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