FluorSeg 0.0.26.dev0

Segment regions from fluorescence images

FlourSeg provides such and such and so and so. You might find it most useful for tasks involving <x> and also <y>. Typical usage often looks like this:

#!/usr/bin/env python

from fluorseg import liffile
from fluorseg import roifile

lif = liffile.LIFFile(path)

#show the path
print(lif.path)

#show the metadata xml object
print(lif.xml_root)

#show the number of images in the file
print(lif.img_count)

#show the number of layers in each zstack for each image
print(lif.z_stack_count)

#show the first image's, 2nd zstack layer
print(type(lif.combined_channel_images[0][1]))

#show the type first channel of the first image's 2nd z-stack layer - each data array is a numpy ndarray
# they are held in nested lists => [image index][z_stack index]
print(type(lif.channel_one_images[0][1]))

#plot the second channel of the first image's 1st z-stack layer [image index][z_stack index]
plt.imshow(lif.channel_two_images[0][0], interpolation='nearest')
plt.show()

#get the maximum projection of each images z-stack for each image
max_projs_channel_one = [liffile.max_proj(z_stacks) for z_stacks in lif.channel_one_images]
max_projs_channel_two = [liffile.max_proj(z_stacks) for z_stacks in lif.channel_two_images]

#plot the first max_proj
plt.imshow(max_projs_channel_one[0], interpolation='nearest')
plt.show()

plt.imshow(max_projs_channel_two[0], interpolation='nearest')
plt.show()

#get the region of interest info
roi_info = roifile.ROIFile(roi_zip_path) #roi for series 001 only

#make the mask for the second region of interest - a polygon
mask = make_polygon_mask(roi_info.rois[1], 512, 512)

#make the mask for the 5th region of interest - an ellipse
oval_mask = make_oval_mask(roi_info.rois[4], 512,512)

#just get the volume in a region of interest - masking done for you
series1_mp_c2 = max_projs_channel_two[0]
get_region_volume(series1_mp_c2, roi_info.rois[4])

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