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import tensorflow as tf |
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import base64 |
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from PIL import Image |
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import io |
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from tqdm import tqdm |
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filenames = tf.io.gfile.glob('./android_control*') |
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raw_dataset = tf.data.TFRecordDataset(filenames, compression_type='GZIP') |
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dataset_iterator = tf.compat.v1.data.make_one_shot_iterator(raw_dataset) |
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for data in tqdm(dataset_iterator): |
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example = tf.train.Example.FromString(data.numpy()) |
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episode_id = example.features.feature['episode_id'].int64_list.value |
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for i, image_data in enumerate(screenshots): |
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image = Image.open(io.BytesIO(image_data)) |
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image.save(f"./images/android_control_episode_{str(episode_id)}_{str(i)}.png") |
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