uib-vfeatures 0.7

Vision features of generalistic use

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. 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

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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

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Demo

We're going to use our library with a mask image .

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.

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.

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}
}