76 lines
2.3 KiB
Python
76 lines
2.3 KiB
Python
import os
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import gdown
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import tensorflow as tf
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from deepface.commons import functions
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# -------------------------------------------
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# pylint: disable=line-too-long
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# -------------------------------------------
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# dependency configuration
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tf_version = int(tf.__version__.split(".", maxsplit=1)[0])
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if tf_version == 1:
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from keras.models import Sequential
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from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Flatten, Dense, Dropout
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elif tf_version == 2:
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import (
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Conv2D,
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MaxPooling2D,
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AveragePooling2D,
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Flatten,
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Dense,
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Dropout,
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)
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# -------------------------------------------
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# Labels for the emotions that can be detected by the model.
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labels = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]
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def loadModel(
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url="https://github.com/serengil/deepface_models/releases/download/v1.0/facial_expression_model_weights.h5",
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):
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num_classes = 7
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model = Sequential()
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# 1st convolution layer
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model.add(Conv2D(64, (5, 5), activation="relu", input_shape=(48, 48, 1)))
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model.add(MaxPooling2D(pool_size=(5, 5), strides=(2, 2)))
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# 2nd convolution layer
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model.add(Conv2D(64, (3, 3), activation="relu"))
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model.add(Conv2D(64, (3, 3), activation="relu"))
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model.add(AveragePooling2D(pool_size=(3, 3), strides=(2, 2)))
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# 3rd convolution layer
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model.add(Conv2D(128, (3, 3), activation="relu"))
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model.add(Conv2D(128, (3, 3), activation="relu"))
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model.add(AveragePooling2D(pool_size=(3, 3), strides=(2, 2)))
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model.add(Flatten())
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# fully connected neural networks
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model.add(Dense(1024, activation="relu"))
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model.add(Dropout(0.2))
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model.add(Dense(1024, activation="relu"))
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model.add(Dropout(0.2))
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model.add(Dense(num_classes, activation="softmax"))
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# ----------------------------
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home = functions.get_deepface_home()
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if os.path.isfile(home + "/.deepface/weights/facial_expression_model_weights.h5") != True:
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print("facial_expression_model_weights.h5 will be downloaded...")
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output = home + "/.deepface/weights/facial_expression_model_weights.h5"
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gdown.download(url, output, quiet=False)
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model.load_weights(home + "/.deepface/weights/facial_expression_model_weights.h5")
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return model
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