从 https://developer.nvidia.com/rdp/cudnn-archive 这个网址下载指定的cudnn版本,这里注意如果直接google然后下载的话只是最新版本,需要点击下面的Archived cuDNN Releases才能够找到以前版本的下载,然后选择cuDNN v×.× Library for Linux
下载下来一个压缩文件。将其解压到某个目录下执行:
sudo cp include/cudnn.h /usr/local/cuda/include
sudo cp lib64/libcudnn* /usr/local/cuda/lib64
然后可以查看cudnn的版本:
cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2
首先从https://opencv.org/releases.html 官网选择需要的opencv版本,然后下载source
下载好之后执行以下语句:
cd $HOME
sudo apt-get install -y \
libglew-dev \
libtiff5-dev \
zlib1g-dev \
libjpeg-dev \
libpng12-dev \
libjasper-dev \
libavcodec-dev \
libavformat-dev \
libavutil-dev \
libpostproc-dev \
libswscale-dev \
libeigen3-dev \
libtbb-dev \
libgtk2.0-dev \
cmake \
pkg-config
# Python 2.7
sudo apt-get install -y python-dev python-numpy python-py python-pytest -y
# GStreamer support
sudo apt-get install -y libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev
cd $HOME/opencv
mkdir build
cd build
# Jetson TX2
cmake \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_INSTALL_PREFIX=/usr \
-DBUILD_PNG=OFF \
-DBUILD_TIFF=OFF \
-DBUILD_TBB=OFF \
-DBUILD_JPEG=OFF \
-DBUILD_JASPER=OFF \
-DBUILD_ZLIB=OFF \
-DBUILD_EXAMPLES=ON \
-DBUILD_opencv_java=OFF \
-DBUILD_opencv_python2=ON \
-DBUILD_opencv_python3=ON \
-DENABLE_PRECOMPILED_HEADERS=OFF \
-DWITH_OPENCL=OFF \
-DWITH_OPENMP=OFF \
-DWITH_FFMPEG=ON \
-DWITH_GSTREAMER=ON \
-DWITH_GSTREAMER_0_10=OFF \
-DWITH_CUDA=ON \
-DWITH_GTK=ON \
-DWITH_VTK=OFF \
-DWITH_TBB=ON \
-DWITH_1394=OFF \
-DWITH_OPENEXR=OFF \
-DCUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-8.0 \
-DCUDA_ARCH_BIN=6.2 \
-DCUDA_ARCH_PTX="" \
-DINSTALL_C_EXAMPLES=ON \
-DINSTALL_TESTS=ON \
-DOPENCV_TEST_DATA_PATH=../opencv_extra/testdata \
../
# Consider using all 6 cores; $ sudo nvpmodel -m 2 or $ sudo nvpmodel -m 0
make -j4
//这里注意,需要注释build/data/cmake_install.cmake文件中的第75行的if语句,再执行下一步
sudo make install
然后检验是否安装成功:
pkg-config --modversion opencv
有版本输出说明安装成功