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verbose off anyway

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Sefik Ilkin Serengil 2022-10-23 17:23:58 +01:00
parent c5fd3fafc4
commit f26bb45b09
1 changed files with 5 additions and 6 deletions
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deepface/DeepFace.py
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@ -367,7 +367,6 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models =
resp_objects = []
disable_option = (False if len(img_paths) > 1 else True) or not prog_bar
verbose = int(not disable_option)
global_pbar = tqdm(range(0,len(img_paths)), desc='Analyzing', disable = disable_option)
@ -396,7 +395,7 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models =
emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
img, region = functions.preprocess_face(img = img_path, target_size = (48, 48), grayscale = True, enforce_detection = enforce_detection, detector_backend = detector_backend, return_region = True)
emotion_predictions = models['emotion'].predict(img, verbose=verbose)[0,:]
emotion_predictions = models['emotion'].predict(img, verbose=0)[0,:]
sum_of_predictions = emotion_predictions.sum()
@ -413,7 +412,7 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models =
if img_224 is None:
img_224, region = functions.preprocess_face(img = img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection, detector_backend = detector_backend, return_region = True)
age_predictions = models['age'].predict(img_224, verbose=verbose)[0,:]
age_predictions = models['age'].predict(img_224, verbose=0)[0,:]
apparent_age = Age.findApparentAge(age_predictions)
resp_obj["age"] = int(apparent_age) #int cast is for the exception - object of type 'float32' is not JSON serializable
@ -423,7 +422,7 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models =
if img_224 is None:
img_224, region = functions.preprocess_face(img = img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection, detector_backend = detector_backend, return_region = True)
gender_predictions = models['gender'].predict(img_224, verbose=verbose)[0,:]
gender_predictions = models['gender'].predict(img_224, verbose=0)[0,:]
gender_labels = ["Woman", "Man"]
resp_obj["gender"] = {}
@ -437,7 +436,7 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models =
elif action == 'race':
if img_224 is None:
img_224, region = functions.preprocess_face(img = img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection, detector_backend = detector_backend, return_region = True) #just emotion model expects grayscale images
race_predictions = models['race'].predict(img_224, verbose=verbose)[0,:]
race_predictions = models['race'].predict(img_224, verbose=0)[0,:]
race_labels = ['asian', 'indian', 'black', 'white', 'middle eastern', 'latino hispanic']
sum_of_predictions = race_predictions.sum()
@ -766,7 +765,7 @@ def represent(img_path, model_name = 'VGG-Face', model = None, enforce_detection
#---------------------------------
#represent
embedding = model.predict(img)[0].tolist()
embedding = model.predict(img, verbose=0)[0].tolist()
return embedding