影象拼接
阿新 • • 發佈:2018-12-12
基本步驟:提取特徵點(這裡用的是SURF提取特徵點),計算特徵向量,訓練一個匹配器,特徵點匹配,根據勞式判據得到優秀的匹配點,計算透視變換矩陣,進行透視變換,計算透視變換後的座標(H*V),計算拼接後圖片的大小,進行拼接,優化連線處。優化連線處的思想:加權處理兩幅圖的畫素,即在重合區域內,畫素點距離重合區域左邊界越近,左影象素比例約大,距離約圓,左影象素比例越小。
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; cout << "V2: " << V2 << endl; cout << "V1: " << V1 << endl; 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; cout << "V2: " << V2 << endl; cout << "V1: " << V1 << endl; 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; cout << "V2: " << V2 << endl; cout << "V1: " << V1 << endl; 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("4.jpg", 1); //右圖 Mat image02 = imread("3.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); cout << imageTransform1.size().width << endl; //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 cout<<"img1:" << img1.channels() << endl; cout<<"trans:" << trans.channels()<<endl; 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); } } }