Face Library is an open source package for accurate and real-time face detection and recognition

Overview

Face Library

Face Library is an open source package for accurate and real-time face detection and recognition. The package is built over OpenCV and using famous models and algorithms for face detection and recognition tasks. Make face detection and recognition with only one line of code. The Library doesn't use heavy frameworks like TensorFlow, Keras and PyTorch so it makes it perfect for production.

Installation

pip install face-library

Usage

Importing

from face_lib import face_lib
FL = face_lib()

The model is built over OpenCV, so it expects cv2 input (i.e. BGR image), it will support PIL in the next version for RGB inputs. At the end there is a piece of code to make PIL image like cv2 image.

Face detection

import cv2

img = cv2.imread(path_to_image)
faces = FL.get_faces(img) #return list of RGB faces image

If you want to get faces locations (coordinates) instead of the faces from the image you can use

no_of_faces, faces_coors = FL.faces_locations(face_img)

Face verfication

img_to_verfiy = cv2.imread(path_to_image_to_verify) #image that contain face you want verify
gt_img = cv2.imread(path_to_image_to_compare) #image of the face to compare with

face_exist, no_faces_detected = FL.recognition_pipeline(img_to_verfiy, gt_image)

You can change the threshold of verfication with the best for your usage or dataset like this :

face_exist, no_faces_detected = FL.recognition_pipeline(img_to_verfiy, gt_image, threshold = 1.1) #default number is 0.92

also if you know that gt_img has only one face and the image is zoomed to that face like this :

You can save computing time and the make the model more faster by using

face_exist, no_faces_detected = FL.recognition_pipeline(img_to_verfiy, gt_image, only_face_gt = True)

Extracting face embeddings

I you want represent the face with vector from face only image, you can use

face_embeddings = FL.face_embeddings(face_only_image)

For PIL images

import cv2
import numpy
from PIL import Image

PIL_img = Image.open(path_to_image)

cv2_img = cv2.cvtColor(numpy.array(PIL_img), cv2.COLOR_RGB2BGR) #now you can use this to be input for face_lib functions

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Support

There are many ways to support a project - starring ⭐️ the GitHub repo is just one.

Licence

Face library is licensed under the MIT License

You might also like...
A real-time speech emotion recognition application using Scikit-learn and gradio
A real-time speech emotion recognition application using Scikit-learn and gradio

Speech-Emotion-Recognition-App A real-time speech emotion recognition application using Scikit-learn and gradio. Requirements librosa==0.6.3 numpy sou

Jetson Nano-based smart camera system that measures crowd face mask usage in real-time.
Jetson Nano-based smart camera system that measures crowd face mask usage in real-time.

MaskCam MaskCam is a prototype reference design for a Jetson Nano-based smart camera system that measures crowd face mask usage in real-time, with all

LaneDet is an open source lane detection toolbox based on PyTorch that aims to pull together a wide variety of state-of-the-art lane detection models
LaneDet is an open source lane detection toolbox based on PyTorch that aims to pull together a wide variety of state-of-the-art lane detection models

LaneDet is an open source lane detection toolbox based on PyTorch that aims to pull together a wide variety of state-of-the-art lane detection models. Developers can reproduce these SOTA methods and build their own methods.

A large-scale face dataset for face parsing, recognition, generation and editing.
A large-scale face dataset for face parsing, recognition, generation and editing.

CelebAMask-HQ [Paper] [Demo] CelebAMask-HQ is a large-scale face image dataset that has 30,000 high-resolution face images selected from the CelebA da

DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition, TPAMI 2021

DVG-Face: Dual Variational Generation for HFR This repo is a PyTorch implementation of DVG-Face: Dual Variational Generation for Heterogeneous Face Re

Code for ACM MM2021 paper "Complementary Trilateral Decoder for Fast and Accurate Salient Object Detection"

CTDNet The PyTorch code for ACM MM2021 paper "Complementary Trilateral Decoder for Fast and Accurate Salient Object Detection" Requirements Python 3.6

A unofficial pytorch implementation of PAN(PSENet2): Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network
A unofficial pytorch implementation of PAN(PSENet2): Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network Requirements pytorch 1.1+ torchvision 0.3+ pyclipper opencv3 gcc

Receptive Field Block Net for Accurate and Fast Object Detection, ECCV 2018
Receptive Field Block Net for Accurate and Fast Object Detection, ECCV 2018

Receptive Field Block Net for Accurate and Fast Object Detection By Songtao Liu, Di Huang, Yunhong Wang Updatas (2021/07/23): YOLOX is here!, stronger

High accurate tool for automatic faces detection with landmarks
High accurate tool for automatic faces detection with landmarks

faces_detanator High accurate tool for automatic faces detection with landmarks. The library is based on public detectors with high accuracy (TinaFace

Comments
  • Face Recognition model corrupted

    Face Recognition model corrupted

    While running the script it says tat "face rcognition model corrupted" and starts to download the model. But it takes too long to download 90 mb file.

    help wanted 
    opened by qnihat 4
  • import issue

    import issue

    This might be a noob python question, but:

    git clone https://github.com/a-akram-98/face_lib.git cd face_lib/ virtualenv -p /usr/bin/python3 venv source venv/bin/activate pip install face-library ...

    (venv) jim@Jim-i9-Win10:src (master)$ python Python 3.6.9 (default, Jan 26 2021, 15:33:00) [GCC 8.4.0] on linux Type "help", "copyright", "credits" or "license" for more information.

    from face_lib import face_lib Traceback (most recent call last): File "", line 1, in File "/home/jim/source/face_lib/src/face_lib/init.py", line 1, in from .face_lib import face_lib File "/home/jim/source/face_lib/src/face_lib/face_lib.py", line 5, in from .BlazeDetector import BlazeFaceDetector File "/home/jim/source/face_lib/src/face_lib/BlazeDetector.py", line 5, in from blazeFaceUtils import gen_anchors, AnchorsOptions ModuleNotFoundError: No module named 'blazeFaceUtils'

    How do I find the modules?

    bug good first issue 
    opened by jvanvorst 2
  • Issue with size of the image

    Issue with size of the image

    I saw that with big image 958x1280 it gave an opencv error! so 9i made a small function to resize it to 500 on the height:

    `from face_lib import face_lib import cv2 import os FL = face_lib()

    maxsize = int(500) #maximum height in pixel to get an opencv error file = "./sacha.jpg" filename, file_extension = os.path.splitext(os.path.basename(file))

    def checksize(file): print("in") if not os.path.isfile(file): print("error file not found") exit(1) #get the filename and extensionof filepath img = cv2.imread(file) #Get the width and Heigth of filepath height, width, channels = img.shape #Get the correction factor if the image is to big if height > maxsize: factor = float(maxsize/height) print(factor) height = round(int(height) * factor) width = round(int(width) * factor) #Resize the image img = cv2.resize(img,(width, height)) return img

    img = checksize(file) no_of_faces, faces_locations = FL.faces_locations(img) x,y,w,h = faces_locations[0] cv2.rectangle(img, (x,y),(x+w,y+h), (255,0,0),2) cv2.imshow(filename, img) cv2.waitKey(0)`

    that can help anyone

    opened by sachadee 0
Releases(v1.0.5)
Owner
null
OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

Build Type Linux MacOS Windows Build Status OpenPose has represented the first real-time multi-person system to jointly detect human body, hand, facia

null 25.7k Jan 9, 2023
Python and C++ implementation of "MarkerPose: Robust real-time planar target tracking for accurate stereo pose estimation". Accepted at LXCV @ CVPR 2021.

MarkerPose: Robust real-time planar target tracking for accurate stereo pose estimation This is a PyTorch and LibTorch implementation of MarkerPose: a

Jhacson Meza 47 Nov 18, 2022
FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networks

FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networks

HKBU High Performance Machine Learning Lab 6 Nov 18, 2022
Real-Time-Student-Attendence-System - Real Time Student Attendence System

Real-Time-Student-Attendence-System The Student Attendance Management System Pro

Rounak Das 1 Feb 15, 2022
Face recognition system using MTCNN, FACENET, SVM and FAST API to track participants of Big Brother Brasil in real time.

BBB Face Recognizer Face recognition system using MTCNN, FACENET, SVM and FAST API to track participants of Big Brother Brasil in real time. Instalati

Rafael Azevedo 232 Dec 24, 2022
Face-Recognition-Attendence-System - This face recognition Attendence system using Python

Face-Recognition-Attendence-System I have developed this face recognition Attend

Riya Gupta 4 May 10, 2022
PaddleRobotics is an open-source algorithm library for robots based on Paddle, including open-source parts such as human-robot interaction, complex motion control, environment perception, SLAM positioning, and navigation.

简体中文 | English PaddleRobotics paddleRobotics是基于paddle的机器人开源算法库集,包括人机交互、复杂运动控制、环境感知、slam定位导航等开源算法部分。 人机交互 主动多模交互技术TFVT-HRI 主动多模交互技术是通过视觉、语音、触摸传感器等输入机器人

null 185 Dec 26, 2022
tsai is an open-source deep learning package built on top of Pytorch & fastai focused on state-of-the-art techniques for time series classification, regression and forecasting.

Time series Timeseries Deep Learning Pytorch fastai - State-of-the-art Deep Learning with Time Series and Sequences in Pytorch / fastai

timeseriesAI 2.8k Jan 8, 2023
Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set (CVPRW 2019). A PyTorch implementation.

Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set —— PyTorch implementation This is an unofficial offici

Sicheng Xu 833 Dec 28, 2022
This is the offical website for paper ''Category-consistent deep network learning for accurate vehicle logo recognition''

The Pytorch Implementation of Category-consistent deep network learning for accurate vehicle logo recognition This is the offical website for paper ''

Wanglong Lu 28 Oct 29, 2022