Metadata-Version: 2.1
Name: uib_vfeatures
Version: 0.7
Summary: Vision features of generalistic use
Home-page: https://github.com/explainingAI/uib_vfeatures
Author: Miquel Miró Nicolau, Bernat Galmés Rubert, Dr. Gabriel Moyà Alcover
Author-email: miquelca32@gmail.com, bernat_galmes@hotmail.com, gabriel_moya@uib.es
License: MIT
Description: <div align="center">
          <img src="https://raw.githubusercontent.com/explainingAI/uib_vfeatures/master/docs/uib-vfeatures.png">
        </div>
        
        
        ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/uib-vfeatures)
        [![PyPI version](https://badge.fury.io/py/uib-vfeatures.svg)](https://badge.fury.io/py/uib-vfeatures)
        
        
        
        **UIB - V Features** is a library to extract useful features of different types(morphological, texture 
        and color). To increase the flexibility of the library all functions can be used with masks or contours. 
        
        UIB - V Features was originally developed by researchers of [UGIVIA](http://ugivia.uib.es/). This research group
        is centered on computer vision and artificial intelligence. 
        
        All the features available are grouped in one iterator to simplify the use of this library.
        
        ### Installation
        
        Install the library is very simple with pip
        
        ```
        $ pip install uib-vfeatures
        ```
        
        ---
        ## List of features
        
        ### Morphological
        
        *   Solidity
        *   Convex hull perimeter
        *   Convex hull area
        *   Bounding box area
        *   Rectangularity
        *   Minor radius
        *   Maximum radius
        *   Feret
        *   Breadh
        *   Circularity
        *   Roundness
        *   Feret Angle
        *   Eccenctricity
        *   Center
        *   Sphericity
        *   Aspect Ratio
        *   Area equivalent diameter
        *   Perimeter equivalent diameter
        *   Equivalent elipse area
        *   Compactness
        *   Area
        *   Convexity
        *   Shape
        *   Perimeter
        
        ### Color
        
        *   Mean of the LAB channels
        *   Mean of the RGB channels
        *   Mean of the HSV channels
        *   Standard deviation of the LAB channels
        *   Standard deviation of the RGB channels
        *   Standard deviation of the HSV channels
        
        
        #### Texture features
        
        +   Contrast
        +   Dissimilarity
        +   Homogeneity
        +   ASN
        +   Energy
        +   Correlation
        
        ---
        
        ## Demo
        
        We're going to use our library with a mask image .
        
        ```python
        from uib_vfeatures.masks import Masks
        from uib_vfeatures import Features_mask as ftrs
        import cv2
        
        ```
        First of all we read the image from a file, then we try our features with visualizations. We only have 
        three features with visualization: the bounding box area, the eccentricity and the solidity. 
        
        ```python
        mask = cv2.imread("mask.jpg")
        
        Masks.bounding_box_area(mask, True)
        
        Masks.eccentricity(mask, True)
        Masks.solidity(mask, True)
        ```
        
        ### Iterator
        
        You can use an iterator and implement every morpholical feature. 
        
        ```python
        features = {}
        
        for key, func in features.items():
            features[key] = func(mask)
        
        ```
        As a result we had a dicctionary of the form *{'Feature_name': value}*
        
        ### Citation
        
        If you use this code, please cite
        
        ```
        @article{PETROVIC2020104027,
            title = {Sickle-cell disease diagnosis support selecting the most appropriate machine learning method: Towards a general and interpretable approach for cell morphology analysis from microscopy images},
            author = {Nataša Petrović and Gabriel Moyà-Alcover and Antoni Jaume-i-Capó and Manuel González-Hidalgo},
            journal = {Computers in Biology and Medicine},
            volume = {126},
            pages = {104027},
            year = {2020},
            issn = {0010-4825},
            doi = {https://doi.org/10.1016/j.compbiomed.2020.104027},
            url = {https://www.sciencedirect.com/science/article/pii/S0010482520303589}
        }
        
        ```
        
Keywords: Features extraction,Machine Learning,Computer Vision
Platform: UNKNOWN
Description-Content-Type: text/markdown
