关于python：将TFRecords转换回JPEG图像


Converting TFRecords back into JPEG Images

我有一个包含数百个TFRecords的文件。每个TFRecord文件包含1,024条记录。每个记录都包含以下信息：

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The Example proto contains the following fields:

image/height: integer, image height in pixels
image/width: integer, image width in pixels
image/colorspace: string, specifying the colorspace, always 'RGB'
image/channels: integer, specifying the number of channels, always 3
image/class/label: integer, specifying the index in a normalized classification layer
image/class/raw: integer, specifying the index in the raw (original) classification layer
image/class/source: integer, specifying the index of the source (creator of the image)
image/class/text: string, specifying the human-readable version of the normalized label
image/format: string, specifying the format, always 'JPEG'
image/filename: string containing the basename of the image file
image/id: integer, specifying the unique id for the image
image/encoded: string, containing JPEG encoded image in RGB colorspace

我将每个TFRecords存储在目录路径/ Data / train中。 python中是否有一种不太复杂的方法将TFRecord中的这些图像转换回JPEG格式并将它们保存到另一个目录/ data / image。我已经看过TensorFlow文档，这些文档看起来很痛苦，而且这个脚本将TFRecord转换为数组，但我遇到了问题。任何帮助，更正或反馈将不胜感激！谢谢。

我正在使用的数据是MARCO图像数据：

https://marco.ccr.buffalo.edu/download

这应该起作用：

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record_iterator = tf.python_io.tf_record_iterator(path_to_tfrecords_file)

    for string_record in record_iterator:
        example = tf.train.Example()
        example.ParseFromString(string_record)

        image = example.features.feature["encoded"].bytes_list.value[0]

        # save image to file
        # ...

我可以用它来查看单个TFRecord。仍在编写循环以遍历多个TFRecords的工作：

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# Read and print data:
sess = tf.InteractiveSession()

# Read TFRecord file
reader = tf.TFRecordReader()
filename_queue =
tf.train.string_input_producer(['marcoTrainData00001.tfrecord'])
_, serialized_example = reader.read(filename_queue)

# Define features
read_features = {
    'image/height': tf.FixedLenFeature([], dtype=tf.int64),
    'image/width': tf.FixedLenFeature([], dtype=tf.int64),
    'image/colorspace': tf.FixedLenFeature([], dtype=tf.string),
    'image/class/label': tf.FixedLenFeature([], dtype=tf.int64),
    'image/class/raw': tf.FixedLenFeature([], dtype=tf.int64),
    'image/class/source': tf.FixedLenFeature([], dtype=tf.int64),
    'image/class/text': tf.FixedLenFeature([], dtype=tf.string),
    'image/format': tf.FixedLenFeature([], dtype=tf.string),
    'image/filename': tf.FixedLenFeature([], dtype=tf.string),
    'image/id': tf.FixedLenFeature([], dtype=tf.int64),
    'image/encoded': tf.FixedLenFeature([], dtype=tf.string)
}

# Extract features from serialized data
read_data = tf.parse_single_example(serialized=serialized_example,
                                features=read_features)

# Many tf.train functions use tf.train.QueueRunner,
# so we need to start it before we read
tf.train.start_queue_runners(sess)

# Print features
for name, tensor in read_data.items():
    print('{}: {}'.format(name, tensor.eval()))