经典推荐：TX2+ubuntu16.04+QT配置opencv3.4.1(完美方案)

opencv3.0开始分成主库+contrib库，一些不稳定的不成熟的涉及专利纠纷的库文件放置在contrib库中，contrib需要用户单独安装。本文采用opencv3.4.1配置TX2，其他opencv版本可类推。具体操作如下：

下载opencv3.4.1及对应的contrib
下载opencv3.4.1主模块source，地址：opencv./releases/page/2/

下载opencv3.4.1对应版本的contrib
地址：github./opencv/opencv_contrib/releases?after=4.0.0-beta

注意：opencv与从contrib版本必须保持一直。创建软件安装目录
2.1在home下创建MySoftware(任意命名)安装软件，创建opencv安装文件夹

    cd /home/nvidia/MySoftware    mkdir opencvcd /home/nvidia/MySoftware/opencv

主：nvidia为TX2的名称，需要替换成自己的

2.2将下载完成的opencv3.4.1解压至/home/nvidia/MySoftware/opencv，将contrib解压至opencv3.4.1，如下图：

创建build目录
进入opencv安装目录/home/nvidia/MySoftware/OpenCv/opencv-3.4.1，创建build文件夹

    cd /home/nvidia/MySoftware/opencv/opencv3.4.1mkdir build

5.打开新终端，安装依赖项
依赖关系：

GCC 4.4.x or later
CMake 2.8.7 or higher
Git
GTK+2.x or higher, including headers (libgtk2.0-dev)
pkg-config
Python 2.6 or later and Numpy 1.5 or later with developer packages (python-dev, python-numpy)
ffmpeg or libav development packages: libavcodec-dev, libavformat-dev, libswscale-dev
[optional] libtbb2 libtbb-dev
[optional] libdc1394 2.x
[optional] libjpeg-dev, libpng-dev, libtiff-dev, libjasper-dev, libdc1394-22-dev
注：官方文档中虽然说其中一些依赖包是可选的，但是最好还是都装上，以防出问题。

以上依赖包可用以下命令安装:

    sudo apt-get install build-essentialsudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev     libswscale-devsudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev  libtiff-dev libjasper-dev libdc1394-22-dev

opencv IPPICV 和 face_landmark_model.dat更改下载源
网络不好时， opencv IPPICV 和 face_landmark_model.dat下载容易失败，可在编译前参考如下博文处理

ubuntu16.04 配置TX2 过程中opencv IPPICV 和 face_landmark_model.dat下载不下来的问题解决
mp.csdn.net/mdeditor/97647529#

CMake Opencv源码：该步骤大约需要2小时左右
进入usr/local，创建opencv341目录，用于install

    cd usr/localmkdir opencv341

 进入build目录：`cd /home/nvidia/MySoftware/opencv/opencv3.4.1/build`在该目录下运行：

cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local/opencv341 -D OPENCV_EXTRA_MODULES_PATH=/home/nvidia/MySoftware/OpenCv/opencv-3.4.1/opencv_contrib-3.4.1/modules/ -D PYTHON_DEFAULT_EXECUTABLE=$(which python3) -D WITH_TBB=ON -D WITH_EIGEN=ON ..

运行完成后如下图，说明没有错误：

运行sudo make

完成后运行sudo make install

一切顺利，没有报错，至此安装完成，下面进行系统配置。
8. 完成上述步骤后，接着配置环境变量等操作

sudo gedit /etc/ld.so.conf.d/opencv.conf

添加安装目录下/usr/local/opencv346的lib目录路径，并保存，输入内容如下：

 sudo ldconfigsudo gedit /etc/bash.bashrc 

在bashrc文件末尾添加

PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/opencv341/lib/pkgconfig  
export PKG_CONFIG_PATH

9. 更新数据库

source /etc/bash.bashrc
sudo updatedb

如果updatedb报错，请参考：
ubuntu16.04 命令sudo updatedb报错: mand not found解决方案blog.csdn.net/qq_41854650/article/details/97638260

查看opencv版本：

pkg-config --modversion opencv 

至此OpenCV 3.4.1+contrib安装+配置完成。
11.测试是否安装成功
opencv3.0开始中SIFT匹配是在opencv_contrib库中的，可以用它来做一个简单的测试。
参考：

cv::xfeatures2d::SIFT Class Reference：docs.opencv./3.4.1/d5/d3c/classcv_1_1xfeatures2d_1_1SIFT.html#gsc.tab=0OpenCV3.1 xfeatures2d::SIFT 使用：blog.csdn.net/lijiang1991/article/details/50855279
QT的pro配置：

TEMPLATE = app
CONFIG += console c++11
CONFIG -= app_bundle
CONFIG -= qtSOURCES += main.cppINCLUDEPATH += /usr/libaarch64-linux-gnuINCLUDEPATH += /usr/local/opencv341/include \
INCLUDEPATH += /usr/local/opencv341/include/opencv \
INCLUDEPATH += /usr/local/opencv341/include/opencv2LIBS += /usr/local/opencv341/lib/libopencv_*.so
LIBS += /usr/local/opencv341/lib/libopencv_*.so.3.4
LIBS += /usr/local/opencv341/lib/libopencv_*.so.3.4.1

程序：

#include <iostream>
#include <opencv2/opencv.hpp>  //头文件
#include <opencv2/xfeatures2d.hpp>
#include <opencv2/xfeatures2d/nonfree.hpp>
using namespace cv;  //包含cv命名空间
using namespace std;int main()
{//Create SIFT class pointerPtr<Feature2D> f2d = xfeatures2d::SIFT::create();//读入图片Mat img_1 = imread("1.jpg");Mat img_2 = imread("2.jpg");//Detect the keypointsvector<KeyPoint> keypoints_1, keypoints_2;f2d->detect(img_1, keypoints_1);f2d->detect(img_2, keypoints_2);//Calculate descriptors (feature vectors)Mat descriptors_1, descriptors_2;f2d->pute(img_1, keypoints_1, descriptors_1);f2d->pute(img_2, keypoints_2, descriptors_2);//Matching descriptor vector using BFMatcherBFMatcher matcher;vector<DMatch> matches;matcher.match(descriptors_1, descriptors_2, matches);//imshow("match", matches);//绘制匹配出的关键点Mat img_matches;drawMatches(img_1, keypoints_1, img_2, keypoints_2, matches, img_matches);imshow("【match图】", img_matches);//等待任意按键按下waitKey(0);
}

输出结果：