OpenCV4入门75：图像均值漂移分割（meanshift）

入门系列索引：OpenCV入门索引目录

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// 功能：代码 7-4 均值漂移图像分割
// 作者：朱伟 zhu1988wei@163.com
// 来源：《OpenCV图像处理编程实例》
// 博客：http://blog.csdn.net/zhuwei1988
// 更新：2016-8-1
// 说明：版权所有，引用或摘录请联系作者，并按照上面格式注明出处，谢谢。//
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
using namespace cv;
using namespace std;
//颜色通道分离
static void MergeSeg(Mat& img
    , const Scalar& colorDiff = Scalar::all(1))
{
    CV_Assert(!img.empty());
    RNG rng = theRNG();
    // 定义掩码图像
    Mat mask(img.rows + 2, img.cols + 2, 
        CV_8UC1, Scalar::all(0));
    for (int y = 0; y < img.rows; y++)
    {
        for (int x = 0; x < img.cols; x++)
        {
            if (mask.at<uchar>(y + 1, x + 1) == 0)
            {
                // 颜色定义
                Scalar newVal(rng(256), rng(256), rng(256));
                // 泛洪合并
                floodFill(img, mask, Point(x, y)
                    , newVal, 0, colorDiff, colorDiff);
            }
        }
    }
}
int main(int argc, char** argv)
{
    cv::Mat srcImg = imread("..\\images\\sea.jpg");
    if (srcImg.empty())
        return -1;
    // 参数设定
    int spatialRad = 20;
    int colorRad = 20;
    int maxPyrLevel = 6;
    cv::Mat resImg;
    // 均值漂移分割
    pyrMeanShiftFiltering(srcImg, resImg, 
        spatialRad, colorRad, maxPyrLevel);
    // 颜色通道分离合并
    MergeSeg(resImg, Scalar::all(2));
    cv::imshow("src", srcImg);
    cv::imshow("resImg", resImg);
    cv::waitKey();
    return 0;
}

动态设置参数：

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//---------------------------------【头文件、命名空间包含部分】----------------------------
//      描述：包含程序所使用的头文件和命名空间
//------------------------------------------------------------------------------------------------
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
using namespace cv;
using namespace std;

//--------------------------------------【help( )函数】--------------------------------------
//       描述：输出一些帮助信息
//----------------------------------------------------------------------------------------------
static void help() {
    cout << "\n\t此程序演示了OpenCV中MeanShift图像分割的使用。\n"
         << "\n\t程序运行后我们可以通过3个滑动条调节分割效果。调节滑动条后可能会有些许卡顿，请耐心"
            "等待\n"
         << "\n\t3个滑动条代表的参数分别为空间窗的半径 "
            "（spatialRad）、色彩窗的半径（colorRad）、最大图像金字塔级别（maxPyrLevel）\n"
         << endl;
}

// This colors the segmentations
static void floodFillPostprocess(Mat &img, const Scalar &colorDiff = Scalar::all(1)) {
    CV_Assert(!img.empty());
    RNG rng = theRNG();
    Mat mask(img.rows + 2, img.cols + 2, CV_8UC1, Scalar::all(0));
    for (int y = 0; y < img.rows; y++) {
        for (int x = 0; x < img.cols; x++) {
            if (mask.at<uchar>(y + 1, x + 1) == 0) {
                Scalar newVal(rng(256), rng(256), rng(256));
                floodFill(img, mask, Point(x, y), newVal, 0, colorDiff, colorDiff);
            }
        }
    }
}

string winName = "meanshift";
int    spatialRad, colorRad, maxPyrLevel;
Mat    img, res;

static void meanShiftSegmentation(int, void *) {
    cout << "spatialRad=" << spatialRad << "; "
         << "colorRad=" << colorRad << "; "
         << "maxPyrLevel=" << maxPyrLevel << endl;
    pyrMeanShiftFiltering(img, res, spatialRad, colorRad, maxPyrLevel);
    floodFillPostprocess(res, Scalar::all(2));
    imshow(winName, res);
}

//-----------------------------------【main( )函数】--------------------------------------------
//      描述：控制台应用程序的入口函数，我们的程序从这里开始
//-------------------------------------------------------------------------------------------------
int main(int argc, char **argv) {

    help();

    img = imread("1.jpg");
    if (img.empty())
        return -1;
    imshow("原始图", img);
    spatialRad  = 10;
    colorRad    = 10;
    maxPyrLevel = 1;

    namedWindow(winName, WINDOW_AUTOSIZE);

    createTrackbar("spatialRad", winName, &spatialRad, 80, meanShiftSegmentation);
    createTrackbar("colorRad", winName, &colorRad, 60, meanShiftSegmentation);
    createTrackbar("maxPyrLevel", winName, &maxPyrLevel, 5, meanShiftSegmentation);

    meanShiftSegmentation(0, 0);
    waitKey();
    return 0;
}