray_p =gray.ptrucha

3、r ();res_p_cur0 =; res_p_curCols =;/结果图第列和最后列置为-1for (int j =1; j Cols; j)/遍历每列if (res_p_cur 0) res_p_cur =0; /不存在 0 | res_p_next 0| res_p_curj - 1 0 | res_p_curj +1 0)/如果标记当前点为0+左点右点上点0+下点0 intensity =gray_p; /强度置为标记点在原图的灰度if (intensity graylevelnum) /如果于256，保存到每个灰度级对应的容器中，将其视为将要操作的点，暂时灰度值设-2为 queu

4、esintensity.push( i,j ); res_p_cur =; int ind =0;for (; ind graylevelnum; ind)if (!queuesind.empty() break; /找到第个储存灰度级的不为空的容器索引if (ind graylevelnum)/如果于最灰度级int lab, t;while (true)if (queuesind.empty()for (; ind graylevelnum; ind)if (!queuesind.empty() break; /灰度级遍历时也会出现为0的灰度级容器，跳过它找出下个索引point2D cur

5、=queuesind.front();/读取当前种点queuesind.pop();/删除栈顶元素/mark current pixel.获取label值，于标记？t =res.atint(cur.r - 1, cur.c);/此处四邻域与上边记录四邻域点存邻域时保持致t =res.atint(cur.r +1, cur.c);/公共区域修建坝？res.atint(cur.r, cur.c) =lab;/当前种点在结果图像标记为lab/std:cout lab: lab std:endl;if (lab !=)/如果标记不是坝，应继续进长漫将原始标记图中未标记的点压容器进标记/check 4

6、neighbors for unmarked r_ind =cur.r - 1, c_ind =cur.c;/rc表与列if (res.atint(r_ind, c_ind) =0)/未进标记的点t =gray.atuchar (r_ind, c_ind);/std:cout t: t std:endl;queuest.push( r_ind,c_ind );res.atint(r_ind, c_ind) =;ind =ind t ? ind : t;/ind为当前遍历的灰度级加梯度图进改进：Sobel(gray_img, sobel_x, CV_64F, 1, 0, 3

7、);Sobel(gray_img, sobel_y, CV_64F, 0, 1, 3);convertScaleAbs(sobel_x, sobel_x);convertScaleAbs(sobel_y, sobel_y);Mat grad =sobel_x +sobel_y;针对梯度图中的背景点并进操作if (lab !=)/如果标记不是坝，应继续进长漫将原始标记图中未标记的点压容器进标记t =gray.atuchar (r_ind, c_ind);/std:cout t: t std:endl;if (t != 0) queuest.push( r_ind,c_ind );res.atin

8、t(r_ind, c_ind) =;ind =ind t ? ind : t;/ind为当前遍历的灰度级t =gray.atuchar (r_ind, c_ind);/std:cout t: t std:endl;if (t != 0) queuest.push( r_ind,c_ind );res.atint(r_ind, c_ind) =;ind =ind t ? ind : t;/ind为当前遍历的灰度级t =gray.atuchar (r_ind, c_ind);/std:cout t: t std:endl;if (t != 0) queuest.push( r_ind,c_ind

9、);res.atint(r_ind, c_ind) =;ind =ind t ? ind : t;/ind为当前遍历的灰度级t =gray.atuchar (r_ind, c_ind);/std:cout t: t std:endl;if (t != 0) queuest.push( r_ind,c_ind );res.atint(r_ind, c_ind) =;ind =ind t ? ind : t;/ind为当前遍历的灰度级主程序：tw or SPACE - run watershed segmentation algorithmntt(before running it, *rough

10、ly* mark the areas to segment on the image)nt (before that, roughly outline several markers on the image)n ;else if (event =EVENT_MOUSEMOVE&(flags &EVENT_FLAG_LBUTTON)line(markerMask, prevPt, pt, Scalar(12,234,12), 40, 8, 0);prevPt =pt;imshow(image, img);int main(int argc, char*argv)cv:CommandLinePa

11、rser parser(argc, argv, help h | | input | fruits.jpg | );if (parser.has(help)help(argv);return 0;/string filename = samples:findFile(parser.get(input);Mat img0 =imread(.), imgGray;if (img0.empty()cout Couldnt open image ;help(argv);return 0;help(argv);/img0 = img0(Rect(1000, 1000, 800, 800);namedWindow(image, WINDOW_NORMAL);img0.copyTo(img);cvtColor(img, markerMask, COLOR_BGR2GRAY);cvtColor(markerMask,