[英]OpenCV cv::findHomography runtime error
我用來編譯和運行Features2D + Homography中的代碼來找到一個已知的對象教程,我得到了這個
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
運行時錯誤。 調試后我發現程序在findHomography函數崩潰了。
Unhandled exception at 0x760ab727 in OpenCVTemplateMatch.exe: Microsoft C++ exception: cv::Exception at memory location 0x0029eb3c..
在OpenCV的介紹中,“cv命名空間”一章說明了這一點
某些當前或未來的OpenCV外部名稱可能與STL或其他庫沖突。 在這種情況下,使用顯式名稱空間說明符來解決名稱沖突:
我改變了我的代碼並使用了所有顯式名稱空間說明符,但問題沒有解決。 如果可以的話,請幫我解決這個問題,或者說找哪個函數和findHomography做同樣的事情,不要崩潰程序。
這是我的代碼
#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; }
今天我遇到了與此示例代碼相同的問題。 @ mathematical- coffee是對的,沒有提取任何特征,因此obj和場景都是空的。 我更換了測試圖片並且工作正常。 從紋理樣式圖像,您無法提取SURF功能。
另一種方法是降低參數minHessianve.g。 `int minHessian = 20;
或通過更改幾行來使用FAST功能檢測器:
//-- Step 1: Detect the keypoints using SURF Detector
int minHessian = 15;
FastFeatureDetector detector( minHessian );
實際答案在錯誤消息中:
npoints >= 0 && points2.checkVector(2) == npoints && points1.type() == points2.type()
人類可讀的翻譯,你必須履行這些斷言:
您的輸入必須具有正數點(實際上,findHomography需要4個或更多點)。
您的“對象”和“場景”點數列表必須具有相同的點數。
您的“對象”和“場景”點列表必須具有相同類型的點。
我有同樣的問題,我按照MMH的解決方案。 只是寫作
cv::Mat H = cv::findHomography( cv::Mat(obj), cv::Mat(scene), CV_RANSAC ); cv::perspectiveTransform( cv::Mat(obj_corners), cv::Mat(scene_corners), H);
解決了這個問題。
更有可能的是,問題在於:
if( matches[i].distance < 3*min_dist)
嚴格的不平等不是你想要的。 如果min_dist == 0
,一個非常好的匹配,你將忽略所有零距離點。 用。。。來代替:
if( matches[i].distance <= 3*min_dist)
並且您應該看到匹配良好的圖像的良好結果。
要優雅地退出,我還要添加,例如:
if (good_matches.size() == 0)
{
std::cout<< " --(!) No good matches found " << std::endl; return -2;
}
你需要在findHomography之前添加一個條件
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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