opencv-gpu版本编译（添加java支持，可选）实现硬解码

opencv gpu版本编译，实现硬解码，加速rtsp视频流读取

前置条件，显卡驱动，cuda 已安装

这里cuda安装路径为 /usr/local/cuda-11.2

1、准备文件

• opencv-4.5.5

• opencv_contrib-4.5.5
  

• nucuvid：官网下载
  

2、复制 NVCUVID 头文件到 cuda 安装目录 include

sudo cp cuviddec.h nvcuid.h nvEncodeAPI. /usr/local/cuda-11.2/include

3、安装相关依赖

• 执行01_install_dependence.sh

#!/bin/bash

sudo apt update
sudo apt upgrade

#sudo apt install -y gcc-10 g++-10
sudo apt install -y build-essential cmake pkg-config yasm git checkinstall
sudo apt install -y pkg-config yasm checkinstall
sudo apt install -y libjpeg-dev libpng-dev libtiff-dev
sudo apt install -y libavcodec-dev libavformat-dev libswscale-dev 
sudo apt install -y libxvidcore-dev x264 libx264-dev libfaac-dev libmp3lame-dev libtheora-dev 
sudo apt install -y libfaac-dev libmp3lame-dev libvorbis-dev
sudo apt install -y libgtkglext1 libgtkglext1-dev
sudo apt-get install -y libavresample-dev libdc1394-22 libdc1394-22-dev libxine2-dev libv4l-dev v4l-utils

4、 执行cmake

• 执行02_install_opencv.sh

cd opencv_build
#unzip opencv.zip
#unzip opencv_contrib.zip

echo "Moving onto the build portion of things"
cd opencv-4.5.5
mkdir build && cd build
cmake -D CMAKE_BUILD_TYPE=RELEASE \
    -D CMAKE_C_COMPILER=/usr/bin/gcc-9 \
    -D CMAKE_INSTALL_PREFIX=../install_dir \
    -D OPENCV_GENERATE_PKGCONFIG=ON \
    -D BUILD_opencv_python3=ON \
    -D CUDA_ARCH_BIN=8.6\
    -D WITH_CUDA=ON \
    -D WITH_CUDNN=ON \
    -D OPENCV_DNN_CUDA=ON \
    -D ENABLE_FAST_MATH=1 \
    -D CUDA_FAST_MATH=1 \
    -D OPENCV_ENABLE_NONFREE=ON \
    -D WTIH_CUBLAS=1 \
    -D WITH_V4L=ON \
	-D WITH_NVCUVID=ON \
	-D WITH_OPENGL=ON \
	-D WITH_FFMPEG=ON \
	-D BUILD_opencv_java=ON \
    -D OPENCV_EXTRA_MODULES_PATH=~/opencv_project/opencv_build/opencv_contrib-4.5.5/modules ..

echo "Configuring build & making OpenCV"
echo 'finished all the shit'

需要注意的地方：

• -D CMAKE_INSTALL_PREFIX=../install_dir：安装路径

• -D CUDA_ARCH_BIN=8.6 ： 显卡算力

• -D WITH_CUDA=ON：启用CUDA支持

• -D WITH_CUDNN=ON：启用CUDNN支持

• -D OPENCV_DNN_CUDA=ON：启用CUDA加速的深度学习模块

• -D WITH_NVCUVID=ON：启用NVCUVID支持，允许OpenCV在NVIDIA GPU上解码视频。

• -D BUILD_opencv_java=ON : 启用 java 支持，需要先配置好java环境变量（jdk，ant），可选项

cmake 执行完成

-- 
--   OpenCV modules:
--     To be built:                 alphamat aruco barcode bgsegm bioinspired calib3d ccalib core cudaarithm cudabgsegm cudacodec cudafeatures2d cudafilters cudaimgproc cudalegacy cudaobjdetect cudaoptflow cudastereo cudawarping cudev datasets dnn dnn_objdetect dnn_superres dpm face features2d flann freetype fuzzy gapi hfs highgui img_hash imgcodecs imgproc intensity_transform java line_descriptor mcc ml objdetect optflow phase_unwrapping photo plot quality rapid reg rgbd saliency shape stereo stitching structured_light superres surface_matching text tracking ts video videoio videostab wechat_qrcode xfeatures2d ximgproc xobjdetect xphoto
--     Disabled:                    world
--     Disabled by dependency:      -
--     Unavailable:                 cvv hdf julia matlab ovis python2 python3 sfm viz
--     Applications:                tests perf_tests apps
--     Documentation:               NO
--     Non-free algorithms:         YES
-- 
--   GUI:                           GTK2
--     GTK+:                        YES (ver 2.24.32)
--       GThread :                  YES (ver 2.64.6)
--       GtkGlExt:                  YES (ver 1.2.0)
--     OpenGL support:              YES (/usr/lib/x86_64-linux-gnu/libGL.so /usr/lib/x86_64-linux-gnu/libGLU.so)
--     VTK support:                 NO
-- 
--   Media I/O: 
--     ZLib:                        /usr/lib/x86_64-linux-gnu/libz.so (ver 1.2.11)
--     JPEG:                        /usr/lib/x86_64-linux-gnu/libjpeg.so (ver 80)
--     WEBP:                        build (ver encoder: 0x020f)
--     PNG:                         /usr/lib/x86_64-linux-gnu/libpng.so (ver 1.6.37)
--     TIFF:                        /usr/lib/x86_64-linux-gnu/libtiff.so (ver 42 / 4.1.0)
--     JPEG 2000:                   build (ver 2.4.0)
--     OpenEXR:                     /usr/lib/x86_64-linux-gnu/libImath.so /usr/lib/x86_64-linux-gnu/libIlmImf.so /usr/lib/x86_64-linux-gnu/libIex.so /usr/lib/x86_64-linux-gnu/libHalf.so /usr/lib/x86_64-linux-gnu/libIlmThread.so (ver 2_3)
--     HDR:                         YES
--     SUNRASTER:                   YES
--     PXM:                         YES
--     PFM:                         YES
-- 
--   Video I/O:
--     DC1394:                      YES (2.2.5)
--     FFMPEG:                      YES
--       avcodec:                   YES (58.54.100)
--       avformat:                  YES (58.29.100)
--       avutil:                    YES (56.31.100)
--       swscale:                   YES (5.5.100)
--       avresample:                YES (4.0.0)
--     GStreamer:                   YES (1.16.3)
--     v4l/v4l2:                    YES (linux/videodev2.h)
-- 
--   Parallel framework:            pthreads
-- 
--   Trace:                         YES (with Intel ITT)
-- 
--   Other third-party libraries:
--     Intel IPP:                   2020.0.0 Gold [2020.0.0]
--            at:                   /home/dell/ljn/opencv_project/opencv_build/opencv-4.5.5/build/3rdparty/ippicv/ippicv_lnx/icv
--     Intel IPP IW:                sources (2020.0.0)
--               at:                /home/dell/ljn/opencv_project/opencv_build/opencv-4.5.5/build/3rdparty/ippicv/ippicv_lnx/iw
--     VA:                          NO
--     Lapack:                      NO
--     Eigen:                       YES (ver 3.3.9)
--     Custom HAL:                  NO
--     Protobuf:                    build (3.19.1)
-- 
--   NVIDIA CUDA:                   YES (ver 11.2, CUFFT CUBLAS NVCUVID FAST_MATH)
--     NVIDIA GPU arch:             86
--     NVIDIA PTX archs:
-- 
--   cuDNN:                         YES (ver 8.6.0)
-- 
--   OpenCL:                        YES (no extra features)
--     Include path:                /home/dell/ljn/opencv_project/opencv_build/opencv-4.5.5/3rdparty/include/opencl/1.2
--     Link libraries:              Dynamic load
-- 
--   Python 3:
--     Interpreter:                 /usr/bin/python3 (ver 3.8.10)
--     Libraries:                   NO
--     numpy:                       NO (Python3 wrappers can not be generated)
--     install path:                -
-- 
--   Python (for build):            /usr/bin/python3
-- 
--   Java:                          
--     ant:                         /usr/local/apache-ant-1.10.13/bin/ant (ver 1.10.13)
--     JNI:                         /usr/lib/jvm/jdk1.8.0_361/include /usr/lib/jvm/jdk1.8.0_361/include/linux /usr/lib/jvm/jdk1.8.0_361/include
--     Java wrappers:               YES
--     Java tests:                  YES
-- 
--   Install to:                    /home/dell/ljn/opencv_project/opencv_build/opencv-4.5.5/install_dir
-- -----------------------------------------------------------------
-- 
-- Configuring done
-- Generating done
-- Build files have been written to: /home/dell/ljn/opencv_project/opencv_build/opencv-4.5.5/build
Configuring build & making OpenCV
finished all the shit

• gpu 加速开启成功
  
• java 支持开启成功
  

5、编译安装

• 进入cmake创建的build目录

  cd opencv_build/opencv-4.5.5/build
  
  #编译，根据电脑核心数选择合适线程
  make -j30  
  

• 安装

  make install
  

6、测试

• 进入目录opencv_gpu_test，编译执行程序，在CMakeLIsts.txt中修改opencv的安装路径

  cd build
  make 
  ./opencv_test
  

• CMakeLIsts.txt

  cmake_minimum_required(VERSION 3.0.2)
  project(opencv_test)
  SET(CMAKE_BUILD_TYPE "Release")
  
  # 安装路径
  find_package(OpenCV 4.5.5 REQUIRED PATHS /home/lenovo/opencv_project/opencv_build/opencv-4.5.5/install_dir)
  
  include_directories(${OpenCV_INCLUDE_DIRS})
  
  add_executable(opencv_test test.cc )
  #add_executable( opencv_test gpu_mat.cpp )
  target_link_libraries( opencv_test
          ${OpenCV_LIBRARIES}
          /usr/lib/x86_64-linux-gnu
          )
  

• test.cc

  #include <iostream>
  #include <string>
  #include <vector>
  #include <algorithm>
  #include <numeric>
  #include "opencv2/opencv_modules.hpp"
  #include <opencv2/core/utility.hpp>
  #include <opencv2/core.hpp>
  #include <opencv2/core/opengl.hpp>
  #include <opencv2/cudacodec.hpp>
  #include <opencv2/highgui.hpp>
  
  int main(int argc, const char* argv[])
  {
     //std::cout<<cv::getBuildInformation()<<std::endl;
     //将这个流改成你自己的
      const std::string fname = "rtsp://admin:abcd1234@192.168.1.110:554/smart264/ch1/main/av_stream";
      const std::string gfname = "rtsp://admin:abcd1234@192.168.1.110:554/Streaming/Channels/2";
      std::cout<<"Set device...."<<std::endl;
      int numDevice = cv::cuda::getCudaEnabledDeviceCount();
      std::cout<<"device count: "<<numDevice<<std::endl;
      int cudaDevice = 0;
      cv::cuda::setDevice(cudaDevice);
      //cv::cuda::setGlDevice(cudaDevice);
      //cv::cuda::setGlDevice(1);
  
      std::cout<<"read rtsp through cpu..."<<std::endl;
      cv::Mat frame;
      cv::VideoCapture reader(fname);
  
      cv::cuda::GpuMat d_frame;
      std::cout<<"read rtsp through cuda..."<<std::endl;
      cv::Ptr<cv::cudacodec::VideoReader> d_reader = cv::cudacodec::createVideoReader(fname);
      cv::TickMeter tm;
      std::vector<double> cpu_times;
      std::vector<double> gpu_times;
      std::cout<<"test"<<std::endl;
      for (int i = 0;i<500;i++)
      {
          tm.reset(); tm.start();
          if (!reader.read(frame))
             break;
          tm.stop();
          cpu_times.push_back(tm.getTimeMilli());
  
           tm.reset(); tm.start();
          if (!d_reader->nextFrame(d_frame))
              break;
           tm.stop();
           gpu_times.push_back(tm.getTimeMilli());
      }
  
      if (!cpu_times.empty() || !gpu_times.empty())
      {
          std::cout << std::endl << "Results:" << std::endl;
  
          //std::sort(cpu_times.begin(), cpu_times.end());
          std::sort(gpu_times.begin(), gpu_times.end());
  
          //double cpu_avg = std::accumulate(cpu_times.begin(), cpu_times.end(), 0.0) / cpu_times.size();
          double gpu_avg = std::accumulate(gpu_times.begin(), gpu_times.end(), 0.0) / gpu_times.size();
  
          //std::cout << "CPU : Avg : " << cpu_avg << " ms FPS : " << 1000.0 / cpu_avg << std::endl;
          std::cout << "GPU : Avg : " << gpu_avg << " ms FPS : " << 1000.0 / gpu_avg << std::endl;
      }
  
      return 0;
  }
  // #endif
  

• 结果如下安装成功

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