dv = mm.displays().get_active_data_viewer()
ds = dv.get_datastore()
cb = mm.data().get_coords_builder()
coord = cb.c(0).t(0).p(0).z(0).build()
img = ds.get_image(coord)
pixels = np.reshape(img.get_raw_pixels(), newshape=[img.get_height(), img.get_width()])
# find organelles using a combination of thresholding and watershed
segmented = find_blobs(pixels, threshold_otsu(pixels), 500, 200)
label_img = label(segmented)
label_img = morphology.remove_small_objects(label_img, 5)
blobs = regionprops(label_img)
# select two sizes, small and large. Select those that are away from the border
centered = select(blobs, 'centroid', img.get_width() / 10, 0.9 * img.get_width())
small = select(centered, 'area', 5, 45)
large = select(centered, 'area', 90, 600)
# for each, select up to 10, but no more than half of the spots, and send them to SLM
nr = 20
projector.enable_point_and_shoot_mode(True)
mm.acquisitions().run_acquisition_nonblocking()
# Trick to get timing right. Wait for Core to report that Sequence is running
while not mmc.is_sequence_running(mmc.get_camera_device()):
time.sleep(0.1)
time.sleep(0.5)
for region_list in [small, large]:
nr_shots = nr if len(region_list) >= (2 * nr) else int(len(region_list) / 2)
shots = random.sample(region_list, nr_shots)
# shots = region_list[0:10]
count -= 1
else:
count = 0
if calibrated:
continue
count += 1
img = mm.live().snap(False).get(0)
pixels = np.reshape(img.get_raw_pixels(),
newshape=[img.get_height(),
img.get_width()])
# find organelles using a combination of thresholding and watershed
_, segmented = find_organelle(pixels, 'local-nucleoli', 500, 200, 10, 1000)
label_img = label(segmented)
label_img = morphology.remove_small_objects(label_img, 5)
blobs = regionprops(label_img)
centered = select(blobs, 'centroid',
img.get_width() / 10, 0.9 * img.get_width())
if len(centered) > (nr // 2):
projector.enable_point_and_shoot_mode(True)
ssb = mm.acquisitions().get_acquisition_settings().copy_builder()
mm.acquisitions().set_acquisition_settings(
ssb.prefix(pos.get_label()).build())
ds = mm.acquisitions().run_acquisition_nonblocking()
# Trick to get timing right. Wait for Core to report that Sequence is running
while not mmc.is_sequence_running(mmc.get_camera_device()):
time.sleep(0.1)
time.sleep(1.5)
for region_list in [centered]:
nr_shots = nr if len(region_list) >= (2 * nr) else int(
len(region_list) / 2)
# reference image of time 0
# if decide to use other image as ref_image
# be sure to check photobleaching correction for all reported intensities
temp = store.get_image(cb.c(data_c).t(0).build())
pix = np.reshape(temp.get_raw_pixels(),
newshape=[temp.get_height(),
temp.get_width()])
_, organelle = find_organelle(pix,
thresholding,
min_size=min_size,
max_size=max_size)
label_organelle = label(organelle, connectivity=1)
blobs = regionprops(label_organelle)
selected = select(blobs, 'centroid',
temp.get_width() / 10, 0.9 * temp.get_width())
print(selected)
spots = obj.select_random_in_label(label_organelle, 1)
if display_mode == 'Y':
print("### Output display ...")
with napari.gui_qt():
# embed mpl widget in napari viewer
mpl_widget = FigureCanvas(Figure(figsize=(5, 3)))
[ax1, ax2, ax3] = mpl_widget.figure.subplots(nrows=1, ncols=3)
viewer = napari.Viewer()
viewer.window.add_dock_widget(mpl_widget)
# napari display