def get_level_from_server(msg, retrieval_mode="volume"):
if retrieval_mode == "slice": # get a slice over http
src_annotations_dataset = DataModel.g.dataset_uri(
msg["level_id"], group="annotations"
)
params = dict(
workpace=True,
src=src_annotations_dataset,
slice_idx=cfg.current_slice,
order=cfg.order,
)
result = Launcher.g.run("annotations", "get_slice", **params)
if result:
src_arr = decode_numpy(result)
elif retrieval_mode == "volume_http": # get a slice over http
src_annotations_dataset = DataModel.g.dataset_uri(
msg["level_id"], group="annotations"
)
params = dict(workpace=True, src=src_annotations_dataset)
result = Launcher.g.run("annotations", "get_volume", **params)
if result:
src_arr = decode_numpy(result)
elif retrieval_mode == "volume": # get entire volume
src = DataModel.g.dataset_uri(msg["level_id"], group="annotations")
with DatasetManager(src, out=None, dtype="float32", fillvalue=0) as DM:
src_annotations_dataset = DM.sources[0][:]
src_arr = get_array_from_dataset(src_annotations_dataset)
return src_arr, src_annotations_dataset
def get_crop(msg):
logger.debug(f"Getting crop roi: {msg}")
features_src = DataModel.g.dataset_uri(msg["feature_id"], group="features")
params = dict(workpace=True, src=features_src, roi=(0, 0, 0, 100, 100, 100))
result = Launcher.g.run("features", "get_crop", **params)
if result:
src_arr = decode_numpy(result)
viewer.add_image(src_arr, name=msg["feature_id"])
def show_slice(self, idx):
params = dict(slice_idx=idx, workspace=True, timeit=True)
params.update(self.viz_params)
result = Launcher.g.run('render', 'render_workspace', **params)
if result:
image = decode_numpy(result)
self.viewer.update_image(image)
self.slider.accept()
def slice_updated(self, idx):
super().slice_updated(idx)
region = self.selection['region']
if region:
region = DataModel.g.dataset_uri(region)
params = dict(workpace=True, src=region, slice_idx=idx)
result = Launcher.g.run('regions', 'get_slice', **params)
if result:
region = decode_numpy(result)
self.annotator.set_region(region)
else:
self.annotator.set_region(None)
def calc_object_stats2(self):
dst = DataModel.g.dataset_uri(self.analyzer_id, group="analyzer")
src = DataModel.g.dataset_uri(self.features_source.value())
all_params = dict(src=src, dst=dst, modal=False)
all_params["workspace"] = DataModel.g.current_workspace
all_params["feature_ids"] = str(self.features_source.value()[-1])
all_params["object_id"] = str(self.objects_source.value())
logger.debug(f"Running analyzer with params {all_params}")
result = Launcher.g.run("analyzer", "object_stats", **all_params)
if result:
src_arr = decode_numpy(result)
sc = MplCanvas(self, width=5, height=4, dpi=100)
sc.axes.imshow(src_arr)
self.add_row(sc, max_height=300)
def calc_object_stats(self):
dst = DataModel.g.dataset_uri(self.analyzer_id, group="analyzer")
src = DataModel.g.dataset_uri(self.features_source.value())
all_params = dict(src=src, dst=dst, modal=False)
all_params["workspace"] = DataModel.g.current_workspace
all_params["feature_ids"] = str(self.features_source.value()[-1])
all_params["object_id"] = str(self.objects_source.value())
all_params["stat_name"] = self.stat_name_combo_box.value()
logger.debug(f"Running analyzer with params {all_params}")
result = Launcher.g.run("analyzer", "object_stats", **all_params)
(point_features, img) = result
if result:
logger.debug(f"Object stats result table {len(point_features)}")
tabledata = []
for i in range(len(point_features)):
entry = (i, point_features[i])
tabledata.append(entry)
tabledata = np.array(
tabledata,
dtype=[
("index", int),
("z", float),
],
)
src_arr = decode_numpy(img)
sc = MplCanvas(self, width=6, height=5, dpi=80)
sc.axes.imshow(src_arr)
sc.axes.axis("off")
self.add_row(sc, max_height=500)
if self.table_control is None:
self.table_control = TableWidget()
self.add_row(self.table_control.w, max_height=500)
self.table_control.set_data(tabledata)
self.collapse()
self.expand()
def jump_to_slice(msg):
cfg.supervoxels_cached = False
cfg.retrieval_mode = "slice"
cfg.current_slice = int(msg["frame"])
existing_feature_layer = [
v for v in viewer.layers if v.name == cfg.current_feature_name
]
if existing_feature_layer:
features_src = DataModel.g.dataset_uri(
cfg.current_feature_name, group="features"
)
params = dict(
workpace=True,
src=features_src,
slice_idx=cfg.current_slice,
order=cfg.order,
)
result = Launcher.g.run("features", "get_slice", **params)
if result:
src_arr = decode_numpy_slice(result)
existing_feature_layer[0].data = src_arr.copy()
existing_regions_layer = [
v for v in viewer.layers if v.name == cfg.current_regions_name
]
if existing_regions_layer:
regions_src = DataModel.g.dataset_uri(
cfg.current_regions_name, group="superregions"
)
params = dict(
workpace=True,
src=regions_src,
slice_idx=cfg.current_slice,
order=cfg.order,
)
result = Launcher.g.run("superregions", "get_slice", **params)
if result:
src_arr = decode_numpy(result)
src_arr = find_boundaries(src_arr) * 1.0
existing_regions_layer[0].data = src_arr.copy()
existing_regions_layer[0].opacity = 0.3
existing_level_layer = [
v for v in viewer.layers if v.name == cfg.current_annotation_name
]
if existing_level_layer and cfg.current_annotation_name is not None:
paint_annotations({"level_id": cfg.current_annotation_name})
existing_pipeline_layer = [
v for v in viewer.layers if v.name == cfg.current_pipeline_name
]
if existing_pipeline_layer:
print(f"loading pipeline {cfg.current_pipeline_name}")
pipeline_src = DataModel.g.dataset_uri(
cfg.current_pipeline_name, group="pipelines"
)
params = dict(
workpace=True,
src=pipeline_src,
slice_idx=cfg.current_slice,
order=cfg.order,
)
result = Launcher.g.run("features", "get_slice", **params)
if result:
src_arr = decode_numpy(result).astype(np.int32)
existing_pipeline_layer[0].data = src_arr.copy()
existing_analyzers_layer = [
v for v in viewer.layers if v.name == cfg.current_analyzers_name
]
if existing_analyzers_layer:
print(f"Jumping to analyzer slice {cfg.current_analyzers_name}")
analyzers_src = DataModel.g.dataset_uri(
cfg.current_analyzers_name, group="analyzer"
)
params = dict(
workpace=True,
src=analyzers_src,
slice_idx=cfg.current_slice,
order=cfg.order,
)
result = Launcher.g.run("features", "get_slice", **params)
if result:
src_arr = decode_numpy(result).astype(np.int32)
existing_analyzers_layer[0].data = src_arr.copy()
def paint_strokes(
msg,
drag_pts,
anno_layer,
parent_level=None,
parent_label_idx=None,
viewer_order=(0, 1, 2),
):
"""
Gather all information required from viewer and build and execute the annotation command
"""
level = msg["level_id"]
anno_layer_shape = anno_layer.data.shape
anno_layer_shape = [anno_layer_shape[i] for i in viewer_order]
if len(viewer_order) == 2:
viewer_order=(0, 1, 2)
if len(drag_pts) == 0:
return
try:
sel_label = int(cfg.label_value["idx"]) - 1
anno_layer.selected_label = sel_label
anno_layer.brush_size = int(cfg.brush_size)
if anno_layer.mode == "erase":
sel_label = 0
cfg.current_mode = "erase"
else:
cfg.current_mode = "paint"
line_x = []
line_y = []
if len(drag_pts[0]) == 2:
px, py = drag_pts[0]
z = cfg.current_slice
#print(f"Using z {z}")
else:
#logger.info(f"drag_pts[0] {drag_pts}")
pt_data = drag_pts[0]
pt_data = [pt_data[i] for i in viewer_order]
z, px, py = pt_data
#logger.debug(f"z px py {z} {px} {py}")
if len(drag_pts[0]) == 2:
# depending on the slice mode we need to handle either 2 or 3 coordinates
for x, y in drag_pts[1:]:
if x < anno_layer.data.shape[0] and y < anno_layer.data.shape[1]:
yy, xx = line(py, px, y, x)
line_x.extend(xx)
line_y.extend(yy)
py, px = y, x
anno_shape = (anno_layer.data.shape[0], anno_layer.data.shape[1])
else: # cfg.retrieval_mode == 'volume':
for zz, x, y in drag_pts[1:]:
pt_data = [zz, x, y]
pt_data = [pt_data[i] for i in viewer_order]
zz, x, y = pt_data
if x < anno_layer_shape[1] and y < anno_layer_shape[2]:
yy, xx = line(py, px, y, x)
line_x.extend(xx)
line_y.extend(yy)
py, px = y, x
anno_shape = (anno_layer_shape[1], anno_layer_shape[2])
line_x = np.array(line_x)
line_y = np.array(line_y)
if len(line_y) > 0:
all_regions = set()
# Check if we are painting using supervoxels, if not, annotate voxels
if cfg.current_supervoxels == None:
#cfg.local_sv = False
if not cfg.three_dim:
line_y, line_x = dilate_annotations(
line_x,
line_y,
anno_shape,
anno_layer.brush_size,
)
params = dict(workspace=True, level=level, label=sel_label)
yy, xx = list(line_y), list(line_x)
yy = [int(e) for e in yy]
xx = [int(e) for e in xx]
# todo: preserve existing
params.update(
slice_idx=int(z),
yy=yy,
xx=xx,
parent_level=parent_level,
parent_label_idx=parent_label_idx,
full=False,
viewer_order=viewer_order,
three_dim = cfg.three_dim,
brush_size = int(cfg.brush_size),
centre_point = (int(z), int(px), int(py))
)
result = Launcher.g.run("annotations", "annotate_voxels", **params)
# we are painting with supervoxels, so check if we have a current supervoxel cache
# if not, get the supervoxels from the server
else:
line_x, line_y = dilate_annotations(
line_x,
line_y,
anno_shape,
anno_layer.brush_size,
)
supervoxel_size = cfg.supervoxel_size * 2
bb = np.array(
[
max(0, int(z) - supervoxel_size),
max(0, min(line_x) - supervoxel_size),
max(0, min(line_y) - supervoxel_size),
int(z) + supervoxel_size,
max(line_x) + supervoxel_size,
max(line_y) + supervoxel_size,
]
)
bb = bb.tolist()
#logger.debug(f"BB: {bb}")
if cfg.supervoxels_cached == False:
if cfg.retrieval_mode == 'volume' or cfg.retrieval_mode == "volume_http":
regions_dataset = DataModel.g.dataset_uri(
cfg.current_regions_name, group="superregions"
)
with DatasetManager(
regions_dataset,
out=None,
dtype="uint32",
fillvalue=0,
) as DM:
src_dataset = DM.sources[0]
sv_arr = src_dataset[:]
else:
regions_dataset = DataModel.g.dataset_uri(
cfg.current_regions_name, group="superregions"
)
params = dict(
workpace=True,
src=regions_dataset,
slice_idx=cfg.current_slice,
order=cfg.order,
)
result = Launcher.g.run("superregions", "get_slice", **params)
if result:
sv_arr = decode_numpy(result)
#logger.debug(f"Loaded superregion array of shape {sv_arr.shape}")
cfg.supervoxels_cache = sv_arr
cfg.supervoxels_cached = True
cfg.current_regions_dataset = regions_dataset
else:
sv_arr = cfg.supervoxels_cache
if cfg.retrieval_mode != 'slice':
viewer_order_str = "".join(map(str, viewer_order))
if viewer_order_str != "012" and len(viewer_order_str) == 3:
sv_arr = np.transpose(sv_arr, viewer_order)
#logger.debug(f"After viewer_order transform {sv_arr.shape}")
for x, y in zip(line_x, line_y):
if cfg.retrieval_mode=='slice':
if (
x >= 0
and x < sv_arr.shape[0]
and y >= 0
and y < sv_arr.shape[1]
):
sv = sv_arr[x, y]
all_regions |= set([sv])
else:
if (
z >= 0
and z < sv_arr.shape[0]
and x >= 0
and x < sv_arr.shape[1]
and y >= 0
and y < sv_arr.shape[2]
):
sv = sv_arr[z, x, y]
all_regions |= set([sv])
# Commit annotation to server
params = dict(workspace=True, level=level, label=sel_label)
if cfg.remote_annotation:
params.update(
region=cfg.current_regions_dataset,
r=list(map(int, all_regions)),
modal=False,
parent_level=parent_level,
parent_label_idx=parent_label_idx,
full=False,
bb=bb,
viewer_order=viewer_order,
)
result = Launcher.g.run("annotations", "annotate_regions", **params)
else:
cfg.local_sv = True
_annotate_regions_local(
cfg.anno_data.copy(),
sv_arr,
list(map(int, all_regions)),
label=sel_label,
parent_level=parent_level,
parent_label_idx=parent_label_idx,
bb=bb,
viewer_order=viewer_order)
except Exception as e:
logger.debug(f"paint_strokes Exception: {e}")