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• face_classification-master
  • src
    • utils
      • visualizer.py
      • preprocessor.py
      • datasets.py
      • __init__.py
      • inference.py
      • data_augmentation.py
      • grad_cam.py
    • video_emotion_color_demo.py
    • web
      • faces.py
      • __init__.py
      • emotion_gender_processor.py
    • image_gradcam_demo.py
    • models
      • cnn.py
      • __init__.py
    • video_emotion_gender_demo.py
    • video_gradcam_demo.py
    • train_emotion_classifier.py
    • train_gender_classifier.py
    • image_emotion_gender_demo.py
    • __init__.py
  • datasets
    • .gitignore
  • images
  • trained_models
    • gender_models
    • detection_models
    • emotion_models
      • KDEF_emotion_training.log
  • LICENSE
  • REQUIREMENTS.txt
  • README.md
  • Dockerfile
  • .gitignore

from keras.layers import Activation, Convolution2D, Dropout, Conv2D
from keras.layers import AveragePooling2D, BatchNormalization
from keras.layers import GlobalAveragePooling2D
from keras.models import Sequential
from keras.layers import Flatten
from keras.models import Model
from keras.layers import Input
from keras.layers import MaxPooling2D
from keras.layers import SeparableConv2D
from keras import layers
from keras.regularizers import l2


def simple_CNN(input_shape, num_classes):

    model = Sequential()
    model.add(Convolution2D(filters=16, kernel_size=(7, 7), padding='same',
                            name='image_array', input_shape=input_shape))
    model.add(BatchNormalization())
    model.add(Convolution2D(filters=16, kernel_size=(7, 7), padding='same'))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(AveragePooling2D(pool_size=(2, 2), padding='same'))
    model.add(Dropout(.5))

    model.add(Convolution2D(filters=32, kernel_size=(5, 5), padding='same'))
    model.add(BatchNormalization())
    model.add(Convolution2D(filters=32, kernel_size=(5, 5), padding='same'))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(AveragePooling2D(pool_size=(2, 2), padding='same'))
    model.add(Dropout(.5))

    model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding='same'))
    model.add(BatchNormalization())
    model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding='same'))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(AveragePooling2D(pool_size=(2, 2), padding='same'))
    model.add(Dropout(.5))

    model.add(Convolution2D(filters=128, kernel_size=(3, 3), padding='same'))
    model.add(BatchNormalization())
    model.add(Convolution2D(filters=128, kernel_size=(3, 3), padding='same'))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(AveragePooling2D(pool_size=(2, 2), padding='same'))
    model.add(Dropout(.5))

    model.add(Convolution2D(filters=256, kernel_size=(3, 3), padding='same'))
    model.add(BatchNormalization())
    model.add(Convolution2D(
        filters=num_classes, kernel_size=(3, 3), padding='same'))
    model.add(GlobalAveragePooling2D())
    model.add(Activation('softmax', name='predictions'))
    return model


def simpler_CNN(input_shape, num_classes):

    model = Sequential()
    model.add(Convolution2D(filters=16, kernel_size=(5, 5), padding='same',
                            name='image_array', input_shape=input_shape))
    model.add(BatchNormalization())
    model.add(Convolution2D(filters=16, kernel_size=(5, 5),
                            strides=(2, 2), padding='same'))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout(.25))

    model.add(Convolution2D(filters=32, kernel_size=(5, 5), padding='same'))
    model.add(BatchNormalization())
    model.add(Convolution2D(filters=32, kernel_size=(5, 5),
                            strides=(2, 2), padding='same'))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout(.25))

    model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding='same'))
    model.add(BatchNormalization())
    model.add(Convolution2D(filters=64, kernel_size=(3, 3),
                            strides=(2, 2), padding='same'))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout(.25))

    model.add(Convolution2D(filters=64, kernel_size=(1, 1), padding='same'))
    model.add(BatchNormalization())
    model.add(Convolution2D(filters=128, kernel_size=(3, 3),
                            strides=(2, 2), padding='same'))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout(.25))

    model.add(Convolution2D(filters=256, kernel_size=(1, 1), padding='same'))
    model.add(BatchNormalization())
    model.add(Convolution2D(filters=128, kernel_size=(3, 3),
                            strides=(2, 2), padding='same'))

    model.add(Convolution2D(filters=256, kernel_size=(1, 1), padding='same'))
    model.add(BatchNormalization())
    model.add(Convolution2D(filters=num_classes, kernel_size=(3, 3),
                            strides=(2, 2), padding='same'))

    model.add(Flatten())
    # model.add(GlobalAveragePooling2D())
    model.add(Activation('softmax', name='predictions'))
    return model


def tiny_XCEPTION(input_shape, num_classes, l2_regularization=0.01):
    regularization = l2(l2_regularization)

    # base
    img_input = Input(input_shape)
    x = Conv2D(5, (3, 3), strides=(1, 1), kernel_regularizer=regularization,
               use_bias=False)(img_input)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2D(5, (3, 3), strides=(1, 1), kernel_regularizer=regularization,
               use_bias=False)(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    # module 1
    residual = Conv2D(8, (1, 1), strides=(2, 2),
                      padding='same', use_bias=False)(x)
    residual = BatchNormalization()(residual)

    x = SeparableConv2D(8, (3, 3), padding='same',
                        kernel_regularizer=regularization,
                        use_bias=False)(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = SeparableConv2D(8, (3, 3), padding='same',
                        kernel_regularizer=regularization,
                        use_bias=False)(x)
    x = BatchNormalization()(x)

    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
    x = layers.add([x, residual])

    # module 2
    residual = Conv2D(16, (1, 1), strides=(2, 2),
                      padding='same', use_bias=False)(x)
    residual = BatchNormalization()(residual)

    x = SeparableConv2D(16, (3, 3), padding='same',
                        kernel_regularizer=regularization,
                        use_bias=False)(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = SeparableConv2D(16, (3, 3), padding='same',
                        kernel_regularizer=regularization,
                        use_bias=False)(x)
    x = BatchNormalization()(x)

    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
    x = layers.add([x, residual])

    # module 3
    residual = Conv2D(32, (1, 1), strides=(2, 2),
                      padding='same', use_bias=False)(x)
    residual = BatchNormalization()(residual)

    x = SeparableConv2D(32, (3, 3), padding='same',
                        kernel_regularizer=regularization,
                        use_bias=False)(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = SeparableConv2D(32, (3, 3), padding='same',
                        kernel_regularizer=regularization,
                        use_bias=False)(x)
    x = BatchNormalization()(x)

    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
    x = layers.add([x, residual])

    # module 4
    residual = Conv2D(64, (1, 1), strides=(2, 2),
                      padding='same', use_bias=False)(x)
    residual = BatchNormalization()(residual)

    x = SeparableConv2D(64, (3, 3), padding='same',
                        kernel_regularizer=regularization,
                        use_bias=False)(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = SeparableConv2D(64, (3, 3), padding='same',
                        kernel_regularizer=regularization,
                        use_bias=False)(x)
    x = BatchNormalization()(x)

    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
    x = layers.add([x, residual])

    x = Conv2D(num_classes, (3, 3),
               # kernel_regularizer=regularization,
               padding='same')(x)
    x = GlobalAveragePooling2D()(x)
    output = Activation('softmax', name='predictions')(x)

    model = Model(img_input, output)
    return model


def mini_XCEPTION(input_shape, num_classes, l2_regularization=0.01):
    regularization = l2(l2_regularization)

    # base
    img_input = Input(input_shape)
    x = Conv2D(8, (3, 3), strides=(1, 1), kernel_regularizer=regularization,
               use_bias=False)(img_input)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2D(8, (3, 3), strides=(1, 1), kernel_regularizer=regularization,
               use_bias=False)(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    # module 1
    residual = Conv2D(16, (1, 1), strides=(2, 2),
                      padding='same', use_bias=False)(x)
    residual = BatchNormalization()(residual)

    x = SeparableConv2D(16, (3, 3), padding='same',
                        kernel_regularizer=regularization,
                        use_bias=False)(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = SeparableConv2D(16, (3, 3), padding='same',
                        kernel_regularizer=regularization,
                        use_bias=False)(x)
    x = BatchNormalization()(x)

    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
    x = layers.add([x, residual])

    # module 2
    residual = Conv2D(32, (1, 1), strides=(2, 2),
                      padding='same', use_bias=False)(x)
    residual = BatchNormalization()(residual)

    x = SeparableConv2D(32, (3, 3), padding='same',
                        kernel_regularizer=regularization,
                        use_bias=False)(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = SeparableConv2D(32, (3, 3), padding='same',
                        kernel_regularizer=regularization,
                        use_bias=False)(x)
    x = BatchNormalization()(x)

    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
    x = layers.add([x, residual])

    # module 3
    residual = Conv2D(64, (1, 1), strides=(2, 2),
                      padding='same', use_bias=False)(x)
    residual = BatchNormalization()(residual)

    x = SeparableConv2D(64, (3, 3), padding='same',
                        kernel_regularizer=regularization,
                        use_bias=False)(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = SeparableConv2D(64, (3, 3), padding='same',
                        kernel_regularizer=regularization,
                        use_bias=False)(x)
    x = BatchNormalization()(x)

    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
    x = layers.add([x, residual])

    # module 4
    residual = Conv2D(128, (1, 1), strides=(2, 2),
                      padding='same', use_bias=False)(x)
    residual = BatchNormalization()(residual)

    x = SeparableConv2D(128, (3, 3), padding='same',
                        kernel_regularizer=regularization,
                        use_bias=False)(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = SeparableConv2D(128, (3, 3), padding='same',
                        kernel_regularizer=regularization,
                        use_bias=False)(x)
    x = BatchNormalization()(x)

    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
    x = layers.add([x, residual])

    x = Conv2D(num_classes, (3, 3),
               # kernel_regularizer=regularization,
               padding='same')(x)
    x = GlobalAveragePooling2D()(x)
    output = Activation('softmax', name='predictions')(x)

    model = Model(img_input, output)
    return model


def big_XCEPTION(input_shape, num_classes):
    img_input = Input(input_shape)
    x = Conv2D(32, (3, 3), strides=(2, 2), use_bias=False)(img_input)
    x = BatchNormalization(name='block1_conv1_bn')(x)
    x = Activation('relu', name='block1_conv1_act')(x)
    x = Conv2D(64, (3, 3), use_bias=False)(x)
    x = BatchNormalization(name='block1_conv2_bn')(x)
    x = Activation('relu', name='block1_conv2_act')(x)

    residual = Conv2D(128, (1, 1), strides=(2, 2),
                      padding='same', use_bias=False)(x)
    residual = BatchNormalization()(residual)

    x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False)(x)
    x = BatchNormalization(name='block2_sepconv1_bn')(x)
    x = Activation('relu', name='block2_sepconv2_act')(x)
    x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False)(x)
    x = BatchNormalization(name='block2_sepconv2_bn')(x)

    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
    x = layers.add([x, residual])

    residual = Conv2D(256, (1, 1), strides=(2, 2),
                      padding='same', use_bias=False)(x)
    residual = BatchNormalization()(residual)

    x = Activation('relu', name='block3_sepconv1_act')(x)
    x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False)(x)
    x = BatchNormalization(name='block3_sepconv1_bn')(x)
    x = Activation('relu', name='block3_sepconv2_act')(x)
    x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False)(x)
    x = BatchNormalization(name='block3_sepconv2_bn')(x)

    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
    x = layers.add([x, residual])
    x = Conv2D(num_classes, (3, 3),
               # kernel_regularizer=regularization,
               padding='same')(x)
    x = GlobalAveragePooling2D()(x)
    output = Activation('softmax', name='predictions')(x)

    model = Model(img_input, output)
    return model


if __name__ == "__main__":
    input_shape = (64, 64, 1)
    num_classes = 7
    # model = tiny_XCEPTION(input_shape, num_classes)
    # model.summary()
    # model = mini_XCEPTION(input_shape, num_classes)
    # model.summary()
    # model = big_XCEPTION(input_shape, num_classes)
    # model.summary()
    model = simple_CNN((48, 48, 1), num_classes)
    model.summary()