Neuroglancer module

This module has several methods for helping create the precomputed data. It also has the main class to convert numpy arrays (images) into the precomputed format.

class library.image_manipulation.neuroglancer_manager.NumpyToNeuroglancer(animal: str, volume, scales, layer_type, data_type, num_channels=1, chunk_size=[64, 64, 1], offset=[0, 0, 0])

Bases: object

Contains collection of methods used to transform Numpy arrays into ‘precomputed cloud volume’ format More info: https://github.com/seung-lab/cloud-volume

add_downsampled_volumes(layer_path, chunk_size=[128, 128, 64], num_mips=3) → None

Augments ‘precomputed’ cloud volume with additional resolutions using chunk calculations tasks = tc.create_downsampling_tasks(cv.layer_cloudpath, mip=mip, num_mips=1, factor=factors, compress=True, chunk_size=chunks)

Parameters

  • chunk_size – list size of chunks

  • num_mips – number of levels in the pyramid

add_rechunking(outputpath, chunks=[64, 64, 64], mip=0, skip_downsamples=True) → None

Augments ‘precomputed’ cloud volume with additional chunk calculations [so format has pointers to location of individual volumes?]

Parameters

  • outpath – path of file location

  • downsample – boolean

  • chunks – list of chunk sizes

add_segment_properties(cloud_volume, segment_properties) → None

Augments ‘precomputed’ cloud volume with attribute tags [resolution, chunk size]

Parameters

  • cloud_volume – Cloudvolume object

  • segment_properties – dictionary of labels, ids

add_segmentation_mesh(layer_path, mip=0) → None

Augments ‘precomputed’ cloud volume with segmentation mesh

Parameters

  • shape – list[int]

  • mip – int, pyramid level

init_precomputed(path: str, volume_size, starting_points=None) → None

Initializes ‘precomputed’ cloud volume format (directory holding multiple volumes)

Parameters

  • path – str of the file location

  • volume_size – size of the volume

  • starting_points – initial starting points

  • progress_id – progress ID

init_volume(path: str) → None

Initializes ‘precomputed’ cloud volume (‘volume’ is a collection image stack with same resolution)

Parameters

path – path of file location

normalize_stack(layer_path, src_path=None, dest_path=None)

This does basically the same thing as our cleaning process.

process_image(file_key)

This reads the image and starts the precomputed data

Parameters

file_key – file_key: tuple

process_image_mesh(file_key)

This reads the image and starts the precomputed data

Parameters

file_key – file_key: tuple

library.image_manipulation.neuroglancer_manager.calculate_chunks(downsample, mip)

Function returns chunk sizes for different ‘precomputed cloud volume’ image stack resolutions. Image stack will be created from full-resolution images but must be chunked for efficient storage and loading into browser. Default values are [64, 64, 64] but may be modified for different resolutions. More info: https://github.com/seung-lab/cloud-volume

Note: highest resolution tier (mip) is 0 and increments

Parameters

  • downsample – boolean

  • mip – integer telling us which pyramid level we want

Return d

dictionary

library.image_manipulation.neuroglancer_manager.calculate_factors(downsample, mip)

Scales get calculated by default by 2x2x1 downsampling

Parameters

  • downsample – boolean

  • mip – which pyramid level to work on

Return list

list of factors

library.image_manipulation.neuroglancer_manager.get_segment_ids(volume)

Gets the unique values of a numpy array. This is used in Neuroglancer for the labels in a mesh

Parameters

volume – numpy array

Return list

list of segment IDs