Calculate offset/skew/rotation of similar images in C++ -


i have multiple images taken simultaneously pointing @ same direction same starting location. however, there still slight offset because these cameras not in exact same place when picture taking. i'm looking way calculate optimal translation/shear/skew/rotation needed apply match 1 image overlay (almost) perfectly.

the images in .raw format reading in 16 bits @ time.

i have been suggested (by employer not programmer [i'm intern btw]) take portion of source image (not @ edges) , brute-force search same-sized portion high correlation in data values. i'm hoping there less-wasteful algorithm.

here short code want (i use opencv 2.2):

  1. suppose have 2 images: srcimage,dstimage, , want align them
  2. the code simple. use basis algorithm.

code:

// detect special points on each image can corresponded     ptr<featuredetector>  detector = new surffeaturedetector(2000);  // detector features  vector<keypoint> srcfeatures;   // detected key points on first image vector<keypoint> dstfeatures; detector->detect(srcimage,srcfeatures); detector->detect(dstimage,dstfeatures);   // extract descriptors of 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. better vector<dmatch> matches; matcher.match(srcdescriptors, dstdescriptors, matches);       // extract pairs of points vector<int> pairofsrckp(matches.size()), pairofdstkp(matches.size()); for( size_t = 0; < 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. pairs used 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 result (only 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) dst (right)", outimg);    cvwaitkey(0); }  // have resulting homography. mean that:  h(srcimage) alligned dstimage. apply h using 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);

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