pymic.io package

Submodules

pymic.io.h5_dataset module

class pymic.io.h5_dataset.H5DataSet(root_dir, sample_list_name, transform=None)

Bases: Dataset

Dataset for loading images stored in h5 format. It generates 4D tensors with dimention order [C, D, H, W] for 3D images, and 3D tensors with dimention order [C, H, W] for 2D images

Args:

root_dir (str): thr root dir of images.

sample_list_name (str): a file name for sample list.

tranform (list): A list of transform objects applied on a sample.

class pymic.io.h5_dataset.TwoStreamBatchSampler(primary_indices, secondary_indices, batch_size, secondary_batch_size)

Bases: Sampler

Iterate two sets of indices

An ‘epoch’ is one iteration through the primary indices. During the epoch, the secondary indices are iterated through as many times as needed.

pymic.io.h5_dataset.grouper(iterable, n)

Collect data into fixed-length chunks or blocks

pymic.io.h5_dataset.iterate_eternally(indices)
pymic.io.h5_dataset.iterate_once(iterable)

pymic.io.image_read_write module

pymic.io.image_read_write.load_image_as_nd_array(image_name)

Load an image and return a 4D array with shape [C, D, H, W], or 3D array with shape [C, H, W].

Parameters:

filename – (str) The input file name

Returns:

A dictionay storing data array, origin, spacing and direction.

pymic.io.image_read_write.load_nifty_volume_as_4d_array(filename)

Read a nifty image and return a dictionay storing data array, origin, spacing and direction.

output[‘data_array’] 4D array with shape [C, D, H, W];

output[‘spacing’] A list of spacing in z, y, x axis;

output[‘direction’] A 3x3 matrix for direction.

Parameters:

filename – (str) The input file name

Returns:

A dictionay storing data array, origin, spacing and direction.

pymic.io.image_read_write.load_rgb_image_as_3d_array(filename)

Read an RGB image and return a dictionay storing data array, origin, spacing and direction.

output[‘data_array’] 3D array with shape [D, H, W];

output[‘spacing’] a list of spacing in z, y, x axis;

output[‘direction’] a 3x3 matrix for direction.

Parameters:

filename – (str) The input file name

Returns:

A dictionay storing data array, origin, spacing and direction.

pymic.io.image_read_write.rotate_nifty_volume_to_LPS(filename_or_image_dict, origin=None, direction=None)

Rotate the axis of a 3D volume to LPS

Parameters:

  • filename_or_image_dict – (str) Filename of the nifty file (str) or image dictionary returned by load_nifty_volume_as_4d_array. If supplied with the former, the flipped image data will be saved to override the original file. If supplied with the later, only flipped image data will be returned.

  • origin – (list/tuple) The origin of the image.

  • direction – (list or tuple) The direction of the image.

Returns:

A dictionary for image data and meta info, with data_array, origin, direction and spacing.

pymic.io.image_read_write.save_array_as_nifty_volume(data, image_name, reference_name=None)

Save a numpy array as nifty image

Parameters:

  • data – (numpy.ndarray) A numpy array with shape [Depth, Height, Width].

  • image_name – (str) The ouput file name.

  • reference_name – (str) File name of the reference image of which meta information is used.

pymic.io.image_read_write.save_array_as_rgb_image(data, image_name)

Save a numpy array as rgb image.

Parameters:

  • data – (numpy.ndarray) A numpy array with shape [3, H, W] or [H, W, 3] or [H, W].

  • image_name – (str) The output file name.

pymic.io.image_read_write.save_nd_array_as_image(data, image_name, reference_name=None)

Save a 3D or 2D numpy array as medical image or RGB image

Parameters:

  • data – (numpy.ndarray) A numpy array with shape [3, H, W] or [H, W, 3] or [H, W].

  • reference_name – (str) File name of the reference image of which meta information is used.

pymic.io.nifty_dataset module

class pymic.io.nifty_dataset.ClassificationDataset(root_dir, csv_file, modal_num=1, class_num=2, with_label=False, transform=None)

Bases: NiftyDataset

Dataset for loading images for classification. It generates 4D tensors with dimention order [C, D, H, W] for 3D images, and 3D tensors with dimention order [C, H, W] for 2D images.

Parameters:

  • root_dir – (str) Directory with all the images.

  • csv_file – (str) Path to the csv file with image names.

  • modal_num – (int) Number of modalities.

  • class_num – (int) Class number of the classificaiton task.

  • with_label – (bool) Load the data with segmentation ground truth or not.

  • transform – (list) List of transforms to be applied on a sample. The built-in transforms can listed in pymic.transform.trans_dict.

class pymic.io.nifty_dataset.NiftyDataset(root_dir, csv_file, modal_num=1, with_label=False, transform=None)

Bases: Dataset

Dataset for loading images for segmentation. It generates 4D tensors with dimention order [C, D, H, W] for 3D images, and 3D tensors with dimention order [C, H, W] for 2D images.

Parameters:

  • root_dir – (str) Directory with all the images.

  • csv_file – (str) Path to the csv file with image names.

  • modal_num – (int) Number of modalities.

  • with_label – (bool) Load the data with segmentation ground truth or not.

  • transform – (list) List of transforms to be applied on a sample. The built-in transforms can listed in pymic.transform.trans_dict.

Module contents