Metadata-Version: 2.1
Name: cuda_slic
Version: 0.0.1a1
Summary: CUDA implementation of the SLIC segmentaion algorithm.
Home-page: https://gitlab.stfc.ac.uk/RosalindFranklinInstitute/cuda-slic
Author: Omar Elamin
Author-email: omar.elamin@diamond.ac.uk
License: UNKNOWN
Description: # cuda-slic: A CUDA implementation of the SLIC Superpixel algorithm
        
        ## SLIC
        SLIC stands for __simple linear iteraticve clustering__. SLIC uses
        tiled k-means clustering to segment an input image into a set
        of superpixels/supervoxels.
        
        This library was designed to segment large 3D images coming from different
        detectors at the [Diamond Light Source](https://diamond.ac.uk). These Images
        can be upto 500 GB so using a serial CPU code is out of the question.
        
        To speed up processing we use GPU acceleratoin to achieve great speed
        improvements over alternative implementations. `cuda-slic` borrows its API
        from `skimage.segmentation.slic`.
        
        ## Install
        ```bash
        pip install cuda-slic
        ```
        `cuda-slic` uses the pycuda which has the following non-python
        build/run dependencies:
        1. gcc and g++/gcc-c++ on Linux.
        2. the cudatoolkit for linking with cuda libraries.
        3. the nvcc compiler. Ships with newer cudatoolkit versions.
        See [pycuda docs](https://wiki.tiker.net/PyCuda/Installation/) for 
        installation instructions.
        
        ## Usage
        ```python
        from cuda_slic import slic
        from skimage import data
        
        img = data.astronaut() # 2D RGB image
        labels = slic(img, n_segments=100, compactness=10)
        
        # To segment 3D gray scale
        vol = data.binary_blobs(length=50, ndim=3, seed=2)
        labels = slic(vol, n_segments=100, multichannel=False, compactness=0.1)
        
        # 3D muli-channel
        vol = data.binary_blobs(length=33, ndim=4, seed=2)
        labels = slic(vol, n_segments=100, multichannel=True, compactness=0.1)
        ```
        
        # Development
        ##### Prerequisites:
        1. gcc and g++/gcc-c++ installed and available on PATH.
        2. cudatoolkit installed and CUDA_HOME pointing to its location.
        3. `nvcc` compiler. Ships with recent versions of cudatoolkit.
        
        ## Dependency Management
        
        We use `conda` as a dependency installer and virtual env manager.
        A development environment can be created with
        ```bash
        conda env create -f environment.yml
        ```
        now you can activate the virtual env with `conda activate cuda-slic`, to deactivate use `conda deactivate`.
        To add a dependency, add it to the [environment.yml](environment.yml) file, then you can run
        ```bash
        conda env update -f environment.yml
        ```
        
        ## Tests
        In the [notebooks](notebooks) folder there are Jupyter notebooks
        where the segmentation algorithm can be visually inspected.
        
        Our unit-testing framework of choice is [Py.test](https://docs.pytest.org/en/latest/). The unit-tests can be run with
        ```bash
        conda activate cuda-slic
        pytest
        ```
        
Keywords: segmentation fast cuda slic clustering kmeans
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.5
Description-Content-Type: text/markdown
