def add_from_folder(folder_path='./', person_name='unknow'):
print('only a face in each image and all image from the same person')
dir_path = Path(folder_path)
if not dir_path.is_dir():
exit('dir does not exists !!')
# create facebank folder if is not exists
save_path = Path(os.path.dirname(os.path.realpath(__file__)),
'models/data/facebank')
if not save_path.exists():
save_path.mkdir()
save_path = Path(f'{save_path}/{person_name}')
if not save_path.exists():
save_path.mkdir()
print('loading ...')
# init detector
detector = FaceDetector()
counter = 0
for img_path in dir_path.iterdir():
img = cv2.imread(str(img_path))
if img is None:
raise Exception('this image has a problem: ', img_path)
face = detector.detect_align(img)[0].cpu().numpy()
if len(face.shape) > 1:
save_name = f'{save_path}/{dir_path.name}_{counter}.jpg'
cv2.imwrite(save_name, face[0])
counter += 1
else:
print(img_path, 'in this image did not detect any face')
print('images saved in this path: ', save_path)
def add_from_webcam(person_name='unknow', camera_index=0):
print('loading ...')
# create facebank folder if is not exists
save_path = Path(os.path.dirname(os.path.realpath(__file__)),
'models/data/facebank')
if not save_path.exists():
save_path.mkdir()
# create a new folder with (name) for new person
save_path = save_path / person_name
if not save_path.exists():
save_path.mkdir()
print('for exit: use q keyword')
# init camera
cap = cv2.VideoCapture(camera_index)
cap.set(3, 1280)
cap.set(4, 720)
# init detector
detector = FaceDetector()
count = 4
print('type q for exit')
while cap.isOpened():
ret, frame = cap.read()
if ret == False:
raise Exception(
'the camera not recognized: change camera_index param to ' +
str(0 if camera_index == 1 else 1))
frame = cv2.putText(frame, f'Press t to take {count} pictures',
(10, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 100, 0),
3, cv2.LINE_AA)
if cv2.waitKey(1) & 0xFF == ord('t'):
count -= 1
faces = detector.detect_align(frame)[0].cpu().numpy()
if len(faces.shape) > 1:
cv2.imwrite(f'{save_path}/{person_name}_{count}.jpg', faces[0])
if count <= 0:
break
else:
print('there is not face in this frame')
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.imshow("add new person from camera", frame)
cap.release()
cv2.destroyAllWindows()
print('images saved in this path: ', save_path)
class WebcamAgeGenderEstimator:
def __init__(
self,
device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")):
print('loading ...')
self.face_detector = FaceDetector(device=device)
self.age_gender_detector = AgeGenderEstimator(device=device)
def run(self, camera_index=0):
cap = cv2.VideoCapture(camera_index)
cap.set(3, 1280)
cap.set(4, 720)
print('type q for exit')
while cap.isOpened():
ret, frame = cap.read()
if ret == False:
raise Exception(
'the camera not recognized: change camera_index param to '
+ str(0 if camera_index == 1 else 1))
faces, boxes, scores, landmarks = self.face_detector.detect_align(
frame)
if len(faces.shape) > 1:
genders, ages = self.age_gender_detector.detect(faces)
for i, b in enumerate(boxes):
special_draw(frame,
b,
landmarks[i],
name=genders[i] + ' ' + str(ages[i]))
cv2.imshow('frame', frame)
if cv2.waitKey(1) == ord('q'):
break
cv2.destroyAllWindows()
def __init__(
self,
update=True,
tta=True,
device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")):
print('loading ...')
self.tta = tta
self.detector = FaceDetector(device=device)
self.conf = get_config()
self.conf.device = device
recognizer = FaceRecognizer(self.conf)
recognizer.model.eval()
self.recognizer = recognizer
if update:
self.targets, self.names = update_facebank(self.conf,
recognizer.model,
self.detector,
tta=self.tta)
else:
self.targets, self.names = load_facebank(self.conf)
def __init__(
self,
device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")):
print('loading ...')
self.face_detector = FaceDetector(face_size=(224, 224), device=device)
self.emotion_detector = EmotionDetector(device=device)
"--update",
default=True,
help="whether perform update the facebank")
parser.add_argument("-tta",
"--tta",
default=True,
help="whether test time augmentation")
parser.add_argument('-m',
'--mobilenet',
default=False,
help="use mobilenet for backbone")
args = parser.parse_args()
conf = get_config(training=False)
detector = FaceDetector(name='mobilenet',
weight_path='../Retinaface/weights/mobilenet.pth',
device=conf.device)
conf.use_mobilfacenet = args.mobilenet
face_rec = FaceRecognizer(conf, inference=True)
face_rec.threshold = args.threshold
face_rec.model.eval()
if args.update:
targets, names = update_facebank(conf,
face_rec.model,
detector,
tta=args.tta)
else:
targets, names = load_facebank(conf)
# init camera
from facelib import FaceDetector
from facelib import EmotionDetector
from facelib import special_draw
import cv2
face_detector = FaceDetector(name='mobilenet',
weight_path='../Retinaface/weights/mobilenet.pth',
device='cuda',
face_size=(224, 224))
emotion_detector = EmotionDetector(name='densnet121',
weight_path='weights/densnet121.pth',
device='cuda')
vid = cv2.VideoCapture(1)
vid.set(3, 1280)
vid.set(4, 720)
while True:
ret, frame = vid.read()
faces, boxes, scores, landmarks = face_detector.detect_align(frame)
if len(faces.shape) > 1:
emotions, emo_probs = emotion_detector.detect_emotion(faces)
for i, b in enumerate(boxes):
special_draw(frame,
b,
landmarks[i],
name=emotions[i],
score=emo_probs[i])
cv2.imshow('frame', frame)
if cv2.waitKey(1) == ord('q'):
break
def __init__(
self,
device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")):
print('loading ...')
self.face_detector = FaceDetector(device=device)
self.age_gender_detector = AgeGenderEstimator(device=device)
class WebcamVerify:
"""
WebcamVerify: face verfication with cv2
if you add new person in to facebank
pass update True
"""
def __init__(
self,
update=True,
tta=True,
device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")):
print('loading ...')
self.tta = tta
self.detector = FaceDetector(device=device)
self.conf = get_config()
self.conf.device = device
recognizer = FaceRecognizer(self.conf)
recognizer.model.eval()
self.recognizer = recognizer
if update:
self.targets, self.names = update_facebank(self.conf,
recognizer.model,
self.detector,
tta=self.tta)
else:
self.targets, self.names = load_facebank(self.conf)
def run(self, camera_index=0):
if len(self.targets) < 1:
raise Exception(
"you don't have any person in facebank: add new person with 'add_from_webcam' or 'add_from_folder' function"
)
cap = cv2.VideoCapture(camera_index)
cap.set(3, 1280)
cap.set(4, 720)
# frame rate 6 due to my laptop is quite slow...
print('type q for exit')
while cap.isOpened():
ret, frame = cap.read()
if ret == False:
raise Exception(
'the camera not recognized: change camera_index param to '
+ str(0 if camera_index == 1 else 1))
faces, boxes, scores, landmarks = self.detector.detect_align(frame)
if len(faces.shape) > 1:
results, score = self.recognizer.infer(self.conf,
faces,
self.targets,
tta=self.tta)
for idx, bbox in enumerate(boxes):
special_draw(frame, bbox, landmarks[idx],
self.names[results[idx] + 1], score[idx])
cv2.imshow('face Capture', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
print('only a face in each image and all image from the same person')
dir_path = Path(args.path)
if not dir_path.is_dir():
exit('dir does not exists !!')
# create facebank folder if is not exists
save_path = Path('models/data/facebank')
if not save_path.exists():
save_path.mkdir()
save_path = Path(f'models/data/facebank/{dir_path.name}')
if not save_path.exists():
save_path.mkdir()
# init detector
detector = FaceDetector(name='mobilenet',
weight_path='../Retinaface/weights/mobilenet.pth',
device='cuda')
counter = 0
for img_path in dir_path.iterdir():
img = cv2.imread(str(img_path))
face = detector.detect_align(img)[0].cpu().numpy()
if len(face.shape) > 1:
save_name = f'{save_path}/{dir_path.name}_{counter}.jpg'
cv2.imwrite(save_name, face[0])
counter += 1
else:
print(img_path, 'in this image did not detect any face')