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：
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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;
}
