mirror of
https://github.com/serengil/deepface.git
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82 lines
2.4 KiB
Python
82 lines
2.4 KiB
Python
# built-in dependencies
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import os
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# 3rd party dependencies
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import gdown
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import numpy as np
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# project dependencies
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from deepface.basemodels import VGGFace
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from deepface.commons import package_utils, folder_utils
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from deepface.models.Demography import Demography
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from deepface.commons import logger as log
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logger = log.get_singletonish_logger()
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# --------------------------
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# pylint: disable=line-too-long
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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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# Labels for the ethnic phenotypes that can be detected by the model.
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labels = ["asian", "indian", "black", "white", "middle eastern", "latino hispanic"]
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# pylint: disable=too-few-public-methods
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class RaceClient(Demography):
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"""
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Race 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 = "Race"
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def predict(self, img: np.ndarray) -> np.ndarray:
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return self.model.predict(img, verbose=0)[0, :]
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def load_model(
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url="https://github.com/serengil/deepface_models/releases/download/v1.0/race_model_single_batch.h5",
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) -> Model:
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"""
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Construct race model, download its weights and load
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"""
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model = VGGFace.base_model()
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# --------------------------
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classes = 6
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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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race_model = Model(inputs=model.input, outputs=base_model_output)
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# --------------------------
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# load weights
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home = folder_utils.get_deepface_home()
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if os.path.isfile(home + "/.deepface/weights/race_model_single_batch.h5") != True:
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logger.info("race_model_single_batch.h5 will be downloaded...")
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output = home + "/.deepface/weights/race_model_single_batch.h5"
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gdown.download(url, output, quiet=False)
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race_model.load_weights(home + "/.deepface/weights/race_model_single_batch.h5")
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return race_model
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