The image mosaic Opencv: Based on Multi FIG Stitcher and the two stitching Surf

Image stitching algorithm, one is stitcher algorithm calls on the opencv, this call is relatively simple, but its internal algorithm is very complex, and this can be seen from cpp, the match continued to run, get results, try the process, compatibility is not very good, is often not the case stitching will appear very slow, a higher error rate. Here achieve a mosaic of four images, the results were good.

code show as below:

#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/stitching.hpp>
using namespace std;
using namespace cv;
vector<Mat> imgs;
int main(int argc, char * argv[])
{
	imgs.push_back(imread("C:/Users/14587/Desktop/图片素材测试/裁剪2.jpg"));
	imgs.push_back(imread("C:/Users/14587/Desktop/图片素材测试/裁剪1.jpg"));
	imgs.push_back(imread("C:/Users/14587/Desktop/图片素材测试/裁剪4.jpg"));
	imgs.push_back(imread("C:/Users/14587/Desktop/图片素材测试/裁剪3.jpg"));
	Stitcher stitcher = Stitcher::createDefault(true);
	// 使用stitch函数进行拼接
	Mat temp;
	Stitcher::Status status = stitcher.stitch(imgs, temp);
	if (status != Stitcher::OK)
	{
		cout << "不能拼接 " << int(status) << endl;
		waitKey(0);
		return -1;
	}
	Mat pano2 = temp.clone();
	// 显示源图像，和结果图像
	namedWindow("全景图像", WINDOW_NORMAL);
	imshow("全景图像", temp);
	waitKey(0);
	if (waitKey() == 27)
		return 0;
}

Surf splicing code is based are as follows:

#include<opencv2/opencv.hpp>
#include<opencv2/highgui/highgui.hpp>
#include<opencv2/xfeatures2d.hpp>
#include<iostream>
#include<math.h>
using namespace cv;
using namespace std;
using namespace cv::xfeatures2d;
Mat srcimage_0, srcimage_1, dstimage;
Mat srcimage0, srcimage1;
Point2f getTransformPoint(const Point2f originalPoint, const Mat &transformMaxtri);
//SURF特征提取及暴力匹配
int main() {
	srcimage_0 = imread("C:\\Users\\14587\\Desktop\\1.jpg");
	srcimage_1 = imread("C:\\Users\\14587\\Desktop\\2.jpg");
	if (!srcimage_0.data) {
		printf("could not looad");
		return -1;
	}
	if (!srcimage_1.data) {
		printf("could not load");
		return -1;
	}
	//cvtColor(srcimage0, srcimage_0, CV_RGB2GRAY);
	//cvtColor(srcimage1, srcimage_1, CV_RGB2GRAY);
	//resize(srcimage_0, srcimage_0, Size(srcimage_0.cols /4, srcimage_0.rows / 4));
	//resize(srcimage_1, srcimage_1, Size(srcimage_1.cols /4, srcimage_1.rows / 4));
	resize(srcimage_0, srcimage_0, Size(416, 416));
	resize(srcimage_1, srcimage_1, Size(416, 416));
	vector<KeyPoint> keypoint0, keypoint1;
	//特征点寻找
	Mat result0, result1,result;
	BFMatcher matcher;
	int minhessian = 800;
	Ptr<SURF> detector = SURF::create(minhessian);
	detector->detect(srcimage_0, keypoint0);
	detector->detect(srcimage_1, keypoint1);
	Ptr<Feature2D> f2d = xfeatures2d::SURF::create();
	f2d->detectAndCompute(srcimage_0,Mat(), keypoint0,result0);
	f2d->detectAndCompute(srcimage_1, Mat(), keypoint1, result1);
	vector<DMatch> matches;
	vector<DMatch> GoodMatchePoints;
	matcher.match(result0, result1, matches,Mat());
	sort(matches.begin(), matches.end());     //从小到大排序
	//最佳匹配点100个
	//调整范围
	int goodmatch =100;
	vector< DMatch > good_matches;
	vector<Point2f> imagePoints0, imagePoints1;
	for (int i = 0; i < goodmatch; i++) {
		good_matches.push_back(matches[i]);
		imagePoints0.push_back(keypoint0[good_matches[i].queryIdx].pt);
		imagePoints1.push_back(keypoint1[good_matches[i].trainIdx].pt);
	}
	//绘制特征点
	//drawKeypoints(srcimage_0, keypoint0, srcimage_0, Scalar::all(-1), DrawMatchesFlags::DEFAULT);
	//drawKeypoints(srcimage_1, keypoint1, srcimage_1, Scalar::all(-1), DrawMatchesFlags::DEFAULT);
	drawMatches(srcimage_0, keypoint0, srcimage_1, keypoint1, good_matches, result, Scalar::all(-1), Scalar::all(-1),
	vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);  //绘制匹配点
	imshow("拼接", result);
	Mat H = findHomography(imagePoints0, imagePoints1, RANSAC, 3, noArray());//最优单映射变换矩阵
	Mat adjustMat = (Mat_<double>(3, 3) << 1.0, 0, srcimage_0.cols, 0, 1.0, 0, 0, 0, 1.0);
	Mat adjustHomo = adjustMat * H;
	Point2f originalLinkPoint, targetLinkPoint, basedImagePoint;	
	originalLinkPoint = keypoint0[good_matches[0].queryIdx].pt;
	targetLinkPoint = getTransformPoint(originalLinkPoint, adjustHomo);
	basedImagePoint = keypoint1[good_matches[0].trainIdx].pt;
	Mat imageTransform1;
	//透视变换
	warpPerspective(srcimage_0, imageTransform1, adjustMat*H,Size(srcimage_1.cols + srcimage_0.cols + 10, srcimage_1.rows));
	Mat ROIMat = srcimage_1(Rect(Point(basedImagePoint.x, 0),Point(srcimage_1.cols, srcimage_1.rows)));
	ROIMat.copyTo(Mat(imageTransform1, Rect(targetLinkPoint.x, 0, srcimage_1.cols - basedImagePoint.x + 1, srcimage_1.rows)));
	imshow("拼接结果", imageTransform1);
	//putText(result, "No Entry Straight Ahead", scene_corners[0] + Point2f((float)srcimage_0.cols, 0),FONT_HERSHEY_COMPLEX,1.0,Scalar(0,0,255));
	waitKey(0);
	return 0;
}
Point2f getTransformPoint(const Point2f originalPoint, const Mat &transformMaxtri) 
{
	Mat originelP, targetP;	
	originelP = (Mat_<double>(3, 1) << originalPoint.x, originalPoint.y, 1.0);
	targetP = transformMaxtri * originelP;	
	float x = targetP.at<double>(0, 0) / targetP.at<double>(2, 0);	
	float y = targetP.at<double>(1, 0) / targetP.at<double>(2, 0);
	return Point2f(x, y);
}

FIG attached to the following tests: