ubuntu16.04+py-faster-rcnn+ZF运行demo.py

1.安装Caffe所有依赖包，由于网络情况，有时候会安装失败，一般重复输入命令，再次安装即可。这里把所有依赖包分开安装便于查看是哪个未安装成功。

$ sudo apt-get install libprotobuf-dev 
$ sudo apt-get install libleveldb-dev
$ sudo apt-get install libsnappy-dev
$ sudo apt-get install libopencv-dev
$ sduo apt-get install libhdf5-serial-dev
$ sudo apt-get install protobuf-compiler
$ sudo apt-get install --noinstall-recommends libboost-all-dev
$ sudo apt-get install libatlas-base-dev
$ sudo apt-get install python-dev
$ sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

2.安装git

$ sudo apt-get install git

3.安装OpenCV

•    github上下载OpenCV  

$ git clone https://github.com/jayrambhia/Install-OpenCV

• 　　对脚本增加可执行权限

$ cd Ubuntu/2.4
$ sudo chmod +x *.sh  

• 　　安装依赖项

$ cd ..
$ sudo ./dependencies.sh

• 安装opencv

$ sudo sh ./opencv2_4_10.sh  

　4.配置caffe

• github上下载caffe

$ git clone https://github.com/bvlc/caffe.git

• 复制一份Makefile.config文件，并修改参数(CPU模式)

$ cd caffe
$ cp Makefile.config.example Makefile.config

• 修改结果：

CPU_ONLY :=1  

注释掉CUDA有关的行：  
#CUDA_DIR := /usr/local/cuda  
#CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \  
#        -gencode arch=compute_20,code=sm_21 \  
#        -gencode arch=compute_30,code=sm_30 \  
#        -gencode arch=compute_35,code=sm_35 \  
#        -gencode arch=compute_50,code=sm_50 \  
#        -gencode arch=compute_50,code=compute_50  

WITH_PYTHON_LAYER := 1  

INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
  
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/i386-linux-gnu/hdf5/serial /usr/lib/x86_64-linux-gnu /usr/lib/x86_64-linux-gnu/hdf5/serial  

#TEST_GPUID := 0  

• 编译

$ make all
$ make test
$ make runtest
$ make pycaffe

• 编译遇到问题：

• 解决办法：

$ sudo find / -name 'Python.h'

• 将Makefile.config文件中PYTHON_LIB的路径替换为找到的‘usr/include’
• 重新编译，然后：

$cd caffe/python  
$python  
>>>import caffe  

成功

5.配置py-faster-rcnn

•  github上下载py-faster-rcnn

$ git clone --recursive  https://github.com/rbgirshick/py-faster-rcnn.git  

• 安装pip

$ sudo apt-get install python-pip

• 安装cython, python-opencv,easydict

$ pip install cython  
$ pip install easydict  
$ apt-get install python-opencv 

• 修改cython的setup.py文件，编译

$ cd py-faster-rcnn/lib/
$ make

• 修改结果

#CUDA = locate_cuda()  
#self.set_executable('compiler_so', CUDA['nvcc'])  
    #Extension('nms.gpu_nms',  
        #['nms/nms_kernel.cu', 'nms/gpu_nms.pyx'],  
        #library_dirs=[CUDA['lib64']],  
        #libraries=['cudart'],  
        #language='c++',  
        #runtime_library_dirs=[CUDA['lib64']],  
        # this syntax is specific to this build system  
        # we're only going to use certain compiler args with nvcc and not with  
        # gcc the implementation of this trick is in customize_compiler() below  
        #extra_compile_args={'gcc': ["-Wno-unused-function"],  
        #                    'nvcc': ['-arch=sm_35',  
        #                             '--ptxas-options=-v',  
        #                             '-c',  
        #                             '--compiler-options',  
        #                             "'-fPIC'"]},  
        #include_dirs = [numpy_include, CUDA['include']]  
    #),  

• 编译py-faster-rcnn
• 参照配置caffe过程修改Makefile.config文件
• 修改CMakeLists.txt

caffe_option(CPU_ONLY  "Build Caffe without CUDA support" ON) 

• 编译

$ cd ~/py-faster-rcnn/caffe-fast-rcnn  
$ make 
$ make pycaffe  

• 下载测试用的数据集

$ cd ~/py-faster-rcnn  
$ ./data/scripts/fetch_faster_rcnn_models.sh  

• 修改/py-faster-rcnn/lib/fast_rcnn/config.py文件

# Use GPU implementation of non-maximum suppression
__C.USE_GPU_NMS = False

• 修改/py-faster-rcnn/tools/test_net.py和 /py-faster-rcnn/tools/train_net.py

caffe.set_mode_cpu()//原为caffe.set_mode_gpu()

• 修改/py-faster-rcnn/lib/fast_rcnn/nms_wrapper.py文件

def nms(dets, thresh, force_cpu=True):

• 运行demo.py

$ cd ~/py-faster-rcnn  
$ ./tools/demo.py --cpu  

• 遇到问题

默认载入的模型是VGG16网络，可能出现内存不足，如果运行过程中出现kill，需要把模型换成ZF。 

$ ./tools/demo.py --cpu  --net zf　

成功~