{
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        "author": "Iv\u00e1n de Paz Centeno",
        "author_email": "ipazc@unileon.es",
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            "Environment :: Console",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "Natural Language :: English",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
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        "description": "MTCNN\n#####\n\n.. image:: https://badge.fury.io/py/mtcnn.svg\n    :target: https://badge.fury.io/py/mtcnn\n.. image:: https://travis-ci.org/ipazc/mtcnn.svg?branch=master\n    :target: https://travis-ci.org/ipazc/mtcnn\n\n\nImplementation of the MTCNN face detector for TensorFlow in Python3.4+. It is written from scratch, using as a reference the implementation of\nMTCNN from David Sandberg (`FaceNet's MTCNN <https://github.com/davidsandberg/facenet/tree/master/src/align>`_) in Facenet. It is based on the paper *Zhang, K et al. (2016)* [ZHANG2016]_.\n\n.. image:: https://github.com/ipazc/mtcnn/raw/master/result.jpg\n\n\nINSTALLATION\n############\n\nCurrently it is only supported Python3.4 onwards. It can be installed through pip:\n\n.. code:: bash\n\n    $ pip3 install mtcnn\n\nThis implementation requires OpenCV>=3.2 and Tensorflow>=1.4.0 installed in the system, with bindings for Python3.\n\nThey can be installed through pip (if pip version >= 9.0.1):\n\n\n.. code:: bash\n\n    $ pip3 install tensorflow==1.4.1 opencv-contrib-python==3.2.0.8\n\nor compiled directly from sources (`OpenCV3 <https://github.com/opencv/opencv/archive/3.4.0.zip>`_, `Tensorflow <https://www.tensorflow.org/install/install_sources>`_).\n\nNote that a tensorflow-gpu version can be used instead if a GPU device is available on the system, which will speedup the results. It can be installed with pip:\n\n.. code:: bash\n\n    $ pip3 install tensorflow-gpu\\>=1.4.0\n\nUSAGE\n#####\n\nThe following example illustrates the ease of use of this package:\n\n\n.. code:: python\n\n    >>> from mtcnn.mtcnn import MTCNN\n    >>> import cv2\n    >>>\n    >>> img = cv2.imread(\"ivan.jpg\")\n    >>> detector = MTCNN()\n    >>> print(detector.detect_faces(img))\n    [{'box': [277, 90, 48, 63], 'keypoints': {'nose': (303, 131), 'mouth_right': (313, 141), 'right_eye': (314, 114), 'left_eye': (291, 117), 'mouth_left': (296, 143)}, 'confidence': 0.99851983785629272}]\n\nThe detector returns a list of JSON objects. Each JSON object contains three main keys: 'box', 'confidence' and 'keypoints':\n\n- The bounding box is formatted as [x, y, width, height] under the key 'box'.\n- The confidence is the probability for a bounding box to be matching a face.\n- The keypoints are formatted into a JSON object with the keys 'left_eye', 'right_eye', 'nose', 'mouth_left', 'mouth_right'. Each keypoint is identified by a pixel position (x, y).\n\nAnother good example of usage can be found in the file \"`example.py`_.\" located in the root of this repository.\n\nBENCHMARK\n=========\n\nThe following tables shows the benchmark of this mtcnn implementation running on an `Intel i7-3612QM CPU @ 2.10GHz <https://www.cpubenchmark.net/cpu.php?cpu=Intel+Core+i7-3612QM+%40+2.10GHz>`_, with a **CPU-based** Tensorflow 1.4.1.\n\n - Pictures containing a single frontal face:\n\n+------------+--------------+---------------+-----+\n| Image size | Total pixels | Process time  | FPS |\n+============+==============+===============+=====+\n| 460x259    | 119,140      | 0.118 seconds | 8.5 |\n+------------+--------------+---------------+-----+\n| 561x561    | 314,721      | 0.227 seconds | 4.5 |\n+------------+--------------+---------------+-----+\n| 667x1000   | 667,000      | 0.456 seconds | 2.2 |\n+------------+--------------+---------------+-----+\n| 1920x1200  | 2,304,000    | 1.093 seconds | 0.9 |\n+------------+--------------+---------------+-----+\n| 4799x3599  | 17,271,601   | 8.798 seconds | 0.1 |\n+------------+--------------+---------------+-----+\n\n - Pictures containing 10 frontal faces:\n\n+------------+--------------+---------------+-----+\n| Image size | Total pixels | Process time  | FPS |\n+============+==============+===============+=====+\n| 474x224    | 106,176      | 0.185 seconds | 5.4 |\n+------------+--------------+---------------+-----+\n| 736x348    | 256,128      | 0.290 seconds | 3.4 |\n+------------+--------------+---------------+-----+\n| 2100x994   | 2,087,400    | 1.286 seconds | 0.7 |\n+------------+--------------+---------------+-----+\n\nMODEL\n#####\n\nBy default the MTCNN bundles a face detection weights model.\n\nThe model is adapted from the Facenet's MTCNN implementation, merged in a single file located inside the folder 'data' relative\nto the module's path. It can be overriden by injecting it into the MTCNN() constructor during instantiation.\n\nThe model must be numpy-based containing the 3 main keys \"pnet\", \"rnet\" and \"onet\", having each of them the weights of each of the layers of the network.\n\nFor more reference about the network definition, take a close look at the paper from *Zhang et al. (2016)* [ZHANG2016]_.\n\nLICENSE\n#######\n\n`MIT License`_.\n\n\nREFERENCE\n=========\n\n.. [ZHANG2016] Zhang, K., Zhang, Z., Li, Z., and Qiao, Y. (2016). Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters, 23(10):1499\u20131503.\n\n.. _example.py: example.py\n.. _MIT license: LICENSE",
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