mirror of
https://github.com/serengil/deepface.git
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97 lines
2.7 KiB
Python
97 lines
2.7 KiB
Python
# stdlib dependencies
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from typing import List, Union
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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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# 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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WEIGHTS_URL="https://github.com/serengil/deepface_models/releases/download/v1.0/gender_model_weights.h5"
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# Labels for the genders that can be detected by the model.
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labels = ["Woman", "Man"]
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# pylint: disable=too-few-public-methods
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class GenderClient(Demography):
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"""
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Gender 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 = "Gender"
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def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray:
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"""
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Predict gender probabilities for single or multiple faces
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Args:
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img: Single image as np.ndarray (224, 224, 3) or
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List of images as List[np.ndarray] or
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Batch of images as np.ndarray (n, 224, 224, 3)
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Returns:
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np.ndarray (n, 2)
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"""
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# Preprocessing input image or image list.
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imgs = self._preprocess_batch_or_single_input(img)
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# Prediction
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predictions = self._predict_internal(imgs)
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return predictions
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def load_model(
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url=WEIGHTS_URL,
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) -> Model:
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"""
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Construct gender 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 = 2
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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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gender_model = Model(inputs=model.inputs, 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="gender_model_weights.h5", source_url=url
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)
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gender_model = weight_utils.load_model_weights(
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model=gender_model, weight_file=weight_file
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)
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return gender_model
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