def __get_spec(self, array_key):
info = self.__get_info(array_key)
roi_min = info['Extended']['MinPoint']
if roi_min is not None:
roi_min = Coordinate(roi_min[::-1])
roi_max = info['Extended']['MaxPoint']
if roi_max is not None:
roi_max = Coordinate(roi_max[::-1])
data_roi = Roi(offset=roi_min, shape=(roi_max - roi_min))
data_dims = Coordinate(data_roi.get_shape())
if self.ndims is None:
self.ndims = len(data_dims)
else:
assert self.ndims == len(data_dims)
if array_key in self.array_specs:
spec = self.array_specs[array_key].copy()
else:
spec = ArraySpec()
if spec.voxel_size is None:
spec.voxel_size = Coordinate(info['Extended']['VoxelSize'])
if spec.roi is None:
spec.roi = data_roi * spec.voxel_size
data_dtype = dvision.DVIDDataInstance(self.hostname, self.port,
self.uuid,
self.datasets[array_key]).dtype
if spec.dtype is not None:
assert spec.dtype == data_dtype, (
"dtype %s provided in array_specs for %s, "
"but differs from instance %s dtype %s" %
(self.array_specs[array_key].dtype, array_key,
self.datasets[array_key], data_dtype))
else:
spec.dtype = data_dtype
if spec.interpolatable is None:
spec.interpolatable = spec.dtype in [
np.float,
np.float32,
np.float64,
np.float128,
np.uint8 # assuming this is not used for labels
]
logger.warning(
"WARNING: You didn't set 'interpolatable' for %s. "
"Based on the dtype %s, it has been set to %s. "
"This might not be what you want.", array_key, spec.dtype,
spec.interpolatable)
return spec
def process(self, batch, request):
points = batch.points[self.points]
mask = self.settings.mask
voxel_size = self.spec[self.array].voxel_size
# get roi used for creating the new array (points_roi does no
# necessarily align with voxel size)
enlarged_vol_roi = points.spec.roi.snap_to_grid(voxel_size)
offset = enlarged_vol_roi.get_begin() / voxel_size
shape = enlarged_vol_roi.get_shape() / voxel_size
data_roi = Roi(offset, shape)
logger.debug("Points in %s", points.spec.roi)
for i, point in points.data.items():
logger.debug("%d, %s", i, point.location)
logger.debug("Data roi in voxels: %s", data_roi)
logger.debug("Data roi in world units: %s", data_roi*voxel_size)
if len(points.data.items()) == 0:
# If there are no points at all, just create an empty matrix.
rasterized_points_data = np.zeros(data_roi.get_shape(),
dtype=self.spec[self.array].dtype)
elif mask is not None:
mask_array = batch.arrays[mask].crop(enlarged_vol_roi)
# get those component labels in the mask, that contain points
labels = []
for i, point in points.data.items():
v = Coordinate(point.location / voxel_size)
v -= data_roi.get_begin()
labels.append(mask_array.data[v])
# Make list unique
labels = list(set(labels))
# zero label should be ignored
if 0 in labels:
labels.remove(0)
if len(labels) == 0:
logger.debug("Points and provided object mask do not overlap. No points to rasterize.")
rasterized_points_data = np.zeros(data_roi.get_shape(),
dtype=self.spec[self.array].dtype)
else:
# create data for the whole points ROI, "or"ed together over
# individual object masks
rasterized_points_data = np.sum(
[
self.__rasterize(
points,
data_roi,
voxel_size,
self.spec[self.array].dtype,
self.settings,
Array(data=mask_array.data==label, spec=mask_array.spec))
for label in labels
],
axis=0)
else:
# create data for the whole points ROI without mask
rasterized_points_data = self.__rasterize(
points,
data_roi,
voxel_size,
self.spec[self.array].dtype,
self.settings)
# fix bg/fg labelling if requested
if (self.settings.bg_value != 0 or
self.settings.fg_value != 1):
replaced = replace(
rasterized_points_data,
[0, 1],
[self.settings.bg_value, self.settings.fg_value])
rasterized_points_data = replaced.astype(self.spec[self.array].dtype)
# create array and crop it to requested roi
spec = self.spec[self.array].copy()
spec.roi = data_roi*voxel_size
rasterized_points = Array(
data=rasterized_points_data,
spec=spec)
batch.arrays[self.array] = rasterized_points.crop(request[self.array].roi)
# restore requested ROI of points
if self.points in request:
request_roi = request[self.points].roi
points.spec.roi = request_roi
points.data = {i: p for i, p in points.data.items() if request_roi.contains(p.location)}
# restore requested mask
if mask is not None:
batch.arrays[mask] = batch.arrays[mask].crop(request[mask].roi)