Ubuntu16.04下配置Basler工业相机（使用QtCre

Basler工业相机网上资料少，写的博客更少，当时为了把这个Basler相机用起来，不知道耗费了我多少心血。
查阅了Basler的官方文档，还有各种语焉不详的博客，终于能够在Linux下调用Basler相机了。
所以我决定写一篇非常详细的博客，好让后来者少踩些坑。

我的第一个工业相机，据说这玩意要近万块钱

其实Linux下配置Basler摄像头时和配置OpenCV时相差不大。
先去 官网下载对应的安装包
https://www.baslerweb.com/cn/sales-support/downloads/software-downloads/#type=pylonsoftware;version=all;os=windows
根据自己的系统选择x86或者x86_64(即x64)版本。

我的相机型号为acA1920-40gc。

下载好后，对压缩包进行解压操作，可以选择解压文件到自己选择的目录，此处我们选择默认当前目录：

$ tar -xzvf  pylon-5.0.***.tar.gz

解压文件后，打开文件，里边还有一个压缩包，此压缩包即为安装文件，解压此文件到/opt目录下：

$ sudo tar -C /opt -xzvf pylon***armhf.tar.gz

安装完毕后就开始在qtcreator中进行配置，以便在Qtcreator中调用该相机。
1、首先打开Qtcreator，如下图所示，创建控制台项目或者空项目：

————————————————————————————
2、然后打开.pro文件，在其中配置Basler相机：

先找到INCLUDEPATH的路径：
（1）点开“计算机”，点开文件夹“opt”

（2）接着打开pylon5文件夹

那么INCLUDEPATH 的内容为：

INCLUDEPATH += /opt/pylon5/include \
/opt/pylon5/include/pylon

例子：（下图中我是把OpenCV和Basler一起配置）

——————————————————————————————————

3、写好了INCLUDEPATH，再来写LIBS。
LIBS（我目前知道的）有2种写法：
第一种是直接写出路径来：

例子：
LIBS += /usr/local/lib/libopencv_calib3d.so \
/usr/local/lib/libopencv_calib3d.so.3.2 \
/usr/local/lib/libopencv_calib3d.so.3.2.0 \
/usr/local/lib/libopencv_core.so \

第二种方法是先写一个总的，再写分的：

例子：
LIBS +=-L/opt/pylon5/lib64 \
-lbxapi-5.0.11 \
-lbxapi \
-lFirmwareUpdate_gcc_v3_0_Basler_pylon_v5_0 \
-lGCBase_gcc_v3_0_Basler_pylon_v5_0 \
-lGenApi_gcc_v3_0_Basler_pylon_v5_0 \
-lgxapi-5.0.11 \

这2种写法都是一样的，是通用的。

先找到LIB所在的路径为 opt / pylon5 / lib64

可以看到里面有很多后缀为.so的文件，把这些文件的路径写到.pro文件中就行了。

这里我把我总的.pro文件内容贴出来，可供参考：
（我这里面同时配置了OpenCV和Basler，注意我的Basler的版本为5.0.11）

TEMPLATE = app
CONFIG += console c++11
CONFIG -= app_bundle
CONFIG -= qt

SOURCES += main.cpp

INCLUDEPATH += /usr/local/include \
/usr/local/include/opencv \
/usr/local/include/opencv2 \
/opt/pylon5/include \
/opt/pylon5/include/pylon

LIBS += /usr/local/lib/libopencv_calib3d.so \
/usr/local/lib/libopencv_calib3d.so.3.2 \
/usr/local/lib/libopencv_calib3d.so.3.2.0 \
/usr/local/lib/libopencv_core.so \
/usr/local/lib/libopencv_core.so.3.2 \
/usr/local/lib/libopencv_core.so.3.2.0 \
/usr/local/lib/libopencv_features2d.so \
/usr/local/lib/libopencv_features2d.so.3.2 \
/usr/local/lib/libopencv_features2d.so.3.2.0 \
/usr/local/lib/libopencv_flann.so \
/usr/local/lib/libopencv_flann.so.3.2 \
/usr/local/lib/libopencv_flann.so.3.2.0 \
/usr/local/lib/libopencv_highgui.so \
/usr/local/lib/libopencv_highgui.so.3.2 \
/usr/local/lib/libopencv_highgui.so.3.2.0 \
/usr/local/lib/libopencv_imgcodecs.so \
/usr/local/lib/libopencv_imgcodecs.so.3.2 \
/usr/local/lib/libopencv_imgcodecs.so.3.2.0 \
/usr/local/lib/libopencv_imgproc.so \
/usr/local/lib/libopencv_imgproc.so.3.2 \
/usr/local/lib/libopencv_imgproc.so.3.2.0 \
/usr/local/lib/libopencv_ml.so \
/usr/local/lib/libopencv_ml.so.3.2 \
/usr/local/lib/libopencv_ml.so.3.2.0 \
/usr/local/lib/libopencv_objdetect.so \
/usr/local/lib/libopencv_objdetect.so.3.2 \
/usr/local/lib/libopencv_objdetect.so.3.2.0 \
/usr/local/lib/libopencv_photo.so \
/usr/local/lib/libopencv_photo.so.3.2 \
/usr/local/lib/libopencv_photo.so.3.2.0 \
/usr/local/lib/libopencv_shape.so \
/usr/local/lib/libopencv_shape.so.3.2 \
/usr/local/lib/libopencv_shape.so.3.2.0 \
/usr/local/lib/libopencv_stitching.so \
/usr/local/lib/libopencv_stitching.so.3.2 \
/usr/local/lib/libopencv_stitching.so.3.2.0 \
/usr/local/lib/libopencv_superres.so \
/usr/local/lib/libopencv_superres.so.3.2 \
/usr/local/lib/libopencv_superres.so.3.2.0 \
/usr/local/lib/libopencv_video.so \
/usr/local/lib/libopencv_video.so.3.2 \
/usr/local/lib/libopencv_video.so.3.2.0 \
/usr/local/lib/libopencv_videoio.so \
/usr/local/lib/libopencv_videoio.so.3.2 \
/usr/local/lib/libopencv_videoio.so.3.2.0 \
/usr/local/lib/libopencv_videostab.so \
/usr/local/lib/libopencv_videostab.so.3.2 \
/usr/local/lib/libopencv_videostab.so.3.2.0 \
/usr/local/lib/libopencv_viz.so \
/usr/local/lib/libopencv_viz.so.3.2 \
/usr/local/lib/libopencv_viz.so.3.2.0 \
-L/opt/pylon5/lib64 \
-lbxapi-5.0.11 \
-lbxapi \
-lFirmwareUpdate_gcc_v3_0_Basler_pylon_v5_0 \
-lGCBase_gcc_v3_0_Basler_pylon_v5_0 \
-lGenApi_gcc_v3_0_Basler_pylon_v5_0 \
-lgxapi-5.0.11 \
-lgxapi \
-llog4cpp_gcc_v3_0_Basler_pylon_v5_0 \
-lLog_gcc_v3_0_Basler_pylon_v5_0 \
-lMathParser_gcc_v3_0_Basler_pylon_v5_0 \
-lNodeMapData_gcc_v3_0_Basler_pylon_v5_0 \
-lpylonbase-5.0.11 \
-lpylonbase \
-lpylonc-5.0.11 \
-lpylonc \
-lpylon_TL_bcon-5.0.11 \
-lpylon_TL_bcon \
-lpylon_TL_camemu-5.0.11 \
-lpylon_TL_camemu \
-lpylon_TL_gige-5.0.11 \
-lpylon_TL_gige \
-lpylon_TL_usb-5.0.11 \
-lpylon_TL_usb \
-lpylonutility-5.0.11 \
-lpylonutility \
-luxapi-5.0.11 \
-luxapi \
-lXmlParser_gcc_v3_0_Basler_pylon_v5_0 \

4、这样配置好了.pro文件，就能开始写程序了。
在Linux中调用Basler摄像头，需要一段比较长的代码，代码如下：

//定义是否保存图片
#define saveImages 0
//定义是否记录视频
#define recordVideo 0

// 加载OpenCV API
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/video/video.hpp>
#include<opencv2/opencv.hpp>

//加载PYLON API.
#include <pylon/PylonIncludes.h>

#include <pylon/gige/BaslerGigEInstantCamera.h> //自动调节

//加载C++ 头文件
#include<iostream>


//命名空间.
using namespace Pylon;
using namespace cv;
using namespace std;

using namespace Basler_GigECameraParams; //自动调节

typedef Pylon::CBaslerGigEInstantCamera Camera_t; //自动调节
typedef Camera_t::GrabResultPtr_t GrabResultPtr_t; //自动调节


static const uint32_t c_countOfImagesToGrab = 2000;

int saveImage_flag=0; //保存一张图片

int main()
{
                 cout<<"000"<<endl;
    //Pylon自动初始化和终止
    Pylon::PylonAutoInitTerm autoInitTerm;

    try
    {
               cout<<"0"<<endl;

        // 原程序对camera的定义
        //CInstantCamera camera(CTlFactory::GetInstance().CreateFirstDevice());

       CDeviceInfo info;
       info.SetDeviceClass( Camera_t::DeviceClass());

       // Create an instant camera object with the first found camera device that matches the specified device class.
       Camera_t camera( CTlFactory::GetInstance().CreateFirstDevice( info));


              cout<<"1"<<endl;

        // 打印相机的名称
        std::cout << "Using device " << camera.GetDeviceInfo().GetModelName() << endl;
                cout<<"2"<<endl;

        //获取相机节点映射以获得相机参数
        GenApi::INodeMap& nodemap = camera.GetNodeMap();
                            cout<<"3"<<endl;

        //打开相机
        camera.Open();
                cout<<"4"<<endl;


        //获取相机成像宽度和高度
        GenApi::CIntegerPtr width = nodemap.GetNode("Width");
        GenApi::CIntegerPtr height = nodemap.GetNode("Height");
                            cout<<"5"<<endl;

        //设置相机最大缓冲区,默认为10
        camera.MaxNumBuffer = 5;
        // 新建pylon ImageFormatConverter对象.
        CImageFormatConverter formatConverter;
                        cout<<"6"<<endl;

        //确定输出像素格式
        formatConverter.OutputPixelFormat = PixelType_BGR8packed;
        // 创建一个Pylonlmage后续将用来创建OpenCV images
        CPylonImage pylonImage;
                    cout<<"7"<<endl;

        //声明一个整形变量用来计数抓取的图像，以及创建文件名索引
        int grabbedlmages = 0;

        // 新建一个OpenCV video creator对象.
        VideoWriter cvVideoCreator;
        //新建一个OpenCV image对象.

        Mat openCvImage;
        // 视频文件名
                    cout<<"8"<<endl;

        std::string videoFileName = "openCvVideo.avi";
        // 定义视频帧大小
        cv::Size frameSize = Size((int)width->GetValue(), (int)height->GetValue());
                        cout<<"9"<<endl;
        cout<<"Width: "<<frameSize.width<<endl;
        cout<<"Height: "<<frameSize.height<<endl;


        //设置视频编码类型和帧率，有三种选择
        // 帧率必须小于等于相机成像帧率!!!!
        cvVideoCreator.open(videoFileName, CV_FOURCC('D', 'I', 'V','X'), 10, frameSize, true);
        //cvVideoCreator.open(videoFileName, CV_F0URCC('M','P',,4','2’), 20, frameSize, true);
        //cvVideoCreator.open(videoFileName, CV_FOURCC('M', '3', 'P', 'G'), 20, frameSize, true);
            cout<<"10"<<endl;

        // 开始抓取c_countOfImagesToGrab images.
        //相机默认设置连续抓取模式
        camera.StartGrabbing(-1, GrabStrategy_LatestImageOnly); //c_countOfImagesToGrab

        //抓取结果数据指针
        CGrabResultPtr ptrGrabResult;

        // 当c_countOfImagesToGrab images获取恢复成功时，Camera.StopGrabbing()
        //被RetrieveResult()方法自动调用停止抓取


        cout << "Initial Gain = " << camera.GainRaw.GetValue() << endl;


        cout << "Initial exposure time = ";
        cout << camera.ExposureTimeAbs.GetValue() << " us" << endl;


        cout << "Initial balance ratio: ";
        camera.BalanceRatioSelector.SetValue(BalanceRatioSelector_Red);
            cout << "R = " << camera.BalanceRatioAbs.GetValue() << "   ";
        camera.BalanceRatioSelector.SetValue(BalanceRatioSelector_Green);
            cout << "G = " << camera.BalanceRatioAbs.GetValue() << "   ";
        camera.BalanceRatioSelector.SetValue(BalanceRatioSelector_Blue);
            cout << "B = " << camera.BalanceRatioAbs.GetValue() << endl;


        while (camera.IsGrabbing())
        {
            // 等待接收和恢复图像，超时时间设置为5000 ms.
            camera.RetrieveResult(5000, ptrGrabResult, TimeoutHandling_ThrowException);

            //如果图像抓取成功
            if (ptrGrabResult->GrabSucceeded())
            {
                // 获取图像数据
             //   cout <<"SizeX: "<<ptrGrabResult->GetWidth()<<endl;
             //   cout <<"SizeY: "<<ptrGrabResult->GetHeight()<<endl;


                //将抓取的缓冲数据转化成pylon image.
                formatConverter.Convert(pylonImage, ptrGrabResult);


                // 将 pylon image转成OpenCV image.
                openCvImage = cv::Mat(ptrGrabResult->GetHeight(), ptrGrabResult->GetWidth(), CV_8UC3, (uint8_t *) pylonImage.GetBuffer());



                //如果需要保存图片
                if (saveImages)
                {
                   std::ostringstream s;
                    // 按索引定义文件名存储图片
                   s << "image_" << grabbedlmages << ".jpg";
                   std::string imageName(s.str());
                    //保存OpenCV image.
                   imwrite(imageName, openCvImage);
                   grabbedlmages++;
                }

                //如果需要记录视频
                if (recordVideo)
                {
                    cvVideoCreator.write(openCvImage);
                }


                //新建OpenCV display window.
                namedWindow("OpenCV Display Window", CV_WINDOW_NORMAL); // other options: CV_AUTOSIZE, CV_FREERATIO
                //显示及时影像.
                  if(!openCvImage.data)
                  {
                      cout<<"opencvImage fail"<<endl;
                      continue;
                  }

                imshow("OpenCV Display Window", openCvImage);

                if(saveImage_flag==0) //只保存一张图片
                {
                    imwrite("/home/fsac/2.jpg",openCvImage);
                    saveImage_flag=1;
                }


                // Define a timeout for customer's input in
                // '0' means indefinite, i.e. the next image will be displayed after closing the window.
                // '1' means live stream

                waitKey(10);

            }
            else
            {
                cout<<"图像读取失败,即ptrGrabResult->GrabSucceeded()未成功"<<endl;
                continue;
            }
        }

        if(!camera.IsGrabbing())
            cout<<"camera.IsGrabbing() is failed"<<endl;


    }
    catch (GenICam::GenericException &e)
    {
        // Error handling.
        cerr << "An exception occurred." << endl
            << e.GetDescription() << endl;
    }
    return 0;
}