{ "info": { "author": "HokutoTateyama", "author_email": "ht235711@yahoo.co.jp", "bugtrack_url": null, "classifiers": [ "Development Status :: 2 - Pre-Alpha", "License :: OSI Approved :: BSD License", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3 :: Only", "Topic :: Scientific/Engineering" ], "description": "# Image Feature Extractor(IFE)\n[![Coverage Status](https://coveralls.io/repos/github/Collonville/ImageFeatureExtractor/badge.svg)](https://coveralls.io/github/Collonville/ImageFeatureExtractor)\n[![Build Status](https://travis-ci.org/Collonville/ImageFeatureExtractor.svg?branch=develop)](https://travis-ci.org/Collonville/ImageFeatureExtractor)\n[![Codacy Badge](https://api.codacy.com/project/badge/Grade/115c65043153459cbfc5026ea53be08d)](https://www.codacy.com/app/Collonville/ImageFeatureExtractor?utm_source=github.com&utm_medium=referral&utm_content=Collonville/ImageFeatureExtractor&utm_campaign=Badge_Grade)\n[![PyPI version](https://badge.fury.io/py/ife.svg)](https://badge.fury.io/py/ife)\n\n## What is this\n`IFE` is a package to get an image feature more easily for Python. It contains many kinds of feature extract algorithms.\n\n## Insatall\n For the latest version are available using pip install.\n```bash\npip install ife\n```\n\n## 1. Features\n### Color Moment\n- Mean, Median, Variance, Skewness, Kurtosis of `RGB, HSV, HSL, CMY`\n### Colourfulness\n- Colourfulness measure of the image\n\n## 2. Examples\nImport the basic image reader of IFE.\n```python\nfrom ife.io.io import ImageReader\n```\n\n### 2.1 Get Moment\nAdd a image file path to `read_from_single_file()`. This will return basic features class.\n\nAnd now! You can get a RGB color moment feature from image!!\n\n### Sample\n```python\n>>> features = ImageReader.read_from_single_file(\"ife/data/small_rgb.jpg\")\n>>> features.moment()\narray([[ 0.57745098, 0.52156863, 0.55980392],\n [ 0.58823529, 0.48823529, 0.54901961],\n [ 0.15220588, 0.12136101, 0.12380911],\n [-0.01944425, 0.18416571, 0.04508015],\n [-1.94196824, -1.55209335, -1.75586748]])\n```\n\nAlso, you can get an `flatten vector, dictionary, or pandas`\n```python\n>>> features.moment(output_type=\"one_col\")\narray([ 0.57745098, 0.52156863, 0.55980392, 0.58823529, 0.48823529,\n 0.54901961, 0.15220588, 0.12136101, 0.12380911, -0.01944425,\n 0.18416571, 0.04508015, -1.94196824, -1.55209335, -1.75586748])\n\n>>> features.moment(output_type=\"dict\")\ndefaultdict(, {'mean': {'R': 0.57745098039215681, 'G': 0.52156862745098043, 'B': 0.55980392156862746}, 'median': {'R': 0.58823529411764708, 'G': 0.48823529411764705, 'B': 0.5490196078431373}, 'var': {'R': 0.15220588235294119, 'G': 0.12136101499423299, 'B': 0.12380911188004615}, 'skew': {'R': -0.019444250980856902, 'G': 0.18416570783012232, 'B': 0.045080152334687214}, 'kurtosis': {'R': -1.9419682406751135, 'G': -1.5520933544103905, 'B': -1.7558674751807395}})\n\n>>> features.moment(output_type=\"pandas\")\n mean median var skew kurtosis\nR 0.577451 0.588235 0.152206 -0.019444 -1.941968\nG 0.521569 0.488235 0.121361 0.184166 -1.552093\nB 0.559804 0.549020 0.123809 0.045080 -1.755867\n```\n\n> No! I want a HSV Color space feature :(\n\nIt can set another color space! Default will be RGB.\n```python\n>>> features.moment(output_type=\"one_col\", color_space=\"CMY\")\narray([ 0.42254902, 0.47843137, 0.44019608, 0.41176471, 0.51176471,\n 0.45098039, 0.15220588, 0.12136101, 0.12380911, 0.01944425,\n -0.18416571, -0.04508015, -1.94196824, -1.55209335, -1.75586748])\n \n>>> features.moment(output_type=\"dict\", color_space=\"HSL\")\ndefaultdict(, {'mean': {'H': 0.50798329143793874, 'S': 0.52775831413836383, 'L': 0.61421568627450984}, 'median': {'H': 0.51915637553935423, 'S': 0.62898601603182969, 'L': 0.52156862745098043}, 'var': {'H': 0.13290200013401141, 'S': 0.10239897927552907, 'L': 0.051550124951941563}, 'skew': {'H': -0.078898095002588917, 'S': -0.83203104238315984, 'L': 1.0202366337483093}, 'kurtosis': {'H': -1.2599104562470791, 'S': -0.87111810912637022, 'L': -0.7502836585891588}})\n\n>>> features.moment(output_type=\"pandas\", color_space=\"HSV\")\n mean median var skew kurtosis\nH 0.507983 0.519156 0.132902 -0.078898 -1.259910\nS 0.595236 0.749543 0.122723 -1.028366 -0.768867\nV 0.855882 0.864706 0.013867 -0.155656 -1.498179\n```\n## 2.2 Colourfulness\n### Reference\nD. Hasler and S.E.Suesstrunk, ``Measuring colorfulness in natural images,\" Human\nVision andElectronicImagingVIII, Proceedings of the SPIE, 5007:87-95, 2003.\n\n### Sample\n```python\n>>> features = ImageReader.read_from_single_file(\"ife/data/strawberry.jpg\")\n>>> features.colourfulness()\n0.18441700366624714\n```\n\n## 3. Future work\n### IO\n- Read from URL links\n- Read from Base64\n- Sliding window\n- Video files\n\n### Color space\n- CMYK\n- CIE Lab\n- XYZ\n\n### Features\n- Value normalize\n- Average Gradient\n- LBP\n- Histogram\n- Color harmony\n- Entropy\n- Brightness measure\n- Contrast measure\n- Saturation measure\n- Naturalness\n- Color fidelity metric\n- Saliency map\n- Fisher vector\n- VGG16, 19 layer feature\n- and more...\n\n## 4. Author\n@Collonville\n\n## 5. 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