90 lines
2.5 KiB
Python
90 lines
2.5 KiB
Python
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# 3rd party dependencies
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import numpy as np
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# project dependencies
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from deepface.models.facial_recognition import VGGFace
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from deepface.commons import package_utils, weight_utils
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from deepface.models.Demography import Demography
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from deepface.commons.logger import Logger
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logger = Logger()
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# ----------------------------------------
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# dependency configurations
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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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# ----------------------------------------
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# pylint: disable=too-few-public-methods
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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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# model.predict causes memory issue when it is called in a for loop
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# age_predictions = self.model.predict(img, verbose=0)[0, :]
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age_predictions = self.model(img, training=False).numpy()[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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# --------------------------
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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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weight_file = weight_utils.download_weights_if_necessary(
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file_name="age_model_weights.h5", source_url=url
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)
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age_model = weight_utils.load_model_weights(
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model=age_model, weight_file=weight_file
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)
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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.arange(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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