SLDC, a generic framework for object detection and classification in large images.
Project description
# SLDC
**_SLDC_** is a framework created for accelerating development of large image analysis workflows. It is especially well
suited for solving more or less complex problems of object detection and classification in multi-gigapixel images.
The framework encapsulates problem-independent logic such as parallelism, memory constraints (due to large image handling)
while providing a concise way of declaring problem-dependent components of the implementer's workflows.
[![Build status](https://travis-ci.org/waliens/sldc.svg?branch=master)](https://travis-ci.org/waliens/sldc)
[![codecov](https://codecov.io/gh/waliens/sldc/branch/master/graph/badge.svg)](https://codecov.io/gh/waliens/sldc)
## Documentation
The algorithm used by the framework as well as some toy examples are presented in the [Wiki](https://github.com/waliens/sldc/wiki).
## Dependencies
The framework currently works under Python 2.7 and 3.5.
The required dependencies are the following :
* Numpy (>= 1.10, might work with earlier versions)
* OpenCV (>= 3.0)
* Pillow (>= 3.1.1)
* joblib (>= 0.9.4)
* Shapely (>= 1.5.13)
* Scipy (>= 0.18.1)
## Install
### With anaconda environment
1) Install Anaconda/Miniconda: https://docs.continuum.io/anaconda/install
2) Set up the environment (replace `__PY_VERSION__` by either `2.7` or `3.5` according to the python version you want to use). For instance, with anaconda:
```bash
# Create envrionment and install packages
conda create -n sldc python=__PY_VERSION__ pillow numpy joblib shapely opencv scipy scikit-image
# Activate environment
source activate sldc
```
3) Install sldc
+ Download the sources
+ Either by cloning the repository: `git clone https://github.com/waliens/sldc.git`
+ Or by downloading an archive: `https://github.com/waliens/sldc/archive/master.zip`
+ Move to the _SLDC_ sources root folder
+ Install sldc: `python setup.py install`
4) Check your install by running `python -c "import sldc"`
## Bindings
The library is image format agnostic and therefore allows you to integrate it with any existing image format by implementing some interfaces. However, some bindings were implemented for integrating SLDC with:
+ [Cytomine](http://www.cytomine.be/): [`cytomine-sldc` repository](https://github.com/cytomine/Cytomine-python-datamining/tree/master/cytomine-datamining/algorithms/sldc)
+ [OpenSlide](http://openslide.org/): [`sldc-openslide` repository](https://github.com/waliens/sldc-openslide)
## References
If you use _SLDC_ in a scientific publication, we would appreciate citations: [Mormont & al., Benelearn, 2016](http://orbi.ulg.ac.be/handle/2268/202624).
The framework was initially developed in the context of [this master thesis](http://hdl.handle.net/2268.2/1314).
**_SLDC_** is a framework created for accelerating development of large image analysis workflows. It is especially well
suited for solving more or less complex problems of object detection and classification in multi-gigapixel images.
The framework encapsulates problem-independent logic such as parallelism, memory constraints (due to large image handling)
while providing a concise way of declaring problem-dependent components of the implementer's workflows.
[![Build status](https://travis-ci.org/waliens/sldc.svg?branch=master)](https://travis-ci.org/waliens/sldc)
[![codecov](https://codecov.io/gh/waliens/sldc/branch/master/graph/badge.svg)](https://codecov.io/gh/waliens/sldc)
## Documentation
The algorithm used by the framework as well as some toy examples are presented in the [Wiki](https://github.com/waliens/sldc/wiki).
## Dependencies
The framework currently works under Python 2.7 and 3.5.
The required dependencies are the following :
* Numpy (>= 1.10, might work with earlier versions)
* OpenCV (>= 3.0)
* Pillow (>= 3.1.1)
* joblib (>= 0.9.4)
* Shapely (>= 1.5.13)
* Scipy (>= 0.18.1)
## Install
### With anaconda environment
1) Install Anaconda/Miniconda: https://docs.continuum.io/anaconda/install
2) Set up the environment (replace `__PY_VERSION__` by either `2.7` or `3.5` according to the python version you want to use). For instance, with anaconda:
```bash
# Create envrionment and install packages
conda create -n sldc python=__PY_VERSION__ pillow numpy joblib shapely opencv scipy scikit-image
# Activate environment
source activate sldc
```
3) Install sldc
+ Download the sources
+ Either by cloning the repository: `git clone https://github.com/waliens/sldc.git`
+ Or by downloading an archive: `https://github.com/waliens/sldc/archive/master.zip`
+ Move to the _SLDC_ sources root folder
+ Install sldc: `python setup.py install`
4) Check your install by running `python -c "import sldc"`
## Bindings
The library is image format agnostic and therefore allows you to integrate it with any existing image format by implementing some interfaces. However, some bindings were implemented for integrating SLDC with:
+ [Cytomine](http://www.cytomine.be/): [`cytomine-sldc` repository](https://github.com/cytomine/Cytomine-python-datamining/tree/master/cytomine-datamining/algorithms/sldc)
+ [OpenSlide](http://openslide.org/): [`sldc-openslide` repository](https://github.com/waliens/sldc-openslide)
## References
If you use _SLDC_ in a scientific publication, we would appreciate citations: [Mormont & al., Benelearn, 2016](http://orbi.ulg.ac.be/handle/2268/202624).
The framework was initially developed in the context of [this master thesis](http://hdl.handle.net/2268.2/1314).
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