利用Meanshift filter和canny边缘检测的效果，可以实现简单的图片的卡通化效果。简单的说，就是用Meanshift filter的结果减去canny算法的结果得到卡通化的效果。

  代码如下：

#include <opencv2/core/core.hpp>

#include <opencv2/highgui//highgui.hpp> #include <opencv2/imgproc/imgproc.hpp>

#include <iostream>

using namespace std; using namespace cv;

int main()

{     cv::Mat img = cv::imread("../lenna.jpg");     cv::namedWindow("image");     cv::imshow("image", img);

    cv::Mat img1;

    img1 = img.clone();

    //meanshift filter

    cv::pyrMeanShiftFiltering(img1.clone(), img1, 10, 30);     cv::namedWindow("image1");     cv::imshow("image1", img1);

    cv::Mat img2;

    cv::Mat img3;     cv::Mat img4;

    //canny

    cv::cvtColor(img, img2, CV_BGR2GRAY);     cv::Canny(img2, img3, 150, 150);     cv::cvtColor(img3, img4, CV_GRAY2BGR);

    cv::namedWindow("image4");

    cv::imshow("image4", img4);

    //卡通化的图片

    img4 = img1 - img4;     cv::namedWindow("image4_1");     cv::imshow("image4_1", img4);

    cv::waitKey(0); }

下面分别为，原始图像，meanshift filter后的图像，canny边缘图像，以及最终的卡通化图像。

下面我们看看meanshift filter算法的原理。

 

在OpenCV中，meanshift filter函数为 pyrMeanShiftFiltering， 它的函数调用格式如下：

C++: void pyrMeanShiftFiltering(InputArray src, OutputArray dst, double sp, double sr, int maxLevel=1, TermCriteriatermcrit=TermCriteria( TermCriteria::MAX_ITER+TermCriteria::EPS,5,1) )

Parameters:

• src – The source 8-bit, 3-channel image. //三通道的输入图像
• dst – The destination image of the same format and the same size as the source. //相同尺寸格式输出图像
• sp – The spatial window radius.  //空间域半径
• sr – The color window radius.  //颜色域半径
• maxLevel – Maximum level of the pyramid for the segmentation. //分割用金字塔的最大层数
• termcrit – Termination criteria: when to stop meanshift iterations. //迭代终止的条件

算法的描述大致如下：

对于输入图像的每个像素点(X,Y) ，在它的半径为sp的空间域，执行meanshift迭代算法，

像素点(X,Y)的颜色值为(R,G,B)， 它的空间邻域点(x,y)的颜色值为(r,g,b)，如果点(x,y)的到(X,Y)的颜色距离小于sr,则满足条件，最终我们求得满足条件点的平均空间坐标(X’，Y’)以及平均颜色向量(R',G',B'),并把它们作为下一次迭代的输入。

迭代结果后，我们把最初输入位置的颜色值用最终迭代的颜色值代替。

算法代码如下：

#include 
    
     #include 
     
      #include 
      
       #include 
       
        using namespace std;using namespace cv;//forward声明void gMeanShift(int x0, int y0, uchar *sptr, uchar *dptr,    int sstep, cv::Size size, int sp, int sr, int maxIter,    float eps, int *tab);void gMeanShiftFilter(const cv::Mat src, cv::Mat &dst, int sp,    int sr, cv::TermCriteria crit = cv::TermCriteria(    cv::TermCriteria::MAX_ITER + cv::TermCriteria::EPS, 5, 1));int main(){    cv::Mat img = cv::imread("../lenna.jpg");    cv::namedWindow("image");    cv::imshow("image", img);    cv::Mat img1;    img1 = img.clone();    //meanshift filter    //转化图像为4BGRA 4通道格式    cv::cvtColor(img1.clone(), img1, CV_BGR2BGRA);    gMeanShiftFilter(img1.clone(), img1, 10, 30);    cv::cvtColor(img1.clone(), img1, CV_BGRA2BGR);    //meanshift filter result image    cv::namedWindow("image1");    cv::imshow("image1", img1);    cv::Mat img2;    cv::Mat img3;    cv::Mat img4;    //canny    cv::cvtColor(img, img2, CV_BGR2GRAY);    cv::Canny(img2, img3, 150, 150);    cv::cvtColor(img3, img4, CV_GRAY2BGR);    cv::namedWindow("image4");    cv::imshow("image4", img4);    img4 = img1 - img4;    cv::namedWindow("image4_1");    cv::imshow("image4_1", img4);    cv::waitKey(0);}void gMeanShift(int x0, int y0, uchar *sptr, uchar *dptr,    int sstep, cv::Size size, int sp, int sr, int maxIter,    float eps, int *tab){    int isr2 = sr * sr;    int c0, c1, c2, c3;    int iter;    uchar *ptr = NULL;    uchar *pstart = NULL;    int revx = 0, revy = 0;    c0 = sptr[0];    c1 = sptr[1];    c2 = sptr[2];    c3 = sptr[3];    /****************************************************************************    * Iterate meanshift procedure                                               *    ****************************************************************************/    for (iter = 0; iter < maxIter; iter++)    {        int count = 0;        int s0 = 0, s1 = 0, s2 = 0, sx = 0, sy = 0;        /****************************************************************************        * mean shift: process pixels in window (p-sigmaSp)x(p+sigmaSp)              *        ****************************************************************************/        int minx = x0 - sp;        int miny = y0 - sp;        int maxx = x0 + sp;        int maxy = y0 + sp;        /****************************************************************************        * Deal with the image boundary.                                             *        ****************************************************************************/        if (minx < 0)        {            minx = 0;        }        if (miny < 0)        {            miny = 0;        }        if (maxx >= size.width)        {            maxx = size.width - 1;        }        if (maxy >= size.height)        {            maxy = size.height - 1;        }        if (iter == 0)        {            pstart = sptr;        }        else        {            pstart = pstart + revy * sstep + (revx << 2); //point to the new position        }        ptr = pstart;        //point to the start in the row        ptr = ptr + (miny - y0) * sstep + ((minx - x0) << 2);        for (int y = miny; y <= maxy; y++, ptr += sstep - ((maxx - minx + 1) << 2))        {            int rowCount = 0;            int temp, temp1;            temp1 = (maxx - minx + 1) << 2;            temp = sstep - ((maxx - minx + 1) << 2);            int x = minx;            for (; x <= maxx; x++, ptr += 4)            {                int t0 = ptr[0], t1 = ptr[1], t2 = ptr[2];                if (tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2)                {                    s0 += t0;                    s1 += t1;                    s2 += t2;                    sx += x;                    rowCount++;                }            }            if (rowCount == 0)            {                continue;            }            count += rowCount;            sy += y * rowCount;        }        if (count == 0)        {            break;        }        int x1 = sx / count;        int y1 = sy / count;        s0 = s0 / count;        s1 = s1 / count;        s2 = s2 / count;        bool stopFlag = (x0 == x1 && y0 == y1) || (abs(x1 - x0) + abs(y1 - y0) +            tab[s0 - c0 + 255] + tab[s1 - c1 + 255] + tab[s2 - c2 + 255] <= eps);        /****************************************************************************        * Revise the pointer corresponding to the new (y0,x0)                       *        ****************************************************************************/        //        revx = x1 - x0;        revy = y1 - y0;        x0 = x1;        y0 = y1;        c0 = s0;        c1 = s1;        c2 = s2;        if (stopFlag)        {            break;        }    }    dptr[0] = (uchar)c0;    dptr[1] = (uchar)c1;    dptr[2] = (uchar)c2;    dptr[3] = (uchar)c3;}void gMeanShiftFilter(const cv::Mat src, cv::Mat &dst,    int sp, int sr, cv::TermCriteria crit){    if (src.empty())    {        cout << "Source is null" << endl;    }    if (!(crit.type & cv::TermCriteria::MAX_ITER))    {        crit.maxCount = 5;    }    int maxIter = std::min(std::max(crit.maxCount, 1), 100);    float eps;    if (!(crit.type & cv::TermCriteria::EPS))    {        eps = 1.f;    }    eps = (float)std::max(crit.epsilon, 0.0);    int tab[512];    for (int i = 0; i < 512; i++)    {        tab[i] = (i - 255) * (i - 255);    }    uchar *sptr = src.data;    uchar *dptr = dst.data;    int sstep = (int)src.step;    int dstep = (int)dst.step;    cv::Size size = src.size();    for (int i = 0; i < size.height; i++, sptr += sstep - (size.width << 2),        dptr += dstep - (size.width << 2))    {        int tt, tt1;        tt = size.width << 2;        tt1 = sstep - (size.width << 2);        for (int j = 0; j < size.width; j++, sptr += 4, dptr += 4)        {            gMeanShift(j, i, sptr, dptr, sstep, size, sp,                sr, maxIter, eps, tab);        }    }}