def process_image_and_series(config, spec, output_ds):
imageds = ImageDataSet(config.ids_uri)
wall_stack = imageds.get_stack(
spec.image_name,
spec.series_name,
0,
config.wall_channel
)
logger.info(f"Loaded {spec.series_name} with shape {wall_stack.shape}")
zoomed_wall_stack = zoom_to_match_scales(wall_stack)
snfmt = spec.series_name.replace(" ", "_").replace("#", "")
level = config.params['level']
logging.info(f"Segmenting with level {level}")
segmentation = sitk_watershed_segmentation(
zoomed_wall_stack,
level=config.params["level"]
)
wall_output_relpath = f"{spec.image_name}_{snfmt}_cell_wall.tif"
seg_output_relpath = f"{spec.image_name}_{snfmt}_segmentation_L{level}.tif"
wall_output_abspath = output_ds.prepare_staging_abspath_promise(wall_output_relpath)
seg_output_abspath = output_ds.prepare_staging_abspath_promise(seg_output_relpath)
segmentation.save(seg_output_abspath)
zoomed_wall_stack.save(wall_output_abspath)
def get_stack_by_imname_sname(ids_uri, imname, sname, channel=0):
ids = ImageDataSet.from_uri(ids_uri)
raw_stack = ids.get_stack(imname, sname, channel=channel)
zoomed_stack = zoom_to_match_scales(raw_stack)
return zoomed_stack
def get_stack_by_name(ids_uri, root_name, channel=0):
ids = ImageDataSet.from_uri(ids_uri)
name_lookup = dict(ids.get_image_series_name_pairs())
series_name = name_lookup[root_name]
raw_stack = ids.get_stack(root_name, series_name, channel=channel)
zoomed_stack = zoom_to_match_scales(raw_stack)
return zoomed_stack
def segment_image_from_dataset(imageds,
image_name,
series_name,
wall_channel,
output_filename,
level=0.664,
nsegments=200):
logging.info("Loading wall stack")
wall_stack = imageds.get_stack(image_name, series_name, 0, wall_channel)
logging.info("Adjusting scales")
zoomed_wall_stack = zoom_to_match_scales(wall_stack)
logging.info("Segmenting image")
segmentation = sitk_watershed_segmentation(zoomed_wall_stack, level=level)
logging.info(f"Filtering segmentation by cell size, max {nsegments} cells")
filtered_segmentation = filter_segmentation_by_size(
segmentation, nsegments)
filtered_segmentation.save(output_filename)
def main(dataset_uri):
logging.basicConfig(level=logging.INFO)
imageds = ImageDataSet(dataset_uri)
print(imageds.all_possible_stack_tuples())
image_name = '20200309_lhp1_W10_T14'
series_name = 'SDB995-5_01'
wall_channel = 1
logging.info("Loading wall stack")
wall_stack = imageds.get_stack(image_name, series_name, 0, wall_channel)
logging.info("Adjusting scales")
zoomed_wall_stack = zoom_to_match_scales(wall_stack)
output_filename = f'{image_name}_{series_name}_wall.tif'
zoomed_wall_stack.save(output_filename)
def load_by_name(self, name):
base_dirpath = os.path.join(self.segmentations_dirpath, name)
file_info_fname = "Root_segments.tif.csv"
segmentation_name = "Root_segments.tif.tif"
segmentation_fpath = os.path.join(base_dirpath, segmentation_name)
volume = volread(segmentation_fpath)
transposed = np.transpose(volume, axes=(1, 2, 0))
segmentation = transposed.view(Segmentation3D)
raw_stack = self.get_stack_by_series_name(name)
measure_stack = zoom_to_match_scales(raw_stack)
file_info_fpath = os.path.join(base_dirpath, file_info_fname)
fids_to_rids = self.load_file_data(file_info_fpath)
return segmentation, measure_stack, fids_to_rids
def get_zoomed_stack_cached(ids_uri, image_name, series_name, channel):
stack = get_stack_cached(ids_uri, image_name, series_name, channel)
return zoom_to_match_scales(stack)