3D视觉(四)：ORB特征提取与匹配
根据维基百科的定义，图像特征是一组与计算任务相关的信息，计算任务取决于具体的应用。简而言之，特征是图像信息的另一种表达形式。
数字图像在计算机中以灰度值矩阵的方式存储，所以最简单的单个图像像素也是一种特征。但是在视觉里程计中，我们希望特征点在相机运动中仍然保持稳定，而灰度值受光照、形变、物体材质的影响严重，在不同图像间变化非常大，不够稳定。
理想情况是，当场景和相机视角发生少量改变时，算法还能从图像中判断哪些地方是同一个点。所以仅凭灰度值是不够的，我们需要对图像提取特征点。
文章目录
一、ORB特征原理
特征点由关键点Key-point和描述子Descriptor两部分组成。例如，当我们说“在一张图像中计算SIFT特征点”时，是指“提取SIFT关键点、计算SIFT描述子”两件事情。
关键点是指该特征点在图像里的位置。描述子通常是一个向量，按照某种人为设计的方式，描述了该关键点周围像素的信息。描述子是按照“外观相似的特征应该有相似的描述子”的原则设计的，因此，只要两个特征点的描述子在向量空间上的距离相近，就可以认为它们是同样的特征点。
ORB特征由关键点和描述子两部分组成。它的关键点称为“Oriented FAST”，是一种改进的FAST角点，它的描述子称为BRIEF，是一种速度极快的二进制描述子。
1.1、FAST关键点
FAST是一种角点，主要检测局部像素灰度变化明显的地方，以速度快著称。它的思想是：如果一个像素与邻域的像素差别较大（过亮或过暗），那么它可能是角点。相比于其他角点检测算法，FAST只需比较像素亮度的大小，十分快捷。
FAST角点检测流程如下：
针对FAST角点不具有方向性和尺度的弱点，ORB添加了尺度和旋转描述。尺度不变性由构建图像金字塔，并在金字塔的每一层上检测角点来实现。而特征的旋转是由灰度质心法实现，计算每个区域块的特征方向向量，将区域块做对应旋转后，再提取相应的描述子。
在旋转方面，我们计算特征点附近的图像灰度质心。所谓质心，是指以图像块灰度值作为权重的中心。灰度质心法的具体步骤如下：
1.2、BRIEF描述子
BRIEF是一种二进制描述子，其描述向量由许多个0和1组成，这里0和1编码了关键点附近两个随机像素（比如p和q）的大小关系：如果p比q大，则取1，反之取0。如果我们取了128个这样的p和q，则最后可以得到128维由0和1组成的向量。
注意，虽然说p和q是随意选取的，实际操作中我们会采用一个固定的模板，从这个模板中选取对应的像素索引。这个模板是研究者们精心设计的，能保证提取出的BRIEF向量有较好的效果。
BRIEF使用了随机选点的比较，速度非常快，而且由于使用了二进制表达，存储起来也十分方便，适用于实时的图像匹配。
1.3、ORB特征匹配
特征匹配是视觉SLAM中极为关键的一步，它解决了SLAM中的数据关联问题(data association）,即确定当前看到的landmark与之前看到的landmark之间的对应关系。通过对图像与图像之间的描述子进行准确匹配，可以为后续姿态估计、优化等操作减轻大量负担。
然而由于图像特征的局部特性，误匹配的情况广泛存在，而且长期一来一直没有得到有效解决，目前已经称为视觉SLAM中制约性能提升的一大瓶颈。部分原因是场景中经常存在大量的重复纹理，使得特征描述非常相似，在这种情况下，仅利用局部特征解决误匹配是非常困难的。
二、算法流程
2.1、提取ORB特征的流程
step 1：提取FAST关键点，得到一系列landmark坐标位置。
step 2：对每个landmark标志点，选定patch，计算图像块的矩，得到旋转方向。
step 3：利用已经设计好的orb pattern，选取特定点对，得到二维点坐标。
step 4：根据旋转方向，对二维点坐标进行旋转，实现特征提取的旋转不变性。
step 5：比较二维点坐标位置的像素值，赋予0或1，重复步骤256次，计算得到BRIEF特征。
2.2、ORB特征匹配的流程
step 1：检测两张图片各自的Oriented FAST角点位置，得到keypoints_1、 keypoints_2。
step 2：根据角点位置，计算各自的BRIEF描述子，得到descriptors_1、descriptors_2。
step 3：对两幅图像中的BRIEF描述子进行匹配，基于Hamming距离，得到匹配点对matches。
step 4：对匹配点对做筛选剔除，保留下正确的匹配，得到good_matches。
三、实验过程
由双目摄像头拍摄得到两张图像：
分别提取各自的ORB特征，得到landmark位置和描述子：
进行特征匹配，得到对应点对：
四、源码
借助OpenCV函数的ORB特征提取：
#include <iostream>
#include <opencv2/core/core.hpp>
// core是opencv的主要头文件，包含数据结构，矩阵运算，数据变换，内存管理，文本、数学等功能
#include <opencv2/features2d/features2d.hpp>
// features2d模块，包含特征提取和匹配等功能
#include <opencv2/highgui/highgui.hpp>
// highgui模块，包含图形界面、视频图像处理等功能
#include <chrono>
// chrono是C++11新加入的方便时间日期操作的标准库，它既是相应的头文件名称，也是std命名空间下的一个子命名空间，所有时间日期相关定义均在std::chrono命名空间下
// 通过这个新的标准库，可以非常方便进行时间日期相关操作。
using namespace std;
using namespace cv;
// ORB特征匹配算法流程：
// 第1步：检测两张图片各自的Oriented FAST角点位置，得到keypoints_1, keypoints_2;
// 第2步：根据角点位置，计算各自的BRIEF描述子，得到descriptors_1, descriptors_2;
// 第3步：对两幅图像中的BRIEF描述子进行匹配，基于Hamming距离，得到匹配点对matches;
// 第4步：对匹配点对做筛选剔除，保留下正确的匹配，得到good_matches;
// 第5步：作图，绘制匹配结果
int main(int argc, char **argv) {
// 读取图像
Mat img_1 = imread("../left_3.jpg");
Mat img_2 = imread("../right_3.jpg");
assert(img_1.data != nullptr && img_2.data != nullptr);
// 创建vector容器，存储特征点
std::vector<KeyPoint> keypoints_1, keypoints_2;
// 创建Mat矩阵，存储特征点的描述子
Mat descriptors_1, descriptors_2;
// 创建特征检测器、描述子提取器、点对匹配器
Ptr<FeatureDetector> detector = ORB::create();
Ptr<DescriptorExtractor> descriptor = ORB::create();
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");
// 第一步:检测 Oriented FAST 角点位置
chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
detector->detect(img_1, keypoints_1);
detector->detect(img_2, keypoints_2);
// 输出提取到的特征点位置
cout << "keypoints_1.size :" << keypoints_1.size() << " keypoints_2.size :" << keypoints_2.size() << endl;
// for(int k=0; k<keypoints_1.size();k++)
// {
// cout << keypoints_1[k].pt.x << " " << keypoints_1[k].pt.y << endl;
// }
chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
chrono::duration<double> time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);
cout << "检测特征点耗时: " << time_used.count() << "s" << endl;
// 作图显示
Mat outimg1, outimg2;
drawKeypoints(img_1, keypoints_1, outimg1, Scalar::all(-1), DrawMatchesFlags::DEFAULT);
drawKeypoints(img_2, keypoints_2, outimg2, Scalar::all(-1), DrawMatchesFlags::DEFAULT);
imshow("ORB features1", outimg1);
imshow("ORB features2", outimg2);
waitKey(0);
cv::imwrite("../demo/ORB_features1.png", outimg1);
cv::imwrite("../demo/ORB_features2.png", outimg2);
// 第二步:根据角点位置计算 BRIEF 描述子
descriptor->compute(img_1, keypoints_1, descriptors_1);
descriptor->compute(img_2, keypoints_2, descriptors_2);
// 这里descriptors是一个 500*32 的二维矩阵，表示有500个landmark，每个标志点特征用32维的向量记录
cout << "第1张图片检测出来的ORB特征的描述子: " << descriptors_1.rows << " " << descriptors_1.cols << endl;
cout << "第2张图片检测出来的ORB特征的描述子: " << descriptors_2.rows << " " << descriptors_2.cols << endl;
// 每个特征点位置，提取得到一个32维度的特征
// cout << descriptors_1 << endl;
// [232, 84, 213, 84, 104, 76, 109, 48, 49, 170, 98, 120, 115, 55, 133, 255, 20, 244, 108, 106, 105, 86, 225, 46, 23, 186, 101, 144, 2, 241, 70, 61;
// 138, 204, 146, 255, 85, 213, 190, 230, 31, 14, 84, 2, 172, 255, 33, 64, 249, 123, 147, 194, 98, 156, 90, 255, 103, 215, 158, 139, 22, 89, 61, 138;
// 11, 159, 50, 223, 53, 183, 30, 246, 95, 73, 92, 38, 172, 186, 166, 228, 223, 58, 150, 128, 134, 152, 113, 255, 47, 127, 154, 131, 22, 204, 184, 15;
// ... ... ...
// 181, 94, 110, 254, 102, 130, 79, 112, 212, 130, 246, 230, 115, 201, 177, 97, 126, 228, 22, 238, 233, 167, 12, 46, 114, 171, 227, 136, 125, 187, 112, 49;
// 66, 178, 187, 186, 41, 237, 57, 87, 175, 93, 177, 125, 190, 158, 74, 123, 167, 51, 253, 1, 151, 73, 178, 209, 211, 209, 122, 251, 8, 4, 135, 83]
// 第三步:对两幅图像中的BRIEF描述子进行匹配，使用 Hamming 距离
vector<DMatch> matches;
t1 = chrono::steady_clock::now();
matcher->match(descriptors_1, descriptors_2, matches);
// matches数据结构包含的内容有：
// size：配对成功的特征点对数
// queryIdx：当前“匹配点”在查询图像的特征在KeyPoints1向量中的索引号，可以据此找到匹配点在查询图像中的位置
// trainIdx：当前“匹配点”在训练（模板）图像的特征在KeyPoints2向量中的索引号，可以据此找到匹配点在训练图像中的位置
// distance：两个特征点之间的欧氏距离，越小表明匹配度越高
// imgIdx：当前匹配点对应训练图像（如果有若干个）的索引，如果只有一个训练图像跟查询图像配对，即两两配对，则imgIdx=0
// 输出匹配好的特征点对的距离和索引
// cout << matches.size() << endl; // 497
// for(int k=0; k<matches.size(); k++)
// {
// cout << matches[k].distance << " " << matches[k].queryIdx << " " << matches[k].trainIdx << " " << matches[k].imgIdx << endl;
// }
t2 = chrono::steady_clock::now();
time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);
cout << "特征点对匹配耗时: " << time_used.count() << "s" << endl;
// 第四步:匹配点对筛选
// 计算最小距离和最大距离
// minmax_element函数，返回指定范围内的最大最小值的元素的迭代器组成的一个pair, 如果最值多于一个，firstf返回的是第一个出现的最小值的迭代器，second返回的是最后一个出现的最大值的迭代器
// 第三个参数cmp可写可不写，max_element() 和 min_element() 默认是从小到大排列，然后 max_element() 输出最后一个值，min_element() 输出第一个值
// 但是如果自定义的cmp函数写的是从大到小排列，那么会导致max_element()和min_element()的两个结果是对调的，这里利用lambda表达式定义了cmp函数
// []后面的代码称为lambda表达式，获取代码{}中的返回值，函数符返回值是一个bool
auto min_max = minmax_element(matches.begin(), matches.end(), [](const DMatch &m1, const DMatch &m2) {
return m1.distance < m2.distance; });
double min_dist = min_max.first->distance;
double max_dist = min_max.second->distance;
cout << "匹配点对的最大距离: " << max_dist << endl;
cout << "匹配点对的最小距离: " << min_dist << endl;
// 当描述子之间的距离大于两倍的最小距离时，即认为匹配有误，但有时候最小距离会非常小，设置一个经验值30作为下限
std::vector<DMatch> good_matches;
for (int i = 0; i < matches.size(); i++)
{
if (matches[i].distance <= max(2 * min_dist, 30.0))
{
good_matches.push_back(matches[i]);
}
}
// 第五步:绘制匹配结果
Mat img_match;
Mat img_goodmatch;
drawMatches(img_1, keypoints_1, img_2, keypoints_2, matches, img_match);
drawMatches(img_1, keypoints_1, img_2, keypoints_2, good_matches, img_goodmatch);
imshow("all matches", img_match);
imshow("good matches", img_goodmatch);
waitKey(0);
cv::imwrite("../demo/all_matches.png", img_match);
cv::imwrite("../demo/good_matches.png", img_goodmatch);
return 0;
}
自己实现的ORB特征提取：
#include <opencv2/opencv.hpp>
// opencv.hpp中包含了OpenCV各模块的头文件，如高层GUI图形用户界面模块头文件highgui.hpp、图像处理模块头文件imgproc.hpp、2D特征模块头文件features2d.hpp等
#include <string>
// string头文件基本已经包含在iostream中，但平时使用建议加上#include <string>，尤其是使用string类型、使用cin、cout语句输入输出string类型变量、使用strlen()、strcpy()等函数时
#include <nmmintrin.h>
// SSE指令集，能大幅优化文本处理速度
#include <chrono>
// chrono是C++11新加入的方便时间日期操作的标准库，它既是相应的头文件名称，也是std命名空间下的一个子命名空间，所有时间日期相关定义均在std::chrono命名空间下
using namespace std;
using namespace cv;
// 提取ORB特征具体细节：
// 1、利用OpenCV的cv::FAST函数，检测出图像中FAST特征标志点landmark;
// 2、对于每个landmark标志点，选定patch，计算图像块的矩，得到旋转方向;
// 3、利用已经设计好的orb pattern，选取特定点对，得到二维点坐标;
// 4、根据旋转方向，对二维点坐标进行旋转;
// 5、比较此时点对的像素值，赋予0或1，重复步骤256次，计算得到BRIEF特征
string first_file = "../1.png";
string second_file = "../2.png";
// 每个unsigned int型含32位，容器中存储8个，则可以表示256维的0-1的BRIEF特征
typedef vector<uint32_t> DescType; // Descriptor type
/**
* compute descriptor of orb keypoints
* @param img input image
* @param keypoints detected fast keypoints
* @param descriptors descriptors
*
* 也就是说，如果FAST算法检测到的landmark在边界附近，无法得到周边的邻域，则也无法计算出BRIEF描述子，此时该landmark将被舍弃忽略
*
* 如果关键点超出边界（8像素），描述符将不会被计算，并将保留为空
*
* NOTE: if a keypoint goes outside the image boundary (8 pixels), descriptors will not be computed and will be left as empty
*
*/
void ComputeORB(const cv::Mat &img, vector<cv::KeyPoint> &keypoints, vector<DescType> &descriptors);
/**
* 根据两幅图像各自提取出来的BRIEF特征描述子，进行特征匹配，并存储于matches中
*
* brute-force match two sets of descriptors
* @param desc1 the first descriptor
* @param desc2 the second descriptor
* @param matches matches of two images
*/
void BfMatch(const vector<DescType> &desc1, const vector<DescType> &desc2, vector<cv::DMatch> &matches);
int main(int argc, char **argv) {
// 加载图像
cv::Mat first_image = cv::imread(first_file);
cv::Mat second_image = cv::imread(second_file);
assert(first_image.data != nullptr && second_image.data != nullptr);
chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
// 调用OpenCV库 FAST算法， 设置参数threshold=40，检测图片中的landmark标志点
vector<cv::KeyPoint> keypoints1;
cv::FAST(first_image, keypoints1, 40);
// 调用自定义的函数，根据检测得到的landmark，提取其对应的BRIEF特征描述子
vector<DescType> descriptor1;
ComputeORB(first_image, keypoints1, descriptor1);
// 同样进行landmark检测、并提取BRIEF特征描述子
vector<cv::KeyPoint> keypoints2;
vector<DescType> descriptor2;
cv::FAST(second_image, keypoints2, 40);
ComputeORB(second_image, keypoints2, descriptor2);
chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
chrono::duration<double> time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);
cout << "提取 ORB 特征耗时: " << time_used.count() << "s. " << endl;
// 根据两幅图像各自提取到的landmark标志点和BRIEF特征描述子，进行匹配
vector<cv::DMatch> matches;
t1 = chrono::steady_clock::now();
BfMatch(descriptor1, descriptor2, matches);
t2 = chrono::steady_clock::now();
time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);
cout << "匹配 ORB 特征耗时: " << time_used.count() << "s. " << endl;
cout << "匹配成功的landmark点对数量: " << matches.size() << endl;
// 作图
cv::Mat image_show;
cv::drawMatches(first_image, keypoints1, second_image, keypoints2, matches, image_show);
cv::imshow("matches", image_show);
cv::imwrite("../matches.png", image_show);
cv::waitKey(0);
return 0;
}
// ORB pattern
// BRIEF算法在提取landmark特征描述子时，需要随机选取邻域附近不同的点对，比较其像素亮度，
// 选取的索引，通过ORB pattern每一行数组元素值实现，共256行，对应256个点对，两两比较得到256维度的0-1特征，
// 这些索引的取法是经过精心设计好的，能够保证良好的特征提取效果。
int ORB_pattern[256 * 4] = {
8, -3, 9, 5/*mean (0), correlation (0)*/,
4, 2, 7, -12/*mean (1.12461e-05), correlation (0.0437584)*/,
-11, 9, -8, 2/*mean (3.37382e-05), correlation (0.0617409)*/,
7, -12, 12, -13/*mean (5.62303e-05), correlation (0.0636977)*/,
2, -13, 2, 12/*mean (0.000134953), correlation (0.085099)*/,
1, -7, 1, 6/*mean (0.000528565), correlation (0.0857175)*/,
-2, -10, -2, -4/*mean (0.0188821), correlation (0.0985774)*/,
-13, -13, -11, -8/*mean (0.0363135), correlation (0.0899616)*/,
-13, -3, -12, -9/*mean (0.121806), correlation (0.099849)*/,
10, 4, 11, 9/*mean (0.122065), correlation (0.093285)*/,
-13, -8, -8, -9/*mean (0.162787), correlation (0.0942748)*/,
-11, 7, -9, 12/*mean (0.21561), correlation (0.0974438)*/,
7, 7, 12, 6/*mean (0.160583), correlation (0.130064)*/,
-4, -5, -3, 0/*mean (0.228171), correlation (0.132998)*/,
-13, 2, -12, -3/*mean (0.00997526), correlation (0.145926)*/,
-9, 0, -7, 5/*mean (0.198234), correlation (0.143636)*/,
12, -6, 12, -1/*mean (0.0676226), correlation (0.16689)*/,
-3, 6, -2, 12/*mean (0.166847), correlation (0.171682)*/,
-6, -13, -4, -8/*mean (0.101215), correlation (0.179716)*/,
11, -13, 12, -8/*mean (0.200641), correlation (0.192279)*/,
4, 7, 5, 1/*mean (0.205106), correlation (0.186848)*/,
5, -3, 10, -3/*mean (0.234908), correlation (0.192319)*/,
3, -7, 6, 12/*mean (0.0709964), correlation (0.210872)*/,
-8, -7, -6, -2/*mean (0.0939834), correlation (0.212589)*/,
-2, 11, -1, -10/*mean (0.127778), correlation (0.20866)*/,
-13, 12, -8, 10/*mean (0.14783), correlation (0.206356)*/,
-7, 3, -5, -3/*mean (0.182141), correlation (0.198942)*/,
-4, 2, -3, 7/*mean (0.188237), correlation (0.21384)*/,
-10, -12, -6, 11/*mean (0.14865), correlation (0.23571)*/,
5, -12, 6, -7/*mean (0.222312), correlation (0.23324)*/,
5, -6, 7, -1/*mean (0.229082), correlation (0.23389)*/,
1, 0, 4, -5/*mean (0.241577), correlation (0.215286)*/,
9, 11, 11, -13/*mean (0.00338507), correlation (0.251373)*/,
4, 7, 4, 12/*mean (0.131005), correlation (0.257622)*/,
2, -1, 4, 4/*mean (0.152755), correlation (0.255205)*/,
-4, -12, -2, 7/*mean (0.182771), correlation (0.244867)*/,
-8, -5, -7, -10/*mean (0.186898), correlation (0.23901)*/,
4, 11, 9, 12/*mean (0.226226), correlation (0.258255)*/,
0, -8, 1, -13/*mean (0.0897886), correlation (0.274827)*/,
-13, -2, -8, 2/*mean (0.148774), correlation (0.28065)*/,
-3, -2, -2, 3/*mean (0.153048), correlation (0.283063)*/,
-6, 9, -4, -9/*mean (0.169523), correlation (0.278248)*/,
8, 12, 10, 7/*mean (0.225337), correlation (0.282851)*/,
0, 9, 1, 3/*mean (0.226687), correlation (0.278734)*/,
7, -5, 11, -10/*mean (0.00693882), correlation (0.305161)*/,
-13, -6, -11, 0/*mean (0.0227283), correlation (0.300181)*/,
10, 7, 12, 1/*mean (0.125517), correlation (0.31089)*/,
-6, -3, -6, 12/*mean (0.131748), correlation (0.312779)*/,
10, -9, 12, -4/*mean (0.144827), correlation (0.292797)*/,
-13, 8, -8, -12/*mean (0.149202), correlation (0.308918)*/,
-13, 0, -8, -4/*mean (0.160909), correlation (0.310013)*/,
3, 3, 7, 8/*mean (0.177755), correlation (0.309394)*/,
5, 7, 10, -7/*mean (0.212337), correlation (0.310315)*/,
-1, 7, 1, -12/*mean (0.214429), correlation (0.311933)*/,
3, -10, 5, 6/*mean (0.235807), correlation (0.313104)*/,
2, -4, 3, -10/*mean (0.00494827), correlation (0.344948)*/,
-13, 0, -13, 5/*mean (0.0549145), correlation (0.344675)*/,
-13, -7, -12, 12/*mean (0.103385), correlation (0.342715)*/,
-13, 3, -11, 8/*mean (0.134222), correlation (0.322922)*/,
-7, 12, -4, 7/*mean (0.153284), correlation (0.337061)*/,
6, -10, 12, 8/*mean (0.154881), correlation (0.329257)*/,
-9, -1, -7, -6/*mean (0.200967), correlation (0.33312)*/,
-2, -5, 0, 12/*mean (0.201518), correlation (0.340635)*/,
-12, 5, -7, 5/*mean (0.207805), correlation (0.335631)*/,
3, -10, 8, -13/*mean (0.224438), correlation (0.34504)*/,
-7, -7, -4, 5/*mean (0.239361), correlation (0.338053)*/,
-3, -2, -1, -7/*mean (0.240744), correlation (0.344322)*/,
2, 9, 5, -11/*mean (0.242949), correlation (0.34145)*/,
-11, -13, -5, -13/*mean (0.244028), correlation (0.336861)*/,
-1, 6, 0, -1/*mean (0.247571), correlation (0.343684)*/,
5, -3, 5, 2/*mean (0.000697256), correlation (0.357265)*/,
-4, -13, -4, 12/*mean (0.00213675), correlation (0.373827)*/,
-9, -6, -9, 6/*mean (0.0126856), correlation (0.373938)*/,
-12, -10, -8, -4/*mean (0.0152497), correlation (0.364237)*/,
10, 2, 12, -3/*mean (0.0299933), correlation (0.345292)*/,
7, 12, 12, 12/*mean (0.0307242), correlation (0.366299)*/,
-7, -13, -6, 5/*mean (0.0534975), correlation (0.368357)*/,
-4, 9, -3, 4/*mean (0.099865), correlation (0.372276)*/,
7, -1, 12, 2/*mean (0.117083), correlation (0.364529)*/,
-7, 6, -5, 1/*mean (0.126125), correlation (0.369606)*/,
-13, 11, -12, 5/*mean (0.130364), correlation (0.358502)*/,
-3, 7, -2, -6/*mean (0.131691), correlation (0.375531)*/,
7, -8, 12, -7/*mean (0.160166), correlation (0.379508)*/,
-13, -7, -11, -12/*mean (0.167848), correlation (0.353343)*/,
1, -3, 12, 12/*mean (0.183378), correlation (0.371916)*/,
2, -6, 3, 0/*mean (0.228711), correlation (0.371761)*/,
-4, 3, -2, -13/*mean (0.247211), correlation (0.364063)*/,
-1, -13, 1, 9/*mean (0.249325), correlation (0.378139)*/,
7, 1, 8, -6/*mean (0.000652272), correlation (0.411682)*/,
1, -1, 3, 12/*mean (0.00248538), correlation (0.392988)*/,
9, 1, 12, 6/*mean (0.0206815), correlation (0.386106)*/,
-1, -9, -1, 3/*mean (0.0364485), correlation (0.410752)*/,
-13, -13, -10, 5/*mean (0.0376068), correlation (0.398374)*/,
7, 7, 10, 12/*mean (0.0424202), correlation (0.405663)*/,
12, -5, 12, 9/*mean (0.0942645), correlation (0.410422)*/,
6, 3, 7, 11/*mean (0.1074), correlation (0.413224)*/,
5, -13, 6, 10/*mean (0.109256), correlation (0.408646)*/,
2, -12, 2, 3/*mean (0.131691), correlation (0.416076)*/,
3, 8, 4, -6/*mean (0.165081), correlation (0.417569)*/,
2, 6, 12, -13/*mean (0.171874), correlation (0.408471)*/,
9, -12, 10, 3/*mean (0.175146), correlation (0.41296)*/,
-8, 4, -7, 9/*mean (0.183682), correlation (0.402956)*/,
-11, 12, -4, -6/*mean (0.184672), correlation (0.416125)*/,
1, 12, 2, -8/*mean (0.191487), correlation (0.386696)*/,
6, -9, 7, -4/*mean (0.192668), correlation (0.394771)*/,
2, 3, 3, -2/*mean (0.200157), correlation (0.408303)*/,
6, 3, 11, 0/*mean (0.204588), correlation (0.411762)*/,
3, -3, 8, -8/*mean (0.205904), correlation (0.416294)*/,
7, 8, 9, 3/*mean (0.213237), correlation (0.409306)*/,
-11, -5, -6, -4/*mean (0.243444), correlation (0.395069)*/,
-10, 11, -5, 10/*mean (0.247672), correlation (0.413392)*/,
-5, -8, -3, 12/*mean (0.24774), correlation (0.411416)*/,
-10, 5, -9, 0/*mean (0.00213675), correlation (0.454003)*/,
8, -1, 12, -6/*mean (0.0293635), correlation (0.455368)*/,
4, -6, 6, -11/*mean (0.0404971), correlation (0.457393)*/,
-10, 12, -8, 7/*mean (0.0481107), correlation (0.448364)*/,
4, -2, 6, 7/*mean (0.050641), correlation (0.455019)*/,
-2, 0, -2, 12/*mean (0.0525978), correlation (0.44338)*/,
-5, -8, -5, 2/*mean (0.0629667), correlation (0.457096)*/,
7, -6, 10, 12/*mean (0.0653846), correlation (0.445623)*/,
-9, -13, -8, -8/*mean (0.0858749), correlation (0.449789)*/,
-5, -13, -5, -2/*mean (0.122402), correlation (0.450201)*/,
8, -8, 9, -13/*mean (0.125416), correlation (0.453224)*/,
-9, -11, -9, 0/*mean (0.130128), correlation (0.458724)*/,
1, -8, 1, -2/*mean (0.132467), correlation (0.440133)*/,
7, -4, 9, 1/*mean (0.132692), correlation (0.454)*/,
-2, 1, -1, -4/*mean (0.135695), correlation (0.455739)*/,
11, -6, 12, -11/*mean (0.142904), correlation (0.446114)*/,
-12, -9, -6, 4/*mean (0.146165), correlation (0.451473)*/,
3, 7, 7, 12/*mean (0.147627), correlation (0.456643)*/,
5, 5, 10, 8/*mean (0.152901), correlation (0.455036)*/,
0, -4, 2, 8/*mean (0.167083), correlation (0.459315)*/,
-9, 12, -5, -13/*mean (0.173234), correlation (0.454706)*/,
0, 7, 2, 12/*mean (0.18312), correlation (0.433855)*/,
-1, 2, 1, 7/*mean (0.185504), correlation (0.443838)*/,
5, 11, 7, -9/*mean (0.185706), correlation (0.451123)*/,
3, 5, 6, -8/*mean (0.188968), correlation (0.455808)*/,
-13, -4, -8, 9/*mean (0.191667), correlation (0.459128)*/,
-5, 9, -3, -3/*mean (0.193196), correlation (0.458364)*/,
-4, -7, -3, -12/*mean (0.196536), correlation (0.455782)*/,
6, 5, 8, 0/*mean (0.1972), correlation (0.450481)*/,
-7, 6, -6, 12/*mean (0.199438), correlation (0.458156)*/,
-13, 6, -5, -2/*mean (0.211224), correlation (0.449548)*/,
1, -10, 3, 10/*mean (0.211718), correlation (0.440606)*/,
4, 1, 8, -4/*mean (0.213034), correlation (0.443177)*/,
-2, -2, 2, -13/*mean (0.234334), correlation (0.455304)*/,
2, -12, 12, 12/*mean (0.235684), correlation (0.443436)*/,
-2, -13, 0, -6/*mean (0.237674), correlation (0.452525)*/,
4, 1, 9, 3/*mean (0.23962), correlation (0.444824)*/,
-6, -10, -3, -5/*mean (0.248459), correlation (0.439621)*/,
-3, -13, -1, 1/*mean (0.249505), correlation (0.456666)*/,
7, 5, 12, -11/*mean (0.00119208), correlation (0.495466)*/,
4, -2, 5, -7/*mean (0.00372245), correlation (0.484214)*/,
-13, 9, -9, -5/*mean (0.00741116), correlation (0.499854)*/,
7, 1, 8, 6/*mean (0.0208952), correlation (0.499773)*/,
7, -8, 7, 6/*mean (0.0220085), correlation (0.501609)*/,
-7, -4, -7, 1/*mean (0.0233806), correlation (0.496568)*/,
-8, 11, -7, -8/*mean (0.0236505), correlation (0.489719)*/,
-13, 6, -12, -8/*mean (0.0268781), correlation (0.503487)*/,
2, 4, 3, 9/*mean (0.0323324), correlation (0.501938)*/,
10, -5, 12, 3/*mean (0.0399235), correlation (0.494029)*/,
-6, -5, -6, 7/*mean (0.0420153), correlation (0.486579)*/,
8, -3, 9, -8/*mean (0.0548021), correlation (0.484237)*/,
2, -12, 2, 8/*mean (0.0616622), correlation (0.496642)*/,
-11, -2, -10, 3/*mean (0.0627755), correlation (0.498563)*/,
-12, -13, -7, -9/*mean (0.0829622), correlation (0.495491)*/,
-11, 0, -10, -5/*mean (0.0843342), correlation (0.487146)*/,
5, -3, 11, 8/*mean (0.0929937), correlation (0.502315)*/,
-2, -13, -1, 12/*mean (0.113327), correlation (0.48941)*/,
-1, -8, 0, 9/*mean (0.132119), correlation (0.467268)*/,
-13, -11, -12, -5/*mean (0.136269), correlation (0.498771)*/,
-10, -2, -10, 11/*mean (0.142173), correlation (0.498714)*/,
-3, 9, -2, -13/*mean (0.144141), correlation (0.491973)*/,
2, -3, 3, 2/*mean (0.14892), correlation (0.500782)*/,
-9, -13, -4, 0/*mean (0.150371), correlation (0.498211)*/,
-4, 6, -3, -10/*mean (0.152159), correlation (0.495547)*/,
-4, 12, -2, -7/*mean (0.156152), correlation (0.496925)*/,
-6, -11, -4, 9/*mean (0.15749), correlation (0.499222)*/,
6, -3, 6, 11/*mean (0.159211), correlation (0.503821)*/,
-13, 11, -5, 5/*mean (0.162427), correlation (0.501907)*/,
11, 11, 12, 6/*mean (0.16652), correlation (0.497632)*/,
7, -5, 12, -2/*mean (0.169141), correlation (0.484474)*/,
-1, 12, 0, 7/*mean (0.169456), correlation (0.495339)*/,
-4, -8, -3, -2/*mean (0.171457), correlation (0.487251)*/,
-7, 1, -6, 7/*mean (0.175), correlation (0.500024)*/,
-13, -12, -8, -13/*mean (0.175866), correlation (0.497523)*/,
-7, -2, -6, -8/*mean (0.178273), correlation (0.501854)*/,
-8, 5, -6, -9/*mean (0.181107), correlation (0.494888)*/,
-5, -1, -4, 5/*mean (0.190227), correlation (0.482557)*/,
-13, 7, -8, 10/*mean (0.196739), correlation (0.496503)*/,
1, 5, 5, -13/*mean (0.19973), correlation (0.499759)*/,
1, 0, 10, -13/*mean (0.204465), correlation (0.49873)*/,
9, 12, 10, -1/*mean (0.209334), correlation (0.49063)*/,
5, -8, 10, -9/*mean (0.211134), correlation (0.503011)*/,
-1, 11, 1, -13/*mean (0.212), correlation (0.499414)*/,
-9, -3, -6, 2/*mean (0.212168), correlation (0.480739)*/,
-1, -10, 1, 12/*mean (0.212731), correlation (0.502523)*/,
-13, 1, -8, -10/*mean (0.21327), correlation (0.489786)*/,
8, -11, 10, -6/*mean (0.214159), correlation (0.488246)*/,
2, -13, 3, -6/*mean (0.216993), correlation (0.50287)*/,
7, -13, 12, -9/*mean (0.223639), correlation (0.470502)*/,
-10, -10, -5, -7/*mean (0.224089), correlation (0.500852)*/,
-10, -8, -8, -13/*mean (0.228666), correlation (0.502629)*/,
4, -6, 8, 5/*mean (0.22906), correlation (0.498305)*/,
3, 12, 8, -13/*mean (0.233378), correlation (0.503825)*/,
-4, 2, -3, -3/*mean (0.234323), correlation (0.476692)*/,
5, -13, 10, -12/*mean (0.236392), correlation (0.475462)*/,
4, -13, 5, -1/*mean (0.236842), correlation (0.504132)*/,
-9, 9, -4, 3/*mean (0.236977), correlation (0.497739)*/,
0, 3, 3, -9/*mean (0.24314), correlation (0.499398)*/,
-12, 1, -6, 1/*mean (0.243297), correlation (0.489447)*/,
3, 2, 4, -8/*mean (0.00155196), correlation (0.553496)*/,
-10, -10, -10, 9/*mean (0.00239541), correlation (0.54297)*/,
8, -13, 12, 12/*mean (0.0034413), correlation (0.544361)*/,
-8, -12, -6, -5/*mean (0.003565), correlation (0.551225)*/,
2, 2, 3, 7/*mean (0.00835583), correlation (0.55285)*/,
10, 6, 11, -8/*mean (0.00885065), correlation (0.540913)*/,
6, 8, 8, -12/*mean (0.0101552), correlation (0.551085)*/,
-7, 10, -6, 5/*mean (0.0102227), correlation (0.533635)*/,
-3, -9, -3, 9/*mean (0.0110211), correlation (0.543121)*/,
-1, -13, -1, 5/*mean (0.0113473), correlation (0.550173)*/,
-3, -7, -3, 4/*mean (0.0140913), correlation (0.554774)*/,
-8, -2, -8, 3/*mean (0.017049), correlation (0.55461)*/,
4, 2, 12, 12/*mean (0.01778), correlation (0.546921)*/,
2, -5, 3, 11/*mean (0.0224022), correlation (0.549667)*/,
6, -9, 11, -13/*mean (0.029161), correlation (0.546295)*/,
3, -1, 7, 12/*mean (0.0303081), correlation (0.548599)*/,
11, -1, 12, 4/*mean (0.0355151), correlation (0.523943)*/,
-3, 0, -3, 6/*mean (0.0417904), correlation (0.543395)*/,
4, -11, 4, 12/*mean (0.0487292), correlation (0.542818)*/,
2, -4, 2, 1/*mean (0.0575124), correlation (0.554888)*/,
-10, -6, -8, 1/*mean (0.0594242), correlation (0.544026)*/,
-13, 7, -11, 1/*mean (0.0597391), correlation (0.550524)*/,
-13, 12, -11, -13/*mean (0.0608974), correlation (0.55383)*/,
6, 0, 11, -13/*mean (0.065126), correlation (0.552006)*/,
0, -1, 1, 4/*mean (0.074224), correlation (0.546372)*/,
-13, 3, -9, -2/*mean (0.0808592), correlation (0.554875)*/,
-9, 8, -6, -3/*mean (0.0883378), correlation (0.551178)*/,
-13, -6, -8, -2/*mean (0.0901035), correlation (0.548446)*/,
5, -9, 8, 10/*mean (0.0949843), correlation (0.554694)*/,
2, 7, 3, -9/*mean (0.0994152), correlation (0.550979)*/,
-1, -6, -1, -1/*mean (0.10045), correlation (0.552714)*/,
9, 5, 11, -2/*mean (0.100686), correlation (0.552594)*/,
11, -3, 12, -8/*mean (0.101091), correlation (0.532394)*/,
3, 0, 3, 5/*mean (0.101147), correlation (0.525576)*/,
-1, 4, 0, 10/*mean (0.105263), correlation (0.531498)*/,
3, -6, 4, 5/*mean (0.110785), correlation (0.540491)*/,
-13, 0, -10, 5/*mean (0.112798), correlation (0.536582)*/,
5, 8, 12, 11/*mean (0.114181), correlation (0.555793)*/,
8, 9, 9, -6/*mean (0.117431), correlation (0.553763)*/,
7, -4, 8, -12/*mean (0.118522), correlation (0.553452)*/,
-10, 4, -10, 9/*mean (0.12094), correlation (0.554785)*/,
7, 3, 12, 4/*mean (0.122582), correlation (0.555825)*/,
9, -7, 10, -2/*mean (0.124978), correlation (0.549846)*/,
7, 0, 12, -2/*mean (0.127002), correlation (0.537452)*/,
-1, -6, 0, -11/*mean (0.127148), correlation (0.547401)*/
};
// 计算landmark标志点对应的BRIEF特征描述子
// half_patch_size = 8，代表以landmark标志点为中心，16个像素的正方形为patch，选定邻域
// 如果关键点超出边界（8像素），描述符将不会被计算，该关键点会被忽略舍弃
void ComputeORB(const cv::Mat &img, vector<cv::KeyPoint> &keypoints, vector<DescType> &descriptors) {
const int half_patch_size = 8;
const int half_boundary = 16;
int bad_points = 0;
for (auto &kp: keypoints) {
// 如果关键点超出边界（16像素），将该landmark标志点push出容器，舍弃
if (kp.pt.x < half_boundary || kp.pt.y < half_boundary ||
kp.pt.x >= img.cols - half_boundary || kp.pt.y >= img.rows - half_boundary) {
bad_points++;
descriptors.push_back({
});
continue;
}
// 通过矩找到图像块的质心，计算得到m10、m01，以周边8个像素为区域块
float m01 = 0, m10 = 0;
for (int dx = -half_patch_size; dx < half_patch_size; ++dx) {
for (int dy = -half_patch_size; dy < half_patch_size; ++dy) {
uchar pixel = img.at<uchar>(kp.pt.y + dy, kp.pt.x + dx);
m10 += dx * pixel;
m01 += dy * pixel;
}
}
// 连接图像块的几何中心O和质心C，计算得到特征点landmark的方向
// angle should be arc tan(m01/m10)
float m_sqrt = sqrt(m01 * m01 + m10 * m10) + 1e-18; // avoid divide by zero
float sin_theta = m01 / m_sqrt;
float cos_theta = m10 / m_sqrt;
// 利用BRIEF算法，提取landmark标志点对应的特征描述子，用8个十进制数值(0-255)存储，则可得到256维度的BRIEF特征
DescType desc(8, 0);
for (int i = 0; i < 8; i++) {
uint32_t d = 0;
for (int k = 0; k < 32; k++) {
// 从ORB_pattern数组中，选出需要进行比较的随机点对坐标
int idx_pq = i * 32 + k;
cv::Point2f p(ORB_pattern[idx_pq * 4], ORB_pattern[idx_pq * 4 + 1]);
cv::Point2f q(ORB_pattern[idx_pq * 4 + 2], ORB_pattern[idx_pq * 4 + 3]);
// 根据landmark标志点的方向，旋转点对坐标
cv::Point2f pp = cv::Point2f(cos_theta * p.x - sin_theta * p.y, sin_theta * p.x + cos_theta * p.y) + kp.pt;
cv::Point2f qq = cv::Point2f(cos_theta * q.x - sin_theta * q.y, sin_theta * q.x + cos_theta * q.y) + kp.pt;
// 比较pp和qq对应的像素值，赋值0或1，最后将8维二进制转化为十进制存储，存储8个十进制数值，则存储得到256维度的BRIEF特征
// c++中1<<k，表示1左移k位,即2^k，2的k次方
// | 表示按位或，是双目运算符，功能是参与运算的两数各对应的二进位（也就是最后一位）相或。只要对应的二个二进位有一个为1时，结果位就为1
if (img.at<uchar>(pp.y, pp.x) < img.at<uchar>(qq.y, qq.x)) {
d |= 1 << k;
}
}
desc[i] = d;
}
descriptors.push_back(desc);
}
cout << "被舍弃的landmark标志点/总landmark标志点: " << bad_points << "/" << keypoints.size() << endl;
}
// 根据不同landmark标志点计算出的BRIEF特征描述子，进行匹配
void BfMatch(const vector<DescType> &desc1, const vector<DescType> &desc2, vector<cv::DMatch> &matches) {
// 当描述子之间的距离大于40时，则认为匹配有误，舍弃
const int d_max = 40;
// 对desc1里的所有标志点，依次计算它与desc2中所有标志点的距离
for (size_t i1 = 0; i1 < desc1.size(); ++i1) {
if (desc1[i1].empty()) continue;
cv::DMatch m{
int(i1), 0, 256};
for (size_t i2 = 0; i2 < desc2.size(); ++i2) {
if (desc2[i2].empty()) continue;
// 利用SSE指令，计算汉明距离
// 对两个字符串进行异或运算，并统计结果为1的个数，那么这个数就是汉明距离
int distance = 0;
for (int k = 0; k < 8; k++) {
distance += _mm_popcnt_u32(desc1[i1][k] ^ desc2[i2][k]);
}
// 存储匹配信息
if (distance < d_max && distance < m.distance) {
m.distance = distance;
m.trainIdx = i2;
}
}
if (m.distance < d_max) {
matches.push_back(m);
}
}
}
五、项目链接
如果代码跑不通，或者想直接使用我自己制作的数据集，可以去下载项目链接：
https://blog.csdn.net/Twilight737