opencv - 如何使用 Homography 在 OpenCV 中转换图片？

Question

我有两张图片(A 和 B)，一张与另一张略有扭曲，它们之间存在平移、旋转和比例差异(例如，这些图片:)

---

Ssoooooooo 我需要在图片 B 中应用一种变换，以便补偿存在的失真/平移/旋转，使两张图片具有相同的大小、方向且没有平移

我已经提取了点并找到了 Homography，如下所示。但是我不知道如何使用 Homography 将 Mat img_B 转换为看起来像 Mat img_A。有什么想法吗？

//-- Localize the object from img_1 in img_2
std::vector<Point2f> obj;
std::vector<Point2f> scene;

for (unsigned int i = 0; i < good_matches.size(); i++) {
    //-- Get the keypoints from the good matches
    obj.push_back(keypoints_object[good_matches[i].queryIdx].pt);
    scene.push_back(keypoints_scene[good_matches[i].trainIdx].pt);
}

Mat H = findHomography(obj, scene, CV_RANSAC);

干杯，

Answer 1

这个问题不需要单应性。您可以改为计算仿射变换。但是，如果您确实想将单应性用于其他目的，您可以查看下面的代码。它是从 this 复制的关于 homography 的详细文章.

C++ 示例

// pts_src and pts_dst are vectors of points in source 
// and destination images. They are of type vector<Point2f>. 
// We need at least 4 corresponding points. 

Mat h = findHomography(pts_src, pts_dst);

// The calculated homography can be used to warp 
// the source image to destination. im_src and im_dst are
// of type Mat. Size is the size (width,height) of im_dst. 

warpPerspective(im_src, im_dst, h, size);

Python 示例

'''
pts_src and pts_dst are numpy arrays of points
in source and destination images. We need at least 
4 corresponding points. 
''' 
h, status = cv2.findHomography(pts_src, pts_dst)

''' 
The calculated homography can be used to warp 
the source image to destination. Size is the 
size (width,height) of im_dst
'''

im_dst = cv2.warpPerspective(im_src, h, size)