OpenCV cv :: findHomography运行时错误

Question

我用来编译和运行Features2D + Homography中的代码来找到一个已知的对象教程，我得到了这个

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函数崩溃了。

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

在OpenCV的介绍中，“cv命名空间”一章说明了这一点

某些当前或未来的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：

我改变了我的代码并使用了所有显式名称空间说明符，但问题没有解决。 如果可以的话，请帮我解决这个问题，或者说找哪个函数和findHomography做同样的事情，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, 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; }

Answer 1

今天我遇到了与此示例代码相同的问题。 @ mathematical- coffee是对的，没有提取任何特征，因此obj和场景都是空的。 我更换了测试图片并且工作正常。 从纹理样式图像，您无法提取SURF功能。

另一种方法是降低参数minHessianve.g。 `int minHessian = 20;

或通过更改几行来使用FAST功能检测器：

  //-- Step 1: Detect the keypoints using SURF Detector
  int minHessian = 15;

  FastFeatureDetector detector( minHessian );

Answer 2

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，你必须履行这些断言：

• 您的输入必须具有正数点（实际上，findHomography需要4个或更多点）。

• 您的“对象”和“场景”点数列表必须具有相同的点数。

• 您的“对象”和“场景”点列表必须具有相同类型的点。

Answer 3

我有同样的问题，我按照MMH的解决方案。 只是写作

cv::Mat H = cv::findHomography( cv::Mat(obj), cv::Mat(scene), CV_RANSAC ); cv::perspectiveTransform( cv::Mat(obj_corners), cv::Mat(scene_corners), H);

解决了这个问题。

Answer 4

更有可能的是，问题在于：

 if( matches[i].distance < 3*min_dist)

严格的不平等不是你想要的。 如果min_dist == 0 ，一个非常好的匹配，你将忽略所有零距离点。 用。。。来代替：

 if( matches[i].distance <= 3*min_dist)

并且您应该看到匹配良好的图像的良好结果。

要优雅地退出，我还要添加，例如：

if (good_matches.size() == 0)
{
  std::cout<< " --(!) No good matches found " << std::endl; return -2; 
}

Answer 5

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 );
}