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26 lines
1.1 KiB
Python
26 lines
1.1 KiB
Python
import tensorflow as tf
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# Définir la fonction pour parser les exemples TFRecord (similaire à ce que vous avez utilisé)
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def _parse_function(proto):
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keys_to_features = {
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'image/encoded': tf.io.FixedLenFeature([], tf.string),
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'image/format': tf.io.FixedLenFeature([], tf.string),
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'image/object/class/label': tf.io.FixedLenFeature([], tf.int64), # Assurez-vous que le type correspond
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'image/object/bbox/xmin': tf.io.FixedLenFeature([], tf.float32),
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'image/object/bbox/ymin': tf.io.FixedLenFeature([], tf.float32),
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'image/object/bbox/xmax': tf.io.FixedLenFeature([], tf.float32),
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'image/object/bbox/ymax': tf.io.FixedLenFeature([], tf.float32),
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}
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parsed_features = tf.io.parse_single_example(proto, keys_to_features)
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return parsed_features
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# Charger les TFRecords
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tfrecords_path = "Pot-plante.tfrecord"
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dataset = tf.data.TFRecordDataset(tfrecords_path)
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# Afficher les informations pour chaque exemple TFRecord
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for raw_record in dataset.take(5): # Prenez les 5 premiers exemples pour illustration
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parsed_record = _parse_function(raw_record.numpy())
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print(parsed_record)
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