二、opencv学习笔记——MAC下安装配置opencv+contrib

在MAC下安装配置opencv+contrib，我也是踩了很多坑。不像Windows可以直接拿别人编译好的库在VS中配置就行，MAC由于最后要在终端配置，会自动查找编译路径，所以必须要自己一步一步来。

下面先说一下我遇到的几个问题：

一、configure过程中会出现文件无法下载问题，由于是外网下载，速度可能很慢会导致无法下载，主要是ippicv文件，有教程说可以先从网上下载对应文件放入根目录configure会通过，我没成功。我是成功下载，虽然速度有点慢。以下是对应文件下载地址：

ippicv等文件下载

二、配置一遍后，要添加对应的contrib库，一定要下载和opencv版本一致的，可在分支里选择，我下的是4.1版本的

contrib库

三、第一遍configure后要选择opencv_enable_nonfree和build_opencv_world

下面开始进入正式教程：

1、安装homebrew，后续终端配置要用到

ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)”

2、下载cmake

cmake下载地址

3、下载opencv和对应opencv contrib
我用的是4.1.0，选择source版本

opencv下载

opencv contrib

4、cmake配置

source code路径就是刚刚下载的opencv，build目录为自己新建的要编译的目录

点击configure，下载ippicv文件时会比较久，等待configuration done，选择opencv_enable_nonfree和build_opencv_world

额外路径选择下载的openc_contrib/modules

点击configure，configuratedone后红色消失，再点击generate，直到generate done。

cmake配置完毕

5、打开终端，cd到刚才你生成的编译路径中
输入make，开始编译，时间会非常长

最后输入sudo make install，输入密码后继续等待，这样opencv+contrib就配置完毕了。
lib在/usr/local/lib（command+shift+g输入/usr/local/lib前往）

由于之前选了build_opencv_world，所以所有库都集成到了这里，后续调用直接调用这个就行了

header在/usr/local/include/opencv4中

6、Xcode配置opencv

build settings输入search，在头路径选择/usr/local/include/opencv4，库路径选择/usr/local/lib

build phases——>link binary with libraries，添加库文件

7、测试，实现全景图拼接

把两张图片路径修改一下就行

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/xfeatures2d.hpp"
#include 
#include 

using namespace cv;
using namespace std;
using namespace cv::xfeatures2d;

void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst);

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)
{
    double v2[] = { 0, 0, 1 };//左上角
    double v1[3];//变换后的坐标值
    Mat V2 = Mat(3, 1, CV_64FC1, v2);  //列向量
    Mat V1 = Mat(3, 1, CV_64FC1, v1);  //列向量
    
    V1 = H * V2;
    //左上角(0,0,1)
    cout << "V2: " << V2 << endl;
    cout << "V1: " << V1 << endl;
    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);  //列向量
    V1 = Mat(3, 1, CV_64FC1, v1);  //列向量
    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);  //列向量
    V1 = Mat(3, 1, CV_64FC1, v1);  //列向量
    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);  //列向量
    V1 = Mat(3, 1, CV_64FC1, v1);  //列向量
    V1 = H * V2;
    corners.right_bottom.x = v1[0] / v1[2];
    corners.right_bottom.y = v1[1] / v1[2];
    
}

int main(int argc, char *argv[])
{
    //Mat image01 = imread("input1.JPG", 1);    //右图
    //Mat image02 = imread("input3.JPG", 1);    //左图
    Mat image01 = imread("/Users/superlinc/工作/xcode work/image_joint/2.png", 1);    //右图
    Mat image02 = imread("/Users/superlinc/工作/xcode work/image_joint/1.png", 1);    //左图
    imshow("p1", image01);
    imshow("p2", image02);
    
    //灰度图转换
    Mat image1, image2;
    cvtColor(image01, image1, COLOR_RGB2GRAY);
    cvtColor(image02, image2, COLOR_RGB2GRAY);
    
    
    //提取特征点
    Ptr surf = xfeatures2d::SURF::create(2000);
    //SurfFeatureDetector Detector(2000);
    vector keyPoint1, keyPoint2;
    surf->detect(image1, keyPoint1);
    surf->detect(image2, keyPoint2);
    
    //特征点描述，为下边的特征点匹配做准备
    //SurfDescriptorExtractor Descriptor;
    Mat imageDesc1, imageDesc2;
    surf->compute(image1, keyPoint1, imageDesc1);
    surf->compute(image2, keyPoint2, imageDesc2);
    
    FlannBasedMatcher matcher;
    vector > matchePoints;
    vector GoodMatchePoints;
    
    vector train_desc(1, imageDesc1);
    matcher.add(train_desc);
    matcher.train();
    
    matcher.knnMatch(imageDesc2, matchePoints, 3);
    cout << "total match points: " << matchePoints.size() << endl;
    
    // Lowe's algorithm,获取优秀匹配点
    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]);
        }
    }
    
    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   寻找从右边到左边的矩阵变换
    Mat homo = findHomography(imagePoints1, imagePoints2, FM_RANSAC);
    也可以使用getPerspectiveTransform方法获得透视变换矩阵，不过要求只能有4个点，效果稍差
    //Mat   homo=getPerspectiveTransform(imagePoints1,imagePoints2);
    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;
    //右边转到左边  矩阵变换
    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);
    
    OptimizeSeam(image02, imageTransform1, dst);
    
    imshow("dst", dst);
    
    waitKey();
    
    return 0;
}


//优化两图的连接处，使得拼接自然
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);
            
        }
    }
    
}

测试结果