在 C++ 中计算相似图像的偏移/倾斜/旋转

Question

我同时拍摄了多张图像，从同一起始位置指向同一方向。然而，仍然存在轻微的偏移，因为拍摄照片时这些相机并不位于完全相同的位置。我正在寻找一种方法来计算将一个图像与另一个图像匹配所需的最佳平移/剪切/倾斜/旋转，以便它们（几乎）完美地重叠。

这些图像采用 .raw 格式，我一次读取 16 位。

我被建议（由我的不是程序员的雇主[顺便说一句，我是实习生]）获取源图像的​​一部分（不在边缘）并强力搜索具有高像素的相同大小的部分。数据值的相关性。我希望有一种更少浪费的算法。

Answer 1

这是一个简短的代码，可以实现您想要的功能（我使用 openCV 2.2）：

1. 假设您有 2 个图像：srcImage，dstImage，并且您想要对齐它们
2. 代码非常简单。将其用作算法的基础。

代码：

// Detect special points on each image that can be corresponded    
Ptr<FeatureDetector>  detector = new SurfFeatureDetector(2000);  // Detector for features

vector<KeyPoint> srcFeatures;   // Detected key points on first image
vector<KeyPoint> dstFeatures;
detector->detect(srcImage,srcFeatures);
detector->detect(dstImage,dstFeatures); 

// Extract descriptors of the features
SurfDescriptorExtractor extractor;  
Mat projDescriptors, camDescriptors;
extractor.compute(srcImage,  srcFeatures, srcDescriptors);
extractor.compute(dstImage , dstFeatures, dstDescriptors );

// Match descriptors of 2 images (find pairs of corresponding points)
BruteForceMatcher<L2<float>> matcher;       // Use FlannBasedMatcher matcher. It is better
vector<DMatch> matches;
matcher.match(srcDescriptors, dstDescriptors, matches);     

// Extract pairs of points
vector<int> pairOfsrcKP(matches.size()), pairOfdstKP(matches.size());
for( size_t i = 0; i < matches.size(); i++ ){
    pairOfsrcKP[i] = matches[i].queryIdx;
    pairOfdstKP[i] = matches[i].trainIdx;
}

vector<Point2f> sPoints; KeyPoint::convert(srcFeatures, sPoints,pairOfsrcKP);
vector<Point2f> dPoints; KeyPoint::convert(dstFeatures, dPoints,pairOfdstKP);

// Matched pairs of 2D points. Those pairs will be used to calculate homography
Mat src2Dfeatures;
Mat dst2Dfeatures;
Mat(sPoints).copyTo(src2Dfeatures);
Mat(dPoints).copyTo(dst2Dfeatures);

// Calculate homography
vector<uchar> outlierMask;
Mat H;
H = findHomography( src2Dfeatures, dst2Dfeatures, outlierMask, RANSAC, 3);

// Show the result (only for debug)
if (debug){
   Mat outimg;
   drawMatches(srcImage, srcFeatures,dstImage, dstFeatures, matches, outimg, Scalar::all(-1), Scalar::all(-1),
               reinterpret_cast<const vector<char>&> (outlierMask));
   imshow("Matches: Src image (left) to dst (right)", outimg);
   cvWaitKey(0);
}

// Now you have the resulting homography. I mean that:  H(srcImage) is alligned to dstImage. Apply H using the below code
Mat AlignedSrcImage;
warpPerspective(srcImage,AlignedSrcImage,H,dstImage.Size(),INTER_LINEAR,BORDER_CONSTANT);
Mat AlignedDstImageToSrc;
warpPerspective(dstImage,AlignedDstImageToSrc,H.inv(),srcImage.Size(),INTER_LINEAR,BORDER_CONSTANT);

Answer 2

图像是否是从相同的位置站立拍摄的，但只是旋转了一点，导致它们没有正确对齐？如果是这样，那么图像通过 同应性 相关 - 即投影变换。给定图像之间的一组对应关系（至少需要 4 对），查找单应性的标准方法是使用 DLT 算法。

Answer 3

使用以下代码避免链接器错误：

#include "cv.h"
#include "highgui.h"
using namespace cv;

// Directives to linker to include openCV lib files.
#pragma comment(lib, "opencv_core220.lib") 
#pragma comment(lib, "opencv_highgui220.lib") 
#pragma comment(lib, "opencv_contrib220.lib") 
#pragma comment(lib, "opencv_imgproc220.lib") 
#pragma comment(lib, "opencv_gpu220.lib") 
#pragma comment(lib, "opencv_video220.lib") 
#pragma comment(lib, "opencv_legacy220.lib") 

#pragma comment(lib, "opencv_ml220.lib") 
#pragma comment(lib, "opencv_objdetect220.lib") 
#pragma comment(lib, "opencv_ffmpeg220.lib") 

#pragma comment(lib, "opencv_flann220.lib") 
#pragma comment(lib, "opencv_features2d220.lib") 
#pragma comment(lib, "opencv_calib3d220.lib") 

// Your code here...
int main(void){
    Mat B = Mat:eye(3,3,CV_8U);
    return -1;
}