利用OpenCV实现图像拼接

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：

1. 对每幅图进行特征点提取
2. 对对特征点进行匹配
3. 进行图像配准
4. 把图像拷贝到另一幅图像的特定位置
5. 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.ptr<uchar>(i);  // 获取第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);vector<KeyPoint> 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;vector<vector<DMatch> > matchePoints;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,获取优秀匹配点vector<DMatch> 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);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);}// 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 <iostream>using 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);vector<Mat> imgs;imgs.push_back(img1);imgs.push_back(img2);Mat pano;Ptr<Stitcher> stitcher = Stitcher::create(Stitcher::PANORAMA);Stitcher::Status status = stitcher->stitch(imgs, pano);if (status != Stitcher::OK){cout << "Can't 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;
}