def test_sr_predict_shape(self, datamodel):
DataModel = datamodel
src = DataModel.g.dataset_uri("__data__", None)
dst = DataModel.g.dataset_uri("001_gaussian_blur", group="features")
result = survos.run_command(
"features", "gaussian_blur", uri=None, src=src, dst=dst
)
with DatasetManager(src, out=dst, dtype="float32", fillvalue=0) as DM:
src_dataset = DM.sources[0]
dst_dataset = DM.out
src_arr = src_dataset[:]
gblur_arr = dst_dataset[:]
result = survos.run_command("superregions", "create", uri=None)
features_src = DataModel.g.dataset_uri("001_gaussian_blur", group="features")
dst = DataModel.g.dataset_uri("001_superregions", group="superregions")
result = supervoxels(
features_src,
dst,
n_segments=8,
compactness=0.5,
spacing=[1, 1, 1],
multichannel=False,
enforce_connectivity=False,
)
with DatasetManager(src, out=dst, dtype="float32", fillvalue=0) as DM:
src_dataset = DM.sources[0]
dst_dataset = DM.out
src_arr = src_dataset[:]
dst_arr = dst_dataset[:]
superseg_cfg = cfg.pipeline
superseg_cfg["type"] = "rf"
superseg_cfg["predict_params"]["clf"] = "Ensemble"
refine = False
lam = (1.0,)
anno_arr = np.ones_like(dst_arr)
anno_arr[2:4, 2:4, 2:4] = 2
feature_arr = view_dataset("001_gaussian_blur", "features", 3)
segmentation = sr_predict(
dst_arr,
anno_arr,
[feature_arr, gblur_arr],
None,
superseg_cfg,
refine,
lam,
)
def run_workflow(workflow_file):
if not os.path.isabs(workflow_file):
workflow_file = os.path.join(os.getcwd(), workflow_file)
with open(workflow_file) as f:
workflows = yaml.safe_load(f.read())
num_workflow_steps = len(workflows.keys())
minVal, maxVal = 0, num_workflow_steps
print(workflows)
for step_idx, k in enumerate(workflows):
workflow = workflows[k]
action = workflow.pop("action")
plugin, command = action.split(".")
params = workflow.pop("params")
src_name = workflow.pop("src")
dst_name = workflow.pop("dst")
if "src_group" in workflow:
plugin = workflow.pop("src_group")
src = DataModel.g.dataset_uri(src_name, group=plugin)
dst = DataModel.g.dataset_uri(dst_name, group=plugin)
all_params = dict(src=src, dst=dst, modal=True)
all_params.update(params)
logger.info(f"Executing workflow {all_params}")
print(
f"+ Running {k}, with {plugin}, {command} on {src}\n to dst {dst} {all_params}\n"
)
import survos
# Launcher.g.run(plugin, command, **all_params)
survos.run_command(plugin, command, uri=None, src=src, dst=dst)
# src_arr = view_dataset(dst_name, plugin, 10)
else:
print("Need input workflow YAML file")
return all_params, params
def test_feature_generation(self, datamodel):
DataModel = datamodel
src = DataModel.g.dataset_uri("__data__", None)
with DatasetManager(src, out=None, dtype="float32", fillvalue=0) as DM:
src_dataset = DM.sources[0]
raw_arr = src_dataset[:]
random_blobs = binary_blobs(length=max(raw_arr.shape), n_dim=3)
random_blobs_anno = np.zeros_like(raw_arr)
random_blobs_anno[
0 : raw_arr.shape[0], 0 : raw_arr.shape[1], 0 : raw_arr.shape[2]
] = random_blobs[
0 : raw_arr.shape[0], 0 : raw_arr.shape[1], 0 : raw_arr.shape[2]
]
result = survos.run_command(
"annotations",
"add_level",
uri=None,
workspace=DataModel.g.current_workspace,
)
assert "id" in result[0]
level_id = result[0]["id"]
label_values = np.unique(random_blobs_anno)
for v in label_values:
params = dict(
level=level_id,
idx=int(v),
name=str(v),
color="#11FF11",
workspace=DataModel.g.current_workspace,
)
label_result = survos.run_command("annotations", "add_label", **params)
dst = DataModel.g.dataset_uri(level_id, group="annotations")
with DatasetManager(dst, out=dst, dtype="uint32", fillvalue=0) as DM:
DM.out[:] = random_blobs_anno
def test_superregions_shape(self, datamodel):
DataModel = datamodel
src = DataModel.g.dataset_uri("__data__", None)
dst = DataModel.g.dataset_uri("001_gaussian_blur", group="features")
result = survos.run_command(
"features", "gaussian_blur", uri=None, src=src, dst=dst
)
result = survos.run_command(
"superregions", "create", uri=None, workspace=DataModel.g.current_workspace
)
assert len(result) == 2
assert result[0]["kind"] == "supervoxels"
features_src = DataModel.g.dataset_uri("001_gaussian_blur", group="features")
dst = DataModel.g.dataset_uri("001_superregions", group="superregions")
n_segments = 8
result = supervoxels(
features_src,
dst,
n_segments=n_segments,
compactness=1,
spacing=[1, 1, 1],
multichannel=False,
enforce_connectivity=False,
)
assert result["n_segments"] == n_segments
with DatasetManager(src, out=dst, dtype="float32", fillvalue=0) as DM:
src_dataset = DM.sources[0]
dst_dataset = DM.out
src_arr = src_dataset[:]
dst_arr = dst_dataset[:]
assert dst_arr.shape == src_arr.shape
assert len(np.unique(dst_arr)) == n_segments
def datamodel():
# make test vol
map_fullpath = os.path.join("./tmp/testvol_4x4x4b.h5")
testvol = np.array(
[
[
[0.1761602, 0.6701295, 0.13151232, 0.95726678],
[0.4795476, 0.48114134, 0.0410548, 0.29893265],
[0.49127266, 0.70298447, 0.42751211, 0.08101552],
[0.73805652, 0.83111601, 0.36852477, 0.38732476],
],
[
[0.2847222, 0.96054574, 0.25430756, 0.35403861],
[0.54439093, 0.65897414, 0.1959487, 0.90714872],
[0.84462152, 0.90754182, 0.02455657, 0.26180662],
[0.1711208, 0.40122666, 0.54562598, 0.01419861],
],
[
[0.59280376, 0.42706895, 0.86637913, 0.87831645],
[0.57991401, 0.31989204, 0.85869799, 0.6333411],
[0.21539274, 0.63780214, 0.64204493, 0.74425482],
[0.1903691, 0.81962537, 0.31774673, 0.34812628],
],
[
[0.40880077, 0.595773, 0.28856063, 0.19316746],
[0.03195766, 0.62475541, 0.50762591, 0.34700798],
[0.98913461, 0.07883111, 0.96534233, 0.57697606],
[0.71496714, 0.70764578, 0.92294417, 0.91300531],
],
]
)
# testvol = np.ones((4,4,4)).astype(np.float32) / 2.0
# print(testvol)
with h5py.File(map_fullpath, "w") as hf:
hf.create_dataset("data", data=testvol)
print(DataModel.g.CHROOT)
tmp_ws_name = "testworkspace_tmp2"
result = survos.run_command("workspace", "get", uri=None, workspace=tmp_ws_name)
if not type(result[0]) == dict:
logger.debug("Creating temp workspace")
survos.run_command("workspace", "create", uri=None, workspace=tmp_ws_name)
else:
logger.debug("tmp exists, deleting and recreating")
survos.run_command("workspace", "delete", uri=None, workspace=tmp_ws_name)
logger.debug("workspace deleted")
survos.run_command("workspace", "create", uri=None, workspace=tmp_ws_name)
logger.debug("workspace recreated")
# add data to workspace
survos.run_command(
"workspace",
"add_data",
uri=None,
workspace=tmp_ws_name,
data_fname=map_fullpath,
dtype="float32",
)
DataModel.g.current_workspace = tmp_ws_name
return DataModel