TFRecord load about raccoon_dataset HOT 1 CLOSED

import tensorflow as tf

def _parse_image_function(example_proto):
    image_feature_description = {
        'image/height':  tf.io.FixedLenFeature([], tf.int64),
        'image/width': tf.io.FixedLenFeature([], tf.int64),
        'image/filename':  tf.io.FixedLenFeature([], tf.string),
        'image/source_id': tf.io.FixedLenFeature([], tf.string),
        'image/encoded':  tf.io.FixedLenFeature([], tf.string),
        'image/format': tf.io.FixedLenFeature([], tf.string),
        'image/object/bbox/xmin':  tf.io.FixedLenSequenceFeature([], tf.float32,allow_missing=True),
        'image/object/bbox/xmax':  tf.io.FixedLenSequenceFeature([], tf.float32,allow_missing=True),
        'image/object/bbox/ymin':  tf.io.FixedLenSequenceFeature([], tf.float32,allow_missing=True),
        'image/object/bbox/ymax':  tf.io.FixedLenSequenceFeature([], tf.float32,allow_missing=True),
        'image/object/class/text': tf.io.FixedLenSequenceFeature([], tf.string,allow_missing=True),
        'image/object/class/label':tf.io.FixedLenSequenceFeature([], tf.int64,allow_missing=True),
    }
    return tf.io.parse_single_example(example_proto, image_feature_description)

filenames="/path/to/records"
raw_image_dataset = tf.data.TFRecordDataset(filenames)

parsed_image_dataset = raw_image_dataset.map(_parse_image_function)
parsed_image_dataset

for image_features in parsed_image_dataset:
    print(image_features['image/height'])
    image_raw = image_features['image/encoded'].numpy()