基于NVIDIA TX2的usb basyer工业相机编码推流
性能：两路2448*2048 basyer相机编码，编码后25fps,延迟200ms左右
编码：调用nvidia底层的api, 参考tegra_multimedia_api下的cuda_encode例子。只需要修改read_from_file()函数的数据为相机输入数据即可
目前只支持输入I420格式,但是basyer相机没有I420的格式，用cuda做转换，代码如下：
// Grab.cpp
/*
Note: Before getting started, Basler recommends reading the Programmer's Guide topic
in the pylon C++ API documentation that gets installed with pylon.
If you are upgrading to a higher major version of pylon, Basler also
strongly recommends reading the Migration topic in the pylon C++ API documentation.
This sample illustrates how to grab and process images using the CInstantCamera class.
The images are grabbed and processed asynchronously, i.e.,
while the application is processing a buffer, the acquisition of the next buffer is done
in parallel.
The CInstantCamera class uses a pool of buffers to retrieve image data
from the camera device. Once a buffer is filled and ready,
the buffer can be retrieved from the camera object for processing. The buffer
and additional image data are collected in a grab result. The grab result is
held by a smart pointer after retrieval. The buffer is automatically reused
when explicitly released or when the smart pointer object is destroyed.
*/
// Include files to use the PYLON API.
#include cout <<"SizeY: "<//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());
//cvtColor(openCvImage,dst_image,CV_RGB2RGBA);
#if 0
unsigned long long timeusd=0;
gettimeofday(&start,NULL);
cvtColor(openCvImage,dst_image,CV_BGR2YUV_I420);
gettimeofday(&end,NULL);
//timeusd=end.tv_sec-start.tv_sec+(end.tv_usec-start.tv_usec)/1000000;
cout<<"convert cost "<1000000<< " sec"<#endif
#if 1
if(!I420Buffer)
{
if(CUDA_FAILED(cudaMalloc((void**)&I420Buffer,m_height*m_width*3/2)))
{
cout<<"cudaMalloc For I420Buffer Error"<#endif
#if 1
if(!RGBBuffer)
{
if(CUDA_FAILED(cudaMalloc((void**)&RGBBuffer,m_height*m_width*3)))
{
cout<<"cudaMalloc For RGBBuffer Error"<#endif
#if 1
if(!BGRABuffer)
{
if(CUDA_FAILED(cudaMalloc((void**)&BGRABuffer,m_height*m_width*4)))
{
cout<<"cudaMalloc For BGRABuffer Error"<#endif
cudaMemcpy(RGBBuffer,pylonImage.GetBuffer(),m_height*m_width*3,cudaMemcpyHostToDevice);
#if 1
if(CUDA_FAILED(cudaRGBToRGBAf((uchar3 *)RGBBuffer,(float4 *)BGRABuffer, m_width,m_height)))
{
cout<<"Failed to Convert RGB2RGBA"<#endif
#if 1
//cudaMemcpy(BGRABuffer,dst_image.data,m_height*m_width*4,cudaMemcpyHostToDevice);
if(CUDA_FAILED(cudaRGBAToI420((uchar4 *)BGRABuffer,I420Buffer,m_width,m_height)))
{
cout<<"Failed to Convert RGBAToI420"<#endif
unsigned char g_buffer[m_height*m_width*3/2];
#if 1
cudaMemcpy(g_buffer,I420Buffer,m_width*m_height*3/2,cudaMemcpyDeviceToHost);
fwrite(g_buffer,2448*2048*3/2,1,fp);
#endif
//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.
cv::imwrite(imageName, openCvImage);
grabbedlmages++;
}
//如果需要记录视频
if (recordVideo)
{
cvVideoCreator.write(openCvImage);
}
//新建OpenCV display window.
//cv::namedWindow("OpenCV Display Window", CV_WINDOW_NORMAL); // other options: CV_AUTOSIZE, CV_FREERATIO
//显示及时影像.
//cv::imshow("OpenCV Display Window", dst_image);
// 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(1);
}
}
fclose(fp);
}
catch (GenICam::GenericException &e)
{
// Error handling.
cerr << "An exception occurred." << endl
<< e.GetDescription() << endl;
}
return exitCode;
}
转换完成，加入编码推流的代码，推流是利用EasyDarwin做流媒体转发服务器：
// Grab.cpp
/*
Note: Before getting started, Basler recommends reading the Programmer's Guide topic
in the pylon C++ API documentation that gets installed with pylon.
If you are upgrading to a higher major version of pylon, Basler also
strongly recommends reading the Migration topic in the pylon C++ API documentation.
This sample illustrates how to grab and process images using the CInstantCamera class.
The images are grabbed and processed asynchronously, i.e.,
while the application is processing a buffer, the acquisition of the next buffer is done
in parallel.
The CInstantCamera class uses a pool of buffers to retrieve image data
from the camera device. Once a buffer is filled and ready,
the buffer can be retrieved from the camera object for processing. The buffer
and additional image data are collected in a grab result. The grab result is
held by a smart pointer after retrieval. The buffer is automatically reused
when explicitly released or when the smart pointer object is destroyed.
*/
// Include files to use the PYLON API.
//gst_rtsp
#include "stdio.h"
#include /* for TCP_NODELAY */
#include
#include return -1;
}
#if 1
int ret;
/*rtsp puser*/
RTSP_Pusher_SetStartPort(50000);
RTSP_Pusher_SetLogCallBack(RtspPushStreamLogCB);
RTSP_Pusher_SetStreamStatusCallback(RtspPushStreamStatueCB, NULL);
TmMediaInfo mediaInfo;
RTSP_Pusher_Handler rtspPusherHandler = RTSP_Pusher_Create();
assert(rtspPusherHandler != NULL);
memset(&mediaInfo,0,sizeof(TmMediaInfo));
mediaInfo.videoCodec = VIDEO_CODEC_H264;
mediaInfo.videoFps = 25;
#if 0
ret = RTSP_Pusher_StartStream(rtspPusherHandler,
testChannelConfigure[0].pushStreamRtspUrl,
RTP_OVER_UDP,
NULL,
NULL,
0,
&mediaInfo);
#endif
memset(testChannelConfigure[0].pushStreamRtspUrl,0,sizeof(testChannelConfigure[0].pushStreamRtspUrl));
strcpy(testChannelConfigure[0].pushStreamRtspUrl,argv[1]);
ret = RTSP_Pusher_StartStream(rtspPusherHandler,
testChannelConfigure[0].pushStreamRtspUrl,
RTP_OVER_UDP,
NULL,
NULL,
0,
&mediaInfo);
if(ret < 0 ) {
printf("fun[%s] Line[%d] chId%d start push stream failed ret[%d].\n",__func__, __LINE__, 0, ret);
return NULL;
}
testChannelConfigure[0].rtspPushStreamHandle = rtspPusherHandler;
#endif
sem_init(&sem_dec_output_read, 0, 0);
sem_init(&sem_dec_output_write, 0, 0);
pthread_mutex_init(&mutex, NULL);
g_buffer = NULL;
g_iReadBufferPos = 0;
TM_video_enc *enc_obj_merge;
enc_context_t enc_cfg;
//enc by TM
enc_obj_merge = new TM_video_enc(0);
if (NULL == enc_obj_merge)
{
return 0;
}
enc_obj_merge->init(ENC_MAX_BUFFER);
memset(&enc_cfg, 0, sizeof(enc_context_t));
enc_cfg.ratecontrol = V4L2_MPEG_VIDEO_BITRATE_MODE_CBR;
enc_cfg.iframe_interval = 25;
enc_cfg.idr_interval = 25;
enc_cfg.level = V4L2_MPEG_VIDEO_H264_LEVEL_5_1;
enc_cfg.fps_n = 25;
enc_cfg.fps_d = 1;
enc_cfg.num_b_frames = (uint32_t) -1;
enc_cfg.nMinQpI = (uint32_t)QP_RETAIN_VAL;
enc_cfg.nMaxQpI = (uint32_t)QP_RETAIN_VAL;
enc_cfg.nMinQpP = (uint32_t)QP_RETAIN_VAL;
enc_cfg.nMaxQpP = (uint32_t)QP_RETAIN_VAL;
enc_cfg.nMinQpB = (uint32_t)QP_RETAIN_VAL;
enc_cfg.nMaxQpB = (uint32_t)QP_RETAIN_VAL;
enc_cfg.in_file_path = "tmp.yuv";
enc_cfg.out_file_path = "tmp.h264";
if (0/*VIDEO_CODEC_H265 == m_strChannelEncCfg.VideoEncodeType*/)
{
enc_cfg.profile = V4L2_MPEG_VIDEO_H265_PROFILE_MAIN10;
enc_cfg.encoder_pixfmt = V4L2_PIX_FMT_H265;
}
else
{
enc_cfg.level = V4L2_MPEG_VIDEO_H264_LEVEL_5_0;
/*����Dz������ѹ���������iframe_interval Ҫ50���ϣ�����profile �ij�HIGH*/
enc_cfg.profile = V4L2_MPEG_VIDEO_H264_PROFILE_MAIN;/*����ij�HIGH �?��¼��elecard ���Ų���*/
enc_cfg.encoder_pixfmt = V4L2_PIX_FMT_H264;
}
/*create v4l2 enc*/
//
Pylon::PylonAutoInitTerm autoInitTerm;
try
{
//创建相机对象（以最先识别的相机）
CTlFactory& tlFactory = CTlFactory::GetInstance();
// Get all attached devices and exit application if
// two cameras aren't found
DeviceInfoList_t devices;
CInstantCamera camera;
if( tlFactory.EnumerateDevices(devices) == 2 )
{
cout << "Found " << devices.size() << " cameras." << endl;
camera.Attach(tlFactory.CreateDevice( devices[0] ) );
if(camera.GetDeviceInfo().GetModelName()=="ML500-35C")
{
// 打印相机的名称
std::cout << "Using device " << camera.GetDeviceInfo().GetModelName() << endl;
camera.Open();
}
else
{
// 打印相机的名称
std::cout << "Using device " << camera.GetDeviceInfo().GetModelName() << endl;
camera.Open();
}
}
else
{
camera.Attach(tlFactory.CreateDevice( devices[0]));
camera.Open();
}
GenApi::INodeMap& nodemap = camera.GetNodeMap();
//打开相机
//获取相机成像宽度和高度
GenApi::CIntegerPtr width = nodemap.GetNode("Width");
GenApi::CIntegerPtr height = nodemap.GetNode("Height");
enc_cfg.width =(int)width->GetValue() /* * g_FaceScaleFactor*/;
enc_cfg.height =(int)height->GetValue() /* * g_FaceScaleFactor*/;
enc_cfg.insert_sps_pps_at_idr = true;/*ǿ��ÿһ��I֡ǰ����SPS PPS*/
enc_cfg.iframe_interval = 25;
enc_cfg.idr_interval = 25;
enc_cfg.fps_n = 25;
enc_cfg.bitrate = 2048 * 1024;
void *pUser = (void *)"c++ duixiang ";
enc_obj_merge->set_config(&enc_cfg, encInput, encOutput, enc_StatueCallBack, (void *)pUser);
enc_obj_merge->start();
//设置相机最大缓冲区,默认为10
camera.MaxNumBuffer = 5;
// 新建pylon ImageFormatConverter对象.
CImageFormatConverter formatConverter;
//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;
//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;
Mat dst_image;
// 视频文件名
std::string videoFileName = "openCvVideo.avi";
// 定义视频帧大小
cv::Size frameSize = Size((int)width->GetValue(), (int)height->GetValue());
//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);
// 开始抓取c_countOfImagesToGrab images.
//相机默认设置连续抓取模式
camera.StartGrabbing(c_countOfImagesToGrab, GrabStrategy_LatestImageOnly);
//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
unsigned long timeusd=0;
FILE *fp=fopen("1.yuv","w+");
struct timeval start,end;
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: "<GetWidth()<cout <<"SizeY: "<GetHeight()<//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());
gettimeofday(&start,NULL);
cvtColor(openCvImage,dst_image,CV_BGR2YUV_I420);
gettimeofday(&end,NULL);
pthread_mutex_lock(&mutex);
g_buffer = dst_image.data;
g_iReadBufferPos = 0;
pthread_mutex_unlock(&mutex);
sem_post(&sem_dec_output_read);
sem_wait(&sem_dec_output_write);
timeusd=start.tv_sec-end.tv_sec+(start.tv_usec-end.tv_usec)/1000000;
//imshow("test",dst_image);
//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.
cv::imwrite(imageName, openCvImage);
grabbedlmages++;
}
//如果需要记录视频
if (recordVideo)
{
cvVideoCreator.write(openCvImage);
}
//新建OpenCV display window.
//cv::namedWindow("OpenCV Display Window", CV_WINDOW_NORMAL); // other options: CV_AUTOSIZE, CV_FREERATIO
//显示及时影像.
//cv::imshow("OpenCV Display Window", dst_image);
// 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(1);
}
}
}
catch (GenICam::GenericException &e)
{
// Error handling.
cerr << "An exception occurred." << endl
<< e.GetDescription() << endl;
}
return exitCode;
}