def reg_out_path(file_path, reg_name, match_ext=False):
"""Generate a path for a file registered to another file.
Args:
file_path: Full path of file registered to. :attr:`config.series`
will be appended unless ``file_path`` is a directory.
reg_name: Suffix for type of registration, eg :const:``IMG_LABELS``.
match_ext: True to change the extension of ``reg_name`` to match
that of ``file_path``.
Returns:
str: Full path with the registered filename including appropriate
extension at the end.
"""
if match_ext:
reg_name = libmag.match_ext(file_path, reg_name)
if os.path.isdir(file_path):
return os.path.join(file_path, reg_name)
else:
file_path_base = importer.filename_to_base(file_path, config.series)
return file_path_base + "_" + reg_name
def _detect_subimgs(
path: str, series: int, subimg_offsets: List[List[int]],
subimg_sizes: List[List[int]]
) -> Tuple[Union[np.ndarray, Any], List[str]]:
"""Detect blobs in an image across sub-image offsets.
Args:
path: Path to image from which MagellanMapper-style paths will
be generated.
series: Image series number.
subimg_offsets: Nested list of sub-image offset sets
given as ``[[offset_z1, offset_y1, offset_x1], ...]``.
subimg_sizes: Nested list of sub-image size sets
given as ``[[offset_z1, offset_y1, offset_x1], ...]`` and
corresponding to ``subimg_offsets``.
Returns:
Summed stats array and concatenated summaries.
"""
stat = np.zeros(3)
# use whole image if sub-image parameters are not set
if subimg_offsets is None:
subimg_offsets = [None]
if subimg_sizes is None:
subimg_sizes = [None]
roi_sizes_len = len(subimg_sizes)
summaries = []
for i in range(len(subimg_offsets)):
size = (subimg_sizes[i] if roi_sizes_len > 1 else subimg_sizes[0])
np_io.setup_images(path, series, subimg_offsets[i], size)
stat_roi, fdbk, _ = stack_detect.detect_blobs_stack(
importer.filename_to_base(path, series), subimg_offsets[i], size)
if stat_roi is not None:
stat = np.add(stat, stat_roi)
summaries.append("Offset {}:\n{}".format(subimg_offsets[i], fdbk))
return stat, summaries
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 setup_dbs():
"""Set up databases for the given image file.
Only sets up each database if it has not been set up already.
"""
# prep filename
filename_base = None
if config.filename:
filename_base = importer.filename_to_base(config.filename,
config.series)
# get any user-supplied truth database path, falling back to name based
# on filename or default name
truth_db_path = config.truth_db_params[config.TruthDB.PATH]
user_dir = config.user_app_dirs.user_data_dir
truth_db_name_base = filename_base if filename_base else os.path.join(
user_dir, sqlite.DB_NAME_BASE)
if config.truth_db_mode is config.TruthDBModes.VIEW:
# loads truth DB as a separate database in parallel with the given
# editable database, with name based on filename by default unless
# truth DB name explicitly given
path = truth_db_path if truth_db_path else truth_db_name_base
try:
sqlite.load_truth_db(path)
except FileNotFoundError as e:
print(e)
print("Could not load truth DB from current image path")
elif config.truth_db_mode is config.TruthDBModes.VERIFY:
if not config.verified_db:
# creates a new verified DB to store all ROC results
config.verified_db = sqlite.ClrDB()
config.verified_db.load_db(
os.path.join(user_dir, sqlite.DB_NAME_VERIFIED), True)
if truth_db_path:
# load truth DB path to verify against if explicitly given
try:
sqlite.load_truth_db(truth_db_path)
except FileNotFoundError as e:
print(e)
print("Could not load truth DB from {}".format(truth_db_path))
elif config.truth_db_mode is config.TruthDBModes.VERIFIED:
# loads verified DB as the main DB, which includes copies of truth
# values with flags for whether they were detected
path = os.path.join(user_dir, sqlite.DB_NAME_VERIFIED)
if truth_db_path: path = truth_db_path
try:
config.db = sqlite.ClrDB()
config.db.load_db(path)
config.verified_db = config.db
except FileNotFoundError as e:
print(e)
print("Could not load verified DB from {}".format(
sqlite.DB_NAME_VERIFIED))
elif config.truth_db_mode is config.TruthDBModes.EDIT:
# loads truth DB as the main database for editing rather than
# loading as a truth database
config.db_path = truth_db_path
if not config.db_path:
config.db_path = "{}{}".format(
os.path.basename(truth_db_name_base), sqlite.DB_SUFFIX_TRUTH)
print("Editing truth database at {}".format(config.db_path))
if config.db is None:
# load the main database
config.db = sqlite.ClrDB()
config.db.load_db(None, False)
def process_file(
path: str,
proc_type: Enum,
proc_val: Optional[Any] = None,
series: Optional[int] = None,
subimg_offset: Optional[List[int]] = None,
subimg_size: Optional[List[int]] = None,
roi_offset: Optional[List[int]] = None,
roi_size: Optional[List[int]] = None
) -> Tuple[Optional[Any], Optional[str]]:
"""Processes a single image file non-interactively.
Assumes that the image has already been set up.
Args:
path: Path to image from which MagellanMapper-style paths will
be generated.
proc_type: Processing type, which should be a one of
:class:`config.ProcessTypes`.
proc_val: Processing value associated with ``proc_type``; defaults to
None.
series: Image series number; defaults to None.
subimg_offset: Sub-image offset as (z,y,x) to load; defaults to None.
subimg_size: Sub-image size as (z,y,x) to load; defaults to None.
roi_offset: Region of interest offset as (x, y, z) to process;
defaults to None.
roi_size: Region of interest size of region to process, given as
``(x, y, z)``; defaults to None.
Returns:
Tuple of stats from processing, or None if no stats, and
text feedback from the processing, or None if no feedback.
"""
# PROCESS BY TYPE
stats = None
fdbk = None
filename_base = importer.filename_to_base(path, series)
print("{}\n".format("-" * 80))
if proc_type is config.ProcessTypes.LOAD:
# loading completed
return None, None
elif proc_type is config.ProcessTypes.LOAD:
# already imported so does nothing
print("imported {}, will exit".format(path))
elif proc_type is config.ProcessTypes.EXPORT_ROIS:
# export ROIs; assumes that info_proc was already loaded to
# give smaller region from which smaller ROIs from the truth DB
# will be extracted
from magmap.io import export_rois
db = config.db if config.truth_db is None else config.truth_db
export_path = naming.make_subimage_name(filename_base, subimg_offset,
subimg_size)
export_rois.export_rois(db, config.image5d, config.channel,
export_path,
config.plot_labels[config.PlotLabels.PADDING],
config.unit_factor, config.truth_db_mode,
os.path.basename(export_path))
elif proc_type is config.ProcessTypes.TRANSFORM:
# transpose, rescale, and/or resize whole large image
transformer.transpose_img(
path,
series,
plane=config.plane,
rescale=config.transform[config.Transforms.RESCALE],
target_size=config.roi_size)
elif proc_type in (config.ProcessTypes.EXTRACT,
config.ProcessTypes.ANIMATED):
# generate animated GIF or extract single plane
export_stack.stack_to_img(config.filenames, roi_offset, roi_size,
series, subimg_offset, subimg_size,
proc_type is config.ProcessTypes.ANIMATED,
config.suffix)
elif proc_type is config.ProcessTypes.EXPORT_BLOBS:
# export blobs to CSV file
from magmap.io import export_rois
export_rois.blobs_to_csv(config.blobs.blobs, filename_base)
elif proc_type in (config.ProcessTypes.DETECT,
config.ProcessTypes.DETECT_COLOC):
# detect blobs in the full image, +/- co-localization
coloc = proc_type is config.ProcessTypes.DETECT_COLOC
stats, fdbk, _ = stack_detect.detect_blobs_stack(
filename_base, subimg_offset, subimg_size, coloc)
elif proc_type is config.ProcessTypes.COLOC_MATCH:
if config.blobs is not None and config.blobs.blobs is not None:
# colocalize blobs in separate channels by matching blobs
shape = subimg_size
if shape is None:
# get shape from loaded image, falling back to its metadata
if config.image5d is not None:
shape = config.image5d.shape[1:]
else:
shape = config.img5d.meta[config.MetaKeys.SHAPE][1:]
matches = colocalizer.StackColocalizer.colocalize_stack(
shape, config.blobs.blobs)
# insert matches into database
colocalizer.insert_matches(config.db, matches)
else:
print("No blobs loaded to colocalize, skipping")
elif proc_type in (config.ProcessTypes.EXPORT_PLANES,
config.ProcessTypes.EXPORT_PLANES_CHANNELS):
# export each plane as a separate image file
export_stack.export_planes(
config.image5d, config.savefig, config.channel,
proc_type is config.ProcessTypes.EXPORT_PLANES_CHANNELS)
elif proc_type is config.ProcessTypes.EXPORT_RAW:
# export the main image as a raw data file
out_path = libmag.combine_paths(config.filename, ".raw", sep="")
libmag.backup_file(out_path)
np_io.write_raw_file(config.image5d, out_path)
elif proc_type is config.ProcessTypes.EXPORT_TIF:
# export the main image as a TIF files for each channel
np_io.write_tif(config.image5d, config.filename)
elif proc_type is config.ProcessTypes.PREPROCESS:
# pre-process a whole image and save to file
# TODO: consider chunking option for larger images
out_path = config.prefix
if not out_path:
out_path = libmag.insert_before_ext(config.filename, "_preproc")
transformer.preprocess_img(config.image5d, proc_val, config.channel,
out_path)
return stats, fdbk
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
def process_file(path,
proc_mode,
series=None,
subimg_offset=None,
subimg_size=None,
roi_offset=None,
roi_size=None):
"""Processes a single image file non-interactively.
Assumes that the image has already been set up.
Args:
path (str): Path to image from which MagellanMapper-style paths will
be generated.
proc_mode (str): Processing mode, which should be a key in
:class:`config.ProcessTypes`, case-insensitive.
series (int): Image series number; defaults to None.
subimg_offset (List[int]): Sub-image offset as (z,y,x) to load;
defaults to None.
subimg_size (List[int]): Sub-image size as (z,y,x) to load;
defaults to None.
roi_offset (List[int]): Region of interest offset as (x, y, z) to
process; defaults to None.
roi_size (List[int]): Region of interest size of region to process,
given as (x, y, z); defaults to None.
Returns:
Tuple of stats from processing, or None if no stats, and
text feedback from the processing, or None if no feedback.
"""
# PROCESS BY TYPE
stats = None
fdbk = None
filename_base = importer.filename_to_base(path, series)
proc_type = libmag.get_enum(proc_mode, config.ProcessTypes)
if proc_type is config.ProcessTypes.LOAD:
# loading completed
return None, None
elif proc_type is config.ProcessTypes.LOAD:
# already imported so does nothing
print("imported {}, will exit".format(path))
elif proc_type is config.ProcessTypes.EXPORT_ROIS:
# export ROIs; assumes that info_proc was already loaded to
# give smaller region from which smaller ROIs from the truth DB
# will be extracted
from magmap.io import export_rois
db = config.db if config.truth_db is None else config.truth_db
export_rois.export_rois(db, config.image5d, config.channel,
filename_base,
config.plot_labels[config.PlotLabels.PADDING],
config.unit_factor, config.truth_db_mode,
os.path.basename(config.filename))
elif proc_type is config.ProcessTypes.TRANSFORM:
# transpose, rescale, and/or resize whole large image
transformer.transpose_img(
path,
series,
plane=config.plane,
rescale=config.transform[config.Transforms.RESCALE],
target_size=config.roi_size)
elif proc_type in (config.ProcessTypes.EXTRACT,
config.ProcessTypes.ANIMATED):
# generate animated GIF or extract single plane
from magmap.io import export_stack
export_stack.stack_to_img(config.filenames, roi_offset, roi_size,
series, subimg_offset, subimg_size,
proc_type is config.ProcessTypes.ANIMATED,
config.suffix)
elif proc_type is config.ProcessTypes.EXPORT_BLOBS:
# export blobs to CSV file
from magmap.io import export_rois
export_rois.blobs_to_csv(config.blobs, filename_base)
elif proc_type is config.ProcessTypes.DETECT:
# detect blobs in the full image
stats, fdbk, segments_all = stack_detect.detect_blobs_large_image(
filename_base, config.image5d, subimg_offset, subimg_size,
config.truth_db_mode is config.TruthDBModes.VERIFY,
not config.grid_search_profile, config.image5d_is_roi)
elif proc_type is config.ProcessTypes.EXPORT_PLANES:
# export each plane as a separate image file
from magmap.io import export_stack
export_stack.export_planes(config.image5d, config.prefix,
config.savefig, config.channel)
elif proc_type is config.ProcessTypes.EXPORT_RAW:
# export the main image as a raw data file
out_path = libmag.combine_paths(config.filename, ".raw", sep="")
libmag.backup_file(out_path)
np_io.write_raw_file(config.image5d, out_path)
elif proc_type is config.ProcessTypes.PREPROCESS:
# pre-process a whole image and save to file
# TODO: consider chunking option for larger images
profile = config.get_roi_profile(0)
out_path = config.prefix
if not out_path:
out_path = libmag.insert_before_ext(config.filename, "_preproc")
transformer.preprocess_img(config.image5d, profile["preprocess"],
config.channel, out_path)
return stats, fdbk
def main(process_args_only=False, skip_dbs=False):
"""Starts the visualization GUI.
Processes command-line arguments.
Args:
process_args_only (bool): Processes command-line arguments and
returns; defaults to False.
skip_dbs (bool): True to skip loading databases; defaults to False.
"""
parser = argparse.ArgumentParser(
description="Setup environment for MagellanMapper")
# image specification arguments
parser.add_argument(
"--img",
nargs="*",
help="Main image path(s); after import, the filename is often "
"given as the original name without its extension")
parser.add_argument(
"--meta",
nargs="*",
help="Metadata path(s), which can be given as multiple files "
"corresponding to each image")
parser.add_argument("--prefix", help="Path prefix")
parser.add_argument("--suffix", help="Filename suffix")
parser.add_argument("--channel", nargs="*", type=int, help="Channel index")
parser.add_argument("--series", help="Series index")
parser.add_argument("--subimg_offset",
nargs="*",
help="Sub-image offset in x,y,z")
parser.add_argument("--subimg_size",
nargs="*",
help="Sub-image size in x,y,z")
parser.add_argument("--offset", nargs="*", help="ROI offset in x,y,z")
parser.add_argument("--size", nargs="*", help="ROI size in x,y,z")
parser.add_argument("--db", help="Database path")
parser.add_argument(
"--cpus",
help="Maximum number of CPUs/processes to use for multiprocessing "
"tasks. Use \"none\" or 0 to auto-detect this number (default).")
# task arguments
parser.add_argument("--proc",
type=str.lower,
choices=libmag.enum_names_aslist(config.ProcessTypes),
help="Image processing mode")
parser.add_argument("--register",
type=str.lower,
choices=libmag.enum_names_aslist(config.RegisterTypes),
help="Image registration task")
parser.add_argument("--df",
type=str.lower,
choices=libmag.enum_names_aslist(config.DFTasks),
help="Data frame task")
parser.add_argument("--plot_2d",
type=str.lower,
choices=libmag.enum_names_aslist(config.Plot2DTypes),
help="2D plot task; see config.Plot2DTypes")
parser.add_argument("--ec2_start",
nargs="*",
help="AWS EC2 instance start")
parser.add_argument("--ec2_list", nargs="*", help="AWS EC2 instance list")
parser.add_argument("--ec2_terminate",
nargs="*",
help="AWS EC2 instance termination")
parser.add_argument(
"--notify",
nargs="*",
help="Notification message URL, message, and attachment strings")
parser.add_argument("--grid_search",
help="Grid search hyperparameter tuning profile(s)")
# profile arguments
parser.add_argument(
"--roi_profile",
nargs="*",
help="ROI profile, which can be separated by underscores "
"for multiple profiles and given as paths to custom profiles "
"in YAML format. Multiple profile groups can be given, which "
"will each be applied to the corresponding channel. See "
"docs/settings.md for more details.")
parser.add_argument(
"--atlas_profile",
help="Atlas profile, which can be separated by underscores "
"for multiple profiles and given as paths to custom profiles "
"in YAML format. See docs/settings.md for more details.")
parser.add_argument(
"--theme",
nargs="*",
type=str.lower,
choices=libmag.enum_names_aslist(config.Themes),
help="UI theme, which can be given as multiple themes to apply "
"on top of one another")
# grouped arguments
parser.add_argument(
"--truth_db",
nargs="*",
help="Truth database; see config.TruthDB for settings and "
"config.TruthDBModes for modes")
parser.add_argument("--labels",
nargs="*",
help=_get_args_dict_help(
"Atlas labels; see config.AtlasLabels.",
config.AtlasLabels))
parser.add_argument("--transform",
nargs="*",
help=_get_args_dict_help(
"Image transformations; see config.Transforms.",
config.Transforms))
parser.add_argument(
"--reg_suffixes",
nargs="*",
help=_get_args_dict_help(
"Registered image suffixes; see config.RegSuffixes for keys "
"and config.RegNames for values", config.RegSuffixes))
parser.add_argument(
"--plot_labels",
nargs="*",
help=_get_args_dict_help(
"Plot label customizations; see config.PlotLabels ",
config.PlotLabels))
parser.add_argument(
"--set_meta",
nargs="*",
help="Set metadata values; see config.MetaKeys for settings")
# image and figure display arguments
parser.add_argument("--plane",
type=str.lower,
choices=config.PLANE,
help="Planar orientation")
parser.add_argument(
"--show",
nargs="?",
const="1",
help="If applicable, show images after completing the given task")
parser.add_argument(
"--alphas",
help="Alpha opacity levels, which can be comma-delimited for "
"multichannel images")
parser.add_argument(
"--vmin",
help="Minimum intensity levels, which can be comma-delimited "
"for multichannel images")
parser.add_argument(
"--vmax",
help="Maximum intensity levels, which can be comma-delimited "
"for multichannel images")
parser.add_argument("--seed", help="Random number generator seed")
# export arguments
parser.add_argument("--save_subimg",
action="store_true",
help="Save sub-image as separate file")
parser.add_argument("--slice", help="Slice given as start,stop,step")
parser.add_argument("--delay", help="Animation delay in ms")
parser.add_argument("--savefig", help="Extension for saved figures")
parser.add_argument("--groups",
nargs="*",
help="Group values corresponding to each image")
parser.add_argument("-v",
"--verbose",
action="store_true",
help="Verbose output to assist with debugging")
args = parser.parse_args()
if args.img is not None:
# set image file path and convert to basis for additional paths
config.filenames = args.img
config.filename = config.filenames[0]
print("Set filenames to {}, current filename {}".format(
config.filenames, config.filename))
if args.meta is not None:
# set metadata paths
config.metadata_paths = args.meta
print("Set metadata paths to", config.metadata_paths)
config.metadatas = []
for path in config.metadata_paths:
# load metadata to dictionary
md, _ = importer.load_metadata(path, assign=False)
config.metadatas.append(md)
if args.channel is not None:
# set the channels
config.channel = args.channel
print("Set channel to {}".format(config.channel))
series_list = [config.series] # list of series
if args.series is not None:
series_split = args.series.split(",")
series_list = []
for ser in series_split:
ser_split = ser.split("-")
if len(ser_split) > 1:
ser_range = np.arange(int(ser_split[0]), int(ser_split[1]) + 1)
series_list.extend(ser_range.tolist())
else:
series_list.append(int(ser_split[0]))
config.series = series_list[0]
print("Set to series_list to {}, current series {}".format(
series_list, config.series))
if args.savefig is not None:
# save figure with file type of this extension; remove leading period
config.savefig = args.savefig.lstrip(".")
print("Set savefig extension to {}".format(config.savefig))
if args.verbose:
# verbose mode, including printing longer Numpy arrays for debugging
config.verbose = args.verbose
np.set_printoptions(linewidth=200, threshold=10000)
print("Set verbose to {}".format(config.verbose))
# parse sub-image offsets and sizes;
# expects x,y,z input but stores as z,y,x by convention
if args.subimg_offset is not None:
config.subimg_offsets = _parse_coords(args.subimg_offset, True)
print("Set sub-image offsets to {} (z,y,x)".format(
config.subimg_offsets))
if args.subimg_size is not None:
config.subimg_sizes = _parse_coords(args.subimg_size, True)
print("Set sub-image sizes to {} (z,y,x)".format(config.subimg_sizes))
# parse ROI offsets and sizes, which are relative to any sub-image;
# expects x,y,z input and output
if args.offset is not None:
config.roi_offsets = _parse_coords(args.offset)
if config.roi_offsets:
config.roi_offset = config.roi_offsets[0]
print("Set ROI offsets to {}, current offset {} (x,y,z)".format(
config.roi_offsets, config.roi_offset))
if args.size is not None:
config.roi_sizes = _parse_coords(args.size)
if config.roi_sizes:
config.roi_size = config.roi_sizes[0]
print("Set ROI sizes to {}, current size {} (x,y,z)".format(
config.roi_sizes, config.roi_size))
if args.cpus is not None:
# set maximum number of CPUs
config.cpus = (None if args.cpus.lower() in ("none",
"0") else int(args.cpus))
print("Set maximum number of CPUs for multiprocessing tasks to",
config.cpus)
# set up main processing mode
if args.proc is not None:
config.proc_type = args.proc
print("processing type set to {}".format(config.proc_type))
proc_type = libmag.get_enum(config.proc_type, config.ProcessTypes)
if config.proc_type and proc_type not in config.ProcessTypes:
libmag.warn("\"{}\" processing type not found".format(
config.proc_type))
if args.set_meta is not None:
# set individual metadata values, currently used for image import
# TODO: take precedence over loaded metadata archives
config.meta_dict = args_to_dict(args.set_meta,
config.MetaKeys,
config.meta_dict,
sep_vals="|")
print("Set metadata values to {}".format(config.meta_dict))
res = config.meta_dict[config.MetaKeys.RESOLUTIONS]
if res:
# set image resolutions, taken as a single set of x,y,z and
# converting to a nested list of z,y,x
res_split = res.split(",")
if len(res_split) >= 3:
res_float = tuple(float(i) for i in res_split)[::-1]
config.resolutions = [res_float]
print("Set resolutions to {}".format(config.resolutions))
else:
res_float = None
print("Resolution ({}) should be given as 3 values (x,y,z)".
format(res))
# store single set of resolutions, similar to input
config.meta_dict[config.MetaKeys.RESOLUTIONS] = res_float
mag = config.meta_dict[config.MetaKeys.MAGNIFICATION]
if mag:
# set objective magnification
config.magnification = mag
print("Set magnification to {}".format(config.magnification))
zoom = config.meta_dict[config.MetaKeys.ZOOM]
if zoom:
# set objective zoom
config.zoom = zoom
print("Set zoom to {}".format(config.zoom))
shape = config.meta_dict[config.MetaKeys.SHAPE]
if shape:
# parse shape, storing only in dict
config.meta_dict[config.MetaKeys.SHAPE] = [
int(n) for n in shape.split(",")[::-1]
]
# set up ROI and register profiles
setup_profiles(args.roi_profile, args.atlas_profile, args.grid_search)
if args.plane is not None:
config.plane = args.plane
print("Set plane to {}".format(config.plane))
if args.save_subimg:
config.save_subimg = args.save_subimg
print("Set to save the sub-image")
if args.labels:
# set up atlas labels
setup_labels(args.labels)
if args.transform is not None:
# image transformations such as flipping, rotation
config.transform = args_to_dict(args.transform, config.Transforms,
config.transform)
print("Set transformations to {}".format(config.transform))
if args.register:
# register type to process in register module
config.register_type = args.register
print("Set register type to {}".format(config.register_type))
if args.df:
# data frame processing task
config.df_task = args.df
print("Set data frame processing task to {}".format(config.df_task))
if args.plot_2d:
# 2D plot type to process in plot_2d module
config.plot_2d_type = args.plot_2d
print("Set plot_2d type to {}".format(config.plot_2d_type))
if args.slice:
# specify a generic slice by command-line, assuming same order
# of arguments as for slice built-in function and interpreting
# "none" string as None
config.slice_vals = args.slice.split(",")
config.slice_vals = [
None if val.lower() == "none" else int(val)
for val in config.slice_vals
]
print("Set slice values to {}".format(config.slice_vals))
if args.delay:
config.delay = int(args.delay)
print("Set delay to {}".format(config.delay))
if args.show:
# show images after task is performed, if supported
config.show = _is_arg_true(args.show)
print("Set show to {}".format(config.show))
if args.groups:
config.groups = args.groups
print("Set groups to {}".format(config.groups))
if args.ec2_start is not None:
# start EC2 instances
config.ec2_start = args_with_dict(args.ec2_start)
print("Set ec2 start to {}".format(config.ec2_start))
if args.ec2_list:
# list EC2 instances
config.ec2_list = args_with_dict(args.ec2_list)
print("Set ec2 list to {}".format(config.ec2_list))
if args.ec2_terminate:
config.ec2_terminate = args.ec2_terminate
print("Set ec2 terminate to {}".format(config.ec2_terminate))
if args.notify:
notify_len = len(args.notify)
if notify_len > 0:
config.notify_url = args.notify[0]
print("Set notification URL to {}".format(config.notify_url))
if notify_len > 1:
config.notify_msg = args.notify[1]
print("Set notification message to {}".format(config.notify_msg))
if notify_len > 2:
config.notify_attach = args.notify[2]
print("Set notification attachment path to {}".format(
config.notify_attach))
if args.prefix:
config.prefix = args.prefix
print("Set path prefix to {}".format(config.prefix))
if args.suffix:
config.suffix = args.suffix
print("Set path suffix to {}".format(config.suffix))
if args.alphas:
# specify alpha levels
config.alphas = [float(val) for val in args.alphas.split(",")]
print("Set alphas to", config.alphas)
if args.vmin:
# specify vmin levels
config.vmins = [libmag.get_int(val) for val in args.vmin.split(",")]
print("Set vmins to", config.vmins)
if args.vmax:
# specify vmax levels and copy to vmax overview used for plotting
# and updated for normalization
config.vmaxs = [libmag.get_int(val) for val in args.vmax.split(",")]
config.vmax_overview = list(config.vmaxs)
print("Set vmaxs to", config.vmaxs)
if args.reg_suffixes is not None:
# specify suffixes of registered images to load
config.reg_suffixes = args_to_dict(args.reg_suffixes,
config.RegSuffixes,
config.reg_suffixes)
print("Set registered image suffixes to {}".format(
config.reg_suffixes))
if args.seed:
# specify random number generator seed
config.seed = int(args.seed)
print("Set random number generator seed to", config.seed)
if args.plot_labels is not None:
# specify general plot labels
config.plot_labels = args_to_dict(args.plot_labels, config.PlotLabels,
config.plot_labels)
print("Set plot labels to {}".format(config.plot_labels))
if args.theme is not None:
# specify themes, currently applied to Matplotlib elements
theme_names = []
for theme in args.theme:
# add theme enum if found
theme_enum = libmag.get_enum(theme, config.Themes)
if theme_enum:
config.rc_params.append(theme_enum)
theme_names.append(theme_enum.name)
print("Set to use themes to {}".format(theme_names))
# prep filename
filename_base = None
if config.filename:
filename_base = importer.filename_to_base(config.filename,
config.series)
if not skip_dbs:
setup_dbs(filename_base, args.db, args.truth_db)
# set multiprocessing start method
chunking.set_mp_start_method()
# POST-ARGUMENT PARSING
if process_args_only:
return
# if command-line driven task specified, start task and shut down
if config.register_type:
register.main()
elif config.notify_url:
notify.main()
elif config.plot_2d_type:
plot_2d.main()
elif config.df_task:
df_io.main()
elif config.grid_search_profile:
_grid_search(series_list)
elif config.ec2_list or config.ec2_start or config.ec2_terminate:
# defer importing AWS module to avoid making its dependencies
# required for MagellanMapper
from magmap.cloud import aws
aws.main()
else:
# set up image and perform any whole image processing tasks;
# do not shut down if not a command-line proc task
_process_files(series_list)
if proc_type is None or proc_type is config.ProcessTypes.LOAD:
return
shutdown()