Ubuntu18.04 + Yolov3 + OpenCV

水深无声 2023-10-07 10:06 16阅读 0赞

# 一、YoloV3安装 #

１．进入[官网][Link 1]

根据说明：下载[Darknet][]：

在自己喜欢的位置解压Darknet，进入Darknet目录并编译：

cd darknet
    make

等待完成即可：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70]  
下载权重文件：`wget https://pjreddie.com/media/files/yolov3.weights`特别慢  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70 1]  
下载完成后，将权重文件yolov3.weights拷贝到Darknet根目录，执行：`./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg`

简单说明：

配置文件路径：cfg/yolov3.cfg

权重文件路径：yolov3.weights

图片问价路径：data/dog.jpg

运行结果：

zhx@zhx:~/Yolo/darknet-master$ ./darknet detect cfg/yolov3.cfg data/yolov3.weights data/dog.jpg
    layer     filters    size              input                output
        0 conv     32  3 x 3 / 1   608 x 608 x   3   ->   608 x 608 x  32  0.639 BFLOPs
        1 conv     64  3 x 3 / 2   608 x 608 x  32   ->   304 x 304 x  64  3.407 BFLOPs
        2 conv     32  1 x 1 / 1   304 x 304 x  64   ->   304 x 304 x  32  0.379 BFLOPs
        3 conv     64  3 x 3 / 1   304 x 304 x  32   ->   304 x 304 x  64  3.407 BFLOPs
        4 res    1                 304 x 304 x  64   ->   304 x 304 x  64
        5 conv    128  3 x 3 / 2   304 x 304 x  64   ->   152 x 152 x 128  3.407 BFLOPs
        6 conv     64  1 x 1 / 1   152 x 152 x 128   ->   152 x 152 x  64  0.379 BFLOPs
        7 conv    128  3 x 3 / 1   152 x 152 x  64   ->   152 x 152 x 128  3.407 BFLOPs
        8 res    5                 152 x 152 x 128   ->   152 x 152 x 128
        9 conv     64  1 x 1 / 1   152 x 152 x 128   ->   152 x 152 x  64  0.379 BFLOPs
       10 conv    128  3 x 3 / 1   152 x 152 x  64   ->   152 x 152 x 128  3.407 BFLOPs
       11 res    8                 152 x 152 x 128   ->   152 x 152 x 128
       12 conv    256  3 x 3 / 2   152 x 152 x 128   ->    76 x  76 x 256  3.407 BFLOPs
       13 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
       14 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
       15 res   12                  76 x  76 x 256   ->    76 x  76 x 256
       16 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
       17 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
       18 res   15                  76 x  76 x 256   ->    76 x  76 x 256
       19 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
       20 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
       21 res   18                  76 x  76 x 256   ->    76 x  76 x 256
       22 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
       23 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
       24 res   21                  76 x  76 x 256   ->    76 x  76 x 256
       25 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
       26 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
       27 res   24                  76 x  76 x 256   ->    76 x  76 x 256
       28 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
       29 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
       30 res   27                  76 x  76 x 256   ->    76 x  76 x 256
       31 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
       32 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
       33 res   30                  76 x  76 x 256   ->    76 x  76 x 256
       34 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
       35 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
       36 res   33                  76 x  76 x 256   ->    76 x  76 x 256
       37 conv    512  3 x 3 / 2    76 x  76 x 256   ->    38 x  38 x 512  3.407 BFLOPs
       38 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
       39 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
       40 res   37                  38 x  38 x 512   ->    38 x  38 x 512
       41 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
       42 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
       43 res   40                  38 x  38 x 512   ->    38 x  38 x 512
       44 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
       45 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
       46 res   43                  38 x  38 x 512   ->    38 x  38 x 512
       47 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
       48 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
       49 res   46                  38 x  38 x 512   ->    38 x  38 x 512
       50 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
       51 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
       52 res   49                  38 x  38 x 512   ->    38 x  38 x 512
       53 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
       54 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
       55 res   52                  38 x  38 x 512   ->    38 x  38 x 512
       56 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
       57 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
       58 res   55                  38 x  38 x 512   ->    38 x  38 x 512
       59 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
       60 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
       61 res   58                  38 x  38 x 512   ->    38 x  38 x 512
       62 conv   1024  3 x 3 / 2    38 x  38 x 512   ->    19 x  19 x1024  3.407 BFLOPs
       63 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
       64 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
       65 res   62                  19 x  19 x1024   ->    19 x  19 x1024
       66 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
       67 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
       68 res   65                  19 x  19 x1024   ->    19 x  19 x1024
       69 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
       70 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
       71 res   68                  19 x  19 x1024   ->    19 x  19 x1024
       72 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
       73 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
       74 res   71                  19 x  19 x1024   ->    19 x  19 x1024
       75 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
       76 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
       77 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
       78 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
       79 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
       80 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
       81 conv    255  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 255  0.189 BFLOPs
       82 yolo
       83 route  79
       84 conv    256  1 x 1 / 1    19 x  19 x 512   ->    19 x  19 x 256  0.095 BFLOPs
       85 upsample            2x    19 x  19 x 256   ->    38 x  38 x 256
       86 route  85 61
       87 conv    256  1 x 1 / 1    38 x  38 x 768   ->    38 x  38 x 256  0.568 BFLOPs
       88 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
       89 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
       90 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
       91 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
       92 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
       93 conv    255  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 255  0.377 BFLOPs
       94 yolo
       95 route  91
       96 conv    128  1 x 1 / 1    38 x  38 x 256   ->    38 x  38 x 128  0.095 BFLOPs
       97 upsample            2x    38 x  38 x 128   ->    76 x  76 x 128
       98 route  97 36
       99 conv    128  1 x 1 / 1    76 x  76 x 384   ->    76 x  76 x 128  0.568 BFLOPs
      100 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
      101 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
      102 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
      103 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
      104 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
      105 conv    255  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 255  0.754 BFLOPs
      106 yolo
    Loading weights from data/yolov3.weights...Done!
    data/dog.jpg: Predicted in 13.315695 seconds.
    dog: 100%
    truck: 92%
    bicycle: 99%
    zhx@zhx:~/Yolo/darknet-master$ ./darknet detect cfg/yolov3.cfg data/yolov3.weights data/dog.jpg

# 二、安装OpenCV #

1.安装cmake（编译器）和依赖库

$ sudo apt-get install cmake #如果已经安装过cmake，则该步骤省略
    $ sudo apt-get install build-essential libgtk2.0-dev libgtk-3-dev libavcodec-dev libavformat-dev libjpeg-dev libswscale-dev libtiff5-dev
    $ sudo apt-get install libgtk2.0-dev pkg-config python-dev python-numpy libdc1394-22 libdc1394-22-dev libjpeg-dev libpng12-dev libtiff5-dev libjasper-dev libavcodec-dev libavformat-dev libswscale-dev libxine2-dev libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev libv4l-dev libtbb-dev libqt4-dev libfaac-dev libmp3lame-dev libopencore-amrnb-dev

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70 2]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70 3]  
2.添加Python3的支持，在终端执行相关命令即可，此处不在截图

# python3支持
    $ sudo apt install python3-dev python3-numpy
    # streamer支持
    $ sudo apt install libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev
    # 可选的依赖
    $ sudo apt install libpng-dev libopenexr-dev libtiff-dev libwebp-dev

３．下载 [OpenCV][] 源文件Sources，并解压缩

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70 4]创建一个build文件夹，并编译

$ mkdir build
    $ cd build/

编译时，OpenCV４默认不使用pkg-config，可以再编译命令中开启生成opencv4.pc文件，支持pkg-config功能，自定义安装位置，方便管理OpenCV(不要遗漏命令末尾的点)

`$ cmake -D CMAKE_BUILD_TYPE=Release -D OPENCV_GENERATE_PKGCONFIG=YES -D CMAKE_INSTALL_PREFIX=/usr/local/opencv4 ..`

开始纠错  
如果出现以下情况，下载失败，可以手动下载[ippicv\_2019\_lnx\_intel64\_general\_20180723.tgz][ippicv_2019_lnx_intel64_general_20180723.tgz]：

其他[版本下载][Link 2]

-- IPPICV: Download: ippicv_2019_lnx_intel64_general_20180723.tgz
    -- Try 1 failed

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70 5]  
下载完成后，修改ippicv.cmake文件，先进入OpenCV根目录在执行：`gedit /3rdparty/ippicv/ippicv.cmake`

在ocv\_download(FILENAME $\{OPENCV\_ICV\_NAME\}上面加入：“set(OPENCV\_IPPICV\_URL “file:///home/zhx/下载/”)”

替换为本地文件路径：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70 6]  
重新执行cmake命令：  
![在这里插入图片描述][20200301195229606.png]  
出现：`-- Looking for ccache - not found`  
解决办法：`sudo apt-get install ccache`  
结果：  
![在这里插入图片描述][20200301214939799.png]

出现：`-- Could NOT find Jasper (missing: JASPER_LIBRARIES JASPER_INCLUDE_DIR)`  
解决办法：

sudo add-apt-repository "deb http://security.ubuntu.com/ubuntu xenial-security main"
    sudo apt update
    sudo apt install libjasper1 libjasper-dev

![在这里插入图片描述][20200301214950207.png]  
出现：  
`--Could not find OpenBLAS include. Turning OpenBLAS_FOUND off -- Could not find OpenBLAS lib. Turning OpenBLAS_FOUND off -- Could NOT find Atlas (missing: Atlas_CBLAS_INCLUDE_DIR Atlas_CLAPACK_INCLUDE_DIR Atlas_CBLAS_LIBRARY Atlas_BLAS_LIBRARY Atlas_LAPACK_LIBRARY)`  
解决办法，依次运行如下命令：

git clone git://github.com/xianyi/OpenBLAS
      cd OpenBLAS
      sudo apt-get install gfortran
      sudo make FC=gfortran
      sudo make install

创建同步文件链接：`sudo ln -s /opt/OpenBLAS/lib/libopenblas.so.0 /usr/lib/libopenblas.so.0`  
![在这里插入图片描述][20200301235532825.png]  
如果出现：`--Could NOT find JNI`  
解决办法：安装Java，并配置环境变量即可，可以参考我另一篇博客  
![在这里插入图片描述][20200302003102417.png]  
如果出现：`VTK is not found.`  
解决办法：

sudo apt install libvtk6-dev python-vtk6
    sudo update-alternatives --install /usr/bin/vtk vtk /usr/bin/vtk6 10
    sudo ln -s /usr/lib/python2.7/dist-packages/vtk/libvtkRenderingPythonTkWidgets.x86_64-linux-gnu.so /usr/lib/x86_64-linux-gnu/libvtkRenderingPythonTkWidgets.so

![在这里插入图片描述][20200302004740404.png]  
如果出现：`-- Checking for module 'libavresample'-- No package 'libavresample' found`  
解决办法：`sudo apt-get install libavresample-dev`  
![在这里插入图片描述][20200302005546723.png]

如果出现：`No package 'libdc1394-2' found`  
解决办法：

sudo apt-get install libdc1394-22
    sudo apt-get install libdc1394-22-dev

![在这里插入图片描述][20200302011856917.png]  
纠错结束

4.进行make编译

在OpenCV根目录下执行：`$ sudo make -j6 # runs 6 jobs in parallel`

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70 7]  
5.进行安装：`$ sudo make install`  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70 8]  
6.配置OpenCV 的 pgk-config环境  
找到：`$ sudo find / -iname opencv4.pc`  
![在这里插入图片描述][20200302014121682.png]  
将/usr/local/OpenCV4/lib/pkgconfig/路径加入PKG\_CONFIG\_PATH：`$ sudo gedit /etc/profile.d/pkgconfig.sh`  
写入以下内容：`export PKG_CONFIG_PATH=/usr/local/OpenCV4/lib/pkgconfig:$PKG_CONFIG_PATH`  
![在这里插入图片描述][20200302014536730.png]  
更新：`$ source /etc/profile`

验证是否配置成功：`$ pkg-config --libs opencv4`

结果如下，配置成功：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70 9]7.配置动态库环境

配置OpenCV动态库环境——程序执行时加载动态库\*.so的路径：`$ sudo gedit /etc/ld.so.conf.d/opencv4.conf`

在该文件（可能是空文件）末尾加上：`/usr/local/OpenCV4/lib`

使配置的路径生效：`$ sudo ldconfig`

8.Python-opencv环境

找到编译好的python cv库：`$sudo find / -iname cv2*.so`，结果如下：

/home/zhx/OpenCV/opencv-4.2.0/build/lib/python3/cv2.cpython-36m-x86_64-linux-gnu.so
    /usr/local/OpenCV4/lib/python3.6/dist-packages/cv2/python-3.6/cv2.cpython-36m-x86_64-linux-gnu.so

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70 10]  
cv2.cpython-35m-x86\_64-linux-gnu.so就是编译好的python3的opencv库  
我们把它复制到对应python解释器的/path/to/dist-packages/(系统自带的python解释器)  
和/path/to/site-packages(用户安装的python解释器)目录下，之后就能在该python解释器中使用python-opencv库

链接到系统自带的python3解释器中：`$ sudo ln -s /usr/local/OpenCV4/lib/python3.6/dist-packages/cv2/python-3.6/cv2.cpython-36m-x86_64-linux-gnu.so /usr/lib/python3/dist-packages/cv2.so`

链接到Anaconda创建的虚拟环境python3解释器中：`ln -s /usr/local/OpenCV4/lib/python3.6/dist-packages/cv2/python-3.6/cv2.cpython-36m-x86_64-linux-gnu.so ~/anaconda3/lib/python3.7/site-packages/cv2.so`

9.测试：

环境配置完成，对我们安装的OpenCV进行测试：

cd 到/opencv-4.0.0/samples/cpp/example\_cmake目录下，查看当前目录内容：  
![在这里插入图片描述][20200302233923641.png]  
通过Makefile测试

因为OpenCV 4.0需要C++11支持，且生成的pkg-config文件名为opencv4.pc，所以需要对当前目录下的Malefile文件进行修改：

$ gedit Makefile

原文如下：

CXX ?= g++
    
    CXXFLAGS += -c -Wall $(shell pkg-config --cflags opencv)
    LDFLAGS += $(shell pkg-config --libs --static opencv)
    
    all: opencv_example
    
    opencv_example: example.o; $(CXX) $< -o $@ $(LDFLAGS)
    
    %.o: %.cpp; $(CXX) $< -o $@ $(CXXFLAGS)
    
    clean: ; rm -f example.o opencv_example

修改为：

CXX ?= g++
     
    CXXFLAGS += -c -std=c++11 -Wall $(shell pkg-config --cflags opencv4)
    LDFLAGS += $(shell pkg-config --libs --static opencv4)
    
    all: opencv_example
    
    opencv_example: example.o; $(CXX) $< -o $@ $(LDFLAGS)
    
    %.o: %.cpp; $(CXX) $< -o $@ $(CXXFLAGS)
    
    clean: ; rm -f example.o opencv_example

当前路径，执行make命令生成可执行文件opencv\_example：

$ make

结果如下：

g++ example.cpp -o example.o -c -std=c++11 -Wall -I/usr/local/opencv4/include/opencv4/opencv -I/usr/local/opencv4/include/opencv4
    g++ example.o -o opencv_example -L/usr/local/opencv4/lib -lopencv_ml -lopencv_dnn -lopencv_video -lopencv_stitching -lopencv_objdetect -lopencv_calib3d -lopencv_features2d -lopencv_highgui -lopencv_videoio -lopencv_imgcodecs -lopencv_flann -lopencv_photo -lopencv_gapi -lopencv_imgproc -lopencv_core -ldl -lm -lpthread -lrt

运行生成的文件opencv\_example：`$ ./opencv_example`

会打开计算机摄像头，并带有Hello OpenCV字样

通过Cmake测试  
因为我们更改了opencv的安装路径（/usr/local/OpenCV4），所以测试前我们需要在CMakeLists.txt文件的14行find\_package(OpenCV REQUIRED)前面加上：`set(OpenCV_DIR /usr/local/OpenCV4/lib/cmake/opencv4)`

修改后文件内容：

# cmake needs this line
    cmake_minimum_required(VERSION 3.1)
    
    # Enable C++11
    set(CMAKE_CXX_STANDARD 11)
    set(CMAKE_CXX_STANDARD_REQUIRED TRUE)
    
    # Define project name
    project(opencv_example_project)
    
    # Find OpenCV, you may need to set OpenCV_DIR variable
    # to the absolute path to the directory containing OpenCVConfig.cmake file
    # via the command line or GUI
    set(OpenCV_DIR /usr/local/OpenCV4/lib/cmake/opencv4)
    find_package(OpenCV REQUIRED)
    
    # If the package has been found, several variables will
    # be set, you can find the full list with descriptions
    # in the OpenCVConfig.cmake file.
    # Print some message showing some of them
    message(STATUS "OpenCV library status:")
    message(STATUS "    config: ${OpenCV_DIR}")
    message(STATUS "    version: ${OpenCV_VERSION}")
    message(STATUS "    libraries: ${OpenCV_LIBS}")
    message(STATUS "    include path: ${OpenCV_INCLUDE_DIRS}")
    
    # Declare the executable target built from your sources
    add_executable(opencv_example example.cpp)
    
    # Link your application with OpenCV libraries
    target_link_libraries(opencv_example PRIVATE ${
        OpenCV_LIBS})

然后当前路径下执行：

$ mkdir build && cd build
    $ cmake ..
    $ make
    $ ./opencv_example

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70 11]弹出和之前一样效果的视频框

10.在Python里引用：

$ python3
    Python 3.5.2 (default, Oct  8 2019, 13:06:37) 
    [GCC 5.4.0 20160609] on linux
    Type "help", "copyright", "credits" or "license" for more information.
    >>> import cv2 as cv
    >>> print(cv.__version__)
    4.2.0

如果报错，缺少`numpy`，就执行`pin3 install numpy`，如果没有pip3，根据提示执行：`sudo apt install python3-pip`  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70 12]

# 三、YoloV3在GPU运行 #

1.进入Darknet路径，修改MakeFile文件：`sudo gedit Makefile`

GPU/CUDNN/OPENCV的值改为1

修改前：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70 13]修改后，如下并保存：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70 14]  
2.找到：

LDFLAGS+= `pkg-config --libs opencv` -lstdc++
    COMMON+= `pkg-config --cflags opencv`

改为：

LDFLAGS+= `pkg-config --libs opencv4` -lstdc++
    COMMON+= `pkg-config --cflags opencv4`

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70 15]  
3.重新编译：

make clean
    make

此时出现这样一个错误：

./src/image_opencv.cpp:12:1: error: ‘IplImage’ does not name a type; did you mean ‘image’?
     IplImage *image_to_ipl(image im)
     ^~~~~~~~
     image
    compilation terminated due to -Wfatal-errors.
    Makefile:86: recipe for target 'obj/image_opencv.o' failed
    make: *** [obj/image_opencv.o] Error 1

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70 16]开始着手解决吧，先找到报错位置：`sudo gedit ./src/image_opencv.cpp`

修改以下两个函数（注释掉的为原代码）：

Mat image_to_mat(image im)
    {
        
        image copy = copy_image(im);
        constrain_image(copy);
        if(im.c == 3) rgbgr_image(copy);
    
    
    
        //IplImage *ipl = image_to_ipl(copy);
        //Mat m = cvarrToMat(ipl, true);
        //cvReleaseImage(&ipl);
        Mat m(cv::Size(im.w,im.h), CV_8UC(im.c));
        int x,y,c;
        
        int step = m.step;
        for(y = 0; y < im.h; ++y){
        
            for(x = 0; x < im.w; ++x){
        
                for(c= 0; c < im.c; ++c){
        
                    float val = im.data[c*im.h*im.w + y*im.w + x];
                    m.data[y*step + x*im.c + c] = (unsigned char)(val*255);
                }
            }
        }
    
        free_image(copy);
        return m;
    }
    
    image mat_to_image(Mat m)
    {
        
        //IplImage ipl = m;
        //image im = ipl_to_image(&ipl);
        
        int h = m.rows;
        int w = m.cols;
        int c = m.channels();
        image im = make_image(w, h, c);
        unsigned char *data = (unsigned char *)m.data;
        int step = m.step;
        int i, j, k;
    
        for(i = 0; i < h; ++i){
        
            for(k= 0; k < c; ++k){
        
                for(j = 0; j < w; ++j){
        
                    im.data[k*w*h + i*w + j] = data[i*step + j*c + k]/255.;
                }
            }
        }
    
        rgbgr_image(im);
        return im;
    }

删除其余所有包含IplImage的函数

在把以：CV\_ 开头的变量，去掉CV\_，新版本不带CV\_

最终修改后的image\_opencv.cpp代码如下：

#ifdef OPENCV
    
    #include "stdio.h"
    #include "stdlib.h"
    #include "opencv2/opencv.hpp"
    #include "image.h"
    
    using namespace cv;
    
    extern "C" {
        
    
    /*
    IplImage *image_to_ipl(image im)
    {
        
        int x,y,c;
        IplImage *disp = cvCreateImage(cvSize(im.w,im.h), IPL_DEPTH_8U, im.c);
        int step = disp->widthStep;
        for(y = 0; y < im.h; ++y){
        
            for(x = 0; x < im.w; ++x){
        
                for(c= 0; c < im.c; ++c){
        
                    float val = im.data[c*im.h*im.w + y*im.w + x];
                    disp->imageData[y*step + x*im.c + c] = (unsigned char)(val*255);
                }
            }
        }
        return disp;
    }
    
    
    image ipl_to_image(IplImage* src)
    {
        
        int h = src->height;
        int w = src->width;
        int c = src->nChannels;
        image im = make_image(w, h, c);
        unsigned char *data = (unsigned char *)src->imageData;
        int step = src->widthStep;
        int i, j, k;
    
        for(i = 0; i < h; ++i){
        
            for(k= 0; k < c; ++k){
        
                for(j = 0; j < w; ++j){
        
                    im.data[k*w*h + i*w + j] = data[i*step + j*c + k]/255.;
                }
            }
        }
        return im;
    }
    */
    
    Mat image_to_mat(image im)
    {
        
        image copy = copy_image(im);
        constrain_image(copy);
        if(im.c == 3) rgbgr_image(copy);
    
    
    
        //IplImage *ipl = image_to_ipl(copy);
        //Mat m = cvarrToMat(ipl, true);
        //cvReleaseImage(&ipl);
        Mat m(cv::Size(im.w,im.h), CV_8UC(im.c));
        int x,y,c;
    
        int step = m.step;
        for(y = 0; y < im.h; ++y){
        
            for(x = 0; x < im.w; ++x){
        
                for(c= 0; c < im.c; ++c){
        
                    float val = im.data[c*im.h*im.w + y*im.w + x];
                    m.data[y*step + x*im.c + c] = (unsigned char)(val*255);
                }
            }
        }
    
        free_image(copy);
        return m;
    }
    
    image mat_to_image(Mat m)
    {
        
        //IplImage ipl = m;
        //image im = ipl_to_image(&ipl);
        
        int h = m.rows;
        int w = m.cols;
        int c = m.channels();
        image im = make_image(w, h, c);
        unsigned char *data = (unsigned char *)m.data;
        int step = m.step;
        int i, j, k;
    
        for(i = 0; i < h; ++i){
        
            for(k= 0; k < c; ++k){
        
                for(j = 0; j < w; ++j){
        
                    im.data[k*w*h + i*w + j] = data[i*step + j*c + k]/255.;
                }
            }
        }
    
        rgbgr_image(im);
        return im;
    }
    
    void *open_video_stream(const char *f, int c, int w, int h, int fps)
    {
        
        VideoCapture *cap;
        if(f) cap = new VideoCapture(f);
        else cap = new VideoCapture(c);
        if(!cap->isOpened()) return 0;
        //if(w) cap->set(CV_CAP_PROP_FRAME_WIDTH, w);
        //if(h) cap->set(CV_CAP_PROP_FRAME_HEIGHT, w);
        //if(fps) cap->set(CV_CAP_PROP_FPS, w);
        if(w) cap->set(CAP_PROP_FRAME_WIDTH, w);
        if(h) cap->set(CAP_PROP_FRAME_HEIGHT, w);
        if(fps) cap->set(CAP_PROP_FPS, w);
        return (void *) cap;
    }
    
    image get_image_from_stream(void *p)
    {
        
        VideoCapture *cap = (VideoCapture *)p;
        Mat m;
        *cap >> m;
        if(m.empty()) return make_empty_image(0,0,0);
        return mat_to_image(m);
    }
    
    image load_image_cv(char *filename, int channels)
    {
        
        int flag = -1;
        if (channels == 0) flag = -1;
        else if (channels == 1) flag = 0;
        else if (channels == 3) flag = 1;
        else {
        
            fprintf(stderr, "OpenCV can't force load with %d channels\n", channels);
        }
        Mat m;
        m = imread(filename, flag);
        if(!m.data){
        
            fprintf(stderr, "Cannot load image \"%s\"\n", filename);
            char buff[256];
            sprintf(buff, "echo %s >> bad.list", filename);
            system(buff);
            return make_image(10,10,3);
            //exit(0);
        }
        image im = mat_to_image(m);
        return im;
    }
    
    int show_image_cv(image im, const char* name, int ms)
    {
        
        Mat m = image_to_mat(im);
        imshow(name, m);
        int c = waitKey(ms);
        if (c != -1) c = c%256;
        return c;
    }
    
    void make_window(char *name, int w, int h, int fullscreen)
    {
        
        namedWindow(name, WINDOW_NORMAL); 
        if (fullscreen) {
        
            //setWindowProperty(name, CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);
            setWindowProperty(name, WND_PROP_FULLSCREEN, WINDOW_FULLSCREEN);
        } else {
        
            resizeWindow(name, w, h);
            if(strcmp(name, "Demo") == 0) moveWindow(name, 0, 0);
        }
    }
    
    }
    
    #endif

再次编译：

make clean
    make

通过：

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70 17]进行YoloV3测试：`./darknet detect cfg/yolov3.cfg data/yolov3.weights data/dog.jpg`

报错：  
`CUDA Error: out of memory darknet: ./src/cuda.c:36: check_error: Assertion 0 failed. 已放弃 (核心已转储)`

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70 18]  
出现这问题的原因就是GPU内存不够大，解决方法就是减小内存的使用：

修改cfg/yolov3.cfg如下：

[net]
    # Testing
    batch=1                   //取消注释
    subdivisions=1            //取消注释
    # Training
    batch=64			     //可以添加注释
    subdivisions=16			 //可以添加注释
    width=608                //可以适当减小width
    height=608               //可以适当减小height

修改后：  
![在这里插入图片描述][20200304223705643.png]

运行通过：

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70 19]

至此，结束结束结束，如有错误，欢迎指正

[Link 1]: https://pjreddie.com/darknet/yolo/
[Darknet]: https://github.com/pjreddie/darknet
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/20200301005220507.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ==,size_16,color_FFFFFF,t_70
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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70 3]: https://img-blog.csdnimg.cn/20200301143532308.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ==,size_16,color_FFFFFF,t_70
[OpenCV]: https://opencv.org/releases/
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70 4]: https://img-blog.csdnimg.cn/20200301150104339.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ==,size_16,color_FFFFFF,t_70
[ippicv_2019_lnx_intel64_general_20180723.tgz]: https://github.com/opencv/opencv_3rdparty/blob/ippicv/master_20180723/ippicv/ippicv_2019_lnx_intel64_general_20180723.tgz
[Link 2]: https://github.com/opencv/opencv_3rdparty/tree/ippicv/master_20180723/ippicv
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ_size_16_color_FFFFFF_t_70 5]: https://img-blog.csdnimg.cn/20200301172646458.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjE3NjYzOQ==,size_16,color_FFFFFF,t_70
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[20200301195229606.png]: https://img-blog.csdnimg.cn/20200301195229606.png
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[20200301214950207.png]: https://img-blog.csdnimg.cn/20200301214950207.png
[20200301235532825.png]: https://img-blog.csdnimg.cn/20200301235532825.png
[20200302003102417.png]: https://img-blog.csdnimg.cn/20200302003102417.png
[20200302004740404.png]: https://img-blog.csdnimg.cn/20200302004740404.png
[20200302005546723.png]: https://img-blog.csdnimg.cn/20200302005546723.png
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[20200302014121682.png]: https://img-blog.csdnimg.cn/20200302014121682.png
[20200302014536730.png]: https://img-blog.csdnimg.cn/20200302014536730.png
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