def img_to_tracks(fname):
# Convert the tracking results saved in an OME-TIFF image to a dictionary of tracks
img_data, order, _ = imread(fname, return_order=True)
track_dict = defaultdict(list)
where = np.where(img_data > 0)
order_idx = {d: i for i, d in enumerate(order)}
for val, point in zip(img_data[where], zip(*where)):
track_dict[val].append([(point[order_idx.get(d, -1)] if d in order_idx else 0) for d in "TXYZ"])
return track_dict
def extract_tiled_annotations(in_tiles, out_path, nj, label_merging=False):
"""
in_images: iterable
List of BiaflowsTile
out_path: str
Path of output tiles
nj: BiaflowsJob
A BIAflows job object
label_merging: bool
True for merging only polygons having the same label. False for merging based on geometry only
"""
# regroup tiles by original images
grouped_tiles = defaultdict(list)
for in_tile in in_tiles:
grouped_tiles[in_tile.in_image.original_filename].append(in_tile)
default_tile_builder = DefaultTileBuilder()
annotations = AnnotationCollection()
for tiles in grouped_tiles.values():
# recreate the topology
in_image = tiles[0].in_image
topology = BiaflowsSldcImage(in_image, is_2d=True).tile_topology(
default_tile_builder,
max_width=nj.flags["tile_width"],
max_height=nj.flags["tile_height"],
overlap=nj.flags["tile_overlap"])
# extract polygons for each tile
ids, polygons, labels = list(), list(), list()
label = -1
for tile in tiles:
out_tile_path = os.path.join(out_path, tile.filename)
slices = mask_to_objects_2d(imread(out_tile_path),
offset=tile.tile.abs_offset[::-1])
ids.append(tile.tile.identifier)
polygons.append([s.polygon for s in slices])
labels.append([s.label for s in slices])
# save label for use after merging
if len(slices) > 0:
label = slices[0]
# merge
merged = SemanticMerger(tolerance=1).merge(
ids, polygons, topology, labels=labels if label_merging else None)
if label_merging:
merged = merged[0]
annotations.extend([
create_annotation_from_slice(
_slice=AnnotationSlice(p, label),
id_image=in_image.object.id,
image_height=in_image.object.height,
id_project=nj.project.id,
) for p in merged
])
return annotations
def extract_annotations_prttrk(out_path, in_image, project_id, track_prefix,
**kwargs):
"""
Parameters:
-----------
out_path: str
in_image: BiaflowsCytomineInput
project_id: int
name_prefix: str
kwargs: dict
"""
image = in_image.object
path = os.path.join(out_path, in_image.filename)
data, dim_order, _ = imread(path, return_order=True)
ndim = get_dimensionality(dim_order)
if ndim != 3:
raise ValueError(
"Annotation extraction for object tracking does not support masks with more than 3 dims..."
)
slices = mask_to_points_3d(data, time=True, assume_unique_labels=True)
time_to_image = get_depth_to_slice(image)
tracks = TrackCollection()
annotations = AnnotationCollection()
for slice_group in slices:
curr_tracks, curr_annots = create_tracking_from_slice_group(
image,
slice_group,
slice2point=lambda _slice: _slice.polygon,
depth2slice=time_to_image,
id_project=project_id,
upload_object=False,
track_prefix=track_prefix + "-particle",
upload_group_id=True)
tracks.extend(curr_tracks)
annotations.extend(curr_annots)
return tracks, annotations
def extract_annotations_pixcla(out_path, in_image, project_id, track_prefix,
**kwargs):
"""
Parameters
----------
out_path: str
in_image: BiaflowsCytomineInput
project_id: int
track_prefix: str
kwargs: dict
"""
image = in_image.object
path = os.path.join(out_path, in_image.filename)
data, dim_order, _ = imread(path, return_order=True)
return mask_convert(data,
image,
project_id,
mask_2d_fn=mask_to_objects_2d,
mask_3d_fn=lambda m: mask_to_objects_3d(
m, background=0, assume_unique_labels=False),
track_prefix=track_prefix + "-object",
upload_group_id=get_dimensionality(dim_order) > 2)
def extract_annotations_objdet(out_path,
in_image,
project_id,
track_prefix,
is_csv=False,
generate_mask=False,
result_file_suffix=".tif",
has_headers=False,
parse_fn=None,
**kwargs):
"""
Parameters:
-----------
out_path: str
in_image: BiaflowsCytomineInput
project_id: int
is_csv: bool
True if the output data are stored in a csv file
generate_mask: bool
If result is in a CSV, True for generating a mask based on the points in the csv. Ignored if is_csv is False.
The mask file is generated in out_path with the name "{in_image.id}.png".
result_file_suffix: str
Suffix of the result filename (prefix being the image id).
has_headers: bool
True if the csv contains some headers (ignored if is_csv is False)
parse_fn: callable
A function for extracting coordinates from the csv file (already separated) line.
track_prefix: str
kwargs: dict
"""
image = in_image.object
file = str(image.id) + result_file_suffix
path = os.path.join(out_path, file)
tracks = TrackCollection()
annotations = AnnotationCollection()
if not os.path.isfile(path):
print("No output file at '{}' for image with id:{}.".format(
path, image.id),
file=sys.stderr)
return annotations
# whether the points are stored in a csv or a mask
if is_csv: # only 2d
if parse_fn is None:
raise ValueError(
"parse_fn shouldn't be 'None' when result file is a CSV.")
points = csv_to_points(path,
has_headers=has_headers,
parse_fn=parse_fn)
annotations.extend([
create_annotation_from_slice(point,
image.id,
image.height,
project_id,
upload_group_id=False)
for point in points
])
if generate_mask:
image, dim_order, _ = imread(in_image.filepath, return_order=True)
mask = slices_to_mask(points, image.shape).squeeze()
imwrite_ome(os.path.join(out_path, in_image.filename),
mask,
dim_order=dim_order)
else:
# points stored in a mask
data, dim_order, _ = imread(path)
tracks, annotations = mask_convert(
data,
image,
project_id,
mask_2d_fn=mask_to_points_2d,
mask_3d_fn=lambda m: mask_to_points_3d(
m, time=False, assume_unique_labels=False),
track_prefix=track_prefix + "-object",
upload_group_id=get_dimensionality(dim_order) > 2)
return tracks, annotations