def load_stack_and_segment(path, imsave):
"""Load a stack from the given path and segment it."""
autof_stack = Stack.from_path(path, channel=2)
nuclear_stack = Stack.from_path(path, channel=2)
return segmentation_from_stacks(nuclear_stack, autof_stack, imsave)
def find_probe_locations(stack_dir, imsave, pchannel):
"""Find probe locations. Given a path, we construct a z stack from the first
channel of the images in that path, and then find probes within that stack.
Returns a list of coordinate pairs, representing x, y locations of probes.
"""
zstack = Stack.from_path(stack_dir, channel=pchannel)
# For comparative purposes (so we save the image)
projection = max_intensity_projection(zstack)
# Normalise each image in the stack
norm_stack = normalise_stack(zstack)
# Now take a maximum intensity projection
norm_projection = max_intensity_projection(norm_stack, 'norm_projection')
# Find edges should show the circle-like probes as annuli
edges = find_edges(norm_projection)
# Find a suitable template image for matching
template = find_best_template(edges, imsave)
match_result = match_template(edges.image_array, template, pad_input=True)
imsave('stage2_match.png', match_result)
# Set a threshold for matched locations
match_thresh = 0.6
print "t,c"
for t in np.arange(0.1, 1, 0.05):
print "{},{}".format(t, len(np.where(match_result > t)[0]))
locs = np.where(match_result > match_thresh)
annotated_edges = grayscale_to_rgb(edges.image_array)
annotated_edges[locs] = edges.image_array.max(), 0, 0
imsave('annotated_edges.png', annotated_edges)
# Find the centroids of the locations where we think there's a probe
cloc_array = match_result > match_thresh
ia_locs = ImageArray(cloc_array, name='new_cloc')
connected_components = find_connected_components(ia_locs)
centroids = component_centroids(connected_components)
probe_locs = zip(*np.where(centroids.image_array != 0))
generate_probe_loc_image(norm_projection, probe_locs, imsave)
return probe_locs
