《opencv学习》之特征检测与匹配

这几天学习SURF特征检测，直接看的视频和书本有点吃不消，现在是基本看懂了，如果写博客记录没有必要，因为网上都差不多，笔记都在书上了，以下是个人认为比较浅显易懂的文章，当然海有很多好文章我没看到。

看第一篇入门就可以，后面讲的不是很好： http://blog.csdn.net/jwh_bupt/article/details/7621681

harris： 　　　　　　　　　　　　　　 http://www.cnblogs.com/ronny/p/4009425.html

Harr：　　　　　　　　　　　　　　　　 http://blog.csdn.net/zouxy09/article/details/7929570/

Box Filters：　　　　　　　　　　　　 http://blog.csdn.net/lanbing510/article/details/28696833

SIFT：　　　　　　　　　　　　　　　　 http://blog.csdn.net/abcjennifer/article/details/7639681/ 　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　 http://blog.csdn.net/pi9nc/article/details/23302075

Harr小波特征：　　　　　　　　　　　　场合不同这个表示也不同，比如在SURF中表示dx、dy，当然也可以表示成其它样子，得看Harr的内核

SURF：　　　　　　　　　　　　　　　看一下毛星云的那本书，其实网上说的差不多。

ORB：　　　　　　　　　　　　　　　　 http://www.aiuxian.com/article/p-1728722.html，里面包括FAST特征点、BRIEF描述子

HOG:　　　　　　　　　　　　　　　　　这个算法相对比较上面容易理解。

LBP：　　　　　　　　　　　　　　　　 http://blog.csdn.net/quincuntial/article/details/50541815这个算法可以手写实现，上面基本自己都实现不了（难度很大）

双线性插值：　　　　　　　　　　　　　百度百科有（先有三角函数计算坐标位置，然后用双线性插值计算像素值）

LBP算子

 经典LBP算法：
 1 int main(int argc, char**argv)
 2 {
 3     Mat input_image, Middle_image,threshold_image;
 4     input_image = imread("1.jpg");
 5     if (input_image.data == NULL) {
 6         return -1; cout << "can't open image.../";
 7     }
 8     cvtColor(input_image, input_image, CV_BGR2GRAY);
 9     Mat output_image;
10     output_image.create(Size(input_image.rows - 2, input_image.cols - 2), input_image.type());
11     output_image.setTo(0, Mat());
12     int width = input_image.cols * input_image.channels();
13     int heigth = input_image.rows;
14     for (size_t i = 1; i < heigth - 1; i++)
15     {
16         //uchar *ptr = input_image.ptr<uchar>(i);
17         for (size_t j = 1; j < width - 1; j++)
18         {
19             uchar code = 0;
20             uchar center = input_image.at<uchar>(i, j);
21             code |= (input_image.at<uchar>(i - 1, j - 1) >= center) << 7;
22             code |= (input_image.at<uchar>(i - 1, j ) >= center) << 6;
23             code |= (input_image.at<uchar>(i - 1, j + 1) >= center) << 5;
24             code |= (input_image.at<uchar>(i , j - 1) >= center) << 4;
25             code |= (input_image.at<uchar>(i , j + 1) >= center) << 3;
26             code |= (input_image.at<uchar>(i + 1, j - 1) >= center) << 2;
27             code |= (input_image.at<uchar>(i + 1, j ) >= center) << 1;
28             code |= (input_image.at<uchar>(i + 1, j + 1) >= center) << 0;
29             output_image.at<uchar>(i - 1, j - 1) = code;
30         }
31     }
32     imshow("LineImage", input_image);
33     imshow("output_image", output_image);
34 
35     waitKey(0);
36     return 0;
37 }

 CLBP：
 1 #include <opencv2/opencv.hpp>
 2 #include <iostream>
 3 
 4 using namespace cv;
 5 using namespace std;
 6 
 7 const int neighbor = 8;//圆周上共几个像素
 8 const int radius = 3;
 9 
10 int main(int argc, char**argv)
11 {
12     Mat input_image, output_image;
13     input_image = imread("1.jpg");
14 
15     if (input_image.data == NULL) {
16         return -1; cout << "can't open image.../";
17     }
18     cvtColor(input_image, input_image, CV_BGR2GRAY);
19     //Mat output_image = Mat::zeros(Size(input_image.cols - 2 * 30, input_image.rows - 2 * 30), CV_8UC1);
20     output_image.create(Size(input_image.cols - 2* radius, input_image.rows - 2*radius), CV_8UC1);
21     output_image.setTo(0);
22      int width = input_image.cols;
23      int height = input_image.rows;
24     
25     
26     for (size_t n = 0; n < neighbor; n++)
27     {
28         //-----计算圆边长点的坐标（这里x、y具体谁用cos和sin的结果一样，看后来计算双线性插值自己怎么理解了）
29         float x = static_cast<float>(radius)  * cos(2.0 * CV_PI*n / static_cast<float>(neighbor));
30         float y = static_cast<float>(-radius) * sin(2.0 * CV_PI*n / static_cast<float>(neighbor));//这里加不加负号效果一样，因为是点的顺序而已。
31         //-----计算圆边上点的像素值，用双线性插值方法
32         int fx = static_cast<int>(floor(x));
33         int fy = static_cast<int>(floor(y));//上采样
34         int cx = static_cast<int>(ceil(x));
35         int cy = static_cast<int>(ceil(y));//下采样
36         
37         float dx = x - fx;
38         float dy = y - fy;
39 
40         float w1 = (1 - dx)*(1 - dy);
41         float w2 = dx*(1 - dy);
42         float w3 = (1 - dx)*dy;
43         float w4 = dx*dy;
44 
45         for (size_t i = radius; i < height - radius; i++)
46         {
47             for (size_t j = radius; j < width - radius; j++)
48             {
49                 float p1 = input_image.at<uchar>(i + fy, j + fx);
50                 float p2 = input_image.at<uchar>(i + fy, j + cx);
51                 float p3 = input_image.at<uchar>(i + cy, j + fx);
52                 float p4 = input_image.at<uchar>(i + cy, j + cx);
53 
54                 float Angle_data = p1*w1 + p2*w2 + p3*w3 + p4*w4;
55                 output_image.at<uchar>(i - radius, j - radius) |= (((input_image.at<uchar>(i, j) >= Angle_data) && (std::abs(Angle_data-input_image.at<uchar>(i,j)) > numeric_limits<float>::epsilon())) << n);
56             }
57 
58         }
59     }
60     
61     imshow("LineImage", input_image);
62     imshow("output_image", output_image);
63 
64     waitKey(0);
65     return 0;
66 }

LBP后面还有很多知识，现在不学习那么深，只是了解一下，以后用到再继续深入。

SIFT算子

 1 #if 1
 2 #include <opencv2/opencv.hpp>
 3 #include <opencv2/xfeatures2d.hpp>
 4 #include <iostream>
 5 #include "math.h"
 6 
 7 using namespace cv::xfeatures2d;
 8 using namespace cv;
 9 using namespace std;
10 
11 void calSIFTFeatureAndCompare(Mat& src1, Mat& src2);
12 
13 int main(int argc, char**argv)
14 {
15     Mat input_image1, input_image2;
16     input_image1 = imread("hand1.jpg");
17     input_image2 = imread("hand2.jpg");
18 
19     if (!input_image1.data && !input_image2.data) {
20         return -1; cout << "can't open image.../";
21     }
22     calSIFTFeatureAndCompare(input_image1, input_image2);
23     /*imshow("input_image1", input_image1);
24     imshow("input_image2", input_image2);*/
25 
26     waitKey(0);
27     return 0;
28 }
29 //-------------特征点：如同harris等检测的点一样（当然加了更多的约束）--------------//
30 //-------------描述子：点形成组织性，有方向和区域等，更能代表整副图像（也就是在特征点上加了更多的约束） --------------//
31 void calSIFTFeatureAndCompare(Mat& src1,Mat& src2)
32 {
33     Mat grayMat1, grayMat2;
34     cvtColor(src1, grayMat1, CV_BGR2GRAY);
35     cvtColor(src2, grayMat2, CV_BGR2GRAY);
36     normalize(grayMat1, grayMat1, 0, 255, CV_MINMAX);
37     normalize(grayMat2, grayMat2, 0, 255, CV_MINMAX);
38     /*//---定义SIFT描述子
39     SiftFeatureDetector        detector; //计算特征点
40     SiftDescriptorExtractor extractor;//计算描述子
41     //---特征点检测
42     vector<KeyPoint> keypoints1;
43     vector<KeyPoint> keypoints2;
44     detector.detect(grayMat1, keypoints1, Mat());
45     detector.detect(grayMat2, keypoints2, Mat());
46     //---计算特征点描述子
47     Mat descriptors1, descriptors2;
48     extractor.compute(grayMat1, keypoints1, descriptors1);
49     extractor.compute(grayMat2, keypoints2, descriptors2);*/
50     //SURF* detector = SURF::create(100);//那道理说是可以的，但是奔溃，不知道原因
51     Ptr<SURF> detector = SURF::create(500);//这个参数hessianThreshold为海深矩阵的阈值T，看SIFT原理就可
52     vector<KeyPoint> keypoints1;
53     vector<KeyPoint> keypoints2;
54     Mat descriptors1, descriptors2;
55     //---特征点和描述子一起检测了
56     detector->detectAndCompute(grayMat1, Mat(), keypoints1, descriptors1);
57     detector->detectAndCompute(grayMat2, Mat(), keypoints2, descriptors2);
58     //---特征点匹配
59     vector<DMatch> matches;//存储匹配结果的类，和keypoint类似
60     BFMatcher matcher(NORM_L1); //NORM_L1:代表SIFT，NORM_L2:代表SURF，默认SURF
61     matcher.match(descriptors1, descriptors2, matches);
62     Mat resultMatch;
63     drawMatches(grayMat1, keypoints1, grayMat2, keypoints2, matches, resultMatch);
64 }
65 
66 #endif

实际图片的匹配与寻找

在原图上截取的一个小图片进行测试---->>>>

　　　　　　　　　　　　　　　　　　　　本文利用SURF算子进行检测，利用FLANN进行匹配！

上代码：

 1 #if 1
 2 #include <opencv2/opencv.hpp>
 3 #include <opencv2/xfeatures2d.hpp>
 4 #include <iostream>
 5 #include "math.h"
 6 
 7 using namespace cv::xfeatures2d;
 8 using namespace cv;
 9 using namespace std;
10 
11 
12 int main(int argc, char**argv)
13 {
14     Mat input_image1, input_image2;
15     input_image1 = imread("2.jpg");
16     input_image2 = imread("1.jpg");
17 
18     if (!input_image1.data && !input_image2.data) {
19         return -1; cout << "can't open image.../";
20     }
21     //-------------keypoints and descriptor detection
22     const int minHessian = 400;
23     Ptr<SURF> detector = SURF::create(minHessian);
24     vector<KeyPoint> srcKeyPoint, dstKeyPoint;
25     Mat srcDescriptor, dstDescriptor;
26     detector->detectAndCompute(input_image1, Mat(), srcKeyPoint, srcDescriptor);
27     detector->detectAndCompute(input_image2, Mat(), dstKeyPoint, dstDescriptor);
28     //------------FLANN algorithm match
29     FlannBasedMatcher matcher;
30     vector<DMatch> matches;
31     matcher.match(srcDescriptor, dstDescriptor, matches);
32     Mat matchImage;
33     drawMatches(input_image1, srcKeyPoint, input_image2, dstKeyPoint, matches, matchImage);
34     //------------calculate best keypoint
35     double maxDistance = 0, minDistance = 10000;
36     for (size_t i = 0; i < matches.size(); i++)
37     {
38         maxDistance = maxDistance < matches[i].distance ? matches[i].distance : maxDistance;
39         minDistance = minDistance < matches[i].distance ? minDistance : matches[i].distance;
40     }
41     vector<DMatch> bestMatches;
42     for (size_t i = 0; i < matches.size(); i++)
43     {
44         if (matches[i].distance < 3 * minDistance)
45         {
46             bestMatches.push_back(matches[i]);
47         }
48     }
49     Mat bestMatchImage;
50     drawMatches(input_image1, srcKeyPoint, input_image2, dstKeyPoint, bestMatches, bestMatchImage);
51     //-----------Find four corner points from bestMatches
52     vector<Point2f> srcPoint;
53     vector<Point2f> dstPoint;
54     for (size_t i = 0; i < bestMatches.size(); i++)//save coordinate to srcPoint/dstPoint from bestMatches
55     {
56         srcPoint.push_back(srcKeyPoint[bestMatches[i].queryIdx].pt);
57         dstPoint.push_back(dstKeyPoint[bestMatches[i].trainIdx].pt);
58     }
59     Mat H = findHomography(srcPoint, dstPoint,CV_RANSAC);//detection perspective Mapping（H映射）
60     vector<Point2f> srcCorners(4);
61     vector<Point2f> dstCorners(4);
62     srcCorners[0] = Point(0, 0);
63     srcCorners[1] = Point(input_image1.cols, 0);
64     srcCorners[2] = Point(0, input_image1.rows);
65     srcCorners[3] = Point(input_image1.cols, input_image1.rows);
66     perspectiveTransform(srcCorners, dstCorners, H);//detection destination Corners
67     line(input_image2, dstCorners[0], dstCorners[1], Scalar(255, 255, 0), 2);
68     line(input_image2, dstCorners[0], dstCorners[2], Scalar(255, 255, 0), 2);
69     line(input_image2, dstCorners[1], dstCorners[3], Scalar(255, 255, 0), 2);
70     line(input_image2, dstCorners[2], dstCorners[3], Scalar(255, 255, 0), 2);
71     waitKey(0);
72     return 0;
73 }
74 
75 
76 #endif

posted on 2017-05-09 08:57 影醉阏轩窗阅读(886) 评论(0) 编辑收藏举报

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