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75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
import os
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import gdown
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from pathlib import Path
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import zipfile
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from deepface.commons import functions
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import tensorflow as tf
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tf_version = int(tf.__version__.split(".")[0])
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if tf_version == 1:
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import keras
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from keras.models import Model, 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 import keras
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from tensorflow.keras.models import Model, Sequential
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from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Flatten, Dense, Dropout
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#url = 'https://drive.google.com/uc?id=13iUHHP3SlNg53qSuQZDdHDSDNdBP9nwy'
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def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/facial_expression_model_weights.h5'):
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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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"""
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#google drive source downloads zip
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output = home+'/.deepface/weights/facial_expression_model_weights.zip'
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gdown.download(url, output, quiet=False)
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#unzip facial_expression_model_weights.zip
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with zipfile.ZipFile(output, 'r') as zip_ref:
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zip_ref.extractall(home+'/.deepface/weights/')
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"""
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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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