骨架提取算法应用

1、引言
根据个人理解，骨架提取（顾名思义）就是根据各个连通区域，将其抽离出与其轮廓近似的单像素表示形态。以便于直观观察、图像的后继处理。因此可以将其视为图像处理中的预处理，其操作是基于二值图。为了更好的提取图像骨架，必要时需要对图像进行相应的预处理（比如去噪、滤波、形态学变换等）。
我的应用主要集中在对一些包含线条型的零件检测，除此之外，骨架提取的应用特别广泛，比如文字的检测/识别、道路观测等。

2、原理
Zhang和Suen提出了一种带有模板匹配的并行细化算法，生成一个像素宽的骨架，不仅保持图像的连通性，并且产生更薄的结果，保持快速的处理速度。
Zhang-Suen细化算法通常是一个迭代算法，整个迭代过程分为两步：

第一步：循环所有前景像素点，对符合如下条件的像素点标记为删除：
1)2<=N(P1)<=6
2)S(P1)=1
3)P2P4P6=0
4)P4P6P8=0
其中N(P1)表示跟P1相邻的8个像素点中，为前景像素点的个数，S(P1)表示从P2-P9-P2像素中出现0-1的累积次数，其中0表示背景，1表示前景，完整的P1-P9的像素位置分布如表1：

第二步：
1)2<=N(P1)<=6
2)S(P1)=1
3)P2P4P8=0
4)P2P6P8=0
循环以上两个步骤，直到两步中没有像素被标记为删除为止，输出的结果即为二值图像细化后的骨架。

3、案例核心代码

//Zhang-Sun细化算法
void SkeletonExtraction()
{
	//原图像名称
	string Img_name = "TEST.png";
	//载入源图像
	Mat Src = imread(Img_name);
	Mat src = Src.clone();
	//灰度化
	cvtColor(src, src, COLOR_RGB2GRAY);
	//Otsu求阈值
	int thre = Otsu(src);
	Mat Img;
	//二值化
	threshold(src, Img, thre, 255, THRESH_BINARY_INV);
	namedWindow("原始二值化图像", 0);
	imshow("原始二值化图像", Img);

	Mat srcImg = Img.clone();
	/****************骨架提取算法：Zhang-Suen法*****检测焊条数量************************/
	vector<Point> deleteList;
	int neighbourhood[9];
	int row = srcImg.rows;
	int col = srcImg.cols;
	bool inOddIterations = true;
	while (true) {
		for (int j = 1; j < (row - 1); j++) {
			uchar* data_last = srcImg.ptr<uchar>(j - 1);
			uchar* data = srcImg.ptr<uchar>(j);
			uchar* data_next = srcImg.ptr<uchar>(j + 1);
			for (int i = 1; i < (col - 1); i++) {
				if (data[i] == 255) {
					int whitePointCount = 0;
					neighbourhood[0] = 1;
					//判断中心点8邻域的像素特征
					if (data_last[i] == 255) neighbourhood[1] = 1;
					else  neighbourhood[1] = 0;
					if (data_last[i + 1] == 255) neighbourhood[2] = 1;
					else  neighbourhood[2] = 0;
					if (data[i + 1] == 255) neighbourhood[3] = 1;
					else  neighbourhood[3] = 0;
					if (data_next[i + 1] == 255) neighbourhood[4] = 1;
					else  neighbourhood[4] = 0;
					if (data_next[i] == 255) neighbourhood[5] = 1;
					else  neighbourhood[5] = 0;
					if (data_next[i - 1] == 255) neighbourhood[6] = 1;
					else  neighbourhood[6] = 0;
					if (data[i - 1] == 255) neighbourhood[7] = 1;
					else  neighbourhood[7] = 0;
					if (data_last[i - 1] == 255) neighbourhood[8] = 1;
					else  neighbourhood[8] = 0;
					for (int k = 1; k < 9; k++) {
						//二进制值为1的个数
						whitePointCount += neighbourhood[k];
					}
					//条件①2<=B(p1)<=6
					if ((whitePointCount >= 2) && (whitePointCount <= 6)) {
						int ap = 0;
						//条件②A(p1)值
						if ((neighbourhood[1] == 0) && (neighbourhood[2] == 1)) ap++;
						if ((neighbourhood[2] == 0) && (neighbourhood[3] == 1)) ap++;
						if ((neighbourhood[3] == 0) && (neighbourhood[4] == 1)) ap++;
						if ((neighbourhood[4] == 0) && (neighbourhood[5] == 1)) ap++;
						if ((neighbourhood[5] == 0) && (neighbourhood[6] == 1)) ap++;
						if ((neighbourhood[6] == 0) && (neighbourhood[7] == 1)) ap++;
						if ((neighbourhood[7] == 0) && (neighbourhood[8] == 1)) ap++;
						if ((neighbourhood[8] == 0) && (neighbourhood[1] == 1)) ap++;
						if (ap == 1) {
							if (inOddIterations && (neighbourhood[3] * neighbourhood[5] * neighbourhood[7] == 0)
								&& (neighbourhood[1] * neighbourhood[3] * neighbourhood[5] == 0)) {
								deleteList.push_back(Point(i, j));
							}
							else if (!inOddIterations && (neighbourhood[1] * neighbourhood[5] * neighbourhood[7] == 0)
								&& (neighbourhood[1] * neighbourhood[3] * neighbourhood[7] == 0)) {
								deleteList.push_back(Point(i, j));
							}
						}
					}
				}
			}
		}
		if (deleteList.size() == 0)
			break;
		for (size_t i = 0; i < deleteList.size(); i++) {
			Point tem;
			tem = deleteList[i];
			uchar* data = srcImg.ptr<uchar>(tem.y);
			data[tem.x] = 0;
		}
		deleteList.clear();

		inOddIterations = !inOddIterations;
	}
	namedWindow("骨架提取", 0);
	imshow("骨架提取", srcImg);
}
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