About

This repository contains Python testing code for words detection, which (including this README) is based on the awesome py-faster-rcnn repository.

License

It is released under the MIT License (as of py-faster-rcnn. Refer to the LICENSE file for details).

Contents

1. Requirements: software
2. Requirements: hardware
3. Basic installation
4. Demo

Requirements: software

1. See requirements for Caffe and pycaffe (Caffe installation instructions)

Note: Caffe must be built with support for Python layers!

# In your Makefile.config, make sure to have this line uncommented
WITH_PYTHON_LAYER := 1
# Unrelatedly, it's also recommended that you use CUDNN
USE_CUDNN := 1

There is a 'Makefile.config' provided for the convenience.

2. Python packages you might not have: cython, python-opencv, easydict

Requirements: hardware

It is strongly recommened to use a morden GPU (e.g., Titan or K40).

Installation

1. Clone the repository

git clone https://github.com/playerkk/map-words-faster-rcnn

2. Build the Cython modules

   # We'll call the directory that you cloned this repository `FRCN_ROOT`. 
   cd $FRCN_ROOT/lib
   make

3. Build Caffe and pycaffe

   cd $FRCN_ROOT/caffe-fast-rcnn
   # Now follow the Caffe installation instructions here:
   #   http://caffe.berkeleyvision.org/installation.html
   
   # If you're experienced with Caffe and have all of the requirements installed
   # and your Makefile.config in place, then simply do:
   make -j8 && make pycaffe

4. Download the pre-computed Faster R-CNN detector

   cd $FRCN_ROOT
   ./pre-trained-models/fetch_pre_trained_models.sh

   This will download a pre-trained words detection model into the `pre-trained-models' folder.

Demo

After successfully completing basic installation, you'll be ready to run the demo.

To run the demo

cd $FRCN_ROOT
./tools/demo.py

The demo performs words detection using a pre-trained VGG16 network. By default, it will use the first GPU on your machine. You can use the '--gpu' flag to specify another one. If you don't have a GPU, you can run the demo using the CPU

./toos/demo.py --cpu

See the detection results in the 'output' folder.