64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
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import os
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import gdown
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import numpy as np
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import tensorflow as tf
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from deepface.basemodels import VGGFace
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from deepface.commons import functions
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# ----------------------------------------
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# dependency configurations
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tf_version = int(tf.__version__.split(".", maxsplit=1)[0])
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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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elif tf_version == 2:
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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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# ----------------------------------------
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def loadModel(
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url="https://github.com/serengil/deepface_models/releases/download/v1.0/age_model_weights.h5",
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):
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model = VGGFace.baseModel()
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# --------------------------
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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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# --------------------------
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age_model = Model(inputs=model.input, outputs=base_model_output)
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# --------------------------
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# load weights
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home = functions.get_deepface_home()
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if os.path.isfile(home + "/.deepface/weights/age_model_weights.h5") != True:
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print("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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# --------------------------
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def findApparentAge(age_predictions):
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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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