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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.models.Demography import Demography
from deepface.commons import logger as log
logger = log.get_singletonish_logger()
# ----------------------------------------
# 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 + "/.deepface/weights/age_model_weights.h5") != True:
logger.info("age_model_weights.h5 will be downloaded...")
output = home + "/.deepface/weights/age_model_weights.h5"
gdown.download(url, output, quiet=False)
age_model.load_weights(home + "/.deepface/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
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