niuchuangnn/Aurora-ASI-KLS

first commit

Weakly Supervised Semantic Segmentation for Join Key Local Structure Localization and Classification of Aurora Image

By Chuang Niu, Jun Zhang, Qian Wang, and Jimin Liang

Introduction

A weakly supervised semantic segmentation method for joint KLS localization and classification of aurora image is implemented in this project, and the related paper is submitted to TGRS. More details will be descibed.

Installation(Tested on x64 Unbuntu 14.04 environment)

Requirements

This project is based on:

• selective search of python version
• fast-rcnn

It is noted that the original codes of selective search and fast-rcnn are not directly uesd by this project, but you must make sure that they can run normally before implementation of this project.

Get started

1. Get the code. We will call the directory that you cloned Aurora-ASI-KLS into $KLS_ROOT

git clone https://github.com/niuchuangnn/Aurora-ASI-KLS
cd $KLS_ROOT/selective_search_py
wget http://cs.brown.edu/~pff/segment/segment.zip; unzip segment.zip; rm segment.zip
cmake .
make

cd &KLS_ROOT/fast-rcnn/caffe-fast-rcnn
make all
make pycaffe
cd &KLS_ROOT/fast-rcnn/lib
make

2. Download the region detection model: vcc_cnn_m_fast_rcnn_b500_iter_10000.caffemodel

cd $KLS_ROOT/Data
mkdir -p region_classification/output

Put the downloaded model into this folder.

3. Run demo.

cd $KLS_ROOT/src/demo
python demo.py

You will see:

classification time: 1.2338631897
segmentation time: 1.77530801296