def create_downsample_scales(layer_path,
mip,
ds_shape,
axis='z',
preserve_chunk_size=False,
chunk_size=None,
encoding=None):
vol = CloudVolume(layer_path, mip)
shape = min2(vol.volume_size, ds_shape)
# sometimes we downsample a base layer of 512x512
# into underlying chunks of 64x64 which permits more scales
underlying_mip = (mip + 1) if (mip + 1) in vol.available_mips else mip
underlying_shape = vol.mip_underlying(underlying_mip).astype(np.float32)
if chunk_size:
underlying_shape = Vec(*chunk_size).astype(np.float32)
toidx = {'x': 0, 'y': 1, 'z': 2}
preserved_idx = toidx[axis]
underlying_shape[preserved_idx] = float('inf')
scales = downsample_scales.compute_plane_downsampling_scales(
size=shape,
preserve_axis=axis,
max_downsampled_size=int(min(*underlying_shape)),
)
scales = scales[1:] # omit (1,1,1)
scales = [
list(map(int, vol.downsample_ratio * Vec(*factor3)))
for factor3 in scales
]
if len(scales) == 0:
print("WARNING: No scales generated.")
for scale in scales:
vol.add_scale(scale, encoding=encoding, chunk_size=chunk_size)
if chunk_size is None:
if preserve_chunk_size or len(scales) == 0:
chunk_size = vol.scales[mip]['chunk_sizes']
else:
chunk_size = vol.scales[mip + 1]['chunk_sizes']
else:
chunk_size = [chunk_size]
if encoding is None:
encoding = vol.scales[mip]['encoding']
for i in range(mip + 1, mip + len(scales) + 1):
vol.scales[i]['chunk_sizes'] = chunk_size
return vol.commit_info()
def create_deletion_tasks(layer_path,
mip=0,
num_mips=5,
shape=None,
bounds=None):
vol = CloudVolume(layer_path, max_redirects=0)
if shape is None:
shape = vol.mip_underlying(mip)[:3]
shape.x *= 2**num_mips
shape.y *= 2**num_mips
else:
shape = Vec(*shape)
if not bounds:
bounds = vol.mip_bounds(mip).clone()
class DeleteTaskIterator(FinelyDividedTaskIterator):
def task(self, shape, offset):
bounded_shape = min2(shape, bounds.maxpt - offset)
return partial(
DeleteTask,
layer_path=layer_path,
shape=bounded_shape.clone(),
offset=offset.clone(),
mip=mip,
num_mips=num_mips,
)
def on_finish(self):
vol = CloudVolume(layer_path, max_redirects=0)
vol.provenance.processing.append({
'method': {
'task': 'DeleteTask',
'mip': mip,
'num_mips': num_mips,
'shape': shape.tolist(),
},
'by':
operator_contact(),
'date':
strftime('%Y-%m-%d %H:%M %Z'),
})
vol.commit_provenance()
return DeleteTaskIterator(bounds, shape)
def create_deletion_tasks(layer_path, mip=0, num_mips=5):
vol = CloudVolume(layer_path)
shape = vol.mip_underlying(mip)[:3]
shape.x *= 2**num_mips
shape.y *= 2**num_mips
class DeleteTaskIterator():
def __len__(self):
return reduce(operator.mul, np.ceil(vol.bounds.size3() / shape))
def __iter__(self):
for startpt in xyzrange(vol.bounds.minpt, vol.bounds.maxpt, shape):
bounded_shape = min2(shape, vol.bounds.maxpt - startpt)
yield DeleteTask(
layer_path=layer_path,
shape=bounded_shape.clone(),
offset=startpt.clone(),
mip=mip,
num_mips=num_mips,
)
vol = CloudVolume(layer_path)
vol.provenance.processing.append({
'method': {
'task': 'DeleteTask',
'mip': mip,
'num_mips': num_mips,
'shape': shape.tolist(),
},
'by':
OPERATOR_CONTACT,
'date':
strftime('%Y-%m-%d %H:%M %Z'),
})
vol.commit_provenance()
return DeleteTaskIterator()
def create_downsample_scales(layer_path,
mip,
ds_shape,
axis='z',
preserve_chunk_size=False):
vol = CloudVolume(layer_path, mip)
shape = min2(vol.volume_size, ds_shape)
# sometimes we downsample a base layer of 512x512
# into underlying chunks of 64x64 which permits more scales
underlying_mip = (mip + 1) if (mip + 1) in vol.available_mips else mip
underlying_shape = vol.mip_underlying(underlying_mip).astype(np.float32)
toidx = {'x': 0, 'y': 1, 'z': 2}
preserved_idx = toidx[axis]
underlying_shape[preserved_idx] = float('inf')
scales = downsample_scales.compute_plane_downsampling_scales(
size=shape,
preserve_axis=axis,
max_downsampled_size=int(min(*underlying_shape)),
)
scales = scales[1:] # omit (1,1,1)
scales = [
list(map(int, vol.downsample_ratio * Vec(*factor3)))
for factor3 in scales
]
for scale in scales:
vol.add_scale(scale)
if preserve_chunk_size:
for i in range(1, len(vol.scales)):
vol.scales[i]['chunk_sizes'] = vol.scales[0]['chunk_sizes']
return vol.commit_info()
