图像细化多用于机器人视觉，OCR字符识别等领域，细化后的图像经过去毛刺就成为了我们常说的图像的骨架。
该图像细化代码依据论文： T. Y. ZHANG and C. Y. SUEN A Fast Parallel Algorithm for Thinning Digital Patterns
代码如下：
void ThinSubiteration1(Mat & pSrc, Mat & pDst) {
int rows = pSrc.rows;
int cols = pSrc.cols;
pSrc.copyTo(pDst);
for(int i = 0; i < rows; i++) {
for(int j = 0; j < cols; j++) {
if(pSrc.at(i, j) == 1.0f) {
/// get 8 neighbors
/// calculate C(p)
int neighbor0 = (int) pSrc.at( i-1, j-1);
int neighbor1 = (int) pSrc.at( i-1, j);
int neighbor2 = (int) pSrc.at( i-1, j+1);
int neighbor3 = (int) pSrc.at( i, j+1);
int neighbor4 = (int) pSrc.at( i+1, j+1);
int neighbor5 = (int) pSrc.at( i+1, j);
int neighbor6 = (int) pSrc.at( i+1, j-1);
int neighbor7 = (int) pSrc.at( i, j-1);
int C = int(~neighbor1 & ( neighbor2 | neighbor3)) +
int(~neighbor3 & ( neighbor4 | neighbor5)) +
int(~neighbor5 & ( neighbor6 | neighbor7)) +
int(~neighbor7 & ( neighbor0 | neighbor1));
if(C == 1) {
/// calculate N
int N1 = int(neighbor0 | neighbor1) +
int(neighbor2 | neighbor3) +
int(neighbor4 | neighbor5) +
int(neighbor6 | neighbor7);
int N2 = int(neighbor1 | neighbor2) +
int(neighbor3 | neighbor4) +
int(neighbor5 | neighbor6) +
int(neighbor7 | neighbor0);
int N = min(N1,N2);
if ((N == 2) || (N == 3)) {
/// calculate criteria 3
int c3 = ( neighbor1 | neighbor2 | ~neighbor4) & neighbor3;
if(c3 == 0) {
pDst.at( i, j) = 0.0f;
}
}
}
}
}
}
}
void ThinSubiteration2(Mat & pSrc, Mat & pDst) {
int rows = pSrc.rows;
int cols = pSrc.cols;
pSrc.copyTo( pDst);
for(int i = 0; i < rows; i++) {
for(int j = 0; j < cols; j++) {
if (pSrc.at( i, j) == 1.0f) {
/// get 8 neighbors
/// calculate C(p)
int neighbor0 = (int) pSrc.at( i-1, j-1);
int neighbor1 = (int) pSrc.at( i-1, j);
int neighbor2 = (int) pSrc.at( i-1, j+1);
int neighbor3 = (int) pSrc.at( i, j+1);
int neighbor4 = (int) pSrc.at( i+1, j+1);
int neighbor5 = (int) pSrc.at( i+1, j);
int neighbor6 = (int) pSrc.at( i+1, j-1);
int neighbor7 = (int) pSrc.at( i, j-1);
int C = int(~neighbor1 & ( neighbor2 | neighbor3)) +
int(~neighbor3 & ( neighbor4 | neighbor5)) +
int(~neighbor5 & ( neighbor6 | neighbor7)) +
int(~neighbor7 & ( neighbor0 | neighbor1));
if(C == 1) {
/// calculate N
int N1 = int(neighbor0 | neighbor1) +
int(neighbor2 | neighbor3) +
int(neighbor4 | neighbor5) +
int(neighbor6 | neighbor7);
int N2 = int(neighbor1 | neighbor2) +
int(neighbor3 | neighbor4) +
int(neighbor5 | neighbor6) +
int(neighbor7 | neighbor0);
int N = min(N1,N2);
if((N == 2) || (N == 3)) {
int E = (neighbor5 | neighbor6 | ~neighbor0) & neighbor7;
if(E == 0) {
pDst.at(i, j) = 0.0f;
}
}
}
}
}
}
}
int main(int argc, char* argv[])
{
Mat src = imread(“D://thinning.png”, 0);
Mat inputarray = src(Rect(10, 10, src.cols – 20, src.rows – 20));
threshold(inputarray, inputarray, 100, 255, CV_THRESH_BINARY);
Mat outputarray(inputarray.rows,inputarray.cols,CV_32FC1);
bool bDone = false;
int rows = inputarray.rows;
int cols = inputarray.cols;
inputarray.convertTo(inputarray, CV_32FC1);
inputarray.copyTo(outputarray);
//outputarray.convertTo(outputarray, CV_32FC1);
/// pad source
Mat p_enlarged_src = Mat(rows + 2, cols + 2, CV_32FC1);
for (int i = 0; i < (rows + 2); i++) {
p_enlarged_src.at(i, 0) = 0.0f;
p_enlarged_src.at(i, cols + 1) = 0.0f;
}
for (int j = 0; j < (cols + 2); j++) {
p_enlarged_src.at(0, j) = 0.0f;
p_enlarged_src.at(rows + 1, j) = 0.0f;
}
for (int i = 0; i < rows; i++) {
for (int j = 0; j < cols; j++) {
if (inputarray.at(i, j) >= 20.0f) {
p_enlarged_src.at(i + 1, j + 1) = 1.0f;
}
else
p_enlarged_src.at(i + 1, j + 1) = 0.0f;
}
}
/// start to thin
Mat p_thinMat1 = Mat::zeros(rows + 2, cols + 2, CV_32FC1);
Mat p_thinMat2 = Mat::zeros(rows + 2, cols + 2, CV_32FC1);
Mat p_cmp = Mat::zeros(rows + 2, cols + 2, CV_8UC1);
while (bDone != true) {
/// sub-iteration 1
ThinSubiteration1(p_enlarged_src, p_thinMat1);
/// sub-iteration 2
//ThinSubiteration2(p_thinMat1, p_thinMat2);
/// compare
compare(p_enlarged_src, p_thinMat1, p_cmp, CV_CMP_EQ);
/// check
int num_non_zero = countNonZero(p_cmp);
if (num_non_zero == (rows + 2) * (cols + 2)) {
bDone = true;
}
/// copy
p_thinMat1.copyTo(p_enlarged_src);
}
// copy result
for (int i = 0; i < rows; i++) {
for (int j = 0; j < cols; j++) {
outputarray.at(i, j) = p_enlarged_src.at(i + 1, j + 1);
}
}
imshow(“src”, inputarray);
imshow(“dst”, p_enlarged_src);
waitKey(0);
return 0;
}
附上效果图：
OpenCV官方教程中文版（For Python） PDF http://www.linuxidc.com/Linux/2015-08/121400.htm
Ubuntu 14.04安装OpenCV2.4.9 http://www.linuxidc.com/Linux/2016-07/132884.htm
Ubuntu 16.04上用CMake图形界面交叉编译树莓派的OpenCV3.0 http://www.linuxidc.com/Linux/2016-10/135914.htm
Ubuntu 16.04中安装OpenCV 2.4.11 http://www.linuxidc.com/Linux/2016-07/132882.htm
Ubuntu 16.04下Matlab2014a+Anaconda2+OpenCV3.1+Caffe安装 http://www.linuxidc.com/Linux/2016-07/132860.htm
Linux上安装和编译OpenCV3.0.0 http://www.linuxidc.com/Linux/2017-07/145446.htm
Ubuntu 16.04下OpenCV安装笔记和例程 http://www.linuxidc.com/Linux/2017-08/146027.htm
Ubuntu 16.04下TensorFlow+Caffe+OpenCV3.1+Theano部署 http://www.linuxidc.com/Linux/2017-01/139503.htm
Ubuntu 16.04 编译安装OpenCV 3.1及OpenCV多版本切换 http://www.linuxidc.com/Linux/2017-01/139325.htm
http://www.linuxidc.com/Linux/2014-12/110045.htm
Ubuntu 安装 OpenCV 2.4.9 http://www.linuxidc.com/Linux/2016-12/138293.htm
Ubuntu 17.04下OpenCV源码编译安装 http://www.linuxidc.com/Linux/2017-10/147316.htm
：请点这里
：请点这里
：http://www.linuxidc.com/Linux/2017-11/148239.htm
