fer 25.10.3
Facial expression recognition from images
License: MIT
Requires Python: >=3.8
Keywords: facial expressions, emotion detection, faces, images

FER
===

Facial expression recognition.

![image](https://github.com/justinshenk/fer/raw/master/result.jpg)

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[![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
```
To extract videos that includes sound, ffmpeg and moviepy packages must be installed with pip:

```bash
$ pip install ffmpeg moviepy 
```

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
[demo.py](demo.py) located in the root of this repository.

To run the examples, install click for command line with `pip install click` and enter `python demo.py [image|video|webcam]` --help.

TF-SERVING
==========

Support running with online TF Serving docker image.

To use: Run `docker-compose up` and initialize FER with `FER(..., tfserving=True)`.

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).