opencv實現雙攝像頭視訊拼接
阿新 • • 發佈：2018-12-11
#include "stdafx.h" #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]; } //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); } } } int _tmain(int argc, _TCHAR* argv[]) { VideoCapture cap1(0);//左 VideoCapture cap2(1);//右 double rate = 60; int delay = 1000 / rate; bool stop(false); Mat frame1; Mat frame2; Mat frame3; Mat frame4; int k = 100; int n = 50; Mat image01; Mat image02; Mat image03; Mat image04; Mat imageTransform1, imageTransform2; Mat homo; namedWindow("cam1", CV_WINDOW_AUTOSIZE); namedWindow("cam2", CV_WINDOW_AUTOSIZE); if (cap1.isOpened() && cap2.isOpened()) { cout << "*** ***" << endl; cout << "攝像頭已啟動！" << endl; } else { cout << "*** ***" << endl; cout << "警告：請檢查攝像頭是否安裝好!" << endl; cout << "程式結束！" << endl << "*** ***" << endl; return -1; } cap1.set(CV_CAP_PROP_FRAME_WIDTH, 640); cap1.set(CV_CAP_PROP_FRAME_HEIGHT, 480); cap2.set(CV_CAP_PROP_FRAME_WIDTH, 640); cap2.set(CV_CAP_PROP_FRAME_HEIGHT, 480); cap1.set(CV_CAP_PROP_FOCUS, 0); cap2.set(CV_CAP_PROP_FOCUS, 0); //獲取兩幅影象，通過這兩幅影象來估計攝像機引數 while (n--) { if (cap1.read(frame1) && cap2.read(frame2)) { imshow("cam1", frame1); imshow("cam2", frame2); imwrite("frame1.bmp", frame1); imwrite("frame2.bmp", frame2); } if (waitKey(1) == 27)//按ESC鍵 { stop = true; cout << "程式結束！" << endl; cout << "*** ***" << endl; } } image02 = imread("frame1.bmp", 1); //左圖 image01 = imread("frame2.bmp", 1); //右圖 //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); 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 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; while (!stop) { double t1 = getTickCount(); if (cap1.read(frame3) && cap2.read(frame4)) { imshow("cam1", frame3); imshow("cam2", frame4); image03 = frame3.clone(); image04 = frame4.clone(); /*imwrite("frame3.bmp", frame1); imwrite("frame4.bmp", frame2);*/ //影象配準 warpPerspective(image04, imageTransform1, homo, Size(MAX(corners.right_top.x, corners.right_bottom.x), image03.rows)); //warpPerspective(image01, imageTransform2, adjustMat*homo, Size(image02.cols*1.3, image02.rows*1.8)); //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 = image03.rows;*/ int dst_height = imageTransform1.rows; int dst1_width = imageTransform1.cols; int dst1_height = corners.right_bottom.y; Mat dst1(dst1_height, dst1_width, CV_8UC3); dst1.setTo(0); Mat dst(dst_height, dst_width, CV_8UC3); dst.setTo(0); imageTransform1.copyTo(dst(Rect(0, 0, imageTransform1.cols, imageTransform1.rows))); image03.copyTo(dst(Rect(0, 0, image03.cols, image03.rows))); OptimizeSeam(image03, imageTransform1, dst); /*dst.rowRange(1,dst1_height).copyTo(dst1.rowRange(1, dst1_height));*/ imshow("dst", dst); if (waitKey(1) == 27)//按ESC鍵 { stop = true; cout << "程式結束！" << endl; cout << "*** ***" << endl; } } cout << "拼接時間: " << ((getTickCount() - t1) / getTickFrequency()) << " sec" << endl; } return 0; }