def ingestImageStack(self):
"""Ingest a TIF image stack"""
# Load a database
with closing(ocpcaproj.OCPCAProjectsDB()) as projdb:
proj = projdb.loadToken(self.token)
with closing(ocpcadb.OCPCADB(proj)) as db:
ch = proj.getChannelObj(self.channel)
# get the dataset configuration
[[ximagesz, yimagesz, zimagesz],
(starttime, endtime)] = proj.datasetcfg.imageSize(self.resolution)
[xcubedim, ycubedim, zcubedim
] = cubedim = proj.datasetcfg.getCubeDims()[self.resolution]
[xoffset, yoffset,
zoffset] = proj.datasetcfg.getOffset()[self.resolution]
if ch.getChannelType() in TIMESERIES_CHANNELS and (
starttime == 0 and endtime == 0):
logger.error("Timeseries Data cannot have timerange (0,0)")
raise OCPCAError("Timeseries Data cannot have timerange (0,0)")
# Get a list of the files in the directories
for timestamp in range(starttime, endtime + 1):
for slice_number in range(zoffset, zimagesz, zcubedim):
slab = np.zeros([zcubedim, yimagesz, ximagesz],
dtype=OCP_dtypetonp.get(ch.getDataType()))
# fetch 16 slices at a time
if ch.getChannelType() in TIMESERIES_CHANNELS:
time_value = timestamp
else:
time_value = None
self.fetchData(
range(slice_number, slice_number +
zcubedim) if slice_number + zcubedim <= zimagesz
else range(slice_number, zimagesz),
time_value=time_value)
for b in range(zcubedim):
if (slice_number + b < zimagesz):
try:
# reading the raw data
file_name = "{}{}".format(
self.path,
self.generateFileName(slice_number + b))
print "Open filename {}".format(file_name)
logger.info(
"Open filename {}".format(file_name))
if ch.getDataType() in [
UINT8, UINT16
] and ch.getChannelType(
) in IMAGE_CHANNELS + TIMESERIES_CHANNELS:
image_data = np.asarray(
Image.open(file_name, 'r'))
slab[b, :, :] = image_data
elif ch.getDataType() in [
UINT32
] and ch.getChannelType(
) in IMAGE_CHANNELS + TIMESERIES_CHANNELS:
image_data = np.asarray(
Image.open(file_name,
'r').convert('RGBA'))
slab[b, :, :] = np.left_shift(
image_data[:, :, 3],
24,
dtype=np.uint32) | np.left_shift(
image_data[:, :, 2],
16,
dtype=np.uint32) | np.left_shift(
image_data[:, :, 1],
8,
dtype=np.uint32) | np.uint32(
image_data[:, :, 0])
elif ch.getChannelType(
) in ANNOTATION_CHANNELS:
image_data = np.asarray(
Image.open(file_name, 'r'))
slab[b, :, :] = image_data
else:
logger.error("Cannot ingest this data yet")
raise OCPCAError(
"Cannot ingest this data yet")
except IOError, e:
logger.warning("IOError {}.".format(e))
slab[b, :, :] = np.zeros((yimagesz, ximagesz),
dtype=np.uint32)
for y in range(0, yimagesz + 1, ycubedim):
for x in range(0, ximagesz + 1, xcubedim):
# Getting a Cube id and ingesting the data one cube at a time
zidx = ocplib.XYZMorton([
x / xcubedim, y / ycubedim,
(slice_number - zoffset) / zcubedim
])
cube = Cube.getCube(cubedim, ch.getChannelType(),
ch.getDataType())
cube.zeros()
xmin, ymin = x, y
xmax = min(ximagesz, x + xcubedim)
ymax = min(yimagesz, y + ycubedim)
zmin = 0
zmax = min(slice_number + zcubedim, zimagesz + 1)
cube.data[0:zmax - zmin, 0:ymax - ymin,
0:xmax - xmin] = slab[zmin:zmax,
ymin:ymax,
xmin:xmax]
if cube.isNotZeros():
if ch.getChannelType() in IMAGE_CHANNELS:
db.putCube(ch,
zidx,
self.resolution,
cube,
update=False)
elif ch.getChannelType(
) in TIMESERIES_CHANNELS:
db.putTimeCube(ch,
zidx,
timestamp,
self.resolution,
cube,
update=False)
elif ch.getChannelType(
) in ANNOTATION_CHANNELS:
corner = map(sub, [x, y, slice_number],
[xoffset, yoffset, zoffset])
db.annotateDense(ch, corner,
self.resolution,
cube.data, 'O')
else:
logger.error(
"Channel type {} not supported".format(
ch.getChannelType()))
raise OCPCAError(
"Channel type {} not supported".format(
ch.getChannelType()))
# clean up the slices fetched
self.cleanData(
range(slice_number, slice_number +
zcubedim) if slice_number + zcubedim <= zimagesz
else range(slice_number, zimagesz))
def buildImageStack(proj, ch, res=None):
"""Build the hierarchy of images"""
with closing(ocpcadb.OCPCADB(proj)) as db:
# pick a resolution
if res is None:
res = 1
high_res = proj.datasetcfg.scalinglevels
scaling = proj.datasetcfg.scalingoption
for cur_res in range (res, high_res+1):
# Get the source database sizes
[[ximagesz, yimagesz, zimagesz], timerange] = proj.datasetcfg.imageSize(cur_res)
[xcubedim, ycubedim, zcubedim] = cubedim = proj.datasetcfg.getCubeDims()[cur_res]
if scaling == ZSLICES:
(xscale, yscale, zscale) = (2, 2, 1)
elif scaling == ISOTROPIC:
(xscale, yscale, zscale) = (2, 2, 2)
else:
logger.error("Invalid scaling option in project = {}".format(scaling))
raise OCPCAError("Invalid scaling option in project = {}".format(scaling))
biggercubedim = [xcubedim*xscale,ycubedim*yscale,zcubedim*zscale]
# Set the limits for iteration on the number of cubes in each dimension
xlimit = (ximagesz-1) / xcubedim + 1
ylimit = (yimagesz-1) / ycubedim + 1
zlimit = (zimagesz-1) / zcubedim + 1
# Iterating over time
for ts in range(timerange[0], timerange[1]+1, 1):
# Iterating over zslice
for z in range(zlimit):
# Iterating over y
for y in range(ylimit):
# Iterating over x
for x in range(xlimit):
# cutout the data at the resolution
if ch.getChannelType() not in TIMESERIES_CHANNELS:
olddata = db.cutout(ch, [x*xscale*xcubedim, y*yscale*ycubedim, z*zscale*zcubedim ], biggercubedim, cur_res-1).data
else:
olddata = db.timecutout(ch, [x*xscale*xcubedim, y*yscale*ycubedim, z*zscale*zcubedim ], biggercubedim, cur_res-1, [ts,ts+1]).data
olddata = olddata[0,:,:,:]
#olddata target array for the new data (z,y,x) order
newdata = np.zeros([zcubedim,ycubedim,xcubedim], dtype=OCP_dtypetonp.get(ch.getDataType()))
for sl in range(zcubedim):
if scaling == ZSLICES:
data = olddata[sl,:,:]
elif scaling == ISOTROPIC:
data = ocplib.isotropicBuild_ctype(olddata[sl*2,:,:], olddata[sl*2+1,:,:])
# Convert each slice to an image
# 8-bit int option
if olddata.dtype == np.uint8:
slimage = Image.frombuffer('L', (xcubedim*2,ycubedim*2), data.flatten(), 'raw', 'L', 0, 1)
# 16-bit int option
elif olddata.dtype == np.uint16:
slimage = Image.frombuffer('I;16', (xcubedim*2,ycubedim*2), data.flatten(), 'raw', 'I;16', 0, 1)
# 32-bit float option
elif olddata.dtype == np.float32:
slimage = Image.frombuffer ( 'F', (xcubedim*2,ycubedim*2), data.flatten(), 'raw', 'F', 0, 1 )
# 32 bit RGBA data
elif olddata.dtype == np.uint32:
slimage = Image.fromarray( data, "RGBA" )
# KL TODO Add support for 32bit and 64bit RGBA data
# Resize the image and put in the new cube array
if olddata.dtype != np.uint32:
newdata[sl, :, :] = np.asarray(slimage.resize([xcubedim,ycubedim]))
else:
tempdata = np.asarray(slimage.resize([xcubedim, ycubedim]))
newdata[sl,:,:] = np.left_shift(tempdata[:,:,3], 24, dtype=np.uint32) | np.left_shift(tempdata[:,:,2], 16, dtype=np.uint32) | np.left_shift(tempdata[:,:,1], 8, dtype=np.uint32) | np.uint32(tempdata[:,:,0])
zidx = ocplib.XYZMorton ([x,y,z])
cube = Cube.getCube(cubedim, ch.getChannelType(), ch.getDataType())
cube.zeros()
cube.data = newdata
if ch.getChannelType() not in TIMESERIES_CHANNELS:
db.putCube(ch, zidx, cur_res, cube, update=True)
else:
db.putTimeCube(ch, zidx, ts, cur_res, cube, update=False)
def ingestImageStack(self):
"""Ingest a TIF image stack"""
# Load a database
with closing(ocpcaproj.OCPCAProjectsDB()) as projdb:
proj = projdb.loadToken(self.token)
with closing(ocpcadb.OCPCADB(proj)) as db:
ch = proj.getChannelObj(self.channel)
# get the dataset configuration
[[ximagesz, yimagesz, zimagesz],
(starttime, endtime)] = proj.datasetcfg.imageSize(self.resolution)
[xcubedim, ycubedim, zcubedim
] = cubedim = proj.datasetcfg.getCubeDims()[self.resolution]
[xoffset, yoffset,
zoffset] = proj.datasetcfg.getOffset()[self.resolution]
if ch.getChannelType() in TIMESERIES_CHANNELS and (
starttime == 0 and endtime == 0):
print "Timeseries Data cannot have timerange (0,0)"
raise
# Get a list of the files in the directories
for timestamp in range(starttime, endtime + 1):
for slice_number in range(zoffset, zimagesz + 1, zcubedim):
slab = np.zeros([zcubedim, yimagesz, ximagesz],
dtype=OCP_dtypetonp.get(ch.getDataType()))
# fetch 16 slices at a time
if ch.getChannelType() in TIMESERIES_CHANNELS:
time_value = timestamp
else:
time_value = None
self.fetchData(
range(slice_number, slice_number +
zcubedim) if slice_number + zcubedim <= zimagesz
else range(slice_number, zimagesz),
time_value=time_value)
for b in range(zcubedim):
if (slice_number + b <= zimagesz):
try:
# reading the raw data
file_name = "{}{}".format(
self.path,
self.generateFileName(slice_number + b))
print "Open filename {}".format(file_name)
slab[b, :, :] = np.asarray(
Image.open(file_name, 'r'))
except IOError, e:
print e
slab[b, :, :] = np.zeros((yimagesz, ximagesz),
dtype=np.uint32)
for y in range(0, yimagesz + 1, ycubedim):
for x in range(0, ximagesz + 1, xcubedim):
# Getting a Cube id and ingesting the data one cube at a time
zidx = ocplib.XYZMorton([
x / xcubedim, y / ycubedim,
(slice_number - zoffset) / zcubedim
])
cube = Cube.getCube(cubedim, ch.getChannelType(),
ch.getDataType())
cube.zeros()
xmin, ymin = x, y
xmax = min(ximagesz, x + xcubedim)
ymax = min(yimagesz, y + ycubedim)
zmin = 0
zmax = min(slice_number + zcubedim, zimagesz + 1)
cube.data[0:zmax - zmin, 0:ymax - ymin,
0:xmax - xmin] = slab[zmin:zmax,
ymin:ymax,
xmin:xmax]
if cube.isNotZeros():
if ch.getChannelType(
) not in TIMESERIES_CHANNELS:
db.putCube(ch,
zidx,
self.resolution,
cube,
update=True)
else:
db.putTimeCube(ch,
zidx,
timestamp,
self.resolution,
cube,
update=True)
# clean up the slices fetched
self.cleanData(
range(slice_number, slice_number +
zcubedim) if slice_number + zcubedim <= zimagesz
else range(slice_number, zimagesz))
def buildAnnoStack ( proj, ch, res=None ):
"""Build the hierarchy for annotations"""
with closing(ocpcadb.OCPCADB (proj)) as db:
# pick a resolution
if res is None:
res = 1
high_res = proj.datasetcfg.scalinglevels
scaling = proj.datasetcfg.scalingoption
for cur_res in range(res, high_res+1):
# Get the source database sizes
[[ximagesz, yimagesz, zimagesz], timerange] = proj.datasetcfg.imageSize(cur_res-1)
[xcubedim, ycubedim, zcubedim] = cubedim = proj.datasetcfg.getCubeDims()[cur_res-1]
# Set the limits for iteration on the number of cubes in each dimension
xlimit = (ximagesz-1) / xcubedim + 1
ylimit = (yimagesz-1) / ycubedim + 1
zlimit = (zimagesz-1) / zcubedim + 1
# Choose constants that work for all resolutions. recall that cube size changes from 128x128x16 to 64*64*64
if scaling == ZSLICES:
outdata = np.zeros ( [ zcubedim*4, ycubedim*2, xcubedim*2 ], dtype=OCP_dtypetonp.get(ch.getDataType()))
elif scaling == ISOTROPIC:
outdata = np.zeros ( [ zcubedim*2, ycubedim*2, xcubedim*2 ], dtype=OCP_dtypetonp.get(ch.getDataType()))
else:
logger.error ( "Invalid scaling option in project = {}".format(scaling) )
raise OCPCAError ( "Invalid scaling option in project = {}".format(scaling))
# Round up to the top of the range
lastzindex = (ocplib.XYZMorton([xlimit,ylimit,zlimit])/64+1)*64
# Iterate over the cubes in morton order
for mortonidx in range(0, lastzindex, 64):
# call the range query
cuboids = db.getCubes(ch, range(mortonidx,mortonidx+64), cur_res-1)
cube = Cube.getCube(cubedim, ch.getChannelType(), ch.getDataType())
# get the first cube
for idx, datastring in cuboids:
xyz = ocplib.MortonXYZ(idx)
cube.fromNPZ(datastring)
if scaling == ZSLICES:
# Compute the offset in the output data cube
# we are placing 4x4x4 input blocks into a 2x2x4 cube
offset = [(xyz[0]%4)*(xcubedim/2), (xyz[1]%4)*(ycubedim/2), (xyz[2]%4)*zcubedim]
# add the contribution of the cube in the hierarchy
ocplib.addDataToZSliceStack_ctype(cube, outdata, offset)
elif scaling == ISOTROPIC:
# Compute the offset in the output data cube
# we are placing 4x4x4 input blocks into a 2x2x2 cube
offset = [(xyz[0]%4)*(xcubedim/2), (xyz[1]%4)*(ycubedim/2), (xyz[2]%4)*(zcubedim/2)]
# use python version for debugging
ocplib.addDataToIsotropicStack_ctype(cube, outdata, offset)
# Get the base location of this batch
xyzout = ocplib.MortonXYZ (mortonidx)
# adjust to output corner for scale.
if scaling == ZSLICES:
outcorner = [ xyzout[0]*xcubedim/2, xyzout[1]*ycubedim/2, xyzout[2]*zcubedim ]
elif scaling == ISOTROPIC:
outcorner = [ xyzout[0]*xcubedim/2, xyzout[1]*ycubedim/2, xyzout[2]*zcubedim/2 ]
# Data stored in z,y,x order dims in x,y,z
outdim = outdata.shape[::-1]
# Preserve annotations made at the specified level
# KL check that the P option preserves annotations? RB changed from O
db.annotateDense(ch, outcorner, cur_res, outdata, 'O')
db.conn.commit()
# zero the output buffer
outdata = np.zeros ([zcubedim*4, ycubedim*2, xcubedim*2], dtype=OCP_dtypetonp.get(ch.getDataType()))
def ingestImageStack(self):
"""Ingest a TIF image stack"""
# Load a database
with closing (ocpcaproj.OCPCAProjectsDB()) as projdb:
proj = projdb.loadToken(self.token)
with closing (ocpcadb.OCPCADB(proj)) as db:
ch = proj.getChannelObj(self.channel)
# get the dataset configuration
[[ximagesz, yimagesz, zimagesz],(starttime,endtime)] = proj.datasetcfg.imageSize(self.resolution)
[xcubedim, ycubedim, zcubedim] = cubedim = proj.datasetcfg.getCubeDims()[self.resolution]
[xoffset, yoffset, zoffset] = proj.datasetcfg.getOffset()[self.resolution]
if ch.getChannelType() in TIMESERIES_CHANNELS and (starttime == 0 and endtime == 0):
logger.error("Timeseries Data cannot have timerange (0,0)")
raise OCPCAError("Timeseries Data cannot have timerange (0,0)")
# Get a list of the files in the directories
for timestamp in range(starttime, endtime+1):
for slice_number in range (zoffset, zimagesz, zcubedim):
slab = np.zeros([zcubedim, yimagesz, ximagesz ], dtype=OCP_dtypetonp.get(ch.getDataType()))
# fetch 16 slices at a time
if ch.getChannelType() in TIMESERIES_CHANNELS:
time_value = timestamp
else:
time_value = None
self.fetchData(range(slice_number,slice_number+zcubedim) if slice_number+zcubedim<=zimagesz else range(slice_number,zimagesz), time_value=time_value)
for b in range(zcubedim):
if (slice_number + b < zimagesz):
try:
# reading the raw data
file_name = "{}{}".format(self.path, self.generateFileName(slice_number+b))
print "Open filename {}".format(file_name)
logger.info("Open filename {}".format(file_name))
if ch.getDataType() in [UINT8, UINT16] and ch.getChannelType() in IMAGE_CHANNELS + TIMESERIES_CHANNELS:
image_data = np.asarray(Image.open(file_name, 'r'))
slab[b,:,:] = image_data
elif ch.getDataType() in [UINT32] and ch.getChannelType() in IMAGE_CHANNELS + TIMESERIES_CHANNELS:
image_data = np.asarray(Image.open(file_name, 'r').convert('RGBA'))
slab[b,:,:] = np.left_shift(image_data[:,:,3], 24, dtype=np.uint32) | np.left_shift(image_data[:,:,2], 16, dtype=np.uint32) | np.left_shift(image_data[:,:,1], 8, dtype=np.uint32) | np.uint32(image_data[:,:,0])
elif ch.getChannelType() in ANNOTATION_CHANNELS:
image_data = np.asarray(Image.open(file_name, 'r'))
slab[b,:,:] = image_data
else:
logger.error("Cannot ingest this data yet")
raise OCPCAError("Cannot ingest this data yet")
except IOError, e:
logger.warning("IOError {}.".format(e))
slab[b,:,:] = np.zeros((yimagesz, ximagesz), dtype=np.uint32)
for y in range ( 0, yimagesz+1, ycubedim ):
for x in range ( 0, ximagesz+1, xcubedim ):
# Getting a Cube id and ingesting the data one cube at a time
zidx = ocplib.XYZMorton ( [x/xcubedim, y/ycubedim, (slice_number-zoffset)/zcubedim] )
cube = Cube.getCube(cubedim, ch.getChannelType(), ch.getDataType())
cube.zeros()
xmin,ymin = x,y
xmax = min ( ximagesz, x+xcubedim )
ymax = min ( yimagesz, y+ycubedim )
zmin = 0
zmax = min(slice_number+zcubedim, zimagesz+1)
cube.data[0:zmax-zmin,0:ymax-ymin,0:xmax-xmin] = slab[zmin:zmax, ymin:ymax, xmin:xmax]
if cube.isNotZeros():
if ch.getChannelType() in IMAGE_CHANNELS:
db.putCube(ch, zidx, self.resolution, cube, update=True)
elif ch.getChannelType() in TIMESERIES_CHANNELS:
db.putTimeCube(ch, zidx, timestamp, self.resolution, cube, update=True)
elif ch.getChannelType() in ANNOTATION_CHANNELS:
corner = map(sub, [x,y,slice_number], [xoffset,yoffset,zoffset])
db.annotateDense(ch, corner, self.resolution, cube.data, 'O')
else:
logger.error("Channel type {} not supported".format(ch.getChannelType()))
raise OCPCAError("Channel type {} not supported".format(ch.getChannelType()))
# clean up the slices fetched
self.cleanData(range(slice_number,slice_number+zcubedim) if slice_number+zcubedim<=zimagesz else range(slice_number,zimagesz))
def ingestImageStack(self):
"""Ingest a TIF image stack"""
# Load a database
with closing (ocpcaproj.OCPCAProjectsDB()) as projdb:
proj = projdb.loadToken(self.token)
with closing (ocpcadb.OCPCADB(proj)) as db:
ch = proj.getChannelObj(self.channel)
# get the dataset configuration
[[ximagesz, yimagesz, zimagesz],(starttime,endtime)] = proj.datasetcfg.imageSize(self.resolution)
[xcubedim, ycubedim, zcubedim] = cubedim = proj.datasetcfg.getCubeDims()[self.resolution]
[xoffset, yoffset, zoffset] = proj.datasetcfg.getOffset()[self.resolution]
if ch.getChannelType() in TIMESERIES_CHANNELS and (starttime == 0 and endtime == 0):
print "Timeseries Data cannot have timerange (0,0)"
raise
# Get a list of the files in the directories
for timestamp in range(starttime, endtime+1):
for slice_number in range (zoffset, zimagesz+1, zcubedim):
slab = np.zeros([zcubedim, yimagesz, ximagesz ], dtype=OCP_dtypetonp.get(ch.getDataType()))
# fetch 16 slices at a time
if ch.getChannelType() in TIMESERIES_CHANNELS:
time_value = timestamp
else:
time_value = None
self.fetchData(range(slice_number,slice_number+zcubedim) if slice_number+zcubedim<=zimagesz else range(slice_number,zimagesz), time_value=time_value)
for b in range(zcubedim):
if (slice_number + b <= zimagesz):
try:
# reading the raw data
file_name = "{}{}".format(self.path, self.generateFileName(slice_number+b))
print "Open filename {}".format(file_name)
slab[b,:,:] = np.asarray(Image.open(file_name, 'r'))
except IOError, e:
print e
slab[b,:,:] = np.zeros((yimagesz, ximagesz), dtype=np.uint32)
for y in range ( 0, yimagesz+1, ycubedim ):
for x in range ( 0, ximagesz+1, xcubedim ):
# Getting a Cube id and ingesting the data one cube at a time
zidx = ocplib.XYZMorton ( [x/xcubedim, y/ycubedim, (slice_number-zoffset)/zcubedim] )
cube = Cube.getCube(cubedim, ch.getChannelType(), ch.getDataType())
cube.zeros()
xmin,ymin = x,y
xmax = min ( ximagesz, x+xcubedim )
ymax = min ( yimagesz, y+ycubedim )
zmin = 0
zmax = min(slice_number+zcubedim, zimagesz+1)
cube.data[0:zmax-zmin,0:ymax-ymin,0:xmax-xmin] = slab[zmin:zmax, ymin:ymax, xmin:xmax]
if cube.isNotZeros():
if ch.getChannelType() not in TIMESERIES_CHANNELS:
db.putCube(ch, zidx, self.resolution, cube, update=True)
else:
db.putTimeCube(ch, zidx, timestamp, self.resolution, cube, update=True)
# clean up the slices fetched
self.cleanData(range(slice_number,slice_number+zcubedim) if slice_number+zcubedim<=zimagesz else range(slice_number,zimagesz))
def buildImageStack(proj, ch, res=None):
"""Build the hierarchy of images"""
with closing(ocpcadb.OCPCADB(proj)) as db:
# pick a resolution
if res is None:
res = 1
high_res = proj.datasetcfg.scalinglevels
scaling = proj.datasetcfg.scalingoption
for cur_res in range(res, high_res + 1):
# Get the source database sizes
[[ximagesz, yimagesz, zimagesz],
timerange] = proj.datasetcfg.imageSize(cur_res)
[xcubedim, ycubedim,
zcubedim] = cubedim = proj.datasetcfg.getCubeDims()[cur_res]
if scaling == ZSLICES:
(xscale, yscale, zscale) = (2, 2, 1)
elif scaling == ISOTROPIC:
(xscale, yscale, zscale) = (2, 2, 2)
else:
logger.error(
"Invalid scaling option in project = {}".format(scaling))
raise OCPCAError(
"Invalid scaling option in project = {}".format(scaling))
biggercubedim = [
xcubedim * xscale, ycubedim * yscale, zcubedim * zscale
]
# Set the limits for iteration on the number of cubes in each dimension
xlimit = (ximagesz - 1) / xcubedim + 1
ylimit = (yimagesz - 1) / ycubedim + 1
zlimit = (zimagesz - 1) / zcubedim + 1
# Iterating over time
for ts in range(timerange[0], timerange[1] + 1, 1):
# Iterating over zslice
for z in range(zlimit):
# Iterating over y
for y in range(ylimit):
# Iterating over x
for x in range(xlimit):
# cutout the data at the resolution
if ch.getChannelType() not in TIMESERIES_CHANNELS:
olddata = db.cutout(ch, [
x * xscale * xcubedim, y * yscale *
ycubedim, z * zscale * zcubedim
], biggercubedim, cur_res - 1).data
else:
olddata = db.timecutout(
ch, [
x * xscale * xcubedim, y * yscale *
ycubedim, z * zscale * zcubedim
], biggercubedim, cur_res - 1,
[ts, ts + 1]).data
olddata = olddata[0, :, :, :]
#olddata target array for the new data (z,y,x) order
newdata = np.zeros([zcubedim, ycubedim, xcubedim],
dtype=OCP_dtypetonp.get(
ch.getDataType()))
for sl in range(zcubedim):
if scaling == ZSLICES:
data = olddata[sl, :, :]
elif scaling == ISOTROPIC:
data = ocplib.isotropicBuild_ctype(
olddata[sl * 2, :, :],
olddata[sl * 2 + 1, :, :])
# Convert each slice to an image
# 8-bit int option
if olddata.dtype == np.uint8:
slimage = Image.frombuffer(
'L', (xcubedim * 2, ycubedim * 2),
data.flatten(), 'raw', 'L', 0, 1)
# 16-bit int option
elif olddata.dtype == np.uint16:
slimage = Image.frombuffer(
'I;16', (xcubedim * 2, ycubedim * 2),
data.flatten(), 'raw', 'I;16', 0, 1)
# 32-bit float option
elif olddata.dtype == np.float32:
slimage = Image.frombuffer(
'F', (xcubedim * 2, ycubedim * 2),
data.flatten(), 'raw', 'F', 0, 1)
# 32 bit RGBA data
elif olddata.dtype == np.uint32:
slimage = Image.fromarray(data, "RGBA")
# KL TODO Add support for 32bit and 64bit RGBA data
# Resize the image and put in the new cube array
if olddata.dtype != np.uint32:
newdata[sl, :, :] = np.asarray(
slimage.resize([xcubedim, ycubedim]))
else:
tempdata = np.asarray(
slimage.resize([xcubedim, ycubedim]))
newdata[sl, :, :] = np.left_shift(
tempdata[:, :, 3], 24,
dtype=np.uint32) | np.left_shift(
tempdata[:, :, 2],
16,
dtype=np.uint32) | np.left_shift(
tempdata[:, :, 1],
8,
dtype=np.uint32) | np.uint32(
tempdata[:, :, 0])
zidx = ocplib.XYZMorton([x, y, z])
cube = Cube.getCube(cubedim, ch.getChannelType(),
ch.getDataType())
cube.zeros()
cube.data = newdata
if ch.getChannelType() not in TIMESERIES_CHANNELS:
db.putCube(ch,
zidx,
cur_res,
cube,
update=True)
else:
db.putTimeCube(ch,
zidx,
ts,
cur_res,
cube,
update=False)
def buildAnnoStack(proj, ch, res=None):
"""Build the hierarchy for annotations"""
with closing(ocpcadb.OCPCADB(proj)) as db:
# pick a resolution
if res is None:
res = 1
high_res = proj.datasetcfg.scalinglevels
scaling = proj.datasetcfg.scalingoption
for cur_res in range(res, high_res + 1):
# Get the source database sizes
[[ximagesz, yimagesz, zimagesz],
timerange] = proj.datasetcfg.imageSize(cur_res - 1)
[xcubedim, ycubedim,
zcubedim] = cubedim = proj.datasetcfg.getCubeDims()[cur_res - 1]
# Set the limits for iteration on the number of cubes in each dimension
xlimit = (ximagesz - 1) / xcubedim + 1
ylimit = (yimagesz - 1) / ycubedim + 1
zlimit = (zimagesz - 1) / zcubedim + 1
# Choose constants that work for all resolutions. recall that cube size changes from 128x128x16 to 64*64*64
if scaling == ZSLICES:
outdata = np.zeros([zcubedim * 4, ycubedim * 2, xcubedim * 2],
dtype=OCP_dtypetonp.get(ch.getDataType()))
elif scaling == ISOTROPIC:
outdata = np.zeros([zcubedim * 2, ycubedim * 2, xcubedim * 2],
dtype=OCP_dtypetonp.get(ch.getDataType()))
else:
logger.error(
"Invalid scaling option in project = {}".format(scaling))
raise OCPCAError(
"Invalid scaling option in project = {}".format(scaling))
# Round up to the top of the range
lastzindex = (ocplib.XYZMorton([xlimit, ylimit, zlimit]) / 64 +
1) * 64
# Iterate over the cubes in morton order
for mortonidx in range(0, lastzindex, 64):
# call the range query
cuboids = db.getCubes(ch, range(mortonidx, mortonidx + 64),
cur_res - 1)
cube = Cube.getCube(cubedim, ch.getChannelType(),
ch.getDataType())
# get the first cube
for idx, datastring in cuboids:
xyz = ocplib.MortonXYZ(idx)
cube.fromNPZ(datastring)
if scaling == ZSLICES:
# Compute the offset in the output data cube
# we are placing 4x4x4 input blocks into a 2x2x4 cube
offset = [(xyz[0] % 4) * (xcubedim / 2),
(xyz[1] % 4) * (ycubedim / 2),
(xyz[2] % 4) * zcubedim]
# add the contribution of the cube in the hierarchy
ocplib.addDataToZSliceStack_ctype(
cube, outdata, offset)
elif scaling == ISOTROPIC:
# Compute the offset in the output data cube
# we are placing 4x4x4 input blocks into a 2x2x2 cube
offset = [(xyz[0] % 4) * (xcubedim / 2),
(xyz[1] % 4) * (ycubedim / 2),
(xyz[2] % 4) * (zcubedim / 2)]
# use python version for debugging
ocplib.addDataToIsotropicStack_ctype(
cube, outdata, offset)
# Get the base location of this batch
xyzout = ocplib.MortonXYZ(mortonidx)
# adjust to output corner for scale.
if scaling == ZSLICES:
outcorner = [
xyzout[0] * xcubedim / 2, xyzout[1] * ycubedim / 2,
xyzout[2] * zcubedim
]
elif scaling == ISOTROPIC:
outcorner = [
xyzout[0] * xcubedim / 2, xyzout[1] * ycubedim / 2,
xyzout[2] * zcubedim / 2
]
# Data stored in z,y,x order dims in x,y,z
outdim = outdata.shape[::-1]
# Preserve annotations made at the specified level
# KL check that the P option preserves annotations? RB changed from O
db.annotateDense(ch, outcorner, cur_res, outdata, 'O')
db.conn.commit()
# zero the output buffer
outdata = np.zeros([zcubedim * 4, ycubedim * 2, xcubedim * 2],
dtype=OCP_dtypetonp.get(ch.getDataType()))
