Blending does not remove seams in OpenCV

Question

I am trying to blend 2 images so that the seams between them disappear.

1st image:

2nd image:

if blending NOT applied:

if blending applied:

I used ALPHA BLENDING; NO seam removed; in fact image STILL SAME BUT DARKER

This is the part where I do the blending

Mat warped1;
warpPerspective(left,warped1,perspectiveTransform,front.size());// Warping may be used for correcting image distortion
imshow("combined1",warped1/2+front/2);
            vector<Mat> imgs;
            imgs.push_back(warped1/2);
            imgs.push_back(front/2);
            double alpha = 0.5; 
            int min_x = ( imgs[0].cols - imgs[1].cols)/2 ;
            int min_y = ( imgs[0].rows -imgs[1].rows)/2 ;
            int width, height;
            if(min_x < 0) {
                min_x = 0; 
                width = (imgs).at(0).cols;
            }
            else         
                width = (imgs).at(1).cols;
            if(min_y < 0) {
                min_y = 0; 
                height = (imgs).at(0).rows - 1;
            }

            else         
                height = (imgs).at(1).rows - 1;
            Rect roi = cv::Rect(min_x, min_y, imgs[1].cols, imgs[1].rows);  
            Mat out_image = imgs[0].clone();
            Mat A_roi= imgs[0](roi);
            Mat out_image_roi = out_image(roi);
            addWeighted(A_roi,alpha,imgs[1],1-alpha,0.0,out_image_roi);
            imshow("foo",imgs[0](roi));

Answer 1

I choose to define the alpha value depending on the distance to the "object center", the further the distance from the object center, the smaller the alpha value. The "object" is defined by a mask.

I've aligned the images with GIMP (similar to your warpPerspective). They need to be in same coordinate system and both images must have same size.

My input images look like this:

int main()
{

cv::Mat i1 = cv::imread("blending/i1_2.png");
cv::Mat i2 = cv::imread("blending/i2_2.png");

cv::Mat m1 = cv::imread("blending/i1_2.png",CV_LOAD_IMAGE_GRAYSCALE);
cv::Mat m2 = cv::imread("blending/i2_2.png",CV_LOAD_IMAGE_GRAYSCALE);

    // works too, for background near white
    //  m1 = m1 < 220;
    //  m2 = m2 < 220;

//    edited:  using OTSU thresholding. If not working you have to create your own masks with a better technique
cv::threshold(m1,m1,255,255,cv::THRESH_BINARY_INV|cv::THRESH_OTSU);
cv::threshold(m2,m2,255,255,cv::THRESH_BINARY_INV|cv::THRESH_OTSU);

cv::Mat out = computeAlphaBlending(i1,m1,i2,m2);

cv::waitKey(-1);
return 0;
}

with blending function: needs some comments and optimizations I guess, I'll add them later.

cv::Mat computeAlphaBlending(cv::Mat image1, cv::Mat mask1, cv::Mat image2, cv::Mat mask2)
{
// edited: find regions where no mask is set
// compute the region where no mask is set at all, to use those color values unblended
cv::Mat bothMasks = mask1 | mask2;
cv::imshow("maskOR",bothMasks);
cv::Mat noMask = 255-bothMasks;
// ------------------------------------------

// create an image with equal alpha values:
cv::Mat rawAlpha = cv::Mat(noMask.rows, noMask.cols, CV_32FC1);
rawAlpha = 1.0f;

// invert the border, so that border values are 0 ... this is needed for the distance transform
cv::Mat border1 = 255-border(mask1);
cv::Mat border2 = 255-border(mask2);

// show the immediate results for debugging and verification, should be an image where the border of the face is black, rest is white
cv::imshow("b1", border1);
cv::imshow("b2", border2);

// compute the distance to the object center
cv::Mat dist1;
cv::distanceTransform(border1,dist1,CV_DIST_L2, 3);

// scale distances to values between 0 and 1
double min, max; cv::Point minLoc, maxLoc;

// find min/max vals
cv::minMaxLoc(dist1,&min,&max, &minLoc, &maxLoc, mask1&(dist1>0));  // edited: find min values > 0
dist1 = dist1* 1.0/max; // values between 0 and 1 since min val should alwaysbe 0

// same for the 2nd image
cv::Mat dist2;
cv::distanceTransform(border2,dist2,CV_DIST_L2, 3);
cv::minMaxLoc(dist2,&min,&max, &minLoc, &maxLoc, mask2&(dist2>0));  // edited: find min values > 0
dist2 = dist2*1.0/max;  // values between 0 and 1


//TODO: now, the exact border has value 0 too... to fix that, enter very small values wherever border pixel is set...

// mask the distance values to reduce information to masked regions
cv::Mat dist1Masked;
rawAlpha.copyTo(dist1Masked,noMask);    // edited: where no mask is set, blend with equal values
dist1.copyTo(dist1Masked,mask1);
rawAlpha.copyTo(dist1Masked,mask1&(255-mask2)); //edited

cv::Mat dist2Masked;
rawAlpha.copyTo(dist2Masked,noMask);    // edited: where no mask is set, blend with equal values
dist2.copyTo(dist2Masked,mask2);
rawAlpha.copyTo(dist2Masked,mask2&(255-mask1)); //edited

cv::imshow("d1", dist1Masked);
cv::imshow("d2", dist2Masked);

// dist1Masked and dist2Masked now hold the "quality" of the pixel of the image, so the higher the value, the more of that pixels information should be kept after blending
// problem: these quality weights don't build a linear combination yet

// you want a linear combination of both image's pixel values, so at the end you have to divide by the sum of both weights
cv::Mat blendMaskSum = dist1Masked+dist2Masked;
//cv::imshow("blendmask==0",(blendMaskSum==0));

// you have to convert the images to float to multiply with the weight
cv::Mat im1Float;
image1.convertTo(im1Float,dist1Masked.type());
cv::imshow("im1Float", im1Float/255.0);

// TODO: you could replace those splitting and merging if you just duplicate the channel of dist1Masked and dist2Masked
// the splitting is just used here to use .mul later... which needs same number of channels
std::vector<cv::Mat> channels1;
cv::split(im1Float,channels1);
// multiply pixel value with the quality weights for image 1
cv::Mat im1AlphaB = dist1Masked.mul(channels1[0]);
cv::Mat im1AlphaG = dist1Masked.mul(channels1[1]);
cv::Mat im1AlphaR = dist1Masked.mul(channels1[2]);

std::vector<cv::Mat> alpha1;
alpha1.push_back(im1AlphaB);
alpha1.push_back(im1AlphaG);
alpha1.push_back(im1AlphaR);
cv::Mat im1Alpha;
cv::merge(alpha1,im1Alpha);
cv::imshow("alpha1", im1Alpha/255.0);

cv::Mat im2Float;
image2.convertTo(im2Float,dist2Masked.type());

std::vector<cv::Mat> channels2;
cv::split(im2Float,channels2);
// multiply pixel value with the quality weights for image 2
cv::Mat im2AlphaB = dist2Masked.mul(channels2[0]);
cv::Mat im2AlphaG = dist2Masked.mul(channels2[1]);
cv::Mat im2AlphaR = dist2Masked.mul(channels2[2]);

std::vector<cv::Mat> alpha2;
alpha2.push_back(im2AlphaB);
alpha2.push_back(im2AlphaG);
alpha2.push_back(im2AlphaR);
cv::Mat im2Alpha;
cv::merge(alpha2,im2Alpha);
cv::imshow("alpha2", im2Alpha/255.0);

// now sum both weighted images and divide by the sum of the weights (linear combination)
cv::Mat imBlendedB = (im1AlphaB + im2AlphaB)/blendMaskSum;
cv::Mat imBlendedG = (im1AlphaG + im2AlphaG)/blendMaskSum;
cv::Mat imBlendedR = (im1AlphaR + im2AlphaR)/blendMaskSum;
std::vector<cv::Mat> channelsBlended;
channelsBlended.push_back(imBlendedB);
channelsBlended.push_back(imBlendedG);
channelsBlended.push_back(imBlendedR);

// merge back to 3 channel image
cv::Mat merged;
cv::merge(channelsBlended,merged);

// convert to 8UC3
cv::Mat merged8U;
merged.convertTo(merged8U,CV_8UC3);

return merged8U;
}

and helper function:

cv::Mat border(cv::Mat mask)
{
cv::Mat gx;
cv::Mat gy;

cv::Sobel(mask,gx,CV_32F,1,0,3);
cv::Sobel(mask,gy,CV_32F,0,1,3);

cv::Mat border;
cv::magnitude(gx,gy,border);

return border > 100;
}

with result:

edit: forgot a function ;) edit: now keeping original background