首先，抱歉我的英语不好。我会尽力表达我的问题。 我正在做一个项目，包括两个图像对齐。我所做的只是检测关键点，匹配这些点并估计这两个图像之间的转换。 这是我的代码：
static void target_region_warping(
Mat IN template_image,
Mat IN input_image,
Mat OUT &warped_image,
int IN method
)
{
vector<KeyPoint> kpt1, kpt2;
vector<Point2f> points1, points2;
Mat desc1, desc2;
vector<Point2f> points, points_transformed;
vector<vector<DMatch> > matches1, matches2;
vector<DMatch> sym_matches, fm_matches;
Mat im_show;
float x, y;
Mat fundemental;
// To avoid NaN's when best match has zero distance we will use inversed ratio.
const float minRatio = 1.0f / 1.5f;
// match scheme, sift + ransac
Ptr<xfeatures2d::SIFT> sift = xfeatures2d::SIFT::create( 1000, 3, 0.004, 20 );
Ptr<flann::IndexParams> indexParams = makePtr<flann::KDTreeIndexParams>(5); // instantiate LSH index parameters
Ptr<flann::SearchParams> searchParams = makePtr<flann::SearchParams>(50); // instantiate flann search parameters
Ptr<DescriptorMatcher> matcher = makePtr<FlannBasedMatcher>(indexParams, searchParams);
sift->detectAndCompute( template_image, noArray(), kpt1, desc1 );
sift->detectAndCompute( input_image, noArray(), kpt2, desc2 );
// step1: match and remove outliers using ratio
// KNN match will return 2 nearest matches for each query descriptor
matcher->knnMatch( desc1, desc2, matches1, 2 );
// for all matches
for ( std::vector<std::vector<cv::DMatch>>::iterator matchIterator= matches1.begin();
matchIterator!= matches1.end(); ++matchIterator )
{
// if 2 NN has been identified
if (matchIterator->size() > 1)
{
// check distance ratio
if ( (*matchIterator)[0].distance /
(*matchIterator)[1].distance > minRatio)
{
matchIterator->clear(); // remove match
}
}
else { // does not have 2 neighbours
matchIterator->clear(); // remove match
}
}
#ifdef TARGET_SHOW
drawMatches( template_image, kpt1, input_image, kpt2, matches1, im_show );
namedWindow( "SIFT matches: image1 -> image2", WINDOW_AUTOSIZE );
imshow( "SIFT matches: image1 -> image2", im_show );
#endif
//step2: image2 -> image1
matcher->knnMatch( desc2, desc1, matches2, 2 );
for ( std::vector<std::vector<cv::DMatch>>::iterator matchIterator= matches2.begin();
matchIterator!= matches2.end(); ++matchIterator )
{
// if 2 NN has been identified
if (matchIterator->size() > 1)
{
// check distance ratio
if ( (*matchIterator)[0].distance/
(*matchIterator)[1].distance > minRatio)
{
matchIterator->clear(); // remove match
}
}
else { // does not have 2 neighbours
matchIterator->clear(); // remove match
}
}
//step3: symmetric matching scheme
// for all matches image 1 -> image 2
for ( vector< vector<DMatch> >::const_iterator matchIterator1= matches1.begin();
matchIterator1!= matches1.end(); ++matchIterator1 )
{
// ignore deleted matches
if (matchIterator1->size() < 2)
continue;
// for all matches image 2 -> image 1
for ( std::vector<std::vector<cv::DMatch>>::const_iterator matchIterator2= matches2.begin();
matchIterator2!= matches2.end(); ++matchIterator2 )
{
// ignore deleted matches
if (matchIterator2->size() < 2)
continue;
// Match symmetry test
if ( ( *matchIterator1)[0].queryIdx == ( *matchIterator2 )[0].trainIdx &&
( *matchIterator2)[0].queryIdx == ( *matchIterator1 )[0].trainIdx )
{
// add symmetrical match
sym_matches.push_back(
cv::DMatch( (*matchIterator1)[0].queryIdx,
(*matchIterator1)[0].trainIdx,
(*matchIterator1)[0].distance));
break; // next match in image 1 -> image 2
}
}
}
#ifdef TARGET_SHOW
drawMatches( template_image, kpt1, input_image, kpt2, sym_matches, im_show );
namedWindow( "SIFT matches: symmetric matching scheme", WINDOW_AUTOSIZE );
imshow( "SIFT matches: symmetric matching scheme", im_show );
#endif
// step4: Identify good matches using RANSAC
// Return fundemental matrix
// first, convert keypoints into Point2f
for ( std::vector<cv::DMatch>::const_iterator it = sym_matches.begin();
it!= sym_matches.end(); ++it )
{
// Get the position of left keypoints
x = kpt1[it->queryIdx].pt.x;
y = kpt1[it->queryIdx].pt.y;
points1.push_back( Point2f( x,y ) );
// Get the position of right keypoints
x = kpt2[it->trainIdx].pt.x;
y = kpt2[it->trainIdx].pt.y;
points2.push_back(cv::Point2f(x,y));
}
// Compute F matrix using RANSAC
std::vector<uchar> inliers(points1.size(),0);
fundemental = findHomography(
Mat(points1),
Mat(points2),
FM_RANSAC,
10,
inliers,
2000,
0.9999 );
// extract the surviving (inliers) matches
vector<uchar>::const_iterator itIn= inliers.begin();
vector<DMatch>::const_iterator itM= sym_matches.begin();
// for all matches
for ( ;itIn!= inliers.end(); ++itIn, ++itM)
{
if (*itIn)
{ // it is a valid match
fm_matches.push_back(*itM);
}
}
#ifdef TARGET_SHOW
drawMatches( template_image, kpt1, input_image, kpt2, fm_matches, im_show );
namedWindow( "SIFT matches: RANSAC matching scheme", WINDOW_AUTOSIZE );
imshow( "SIFT matches: RANSAC matching scheme", im_show );
#endif
// step5: warp image 1 to image 2
cv::warpPerspective( input_image, // input image
warped_image, // output image
fundemental, // homography
input_image.size(),
cv::WARP_INVERSE_MAP | cv::INTER_CUBIC ); // size of output image
}
我的代码中的step5有一些问题。也就是说，通过估计从template_image到input_image的转换得到矩阵“fundemental”。所以正确的调用方法应该是
// may I sign this "1"
cv::warpPerspective( template_image, // input image
warped_image, // output image
fundemental, // homography
input_image.size(),
cv::WARP_INVERSE_MAP | cv::INTER_CUBIC ); // size of output image
而不是
// I sign this "2"
cv::warpPerspective( input_image, // input image
warped_image, // output image
fundemental, // homography
input_image.size(),
cv::WARP_INVERSE_MAP | cv::INTER_CUBIC ); // size of output image
然而，实际上当我使用absdiff方法来测试这样的结果时：
// test method "1"
absdiff( warped_image, input_image, diff_image );
// test method "2"
absdiff( warped_image, template_image, diff_image );
我惊奇地发现错误的调用方法“2”产生了更好的结果，即“2”中的diff_image在“1”中有更多的零元素。 我不知道出了什么问题，在理解“findHomograhpy”方法时我有些错误吗？我需要一些帮助，谢谢！
答案 0 :(得分：1)
请尝试以下两个版本：
cv::warpPerspective( template_image, // input image
warped_image, // output image
fundemental, // homography
input_image.size(), // size of output image
cv::INTER_CUBIC ); // HERE, INVERSE FLAG IS REMOVED
和
cv::warpPerspective( template_image, // input image
warped_image, // output image
fundemental.inv(), // homography, HERE: INVERTED HOMOGRAPHY AS INPUT
input_image.size(), // size of output image
cv::WARP_INVERSE_MAP | cv::INTER_CUBIC );
标志cv::WARP_INVERSE_MAP表示已经传递了反转变换的openCV函数。
图像变形总是反向进行，因为您要确保每个输出图像的像素只有一个合法值。
因此，要从源图像到目标图像扭曲，您要么提供从源到目标图像的单应性，这意味着openCV将反转该变换，或者您提供从目标到源的单应性并且将openCV信号化为已经被反转
见
设置标志WARP_INVERSE_MAP时。否则，首先使用invert（）反转变换，然后将其放入上面的公式而不是M。