diff --git a/deepface/DeepFace.py b/deepface/DeepFace.py index 7d8040c9f..39165668e 100644 --- a/deepface/DeepFace.py +++ b/deepface/DeepFace.py @@ -367,6 +367,7 @@ 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) @@ -395,7 +396,7 @@ 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)[0,:] + emotion_predictions = models['emotion'].predict(img, verbose=verbose)[0,:] 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: 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) 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: 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"] resp_obj["gender"] = {} @@ -436,7 +437,7 @@ 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)[0,:] + race_predictions = models['race'].predict(img_224, verbose=verbose)[0,:] race_labels = ['asian', 'indian', 'black', 'white', 'middle eastern', 'latino hispanic'] sum_of_predictions = race_predictions.sum()