multiScaleAnalysis.Stitching package

Submodules

multiScaleAnalysis.Stitching.multiScale_stitching module

class multiScaleAnalysis.Stitching.multiScale_stitching.image_stitcher

Bases: object

This class stitches images from the low-resolution data acquisiton of the self-driving, multi-scale microscope.

__init__()

Initialize stitcher

crop_images(image_crops_x, image_crops_y)

Remove/crop parts of the images and update self.rawimage array with cropped images. This is function is helpful, e.g. to cut away parts of the image that were not properly illuminated.

Parameters:

• image_crops_x – For every image specify cropping in x direction [startpoint, endpoint], e.g. [[396, 396 + 3300], [208, 208 + 3236], [200, 200 + 3052]]

• image_crops_y – For every image specify cropping in y direction [startpoint, endpoint], e.g. [[396, 396 + 3300], [208, 208 + 3236], [200, 200 + 3052]]

iterate_overfolder(experimentfolder_parent, experimentfolder_result, channelnames, region, sample, image_crops_y, image_crops_x, displacement_x, displacement_y, optimize_alignment=1, limit_search=[], use_reference=0)

Iterate over the output of the low-resolution imaging of the multi-scale microscope to stitch all the data based on an initial guess of the translational shift (from the stage positions).

Parameters:

• experimentfolder_parent – Folder where all timepoints are in

• experimentfolder_result – Folder to save the result of Stitching

• channelnames – List of channels to stitch

• region – List of regions, e.g. “low_stack000”

• sample – Which fish/sample are you Stitching, e.g. “fish3”

• image_crops_y – Cropping parameters so that empty regions/at border can be removed, e.g. [[396, 396 + 3336], [308, 308 + 3336], [212, 212 + 3052]]

• image_crops_x – Cropping parameters so that empty regions/at border can be removed, e.g. [[200, 2200], [200, 2200], [200, 2200]]

• displacement_x – Approximate displacement in x

• displacement_y – Approximate displacement in y

• optimize_alignment – (Optional) do you want to optimize the alignment per timepoint or apply the same parameters to whole timelapse

• limit_search – (Optional) do you want to restrict the finding of optimal Stitching to a small overlap window? if yes, give a range: [100, 15]

• use_reference – Use a reference for individual timepoints

load_image(pathlist)

Load the images specified by the path list and populate the self.rawimages array.

Parameters:

pathlist – Array of image paths to open

make_maxprojections()

Make maximum projections of the images in the self.rawimages array and save them in the self.maxprojections array.

optimize_overlap(limit_search=[], extendfactor=100)

Optimizes the provided overlap, based on the maximum projections.

Parameters:

• limit_search – Constrain the search around the center of a predefined position, e.g. what maximum displacement is expected

• extendfactor – Determines how large the overlap region is on which you calculate the overlap.

Returns:

The optimized translation values (translation_values_x, translation_values_y).

perform_2D_maximum_fusion(imagearrays)

Perform image fusion by selecting the maximum intensity of both images.

Parameters:

imagearrays – list of images to fuse, translation based on self.calculated_displacement

Returns:

fused maximum projection image of imagearrays

perform_2D_weighted_average_fusion(imagearrays)

Perform fusion based on a sigmoidal blending curve of both images.

Parameters:

imagearrays – list of images to fuse

Returns:

weighted average fused image

perform_3D_fusion(mode='weighted')

Perform plane-by-plane wise fusion on 3D stack.

Parameters:

mode – select mode of image fusion (maximum or weighted)

Returns:

fused_image. 3D image fused stack.

stitching_workflow(imagelist, image_crops_x=None, image_crops_y=None, optimizeAlignment=1, limit_search=[], clipped=1, use_reference=1)

Process one set of filepaths, load images, (optionally) crop them, (optionally) improve alignment, fuse data and (optionally) clip data.

Parameters:

• imagelist – Filepaths of images to stitch

• image_crops_x – (Optional) crop images before Stitching

• image_crops_y – (Optional) crop images before Stitching

• optimizeAlignment – (Optional) run optimization of alignment for stack (1 = yes, 0 = no)

• limit_search – (Optional) if you want to limit the search to a region around a pre-aligned setting

• clipped – (Optional) clip stack so that no zero value pixels are in the image (1 = yes, 0 = no)

• use_reference – (Optional) use a reference set at the beginning for Stitching so that parameters don’t drift outside expected values

Returns:

Fused image of files in imagelist

Module contents