1. 程式人生 > >OpenCV探索之路(二十四)影象拼接和影象融合技術

OpenCV探索之路(二十四)影象拼接和影象融合技術

影象拼接在實際的應用場景很廣,比如無人機航拍,遙感影象等等,影象拼接是進一步做影象理解基礎步驟,拼接效果的好壞直接影響接下來的工作,所以一個好的影象拼接演算法非常重要。

再舉一個身邊的例子吧,你用你的手機對某一場景拍照,但是你沒有辦法一次將所有你要拍的景物全部拍下來,所以你對該場景從左往右依次拍了好幾張圖,來把你要拍的所有景物記錄下來。那麼我們能不能把這些影象拼接成一個大圖呢?我們利用opencv就可以做到影象拼接的效果!

比如我們有對這兩張圖進行拼接。

1093303-20170822154321308-71491510.png

從上面兩張圖可以看出,這兩張圖有比較多的重疊部分,這也是拼接的基本要求。

那麼要實現影象拼接需要那幾步呢?簡單來說有以下幾步:

  1. 對每幅圖進行特徵點提取
  2. 對對特徵點進行匹配
  3. 進行影象配準
  4. 把影象拷貝到另一幅影象的特定位置
  5. 對重疊邊界進行特殊處理

好吧,那就開始正式實現影象配準。

第一步就是特徵點提取。現在CV領域有很多特徵點的定義,比如sift、surf、harris角點、ORB都是很有名的特徵因子,都可以用來做影象拼接的工作,他們各有優勢。本文將使用ORB和SURF進行影象拼接,用其他方法進行拼接也是類似的。

基於SURF的影象拼接

用SIFT演算法來實現影象拼接是很常用的方法,但是因為SIFT計算量很大,所以在速度要求很高的場合下不再適用。所以,它的改進方法SURF因為在速度方面有了明顯的提高(速度是SIFT的3倍),所以在影象拼接領域還是大有作為。雖說SURF精確度和穩定性不及SIFT,但是其綜合能力還是優越一些。下面將詳細介紹拼接的主要步驟。

1.特徵點提取和匹配

特徵點提取和匹配的方法我在上一篇文章《OpenCV探索之路(二十三):特徵檢測和特徵匹配方法彙總》中做了詳細的介紹,在這裡直接使用上文所總結的SURF特徵提取和特徵匹配的方法。

//提取特徵點    
SurfFeatureDetector Detector(2000);  
vector<KeyPoint> keyPoint1, keyPoint2;
Detector.detect(image1, keyPoint1);
Detector.detect(image2, keyPoint2);

//特徵點描述,為下邊的特徵點匹配做準備    
SurfDescriptorExtractor Descriptor;
Mat imageDesc1, imageDesc2;
Descriptor.compute(image1, keyPoint1, imageDesc1);
Descriptor.compute(image2, keyPoint2, imageDesc2);

FlannBasedMatcher matcher;
vector<vector<DMatch> > matchePoints;
vector<DMatch> GoodMatchePoints;

vector<Mat> train_desc(1, imageDesc1);
matcher.add(train_desc);
matcher.train();

matcher.knnMatch(imageDesc2, matchePoints, 2);
cout << "total match points: " << matchePoints.size() << endl;

// Lowe's algorithm,獲取優秀匹配點
for (int i = 0; i < matchePoints.size(); i++)
{
    if (matchePoints[i][0].distance < 0.4 * matchePoints[i][1].distance)
    {
        GoodMatchePoints.push_back(matchePoints[i][0]);
    }
}

Mat first_match;
drawMatches(image02, keyPoint2, image01, keyPoint1, GoodMatchePoints, first_match);
imshow("first_match ", first_match);

1093303-20170822154344449-509144423.png

2.影象配準

這樣子我們就可以得到了兩幅待拼接圖的匹配點集,接下來我們進行影象的配準,即將兩張影象轉換為同一座標下,這裡我們需要使用findHomography函式來求得變換矩陣。但是需要注意的是,findHomography函式所要用到的點集是Point2f型別的,所有我們需要對我們剛得到的點集GoodMatchePoints再做一次處理,使其轉換為Point2f型別的點集。

vector<Point2f> imagePoints1, imagePoints2;

for (int i = 0; i<GoodMatchePoints.size(); i++)
{
    imagePoints2.push_back(keyPoint2[GoodMatchePoints[i].queryIdx].pt);
    imagePoints1.push_back(keyPoint1[GoodMatchePoints[i].trainIdx].pt);
}

這樣子,我們就可以拿著imagePoints1, imagePoints2去求變換矩陣了,並且實現影象配準。值得注意的是findHomography函式的引數中我們選澤了CV_RANSAC,這表明我們選擇RANSAC演算法繼續篩選可靠地匹配點,這使得匹配點解更為精確。

//獲取影象1到影象2的投影對映矩陣 尺寸為3*3  
Mat homo = findHomography(imagePoints1, imagePoints2, CV_RANSAC);
////也可以使用getPerspectiveTransform方法獲得透視變換矩陣,不過要求只能有4個點,效果稍差  
//Mat   homo=getPerspectiveTransform(imagePoints1,imagePoints2);  
cout << "變換矩陣為:\n" << homo << endl << endl; //輸出對映矩陣     

//影象配準  
Mat imageTransform1, imageTransform2;
warpPerspective(image01, imageTransform1, homo, Size(MAX(corners.right_top.x, corners.right_bottom.x), image02.rows));
//warpPerspective(image01, imageTransform2, adjustMat*homo, Size(image02.cols*1.3, image02.rows*1.8));
imshow("直接經過透視矩陣變換", imageTransform1);
imwrite("trans1.jpg", imageTransform1);

1093303-20170822154403277-379911836.png

3. 影象拷貝

拷貝的思路很簡單,就是將左圖直接拷貝到配準圖上就可以了。

//建立拼接後的圖,需提前計算圖的大小
int dst_width = imageTransform1.cols;  //取最右點的長度為拼接圖的長度
int dst_height = image02.rows;

Mat dst(dst_height, dst_width, CV_8UC3);
dst.setTo(0);

imageTransform1.copyTo(dst(Rect(0, 0, imageTransform1.cols, imageTransform1.rows)));
image02.copyTo(dst(Rect(0, 0, image02.cols, image02.rows)));

imshow("b_dst", dst);

1093303-20170822154425449-1866547572.png

4.影象融合(去裂縫處理)

從上圖可以看出,兩圖的拼接並不自然,原因就在於拼接圖的交界處,兩圖因為光照色澤的原因使得兩圖交界處的過渡很糟糕,所以需要特定的處理解決這種不自然。這裡的處理思路是加權融合,在重疊部分由前一幅影象慢慢過渡到第二幅影象,即將影象的重疊區域的畫素值按一定的權值相加合成新的影象。


//優化兩圖的連線處,使得拼接自然
void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst)
{
    int start = MIN(corners.left_top.x, corners.left_bottom.x);//開始位置,即重疊區域的左邊界  

    double processWidth = img1.cols - start;//重疊區域的寬度  
    int rows = dst.rows;
    int cols = img1.cols; //注意,是列數*通道數
    double alpha = 1;//img1中畫素的權重  
    for (int i = 0; i < rows; i++)
    {
        uchar* p = img1.ptr<uchar>(i);  //獲取第i行的首地址
        uchar* t = trans.ptr<uchar>(i);
        uchar* d = dst.ptr<uchar>(i);
        for (int j = start; j < cols; j++)
        {
            //如果遇到影象trans中無畫素的黑點,則完全拷貝img1中的資料
            if (t[j * 3] == 0 && t[j * 3 + 1] == 0 && t[j * 3 + 2] == 0)
            {
                alpha = 1;
            }
            else
            {
                //img1中畫素的權重,與當前處理點距重疊區域左邊界的距離成正比,實驗證明,這種方法確實好  
                alpha = (processWidth - (j - start)) / processWidth;
            }

            d[j * 3] = p[j * 3] * alpha + t[j * 3] * (1 - alpha);
            d[j * 3 + 1] = p[j * 3 + 1] * alpha + t[j * 3 + 1] * (1 - alpha);
            d[j * 3 + 2] = p[j * 3 + 2] * alpha + t[j * 3 + 2] * (1 - alpha);

        }
    }

}

1093303-20170822154445464-413726039.png

多嘗試幾張,驗證拼接效果

測試一

1093303-20170822154454839-1253938282.png

1093303-20170822154506605-1380482375.png

測試二

1093303-20170822154518449-1425129916.png

1093303-20170822154535324-2137232231.png

測試三

1093303-20170822154551746-1673577698.png

1093303-20170822154603746-881176266.png

最後給出完整的SURF演算法實現的拼接程式碼。

#include "highgui/highgui.hpp"    
#include "opencv2/nonfree/nonfree.hpp"    
#include "opencv2/legacy/legacy.hpp"   
#include <iostream>  

using namespace cv;
using namespace std;

void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst);

typedef struct
{
    Point2f left_top;
    Point2f left_bottom;
    Point2f right_top;
    Point2f right_bottom;
}four_corners_t;

four_corners_t corners;

void CalcCorners(const Mat& H, const Mat& src)
{
    double v2[] = { 0, 0, 1 };//左上角
    double v1[3];//變換後的座標值
    Mat V2 = Mat(3, 1, CV_64FC1, v2);  //列向量
    Mat V1 = Mat(3, 1, CV_64FC1, v1);  //列向量

    V1 = H * V2;
    //左上角(0,0,1)
    cout << "V2: " << V2 << endl;
    cout << "V1: " << V1 << endl;
    corners.left_top.x = v1[0] / v1[2];
    corners.left_top.y = v1[1] / v1[2];

    //左下角(0,src.rows,1)
    v2[0] = 0;
    v2[1] = src.rows;
    v2[2] = 1;
    V2 = Mat(3, 1, CV_64FC1, v2);  //列向量
    V1 = Mat(3, 1, CV_64FC1, v1);  //列向量
    V1 = H * V2;
    corners.left_bottom.x = v1[0] / v1[2];
    corners.left_bottom.y = v1[1] / v1[2];

    //右上角(src.cols,0,1)
    v2[0] = src.cols;
    v2[1] = 0;
    v2[2] = 1;
    V2 = Mat(3, 1, CV_64FC1, v2);  //列向量
    V1 = Mat(3, 1, CV_64FC1, v1);  //列向量
    V1 = H * V2;
    corners.right_top.x = v1[0] / v1[2];
    corners.right_top.y = v1[1] / v1[2];

    //右下角(src.cols,src.rows,1)
    v2[0] = src.cols;
    v2[1] = src.rows;
    v2[2] = 1;
    V2 = Mat(3, 1, CV_64FC1, v2);  //列向量
    V1 = Mat(3, 1, CV_64FC1, v1);  //列向量
    V1 = H * V2;
    corners.right_bottom.x = v1[0] / v1[2];
    corners.right_bottom.y = v1[1] / v1[2];

}

int main(int argc, char *argv[])
{
    Mat image01 = imread("g5.jpg", 1);    //右圖
    Mat image02 = imread("g4.jpg", 1);    //左圖
    imshow("p2", image01);
    imshow("p1", image02);

    //灰度圖轉換  
    Mat image1, image2;
    cvtColor(image01, image1, CV_RGB2GRAY);
    cvtColor(image02, image2, CV_RGB2GRAY);


    //提取特徵點    
    SurfFeatureDetector Detector(2000);  
    vector<KeyPoint> keyPoint1, keyPoint2;
    Detector.detect(image1, keyPoint1);
    Detector.detect(image2, keyPoint2);

    //特徵點描述,為下邊的特徵點匹配做準備    
    SurfDescriptorExtractor Descriptor;
    Mat imageDesc1, imageDesc2;
    Descriptor.compute(image1, keyPoint1, imageDesc1);
    Descriptor.compute(image2, keyPoint2, imageDesc2);

    FlannBasedMatcher matcher;
    vector<vector<DMatch> > matchePoints;
    vector<DMatch> GoodMatchePoints;

    vector<Mat> train_desc(1, imageDesc1);
    matcher.add(train_desc);
    matcher.train();

    matcher.knnMatch(imageDesc2, matchePoints, 2);
    cout << "total match points: " << matchePoints.size() << endl;

    // Lowe's algorithm,獲取優秀匹配點
    for (int i = 0; i < matchePoints.size(); i++)
    {
        if (matchePoints[i][0].distance < 0.4 * matchePoints[i][1].distance)
        {
            GoodMatchePoints.push_back(matchePoints[i][0]);
        }
    }

    Mat first_match;
    drawMatches(image02, keyPoint2, image01, keyPoint1, GoodMatchePoints, first_match);
    imshow("first_match ", first_match);

    vector<Point2f> imagePoints1, imagePoints2;

    for (int i = 0; i<GoodMatchePoints.size(); i++)
    {
        imagePoints2.push_back(keyPoint2[GoodMatchePoints[i].queryIdx].pt);
        imagePoints1.push_back(keyPoint1[GoodMatchePoints[i].trainIdx].pt);
    }



    //獲取影象1到影象2的投影對映矩陣 尺寸為3*3  
    Mat homo = findHomography(imagePoints1, imagePoints2, CV_RANSAC);
    ////也可以使用getPerspectiveTransform方法獲得透視變換矩陣,不過要求只能有4個點,效果稍差  
    //Mat   homo=getPerspectiveTransform(imagePoints1,imagePoints2);  
    cout << "變換矩陣為:\n" << homo << endl << endl; //輸出對映矩陣      

   //計算配準圖的四個頂點座標
    CalcCorners(homo, image01);
    cout << "left_top:" << corners.left_top << endl;
    cout << "left_bottom:" << corners.left_bottom << endl;
    cout << "right_top:" << corners.right_top << endl;
    cout << "right_bottom:" << corners.right_bottom << endl;

    //影象配準  
    Mat imageTransform1, imageTransform2;
    warpPerspective(image01, imageTransform1, homo, Size(MAX(corners.right_top.x, corners.right_bottom.x), image02.rows));
    //warpPerspective(image01, imageTransform2, adjustMat*homo, Size(image02.cols*1.3, image02.rows*1.8));
    imshow("直接經過透視矩陣變換", imageTransform1);
    imwrite("trans1.jpg", imageTransform1);


    //建立拼接後的圖,需提前計算圖的大小
    int dst_width = imageTransform1.cols;  //取最右點的長度為拼接圖的長度
    int dst_height = image02.rows;

    Mat dst(dst_height, dst_width, CV_8UC3);
    dst.setTo(0);

    imageTransform1.copyTo(dst(Rect(0, 0, imageTransform1.cols, imageTransform1.rows)));
    image02.copyTo(dst(Rect(0, 0, image02.cols, image02.rows)));

    imshow("b_dst", dst);


    OptimizeSeam(image02, imageTransform1, dst);


    imshow("dst", dst);
    imwrite("dst.jpg", dst);

    waitKey();

    return 0;
}


//優化兩圖的連線處,使得拼接自然
void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst)
{
    int start = MIN(corners.left_top.x, corners.left_bottom.x);//開始位置,即重疊區域的左邊界  

    double processWidth = img1.cols - start;//重疊區域的寬度  
    int rows = dst.rows;
    int cols = img1.cols; //注意,是列數*通道數
    double alpha = 1;//img1中畫素的權重  
    for (int i = 0; i < rows; i++)
    {
        uchar* p = img1.ptr<uchar>(i);  //獲取第i行的首地址
        uchar* t = trans.ptr<uchar>(i);
        uchar* d = dst.ptr<uchar>(i);
        for (int j = start; j < cols; j++)
        {
            //如果遇到影象trans中無畫素的黑點,則完全拷貝img1中的資料
            if (t[j * 3] == 0 && t[j * 3 + 1] == 0 && t[j * 3 + 2] == 0)
            {
                alpha = 1;
            }
            else
            {
                //img1中畫素的權重,與當前處理點距重疊區域左邊界的距離成正比,實驗證明,這種方法確實好  
                alpha = (processWidth - (j - start)) / processWidth;
            }

            d[j * 3] = p[j * 3] * alpha + t[j * 3] * (1 - alpha);
            d[j * 3 + 1] = p[j * 3 + 1] * alpha + t[j * 3 + 1] * (1 - alpha);
            d[j * 3 + 2] = p[j * 3 + 2] * alpha + t[j * 3 + 2] * (1 - alpha);

        }
    }

}

基於ORB的影象拼接

利用ORB進行影象拼接的思路跟上面的思路基本一樣,只是特徵提取和特徵點匹配的方式略有差異罷了。這裡就不再詳細介紹思路了,直接貼程式碼看效果。

#include "highgui/highgui.hpp"    
#include "opencv2/nonfree/nonfree.hpp"    
#include "opencv2/legacy/legacy.hpp"   
#include <iostream>  

using namespace cv;
using namespace std;

void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst);

typedef struct
{
    Point2f left_top;
    Point2f left_bottom;
    Point2f right_top;
    Point2f right_bottom;
}four_corners_t;

four_corners_t corners;

void CalcCorners(const Mat& H, const Mat& src)
{
    double v2[] = { 0, 0, 1 };//左上角
    double v1[3];//變換後的座標值
    Mat V2 = Mat(3, 1, CV_64FC1, v2);  //列向量
    Mat V1 = Mat(3, 1, CV_64FC1, v1);  //列向量

    V1 = H * V2;
    //左上角(0,0,1)
    cout << "V2: " << V2 << endl;
    cout << "V1: " << V1 << endl;
    corners.left_top.x = v1[0] / v1[2];
    corners.left_top.y = v1[1] / v1[2];

    //左下角(0,src.rows,1)
    v2[0] = 0;
    v2[1] = src.rows;
    v2[2] = 1;
    V2 = Mat(3, 1, CV_64FC1, v2);  //列向量
    V1 = Mat(3, 1, CV_64FC1, v1);  //列向量
    V1 = H * V2;
    corners.left_bottom.x = v1[0] / v1[2];
    corners.left_bottom.y = v1[1] / v1[2];

    //右上角(src.cols,0,1)
    v2[0] = src.cols;
    v2[1] = 0;
    v2[2] = 1;
    V2 = Mat(3, 1, CV_64FC1, v2);  //列向量
    V1 = Mat(3, 1, CV_64FC1, v1);  //列向量
    V1 = H * V2;
    corners.right_top.x = v1[0] / v1[2];
    corners.right_top.y = v1[1] / v1[2];

    //右下角(src.cols,src.rows,1)
    v2[0] = src.cols;
    v2[1] = src.rows;
    v2[2] = 1;
    V2 = Mat(3, 1, CV_64FC1, v2);  //列向量
    V1 = Mat(3, 1, CV_64FC1, v1);  //列向量
    V1 = H * V2;
    corners.right_bottom.x = v1[0] / v1[2];
    corners.right_bottom.y = v1[1] / v1[2];

}

int main(int argc, char *argv[])
{
    Mat image01 = imread("t1.jpg", 1);    //右圖
    Mat image02 = imread("t2.jpg", 1);    //左圖
    imshow("p2", image01);
    imshow("p1", image02);

    //灰度圖轉換  
    Mat image1, image2;
    cvtColor(image01, image1, CV_RGB2GRAY);
    cvtColor(image02, image2, CV_RGB2GRAY);


    //提取特徵點    
    OrbFeatureDetector  surfDetector(3000);  
    vector<KeyPoint> keyPoint1, keyPoint2;
    surfDetector.detect(image1, keyPoint1);
    surfDetector.detect(image2, keyPoint2);

    //特徵點描述,為下邊的特徵點匹配做準備    
    OrbDescriptorExtractor  SurfDescriptor;
    Mat imageDesc1, imageDesc2;
    SurfDescriptor.compute(image1, keyPoint1, imageDesc1);
    SurfDescriptor.compute(image2, keyPoint2, imageDesc2);

    flann::Index flannIndex(imageDesc1, flann::LshIndexParams(12, 20, 2), cvflann::FLANN_DIST_HAMMING);

    vector<DMatch> GoodMatchePoints;

    Mat macthIndex(imageDesc2.rows, 2, CV_32SC1), matchDistance(imageDesc2.rows, 2, CV_32FC1);
    flannIndex.knnSearch(imageDesc2, macthIndex, matchDistance, 2, flann::SearchParams());

    // Lowe's algorithm,獲取優秀匹配點
    for (int i = 0; i < matchDistance.rows; i++)
    {
        if (matchDistance.at<float>(i, 0) < 0.4 * matchDistance.at<float>(i, 1))
        {
            DMatch dmatches(i, macthIndex.at<int>(i, 0), matchDistance.at<float>(i, 0));
            GoodMatchePoints.push_back(dmatches);
        }
    }

    Mat first_match;
    drawMatches(image02, keyPoint2, image01, keyPoint1, GoodMatchePoints, first_match);
    imshow("first_match ", first_match);

    vector<Point2f> imagePoints1, imagePoints2;

    for (int i = 0; i<GoodMatchePoints.size(); i++)
    {
        imagePoints2.push_back(keyPoint2[GoodMatchePoints[i].queryIdx].pt);
        imagePoints1.push_back(keyPoint1[GoodMatchePoints[i].trainIdx].pt);
    }



    //獲取影象1到影象2的投影對映矩陣 尺寸為3*3  
    Mat homo = findHomography(imagePoints1, imagePoints2, CV_RANSAC);
    ////也可以使用getPerspectiveTransform方法獲得透視變換矩陣,不過要求只能有4個點,效果稍差  
    //Mat   homo=getPerspectiveTransform(imagePoints1,imagePoints2);  
    cout << "變換矩陣為:\n" << homo << endl << endl; //輸出對映矩陣      

                                                //計算配準圖的四個頂點座標
    CalcCorners(homo, image01);
    cout << "left_top:" << corners.left_top << endl;
    cout << "left_bottom:" << corners.left_bottom << endl;
    cout << "right_top:" << corners.right_top << endl;
    cout << "right_bottom:" << corners.right_bottom << endl;

    //影象配準  
    Mat imageTransform1, imageTransform2;
    warpPerspective(image01, imageTransform1, homo, Size(MAX(corners.right_top.x, corners.right_bottom.x), image02.rows));
    //warpPerspective(image01, imageTransform2, adjustMat*homo, Size(image02.cols*1.3, image02.rows*1.8));
    imshow("直接經過透視矩陣變換", imageTransform1);
    imwrite("trans1.jpg", imageTransform1);


    //建立拼接後的圖,需提前計算圖的大小
    int dst_width = imageTransform1.cols;  //取最右點的長度為拼接圖的長度
    int dst_height = image02.rows;

    Mat dst(dst_height, dst_width, CV_8UC3);
    dst.setTo(0);

    imageTransform1.copyTo(dst(Rect(0, 0, imageTransform1.cols, imageTransform1.rows)));
    image02.copyTo(dst(Rect(0, 0, image02.cols, image02.rows)));

    imshow("b_dst", dst);


    OptimizeSeam(image02, imageTransform1, dst);


    imshow("dst", dst);
    imwrite("dst.jpg", dst);

    waitKey();

    return 0;
}


//優化兩圖的連線處,使得拼接自然
void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst)
{
    int start = MIN(corners.left_top.x, corners.left_bottom.x);//開始位置,即重疊區域的左邊界  

    double processWidth = img1.cols - start;//重疊區域的寬度  
    int rows = dst.rows;
    int cols = img1.cols; //注意,是列數*通道數
    double alpha = 1;//img1中畫素的權重  
    for (int i = 0; i < rows; i++)
    {
        uchar* p = img1.ptr<uchar>(i);  //獲取第i行的首地址
        uchar* t = trans.ptr<uchar>(i);
        uchar* d = dst.ptr<uchar>(i);
        for (int j = start; j < cols; j++)
        {
            //如果遇到影象trans中無畫素的黑點,則完全拷貝img1中的資料
            if (t[j * 3] == 0 && t[j * 3 + 1] == 0 && t[j * 3 + 2] == 0)
            {
                alpha = 1;
            }
            else
            {
                //img1中畫素的權重,與當前處理點距重疊區域左邊界的距離成正比,實驗證明,這種方法確實好  
                alpha = (processWidth - (j - start)) / processWidth;
            }

            d[j * 3] = p[j * 3] * alpha + t[j * 3] * (1 - alpha);
            d[j * 3 + 1] = p[j * 3 + 1] * alpha + t[j * 3 + 1] * (1 - alpha);
            d[j * 3 + 2] = p[j * 3 + 2] * alpha + t[j * 3 + 2] * (1 - alpha);

        }
    }

}

看一看拼接效果,我覺得還是不錯的。

1093303-20170822154629152-1101551803.png

1093303-20170822154650761-693802456.png

1093303-20170822154706636-1756490695.png

看一下這一組圖片,這組圖片產生了鬼影,為什麼?因為兩幅圖中的人物走動了啊!所以要做影象拼接,儘量保證使用的是靜態圖片,不要加入一些動態因素干擾拼接。

1093303-20170822154718027-805234486.png

1093303-20170822154727636-1873510992.png

1093303-20170822154736230-1197649063.png

opencv自帶的拼接演算法stitch

opencv其實自己就有實現影象拼接的演算法,當然效果也是相當好的,但是因為其實現很複雜,而且程式碼量很龐大,其實在一些小應用下的拼接有點殺雞用牛刀的感覺。最近在閱讀sticth原始碼時,發現其中有幾個很有意思的地方。

1.opencv stitch選擇的特徵檢測方式

一直很好奇opencv stitch演算法到底選用了哪個演算法作為其特徵檢測方式,是ORB,SIFT還是SURF?讀原始碼終於看到答案。

#ifdef HAVE_OPENCV_NONFREE
        stitcher.setFeaturesFinder(new detail::SurfFeaturesFinder());
#else
        stitcher.setFeaturesFinder(new detail::OrbFeaturesFinder());
#endif

在原始碼createDefault函式中(預設設定),第一選擇是SURF,第二選擇才是ORB(沒有NONFREE模組才選),所以既然大牛們這麼選擇,必然是經過綜合考慮的,所以應該SURF演算法在影象拼接有著更優秀的效果。

2.opencv stitch獲取匹配點的方式

以下程式碼是opencv stitch原始碼中的特徵點提取部分,作者使用了兩次特徵點提取的思路:先對圖一進行特徵點提取和篩選匹配(1->2),再對圖二進行特徵點的提取和匹配(2->1),這跟我們平時的一次提取的思路不同,這種二次提取的思路可以保證更多的匹配點被選中,匹配點越多,findHomography求出的變換越準確。這個思路值得借鑑。


matches_info.matches.clear();

Ptr<flann::IndexParams> indexParams = new flann::KDTreeIndexParams();
Ptr<flann::SearchParams> searchParams = new flann::SearchParams();

if (features2.descriptors.depth() == CV_8U)
{
    indexParams->setAlgorithm(cvflann::FLANN_INDEX_LSH);
    searchParams->setAlgorithm(cvflann::FLANN_INDEX_LSH);
}

FlannBasedMatcher matcher(indexParams, searchParams);
vector< vector<DMatch> > pair_matches;
MatchesSet matches;

// Find 1->2 matches
matcher.knnMatch(features1.descriptors, features2.descriptors, pair_matches, 2);
for (size_t i = 0; i < pair_matches.size(); ++i)
{
    if (pair_matches[i].size() < 2)
        continue;
    const DMatch& m0 = pair_matches[i][0];
    const DMatch& m1 = pair_matches[i][1];
    if (m0.distance < (1.f - match_conf_) * m1.distance)
    {
        matches_info.matches.push_back(m0);
        matches.insert(make_pair(m0.queryIdx, m0.trainIdx));
    }
}
LOG("\n1->2 matches: " << matches_info.matches.size() << endl);

// Find 2->1 matches
pair_matches.clear();
matcher.knnMatch(features2.descriptors, features1.descriptors, pair_matches, 2);
for (size_t i = 0; i < pair_matches.size(); ++i)
{
    if (pair_matches[i].size() < 2)
        continue;
    const DMatch& m0 = pair_matches[i][0];
    const DMatch& m1 = pair_matches[i][1];
    if (m0.distance < (1.f - match_conf_) * m1.distance)
        if (matches.find(make_pair(m0.trainIdx, m0.queryIdx)) == matches.end())
            matches_info.matches.push_back(DMatch(m0.trainIdx, m0.queryIdx, m0.distance));
}
LOG("1->2 & 2->1 matches: " << matches_info.matches.size() << endl);

這裡我仿照opencv原始碼二次提取特徵點的思路對我原有拼接程式碼進行改寫,實驗證明獲取的匹配點確實較一次提取要多。

//提取特徵點    
SiftFeatureDetector Detector(1000);  // 海塞矩陣閾值,在這裡調整精度,值越大點越少,越精準 
vector<KeyPoint> keyPoint1, keyPoint2;
Detector.detect(image1, keyPoint1);
Detector.detect(image2, keyPoint2);

//特徵點描述,為下邊的特徵點匹配做準備    
SiftDescriptorExtractor Descriptor;
Mat imageDesc1, imageDesc2;
Descriptor.compute(image1, keyPoint1, imageDesc1);
Descriptor.compute(image2, keyPoint2, imageDesc2);

FlannBasedMatcher matcher;
vector<vector<DMatch> > matchePoints;
vector<DMatch> GoodMatchePoints;

MatchesSet matches;

vector<Mat> train_desc(1, imageDesc1);
matcher.add(train_desc);
matcher.train();

matcher.knnMatch(imageDesc2, matchePoints, 2);

// Lowe's algorithm,獲取優秀匹配點
for (int i = 0; i < matchePoints.size(); i++)
{
    if (matchePoints[i][0].distance < 0.4 * matchePoints[i][1].distance)
    {
        GoodMatchePoints.push_back(matchePoints[i][0]);
        matches.insert(make_pair(matchePoints[i][0].queryIdx, matchePoints[i][0].trainIdx));
    }
}
cout<<"\n1->2 matches: " << GoodMatchePoints.size() << endl;

#if 1

FlannBasedMatcher matcher2;
matchePoints.clear();
vector<Mat> train_desc2(1, imageDesc2);
matcher2.add(train_desc2);
matcher2.train();

matcher2.knnMatch(imageDesc1, matchePoints, 2);
// Lowe's algorithm,獲取優秀匹配點
for (int i = 0; i < matchePoints.size(); i++)
{
    if (matchePoints[i][0].distance < 0.4 * matchePoints[i][1].distance)
    {
        if (matches.find(make_pair(matchePoints[i][0].trainIdx, matchePoints[i][0].queryIdx)) == matches.end())
        {
            GoodMatchePoints.push_back(DMatch(matchePoints[i][0].trainIdx, matchePoints[i][0].queryIdx, matchePoints[i][0].distance));
        }
        
    }
}
cout<<"1->2 & 2->1 matches: " << GoodMatchePoints.size() << endl;
#endif

最後再看一下opencv stitch的拼接效果吧~速度雖然比較慢,但是效果還是很好的。

#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/stitching/stitcher.hpp>
using namespace std;
using namespace cv;
bool try_use_gpu = false;
vector<Mat> imgs;
string result_name = "dst1.jpg";
int main(int argc, char * argv[])
{
    Mat img1 = imread("34.jpg");
    Mat img2 = imread("35.jpg");

    imshow("p1", img1);
    imshow("p2", img2);

    if (img1.empty() || img2.empty())
    {
        cout << "Can't read image" << endl;
        return -1;
    }
    imgs.push_back(img1);
    imgs.push_back(img2);


    Stitcher stitcher = Stitcher::createDefault(try_use_gpu);
    // 使用stitch函式進行拼接
    Mat pano;
    Stitcher::Status status = stitcher.stitch(imgs, pano);
    if (status != Stitcher::OK)
    {
        cout << "Can't stitch images, error code = " << int(status) << endl;
        return -1;
    }
    imwrite(result_name, pano);
    Mat pano2 = pano.clone();
    // 顯示源影象,和結果影象
    imshow("全景影象", pano);
    if (waitKey() == 27)
        return 0;
}

1093303-20170822154805043-430469339.png

1093303-20170822154823308-950402280.png