python3.7安装opencv失败 cuda10.0_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。图1 各种Net的检测效率和精度

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

git clone https://github.com/AlexeyAB/darknet

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 testfiles

3.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：图2 CPU测试的结果

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：图3 GPU使用效果

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 update

3.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-440

4.重启和检测

reboot

nvidia-smi

5.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 Archive​developer.nvidia.com

wget http://developer.download.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda_10.1.243_418.87.00_linux.run图3 Cuda选择参考

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)

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/opencv​github.comopencv/opencv_contrib​github.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

Atlas_BLAS_LIBRARY Atlas_BLAS_LIBRARY-NOTFOUND

Atlas_CBLAS_INCLUDE_DIR /usr/include/x86_64-linux-gnu

Atlas_CBLAS_LIBRARY Atlas_CBLAS_LIBRARY-NOTFOUND

Atlas_CLAPACK_INCLUDE_DIR Atlas_CLAPACK_INCLUDE_DIR-NOTFOUND

Atlas_LAPACK_LIBRARY /usr/lib/x86_64-linux-gnu/liblapack.so

BUILD_CUDA_STUBS OFF

BUILD_DOCS OFF

BUILD_EXAMPLES ON

BUILD_IPP_IW ON

BUILD_ITT ON

BUILD_JASPER ON

BUILD_JAVA ON

BUILD_JPEG ON

BUILD_LIST

BUILD_OPENEXR OFF

BUILD_PACKAGE ON

BUILD_PERF_TESTS ON

BUILD_PNG OFF

BUILD_PROTOBUF ON

BUILD_SHARED_LIBS ON

BUILD_TBB ON

BUILD_TESTS ON

BUILD_TIFF OFF

BUILD_USE_SYMLINKS OFF

BUILD_WEBP OFF

BUILD_WITH_DEBUG_INFO OFF

BUILD_WITH_DYNAMIC_IPP OFF

BUILD_ZLIB OFF

BUILD_opencv_apps ON

BUILD_opencv_aruco ON

BUILD_opencv_bgsegm ON

BUILD_opencv_bioinspired ON

BUILD_opencv_calib3d ON

BUILD_opencv_ccalib ON

BUILD_opencv_core ON

BUILD_opencv_cudaarithm ON

BUILD_opencv_cudabgsegm ON

BUILD_opencv_cudacodec ON

BUILD_opencv_cudafeatures2d ON

BUILD_opencv_cudafilters ON

BUILD_opencv_cudaimgproc ON

BUILD_opencv_cudalegacy ON

BUILD_opencv_cudaobjdetect ON

BUILD_opencv_cudaoptflow ON

BUILD_opencv_cudastereo ON

BUILD_opencv_cudawarping ON

BUILD_opencv_cudev ON

BUILD_opencv_datasets ON

BUILD_opencv_dnn ON

BUILD_opencv_dnn_objdetect ON

BUILD_opencv_dpm ON

BUILD_opencv_face ON

BUILD_opencv_features2d ON

BUILD_opencv_flann ON

BUILD_opencv_freetype ON

BUILD_opencv_fuzzy ON

BUILD_opencv_hdf ON

BUILD_opencv_hfs ON

BUILD_opencv_highgui ON

BUILD_opencv_img_hash ON

BUILD_opencv_imgcodecs ON

BUILD_opencv_imgproc ON

BUILD_opencv_java_bindings_gen ON

BUILD_opencv_js OFF

BUILD_opencv_line_descriptor ON

BUILD_opencv_ml ON

BUILD_opencv_objdetect ON

BUILD_opencv_optflow ON

BUILD_opencv_phase_unwrapping ON

BUILD_opencv_photo ON

BUILD_opencv_plot ON

BUILD_opencv_python2 ON

BUILD_opencv_python3 ON

BUILD_opencv_python_bindings_g ON

BUILD_opencv_python_tests ON

BUILD_opencv_reg ON

BUILD_opencv_rgbd ON

BUILD_opencv_saliency ON

BUILD_opencv_shape ON

BUILD_opencv_stereo ON

BUILD_opencv_stitching ON

BUILD_opencv_structured_light ON

BUILD_opencv_superres ON

BUILD_opencv_surface_matching ON

BUILD_opencv_text ON

BUILD_opencv_tracking ON

BUILD_opencv_ts ON

BUILD_opencv_video ON

BUILD_opencv_videoio ON

BUILD_opencv_videostab ON

BUILD_opencv_viz ON

BUILD_opencv_world OFF

BUILD_opencv_xfeatures2d ON

BUILD_opencv_ximgproc ON

BUILD_opencv_xobjdetect ON

BUILD_opencv_xphoto ON

CCACHE_PROGRAM CCACHE_PROGRAM-NOTFOUND

CLAMDBLAS_INCLUDE_DIR CLAMDBLAS_INCLUDE_DIR-NOTFOUND

CLAMDBLAS_ROOT_DIR CLAMDBLAS_ROOT_DIR-NOTFOUND

CLAMDFFT_INCLUDE_DIR CLAMDFFT_INCLUDE_DIR-NOTFOUND

CLAMDFFT_ROOT_DIR CLAMDFFT_ROOT_DIR-NOTFOUND

CMAKE_BUILD_TYPE

CMAKE_CONFIGURATION_TYPES Debug;Release

CMAKE_INSTALL_PREFIX /usr/local

CPU_BASELINE SSE3

CPU_DISPATCH SSE4_1;SSE4_2;AVX;FP16;AVX2;AVX512_SKX

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

CV_ENABLE_INTRINSICS ON

CV_TRACE ON

Caffe_INCLUDE_DIR Caffe_INCLUDE_DIR-NOTFOUND

Caffe_LIBS Caffe_LIBS-NOTFOUND

Ceres_DIR Ceres_DIR-NOTFOUND

ENABLE_BUILD_HARDENING OFF

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ENABLE_OMIT_FRAME_POINTER ON

ENABLE_PIC ON

ENABLE_PRECOMPILED_HEADERS OFF

ENABLE_PROFILING OFF

ENABLE_PYLINT OFF

ENABLE_SOLUTION_FOLDERS OFF

EXECUTABLE_OUTPUT_PATH /software/opencv-3.4.10/build/bin

Eigen3_DIR /usr/local/share/eigen3/cmake

GENERATE_ABI_DESCRIPTOR OFF

GFLAGS_INCLUDE_DIR GFLAGS_INCLUDE_DIR-NOTFOUND

GFLAGS_NAMESPACE

GLOG_INCLUDE_DIR GLOG_INCLUDE_DIR-NOTFOUND

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

INSTALL_C_EXAMPLES OFF

INSTALL_PYTHON_EXAMPLES OFF

INSTALL_TESTS OFF

INSTALL_TO_MANGLED_PATHS OFF

LAPACKE_INCLUDE_DIR LAPACKE_INCLUDE_DIR-NOTFOUND

LAPACK_CBLAS_H

LAPACK_IMPL Unknown

LAPACK_INCLUDE_DIR

LAPACK_LAPACKE_H

LAPACK_LIBRARIES

Lept_LIBRARY Lept_LIBRARY-NOTFOUND

MKL_INCLUDE_DIRS MKL_ROOT_DIR-NOTFOUND/include

MKL_LAPACKE_INCLUDE_DIR MKL_LAPACKE_INCLUDE_DIR-NOTFOUND

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MKL_WITH_OPENMP OFF

MKL_WITH_TBB ON

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OPENCL_FOUND ON

OPENCV_CONFIG_FILE_INCLUDE_DIR /software/opencv-3.4.10/build

OPENCV_DNN_OPENCL ON

OPENCV_DOWNLOAD_PATH /software/opencv-3.4.10/.cache

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OPENCV_ENABLE_ALLOCATOR_STATS ON

OPENCV_ENABLE_ATOMIC_LONG_LONG ON

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OPENCV_EXTRA_MODULES_PATH /software/opencv_contrib/modules

OPENCV_FORCE_3RDPARTY_BUILD OFF

OPENCV_FORCE_PYTHON_LIBS OFF

OPENCV_GENERATE_PKGCONFIG ON

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OPENCV_JAVA_SOURCE_VERSION

OPENCV_JAVA_TARGET_VERSION

OPENCV_MATHJAX_RELPATH https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0

OPENCV_PYTHON3_VERSION OFF

OPENCV_TIMESTAMP 2020-06-26T13:34:43Z

OPENCV_WARNINGS_ARE_ERRORS OFF

OPENEXR_INCLUDE_PATH /usr/include/OpenEXR

OpenCV_HAL_DIR OpenCV_HAL_DIR-NOTFOUND

PROTOBUF_UPDATE_FILES OFF

PYTHON2_EXECUTABLE /usr/bin/python2.7

PYTHON2_INCLUDE_DIR /usr/include/python2.7

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

PYTHON3_NUMPY_INCLUDE_DIRS /usr/lib/python3/dist-packages/numpy/core/include

PYTHON3_PACKAGES_PATH lib/python3.6/dist-packages

TBB_VER_FILE /software/opencv-3.4.10/build/3rdparty/tbb/oneTBB-2020.1/include/tbb/tbb_stddef.h

Tesseract_DIR Tesseract_DIR-NOTFOUND

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Tesseract_LIBRARY Tesseract_LIBRARY-NOTFOUND

VTK_DIR /usr/local/lib/cmake/vtk-7.1

WITH_1394 ON

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WITH_CUBLAS ON

WITH_CUDA ON

WITH_CUFFT ON

WITH_EIGEN ON

WITH_FFMPEG ON

WITH_GDAL OFF

WITH_GDCM OFF

WITH_GIGEAPI OFF

WITH_GPHOTO2 OFF

WITH_GSTREAMER ON

WITH_GSTREAMER_0_10 OFF

WITH_GTK ON

WITH_GTK_2_X OFF

WITH_HALIDE OFF

WITH_IMGCODEC_HDR ON

WITH_IMGCODEC_PXM ON

WITH_IMGCODEC_SUNRASTER ON

WITH_INF_ENGINE OFF

WITH_IPP ON

WITH_ITT ON

WITH_JASPER ON

WITH_JPEG ON

WITH_LAPACK ON

WITH_LIBV4L OFF

WITH_MATLAB OFF

WITH_MFX OFF

WITH_NGRAPH OFF

WITH_NVCUVID ON

WITH_OPENCL ON

WITH_OPENCLAMDBLAS ON

WITH_OPENCLAMDFFT ON

WITH_OPENCL_SVM OFF

WITH_OPENEXR ON

WITH_OPENGL ON

WITH_OPENMP OFF

WITH_OPENNI OFF

WITH_OPENNI2 OFF

WITH_OPENVX OFF

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：