Question

您好我正在使用OpenCV库编写基本的C ++应用程序，以从背景中分割图像的主题。应用程序读入图像文件并使用分水岭算法根据边缘找到的数据和图像中心找到的数据生成遮罩。

（开始我创建了一个整体值为-1的图像对象。然后我在一个空图像周围创建了一个边界，其值为1.然后我创建了一个大致位于图像中心的矩形值为2.边框和矩形不接触。）

我尝试使用生成的蒙版使用原始图像和自动生成的蒙版之间的按位AND从图像中删除数据。

我用C ++编写过这篇文章，如果有人能快速查看我的代码，我将非常感激。我能找到的唯一类似示例是使用Python的原生OpenCV绑定。

样品面膜：http://i.imgur.com/a0SUwy3.png

示例图片：http://i.imgur.com/FQywu6P.png

// Usage: ./app input.jpg
#include "opencv2/opencv.hpp"
#include <string>

using namespace cv;
using namespace std;

class WatershedSegmenter{
private:
    cv::Mat markers;
public:
    void setMarkers(cv::Mat& markerImage)
    {
        markerImage.convertTo(markers, CV_32S);
    }

    cv::Mat process(cv::Mat &image)
    {
        cv::watershed(image, markers);
        markers.convertTo(markers,CV_8U);
        return markers;
    }
};


int main(int argc, char* argv[])
{
    cv::Mat image = cv::imread(argv[1]);
    cv::Mat blank(image.size(),CV_8U,cv::Scalar(0xFF));
    cv::Mat dest(image.size(),CV_8U,cv::Scalar(0xFF));
    imshow("originalimage", image);

    // Create markers image
    cv::Mat markers(image.size(),CV_8U,cv::Scalar(-1));
    //Rect(topleftcornerX, topleftcornerY, width, height);
    //top rectangle
    markers(Rect(0,0,image.cols, 5)) = Scalar::all(1);
    //bottom rectangle
    markers(Rect(0,image.cols-5,image.cols, 5)) = Scalar::all(1);
    //left rectangle
    markers(Rect(0,0,5,image.rows)) = Scalar::all(1);
    //right rectangle
    markers(Rect(image.cols-5,0,5,image.rows)) = Scalar::all(1);
    //centre rectangle
    markers(Rect(image.cols/2,image.rows/2,50, 50)) = Scalar::all(2);


    //Create watershed segmentation object
    WatershedSegmenter segmenter;
    segmenter.setMarkers(markers);
    cv::Mat result = segmenter.process(image);
    result.convertTo(result,CV_8U);

    bitwise_and(image, blank, dest, result);
    imshow("final_result", dest);

    cv::waitKey(0);

    return 0;
}

Answer 1

搞定了！

// Usage: ./app input.jpg
#include "opencv2/opencv.hpp"
#include <string>

using namespace cv;
using namespace std;

class WatershedSegmenter{
private:
    cv::Mat markers;
public:
    void setMarkers(cv::Mat& markerImage)
    {
        markerImage.convertTo(markers, CV_32S);
    }

    cv::Mat process(cv::Mat &image)
    {
        cv::watershed(image, markers);
        markers.convertTo(markers,CV_8U);
        return markers;
    }
};


int main(int argc, char* argv[])
{
    cv::Mat image = cv::imread(argv[1]);
    cv::Mat blank(image.size(),CV_8U,cv::Scalar(0xFF));
    cv::Mat dest;
    imshow("originalimage", image);

    // Create markers image
    cv::Mat markers(image.size(),CV_8U,cv::Scalar(-1));
    //Rect(topleftcornerX, topleftcornerY, width, height);
    //top rectangle
    markers(Rect(0,0,image.cols, 5)) = Scalar::all(1);
    //bottom rectangle
    markers(Rect(0,image.rows-5,image.cols, 5)) = Scalar::all(1);
    //left rectangle
    markers(Rect(0,0,5,image.rows)) = Scalar::all(1);
    //right rectangle
    markers(Rect(image.cols-5,0,5,image.rows)) = Scalar::all(1);
    //centre rectangle
    int centreW = image.cols/4;
    int centreH = image.rows/4;
    markers(Rect((image.cols/2)-(centreW/2),(image.rows/2)-(centreH/2), centreW, centreH)) = Scalar::all(2);
    markers.convertTo(markers,CV_BGR2GRAY);
    imshow("markers", markers);

    //Create watershed segmentation object
    WatershedSegmenter segmenter;
    segmenter.setMarkers(markers);
    cv::Mat wshedMask = segmenter.process(image);
    cv::Mat mask;
    convertScaleAbs(wshedMask, mask, 1, 0);
    double thresh = threshold(mask, mask, 1, 255, THRESH_BINARY);
    bitwise_and(image, image, dest, mask);
    dest.convertTo(dest,CV_8U);

    imshow("final_result", dest);
    cv::waitKey(0);

    return 0;
}

使用Watershed在C ++中进行OpenCV图像分割

1 个答案: