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import os |
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import gdown |
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from deepface.commons import package_utils, folder_utils |
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from deepface.models.FacialRecognition import FacialRecognition |
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from deepface.commons import logger as log |
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logger = log.get_singletonish_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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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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home = folder_utils.get_deepface_home() |
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if os.path.isfile(home + "/.deepface/weights/deepid_keras_weights.h5") != True: |
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logger.info("deepid_keras_weights.h5 will be downloaded...") |
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output = home + "/.deepface/weights/deepid_keras_weights.h5" |
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gdown.download(url, output, quiet=False) |
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model.load_weights(home + "/.deepface/weights/deepid_keras_weights.h5") |
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return model |
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