c – OpenCV cv :: findHomography运行时错误

我正在使用从 Features2D + Homography to find a known object教程编译和运行代码,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:\Users\vp\wor
k\ocv\opencv\modules\calib3d\src\fundam.cpp,line 1062

运行时错误.调试后我发现程序在findHomography功能崩溃.

Unhandled exception at 0x760ab727 in OpenCVTemplateMatch.exe: Microsoft C++ exception: cv::Exception at memory location 0x0029eb3c..

在OpenCV的Introduction中,“cv命名空间”一章说

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:

我改变了我的代码,并使用明确的命名空间说明符,但问题没有解决.如果可以,请帮我解决这个问题,或者说哪个功能和findHomography相同,不要崩溃程序.

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,img_scene,good_matches,img_matches,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,cv::Scalar(0,255,4 );
    cv::line( img_matches,scene_corners[2] + cv::Point2f( img_object.cols,cv::Scalar( 0,scene_corners[3] + cv::Point2f( img_object.cols,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; }

解决方法

今天我遇到与此示例代码相同的问题. @数学咖啡是没有提取的功能,因此obj和场景是空的.我取代了测试图片,它的工作.从纹理样式图像,您不能提取SURF功能.

另一个方法是降低参数minHessianve.g. `int minHessian = 20;

或者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 );