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我試圖縫合android中的圖像像全景視圖。我試圖使用衝浪描述符拼接images.I使用2.4.0版本的openCv因爲上版本不包含非遊離文件夾衝浪descriptor.I也已包括OpenCV未定義的引用到cv :: SURF :: SURF（）eclipse
#include "opencv2/nonfree/features2d.hpp"
#include "opencv2/nonfree/nonfree.hpp"
在報頭和也給定的路徑.. \ JNI \包括和.. \天然\庫\ armeabi-V7A在GNU路徑中包含所有.a files.Below是我的代碼，我從OpenCV示例
#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/nonfree/features2d.hpp"
#include "opencv2/nonfree/nonfree.hpp"
using namespace std;
using namespace cv;
void readme();
int main(int argc, char** argv)
{
if(argc != 3)
{
readme(); return -1;
}
Mat img_object = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
Mat img_scene = imread(argv[2], CV_LOAD_IMAGE_GRAYSCALE);
if(!img_object.data || !img_scene.data)
{ std::cout<< " --(!) Error reading images " << std::endl; return -1; }
//-- Step 1: Detect the keypoints using SURF Detector
int minHessian = 400;
SurfFeatureDetector detector(400); //error:undefined reference to cv::SURF::SURF(double, int, int, bool, bool)
std::vector<KeyPoint> keypoints_object, keypoints_scene;
detector.detect(img_object, keypoints_object);
detector.detect(img_scene, keypoints_scene);
//-- Step 2: Calculate descriptors (feature vectors)
SurfDescriptorExtractor extractor; //error:undefined reference to cv::SURF::SURF()
Mat descriptors_object, descriptors_scene;
extractor.compute(img_object, keypoints_object, descriptors_object);
extractor.compute(img_scene, keypoints_scene, descriptors_scene);
//-- Step 3: Matching descriptor vectors using FLANN matcher
FlannBasedMatcher matcher;
std::vector<DMatch> matches;
matcher.match(descriptors_object, descriptors_scene, matches);
double max_dist = 0; double min_dist = 100;
//-- Quick calculation of max and min distances between keypoints
for(int i = 0; i < descriptors_object.rows; i++)
{ double dist = matches[i].distance;
if(dist < min_dist) min_dist = dist;
if(dist > max_dist) max_dist = dist;}
printf("-- Max dist : %f \n", max_dist);
printf("-- Min dist : %f \n", min_dist);
//-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist)
std::vector<DMatch> good_matches;
for(int i = 0; i < descriptors_object.rows; i++)
{ if(matches[i].distance < 3*min_dist)
{ good_matches.push_back(matches[i]); }
}
Mat img_matches;
drawMatches(img_object, keypoints_object, img_scene, keypoints_scene,
good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
//-- Localize the object from img_1 in img_2
std::vector<Point2f> obj;
std::vector<Point2f> scene;
for(size_t i = 0; i < good_matches.size(); i++)
{
//-- Get the keypoints from the good matches
obj.push_back(keypoints_object[ good_matches[i].queryIdx ].pt);
scene.push_back(keypoints_scene[ good_matches[i].trainIdx ].pt);
}
Mat H = findHomography(obj, scene, CV_RANSAC);
//-- Get the corners from the image_1 (the object to be "detected")
std::vector<Point2f> obj_corners(4);
obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint(img_object.cols, 0);
obj_corners[2] = cvPoint(img_object.cols, img_object.rows); obj_corners[3] = cvPoint(0, img_object.rows);
std::vector<Point2f> scene_corners(4);
perspectiveTransform(obj_corners, scene_corners, H);
//-- Draw lines between the corners (the mapped object in the scene - image_2)
Point2f offset((float)img_object.cols, 0);
line(img_matches, scene_corners[0] + offset, scene_corners[1] + offset, Scalar(0, 255, 0), 4);
line(img_matches, scene_corners[1] + offset, scene_corners[2] + offset, Scalar(0, 255, 0), 4);
line(img_matches, scene_corners[2] + offset, scene_corners[3] + offset, Scalar(0, 255, 0), 4);
line(img_matches, scene_corners[3] + offset, scene_corners[0] + offset, Scalar(0, 255, 0), 4);
//-- Show detected matches
imshow("Good Matches & Object detection", img_matches);
waitKey(0);
return 0;}
/**
* @function readme
*/
void readme()
{ std::cout << " Usage: ./SURF_Homography <img1> <img2>" << std::endl; }
我在那裏SurfFeatureDetector和SurfDescriptorExtractor declared.I在comment.Anybody已經寫了錯誤的錯誤有任何關於本那麼請suggest.I真的很堅持這個問題的任何想法。:(在此先感謝。