Ubuntu14.04 + Text-Detection-with-FRCN(CPU)

操作系统：

yt@yt-MS-:~$ cat /etc/issue

Ubuntu 14.04. LTS \n \l

Python版本：

yt@yt-MS-:~$ python --version

Python 2.7.

pip版本：

yt@yt-MS-:~$ pip --version

pip 1.5. from /usr/lib/python2./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.

numpy>=1.7.

scipy>=0.13.

scikit-image>=0.9.

matplotlib>=1.3.

ipython>=3.0.

h5py>=2.2.

leveldb>=0.191

networkx>=1.8.

nose>=1.3.

pandas>=0.12.

python-dateutil>=1.4,<

protobuf>=2.5.

python-gflags>=2.0

pyyaml>=3.10

Pillow>=2.3.

six>=1.1.

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 :=

WITH_PYTHON_LAYER :=

# 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. \

                  /usr/lib64/python2./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('/')[-] + "_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