Face Analysis: Detection, Age Gender Estimation & Recognition
Used for face detection, facial expression, AgeGender estimation and recognition with PyTorch.
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.
from facelib import WebcamFaceDetector
detector = WebcamFaceDetector()
detector.run()
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.
detect_align
function. 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.
from facelib import WebcamAgeGenderEstimator
estimator = WebcamAgeGenderEstimator()
estimator.run()
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).
from facelib import WebcamEmotionDetector
detector = WebcamEmotionDetector()
detector.run()
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)
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 |
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:
add_from_webcam
: it takes 4 images and saves them on facebank. from facelib import add_from_webcam
add_from_webcam(person_name='sajjad')
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
from facelib import WebcamVerify
verifier = WebcamVerify(update=True)
verifier.run()
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