agx 安装ros opencv_Yolov4配置-Ubuntu18.04-opencv3.4.10-Cuda10.1-(非ROS)
一、前言
最近需要使用object detection，就把yolo4配置一下，这个检测效率和效果还是非常到位的。和其他各种Net对比参考图1，当然在这个时候，我也测试了Yolov5,后面会再出一个文档来讲如何配置Yolov5。对于Yolov4这会是一个系列的文章，主要分两个方向，基于ROS和非ROS的。
至于为什么会有两个方向，由于我们主要研究机器人控制系统，所以配置Yolo主要用在机器人系统上，哪肯定要和ROS结合的，所以本文先描述如何配置，后面会陆陆续续出一些关于如何进行标注自己的数据集，然后进行迁移学习，移植到自己的项目上。
二、安装YoloV4
- 克隆下载，官网：https://github.com/AlexeyAB/darknet
git 2. 非GPU编译，并建立weights，testfiles文件夹，用于后面测试和放置权重文件
cd darknet
make
mkdir weights
mkdir testfiles3.非GPU测试：
需要下载权重文件yolov4.weights ，这是作者训练好的，我们可以直接用来测试。
下载链接：
Yolov4-Resource.zip-机器学习文档类资源-CSDN下载download.csdn.net
测试命令如下：
./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights
data/dog.jpg效果如下花费大概25s左右：
4.GPU测试，命令是一样的，但在这之前你需要安装Cuda,Cudacnn,Opencv等
安装参考步骤三.
测试命令：
./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights
data/dog.jpg计算过程：
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.05
nms_kind: greedynms (1), beta = 0.600000
Total BFLOPS 128.459
avg_outputs = 1068395
Allocate additional workspace_size = 52.43 MB
Loading weights from ./weights/yolov4.weights...
seen 64, trained: 32032 K-images (500 Kilo-batches_64)
Done! Loaded 162 layers from weights-file
./data/dog.jpg: Predicted in 26.903000 milli-seconds.
bicycle: 92%
dog: 98%
truck: 92%
pottedplant: 33%测试效果：
三、安装Cuda10.1,Cudacnn7.6.5,Nvidia driver,opencv 3.4.10和opencv_contrib
[1] Nvidia driver 安装
- 删除之前驱动
sudo apt-get purge nvidia*
sudo apt --purge remove "cublas*" "cuda*"2.添加源
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update3.安装
ubuntu-drivers devices
最后根据自己需求安装，这里我安装的是：
sudo apt-get install --no-install-recommends nvidia-driver-4404.重启和检测
reboot
nvidia-smi5.出现如下说明安装完毕。
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.100 Driver Version: 440.100 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 108... Off | 00000000:01:00.0 Off | N/A |
| 0% 53C P5 16W / 275W | 1422MiB / 11178MiB | 3% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1379 G /usr/lib/xorg/Xorg 36MiB |
| 0 1469 G /usr/bin/gnome-shell 49MiB |
| 0 2500 G /usr/lib/xorg/Xorg 451MiB |
| 0 2626 G /usr/bin/gnome-shell 322MiB |
| 0 9746 G ...AAAAAAAAAAAACAAAAAAAAAA= --shared-files 181MiB |
| 0 10883 G ...uest-channel-token=15615578348179024402 374MiB |
+-----------------------------------------------------------------------------+[2] Cuda10.1
1.根据自己的系统选择Cuda，这里我选择Cuda10.1（选择参考图3），如果下载慢可以直接这里下载
CUDA Toolkit 10.1 original Archivedeveloper.nvidia.comwget http://developer.download.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda_10.1.243_418.87.00_linux.run
2.安装，注意这里需要把NVIDIA驱动去掉，因为我们之前已经安装了这里不需要再安装
sudo sh cuda_10.1.243_418.87.00_linux.run这里默认安装路径于：/usr/local/cuda-10.1
3.添加到bashrc让启动terminal可找到，也就是添加环境变量
sudo vim ~/.bashrc在最后插入如下环境变量
#added by cuda10.1 installer
export CUDA_HOME=/usr/local/cuda-10.1
export PATH=$CUDA_HOME/bin:$PATH
export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH使用如下命令检查是否生效
source ~/.bashrc
nvcc -V
输出：
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Fri_Feb__8_19:08:17_CDT_2019
Cuda compilation tools, release 10.1, V10.1.105[3] 安装Cudacnn7.6.5
1.下载Cudacnn,这里你需要注册才能下载：
Cudacnn下载地址developer.nvidia.com这里选择Cuda10.1的7.6.5Cudacnn
Download cuDNN v8.0.1 RC2 (June 26th, 2020), for CUDA 11.0
Download cuDNN v8.0.1 RC2 (June 26th, 2020), for CUDA 10.2
Download cuDNN v7.6.5 (November 18th, 2019), for CUDA 10.2
Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.1
Library for Windows, Mac, Linux, Ubuntu and RedHat/Centos(x86_64architecture)
cuDNN Runtime Library for Ubuntu18.04 (Deb)<------[选择这个]2.拷贝到Cuda文件夹
sudo cp cuda/include/cudnn.h /usr/local/cuda/include/
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64/
sudo chmod a+r /usr/local/cuda/include/cudnn.h
sudo chmod a+r /usr/local/cuda/lib64/libcudnn*3.最后检测是否成功安装和查询安装版本
cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2[4] 编译安装Opencv3.4.10和opencv_contrib
1.下载opencv3.4.10和对应版本的opencv_contrib，这里一定要下载对应版本，不然很容易遇到错误。
opencv/opencvgithub.com
2.编译opencv 编译命令如下：
编译方式一：
第一步：进入对应路径
cd opencv-3.4.10
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=RELEASE
-D CMAKE_INSTALL_PREFIX=/usr/local
-D INSTALL_PYTHON_EXAMPLES=ON
-D INSTALL_C_EXAMPLES=OFF
-D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib-3.4.10/modules #注意这里选择自己对应路径
-D PYTHON_EXCUTABLE=/usr/bin/python
-D WITH_CUDA=ON # 使用CUDA
-D WITH_CUBLAS=ON
-D DCUDA_NVCC_FLAGS="-D_FORCE_INLINES"
-D CUDA_ARCH_BIN="7.5" # 需要去官网查询自己显卡的算力
-D CUDA_ARCH_PTX=""
-D CUDA_FAST_MATH=ON
-D WITH_TBB=ON
-D WITH_V4L=ON
-D WITH_QT=ON
-D WITH_GTK=ON
-D WITH_OPENGL=ON
-D BUILD_EXAMPLES=ON
最后再：make
sudo make install编译方式二：
直接使用如下命令进行：
cd opencv-3.4.10
mkdir build
cd build
ccmake .. 然后选择对应的模块选择参考如下，然后按c键保存，按g键生成对应的makefile。如果是warnning 可以忽略。
ANT_EXECUTABLE ANT_EXECUTABLE-NOTFOUND
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Atlas_CLAPACK_INCLUDE_DIR Atlas_CLAPACK_INCLUDE_DIR-NOTFOUND
Atlas_LAPACK_LIBRARY /usr/lib/x86_64-linux-gnu/liblapack.so
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BUILD_opencv_java_bindings_gen ON
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BUILD_opencv_ximgproc ON
BUILD_opencv_xobjdetect ON
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CLAMDBLAS_ROOT_DIR CLAMDBLAS_ROOT_DIR-NOTFOUND
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CLAMDFFT_ROOT_DIR CLAMDFFT_ROOT_DIR-NOTFOUND
CMAKE_BUILD_TYPE
CMAKE_CONFIGURATION_TYPES Debug;Release
CMAKE_INSTALL_PREFIX /usr/local
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CUDA_ARCH_BIN 3.0 3.5 3.7 5.0 5.2 6.0 6.1 7.0 7.5
CUDA_ARCH_PTX
CUDA_FAST_MATH ON
CUDA_GENERATION
CUDA_HOST_COMPILER /usr/bin/cc
CUDA_TOOLKIT_ROOT_DIR /usr/local/cuda-10.0
CUDA_USE_STATIC_CUDA_RUNTIME ON
CUDA_rt_LIBRARY /usr/lib/x86_64-linux-gnu/librt.so
CV_DISABLE_OPTIMIZATION OFF
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Glog_LIBS Glog_LIBS-NOTFOUND
HDF5_C_LIBRARY_dl /usr/lib/x86_64-linux-gnu/libdl.so
HDF5_C_LIBRARY_hdf5 /usr/lib/x86_64-linux-gnu/hdf5/serial/libhdf5.so
HDF5_C_LIBRARY_m /usr/lib/x86_64-linux-gnu/libm.so
HDF5_C_LIBRARY_pthread /usr/lib/x86_64-linux-gnu/libpthread.so
HDF5_C_LIBRARY_sz /usr/lib/x86_64-linux-gnu/libsz.so
HDF5_C_LIBRARY_z /usr/lib/x86_64-linux-gnu/libz.so
INSTALL_CREATE_DISTRIB OFF
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PYTHON2_INCLUDE_DIR2
PYTHON2_LIBRARY /usr/lib/x86_64-linux-gnu/libpython2.7.so
PYTHON2_LIBRARY_DEBUG
PYTHON2_NUMPY_INCLUDE_DIRS /home/yue/.local/lib/python2.7/site-packages/numpy/core/include
PYTHON2_PACKAGES_PATH lib/python2.7/dist-packages
PYTHON3_EXECUTABLE /usr/bin/python3
PYTHON3_INCLUDE_DIR /usr/include/python3.6m
PYTHON3_INCLUDE_DIR2
PYTHON3_LIBRARY /usr/lib/x86_64-linux-gnu/libpython3.6m.so
PYTHON3_LIBRARY_DEBUG
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WITH_OPENCL_SVM OFF
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WITH_OPENMP OFF
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WITH_PNG ON
WITH_PROTOBUF ON
WITH_PTHREADS_PF ON
WITH_PVAPI OFF
WITH_QT OFF
WITH_QUIRC ON
WITH_TBB ON
WITH_TESSERACT ON
WITH_TIFF ON
WITH_UNICAP OFF
WITH_V4L ON
WITH_VA OFF
WITH_VA_INTEL OFF
WITH_VTK ON
WITH_WEBP ON
WITH_XIMEA OFF
WITH_XINE OFF
gflags_DIR gflags_DIR-NOTFOUND
opencv_dnn_PERF_CAFFE OFF
opencv_dnn_PERF_CLCAFFE OFF
后记：
到这里基本都编译完毕，就可以进行下一步了，当然了编译过程中会遇到各种各样的问题，大家可以留言哈，有空我就会回的。大家一起加油，后续序列会出如何根据自己的数据集，进行标注并且训练得出自己的权重信息。同时会出如何和ROS结合的版本，欢迎大家继续关注。
参考链接：
[1]https://blog.csdn.net/jgj123321/article/details/104774786/
[2]https://developer.nvidia.com/cuda-10.1-download-archive-base?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=1804&target_type=runfilelocal
[3]https://developer.nvidia.com/rdp/cudnn-download
[4] https://blog.csdn.net/JackSparrow_sjl/article/details/81911855
[5]https://github.com/AlexeyAB/darknet