def setup_images(path=None,
series=None,
offset=None,
size=None,
proc_mode=None,
allow_import=True):
"""Sets up an image and all associated images and metadata.
Paths for related files such as registered images will generally be
constructed from ``path``. If :attr:`config.prefix` is set, it will
be used in place of ``path`` for registered labels.
Args:
path (str): Path to image from which MagellanMapper-style paths will
be generated.
series (int): Image series number; defaults to None.
offset (List[int]): Sub-image offset given in z,y,x; defaults to None.
size (List[int]): Sub-image shape given in z,y,x; defaults to None.
proc_mode (str): Processing mode, which should be a key in
:class:`config.ProcessTypes`, case-insensitive; defaults to None.
allow_import (bool): True to allow importing the image if it
cannot be loaded; defaults to True.
"""
def add_metadata():
# override metadata set from command-line metadata args if available
md = {
config.MetaKeys.RESOLUTIONS:
config.meta_dict[config.MetaKeys.RESOLUTIONS],
config.MetaKeys.MAGNIFICATION:
config.meta_dict[config.MetaKeys.MAGNIFICATION],
config.MetaKeys.ZOOM:
config.meta_dict[config.MetaKeys.ZOOM],
config.MetaKeys.SHAPE:
config.meta_dict[config.MetaKeys.SHAPE],
config.MetaKeys.DTYPE:
config.meta_dict[config.MetaKeys.DTYPE],
}
for key, val in md.items():
if val is not None:
# explicitly set metadata takes precedence over extracted vals
import_md[key] = val
# LOAD MAIN IMAGE
# reset image5d
config.image5d = None
config.image5d_is_roi = False
load_subimage = offset is not None and size is not None
config.resolutions = None
# reset label images
config.labels_img = None
config.borders_img = None
filename_base = importer.filename_to_base(path, series)
subimg_base = None
if load_subimage and not config.save_subimg:
# load a saved sub-image file if available and not set to save one
subimg_base = stack_detect.make_subimage_name(filename_base, offset,
size)
filename_subimg = libmag.combine_paths(subimg_base,
config.SUFFIX_SUBIMG)
try:
# load sub-image if available
config.image5d = np.load(filename_subimg, mmap_mode="r")
config.image5d = importer.roi_to_image5d(config.image5d)
config.image5d_is_roi = True
config.image5d_io = config.LoadIO.NP
print("Loaded sub-image from {} with shape {}".format(
filename_subimg, config.image5d.shape))
# after loading sub-image, load original image's metadata
# for essential data such as vmin/vmax; will only warn if
# fails to load since metadata could be specified elsewhere
_, orig_info = importer.make_filenames(path, series)
print("load original image metadata from:", orig_info)
importer.load_metadata(orig_info)
except IOError:
print("Ignored sub-image file from {} as unable to load".format(
filename_subimg))
proc_type = libmag.get_enum(proc_mode, config.ProcessTypes)
if proc_type in (config.ProcessTypes.LOAD, config.ProcessTypes.EXPORT_ROIS,
config.ProcessTypes.EXPORT_BLOBS,
config.ProcessTypes.DETECT):
# load a blobs archive
try:
if subimg_base:
try:
# load blobs generated from sub-image
config.blobs = load_blobs(subimg_base)
except (FileNotFoundError, KeyError):
# fallback to loading from full image blobs and getting
# a subset, shifting them relative to sub-image offset
print("Unable to load blobs file based on {}, will try "
"from {}".format(subimg_base, filename_base))
config.blobs = load_blobs(filename_base)
config.blobs, _ = detector.get_blobs_in_roi(config.blobs,
offset,
size,
reverse=False)
detector.shift_blob_rel_coords(config.blobs,
np.multiply(offset, -1))
else:
# load full image blobs
config.blobs = load_blobs(filename_base)
except (FileNotFoundError, KeyError) as e2:
print("Unable to load blobs file")
if proc_type in (config.ProcessTypes.LOAD,
config.ProcessTypes.EXPORT_BLOBS):
# blobs expected but not found
raise e2
if path and config.image5d is None:
# load or import the main image stack
print("Loading main image")
try:
if path.endswith(sitk_io.EXTS_3D):
# attempt to format supported by SimpleITK and prepend time axis
config.image5d = sitk_io.read_sitk_files(path)[None]
config.image5d_io = config.LoadIO.SITK
else:
# load or import from MagellanMapper Numpy format
import_only = proc_type is config.ProcessTypes.IMPORT_ONLY
if not import_only:
# load previously imported image
config.image5d = importer.read_file(path, series)
if allow_import:
# re-import over existing image or import new image
if os.path.isdir(path) and all(
[r is None for r in config.reg_suffixes.values()]):
# import directory of single plane images to single
# stack if no register suffixes are set
chls, import_md = importer.setup_import_dir(path)
add_metadata()
prefix = config.prefix
if not prefix:
prefix = os.path.join(
os.path.dirname(path),
importer.DEFAULT_IMG_STACK_NAME)
config.image5d = importer.import_planes_to_stack(
chls, prefix, import_md)
elif import_only or config.image5d is None:
# import multi-plane image
chls, import_path = importer.setup_import_multipage(
path)
prefix = config.prefix if config.prefix else import_path
import_md = importer.setup_import_metadata(
chls, config.channel, series)
add_metadata()
config.image5d = importer.import_multiplane_images(
chls,
prefix,
import_md,
series,
channel=config.channel)
config.image5d_io = config.LoadIO.NP
except FileNotFoundError as e:
print(e)
print("Could not load {}, will fall back to any associated "
"registered image".format(path))
if config.metadatas and config.metadatas[0]:
# assign metadata from alternate file if given to supersede settings
# for any loaded image5d
# TODO: access metadata directly from given image5d's dict to allow
# loading multiple image5d images simultaneously
importer.assign_metadata(config.metadatas[0])
# main image is currently required since many parameters depend on it
atlas_suffix = config.reg_suffixes[config.RegSuffixes.ATLAS]
if atlas_suffix is None and config.image5d is None:
# fallback to atlas if main image not already loaded
atlas_suffix = config.RegNames.IMG_ATLAS.value
print(
"main image is not set, falling back to registered "
"image with suffix", atlas_suffix)
# use prefix to get images registered to a different image, eg a
# downsampled version, or a different version of registered images
path = config.prefix if config.prefix else path
if path and atlas_suffix is not None:
try:
# will take the place of any previously loaded image5d
config.image5d = sitk_io.read_sitk_files(
path, reg_names=atlas_suffix)[None]
config.image5d_io = config.LoadIO.SITK
except FileNotFoundError as e:
print(e)
annotation_suffix = config.reg_suffixes[config.RegSuffixes.ANNOTATION]
if annotation_suffix is not None:
# load labels image, set up scaling, and load labels file
try:
# TODO: need to support multichannel labels images
config.labels_img = sitk_io.read_sitk_files(
path, reg_names=annotation_suffix)
if config.image5d is not None:
config.labels_scaling = importer.calc_scaling(
config.image5d, config.labels_img)
if config.load_labels is not None:
labels_ref = ontology.load_labels_ref(config.load_labels)
if isinstance(labels_ref, pd.DataFrame):
# parse CSV files loaded into data frame
config.labels_ref_lookup = ontology.create_lookup_pd(
labels_ref)
else:
# parse dict from ABA JSON file
config.labels_ref_lookup = (
ontology.create_aba_reverse_lookup(labels_ref))
except FileNotFoundError as e:
print(e)
borders_suffix = config.reg_suffixes[config.RegSuffixes.BORDERS]
if borders_suffix is not None:
# load borders image, which can also be another labels image
try:
config.borders_img = sitk_io.read_sitk_files(
path, reg_names=borders_suffix)
except FileNotFoundError as e:
print(e)
if (config.atlas_labels[config.AtlasLabels.ORIG_COLORS]
and config.load_labels is not None):
# load original labels image from same directory as ontology
# file for consistent ID-color mapping, even if labels are missing
try:
config.labels_img_orig = sitk_io.load_registered_img(
config.load_labels, config.RegNames.IMG_LABELS.value)
except FileNotFoundError as e:
print(e)
libmag.warn(
"could not load original labels image; colors may differ"
"differ from it")
load_rot90 = config.roi_profile["load_rot90"]
if load_rot90 and config.image5d is not None:
# rotate main image specified num of times x90deg after loading since
# need to rotate images output by deep learning toolkit
config.image5d = np.rot90(config.image5d, load_rot90, (2, 3))
if (config.image5d is not None and load_subimage
and not config.image5d_is_roi):
# crop full image to bounds of sub-image
config.image5d = plot_3d.prepare_subimg(config.image5d, size,
offset)[None]
config.image5d_is_roi = True
# add any additional image5d thresholds for multichannel images, such
# as those loaded without metadata for these settings
colormaps.setup_cmaps()
num_channels = get_num_channels(config.image5d)
config.near_max = libmag.pad_seq(config.near_max, num_channels, -1)
config.near_min = libmag.pad_seq(config.near_min, num_channels, 0)
config.vmax_overview = libmag.pad_seq(config.vmax_overview, num_channels)
colormaps.setup_colormaps(num_channels)
def transpose_img(filename,
series,
plane=None,
rescale=None,
target_size=None):
"""Transpose Numpy NPY saved arrays into new planar orientations and
rescaling or resizing.
Rescaling/resizing take place in multiprocessing. Files are saved
through memmap-based arrays to minimize RAM usage. Output filenames
are based on the ``make_modifer_[task]`` functions. Currently transposes
all channels, ignoring :attr:``config.channel`` parameter.
Args:
filename: Full file path in :attribute:cli:`filename` format.
series: Series within multi-series file.
plane: Planar orientation (see :attribute:plot_2d:`PLANES`). Defaults
to None, in which case no planar transformation will occur.
rescale: Rescaling factor; defaults to None. Takes precedence over
``target_size``.
target_size (List[int]): Target shape in x,y,z; defaults to None,
in which case the target size will be extracted from the register
profile if available if available.
"""
if target_size is None:
target_size = config.atlas_profile["target_size"]
if plane is None and rescale is None and target_size is None:
print("No transposition to perform, skipping")
return
time_start = time()
# even if loaded already, reread to get image metadata
# TODO: consider saving metadata in config and retrieving from there
img5d = importer.read_file(filename, series)
info = img5d.meta
image5d = img5d.img
sizes = info["sizes"]
# make filenames based on transpositions
modifier = ""
if plane is not None:
modifier = make_modifier_plane(plane)
# either rescaling or resizing
if rescale is not None:
modifier += make_modifier_scale(rescale)
elif target_size:
# target size may differ from final output size but allows a known
# size to be used for finding the file later
modifier += make_modifier_resized(target_size)
filename_image5d_npz, filename_info_npz = importer.make_filenames(
filename, series, modifier=modifier)
# TODO: image5d should assume 4/5 dimensions
offset = 0 if image5d.ndim <= 3 else 1
multichannel = image5d.ndim >= 5
image5d_swapped = image5d
if plane is not None and plane != config.PLANE[0]:
# swap z-y to get (y, z, x) order for xz orientation
image5d_swapped = np.swapaxes(image5d_swapped, offset, offset + 1)
config.resolutions[0] = libmag.swap_elements(config.resolutions[0], 0,
1)
if plane == config.PLANE[2]:
# swap new y-x to get (x, z, y) order for yz orientation
image5d_swapped = np.swapaxes(image5d_swapped, offset, offset + 2)
config.resolutions[0] = libmag.swap_elements(
config.resolutions[0], 0, 2)
scaling = None
if rescale is not None or target_size is not None:
# rescale based on scaling factor or target specific size
rescaled = image5d_swapped
# TODO: generalize for more than 1 preceding dimension?
if offset > 0:
rescaled = rescaled[0]
max_pixels = [100, 500, 500]
sub_roi_size = None
if target_size:
# to avoid artifacts from thin chunks, fit image into even
# number of pixels per chunk by rounding up number of chunks
# and resizing each chunk by ratio of total size to chunk num
target_size = target_size[::-1] # change to z,y,x
shape = rescaled.shape[:3]
num_chunks = np.ceil(np.divide(shape, max_pixels))
max_pixels = np.ceil(np.divide(shape, num_chunks)).astype(np.int)
sub_roi_size = np.floor(np.divide(target_size,
num_chunks)).astype(np.int)
print("Resizing image of shape {} to target_size: {}, using "
"num_chunks: {}, max_pixels: {}, sub_roi_size: {}".format(
rescaled.shape, target_size, num_chunks, max_pixels,
sub_roi_size))
else:
print("Rescaling image of shape {} by factor of {}".format(
rescaled.shape, rescale))
# rescale in chunks with multiprocessing
sub_roi_slices, _ = chunking.stack_splitter(rescaled.shape, max_pixels)
is_fork = chunking.is_fork()
if is_fork:
Downsampler.set_data(rescaled)
sub_rois = np.zeros_like(sub_roi_slices)
pool = chunking.get_mp_pool()
pool_results = []
for z in range(sub_roi_slices.shape[0]):
for y in range(sub_roi_slices.shape[1]):
for x in range(sub_roi_slices.shape[2]):
coord = (z, y, x)
slices = sub_roi_slices[coord]
args = [coord, slices, rescale, sub_roi_size, multichannel]
if not is_fork:
# pickle chunk if img not directly available
args.append(rescaled[slices])
pool_results.append(
pool.apply_async(Downsampler.rescale_sub_roi,
args=args))
for result in pool_results:
coord, sub_roi = result.get()
print("replacing sub_roi at {} of {}".format(
coord, np.add(sub_roi_slices.shape, -1)))
sub_rois[coord] = sub_roi
pool.close()
pool.join()
rescaled_shape = chunking.get_split_stack_total_shape(sub_rois)
if offset > 0:
rescaled_shape = np.concatenate(([1], rescaled_shape))
print("rescaled_shape: {}".format(rescaled_shape))
# rescale chunks directly into memmap-backed array to minimize RAM usage
image5d_transposed = np.lib.format.open_memmap(
filename_image5d_npz,
mode="w+",
dtype=sub_rois[0, 0, 0].dtype,
shape=tuple(rescaled_shape))
chunking.merge_split_stack2(sub_rois, None, offset, image5d_transposed)
if rescale is not None:
# scale resolutions based on single rescaling factor
config.resolutions = np.multiply(config.resolutions, 1 / rescale)
else:
# scale resolutions based on size ratio for each dimension
config.resolutions = np.multiply(config.resolutions,
(image5d_swapped.shape /
rescaled_shape)[1:4])
sizes[0] = rescaled_shape
scaling = importer.calc_scaling(image5d_swapped, image5d_transposed)
else:
# transfer directly to memmap-backed array
image5d_transposed = np.lib.format.open_memmap(
filename_image5d_npz,
mode="w+",
dtype=image5d_swapped.dtype,
shape=image5d_swapped.shape)
if plane == config.PLANE[1] or plane == config.PLANE[2]:
# flip upside-down if re-orienting planes
if offset:
image5d_transposed[0, :] = np.fliplr(image5d_swapped[0, :])
else:
image5d_transposed[:] = np.fliplr(image5d_swapped[:])
else:
image5d_transposed[:] = image5d_swapped[:]
sizes[0] = image5d_swapped.shape
# save image metadata
print("detector.resolutions: {}".format(config.resolutions))
print("sizes: {}".format(sizes))
image5d.flush()
importer.save_image_info(
filename_info_npz, info["names"], sizes, config.resolutions,
info["magnification"], info["zoom"],
*importer.calc_intensity_bounds(image5d_transposed), scaling, plane)
print("saved transposed file to {} with shape {}".format(
filename_image5d_npz, image5d_transposed.shape))
print("time elapsed (s): {}".format(time() - time_start))
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
def setup_images(path: str,
series: Optional[int] = None,
offset: Optional[Sequence[int]] = None,
size: Optional[Sequence[int]] = None,
proc_type: Optional["config.ProcessTypes"] = None,
allow_import: bool = True,
fallback_main_img: bool = True):
"""Sets up an image and all associated images and metadata.
Paths for related files such as registered images will generally be
constructed from ``path``. If :attr:`config.prefix` is set, it will
be used in place of ``path`` for registered labels.
Args:
path: Path to image from which MagellanMapper-style paths will
be generated.
series: Image series number; defaults to None.
offset: Sub-image offset given in z,y,x; defaults to None.
size: Sub-image shape given in z,y,x; defaults to None.
proc_type: Processing type.
allow_import: True to allow importing the image if it
cannot be loaded; defaults to True.
fallback_main_img: True to fall back to loading a registered image
if possible if the main image could not be loaded; defaults to True.
"""
def add_metadata():
# override metadata set from command-line metadata args if available
md = {
config.MetaKeys.RESOLUTIONS:
config.meta_dict[config.MetaKeys.RESOLUTIONS],
config.MetaKeys.MAGNIFICATION:
config.meta_dict[config.MetaKeys.MAGNIFICATION],
config.MetaKeys.ZOOM:
config.meta_dict[config.MetaKeys.ZOOM],
config.MetaKeys.SHAPE:
config.meta_dict[config.MetaKeys.SHAPE],
config.MetaKeys.DTYPE:
config.meta_dict[config.MetaKeys.DTYPE],
}
for key, val in md.items():
if val is not None:
# explicitly set metadata takes precedence over extracted vals
import_md[key] = val
res = import_md[config.MetaKeys.RESOLUTIONS]
if res is None:
# default to 1 for x,y,z since image resolutions are required
res = [1] * 3
import_md[config.MetaKeys.RESOLUTIONS] = res
_logger.warn("No image resolutions found. Defaulting to: %s", res)
# LOAD MAIN IMAGE
# reset image5d
config.image5d = None
config.image5d_is_roi = False
config.img5d = Image5d()
load_subimage = offset is not None and size is not None
config.resolutions = None
# reset label images
config.labels_img = None
config.labels_img_sitk = None
config.labels_img_orig = None
config.borders_img = None
config.labels_meta = None
config.labels_ref = None
# reset blobs
config.blobs = None
filename_base = importer.filename_to_base(path, series)
subimg_base = None
blobs = None
# registered images set to load
atlas_suffix = config.reg_suffixes[config.RegSuffixes.ATLAS]
annotation_suffix = config.reg_suffixes[config.RegSuffixes.ANNOTATION]
borders_suffix = config.reg_suffixes[config.RegSuffixes.BORDERS]
if load_subimage and not config.save_subimg:
# load a saved sub-image file if available and not set to save one
subimg_base = naming.make_subimage_name(filename_base, offset, size)
filename_subimg = libmag.combine_paths(subimg_base,
config.SUFFIX_SUBIMG)
try:
# load sub-image if available
config.image5d = np.load(filename_subimg, mmap_mode="r")
config.image5d = importer.roi_to_image5d(config.image5d)
config.image5d_is_roi = True
config.img5d.img = config.image5d
config.img5d.path_img = filename_subimg
config.img5d.img_io = config.LoadIO.NP
config.img5d.subimg_offset = offset
config.img5d.subimg_size = size
print("Loaded sub-image from {} with shape {}".format(
filename_subimg, config.image5d.shape))
# after loading sub-image, load original image's metadata
# for essential data such as vmin/vmax; will only warn if
# fails to load since metadata could be specified elsewhere
_, orig_info = importer.make_filenames(path, series)
print("load original image metadata from:", orig_info)
importer.load_metadata(orig_info)
except IOError:
print("Ignored sub-image file from {} as unable to load".format(
filename_subimg))
if config.load_data[config.LoadData.BLOBS] or proc_type in (
config.ProcessTypes.LOAD, config.ProcessTypes.COLOC_MATCH,
config.ProcessTypes.EXPORT_ROIS, config.ProcessTypes.EXPORT_BLOBS):
# load a blobs archive
blobs = detector.Blobs()
try:
if subimg_base:
try:
# load blobs generated from sub-image
config.blobs = blobs.load_blobs(
img_to_blobs_path(subimg_base))
except (FileNotFoundError, KeyError):
# fallback to loading from full image blobs and getting
# a subset, shifting them relative to sub-image offset
print("Unable to load blobs file based on {}, will try "
"from {}".format(subimg_base, filename_base))
config.blobs = blobs.load_blobs(
img_to_blobs_path(filename_base))
blobs.blobs, _ = detector.get_blobs_in_roi(blobs.blobs,
offset,
size,
reverse=False)
detector.Blobs.shift_blob_rel_coords(
blobs.blobs, np.multiply(offset, -1))
else:
# load full image blobs
config.blobs = blobs.load_blobs(
img_to_blobs_path(filename_base))
except (FileNotFoundError, KeyError) as e2:
print("Unable to load blobs file")
if proc_type in (config.ProcessTypes.LOAD,
config.ProcessTypes.EXPORT_BLOBS):
# blobs expected but not found
raise e2
if path and config.image5d is None and not atlas_suffix:
# load or import the main image stack
print("Loading main image")
try:
path_lower = path.lower()
import_only = proc_type is config.ProcessTypes.IMPORT_ONLY
if path_lower.endswith(sitk_io.EXTS_3D):
# load format supported by SimpleITK and prepend time axis;
# if 2D, convert to 3D
img5d = sitk_io.read_sitk_files(path, make_3d=True)
elif not import_only and path_lower.endswith((".tif", ".tiff")):
# load TIF file directly
img5d, meta = read_tif(path)
config.resolutions = meta[config.MetaKeys.RESOLUTIONS]
else:
# load or import from MagellanMapper Numpy format
img5d = None
if not import_only:
# load previously imported image
img5d = importer.read_file(path, series)
if allow_import and (img5d is None or img5d.img is None):
# import image; will re-import over any existing image file
if os.path.isdir(path) and all(
[r is None for r in config.reg_suffixes.values()]):
# import directory of single plane images to single
# stack if no register suffixes are set
chls, import_md = importer.setup_import_dir(path)
add_metadata()
prefix = config.prefix
if not prefix:
prefix = os.path.join(
os.path.dirname(path),
importer.DEFAULT_IMG_STACK_NAME)
img5d = importer.import_planes_to_stack(
chls, prefix, import_md)
elif import_only:
# import multi-plane image
chls, import_path = importer.setup_import_multipage(
path)
prefix = config.prefix if config.prefix else import_path
import_md = importer.setup_import_metadata(
chls, config.channel, series)
add_metadata()
img5d = importer.import_multiplane_images(
chls,
prefix,
import_md,
series,
channel=config.channel)
if img5d is not None:
# set loaded main image in config
config.img5d = img5d
config.image5d = config.img5d.img
except FileNotFoundError as e:
_logger.exception(e)
_logger.info("Could not load %s", path)
if config.metadatas and config.metadatas[0]:
# assign metadata from alternate file if given to supersede settings
# for any loaded image5d
# TODO: access metadata directly from given image5d's dict to allow
# loading multiple image5d images simultaneously
importer.assign_metadata(config.metadatas[0])
# main image is currently required since many parameters depend on it
if fallback_main_img and atlas_suffix is None and config.image5d is None:
# fallback to atlas if main image not already loaded
atlas_suffix = config.RegNames.IMG_ATLAS.value
_logger.info(
"Main image is not set, falling back to registered image with "
"suffix %s", atlas_suffix)
# use prefix to get images registered to a different image, eg a
# downsampled version, or a different version of registered images
path = config.prefix if config.prefix else path
if path and atlas_suffix is not None:
try:
# will take the place of any previously loaded image5d
config.img5d = sitk_io.read_sitk_files(path,
atlas_suffix,
make_3d=True)
config.image5d = config.img5d.img
except FileNotFoundError as e:
print(e)
# load metadata related to the labels image
config.labels_metadata = labels_meta.LabelsMeta(
f"{path}." if config.prefix else path).load()
# load labels reference file, prioritizing path given by user
# and falling back to any extension matching PATH_LABELS_REF
path_labels_refs = [config.load_labels]
labels_path_ref = config.labels_metadata.path_ref
if labels_path_ref:
path_labels_refs.append(labels_path_ref)
labels_ref = None
for ref in path_labels_refs:
if not ref: continue
try:
# load labels reference file
labels_ref = ontology.LabelsRef(ref).load()
if labels_ref.ref_lookup is not None:
config.labels_ref = labels_ref
_logger.debug("Loaded labels reference file from %s", ref)
break
except (FileNotFoundError, KeyError):
pass
if path_labels_refs and (labels_ref is None
or labels_ref.ref_lookup is None):
# warn if labels path given but none found
_logger.warn(
"Unable to load labels reference file from '%s', skipping",
path_labels_refs)
if annotation_suffix is not None:
try:
# load labels image
# TODO: need to support multichannel labels images
img5d, config.labels_img_sitk = sitk_io.read_sitk_files(
path, annotation_suffix, True, True)
config.labels_img = img5d.img[0]
except FileNotFoundError as e:
print(e)
if config.image5d is not None:
# create a blank labels images for custom annotation; colormap
# can be generated for the original labels loaded below
config.labels_img = np.zeros(config.image5d.shape[1:4],
dtype=int)
print("Created blank labels image from main image")
if config.image5d is not None and config.labels_img is not None:
# set up scaling factors by dimension between intensity and
# labels images
config.labels_scaling = importer.calc_scaling(
config.image5d, config.labels_img)
if borders_suffix is not None:
# load borders image, which can also be another labels image
try:
config.borders_img = sitk_io.read_sitk_files(path,
borders_suffix,
make_3d=True).img[0]
except FileNotFoundError as e:
print(e)
if config.atlas_labels[config.AtlasLabels.ORIG_COLORS]:
labels_orig_ids = config.labels_metadata.region_ids_orig
if labels_orig_ids is None:
if config.load_labels is not None:
# load original labels image from same directory as ontology
# file for consistent ID-color mapping, even if labels are missing
try:
config.labels_img_orig = sitk_io.load_registered_img(
config.load_labels, config.RegNames.IMG_LABELS.value)
except FileNotFoundError as e:
print(e)
if config.labels_img is not None and config.labels_img_orig is None:
_logger.warn(
"Could not load original labels image IDs; colors may "
"differ from the original image")
load_rot90 = config.roi_profile["load_rot90"]
if load_rot90 and config.image5d is not None:
# rotate main image specified num of times x90deg after loading since
# need to rotate images output by deep learning toolkit
config.image5d = np.rot90(config.image5d, load_rot90, (2, 3))
if (config.image5d is not None and load_subimage
and not config.image5d_is_roi):
# crop full image to bounds of sub-image
config.image5d = plot_3d.prepare_subimg(config.image5d, offset,
size)[None]
config.image5d_is_roi = True
# add any additional image5d thresholds for multichannel images, such
# as those loaded without metadata for these settings
colormaps.setup_cmaps()
num_channels = get_num_channels(config.image5d)
config.near_max = libmag.pad_seq(config.near_max, num_channels, -1)
config.near_min = libmag.pad_seq(config.near_min, num_channels, 0)
config.vmax_overview = libmag.pad_seq(config.vmax_overview, num_channels)
colormaps.setup_colormaps(num_channels)
if config.labels_img is not None:
# make discrete colormap for labels image
config.cmap_labels = colormaps.setup_labels_cmap(config.labels_img)
if (blobs is not None and blobs.blobs is not None
and config.img5d.img is not None and blobs.roi_size is not None):
# scale blob coordinates to main image if shapes differ
scaling = np.divide(config.img5d.img.shape[1:4], blobs.roi_size)
# scale radius by mean of other dimensions' scaling
scaling = np.append(scaling, np.mean(scaling))
if not np.all(scaling == 1):
_logger.debug("Scaling blobs to main image by factor: %s", scaling)
blobs.blobs[:, :4] = ontology.scale_coords(blobs.blobs[:, :4],
scaling)
blobs.scaling = scaling