ImageCaption/task3.py at master · songdongqing/ImageCaption · GitHub

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from keras.preprocessing.sequence import pad_sequences
from keras.utils import to_categorical
from numpy import array
from pickle import load

def create_input_data(tokenizer, max_length, descriptions, photos_features, vocab_size):
    """
    从输入的图片标题list和图片特征构造LSTM的一组输入

    Args:
    :param tokenizer: 英文单词和整数转换的工具keras.preprocessing.text.Tokenizer
    :param max_length: 训练数据集中最长的标题的长度
    :param descriptions: dict, key为图像的名(不带.jpg后缀), value为list, 包含一个图像的几个不同的描述
    :param photos_features: dict, key 为图像的名(不带.jpg后缀), value 为numpy array 图像的特征
    :param vocab_size: 训练集中表的单词数量
    :return: tuple:
            第一个元素为 numpy array, 元素为图像的特征, 它本身也是 numpy.array
            第二个元素为 numpy array, 元素为图像标题的前缀, 它自身也是 numpy.array
            第三个元素为 numpy array, 元素为图像标题的下一个单词(根据图像特征和标题的前缀产生) 也为numpy.array

    Examples:
        from pickle import load
        tokenizer = load(open('tokenizer.pkl', 'rb'))
        max_length = 6
        descriptions = {'1235345':['startseq one bird on tree endseq', "startseq red bird on tree endseq"],
                        '1234546':['startseq one boy play water endseq', "startseq one boy run across water endseq"]}
        photo_features = {'1235345':[ 0.434,  0.534,  0.212,  0.98 ],
                          '1234546':[ 0.534,  0.634,  0.712,  0.28 ]}
        vocab_size = 7378
        print(create_input_data(tokenizer, max_length, descriptions, photo_features, vocab_size))
(array([[ 0.434,  0.534,  0.212,  0.98 ],
       [ 0.434,  0.534,  0.212,  0.98 ],
       [ 0.434,  0.534,  0.212,  0.98 ],
       [ 0.434,  0.534,  0.212,  0.98 ],
       [ 0.434,  0.534,  0.212,  0.98 ],
       [ 0.434,  0.534,  0.212,  0.98 ],
       [ 0.434,  0.534,  0.212,  0.98 ],
       [ 0.434,  0.534,  0.212,  0.98 ],
       [ 0.434,  0.534,  0.212,  0.98 ],
       [ 0.434,  0.534,  0.212,  0.98 ],
       [ 0.534,  0.634,  0.712,  0.28 ],
       [ 0.534,  0.634,  0.712,  0.28 ],
       [ 0.534,  0.634,  0.712,  0.28 ],
       [ 0.534,  0.634,  0.712,  0.28 ],
       [ 0.534,  0.634,  0.712,  0.28 ],
       [ 0.534,  0.634,  0.712,  0.28 ],
       [ 0.534,  0.634,  0.712,  0.28 ],
       [ 0.534,  0.634,  0.712,  0.28 ],
       [ 0.534,  0.634,  0.712,  0.28 ],
       [ 0.534,  0.634,  0.712,  0.28 ],
       [ 0.534,  0.634,  0.712,  0.28 ]]),
array([[  0,   0,   0,   0,   0,   2],
       [  0,   0,   0,   0,   2,  59],
       [  0,   0,   0,   2,  59, 254],
       [  0,   0,   2,  59, 254,   6],
       [  0,   2,  59, 254,   6, 134],
       [  0,   0,   0,   0,   0,   2],
       [  0,   0,   0,   0,   2,  26],
       [  0,   0,   0,   2,  26, 254],
       [  0,   0,   2,  26, 254,   6],
       [  0,   2,  26, 254,   6, 134],
       [  0,   0,   0,   0,   0,   2],
       [  0,   0,   0,   0,   2,  59],
       [  0,   0,   0,   2,  59,  16],
       [  0,   0,   2,  59,  16,  82],
       [  0,   2,  59,  16,  82,  24],
       [  0,   0,   0,   0,   0,   2],
       [  0,   0,   0,   0,   2,  59],
       [  0,   0,   0,   2,  59,  16],
       [  0,   0,   2,  59,  16, 165],
       [  0,   2,  59,  16, 165, 127],
       [  2,  59,  16, 165, 127,  24]]),
array([[ 0.,  0.,  0., ...,  0.,  0.,  0.],
       [ 0.,  0.,  0., ...,  0.,  0.,  0.],
       [ 0.,  0.,  0., ...,  0.,  0.,  0.],
       ...,
       [ 0.,  0.,  0., ...,  0.,  0.,  0.],
       [ 0.,  0.,  0., ...,  0.,  0.,  0.],
       [ 0.,  0.,  0., ...,  0.,  0.,  0.]]))
    """
    pass