所用版本为VS2019,OPENCV3.10
注意opencv标准包中不包含sift和ransac，需要手动自己安装附加包，可利用cmake编译安装，具体教程可参考这里
代码亲自调试通过，运行正常，并附有详细注释
#include#include #include #include #include #include #include #include #include using namespace cv; using namespace std; //using namespace cv::xfeatures2d; //只有加上这句命名空间，SiftFeatureDetector and SiftFeatureExtractor才可以使用 int main() { //Create SIFT class pointer Ptr f2d = xfeatures2d::SIFT::create(); //Loading images Mat img1 = imread("1.jpg"); Mat img2 = imread("2.jpg"); if (!img1.data || !img2.data) { cout << "Reading picture error！" << endl; return false; } //Detect the 特征点 double t0 = getTickCount(); //当前时间 vector m_LeftKey, m_RightKey; f2d->detect(img1, m_LeftKey); f2d->detect(img2, m_RightKey); cout << "The keypoints number of img1 is:" << m_LeftKey.size() << endl; cout << "The keypoints number of img2 is:" << m_RightKey.size() << endl; //Calculate descriptors (feature vectors) 描述子 Mat descriptors_1, descriptors_2; f2d->compute(img1, m_LeftKey, descriptors_1); f2d->compute(img2, m_RightKey, descriptors_2); double freq = getTickFrequency(); double tt = ((double)getTickCount() - t0) / freq; cout << "Extract SIFT Time:" << tt << "ms" << endl; //画关键点 Mat img_keypoints_1, img_keypoints_2; drawKeypoints(img1, m_LeftKey, img_keypoints_1, Scalar::all(-1), 0); drawKeypoints(img2, m_RightKey, img_keypoints_2, Scalar::all(-1), 0); imshow("img_keypoints_1", img_keypoints_1); imshow("img_keypoints_2", img_keypoints_2); //Matching descriptor vector using BFMatcher 全部匹配点 暴力匹配 BFMatcher matcher; vector matches; //存储匹配出的对应点索引和距离 matcher.match(descriptors_1, descriptors_2, matches); cout << "The number of match:" << matches.size() << endl; //计算匹配结果中距离的最大和最小值 //距离是指两个特征向量间的欧式距离，表明两个特征的差异，值越小表明两个特征点越接近 double max_dist = 0; double min_dist = 100; for (int i = 0; i < matches.size(); i++) { double dist = matches[i].distance; if (dist < min_dist) min_dist = dist; if (dist > max_dist) max_dist = dist; } cout << "最大距离：" << max_dist << endl; cout << "最小距离：" << min_dist << endl; //筛选出较好的匹配点 vector goodMatches; for (int i = 0; i < matches.size(); i++) { if (matches[i].distance < 0.2 * max_dist) { goodMatches.push_back(matches[i]); } } cout << "goodMatch个数：" << goodMatches.size() << endl; //画出匹配结果 Mat img_matches; //红色连接的是匹配的特征点对，绿色是未匹配的特征点 drawMatches(img1, m_LeftKey, img2, m_RightKey, goodMatches, img_matches,Scalar::all(-1), CV_RGB(0, 255, 0), Mat(), 2); imshow("MatchSIFT筛选后", img_matches); IplImage result = img_matches; waitKey(0); //RANSAC匹配过程 使用筛选后的sift匹配点 vector m_Matches = goodMatches; // 分配空间 int ptCount = (int)m_Matches.size(); Mat p1(ptCount, 2, CV_32F); //存储左图匹配点坐标 Mat p2(ptCount, 2, CV_32F); //存储右图匹配点坐标 // 把Keypoint转换为Mat Point2f pt; for (int i = 0; i < ptCount; i++) { pt = m_LeftKey[m_Matches[i].queryIdx].pt; p1.at (i, 0) = pt.x; p1.at (i, 1) = pt.y; pt = m_RightKey[m_Matches[i].trainIdx].pt; p2.at (i, 0) = pt.x; p2.at (i, 1) = pt.y; } // 用RANSAC方法计算F Mat m_Fundamental; vector m_RANSACStatus; // 这个变量用于存储RANSAC后每个点的状态 findFundamentalMat(p1, p2, m_RANSACStatus, FM_RANSAC); // 计算野点个数 未匹配点 int OutlinerCount = 0; for (int i = 0; i < ptCount; i++) { if (m_RANSACStatus[i] == 0) // 状态为0表示野点 { OutlinerCount++; } } int InlinerCount = ptCount - OutlinerCount; // 计算内点个数 cout << "内点数为：" << InlinerCount << endl; // 这三个变量用于保存匹配点（内点）和匹配关系 vector m_LeftInlier; //左图匹配点 vector m_RightInlier; //右图匹配点 vector m_InlierMatches; //匹配关系 m_InlierMatches.resize(InlinerCount); m_LeftInlier.resize(InlinerCount); m_RightInlier.resize(InlinerCount); InlinerCount = 0; float inlier_minRx = img1.cols; //用于存储内点中右图最小横坐标，以便后续融合 for (int i = 0; i < ptCount; i++) { if (m_RANSACStatus[i] != 0) { //左图匹配点坐标 m_LeftInlier[InlinerCount].x = p1.at (i, 0); m_LeftInlier[InlinerCount].y = p1.at (i, 1); //右图匹配点坐标 m_RightInlier[InlinerCount].x = p2.at (i, 0); m_RightInlier[InlinerCount].y = p2.at (i, 1); //匹配关系 m_InlierMatches[InlinerCount].queryIdx = InlinerCount; m_InlierMatches[InlinerCount].trainIdx = InlinerCount; //存储匹配点中右图最小横坐标 if (m_RightInlier[InlinerCount].x < inlier_minRx) inlier_minRx = m_RightInlier[InlinerCount].x; InlinerCount++; } } // 把内点转换为drawMatches可以使用的格式 vector key1(InlinerCount); vector key2(InlinerCount); KeyPoint::convert(m_LeftInlier, key1); KeyPoint::convert(m_RightInlier, key2); // 显示计算F过后的内点匹配 Mat OutImage; drawMatches(img1, key1, img2, key2, m_InlierMatches, OutImage); // cvNamedWindow("Match features", 1); // cvShowImage("Match features", OutImage); // waitKey(0); cvDestroyAllWindows(); //矩阵H用以存储RANSAC得到的单应矩阵 Mat H = findHomography(m_LeftInlier, m_RightInlier, RANSAC); //两平面之间的转换矩阵 //存储左图四角，及其变换到右图位置 std::vector obj_corners(4); obj_corners[0] = Point(0, 0); obj_corners[1] = Point(img1.cols, 0); obj_corners[2] = Point(img1.cols, img1.rows); obj_corners[3] = Point(0, img1.rows); std::vector scene_corners(4); //左图四角变换到右图的位置 //转换 perspectiveTransform(obj_corners, scene_corners, H); //投影到右侧的新视角 按匹配的特征点位置进行转换投影 //画出变换后图像位置 Point2f offset((float)img1.cols, 0); //Point2f offset(0, 0); line(OutImage, scene_corners[0] + offset, scene_corners[1] + offset, Scalar(0, 255, 0), 4); line(OutImage, scene_corners[1] + offset, scene_corners[2] + offset, Scalar(0, 255, 0), 4); line(OutImage, scene_corners[2] + offset, scene_corners[3] + offset, Scalar(0, 255, 0), 4); line(OutImage, scene_corners[3] + offset, scene_corners[0] + offset, Scalar(0, 255, 0), 4); imshow("Good Matches & Object detection左图转换后的位置标注", OutImage); waitKey(0); int drift = scene_corners[1].x; //储存偏移量 //新建一个矩阵存储配准后四角的位置 int width = int(max(abs(scene_corners[1].x), abs(scene_corners[2].x))); int height = img1.rows; //或者：int height = int(max(abs(scene_corners[2].y), abs(scene_corners[3].y))); float origin_x = 0, //左图转换后超出0坐标左侧的图像的宽度大小 origin_y = 0; //计算左图转换之后的宽和高 if (scene_corners[0].x < 0) { if (scene_corners[3].x < 0) origin_x += min(scene_corners[0].x, scene_corners[3].x); else origin_x += scene_corners[0].x; } width -= int(origin_x); if (scene_corners[0].y < 0) { if (scene_corners[1].y) origin_y += min(scene_corners[0].y, scene_corners[1].y); else origin_y += scene_corners[0].y; } //可选：height-=int(origin_y); Mat imageturn = Mat::zeros(width, height, img1.type()); //左图变换后的 //获取新的变换矩阵，使图像完整显示 for (int i = 0;i < 4;i++) { scene_corners[i].x -= origin_x; //补偿显示不全的位置，超出0坐标左侧的图像宽度为origin.x //可选：scene_corners[i].y -= (float)origin_y; } } Mat H1 = getPerspectiveTransform(obj_corners, scene_corners); //重新计算左图的实际转换矩阵 //进行左图图像变换，显示效果 左侧图像变换的基准是左右侧对应点，将左侧对应点在图像中的位置转换到该对应点在右图中的位置 warpPerspective(img1, imageturn, H1, Size(width, height)); imshow("image_Perspective左图变换后", imageturn); waitKey(0); //图像融合 int width_ol = width - int(inlier_minRx - origin_x); //重叠区域的宽 int start_x = int(inlier_minRx - origin_x); //imageturn上的重叠区域的起始坐标 右侧内点的最小点-origin_x //重叠区域开始的横坐标为右图上的最小横坐标的内点在imageturn上的坐标，右侧为左图的右边界 cout << "width: " << width << endl; cout << "img1.width: " << img1.cols << endl; cout << "start_x: " << start_x << endl; cout << "width_ol: " << width_ol << endl; uchar* ptr = imageturn.data; //左侧图像转换之后的图像数据 double alpha = 0, beta = 1; //定义权重 //按权重计算重合部分的像素值 for (int row = 0;row < height;row++) //图像转换之后的高度 { //step可以理解为Mat矩阵中每一行的“步长”，以字节为基本单位，每一行中所有元素的字节总量，是累计了一行中所有元素、所有通道、所有通道的elemSize1之后的值 //elemSize: 通道数 ptr = imageturn.data + row * imageturn.step + (start_x)*imageturn.elemSize(); //左图第一通道 从重叠部分开始 for (int col = 0;col < width_ol;col++) { uchar* ptr_c1 = ptr + imageturn.elemSize1(); //左图第二通道 uchar* ptr_c2 = ptr_c1 + imageturn.elemSize1(); //左图第三通道 uchar* ptr2 = img2.data + row * img2.step + (col + int(inlier_minRx)) * img2.elemSize(); //右图第一通道 从重合部分开始计算 uchar* ptr2_c1 = ptr2 + img2.elemSize1(); //右图第二通道 uchar* ptr2_c2 = ptr2_c1 + img2.elemSize1();//右图第三通道 alpha = double(col) / double(width_ol); beta = 1 - alpha; //左图中转换了之后的无像素值的黑点，则完全拷贝右侧图的像素值 if (*ptr == 0 && *ptr_c1 == 0 && *ptr_c2 == 0) { *ptr = (*ptr2); //左图该像素第一通道 *ptr_c1 = (*ptr2_c1); //左图该像素第二通道 *ptr_c2 = (*ptr2_c2); //左图该像素第三通道 } //不是黑点的按权重计算 *ptr = (*ptr) * beta + (*ptr2) * alpha;//左图通道1的像素值乘以权重+右侧通道1像素值乘以权重 *ptr_c1 = (*ptr_c1) * beta + (*ptr2_c1) * alpha; *ptr_c2 = (*ptr_c2) * beta + (*ptr2_c2) * alpha; //指针后移 ptr += imageturn.elemSize(); } } // imshow("image_overlap", imageturn); // waitKey(0); Mat img_result = Mat::zeros(height, width + img2.cols - drift, img1.type()); //int drift = scene_corners[1].x; uchar* ptr_r = imageturn.data; for (int row = 0;row < height;row++) { //将左侧图像融入结果图像 ptr_r = img_result.data + row * img_result.step;//指向结果图像的指针 第一通道 for (int col = 0;col < imageturn.cols;col++) { uchar* ptr_rc1 = ptr_r + imageturn.elemSize1();//指向结果图像的指针 第二通道 uchar* ptr_rc2 = ptr_rc1 + imageturn.elemSize1(); 指向结果图像的指针 第三通道 uchar* ptr = imageturn.data + row * imageturn.step + col * imageturn.elemSize();//指向 左侧图像的指针 第一通道 uchar* ptr_c1 = ptr + imageturn.elemSize1();//第二通道 uchar* ptr_c2 = ptr_c1 + imageturn.elemSize1();//第三通道 *ptr_r = *ptr; //全部赋值为左侧图像的像素值 *ptr_rc1 = *ptr_c1; *ptr_rc2 = *ptr_c2; ptr_r += img_result.elemSize(); } //将右侧图像融合进结果图像 ptr_r = img_result.data + row * img_result.step + imageturn.cols * img_result.elemSize();//指向结果图像的指针 从左侧图像的右边界开始计算 for (int col = imageturn.cols;col < img_result.cols;col++) { uchar* ptr_rc1 = ptr_r + imageturn.elemSize1();//指向结果图像指针 第二通道 uchar* ptr_rc2 = ptr_rc1 + imageturn.elemSize1();//指向结果图像 第三通道 uchar* ptr2 = img2.data + row * img2.step + (col - imageturn.cols + drift) * img2.elemSize(); //指向右侧图像 从重合部分开始计算 重合部分起点为右侧图像中的第一个匹配点对 uchar* ptr2_c1 = ptr2 + img2.elemSize1();//指向右侧图像 第二通道 uchar* ptr2_c2 = ptr2_c1 + img2.elemSize1();//指向右侧图像 第三通道 *ptr_r = *ptr2; *ptr_rc1 = *ptr2_c1; *ptr_rc2 = *ptr2_c2; ptr_r += img_result.elemSize(); } } imshow("image_result融合结果", img_result); while (1) { if (waitKey(100) == 19) //cvSaveImage("E:\final_result.jpg", &IplImage(img_result)); cv::imwrite("E:\final_result.jpg", img_result); if (waitKey(100) == 27) break; //按esc退出，ctl+s保存图像 } return 0; }
