一、介紹

? ? ?圖像拼接.

二、分步實(shí)現(xiàn)

? ? ?要實(shí)現(xiàn)圖像拼接，簡(jiǎn)單來說有以下幾步：

1. 對(duì)每幅圖進(jìn)行特征點(diǎn)提取
2. 對(duì)對(duì)特征點(diǎn)進(jìn)行匹配
3. 進(jìn)行圖像配準(zhǔn)
4. 把圖像拷貝到另一幅圖像的特定位置
5. 對(duì)重疊邊界進(jìn)行特殊處理

? ? ?PS：需要使用低版本的opencv，否則無法使用特征角點(diǎn)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);//開始位置，即重疊區(qū)域的左邊界  

    double processWidth = img1.cols - start; // 重疊區(qū)域的寬度  
    int rows = dst.rows;
    int cols = img1.cols; // 注意，是列數(shù)*通道數(shù)
    double alpha = 1; // img1中像素的權(quán)重  
    for (int i = 0; i < rows; i++)
    {
        uchar* p = img1.ptr<uchar>(i);  // 獲取第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++)
        {
            // 如果遇到圖像trans中無像素的黑點(diǎn)，則完全拷貝img1中的數(shù)據(jù)
            if (t[j * 3] == 0 && t[j * 3 + 1] == 0 && t[j * 3 + 2] == 0)
            {
                alpha = 1;
            }
            else
            {
                // img1中像素的權(quán)重，與當(dāng)前處理點(diǎn)距重疊區(qū)域左邊界的距離成正比，實(shí)驗(yàn)證明，這種方法確實(shí)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); //右圖
    Mat image02 = imread("image1.png", 1); //左圖
    imshow("p2", image01);
    imshow("p1", image02);

    // 灰度圖轉(zhuǎn)換  
    Mat image1, image2;
    cvtColor(image01, image1, CV_RGB2GRAY);
    cvtColor(image02, image2, CV_RGB2GRAY);

    // 提取特征點(diǎn)
    SurfFeatureDetector Detector(2000);
    vector<KeyPoint> keyPoint1, keyPoint2;
    Detector.detect(image1, keyPoint1);
    Detector.detect(image2, keyPoint2);

    // 特征點(diǎn)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,獲取優(yōu)秀匹配點(diǎn)
    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 match
    Mat 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);
    }

    // 獲取圖像1到圖像2的投影映射矩陣 尺寸為3*3  
    Mat homo = findHomography(imagePoints1, imagePoints2, CV_RANSAC);
    cout << "變換矩陣為：\n" << homo << endl << endl; // 輸出映射矩陣      

   // 計(jì)算配準(zhǔn)圖的四個(gè)頂點(diǎn)坐標(biāo)
    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;

    // 圖像配準(zhǔn)  
    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("直接經(jīng)過透視矩陣變換", imageTransform1);

    // 創(chuàng)建拼接后的圖,需提前計(jì)算圖的大小
    int dst_width = imageTransform1.cols;  // 取最右點(diǎn)的長(zhǎng)度為拼接圖的長(zhǎng)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);

    // 優(yōu)化拼接處
    OptimizeSeam(image02, imageTransform1, dst);
    imshow("dst", dst);

    waitKey();
    return 0;
}

??

?

三、利用stitch實(shí)現(xiàn)

#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;
}

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