使用opencv的图像边缘平滑 [英] Image edge smoothing with opencv

问题描述

我试图使用opencv框架平滑输出图像边缘，我正在尝试以下步骤。从此处采取的步骤

I am trying to smooth output image edges using opencv framework, I am trying following steps. Steps took from here http://stackoverflow.com/a/17175381/790842

int lowThreshold = 10.0;
int ratio = 3;
int kernel_size = 3;

Mat src_gray,detected_edges,dst,blurred;

/// Convert the image to grayscale
cvtColor( result, src_gray, CV_BGR2GRAY );

/// Reduce noise with a kernel 3x3
cv::blur( src_gray, detected_edges, cv::Size(5,5) );

/// Canny detector
cv::Canny( detected_edges, detected_edges, lowThreshold, lowThreshold*ratio, kernel_size );

//Works fine upto here I am getting perfect edge mask    

cv::dilate(detected_edges, blurred, result);

//I get Assertion failed (src.channels() == 1 && func != 0) in countNonZero ERROR while doing dilate

result.copyTo(blurred, blurred);

cv::blur(blurred, blurred, cv::Size(3.0,3.0));

blurred.copyTo(result, detected_edges);

UIImage *image = [UIImageCVMatConverter UIImageFromCVMat:result];

I want help whether if I am going in right way, or what am I missing?

Thanks for any suggestion and help.

Updated:

I have got an image like below got from grabcut algorithm, now I want to apply edge smoothening to the image, as you can see the image is not smooth.

解决方案

Do you want to get something like this?

If yes, then here is the code:

#include <iostream>
#include <vector>
#include <string>
#include <fstream>
#include <opencv2/opencv.hpp>

using namespace cv;
using namespace std;

int main(int argc, char **argv)
{
    cv::namedWindow("result");
    Mat img=imread("TestImg.png");
    Mat whole_image=imread("D:\\ImagesForTest\\lena.jpg");
    whole_image.convertTo(whole_image,CV_32FC3,1.0/255.0);
    cv::resize(whole_image,whole_image,img.size());
    img.convertTo(img,CV_32FC3,1.0/255.0);

    Mat bg=Mat(img.size(),CV_32FC3);
    bg=Scalar(1.0,1.0,1.0);

    // Prepare mask
    Mat mask;
    Mat img_gray;
    cv::cvtColor(img,img_gray,cv::COLOR_BGR2GRAY);
    img_gray.convertTo(mask,CV_32FC1);
    threshold(1.0-mask,mask,0.9,1.0,cv::THRESH_BINARY_INV);

    cv::GaussianBlur(mask,mask,Size(21,21),11.0);
    imshow("result",mask);
    cv::waitKey(0);


        // Reget the image fragment with smoothed mask
    Mat res;

    vector<Mat> ch_img(3);
    vector<Mat> ch_bg(3);
    cv::split(whole_image,ch_img);
    cv::split(bg,ch_bg);
    ch_img[0]=ch_img[0].mul(mask)+ch_bg[0].mul(1.0-mask);
    ch_img[1]=ch_img[1].mul(mask)+ch_bg[1].mul(1.0-mask);
    ch_img[2]=ch_img[2].mul(mask)+ch_bg[2].mul(1.0-mask);
    cv::merge(ch_img,res);
    cv::merge(ch_bg,bg);

    imshow("result",res);
    cv::waitKey(0);
    cv::destroyAllWindows();
}

And I think this link will be interestiong for you too: Poisson Blending

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