def test_find_closest_divisor():
size = lib.find_closest_divisor((128, 128, 128), (64, 64, 64))
assert tuple(size) == (64, 64, 64)
size = lib.find_closest_divisor((240, 240, 240), (64, 64, 64))
assert tuple(size) == (60, 60, 60)
size = lib.find_closest_divisor((224, 224, 224), (64, 64, 64))
assert tuple(size) == (56, 56, 56)
size = lib.find_closest_divisor((73, 73, 73), (64, 64, 64))
assert tuple(size) == (73, 73, 73)
def create_info_file_from_build(layer_path, layer_type, resolution, encoding):
assert layer_type in ('image', 'segmentation', 'affinities')
with Storage(layer_path) as storage:
bounds, build_chunk_size = compute_build_bounding_box(storage)
data_type, num_channels = get_build_data_type_and_shape(storage)
neuroglancer_chunk_size = find_closest_divisor(build_chunk_size,
closest_to=[64, 64, 64])
info = CloudVolume.create_new_info(
num_channels=num_channels,
layer_type=layer_type,
data_type=data_type,
encoding=encoding,
resolution=resolution,
voxel_offset=bounds.minpt.tolist(),
volume_size=bounds.size3(),
mesh=(layer_type == 'segmentation'),
chunk_size=list(map(int, neuroglancer_chunk_size)),
)
vol = CloudVolume(layer_path, mip=0, info=info).commit_info()
vol = create_downsample_scales(layer_path,
mip=0,
ds_shape=build_chunk_size,
axis='z')
return vol.info
def create_volume_from_image(image, offset, layer_path, layer_type, resolution, encoding):
assert layer_type in ('image', 'segmentation', 'affinities')
offset = Vec(*offset)
volsize = Vec(*image.shape[:3])
data_type = str(image.dtype)
bounds = Bbox(offset, offset + volsize)
neuroglancer_chunk_size = find_closest_divisor(image.shape[:3], closest_to=[64,64,64])
info = CloudVolume.create_new_info(
num_channels=1, # Increase this number when we add more tests for RGB
layer_type=layer_type,
data_type=data_type,
encoding=encoding,
resolution=resolution,
voxel_offset=bounds.minpt,
volume_size=bounds.size3(),
mesh=(layer_type == 'segmentation'),
chunk_size=neuroglancer_chunk_size,
)
vol = CloudVolume(layer_path, mip=0, info=info)
vol.commit_info()
vol[:,:,:] = image
return vol
def upload_image(image, offset, layer_type, layer_name, encoding):
lpath = 'file://{}'.format(os.path.join(layer_path, layer_name))
neuroglancer_chunk_size = find_closest_divisor(image.shape[:3], closest_to=[64,64,64])
# Jpeg encoding is lossy so it won't work
vol = CloudVolume.from_numpy(
image,
vol_path=lpath,
resolution=(1,1,1),
voxel_offset=offset,
chunk_size=neuroglancer_chunk_size,
layer_type=layer_type,
encoding=encoding,
)
return vol
