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        "author": "Miquel Mir\u00f3 Nicolau, Bernat Galm\u00e9s Rubert, Dr. Gabriel Moy\u00e0 Alcover",
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        "description": "<div align=\"center\">\n  <img src=\"https://raw.githubusercontent.com/explainingAI/uib_vfeatures/master/docs/uib-vfeatures.png\">\n</div>\n\n\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/uib-vfeatures)\n[![PyPI version](https://badge.fury.io/py/uib-vfeatures.svg)](https://badge.fury.io/py/uib-vfeatures)\n\n\n\n**UIB - V Features** is a library to extract useful features of different types(morphological, texture \nand color). To increase the flexibility of the library all functions can be used with masks or contours. \n\nUIB - V Features was originally developed by researchers of [UGIVIA](http://ugivia.uib.es/). This research group\nis centered on computer vision and artificial intelligence. \n\nAll the features available are grouped in one iterator to simplify the use of this library.\n\n### Installation\n\nInstall the library is very simple with pip\n\n```\n$ pip install uib-vfeatures\n```\n\n---\n## List of features\n\n### Morphological\n\n*   Solidity\n*   Convex hull perimeter\n*   Convex hull area\n*   Bounding box area\n*   Rectangularity\n*   Minor radius\n*   Maximum radius\n*   Feret\n*   Breadh\n*   Circularity\n*   Roundness\n*   Feret Angle\n*   Eccenctricity\n*   Center\n*   Sphericity\n*   Aspect Ratio\n*   Area equivalent diameter\n*   Perimeter equivalent diameter\n*   Equivalent elipse area\n*   Compactness\n*   Area\n*   Convexity\n*   Shape\n*   Perimeter\n\n### Color\n\n*   Mean of the LAB channels\n*   Mean of the RGB channels\n*   Mean of the HSV channels\n*   Standard deviation of the LAB channels\n*   Standard deviation of the RGB channels\n*   Standard deviation of the HSV channels\n\n\n#### Texture features\n\n+   Contrast\n+   Dissimilarity\n+   Homogeneity\n+   ASN\n+   Energy\n+   Correlation\n\n---\n\n## Demo\n\nWe're going to use our library with a mask image .\n\n```python\nfrom uib_vfeatures.masks import Masks\nfrom uib_vfeatures import Features_mask as ftrs\nimport cv2\n\n```\nFirst of all we read the image from a file, then we try our features with visualizations. We only have \nthree features with visualization: the bounding box area, the eccentricity and the solidity. \n\n```python\nmask = cv2.imread(\"mask.jpg\")\n\nMasks.bounding_box_area(mask, True)\n\nMasks.eccentricity(mask, True)\nMasks.solidity(mask, True)\n```\n\n### Iterator\n\nYou can use an iterator and implement every morpholical feature. \n\n```python\nfeatures = {}\n\nfor key, func in features.items():\n    features[key] = func(mask)\n\n```\nAs a result we had a dicctionary of the form *{'Feature_name': value}*\n\n### Citation\n\nIf you use this code, please cite\n\n```\n@article{PETROVIC2020104027,\n    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},\n    author = {Nata\u0161a Petrovi\u0107 and Gabriel Moy\u00e0-Alcover and Antoni Jaume-i-Cap\u00f3 and Manuel Gonz\u00e1lez-Hidalgo},\n    journal = {Computers in Biology and Medicine},\n    volume = {126},\n    pages = {104027},\n    year = {2020},\n    issn = {0010-4825},\n    doi = {https://doi.org/10.1016/j.compbiomed.2020.104027},\n    url = {https://www.sciencedirect.com/science/article/pii/S0010482520303589}\n}\n\n```",
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