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")