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   

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(i);  // 获取第i行的首地址
        uchar* t = trans.ptr(i);
        uchar* d = dst.ptr(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 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 > matchePoints;
    vector 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 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 match
    Mat first_match;
    drawMatches(image02, keyPoint2, image01, keyPoint1, GoodMatchePoints, first_match);
    imshow("first_match ", first_match);

    vector 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 

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 imgs;
	imgs.push_back(img1);
	imgs.push_back(img2);

	Mat pano;
	Ptr 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;
}