Metadata-Version: 1.1
Name: arip
Version: 1.0.1
Summary: ARIP, software to quantify bacterial resistance to antibiotics by analysing picture of phenotypic plates
Home-page: https://github.com/mazeitor/antibiotic-resistance-image-process
Author: oriol mazariegos
Author-email: mazeitor@gmail.com
License: GPLv3
Description: Antibiotic Resistance Image Process - ARIP
        ==========================================
        
        This software is aimed to quantify bacterial resistance to antibiotics
        by analysing pictures of phenotypic plates. Currently it supports 96
        well plates where different bacteria are cultured with different
        concentrations of antibiotics, but the application adapt to different
        plates size in rows and columns. Computer vision algorithms have been
        implemented in order to detect different levels of bacterial growth. As
        a result, the software generates a report providing quantitative
        information for each well of the plate. Pictures should be taken so that
        the plate is square with the picture frame, the algorithm should be able
        to cope with a slight rotation of the plate.
        
        
        plate
        .. image:: arip/images/sinteticplatebac.jpg 
        
        segmentated wells
        .. image:: arip/output/inteticplatebac/output2.jpg 
        
        extracted resistance
        .. image:: arip/output/report.png 
        
        report
        .. image:: arip/output/report_json.png 
        
        
        Key methods:
        ------------
        
        -  Hough Circles method to detect circles in an image
           `doc <http://docs.opencv.org/2.4/doc/tutorials/imgproc/imgtrans/hough_circle/hough_circle.html>`__
        -  Wells segmentation using threshold feature of opencv
           `doc <http://docs.opencv.org/2.4/modules/imgproc/doc/miscellaneous_transformations.html#threshold>`__
           combining binary and otsu threshold
        -  Quality detection using a grid model by rows and columns and
           clustering them, robust to scale and sensible rotation.
        
        Execution:
        ----------
        
        There are two ways for executing the process: binary or library \*
        Binary using arip.py file allocated in the project:
        
        .. code:: bash
        
            python arip.py --image images/\<platename\>.png
        
        -  Library installing as described below:
        
           .. code:: bash
        
               import arip
               arip.process({'image': 'images/sinteticplate.jpg'})
        
        input:
        ~~~~~~
        
        images/<platename>.png with a plate and ninety six wells
        
        output:
        ~~~~~~~
        
        -  Image with extracted wells: images/<platename>/outputXXX.png
        -  Cropped image of extracted well:
           images/<platename>/<row>\ *<column>*\ <resistance>\_<density>.png
        -  Report in json format: images/<platename>/report.json
        -  Log: images/<platename>/log.txt
        
        description of schema: \* row: well row index \* column: well colmun
        index \* total: well area in pixels \* resistance: absolute resistance
        found in pixels \* density: density of the resistance found
        
        report example:
        
        ::
        
               "7-J":{  
                  "density":0.17,
                  "column":"A",
                  "resistance":122,
                  "total":706,
                  "row":"4"
               }
        
        output images example:
        
        ::
        
            4-A_122-0.23, is the well 4-A, with 122 pixels found as resistance with density of 17%
        
        output log example:
        
        ::
        
            customizing scale well: found False, num wells 93, min radius value 18, max radius value 23
            customizing scale well: found False, num wells 96, min radius value 18, max radius value 24
            customizing grid matching: found False, num wells recognized 96
            Succesfully processed plate, found 96 wells
        
        Installing dependencies
        -----------------------
        
        pip
        ~~~
        
        sudo apt-get install python-pip ### opencv sudo apt-get install
        build-essential sudo apt-get install cmake git libgtk2.0-dev pkg-config
        libavcodec-dev libavformat-dev libswscale-dev sudo apt-get install
        python-opencv ### scilab sudo apt-get install python-scipy
        
        Installing arip
        ---------------
        
        There are two ways of installing pynteractive: \* Cloning the project
        
        .. code:: bash
        
            $ git clone https://github.com/mazeitor/antibiotic-resistance-process.git
            $ cd antibiotic-resistance-process
            $ python setup.py install  ### (as root)
        
        -  Via `Python package index <https://pypi.python.org/pypi/pip>`__
           (pip), TODO
        
           .. code:: bash
        
               $ pip install arip
        
        TODO
        ----
        
        -  Normalizing radius by neighborhood instead of general average
        -  Working with static grids or masks
        
        
Keywords: medical image processing antibiotic resistance phenotypic plate
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
