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fer-22.5.0
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مشاهده بیشترتوضیحات
Facial expression recognition from images
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fer-22.5.0 |
| نام | fer |
| نسخه کتابخانه | 22.5.0 |
| نگهدارنده | ['Justin Shenk'] |
| ایمیل نگهدارنده | ['shenkjustin@gmail.com'] |
| نویسنده | Justin Shenk |
| ایمیل نویسنده | shenkjustin@gmail.com |
| آدرس صفحه اصلی | https://github.com/justinshenk/fer |
| آدرس اینترنتی | https://pypi.org/project/fer/ |
| مجوز | MIT |
FER
===
Facial expression recognition.
[](https://badge.fury.io/py/fer) [](https://travis-ci.org/justinshenk/fer) [](https://pepy.tech/project/fer)
[](http://colab.research.google.com/github/justinshenk/fer/blob/master/fer-video-demo.ipynb)
[](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).
نیازمندی
| مقدار | نام |
|---|---|
| - | matplotlib |
| - | opencv-contrib-python |
| >=2.0.0 | keras |
| - | pandas |
| - | requests |
| - | facenet-pytorch |
| - | tqdm |
| - | coverage |
| - | pytest |
| - | sphinx |
| - | wheel |
| - | pre-commit |
| - | sphinx |
| - | coverage |
| - | pytest |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >= 3.6 | Python |
نحوه نصب
نصب پکیج whl fer-22.5.0:
pip install fer-22.5.0.whl
نصب پکیج tar.gz fer-22.5.0:
pip install fer-22.5.0.tar.gz