单纯记录一下之前的代码#include <opencv2/imgproc/imgproc.hpp> #include <opencv2/opencv.hpp> #include<opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <cv.h> #include <iostream> #include<fstream> #include <stdio.h> //#include<opencv2/legacy/legacy.hpp> #include <string> #include <vector> #include<opencv2/xfeatures2d.hpp> #include<algorithm> using namespace cv; using namespace std; using namespace cv::xfeatures2d; using namespace cv::ml; int main() { int64 begintime = getTickCount(); ofstream outfile1, outfile2; outfile1.open("matrix.txt"); //outfile2.open("mmright.txt"); //读入图像并进行灰度处理 //Mat img1 = imread("D:/SU/Tao13_LF_Depth_Light/DEPTH_FROM_CORR_DEFOC_JPEGONLY/Desk/9168/AllFocus.tif"); //Mat img2 = imread("D:/SU/Tao13_LF_Depth_Light/DEPTH_FROM_CORR_DEFOC_JPEGONLY/Desk/9169/AllFocus.tif"); Mat img1 = imread("panoLeft.tif"); Mat img2 = imread("panoRight.tif"); //第一步，用SIFT算子检测关键点； Ptr<SiftFeatureDetector>detector=SiftFeatureDetector::create(); std::vector<KeyPoint> m_LeftKey, m_RightKey;//构造2个专门由点组成的点向量用来存储特征点 detector->detect(img1, m_LeftKey);//将img1图像中检测到的特征点存储起来放在m_LeftKey中 detector->detect(img2, m_RightKey);//同理 cout << "图像1特征点的个数：" << m_LeftKey.size() << endl; cout << "图像2特征点的个数：" << m_RightKey.size() << endl; //计算特征向量 cv::Mat descriptors1, descriptors2;//存放特征向量的矩阵 detector->compute(img1, m_LeftKey, descriptors1); detector->compute(img2, m_RightKey, descriptors2); //画出特征点 Mat img_m_LeftKey, img_m_RightKey; drawKeypoints(img1, m_LeftKey, img_m_LeftKey, Scalar::all(-1), 0); //cvScalar(255,0,0)画的圈圈是蓝色，对应于特征点的颜色,DrawMatchesFlags::DRAW_RICH_KEYPOINTS表示关键点上圆圈的尺寸与特征的尺度成正比，对应于0，是“标志位”的意思 drawKeypoints(img2, m_RightKey, img_m_RightKey, Scalar::all(-1), 0); imshow("Keysrc1", img_m_LeftKey); imshow("Keysrc2", img_m_RightKey); imwrite("图像2的特征点.tif", img_m_LeftKey); imwrite("图像3的特征点.tif", img_m_RightKey); BFMatcher matcher(NORM_L2, false); vector<vector<DMatch>> matches2; vector<DMatch>matches; matcher.match(descriptors1, descriptors2, matches); const float ratio = 0.7; matches.clear(); matcher.knnMatch(descriptors1, descriptors2, matches2, 2); for (int n = 0; n < matches2.size(); n++) { DMatch& bestmatch = matches2[n][0]; DMatch& bettermatch = matches2[n][1]; if (bestmatch.distance < ratio*bettermatch.distance) { matches.push_back(bestmatch); } } cout << "match个数:" << matches.size() << endl; //画出直接匹配结果 Mat key_matches; drawMatches(img1, m_LeftKey, img2, m_RightKey, matches, key_matches, CV_RGB(255, 0, 255), CV_RGB(0, 255, 0), Mat(), 2); imshow("ratio_matches", key_matches); imwrite("ratio_matches.tif", key_matches); //RANSAC匹配过程 vector<DMatch> m_Matches =matches; //cout<<"m_Matches="<<m_Matches.size()<<endl; // 分配空间 int ptCount = (int)m_Matches.size(); //cout<<"m_Matches="<<ptCount<<endl; Mat p1(ptCount, 2, CV_32F); Mat p2(ptCount, 2, CV_32F); //cout<<"p1="<<p1<<endl; // 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<float>(i, 0) = pt.x; p1.at<float>(i, 1) = pt.y; pt = m_RightKey[m_Matches[i].trainIdx].pt; p2.at<float>(i, 0) = pt.x; p2.at<float>(i, 1) = pt.y; } //cout<<"p1="<<p1<<endl;//图1的匹配点坐标 //cout<<"p2="<<p2<<endl;//图2的匹配点坐标 // 用RANSAC方法计算F(基础矩阵) Mat m_Fundamental; vector<uchar> 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); // 计算内点个数 int OutlinerCount = 0; for (int i = 0; i<ptCount; i++) { if (m_RANSACStatus[i] == 0) // 状态为0表示外点 { OutlinerCount++; } } int InlinerCount = ptCount - OutlinerCount; // 计算内点 cout << "内点数为：" << InlinerCount << endl; cout << "外点数为：" << OutlinerCount << endl; // 这三个变量用于保存内点和匹配关系 vector<Point2f> m_LeftInlier; vector<Point2f> m_RightInlier; vector<DMatch> m_InlierMatches; 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 //cout<<"inlier="<<inlier_minRx<<endl; for (int i = 0; i<ptCount; i++) { if (m_RANSACStatus[i] != 0) { m_LeftInlier[InlinerCount].x = p1.at<float>(i, 0); m_LeftInlier[InlinerCount].y = p1.at<float>(i, 1); m_RightInlier[InlinerCount].x = p2.at<float>(i, 0); m_RightInlier[InlinerCount].y = p2.at<float>(i, 1); m_InlierMatches[InlinerCount].queryIdx = InlinerCount; m_InlierMatches[InlinerCount].trainIdx = InlinerCount; if (m_RightInlier[InlinerCount].x<inlier_minRx) inlier_minRx = m_RightInlier[InlinerCount].x; //存储内点中右图最小横坐标 InlinerCount++; } } cout<<"inlier="<<inlier_minRx<<endl;//最小横坐标 // 把内点转换为drawMatches可以使用的格式 vector<KeyPoint> key1(InlinerCount); vector<KeyPoint> 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, CV_RGB(255, 0, 255), CV_RGB(0, 255, 0)); imshow("RANSAC match features", OutImage); imwrite("RansacMatchb.tif", OutImage); //矩阵H用以存储RANSAC得到的单应矩阵 Mat H = findHomography(m_LeftInlier, m_RightInlier, RANSAC); outfile1 << H << endl; cout << H << endl; //存储左图四角，及其变换到右图位置 std::vector<Point2f> 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<Point2f> scene_corners(4); perspectiveTransform(obj_corners, scene_corners, H); /* //画出变换后图像位置 Point2f offset((float)img1.cols, 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", OutImage); imwrite("transform.tif", OutImage);*/ int drift = scene_corners[1].x; //储存偏移量 cout << "scene=" << scene_corners << endl; cout << "scene0=" << scene_corners[0].x << endl; cout << "scene1=" << scene_corners[1].x << endl; cout << "scene2=" << scene_corners[2].x << endl; cout << "scene3=" << scene_corners[3].x << endl; //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)));//等于左图右边两个顶点中横坐标较大的那个 int height = img1.rows; //或者：int height = int(max(abs(scene_corners[2].y), abs(scene_corners[3].y))); float origin_x = 0, origin_y = 0; 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);//图像的宽度变为左图变换之后左边的定点坐标小的数值+右边的横坐标较大的数值 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; } //height-=int(origin_y); //imageturn为变换只有的左图的大小 Mat imageturn = Mat::zeros(width, height, img1.type()); cout << "PerspectiveWidth: " << width << endl; cout << "PerspectiveHeight: " << height << endl; cout << "img1.type(): " << img1.type() << endl; //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; }//相当于把整个图像往右移动至右图上，移动距离为初次变换之后最小的横坐标 Mat H1 = getPerspectiveTransform(obj_corners, scene_corners);//然后移动之后的与右图再求一次矩阵 //进行图像变换，显示效果 warpPerspective(img1, imageturn, H1, Size(width, height)); imshow("image_Perspective", imageturn); imwrite("perpective.tif", imageturn); cout << "origin_x=" << origin_x << endl; cout << "origin_y=" << origin_y << endl; cout << "img1.width=" << img1.cols << endl; cout << "inlier_minRx=" << inlier_minRx << endl; cout << "scene_corners= " << scene_corners << endl; cout << "单应矩阵H=" << H << endl; cout << "变换矩阵H1=" << H1 << endl; //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 cout << "start_X:" << start_x << endl; /*//imageturn为左图图转化之后的 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 Mat subValue_image = Mat::zeros(imageturn.rows, width_ol, imageturn.type());//创建一个图用于保存差值图像 Mat sub1 = imageturn(Rect(start_x, 0, width_ol, imageturn.rows));//start_x 重叠区域的宽 高不变 Mat sub2 = img2(Rect(inlier_minRx, 0, width_ol, img2.rows)); //求两个图像插值 absdiff(sub1, sub2, subValue_image); imshow("差值图", subValue_image); //创建一个mask Mat mask = Mat::zeros(height, width_ol, img1.type());//重叠区域大小 Mat imgL = imageturn(Rect(start_x, 0, width_ol, height)); Mat imgR = img2(Rect(0, 0, width_ol, height)); mask(Rect(0, 0, width_ol*0.5, height)).setTo(255); imshow("mask", mask); Point Center(imgL.cols*0.5, imgL.rows*0.5); Mat resultImage; resultImage.create(imgL.size(), imgL.type()); seamlessClone(imgL, imgR, mask, Center, resultImage, NORMAL_CLONE); imshow("blending", resultImage);*/ uchar* ptr = imageturn.data;//perspective的图像数据 double alpha = 0, beta = 1;//定义权重 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； ptr = imageturn.data + row*imageturn.step + (start_x)*imageturn.elemSize(); //data uchar类型的指针，指向Mat数据矩阵的首地址。可以理解为标示一个房屋的门牌号； for (int col = 0; col<width_ol; col++)//图像转换之后宽度 { uchar* ptr_c1 = ptr + imageturn.elemSize1(); uchar* ptr_c2 = ptr_c1 + imageturn.elemSize1(); uchar* ptr2 = img2.data + row*img2.step + (col + int(inlier_minRx))*img2.elemSize(); uchar* ptr2_c1 = ptr2 + img2.elemSize1(); uchar* ptr2_c2 = ptr2_c1 + img2.elemSize1(); alpha = double(col) / double(width_ol); beta = 1 - alpha; if (*ptr == 0 && *ptr_c1 == 0 && *ptr_c2 == 0) {//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 *ptr = (*ptr2);//该像素第一通道 *ptr_c1 = (*ptr2_c1);//该像素第二通道 *ptr_c2 = (*ptr2_c2);//该像素第三通道 } //如不为0 *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; ptr += imageturn.elemSize();//指针指向下一个元素 } } imshow("image_overlap", imageturn); Mat img_result = Mat::zeros(height, width + img2.cols - drift, img1.type());//int drift = scene_corners[1].x; cout << "宽：" << width + img2.cols - drift << endl; cout << "高：" << height << endl; uchar* ptr_r = imageturn.data; for (int row = 0; row<height; row++) { ptr_r = img_result.data + row*img_result.step; for (int col = 0; col<imageturn.cols; col++) { uchar* ptr_rc1 = ptr_r + imageturn.elemSize1(); uchar* ptr_rc2 = ptr_rc1 + imageturn.elemSize1(); uchar* ptr = imageturn.data + row*imageturn.step + col*imageturn.elemSize(); uchar* ptr_c1 = ptr + imageturn.elemSize1(); uchar* ptr_c2 = ptr_c1 + imageturn.elemSize1(); *ptr_r = *ptr; *ptr_rc1 = *ptr_c1; *ptr_rc2 = *ptr_c2; ptr_r += img_result.elemSize(); } ptr_r = img_result.data + row*img_result.step + imageturn.cols*img_result.elemSize(); for (int col = imageturn.cols; col<img_result.cols; col++) { uchar* ptr_rc1 = ptr_r + imageturn.elemSize1(); uchar* ptr_rc2 = ptr_rc1 + imageturn.elemSize1(); uchar* ptr2 = img2.data + row*img2.step + (col - imageturn.cols + drift)*img2.elemSize(); uchar* ptr2_c1 = ptr2 + img2.elemSize1(); uchar* ptr2_c2 = ptr2_c1 + img2.elemSize1(); *ptr_r = *ptr2; *ptr_rc1 = *ptr2_c1; *ptr_rc2 = *ptr2_c2; ptr_r += img_result.elemSize(); } } imshow("image_result", img_result); imwrite("panoAll.tif", img_result); //imwrite("14.jpg",img_result); int64 endtime = getTickCount(); cout << "时间：" << (endtime - begintime) / getTickFrequency() << "s" << endl; waitKey(0); return 0; }
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