FaceLib: Face Analysis
Used for face detection, facial expression, AgeGender estimation and recognition with PyTorch.
- Instalation:
pip install git+https://github.com/sajjjadayobi/FaceLib.git
Check this example_notebook or take a look at the following sections
You can use these backbone networks: Resnet50, mobilenet. Default model is mobilenet and it will be automatically downloaded.
- The following example illustrates the ease of use of this package on your webcam:
from facelib import WebcamFaceDetector
detector = WebcamFaceDetector()
detector.run()- Low-level access to bounding boxes and face landmarks
from facelib import FaceDetector
detector = FaceDetector()
boxes, scores, landmarks = detector.detect_faces(image)For face aligment always use the detect_align function it gives you better performance.
- Face detection and aligment using the
detect_alignfunction.
from facelib import FaceDetector
detector = FaceDetector()
faces, boxes, scores, landmarks = detector.detect_align(image)| Original | Aligned & Resized | Original | Aligned & Resized |
|---|---|---|---|
 |
 |
 |
 |
ShufflenetFull is the default model, and it will be automatically downloaded.
- Age and gender estimation live on your webcam (or any camera)
from facelib import WebcamAgeGenderEstimator
estimator = WebcamAgeGenderEstimator()
estimator.run()- Low-lvel access to ages and genders
from facelib import FaceDetector, AgeGenderEstimator
face_detector = FaceDetector()
age_gender_detector = AgeGenderEstimator()
faces, boxes, scores, landmarks = face_detector.detect_align(image)
genders, ages = age_gender_detector.detect(faces)
print(genders, ages)The default model is densnet121 and it will be automatically downloaded. Note that face size must be (224, 224).
- Emotion detector live on your webcam
from facelib import WebcamEmotionDetector
detector = WebcamEmotionDetector()
detector.run()- Emotions as an array with their probabilities
from facelib import FaceDetector, EmotionDetector
face_detector = FaceDetector(face_size=(224, 224))
emotion_detector = EmotionDetector()
faces, boxes, scores, landmarks = face_detector.detect_align(image)
emotions, probab = emotion_detector.detect_emotion(faces)- on my Webcam 🙂
- This module is a pytorch reimplementation of Arcface(paper), or Insightface(Github)
- IR-SE50
| LFW(%) | CFP-FF(%) | CFP-FP(%) | AgeDB-30(%) | calfw(%) | cplfw(%) | vgg2_fp(%) |
|---|---|---|---|---|---|---|
| 0.9952 | 0.9962 | 0.9504 | 0.9622 | 0.9557 | 0.9107 | 0.9386 |
- Mobilefacenet
| LFW(%) | CFP-FF(%) | CFP-FP(%) | AgeDB-30(%) | calfw(%) | cplfw(%) | vgg2_fp(%) |
|---|---|---|---|---|---|---|
| 0.9918 | 0.9891 | 0.8986 | 0.9347 | 0.9402 | 0.866 | 0.9100 |
Save the images of the faces you want to detect in this folder
Insightface/models/data/facebank/
---> person_1/
---> img_1.jpg
---> img_2.jpg
---> person_2/
---> img_1.jpg
---> img_2.jpg
You can save a new preson in facebank with 2 ways:
- Use
add_from_webcam: it takes 4 images and saves them on facebank.
from facelib import add_from_webcam
add_from_webcam(person_name='sajjad')- use
add_from_folder: it takes a path with some images from just a person.
from facelib import add_from_folder
add_from_folder(folder_path='./', person_name='sajjad')The default model is mobilenet and it will be automatically downloaded
- Face Recognition live on your webcam
from facelib import WebcamVerify
verifier = WebcamVerify(update=True)
verifier.run()- Low-level access to your images
import cv2
from facelib import FaceRecognizer, FaceDetector
from facelib import update_facebank, load_facebank, special_draw, get_config
conf = get_config()
# conf.use_mobilenet=False # if you want to use the bigger model
detector = FaceDetector(device=conf.device)
face_rec = FaceRecognizer(conf)
# set True when you add someone new to the facebank
update_facebank_for_add_new_person = False
if update_facebank_for_add_new_person:
targets, names = update_facebank(conf, face_rec.model, detector)
else:
targets, names = load_facebank(conf)
image = cv2.imread(your_path)
faces, boxes, scores, landmarks = detector.detect_align(image)
results, score = face_rec.infer(faces, targets)
print(names[results.cpu()])
for idx, bbox in enumerate(boxes):
special_draw(image, bbox, landmarks[idx], names[results[idx]+1], score[idx])
Reference: InsightFace
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