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YOLO+yolo9000配置使用darknet

1.直接设置使用，编译通过

git clone https://github.com/pjreddie/darknet.git
cd darknet
make

2. 下载权重测试

    wget http://pjreddie.com/media/files/yolo.weights  
    ./darknet yolo test cfg/yolo.cfg yolo.weights data/dog.jpg

3.测试结果：

dsp@dsp:/media/dsp/学习/yolo_darknet/darknet$ ./darknet yolo test cfg/yolo.cfg yolo.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
    1 max          2 x 2 / 2   608 x 608 x  32   ->   304 x 304 x  32
    2 conv     64  3 x 3 / 1   304 x 304 x  32   ->   304 x 304 x  64
    3 max          2 x 2 / 2   304 x 304 x  64   ->   152 x 152 x  64
    4 conv    128  3 x 3 / 1   152 x 152 x  64   ->   152 x 152 x 128
    5 conv     64  1 x 1 / 1   152 x 152 x 128   ->   152 x 152 x  64
    6 conv    128  3 x 3 / 1   152 x 152 x  64   ->   152 x 152 x 128
    7 max          2 x 2 / 2   152 x 152 x 128   ->    76 x  76 x 128
    8 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256
    9 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128
   10 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256
   11 max          2 x 2 / 2    76 x  76 x 256   ->    38 x  38 x 256
   12 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512
   13 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256
   14 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512
   15 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256
   16 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512
   17 max          2 x 2 / 2    38 x  38 x 512   ->    19 x  19 x 512
   18 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024
   19 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512
   20 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024
   21 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512
   22 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024
   23 conv   1024  3 x 3 / 1    19 x  19 x1024   ->    19 x  19 x1024
   24 conv   1024  3 x 3 / 1    19 x  19 x1024   ->    19 x  19 x1024
   25 route  16
   26 conv     64  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x  64
   27 reorg              / 2    38 x  38 x  64   ->    19 x  19 x 256
   28 route  27 24
   29 conv   1024  3 x 3 / 1    19 x  19 x1280   ->    19 x  19 x1024
   30 conv    425  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 425
   31 detection
mask_scale: Using default '1.000000'
Loading weights from yolo.weights...Done!
data/dog.jpg: Predicted in 9.566333 seconds.
Not compiled with OpenCV, saving to predictions.png instead

- 区分上下连个命令：

 ./darknet yolo test cfg/yolo.cfg yolo.weights data/dog.jpg  
./darknet detect cfg/yolo.cfg yolo.weights data/dog.jpg

-/darknet detect cfg/yolo.cfg yolo.weights data/dog.jpg 结果：

4.设置GPU和opencv

GPU=1
OPENCV=1

-编译错误

/usr/bin/ld: cannot find -lippicv
collect2: error: ld returned 1 exit status
Makefile:76: recipe for target 'darknet' failed
make: *** [darknet] Error 1
/usr/bin/ld: cannot find -make: *** Waiting for unfinished jobs....
lippicv
collect2: error: ld returned 1 exit status
Makefile:82: recipe for target 'libdarknet.so' failed
make: *** [libdarknet.so] Error 1

 - opencv安装的问题，于是重新安装

https://github.com/pjreddie/darknet/issues/290:  /usr/bin/ld: cannot find -lippicv collect2: error: ld returned 1 exit status

https://github.com/opencv/opencv/issues/5852这篇文章中也遇到了这个错误. 可以在cmake时加上"cmake -DINSTALL_CREATE_DISTRIB=ON" 这句,就不会有这个错误了.

 - 首先卸载opencv，然后在安装 ：卸载参考：Ubuntu16.04 上openCV的卸载与 opencv3.0.2安装记录 

cd /home/hy/opencv/build
make uninstall//卸载掉配置路径中的文件
sudo rm -r build//删除build文件
//删除掉环境中有关的其余包
sudo rm -r /usr/local/include/opencv2 /usr/local/include/opencv /usr/include/opencv /usr/include/opencv2 /usr/local/share/opencv /usr/local/share/OpenCV /usr/share/opencv /usr/share/OpenCV /usr/local/bin/opencv* /usr/local/lib/libopencv*

-安装，安装有图割错误，以前解决办法

dsp@dsp:~/opencv-3.1.0/build$ sudo cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local .. -DWITH_IPP=ON -DINSTALL_CREATE_DISTRIB=ON

dsp@dsp:~/opencv-3.1.0/build$ sudo make -j$(nproc)

5. 在编译darknet

dart -lcublas -lcurand -lcudnn -lstdc++  libdarknet.a
/usr/local/lib/libopencv_imgcodecs.so: undefined reference to `TIFFReadRGBAStrip@LIBTIFF_4.0'
/usr/local/lib/libopencv_imgcodecs.so: undefined reference to `TIFFReadDirectory@LIBTIFF_4.0'
/usr/local/lib/libopencv_imgcodecs.so: undefined reference to `TIFFWriteEncodedStrip@LIBTIFF_4.0'
/usr/local/lib/libopencv_imgcodecs.so: undefined reference to `TIFFIsTiled@LIBTIFF_4.0'
/usr/local/lib/libopencv_imgcodecs.so: undefined reference to `TIFFWriteScanline@LIBTIFF_4.0'
/usr/local/lib/libopencv_imgcodecs.so: undefined reference to `TIFFGetField@LIBTIFF_4.0'
/usr/local/lib/libopencv_imgcodecs.so: undefined reference to `TIFFNumberOfStrips@LIBTIFF_4.0'
/usr/local/lib/libopencv_imgcodecs.so: undefined reference to `TIFFScanlineSize@LIBTIFF_4.0'
/usr/local/lib/libopencv_imgcodecs.so: undefined reference to `TIFFReadEncodedTile@LIBTIFF_4.0'
/usr/local/lib/libopencv_imgcodecs.so: undefined reference to `TIFFReadRGBATile@LIBTIFF_4.0'
/usr/local/lib/libopencv_imgcodecs.so: undefined reference to `TIFFClose@LIBTIFF_4.0'
/usr/local/lib/libopencv_imgcodecs.so: undefined reference to `TIFFRGBAImageOK@LIBTIFF_4.0'
/usr/local/lib/libopencv_imgcodecs.so: undefined reference to `TIFFOpen@LIBTIFF_4.0'
/usr/local/lib/libopencv_imgcodecs.so: undefined reference to `TIFFReadEncodedStrip@LIBTIFF_4.0'
/usr/local/lib/libopencv_imgcodecs.so: undefined reference to `TIFFSetField@LIBTIFF_4.0'
/usr/local/lib/libopencv_imgcodecs.so: undefined reference to `TIFFSetWarningHandler@LIBTIFF_4.0'
/usr/local/lib/libopencv_imgcodecs.so: undefined reference to `TIFFSetErrorHandler@LIBTIFF_4.0'
collect2: error: ld returned 1 exit status
Makefile:76: recipe for target 'darknet' failed
make: *** [darknet] Error 1

//权限问题，sudo即可解决
这个可能是权限问题，采用以下指令：
sudo su；
一切都能顺利解决。

6. 运行yolo和yolo9000，可以实现demo运行，并实现视频检测

YOLOv2是Joseph Redmon提出的针对YOLO算法不足的改进版本，作者使用了一系列的方法对原来的YOLO多目标检测框架进行了改进，在保持原有速度的优势之下，精度上得以提升，此外作者提出了一种目标分类与检测的联合训练方法，通过这种方法YOLO9000可以同时在COCO和ImageNet数据集中进行训练，训练后的模型可以实现多达9000种物体的实时检测。
posted @ 2017-12-19 21:04  ranjiewen  阅读(9047)  评论(0)  编辑  收藏  举报