视频截帧+光流 基于CUDA9+OpenCV3

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在计算机视觉领域中,视频算法是重要的一个部分,不同于图像,视频需要含有时序特征的多帧图像,同时,还包括一定的运动信息,如光流。在预处理时,需要将视频中的图像和光流提取出来,开源工程dense_flow已经实现这个功能,支持GPU操作。

在CUDA 9和OpenCV 3中,配置dense_flow工程,高级版本temporal-segment-networks。同时,推荐视频的Benchmark工程mmaction

参考:

dense_flow: https://github.com/yjxiong/dense_flow
temporal-segment-networks: https://github.com/yjxiong/temporal-segment-networks
mmaction: https://github.com/open-mmlab/mmaction

编译OpenCV

OpenCV的编译步骤如下:

  1. 下载opencv,下载opencv_contrib;
  2. 修改2个cmake文件,和1个hpp文件;
  3. 修改hdf5、ffmpeg、nonfree(可选);
  4. make,安装opencv;

OpenCV 3

CUDA 9不支持OpenCV2.x,只能选用3.x,如3.1.0,参考

CUDA9

OpenCV源码

下载OpenCV源码文件,并解压:

wget https://github.com/opencv/opencv/archive/3.1.0.zip

unzip 3.1.0.zip

cd opencv-3.1.0

opencv_contrib

在opencv-3.1.0中,下载opencv_contrib文件,并解压:

wget https://github.com/opencv/opencv_contrib/archive/3.1.0.zip

unzip 3.1.0.zip

位置如下:

opencv_contrib

原因是,SURF或SIFT算法移入opencv_contrib,需要参于源码编译,在dense_flow中,调用SURF算法,否则无法找到SURF,参考

Error:

undefined reference to `cv::xfeatures2d::SURF::create(double, int, int, bool, bool)'

修改cmake文件

CMake Error:

CMake Error: The following variables are used in this project, but they are set to NOTFOUND.
Please set them or make sure they are set and tested correctly in the CMake files:
CUDA_nppi_LIBRARY (ADVANCED)

原因是,nppi已经废弃,需要替换其他的CUDA,同时,CUDA 2.0已经不兼容当前版本,需要删除。

需要修改cmake文件夹下的FindCUDA.cmake和OpenCVDetectCUDA.cmake,还有修改common.hpp。

修改FindCUDA.cmake文件,3处替换:

替换

find_cuda_helper_libs(nppi)

  find_cuda_helper_libs(nppial)
  find_cuda_helper_libs(nppicc)
  find_cuda_helper_libs(nppicom)
  find_cuda_helper_libs(nppidei)
  find_cuda_helper_libs(nppif)
  find_cuda_helper_libs(nppig)
  find_cuda_helper_libs(nppim)
  find_cuda_helper_libs(nppist)
  find_cuda_helper_libs(nppisu)
  find_cuda_helper_libs(nppitc)

替换

set(CUDA_npp_LIBRARY "${CUDA_nppc_LIBRARY};${CUDA_nppi_LIBRARY};${CUDA_npps_LIBRARY}")

set(CUDA_npp_LIBRARY "${CUDA_nppc_LIBRARY};${CUDA_nppial_LIBRARY};${CUDA_nppicc_LIBRARY};${CUDA_nppicom_LIBRARY};${CUDA_nppidei_LIBRARY};${CUDA_nppif_LIBRARY};${CUDA_nppig_LIBRARY};${CUDA_nppim_LIBRARY};${CUDA_nppist_LIBRARY};${CUDA_nppisu_LIBRARY};${CUDA_nppitc_LIBRARY};${CUDA_npps_LIBRARY}")

替换

unset(CUDA_nppi_LIBRARY CACHE)

  unset(CUDA_nppial_LIBRARY CACHE)
  unset(CUDA_nppicc_LIBRARY CACHE)
  unset(CUDA_nppicom_LIBRARY CACHE)
  unset(CUDA_nppidei_LIBRARY CACHE)
  unset(CUDA_nppif_LIBRARY CACHE)
  unset(CUDA_nppig_LIBRARY CACHE)
  unset(CUDA_nppim_LIBRARY CACHE)
  unset(CUDA_nppist_LIBRARY CACHE)
  unset(CUDA_nppisu_LIBRARY CACHE)
  unset(CUDA_nppitc_LIBRARY CACHE)

修改OpenCVDetectCUDA.cmake文件,2处删除:

将"Fermi"注释,将"Kepler"提前,即删除"Fermi"的if分支,主要是为了删除CUDA的2.0版本兼容。

  set(__cuda_arch_ptx "")
  if(CUDA_GENERATION STREQUAL "Fermi")
    set(__cuda_arch_bin "2.0")
  elseif(CUDA_GENERATION STREQUAL "Kepler")
    set(__cuda_arch_bin "3.0 3.5 3.7")

修改为

  set(__cuda_arch_ptx "")
  if(CUDA_GENERATION STREQUAL "Kepler")
    set(__cuda_arch_bin "3.0 3.5 3.7")

在CUDA版本大于6.5时,删除2.0版本的兼容,修改完成如下:

      elseif(${CUDA_VERSION} VERSION_GREATER "6.5")
        set(__cuda_arch_bin "3.0 3.5")

opencv-3.1.0/modules/cudev/include/opencv2/cudev/common.hpp的头文件中,添加:

#include <cuda_fp16.h>

参考

hdf5 error

Error:

hdf5.hpp:40:18: fatal error: hdf5.h: No such file or directory

修改opencv-3.1.0/modules/python/common.cmake文件,在文件头部中,添加

find_package(HDF5)
include_directories(${HDF5_INCLUDE_DIRS})

参考

nonfree error

Error:

fatal error: opencv2/nonfree/nonfree.hpp: No such file or directory

安装包libopencv-nonfree-dev:

sudo apt-get update
sudo add-apt-repository --yes ppa:xqms/opencv-nonfree
sudo apt-get update
sudo apt-get install libopencv-nonfree-dev

如果不成功,更换ppa的源:

sudo add-apt-repository --remove ppa:xqms/opencv-nonfree
sudo add-apt-repository --yes ppa:jeff250/opencv
sudo apt-get update
sudo apt-get install libopencv-dev
sudo apt-get install libopencv-nonfree-dev

参考参考

ffmpeg error

Error:

c->flags |= CODEC_FLAG_GLOBAL_HEADER

opencv-3.1.0/modules/videoio/src/cap_ffmpeg_impl.hpp中,添加:

#define AV_CODEC_FLAG_GLOBAL_HEADER (1 << 22)
#define CODEC_FLAG_GLOBAL_HEADER AV_CODEC_FLAG_GLOBAL_HEADER
#define AVFMT_RAWPICTURE 0x0020

参考

make

执行make操作,在OPENCV_EXTRA_MODULES_PATH中,需要引入opencv_contrib

make -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D OPENCV_EXTRA_MODULES_PATH=/data1/wcl/workspace/opencv-3.1.0/opencv_contrib-3.1.0/modules/ ..

执行make,32进程:

make -j32  

安装,并且将opencv导入系统环境。

sudo make install  
sudo /bin/bash -c 'echo "/usr/local/lib" > /etc/ld.so.conf.d/opencv.conf'
sudo ldconfig

编译DenseFlow

参考

安装libzip-dev

apt-get install libzip-dev

下载dense_flow工程,切换OpenCV的3.1分支:

git clone --recursive http://github.com/yjxiong/dense_flow
git checkout remotes/origin/opencv-3.1

指定OpenCV_DIR,编译工程:

mkdir build && cd build
OpenCV_DIR=/opencv-3.1.0/build cmake .. -DCUDA_USE_STATIC_CUDA_RUNTIME=OFF
make -j32

编译成功之后,在build文件夹中:

  1. 869956383.mp4为测试视频,建立tmp文件夹。
  2. 执行命令,注意空格全部替换为“=”,参考参考
  3. 在tmp文件夹中,生成视频帧以image前缀,x轴光流以flow_x前缀,y轴光流以flow_y前缀,其余参数参考
./extract_gpu -f=980044841.mp4 -x=./tmp/flow_x -y=./tmp/flow_y -i=./tmp/image -b=20 -t=1 -d=0 -s=1 -o=dir

Error,提示无法打开视频,将空格替换为“=”即可。

FATAL [default] Check failed: [video_stream.isOpened()]

测试视频:

Test

输出结果:

Result

GPU使用情况

GPU


OK, that's all! Enjoy it!