def test_iteration(self):
"""Reads 32 images at a time and checks the final result is equal to original.
"""
zplanes = 503
arr = np.random.randint(255, size=(zplanes, 233, 112)).astype(np.uint8)
filterstr, formatstr, fnames = writeImages(arr)
schema = partitionSchema(PartitionDims(32, 64, 64), padding=32)
imgreader = imagefileSrc(schema,
formatstr,
minmaxplane=(0, zplanes),
offset=VolumeOffset(35, 21, 55))
# use the iterator, the iteration size is determiend by the Z partition size
partitions = []
for partitions_iter in imgreader:
partitions.extend(partitions_iter)
for fname in fnames:
os.remove(fname)
self.assertEqual(len(partitions), 192)
schema = partitionSchema(PartitionDims(0, 0, 0))
res = schema.partition_data(partitions)
self.assertEqual(len(res), 1)
# data that comes back will be padded by 32
origvol = np.zeros((512, 256, 160), dtype=np.uint8)
origvol[3:506, 21:254, 23:135] = arr
finalvol = res[0][1]
match = np.array_equal(origvol, finalvol)
self.assertEqual(match, True)
def test_largeimportandreverse(self):
"""Imports a large volume shifted, partitions, then unpartitions.
"""
zplanes = 503
arr = np.random.randint(255, size=(zplanes, 233, 112)).astype(np.uint8)
filterstr, formatstr, fnames = writeImages(arr)
schema = partitionSchema(PartitionDims(32, 64, 64), padding=32)
imgreader = imagefileSrc(schema,
formatstr,
minmaxplane=(0, zplanes),
offset=VolumeOffset(35, 21, 55))
partitions = imgreader.extract_volume()
for fname in fnames:
os.remove(fname)
self.assertEqual(len(partitions), 192)
schema = partitionSchema(PartitionDims(0, 0, 0))
res = schema.partition_data(partitions)
self.assertEqual(len(res), 1)
# data that comes back will be padded by 32
origvol = np.zeros((512, 256, 160), dtype=np.uint8)
origvol[3:506, 21:254, 23:135] = arr
finalvol = res[0][1]
match = np.array_equal(origvol, finalvol)
self.assertEqual(match, True)
def test_creatinglargevol(self):
"""Take small partitions and group into one large partition.
"""
arr = np.random.randint(1025, size=(4, 6, 4)).astype(np.uint16)
schema = partitionSchema(PartitionDims(2, 0, 0),
blank_delimiter=1111,
padding=2)
volpart = volumePartition(0, VolumeOffset(1, 1, 1))
res = schema.partition_data([(volpart, arr)])
self.assertEqual(len(res), 3) # 3 partitions with zsize=2 each
# make a new volume and pad
arrcomp = np.zeros((6, 8, 6), dtype=np.uint16)
arrcomp[:] = 1111
arrcomp[1, 1:7, 1:5] = arr[0, :, :]
arrcomp[2, 1:7, 1:5] = arr[1, :, :]
arrcomp[3, 1:7, 1:5] = arr[2, :, :]
arrcomp[4, 1:7, 1:5] = arr[3, :, :]
# reverse procedure should be same as the original
schemaglb = partitionSchema(PartitionDims(0, 0, 0))
res2 = schemaglb.partition_data(res)
self.assertEqual(len(res2), 1) # 3 partitions with zsize=2 each
match = np.array_equal(arrcomp, res2[0][1])
self.assertEqual(match, True)
def test_fixedpartitionandreloffset(self):
"""Test rigid partition sizes and relative offsets.
No padding should be needed and mask should be None.
"""
arr = np.random.randint(1025, size=(4, 6, 4)).astype(np.uint16)
schema = partitionSchema(PartitionDims(2, 8, 8),
blank_delimiter=1111,
padding=2,
enablemask=True)
volpart = volumePartition(0,
VolumeOffset(1, 1, 1),
reloffset=VolumeOffset(1, 1, 1))
res = schema.partition_data([[volpart, arr]])
self.assertEqual(len(res), 2) # 2 partitions with zsize=2 each
for partpair in res:
part, partvol = partpair
zidx = part.get_offset().z
# this mask should be all 1
self.assertEqual(part.get_reloffset().x, 2)
self.assertEqual(part.get_reloffset().y, 2)
self.assertEqual(part.get_reloffset().z, 0)
match = np.array_equal(arr[zidx - 2:zidx, :, :], partvol)
self.assertEqual(match, True)
self.assertEqual(part.mask, None)
def test_createtiles(self):
"""Take a 3D volume and transform into a series of slices.
"""
arr = np.random.randint(255, size=(45, 25, 13)).astype(np.uint8)
#arr = np.random.randint(255, size=(2,3,4)).astype(np.uint8)
schema = partitionSchema(PartitionDims(1, 0, 0))
volpart = volumePartition(0, VolumeOffset(0, 0, 0))
res = schema.partition_data([[volpart, arr]])
self.assertEqual(len(res), 45)
for tilepair in res:
part, tile = tilepair
zplane = part.get_offset().z
match = np.array_equal(tile[0, :, :], arr[zplane, :, :])
self.assertEqual(match, True)
def test_retrieve_shiftedpaddedimages(self):
"""Converts 3d numpy array and imports into shifted global space with padding.
Note:
Tests min/max plane format string as well.
"""
zplanes = 32
arr = np.random.randint(255, size=(zplanes, 25, 13)).astype(np.uint8)
filterstr, formatstr, fnames = writeImages(arr, 5)
schema = partitionSchema(PartitionDims(32, 0, 0), padding=8)
imgreader = imagefileSrc(schema,
formatstr,
minmaxplane=(5, 5 + zplanes),
offset=VolumeOffset(1, 3, 2))
partitions = imgreader.extract_volume()
for fname in fnames:
os.remove(fname)
self.assertEqual(len(partitions), 2)
origvol = np.zeros((40, 32, 16), dtype=np.uint8)
origvol[1:33, 3:28, 2:15] = arr
zoff = partitions[0][0].get_offset().z
if zoff == 0:
finalvol = partitions[0][1]
match = np.array_equal(origvol[0:32, 0:32, 0:16], finalvol)
self.assertEqual(match, True)
finalvol = partitions[1][1]
match = np.array_equal(origvol[32:40, 0:32, 0:16], finalvol)
self.assertEqual(match, True)
else:
finalvol = partitions[1][1]
match = np.array_equal(origvol[0:32, 0:32, 0:16], finalvol)
self.assertEqual(match, True)
finalvol = partitions[0][1]
match = np.array_equal(origvol[32:40, 0:32, 0:16], finalvol)
self.assertEqual(match, True)
def test_dvidpadlabels(self):
"""Check padding data with DVID labels.
"""
service = DVIDServerService(dvidserver)
uuid = service.create_new_repo("foo", "bar")
ns = DVIDNodeService(dvidserver, uuid)
ns.create_labelblk("labels")
arr = np.random.randint(12442, size=(58, 58, 58)).astype(np.uint64)
arr2 = np.zeros((64, 64, 64), np.uint64)
arr2[0:58, 0:58, 0:58] = arr
# load gray data
ns.put_labels3D("labels", arr2, (0, 0, 0))
# load shifted data for comparison
arr2[6:64, 6:64, 6:64] = arr
# read and pad data
schema = partitionSchema(PartitionDims(32, 64, 64),
enablemask=True,
padding=8,
blank_delimiter=99999)
volpart = volumePartition(0, VolumeOffset(6, 6, 6))
partitions = schema.partition_data([(volpart, arr)])
# fetch with mask
dvidreader = dvidSrc(dvidserver, uuid, "labels", partitions)
newparts = dvidreader.extract_volume()
self.assertEqual(len(newparts), 2)
for (part, vol) in newparts:
if part.get_offset().z == 0:
match = np.array_equal(arr2[0:32, :, :], vol)
self.assertTrue(match)
else:
match = np.array_equal(arr2[32:64, :, :], vol)
self.assertTrue(match)
def test_createtilesshifted(self):
"""Take a 3d volume with offset and transform into a series of slices.
The size of each tile should be the same even with the shift.
"""
arr = np.random.randint(255, size=(45, 25, 13)).astype(np.uint8)
#arr = np.random.randint(255, size=(2,3,4)).astype(np.uint8)
schema = partitionSchema(PartitionDims(1, 0, 0))
volpart = volumePartition(0, VolumeOffset(4, 2, 3))
res = schema.partition_data([[volpart, arr]])
self.assertEqual(len(res), 45)
for tilepair in res:
part, tile = tilepair
zplane = part.get_offset().z - 4
self.assertEqual(part.get_offset().x, 0)
self.assertEqual(part.get_offset().y, 0)
match = np.array_equal(tile[0, :, :], arr[zplane, :, :])
self.assertEqual(match, True)
def test_extractpartitionaligned(self):
"""Imports images shifted into partitioned space that is padded entire Z size.
"""
zplanes = 33
arr = np.random.randint(255, size=(zplanes, 25, 13)).astype(np.uint8)
filterstr, formatstr, fnames = writeImages(arr, 5)
schema = partitionSchema(PartitionDims(32, 0, 0), padding=32)
imgreader = imagefileSrc(schema,
formatstr,
minmaxplane=(5, 5 + zplanes),
offset=VolumeOffset(1, 3, 2))
partitions = imgreader.extract_volume()
for fname in fnames:
os.remove(fname)
self.assertEqual(len(partitions), 2)
origvol = np.zeros((64, 32, 32), dtype=np.uint8)
origvol[1:34, 3:28, 2:15] = arr
zoff = partitions[0][0].get_offset().z
if zoff == 0:
finalvol = partitions[0][1]
match = np.array_equal(origvol[0:32, :, :], finalvol)
self.assertEqual(match, True)
finalvol = partitions[1][1]
match = np.array_equal(origvol[32:64, :, :], finalvol)
self.assertEqual(match, True)
else:
finalvol = partitions[1][1]
match = np.array_equal(origvol[0:32, :, :], finalvol)
self.assertEqual(match, True)
finalvol = partitions[0][1]
match = np.array_equal(origvol[32:64, :, :], finalvol)
self.assertEqual(match, True)
def test_createvolshiftandpad(self):
"""Take a 3d volume with offset and transform into a series of subvolumes with padding.
The data is moved to the proper partition and is padded so that the x, y, z
of the stored data is lined up to a grid determined by the padding specified.
Also, tests >8bit data and the data mask.
"""
arr = np.random.randint(1025, size=(4, 6, 4)).astype(np.uint16)
#arr = np.random.randint(255, size=(2,3,4)).astype(np.uint8)
schema = partitionSchema(PartitionDims(2, 0, 0),
blank_delimiter=1111,
padding=2,
enablemask=True)
volpart = volumePartition(0, VolumeOffset(1, 1, 1))
res = schema.partition_data([[volpart, arr]])
self.assertEqual(len(res), 3) # 3 partitions with zsize=2 each
arrcomp = np.zeros((6, 8, 6), dtype=np.uint16)
arrcomp[:] = 1111
arrcomp[1, 1:7, 1:5] = arr[0, :, :]
arrcomp[2, 1:7, 1:5] = arr[1, :, :]
arrcomp[3, 1:7, 1:5] = arr[2, :, :]
arrcomp[4, 1:7, 1:5] = arr[3, :, :]
for partpair in res:
part, partvol = partpair
zidx = part.get_offset().z
# make a mask
mask = arrcomp[zidx:zidx + 2, :, :].copy()
mask[mask != 1111] = 1
mask[mask == 1111] = 0
match = np.array_equal(arrcomp[zidx:zidx + 2, :, :], partvol)
matchmask = np.array_equal(mask, part.mask)
self.assertEqual(match, True)
self.assertEqual(matchmask, True)
def test_retrieve_wholevolume(self):
"""Converts 3d numpy array to 2D slices and imports these slices as a 3D volume.
Note:
Also checks that volume offset works properly.
"""
zplanes = 5
arr = np.random.randint(255, size=(zplanes, 25, 13)).astype(np.uint8)
filterstr, formatstr, fnames = writeImages(arr, 10)
schema = partitionSchema(PartitionDims(0, 0, 0))
imgreader = imagefileSrc(schema,
filterstr,
offset=VolumeOffset(1, 0, 0))
partitions = imgreader.extract_volume()
for fname in fnames:
os.remove(fname)
self.assertEqual(len(partitions), 1)
finalvol = partitions[0][1]
match = np.array_equal(arr, finalvol)
self.assertEqual(match, True)
def execute(self):
"""
Execute spark workflow.
"""
self._sanitize_config()
session = default_dvid_session()
dvid_info = self.config_data["dvid-info"]
options = self.config_data["options"]
block_shape = 3 * (options["blocksize"], )
self.partition_size = options["blockwritelimit"] * options["blocksize"]
# ?? num parallel requests might be really small at high levels of pyramids
# xdim is unbounded or very large
partition_dims = PartitionDims(options["blocksize"],
options["blocksize"],
self.partition_size)
partition_schema = partitionSchema(
partition_dims,
blank_delimiter=options["blankdelimiter"],
padding=options["blocksize"],
enablemask=options["has-dvidmask"])
offset_zyx = np.array(options["offset"][::-1])
offset_zyx[0] += options["minslice"]
imgreader = imagefileSrc(partition_schema, options["basename"],
(options["minslice"], options["maxslice"]),
VolumeOffset(*offset_zyx), self.sc)
# !! hack: override iteration size that is set to partition size, TODO: add option
# this just makes the downstream processing a little more convenient, and reduces
# unnecessary DVID patching if that is enabled.
# (must be a multiple of block size)
imgreader.iteration_size = options["num-tasks"]
# get dims from image (hackage)
from PIL import Image
import requests
if '%' in options["basename"]:
minslice_name = options["basename"] % options["minslice"]
elif '{' in options["basename"]:
minslice_name = options["basename"].format(options["minslice"])
else:
raise RuntimeError(
f"Unrecognized format string for image basename: {options['basename']}"
)
img = Image.open(minslice_name)
volume_shape = (1 + options["maxslice"] - options["minslice"],
img.height, img.width)
del img
global_box_zyx = np.zeros((2, 3), dtype=int)
global_box_zyx[0] = options["offset"]
global_box_zyx[0] += (options["minslice"], 0, 0)
global_box_zyx[1] = global_box_zyx[0] + volume_shape
if options["create-pyramid"]:
if is_datainstance(dvid_info["dvid-server"], dvid_info["uuid"],
dvid_info["dataname"]):
logger.info(
"'{dataname}' already exists, skipping creation".format(
**dvid_info))
else:
# create data instance and disable dvidmask
# !! assume if data instance exists and mask is set that all pyramid
# !! also exits, meaning the mask should be used.
options["has-dvidmask"] = False
if options["disable-original"]:
logger.info(
"Not creating '{dataname}' due to 'disable-original' config setting"
.format(**dvid_info))
elif 0 in options["skipped-pyramid-levels"]:
logger.info(
"Not creating '{dataname}' due to 'skipped-pyramid-levels' config setting"
.format(**dvid_info))
else:
if options["is-rawarray"]:
create_rawarray8(dvid_info["dvid-server"],
dvid_info["uuid"],
dvid_info["dataname"], block_shape)
else:
create_label_instance(dvid_info["dvid-server"],
dvid_info["uuid"],
dvid_info["dataname"], 0,
block_shape)
if not options["disable-original"] and 0 not in options[
"skipped-pyramid-levels"]:
update_extents(dvid_info["dvid-server"], dvid_info["uuid"],
dvid_info["dataname"], global_box_zyx)
# Bottom level of pyramid is listed as neuroglancer-compatible
extend_list_value(dvid_info["dvid-server"], dvid_info["uuid"],
'.meta', 'neuroglancer',
[dvid_info["dataname"]])
# determine number of pyramid levels if not specified
if options["create-pyramid"] or options["create-pyramid-jpeg"]:
if options["pyramid-depth"] == -1:
options["pyramid-depth"] = 0
zsize = options["maxslice"] - options["minslice"] + 1
while zsize > 512:
options["pyramid-depth"] += 1
zsize /= 2
# NeuTu doesn't work well if there aren't at least a few pyramid levels.
# Even for small volumes, use at least a few pyramid levels,
# unless the depth was explicit in the config.
options["pyramid-depth"] = max(options["pyramid-depth"], 4)
# create pyramid data instances
if options["create-pyramid-jpeg"]:
dataname_jpeg = dvid_info["dataname"] + self.JPEGPYRAMID_NAME
if 0 in options["skipped-pyramid-levels"]:
logger.info(
"Not creating '{}' due to 'skipped-pyramid-levels' config setting"
.format(dataname_jpeg))
else:
if is_datainstance(dvid_info["dvid-server"], dvid_info["uuid"],
dataname_jpeg):
logger.info(
"'{}' already exists, skipping creation".format(
dataname_jpeg))
else:
create_rawarray8(dvid_info["dvid-server"],
dvid_info["uuid"], dataname_jpeg,
block_shape, Compression.JPEG)
update_extents(dvid_info["dvid-server"], dvid_info["uuid"],
dataname_jpeg, global_box_zyx)
# Bottom level of pyramid is listed as neuroglancer-compatible
extend_list_value(dvid_info["dvid-server"], dvid_info["uuid"],
'.meta', 'neuroglancer', [dataname_jpeg])
if options["create-pyramid"]:
for level in range(1, 1 + options["pyramid-depth"]):
downsampled_box_zyx = global_box_zyx // (2**level)
downname = dvid_info["dataname"] + "_%d" % level
if level in options["skipped-pyramid-levels"]:
logger.info(
"Not creating '{}' due to 'skipped-pyramid-levels' config setting"
.format(downname))
continue
if is_datainstance(dvid_info["dvid-server"], dvid_info["uuid"],
downname):
logger.info(
"'{}' already exists, skipping creation".format(
downname))
else:
if options["is-rawarray"]:
create_rawarray8(dvid_info["dvid-server"],
dvid_info["uuid"], downname,
block_shape)
else:
create_label_instance(dvid_info["dvid-server"],
dvid_info["uuid"], downname, 0,
block_shape)
update_extents(dvid_info["dvid-server"], dvid_info["uuid"],
downname, downsampled_box_zyx)
# Higher-levels of the pyramid should not appear in the DVID-lite console.
extend_list_value(dvid_info["dvid-server"], dvid_info["uuid"],
'.meta', 'restrictions', [downname])
if options["create-pyramid-jpeg"]:
for level in range(1, 1 + options["pyramid-depth"]):
downsampled_box_zyx = global_box_zyx // (2**level)
downname = dvid_info[
"dataname"] + self.JPEGPYRAMID_NAME + "_%d" % level
if level in options["skipped-pyramid-levels"]:
logger.info(
"Not creating '{}' due to 'skipped-pyramid-levels' config setting"
.format(downname))
continue
if is_datainstance(dvid_info["dvid-server"], dvid_info["uuid"],
downname):
logger.info(
"'{}' already exists, skipping creation".format(
downname))
else:
create_rawarray8(dvid_info["dvid-server"],
dvid_info["uuid"], downname, block_shape,
Compression.JPEG)
update_extents(dvid_info["dvid-server"], dvid_info["uuid"],
downname, downsampled_box_zyx)
# Higher-levels of the pyramid should not appear in the DVID-lite console.
extend_list_value(dvid_info["dvid-server"], dvid_info["uuid"],
'.meta', 'restrictions', [downname])
# create tiles
if options["create-tiles"] or options["create-tiles-jpeg"]:
MinTileCoord = global_box_zyx[0][::-1] // options["tilesize"]
MaxTileCoord = global_box_zyx[1][::-1] // options["tilesize"]
# get max level by just finding max tile coord
maxval = max(MaxTileCoord) - min(MinTileCoord) + 1
import math
self.maxlevel = int(math.log(maxval) / math.log(2))
tilemeta = {}
tilemeta["MinTileCoord"] = MinTileCoord.tolist()
tilemeta["MaxTileCoord"] = MaxTileCoord.tolist()
tilemeta["Levels"] = {}
currres = 8.0 # just use as placeholder for now
for level in range(0, self.maxlevel + 1):
tilemeta["Levels"][str(level)] = {
"Resolution": 3 * [currres],
"TileSize": 3 * [options["tilesize"]]
}
currres *= 2
if options["create-tiles"]:
session.post("{dvid-server}/api/repo/{uuid}/instance".format(
**dvid_info),
json={
"typename": "imagetile",
"dataname":
dvid_info["dataname"] + self.TILENAME,
"source": dvid_info["dataname"],
"format": "png"
})
session.post(
"{dvid-server}/api/repo/{uuid}/{dataname}{tilename}/metadata"
.format(tilename=self.TILENAME, **dvid_info),
json=tilemeta)
if options["create-tiles-jpeg"]:
session.post("{dvid-server}/api/repo/{uuid}/instance".format(
**dvid_info),
json={
"typename": "imagetile",
"dataname":
dvid_info["dataname"] + self.JPEGTILENAME,
"source": dvid_info["dataname"],
"format": "jpg"
})
session.post(
"{dvid-server}/api/repo/{uuid}/{dataname_jpeg_tile}/metadata"
.format(dataname_jpeg_tile=dvid_info["dataname"] +
self.JPEGTILENAME,
**dvid_info),
json=tilemeta)
if dvid_info["dvid-server"].startswith("http://127.0.0.1"):
def reload_meta():
reload_server_metadata(dvid_info["dvid-server"])
self.run_on_each_worker(reload_meta)
# TODO Validation: should verify syncs exist, should verify pyramid depth
# TODO: set syncs for pyramids, tiles if base datatype exists
# syncs should be removed before ingestion and added afterward
levels_cache = {}
# iterate through each partition
for arraypartition in imgreader:
# DVID pad if necessary
if options["has-dvidmask"]:
dvidsrc = dvidSrc(dvid_info["dvid-server"],
dvid_info["uuid"],
dvid_info["dataname"],
arraypartition,
resource_server=self.resource_server,
resource_port=self.resource_port)
arraypartition = dvidsrc.extract_volume()
# potentially need for future iterations
arraypartition.persist()
# check for final layer
finallayer = imgreader.curr_slice > imgreader.end_slice
if not options["disable-original"]:
# Write level-0 of the raw data, even if we aren't writing the rest of the pyramid.
dataname = datanamelossy = None
if options["create-pyramid"]:
dataname = dvid_info["dataname"]
if options["create-pyramid-jpeg"]:
datanamelossy = dvid_info[
"dataname"] + self.JPEGPYRAMID_NAME
if (dataname or datanamelossy
) and 0 not in options["skipped-pyramid-levels"]:
self._write_blocks(arraypartition, dataname, datanamelossy)
if options["create-tiles"] or options["create-tiles-jpeg"]:
# repartition into tiles
schema = partitionSchema(PartitionDims(1, 0, 0))
tilepartition = schema.partition_data(arraypartition)
# write unpadded tilesize (will pad with delimiter if needed)
self._writeimagepyramid(tilepartition)
if options["create-pyramid"] or options["create-pyramid-jpeg"]:
if 0 not in levels_cache:
levels_cache[0] = []
levels_cache[0].append(arraypartition)
curr_level = 1
downsample_factor = 2
# should be a multiple of Z blocks or the final fetch
assert imgreader.curr_slice % options["blocksize"] == 0
while ((((imgreader.curr_slice // options["blocksize"]) %
downsample_factor) == 0) or
finallayer) and curr_level <= options["pyramid-depth"]:
partlist = levels_cache[curr_level - 1]
part = partlist[0]
# union all RDDs from the same level
for iter1 in range(1, len(partlist)):
part = part.union(partlist[iter1])
# downsample map
israw = options["is-rawarray"]
def downsample(part_vol):
part, vol = part_vol
if not israw:
vol = downsample_3Dlabels(vol)[0]
else:
vol = downsample_raw(vol)[0]
return (part, vol)
downsampled_array = part.map(downsample)
# repart (vol and offset will always be power of two because of padding)
def repartition_down(part_volume):
part, volume = part_volume
downsampled_offset = np.array(part.get_offset()) // 2
downsampled_reloffset = np.array(
part.get_reloffset()) // 2
offsetnew = VolumeOffset(*downsampled_offset)
reloffsetnew = VolumeOffset(*downsampled_reloffset)
partnew = volumePartition(
(offsetnew.z, offsetnew.y, offsetnew.x),
offsetnew,
reloffset=reloffsetnew)
return partnew, volume
downsampled_array = downsampled_array.map(repartition_down)
# repartition downsample data
partition_dims = PartitionDims(options["blocksize"],
options["blocksize"],
self.partition_size)
schema = partitionSchema(
partition_dims,
blank_delimiter=options["blankdelimiter"],
padding=options["blocksize"],
enablemask=options["has-dvidmask"])
downsampled_array = schema.partition_data(
downsampled_array)
# persist before padding if there are more levels
if curr_level < options["pyramid-depth"]:
downsampled_array.persist()
if curr_level not in levels_cache:
levels_cache[curr_level] = []
levels_cache[curr_level].append(downsampled_array)
# pad from DVID (move before persist will allow multi-ingest
# but will lead to slightly non-optimal downsampling boundary
# effects if using a lossy compression only.
if options["has-dvidmask"]:
padname = dvid_info["dataname"]
if options[
"create-pyramid-jpeg"]: # !! should pad with orig if computing
# pad with jpeg
padname += self.JPEGPYRAMID_NAME
padname += "_%d" % curr_level
dvidsrc = dvidSrc(dvid_info["dvid-server"],
dvid_info["uuid"],
padname,
downsampled_array,
resource_server=self.resource_server,
resource_port=self.resource_port)
downsampled_array = dvidsrc.extract_volume()
# write result
downname = None
downnamelossy = None
if options["create-pyramid"]:
downname = dvid_info["dataname"] + "_%d" % curr_level
if options["create-pyramid-jpeg"]:
downnamelossy = dvid_info[
"dataname"] + self.JPEGPYRAMID_NAME + "_%d" % curr_level
if curr_level not in options["skipped-pyramid-levels"]:
self._write_blocks(downsampled_array, downname,
downnamelossy)
# remove previous level
del levels_cache[curr_level - 1]
curr_level += 1
downsample_factor *= 2
def test_dvidfetchgray(self):
"""Check reading grayscale from DVID from partitions.
This also checks basic iteration and overwrite of
previous data.
"""
service = DVIDServerService(dvidserver)
uuid = service.create_new_repo("foo", "bar")
ns = DVIDNodeService(dvidserver, uuid)
ns.create_grayscale8("gray")
arr = np.random.randint(255, size=(64, 64, 64)).astype(np.uint8)
# load gray data
ns.put_gray3D("gray", arr, (0, 0, 0))
# read data
schema = partitionSchema(PartitionDims(32, 64, 64))
volpart = volumePartition(0, VolumeOffset(0, 0, 0))
overwrite = np.random.randint(255, size=(64, 64, 64)).astype(np.uint8)
partitions = schema.partition_data([(volpart, overwrite)])
dvidreader = dvidSrc(dvidserver,
uuid,
"gray",
partitions,
maskonly=False)
newparts = dvidreader.extract_volume()
self.assertEqual(len(newparts), 2)
for (part, vol) in newparts:
if part.get_offset().z == 0:
match = np.array_equal(arr[0:32, :, :], vol)
self.assertTrue(match)
else:
match = np.array_equal(arr[32:64, :, :], vol)
self.assertTrue(match)
# test iteration
dvidreader2 = dvidSrc(dvidserver,
uuid,
"gray",
partitions,
maskonly=False)
newparts2 = []
for newpart in dvidreader2:
self.assertEqual(len(newpart), 1)
newparts2.extend(newpart)
self.assertEqual(len(newparts2), 2)
for (part, vol) in newparts2:
if part.get_offset().z == 0:
match = np.array_equal(arr[0:32, :, :], vol)
self.assertTrue(match)
else:
match = np.array_equal(arr[32:64, :, :], vol)
self.assertTrue(match)
def doit():
partitionSchema(PartitionDims(2, 0, 0),
blank_delimiter=1111,
padding=3,
enablemask=True)