def test_luminance_levels_task():
directory = '/tmp/removeme/luminance_levels/'
layer_path = 'file://' + directory
delete_layer(layer_path)
storage, imgd = create_layer(size=(256, 256, 128, 1),
offset=(0, 0, 0),
layer_type="image",
layer_name='luminance_levels')
tq = MockTaskQueue()
tasks = tc.create_luminance_levels_tasks(layer_path=layer_path,
coverage_factor=0.01,
shape=None,
offset=(0, 0, 0),
mip=0)
tq.insert_all(tasks)
gt = [0] * 256
for x, y, z in lib.xyzrange((0, 0, 0), list(imgd.shape[:2]) + [1]):
gt[imgd[x, y, 0, 0]] += 1
with open('/tmp/removeme/luminance_levels/levels/0/0', 'rt') as f:
levels = f.read()
levels = json.loads(levels)
assert levels['coverage_ratio'] == 1.0
assert levels['levels'] == gt
def test_downsample_w_missing():
delete_layer()
storage, data = create_layer(size=(512,512,128,1), offset=(3,7,11))
cv = CloudVolume(storage.layer_path)
assert len(cv.scales) == 4
assert len(cv.available_mips) == 4
delete_layer()
cv.commit_info()
try:
create_downsampling_tasks(MockTaskQueue(), storage.layer_path, mip=0, num_mips=3, fill_missing=False)
except EmptyVolumeException:
pass
create_downsampling_tasks(MockTaskQueue(), storage.layer_path, mip=0, num_mips=3, fill_missing=True)
cv.refresh_info()
assert len(cv.available_mips) == 4
assert np.array_equal(cv.mip_volume_size(0), [ 512, 512, 128 ])
assert np.array_equal(cv.mip_volume_size(1), [ 256, 256, 128 ])
assert np.array_equal(cv.mip_volume_size(2), [ 128, 128, 128 ])
assert np.array_equal(cv.mip_volume_size(3), [ 64, 64, 128 ])
assert np.all(cv.mip_voxel_offset(3) == (0,0,11))
cv.mip = 0
cv.fill_missing = True
assert np.count_nonzero(cv[3:67, 7:71, 11:75]) == 0
def downsample_dataset(dataset_name, from_mip=-1, num_mips=1, local=False,
n_download_workers=1, n_threads=32):
if dataset_name == "pinky":
ws_path = "gs://neuroglancer/svenmd/pinky40_v11/watershed/"
elif dataset_name == "basil":
ws_path = "gs://neuroglancer/svenmd/basil_4k_oldnet_cg/watershed/"
elif dataset_name == "pinky100":
ws_path = "gs://neuroglancer/nkem/pinky100_v0/ws/lost_no-random/bbox1_0/"
else:
raise Exception("Dataset unknown")
if local:
if n_threads == 1:
with MockTaskQueue() as task_queue:
tc.create_downsampling_tasks(task_queue, ws_path, mip=from_mip,
fill_missing=True, num_mips=num_mips,
n_download_workers=n_download_workers,
preserve_chunk_size=True)
else:
with LocalTaskQueue(parallel=n_threads) as task_queue:
tc.create_downsampling_tasks(task_queue, ws_path, mip=from_mip,
fill_missing=True, num_mips=num_mips,
n_download_workers=n_download_workers,
preserve_chunk_size=True)
else:
with TaskQueue(queue_server='sqs',
qurl="https://sqs.us-east-1.amazonaws.com/098703261575/nkem-igneous") as task_queue:
tc.create_downsampling_tasks(task_queue, ws_path, mip=from_mip,
fill_missing=True, num_mips=num_mips,
n_download_workers=n_download_workers,
preserve_chunk_size=True)
def test_downsample_with_offset():
delete_layer()
storage, data = create_layer(size=(512,512,128,1), offset=(3,7,11))
cv = CloudVolume(storage.layer_path)
assert len(cv.scales) == 4
assert len(cv.available_mips) == 4
cv.commit_info()
create_downsampling_tasks(MockTaskQueue(), storage.layer_path, mip=0, num_mips=3)
cv.refresh_info()
assert len(cv.available_mips) == 4
assert np.array_equal(cv.mip_volume_size(0), [ 512, 512, 128 ])
assert np.array_equal(cv.mip_volume_size(1), [ 256, 256, 128 ])
assert np.array_equal(cv.mip_volume_size(2), [ 128, 128, 128 ])
assert np.array_equal(cv.mip_volume_size(3), [ 64, 64, 128 ])
assert np.all(cv.mip_voxel_offset(3) == (0,0,11))
cv.mip = 0
assert np.all(cv[3:67, 7:71, 11:75] == data[0:64, 0:64, 0:64])
data_ds1 = downsample.downsample_with_averaging(data, factor=[2, 2, 1, 1])
cv.mip = 1
assert np.all(cv[1:33, 3:35, 11:75] == data_ds1[0:32, 0:32, 0:64])
data_ds2 = downsample.downsample_with_averaging(data_ds1, factor=[2, 2, 1, 1])
cv.mip = 2
assert np.all(cv[0:16, 1:17, 11:75] == data_ds2[0:16, 0:16, 0:64])
data_ds3 = downsample.downsample_with_averaging(data_ds2, factor=[2, 2, 1, 1])
cv.mip = 3
assert np.all(cv[0:8, 0:8, 11:75] == data_ds3[0:8,0:8,0:64])
def test_downsample_no_offset(compression_method):
delete_layer()
storage, data = create_layer(size=(1024, 1024, 128, 1), offset=(0, 0, 0))
cv = CloudVolume(storage.layer_path)
assert len(cv.scales) == 1
assert len(cv.available_mips) == 1
cv.commit_info()
tq = MockTaskQueue()
tasks = create_downsampling_tasks(storage.layer_path,
mip=0,
num_mips=4,
compress=compression_method)
tq.insert_all(tasks)
cv.refresh_info()
assert len(cv.available_mips) == 5
assert np.array_equal(cv.mip_volume_size(0), [1024, 1024, 128])
assert np.array_equal(cv.mip_volume_size(1), [512, 512, 128])
assert np.array_equal(cv.mip_volume_size(2), [256, 256, 128])
assert np.array_equal(cv.mip_volume_size(3), [128, 128, 128])
assert np.array_equal(cv.mip_volume_size(4), [64, 64, 128])
slice64 = np.s_[0:64, 0:64, 0:64]
cv.mip = 0
assert np.all(cv[slice64] == data[slice64])
data_ds1, = tinybrain.downsample_with_averaging(data, factor=[2, 2, 1, 1])
cv.mip = 1
assert np.all(cv[slice64] == data_ds1[slice64])
data_ds2, = tinybrain.downsample_with_averaging(data, factor=[4, 4, 1, 1])
cv.mip = 2
assert np.all(cv[slice64] == data_ds2[slice64])
data_ds3, = tinybrain.downsample_with_averaging(data, factor=[8, 8, 1, 1])
cv.mip = 3
assert np.all(cv[slice64] == data_ds3[slice64])
data_ds4, = tinybrain.downsample_with_averaging(data,
factor=[16, 16, 1, 1])
cv.mip = 4
assert np.all(cv[slice64] == data_ds4[slice64])
def test_downsample_higher_mip():
delete_layer()
storage, data = create_layer(size=(512,512,64,1), offset=(3,7,11))
cv = CloudVolume(storage.layer_path)
cv.info['scales'] = cv.info['scales'][:1]
cv.commit_info()
create_downsampling_tasks(MockTaskQueue(), storage.layer_path, mip=0, num_mips=2)
cv.refresh_info()
assert len(cv.available_mips) == 3
create_downsampling_tasks(MockTaskQueue(), storage.layer_path, mip=1, num_mips=2)
cv.refresh_info()
assert len(cv.available_mips) == 4
cv.mip = 3
assert cv[:,:,:].shape == (64,64,64,1)
def test_skeletonization_task():
directory = '/tmp/removeme/skeleton/'
layer_path = 'file://' + directory
delete_layer(layer_path)
img = np.ones((256,256,256), dtype=np.uint64)
img[:,:,:] = 2
cv = CloudVolume.from_numpy(
img,
layer_type='segmentation',
vol_path=layer_path,
)
tq = MockTaskQueue()
tasks = tc.create_skeletonizing_tasks(layer_path, mip=0, teasar_params={
'scale': 10,
'const': 10,
})
tq.insert_all(tasks)
def test_downsample_no_offset_2x2x2():
delete_layer()
cf, data = create_layer(size=(512,512,512,1), offset=(0,0,0))
cv = CloudVolume(cf.cloudpath)
assert len(cv.scales) == 1
assert len(cv.available_mips) == 1
cv.commit_info()
tq = MockTaskQueue()
tasks = create_downsampling_tasks(
cf.cloudpath, mip=0, num_mips=3,
compress=None, factor=(2,2,2)
)
tq.insert_all(tasks)
cv.refresh_info()
assert len(cv.available_mips) == 4
assert np.array_equal(cv.mip_volume_size(0), [ 512, 512, 512 ])
assert np.array_equal(cv.mip_volume_size(1), [ 256, 256, 256 ])
assert np.array_equal(cv.mip_volume_size(2), [ 128, 128, 128 ])
assert np.array_equal(cv.mip_volume_size(3), [ 64, 64, 64 ])
slice64 = np.s_[0:64, 0:64, 0:64]
cv.mip = 0
assert np.all(cv[slice64] == data[slice64])
data_ds1, = tinybrain.downsample_with_averaging(data, factor=[2, 2, 2, 1])
cv.mip = 1
assert np.all(cv[slice64] == data_ds1[slice64])
data_ds2, = tinybrain.downsample_with_averaging(data, factor=[4, 4, 4, 1])
cv.mip = 2
assert np.all(cv[slice64] == data_ds2[slice64])
data_ds3, = tinybrain.downsample_with_averaging(data, factor=[8, 8, 8, 1])
cv.mip = 3
assert np.all(cv[slice64] == data_ds3[slice64])
def mesh(opt):
gs_path = opt.gs_output
# Mesh
if opt.mesh:
assert opt.vol_type == 'segmentation'
# Create mesh
with LocalTaskQueue(parallel=opt.parallel) as tq:
tasks = tc.create_meshing_tasks(gs_path, mip=opt.mesh_mip)
tq.insert_all(tasks)
# Manifest
with MockTaskQueue() as tq:
tasks = tc.create_mesh_manifest_tasks(gs_path)
tq.insert_all(tasks)
def test_downsample_no_offset():
delete_layer()
storage, data = create_layer(size=(1024,1024,128,1), offset=(0,0,0))
cv = CloudVolume(storage.layer_path)
assert len(cv.scales) == 5
assert len(cv.available_mips) == 5
cv.commit_info()
create_downsampling_tasks(MockTaskQueue(), storage.layer_path, mip=0, num_mips=4)
cv.refresh_info()
assert len(cv.available_mips) == 5
assert np.array_equal(cv.mip_volume_size(0), [ 1024, 1024, 128 ])
assert np.array_equal(cv.mip_volume_size(1), [ 512, 512, 128 ])
assert np.array_equal(cv.mip_volume_size(2), [ 256, 256, 128 ])
assert np.array_equal(cv.mip_volume_size(3), [ 128, 128, 128 ])
assert np.array_equal(cv.mip_volume_size(4), [ 64, 64, 128 ])
slice64 = np.s_[0:64, 0:64, 0:64]
cv.mip = 0
assert np.all(cv[slice64] == data[slice64])
data_ds1 = downsample.downsample_with_averaging(data, factor=[2, 2, 1, 1])
cv.mip = 1
assert np.all(cv[slice64] == data_ds1[slice64])
data_ds2 = downsample.downsample_with_averaging(data_ds1, factor=[2, 2, 1, 1])
cv.mip = 2
assert np.all(cv[slice64] == data_ds2[slice64])
data_ds3 = downsample.downsample_with_averaging(data_ds2, factor=[2, 2, 1, 1])
cv.mip = 3
assert np.all(cv[slice64] == data_ds3[slice64])
data_ds4 = downsample.downsample_with_averaging(data_ds3, factor=[2, 2, 1, 1])
cv.mip = 4
assert np.all(cv[slice64] == data_ds4[slice64])
def test_downsample_higher_mip():
delete_layer()
cf, data = create_layer(size=(512,512,64,1), offset=(3,7,11))
cv = CloudVolume(cf.cloudpath)
cv.info['scales'] = cv.info['scales'][:1]
tq = MockTaskQueue()
cv.commit_info()
tasks = create_downsampling_tasks(cf.cloudpath, mip=0, num_mips=2)
tq.insert_all(tasks)
cv.refresh_info()
assert len(cv.available_mips) == 3
tasks = create_downsampling_tasks(cf.cloudpath, mip=1, num_mips=2)
tq.insert_all(tasks)
cv.refresh_info()
assert len(cv.available_mips) == 4
cv.mip = 3
assert cv[:,:,:].shape == (64,64,64,1)
def test_contrast_normalization_task():
directory = '/tmp/removeme/contrast_normalization/'
src_path = 'file://' + directory
dest_path = src_path[:-1] + '2'
delete_layer(src_path)
delete_layer(dest_path)
cf, imgd = create_layer(
size=(300,300,129,1), offset=(0,0,0),
layer_type="image", layer_name='contrast_normalization'
)
tq = MockTaskQueue()
tasks = tc.create_luminance_levels_tasks(
layer_path=src_path,
coverage_factor=0.01,
shape=None,
offset=(0,0,0),
mip=0
)
tq.insert_all(tasks)
tasks = tc.create_contrast_normalization_tasks(
src_path=src_path,
dest_path=dest_path,
levels_path=None,
shape=None,
mip=0,
clip_fraction=0.01,
fill_missing=False,
translate=(0,0,0),
minval=None,
maxval=None,
bounds=None,
bounds_mip=0,
)
tq.insert_all(tasks)
volume,
offset=[0, 0, 0],
build_chunk_size=[1024, 1024, 128]):
offset = Vec(*offset)
shape = Vec(*volume.shape[:3])
build_chunk_size = Vec(*build_chunk_size)
for spt in xyzrange((0, 0, 0), shape, build_chunk_size):
ept = min2(spt + build_chunk_size, shape)
bbox = Bbox(spt, ept)
chunk = volume[bbox.to_slices()]
bbox += offset
filename = 'build/{}'.format(bbox.to_filename())
storage.put_file(filename, chunks.encode_npz(chunk))
storage.wait()
def cascade(tq, fnlist):
for fn in fnlist:
fn(tq)
N = tq.enqueued
while N > 0:
N = tq.enqueued
print('\r {} remaining'.format(N), end='')
time.sleep(2)
if __name__ == '__main__':
with MockTaskQueue() as task_queue:
pass
def tq():
return MockTaskQueue()