imagepipe package

Submodules

imagepipe.core_functions module

imagepipe.core_functions.doublewrap(f)

a decorator decorator, allowing the decorator to be used as: @decorator(with, arguments, and=kwargs) or @decorator

credits: http://stackoverflow.com/questions/653368/how-to-create-a-python-decorator-that-can-be-used-either-with-or-without-paramet

imagepipe.core_functions.for_each(outer_generator, embedded_transformer, inside, **kwargs)
imagepipe.core_functions.generator_wrapper(*args, **kwargs)
imagepipe.core_functions.pad_skipping_iterator(secondary_namespace)
imagepipe.core_functions.paint_from_mask(outer_generator, based_on, in_anchor, out_channel=None)
imagepipe.core_functions.splitter(outer_generator, to, sources, mask)

Creates a secondary namespace by using mask as a pad to conserve only certain segments in sources

Parameters:
  • outer_generator –
  • to –
  • sources –
  • mask –
Returns:
imagepipe.core_functions.tile_from_mask(outer_generator, based_on, in_anchor, out_channel=None)

imagepipe.debug_renders module

imagepipe.density_plot module

imagepipe.density_plot.better2D_desisty_plot(xdat, ydat, thresh=3, bins=(100, 100))

imagepipe.iterate_and_check_img_files module

imagepipe.iterate_and_check_img_files.iterate_and_check_img(filename)

imagepipe.raw_functions module

This module contains raw function definitions that can be either used directly or wrapped and then assembled in pipelines

imagepipe.raw_functions.agreeing_skeletons(float_surface, mito_labels)

Calculates agreeing skeletonization by both median and morphological skeletons

Parameters:
  • float_surface – float volume on which we need to calculate the values
  • mito_labels – labels that will be skeletonized
Returns:
imagepipe.raw_functions.average_qualifying_value_per_region(region_labels, image_2d, qualifying_mask)

Calculates average qualifying value per region of interest

Parameters:
  • region_labels –
  • image_2d –
  • qualifying_mask –
Returns:

np.array list of average values, 2d pad of average values
imagepipe.raw_functions.binarize_2d(float_surface, cutoff_type='static', mcc_cutoff=None)

Performs a 2d binarization based on several possible methods

Parameters:
  • float_surface –
  • cutoff_type – [‘otsu’, ‘local_otsu’, ‘static’, ‘log-otsu”]. Local Otsu is done with 5px mask
  • mcc_cutoff – is cutoff_type is ‘static’, this will be the cutoff threshold
Returns:

binary labels
imagepipe.raw_functions.binarize_3d(floats_volume, cutoff)

Performs a 3d binarization

Parameters:
  • floats_volume –
  • cutoff –
Returns:
imagepipe.raw_functions.classify_fragmentation_for_mitochondria(label_mask, skeletons)

Performs mitochondria fragmentation based off the labels mask and skeletons mask

Parameters:
  • label_mask –
  • skeletons –
Returns:
imagepipe.raw_functions.clear_based_on_2d_mask(stack, mask)

Sets to 0 in 3d everything covered by the mask along the z axis

Parameters:
  • stack – 3d image stack
  • mask –
Returns:
imagepipe.raw_functions.detect_upper_outliers(value_list)

Performs upper outlier detection based on the extreme value distribution intuition. Works best with over 15 data elements

Parameters:value_list –
Returns:positions of non-outliers, baseline curve of sorted averages, error margins
imagepipe.raw_functions.exclude_region(exclusion_mask, base_image, _dilation=5)

Excludes the region where exclusion_mask is true from the base image.

Parameters:
  • exclusion_mask –
  • base_image –
  • _dilation – if set to anything other than 0, would perform a morphological dilation using this parameter value as size on the exclusion mask
Returns:
imagepipe.raw_functions.f_2d_stack_2d_filter(_2d_stack, _2d_filter)

helper function to apply the 2d filter to a 2d image while creating an image copy

Parameters:
  • _2d_stack –
  • _2d_filter –
Returns:
imagepipe.raw_functions.f_3d_stack_2d_filter(_3d_stack, _2d_filter)

helper function to apply 2d filter to a 3d image along the z axis

Parameters:
  • _3d_stack –
  • _2d_filter –
Returns:
imagepipe.raw_functions.filter_labels(labels, binary_mask, min_feature_size=10)

Applies the binary mask to labels, than filters out all the labels with feature size less than min_feature_size

Parameters:
  • labels –
  • binary_mask –
  • min_feature_size –
Returns:
imagepipe.raw_functions.gamma_stabilize(image, alpha_clean=5, floor_method='min')

Normalizes the luma curve. floor intensity becomes 0 and max allowed by the bit number - 1

Parameters:
  • image –
  • alpha_clean – size of features that would be removed if surrounded by a majority of
  • floor_method – [‘min’, ‘1q’, ‘5p’, ‘median’] method of setting the floor intensity. 1q is first quartile, 1p is the first percentile
Returns:
imagepipe.raw_functions.improved_watershed(binary_base, intensity, expected_separation=10)

Improved watershed method that takes in account minimum intensity as well as minimal size of separation between the elements

Parameters:
  • binary_base – support for watershedding
  • intensity – intensity value used to exclude watershed points with too low of intensity
  • expected_separation – expected minimal separation (in pixels) between watershed centers
Returns:
imagepipe.raw_functions.in_contact(mask1, mask2, distance=10)

Finds if two binary masks are in contact or proximity. distance of detection is defined by the distance parameter

Parameters:
  • mask1 –
  • mask2 –
  • distance –
Returns:

two arrays of the same shape as masks, each with ones for labels that overlap.
imagepipe.raw_functions.label_and_correct(binary_channel, value_channel, min_px_radius=3, min_intensity=0, mean_diff=10)
Labelling of a binary image, with constraints on minimal feature size, minimal intensity of area
covered by a binary label or minimal mean difference from background
Parameters:
  • binary_channel –
  • value_channel – used to compute total intensity
  • min_px_radius – minimal feature size
  • min_intensity – minimal total intensity
  • mean_diff – minimal (multiplicative) difference from the background
Returns:
imagepipe.raw_functions.label_based_aq(labels, field_of_interest)

Calculates average qualifying intensity of field of interest based on the labels mask

Parameters:
  • labels –
  • field_of_interest –
Returns:

list of averages, pad of averages
imagepipe.raw_functions.locally_normalize(channel, local_xy_pool=5, local_z_pool=2)

Performs a per- zslice local normalization of a 3d channel

Parameters:
  • channel –
  • local_xy_pool – size of the neighborhood to be considered for normalization
  • local_z_pool – placeholder, currently unused
Returns:

normalized 3d channel
imagepipe.raw_functions.mask_filter_2d(base, _filter)

Applies a filter a base mask in 2d

Parameters:
  • base –
  • _filter –
Returns:
imagepipe.raw_functions.max_projection(current_image)

Max projection along z axis

Parameters:current_image –
Returns:
imagepipe.raw_functions.otsu_tresholding(shape_base)

perofrms an otsu thresholding based of the shape_base

Parameters:shape_base –
Returns:
imagepipe.raw_functions.paint_mask(label_masks, labels_to_paint)

Paints a labeled mask based off a numpy list of values assigned to labels to paint.

Parameters:
  • label_masks –
  • labels_to_paint – 1d numpy array with values that need to be painted on the labels.
Returns:
imagepipe.raw_functions.qualifying_gfp(max_sum_projection)

Creates a binary mask for qualifying gfp

Parameters:max_sum_projection –
Returns:binary mask
imagepipe.raw_functions.random_walker_binarize(base_image, _dilation=0)

Improved random walker binarization based on the the scikits image library

Parameters:
  • base_image –
  • _dilation – if set to anything other than 0, would perform a morphological dilation using this parameter value as size
Returns:

binary labels
imagepipe.raw_functions.robust_binarize(base_image, _dilation=0, heterogeity_size=10, feature_size=50)

Robust binarization algorithm based off random walker clustering

Parameters:
  • base_image –
  • _dilation – if set to anything other than 0, would perform a morphological dilation using this parameter value as size
  • heterogeity_size – size of the feature (px) that the method will try to eliminate by smoothing
  • feature_size – size of the feature (px) that the method will try to segment out
Returns:

binary_labels
imagepipe.raw_functions.smooth(image, smoothing_px=1.5)

Gaussian smoothing of the image

Parameters:
  • image –
  • smoothing_px –
Returns:
imagepipe.raw_functions.smooth_2d(image, smoothing_px=1.5)

Gaussian smoothing of a 2d image

Parameters:
  • image –
  • smoothing_px –
Returns:
imagepipe.raw_functions.split_and_trim(path_prefix, main_root)

helper function for OS Path trimming routine that accounts for the trailing separator

Parameters:
  • path_prefix – [str]
  • main_root – [str]

:return:[list]

imagepipe.raw_functions.sum_projection(image)

Sum projection along z axis

Parameters:image –
Returns:
imagepipe.raw_functions.volume_aqvi(float_volume, binary_volume)

Calculates the average of the float volume after filtering it through the binary volume

Parameters:
  • float_volume –
  • binary_volume –
Returns:
imagepipe.raw_functions.volume_mqvi(float_volume, binary_volume)

Calculates the median of the float volume after filtering it through the binary volume

Parameters:
  • float_volume –
  • binary_volume –
Returns:
imagepipe.raw_functions.voronoi_segment_labels(binary_labels)

Performs a Voronoi segmentation on binary labels (assuming background is set to 0)

Parameters:binary_labels –
Returns:pad with labels of segmentation of the same size as binary_labels

imagepipe.traversals module

imagepipe.traversals.color_based_traversal(main_root, coding_separator='.', group_anchor=1)

Traverses the main root directory pulling the data from the images. different layers are assumed to be different colors, images are assumed to be 2D.

Parameters:
  • main_root –
  • coding_separator – character used to derived codename of the image
  • group_anchor – position where the group information is stored
Returns:
imagepipe.traversals.name_channels(stack_group_generator, channel_names)

Assigns names to the channel for the future processing and bundles them together

Parameters:
  • stack_group_generator – generator returning image stack,
  • channel_names –
Returns:
imagepipe.traversals.z_stack_based_traversal(main_root, matching_rule='c', matching_map=None)

Traverses the main_root directory, looking for all the ‘.tif/.TIF’ files, performs name matching then iterates through the resulting matched dictironary.

Matching assumption is that except for the matching keys, the names are identical

Parameters:
  • main_root – folder from which will be traversed in depth
  • matching_rule – name modification to type mapping. Currently ‘’ for no matching, ‘color’ for colors
  • matching_map – {‘pattern in the file name’: color channel number}
Returns:

imagepipe.wrapped_functions module

This module essentially wraps the functions to be compatible with the usage inside the pipelines

Module contents