简单的图像拼接实现

编译环境VS2013和opencv2.4.1；

需要包含一下stdafx.h文件，然后直接main.cpp里编译以下代码即可。

运行的时候，需要在终端下运行，找到编译好的可执行程序，如下图所示：

输入图片：

       

输出图片：

算法实现：

#include <stdio.h>#include <iostream>#include "opencv2/core/core.hpp"#include "opencv2/features2d/features2d.hpp"#include "opencv2/highgui/highgui.hpp"#include "opencv2/nonfree/nonfree.hpp"#include "opencv2/calib3d/calib3d.hpp"#include "opencv2/imgproc/imgproc.hpp"using namespace cv;using namespace std;void readme();double get_avg_gray(IplImage *img);void set_avg_gray(IplImage *img, IplImage *out, double avg_gray);/** @function main */int main(int argc, char** argv){if (argc != 3){readme(); return -1;}// Load the imagesMat image1 = imread(argv[2]);Mat image2 = imread(argv[1]);resize(image1, image1, Size(256, 320), 0, 0, CV_INTER_LINEAR);resize(image2, image2, Size(256, 320), 0, 0, CV_INTER_LINEAR);Mat gray_image1;Mat gray_image2;// Convert to GrayscalecvtColor(image1, gray_image1, CV_RGB2GRAY);cvtColor(image2, gray_image2, CV_RGB2GRAY);    imshow("first image", image2);imshow("second image", image1);if (!gray_image1.data || !gray_image2.data){std::cout << " --(!) Error reading images " << std::endl; return -1;}//-- Step 1: Detect the keypoints using SURF Detectorint minHessian = 400;SurfFeatureDetector detector(minHessian);std::vector< KeyPoint > keypoints_object, keypoints_scene;detector.detect(gray_image1, keypoints_object);detector.detect(gray_image2, keypoints_scene);//-- Step 2: Calculate descriptors (feature vectors)SurfDescriptorExtractor extractor;Mat descriptors_object, descriptors_scene;extractor.compute(gray_image1, keypoints_object, descriptors_object);extractor.compute(gray_image2, keypoints_scene, descriptors_scene);//-- Step 3: Matching descriptor vectors using FLANN matcherFlannBasedMatcher matcher;std::vector< DMatch > matches;matcher.match(descriptors_object, descriptors_scene, matches);double max_dist = 0; double min_dist = 100;//-- Quick calculation of max and min distances between keypointsfor (int i = 0; i < descriptors_object.rows; i++){double dist = matches[i].distance;if (dist < min_dist) min_dist = dist;if (dist > max_dist) max_dist = dist;}printf("-- Max dist : %f \n", max_dist);printf("-- Min dist : %f \n", min_dist);//-- Use only "good" matches (i.e. whose distance is less than 3*min_dist )std::vector< DMatch > good_matches;for (int i = 0; i < descriptors_object.rows; i++){if (matches[i].distance < 3 * min_dist){good_matches.push_back(matches[i]);}}std::vector< Point2f > obj;std::vector< Point2f > scene;for (int i = 0; i < good_matches.size(); i++){//-- Get the keypoints from the good matchesobj.push_back(keypoints_object[good_matches[i].queryIdx].pt);scene.push_back(keypoints_scene[good_matches[i].trainIdx].pt);}// Find the Homography MatrixMat H = findHomography(obj, scene, CV_RANSAC);// Use the Homography Matrix to warp the imagescv::Mat result;//按列拼接 accoring to columswarpPerspective(image1, result, H, cv::Size(image1.cols + image2.cols, image1.rows));cv::Mat half(result, cv::Rect(0, 0, image2.cols, image2.rows));//按行拼接 accoring to rows//warpPerspective(image1, result, H, cv::Size(image1.cols, image1.rows + image2.rows));//cv::Mat half(result, cv::Rect(0, 0, image2.cols, image2.rows));image2.copyTo(half);//GaussianBlur Filter//GaussianBlur(result, result, Size(5, 5), 0, 0);imshow("Result", result);waitKey(0);return 0;}/** @function readme */void readme(){std::cout << " Usage: Panorama < img1 > < img2 >" << std::endl;}