YOLO+yolo9000配置使用darknet

• Installing Darknet

1.直接設置使用，編譯通過

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

2. 下載權重測試

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

3.測試結果：

dsp@dsp:/media/dsp/學習/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

- 區分上下連個命令：

 ./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 結果：

4.設置GPU和opencv

GPU=1
OPENCV=1

-編譯錯誤

/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安裝的問題，於是重新安裝

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這篇文章中也遇到了這個錯誤. 可以在cmake時加上"cmake -DINSTALL_CREATE_DISTRIB=ON" 這句,就不會有這個錯誤了.

 - 首先卸載opencv，然后在安裝 ：卸載參考：Ubuntu16.04 上openCV的卸載與 opencv3.0.2安裝記錄

cd /home/hy/opencv/build
make uninstall//卸載掉配置路徑中的文件
sudo rm -r build//刪除build文件
//刪除掉環境中有關的其余包
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*

-安裝，安裝有圖割錯誤，以前解決辦法

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. 在編譯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

//權限問題，sudo即可解決
這個可能是權限問題，采用以下指令：
sudo su；
一切都能順利解決。

6. 運行yolo和yolo9000，可以實現demo運行，並實現視頻檢測

• philipperemy/yolo-9000

YOLOv2是Joseph Redmon提出的針對YOLO算法不足的改進版本，作者使用了一系列的方法對原來的YOLO多目標檢測框架進行了改進，在保持原有速度的優勢之下，精度上得以提升，此外作者提出了一種目標分類與檢測的聯合訓練方法，通過這種方法YOLO9000可以同時在COCO和ImageNet數據集中進行訓練，訓練后的模型可以實現多達9000種物體的實時檢測。

• 第一種：為darknet添加Python接口

  Github：https://github.com/SidHard/py-yolo2

• 第二種：使用keras

  Github：https://github.com/allanzelener/YAD2K