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 
#include 

#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 images
	Mat 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 Grayscale
	cvtColor(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 Detector
	int 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 matcher
	FlannBasedMatcher 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 keypoints
	for (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 matches
		obj.push_back(keypoints_object[good_matches[i].queryIdx].pt);
		scene.push_back(keypoints_scene[good_matches[i].trainIdx].pt);
	}

	// Find the Homography Matrix
	Mat H = findHomography(obj, scene, CV_RANSAC);
	// Use the Homography Matrix to warp the images
	cv::Mat result;
	//按列拼接 accoring to colums
	warpPerspective(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;
}