OpenCV + CPP 系列（卌一）图像特征匹配（ FLANN 匹配）

文章目录

一、FLANN简介
- 演示SURF--Flann
二、单应性矩阵

一、FLANN简介

FLANN库全称是Fast Library for Approximate Nearest Neighbors（快速最近邻逼进搜索库），是一个在高维空间快速搜索近邻的库，它是目前最完整的（近似）最近邻开源库。不但实现了一系列查找算法，还包含了一种自动选取最快算法的机制。

主要优势：实现了包含kd-tree等在内的搜索算法集合，并且可以根据数据集本身特点，自动选取最适合的算法来完成k近邻搜索任务（更加确切的说是高维特征向量的匹配任务），提供了c++、c、python、matlab的接口。

FLANN 算法官网：https://www.cs.ubc.ca/paper/flann
FLANN github地址：https://github.com/flann-lib/flann

关于算法详情查看：https://blog.csdn.net/Bluenapa/article/details/88371512
关于特征存储查看：OpenCV—python 角点特征检测之三（FLANN匹配）
关于构造函数查看：https://www.jianshu.com/p/d70d9c8b2bec

主要介绍一下indexParams的参数

Ptr<flann::IndexParams> indexParams; 索引参数
Ptr<flann::SearchParams> searchParams; 搜索参数
Ptr<flann::Index> flannIndex; 创建索引

头文件 image_feature_all.h：声明类与公共函数

#pragma once
#include <opencv2/opencv.hpp>
#include <iostream>
#include <opencv2/xfeatures2d.hpp>　　//新增引入库using namespace cv;
using namespace std;class ImageFeature {public:void flann_demo(Mat& image, Mat& image2);};

主函数main.cpp调用该类的公共成员函数

#include "image_feature_all.h"int main(int argc, char** argv) {const char* img_path = "D:\\Desktop\\match_dst.jpg";const char* img_path2 = "D:\\Desktop\\match_raw.jpg";Mat image = imread(img_path, IMREAD_GRAYSCALE);Mat image2 = imread(img_path2, IMREAD_GRAYSCALE);if (image.empty() || image2.empty()) {cout << "图像数据为空，读取文件失败！" << endl;}ImageFeature imgfeature;imgfeature.flann_demo(image, image2);waitKey(0);destroyAllWindows();return 0;
}

演示SURF–Flann

源文件 feature_extract.cpp：实现类与公共函数

void ImageFeature::flann_demo(Mat& image, Mat& image2) {//SURF 特征提取int minHessian = 400;Ptr<xfeatures2d::SURF> detector = xfeatures2d::SURF::create(minHessian);vector<KeyPoint> keypoint_obj, keypoint_scene;Mat descriptor_obj, descriptor_scens;detector->detectAndCompute(image, Mat(), keypoint_obj, descriptor_obj);detector->detectAndCompute(image2, Mat(), keypoint_scene, descriptor_scens);//SURF 特征匹配FlannBasedMatcher matcher;vector<DMatch> matches;matcher.match(descriptor_obj, descriptor_scens, matches);//查找最大最小距离double minDist = 1000;double maxDist = 0;for (size_t i = 0; i < descriptor_obj.rows; i++) {double dist = matches[i].distance;if (dist > maxDist) { maxDist = dist; }if (dist < minDist) { minDist = dist; }}cout << "minDist = " << minDist << endl;cout << "maxDist = " << maxDist << endl;// 筛选最佳匹配（小于最小距离的3倍）vector<DMatch> goodMatchs;for (size_t i = 0; i < descriptor_obj.rows; i++) {double dist = matches[i].distance;if (dist < max(3 * minDist, 0.02)) {goodMatchs.push_back(matches[i]);}}//绘制特征点Mat matchImg;drawMatches(image, keypoint_obj,image2, keypoint_scene, goodMatchs, matchImg,Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);imshow("matchImg", matchImg);//查看goodMatchsfor (size_t i = 0; i < goodMatchs.size(); i++){cout << "goodMatchs "<<to_string(i) << "\tKeypoint1, Keypoint2 = " << goodMatchs[i].queryIdx <<", " << goodMatchs[i].trainIdx << endl;}
}

二、单应性矩阵

从场景图像上得到特征匹配图像的四点坐标：详情请查看

函数介绍：findHomography有三个重载函数，这里只说明一个

Mat findHomography(

InputArray srcPoints, 　　　　　　　特征点集合，一般是来自目标图像
InputArray dstPoints,　　　　　　　特征点集合，一般是来自场景图像
int method = 0, 　　　　　　　　　　配准方法，支持有四种方法（如下）
double ransacReprojThreshold = 3,　　　　　　
OutputArray mask=noArray(), 　　　　掩码
const int maxIters = 2000,　　　　　　最大迭代次数，当使用RANSAC方法
const double confidence = 0.995　　　置信参数
);

method: 0 – 使用所有的点，比如最小二乘
RANSAC – 基于随机样本一致性
LMEDS – 最小中值
RHO –基于渐近样本一致性

void perspectiveTransform(: InputArray src, 　　　　输入四个顶点
OutputArray dst, 　　　输出四个顶点（变换后）
InputArray m 　　　　　仿射变换矩阵
)

void ImageFeature::flann_demo(Mat& image, Mat& image2) {//SURF 特征提取int minHessian = 400;Ptr<xfeatures2d::SURF> detector = xfeatures2d::SURF::create(minHessian);vector<KeyPoint> keypoint_obj, keypoint_scene;Mat descriptor_obj, descriptor_scens;detector->detectAndCompute(image, Mat(), keypoint_obj, descriptor_obj);detector->detectAndCompute(image2, Mat(), keypoint_scene, descriptor_scens);//SURF 特征匹配FlannBasedMatcher matcher;vector<DMatch> matches;matcher.match(descriptor_obj, descriptor_scens, matches);//查找最大最小距离double minDist = 1000;double maxDist = 0;for (size_t i = 0; i < descriptor_obj.rows; i++) {double dist = matches[i].distance;if (dist > maxDist) { maxDist = dist; }if (dist < minDist) { minDist = dist; }}cout << "minDist = " << minDist << endl;cout << "maxDist = " << maxDist << endl;// 筛选最佳匹配（小于最小距离的3倍）vector<DMatch> goodMatchs;for (size_t i = 0; i < descriptor_obj.rows; i++) {double dist = matches[i].distance;if (dist < max(3 * minDist, 0.02)) {goodMatchs.push_back(matches[i]);}}//绘制特征点Mat matchImg;drawMatches(image, keypoint_obj,image2, keypoint_scene, goodMatchs, matchImg,Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);imshow("matchImg", matchImg);//查看goodMatchsfor (size_t i = 0; i < goodMatchs.size(); i++){cout << "goodMatchs "<<to_string(i) << "\tKeypoint1, Keypoint2 = " << goodMatchs[i].queryIdx <<", " << goodMatchs[i].trainIdx << endl;}//获取goodMatchs坐标,并获取变换矩阵vector<Point2f> obj;vector<Point2f> scens;for (size_t i = 0; i < goodMatchs.size(); i++) {obj.push_back(keypoint_obj[goodMatchs[i].queryIdx].pt);scens.push_back(keypoint_scene[goodMatchs[i].trainIdx].pt);}Mat H = findHomography(obj, scens, RANSAC);vector<Point2f> obj_corners(4);vector<Point2f> scens_corners(4);obj_corners[0] = Point(0, 0);obj_corners[1] = Point(image.cols, 0);obj_corners[2] = Point(image.cols,image.rows);obj_corners[3] = Point(0, image.rows);perspectiveTransform(obj_corners, scens_corners, H);//可视化line(matchImg, scens_corners[0] + obj_corners[1], scens_corners[1] + obj_corners[1], Scalar(0, 0, 255));line(matchImg, scens_corners[1] + obj_corners[1], scens_corners[2] + obj_corners[1], Scalar(0, 0, 255));line(matchImg, scens_corners[2] + obj_corners[1], scens_corners[3] + obj_corners[1], Scalar(0, 0, 255));line(matchImg, scens_corners[3] + obj_corners[1], scens_corners[0] + obj_corners[1], Scalar(0, 0, 255));imshow("matchImg2", matchImg);//绘制到背景图上Mat image2_BGR;cvtColor(image2, image2_BGR, COLOR_GRAY2BGR);line(image2_BGR, scens_corners[0], scens_corners[1], Scalar(0, 0, 255));line(image2_BGR, scens_corners[1], scens_corners[2], Scalar(0, 0, 255));line(image2_BGR, scens_corners[2], scens_corners[3], Scalar(0, 0, 255));line(image2_BGR, scens_corners[3], scens_corners[0], Scalar(0, 0, 255));imshow("image2_BGR", image2_BGR);
}