Visualize & Check                 Data

After creating your TFRecords Dataset (labeled or unlabeled), you would like to check and glance through your dataset. For this import, visualize_and_check_data from quick_ml.
To get started, write the following line of code. :-

Available methods are :-

  1. check_one_image_and_label

  2. check_batch_and_labels

  3. check_one_image_and_id

  4. check_batch_and_ids

1. check_one_image_and_label

Use this for checking labeled TFRecords Dataset. It displays only one image along with its label when the labeled TFRecords dataset is passed as an argument.

Description =>
Displays one image along with its label.
Pass the tfrecord_filename as the argument. It will display one image along with its label from the tfrecords dataset.

2. check_batch_and_labels
Use this for checking labeled TFRecords Dataset. It displays a grid of images along with their labels given the tfrecords dataset passed as an argument.

Description =>
Displays a grid of images along with their labels.
Pass the tfrecord_filename, the number of examples to see (n_examples), divide the n_examples into product of rows (grid_rows) and columns (grid_columns) such that n_examples = grid_rows * grid_columns. Finally the grid_size as a tuple, Default (8,8) as an argument. It will display a grid of images along with their labels from the tfrecords dataset.

3. check_one_image_and_id

Use this for checking unlabeled TFRecords Dataset. It displays only one image along with its id when the unlabeled TFRecords dataset is passed as an argument.

Description =>
Displays one image along with its id.
Pass the tfrecord_filename as the argument. It will display one image along with its id from the tfrecords dataset.

 

4. check_batch_and_ids
Use this for checking unlabeled TFRecords Dataset. It displays a grid of images along with their ids given the tfrecords dataset passed as an argument.

Description =>
Displays a grid of images along with their ids.
Pass the tfrecord_filename, the number of examples to see (n_examples), divide the n_examples into product of rows (grid_rows) and columns (grid_columns) such that n_examples = grid_rows * grid_columns. Finally the grid_size as a tuple, Default (8,8) as an argument. It will display a grid of images along with their ids from the tfrecords dataset.

from quick_ml.visualize_and_check_data import check_one_image_and_label, check_batch_and_labels, check_one_image_and_id, check_batch_and_ids

check_one_image_and_label(tfrecord_filename)

check_batch_and_labels(tfrecord_filename, n_examples, grid_rows, grid_columns, grid_size = (8,8))

check_one_image_and_id(tfrecord_filename)

check_batch_and_ids(tfrecord_filename, n_examples, grid_rows, grid_columns, grid_size = (8,8)

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