def saveResult(save_path, nparray, polygons, source_dir_list, prefix=''):
if not os.path.exists(save_path):
os.makedirs(save_path)
for (filename, image, poly) in zip(source_dir_list, nparray, polygons):
imsave(
os.path.join(save_path,
prefix + os.path.splitext(filename)[0] + '.tif'),
image, polygons)
export_imagej_rois(
os.path.join(save_path, prefix + os.path.splitext(filename)[0]),
poly['coord'])
def _predict_stardist(self, model, file, T, channel, prob_thresh,
nms_thresh, out_dir):
axes = 'TCYX'
# if T.ndim==3:
# timelapse = T
if T.ndim == 4:
timelapse = T[:, channel]
else:
raise ValueError(
'Data has unexpected number of dimensions. Weird.')
# normalise
print(f'Normalizing each frame to run Stardist', flush=True)
timelapse = np.stack(
[normalize(frame, 1, 99.8) for frame in timelapse])
print(
f"Timelapse has axes {axes.replace('C','')} with shape {timelapse.shape}"
)
polygons = [
model.predict_instances(frame,
nms_thresh=nms_thresh,
prob_thresh=prob_thresh)[1]
for frame in tqdm(timelapse)
]
if prob_thresh is None:
prob_string = 'default'
else:
prob_string = f'{prob_thresh:.2f}'
if nms_thresh is None:
nms_string = 'default'
else:
nms_string = f'{nms_thresh:.2f}'
roi_path = out_dir / f"{file.stem}_prob={prob_string}_nms={nms_string}"
roi_path.parent.mkdir(parents=True, exist_ok=True)
rois_python = Path(str(roi_path) + '.npz')
rois_imagej = Path(str(roi_path) + '.zip')
print(f'Saving ImageJ ROIs to {rois_imagej}')
export_imagej_rois(str(rois_imagej),
[poly['coord'] for poly in polygons])
print(f'Saving Python rois to {rois_python}')
np.savez(
str(rois_python),
coord=[p['coord'] for p in polygons],
points=[p['points'] for p in polygons],
prob=[p['prob'] for p in polygons],
)
flow_threshold=None,
channels=channels,
net_avg=net_avg)
for one_mask, image_file in zip(masks, image_files):
polygons = []
slices = find_objects(one_mask.astype(int))
for i, si in enumerate(slices):
if si is not None:
coords = [[], []]
sr, sc = si
mask = (one_mask[sr, sc] == (i + 1)).astype(np.uint8)
contours = cv.findContours(mask, cv.RETR_EXTERNAL,
cv.CHAIN_APPROX_NONE)
pvc, pvr = np.concatenate(contours[-2], axis=0).squeeze().T
vr, vc = pvr + sr.start, pvc + sc.start
coords[0] = vr
coords[1] = vc
#sub_polygons.append(coords)
polygons.append(coords)
roi_path = image_folder / 'rois' / (str(image_file.name) + '_rois')
roi_path.parent.mkdir(exist_ok=True)
print(' Exporting Polygons')
export_imagej_rois(roi_path, [polygons])
print('Script Finished!')
model = StarDist2D(None, name=model_name, basedir=model_folder)
# Read All Images
image_files = sorted(image_folder.glob('*.tif'))
# Normalize Images and predict
for image_file in image_files:
print('Processing Image: ', str(image_file.name), '...')
image = imread(str(image_file))
if (is_norm_provided):
norm = normalize_mi_ma(image, min, max)
else:
norm = normalize(image, 1, 99.8, axis=(0, 1))
labels, polygons = model.predict_instances(norm,
prob_thresh=prob_thresh,
nms_thresh=nms_thresh)
# Create a 'rois' folder inside the same directory and make sure that
# the images are saved with the same name as the image (with extension), with suffix `_rois.zip`
roi_path = image_folder / 'rois' / (str(image_file.name) + '_rois')
roi_path.parent.mkdir(exist_ok=True)
if (polygons['coord'].shape[0] > 0):
print('Exporting Polygons')
export_imagej_rois(roi_path, polygons['coord'])
print('Script Finished!')