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OpenCV探索：圖像拼接和圖像融合技術(shù)
圖像拼接在實際的應用場景很廣，比如無人機航拍，遙感圖像等等，圖像拼接是進一步做圖像理解基礎(chǔ)步驟，拼接效果的好壞直接影響接下來的工作，所以一個好的圖像拼接算法非常重要。 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。那么我們能不能把這些圖像拼接成一個大圖呢？我們利用opencv就可以做到圖像拼接的效果！ 比如我們有對這兩張圖進行拼接。 
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。 那么要實現(xiàn)圖像拼接需要那幾步呢？簡單來說有以下幾步： 對每幅圖進行特征點提取 對對特征點進行匹配 進行圖像配準 把圖像拷貝到另一幅圖像的特定位置 對重疊邊界進行特殊處理
好吧，那就開始正式實現(xiàn)圖像配準。 第一步就是特征點提取?，F(xiàn)在CV領(lǐng)域有很多特征點的定義，比如sift、surf、harris角點、ORB都是很有名的特征因子，都可以用來做圖像拼接的工作，他們各有優(yōu)勢。本文將使用ORB和SURF進行圖像拼接，用其他方法進行拼接也是類似的。 基于SURF的圖像拼接用SIFT算法來實現(xiàn)圖像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），所以在圖像拼接領(lǐng)域還是大有作為。雖說SURF精確度和穩(wěn)定性不及SIFT，但是其綜合能力還是優(yōu)越一些。下面將詳細介紹拼接的主要步驟。 1.特征點提取和匹配特征點提取和匹配的方法我在上一篇文章《OpenCV探索之路（二十三）：特征檢測和特征匹配方法匯總》中做了詳細的介紹，在這里直接使用上文所總結(jié)的SURF特征提取和特征匹配的方法。 //提取特征點 SurfFeatureDetector Detector(2000); vector keyPoint1, keyPoint2;Detector.detect(image1, keyPoint1);Detector.detect(image2, keyPoint2);//特征點描述，為下邊的特征點匹配做準備 SurfDescriptorExtractor Descriptor;Mat imageDesc1, imageDesc2;Descriptor.compute(image1, keyPoint1, imageDesc1);Descriptor.compute(image2, keyPoint2, imageDesc2);FlannBasedMatcher matcher;vectorvector > matchePoints;vector GoodMatchePoints;vector 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(yōu)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);
2.圖像配準這樣子我們就可以得到了兩幅待拼接圖的匹配點集，接下來我們進行圖像的配準，即將兩張圖像轉(zhuǎn)換為同一坐標下，這里我們需要使用findHomography函數(shù)來求得變換矩陣。但是需要注意的是，findHomography函數(shù)所要用到的點集是Point2f類型的，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(zhuǎn)換為Point2f類型的點集。 vector imagePoints1, imagePoints2;for (int i = 0; i<>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，并且實現(xiàn)圖像配準。值得注意的是findHomography函數(shù)的參數(shù)中我們選澤了CV_RANSAC，這表明我們選擇RANSAC算法繼續(xù)篩選可靠地匹配點，這使得匹配點解更為精確。 //獲取圖像1到圖像2的投影映射矩陣 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; //輸出映射矩陣 //圖像配準 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(jīng)過透視矩陣變換', imageTransform1);imwrite('trans1.jpg', imageTransform1);
3. 圖像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。 //創(chuàng)建拼接后的圖,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);
4.圖像融合（去裂縫處理）從上圖可以看出，兩圖的拼接并不自然，原因就在于拼接圖的交界處，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。這里的處理思路是加權(quán)融合，在重疊部分由前一幅圖像慢慢過渡到第二幅圖像，即將圖像的重疊區(qū)域的像素值按一定的權(quán)值相加合成新的圖像。 //優(yōu)化兩圖的連接處，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(qū)域的左邊界 double processWidth = img1.cols - start;//重疊區(qū)域的寬度 int rows = dst.rows; int cols = img1.cols; //注意，是列數(shù)*通道數(shù) double alpha = 1;//img1中像素的權(quán)重 for (int i = 0; i < rows;="" i++)="" ="" {="" ="" ="" ="" uchar*="" p="">(i); //獲取第i行的首地址 uchar* t = trans.ptr(i); uchar* d = dst.ptr(i); for (int j = start; j < cols;="" j++)="" ="" ="" ="" {="" ="" ="" ="" ="" ="">//如果遇到圖像trans中無像素的黑點，則完全拷貝img1中的數(shù)據(jù) if (t[j * 3] == 0 && t[j * 3 + 1] == 0 && t[j * 3 + 2] == 0) { alpha = 1; } else { //img1中像素的權(quán)重，與當前處理點距重疊區(qū)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); } }}
多嘗試幾張，驗證拼接效果 測試一 
測試二 
測試三 
最后給出完整的SURF算法實現(xiàn)的拼接代碼。 #include 'highgui/highgui.hpp' #include 'opencv2/nonfree/nonfree.hpp' #include 'opencv2/legacy/legacy.hpp' #include 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];//變換后的坐標值 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(zhuǎn)換 Mat image1, image2; cvtColor(image01, image1, CV_RGB2GRAY); cvtColor(image02, image2, CV_RGB2GRAY); //提取特征點 SurfFeatureDetector Detector(2000); vector keyPoint1, keyPoint2; Detector.detect(image1, keyPoint1); Detector.detect(image2, keyPoint2); //特征點描述，為下邊的特征點匹配做準備 SurfDescriptorExtractor Descriptor; Mat imageDesc1, imageDesc2; Descriptor.compute(image1, keyPoint1, imageDesc1); Descriptor.compute(image2, keyPoint2, imageDesc2); FlannBasedMatcher matcher; vectorvector > matchePoints; vector GoodMatchePoints; vector 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(yōu)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 imagePoints1, imagePoints2; for (int i = 0; i//獲取圖像1到圖像2的投影映射矩陣 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; //輸出映射矩陣 //計算配準圖的四個頂點坐標 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; //圖像配準 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(jīng)過透視矩陣變換', imageTransform1); imwrite('trans1.jpg', imageTransform1); //創(chuàng)建拼接后的圖,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(yōu)化兩圖的連接處，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(qū)域的左邊界 double processWidth = img1.cols - start;//重疊區(qū)域的寬度 int rows = dst.rows; int cols = img1.cols; //注意，是列數(shù)*通道數(shù) double alpha = 1;//img1中像素的權(quán)重 for (int i = 0; i < rows;="" i++)="" ="" {="" ="" ="" ="" uchar*="" p="">(i); //獲取第i行的首地址 uchar* t = trans.ptr(i); uchar* d = dst.ptr(i); for (int j = start; j < cols;="" j++)="" ="" ="" ="" {="" ="" ="" ="" ="" ="">//如果遇到圖像trans中無像素的黑點，則完全拷貝img1中的數(shù)據(jù) if (t[j * 3] == 0 && t[j * 3 + 1] == 0 && t[j * 3 + 2] == 0) { alpha = 1; } else { //img1中像素的權(quán)重，與當前處理點距重疊區(qū)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); } }}基于ORB的圖像拼接利用ORB進行圖像拼接的思路跟上面的思路基本一樣，只是特征提取和特征點匹配的方式略有差異罷了。這里就不再詳細介紹思路了，直接貼代碼看效果。 #include 'highgui/highgui.hpp' #include 'opencv2/nonfree/nonfree.hpp' #include 'opencv2/legacy/legacy.hpp' #include 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];//變換后的坐標值 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="">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(zhuǎn)換 Mat image1, image2; cvtColor(image01, image1, CV_RGB2GRAY); cvtColor(image02, image2, CV_RGB2GRAY); //提取特征點 OrbFeatureDetector surfDetector(3000); vector keyPoint1, keyPoint2; surfDetector.detect(image1, keyPoint1); surfDetector.detect(image2, keyPoint2); //特征點描述，為下邊的特征點匹配做準備 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 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(yōu)秀匹配點 for (int i = 0; i < matchdistance.rows;="" i++)="" ="" {="" ="" ="" ="">if (matchDistance.atfloat>(i, 0) <>0.4 * matchDistance.atfloat>(i, 1)) { DMatch dmatches(i, macthIndex.atint>(i, 0), matchDistance.atfloat>(i, 0)); GoodMatchePoints.push_back(dmatches); } } Mat first_match; drawMatches(image02, keyPoint2, image01, keyPoint1, GoodMatchePoints, first_match); imshow('first_match ', first_match); vector imagePoints1, imagePoints2; for (int i = 0; i//獲取圖像1到圖像2的投影映射矩陣 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;="">//輸出映射矩陣 //計算配準圖的四個頂點坐標 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;="" ="">//圖像配準 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(jīng)過透視矩陣變換', imageTransform1); imwrite('trans1.jpg', imageTransform1); //創(chuàng)建拼接后的圖,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(yōu)化兩圖的連接處，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(qū)域的左邊界 double processWidth = img1.cols - start;//重疊區(qū)域的寬度 int rows = dst.rows; int cols = img1.cols; //注意，是列數(shù)*通道數(shù) double alpha = 1;//img1中像素的權(quán)重 for (int i = 0; i < rows;="" i++)="" ="" {="" ="" ="" ="" uchar*="" p="">(i); //獲取第i行的首地址 uchar* t = trans.ptr(i); uchar* d = dst.ptr(i); for (int j = start; j < cols;="" j++)="" ="" ="" ="" {="" ="" ="" ="" ="" ="">//如果遇到圖像trans中無像素的黑點，則完全拷貝img1中的數(shù)據(jù) if (t[j * 3] == 0 && t[j * 3 + 1] == 0 && t[j * 3 + 2] == 0) { alpha = 1; } else { //img1中像素的權(quán)重，與當前處理點距重疊區(qū)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。
看一下這一組圖片，這組圖片產(chǎn)生了鬼影，為什么？因為兩幅圖中的人物走動了啊！所以要做圖像拼接，盡量保證使用的是靜態(tài)圖片，不要加入一些動態(tài)因素干擾拼接。
opencv自帶的拼接算法stitchopencv其實自己就有實現(xiàn)圖像拼接的算法，當然效果也是相當好的，但是因為其實現(xiàn)很復雜，而且代碼量很龐大，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源碼時，發(fā)現(xiàn)其中有幾個很有意思的地方。 1.opencv stitch選擇的特征檢測方式一直很好奇opencv stitch算法到底選用了哪個算法作為其特征檢測方式，是ORB，SIFT還是SURF？讀源碼終于看到答案。 #ifdef HAVE_OPENCV_NONFREE stitcher.setFeaturesFinder(new detail::SurfFeaturesFinder());#else stitcher.setFeaturesFinder(new detail::OrbFeaturesFinder());#endif
在源碼createDefault函數(shù)中（默認設置），第一選擇是SURF,第二選擇才是ORB（沒有NONFREE模塊才選），所以既然大牛們這么選擇，必然是經(jīng)過綜合考慮的，所以應該SURF算法在圖像拼接有著更優(yōu)秀的效果。 2.opencv stitch獲取匹配點的方式以下代碼是opencv stitch源碼中的特征點提取部分，作者使用了兩次特征點提取的思路：先對圖一進行特征點提取和篩選匹配（1->2），再對圖二進行特征點的提取和匹配（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。這個思路值得借鑒。 matches_info.matches.clear();Ptr indexParams = new flann::KDTreeIndexParams();Ptr 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 > pair_matches;MatchesSet matches;// Find 1->2 matchesmatcher.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 matchespair_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);
這里我仿照opencv源碼二次提取特征點的思路對我原有拼接代碼進行改寫，實驗證明獲取的匹配點確實較一次提取要多。 //提取特征點 SiftFeatureDetector Detector(1000); // 海塞矩陣閾值，在這里調(diào)整精度，值越大點越少，越精準 vector keyPoint1, keyPoint2;Detector.detect(image1, keyPoint1);Detector.detect(image2, keyPoint2);//特征點描述，為下邊的特征點匹配做準備 SiftDescriptorExtractor Descriptor;Mat imageDesc1, imageDesc2;Descriptor.compute(image1, keyPoint1, imageDesc1);Descriptor.compute(image2, keyPoint2, imageDesc2);FlannBasedMatcher matcher;vectorvector > matchePoints;vector GoodMatchePoints;MatchesSet matches;vector train_desc(1, imageDesc1);matcher.add(train_desc);matcher.train();matcher.knnMatch(imageDesc2, matchePoints, 2);// Lowe's algorithm,獲取優(yōu)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 1FlannBasedMatcher matcher2;matchePoints.clear();vector train_desc2(1, imageDesc2);matcher2.add(train_desc2);matcher2.train();matcher2.knnMatch(imageDesc1, matchePoints, 2);// Lowe's algorithm,獲取優(yōu)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
最后再看一下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 #include #include #include #include using namespace std;using namespace cv;bool try_use_gpu = false;vector 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函數(shù)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(); // 顯示源圖像，和結(jié)果圖像 imshow('全景圖像', pano); if (waitKey() == 27) return 0;}
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