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face recognition models as global

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Sefik Ilkin Serengil 2021-06-24 00:11:06 +03:00
parent 9f31012ef4
commit e0809bf490
2 changed files with 17 additions and 11 deletions
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15
deepface/DeepFace.py
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@ -35,6 +35,8 @@ def build_model(model_name):
built deepface model built deepface model
""" """
global model_obj, model_label
models = { models = {
'VGG-Face': VGGFace.loadModel, 'VGG-Face': VGGFace.loadModel,
'OpenFace': OpenFace.loadModel, 'OpenFace': OpenFace.loadModel,
@ -43,22 +45,25 @@ def build_model(model_name):
'DeepID': DeepID.loadModel, 'DeepID': DeepID.loadModel,
'Dlib': DlibWrapper.loadModel, 'Dlib': DlibWrapper.loadModel,
'ArcFace': ArcFace.loadModel, 'ArcFace': ArcFace.loadModel,
'Emotion': Emotion.loadModel, 'Emotion': Emotion.loadModel,
'Age': Age.loadModel, 'Age': Age.loadModel,
'Gender': Gender.loadModel, 'Gender': Gender.loadModel,
'Race': Race.loadModel 'Race': Race.loadModel
} }
model = models.get(model_name) if not "model_obj" in globals() or model_label != model_name:
if model: model_obj = models.get(model_name)
model = model()
if model_obj:
model_obj = model_obj()
model_label = model_name
#print('Using {} model backend'.format(model_name)) #print('Using {} model backend'.format(model_name))
return model
else: else:
raise ValueError('Invalid model_name passed - {}'.format(model_name)) raise ValueError('Invalid model_name passed - {}'.format(model_name))
return model_obj
def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'opencv', align = True): def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'opencv', align = True):
""" """

7
tests/unit_tests.py
View File

@ -186,8 +186,8 @@ metrics = ['cosine', 'euclidean', 'euclidean_l2']
passed_tests = 0; test_cases = 0 passed_tests = 0; test_cases = 0
for model in models: for model in models:
prebuilt_model = DeepFace.build_model(model) #prebuilt_model = DeepFace.build_model(model)
print(model," is built") #print(model," is built")
for metric in metrics: for metric in metrics:
for instance in dataset: for instance in dataset:
img1 = instance[0] img1 = instance[0]
@ -195,7 +195,8 @@ for model in models:
result = instance[2] result = instance[2]
resp_obj = DeepFace.verify(img1, img2 resp_obj = DeepFace.verify(img1, img2
, model_name = model, model = prebuilt_model , model_name = model
#, model = prebuilt_model
, distance_metric = metric) , distance_metric = metric)
prediction = resp_obj["verified"] prediction = resp_obj["verified"]