会建网站的人,wordpress安装系统,外贸网站建设便宜,现货市场交易平台一、介绍 图像拼接.
二、分步实现 要实现图像拼接#xff0c;简单来说有以下几步#xff1a;
对每幅图进行特征点提取对对特征点进行匹配进行图像配准把图像拷贝到另一幅图像的特定位置对重叠边界进行特殊处理 PS#xff1a;需要使用低版本的opencv#xff0c;否则无法使…一、介绍 图像拼接.
二、分步实现 要实现图像拼接简单来说有以下几步
对每幅图进行特征点提取对对特征点进行匹配进行图像配准把图像拷贝到另一幅图像的特定位置对重叠边界进行特殊处理 PS需要使用低版本的opencv否则无法使用特征角点提取算子。
#include highgui/highgui.hpp
#include opencv2/nonfree/nonfree.hpp
#include opencv2/legacy/legacy.hpp
#include iostream using namespace cv;
using namespace std;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)
{// 左上角(0, 0, 1)double v2[3] { 0, 0, 1 };double v1[3] { 0 };Mat V2 Mat(3, 1, CV_64FC1, v2);Mat V1 Mat(3, 1, CV_64FC1, v1);V1 H * V2;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);V1 Mat(3, 1, CV_64FC1, v1);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);V1 Mat(3, 1, CV_64FC1, v1);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);V1 Mat(3, 1, CV_64FC1, v1);V1 H * V2;corners.right_bottom.x v1[0] / v1[2];corners.right_bottom.y v1[1] / v1[2];
}void OptimizeSeam(Mat img1, Mat trans, Mat dst)
{int start MIN(corners.left_top.x, corners.left_bottom.x);//开始位置即重叠区域的左边界 double processWidth img1.cols - start; // 重叠区域的宽度 int rows dst.rows;int cols img1.cols; // 注意是列数*通道数double alpha 1; // img1中像素的权重 for (int i 0; i rows; i){uchar* p img1.ptruchar(i); // 获取第i行的首地址uchar* t trans.ptruchar(i);uchar* d dst.ptruchar(i);for (int j start; j cols; j){// 如果遇到图像trans中无像素的黑点则完全拷贝img1中的数据if (t[j * 3] 0 t[j * 3 1] 0 t[j * 3 2] 0){alpha 1;}else{// img1中像素的权重与当前处理点距重叠区域左边界的距离成正比实验证明这种方法确实好 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 main(int argc, char* argv[])
{Mat image01 imread(image2.png, 1); //右图Mat image02 imread(image1.png, 1); //左图imshow(p2, image01);imshow(p1, image02);// 灰度图转换 Mat image1, image2;cvtColor(image01, image1, CV_RGB2GRAY);cvtColor(image02, image2, CV_RGB2GRAY);// 提取特征点SurfFeatureDetector Detector(2000);vectorKeyPoint keyPoint1, keyPoint2;Detector.detect(image1, keyPoint1);Detector.detect(image2, keyPoint2);// 特征点描述SurfDescriptorExtractor Descriptor;Mat imageDesc1, imageDesc2;Descriptor.compute(image1, keyPoint1, imageDesc1);Descriptor.compute(image2, keyPoint2, imageDesc2);FlannBasedMatcher matcher;vectorvectorDMatch matchePoints;vectorMat train_desc(1, imageDesc1);matcher.add(train_desc);matcher.train();matcher.knnMatch(imageDesc2, matchePoints, 2);cout total match points: matchePoints.size() endl;// Lowes algorithm,获取优秀匹配点vectorDMatch GoodMatchePoints;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]);}}// draw matchMat first_match;drawMatches(image02, keyPoint2, image01, keyPoint1, GoodMatchePoints, first_match);imshow(first_match , first_match);vectorPoint2f 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);}// 获取图像1到图像2的投影映射矩阵 尺寸为3*3 Mat homo findHomography(imagePoints1, imagePoints2, CV_RANSAC);cout 变换矩阵为\n homo endl endl; // 输出映射矩阵 // 计算配准图的四个顶点坐标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;// 图像配准 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(直接经过透视矩阵变换, imageTransform1);// 创建拼接后的图,需提前计算图的大小int dst_width imageTransform1.cols; // 取最右点的长度为拼接图的长度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);// 优化拼接处OptimizeSeam(image02, imageTransform1, dst);imshow(dst, dst);waitKey();return 0;
}三、利用stitch实现
#include opencv2/imgcodecs.hpp
#include opencv2/highgui.hpp
#include opencv2/stitching.hpp
#include iostreamusing namespace std;
using namespace cv;int main(int argc, char* argv[])
{Mat img1 imread(image1.png, cv::IMREAD_COLOR);Mat img2 imread(image2.png, cv::IMREAD_COLOR);vectorMat imgs;imgs.push_back(img1);imgs.push_back(img2);Mat pano;PtrStitcher stitcher Stitcher::create(Stitcher::PANORAMA);Stitcher::Status status stitcher-stitch(imgs, pano);if (status ! Stitcher::OK){cout Cant stitch images, error code int(status) endl;return EXIT_FAILURE;}string result_name result1.jpg;imwrite(result_name, pano);cout stitching completed successfully\n result_name saved!;return EXIT_SUCCESS;
}
