图像拼接|OpenCV3.4 stitching源码分析（一）

前言

图像拼接|——OpenCV3.4 stitching模块分析（一）特征点检测

参考opencv_赵春江的专栏_zhaocj-CSDN博客

特征点检测

OpenCV3.4中实现了surf、orb、sift等特征检测算法，默认的是surf。

特征检测算法的实现主要包含在 “opencv2/stitching/detail/matchers.hpp” 中。

源码

首先定义了一个ImageFeatures结构体用于存放特征信息，包括图片序号、尺寸、特征点信息及描述子矩阵：

struct CV_EXPORTS ImageFeatures{int img_idx;Size img_size;std::vector<KeyPoint> keypoints;UMat descriptors;};

定义FeaturesFinder基类，其中的成员函数find()是用来寻找特征信息：

class CV_EXPORTS FeaturesFinder{public:virtual ~FeaturesFinder() {}//重载void operator ()(InputArray image, ImageFeatures &features);//寻找给定图像特征void operator ()(InputArray image, ImageFeatures &features, const std::vector<cv::Rect> &rois);//并行寻找给定图像特征void operator ()(InputArrayOfArrays images, std::vector<ImageFeatures> &features,const std::vector<std::vector<cv::Rect> > &rois);//重载void operator ()(InputArrayOfArrays images, std::vector<ImageFeatures> &features);//释放内存virtual void collectGarbage() {}protected://纯虚函数，根据用户所选取的特征类别，调用不同子类的find函数virtual void find(InputArray image, ImageFeatures &features) = 0;//判断是否可以并行bool isThreadSafe() const;};

FeaturesFinder基类派生出SurfFeaturesFinder、SiftFeaturesFinder、OrbFeaturesFinder等派生类。

SurfFeaturesFinder派生类定义：

class CV_EXPORTS SurfFeaturesFinder : public FeaturesFinder{public:SurfFeaturesFinder(double hess_thresh = 300., int num_octaves = 3, int num_layers = 4,int num_octaves_descr = /*4*/3, int num_layers_descr = /*2*/4);private:void find(InputArray image, ImageFeatures &features) CV_OVERRIDE;Ptr<FeatureDetector> detector_;Ptr<DescriptorExtractor> extractor_;Ptr<Feature2D> surf;};

SiftFeaturesFinder派生类定义：

class CV_EXPORTS SiftFeaturesFinder : public FeaturesFinder{public:SiftFeaturesFinder();private:void find(InputArray image, ImageFeatures &features) CV_OVERRIDE;Ptr<Feature2D> sift;};

OrbFeaturesFinder派生类定义：

class CV_EXPORTS OrbFeaturesFinder : public FeaturesFinder{public:OrbFeaturesFinder(Size _grid_size = Size(3,1), int nfeatures=1500, float scaleFactor=1.3f, int nlevels=5);private:void find(InputArray image, ImageFeatures &features) CV_OVERRIDE;Ptr<ORB> orb;Size grid_size;};

关于各个特征检测算法的具体实现，就不具体展开了，一般只要知道怎样调用高级API即可。

应用

#include <iostream>#include "opencv2/highgui.hpp"#include "opencv2/stitching/detail/matchers.hpp"using namespace std;using namespace cv;using namespace cv::detail;int main(){Mat img = imread("4.jpg");Ptr<FeaturesFinder> finder1, finder2, finder3;finder1 = makePtr<SurfFeaturesFinder>();finder2 = makePtr<OrbFeaturesFinder>();finder3 = makePtr<SiftFeaturesFinder>();ImageFeatures features1, features2, features3;(*finder1)(img, features1);(*finder2)(img, features2);(*finder3)(img, features3);Mat output_img1, output_img2, output_img3;//调用drawKeypoints函数绘制特征drawKeypoints(img, features1.keypoints, output_img1, Scalar(255, 0, 0));drawKeypoints(img, features2.keypoints, output_img2, Scalar(255, 0, 0));drawKeypoints(img, features3.keypoints, output_img3, Scalar(255, 0, 0));cout << "Number of surf keypoints: " << features1.keypoints.size() << endl;cout << "Number of orb keypoints: " << features2.keypoints.size() << endl;cout << "Number of sift keypoints: " << features3.keypoints.size() << endl;namedWindow("surf");imshow("surf", output_img1);namedWindow("orb");imshow("orb", output_img2);namedWindow("sift");imshow("sift", output_img3);waitKey(0);return 0;}

运行结果：

Number of surf keypoints: 1403

Number of orb keypoints: 1346

Number of sift keypoints: 1287

主观视觉上还是SIFT更稳健一些，不过stitching默认使用的是surf。

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