Image edge smoothing with opencv

Question

I am trying to smooth output image edges using opencv framework, I am trying following steps. Steps took from here https://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.

Answer 1

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