def preprocess_img(image5d, preprocs, channel, out_path):
"""Pre-process an image in 3D.
Args:
image5d (:obj:`np.ndarray`): 5D array in t,z,y,x[,c].
preprocs (List[:obj:`profiles.PreProcessKeys`): Sequence of
pre-processing tasks to perform in the order given.
channel (int): Channel to preprocess, or None for all channels.
out_path (str): Output base path.
Returns:
:obj:`np.ndarray`: The pre-processed image array.
"""
if preprocs is None:
print("No preprocessing tasks to perform, skipping")
return
roi = image5d[0]
for preproc in preprocs:
# perform global pre-processing task
print("Pre-processing task:", preproc)
if preproc is profiles.PreProcessKeys.SATURATE:
roi = plot_3d.saturate_roi(roi, channel=channel)
elif preproc is profiles.PreProcessKeys.DENOISE:
roi = plot_3d.denoise_roi(roi, channel)
elif preproc is profiles.PreProcessKeys.REMAP:
roi = plot_3d.remap_intensity(roi, channel)
elif preproc is profiles.PreProcessKeys.ROTATE:
roi = rotate_img(roi)
# save to new file
image5d = importer.roi_to_image5d(roi)
importer.save_np_image(image5d, out_path)
return image5d
def preprocess_img(image5d, preprocs, channel, out_path):
"""Pre-process an image in 3D.
Args:
image5d (:obj:`np.ndarray`): 5D array in t,z,y,x[,c].
preprocs (Union[str, list[str]]): Pre-processing tasks that will be
converted to enums in :class:`config.PreProcessKeys` to perform
in the order given.
channel (int): Channel to preprocess, or None for all channels.
out_path (str): Output base path.
Returns:
:obj:`np.ndarray`: The pre-processed image array.
"""
if preprocs is None:
print("No preprocessing tasks to perform, skipping")
return
if not libmag.is_seq(preprocs):
preprocs = [preprocs]
roi = image5d[0]
for preproc in preprocs:
# perform global pre-processing task
task = libmag.get_enum(preproc, config.PreProcessKeys)
_logger.info("Pre-processing task: %s", task)
if task is config.PreProcessKeys.SATURATE:
roi = plot_3d.saturate_roi(roi, channel=channel)
elif task is config.PreProcessKeys.DENOISE:
roi = plot_3d.denoise_roi(roi, channel)
elif task is config.PreProcessKeys.REMAP:
roi = plot_3d.remap_intensity(roi, channel)
elif task is config.PreProcessKeys.ROTATE:
roi = rotate_img(roi)
else:
_logger.warn("No preprocessing task found for: %s", preproc)
# save to new file
image5d = importer.roi_to_image5d(roi)
importer.save_np_image(image5d, out_path)
return image5d
def detect_sub_roi(cls, coord, offset, last_coord, denoise_max_shape,
exclude_border, sub_roi, channel, img_path=None,
coloc=False):
"""Perform 3D blob detection within a sub-ROI without accessing
class attributes, such as for spawned multiprocessing.
Args:
coord (Tuple[int]): Coordinate of the sub-ROI in the order z,y,x.
offset (Tuple[int]): Offset of the sub-ROI within the full ROI,
in z,y,x.
last_coord (:obj:`np.ndarray`): See attributes.
denoise_max_shape (Tuple[int]): See attributes.
exclude_border (bool): See attributes.
sub_roi (:obj:`np.ndarray`): Array in which to perform detections.
img_path (str): Path from which to load metadatat; defaults to None.
If given, the command line arguments will be reloaded to
set up the image and processing parameters.
coloc (bool): True to perform blob co-localizations; defaults
to False.
channel (Sequence[int]): Sequence of channels, where None detects
in all channels.
Returns:
Tuple[int], :obj:`np.ndarray`: The coordinate given back again to
identify the sub-ROI position and an array of detected blobs.
"""
if img_path:
# reload command-line parameters and image metadata, which is
# required if run from a spawned (not forked) process
cli.process_cli_args()
_, orig_info = importer.make_filenames(img_path)
importer.load_metadata(orig_info)
print("detecting blobs in sub-ROI at {} of {}, offset {}, shape {}..."
.format(coord, last_coord, tuple(offset.astype(int)),
sub_roi.shape))
if denoise_max_shape is not None:
# further split sub-ROI for preprocessing locally
denoise_roi_slices, _ = chunking.stack_splitter(
sub_roi.shape, denoise_max_shape)
for z in range(denoise_roi_slices.shape[0]):
for y in range(denoise_roi_slices.shape[1]):
for x in range(denoise_roi_slices.shape[2]):
denoise_coord = (z, y, x)
denoise_roi = sub_roi[denoise_roi_slices[denoise_coord]]
_logger.debug(
f"preprocessing sub-sub-ROI {denoise_coord} of "
f"{np.subtract(denoise_roi_slices.shape, 1)} "
f"(shape {denoise_roi.shape} within sub-ROI shape "
f"{sub_roi.shape})")
denoise_roi = plot_3d.saturate_roi(
denoise_roi, channel=channel)
denoise_roi = plot_3d.denoise_roi(
denoise_roi, channel=channel)
# replace slices with denoised ROI
denoise_roi_slices[denoise_coord] = denoise_roi
# re-merge back into the sub-ROI
merged_shape = chunking.get_split_stack_total_shape(
denoise_roi_slices)
merged = np.zeros(
tuple(merged_shape), dtype=denoise_roi_slices[0, 0, 0].dtype)
chunking.merge_split_stack2(denoise_roi_slices, None, 0, merged)
sub_roi = merged
if exclude_border is None:
exclude = None
else:
exclude = np.array([exclude_border, exclude_border])
exclude[0, np.equal(coord, 0)] = 0
exclude[1, np.equal(coord, last_coord)] = 0
segments = detector.detect_blobs(sub_roi, channel, exclude)
if coloc and segments is not None:
# co-localize blobs and append to blobs array
colocs = colocalizer.colocalize_blobs(sub_roi, segments)
segments = np.hstack((segments, colocs))
#print("segs before (offset: {}):\n{}".format(offset, segments))
if segments is not None:
# shift both coordinate sets (at beginning and end of array) to
# absolute positioning, using the latter set to store shifted
# coordinates based on duplicates and the former for initial
# positions to check for multiple duplicates
detector.shift_blob_rel_coords(segments, offset)
detector.shift_blob_abs_coords(segments, offset)
#print("segs after:\n{}".format(segments))
return coord, segments