全景形象建设

Question

我试图从不同的图像构建全景图。最初，我试图缝合两幅图片，作为全景结构的一部分。我试图缝制的两幅输入图像是：

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我使用ORB特征描述符在图像中找到特征，然后找出这两幅图像之间的单形矩阵。我的代码是：

int main(int argc, char **argv){

Mat img1 = imread(argv[1],1);
Mat img2 = imread(argv[2],1);
//-- Step 1: Detect the keypoints using orb Detector
std::vector<KeyPoint> kp2,kp1;

// Default parameters of ORB
int nfeatures=500;
float scaleFactor=1.2f;
int nlevels=8;
int edgeThreshold=15; // Changed default (31);
int firstLevel=0;
int WTA_K=2;
int scoreType=ORB::HARRIS_SCORE;
int patchSize=31;
int fastThreshold=20;

Ptr<ORB> detector = ORB::create(
        nfeatures,
        scaleFactor,
        nlevels,
        edgeThreshold,
        firstLevel,
        WTA_K,
        scoreType,
        patchSize,
        fastThreshold );
Mat descriptors_img1, descriptors_img2;


//-- Step 2: Calculate descriptors (feature vectors)
detector->detect(img1, kp1,descriptors_img1);
detector->detect(img2, kp2,descriptors_img2);


Ptr<DescriptorExtractor> extractor = ORB::create();
extractor->compute(img1, kp1, descriptors_img1 );
extractor->compute(img2, kp2, descriptors_img2 );
//-- Step 3: Matching descriptor vectors using FLANN matcher
if ( descriptors_img1.empty() )
    cvError(0,"MatchFinder","1st descriptor empty",__FILE__,__LINE__);
if ( descriptors_img2.empty() )
    cvError(0,"MatchFinder","2nd descriptor empty",__FILE__,__LINE__);
descriptors_img1.convertTo(descriptors_img1, CV_32F);
descriptors_img2.convertTo(descriptors_img2, CV_32F);
FlannBasedMatcher matcher;
std::vector<DMatch> matches;
matcher.match(descriptors_img1,descriptors_img2,matches);
double max_dist = 0; double min_dist = 100;

//-- Quick calculation of max and min distances between keypoints
for( int i = 0; i < descriptors_img1.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< DMatch > good_matches;

for( int i = 0; i < descriptors_img1.rows; i++ )
{
    if( matches[i].distance < 3*min_dist )
    {
        good_matches.push_back( matches[i]);
    }
}
Mat img_matches;
drawMatches(img1,kp1,img2,kp2,good_matches,img_matches,Scalar::all(-1),
        Scalar::all(-1),vector<char>(),DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
std::vector<Point2f> obj;
std::vector<Point2f> scene;
for( int i = 0; i < good_matches.size(); i++ )
{
    //-- Get the keypoints from the good matches
    obj.push_back( kp1[ good_matches[i].queryIdx ].pt );
    scene.push_back( kp2[ good_matches[i].trainIdx ].pt );
}

Mat H = findHomography( obj, scene, CV_RANSAC );

在病房之后，一些人告诉我要包括以下代码

cv::Mat result;

warpPerspective( img1, result, H, cv::Size( img1.cols+img2.cols, img1.rows) );
cv::Mat half(result, cv::Rect(0, 0, img2.cols, img2.rows) );
img2.copyTo(half);
imshow("result",result);

我得到的结果是

​

我还尝试使用内置的opencv缝纫功能。我得到了结果

​

我试图实现缝纫功能，所以我不想使用内置的opencv缝纫功能。有人能告诉我我哪里出了问题并提前纠正我的code.Thanks吗？

Answer 1

图像拼接包括以下步骤：

1. 特征查找
2. 寻找摄像机参数
3. 翘曲
4. 曝光补偿
5. 煤层发现
6. 混合

为了得到完美的结果，你必须做所有这些步骤。在您的代码中，您只完成了第一部分，即功能查找。

您可以找到关于图像拼接在学习OpenCV中如何工作的详细说明。

此外，我还有Github上的代码

希望这能有所帮助。