OpenCV SURF SIFT特征提取及RANSAC算法

参考了一下原文写的代码，加了些头文件可以成功运行。OpenCV2.4.13+VS2013

看到OpenCV2.4.6里面ORB特征提取算法也在里面了，套用给的SURF特征例子程序改为ORB特征一直提示错误，类型不匹配神马的，由于没有找到示例程序，只能自己找答案。

（ORB特征论文：ORB： an efficient alternative to SIFT or SURF.点击下载论文）

经过查找发现：

描述符数据类型有是float的，比如说SIFT，SURF描述符，还有是uchar的，比如说有ORB，BRIEF

对于float 匹配方式有：

FlannBased

BruteForce >

BruteForce >

BruteForce >

对于uchar有：

BruteForce

BruteForce

BruteForceMatcher< L2 > matcher;//改动的地方

BruteForceMatcher< L2 > matcher;//改动的地方

#include"opencv2/opencv.hpp"
#include"opencv2/features2d/features2d.hpp"
#include"opencv2/highgui/highgui.hpp"
#include"opencv2/core/core.hpp"
#include"opencv2/calib3d/calib3d.hpp"
#include"opencv2/nonfree/nonfree.hpp"
#include
using namespace cv;
using namespace std;






int main(int argc, char** argv)
{
initModule_nonfree();//初始化模块，使用SIFT或SURF时用到      
Ptr detector = FeatureDetector::create("SURF");//创建SIFT特征检测器，可改成SURF/ORB     
Ptr descriptor_extractor = DescriptorExtractor::create("SURF");//创建特征向量生成器，可改成SURF/ORB     
Ptr descriptor_matcher = DescriptorMatcher::create("BruteForce");//创建特征匹配器       
if (detector.empty() || descriptor_extractor.empty())
cout << "fail to create detector!";


//读入图像       
Mat img1 = imread("E:\\卫星机械臂项目\\测试图像\\模拟工件\\模板\\2模板.jpg");
Mat img2 = imread("E:\\卫星机械臂项目\\测试图像\\模拟工件\\目标工件\\尺寸减半\\1 (7).jpg");


Size imgSize(320, 240);
resize(img1, img1, imgSize);
resize(img2, img2, imgSize);


//特征点检测       
double t = getTickCount();//当前滴答数       
vector m_LeftKey, m_RightKey;
detector->detect(img1, m_LeftKey);//检测img1中的SIFT特征点，存储到m_LeftKey中       
detector->detect(img2, m_RightKey);
cout << "图像1特征点个数:" << m_LeftKey.size() << endl;
cout << "图像2特征点个数:" << m_RightKey.size() << endl;


//根据特征点计算特征描述子矩阵，即特征向量矩阵       
Mat descriptors1, descriptors2;
descriptor_extractor->compute(img1, m_LeftKey, descriptors1);
descriptor_extractor->compute(img2, m_RightKey, descriptors2);
t = ((double)getTickCount() - t) / getTickFrequency();
cout << "SIFT算法用时：" << t << "秒" << endl;


cout << "图像1特征描述矩阵大小：" << descriptors1.size()
<< "，特征向量个数：" << descriptors1.rows << "，维数：" << descriptors1.cols << endl;
cout << "图像2特征描述矩阵大小：" << descriptors2.size()
<< "，特征向量个数：" << descriptors2.rows << "，维数：" << descriptors2.cols << endl;


//画出特征点       
Mat img_m_LeftKey, img_m_RightKey;
drawKeypoints(img1, m_LeftKey, img_m_LeftKey, Scalar::all(-1), 0);
drawKeypoints(img2, m_RightKey, img_m_RightKey, Scalar::all(-1), 0);
//imshow("Src1",img_m_LeftKey);       
//imshow("Src2",img_m_RightKey);       


//特征匹配       
vector matches;//匹配结果       
descriptor_matcher->match(descriptors1, descriptors2, matches);//匹配两个图像的特征矩阵       
cout << "Match个数：" << matches.size() << endl;


//计算匹配结果中距离的最大和最小值       
//距离是指两个特征向量间的欧式距离，表明两个特征的差异，值越小表明两个特征点越接近       
double max_dist = 0;
double min_dist = 100;
for (int i = 0; i {
double dist = matches[i].distance;
if (dist < min_dist) min_dist = dist;
if (dist > max_dist) max_dist = dist;
}
cout << "最大距离：" << max_dist << endl;
cout << "最小距离：" << min_dist << endl;


//筛选出较好的匹配点       
vector goodMatches;
for (int i = 0; i {
if (matches[i].distance < 0.6 * max_dist)
{
goodMatches.push_back(matches[i]);
}
}
cout << "goodMatch个数：" << goodMatches.size() << endl;


//画出匹配结果       
Mat img_matches;
//红色连接的是匹配的特征点对，绿色是未匹配的特征点       
drawMatches(img1, m_LeftKey, img2, m_RightKey, goodMatches, img_matches,
Scalar::all(-1)/*CV_RGB(255,0,0)*/, CV_RGB(0, 255, 0), Mat(), 2);
namedWindow("MatchSIFT", 0);
imshow("MatchSIFT", img_matches);
IplImage result = img_matches;


waitKey(10);




//RANSAC匹配过程     
vector m_Matches = goodMatches;
// 分配空间     
int ptCount = (int)m_Matches.size();


if (ptCount<10)
{
cout << "没有找到足够的匹配点" << endl;
waitKey(0);
return 0;
}
Mat p1(ptCount, 2, CV_32F);
Mat p2(ptCount, 2, CV_32F);


// 把Keypoint转换为Mat     
Point2f pt;
for (int i = 0; i {
pt = m_LeftKey[m_Matches[i].queryIdx].pt;
p1.at(i, 0) = pt.x;
p1.at(i, 1) = pt.y;


pt = m_RightKey[m_Matches[i].trainIdx].pt;
p2.at(i, 0) = pt.x;
p2.at(i, 1) = pt.y;
}


// 用RANSAC方法计算F     
Mat m_Fundamental;
vector m_RANSACStatus;       // 这个变量用于存储RANSAC后每个点的状态     
findFundamentalMat(p1, p2, m_RANSACStatus, FM_RANSAC);


// 计算野点个数     


int OutlinerCount = 0;
for (int i = 0; i {
if (m_RANSACStatus[i] == 0)    // 状态为0表示野点     
{
OutlinerCount++;
}
}
int InlinerCount = ptCount - OutlinerCount;   // 计算内点     
cout << "内点数为：" << InlinerCount << endl;




// 这三个变量用于保存内点和匹配关系     
vector m_LeftInlier;
vector m_RightInlier;
vector m_InlierMatches;


m_InlierMatches.resize(InlinerCount);
m_LeftInlier.resize(InlinerCount);
m_RightInlier.resize(InlinerCount);
InlinerCount = 0;
float inlier_minRx = img1.cols;        //用于存储内点中右图最小横坐标，以便后续融合     


for (int i = 0; i {
if (m_RANSACStatus[i] != 0)
{
m_LeftInlier[InlinerCount].x = p1.at(i, 0);
m_LeftInlier[InlinerCount].y = p1.at(i, 1);
m_RightInlier[InlinerCount].x = p2.at(i, 0);
m_RightInlier[InlinerCount].y = p2.at(i, 1);
m_InlierMatches[InlinerCount].queryIdx = InlinerCount;
m_InlierMatches[InlinerCount].trainIdx = InlinerCount;


if (m_RightInlier[InlinerCount].x

InlinerCount++;
}
}


// 把内点转换为drawMatches可以使用的格式     
vector key1(InlinerCount);
vector key2(InlinerCount);
KeyPoint::convert(m_LeftInlier, key1);
KeyPoint::convert(m_RightInlier, key2);


// 显示计算F过后的内点匹配     
Mat OutImage;
drawMatches(img1, key1, img2, key2, m_InlierMatches, OutImage);
cvNamedWindow("Match features", 1);
cvShowImage("Match features", &IplImage(OutImage));
waitKey(10);


cvDestroyAllWindows();


//矩阵H用以存储RANSAC得到的单应矩阵     
Mat H = findHomography(m_LeftInlier, m_RightInlier, RANSAC);


//存储左图四角，及其变换到右图位置     
std::vector obj_corners(4);
obj_corners[0] = Point(0, 0); obj_corners[1] = Point(img1.cols, 0);
obj_corners[2] = Point(img1.cols, img1.rows); obj_corners[3] = Point(0, img1.rows);
std::vector scene_corners(4);
perspectiveTransform(obj_corners, scene_corners, H);


//画出变换后图像位置     
Point2f offset((float)img1.cols, 0);
line(OutImage, scene_corners[0] + offset, scene_corners[1] + offset, Scalar(0, 255, 0), 4);
line(OutImage, scene_corners[1] + offset, scene_corners[2] + offset, Scalar(0, 255, 0), 4);
line(OutImage, scene_corners[2] + offset, scene_corners[3] + offset, Scalar(0, 255, 0), 4);
line(OutImage, scene_corners[3] + offset, scene_corners[0] + offset, Scalar(0, 255, 0), 4);
namedWindow("Good Matches & Object detection", 0);
imshow("Good Matches & Object detection", OutImage);


waitKey(10);
imwrite("warp_position.jpg", OutImage);




int drift = scene_corners[1].x;                                                        //储存偏移量     


//新建一个矩阵存储配准后四角的位置     
int width = int(max(abs(scene_corners[1].x), abs(scene_corners[2].x)));
int height = img1.rows;                                                                  //或者：int height = int(max(abs(scene_corners[2].y), abs(scene_corners[3].y)));     
float origin_x = 0, origin_y = 0;
if (scene_corners[0].x<0) {
if (scene_corners[3].x<0) origin_x += min(scene_corners[0].x, scene_corners[3].x);
else origin_x += scene_corners[0].x;
}
width -= int(origin_x);
if (scene_corners[0].y<0) {
if (scene_corners[1].y) origin_y += min(scene_corners[0].y, scene_corners[1].y);
else origin_y += scene_corners[0].y;
}
//可选：height-=int(origin_y);     
Mat imageturn = Mat::zeros(width, height, img1.type());


//获取新的变换矩阵，使图像完整显示     
for (int i = 0; i<4; i++) { scene_corners[i].x -= origin_x; }     //可选：scene_corners[i].y -= (float)origin_y; }     
Mat H1 = getPerspectiveTransform(obj_corners, scene_corners);


//进行图像变换，显示效果     
warpPerspective(img1, imageturn, H1, Size(width, height));
namedWindow("image_Perspective", 0);
imshow("image_Perspective", imageturn);
waitKey(10);




//图像融合     
int width_ol = width - int(inlier_minRx - origin_x);
int start_x = int(inlier_minRx - origin_x);
cout << "width: " << width << endl;
cout << "img1.width: " << img1.cols << endl;
cout << "start_x: " << start_x << endl;
cout << "width_ol: " << width_ol << endl;


uchar* ptr = imageturn.data;
double alpha = 0, beta = 1;
for (int row = 0; row {
ptr = imageturn.data + row*imageturn.step + (start_x)*imageturn.elemSize();
for (int col = 0; col {
uchar* ptr_c1 = ptr + imageturn.elemSize1();
uchar*  ptr_c2 = ptr_c1 + imageturn.elemSize1();
uchar* ptr2 = img2.data + row*img2.step + (col + int(inlier_minRx))*img2.elemSize();
uchar* ptr2_c1 = ptr2 + img2.elemSize1();
uchar* ptr2_c2 = ptr2_c1 + img2.elemSize1();


alpha = double(col) / double(width_ol); beta = 1 - alpha;


if (*ptr == 0 && *ptr_c1 == 0 && *ptr_c2 == 0)
{
*ptr = (*ptr2);
*ptr_c1 = (*ptr2_c1);
*ptr_c2 = (*ptr2_c2);
}


*ptr = (*ptr)*beta + (*ptr2)*alpha;
*ptr_c1 = (*ptr_c1)*beta + (*ptr2_c1)*alpha;
*ptr_c2 = (*ptr_c2)*beta + (*ptr2_c2)*alpha;


ptr += imageturn.elemSize();
}
}
namedWindow("image_overlap", 0);
imshow("image_overlap", imageturn);     
//waitKey(0);     


Mat img_result = Mat::zeros(height, width + img2.cols - drift, img1.type());
uchar* ptr_r = imageturn.data;


for (int row = 0; row {
ptr_r = img_result.data + row*img_result.step;


for (int col = 0; col {
uchar* ptr_rc1 = ptr_r + imageturn.elemSize1();
uchar* ptr_rc2 = ptr_rc1 + imageturn.elemSize1();


uchar* ptr = imageturn.data + row*imageturn.step + col*imageturn.elemSize();
uchar* ptr_c1 = ptr + imageturn.elemSize1();
uchar* ptr_c2 = ptr_c1 + imageturn.elemSize1();


*ptr_r = *ptr;
*ptr_rc1 = *ptr_c1;
*ptr_rc2 = *ptr_c2;


ptr_r += img_result.elemSize();
}


ptr_r = img_result.data + row*img_result.step + imageturn.cols*img_result.elemSize();
for (int col = imageturn.cols; col {
uchar* ptr_rc1 = ptr_r + imageturn.elemSize1();
uchar*  ptr_rc2 = ptr_rc1 + imageturn.elemSize1();


uchar* ptr2 = img2.data + row*img2.step + (col - imageturn.cols + drift)*img2.elemSize();
uchar* ptr2_c1 = ptr2 + img2.elemSize1();
uchar* ptr2_c2 = ptr2_c1 + img2.elemSize1();


*ptr_r = *ptr2;
*ptr_rc1 = *ptr2_c1;
*ptr_rc2 = *ptr2_c2;


ptr_r += img_result.elemSize();
}
}
namedWindow("image_result", 0);
imshow("image_result", img_result);    
//imwrite("final_result.jpg",img_result);    
waitKey(0);


return 0;
}