1 K3M
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/objdetect/objdetect.hpp>
#include <opencv2/highgui/highgui.hpp>
#include<list>
#include<iostream>
#include<algorithm>
#include<set>
using namespace std;
set<int> GetAi(int a[], int length)//获取A0~A5
{
set<int> vec;
int neighbour[] = { 1,2,4,8,16,32,64,128,1,2,4,8,16,32,64 };
for (int i = 0; i<length; i++)
for (int j = 0; j<8; j++)
{
int sum = 0;
for (int k = j; k <= j + a[i]; k++)
sum += neighbour[k];
vec.insert(sum);
std::cout << sum << " ";
}
std::cout << std::endl;
return vec;
}
//迭代腐蚀
bool erodephase(list<cv::Point> &border, cv::Mat&Input, int neighbour[][3], const set<int>& A)
{
auto pt = border.begin();
bool result = false;
while(pt!= border.end())
{
int weight = 0;
for (int j = -1; j <= 1; ++j)
for (int k = -1; k <= 1; k++)
weight += neighbour[j + 1][k + 1] * Input.at<uchar>(pt->y + j, pt->x + k);
if (std::find(A.begin(), A.end(), weight) != A.end())
{
Input.at<uchar>(pt->y , pt->x ) = 0;
pt=border.erase(pt);
result = true;
}
else
++pt;
}
return result;
}
//找边界
void findborder(list<cv::Point2i>& border, const cv::Mat&Input)
{
int cnt = 0;
int rows = Input.rows;
int cols = Input.cols;
cv::Mat bordermat = Input.clone();
for (int row = 1; row<rows - 1; ++row)
for (int col = 1; col<cols - 1; ++col)
{
int weight = 0;
for (int j = -1; j <= 1; ++j)
for (int k = -1; k <= 1; k++)
{
if (Input.at<uchar>(row + j, col + k) == 1)
++cnt;
}
if (cnt == 9)
bordermat.at<uchar>(row, col) = 0;
cnt = 0;
}
for (int row = 1; row<rows - 1; ++row)
for (int col = 1; col < cols - 1; ++col)
{
if (bordermat.at<uchar>(row, col) == 1)
border.push_back(cv::Point2i(col, row));
}
}
//最后一步，得到骨架
void finalerode( cv::Mat&Input ,int neighbour[][3], const set<int>& A)
{
int rows = Input.rows;
int cols = Input.cols;
for (int m = 1; m<rows - 1; ++m)
for (int n = 1; n<cols - 1; ++n)
{
int weight = 0;
for (int j = -1; j <= 1; ++j)
for (int k = -1; k <= 1; k++)
{
weight += neighbour[j + 1][k + 1] * Input.at<uchar>(m + j, n + k);
}
if (std::find(A.begin(), A.end(), weight) != A.end())
Input.at<uchar>(m, n) = 0;
}
}
void thin(cv::Mat &Input) //Input是二值图像
{
int a0[] = { 1,2,3,4,5,6 };
int a1[] = { 2 };
int a2[] = { 2,3 };
int a3[] = { 2,3,4 };
int a4[] = { 2,3,4,5 };
int a5[] = { 2,3,4,5,6 };
set<int> A0 = GetAi(a0, 6);
set<int> A1 = GetAi(a1, 1);
set<int> A2 = GetAi(a2, 2);
set<int> A3 = GetAi(a3, 3);
set<int> A4 = GetAi(a4, 4);
set<int> A5 = GetAi(a5, 5);
list<cv::Point2i> border;
bool continue_ = true;
int neighbour[3][3] = {
{ 128,1,2 },
{ 64,0,4 },
{ 32,16,8 }
};
while (continue_)
{
//找边界，对原文方法做了小改变，但影响不大。
continue_ = false;
findborder(border, Input);//Phase0
//可以在下面每一步打印结果，看每一步对提取骨架的贡献
erodephase(border, Input, neighbour,A1);//Phase1
erodephase(border, Input, neighbour, A2);//Phase2
erodephase(border, Input, neighbour, A3);//Phase3
erodephase(border, Input, neighbour, A4);//Phase4
continue_ =erodephase(border, Input, neighbour, A5);//Phase5
border.clear();
}
finalerode(Input, neighbour, A0);//最后一步
}
int main()
{
cv::Mat raw = cv::imread("test.png",0);
cv::Mat binaryImage;
cv::threshold(raw, binaryImage, 180, 1, CV_THRESH_BINARY_INV);
cv::imshow("二值化图像", binaryImage * 255);
thin(binaryImage);
cv::imshow("骨架图像", binaryImage*255);
cv::cvtColor(raw, raw, CV_GRAY2BGR);
for(int row=0;row<binaryImage.rows;row++)
for (int col = 0; col < binaryImage.cols; col++)
{
if (binaryImage.at<uchar>(row, col) == 1)
{
raw.at<cv::Vec3b>(row, col)[0] = 0;
raw.at<cv::Vec3b>(row, col)[1] = 255;
raw.at<cv::Vec3b>(row, col)[1] = 0;
}
}
cv::imshow("骨架在原图的位置", raw);
cv::waitKey(0);
return 0;
}2 Zhang-Suen algorithm
/**
* Code for thinning a binary image using Zhang-Suen algorithm.
*
* Author: Nash (nash [at] opencv-code [dot] com)
* Website: http://opencv-code.com
*/
#include <opencv2/opencv.hpp>
#include<iostream>
/**
* Perform one thinning iteration.
* Normally you wouldn't call this function directly from your code.
*
* Parameters:
* im Binary image with range = [0,1]
* iter 0=even, 1=odd
*/
void thinningIteration(cv::Mat& img, int iter)
{
CV_Assert(img.channels() == 1);
CV_Assert(img.depth() != sizeof(uchar));
CV_Assert(img.rows > 3 && img.cols > 3);
cv::Mat marker = cv::Mat::zeros(img.size(), CV_8UC1);
int nRows = img.rows;
int nCols = img.cols;
if (img.isContinuous()) {
nCols *= nRows;
nRows = 1;
}
int x, y;
uchar *pAbove;
uchar *pCurr;
uchar *pBelow;
uchar *nw, *no, *ne; // north (pAbove)
uchar *we, *me, *ea;
uchar *sw, *so, *se; // south (pBelow)
uchar *pDst;
// initialize row pointers
pAbove = NULL;
pCurr = img.ptr<uchar>(0);
pBelow = img.ptr<uchar>(1);
for (y = 1; y < img.rows-1; ++y) {
// shift the rows up by one
pAbove = pCurr;
pCurr = pBelow;
pBelow = img.ptr<uchar>(y+1);
pDst = marker.ptr<uchar>(y);
// initialize col pointers
no = &(pAbove[0]);
ne = &(pAbove[1]);
me = &(pCurr[0]);
ea = &(pCurr[1]);
so = &(pBelow[0]);
se = &(pBelow[1]);
for (x = 1; x < img.cols-1; ++x) {
// shift col pointers left by one (scan left to right)
nw = no;
no = ne;
ne = &(pAbove[x+1]);
we = me;
me = ea;
ea = &(pCurr[x+1]);
sw = so;
so = se;
se = &(pBelow[x+1]);
int A = (*no == 0 && *ne == 1) + (*ne == 0 && *ea == 1) +
(*ea == 0 && *se == 1) + (*se == 0 && *so == 1) +
(*so == 0 && *sw == 1) + (*sw == 0 && *we == 1) +
(*we == 0 && *nw == 1) + (*nw == 0 && *no == 1);
int B = *no + *ne + *ea + *se + *so + *sw + *we + *nw;
int m1 = iter == 0 ? (*no * *ea * *so) : (*no * *ea * *we);
int m2 = iter == 0 ? (*ea * *so * *we) : (*no * *so * *we);
if (A == 1 && (B >= 2 && B <= 6)&& m1 == 0 && m2 == 0)//腐蚀四个条件
pDst[x] = 1;
}
}
img &= ~marker;
}
/**
* Function for thinning the given binary image
*
* Parameters:
* src The source image, binary with range = [0,255]
* dst The destination image
*/
void thinning(const cv::Mat& src, cv::Mat& dst)
{
dst = src.clone();
dst /= 255; // convert to binary image
cv::Mat prev = cv::Mat::zeros(dst.size(), CV_8UC1);
cv::Mat diff;
do {
thinningIteration(dst, 0);
thinningIteration(dst, 1);
cv::absdiff(dst, prev, diff);
dst.copyTo(prev);
static int ii = 0;
std::cout << ++ii << std::endl;
}
while (cv::countNonZero(diff) >0);//迭代终止条件
dst *= 255;
}
/**
* This is an example on how to call the thinning funciton above
*/
int main()
{
cv::Mat src = cv::imread("test.png",0);
if (!src.data)
return -1;
cv::Mat binary;
cv::threshold(src, binary, 180, 255, CV_THRESH_BINARY_INV);
cv::imshow("二值化", binary);
thinning(binary, binary);
cv::imshow("src", src);
cv::imshow("dst", binary);
cv::waitKey();
return 0;
}