用C++重写了OPENCV SURF算法例子

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用C++重写了OPENCV SURF算法例子(find_obj)
2012-2-7 创建

OPENCV 2.0后大量使用了标准模板类STL，而且图像结构统一为Mat，并提供了SURF类，而OPENCV2.2中带有的find_obj.cpp实际上还是主要使用c结构编写的，我也一直没有找到相对应C++例子
因此花时间改写了一下，为了例子的简洁，删除了不使用FLANN的brute force matching，而且没有进行平面映射，代码仅供参考


#include <iostream>#include <opencv2/opencv.hpp>using namespace cv;using namespace std;void newFlannFindPairs(const vector<KeyPoint>& obj_keypoints,     const vector<float>& obj_descriptors,      const vector<KeyPoint>& img_keypoints,      const vector<float>& img_descriptors,      vector<int>& ptpairs ){// find nearest neighbors using FLANNint length = obj_descriptors.size() / obj_keypoints.size();cv::Mat m_object(obj_keypoints.size(), length, CV_32F);cv::Mat m_image(img_keypoints.size(), length, CV_32F);cv::Mat temp_object(obj_descriptors, true);cv::Mat temp_image(img_descriptors, true);//Use memcpy to keep Mat sizememcpy(m_object.data, temp_object.data, temp_object.total() * sizeof(float));memcpy(m_image.data, temp_image.data, temp_image.total() * sizeof(float));//FLANNcv::Mat m_indices(obj_keypoints.size(), 2, CV_32S);cv::Mat m_dists(obj_keypoints.size(), 2, CV_32F);cv::flann::Index flann_index(m_image, cv::flann::KDTreeIndexParams(4));  // using 4 randomized kdtreesflann_index.knnSearch(m_object, m_indices, m_dists, 2, cv::flann::SearchParams(64) ); // maximum number of leafs checked//Save to point pairsint* indices_ptr = m_indices.ptr<int>(0);float* dists_ptr = m_dists.ptr<float>(0);for (int i=0;i<m_indices.rows;++i) {if (dists_ptr[2*i]<0.6*dists_ptr[2*i+1]) {ptpairs.push_back(i);ptpairs.push_back(indices_ptr[2*i]);}}}int surfTest(const char* object_filename, const char* scene_filename){double tt = (double)cvGetTickCount();Mat obj = imread(object_filename,0);Mat img = imread(scene_filename,0);if( (img.data == NULL) || (obj.data == NULL) ){cout<<"Unable to read images "<<endl;return 1;}// Create the SURF objectMat obj_mask = 255 * Mat::ones(obj.rows,obj.cols,CV_8U);Mat img_mask = 255 * Mat::ones(img.rows,img.cols,CV_8U);const int HessianThreshold = 500;SURF surf(HessianThreshold);vector<KeyPoint> obj_keypoints;vector<float> obj_descriptors;vector<KeyPoint> img_keypoints;vector<float> img_descriptors;surf(obj, obj_mask, obj_keypoints, obj_descriptors);surf(img, img_mask, img_keypoints, img_descriptors);// Plot the keypointsconst int radius = 1, thickness = 2;const Scalar obj_color(255,0,0,0);const Scalar img_color(0, 255,0,0);vector<int> ptpairs;newFlannFindPairs(obj_keypoints, obj_descriptors, img_keypoints, img_descriptors, ptpairs);double tt_f = (double)cvGetTickCount() - tt;printf( "Extraction pair point time = %gms\n", tt_f/(cvGetTickFrequency()*1000.));Mat correspond = Mat::zeros(img.rows + obj.rows, img.cols, CV_8U);cv::Rect objRect(Point(0, 0), obj.size());Mat objROI = correspond(objRect);obj.copyTo(objROI);cv::Rect imgRect(Point(0, objRect.height), img.size());Mat imgROI = correspond(imgRect);img.copyTo(imgROI);for( int i = 0; i < (int)ptpairs.size(); i += 2 ){Point p1 = Point(cvRound(obj_keypoints[ptpairs[i]].pt.x), cvRound(obj_keypoints[ptpairs[i]].pt.y));Point p2 = Point(cvRound(img_keypoints[ptpairs[i+1]].pt.x), cvRound(img_keypoints[ptpairs[i+1]].pt.y + objRect.height));line(correspond, p1, p2, obj_color);}imshow("SURF_CORRESPOND", correspond);waitKey(0);return 0;}int main(int argc, char** argv){const char* object_filename = argc == 3 ? argv[1] : "../debug/box.png";const char* scene_filename = argc == 3 ? argv[2] : "../debug/box_in_scene.png";return surfTest(object_filename, scene_filename);}

 
 

使用例子带有的图片，性能如下：
OPENCV自带的C可执行程序：
Object Descriptors: 593
Image Descriptors: 782
Extraction time = 363.425ms
Using approximate nearest neighbor search
重新编译的DEBUG版
Object Descriptors: 593
Image Descriptors: 782
Extraction time = 1316.22ms
Using approximate nearest neighbor search
重新改写的C++代码
注意功能比原例子减少了平面映射，只提取对应点并显示
DEBUG 1820.17ms  
RELEASE 402.799ms
	
					
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