group = 'NT'
else:
'Print No Group'
table['image'] = name
table['group'] = group
#Reading Image
green = io.imread(path + file_name[0:-4] + '.tif')
#Creating ztsack of intesity image and color zstack
green_color = np.zeros(
(green.shape[0], green.shape[1], green.shape[2], 3),
dtype=np.uint8)
green_color[:, :, :, 1] = green.copy()
ct = cellseg.cell_tracking(green, green, green_color)
ct.positions_table = table.copy()
ct.track_with_blob(75)
ct.set_segment_param(enhance=False,
blur=True,
kernel=31,
n_intensities=2)
#Setting Segmentation Parameters
#fig, ax1 = plt.subplots(nrows=1,ncols=1, figsize=(2.5,2.5))
g = sns.lmplot(x="z",
y="mean_intensity",
hue="label",
data=ct.positions_table,
fit_reg=False,
size=5,
legend=False)
io.imsave(path_results + 'Z-level_Blobs_' + str(z) + '.tif',
zlevel_image_color_marked)
#Get Measurements
positions_blobs = cellseg.blob_log_measure(cl1,
zlevel_image_erk,
max_sigma=30,
min_sigma=20,
num_sigma=10,
threshold=.01,
overlap=0.6)
#%%
# 2) Cell Tracking With Blob Detection, Note: This can take a long time to run
ct = cellseg.cell_tracking(erk, dapi, erk_color)
# Setting Segmentation Parameters
ct.set_segment_param(enhance=False, blur=False, n_intensities=2)
# Setting Blob Parameters
ct.set_blob_param(max_sigma=30,
min_sigma=20,
num_sigma=10,
threshold=.01,
overlap=0.6)
# Track with Blob
ct.track_with_blob(min_slices=1, color_blobs=(255, 0, 0))
ct.draw_trajectories(color_trajectory=(255, 255, 0))