def find_segmented_regions(seeds, autof_stack, imsave):
min_autof_proj = min_intensity_projection(autof_stack)
equal_autof = equalize_adaptive(min_autof_proj, "equal_autof")
smoothed_autof = gaussian_filter(equal_autof, sigma=5, name="smooth_autof")
edge_autof = find_edges(smoothed_autof, name="edge_autof")
thresh_autof = threshold_otsu(smoothed_autof, mult=0.6, name="thresh_autof")
# ndfeed = skimage.img_as_uint(edge_autof.image_array & thresh_autof)
# imsave('ndfeed.png', ndfeed)
# altseg = watershed_ift(ndfeed, seeds.image_array)
# imsave('altseg.png', altseg)
# segmentation = watershed_with_seeds(smoothed_autof, ImageArray(altseg, 'atseg'),
segmentation = watershed_with_seeds(smoothed_autof, seeds, mask_image=thresh_autof)
# my_maker = make_named_transform('hughbert')
# my_filter = my_maker(filter_segmentation)
# filtered_segmentation = my_filter(segmentation)
filtered_segmentation = filter_segmentation(segmentation)
re_watershed = watershed_with_seeds(
smoothed_autof, filtered_segmentation, mask_image=thresh_autof, name="re_watershed"
)
return re_watershed
def generate_segmentation_seeds(nuclear_stack):
"""Given the nuclear fluorescence channel, find markers representing the
locations of those nuclei so that they can be used to seed a segmentation.
"""
normed_stack = normalise_stack(nuclear_stack)
max_nuclear_proj = max_intensity_projection(normed_stack)
eq_proj = equalize_adaptive(max_nuclear_proj, n_tiles=16, name="equalized_nuclear_proj")
gauss = gaussian_filter(eq_proj, sigma=3)
edges = find_edges(gauss, name="seed_edges")
thresh = threshold_otsu(edges, mult=1)
nosmall = remove_small_objects(thresh, min_size=500)
# dilated = dilate_simple(nosmall)
connected_components = find_connected_components(nosmall, background=0, name="conn_seeds")
seeds = component_centroids(connected_components, name="seed_centroids")
return seeds
def hough_stuff(imsave):
"""Deprecated. Template matching works better."""
thresh = threshold_otsu(edges, mult=1.5)
hough_radii = np.arange(1, 3, 1)
hough_res = skimage.transform.hough_circle(thresh.image_array, hough_radii)
hough_data = hough_res[0] + hough_res[1]
#loc_array = peak_local_max(hough_data, min_distance=5, threshold_rel=0.5)
cloc_array = peak_local_max(hough_data, min_distance=5, threshold_rel=0.5, indices=False)
imsave('cloc.png', cloc_array)
connected_components, n_cc = skimage.measure.label(cloc_array, neighbors=8, return_num=True)
labels = np.unique(connected_components)
annot = np.zeros((512, 512, 3), dtype=np.uint8)
annot[:,:] = 255, 255, 255
annot[np.where(thresh.image_array)] = [0, 0, 0]
def draw_cross(x, y, c):
for xo in np.arange(-4, 5, 1):
annot[x+xo, y] = c
for yo in np.arange(-4, 5, 1):
annot[x,y+yo] = c
probe_locs = []
for label in labels:
coord_list = zip(*np.where(connected_components == label))
probe_locs.append(coord_list[0])
for coords in probe_locs:
x, y = coords
c = random_rgb()
draw_cross(x, y, c)
imsave('probe_locations.png', annot)
#print '\n'.join(thresh.history)
return probe_locs