niftynet.contrib.csv_reader.sampler_csv_rows module

class ImageWindowDatasetCSV(reader, csv_reader=None, window_sizes=None, batch_size=10, windows_per_image=1, shuffle=True, queue_length=10, num_threads=4, epoch=-1, smaller_final_batch_mode='pad', name='random_vector_sampler')

Bases: niftynet.engine.image_window_dataset.ImageWindowDataset

Extending the default sampler to include csv data

layer_op(idx=None)

Generating each image as a window. Overriding this function to create new image sampling strategies.

This function should either yield a dictionary (for single window per image):

yield a dictionary

{
 'image_name': a numpy array,
 'image_name_location': (image_id,
                         x_start, y_start, z_start,
                         x_end, y_end, z_end)
}

or return a dictionary (for multiple windows per image):

return a dictionary:
{
 'image_name': a numpy array,
 'image_name_location': [n_samples, 7]
}

where the 7-element location vector encode the image_id, starting and ending coordinates of the image window.

Following the same notation, the dictionary can be extended to multiple modalities; the keys will be:

{'image_name_1', 'image_name_location_1',
 'image_name_2', 'image_name_location_2', ...}

Parameters:idx – image_id used to load the image at the i-th row of the input Returns:a image data dictionary

tf_shapes

returns a dictionary of sampler output tensor shapes

tf_dtypes

returns a dictionary of sampler output tensorflow dtypes