def downsample_and_upload(image,
bounds,
vol,
ds_shape,
mip=0,
axis='z',
skip_first=False,
sparse=False,
factor=None):
ds_shape = min2(vol.volume_size, ds_shape[:3])
underlying_mip = (mip + 1) if (mip + 1) in vol.available_mips else mip
chunk_size = vol.meta.chunk_size(underlying_mip).astype(np.float32)
if factor is None:
factor = downsample_scales.axis_to_factor(axis)
factors = downsample_scales.compute_factors(ds_shape, factor, chunk_size)
if len(factors) == 0:
print(
"No factors generated. Image Shape: {}, Downsample Shape: {}, Volume Shape: {}, Bounds: {}"
.format(image.shape, ds_shape, vol.volume_size, bounds))
vol.mip = mip
if not skip_first:
vol[bounds.to_slices()] = image
if len(factors) == 0:
return
num_mips = len(factors)
mips = []
if vol.layer_type == 'image':
mips = tinybrain.downsample_with_averaging(image,
factors[0],
num_mips=num_mips)
elif vol.layer_type == 'segmentation':
mips = tinybrain.downsample_segmentation(image,
factors[0],
num_mips=num_mips,
sparse=sparse)
else:
mips = tinybrain.downsample_with_striding(image,
factors[0],
num_mips=num_mips)
new_bounds = bounds.clone()
for factor3 in factors:
vol.mip += 1
new_bounds //= factor3
mipped = mips.pop(0)
new_bounds.maxpt = new_bounds.minpt + Vec(*mipped.shape[:3])
vol[new_bounds] = mipped
def ds_shape(mip, chunk_size=None, factor=None):
if chunk_size:
shape = Vec(*chunk_size)
else:
shape = vol.meta.chunk_size(mip)[:3]
if factor is None:
factor = downsample_scales.axis_to_factor(axis)
shape.x *= factor[0]**num_mips
shape.y *= factor[1]**num_mips
shape.z *= factor[2]**num_mips
return shape