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fer-22.5.0


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توضیحات

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. ![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 ``` 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