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import os |
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import gdown |
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import numpy as np |
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from deepface.basemodels import VGGFace |
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from deepface.commons import package_utils, folder_utils |
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from deepface.models.Demography import Demography |
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from deepface.commons import logger as log |
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logger = log.get_singletonish_logger() |
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tf_version = package_utils.get_tf_major_version() |
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if tf_version == 1: |
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from keras.models import Model, Sequential |
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from keras.layers import Convolution2D, Flatten, Activation |
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else: |
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from tensorflow.keras.models import Model, Sequential |
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from tensorflow.keras.layers import Convolution2D, Flatten, Activation |
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class ApparentAgeClient(Demography): |
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""" |
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Age model class |
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""" |
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def __init__(self): |
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self.model = load_model() |
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self.model_name = "Age" |
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def predict(self, img: np.ndarray) -> np.float64: |
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age_predictions = self.model.predict(img, verbose=0)[0, :] |
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return find_apparent_age(age_predictions) |
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def load_model( |
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url="https://github.com/serengil/deepface_models/releases/download/v1.0/age_model_weights.h5", |
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) -> Model: |
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""" |
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Construct age model, download its weights and load |
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Returns: |
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model (Model) |
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""" |
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model = VGGFace.base_model() |
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classes = 101 |
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base_model_output = Sequential() |
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base_model_output = Convolution2D(classes, (1, 1), name="predictions")(model.layers[-4].output) |
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base_model_output = Flatten()(base_model_output) |
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base_model_output = Activation("softmax")(base_model_output) |
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age_model = Model(inputs=model.input, outputs=base_model_output) |
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home = folder_utils.get_deepface_home() |
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if os.path.isfile(home + "/.deepface/weights/age_model_weights.h5") != True: |
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logger.info("age_model_weights.h5 will be downloaded...") |
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output = home + "/.deepface/weights/age_model_weights.h5" |
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gdown.download(url, output, quiet=False) |
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age_model.load_weights(home + "/.deepface/weights/age_model_weights.h5") |
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return age_model |
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def find_apparent_age(age_predictions: np.ndarray) -> np.float64: |
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""" |
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Find apparent age prediction from a given probas of ages |
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Args: |
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age_predictions (?) |
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Returns: |
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apparent_age (float) |
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""" |
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output_indexes = np.array(list(range(0, 101))) |
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apparent_age = np.sum(age_predictions * output_indexes) |
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return apparent_age |
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