def cluster_by_label(cls,
blobs,
labels_img_np,
blobs_lbl_scaling,
blobs_iso_scaling,
all_labels=False):
coord_scaled = ontology.scale_coords(blobs, blobs_lbl_scaling)
blobs_lbls = ontology.get_label_ids_from_position(
coord_scaled, labels_img_np)
blobs = np.multiply(blobs[:, :3], blobs_iso_scaling)
blobs_clus = np.zeros((len(blobs), 5), dtype=int)
blobs_clus[:, :3] = blobs
blobs_clus[:, 3] = blobs_lbls
cls.blobs = blobs_clus
print(np.unique(blobs_clus[:, 3]))
print(cls.blobs)
# TODO: shift to separate func once load blobs without req labels img
label_ids = np.unique(labels_img_np)
cluster_settings = config.atlas_profile[
profiles.RegKeys.METRICS_CLUSTER]
eps = cluster_settings[profiles.RegKeys.DBSCAN_EPS]
minpts = cluster_settings[profiles.RegKeys.DBSCAN_MINPTS]
if all_labels:
# cluster all labels together
# TODO: n_jobs appears to be ignored despite reported fixes
_, labels = cls.cluster_within_label(None, eps, minpts, -1)
cls.blobs[:, 4] = labels
else:
# cluster by individual label
pool = chunking.get_mp_pool()
pool_results = []
for label_id in label_ids:
# add rotation argument if necessary
pool_results.append(
pool.apply_async(cls.cluster_within_label,
args=(label_id, eps, minpts, None)))
for result in pool_results:
label_id, labels = result.get()
if labels is not None:
cls.blobs[cls.blobs[:, 3] == label_id, 4] = labels
pool.close()
pool.join()
cls.blobs[:, :3] = np.divide(blobs[:, :3], blobs_iso_scaling)
return cls.blobs
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 make_heat_map():
# build heat map to store densities per label px and save to file
coord_scaled = ontology.scale_coords(blobs_chl[:, :3], scaling,
labels_img.shape)
_logger.debug("Scaled coords:\n%s", coord_scaled)
return cv_nd.build_heat_map(labels_img.shape, coord_scaled)
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
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.borders_img = None
# reset blobs
config.blobs = None
filename_base = importer.filename_to_base(path, series)
subimg_base = None
blobs = 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 = 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))
proc_type = libmag.get_enum(proc_mode, config.ProcessTypes)
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,
config.ProcessTypes.DETECT):
# 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.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:
# 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.img5d.img = config.image5d
config.img5d.path_img = path
config.img5d.img_io = config.LoadIO.SITK
else:
# load or import from MagellanMapper Numpy format
import_only = proc_type is config.ProcessTypes.IMPORT_ONLY
img5d = None
if not import_only:
# load previously imported image
img5d = 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)
img5d = importer.import_planes_to_stack(
chls, prefix, import_md)
elif import_only or img5d 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()
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:
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.img5d.img = config.image5d
config.img5d.img_io = config.LoadIO.SITK
except FileNotFoundError as e:
print(e)
annotation_suffix = config.reg_suffixes[config.RegSuffixes.ANNOTATION]
if annotation_suffix is not None:
try:
# load labels image
# TODO: need to support multichannel labels images
config.labels_img, config.labels_img_sitk = sitk_io.read_sitk_files(
path, reg_names=annotation_suffix, return_sitk=True)
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)
try:
if config.load_labels is not None:
# load labels reference file
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, 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):
# scale blob coordinates to main image if shapes differ
scaling = np.divide(config.img5d.img.shape[1:4], blobs.roi_size)
if not np.all(scaling == 1):
print("Scaling blobs to main image by factor:", scaling)
blobs.blobs[:, :3] = ontology.scale_coords(
blobs.blobs[:, :3], scaling)