問題描述

我一直在嘗試混合兩個圖像.我目前采用的方法是，我獲取兩個圖像的重疊區域的坐標，并且僅對于重疊區域，我在添加之前與 0.5 的硬編碼 alpha 混合.所以基本上我只是從兩個圖像的重疊區域中獲取每個像素值的一半，然后添加它們.這并沒有給我一個完美的融合，因為 alpha 值被硬編碼為 0.5.這是 3 張圖像混合的結果:

如您所見，從一張圖像到另一張圖像的過渡仍然可見.如何獲得可以消除這種可見過渡的完美 alpha 值?還是沒有這樣的事情，我采取了錯誤的方法?

這是我目前進行混合的方式:

for i in range(3):base_img_warp[overlap_coords[0]，overlap_coords[1]，i] = base_img_warp[overlap_coords[0]，overlap_coords[1]，i]*0.5next_img_warp[overlap_coords[0]，overlap_coords[1]，i] = next_img_warp[overlap_coords[0]，overlap_coords[1],i]*0.5final_img = cv2.add(base_img_warp, next_img_warp)

如果有人想試一試，這里有兩張扭曲的圖像，以及它們重疊區域的蒙版:

混合蒙版(只是作為一種印象，必須是浮點矩陣):

圖像馬賽克:

I've been trying to blend two images. The current approach I'm taking is, I obtain the coordinates of the overlapping region of the two images, and only for the overlapping regions, I blend with a hardcoded alpha of 0.5, before adding it. SO basically I'm just taking half the value of each pixel from overlapping regions of both the images, and adding them. That doesn't give me a perfect blend because the alpha value is hardcoded to 0.5. Here's the result of blending of 3 images:

As you can see, the transition from one image to another is still visible. How do I obtain the perfect alpha value that would eliminate this visible transition? Or is there no such thing, and I'm taking a wrong approach?

Here's how I'm currently doing the blending:

for i in range(3):
            base_img_warp[overlap_coords[0], overlap_coords[1], i] = base_img_warp[overlap_coords[0], overlap_coords[1],i]*0.5
            next_img_warp[overlap_coords[0], overlap_coords[1], i] = next_img_warp[overlap_coords[0], overlap_coords[1],i]*0.5
final_img = cv2.add(base_img_warp, next_img_warp)

If anyone would like to give it a shot, here are two warped images, and the mask of their overlapping region: http://imgur.com/a/9pOsQ

Here is the way I would do it in general:

int main(int argc, char* argv[])
{
    cv::Mat input1 = cv::imread("C:/StackOverflow/Input/pano1.jpg");
    cv::Mat input2 = cv::imread("C:/StackOverflow/Input/pano2.jpg");

    // compute the vignetting masks. This is much easier before warping, but I will try...
    // it can be precomputed, if the size and position of your ROI in the image doesnt change and can be precomputed and aligned, if you can determine the ROI for every image
    // the compression artifacts make it a little bit worse here, I try to extract all the non-black regions in the images.
    cv::Mat mask1;
    cv::inRange(input1, cv::Vec3b(10, 10, 10), cv::Vec3b(255, 255, 255), mask1);
    cv::Mat mask2;
    cv::inRange(input2, cv::Vec3b(10, 10, 10), cv::Vec3b(255, 255, 255), mask2);


    // now compute the distance from the ROI border:
    cv::Mat dt1;
    cv::distanceTransform(mask1, dt1, CV_DIST_L1, 3);
    cv::Mat dt2;
    cv::distanceTransform(mask2, dt2, CV_DIST_L1, 3);

    // now you can use the distance values for blending directly. If the distance value is smaller this means that the value is worse (your vignetting becomes worse at the image border)
    cv::Mat mosaic = cv::Mat(input1.size(), input1.type(), cv::Scalar(0, 0, 0));
    for (int j = 0; j < mosaic.rows; ++j)
    for (int i = 0; i < mosaic.cols; ++i)
    {
        float a = dt1.at<float>(j, i);
        float b = dt2.at<float>(j, i);

        float alpha = a / (a + b); // distances are not between 0 and 1 but this value is. The "better" a is, compared to b, the higher is alpha.
        // actual blending: alpha*A + beta*B
        mosaic.at<cv::Vec3b>(j, i) = alpha*input1.at<cv::Vec3b>(j, i) + (1 - alpha)* input2.at<cv::Vec3b>(j, i);
    }

    cv::imshow("mosaic", mosaic);

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

Basically you compute the distance from your ROI border to the center of your objects and compute the alpha from both blending mask values. So if one image has a high distance from the border and other one a low distance from border, you prefer the pixel that is closer to the image center. It would be better to normalize those values for cases where the warped images aren't of similar size. But even better and more efficient is to precompute the blending masks and warp them. Best would be to know the vignetting of your optical system and choose and identical blending mask (typically lower values of the border).

From the previous code you'll get these results: ROI masks:

Blending masks (just as an impression, must be float matrices instead):

image mosaic:

這篇關于如何獲得正確的 alpha 值以完美融合兩個圖像?的文章就介紹到這了，希望我們推薦的答案對大家有所幫助，也希望大家多多支持html5模板網！