OpenCV cv::findHomography运行时错误
OpenCV cv::findHomography runtime error
我用编译和运行代码从Features2D + Homography找到一个已知的对象教程,我得到这个
OpenCV Error: Assertion failed (npoints >= 0 && points2.checkVector(2) == npoint
s && points1.type() == points2.type()) in unknown function, file c:Usersvpwor
kocvopencvmodulescalib3dsrcfundam.cpp, line 1062
运行时错误。调试后,我发现程序在findHomography函数处崩溃。
Unhandled exception at 0x760ab727 in OpenCVTemplateMatch.exe: Microsoft C++ exception: cv::Exception at memory location 0x0029eb3c..
在OpenCV简介中,"cv命名空间"一章说
一些当前或未来的OpenCV外部名称可能与STL或其他库冲突。在这种情况下,使用显式名称空间说明符来解决名称冲突:
我修改了代码,到处使用显式命名空间说明符,但问题没有解决。如果可以,请帮助我解决这个问题,或者告诉我哪个函数和findHomography做同样的事情,并且不要使程序崩溃。
这是我的代码#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; }
今天我在这个示例代码中遇到了同样的问题。@mathematical-coffee是对的,没有提取特征,因此obj和scene是空的。我更换了测试图片,它起作用了。从纹理样式图像中你不能提取SURF特征。
另一种方法是降低参数minHessianve.g。
或通过更改几行来使用FAST特征检测器:
//-- Step 1: Detect the keypoints using SURF Detector
int minHessian = 15;
FastFeatureDetector detector( minHessian );
实际答案在错误消息中:
npoints >= 0 && points2.checkVector(2) == npoints && points1.type() == points2.type()
人类可读的翻译,你必须满足这些断言:
你的输入必须有正数的点(在实践中,findHomography需要4个或更多的点)
您的"对象"answers"场景"点列表必须具有相同数量的点
您的"对象"answers"场景"点列表必须具有相同类型的点
我也有同样的问题,我遵循了MMH的解决方案。只是写
cv::Mat H = cv::findHomography( cv::Mat(obj), cv::Mat(scene), CV_RANSAC );
cv::perspectiveTransform( cv::Mat(obj_corners), cv::Mat(scene_corners), H);
解决了问题
更有可能的是,问题在这里:
if( matches[i].distance < 3*min_dist)
严格的不等式不是你想要的。如果min_dist == 0
是一个非常好的匹配,你将忽略所有零距离点。替换为:
if( matches[i].distance <= 3*min_dist)
,您应该会看到匹配良好的图像的良好结果。
要优雅地退出,我还会添加,例如:
if (good_matches.size() == 0)
{
std::cout<< " --(!) No good matches found " << std::endl; return -2;
}
你需要在findHomography
之前添加一个条件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 );
}
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