Metadata-Version: 2.1
Name: fer
Version: 0.0.5
Summary: Facial Expression Recognition based on Keras
Home-page: https://github.com/justinshenk/fer
Author: Justin Shenk
Author-email: shenk.justin@gmail.com
License: MIT
Keywords: expression emotion detection tensorflow pip package
Platform: UNKNOWN
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
Requires-Dist: matplotlib
Requires-Dist: tensorflow
Requires-Dist: opencv-contrib-python
Requires-Dist: keras
Requires-Dist: pandas

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)

INSTALLATION
============

Currently FER only supports Python3.4 onwards. It can be installed
through pip:

```bash
$ pip3 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
$ pip3 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
$ pip3 install tensorflow-gpu\>=1.7.0
```

USAGE
=====

The following example illustrates the ease of use of this package:

```python
>>> from fer.fer import FER
>>> import cv2
>>>
>>> img = cv2.imread("justin.jpg")
>>> detector = FER()
>>> print(detector.detect_emotions(img))
[{'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}]
```

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
=====

By default the FER bundles a face detection 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 Octavia Arriaga's [facial expression recognition
repo](https://github.com/oarriaga/face_classification/).

REFERENCE
---------


