OpenCV实现二值图细化（骨架提取）

对二值图进行细化（骨架提取），也就是把每根线条细化到一个像素的宽度。有两个比较成熟的算法实现此功能，分别是Zhang-Suen算法和Guo-Hall算法。

我们下面使用OpenCVSharp，使用C#实现上述两个算法：

cs 复制代码

private static Mat ThinningIteration(Mat img, int iter, int thinningType)
{
    Mat marker = Mat.Zeros(img.Size(), MatType.CV_8UC1);
    int rows = img.Rows;
    int cols = img.Cols;
    marker.Col(0).SetTo(1);
    marker.Col(cols - 1).SetTo(1);
    marker.Row(0).SetTo(1);
    marker.Row(rows - 1).SetTo(1);

    //THINNING_ZHANGSUEN
    if (thinningType == 1)
    {
        marker.ForEachAsByte((byte* value, int* position) =>
        {
            int i = position[0];
            int j = position[1];
            if (i == 0 || j == 0 || i == rows - 1 || j == cols - 1)
                return;

            var ptr = (byte*)img.Ptr(i, j).ToPointer();

            // p9 p2 p3
            // p8 p1 p4
            // p7 p6 p5
            byte p2 = ptr[-cols];
            byte p3 = ptr[-cols + 1];
            byte p4 = ptr[1];
            byte p5 = ptr[cols + 1];
            byte p6 = ptr[cols];
            byte p7 = ptr[cols - 1];
            byte p8 = ptr[-1];
            byte p9 = ptr[-cols - 1];

            int neighbors = p9 | (p2 << 1) | (p3 << 2) | (p4 << 3) | (p5 << 4) | (p6 << 5) | (p7 << 6) | (p8 << 7);

            if (iter == 0)
                *value = lut_zhang_iter0[neighbors];
            else
                *value = lut_zhang_iter1[neighbors];
        });
    }
    //THINNING_GUOHALL
    else if (thinningType == 2)
    {
        marker.ForEachAsByte((byte* value, int* position) =>
        {
            int i = position[0];
            int j = position[1];
            if (i == 0 || j == 0 || i == rows - 1 || j == cols - 1)
                return;

            var ptr = (byte*)img.Ptr(i, j).ToPointer();

            // p9 p2 p3
            // p8 p1 p4
            // p7 p6 p5
            byte p2 = ptr[-cols];
            byte p3 = ptr[-cols + 1];
            byte p4 = ptr[1];
            byte p5 = ptr[cols + 1];
            byte p6 = ptr[cols];
            byte p7 = ptr[cols - 1];
            byte p8 = ptr[-1];
            byte p9 = ptr[-cols - 1];

            int neighbors = p9 | (p2 << 1) | (p3 << 2) | (p4 << 3) | (p5 << 4) | (p6 << 5) | (p7 << 6) | (p8 << 7);

            if (iter == 0)
                *value = lut_guo_iter0[neighbors];
            else
                *value = lut_guo_iter1[neighbors];
        });
    }

    img &= marker;
    return img;
}

public static void Thinning(InputArray input, OutputArray output, int thinningType)
{
    Mat processed = input.GetMat().Clone();
    processed /= 255;

    Mat prev = processed.Clone();
    Mat diff = new Mat();
    do
    {
        processed = ThinningIteration(processed, 0, thinningType);
        processed = ThinningIteration(processed, 1, thinningType);
        Cv2.Absdiff(processed, prev, diff);
        if (diff.CountNonZero() == 0) break;
        processed.CopyTo(prev);
    }
    while (true);

    processed *= 255;

    processed.CopyTo(output);
}

我们的测试框图如下图所示：

原二值图如下图所示：

细化（提取骨骼）之后的结果图如下：

Zhang-Suen算法

Guo-Hall算法