97 lines
2.4 KiB
Python
97 lines
2.4 KiB
Python
# project dependencies
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from deepface.commons import package_utils, weight_utils
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from deepface.models.FacialRecognition import FacialRecognition
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from deepface.commons.logger import Logger
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logger = 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
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from keras.layers import (
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Conv2D,
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Activation,
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Input,
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Add,
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MaxPooling2D,
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Flatten,
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Dense,
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Dropout,
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)
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else:
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from tensorflow.keras.models import Model
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from tensorflow.keras.layers import (
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Conv2D,
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Activation,
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Input,
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Add,
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MaxPooling2D,
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Flatten,
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Dense,
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Dropout,
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)
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# pylint: disable=line-too-long
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# -------------------------------------
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# pylint: disable=too-few-public-methods
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class DeepIdClient(FacialRecognition):
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"""
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DeepId 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 = "DeepId"
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self.input_shape = (47, 55)
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self.output_shape = 160
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def load_model(
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url="https://github.com/serengil/deepface_models/releases/download/v1.0/deepid_keras_weights.h5",
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) -> Model:
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"""
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Construct DeepId model, download its weights and load
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"""
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myInput = Input(shape=(55, 47, 3))
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x = Conv2D(20, (4, 4), name="Conv1", activation="relu", input_shape=(55, 47, 3))(myInput)
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x = MaxPooling2D(pool_size=2, strides=2, name="Pool1")(x)
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x = Dropout(rate=0.99, name="D1")(x)
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x = Conv2D(40, (3, 3), name="Conv2", activation="relu")(x)
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x = MaxPooling2D(pool_size=2, strides=2, name="Pool2")(x)
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x = Dropout(rate=0.99, name="D2")(x)
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x = Conv2D(60, (3, 3), name="Conv3", activation="relu")(x)
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x = MaxPooling2D(pool_size=2, strides=2, name="Pool3")(x)
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x = Dropout(rate=0.99, name="D3")(x)
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x1 = Flatten()(x)
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fc11 = Dense(160, name="fc11")(x1)
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x2 = Conv2D(80, (2, 2), name="Conv4", activation="relu")(x)
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x2 = Flatten()(x2)
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fc12 = Dense(160, name="fc12")(x2)
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y = Add()([fc11, fc12])
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y = Activation("relu", name="deepid")(y)
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model = Model(inputs=[myInput], outputs=y)
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# ---------------------------------
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weight_file = weight_utils.download_weights_if_necessary(
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file_name="deepid_keras_weights.h5", source_url=url
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)
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model = weight_utils.load_model_weights(
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model=model, weight_file=weight_file
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)
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return model
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