opencv上gpu版surf特征点与orb特征点提取及匹配实例
一、前言
本文主要实现了使用opencv里的gpu版surf特征检测器和gpu版orb检测器,分别对图片进行特征点提取及匹配,并对寻获的特征点进行了距离筛选,将匹配较为好的特征点进行展示
二、实现代码
我不生产代码,我只是代码的搬运工和修改工
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//main.cpp//
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#include <opencv2/core/core.hpp>
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#include <opencv2/imgproc/imgproc.hpp>
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#include <opencv2/highgui/highgui.hpp>
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#include <opencv2/gpu/gpu.hpp>
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#include <opencv2/nonfree/gpu.hpp>
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#include <opencv2/nonfree/features2d.hpp>
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#include <iostream>
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using namespace std;
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using namespace cv;
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Mat rotatedImage(const Mat & _src, double _degree)
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{
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int width_src = _src.cols;
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int height_src = _src.rows;
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float center_x = width_src / 2.0;
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float center_y = height_src / 2.0;
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double angle = _degree * CV_PI / 180.;
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double a = sin(angle), b = cos(angle);
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Mat map_matrix = getRotationMatrix2D(Point2f(center_x, center_y), _degree, 1.0);//获得旋转矩阵
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int height_rotated = height_src*fabs(b) + width_src*fabs(a);
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int width_rotated = height_src*fabs(a) + width_src*fabs(b);
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map_matrix.at<double>(0, 2) += (width_rotated - width_src) / 2.0; //将坐标移到中点
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map_matrix.at<double>(1, 2) += (height_rotated - height_src) / 2.0; //将坐标移到中点
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Mat dst;
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warpAffine(_src, dst, map_matrix, Size(width_rotated, height_rotated),
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CV_INTER_CUBIC | CV_WARP_FILL_OUTLIERS, BORDER_CONSTANT, cvScalarAll(0));
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return dst;
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}
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//主要获得surf特征点、描述子、及特征点匹配
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void surfExtractor(Mat& _src_Img, Mat& _dst_Img )
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{
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gpu::GpuMat src_gpu(_src_Img);
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gpu::GpuMat dst_gpu(_dst_Img);
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std::vector<KeyPoint> keypoints_src;
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std::vector<KeyPoint> keypoints_dst;
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std::vector<DMatch> matches;
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gpu::SURF_GPU FeatureFinder_gpu(500);
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gpu::GpuMat keypoints_gpu_src, keypoints_gpu_dst;
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gpu::GpuMat descriptors_gpu_src, descriptors_gpu_dst;
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std::vector<float> descriptors_v1, descriptors_v2;
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//计算特征点和特征描述子
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FeatureFinder_gpu(src_gpu, gpu::GpuMat(), keypoints_gpu_src, descriptors_gpu_src);
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FeatureFinder_gpu(dst_gpu, gpu::GpuMat(), keypoints_gpu_dst, descriptors_gpu_dst);
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//将特征点下载回cpu,便于画图使用
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FeatureFinder_gpu.downloadKeypoints(keypoints_gpu_src, keypoints_src);
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FeatureFinder_gpu.downloadKeypoints(keypoints_gpu_dst, keypoints_dst);
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//使用gpu提供的BruteForceMatcher进行特征点匹配
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gpu::BruteForceMatcher_GPU< L2<float> > matcher_lk;
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matcher_lk.match(descriptors_gpu_src, descriptors_gpu_dst, matches, gpu::GpuMat());
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float max_distance = 0.2; //定义特征点好坏衡量距离
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std::vector<DMatch> good_matches; //收集较好的匹配点
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for (int i = 0; i < descriptors_gpu_src.rows; i++) {
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if (matches[i].distance < max_distance) {
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good_matches.push_back(matches[i]);
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}
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}
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Mat image_matches;
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drawMatches(_src_Img, keypoints_src, _dst_Img, keypoints_dst, good_matches,
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image_matches, Scalar(0, 255, 0) , Scalar::all(-1), vector<char>(), 0);
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imshow("Gpu Surf", image_matches);
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}
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void orbExtractor(Mat& _src_Img, Mat& _dst_Img)
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{
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gpu::GpuMat src_gpu(_src_Img);
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gpu::GpuMat dst_gpu(_dst_Img);
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std::vector<KeyPoint> keypoints_src, keypoints_dst;
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gpu::GpuMat descriptors_gpu_src, descriptors_gpu_dst;
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std::vector<DMatch> matches;
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gpu::ORB_GPU orb_finder(500);
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orb_finder.blurForDescriptor = true; //设置模糊
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cv::gpu::GpuMat fullmask_1(src_gpu.size(), CV_8U, 0xFF);
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cv::gpu::GpuMat fullmask_2(dst_gpu.size(), CV_8U, 0xFF);
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orb_finder(src_gpu, fullmask_1, keypoints_src, descriptors_gpu_src);
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orb_finder(dst_gpu, fullmask_2, keypoints_dst, descriptors_gpu_dst);
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//使用gpu提供的BruteForceMatcher进行特征点匹配
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gpu::BruteForceMatcher_GPU< HammingLUT > matcher_lk;
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matcher_lk.match(descriptors_gpu_src, descriptors_gpu_dst, matches, gpu::GpuMat());
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float max_distance = 60; //定义特征点好坏衡量距离
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std::vector<DMatch> good_matches; //收集较好的匹配点
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for (int i = 0; i < descriptors_gpu_src.rows; i++) {
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if (matches[i].distance < max_distance) {
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good_matches.push_back(matches[i]);
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}
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}
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Mat image_matches;
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drawMatches(_src_Img, keypoints_src, _dst_Img, keypoints_dst, good_matches,
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image_matches, Scalar(255, 0, 0), Scalar::all(-1), vector<char>(), 0);
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imshow("Gpu ORB", image_matches);
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}
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int main()
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{
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int num_devices = cv::gpu::getCudaEnabledDeviceCount();
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if (num_devices <= 0)
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{
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std::cerr << "There is no device." << std::endl;
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return -1;
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}
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int enable_device_id = -1;
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for (int i = 0; i < num_devices; i++)
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{
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cv::gpu::DeviceInfo dev_info(i);
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if (dev_info.isCompatible())
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{
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enable_device_id = i;
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}
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}
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if (enable_device_id < 0)
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{
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std::cerr << "GPU module isn't built for GPU" << std::endl;
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return -1;
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}
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gpu::setDevice(enable_device_id);
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Mat src_Img = imread("book.bmp" , 0);
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Mat dst_Img = rotatedImage(src_Img, -30.0);
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surfExtractor(src_Img, dst_Img);
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orbExtractor(src_Img, dst_Img);
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cv::waitKey(0);
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return 0;
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}
三、运行结果
运行环境为vs2013+opencv2.4.9+cuda7.0,结果展示如下,orb算法寻找特征点及计算描述子速度较快,gpu版的surf特征点对输入图片大小有要求,不能太小