Linux系统带有桌面操作、basler相机（GIge接口）、路由器、Python编程环境（之前的帖子有安装步骤）
2.将系统和相机利用路由器连接在同一个局域网内，打开软件先选择Tools，搜索相机IP并改成固定IP，就可以打开相机看实时图像了。
3.利用C++和OpenCV开发，这里是因为pypylon没有linux版本的，Windows下才支持pypylon(可以看之前的博客)。先讲一下opencv的安装:pip install opencv-python这是安装最新版的opencv,要根据自己的Python环境来安装（之前帖子有将linux下pychar+anaconda的安装）在安装opencv之前要保证numpy和scipy安装好的。
这说明安装成功。之后再来安装eclipse+C++环境（之前帖子也有）。
下面正式进入开发：打开eclipse创建一个新的c++环境，下面用图来说明配置include文件和依赖库
上面三张图就是添加文件路径和依赖库的步骤。
最后给出代码和效果图，这里的代码参考的是官网C++例程：
/* * Grab.cpp * * Created on: 2018年11月30日 * Author: wenhan */ #define saveImages 1 // Include files to use the PYLON API. #include <pylon/PylonIncludes.h> #ifdef PYLON_WIN_BUILD # include <pylon/PylonGUI.h> #endif #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/video/video.hpp> // Namespace for using pylon objects. using namespace Pylon; // Namespace for using cout. using namespace std; using namespace cv; // Number of images to be grabbed. static const uint32_t c_countOfImagesToGrab = 100; // int main(int argc, char* argv[]) { Mat src; CImageFormatConverter formatConverter; formatConverter.OutputPixelFormat = PixelType_BGR8packed; int grabbedlmages = 0; // 创建一个Pylonlmage后续将用来创建OpenCV images CPylonImage pylonImage; // The exit code of the sample application. int exitCode = 0; // Before using any pylon methods, the pylon runtime must be initialized. PylonInitialize(); try { // Create an instant camera object with the camera device found first. CInstantCamera camera( CTlFactory::GetInstance().CreateFirstDevice()); // Print the model name of the camera. cout << "Using device " << camera.GetDeviceInfo().GetModelName() << endl; // The parameter MaxNumBuffer can be used to control the count of buffers // allocated for grabbing. The default value of this parameter is 10. camera.MaxNumBuffer = 5; // Start the grabbing of c_countOfImagesToGrab images. // The camera device is parameterized with a default configuration which // sets up free-running continuous acquisition. camera.StartGrabbing( c_countOfImagesToGrab); // This smart pointer will receive the grab result data. CGrabResultPtr ptrGrabResult; // Camera.StopGrabbing() is called automatically by the RetrieveResult() method // when c_countOfImagesToGrab images have been retrieved. while ( camera.IsGrabbing()) { // Wait for an image and then retrieve it. A timeout of 5000 ms is used. camera.RetrieveResult( 5000, ptrGrabResult, TimeoutHandling_ThrowException); // Image grabbed successfully? if (ptrGrabResult->GrabSucceeded()) { // Access the image data. cout << "SizeX: " << ptrGrabResult->GetWidth() << endl; cout << "SizeY: " << ptrGrabResult->GetHeight() << endl; formatConverter.Convert(pylonImage, ptrGrabResult); src = 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 << "/home/wenhan/img/image_" << grabbedlmages << ".jpg"; std::string imageName(s.str()); //保存OpenCV image. imwrite(imageName, src); grabbedlmages++; } //新建OpenCV display window. namedWindow("OpenCV Display Window", CV_WINDOW_NORMAL); // other options: CV_AUTOSIZE, CV_FREERATIO //显示及时影像. imshow("OpenCV Display Window", src); waitKey(1); const uint8_t *pImageBuffer = (uint8_t *) ptrGrabResult->GetBuffer(); cout << "Gray value of first pixel: " << (uint32_t) pImageBuffer[0] << endl << endl; #ifdef PYLON_WIN_BUILD // Display the grabbed image. Pylon::DisplayImage(1, ptrGrabResult); #endif } else { cout << "Error: " << ptrGrabResult->GetErrorCode() << " " << ptrGrabResult->GetErrorDescription() << endl; } } } catch (const GenericException &e) { // Error handling. cerr << "An exception occurred." << endl << e.GetDescription() << endl; exitCode = 1; } // Comment the following two lines to disable waiting on exit. cerr << endl << "Press Enter to exit." << endl; while( cin.get() != '\n'); // Releases all pylon resources. PylonTerminate(); return exitCode; } 效果图：
最后可以直接运行c++的程序：
cd (c++程序debug下)