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import os
import gdown
from deepface.commons import package_utils, folder_utils
from deepface.models.FacialRecognition import FacialRecognition
from deepface.commons import logger as log
logger = log.get_singletonish_logger()
tf_version = package_utils.get_tf_major_version()
if tf_version == 1:
from keras.models import Model
from keras.layers import (
Conv2D,
Activation,
Input,
Add,
MaxPooling2D,
Flatten,
Dense,
Dropout,
)
else:
from tensorflow.keras.models import Model
from tensorflow.keras.layers import (
Conv2D,
Activation,
Input,
Add,
MaxPooling2D,
Flatten,
Dense,
Dropout,
)
# pylint: disable=line-too-long
# -------------------------------------
# pylint: disable=too-few-public-methods
class DeepIdClient(FacialRecognition):
"""
DeepId model class
"""
def __init__(self):
self.model = load_model()
self.model_name = "DeepId"
self.input_shape = (47, 55)
self.output_shape = 160
def load_model(
url="https://github.com/serengil/deepface_models/releases/download/v1.0/deepid_keras_weights.h5",
) -> Model:
"""
Construct DeepId model, download its weights and load
"""
myInput = Input(shape=(55, 47, 3))
x = Conv2D(20, (4, 4), name="Conv1", activation="relu", input_shape=(55, 47, 3))(myInput)
x = MaxPooling2D(pool_size=2, strides=2, name="Pool1")(x)
x = Dropout(rate=0.99, name="D1")(x)
x = Conv2D(40, (3, 3), name="Conv2", activation="relu")(x)
x = MaxPooling2D(pool_size=2, strides=2, name="Pool2")(x)
x = Dropout(rate=0.99, name="D2")(x)
x = Conv2D(60, (3, 3), name="Conv3", activation="relu")(x)
x = MaxPooling2D(pool_size=2, strides=2, name="Pool3")(x)
x = Dropout(rate=0.99, name="D3")(x)
x1 = Flatten()(x)
fc11 = Dense(160, name="fc11")(x1)
x2 = Conv2D(80, (2, 2), name="Conv4", activation="relu")(x)
x2 = Flatten()(x2)
fc12 = Dense(160, name="fc12")(x2)
y = Add()([fc11, fc12])
y = Activation("relu", name="deepid")(y)
model = Model(inputs=[myInput], outputs=y)
# ---------------------------------
home = folder_utils.get_deepface_home()
if os.path.isfile(home + "/.deepface/weights/deepid_keras_weights.h5") != True:
logger.info("deepid_keras_weights.h5 will be downloaded...")
output = home + "/.deepface/weights/deepid_keras_weights.h5"
gdown.download(url, output, quiet=False)
model.load_weights(home + "/.deepface/weights/deepid_keras_weights.h5")
return model
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