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    "info": {
        "author": "Paolo Ripamonti",
        "author_email": "paolo.ripamonti93@gmail.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
        ],
        "description": "# Face Recognition\n\nSimple library to recognize faces from given images\n\n[<img src=\"https://www.kaggleusercontent.com/kf/21132673/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0..Hig7ClqLMXecFinIUQqE_g.Fg1LGL5rc0-PLv7twHxDXzUI8oK3nZGyfpUDv_P-le31C2I2-qoFjWzaxz1n1WlhJqaFyzkqMlfptZy65zaCvk5bxQ5h4bEeS6AFTcORFUdYfMqBM5cYRvWKg4KX-sM-SeWRPRh_s-HWIxe4m2gZWw.l8bObQ1Fhvykp8XMeIAbXw/__results___files/__results___23_0.png\">](https://www.kaggle.com/paoloripamonti/face-recogniton)\n\n### Face Recognition pipeline\n\nBelow the pipeline for face recognition:\n- **Face Detection**: the [MTCNN](https://github.com/ipazc/mtcnn) algorithm is used to do face detection\n- **Face Alignement** Align face by eyes line\n- **Face Encoding** Extract encoding from face using [FaceNet](https://github.com/faustomorales/keras-facenet)\n- **Face Classification** Classify face via eculidean distrances between face encodings\n\n### How to install\n```git\npip install git+https://github.com/paoloripamonti/face-recognition\n```\n\n### How to train custom model\n\nInitialize model\n```python\nfrom face_recognition import FaceRecognition\n\nfr = FaceRecognition()\n```\n\n#### Train model with pandas DataFrame:\n\n```python\nfr = FaceRecognition()\n\nfr.fit_from_dataframe(df)\n```\n\nwhere 'df' is pandas DataFrame with column **person** (person name) and column **path** (image path)\n\n#### Train model with folder:\n\n```python\nfr = FaceRecognition()\n\nfr.fit('/path/root/')\n```\n\nthe root folder must have the following structure:\n\n```\nroot\\\n    Person_1\\\n        image.jpg\n        ...\n        image.jpg\n    Person_2\\\n        image.jpg\n        ...\n        image.jpg\n    ...\n        \n```\n\n### Save and load model\n\nyou can save and load model as **pickle** file.\n\n\n```python\nfr.save('model.pkl')\n```\n\n```python\nfr = FaceRecognition()\n\nfr.load('model.pkl')\n```\n\n\n### Predict image\n\n```python\nfr.predict('/path/image.jpg')\n```\n\nRecognize faces from given image.\nThe output is a JSON with folling structure:\n\n```\n{\n  \"frame\": \"image data\", # base64 image with bounding boxes\n  \"elapsed_time\": time, # elapsed time in seconds\n  \"predictions\": [\n      {\n        \"person\": \"Person\", # person name\n        \"confidence\": float, # prediction confidence\n        \"box\": (x1, y1, x2, y2) # face bounding box\n      }\n  ]\n}\n```\n\n### Example\n\n[<img src=\"https://www.kaggleusercontent.com/kf/21129215/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0..HbixuFgKpuPhmZNn6lkvkQ.ll6VOKAnA0aeJQpm-v9L0hYYZzIAfvvTa1TAxVzJP-bRDPwEpH1WYyrgrX4Vr_ADSI929jwLSGSuuq9KGJnQogJbYVPTRkGW5pBxO8R4rhxrSkg8IoQ6pokYR6ZtehZvjKbK01Bjkow6ykbFWZmZcA.xUV-JT8XBPEHdZJm-yasZQ/__results___files/__results___20_0.png\">](https://www.kaggle.com/paoloripamonti/face-recogniton)\n\n[<img src=\"https://www.kaggleusercontent.com/kf/21129215/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0..HbixuFgKpuPhmZNn6lkvkQ.ll6VOKAnA0aeJQpm-v9L0hYYZzIAfvvTa1TAxVzJP-bRDPwEpH1WYyrgrX4Vr_ADSI929jwLSGSuuq9KGJnQogJbYVPTRkGW5pBxO8R4rhxrSkg8IoQ6pokYR6ZtehZvjKbK01Bjkow6ykbFWZmZcA.xUV-JT8XBPEHdZJm-yasZQ/__results___files/__results___21_0.png\">](https://www.kaggle.com/paoloripamonti/face-recogniton)\n\n\nFor more details you can see: [https://www.kaggle.com/paoloripamonti/face-recogniton](https://www.kaggle.com/paoloripamonti/face-recogniton)",
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