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I am using OpenCv along with Eclipse to implement matching with homography. I am getting the following error while building it:
/usr/bin/ld: ./src/flann.o: undefined reference to symbol
'_ZN2cv5flann12SearchParamsC1Eifb'
An image of compilation error.
Here's my code:
#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/nonfree.hpp"
using namespace cv;
void readme();
/** #function main */
int main()
{
Mat img_object = imread( "/home/gaps/Desktop/DSC00268.JPG");
Mat img_scene = imread( "/home/gaps/Desktop/chart_1-2_1816185b.jpg" );
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( minHessian );
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;
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
std::vector<Point2f> obj;
std::vector<Point2f> scene;
for( int 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 )
line( img_matches, scene_corners[0] + Point2f( img_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 );
line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), 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_descriptor <img1> <img2>" << std::endl;
}
You are not linking libopencv_flann, in which the undefined symbol is defined. Add -lopencv_flann to your linker options.
Related
I'm trying to recognize objects by using SURF algorithm. Since I had some problems while installing the nonfree module, I decided to use an older version of OpenCV(2.4.11 I'm running it in Visual Studio 2013).
Now, I have some errors that aren't related to the sintax and I don't know what to do. Here is the code:
#include <stdio.h>
#include <iostream>
#include <fstream>
#include <string>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/nonfree/features2d.hpp"
#include "opencv2/nonfree/nonfree.hpp"
using namespace cv;
using namespace std;
char key = 'a';
int framecount = 0;
SurfFeatureDetector detector( 500 );
SurfDescriptorExtractor extractor;
FlannBasedMatcher matcher;
Mat frame, des_object, image;
Mat des_image, img_matches, H;
std::vector<KeyPoint> kp_object;
std::vector<Point2f> obj_corners(4);
std::vector<KeyPoint> kp_image;
std::vector<vector<DMatch > > matches;
std::vector<DMatch > good_matches;
std::vector<Point2f> obj;
std::vector<Point2f> scene;
std::vector<Point2f> scene_corners(4);
int main()
{
//reference image
Mat object = imread( "C:\\Users\\patri\\Desktop\\test.jpg", CV_LOAD_IMAGE_GRAYSCALE );
if( !object.data )
{
std::cout<< "Error reading object " << std::endl;
return -1;
}
//compute detectors and descriptors of reference image
detector.detect( object, kp_object );
extractor.compute( object, kp_object, des_object );
//create video capture object
VideoCapture cap(0);
//Get the corners from the object
obj_corners[0] = cvPoint(0,0);
obj_corners[1] = cvPoint( object.cols, 0 );
obj_corners[2] = cvPoint( object.cols, object.rows );
obj_corners[3] = cvPoint( 0, object.rows );
//wile loop for real time detection
while (key != 27)
{
//capture one frame from video and store it into image object name 'frame'
cap >> frame;
if (framecount < 5)
{
framecount++;
continue;
}
//converting captured frame into gray scale
cvtColor(frame, image, CV_RGB2GRAY);
//extract detectors and descriptors of captured frame
detector.detect( image, kp_image );
extractor.compute( image, kp_image, des_image );
//find matching descriptors of reference and captured image
matcher.knnMatch(des_object, des_image, matches, 2);
//finding matching keypoints with Euclidean distance 0.6 times the distance of next keypoint
//used to find right matches
for(int i = 0; i < min(des_image.rows-1,(int) matches.size()); i++)
{
if((matches[i][0].distance < 0.6*(matches[i][1].distance)) && ((int) matches[i].size()<=2 && (int) matches[i].size()>0))
{
good_matches.push_back(matches[i][0]);
}
}
//Draw only "good" matches
drawMatches( object, kp_object, frame, kp_image, good_matches, img_matches,
Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
//3 good matches are enough to describe an object as a right match.
if (good_matches.size() >= 3)
{
for( int i = 0; i < good_matches.size(); i++ )
{
//Get the keypoints from the good matches
obj.push_back( kp_object[ good_matches[i].queryIdx ].pt );
scene.push_back( kp_image[ good_matches[i].trainIdx ].pt );
}
try
{
H = findHomography( obj, scene, CV_RANSAC );
}
catch(Exception e){}
perspectiveTransform( obj_corners, scene_corners, H);
//Draw lines between the corners (the mapped object in the scene image )
line( img_matches, scene_corners[0] + Point2f( object.cols, 0), scene_corners[1] + Point2f( object.cols, 0), Scalar(0, 255, 0), 4 );
line( img_matches, scene_corners[1] + Point2f( object.cols, 0), scene_corners[2] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[2] + Point2f( object.cols, 0), scene_corners[3] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[3] + Point2f( object.cols, 0), scene_corners[0] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
}
//Show detected matches
imshow( "Good Matches", img_matches );
//clear array
good_matches.clear();
key = waitKey(1);
}
return 0;
}
I'm also attaching a picture of the errors I get. Please help me find the problem.
errors
About the warnings ("conversion from int to float"), I suppose the problem is the use of integer initializing cv::Point2f(); so, in lines 112-115, you should use Point2f( object.cols, 0.0) instead of Point2f( object.cols, 0).
But are only warnings.
The real problem is the linker error: you need the library with cv::SURF::SURF() (used by SurfFeatureDetector detector( 500 ); and SurfDescriptorExtractor extractor;).
You should add a library that I suppose is, in you case, opencv_nonfree241.lib. Or a similar name.
I am using the below code provided at opencv site to find the scale and rotation invariant template matching,but I want to calculate exact pixel co-ordinates of template image in source image,especially for rotated image.
#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/nonfree.hpp"
using namespace cv;
void readme();
/** #function main */
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( minHessian );
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;
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
std::vector<Point2f> obj;
std::vector<Point2f> scene;
for( int 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 )
line( img_matches, scene_corners[0] + Point2f( img_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 );
line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), 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_descriptor <img1> <img2>" << std::endl;
}
You already have these coordinates in scene_corners after computing the perspective transform. The four points in scene_corners make up a rectangle that is the template match found in the scene, so these four corners are what you're looking for.
Further, if you wish to find the trasnformed coordinates of any point from the template in the scene, you just need to apply the same homography, via the perspectiveTransform function, to that point.
the function stitches two images...that works fine...
function:
for( int i = 1; i<(4); i++ )
{
test=Bildpaar(test,image1[2+z]);
z=z+1;
}
imwrite("../superrechts.jpg",test);
but the second iteration fails because there is a Black part of the pano image and I can only see the third image...
my function
Mat Bildpaar(Mat Bild1,Mat Bild2)
{
Mat img_keypoints_1;
std::vector<KeyPoint> keypoints_1,keypoints_2;//anzahl der keypoint vektoren
int minHessian = 50;
FastFeatureDetector detector( minHessian);
detector.detect( Bild1, keypoints_1 );
detector.detect( Bild2, keypoints_2 );
//-- Draw keypoints
drawKeypoints( Bild1, keypoints_1, img_keypoints_1, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
//%%%%%%%%%%%%%%%%%%%%%%%%&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&
//Discriptor
//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
SiftDescriptorExtractor extractor; //Zeit
Mat descriptors_1,descriptors_2;
extractor.compute( Bild1, keypoints_1, descriptors_1 );
extractor.compute( Bild2, keypoints_2, descriptors_2 );
// waitKey(0);
//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
//Matcher
//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
//-- Step 3: Matching descriptor vectors using FLANN matcher
std::vector< DMatch > matches;
FlannBasedMatcher matcher;
matcher.match( descriptors_1, descriptors_2, matches );
double max_dist = 0; double min_dist = 100;
Mat img_matches;
drawMatches(Bild1, keypoints_1, Bild2, keypoints_2, matches, img_matches);
//namedWindow( filename, CV_WINDOW_NORMAL );// Create a window for display.
//imshow(filename, img_matches[i]);
std::vector< DMatch > good_matches;
for( int i = 0; i < descriptors_1.rows; i++ )
{ double dist = matches[i].distance;
if( dist < min_dist ) min_dist = dist;
if( dist > max_dist ) max_dist = dist;
}
for( int i = 0; i < descriptors_1.rows; i++ )
{ if( matches[i].distance < 2*min_dist )
{ good_matches.push_back( matches[i]);
}
}
Mat img_matches1;
drawMatches( Bild1, keypoints_1, Bild2, keypoints_2,
good_matches, img_matches1, Scalar::all(-1), Scalar::all(-1),
vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
//bis hierhin geht es
//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
std::vector< Point2f > obj1;
std::vector< Point2f > scene1;
for( int i = 0; i < good_matches.size(); i++ )
{
//-- Get the keypoints from the good matches
obj1.push_back( keypoints_1[ good_matches[i].queryIdx ].pt );
scene1.push_back( keypoints_2[ good_matches[i].trainIdx ].pt );
}
//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
// Find the Homography Matrix
//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%#
Mat H;
H= findHomography( scene1, obj1, CV_RANSAC,5 );
std::vector<Point2f> obj_corners(4);
obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( Bild1.cols, 0 );
obj_corners[2] = cvPoint( Bild1.cols, Bild1.rows ); obj_corners[3] = cvPoint( 0, Bild1.rows );
std::vector<Point2f> scene_corners(4);
perspectiveTransform( obj_corners, scene_corners, H);
//-- Show detected matches
//-- Draw lines between the corners (the mapped object in the scene - image_2 )
line( img_matches1, scene_corners[0] + Point2f( Bild1.cols, 0), scene_corners[1] + Point2f( Bild1.cols, 0), Scalar(0, 255, 0), 4 );
line( img_matches1, scene_corners[1] + Point2f( Bild1.cols, 0), scene_corners[2] + Point2f( Bild1.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches1, scene_corners[2] + Point2f( Bild1.cols, 0), scene_corners[3] + Point2f( Bild1.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches1, scene_corners[3] + Point2f( Bild1.cols, 0), scene_corners[0] + Point2f( Bild1.cols, 0), Scalar( 0, 255, 0), 4 );
//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
//Warp images
//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Mat result;
warpPerspective(Bild2, result, H, Size(Bild2.cols*2, Bild2.rows), INTER_CUBIC);
Mat final(Size(Bild2.cols + Bild1.cols, Bild2.rows),CV_8UC3);//richtig //testen ob Spaltenanzahl auswirkungen auf das ergebnis hat
//velikost img1
Mat roi1(final, Rect(0, 0, Bild1.cols, Bild1.rows));
Mat roi2(final, Rect(0, 0, result.cols, result.rows));
result.copyTo(roi2);
Bild1.copyTo(roi1);
return final;
}
In my view I make some misstakes with the roi...
what can I do I try blending but it works only of the images have the same size...or I can remove the black part...What is the best thing I can do?
Maybe I copy one roi over another I try to change the order of
result.copyTo(roi2);
Bild1.copyTo(roi1);
In one case I saw the left picture and in the other case the right picture...how can I see both together?
best regards
I found one way (but its not a good way to do it).
Firstly you have stitch 2 images (called final1)
Secondly when u stitch final1 and image 3 you have to blend the rois then it works...and you have to play arround with alpha...
Mat blendo;
double alpha=0.3;
double beta = ( 1.0 - alpha );
addWeighted( roi1, alpha, roi2, beta, 0.0, blendo);
The thing is I am unable to implement the center of mass with the existing code, which image object to use etc after the detected object is bounded by the rectangle so that I may get the trajectory of the path.
I am using Opencv2.3 .I found out there are 2 methods - Link1 and Link2 talk about the usage of moments. And the other method is to use the information of the bounding box Link3. The method of moments requires image thresholding. However, when using SURF the image is in gray scale. So, on passing a gray image for thresholding displays a white image! Now, I am having a tough time in understanding how I should calculate the centroid using the code below (esp what should I use instead of points[i].x since I am using
obj.push_back( kp_object[ good_matches[i].queryIdx ].pt );
scene.push_back( kp_image[ good_matches[i].trainIdx ].pt )
where in my case numPoints=good_matches.size(), denoting the number of feature points) as mentioned in the documentation. If anyone can put up an implementation of how to use SURF with centroid then it will be helpful.
#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/calib3d/calib3d.hpp"
using namespace cv;
int main()
{
Mat object = imread( "object.png", CV_LOAD_IMAGE_GRAYSCALE );
if( !object.data )
{
std::cout<< "Error reading object " << std::endl;
return -1;
}
//Detect the keypoints using SURF Detector
int minHessian = 500;
SurfFeatureDetector detector( minHessian );
std::vector<KeyPoint> kp_object;
detector.detect( object, kp_object );
//Calculate descriptors (feature vectors)
SurfDescriptorExtractor extractor;
Mat des_object;
extractor.compute( object, kp_object, des_object );
FlannBasedMatcher matcher;
VideoCapture cap(0);
namedWindow("Good Matches");
std::vector<Point2f> obj_corners(4);
//Get the corners from the object
obj_corners[0] = cvPoint(0,0);
obj_corners[1] = cvPoint( object.cols, 0 );
obj_corners[2] = cvPoint( object.cols, object.rows );
obj_corners[3] = cvPoint( 0, object.rows );
char key = 'a';
int framecount = 0;
while (key != 27)
{
Mat frame;
cap >> frame;
if (framecount < 5)
{
framecount++;
continue;
}
Mat des_image, img_matches;
std::vector<KeyPoint> kp_image;
std::vector<vector<DMatch > > matches;
std::vector<DMatch > good_matches;
std::vector<Point2f> obj;
std::vector<Point2f> scene;
std::vector<Point2f> scene_corners(4);
Mat H;
Mat image;
cvtColor(frame, image, CV_RGB2GRAY);
detector.detect( image, kp_image );
extractor.compute( image, kp_image, des_image );
matcher.knnMatch(des_object, des_image, matches, 2);
for(int i = 0; i < min(des_image.rows-1,(int) matches.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
{
if((matches[i][0].distance < 0.6*(matches[i][4].distance)) && ((int) matches[i].size()<=2 && (int) matches[i].size()>0))
{
good_matches.push_back(matches[i][0]);
}
}
//Draw only "good" matches
drawMatches( object, kp_object, image, kp_image, good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
if (good_matches.size() >= 4)
{
for( int i = 0; i < good_matches.size(); i++ )
{
//Get the keypoints from the good matches
obj.push_back( kp_object[ good_matches[i].queryIdx ].pt );
scene.push_back( kp_image[ good_matches[i].trainIdx ].pt );
}
H = findHomography( obj, scene, CV_RANSAC );
perspectiveTransform( obj_corners, scene_corners, H);
//Draw lines between the corners (the mapped object in the scene image )
line( img_matches, scene_corners[0] + Point2f( object.cols, 0), scene_corners[1] + Point2f( object.cols, 0), Scalar(0, 255, 0), 4 );
line( img_matches, scene_corners[1] + Point2f( object.cols, 0), scene_corners[2] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[2] + Point2f( object.cols, 0), scene_corners[3] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[3] + Point2f( object.cols, 0), scene_corners[0] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
}
//Show detected matches
imshow( "Good Matches", img_matches );
key = waitKey(1);
}
return 0;
}
so, you already got your pointlists,
obj.push_back( kp_object[ good_matches[i].queryIdx ].pt );
scene.push_back( kp_image[ good_matches[i].trainIdx ].pt );
i think, it's perfectly valid, to calc the centroid based on that, no further image processing nessecary.
there's 2 methods, the 'center of mass' way, that's just the mean position of all points, like this:
Point2f cen(0,0);
for ( size_t i=0; i<scene.size(); i++ )
{
cen.x += scene[i].x;
cen.y += scene[i].y;
}
cen.x /= scene.size();
cen.y /= scene.size();
and the 'center of bbox' way
Point2f pmin(1000000,1000000);
Point2f pmax(0,0);
for ( size_t i=0; i<scene.size(); i++ )
{
if ( scene[i].x < pmin.x ) pmin.x = scene[i].x;
if ( scene[i].y < pmin.y ) pmin.y = scene[i].y;
if ( scene[i].x > pmax.x ) pmax.x = scene[i].x;
if ( scene[i].y > pmax.y ) pmax.y = scene[i].y;
}
Point2f cen( (pmax.x-pmin.x)/2, (pmax.y-pmin.y)/2);
note, that the results will be different ! they're only the same for circles & squares, point symmetric objects
// now draw a circle around the centroid:
cv::circle( img, cen, 10, Scalar(0,0,255), 2 );
// and a line connecting the query and train points:
cv::line( img, scene[i], obj[i], Scalar(255,0,0), 2 );
I am using to compile and run code from Features2D + Homography to find a known object tutorial, and I am getting this
OpenCV Error: Assertion failed (npoints >= 0 && points2.checkVector(2) == npoint
s && points1.type() == points2.type()) in unknown function, file c:\Users\vp\wor
k\ocv\opencv\modules\calib3d\src\fundam.cpp, line 1062
run-time error. after debugging I find that the program is crashing at findHomography function.
Unhandled exception at 0x760ab727 in OpenCVTemplateMatch.exe: Microsoft C++ exception: cv::Exception at memory location 0x0029eb3c..
in the Introduction of OpenCV, the "cv Namespace" chapter says that
Some of the current or future OpenCV external names may conflict with STL or other libraries. In this case, use explicit namespace specifiers to resolve the name conflicts:
I changed my code and use everywhere explicit namespace specifiers, but problem did not solved. If you can, please help me in this problem, or say which function do same thing as findHomography, and do not crash program.
And this is my code
#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"
void readme();
/** #function main */
int main( int argc, char** argv )
{
if( argc != 3 )
{ readme(); return -1; }
cv::Mat img_object = cv::imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
cv::Mat img_scene = cv::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;
cv::SurfFeatureDetector detector( minHessian );
std::vector<cv::KeyPoint> keypoints_object, keypoints_scene;
detector.detect( img_object, keypoints_object );
detector.detect( img_scene, keypoints_scene );
//-- Step 2: Calculate descriptors (feature vectors)
cv::SurfDescriptorExtractor extractor;
cv::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
cv::FlannBasedMatcher matcher;
std::vector< cv::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< cv::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]); }
}
cv::Mat img_matches;
cv::drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
good_matches, img_matches, cv::Scalar::all(-1), cv::Scalar::all(-1),
std::vector<char>(), cv::DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
//-- Localize the object
std::vector<cv::Point2f> obj;
std::vector<cv::Point2f> scene;
for( int 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 );
}
cv::Mat H = cv::findHomography( obj, scene, CV_RANSAC );
//-- Get the corners from the image_1 ( the object to be "detected" )
std::vector<cv::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<cv::Point2f> scene_corners(4);
cv::perspectiveTransform( obj_corners, scene_corners, H);
//-- Draw lines between the corners (the mapped object in the scene - image_2 )
cv::line( img_matches, scene_corners[0] + cv::Point2f( img_object.cols, 0), scene_corners[1] + cv::Point2f( img_object.cols, 0), cv::Scalar(0, 255, 0), 4 );
cv::line( img_matches, scene_corners[1] + cv::Point2f( img_object.cols, 0), scene_corners[2] + cv::Point2f( img_object.cols, 0), cv::Scalar( 0, 255, 0), 4 );
cv::line( img_matches, scene_corners[2] + cv::Point2f( img_object.cols, 0), scene_corners[3] + cv::Point2f( img_object.cols, 0), cv::Scalar( 0, 255, 0), 4 );
cv::line( img_matches, scene_corners[3] + cv::Point2f( img_object.cols, 0), scene_corners[0] + cv::Point2f( img_object.cols, 0), cv::Scalar( 0, 255, 0), 4 );
//-- Show detected matches
cv::imshow( "Good Matches & Object detection", img_matches );
cv::waitKey(0);
return 0;
}
/** #function readme */
void readme()
{ std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }
Today I run into the same problem with this example code. #mathematical-coffee was right there were no features extracted, thus obj and scene were empty. I replaced the test pictures and it worked. From texture style images you can't extract SURF features.
Another way to is to lower the parameter minHessianve.g. `int minHessian = 20;
or use the FAST feature detector by changing a few lines:
//-- Step 1: Detect the keypoints using SURF Detector
int minHessian = 15;
FastFeatureDetector detector( minHessian );
The actual answer is within the error message:
npoints >= 0 && points2.checkVector(2) == npoints && points1.type() == points2.type()
Human readable translation, you have to fulfil these assertions:
Your input must have a positive number of points (in practice an findHomography needs 4 or more points).
Your 'object' and 'scene' list of points must have the same number of points.
Your 'object' and 'scene' list of points must have the same type of points.
I had the same issue and I followed the solution by MMH. Just writing
cv::Mat H = cv::findHomography( cv::Mat(obj), cv::Mat(scene), CV_RANSAC );
cv::perspectiveTransform( cv::Mat(obj_corners), cv::Mat(scene_corners), H);
solved the problem.
More likely, the problem is here:
if( matches[i].distance < 3*min_dist)
The strict inequality is not what you want. If min_dist == 0, a very good match, you will disregard all zero-distance points. Replace with:
if( matches[i].distance <= 3*min_dist)
and you should see good results for images that match well.
To exit gracefully, I would also add, e.g.:
if (good_matches.size() == 0)
{
std::cout<< " --(!) No good matches found " << std::endl; return -2;
}
you need to add a condition before findHomography
if(obj.size()>3){
///-- Get the corners from the image_1 ( the object to be "detected" )
vector<Point2f> obj_corners(4);
obj_corners[0] = Point(0,0); obj_corners[1] = Point( img_object.cols, 0 );
obj_corners[2] = Point( img_object.cols, img_object.rows ); obj_corners[3] = Point( 0, img_object.rows );
Mat H = findHomography( obj, scene,CV_RANSAC );
perspectiveTransform( obj_corners, scene_corners, H);
///-- Draw lines between the corners (the mapped object in the scene - image_2 )
for(int i = 0; i < 4; ++i)
line( fram_tmp, scene_corners[i]+offset, scene_corners[(i + 1) % 4]+offset, Scalar(0, 255, 0), 4 );
}