def save_timepoint_as_tiff(dask_array, idx):
# write your saving code here
writer = OmeTiffWriter(
f"//allen/aics/animated-cell/Dan/LLS2/T{idx}.ome.tif",
overwrite_file=True)
imgdata = dask_array.compute()
writer.save(imgdata, dimension_order="CZYX")
def save_segmentation(
bw: np.ndarray,
contour_flag: bool,
output_path: Path,
fn: str,
suffix: str = "_struct_segmentation",
):
"""save the segmentation into a tiff file
Parameters:
------------
bw: np.ndarray
the segmentation to save
contour_flag: book
whether to also save segmentation contour
output_path: Path
the path to save
fn: str
the core file name to use, for example, "img_102", then
after a suffix (say "_seg") is added, the file name of the output
is "img_101_seg.tiff"
suffix: str
the suffix to add to the output filename
"""
with OmeTiffWriter(str(output_path / (fn + suffix + ".tiff"))) as writer:
writer.save(bw)
if contour_flag:
bd = generate_segmentation_contour(bw)
out_fn = str(output_path / (fn + suffix + "_contour.tiff"))
with OmeTiffWriter(out_fn) as writer:
writer.save(bd)
def execute(self, args):
if not args.data_type.startswith('.'):
args.data_type = '.' + args.data_type
filenames = glob(args.raw_path + os.sep + '*' + args.data_type)
filenames.sort()
existing_files = glob(args.train_path + os.sep + 'img_*.ome.tif')
print(len(existing_files))
training_data_count = len(existing_files) // 3
for _, fn in enumerate(filenames):
training_data_count += 1
# load raw
reader = AICSImage(fn)
struct_img = reader.get_image_data("CZYX",
S=0,
T=0,
C=[args.input_channel
]).astype(np.float32)
struct_img = input_normalization(img, args)
# load seg
seg_fn = args.seg_path + os.sep + os.path.basename(
fn)[:-1 * len(args.data_type)] + '_struct_segmentation.tiff'
reader = AICSImage(seg_fn)
seg = reader.get_image_data("ZYX", S=0, T=0, C=0) > 0.01
seg = seg.astype(np.uint8)
seg[seg > 0] = 1
# excluding mask
cmap = np.ones(seg.shape, dtype=np.float32)
mask_fn = args.mask_path + os.sep + os.path.basename(
fn)[:-1 * len(args.data_type)] + '_mask.tiff'
if os.path.isfile(mask_fn):
reader = AICSImage(mask_fn)
mask = reader.get_image_data("ZYX", S=0, T=0, C=0)
cmap[mask == 0] = 0
with OmeTiffWriter(args.train_path + os.sep + 'img_' +
f'{training_data_count:03}' +
'.ome.tif') as writer:
writer.save(struct_img)
with OmeTiffWriter(args.train_path + os.sep + 'img_' +
f'{training_data_count:03}' +
'_GT.ome.tif') as writer:
writer.save(seg)
with OmeTiffWriter(args.train_path + os.sep + 'img_' +
f'{training_data_count:03}' +
'_CM.ome.tif') as writer:
writer.save(cmap)
def save_segmentation(bw, contour_flag, output_path, fn):
with OmeTiffWriter(str(output_path /
(fn + '_struct_segmentation.tiff'))) as writer:
writer.save(bw)
if contour_flag:
bd = generate_segmentation_contour(bw)
with OmeTiffWriter(str(output_path /
(fn + '_struct_contour.tiff'))) as writer:
writer.save(bd)
def test_overwriteFile(resources_dir):
"""
Test to check if save() can overwrite a file
"""
with OmeTiffWriter(resources_dir / filename,
overwrite_file=True) as writer:
writer.save(image)
def combineFiles(files, out, channel_names=None):
finalimage = None
for f in files:
ai = AICSImage(f)
# ai.data is 6d.
image = ai.data
if image.dtype == numpy.float32:
# normalizes data in range 0 - uint16max
image = image.clip(min=0.0)
image = image / image.max()
image = 65535 * image
# convert float to uint16
image = image.astype(numpy.uint16)
if finalimage is None:
finalimage = [image[0][0]]
else:
finalimage = numpy.append(finalimage, [image[0][0]], axis=1)
print(finalimage.shape)
finalimage = finalimage.transpose([0, 2, 1, 3, 4])
with OmeTiffWriter(file_path=out, overwrite_file=True) as writer:
writer.save(
finalimage,
channel_names=channel_names,
pixels_physical_size=[0.108, 0.108, 0.290],
)
def create_multichtiff_data(path_root: Path, dims_zyx: Sequence[int],
n_ch_in: int, n_ch_out: int, n_items: int) -> Path:
assert len(dims_zyx) == 3
path_data = create_data_dir(path_root)
records = []
for idx in range(n_items):
path_x = path_data / f"{idx:02}.tif"
data_x = np.random.randint(128,
size=[n_ch_in + n_ch_out] + list(dims_zyx),
dtype=np.uint8)
with OmeTiffWriter(path_x) as writer:
writer.save(data_x,
dimension_order="CZYX") # should be a numpy array
records.append({
"dummy_id":
idx,
"path_tiff":
path_x,
"channel_signal":
list(np.arange(0, n_ch_in)),
"channel_target":
list(np.arange(0, n_ch_out) + n_ch_in),
})
path_csv = path_root / "dummy.csv"
pd.DataFrame(records).set_index("dummy_id").to_csv(path_csv)
return path_csv
def test_dontOverwriteFile(resources_dir):
"""
Test to check if save() will raise error when user does not want to overwrite a file that exists
"""
with pytest.raises(Exception):
with OmeTiffWriter(resources_dir / filename) as writer:
writer.save(image)
def convert_combined():
inroot = "\\\\allen\\aics\\modeling\\cheko\\for_others\\2018-02-14_dan_vday_mitosis\\timelapse_wt2_s2\\"
for j in range(0, 20):
finalimage = None
for i in range(0, len(INFILES)):
infilepath = inroot + str(j).zfill(2) + "\\" + INFILES[i]
image = TiffReader(infilepath).data
image = image.transpose([1, 0, 2, 3])
# normalizes data in range 0 - uint16max
image = image.clip(min=0.0)
image = image / image.max()
image = 65535 * image
# convert float to uint16
image = image.astype(numpy.uint16)
# axis=2 is the C axis
if finalimage is None:
finalimage = [image]
else:
finalimage = numpy.append(finalimage, [image], axis=2)
with OmeTiffWriter(
file_path=OUTROOT + "combined_frame_" + str(j).zfill(2) +
".ome.tiff",
overwrite_file=True,
) as writer:
writer.save(
finalimage,
channel_names=CHNAMES,
pixels_physical_size=[0.290, 0.290, 0.290],
)
def test_loadAssertionError(resources_dir):
"""
Test to check if save() will only accept 3, 4, 5 dimensions for data
"""
image_to_save = np.ones((1, 2, 3, 4, 5, 6))
with pytest.raises(Exception):
with OmeTiffWriter(resources_dir / filename,
overwrite_file=True) as writer:
writer.save(image_to_save)
def output_hook(im, names, out_flag, output_path, fn):
assert len(im) == len(names) and len(names) == len(out_flag)
for i in range(len(out_flag)):
if out_flag[i]:
if names[i].startswith('bw_'):
segmentation_type = names[i]
bw = im[i].astype(np.uint8)
bw[bw > 0] = 255
with OmeTiffWriter(
str(output_path /
(fn + '_bw_' + segmentation_type[3:] +
'.tiff'))) as writer:
writer.save(bw)
else:
with OmeTiffWriter(
str(output_path /
(fn + '_' + names[i] + '.tiff'))) as writer:
writer.save(im[i])
def output_hook(im, names, out_flag, output_path, fn):
""" general hook for cutomized output """
assert len(im) == len(names) and len(names) == len(out_flag)
for i in range(len(out_flag)):
if out_flag[i]:
if names[i].startswith("bw_"):
segmentation_type = names[i]
bw = im[i].astype(np.uint8)
bw[bw > 0] = 255
with OmeTiffWriter(
str(output_path /
(fn + "_bw_" + segmentation_type[3:] +
".tiff"))) as writer:
writer.save(bw)
else:
with OmeTiffWriter(
str(output_path /
(fn + "_" + names[i] + ".tiff"))) as writer:
writer.save(im[i])
def test_writerShapeComparison(resources_dir):
"""
Test to check that OmeTiffWriter saves arrays that are reflexive with OmeTiffReader
"""
with OmeTiffWriter(resources_dir / filename,
overwrite_file=True) as writer:
writer.save(image)
output = OmeTiffReader(resources_dir / filename).data
assert output.shape == image.shape[1:]
def test_noopOverwriteFile(resources_dir):
"""
Test to check if save() silently no-ops when user does not want to overwrite exiting file
"""
with open(resources_dir / filename, "w") as f:
f.write("test")
with OmeTiffWriter(resources_dir / filename,
overwrite_file=False) as writer:
writer.save(image)
with open(resources_dir / filename, "r") as f:
line = f.readline().strip()
assert "test" == line
def convert_tiff_to_ome_tiff_1ch(filepathin, filepathout):
image = TiffReader(filepathin).data
image = image.transpose([1, 0, 2, 3])
# normalizes data in range 0 - uint16max
image = image.clip(min=0.0)
image = image / image.max()
image = 65535 * image
# convert float to uint16
image = image.astype(numpy.uint16)
with OmeTiffWriter(file_path=filepathout, overwrite_file=True) as writer:
writer.save(image,
channel_names=["dna"],
pixels_physical_size=[0.290, 0.290, 0.290])
def test_dimensionOrder(resources_dir, dims, expected_t, expected_c,
expected_z, expected_y, expected_x):
with OmeTiffWriter(resources_dir / filename,
overwrite_file=True) as writer:
writer.save(image, dimension_order=dims)
reader = OmeTiffReader(resources_dir / filename)
output = reader.data
t = reader.size_t()
c = reader.size_c()
z = reader.size_z()
y = reader.size_y()
x = reader.size_x()
os.remove(resources_dir / filename)
assert output.shape == image.shape[1:]
assert x == expected_x
assert y == expected_y
assert z == expected_z
assert c == expected_c
assert t == expected_t
def _generate_single_cell_images(
row_index: int,
row: pd.Series,
cell_ceiling_adjustment: int,
bounding_box: np.ndarray,
projection_method: str,
cell_images_3d_dir: Path,
cell_images_2d_all_proj_dir: Path,
cell_images_2d_yx_proj_dir: Path,
overwrite: bool,
) -> Union[CellImagesResult, CellImagesError]:
# Don't use dask for image reading
aicsimageio.use_dask(False)
# Get the ultimate end save paths for this cell
cell_image_3d_save_path = cell_images_3d_dir / f"{row.CellId}.ome.tiff"
cell_image_2d_all_proj_save_path = (cell_images_2d_all_proj_dir /
f"{row.CellId}.png")
cell_image_2d_yx_proj_save_path = (cell_images_2d_yx_proj_dir /
f"{row.CellId}.png")
# Check skip
if (not overwrite
# Only skip if all images exist for this cell
and all(p.is_file() for p in [
cell_image_3d_save_path,
cell_image_2d_all_proj_save_path,
cell_image_2d_yx_proj_save_path,
])):
log.info(
f"Skipping single cell image generation for CellId: {row.CellId}"
)
return CellImagesResult(
row.CellId,
cell_image_3d_save_path,
cell_image_2d_all_proj_save_path,
cell_image_2d_yx_proj_save_path,
)
# Overwrite or didn't exist
log.info(
f"Beginning single cell image generation for CellId: {row.CellId}")
# Wrap errors for debugging later
try:
# Initialize image object with standardized FOV
standardized_image = AICSImage(row.StandardizedFOVPath)
channels = standardized_image.get_channel_names()
# Preload image data
standardized_image.data
# Select and adjust cell shape ceiling for this cell
image = image_utils.select_and_adjust_segmentation_ceiling(
# Unlike most other operations, we can read in normal "CZYX" dimension
# order here as all future operations are expecting it
image=standardized_image.get_image_data("CYXZ", S=0, T=0),
cell_index=row.CellIndex,
cell_ceiling_adjustment=cell_ceiling_adjustment,
)
# Perform a rigid registration on the image
image, _, _ = proc.cell_rigid_registration(
image,
# Reorder bounding box as image is currently CYXZ
bbox_size=bounding_box[[0, 2, 3, 1]],
)
# Reduce size
crop_3d = image * 255
crop_3d = crop_3d.astype(np.uint8)
# Transpose to CZYX for saving
crop_3d = transforms.transpose_to_dims(crop_3d, "CYXZ", "CZYX")
# Save to OME-TIFF
with OmeTiffWriter(cell_image_3d_save_path,
overwrite_file=True) as writer:
writer.save(
crop_3d,
dimension_order="CZYX",
channel_names=standardized_image.get_channel_names(),
pixels_physical_size=standardized_image.
get_physical_pixel_size(),
)
# Generate 2d image projections
# Crop raw channels using segmentations
image = image_utils.crop_raw_channels_with_segmentation(
image, channels)
# Transpose to CZYX for projections
image = transforms.transpose_to_dims(image, "CYXZ", "CZYX")
# Select the DNA, Membrane, and Structure channels
image = image[[
channels.index(target) for target in
[Channels.DNA, Channels.Membrane, Channels.Structure]
]]
# Set RGB colors
# This will set:
# DNA to Blue
# Membrane to Red
# Structure to Green
colors = [[0, 0, 1], [1, 0, 0], [0, 1, 0]]
# Get all axes projection image
all_proj = proc.imgtoprojection(
image,
proj_all=True,
proj_method=projection_method,
local_adjust=False,
global_adjust=True,
colors=colors,
)
# Convert to YXC for PNG writing
all_proj = transforms.transpose_to_dims(all_proj, "CYX", "YXC")
# Drop size to uint8
all_proj = all_proj.astype(np.uint8)
# Save to PNG
imwrite(cell_image_2d_all_proj_save_path, all_proj)
# Get YX axes projection image
yx_proj = proc.imgtoprojection(
image,
proj_all=False,
proj_method=projection_method,
local_adjust=False,
global_adjust=True,
colors=colors,
)
# Convert to YXC for PNG writing
yx_proj = transforms.transpose_to_dims(yx_proj, "CYX", "YXC")
# Drop size to uint8
yx_proj = yx_proj.astype(np.uint8)
# Save to PNG
imwrite(cell_image_2d_yx_proj_save_path, yx_proj)
log.info(
f"Completed single cell image generation for CellId: {row.CellId}"
)
# Return ready to save image
return CellImagesResult(
row.CellId,
cell_image_3d_save_path,
cell_image_2d_all_proj_save_path,
cell_image_2d_yx_proj_save_path,
)
# Catch and return error
except Exception as e:
log.info(
f"Failed single cell image generation for CellId: {row.CellId}. "
"Error: {e}")
return CellImagesError(row.CellId, str(e))
img_rna_smooth = image_smoothing_gaussian_3d(imgs_rna[i], sigma=1)
s3_param = [[1, 0.75*t_spots[i]], [2, 0.75*t_spots[i]]]
print('Identifying spots...')
bw = dot_3d_wrapper(img_rna_smooth, s3_param)
# watershed
minArea = 4
Mask = morphology.remove_small_objects(bw>0, min_size=minArea, connectivity=1, in_place=False)
labeled_mask = measure.label(Mask)
print('Performing watershed segmentation...')
peaks = feature.peak_local_max(imgs_rna[i],labels=labeled_mask, min_distance=2, indices=False)
Seed = morphology.binary_dilation(peaks, selem=morphology.ball(1))
Watershed_Map = -1*distance_transform_edt(bw)
seg = morphology.watershed(Watershed_Map, measure.label(Seed), mask=Mask, watershed_line=True)
seg = morphology.remove_small_objects(seg>0, min_size=minArea, connectivity=1, in_place=False)
print('Exporting mask...')
outname = img_name + '_mask^' + g + '.tiff'
p_out = os.path.join(outdir, 'masks', outname)
seg = seg > 0
out=seg.astype(np.uint8)
out[out>0]=255
writer = OmeTiffWriter(p_out, overwrite_file=True)
writer.save(out)
# output animation
print('Exporting animation...')
anim_name = img_name + '_mask^' + g + '.gif'
su.animate_zstacks([imgs_rna[i].transpose(1,2,0), seg.astype(int).transpose(1,2,0)], vmax=[0.2, 1], gif_name=os.path.join(outdir, 'anim', anim_name))
def execute(self, args):
global draw_mask
# part 1: do sorting
df = pd.read_csv(args.csv_name, index_col=False)
for index, row in df.iterrows():
if not np.isnan(row['score']) and (row['score']==1 or row['score']==0):
continue
reader = AICSImage(row['raw'])
struct_img = reader.get_image_data("ZYX", S=0, T=0, C=args.input_channel)
struct_img[struct_img>5000] = struct_img.min() # adjust contrast
raw_img = (struct_img- struct_img.min() + 1e-8)/(struct_img.max() - struct_img.min() + 1e-8)
raw_img = 255 * raw_img
raw_img = raw_img.astype(np.uint8)
seg = np.squeeze(imread(row['seg']))
score = gt_sorting(raw_img, seg)
if score == 1:
df['score'].iloc[index]=1
need_mask = input('Do you need to add a mask for this image, enter y or n: ')
if need_mask == 'y':
create_mask(raw_img, seg.astype(np.uint8))
mask_fn = args.mask_path + os.sep + os.path.basename(row['raw'])[:-5] + '_mask.tiff'
crop_mask = np.zeros(seg.shape, dtype=np.uint8)
for zz in range(crop_mask.shape[0]):
crop_mask[zz,:,:] = draw_mask[:crop_mask.shape[1],:crop_mask.shape[2]]
crop_mask = crop_mask.astype(np.uint8)
crop_mask[crop_mask>0]=255
with OmeTiffWriter(mask_fn) as writer:
writer.save(crop_mask)
df['mask'].iloc[index]=mask_fn
else:
df['score'].iloc[index]=0
df.to_csv(args.csv_name, index=False)
#########################################
# generate training data:
# (we want to do this step after "sorting"
# (is mainly because we want to get the sorting
# step as smooth as possible, even though
# this may waster i/o time on reloading images)
# #######################################
print('finish merging, start building the training data ...')
existing_files = glob(args.train_path+os.sep+'img_*.ome.tif')
print(len(existing_files))
training_data_count = len(existing_files)//3
for index, row in df.iterrows():
if row['score']==1:
training_data_count += 1
# load raw image
reader = AICSImage(row['raw'])
img = reader.get_image_data("CZYX", S=0, T=0, C=[args.input_channel]).astype(np.float32)
struct_img = input_normalization(img, args)
struct_img= struct_img[0,:,:,:]
# load segmentation gt
seg = np.squeeze(imread(row['seg'])) > 0.01
seg = seg.astype(np.uint8)
seg[seg>0]=1
cmap = np.ones(seg.shape, dtype=np.float32)
if os.path.isfile(str(row['mask'])):
# load segmentation gt
mask = np.squeeze(imread(row['mask']))
cmap[mask>0]=0
with OmeTiffWriter(args.train_path + os.sep + 'img_' + f'{training_data_count:03}' + '.ome.tif') as writer:
writer.save(struct_img)
with OmeTiffWriter(args.train_path + os.sep + 'img_' + f'{training_data_count:03}' + '_GT.ome.tif') as writer:
writer.save(seg)
with OmeTiffWriter(args.train_path + os.sep + 'img_' + f'{training_data_count:03}' + '_CM.ome.tif') as writer:
writer.save(cmap)
print('training data is ready')
def field_worker(
image_path,
image_dims="CYX",
out_dir=None,
contrast_method="simple_quantile",
contrast_kwargs=DEFAULT_CONTRAST_KWARGS,
channels=DEFAULT_CHANNELS,
channel_groups=DEFAULT_CHANNEL_GROUPS,
verbose=False,
):
r"""
Process an entire field -- autocontrast + save, log info to df
Args:
image_path (str): location of input tiff image
image_dims (str): input image dimension ordering, default="CYX"
out_dir (str): where to save output images, default=None
contrast_method (str): method for autocontrasting, default=="simple_quantile"
contrast_kwargs (dict):, default=DEFAULT_CONTRAST_KWARGS
channels (dict): {"name":index} map for input tiff, default=DEFAULT_CHANNELS
channel_groups (dict): fluor/bf/seg grouping, default=DEFAULT_CHANNEL_GROUPS
verbose (bool): print info while processing or not, default=False
Returns:
field_info_df (pd.DataFrame): info for each field, merged with info for each cell
"""
# early exit if file does not exist -- return df of just
image_path = Path(image_path)
if not image_path.is_file():
return pd.DataFrame({"field_image_path": [image_path]})
# set out dir if needed
if out_dir is None:
out_dir = Path.cwd()
else:
out_dir = Path(out_dir)
out_dir = out_dir.absolute()
# set out dir for rescaled images and create if needed
output_field_image_dir = out_dir.joinpath("output_field_images")
output_field_image_dir.mkdir(exist_ok=True)
# contrast stretch the field channels
Cmaxs, Cautos = read_and_contrast_image(
image_path,
contrast_method=contrast_method,
contrast_kwargs=contrast_kwargs,
channel_groups=channel_groups,
image_dims=image_dims,
verbose=verbose,
)
# save original and rescaled field data
rescaled_out_path = output_field_image_dir.joinpath(
f"{image_path.stem}_rescaled.ome.tiff")
field_info_df = pd.DataFrame({
"2D_fov_tiff_path": [image_path],
"rescaled_2D_fov_tiff_path": [rescaled_out_path],
})
# Reshape prior to sending to save
out_data = np.array(Cautos)
out_data = transforms.reshape_data(out_data, "CYX", "TZCYX", S=0)
# Write with channel metadata
channel_names = [
k for k, v in sorted(channels.items(), key=lambda kv: kv[1])
]
with OmeTiffWriter(rescaled_out_path, overwrite_file=True) as writer:
writer.save(out_data, channel_names=channel_names)
# extract napari annotation channel and grab unique labels for cells
label_image = Cautos[channels["cell"]]
labels = np.unique(label_image)
cell_labels = np.sort(labels[labels > 0])
assert (cell_labels[0], cell_labels[-1]) == (1, len(cell_labels))
if verbose:
print(f"processing {image_path}")
print(f"found {len(cell_labels)} segmented cells")
# partial function for iterating over all cells in an image with map
_cell_worker_partial = partial(
cell_worker,
Cautos=Cautos,
label_channel="cell",
field_image_path=image_path,
out_dir=out_dir,
channels=channels,
verbose=verbose,
)
# iterate through all cells in an image
all_cell_info_df = pd.concat(map(_cell_worker_partial, cell_labels),
axis="rows",
ignore_index=True)
# merge cell-wise data into field-wise data
all_cell_info_df["2D_fov_tiff_path"] = image_path
field_info_df = field_info_df.merge(all_cell_info_df, how="inner")
return field_info_df
def create_random_source_image():
random_array = np.random.rand(*BASE_IMAGE_DIM)
# write numpy array to .tiff file
with OmeTiffWriter(TEST_IMG_DIR + "random_input.tiff") as writer:
writer.save(random_array)
def test_big_tiff():
x = np.zeros((10, 10))
assert OmeTiffWriter._size_of_ndarray(data=x) == 10 * 10 * x.itemsize
def _generate_standardized_fov_array(
row_index: int,
row: pd.Series,
current_pixel_sizes: Optional[Tuple[float]],
desired_pixel_sizes: Optional[Tuple[float]],
save_dir: Path,
overwrite: bool,
) -> Union[StandardizeFOVArrayResult, StandardizeFOVArrayError]:
# Don't use dask for image reading
aicsimageio.use_dask(False)
# Get the ultimate end save path for this cell
save_path = save_dir / f"{row.FOVId}.ome.tiff"
# Check skip
if not overwrite and save_path.is_file():
log.info(
f"Skipping standardized FOV generation for FOVId: {row.FOVId}")
return StandardizeFOVArrayResult(row.FOVId, save_path)
# Overwrite or didn't exist
log.info(
f"Beginning standardized FOV generation for FOVId: {row.FOVId}")
# Wrap errors for debugging later
try:
# Get normalized image array
normalized_img, channels, pixel_sizes = image_utils.get_normed_image_array(
raw_image=row.SourceReadPath,
nucleus_seg_image=row.NucleusSegmentationReadPath,
membrane_seg_image=row.MembraneSegmentationReadPath,
dna_channel_index=row.ChannelIndexDNA,
membrane_channel_index=row.ChannelIndexMembrane,
structure_channel_index=row.ChannelIndexStructure,
brightfield_channel_index=row.ChannelIndexBrightfield,
nucleus_seg_channel_index=row.ChannelIndexNucleusSegmentation,
membrane_seg_channel_index=row.
ChannelIndexMembraneSegmentation,
current_pixel_sizes=current_pixel_sizes,
desired_pixel_sizes=desired_pixel_sizes,
)
# Reshape data for serialization
reshaped = transforms.transpose_to_dims(normalized_img, "CYXZ",
"CZYX")
# Save array as OME Tiff
with OmeTiffWriter(save_path, overwrite_file=True) as writer:
writer.save(
data=reshaped,
dimension_order="CZYX",
channel_names=channels,
pixels_physical_size=pixel_sizes,
)
log.info(
f"Completed standardized FOV generation for FOVId: {row.FOVId}"
)
return StandardizeFOVArrayResult(row.FOVId, save_path)
# Catch and return error
except Exception as e:
log.info(
f"Failed standardized FOV generation for FOVId: {row.FOVId}. Error: {e}"
)
return StandardizeFOVArrayError(row.FOVId, str(e))
def cell_worker(
cell_label_value,
Cautos=[],
label_channel="cell",
field_image_path="unnamed_image_field.tiff",
out_dir=None,
channels=DEFAULT_CHANNELS,
verbose=False,
):
r"""
segment single cells + save, log info to df
Args:
cell_label_value (int): integer mask value in segmentation for this cell,
Cautos (list): auto-contrasted images from single iimage field, default=[],
label_channel (str): name of channel to use as the image mask, default=="cell",
field_image_path (str): location of input tiff imagee field, default=="unnamed_image_field.tiff",
out_dir (str): where to save output images, default==None,
channels (dict): {"name":index} map for input tiff, default=DEFAULT_CHANNELS
verbose (bool): print info while processing or not, default=False
Returns:
cell_info_df (pd.DataFrame): info for each cell
"""
field_image_path = Path(field_image_path)
if out_dir is None:
out_dir = Path.cwd()
else:
out_dir = Path(out_dir)
out_dir = out_dir.absolute()
img_out_dir = out_dir.joinpath("output_single_cell_images")
img_out_dir.mkdir(exist_ok=True)
# crop to single cell boundaries
label_image = Cautos[channels[label_channel]]
y, x = np.where(label_image == cell_label_value)
crop_slice = np.s_[min(y):max(y) + 1, min(x):max(x) + 1]
label_crop = label_image[crop_slice]
mask = (label_crop == cell_label_value).astype(np.float64)
out_data_sc = np.array([
img_as_ubyte_nowarn(rescale_intensity(c[crop_slice] * mask))
for c in Cautos
])
# Reshape prior to sending to save
out_data_sc = transforms.reshape_data(out_data_sc, "CYX", "TZCYX", S=0)
# save input field path, cell label value, and output single cell path
out_image_path = img_out_dir.joinpath(
f"{field_image_path.stem}_rescaled_cell{cell_label_value}.ome.tiff")
cell_info_df = pd.DataFrame({
"2D_fov_tiff_path": [field_image_path],
"cell_label_value": [cell_label_value],
"rescaled_2D_single_cell_tiff_path": [out_image_path],
})
# Write tiff with channel metadata
channel_names = [
k for k, v in sorted(channels.items(), key=lambda kv: kv[1])
]
with OmeTiffWriter(out_image_path, overwrite_file=True) as writer:
writer.save(out_data_sc, channel_names=channel_names)
if verbose:
print(f"cell_label_value = {cell_label_value}")
return cell_info_df
