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        "author": "Manivannan",
        "author_email": "manivannanmca2012@gmail.com",
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            "License :: OSI Approved :: MIT License",
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            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
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        "description": "# This Package used for Face Recognition with Machine Algorithm\n\n## Installing Steps for requirements python package\n### Installing dlib on Ubuntu\nThe following instructions were gathered on Ubuntu 16.04 but should work on newer versions of Ubuntu as well.\n\nTo get started, let\u2019s install our required dependencies:\n\n```\nsudo apt-get update\nsudo apt-get install build-essential cmake\nsudo apt-get install libopenblas-dev liblapack-dev\nsudo apt-get install libx11-dev libgtk-3-dev\nsudo apt-get install python python-dev python-pip\nsudo apt-get install python3 python3-dev python3-pip\n```\nafter\n```\npip install dlib\n```\n### Installing pyfacy models on Ubuntu\n\n```\npip install pyfacy_dlib_models\n```\n### Installing imutils on Ubuntu\n```\npip install imutils\n```\n### Installing numpy, scipy and sklearn\n```\npip install numpy\npip install scipy\npip install scikit-learn\n```\n\n## Examples:\n\n#### Read Image\n```\nfrom pyfacy import utils\nimg = utils.load_image('<image src>')\nex:\nimg = utils.load_image('manivannan.jpg')\n```\n\n### Face Encodings:\n```\nfrom pyfacy import utils\nimg = utils.load_image('<image src>')\nencodings = utils.img_to_encodings(img)\n```\n\n### Compare Two faces\n```\nfrom pyfacy import utils\nimage1 = utils.load_image('<image1 src>')\nimage2 = utils.load_image('<image2 src>')\nmatching,distance = utils.compare_faces(image1,image2)\n```\n> Note: The compare_faces return Boolean and Distance between two faces\n\n# Example for Face Recognition using ML\n\n## Implementing Algorithms\n\n1. KNN - K-Nearest Neighbors\n2. LOG_REG_BIN - Logistic Regression with two classes\n3. LOG_REG_MUL - Logistic Regression with more than two classes\n4. LDA - Linear Discriminant Analysis\n\n### Training , Save model and Predict Image\n```\nfrom pyfacy import face_recog\nfrom pyfacy import utils\nmdl = face_recog.Face_Recog_Algorithm()\n# Train the Model\n# Implemented only four algorithms above mentioned and put the shortform\nmdl.train('pyfacy/Test_DS',alg='LOG_REG_MUL')\n# Save the Model\nmdl.save_model()\n# Predicting Image\nimg = utils.load_image('<image src>')\nmdl.predict(img)\n```\n\n### Loading model and Predict Image\n```\nfrom pyfacy import face_recog\nfrom pyfacy import utils\nmdl = face_recog.Face_Recog_Algorithm()\n# Load Model\nmdl.load_model('model.pkl')\n# Predicting Image\nimg = utils.load_image('<image src>')\nmdl.predict(img)\n```\n\n# Face Clustering\n### Cluster the image_src\n```\nfrom pyfacy import face_clust\n# Create object for Cluster class with your source path(only contains jpg images)\nmdl = face_clust.Face_Clust_Algorithm('./pyfacy/cluster')\n# Load the faces to the algorithm\nmdl.load_faces()\n# Save the group of images to custom location(if the arg is empty store to current location)\nmdl.save_faces('./pyfacy')\n\n\n",
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