move example code to source file for ease of runnability
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@ -78,25 +78,7 @@ process new data sets, in the documentation of
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PYTHONPATH="${PYTHONPATH};"+`pwd`
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```
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and then you can import the data set in Python.
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```python
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import tensorflow as tf
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from mediapipe.examples.desktop.media_sequence.demo_dataset import DemoDataset
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demo_data_path = '/tmp/demo_data/'
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with tf.Graph().as_default():
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d = DemoDataset(demo_data_path)
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dataset = d.as_dataset('test')
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# implement additional processing and batching here
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dataset_output = dataset.make_one_shot_iterator().get_next()
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images = dataset_output['images']
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labels = dataset_output['labels']
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with tf.Session() as sess:
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images_, labels_ = sess.run([images, labels])
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print('The shape of images_ is %s' % str(images_.shape))
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print('The shape of labels_ is %s' % str(labels_.shape))
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```
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and then you can import the data set in Python using [read_demo_dataset.py](mediapipe/examples/desktop/media_sequence/read_demo_dataset.py)
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### Preparing a practical data set
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As an example of processing a practical data set, a similar set of commands will
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@ -0,0 +1,15 @@
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import tensorflow as tf
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from mediapipe.examples.desktop.media_sequence.demo_dataset import DemoDataset
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demo_data_path = '/tmp/demo_data/'
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with tf.Graph().as_default():
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d = DemoDataset(demo_data_path)
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dataset = d.as_dataset('test')
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# implement additional processing and batching here
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dataset_output = dataset.make_one_shot_iterator().get_next()
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images = dataset_output['images']
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labels = dataset_output['labels']
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with tf.Session() as sess:
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images_, labels_ = sess.run([images, labels])
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print('The shape of images_ is %s' % str(images_.shape))
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print('The shape of labels_ is %s' % str(labels_.shape))
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