Metadata-Version: 2.1
Name: uib_vfeatures
Version: 0.6.1
Summary: Vision features of generalistic use
Home-page: https://gitlab.com/miquelca32/features
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: # UIB - V Features
        
        ## Introduction 
        
        *UIB - V Features* provide a set of useful features. With three types of features: morphological, texture 
        and color. All the features can be used with mask or with the contours. Every feature is a numerical value 
        that used in ML can improve their results. 
         
        The morphological features are all grouped in one iterator, so you can calculate all the features inside 
        a loop easily.
        
        The library use mask and contours. Masks are two value image, where the object has a value diferent than the rest of 
        the image. A contour is a 2D vector of points that define a contour. To calculate a contour normally is used the OpenCV
        function.
        
        ### Installation
        
        Install the library is very simple with pip
        
        ```
        pip install uib-vfeatures
        ``` 
        
        ---
        ## List of features
        
        ### Morphological
        
        ##### All this features are in the iterator
        
        *   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
        
        
        ### Textures
        
        The texture features depends on the parameter of a unique function. The first two parameter define the texture,
        with the distance and the angle of the texture. The third defines the feature to extract and the last one is 
        a grey-scale image.
        
        #### 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}*
        
Keywords: Features extraction,Machine Learning,Computer Vision
Platform: UNKNOWN
Description-Content-Type: text/markdown
