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global model building function created
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serengil
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@ -16,9 +16,29 @@ from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID
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from deepface.extendedmodels import Age, Gender, Race, Emotion
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from deepface.commons import functions, realtime, distance as dst
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def DlibResNet_():
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from deepface.basemodels.DlibResNet import DlibResNet
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return DlibResNet()
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def build_model(model_name):
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models = {
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'VGG-Face': VGGFace.loadModel,
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'OpenFace': OpenFace.loadModel,
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'Facenet': Facenet.loadModel,
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'DeepFace': FbDeepFace.loadModel,
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'DeepID': DeepID.loadModel,
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'Dlib': DlibResNet_,
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'Emotion': Emotion.loadModel,
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'Age': Age.loadModel,
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'Gender': Gender.loadModel,
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'Race': Race.loadModel
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}
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model = models.get(model_name)
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if model:
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model = model()
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print('Using {} model backend'.format(model_name))
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return model
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else:
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raise ValueError('Invalid model_name passed - {}'.format(model_name))
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def verify(img1_path, img2_path = '', model_name='VGG-Face', distance_metric='cosine',
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model=None, enforce_detection=True, detector_backend = 'opencv'):
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@ -55,16 +75,16 @@ def verify(img1_path, img2_path = '', model_name='VGG-Face', distance_metric='co
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if index == 0:
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model_pbar.set_description("Loading VGG-Face")
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model["VGG-Face"] = VGGFace.loadModel()
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model["VGG-Face"] = build_model('VGG-Face')
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elif index == 1:
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model_pbar.set_description("Loading Google FaceNet")
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model["Facenet"] = Facenet.loadModel()
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model["Facenet"] = build_model('Facenet')
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elif index == 2:
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model_pbar.set_description("Loading OpenFace")
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model["OpenFace"] = OpenFace.loadModel()
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model["OpenFace"] = build_model('OpenFace')
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elif index == 3:
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model_pbar.set_description("Loading Facebook DeepFace")
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model["DeepFace"] = FbDeepFace.loadModel()
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model["DeepFace"] = build_model('DeepFace')
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#--------------------------
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#validate model dictionary because it might be passed from input as pre-trained
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@ -204,22 +224,7 @@ def verify(img1_path, img2_path = '', model_name='VGG-Face', distance_metric='co
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#ensemble learning disabled
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if model == None:
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models = {
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'VGG-Face': VGGFace.loadModel,
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'OpenFace': OpenFace.loadModel,
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'Facenet': Facenet.loadModel,
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'DeepFace': FbDeepFace.loadModel,
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'DeepID': DeepID.loadModel,
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'Dlib': DlibResNet_
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}
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model = models.get(model_name)
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if model:
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model = model()
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print('Using {} model backend and {} distance'.format(model_name, distance_metric))
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else:
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raise ValueError('Invalid model_name passed - {}'.format(model_name))
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model = build_model(model_name)
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else: #model != None
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print("Already built model is passed")
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@ -350,6 +355,12 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True, detec
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#---------------------------------
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#build mtcnn model once
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if detector_backend == 'mtcnn':
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functions.load_mtcnn()
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#---------------------------------
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#if a specific target is not passed, then find them all
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if len(actions) == 0:
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actions= ['emotion', 'age', 'gender', 'race']
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@ -363,28 +374,28 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True, detec
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print("already built emotion model is passed")
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emotion_model = models['emotion']
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else:
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emotion_model = Emotion.loadModel()
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emotion_model = build_model('Emotion')
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if 'age' in actions:
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if 'age' in models:
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#print("already built age model is passed")
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age_model = models['age']
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else:
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age_model = Age.loadModel()
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age_model = build_model('Age')
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if 'gender' in actions:
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if 'gender' in models:
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print("already built gender model is passed")
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gender_model = models['gender']
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else:
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gender_model = Gender.loadModel()
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gender_model = build_model('Gender')
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if 'race' in actions:
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if 'race' in models:
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print("already built race model is passed")
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race_model = models['race']
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else:
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race_model = Race.loadModel()
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race_model = build_model('Race')
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#---------------------------------
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resp_objects = []
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@ -508,6 +519,11 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True, detec
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#return resp_objects
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def detectFace(img_path, detector_backend = 'opencv'):
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#build mtcnn model once
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if detector_backend == 'mtcnn':
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functions.load_mtcnn()
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img = functions.preprocess_face(img = img_path, detector_backend = detector_backend)[0] #preprocess_face returns (1, 224, 224, 3)
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return img[:, :, ::-1] #bgr to rgb
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@ -525,31 +541,21 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
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bulkProcess = False
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img_paths = [img_path]
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#-------------------------------
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#build mtcnn model once
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if detector_backend == 'mtcnn':
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functions.load_mtcnn()
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#-------------------------------
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if os.path.isdir(db_path) == True:
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#---------------------------------------
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if model == None:
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if model_name == 'VGG-Face':
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print("Using VGG-Face model backend and", distance_metric,"distance.")
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model = VGGFace.loadModel()
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elif model_name == 'OpenFace':
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print("Using OpenFace model backend", distance_metric,"distance.")
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model = OpenFace.loadModel()
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elif model_name == 'Facenet':
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print("Using Facenet model backend", distance_metric,"distance.")
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model = Facenet.loadModel()
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elif model_name == 'DeepFace':
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print("Using FB DeepFace model backend", distance_metric,"distance.")
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model = FbDeepFace.loadModel()
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elif model_name == 'DeepID':
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print("Using DeepID model backend", distance_metric,"distance.")
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model = DeepID.loadModel()
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elif model_name == 'Dlib':
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print("Using Dlib ResNet model backend", distance_metric,"distance.")
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from deepface.basemodels.DlibResNet import DlibResNet #this is not a must because it is very huge
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model = DlibResNet()
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elif model_name == 'Ensemble':
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if model_name == 'Ensemble':
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print("Ensemble learning enabled")
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#TODO: include DeepID in ensemble method
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@ -562,19 +568,20 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
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for index in pbar:
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if index == 0:
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pbar.set_description("Loading VGG-Face")
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models['VGG-Face'] = VGGFace.loadModel()
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models['VGG-Face'] = build_model('VGG-Face')
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elif index == 1:
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pbar.set_description("Loading FaceNet")
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models['Facenet'] = Facenet.loadModel()
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models['Facenet'] = build_model('Facenet')
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elif index == 2:
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pbar.set_description("Loading OpenFace")
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models['OpenFace'] = OpenFace.loadModel()
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models['OpenFace'] = build_model('OpenFace')
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elif index == 3:
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pbar.set_description("Loading DeepFace")
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models['DeepFace'] = FbDeepFace.loadModel()
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models['DeepFace'] = build_model('DeepFace')
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else: #model is not ensemble
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model = build_model(model_name)
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else:
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raise ValueError("Invalid model_name passed - ", model_name)
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else: #model != None
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print("Already built model is passed")
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@ -864,6 +871,10 @@ def allocateMemory():
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print("Analyzing your system...")
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functions.allocateMemory()
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def DlibResNet_():
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#this is not a regular Keras model.
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from deepface.basemodels.DlibResNet import DlibResNet
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return DlibResNet()
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#---------------------------
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#main
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@ -244,8 +244,8 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
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elif detector_backend == 'mtcnn':
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# mtcnn_detector = MTCNN()
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img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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# mtcnn_detector = MTCNN() #this is a global variable now
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img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) #mtcnn expects RGB but OpenCV read BGR
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detections = mtcnn_detector.detect_faces(img_rgb)
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if len(detections) > 0:
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@ -399,8 +399,8 @@ def align_face(img, detector_backend = 'opencv'):
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elif detector_backend == 'mtcnn':
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# mtcnn_detector = MTCNN()
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img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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# mtcnn_detector = MTCNN() #this is a global variable now
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img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) #mtcnn expects RGB but OpenCV read BGR
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detections = mtcnn_detector.detect_faces(img_rgb)
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if len(detections) > 0:
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