Metadata-Version: 2.1
Name: fer
Version: 22.1.1
Summary: Facial expression recognition from images
Home-page: https://github.com/justinshenk/fer
Author: Justin Shenk
Author-email: shenkjustin@gmail.com
Maintainer: Justin Shenk
Maintainer-email: shenkjustin@gmail.com
License: MIT
Description: FER
        ===
        
        Facial expression recognition.
        
        ![image](https://github.com/justinshenk/fer/raw/master/result.jpg)
        
        [![PyPI version](https://badge.fury.io/py/fer.svg)](https://badge.fury.io/py/fer) [![Build Status](https://travis-ci.org/justinshenk/fer.svg?branch=master)](https://travis-ci.org/justinshenk/fer) [![Downloads](https://pepy.tech/badge/fer)](https://pepy.tech/project/fer)
        
        [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/justinshenk/fer/blob/master/fer-video-demo.ipynb)
        
        [![DOI](https://zenodo.org/badge/150107943.svg)](https://zenodo.org/badge/latestdoi/150107943)
        
        
        INSTALLATION
        ============
        
        Currently FER only supports Python 3.6 onwards. It can be installed
        through pip:
        
        ```bash
        $ pip install fer
        ```
        
        This implementation requires OpenCV\>=3.2 and Tensorflow\>=1.7.0
        installed in the system, with bindings for Python3.
        
        They can be installed through pip (if pip version \>= 9.0.1):
        
        ```bash
        $ pip install tensorflow>=1.7 opencv-contrib-python==3.3.0.9
        ```
        
        or compiled directly from sources
        ([OpenCV3](https://github.com/opencv/opencv/archive/3.4.0.zip),
        [Tensorflow](https://www.tensorflow.org/install/install_sources)).
        
        Note 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:
        
        ```bash
        $ pip install tensorflow-gpu\>=1.7.0
        ```
        
        USAGE
        =====
        
        The following example illustrates the ease of use of this package:
        
        ```python
        from fer import FER
        import cv2
        
        img = cv2.imread("justin.jpg")
        detector = FER()
        detector.detect_emotions(img)
        ```
        
        Sample output:
        ```
        [{'box': [277, 90, 48, 63], 'emotions': {'angry': 0.02, 'disgust': 0.0, 'fear': 0.05, 'happy': 0.16, 'neutral': 0.09, 'sad': 0.27, 'surprise': 0.41}]
        ```
        
        Pretty print it with `import pprint; pprint.pprint(result)`.
        
        Just want the top emotion? Try:
        
        ```python
        emotion, score = detector.top_emotion(img) # 'happy', 0.99
        ```
        
        #### MTCNN Facial Recognition
        
        Faces by default are detected using OpenCV's Haar Cascade classifier. To use the more accurate MTCNN network,
        add the parameter:
        
        ```python
        detector = FER(mtcnn=True)
        ```
        
        #### Video
        For recognizing facial expressions in video, the `Video` class splits video into frames. It can use a local Keras model (default) or Peltarion API for the backend:
        
        ```python
        from fer import Video
        from fer import FER
        
        video_filename = "tests/woman2.mp4"
        video = Video(video_filename)
        
        # Analyze video, displaying the output
        detector = FER(mtcnn=True)
        raw_data = video.analyze(detector, display=True)
        df = video.to_pandas(raw_data)
        ```
        
        The detector returns a list of JSON objects. Each JSON object contains
        two keys: 'box' and 'emotions':
        
        -   The bounding box is formatted as [x, y, width, height] under the key
            'box'.
        -   The emotions are formatted into a JSON object with the keys 'anger',
            'disgust', 'fear', 'happy', 'sad', surprise', and 'neutral'.
        
        Other good examples of usage can be found in the files
        [example.py](example.py) and [video-example.py](video-example.py)
        located in the root of this repository.
        
        MODEL
        =====
        
        FER bundles a Keras model.
        
        The model is a convolutional neural network with weights saved to HDF5
        file in the `data` folder relative to the module's path. It can be
        overriden by injecting it into the `FER()` constructor during
        instantiation with the `emotion_model` parameter.
        
        LICENSE
        =======
        
        [MIT License](LICENSE).
        
        CREDIT
        ======
        
        This code includes methods and package structure copied or derived from
        Iván de Paz Centeno's [implementation](https://github.com/ipazc/mtcnn/)
        of MTCNN and Octavio Arriaga's [facial expression recognition
        repo](https://github.com/oarriaga/face_classification/).
        
        REFERENCE
        ---------
        
        FER 2013 dataset curated by Pierre Luc Carrier and Aaron Courville, described in:
        
        "Challenges in Representation Learning: A report on three machine learning contests," by Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron Courville, Mehdi Mirza, Ben Hamner, Will Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, Yingbo Zhou, Chetan Ramaiah, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Zhang Chuang, and Yoshua Bengio, [arXiv:1307.0414](https://arxiv.org/abs/1307.0414).
        
Keywords: facial expressions,emotion detection,faces,images
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >= 3.6
Description-Content-Type: text/markdown
Provides-Extra: docs
Provides-Extra: tests
Provides-Extra: dev
