auto_size_text=True,
font='helvetica',
element_justification='center',
icon=icon_logo64).Layout(layout)
# Event Loop
while True:
event, values = window.Read()
if event in (None, 'Exit'):
break
if event == 'Submit':
# Update the "output" text element to be the value of "input" element
window['-OUTPUT-'].update(values['-INPUT-'])
elif event == 'Start Human Emotion Detection':
DeepFace.stream("DATABASE")
text_input = values['-INPUT-']
elif event == "Upload Subject's Image":
sg.popup_get_file('Select Image:',
"Upload Subject's Image",
icon=icon_logo64)
elif event == 'Onboard Subject':
text_input = values['-INPUT-']
subject_onboarding(text_input)
# Close Window
window.Close()
#pip install deepface
from deepface import DeepFace
DeepFace.stream('dataset')
from deepface import DeepFace
DeepFace.stream("dataset")
from deepface import DeepFace
# result = DeepFace.stream("face_images/Trump/4.jpg")
result = DeepFace.stream("face_images/Gates/1.jpg")
def faceStream(database):
DeepFace.stream(database)
from deepface import DeepFace
DeepFace.stream("database")
from deepface import DeepFace
DeepFace.stream(
'dataset',
model_name='Facenet',
)
from deepface import DeepFace
DeepFace.stream("dataset") #opencv
#DeepFace.stream("dataset", detector_backend = 'opencv')
#DeepFace.stream("dataset", detector_backend = 'ssd')
#DeepFace.stream("dataset", detector_backend = 'mtcnn')
#DeepFace.stream("dataset", detector_backend = 'dlib')
#DeepFace.stream("dataset", detector_backend = 'retinaface')
from deepface import DeepFace DeepFace.stream(db_path="face_data", model_name="VGG-Face")
