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feat(verbose): Silent tensorflow models output to stdout

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Hoan Nguyen 2022-10-21 10:01:12 +02:00 committed by GitHub
parent 56adc351f2
commit a807284e2a
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1 changed files with 5 additions and 4 deletions
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deepface/DeepFace.py
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@ -367,6 +367,7 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models =
resp_objects = [] resp_objects = []
disable_option = (False if len(img_paths) > 1 else True) or not prog_bar 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) global_pbar = tqdm(range(0,len(img_paths)), desc='Analyzing', disable = disable_option)
@ -395,7 +396,7 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models =
emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'] 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) 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)[0,:] emotion_predictions = models['emotion'].predict(img, verbose=verbose)[0,:]
sum_of_predictions = emotion_predictions.sum() sum_of_predictions = emotion_predictions.sum()
@ -412,7 +413,7 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models =
if img_224 is None: 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) 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)[0,:] age_predictions = models['age'].predict(img_224, verbose=verbose)[0,:]
apparent_age = Age.findApparentAge(age_predictions) 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 resp_obj["age"] = int(apparent_age) #int cast is for the exception - object of type 'float32' is not JSON serializable
@ -422,7 +423,7 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models =
if img_224 is None: 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) 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)[0,:] gender_predictions = models['gender'].predict(img_224, verbose=verbose)[0,:]
gender_labels = ["Woman", "Man"] gender_labels = ["Woman", "Man"]
resp_obj["gender"] = {} resp_obj["gender"] = {}
@ -436,7 +437,7 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models =
elif action == 'race': elif action == 'race':
if img_224 is None: 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 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)[0,:] race_predictions = models['race'].predict(img_224, verbose=verbose)[0,:]
race_labels = ['asian', 'indian', 'black', 'white', 'middle eastern', 'latino hispanic'] race_labels = ['asian', 'indian', 'black', 'white', 'middle eastern', 'latino hispanic']
sum_of_predictions = race_predictions.sum() sum_of_predictions = race_predictions.sum()