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real time implementation

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serengil 2020-12-04 16:50:11 +03:00
parent e44e418eba
commit b1245e7055
2 changed files with 24 additions and 45 deletions
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13
deepface/DeepFace.py
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@ -584,11 +584,20 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
return None
def stream(db_path = '', model_name ='VGG-Face', distance_metric = 'cosine', enable_face_analysis = True):
def stream(db_path = '', model_name ='VGG-Face', distance_metric = 'cosine'
, enable_face_analysis = True
, source = 0, time_threshold = 5, frame_threshold = 5):
if time_threshold < 1:
raise ValueError("time_threshold must be greater than the value 1 but you passed "+str(time_threshold))
if frame_threshold < 1:
raise ValueError("frame_threshold must be greater than the value 1 but you passed "+str(frame_threshold))
functions.initialize_detector(detector_backend = 'opencv')
realtime.analysis(db_path, model_name, distance_metric, enable_face_analysis)
realtime.analysis(db_path, model_name, distance_metric, enable_face_analysis
, source = source, time_threshold = time_threshold, frame_threshold = frame_threshold)
def detectFace(img_path, detector_backend = 'mtcnn'):

56
deepface/commons/realtime.py
View File

@ -9,14 +9,14 @@ import re
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID
from deepface.extendedmodels import Age, Gender, Race, Emotion
from deepface import DeepFace
from deepface.extendedmodels import Age
from deepface.commons import functions, realtime, distance as dst
def analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
def analysis(db_path, model_name, distance_metric, enable_face_analysis = True
, source = 0, time_threshold = 5, frame_threshold = 5):
input_shape = (224, 224)
input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
input_shape = (224, 224); input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
text_color = (255,255,255)
@ -37,41 +37,13 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
#------------------------
if len(employees) > 0:
if model_name == 'VGG-Face':
print("Using VGG-Face model backend and", distance_metric,"distance.")
model = VGGFace.loadModel()
input_shape = (224, 224)
elif model_name == 'OpenFace':
print("Using OpenFace model backend", distance_metric,"distance.")
model = OpenFace.loadModel()
input_shape = (96, 96)
model = DeepFace.build_model(model_name)
print(model_name," is built")
elif model_name == 'Facenet':
print("Using Facenet model backend", distance_metric,"distance.")
model = Facenet.loadModel()
input_shape = (160, 160)
elif model_name == 'DeepFace':
print("Using FB DeepFace model backend", distance_metric,"distance.")
model = FbDeepFace.loadModel()
input_shape = (152, 152)
elif model_name == 'DeepID':
print("Using DeepID model backend", distance_metric,"distance.")
model = DeepID.loadModel()
input_shape = (55, 47)
elif model_name == 'Dlib':
print("Using Dlib model backend", distance_metric,"distance.")
from deepface.basemodels.DlibResNet import DlibResNet
model = DlibResNet()
input_shape = (150, 150)
else:
raise ValueError("Invalid model_name passed - ", model_name)
#------------------------
input_shape = functions.find_input_shape(model)
input_shape_x = input_shape[0]
input_shape_y = input_shape[1]
@ -84,14 +56,14 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
if enable_face_analysis == True:
tic = time.time()
emotion_model = Emotion.loadModel()
emotion_model = DeepFace.build_model('Emotion')
print("Emotion model loaded")
age_model = Age.loadModel()
age_model = DeepFace.build_model('Age')
print("Age model loaded")
gender_model = Gender.loadModel()
gender_model = DeepFace.build_model('Gender')
print("Gender model loaded")
toc = time.time()
@ -128,7 +100,6 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
#-----------------------
time_threshold = 5; frame_threshold = 5
pivot_img_size = 112 #face recognition result image
#-----------------------
@ -145,8 +116,7 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
freezed_frame = 0
tic = time.time()
cap = cv2.VideoCapture(0) #webcam
#cap = cv2.VideoCapture("C:/Users/IS96273/Desktop/skype-video-1.mp4") #video
cap = cv2.VideoCapture(source) #webcam
while(True):
ret, img = cap.read()