def main(imgPath: pathlib.Path,
stitchPath: pathlib.Path,
outDir: pathlib.Path,
timesliceNaming: typing.Optional[bool]
) -> None:
'''Setup stitching variables/objects'''
# Get a list of stitching vectors
vectors = list(stitchPath.iterdir())
vectors.sort()
# Try to infer a filepattern from the files on disk for faster matching later
global fp # make the filepattern global to share between processes
try:
pattern = filepattern.infer_pattern([f.name for f in imgPath.iterdir()])
logger.info(f'Inferred file pattern: {pattern}')
fp = filepattern.FilePattern(imgPath,pattern)
# Pattern inference didn't work, so just get a list of files
except:
logger.info(f'Unable to infer pattern, defaulting to: .*')
fp = filepattern.FilePattern(imgPath,'.*')
'''Run stitching jobs in separate processes'''
ProcessManager.init_processes('main','asmbl')
for v in vectors:
# Check to see if the file is a valid stitching vector
if 'img-global-positions' not in v.name:
continue
ProcessManager.submit_process(assemble_image,v,outDir)
ProcessManager.join_processes()
def _parse_stitch(stitchPath: pathlib.Path,
timepointName: bool = False) -> dict:
""" Load and parse image stitching vectors
This function parses the data from a stitching vector, then extracts the
relevant image sizes for each image in the stitching vector to obtain a
stitched image size. This function also infers an output file name.
Args:
stitchPath: A path to stitching vectors
timepointName: Use the vector timeslice as the image name
Returns:
Dictionary with keys (width, height, name, filePos)
"""
# Initialize the output
out_dict = { 'width': int(0),
'height': int(0),
'name': '',
'filePos': []}
# Try to parse the stitching vector using the infered file pattern
if fp.pattern != '.*':
vp = filepattern.VectorPattern(stitchPath,fp.pattern)
unique_vals = {k.upper():v for k,v in vp.uniques.items() if len(v)==1}
files = fp.get_matching(**unique_vals)
else:
# Try to infer a pattern from the stitching vector
try:
vector_files = filepattern.VectorPattern(stitchPath,'.*')
pattern = filepattern.infer_pattern([v[0]['file'] for v in vector_files()])
vp = filepattern.VectorPattern(stitchPath,pattern)
# Fall back to universal filepattern
except ValueError:
vp = filepattern.VectorPattern(stitchPath,'.*')
files = fp.files
file_names = [f['file'].name for f in files]
for file in vp():
if file[0]['file'] not in file_names:
continue
stitch_groups = {k:get_number(v) for k,v in file[0].items()}
stitch_groups['file'] = files[0]['file'].with_name(stitch_groups['file'])
# Get the image size
stitch_groups['width'], stitch_groups['height'] = BioReader.image_size(stitch_groups['file'])
# Set the stitching vector values in the file dictionary
out_dict['filePos'].append(stitch_groups)
# Calculate the output image dimensions
out_dict['width'] = max([f['width'] + f['posX'] for f in out_dict['filePos']])
out_dict['height'] = max([f['height'] + f['posY'] for f in out_dict['filePos']])
# Generate the output file name
if timepointName:
global_regex = ".*global-positions-([0-9]+).txt"
name = re.match(global_regex,pathlib.Path(stitchPath).name).groups()[0]
name += '.ome.tif'
out_dict['name'] = name
ProcessManager.job_name(out_dict['name'])
ProcessManager.log(f'Setting output name to timepoint slice number.')
else:
# Try to infer a good filename
try:
out_dict['name'] = vp.output_name()
ProcessManager.job_name(out_dict['name'])
ProcessManager.log(f'Inferred output file name from vector.')
# A file name couldn't be inferred, default to the first image name
except:
ProcessManager.job_name(out_dict['name'])
ProcessManager.log(f'Could not infer output file name from vector, using first file name in the stitching vector as an output file name.')
for file in vp():
out_dict['name'] = file[0]['file']
break
return out_dict
def basic(files: typing.List[Path],
out_dir: Path,
metadata_dir: typing.Optional[Path] = None,
darkfield: bool = False,
photobleach: bool = False):
# Try to infer a filename
try:
pattern = infer_pattern([f['file'].name for f in files])
fp = FilePattern(files[0]['file'].parent,pattern)
base_output = fp.output_name()
# Fallback to the first filename
except:
base_output = files[0]['file'].name
extension = ''.join(files[0]['file'].suffixes)
with ProcessManager.process(base_output):
# Load files and sort
ProcessManager.log('Loading and sorting images...')
img_stk,X,Y = _get_resized_image_stack(files)
img_stk_sort = np.sort(img_stk)
# Initialize options
new_options = _initialize_options(img_stk_sort,darkfield,OPTIONS)
# Initialize flatfield/darkfield matrices
ProcessManager.log('Beginning flatfield estimation')
flatfield_old = np.ones((new_options['size'],new_options['size']),dtype=np.float64)
darkfield_old = np.random.normal(size=(new_options['size'],new_options['size'])).astype(np.float64)
# Optimize until the change in values is below tolerance or a maximum number of iterations is reached
for w in range(new_options['max_reweight_iterations']):
# Optimize using inexact augmented Legrangian multiplier method using L1 loss
A, E1, A_offset = _inexact_alm_l1(copy.deepcopy(img_stk_sort),new_options)
# Calculate the flatfield/darkfield images and update training weights
flatfield, darkfield, new_options = _get_flatfield_and_reweight(A,E1,A_offset,new_options)
# Calculate the change in flatfield and darkfield images between iterations
mad_flat = np.sum(np.abs(flatfield-flatfield_old))/np.sum(np.abs(flatfield_old))
temp_diff = np.sum(np.abs(darkfield - darkfield_old))
if temp_diff < 10**-7:
mad_dark =0
else:
mad_dark = temp_diff/np.max(np.sum(np.abs(darkfield_old)),initial=10**-6)
flatfield_old = flatfield
darkfield_old = darkfield
# Stop optimizing if the change in flatfield/darkfield is below threshold
ProcessManager.log('Iteration {} loss: {}'.format(w+1,mad_flat))
if np.max(mad_flat,initial=mad_dark) < new_options['reweight_tol']:
break
# Calculate photobleaching effects if specified
if photobleach:
pb = _get_photobleach(copy.deepcopy(img_stk),flatfield,darkfield)
# Resize images back to original image size
ProcessManager.log('Saving outputs...')
flatfield = cv2.resize(flatfield,(Y,X),interpolation=cv2.INTER_CUBIC).astype(np.float32)
if new_options['darkfield']:
darkfield = cv2.resize(darkfield,(Y,X),interpolation=cv2.INTER_CUBIC).astype(np.float32)
# Export the flatfield image as a tiled tiff
flatfield_out = base_output.replace(extension,'_flatfield' + extension)
with BioReader(files[0]['file'],max_workers=2) as br:
metadata = br.metadata
with BioWriter(out_dir.joinpath(flatfield_out),metadata=metadata,max_workers=2) as bw:
bw.dtype = np.float32
bw.x = X
bw.y = Y
bw[:] = np.reshape(flatfield,(Y,X,1,1,1))
# Export the darkfield image as a tiled tiff
if new_options['darkfield']:
darkfield_out = base_output.replace(extension,'_darkfield' + extension)
with BioWriter(out_dir.joinpath(darkfield_out),metadata=metadata,max_workers=2) as bw:
bw.dtype = np.float32
bw.x = X
bw.y = Y
bw[:] = np.reshape(darkfield,(Y,X,1,1,1))
# Export the photobleaching components as csv
if photobleach:
offsets_out = base_output.replace(extension,'_offsets.csv')
with open(metadata_dir.joinpath(offsets_out),'w') as fw:
fw.write('file,offset\n')
for f,o in zip(files,pb[0,:].tolist()):
fw.write("{},{}\n".format(f,o))
def main(stitch_dir: Path, collection_dir: Path, output_dir: Path,
pattern: str):
if pattern in [None, ".+", ".*"]:
pattern = filepattern.infer_pattern(
[f.name for f in collection_dir.iterdir()])
logger.info(f"Inferred filepattern: {pattern}")
# Parse files in the image collection
fp = filepattern.FilePattern(collection_dir, pattern)
# Get valid stitching vectors
vectors = [
v for v in Path(stitch_dir).iterdir()
if Path(v).name.startswith("img-global-positions")
]
"""Get filepatterns for each stitching vector
This section of code creates a filepattern for each stitching vector, and while
traversing the stitching vectors analyzes the patterns to see which values in the
filepattern are static or variable within a single stitching vector and across
stitching vectors. The `singulars` variable determines which case each variable is:
`singulars[v]==-1` when the variable, v, changes within a stitching vector.
`singulars[v]==None` when the variable, v, changes across stitching vectors.
`singulars[v]==int` when the variable, v, doesn't change.
The variables that change across stitching vectors are grouping variables for the
filepattern iterator.
"""
singulars = {}
vps = {}
for vector in vectors:
vps[vector.name] = filepattern.VectorPattern(vector, pattern)
for variable in vps[vector.name].variables:
if variable not in singulars.keys():
if len(vps[vector.name].uniques[variable]) == 1:
singulars[variable] = vps[vector.name].uniques[variable]
else:
singulars[variable] = -1
elif (variable in singulars.keys() and
vps[vector.name].uniques[variable] != singulars[variable]):
singulars[variable] = None if singulars[variable] != -1 else -1
group_by = "".join([k for k, v in singulars.items() if v == -1])
vector_count = 1
for vector in vectors:
logger.info("Processing vector: {}".format(str(vector.absolute())))
sp = vps[vector.name]
# Define the variables used in the current vector pattern so that corresponding
# files can be located from files in the image collection with filepattern.
matching = {
k.upper(): sp.uniques[k][0]
for k, v in singulars.items() if v is None
}
vector_groups = [
k for k, v in singulars.items() if v not in [None, -1]
]
# Vector output dictionary
vector_dict = {}
# Loop through lines in the stitching vector, generate new vectors
for v in sp():
variables = {
key.upper(): value
for key, value in v[0].items() if key in group_by
}
variables.update(matching)
for files in fp(**variables):
for f in files:
# Get the file writer, create it if it doesn't exist
temp_dict = vector_dict
for key in vector_groups:
if f[key] not in temp_dict.keys():
if vector_groups[-1] != key:
temp_dict[f[key]] = {}
else:
fname = "img-global-positions-{}.txt".format(
vector_count)
vector_count += 1
logger.info(
"Creating vector: {}".format(fname))
temp_dict[f[key]] = open(
str(
Path(output_dir).joinpath(
fname).absolute()),
"w",
)
temp_dict = temp_dict[f[key]]
# If the only grouping variables are positional (xyp), then create an output file
fw = temp_dict
fw.write(
"file: {}; corr: {}; position: ({}, {}); grid: ({}, {});\n"
.format(
Path(f["file"]).name,
v[0]["correlation"],
v[0]["posX"],
v[0]["posY"],
v[0]["gridX"],
v[0]["gridY"],
))
# Close all open stitching vectors
close_vectors(vector_dict)
logger.info("Plugin completed all operations!")
def test_alphanumeric_variable_width(self):
pattern = infer_pattern(self.data['variable'])
self.assertEqual(pattern,'S1_R{r}_C1-C11_A1_y{t+}_x{c+}_c{z+}.ome.tif')
def test_alphanumeric_fixed_width(self):
pattern = infer_pattern(self.data['brain'])
self.assertEqual(pattern,'S1_R{r}_C1-C11_A1_y0{tt}_x0{cc}_c0{zz}.ome.tif')
def test_numeric_fixed_width(self):
pattern = infer_pattern(self.data['robot'])
self.assertEqual(pattern,'00{r}0{tt}-{c}-00100100{z}.tif')