YM_labels_matrix.py 文件源码

def get_frame_input_feature(input_file):
    features = []
    record_iterator = tf.python_io.tf_record_iterator(path=input_file)
    for i, string_record in enumerate(record_iterator):
        example = tf.train.SequenceExample()
        example.ParseFromString(string_record)

        # traverse the Example format to get data
        video_id = example.context.feature['video_id'].bytes_list.value[0]
        label = example.context.feature['labels'].int64_list.value[:]
        rgbs = []
        audios = []
        rgb_feature = example.feature_lists.feature_list['rgb'].feature
        for i in range(len(rgb_feature)):
            rgb = np.fromstring(rgb_feature[i].bytes_list.value[0], dtype=np.uint8).astype(np.float32)
            rgb = utils.Dequantize(rgb, 2, -2)
            rgbs.append(rgb)
        audio_feature = example.feature_lists.feature_list['audio'].feature
        for i in range(len(audio_feature)):
            audio = np.fromstring(audio_feature[i].bytes_list.value[0], dtype=np.uint8).astype(np.float32)
            audio = utils.Dequantize(audio, 2, -2)
            audios.append(audio)
        rgbs = np.array(rgbs)
        audios = np.array(audios)
        features.append((video_id, label, rgbs, audios))
    return features