上篇文章中我们讲了“Sift+TPS”的配准方法：
我们知道，TPS薄板样条变换（简称TPS变换）与FFD变换均具有一定的局部形变特性。不过对于TPS变换，只要其输入的匹配点对中有一组发生变化，其整体的形变可能会发生很大改变，因此有时候TPS变换并不能很好地矫正局部形变。而对于FFD变换，每个点的形变坐标偏移只与其周围4*4个控制点的控制参数有关，与其它控制点无关，因此FFD变换的局部形变特性比TPS变换更好，然而“FFD+梯度下降”配准方法的输入参数通常很多，也即要优化的参数很多，优化参数时较容易陷入局部极值，且优化参数的过程很耗时：
基于此原因，有研究员提出了层次FFD的方法，我们前面已经讲过：
相比来说“Sift+TPS”的配准方法更稳定更快，为了使配准效果更好、更快、更稳定，因此后来又有人提出进一步把“层次FFD”与“Sift+TPS”结合起来，使配准效果更好、更快、更稳定。如下图所示：
下面上代码：
(1) 层次FFD代码
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void level_ffd_match(Mat img1, Mat img2, Mat &outffd)
-
{
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float min =
-0.01;
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float max =
0.01;
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//第一层
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int row_block_num =
8;
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int col_block_num =
8;
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Mat grid_points;
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init_bpline_para(img1, row_block_num, col_block_num, grid_points, min, max);
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Mat
out;
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bpline_match(img1, img2,
out, row_block_num, col_block_num, grid_points);
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-
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//第二层
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row_block_num =
16;
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col_block_num =
16;
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init_bpline_para(img1, row_block_num, col_block_num, grid_points, min, max);
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Mat out1;
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bpline_match(img1,
out, out1, row_block_num, col_block_num, grid_points);
-
-
-
-
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//第三层
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row_block_num =
30;
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col_block_num =
30;
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init_bpline_para(img1, row_block_num, col_block_num, grid_points, min, max);
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Mat out2;
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bpline_match(img1, out1, out2, row_block_num, col_block_num, grid_points);
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-
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out2.copyTo(outffd);
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}
(2) “Sift+TPS+层次FFD”代码
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void Sift_Tps_test(void)
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{
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Mat img1 = imread(
"lena.jpg", CV_LOAD_IMAGE_GRAYSCALE);
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Mat img2 = imread(
"lena_ffd.jpg", CV_LOAD_IMAGE_GRAYSCALE);
-
-
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imshow(
"image before", img1);
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imshow(
"image2 before", img2);
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-
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// SIFT - 检测关键点并在原图中绘制
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vector<KeyPoint> kp1, kp2;
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kp1 = detect_sift_block(img1,
50,
5,
5);
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kp2 = detect_sift_block(img2,
50,
5,
5);
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Mat outimg1, outimg2;
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drawKeypoints(img1, kp1, outimg1);
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drawKeypoints(img2, kp2, outimg2);
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imshow(
"image1 keypoints", outimg1);
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imshow(
"image2 keypoints", outimg2);
-
-
-
// SIFT - 特征向量提取
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Ptr<SiftDescriptorExtractor> extractor = SiftDescriptorExtractor::create();
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Mat descriptor1, descriptor2;
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extractor->compute(img1, kp1, descriptor1);
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extractor->compute(img2, kp2, descriptor2);
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-
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// 两张图像的特征匹配
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Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create(
"BruteForce");
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vector<DMatch> matches;
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Mat img_matches;
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matcher->match(descriptor1, descriptor2, matches);
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//计算匹配结果中距离最大和距离最小值
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double min_dist = matches[
0].distance, max_dist = matches[
0].distance;
-
for (
int m =
0; m < matches.size(); m++)
-
{
-
if (matches[m].distance < min_dist)
-
{
-
min_dist = matches[m].distance;
-
}
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if (matches[m].distance > max_dist)
-
{
-
max_dist = matches[m].distance;
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}
-
}
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cout <<
"min dist=" << min_dist <<
endl;
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cout <<
"max dist=" << max_dist <<
endl;
-
-
-
-
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vector<DMatch> goodMatches;
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for (
int i =
0; i < matches.size(); i++)
//筛选出较好的匹配点
-
{
-
if (matches[i].distance <
0.35*max_dist)
-
{
-
goodMatches.push_back(matches[i]);
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}
-
}
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cout <<
"The number of good matches:" << goodMatches.size() <<
endl;
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-
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//坐标转换为float类型
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vector <KeyPoint> good_kp1, good_kp2;
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for (
int i =
0; i < goodMatches.size(); i++)
-
{
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good_kp1.push_back(kp1[goodMatches[i].queryIdx]);
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good_kp2.push_back(kp2[goodMatches[i].trainIdx]);
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}
-
-
-
//坐标变换
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vector <Point2f> p01, p02;
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for (
int i =
0; i < goodMatches.size(); i++)
-
{
-
p01.push_back(good_kp1[i].pt);
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p02.push_back(good_kp2[i].pt);
-
}
-
-
-
vector<uchar> RANSACStatus;
//用以标记每一个匹配点的状态，等于0则为外点，等于1则为内点。
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findFundamentalMat(p01, p02, RANSACStatus, CV_FM_RANSAC,
4.5);
//p1 p2必须为float型
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vector<Point2f> f1_features_ok;
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vector<Point2f> f2_features_ok;
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for (
int i =
0; i < p01.size(); i++)
//剔除跟踪失败点
-
{
-
if (RANSACStatus[i])
-
{
-
f1_features_ok.push_back(p01[i]);
//基准图特征点
-
f2_features_ok.push_back(p02[i]);
//流动图特征点
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}
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}
-
-
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Mat Tx, Ty;
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Tps_TxTy(f1_features_ok, f2_features_ok, img2.rows, img2.cols, Tx, Ty);
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Mat tps_out;
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remap(img2, tps_out, Tx, Ty, INTER_CUBIC);
//Sift+TPS粗配准结果
-
-
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Mat ffd_out;
-
level_ffd_match(img1, tps_out, ffd_out);
//层次FFD细配准结果
-
-
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imshow(
"img2-img1",
abs(img2-img1));
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imshow(
"tps_out-img1",
abs(tps_out - img1));
-
imshow(
"ffd_out-img1",
abs(ffd_out - img1));
-
imshow(
"tps_out", tps_out);
-
imshow(
"ffd_out", ffd_out);
-
cv::waitKey(
0);
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}
运行上述代码，对扭曲的Lena图像进行配准，结果如下。由以下结果可知，细配准图像比粗配准图像的形变矫正效果好多了。因此结合两种方法的配准方法的配准效果更好。
原图
浮动图像
粗配准图像
细配准图像
浮动图像与参考图像的差值图
粗配准图像与参考图像的差值图
细配准图像与参考图像的差值图
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转载：https://blog.csdn.net/shandianfengfan/article/details/114422615
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