TFRecord for video data

最新推荐文章于 2020-11-24 03:02:39 发布

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最新推荐文章于 2020-11-24 03:02:39 发布 · 819 阅读

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这篇博客介绍了如何利用TensorFlow的tf.train.SequenceExample处理序列化视频数据。SequenceExample包含context和feature_lists参数，用于存储视频的元数据和帧数据。通过示例代码，展示了如何将视频每一帧作为feature存储。

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https://github.com/tensorflow/magenta/blob/master/magenta/common/sequence_example_lib.py
https://www.tensorflow.org/api_docs/python/tf/train/SequenceExample
https://github.com/tensorflow/tensorflow/blob/r1.5/tensorflow/core/example/example.proto
http://leix.me/2017/01/09/tensorflow-practical-guides/#%E5%BC%95%E8%A8%80

`tf.train.SequenceExample`

tf.train.SequenceExample用于处理序列化数据。比如视频。

tf.train.SequenceExample(
context=context,
featureList = featurelist
)

SequenceExample的参数有两个，分别是：context和feature_lists, 创建SequenceExample的原型如下，其中的feat类型是python列表。

官方的说明文档是这样写的：原文点这里

// A SequenceExample is an Example representing one or more sequences, and
// some context.  The context contains features which apply to the entire
// example. The feature_lists contain a key, value map where each key is
// associated with a repeated set of Features (a FeatureList).
// A FeatureList thus represents the values of a feature identified by its key
// over time / frames.