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

Last update: Nov 09, 2022

Related tags

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

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

6 Oct 4, 2022

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

212 Dec 29, 2022

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.

405 Jan 4, 2023

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

1.7k Dec 26, 2022

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

52 Dec 30, 2022

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

28 Oct 20, 2022

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

400 Dec 26, 2022

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

1.4k Dec 21, 2022

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

7 May 10, 2022

Comments

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

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) [email protected]: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

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)

v1.0.5(Oct 17, 2021)

This release hold the Face Recognition model
Source code(tar.gz)
Source code(zip)
graph_final.pb(89.67 MB)

Owner

GitHub Repository

A tensorflow=1.13 implementation of Deconvolutional Networks on Graph Data (NeurIPS 2021)

GDN A tensorflow=1.13 implementation of Deconvolutional Networks on Graph Data (NeurIPS 2021) Abstract In this paper, we consider an inverse problem i

4 Sep 13, 2022

STEAL - Learning Semantic Boundaries from Noisy Annotations (CVPR 2019)

STEAL This is the official inference code for: Devil Is in the Edges: Learning Semantic Boundaries from Noisy Annotations David Acuna, Amlan Kar, Sanj

469 Dec 26, 2022

Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces

This repository contains source code for the paper Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces a

9 Nov 21, 2022

MoCoGAN: Decomposing Motion and Content for Video Generation

MoCoGAN: Decomposing Motion and Content for Video Generation This repository contains an implementation and further details of MoCoGAN: Decomposing Mo

514 Dec 18, 2022

DiffWave is a fast, high-quality neural vocoder and waveform synthesizer.

DiffWave DiffWave is a fast, high-quality neural vocoder and waveform synthesizer. It starts with Gaussian noise and converts it into speech via itera

498 Jan 03, 2023

A PyTorch Implementation of Single Shot MultiBox Detector

SSD: Single Shot MultiBox Object Detector, in PyTorch A PyTorch implementation of Single Shot MultiBox Detector from the 2016 paper by Wei Liu, Dragom

4.8k Jan 07, 2023

Fast Axiomatic Attribution for Neural Networks (NeurIPS*2021)

Fast Axiomatic Attribution for Neural Networks This is the official repository accompanying the NeurIPS 2021 paper: R. Hesse, S. Schaub-Meyer, and S.

11 Nov 21, 2022

The story of Chicken for Club Bing

Chicken Story tl;dr: The time when Microsoft banned my entire country for cheating at Club Bing. (A lot of the details are from memory so I've recreat

142 May 16, 2022

Image De-raining Using a Conditional Generative Adversarial Network

Image De-raining Using a Conditional Generative Adversarial Network [Paper Link] [Project Page] He Zhang, Vishwanath Sindagi, Vishal M. Patel In this

216 Dec 18, 2022

A clear, concise, simple yet powerful and efficient API for deep learning.

The Gluon API Specification The Gluon API specification is an effort to improve speed, flexibility, and accessibility of deep learning technology for

2.3k Dec 17, 2022

Official PyTorch implementation of the paper "Graph-based Generative Face Anonymisation with Pose Preservation" in ICIAP 2021

Contents AnonyGAN Installation Dataset Preparation Generating Images Using Pretrained Model Train and Test New Models Evaluation Acknowledgments Citat

10 May 24, 2022