概述
本示例看完后，可基本掌握以下内容
1、利用opencv+gstreamer拉流推流的基本步骤
2、可学习gstreamer，图像混合的一些插件用法
3、TX2 NX上的视频编解码与硬件加速
4、linux下如何提高线程优先级
我需要实现的功能是在TX2 NX，拉取摄像头数据，并再摄像头数据上与另一张图片混合
混合后推流到rtsp服务器。
由于混合的时候需要保留透明度，但是OpenCV不支持四通道的数据写入。硬件加速混合插件无法剔除透明度。当然可以用内存中剔除指定颜色的插件实现，但效率太低
所以只能利用VideoCapture先拉到摄像头数据，利用要混合的图片，手动计算叠加。
叠加后利用VideoWriter推流
为提高效率，通过将线程绑定在固定CPU上，提高了线程优先级。
具体流程：
首先搭建rtsp服务端，可利用rtsp-simple-serve这个项目在局域网部署一套
安装gstreamer后
由于用到了rtspclientsink插件，所以需要安装rtsp插件
// sudo apt-get update
// sudo apt-get upgrade
// sudo apt install gstreamer1.0-rtsp
下面是利用VideoWriter推流的过程
//cpp 实现
#include "rtsp_push_stream.h"
RtspPushStream::RtspPushStream() : active_(true) {}
RtspPushStream::~RtspPushStream() {}
void RtspPushStream::start() {
string appsrcpipeline =
"appsrc ! video/x-raw, format=BGR ! queue ! videoconvert ! video/x-raw,format=RGBA ! nvvidconv ! nvv4l2h264enc "
"! h264parse ! qtmux ! filesink location={filename} sync=false";
//使用 rtspclientsink 需要安装插件
// sudo apt-get update
// sudo apt-get upgrade
// sudo apt install gstreamer1.0-rtsp
std::string pipeline_useglvideomixer =
"appsrc "
"! video/x-raw, format=BGR "
"! videoconvert "
"! video/x-raw,format=(string)RGBA , width=(int)1024, height=(int)600"
"! queue2"
"! alpha method=blue "
"! glvideomixer name = compos sink_0::zorder=1 sink_0::alpha=0.85 sink_1::alpha=1 sink_1::zorder=0 "
"sink_1::width=1024 sink_1::height=600 "
"! nvvidconv"
"! video/x-raw(memory:NVMM), format=(string)I420, width=(int)1024, height=(int)600"
"! nvv4l2h264enc "
"! rtspclientsink location=rtsp://192.168.20.99:8554/my_pipeline"
" nvarguscamerasrc"
"! video/x-raw(memory:NVMM),format=(string)NV12, width=(int)1640, height=(int)1232, framerate=(fraction)25/1"
"! queue2"
"! nvvidconv left=0 right=1640 top=136 bottom=1096 "
"! compos. ";
// nvcompsositor 的两个输入必须是一样的图片格式和内存形式,需要将云图透明部分用白色(255,255,255)填充
std::string pipeline_nvcompsositor =
"appsrc "
"! video/x-raw, format=BGR "
"! videoconvert "
"! video/x-raw,format=(string)RGBA, width=(int)1024, height=(int)600"
"! nvvidconv "
"! queue2"
"! nvcompositor name = compos sink_0::zorder=1 sink_0::alpha=0.5 "
"sink_1::alpha=1 "
"sink_1::zorder=0 sink_1::width=1024 sink_1::height=600 "
"! nvvidconv "
"! nvv4l2h264enc "
"! rtspclientsink location=rtsp://192.168.20.99:8554/my_pipeline"
" nvarguscamerasrc "
"! video/x-raw(memory:NVMM), format=(string)NV12, width=(int)1640, height=(int)1232,framerate=(fraction)25/1 "
"! nvvidconv left=0 right=1640 top=136 bottom=1096 "
"! video/x-raw,format=(string)RGBA, width=(int)1024, height=(int)600 "
"! videobalance brightness=0.3 "
"! nvvidconv "
"! queue2"
"! compos. ";
video_writer_.open(pipeline_nvcompsositor, cv::CAP_GSTREAMER, 0, 25, cv::Size(1024, 600));
mat_ = cv::imread("test.jpg");
write_thread_ = make_shared<thread>(&RtspPushStream::run, this);
write_thread_->join();
}
void RtspPushStream::run() {
int id = 0;
while (active_) {
space_mat_ = cv::Mat(600, 1024, CV_8UC3, cv::Scalar(0, 0, 0));
video_writer_.write(space_mat_);
std::this_thread::sleep_for(std::chrono::milliseconds(3));
}
}
void RtspPushStream::end() {
active_ = false;
//LOG::info("RtspPushStream::end()!");
}
void RtspPushStream::start_capture() {
std::string pipeline_camear_capture =
" nvarguscamerasrc "
"! video/x-raw(memory:NVMM), format=(string)NV12, width=(int)1640, "
"height=(int)1232,framerate=(fraction)30/1 "
"! nvvidconv left=0 right=1640 top=136 bottom=1096 "
"! video/x-raw,format=(string)I420, width=(int)1024, height=(int)600 "
"! videoconvert "
"! video/x-raw,format=(string)BGR "
"! appsink";
video_capture_.open(pipeline_camear_capture, cv::CAP_GSTREAMER);
if (!video_capture_.isOpened()) {
//LOG::error("Failed to open VideoCapture");
return;
}
mat_ = cv::imread("test.jpg");
std::string pipeline_video_writer =
"appsrc "
"! video/x-raw, format=BGR "
"! videoconvert "
"! nvvidconv "
"! nvv4l2h264enc "
"! rtspclientsink location=rtsp://192.168.20.99:8554/my_pipeline";
video_writer_.open(pipeline_video_writer, cv::CAP_GSTREAMER, 0, 20, cv::Size(1024, 600));
cap_thread_ = make_shared<thread>(&RtspPushStream::run_capture, this);
cap_thread_->detach();
}
void RtspPushStream::run_capture() {
bool use_affinity = true;
int cpu = 1;
if (use_affinity) {
cpu_set_t cpuset;
CPU_ZERO(&cpuset);
CPU_SET(cpu, &cpuset);
pthread_t current_thread = pthread_self();
int ret = pthread_setaffinity_np(current_thread, sizeof(cpu_set_t), &cpuset);
if (ret != 0) {
//LOG::error("run_capture pthread_setaffinity_np failed, {}", ret);
} else {
//LOG::info("run_capture pthread_setaffinity_np success, bind recv thread to cpu 1");
}
}
int id = 0;
cv::Mat src;
while (active_) {
video_capture_ >> src;
//在这里自定义融合算法就行了
//可以利用opencv给src写入当前时间用来观察
video_writer_.write(src);
src.release();
}
//LOG::info("RtspPushStream::run_capture() end!");
}
//头文件
#ifndef RTSHPUSHSTREAM_H
#define RTSHPUSHSTREAM_H
#include <chrono>
#include <iostream>
#include <memory>
#include <mutex>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/videoio/videoio.hpp>
#include <string>
#include <thread>
#include <vector>
using namespace std;
class RtspPushStream {
public:
RtspPushStream();
~RtspPushStream();
void start();
void write_image(cv::Mat image);
void run();
void start_capture();
void run_capture();
void end();
private:
mutex mutex_;
cv::VideoWriter video_writer_;
list<cv::Mat> img_mats_;
bool active_;
cv::Mat mat_;
cv::Mat space_mat_;
shared_ptr<thread> cap_thread_;
shared_ptr<thread> write_thread_;
cv::VideoCapture video_capture_;
std::vector<cv::Mat> push_frames_;
std::mutex push_frames_lock_;
};
#endif
推流后用相关视频软件拉流就能看到效果，如PotPlayer 64 bit，VLC等等
最后
以上就是可耐小鸭子为你收集整理的使用Gstreamer+OpenCV实现两路图像数据混合拉流推流的全部内容，希望文章能够帮你解决使用Gstreamer+OpenCV实现两路图像数据混合拉流推流所遇到的程序开发问题。
如果觉得靠谱客网站的内容还不错，欢迎将靠谱客网站推荐给程序员好友。
本图文内容来源于网友提供，作为学习参考使用，或来自网络收集整理，版权属于原作者所有。
发表评论 取消回复