问题
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; }
回答1:
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 );
回答2:
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.
回答3:
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.
回答4:
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;
}
回答5:
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 );
}
来源：https://stackoverflow.com/questions/8520250/opencv-cvfindhomography-runtime-error