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。。。。