def init_brickwall(self, volume_service, subset_labels, roi):
sbm = None
if roi["name"]:
base_service = volume_service.base_service
if not roi["server"] or not roi["uuid"]:
assert isinstance(base_service, DvidVolumeService), \
"Since you aren't using a DVID input source, you must specify the ROI server and uuid."
roi["server"] = (roi["server"] or volume_service.server)
roi["uuid"] = (roi["uuid"] or volume_service.uuid)
if roi["scale"] is not None:
scale = roi["scale"]
elif isinstance(volume_service, ScaledVolumeService):
scale = volume_service.scale_delta
assert scale <= 5, \
"The 'roi' option doesn't support volumes downscaled beyond level 5"
else:
scale = 0
brick_shape = volume_service.preferred_message_shape
assert not (brick_shape % 2**(5-scale)).any(), \
"If using an ROI, select a brick shape that is divisible by 32"
seg_box = volume_service.bounding_box_zyx
seg_box = round_box(seg_box, 2**(5-scale))
seg_box_s0 = seg_box * 2**scale
seg_box_s5 = seg_box // 2**(5-scale)
with Timer(f"Fetching mask for ROI '{roi['name']}' ({seg_box_s0[:, ::-1].tolist()})", logger):
roi_mask_s5, _ = fetch_roi(roi["server"], roi["uuid"], roi["name"], format='mask', mask_box=seg_box_s5)
# SBM 'full-res' corresponds to the input service voxels, not necessarily scale-0.
sbm = SparseBlockMask(roi_mask_s5, seg_box, 2**(5-scale))
elif subset_labels:
try:
sbm = volume_service.sparse_block_mask_for_labels([*subset_labels])
if ((sbm.box[1] - sbm.box[0]) == 0).any():
raise RuntimeError("Could not find sparse masks for any of the subset-labels")
except NotImplementedError:
sbm = None
with Timer("Initializing BrickWall", logger):
# Aim for 2 GB RDD partitions when loading segmentation
GB = 2**30
target_partition_size_voxels = 2 * GB // np.uint64().nbytes
# Apply halo WHILE downloading the data.
# TODO: Allow the user to configure whether or not the halo should
# be fetched from the outset, or added after the blocks are loaded.
halo = self.config["connectedcomponents"]["halo"]
brickwall = BrickWall.from_volume_service(volume_service, 0, None, self.client, target_partition_size_voxels, halo, sbm, compression='lz4_2x')
return brickwall
def init_boxes(self, volume_service, subset_labels, roi):
sbm = None
if roi:
base_service = volume_service.base_service
assert isinstance(base_service, DvidVolumeService), \
"Can't specify an ROI unless you're using a dvid input"
assert isinstance(volume_service, (ScaledVolumeService, DvidVolumeService)), \
"The 'roi' option doesn't support adapters other than 'rescale-level'"
scale = 0
if isinstance(volume_service, ScaledVolumeService):
scale = volume_service.scale_delta
assert scale <= 5, \
"The 'roi' option doesn't support volumes downscaled beyond level 5"
server, uuid, _seg_instance = base_service.instance_triple
brick_shape = volume_service.preferred_message_shape
assert not (brick_shape % 2**(5-scale)).any(), \
"If using an ROI, select a brick shape that is divisible by 32"
seg_box = volume_service.bounding_box_zyx
seg_box = round_box(seg_box, brick_shape)
seg_box_s0 = seg_box * 2**scale
seg_box_s5 = seg_box // 2**(5 - scale)
with Timer(
f"Fetching mask for ROI '{roi}' ({seg_box_s0[:, ::-1].tolist()})",
logger):
roi_mask_s5, _ = fetch_roi(server,
uuid,
roi,
format='mask',
mask_box=seg_box_s5)
# SBM 'full-res' corresponds to the input service voxels, not necessarily scale-0.
sbm = SparseBlockMask.create_from_highres_mask(
roi_mask_s5, 2**(5 - scale), seg_box, brick_shape)
elif subset_labels:
try:
sbm = volume_service.sparse_block_mask_for_labels(
[*subset_labels])
if ((sbm.box[1] - sbm.box[0]) == 0).any():
raise RuntimeError(
"Could not find sparse masks for any of the subset-labels"
)
except NotImplementedError:
sbm = None
if sbm is None:
boxes = boxes_from_grid(volume_service.bounding_box_zyx,
volume_service.preferred_message_shape,
clipped=True)
return np.array([*boxes])
else:
return sbm.sparse_boxes(brick_shape)
def init_boxes(self, volume_service, roi):
if not roi["name"]:
boxes = boxes_from_grid(volume_service.bounding_box_zyx,
volume_service.preferred_message_shape,
clipped=True)
return np.array([*boxes])
base_service = volume_service.base_service
if not roi["server"] or not roi["uuid"]:
assert isinstance(base_service, DvidVolumeService), \
"Since you aren't using a DVID input source, you must specify the ROI server and uuid."
roi["server"] = (roi["server"] or volume_service.server)
roi["uuid"] = (roi["uuid"] or volume_service.uuid)
if roi["scale"] is not None:
scale = roi["scale"]
elif isinstance(volume_service, ScaledVolumeService):
scale = volume_service.scale_delta
assert scale <= 5, \
"The 'roi' option doesn't support volumes downscaled beyond level 5"
else:
scale = 0
brick_shape = volume_service.preferred_message_shape
assert not (brick_shape % 2**(5-scale)).any(), \
"If using an ROI, select a brick shape that is divisible by 32"
seg_box = volume_service.bounding_box_zyx
seg_box = round_box(seg_box, 2**(5 - scale))
seg_box_s0 = seg_box * 2**scale
seg_box_s5 = seg_box // 2**(5 - scale)
with Timer(
f"Fetching mask for ROI '{roi['name']}' ({seg_box_s0[:, ::-1].tolist()})",
logger):
roi_mask_s5, _ = fetch_roi(roi["server"],
roi["uuid"],
roi["name"],
format='mask',
mask_box=seg_box_s5)
# SBM 'full-res' corresponds to the input service voxels, not necessarily scale-0.
sbm = SparseBlockMask(roi_mask_s5, seg_box, 2**(5 - scale))
boxes = sbm.sparse_boxes(brick_shape)
# Clip boxes to the true (not rounded) bounding box
boxes[:, 0] = np.maximum(boxes[:, 0],
volume_service.bounding_box_zyx[0])
boxes[:, 1] = np.minimum(boxes[:, 1],
volume_service.bounding_box_zyx[1])
return boxes
def init_boxes(self, volume_service, roi):
if not roi:
boxes = boxes_from_grid(volume_service.bounding_box_zyx,
volume_service.preferred_message_shape,
clipped=True)
return np.array([*boxes])
base_service = volume_service.base_service
assert isinstance(base_service, DvidVolumeService), \
"Can't specify an ROI unless you're using a dvid input"
assert isinstance(volume_service, (ScaledVolumeService, DvidVolumeService)), \
"The 'roi' option doesn't support adapters other than 'rescale-level'"
scale = 0
if isinstance(volume_service, ScaledVolumeService):
scale = volume_service.scale_delta
assert scale <= 5, \
"The 'roi' option doesn't support volumes downscaled beyond level 5"
server, uuid, _seg_instance = base_service.instance_triple
brick_shape = volume_service.preferred_message_shape
assert not (brick_shape % 2**(5-scale)).any(), \
"If using an ROI, select a brick shape that is divisible by 32"
seg_box = volume_service.bounding_box_zyx
seg_box = round_box(seg_box, 2**(5 - scale))
seg_box_s0 = seg_box * 2**scale
seg_box_s5 = seg_box // 2**(5 - scale)
with Timer(
f"Fetching mask for ROI '{roi}' ({seg_box_s0[:, ::-1].tolist()})",
logger):
roi_mask_s5, _ = fetch_roi(server,
uuid,
roi,
format='mask',
mask_box=seg_box_s5)
# SBM 'full-res' corresponds to the input service voxels, not necessarily scale-0.
sbm = SparseBlockMask(roi_mask_s5, seg_box, 2**(5 - scale))
boxes = sbm.sparse_boxes(brick_shape)
# Clip boxes to the true (not rounded) bounding box
boxes[:, 0] = np.maximum(boxes[:, 0],
volume_service.bounding_box_zyx[0])
boxes[:, 1] = np.minimum(boxes[:, 1],
volume_service.bounding_box_zyx[1])
return boxes
def init_boxes(self, volume_service, roi, chunk_shape_s0):
"""
Return a set of bounding boxes to tile the given ROI.
Scale 0 of the volume service should correspond to full-res data,
which is 32x higher-res than ROI resolution.
"""
if not roi["name"]:
boxes = boxes_from_grid(volume_service.bounding_box_zyx,
chunk_shape_s0,
clipped=True)
return np.array([*boxes])
base_service = volume_service.base_service
if not roi["server"] or not roi["uuid"]:
assert isinstance(base_service, DvidVolumeService), \
"Since you aren't using a DVID input source, you must specify the ROI server and uuid."
roi["server"] = (roi["server"] or volume_service.server)
roi["uuid"] = (roi["uuid"] or volume_service.uuid)
assert not (chunk_shape_s0 % 2**5).any(), \
"If using an ROI, select a chunk shape that is divisible by 32"
seg_box_s0 = volume_service.bounding_box_zyx
seg_box_s0 = round_box(seg_box_s0, 2**5)
seg_box_s5 = seg_box_s0 // 2**5
with Timer(
f"Fetching mask for ROI '{roi['name']}' ({seg_box_s0[:, ::-1].tolist()})",
logger):
roi_mask_s5, _ = fetch_roi(roi["server"],
roi["uuid"],
roi["name"],
format='mask',
mask_box=seg_box_s5)
# SBM 'full-res' corresponds to the input service voxels, not necessarily scale-0.
sbm = SparseBlockMask(roi_mask_s5, seg_box_s0, 2**5)
boxes = sbm.sparse_boxes(chunk_shape_s0)
# Clip boxes to the true (not rounded) bounding box
boxes[:, 0] = np.maximum(boxes[:, 0],
volume_service.bounding_box_zyx[0])
boxes[:, 1] = np.minimum(boxes[:, 1],
volume_service.bounding_box_zyx[1])
return boxes
def init_boxes(self, volume_service, roi):
if not roi["name"]:
boxes = boxes_from_grid(volume_service.bounding_box_zyx,
volume_service.preferred_message_shape,
clipped=True)
return np.array([*boxes])
server, uuid, roi_name = roi["server"], roi["uuid"], roi["name"]
roi_scale = roi["relative-scale"]
brick_shape = volume_service.preferred_message_shape
assert not (brick_shape % 2**roi_scale).any(), \
"If using an ROI, select a brick shape that is divisible by 32"
seg_box = volume_service.bounding_box_zyx
seg_box = round_box(seg_box, 2**roi_scale)
seg_box_s5 = seg_box // 2**roi_scale
with Timer(
f"Fetching mask for ROI '{roi_name}' ({seg_box[:, ::-1].tolist()})",
logger):
roi_mask_s5, _ = fetch_roi(server,
uuid,
roi_name,
format='mask',
mask_box=seg_box_s5)
# SBM 'full-res' corresponds to the input service voxels, not necessarily scale-0.
sbm = SparseBlockMask(roi_mask_s5, seg_box, 2**roi_scale)
boxes = sbm.sparse_boxes(brick_shape)
# Clip boxes to the true (not rounded) bounding box
boxes[:, 0] = np.maximum(boxes[:, 0],
volume_service.bounding_box_zyx[0])
boxes[:, 1] = np.minimum(boxes[:, 1],
volume_service.bounding_box_zyx[1])
return boxes
def main():
configure_default_logging()
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('--no-downres', action='store_true')
parser.add_argument('--only-within-roi')
parser.add_argument('--not-within-roi')
parser.add_argument('dvid_server')
parser.add_argument('uuid')
parser.add_argument('labelmap_instance')
parser.add_argument('sparsevol_files', nargs='+')
args = parser.parse_args()
instance_info = (args.dvid_server, args.uuid, args.labelmap_instance)
assert not args.only_within_roi or not args.not_within_roi, \
"Can't supply both --only-within-roi and --not-within-roi. Pick one or the other (or neither)."
roi = args.only_within_roi or args.not_within_roi
invert_roi = (args.not_within_roi is not None)
if roi:
roi_mask, mask_box = fetch_roi(args.dvid_server,
args.uuid,
roi,
format='mask')
roi_sbm = SparseBlockMask(roi_mask, mask_box * (2**5),
2**5) # ROIs are provided at scale 5
else:
roi_sbm = None
# Ideally, we would choose the max label for the node we're writing to,
# but the /maxlabel endpoint doesn't work for all nodes
# instead, we'll use the repo-wide maxlabel from the /info JSON.
#maxlabel = fetch_maxlabel(args.dvid_server, args.uuid, args.labelmap_instance)
maxlabel = fetch_instance_info(
args.dvid_server, args.uuid,
args.labelmap_instance)["Extended"]["MaxRepoLabel"]
for i, path in enumerate(args.sparsevol_files):
maxlabel += 1
name = os.path.split(path)[1]
prefix_logger = PrefixedLogger(logger, f"Vol #{i:02d} {name}: ")
with Timer(f"Pasting {name} as {maxlabel}", logger):
overwritten_labels = overwrite_sparsevol(*instance_info, maxlabel,
path, roi_sbm, invert_roi,
args.no_downres,
prefix_logger)
results_path = os.path.splitext(path)[0] + '.json'
with open(results_path, 'w') as f:
results = {
'new-label': maxlabel,
'overwritten_labels': sorted(overwritten_labels)
}
json.dump(results, f, indent=2, cls=NumpyConvertingEncoder)
logger.info(f"Done.")
def _init_mask(self):
"""
- read the mask ROI as a volume
- dilate/erode it if necessary
- invert it if necessary
- save to .h5 (just for offline debug)
- return the scale-5 mask and its scale-5 bounding-box
"""
options = self.config["masksegmentation"]
roi = options["mask-roi"]
invert_mask = options["invert-mask"]
max_scale = options["max-pyramid-scale"]
roi_dilation = options["dilate-roi"]
roi_erosion = options["erode-roi"]
seg_dilation = options["dilate-segmentation"]
block_width = self.output_service.block_width
# Select a mask_box that's large enough to divide evenly into the
# block width even when reduced to the highest scale we'll be processing.
seg_box = round_box(self.input_service.bounding_box_zyx,
block_width * 2**max_scale)
seg_box_s5 = round_box(seg_box, 2**5) // (2**5)
with Timer(f"Loading ROI '{roi}'", logger):
roi_mask, _ = fetch_roi(self.input_service.server,
self.input_service.uuid,
roi,
format='mask',
mask_box=seg_box_s5)
with h5py.File('roi-mask.h5', 'w') as f:
f.create_dataset('mask',
data=roi_mask.view(np.uint8),
chunks=(128, 128, 128))
assert not (roi_dilation and roi_erosion)
if roi_dilation > 0:
with Timer(f"Dilating ROI by {roi_dilation}", logger):
roi_mask = vigra.filters.multiBinaryDilation(
roi_mask, roi_dilation)
with h5py.File('dilated-roi-mask.h5', 'w') as f:
f.create_dataset('mask',
data=roi_mask.view(np.uint8),
chunks=(128, 128, 128))
if roi_erosion > 0:
with Timer(f"Eroding ROI by {roi_erosion}", logger):
roi_mask = vigra.filters.multiBinaryErosion(
roi_mask, roi_erosion)
with h5py.File('eroded-roi-mask.h5', 'w') as f:
f.create_dataset('mask',
data=roi_mask.view(np.uint8),
chunks=(128, 128, 128))
assert not seg_dilation or invert_mask, \
"Can't use 'dilate-segmentation'. The segmentation isn't downloaded unless 'invert-mask' is used."
if invert_mask:
with Timer("Inverting mask", logger):
# Initialize the mask with entire segmentation at scale 5,
# then subtract the roi from it.
boxes = [
*boxes_from_grid(seg_box_s5, (64, 64, 2048), clipped=True)
]
input_service = self.input_service
def fetch_seg_mask_s5(box_s5):
seg_s5 = input_service.get_subvolume(box_s5, scale=5)
return box_s5, (seg_s5 != 0)
boxes_and_mask = dask.bag.from_sequence(
boxes, 1).map(fetch_seg_mask_s5).compute()
seg_mask = np.zeros(box_shape(seg_box_s5), bool)
for box_s5, box_mask in boxes_and_mask:
overwrite_subvol(seg_mask, box_s5, box_mask)
if seg_dilation == 0:
with h5py.File('segmentation-mask.h5', 'w') as f:
f.create_dataset('mask',
data=seg_mask.view(np.uint8),
chunks=(128, 128, 128))
else:
with Timer(f"Dilating segmentation by {seg_dilation}",
logger):
seg_mask = vigra.filters.multiBinaryDilation(
seg_mask, seg_dilation)
with h5py.File('dilated-segmentation-mask.h5', 'w') as f:
f.create_dataset('mask',
data=seg_mask.view(np.uint8),
chunks=(128, 128, 128))
seg_mask[roi_mask] = False
roi_mask = seg_mask
with h5py.File('final-mask.h5', 'w') as f:
f.create_dataset('mask',
data=roi_mask.view(np.uint8),
chunks=(128, 128, 128))
# Downsample the roi_mask to dvid-block resolution, just to see how many blocks it touches.
block_mask = view_as_blocks(roi_mask, (2, 2, 2)).any(axis=(3, 4, 5))
blocks_touched = block_mask.sum()
voxel_total = blocks_touched * (block_width**3)
logger.info(
f"Mask touches {blocks_touched} blocks ({voxel_total / 1e9:.1f} Gigavoxels)"
)
return roi_mask, seg_box_s5
def export_roi(server, uuid, roi_name, scale, scaled_shape_zyx, parent_output_dir):
"""
Export the ROI to a PNG stack (as binary images) at the requested scale.
Args:
server, uuid, roi_name:
ROI instance to read
scale:
What scale to export as, relative to the full-res grayscale.
Must be no greater than 5.
(ROIs are natively at scale=5, so using that scale will result in no upscaling.)
scaled_shape_zyx:
The max shape of the exported volume, in scaled coordinates.
The PNG stack files always start at (0,0,0), and extend to this shape.
Any ROI blocks below 0 or above this shape are silently ignored.
parent_output_dir:
Where to write the directory of PNG images.
(A child directory will be created here and named after the ROI instance.)
"""
from neuclease.util import view_as_blocks
assert not ((scaled_shape_zyx * 2**scale) % 64).any(), \
"The code below assumes that the volume shape is block aligned"
# Fetch the ROI-block coords (always scale 5)
roi_coords = fetch_roi((server, uuid, roi_name), format='coords')
if len(roi_coords) == 0:
return
output_dir = f'{parent_output_dir}/{roi_name}-mask-scale-{scale}'
os.makedirs(output_dir, exist_ok=True)
# Create a mask for the scale we're using (hopefully it fits in RAM...)
scaled_mask = np.zeros(scaled_shape_zyx, np.uint8)
# Create a view of the scaled mask that allows us to broadcast on a per-block basis,
# indexed as follows: scaled_mask_view[Bz,By,Bx,f,f,f],
# where f = scale_diff_factor = 32 / (2**SCALE)
# (ROIs are returned at scale 5!!)
scale_diff_factor = (2**5) // (2**scale)
scaled_mask_view = view_as_blocks(scaled_mask, 3*(scale_diff_factor,))
roi_box = np.array([roi_coords.min(axis=0), 1+roi_coords.max(axis=0)])
if (roi_box[0] < 0).any() or (roi_box[1] > scaled_mask_view.shape[:3]).any():
# Drop coordinates outside the volume.
# (Some ROIs extend beyond our sample.)
(Z, Y, X) = scaled_mask_view.shape[:3] #@UnusedVariable
roi_coords_df = pd.DataFrame(roi_coords, columns=list('zyx'))
roi_coords_df.query('x >= 0 and y >= 0 and z >= 0 and x < @X and y < @Y and z < @Z', inplace=True)
roi_coords = roi_coords_df.values
roi_box = np.array([roi_coords.min(axis=0), 1+roi_coords.max(axis=0)])
# Apply to the mask
scaled_mask_view[tuple(roi_coords.transpose())] = 1
scaled_mask = vigra.taggedView(scaled_mask, 'zyx')
for z, z_slice in enumerate(tqdm(scaled_mask, leave=False)):
vigra.impex.writeImage(z_slice, f'{output_dir}/{z:05d}.png', 'UINT8')
def init_boxes(self, volume_service, subset_labels, roi):
sbm = None
if roi:
base_service = volume_service.base_service
assert isinstance(base_service, DvidVolumeService), \
"Can't specify an ROI unless you're using a dvid input"
assert isinstance(volume_service, (ScaledVolumeService, DvidVolumeService)), \
"The 'roi' option doesn't support adapters other than 'rescale-level'"
scale = 0
if isinstance(volume_service, ScaledVolumeService):
scale = volume_service.scale_delta
assert scale <= 5, \
"The 'roi' option doesn't support volumes downscaled beyond level 5"
server, uuid, _seg_instance = base_service.instance_triple
brick_shape = volume_service.preferred_message_shape
assert not (brick_shape % 2**(5-scale)).any(), \
"If using an ROI, select a brick shape that is divisible by 32"
seg_box = volume_service.bounding_box_zyx
seg_box = round_box(seg_box, brick_shape)
seg_box_s5 = seg_box // 2**(5 - scale)
with Timer(f"Fetching mask for ROI '{roi}'", logger):
roi_mask_s5, roi_box_s5 = fetch_roi(server,
uuid,
roi,
format='mask')
# Restrict to input bounding box
clipped_roi_box_s5 = box_intersection(seg_box_s5, roi_box_s5)
clipped_roi_mask_s5 = extract_subvol(
roi_mask_s5, clipped_roi_box_s5 - roi_box_s5[0])
# Align to brick grid
aligned_roi_box_s5 = round_box(clipped_roi_box_s5,
brick_shape // 2**5, 'out')
padding = (aligned_roi_box_s5 - clipped_roi_box_s5)
padding[0] *= -1
aligned_roi_mask_s5 = np.pad(clipped_roi_mask_s5,
padding.transpose())
# At the service native scale
aligned_roi_box = (2**(5 - scale) * aligned_roi_box_s5)
logger.info(
f"Brick-aligned ROI '{roi}' has bounding-box {aligned_roi_box[:, ::-1].tolist()}"
)
# SBM 'full-res' corresponds to the input service voxels, not necessarily scale-0.
sbm = SparseBlockMask.create_from_highres_mask(
aligned_roi_mask_s5, 2**(5 - scale), aligned_roi_box,
brick_shape)
elif subset_labels:
try:
sbm = volume_service.sparse_block_mask_for_labels(
[*subset_labels])
if ((sbm.box[1] - sbm.box[0]) == 0).any():
raise RuntimeError(
"Could not find sparse masks for any of the subset-labels"
)
except NotImplementedError:
sbm = None
if sbm is None:
boxes = boxes_from_grid(volume_service.bounding_box_zyx,
volume_service.preferred_message_shape,
clipped=True)
return np.array([*boxes])
else:
boxes = sbm.sparse_boxes(brick_shape)
boxes = np.array(boxes)
# Clip
boxes[:, 0, :] = np.maximum(volume_service.bounding_box_zyx[0],
boxes[:, 0, :])
boxes[:, 1, :] = np.minimum(volume_service.bounding_box_zyx[1],
boxes[:, 1, :])
assert (boxes[:,0,:] < boxes[:,1,:]).all(), \
"After cropping to input volume, some bricks disappeared."
return boxes
def check_in_rois(server, uuid, synapse_df, rois):
"""
Adds a column 'in_roi' to the given points dataframe
indicating whether or not each point is covered by a ROI in the given list of ROIs.
Adds the column (IN-PLACE).
Args:
server:
dvid server
uuid:
Where to pull the rois from
synapse_df:
A DataFrame with at least columns ['x', 'y', 'z', 'body']
rois:
list of strings (roi instance names)
Returns:
None (Operates in-place)
"""
num_bodies = len(pd.unique(synapse_df['body']))
logging.info(
f"Checking in {len(rois)} ROIs for {len(synapse_df)} synapses from {num_bodies} bodies"
)
masks_and_boxes = []
for roi in rois:
logger.info(f"Fetching ROI '{roi}'")
mask, box = fetch_roi(server, uuid, roi, format='mask')
masks_and_boxes.append((mask, box))
_masks, boxes = zip(*masks_and_boxes)
boxes = np.array(boxes)
# box/shape is in scale-5 coordinates
logger.info("Combining ROIs into a single mask")
combined_box = (boxes[:, 0, :].min(axis=0), boxes[:, 1, :].max(axis=0))
combined_shape = (combined_box[1] - combined_box[0])
combined_mask = np.zeros(combined_shape, dtype=bool)
for mask, box in masks_and_boxes:
offset_box = box - combined_box[0]
combined_mask[box_to_slicing(*offset_box)] |= mask
# Rescale points to scale 5 (ROIs are given at scale 5)
logger.info("Scaling points")
downsampled_coords_zyx = synapse_df[['z', 'y', 'x']] // (2**5)
# Drop everything outside the combined_box
logger.info("Excluding OOB points")
min_z, min_y, min_x = combined_box[0] #@UnusedVariable
max_z, max_y, max_x = combined_box[1] #@UnusedVariable
q = 'z >= @min_z and y >= @min_y and x >= @min_x and z < @max_z and y < @max_y and x < @max_x'
downsampled_coords_zyx.query(q, inplace=True)
logging.info("Extracting mask values")
synapse_df['in_roi'] = False
downsampled_coords_zyx -= combined_box[0]
synapse_df.loc[downsampled_coords_zyx.index,
'in_roi'] = combined_mask[tuple(
downsampled_coords_zyx.values.transpose())]
roi_synapses = synapse_df['in_roi'].sum()
roi_bodies = len(pd.unique(synapse_df['body'][synapse_df['in_roi']]))
logging.info(
f"Found {roi_synapses} synapses in the ROI from {roi_bodies} bodies")
