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common boosting function

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serengil 2020-11-30 14:20:17 +03:00
parent 5963ea4d2b
commit 0b257841cb
3 changed files with 23 additions and 44 deletions
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33
deepface/DeepFace.py
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@ -145,23 +145,10 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
ensemble_features_string += "]" ensemble_features_string += "]"
#------------------------------- #-------------------------------
#find deepface path
deepface_ensemble = functions.boosting_method()
home = str(Path.home()) #---------------------------
if os.path.isfile(home+'/.deepface/weights/face-recognition-ensemble-model.txt') != True:
print("face-recognition-ensemble-model.txt will be downloaded...")
url = 'https://raw.githubusercontent.com/serengil/deepface/master/deepface/models/face-recognition-ensemble-model.txt'
output = home+'/.deepface/weights/face-recognition-ensemble-model.txt'
gdown.download(url, output, quiet=False)
ensemble_model_path = home+'/.deepface/weights/face-recognition-ensemble-model.txt'
#print(ensemble_model_path)
#-------------------------------
deepface_ensemble = lgb.Booster(model_file = ensemble_model_path)
prediction = deepface_ensemble.predict(np.expand_dims(np.array(ensemble_features), axis=0))[0] prediction = deepface_ensemble.predict(np.expand_dims(np.array(ensemble_features), axis=0))[0]
@ -680,18 +667,8 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
x = df[feature_names].values x = df[feature_names].values
#---------------------------------- #----------------------------------
#lightgbm model #lightgbm model
home = str(Path.home()) deepface_ensemble = functions.boosting_method()
if os.path.isfile(home+'/.deepface/weights/face-recognition-ensemble-model.txt') != True:
print("face-recognition-ensemble-model.txt will be downloaded...")
url = 'https://raw.githubusercontent.com/serengil/deepface/master/deepface/models/face-recognition-ensemble-model.txt'
output = home+'/.deepface/weights/face-recognition-ensemble-model.txt'
gdown.download(url, output, quiet=False)
ensemble_model_path = home+'/.deepface/weights/face-recognition-ensemble-model.txt'
deepface_ensemble = lgb.Booster(model_file = ensemble_model_path)
y = deepface_ensemble.predict(x) y = deepface_ensemble.predict(x)

18
deepface/commons/functions.py
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@ -447,3 +447,21 @@ def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_dete
img_pixels /= 255 #normalize input in [0, 1] img_pixels /= 255 #normalize input in [0, 1]
return img_pixels return img_pixels
def boosting_method():
import lightgbm as lgb #lightgbm==2.3.1
home = str(Path.home())
if os.path.isfile(home+'/.deepface/weights/face-recognition-ensemble-model.txt') != True:
print("face-recognition-ensemble-model.txt will be downloaded...")
url = 'https://raw.githubusercontent.com/serengil/deepface/master/deepface/models/face-recognition-ensemble-model.txt'
output = home+'/.deepface/weights/face-recognition-ensemble-model.txt'
gdown.download(url, output, quiet=False)
ensemble_model_path = home+'/.deepface/weights/face-recognition-ensemble-model.txt'
deepface_ensemble = lgb.Booster(model_file = ensemble_model_path)
return deepface_ensemble

16
tests/unit_tests.py
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@ -242,19 +242,3 @@ print(df)
#----------------------------------- #-----------------------------------
print("--------------------------") print("--------------------------")
print("Different face detector backends")
backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
for backend in backends:
tic = time.time()
processed_img = functions.preprocess_face(img = "dataset/img11.jpg", detector_backend = backend)
toc = time.time()
print("Backend ", backend, " is done in ", toc-tic," seconds")
#-----------------------------------
print("--------------------------")