MTCNN.py 文件源码

def create_Kao_Rnet (weight_path = 'model24.h5'):
    input = Input(shape=[24, 24, 3])  # change this shape to [None,None,3] to enable arbitraty shape input
    x = Conv2D(28, (3, 3), strides=1, padding='valid', name='conv1')(input)
    x = PReLU(shared_axes=[1, 2], name='prelu1')(x)
    x = MaxPool2D(pool_size=3,strides=2, padding='same')(x)

    x = Conv2D(48, (3, 3), strides=1, padding='valid', name='conv2')(x)
    x = PReLU(shared_axes=[1, 2], name='prelu2')(x)
    x = MaxPool2D(pool_size=3, strides=2)(x)

    x = Conv2D(64, (2, 2), strides=1, padding='valid', name='conv3')(x)
    x = PReLU(shared_axes=[1, 2], name='prelu3')(x)
    x = Permute((3, 2, 1))(x)
    x = Flatten()(x)
    x = Dense(128, name='conv4')(x)
    x = PReLU( name='prelu4')(x)
    classifier = Dense(2, activation='softmax', name='conv5-1')(x)
    bbox_regress = Dense(4, name='conv5-2')(x)
    model = Model([input], [classifier, bbox_regress])
    model.load_weights(weight_path, by_name=True)
    return model