Segment regions from fluorescence images
Project description
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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