def get_roi_mask(self, cell_specimen_ids=None):
''' Returns an array of all the ROI masks
Parameters
----------
cell specimen IDs: list or array (optional)
List of cell IDs to return traces for. If this is None (default)
then all are returned
Returns
-------
List of ROI_Mask objects
'''
all_cell_specimen_ids = self.get_cell_specimen_ids()
with h5py.File(self.nwb_file, 'r') as f:
mask_loc = f['processing'][self.PIPELINE_DATASET]['ImageSegmentation']['imaging_plane_1']
roi_list = f['processing'][self.PIPELINE_DATASET]['ImageSegmentation']['imaging_plane_1']['roi_list'].value
inds = None
if cell_specimen_ids is None:
inds = range(len(all_cell_specimen_ids))
else:
inds = [ list(all_cell_specimen_ids).index(i) for i in cell_specimen_ids ]
roi_array = []
for i in inds:
v = roi_list[i]
m = roi.create_roi_mask(self.MOVIE_FOV_PX[0], self.MOVIE_FOV_PX[1], [0,0,0,0], pix_list=mask_loc[v]["pix_mask"].value, label=v)
roi_array.append(m)
return roi_array
def create_mask_objects(masks, motion_border, roi_ids, union_threshold=0.7):
"""
create_mask_objects(masks, motion_border, roi_ids)
Returns mask objects, labeled for overlapping the motion border, as well
as for labels, duplicates and being empty.
Required args:
- masks (3D array) : ROI masks, structured as ROI x height x width
- motion border (list): motion border values for [x0, x1, y1, y0]
(right, left, down, up shifts)
- roi_ids (list) : ID for each ROI
Returns:
- all_mask_objs (list) : list of ROI Mask objects, with exclusion labels
(allensdk roi_masks.py)
"""
all_mask_objs = []
hei, wid = masks.shape[1:]
for _, (mask, roi_id) in enumerate(zip(masks, roi_ids)):
all_mask_objs.append(roi_masks.create_roi_mask(
wid, hei, motion_border, roi_mask=mask, label=str(roi_id)))
# add empty list of labels
all_mask_objs[-1].labels = []
all_mask_objs = label_unions_and_duplicates(all_mask_objs, masks,
union_threshold=0.7)
return all_mask_objs
def get_roi_mask(self, cell_specimen_ids=None):
''' Returns an array of all the ROI masks
Parameters
----------
cell specimen IDs: list or array (optional)
List of cell IDs to return traces for. If this is None (default)
then all are returned
Returns
-------
List of ROI_Mask objects
'''
with h5py.File(self.nwb_file, 'r') as f:
mask_loc = f['processing'][self.PIPELINE_DATASET][
'ImageSegmentation']['imaging_plane_1']
roi_list = f['processing'][self.PIPELINE_DATASET][
'ImageSegmentation']['imaging_plane_1']['roi_list'].value
inds = None
if cell_specimen_ids is None:
inds = range(self.number_of_cells)
else:
inds = self.get_cell_specimen_indices(cell_specimen_ids)
roi_array = []
for i in inds:
v = roi_list[i]
roi_mask = mask_loc[v]["img_mask"].value
m = roi.create_roi_mask(roi_mask.shape[1], roi_mask.shape[0],
[0, 0, 0, 0], roi_mask=roi_mask, label=v)
roi_array.append(m)
return roi_array
def test_create_neuropil_mask():
image_width = 100
image_height = 80
# border = [image_width-1, 0, image_height-1, 0]
border = [5, 5, 5, 5]
roi_mask = np.zeros((image_height, image_width), dtype=np.uint8)
roi_mask[40:45, 30:35] = 1
combined_binary_mask = np.zeros((image_height, image_width),
dtype=np.uint8)
combined_binary_mask[:, 45:] = 1
roi = roi_masks.create_roi_mask(image_w=image_width,
image_h=image_height,
border=border,
roi_mask=roi_mask)
obtained = roi_masks.create_neuropil_mask(roi, border,
combined_binary_mask)
expected_mask = np.zeros((58 - 27, 45 - 17), dtype=np.uint8)
expected_mask[:, :] = 1
assert np.allclose(expected_mask, obtained.mask)
assert obtained.x == 17
assert obtained.y == 27
assert obtained.width == 28
assert obtained.height == 31
def get_roi_mask(self, cell_specimen_ids=None):
''' Returns an array of all the ROI masks
Parameters
----------
cell specimen IDs: list or array (optional)
List of cell IDs to return traces for. If this is None (default)
then all are returned
Returns
-------
List of ROI_Mask objects
'''
with h5py.File(self.nwb_file, 'r') as f:
mask_loc = f['processing'][self.PIPELINE_DATASET][
'ImageSegmentation']['imaging_plane_1']
roi_list = f['processing'][self.PIPELINE_DATASET][
'ImageSegmentation']['imaging_plane_1']['roi_list'].value
inds = None
if cell_specimen_ids is None:
inds = range(self.number_of_cells)
else:
inds = self.get_cell_specimen_indices(cell_specimen_ids)
roi_array = []
for i in inds:
v = roi_list[i]
roi_mask = mask_loc[v]["img_mask"].value
m = roi.create_roi_mask(roi_mask.shape[1], roi_mask.shape[0],
[0, 0, 0, 0], roi_mask=roi_mask, label=v)
roi_array.append(m)
return roi_array
def test_init_by_pixels_large():
a = np.random.random((512, 512))
a[a > 0.5] = 1
m = roi_masks.create_roi_mask(512, 512, [0, 0, 0, 0], pix_list=np.argwhere(a))
npx = len(np.where(a)[0])
assert npx == len(np.where(m.get_mask_plane())[0])
def test_init_by_pixels_large():
a = np.random.random((512, 512))
a[a > 0.5] = 1
m = roi_masks.create_roi_mask(
512, 512, [0, 0, 0, 0], pix_list=np.argwhere(a))
npx = len(np.where(a)[0])
assert npx == len(np.where(m.get_mask_plane())[0])
def test_init_by_pixels_with_border():
a = np.array([[1, 1], [2, 1]])
m = roi_masks.create_roi_mask(3, 3, [1, 1, 1, 1], pix_list=a)
assert m.x == 1
assert m.width == 2
assert m.y == 1
assert m.height == 1
assert m.valid is False
def test_init_by_pixels_with_border():
a = np.array([[1, 1], [2, 1]])
m = roi_masks.create_roi_mask(3, 3, [1, 1, 1, 1], pix_list=a)
assert m.x == 1
assert m.width == 2
assert m.y == 1
assert m.height == 1
assert m.valid is False
def test_init_by_pixels_with_border():
a = np.array([[1, 1], [2, 1]])
m = roi_masks.create_roi_mask(3, 3, [1, 1, 1, 1], pix_list=a)
assert m.x == 1
assert m.width == 2
assert m.y == 1
assert m.height == 1
assert m.overlaps_motion_border is True
def get_roi_mask(self, cell_specimen_ids=None):
segmentations_df = self.nwb_session.modules[self.BRAIN_OBSERVATORY_PIPELINE]['ImageSegmentation']['PlaneSegmentation'].to_dataframe()
if cell_specimen_ids is not None:
segmentations_df = segmentations_df.loc[cell_specimen_ids]
roi_array = []
for roi_id, roi_seg_df in segmentations_df.iterrows():
roi_mask = roi_seg_df['image_mask']
m = roi.create_roi_mask(roi_mask.shape[1], roi_mask.shape[0], [0, 0, 0, 0], roi_mask=roi_mask, label=roi_id)
roi_array.append(m)
return roi_array
def get_cell_specimen_table(self):
cell_specimen_table = pd.DataFrame.from_dict(self.get_raw_cell_specimen_table_dict()).set_index('cell_roi_id').sort_index()
fov_width, fov_height = self.get_field_of_view_shape()['width'], self.get_field_of_view_shape()['height']
image_mask_list = []
for sub_mask in cell_specimen_table['image_mask'].values:
curr_roi = roi.create_roi_mask(fov_width, fov_height, [(fov_width - 1), 0, (fov_height - 1), 0], roi_mask=np.array(sub_mask, dtype=np.bool))
image_mask_list.append(curr_roi.get_mask_plane().astype(np.bool))
cell_specimen_table['image_mask'] = image_mask_list
cell_specimen_table = cell_specimen_table[sorted(cell_specimen_table.columns)]
cell_specimen_table.index.rename('cell_roi_id', inplace=True)
cell_specimen_table.reset_index(inplace=True)
cell_specimen_table.set_index('cell_specimen_id', inplace=True)
return cell_specimen_table
def roi_mask_list(image_dims, motion_border):
base_pixels = np.argwhere(np.ones((10, 10)))
masks = []
for ii in range(10):
pixels = base_pixels + ii * 10
masks.append(
roi_masks.create_roi_mask(image_dims['width'],
image_dims['height'],
motion_border,
pix_list=pixels,
label=str(ii),
mask_group=-1))
return masks
def test_init_by_pixels():
a = np.array([[0, 0], [1, 1], [1, 0]])
m = roi_masks.create_roi_mask(2, 2, [0, 0, 0, 0], pix_list=a)
mp = m.get_mask_plane()
assert mp[0, 0] == 1
assert mp[1, 1] == 1
assert mp[1, 0] == 0
assert mp[1, 1] == 1
assert m.x == 0
assert m.width == 2
assert m.y == 0
assert m.height == 2
def test_init_by_pixels():
a = np.array([[0, 0], [1, 1], [1, 0]])
m = roi_masks.create_roi_mask(2, 2, [0, 0, 0, 0], pix_list=a)
mp = m.get_mask_plane()
assert mp[0, 0] == 1
assert mp[1, 1] == 1
assert mp[1, 0] == 0
assert mp[1, 1] == 1
assert m.x == 0
assert m.width == 2
assert m.y == 0
assert m.height == 2
def get_roi_mask(self, cell_specimen_ids=None):
''' Returns an array of all the ROI masks
Parameters
----------
cell specimen IDs: list or array (optional)
List of cell IDs to return traces for. If this is None (default)
then all are returned
Returns
-------
List of ROI_Mask objects
'''
all_cell_specimen_ids = self.get_cell_specimen_ids()
with h5py.File(self.nwb_file, 'r') as f:
mask_loc = f['processing'][
self.PIPELINE_DATASET]['ImageSegmentation']['imaging_plane_1']
roi_list = f['processing'][self.PIPELINE_DATASET][
'ImageSegmentation']['imaging_plane_1']['roi_list'].value
inds = None
if cell_specimen_ids is None:
inds = range(len(all_cell_specimen_ids))
else:
inds = [
list(all_cell_specimen_ids).index(i)
for i in cell_specimen_ids
]
roi_array = []
for i in inds:
v = roi_list[i]
m = roi.create_roi_mask(self.MOVIE_FOV_PX[0],
self.MOVIE_FOV_PX[1], [0, 0, 0, 0],
pix_list=mask_loc[v]["pix_mask"].value,
label=v)
roi_array.append(m)
return roi_array
def get_rois(segmentation_stack, border=None):
'''Extract a list of rois from the segmentation data array.
Parameters
----------
segmentation_stack : numpy.ndarray
The array from the maxInt_masks file showing the object masks.
border : list
[right_shift, left_shift, down_shift, up_shift] bounding box
determined from motion correction.
Returns
-------
list
List of RoiMask objects.
'''
rois = []
if border is None:
border = [0, 0, 0, 0]
height = segmentation_stack.shape[1]
width = segmentation_stack.shape[2]
for i in range(segmentation_stack.shape[0]):
page = segmentation_stack[i, :, :]
label_mask, num_labels = measurements.label(page,
structure=[[1, 1, 1],
[1, 1, 1],
[1, 1, 1]])
for label in range(1, num_labels + 1):
img_mask = label_mask == label
mask = create_roi_mask(width,
height,
border,
roi_mask=img_mask,
label="ROI {}:{}".format(i, label),
mask_group=i)
mask.labels = []
if mask.overlaps_motion_border:
mask.labels.append("motion_border")
rois.append(mask)
return rois
def create_roi_masks(rois, w, h, motion_border):
roi_list = []
for roi in rois:
mask = np.array(roi["mask"], dtype=bool)
px = np.argwhere(mask)
px[:, 0] += roi["y"]
px[:, 1] += roi["x"]
mask = roi_masks.create_roi_mask(w,
h,
motion_border,
pix_list=px[:, [1, 0]],
label=str(roi["id"]),
mask_group=roi.get("mask_page", -1))
roi_list.append(mask)
# sort by roi id
roi_list.sort(key=lambda x: x.label)
return roi_list
def test_create_empty_neuropil_mask():
image_width = 100
image_height = 80
# border = [image_width-1, 0, image_height-1, 0]
border = [5, 5, 5, 5]
roi_mask = np.zeros((image_height, image_width), dtype=np.uint8)
roi_mask[40:45, 30:35] = 1
combined_binary_mask = np.zeros((image_height, image_width),
dtype=np.uint8)
combined_binary_mask[:, :] = 1
roi = roi_masks.create_roi_mask(image_w=image_width,
image_h=image_height,
border=border,
roi_mask=roi_mask)
obtained = roi_masks.create_neuropil_mask(roi, border,
combined_binary_mask)
assert obtained.mask is None
assert 'zero_pixels' in obtained.flags
def add_cell_specimen_table(nwbfile: NWBFile,
cell_specimen_table: pd.DataFrame):
"""
This function takes the cell specimen table and writes the ROIs
contained within. It writes these to a new NWB imaging plane
based off the previously supplied metadata
Parameters
----------
nwbfile: NWBFile
this is the in memory NWBFile currently being written to which ROI data
is added
cell_specimen_table: pd.DataFrame
this is the DataFrame containing the cells segmented from a ophys
experiment, stored in json file and loaded.
example: /home/nicholasc/projects/allensdk/allensdk/test/
brain_observatory/behavior/cell_specimen_table_789359614.json
Returns
-------
nwbfile: NWBFile
The altered in memory NWBFile object that now has a specimen table
"""
cell_roi_table = cell_specimen_table.reset_index().set_index('cell_roi_id')
# Device:
device_name = nwbfile.lab_meta_data['metadata'].rig_name
nwbfile.create_device(device_name,
"Allen Brain Observatory")
device = nwbfile.get_device(device_name)
# Location:
location_description = "Area: {}, Depth: {} um".format(
nwbfile.lab_meta_data['metadata'].targeted_structure,
nwbfile.lab_meta_data['metadata'].imaging_depth)
# FOV:
fov_width = nwbfile.lab_meta_data['metadata'].field_of_view_width
fov_height = nwbfile.lab_meta_data['metadata'].field_of_view_height
imaging_plane_description = "{} field of view in {} at depth {} um".format(
(fov_width, fov_height),
nwbfile.lab_meta_data['metadata'].targeted_structure,
nwbfile.lab_meta_data['metadata'].imaging_depth)
# Optical Channel:
optical_channel = OpticalChannel(
name='channel_1',
description='2P Optical Channel',
emission_lambda=nwbfile.lab_meta_data['metadata'].emission_lambda)
# Imaging Plane:
imaging_plane = nwbfile.create_imaging_plane(
name='imaging_plane_1',
optical_channel=optical_channel,
description=imaging_plane_description,
device=device,
excitation_lambda=nwbfile.lab_meta_data['metadata'].excitation_lambda,
imaging_rate=nwbfile.lab_meta_data['metadata'].ophys_frame_rate,
indicator=nwbfile.lab_meta_data['metadata'].indicator,
location=location_description,
manifold=[], # Should this be passed in for future support?
conversion=1.0,
unit='unknown', # Should this be passed in for future support?
reference_frame='unknown') # Should this be passed in for future support?
# Image Segmentation:
image_segmentation = ImageSegmentation(name="image_segmentation")
if 'two_photon_imaging' not in nwbfile.modules:
two_photon_imaging_module = ProcessingModule('two_photon_imaging', '2P processing module')
nwbfile.add_processing_module(two_photon_imaging_module)
else:
two_photon_imaging_module = nwbfile.modules['two_photon_imaging']
two_photon_imaging_module.add_data_interface(image_segmentation)
# Plane Segmentation:
plane_segmentation = image_segmentation.create_plane_segmentation(
name='cell_specimen_table',
description="Segmented rois",
imaging_plane=imaging_plane)
for col_name in cell_roi_table.columns:
# the columns 'image_mask', 'pixel_mask', and 'voxel_mask' are already defined
# in the nwb.ophys::PlaneSegmentation Object
if col_name not in ['id', 'mask_matrix', 'image_mask', 'pixel_mask', 'voxel_mask']:
# This builds the columns with name of column and description of column
# both equal to the column name in the cell_roi_table
plane_segmentation.add_column(col_name,
CELL_SPECIMEN_COL_DESCRIPTIONS.get(col_name,
"No Description Available"))
# go through each roi and add it to the plan segmentation object
for cell_roi_id, row in cell_roi_table.iterrows():
sub_mask = np.array(row.pop('image_mask'))
curr_roi = roi.create_roi_mask(fov_width, fov_height, [(fov_width - 1), 0, (fov_height - 1), 0],
roi_mask=sub_mask)
mask = curr_roi.get_mask_plane()
csid = row.pop('cell_specimen_id')
row['cell_specimen_id'] = -1 if csid is None else csid
row['id'] = cell_roi_id
plane_segmentation.add_roi(image_mask=mask, **row.to_dict())
return nwbfile
def add_cell_specimen_table(nwbfile, cell_specimen_table):
cell_roi_table = cell_specimen_table.reset_index().set_index('cell_roi_id')
# Device:
device_name = nwbfile.lab_meta_data['metadata'].rig_name
nwbfile.create_device(device_name, "Allen Brain Observatory")
device = nwbfile.get_device(device_name)
# Location:
location_description = "Area: {}, Depth: {} um".format(
nwbfile.lab_meta_data['metadata'].targeted_structure,
nwbfile.lab_meta_data['metadata'].imaging_depth)
# FOV:
fov_width = nwbfile.lab_meta_data['metadata'].field_of_view_width
fov_height = nwbfile.lab_meta_data['metadata'].field_of_view_height
imaging_plane_description = "{} field of view in {} at depth {} um".format(
(fov_width, fov_height),
nwbfile.lab_meta_data['metadata'].targeted_structure,
nwbfile.lab_meta_data['metadata'].imaging_depth)
# Optical Channel:
optical_channel = OpticalChannel(
name='channel_1',
description='2P Optical Channel',
emission_lambda=nwbfile.lab_meta_data['metadata'].emission_lambda)
# Imaging Plane:
imaging_plane = nwbfile.create_imaging_plane(
name='imaging_plane_1',
optical_channel=optical_channel,
description=imaging_plane_description,
device=device,
excitation_lambda=nwbfile.lab_meta_data['metadata'].excitation_lambda,
imaging_rate=nwbfile.lab_meta_data['metadata'].ophys_frame_rate,
indicator=nwbfile.lab_meta_data['metadata'].indicator,
location=location_description,
manifold=[], # Should this be passed in for future support?
conversion=1.0,
unit='unknown', # Should this be passed in for future support?
reference_frame='unknown'
) # Should this be passed in for future support?
# Image Segmentation:
image_segmentation = ImageSegmentation(name="image_segmentation")
if 'two_photon_imaging' not in nwbfile.modules:
two_photon_imaging_module = ProcessingModule('two_photon_imaging',
'2P processing module')
nwbfile.add_processing_module(two_photon_imaging_module)
else:
two_photon_imaging_module = nwbfile.modules['two_photon_imaging']
two_photon_imaging_module.add_data_interface(image_segmentation)
# Plane Segmentation:
plane_segmentation = image_segmentation.create_plane_segmentation(
name='cell_specimen_table',
description="Segmented rois",
imaging_plane=imaging_plane)
for c in [
c for c in cell_roi_table.columns
if c not in ['id', 'mask_matrix']
]:
plane_segmentation.add_column(c, c)
for cell_roi_id, row in cell_roi_table.iterrows():
sub_mask = np.array(row.pop('image_mask'))
curr_roi = roi.create_roi_mask(fov_width,
fov_height, [(fov_width - 1), 0,
(fov_height - 1), 0],
roi_mask=sub_mask)
mask = curr_roi.get_mask_plane()
csid = row.pop('cell_specimen_id')
row['cell_specimen_id'] = -1 if csid is None else csid
row['id'] = cell_roi_id
plane_segmentation.add_roi(image_mask=mask, **row.to_dict())
return nwbfile
