操作系统：
yt@yt-MS-7816:~$ cat /etc/issue Ubuntu 14.04.4 LTS l
Python版本：
yt@yt-MS-7816:~$ python --version Python 2.7.6
pip版本：
yt@yt-MS-7816:~$ pip --version pip 1.5.4 from /usr/lib/python2.7/dist-packages (python 2.7)
源文件：
git clone --recursive https://github.com/jugg1024/Text-Detection-with-FRCN.git
1. 安装Caffe需要的依赖包：
sudo apt-get install build-essential # basic requirement
sudo apt-get install libblas-dev libopenblas-base liblapack-dev libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler #required by caffe
将caffe-fast-rcnn/python目录下的requirements下的依赖都装一遍：
cd Text-Detection-with-FRCN/py-faster-rcnn/caffe-fast-rcnn/python cat requirements.txt for req in $(cat requirements.txt); do pip install $req; done
Cython>=0.19.2 numpy>=1.7.1 scipy>=0.13.2 scikit-image>=0.9.3 matplotlib>=1.3.1 ipython>=3.0.0 h5py>=2.2.0 leveldb>=0.191 networkx>=1.8.1 nose>=1.3.0 pandas>=0.12.0 python-dateutil>=1.4,<2 protobuf>=2.5.0 python-gflags>=2.0 pyyaml>=3.10 Pillow>=2.3.0 six>=1.1.0
这里有一个小技巧，因为pip这个工具对应的网络非常的烂，这个时候，可以将其改为国内的镜像网站，速度将提升几个数量级，方法如下：
sudo pip install ipython -i http://pypi.douban.com/simple
如果出现有依赖包安装失败可以使用这种形式安装
sudo apt-get install python-matplotlib
除此之外还有以下依赖包：
sudo pip install easydict sudo pip install opencv_python
2. 编译
编译 py-faster-rcnn
2.1 change the branch of py-faster-rcnn to text-detection-demo.
cd Text-Detection-with-FRCN/py-faster-rcnn
git checkout text-detection
2.2 Build Caffe and pycaffe.
cd Text-Detection-with-FRCN/py-faster-rcnn/caffe-fast-rcnn cp Makefile.config.example Makefile.config
修改 Makefile.config
CPU_ONLY := 1 WITH_PYTHON_LAYER := 1
# 以下可选择性改变 BLAS_INCLUDE := /usr/include/atlas-x86_64-base BLAS_LIB := /usr/lib64/atlas PYTHON_INCLUDE := /usr/include/python2.7 /usr/lib64/python2.7/site-packages/numpy/core/include PYTHON_LIB := /usr/lib64 # Whatever else you find you need goes here. INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/include LIBRARY_DIRS := $(PYTHON_LIB) /usr/lib64
编译(也可以直接 make , make pycaffe )
make -j16 && make pycaffe # here only python api is used.
测试下
cd python
python >>> import caffe >>> caffe.__version__ '1.0.0-rc3'
2.2 Build the Cython modules.
cd Text-Detection-with-FRCN/py-faster-rcnn/lib
修改 setup.py 把所以关于gpu的部分注释掉
# 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']] # ),
修改 ./fast_rcnn/nms_wrapper.py
#from nms.gpu_nms import gpu_nms def nms(dets, thresh, force_cpu=True):
修改 ./fast_rcnn/config.py
__C.USE_GPU_NMS = False
修改 py-faster-rcnn/tools/test_net.py和 py-faster-rcnn/tools/train_net.py
将 caffe.set_mode_gpu()修改为caffe.set_mode_cpu().
编译
make
3. Run demo
- Run text detection demo
1. 下载训练好的模型,解压放到 Text-Detection-with-FRCN/models
URL: http://pan.baidu.com/s/1dE2Ori5 Extract Code: phxk
2. run demo，检测结果会保存在Text-Detection-with-FRCN/output_img
cd Text-Detection-with-FRCN/ ./script/text_detect_demo.sh
3. training, if you think the model is not ok, then you can trainning with your own dataset, take coco-text for example.
3.1 download coco-text dataset
cd Text-Detection-with-FRCN/datasets/script ./fetch_dataset.sh coco-text # download it takes long! # ensure you have both data and label # for coco-text label is in COCO-text.json, and data is in train2014.zip
3.2 download pre-train model
# finetune on this model, you can also use one model you train before cd Text-Detection-with-FRCN/py-faster-rcnn ./data/scripts/fetch_imagenet_models.sh # download it takes long!
3.3 format the data(you should write your code here)
# format the raw image and label into the type of pascal_voc # follow the code in $Text-Detection-with-FRCN/datasets/script/format_annotation.py cd Text-Detection-with-FRCN/datasets/script ./format_annotation.py --dataset coco-text
3.4 create a softlink the formatted data to working directorry
# link your data folder to train_data cd Text-Detection-with-FRCN/datasets/ ln -s train_data coco-text # $YOUR_DATA
3.5 training
cd Text-Detection-with-FRCN/py-faster-rcnn/ ./experiments/scripts/faster_rcnn_end2end.sh [gpu-id] [net](VGG16) [label_type](must be pascal_voc)
- Run text detection demo
1. 下载训练好的模型,解压放到 Text-Detection-with-FRCN/py-faster-rcnn/data/faster_rcnn_models
cd Text-Detection-with-FRCN/py-faster-rcnn/data/scripts ./ fetch_faster_rcnn_models.sh
2. 修改 demo.py, 添加保存输出语句
cd Text-Detection-with-FRCN/py-faster-rcnn/tools
def demo(net, image_name):
'''''' output_dir = os.path.join(cfg.ROOT_DIR, '..', 'output_img', image_name.split('/')[-1] + "_detect_rst.jpg") plt.savefig(output_dir)3. run demo，检测结果会保存在Text-Detection-with-FRCN/output_img
cd Text-Detection-with-FRCN/py-faster-rcnn/tools ./demo.py --cpu
参考：
- http://www.cnblogs.com/justinzhang/p/5386837.html
- http://blog.sina.com.cn/s/blog_679f93560102wpyf.html
- http://blog.csdn.net/wuzuyu365/article/details/5189525
- http://blog.csdn.net/u011762313/article/details/47262549