我正在尝试混合 2 个图像，以便它们之间的接缝消失。

第一张图片:

第二张图片:

如果混合不应用:

如果应用了混合:

我使用了阿尔法混合； NO 接缝被移除；事实上图像仍然相同，但更暗

这是我进行混合的部分

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));

最佳答案

我选择根据到“物体中心”的距离来定义alpha值，离物体中心越远，alpha值越小。 “对象”由掩码定义。

我已将图像与 GIMP 对齐(类似于您的 warpPerspective)。它们需要在同一个坐标系中，并且两个图像必须具有相同的大小。

我的输入图像如下所示:

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;
}

具有混合功能: 我想需要一些评论和优化，我稍后会添加它们。

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;
}

和辅助函数:

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;
}

结果:

编辑:忘记了一个功能；) 编辑:现在保留原始背景

关于c++ - 混合不会消除 OpenCV 中的接缝，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/22315904/