opencv 图像细化
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 = ; i < rows; i++) {
for(int j = ; j < cols; j++) {
if(pSrc.at<float>(i, j) == 1.0f) {
/// get 8 neighbors
/// calculate C(p)
int neighbor0 = (int) pSrc.at<float>( i-, j-);
int neighbor1 = (int) pSrc.at<float>( i-, j);
int neighbor2 = (int) pSrc.at<float>( i-, j+);
int neighbor3 = (int) pSrc.at<float>( i, j+);
int neighbor4 = (int) pSrc.at<float>( i+, j+);
int neighbor5 = (int) pSrc.at<float>( i+, j);
int neighbor6 = (int) pSrc.at<float>( i+, j-);
int neighbor7 = (int) pSrc.at<float>( i, j-);
int C = int(~neighbor1 & ( neighbor2 | neighbor3)) +
int(~neighbor3 & ( neighbor4 | neighbor5)) +
int(~neighbor5 & ( neighbor6 | neighbor7)) +
int(~neighbor7 & ( neighbor0 | neighbor1));
if(C == ) {
/// 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 == ) || (N == )) {
/// calculate criteria 3
int c3 = ( neighbor1 | neighbor2 | ~neighbor4) & neighbor3;
if(c3 == ) {
pDst.at<float>( i, j) = 0.0f;
}
}
}
}
}
}
} void ThinSubiteration2(Mat & pSrc, Mat & pDst) {
int rows = pSrc.rows;
int cols = pSrc.cols;
pSrc.copyTo( pDst);
for(int i = ; i < rows; i++) {
for(int j = ; j < cols; j++) {
if (pSrc.at<float>( i, j) == 1.0f) {
/// get 8 neighbors
/// calculate C(p)
int neighbor0 = (int) pSrc.at<float>( i-, j-);
int neighbor1 = (int) pSrc.at<float>( i-, j);
int neighbor2 = (int) pSrc.at<float>( i-, j+);
int neighbor3 = (int) pSrc.at<float>( i, j+);
int neighbor4 = (int) pSrc.at<float>( i+, j+);
int neighbor5 = (int) pSrc.at<float>( i+, j);
int neighbor6 = (int) pSrc.at<float>( i+, j-);
int neighbor7 = (int) pSrc.at<float>( i, j-);
int C = int(~neighbor1 & ( neighbor2 | neighbor3)) +
int(~neighbor3 & ( neighbor4 | neighbor5)) +
int(~neighbor5 & ( neighbor6 | neighbor7)) +
int(~neighbor7 & ( neighbor0 | neighbor1));
if(C == ) {
/// 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 == ) || (N == )) {
int E = (neighbor5 | neighbor6 | ~neighbor0) & neighbor7;
if(E == ) {
pDst.at<float>(i, j) = 0.0f;
}
}
}
}
}
}
}
int main(int argc, char* argv[])
{
Mat src = imread("D://thinning.png", );
Mat inputarray = src(Rect(, , src.cols - , src.rows - ));
threshold(inputarray, inputarray, , , 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 + , cols + , CV_32FC1);
for (int i = ; i < (rows + ); i++) {
p_enlarged_src.at<float>(i, ) = 0.0f;
p_enlarged_src.at<float>(i, cols + ) = 0.0f;
}
for (int j = ; j < (cols + ); j++) {
p_enlarged_src.at<float>(, j) = 0.0f;
p_enlarged_src.at<float>(rows + , j) = 0.0f;
}
for (int i = ; i < rows; i++) {
for (int j = ; j < cols; j++) {
if (inputarray.at<float>(i, j) >= 20.0f) {
p_enlarged_src.at<float>(i + , j + ) = 1.0f;
}
else
p_enlarged_src.at<float>(i + , j + ) = 0.0f;
}
} /// start to thin
Mat p_thinMat1 = Mat::zeros(rows + , cols + , CV_32FC1);
Mat p_thinMat2 = Mat::zeros(rows + , cols + , CV_32FC1);
Mat p_cmp = Mat::zeros(rows + , cols + , 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 + ) * (cols + )) {
bDone = true;
}
/// copy
p_thinMat1.copyTo(p_enlarged_src);
}
// copy result
for (int i = ; i < rows; i++) {
for (int j = ; j < cols; j++) {
outputarray.at<float>(i, j) = p_enlarged_src.at<float>(i + , j + );
}
}
imshow("src", inputarray);
imshow("dst", p_enlarged_src);
waitKey(); return ; }
附上效果图:
未完待续。。。。
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