seg, num_obj = skimage.measure.label(seg > 0, return_num=True)
# Determine if the segmentation should be saved.
if i == rand:
mscl.save_seg(
'output/{0}_{1}_example_segmentation.png'.format(DATE, RBS), ph, seg)
if num_obj > 0:
# Compute the background information
mean_bg = mscl.compute_mean_bg(ph, ff_ims[i])
mean_bg = mean_bg / ip_dist**2
exposure = mscl.scrape_metadata(metadata[i], return_date=False)
# For shock experiments, link the markers.
if EXP.lower() == 'shock':
_markers = mscl.marker_parse(markers[i])
_df = mscl.link_markers(_markers, seg, ff_ims[i])
_df['flow_rate'] = FLOW_RATE
_df.drop(['x_pos', 'y_pos', 'mask_label', 'label_cent_x',
'label_cent_y', 'dist'], axis=1, inplace=True)
else:
props = skimage.measure.regionprops(seg, ff_Ims[i])
intensity = [prop.mean_intensity / ip_dist**2 for prop in props]
area = [prop.area * ip_dist**2 for prop in props]
_df = pd.DataFrame(dict(intensity=intensity, area=area))
# Insert the other necessary information.
_df['date'] = DATE
_df['rbs'] = RBS
_df['mean_bg'] = mean_bg
seg)
if num_obj > 0:
# Compute the background information
mean_bg = mscl.compute_mean_bg(ph, ff_ims[i])
mean_bg = mean_bg / ip_dist**2
exposure = mscl.scrape_metadata(metadata[i], return_date=False)
# For shock experiments, link the markers.
if EXP.lower() == 'shock':
for m in markers:
m_pos = m.split('/')[-1].split('.')[0]
ph_pos = tif_files[i].split('/')[-1].split('_')[-1].split(
'.')[0]
if m_pos == ph_pos:
_markers = mscl.marker_parse(m)
_df = mscl.link_markers(_markers, seg, ff_ims[i])
_df.drop([
'x_pos', 'y_pos', 'mask_label', 'label_cent_x',
'label_cent_y', 'dist'
],
axis=1,
inplace=True)
else:
props = skimage.measure.regionprops(seg, ff_ims[i])
intensity = [
prop.mean_intensity / ip_dist**2 for prop in props
]
area = [prop.area * ip_dist**2 for prop in props]
_df = pd.DataFrame(dict(intensity=intensity, area=area))
_df['survival'] = False
