def read_similarities(self, directory):
if not ph.directory_exists(directory):
raise IOError("Directory '%s' does not exist." % directory)
# Get filename paths
path_to_file_filenames, path_to_file_similarities = self._get_filename_paths(
directory)
for f in [path_to_file_filenames, path_to_file_similarities]:
if not ph.file_exists(path_to_file_filenames):
raise IOError("File '%s' does not exist" % f)
lines = ph.read_file_line_by_line(path_to_file_filenames)
# Get image filenames
image_names = [re.sub("\n", "", f) for f in lines[2:]]
# Get computed measures
measures = lines[1]
measures = re.sub("# ", "", measures)
measures = re.sub("\n", "", measures)
self._measures = measures.split("\t")
# Get computed similarities
similarities_nda = np.loadtxt(path_to_file_similarities, skiprows=2)
# Reconstruct similarity dictionary
self._similarities = {}
self._similarities["filenames"] = image_names
for i_m, m in enumerate(self._measures):
self._similarities[m] = similarities_nda[:, i_m]
def read_data(self):
if not ph.directory_exists(self._path_to_directory):
raise exceptions.DirectoryNotExistent(self._path_to_directory)
abs_path_to_directory = os.path.abspath(self._path_to_directory)
# Get data filenames of images by finding the prefixes associated
# to the slices which are build as filename_slice[0-9]+.nii.gz
pattern = "(" + REGEX_FILENAMES + ")" + \
self._prefix_slice + "[0-9]+[.]" + REGEX_FILENAME_EXTENSIONS
p = re.compile(pattern)
dic_filenames = {
p.match(f).group(1): p.match(f).group(0)
for f in os.listdir(abs_path_to_directory) if p.match(f)
}
# Filenames without filename ending as sorted list
filenames = natsort.natsorted(dic_filenames.keys(),
key=lambda y: y.lower())
# Reduce filenames to be read to selection only
if self._image_selection is not None:
filenames = [f for f in self._image_selection if f in filenames]
self._stacks = [None] * len(filenames)
self._slice_transforms_sitk = [None] * len(filenames)
for i, filename in enumerate(filenames):
# Get slice names associated to stack
pattern = "(" + filenames[i] + self._prefix_slice + \
")([0-9]+)[.]" + REGEX_FILENAME_EXTENSIONS
p = re.compile(pattern)
# Dictionary linking slice number with filename (without extension)
dic_slice_filenames = {
int(p.match(f).group(2)):
p.match(f).group(1) + p.match(f).group(2)
for f in os.listdir(abs_path_to_directory) if p.match(f)
}
# Build stack from image and its found slices
self._stacks[i] = st.Stack.from_slice_filenames(
dir_input=self._path_to_directory,
prefix_stack=filename,
suffix_mask=self._suffix_mask,
dic_slice_filenames=dic_slice_filenames)
# Read
self._slice_transforms_sitk[i] = [
sitk.ReadTransform(
os.path.join(self._path_to_directory,
"%s.tfm" % dic_slice_filenames[k]))
for k in sorted(dic_slice_filenames.keys())
]
def create_video(self, path_to_video, dir_input_slices, fps=1):
dir_output_video = os.path.dirname(path_to_video)
filename = os.path.basename(path_to_video).split(".")[0]
path_to_slices = "%s*.png" % os.path.join(dir_input_slices, filename)
path_to_video = os.path.join(dir_output_video, "%s.mp4" % filename)
path_to_video_tmp = os.path.join(
dir_output_video, "%s_tmp.mp4" % filename)
# Check that folder containing the slices exist
if not ph.directory_exists(dir_input_slices):
raise IOError("Folder '%s' meant to contain exported slices does "
"not exist" % dir_input_slices)
# Check that the folder contains exported slices as png files
# if not ph.file_exists(os.path.join(
# dir_input_slices, self._get_filename_slice(filename, 1))):
# raise IOError(
# "Slices '%s' need to be generated first using "
# "'export_slices'" % (path_to_slices))
# Create output folder for video
ph.create_directory(dir_output_video)
# ---------------Create temp video from exported slices----------------
cmd_args = []
cmd_args.append("-monitor")
cmd_args.append("-delay %d" % (100. / fps))
cmd_exe = "convert"
cmd = "%s %s %s %s" % (
cmd_exe, (" ").join(cmd_args), path_to_slices, path_to_video_tmp)
flag = ph.execute_command(cmd)
if flag != 0:
raise RuntimeError("Unable to create video from slices")
# ----------------------Use more common codec (?)----------------------
cmd_args = []
# overwrite possibly existing image
cmd_args.append("-y")
# Define input video to be converted
cmd_args.append("-i %s" % path_to_video_tmp)
# Use H.264 codec for video compression of MP4 file
cmd_args.append("-vcodec libx264")
# Define used pixel format
cmd_args.append("-pix_fmt yuv420p")
# Avoid error message associated to odd rows
# (https://stackoverflow.com/questions/20847674/ffmpeg-libx264-height-not-divisible-by-2)
cmd_args.append("-vf 'scale=trunc(iw/2)*2:trunc(ih/2)*2'")
cmd = "ffmpeg %s %s" % ((" ").join(cmd_args), path_to_video)
ph.execute_command(cmd)
# Delete temp video
os.remove(path_to_video_tmp)
def read_data(self):
if not ph.directory_exists(self._path_to_directory):
raise exceptions.DirectoryNotExistent(self._path_to_directory)
abs_path_to_directory = os.path.abspath(self._path_to_directory)
# Get data filenames of images without filename extension
pattern = "(" + REGEX_FILENAMES + ")[.]" + REGEX_FILENAME_EXTENSIONS
pattern_mask = "(" + REGEX_FILENAMES + ")" + self._suffix_mask + \
"[.]" + REGEX_FILENAME_EXTENSIONS
p = re.compile(pattern)
p_mask = re.compile(pattern_mask)
# TODO:
# - If folder contains A.nii and A.nii.gz that ambiguity will not
# be detected
# - exclude potential mask filenames
# - hidden files are not excluded
dic_filenames = {
p.match(f).group(1): p.match(f).group(0)
for f in os.listdir(abs_path_to_directory)
if p.match(f) and not p_mask.match(f)
}
dic_filenames_mask = {
p_mask.match(f).group(1): p_mask.match(f).group(0)
for f in os.listdir(abs_path_to_directory) if p_mask.match(f)
}
# Filenames without filename ending as sorted list
filenames = natsort.natsorted(dic_filenames.keys(),
key=lambda y: y.lower())
self._stacks = [None] * len(filenames)
for i, filename in enumerate(filenames):
abs_path_image = os.path.join(abs_path_to_directory,
dic_filenames[filename])
if filename in dic_filenames_mask.keys():
abs_path_mask = os.path.join(abs_path_to_directory,
dic_filenames_mask[filename])
else:
ph.print_info("No mask found for '%s'." % (abs_path_image))
abs_path_mask = None
self._stacks[i] = st.Stack.from_filename(
abs_path_image,
abs_path_mask,
extract_slices=self._extract_slices)
def _get_path_to_potential_mask(self, file_path):
# Build absolute path to directory of image
path_to_directory = os.path.dirname(file_path)
filename = os.path.basename(file_path)
if not ph.directory_exists(path_to_directory):
raise exceptions.DirectoryNotExistent(path_to_directory)
abs_path_to_directory = os.path.abspath(path_to_directory)
# Get absolute path mask to image
pattern = "(" + REGEX_FILENAMES + \
")[.]" + REGEX_FILENAME_EXTENSIONS
p = re.compile(pattern)
# filename = [p.match(f).group(1) if p.match(file_path)][0]
if not file_path.endswith(tuple(ALLOWED_EXTENSIONS)):
raise IOError("Input image type not correct. Allowed types %s" %
"(" + (", or ").join(ALLOWED_EXTENSIONS) + ")")
# Strip extension from filename to find associated mask
filename = [
re.sub("." + ext, "", filename) for ext in ALLOWED_EXTENSIONS
if file_path.endswith(ext)
][0]
pattern_mask = filename + self._suffix_mask + "[.]" + \
REGEX_FILENAME_EXTENSIONS
p_mask = re.compile(pattern_mask)
filename_mask = [
p_mask.match(f).group(0) for f in os.listdir(abs_path_to_directory)
if p_mask.match(f)
]
if len(filename_mask) == 0:
abs_path_mask = None
else:
# exclude non-integer valued image as candidate (to avoid using
# the same image as mask in case of suffix_mask = '')
candidate = os.path.join(abs_path_to_directory, filename_mask[0])
candidate_sitk = sitk.ReadImage(candidate)
if "int" in candidate_sitk.GetPixelIDTypeAsString():
abs_path_mask = candidate
else:
abs_path_mask = None
return abs_path_mask
def read_data(self):
self._stacks = [None] * len(self._file_paths)
for i, file_path in enumerate(self._file_paths):
# Build absolute path to directory of image
path_to_directory = os.path.dirname(file_path)
filename = os.path.basename(file_path)
if not ph.directory_exists(path_to_directory):
raise exceptions.DirectoryNotExistent(path_to_directory)
abs_path_to_directory = os.path.abspath(path_to_directory)
# Get absolute path mask to image
pattern = "(" + REGEX_FILENAMES + \
")[.]" + REGEX_FILENAME_EXTENSIONS
p = re.compile(pattern)
# filename = [p.match(f).group(1) if p.match(file_path)][0]
if not file_path.endswith(tuple(ALLOWED_EXTENSIONS)):
raise IOError("Input image type not correct. Allowed types %s"
% "(" + (", or ").join(ALLOWED_EXTENSIONS) + ")")
# Strip extension from filename to find associated mask
filename = [re.sub("." + ext, "", filename)
for ext in ALLOWED_EXTENSIONS
if file_path.endswith(ext)][0]
pattern_mask = filename + self._suffix_mask + "[.]" + \
REGEX_FILENAME_EXTENSIONS
p_mask = re.compile(pattern_mask)
filename_mask = [p_mask.match(f).group(0)
for f in os.listdir(abs_path_to_directory)
if p_mask.match(f)]
if len(filename_mask) == 0:
abs_path_mask = None
else:
abs_path_mask = os.path.join(abs_path_to_directory,
filename_mask[0])
self._stacks[i] = st.Stack.from_filename(
file_path,
abs_path_mask,
extract_slices=self._extract_slices)
def read_data(self):
if not ph.directory_exists(self._directory):
raise exceptions.DirectoryNotExistent(self._directory)
# Create absolute path for directory
directory = os.path.abspath(self._directory)
pattern = "(" + REGEX_FILENAMES + \
")%s([0-9]+)[.]tfm" % self._suffix_slice
p = re.compile(pattern)
dic_tmp = {(p.match(f).group(1), int(p.match(f).group(2))):
os.path.join(directory,
p.match(f).group(0))
for f in os.listdir(directory) if p.match(f)}
fnames = list(set([k[0] for k in dic_tmp.keys()]))
self._transforms_sitk = {fname: {} for fname in fnames}
for (fname, slice_number), path in six.iteritems(dic_tmp):
self._transforms_sitk[fname][slice_number] = \
self._get_sitk_transform_from_filepath(path)
def read_slice_similarities(self, directory, ext="txt"):
if not ph.directory_exists(directory):
raise IOError("Given directory '%s' does not exist" % (directory))
pattern = "([a-zA-Z0-9_\+\-]+)[.]%s" % ext
p = re.compile(pattern)
stack_names = [
p.match(f).group(1) for f in os.listdir(directory) if p.match(f)
]
self._slice_similarities = {
stack_name: {}
for stack_name in stack_names
}
for stack_name in stack_names:
path_to_file = os.path.join(directory, "%s.%s" % (stack_name, ext))
# Read computed measures
self._measures = ph.read_file_line_by_line(path_to_file)[1]
self._measures = re.sub("# ", "", self._measures)
self._measures = re.sub("\n", "", self._measures)
self._measures = self._measures.split("\t")
# Read array
array = np.loadtxt(path_to_file, skiprows=2)
# Ensure correct shape in case only a single slice available
array = array.reshape(-1, len(self._measures))
if array.ndim == 1:
array = array.reshape(len(array), 1)
for i_m, m in enumerate(self._measures):
self._slice_similarities[stack_name][m] = array[:, i_m]
def run(self):
if not ph.directory_exists(self._dir_motion_correction):
raise exceptions.DirectoryNotExistent(self._dir_motion_correction)
abs_path_to_directory = os.path.abspath(self._dir_motion_correction)
for i in range(len(self._stacks)):
stack_name = self._stacks[i].get_filename()
# update stack position
path_to_stack_transform = os.path.join(abs_path_to_directory,
"%s.tfm" % stack_name)
if ph.file_exists(path_to_stack_transform):
transform_stack_sitk = sitkh.read_transform_sitk(
path_to_stack_transform)
transform_stack_sitk_inv = sitkh.read_transform_sitk(
path_to_stack_transform, inverse=True)
self._stacks[i].update_motion_correction(transform_stack_sitk)
ph.print_info("Stack '%s': Stack position updated" %
stack_name)
else:
transform_stack_sitk_inv = sitk.Euler3DTransform()
# update slice positions
pattern_trafo_slices = stack_name + self._prefix_slice + \
"([0-9]+)[.]tfm"
p = re.compile(pattern_trafo_slices)
dic_slice_transforms = {
int(p.match(f).group(1)): os.path.join(abs_path_to_directory,
p.match(f).group(0))
for f in os.listdir(abs_path_to_directory) if p.match(f)
}
slices = self._stacks[i].get_slices()
for i_slice in range(self._stacks[i].get_number_of_slices()):
if i_slice in dic_slice_transforms.keys():
transform_slice_sitk = sitkh.read_transform_sitk(
dic_slice_transforms[i_slice])
transform_slice_sitk = \
sitkh.get_composite_sitk_affine_transform(
transform_slice_sitk, transform_stack_sitk_inv)
slices[i_slice].update_motion_correction(
transform_slice_sitk)
# # ------------------------- HACK -------------------------
# # 18 Jan 2019
# # HACK to use results of a previous version where image
# # slices were still exported
# # (Bug was that after stack intensity correction, the
# # previous v2v-reg was not passed on to the final
# # registration transform):
# import niftymic.base.slice as sl
# path_to_slice = re.sub(
# ".tfm", ".nii.gz", dic_slice_transforms[i_slice])
# path_to_slice_mask = re.sub(
# ".tfm", "_mask.nii.gz", dic_slice_transforms[i_slice])
# slice_sitk = sitk.ReadImage(path_to_slice)
# slice_sitk_mask = sitk.ReadImage(path_to_slice_mask)
# hack = sl.Slice.from_sitk_image(
# # slice_sitk=slice_sitk,
# slice_sitk=slice_sitk_mask, # mask for Mask-SRR!
# slice_sitk_mask=slice_sitk_mask,
# slice_number=slices[i_slice].get_slice_number(),
# slice_thickness=slices[i_slice].get_slice_thickness(),
# )
# self._stacks[i]._slices[i_slice] = hack
# # --------------------------------------------------------
else:
self._stacks[i].delete_slice(slices[i_slice])
# print update information
ph.print_info("Stack '%s': Slice positions updated "
"(%d/%d slices deleted)" % (
stack_name,
len(self._stacks[i].get_deleted_slice_numbers()),
self._stacks[i].sitk.GetSize()[-1],
))
# delete entire stack if all slices were rejected
if self._stacks[i].get_number_of_slices() == 0:
ph.print_info("Stack '%s' removed as all slices were deleted" %
stack_name)
self._stacks[i] = None
# only return maintained stacks
self._stacks = [s for s in self._stacks if s is not None]
if len(self._stacks) == 0:
raise RuntimeError(
"All stacks removed. "
"Did you check that the correct motion-correction directory "
"was provided?")
def run(self, older_than_v3=False):
if not ph.directory_exists(self._dir_motion_correction):
raise exceptions.DirectoryNotExistent(
self._dir_motion_correction)
abs_path_to_directory = os.path.abspath(
self._dir_motion_correction)
path_to_rejected_slices = os.path.join(
abs_path_to_directory, "rejected_slices.json")
if ph.file_exists(path_to_rejected_slices):
self._rejected_slices = ph.read_dictionary_from_json(
path_to_rejected_slices)
bool_check = True
else:
self._rejected_slices = None
bool_check = False
for i in range(len(self._stacks)):
stack_name = self._stacks[i].get_filename()
if not older_than_v3:
# update stack position
path_to_stack_transform = os.path.join(
abs_path_to_directory, "%s.tfm" % stack_name)
if ph.file_exists(path_to_stack_transform):
transform_stack_sitk = sitkh.read_transform_sitk(
path_to_stack_transform)
transform_stack_sitk_inv = sitkh.read_transform_sitk(
path_to_stack_transform, inverse=True)
self._stacks[i].update_motion_correction(
transform_stack_sitk)
ph.print_info(
"Stack '%s': Stack position updated" % stack_name)
else:
transform_stack_sitk_inv = sitk.Euler3DTransform()
if self._volume_motion_only:
continue
# update slice positions
pattern_trafo_slices = stack_name + self._prefix_slice + \
"([0-9]+)[.]tfm"
p = re.compile(pattern_trafo_slices)
dic_slice_transforms = {
int(p.match(f).group(1)): os.path.join(
abs_path_to_directory, p.match(f).group(0))
for f in os.listdir(abs_path_to_directory) if p.match(f)
}
slices = self._stacks[i].get_slices()
for i_slice in range(self._stacks[i].get_number_of_slices()):
if i_slice in dic_slice_transforms.keys():
transform_slice_sitk = sitkh.read_transform_sitk(
dic_slice_transforms[i_slice])
transform_slice_sitk = \
sitkh.get_composite_sitk_affine_transform(
transform_slice_sitk, transform_stack_sitk_inv)
slices[i_slice].update_motion_correction(
transform_slice_sitk)
else:
self._stacks[i].delete_slice(slices[i_slice])
# ----------------------------- HACK -----------------------------
# 18 Jan 2019
# HACK to use results of a previous version where image slices were
# still exported.
# (There was a bug after stack intensity correction, which resulted
# in v2v-reg transforms not being part of in the final registration
# transforms; Thus, slice transformations (tfm's) were flawed and
# could not be used):
else:
# Recover suffix for mask
pattern = stack_name + self._prefix_slice + \
"[0-9]+[_]([a-zA-Z]+)[.]nii.gz"
pm = re.compile(pattern)
matches = list(set([pm.match(f).group(1) for f in os.listdir(
abs_path_to_directory) if pm.match(f)]))
if len(matches) > 1:
raise RuntimeError("Suffix mask cannot be determined")
suffix_mask = "_%s" % matches[0]
# Recover stack
path_to_stack = os.path.join(
abs_path_to_directory, "%s.nii.gz" % stack_name)
path_to_stack_mask = os.path.join(
abs_path_to_directory, "%s%s.nii.gz" % (
stack_name, suffix_mask))
stack = st.Stack.from_filename(
path_to_stack, path_to_stack_mask)
# Recover slices
pattern_trafo_slices = stack_name + self._prefix_slice + \
"([0-9]+)[.]tfm"
p = re.compile(pattern_trafo_slices)
dic_slice_transforms = {
int(p.match(f).group(1)): os.path.join(
abs_path_to_directory, p.match(f).group(0))
for f in os.listdir(abs_path_to_directory) if p.match(f)
}
slices = self._stacks[i].get_slices()
for i_slice in range(self._stacks[i].get_number_of_slices()):
if i_slice in dic_slice_transforms.keys():
path_to_slice = re.sub(
".tfm", ".nii.gz", dic_slice_transforms[i_slice])
path_to_slice_mask = re.sub(
".tfm", "%s.nii.gz" % suffix_mask,
dic_slice_transforms[i_slice])
slice_sitk = sitk.ReadImage(path_to_slice)
slice_sitk_mask = sitk.ReadImage(path_to_slice_mask)
hack = sl.Slice.from_sitk_image(
slice_sitk=slice_sitk,
# slice_sitk=slice_sitk_mask, # mask for Mask-SRR!
slice_sitk_mask=slice_sitk_mask,
slice_number=slices[i_slice].get_slice_number(),
slice_thickness=slices[
i_slice].get_slice_thickness(),
)
self._stacks[i]._slices[i_slice] = hack
else:
self._stacks[i].delete_slice(slices[i_slice])
self._stacks[i].sitk = stack.sitk
self._stacks[i].sitk_mask = stack.sitk_mask
self._stacks[i].itk = stack.itk
self._stacks[i].itk_mask = stack.itk_mask
# -----------------------------------------------------------------
# print update information
ph.print_info(
"Stack '%s': Slice positions updated "
"(%d/%d slices deleted)" % (
stack_name,
len(self._stacks[i].get_deleted_slice_numbers()),
self._stacks[i].sitk.GetSize()[-1],
)
)
# delete entire stack if all slices were rejected
if self._stacks[i].get_number_of_slices() == 0:
ph.print_info(
"Stack '%s' removed as all slices were deleted" %
stack_name)
self._stacks[i] = None
# only return maintained stacks
self._stacks = [s for s in self._stacks if s is not None]
if len(self._stacks) == 0:
raise RuntimeError(
"All stacks removed. "
"Did you check that the correct motion-correction directory "
"was provided?")
def main():
input_parser = InputArgparser(
description="Script to show the evaluated similarity between "
"simulated stack from obtained reconstruction and original stack. "
"This function takes the result of "
"evaluate_simulated_stack_similarity.py as input. "
"Provide --dir-output in order to save the results."
)
input_parser.add_dir_input(required=True)
input_parser.add_dir_output(required=False)
args = input_parser.parse_args()
input_parser.print_arguments(args)
if not ph.directory_exists(args.dir_input):
raise exceptions.DirectoryNotExistent(args.dir_input)
# --------------------------------Read Data--------------------------------
pattern = "Similarity_(" + REGEX_FILENAMES + ")[.]txt"
p = re.compile(pattern)
dic_filenames = {
p.match(f).group(1): p.match(f).group(0)
for f in os.listdir(args.dir_input) if p.match(f)
}
dic_stacks = {}
for filename in dic_filenames.keys():
path_to_file = os.path.join(args.dir_input, dic_filenames[filename])
# Extract evaluated measures written as header in second line
measures = open(path_to_file).readlines()[1]
measures = re.sub("#\t", "", measures)
measures = re.sub("\n", "", measures)
measures = measures.split("\t")
# Extract errors
similarities = np.loadtxt(path_to_file, skiprows=2)
# Build dictionary holding all similarity information for stack
dic_stack_similarity = {
measures[i]: similarities[:, i] for i in range(len(measures))
}
# dic_stack_similarity["measures"] = measures
# Store information of to dictionary
dic_stacks[filename] = dic_stack_similarity
# -----------Visualize stacks individually per similarity measure----------
ctr = [0]
N_stacks = len(dic_stacks)
N_measures = len(measures)
rows = 2 if N_measures < 6 else 3
filenames = natsorted(dic_stacks.keys(), key=lambda y: y.lower())
for i, filename in enumerate(filenames):
fig = plt.figure(ph.add_one(ctr))
fig.clf()
for m, measure in enumerate(measures):
ax = plt.subplot(rows, np.ceil(N_measures/float(rows)), m+1)
y = dic_stacks[filename][measure]
x = range(1, y.size+1)
lines = plt.plot(x, y)
line = lines[0]
line.set_linestyle("")
line.set_marker(ph.MARKERS[0])
# line.set_markerfacecolor("w")
plt.xlabel("Slice")
plt.ylabel(measure)
ax.set_xticks(x)
if measure in ["SSIM", "NCC"]:
ax.set_ylim([0, 1])
plt.suptitle(filename)
try:
# Open windows (and also save them) in full screen
manager = plt.get_current_fig_manager()
manager.full_screen_toggle()
except:
pass
plt.show(block=False)
if args.dir_output is not None:
filename = "Similarity_%s.pdf" % filename
ph.save_fig(fig, args.dir_output, filename)
# -----------All in one (meaningful in case of similar scaling)----------
fig = plt.figure(ph.add_one(ctr))
fig.clf()
data = {}
for m, measure in enumerate(measures):
for i, filename in enumerate(filenames):
similarities = dic_stacks[filename][measure]
labels = [filename] * similarities.size
if m == 0:
if "Stack" not in data.keys():
data["Stack"] = labels
else:
data["Stack"] = np.concatenate((data["Stack"], labels))
if measure not in data.keys():
data[measure] = similarities
else:
data[measure] = np.concatenate(
(data[measure], similarities))
df_melt = pd.DataFrame(data).melt(
id_vars="Stack",
var_name="",
value_name=" ",
value_vars=measures,
)
ax = plt.subplot(1, 1, 1)
b = sns.boxplot(
data=df_melt,
hue="Stack", # different colors for different "Stack"
x="",
y=" ",
order=measures,
)
ax.set_axisbelow(True)
try:
# Open windows (and also save them) in full screen
manager = plt.get_current_fig_manager()
manager.full_screen_toggle()
except:
pass
plt.show(block=False)
if args.dir_output is not None:
filename = "Boxplot.pdf"
ph.save_fig(fig, args.dir_output, filename)
# # -------------Boxplot: Plot individual similarity measures v1----------
# for m, measure in enumerate(measures):
# fig = plt.figure(ph.add_one(ctr))
# fig.clf()
# data = {}
# for i, filename in enumerate(filenames):
# similarities = dic_stacks[filename][measure]
# labels = [filename] * similarities.size
# if "Stack" not in data.keys():
# data["Stack"] = labels
# else:
# data["Stack"] = np.concatenate((data["Stack"], labels))
# if measure not in data.keys():
# data[measure] = similarities
# else:
# data[measure] = np.concatenate(
# (data[measure], similarities))
# df_melt = pd.DataFrame(data).melt(
# id_vars="Stack",
# var_name="",
# value_name=measure,
# )
# ax = plt.subplot(1, 1, 1)
# b = sns.boxplot(
# data=df_melt,
# hue="Stack", # different colors for different "Stack"
# x="",
# y=measure,
# )
# ax.set_axisbelow(True)
# plt.show(block=False)
# # -------------Boxplot: Plot individual similarity measures v2----------
# for m, measure in enumerate(measures):
# fig = plt.figure(ph.add_one(ctr))
# fig.clf()
# data = {}
# for i, filename in enumerate(filenames):
# similarities = dic_stacks[filename][measure]
# labels = [filename] * len(filenames)
# if filename not in data.keys():
# data[filename] = similarities
# else:
# data[filename] = np.concatenate(
# (data[filename], similarities))
# for filename in filenames:
# data[filename] = pd.Series(data[filename])
# df = pd.DataFrame(data)
# df_melt = df.melt(
# var_name="",
# value_name=measure,
# value_vars=filenames,
# )
# ax = plt.subplot(1, 1, 1)
# b = sns.boxplot(
# data=df_melt,
# x="",
# y=measure,
# order=filenames,
# )
# ax.set_axisbelow(True)
# plt.show(block=False)
return 0
def main():
time_start = ph.start_timing()
flag_individual_cases_only = 1
flag_batch_script = 0
batch_ctr = [32]
flag_correct_bias_field = 0
# flag_correct_intensities = 0
flag_collect_segmentations = 0
flag_select_images_segmentations = 0
flag_reconstruct_volume_subject_space = 0
flag_reconstruct_volume_subject_space_irtk = 0
flag_reconstruct_volume_subject_space_show_comparison = 0
flag_register_to_template = 0
flag_register_to_template_irtk = 0
flag_show_srr_template_space = 0
flag_reconstruct_volume_template_space = 0
flag_collect_volumetric_reconstruction_results = 0
flag_show_volumetric_reconstruction_results = 0
flag_rsync_stuff = 0
# Analysis
flag_remove_failed_cases_for_analysis = 1
flag_postop = 2 # 0... preop, 1...postop, 2... pre+postop
flag_evaluate_image_similarities = 0
flag_analyse_image_similarities = 1
flag_evaluate_slice_residual_similarities = 0
flag_analyse_slice_residual_similarities = 0
flag_analyse_stacks = 0
flag_analyse_qualitative_assessment = 0
flag_collect_data_blinded_analysis = 0
flag_anonymize_data_blinded_analysis = 0
provide_comparison = 0
intensity_correction = 1
isotropic_resolution = 0.75
alpha = 0.02
outlier_rejection = 1
threshold = 0.7
threshold_first = 0.6
# metric = "ANTSNeighborhoodCorrelation"
# metric_radius = 5
# multiresolution = 0
prefix_srr = "srr_"
prefix_srr_qa = "masked_"
# ----------------------------------Set Up---------------------------------
if flag_correct_bias_field:
dir_batch = os.path.join(utils.DIR_BATCH_ROOT, "BiasFieldCorrection")
elif flag_reconstruct_volume_subject_space:
dir_batch = os.path.join(utils.DIR_BATCH_ROOT,
"VolumetricReconstructionSubjectSpace")
elif flag_register_to_template:
dir_batch = os.path.join(utils.DIR_BATCH_ROOT,
"VolumetricReconstructionRegisterToTemplate")
elif flag_reconstruct_volume_template_space:
dir_batch = os.path.join(utils.DIR_BATCH_ROOT,
"VolumetricReconstructionTemplateSpace")
else:
dir_batch = os.path.join(utils.DIR_BATCH_ROOT, "foo")
file_prefix_batch = os.path.join(dir_batch, "command")
if flag_batch_script:
verbose = 0
else:
verbose = 1
data_reader = dr.ExcelSheetDataReader(utils.EXCEL_FILE)
data_reader.read_data()
cases = data_reader.get_data()
if flag_analyse_qualitative_assessment:
data_reader = dr.ExcelSheetQualitativeAssessmentReader(utils.QA_FILE)
data_reader.read_data()
qualitative_assessment = data_reader.get_data()
statistical_evaluation = se.StatisticalEvaluation(
qualitative_assessment)
statistical_evaluation.run_tests(ref="seg_manual")
ph.exit()
cases_similarities = []
cases_stacks = []
if flag_individual_cases_only:
N_cases = len(INDIVIDUAL_CASE_IDS)
else:
N_cases = len(cases.keys())
i_case = 0
for case_id in sorted(cases.keys()):
if flag_individual_cases_only and case_id not in INDIVIDUAL_CASE_IDS:
continue
if not flag_analyse_image_similarities and \
not flag_analyse_slice_residual_similarities:
i_case += 1
ph.print_title("%d/%d: %s" % (i_case, N_cases, case_id))
if flag_rsync_stuff:
dir_output = utils.get_directory_case_recon_seg_mode(
case_id=case_id, recon_space="template_space", seg_mode="")
dir_input = re.sub("Volumes/spina/",
"Volumes/medic-volumetric_res/SpinaBifida/",
dir_output)
cmd = "rsync -avuhn --exclude 'motion_correction' %sseg_manual %s" % (
dir_input, dir_output)
ph.print_execution(cmd)
# ph.execute_command(cmd)
# -------------------------Correct Bias Field--------------------------
if flag_correct_bias_field:
filenames = utils.get_filenames_preprocessing_bias_field(case_id)
paths_to_filenames = [
os.path.join(utils.get_directory_case_original(case_id), f)
for f in filenames
]
dir_output = utils.get_directory_case_preprocessing(
case_id, stage="01_N4ITK")
# no image found matching the pattern
if len(paths_to_filenames) == 0:
continue
cmd_args = []
cmd_args.append("--filenames %s" % " ".join(paths_to_filenames))
cmd_args.append("--dir-output %s" % dir_output)
cmd_args.append("--prefix-output ''")
cmd = "niftymic_correct_bias_field %s" % (" ").join(cmd_args)
ph.execute_command(cmd,
flag_print_to_file=flag_batch_script,
path_to_file="%s%d.txt" %
(file_prefix_batch, ph.add_one(batch_ctr)))
# # Skip case in case segmentations have not been provided yet
# if not ph.directory_exists(utils.get_directory_case_segmentation(
# case_id, utils.SEGMENTATION_INIT, SEG_MODES[0])):
# continue
# ------------------------Collect Segmentations------------------------
if flag_collect_segmentations:
# Skip case in case segmentations have been collected already
if ph.directory_exists(
utils.get_directory_case_segmentation(
case_id, utils.SEGMENTATION_SELECTED, SEG_MODES[0])):
ph.print_info("skipped")
continue
filenames = utils.get_segmented_image_filenames(
case_id, subfolder=utils.SEGMENTATION_INIT)
for i_seg_mode, seg_mode in enumerate(SEG_MODES):
directory_selected = utils.get_directory_case_segmentation(
case_id, utils.SEGMENTATION_SELECTED, seg_mode)
ph.create_directory(directory_selected)
paths_to_filenames_init = [
os.path.join(
utils.get_directory_case_segmentation(
case_id, utils.SEGMENTATION_INIT, seg_mode), f)
for f in filenames
]
paths_to_filenames_selected = [
os.path.join(directory_selected, f) for f in filenames
]
for i in range(len(filenames)):
cmd = "cp -p %s %s" % (paths_to_filenames_init[i],
paths_to_filenames_selected[i])
# ph.print_execution(cmd)
ph.execute_command(cmd)
if flag_select_images_segmentations:
filenames = utils.get_segmented_image_filenames(
case_id, subfolder=utils.SEGMENTATION_SELECTED)
paths_to_filenames = [
os.path.join(
utils.get_directory_case_preprocessing(case_id,
stage="01_N4ITK"),
f) for f in filenames
]
paths_to_filenames_masks = [
os.path.join(
utils.get_directory_case_segmentation(
case_id, utils.SEGMENTATION_SELECTED, "seg_manual"), f)
for f in filenames
]
for i in range(len(filenames)):
ph.show_niftis(
[paths_to_filenames[i]],
segmentation=paths_to_filenames_masks[i],
# viewer="fsleyes",
)
ph.pause()
ph.killall_itksnap()
# # -------------------------Correct Intensities-----------------------
# if flag_correct_intensities:
# filenames = utils.get_segmented_image_filenames(case_id)
# paths_to_filenames_bias = [os.path.join(
# utils.get_directory_case_preprocessing(
# case_id, stage="01_N4ITK"), f) for f in filenames]
# print paths_to_filenames_bias
# -----------------Reconstruct Volume in Subject Space-----------------
if flag_reconstruct_volume_subject_space:
filenames = utils.get_segmented_image_filenames(
case_id, subfolder=utils.SEGMENTATION_SELECTED)
# filenames = filenames[0:2]
paths_to_filenames = [
os.path.join(
utils.get_directory_case_preprocessing(case_id,
stage="01_N4ITK"),
f) for f in filenames
]
# Estimate target stack
target_stack_index = utils.get_target_stack_index(
case_id, utils.SEGMENTATION_SELECTED, "seg_auto", filenames)
for i, seg_mode in enumerate(SEG_MODES):
# Get mask filenames
paths_to_filenames_masks = [
os.path.join(
utils.get_directory_case_segmentation(
case_id, utils.SEGMENTATION_SELECTED, seg_mode), f)
for f in filenames
]
if flag_reconstruct_volume_subject_space_irtk:
if seg_mode != "seg_manual":
continue
utils.export_irtk_call_to_workstation(
case_id=case_id,
filenames=filenames,
seg_mode=seg_mode,
isotropic_resolution=isotropic_resolution,
target_stack_index=target_stack_index,
kernel_mask_dilation=(15, 15, 4))
else:
dir_output = utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="subject_space",
seg_mode=seg_mode)
# dir_output = "/tmp/foo"
cmd_args = []
cmd_args.append("--filenames %s" %
" ".join(paths_to_filenames))
cmd_args.append("--filenames-masks %s" %
" ".join(paths_to_filenames_masks))
cmd_args.append("--dir-output %s" % dir_output)
cmd_args.append("--use-masks-srr 0")
cmd_args.append("--isotropic-resolution %f" %
isotropic_resolution)
cmd_args.append("--target-stack-index %d" %
target_stack_index)
cmd_args.append("--intensity-correction %d" %
intensity_correction)
cmd_args.append("--outlier-rejection %d" %
outlier_rejection)
cmd_args.append("--threshold-first %f" % threshold_first)
cmd_args.append("--threshold %f" % threshold)
# cmd_args.append("--metric %s" % metric)
# cmd_args.append("--multiresolution %d" % multiresolution)
# cmd_args.append("--metric-radius %s" % metric_radius)
# if i > 0:
# cmd_args.append("--reconstruction-space %s" % (
# utils.get_path_to_recon(
# utils.get_directory_case_recon_seg_mode(
# case_id, "seg_manual"))))
# cmd_args.append("--two-step-cycles 0")
cmd_args.append("--verbose %d" % verbose)
cmd_args.append("--provide-comparison %d" %
provide_comparison)
# cmd_args.append("--iter-max 1")
cmd = "niftymic_reconstruct_volume %s" % (
" ").join(cmd_args)
ph.execute_command(
cmd,
flag_print_to_file=flag_batch_script,
path_to_file="%s%d.txt" %
(file_prefix_batch, ph.add_one(batch_ctr)))
if flag_reconstruct_volume_subject_space_show_comparison:
recon_paths = []
for seg_mode in SEG_MODES:
path_to_recon = utils.get_path_to_recon(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="subject_space",
seg_mode=seg_mode))
recon_paths.append(path_to_recon)
recon_path_irtk = os.path.join(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="subject_space",
seg_mode="IRTK"), "IRTK_SRR.nii.gz")
show_modes = list(SEG_MODES)
if ph.file_exists(recon_path_irtk):
recon_paths.append(recon_path_irtk)
show_modes.append("irtk")
ph.show_niftis(recon_paths)
ph.print_info("Sequence: %s" % (" -- ").join(show_modes))
ph.pause()
ph.killall_itksnap()
# -------------------------Register to template------------------------
if flag_register_to_template:
for seg_mode in SEG_MODES:
cmd_args = []
# register seg_auto-recon to template space
if seg_mode == "seg_auto":
path_to_recon = utils.get_path_to_recon(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="subject_space",
seg_mode=seg_mode))
template_stack_estimator = \
tse.TemplateStackEstimator.from_mask(
ph.append_to_filename(path_to_recon, "_mask"))
path_to_reference = \
template_stack_estimator.get_path_to_template()
dir_input_motion_correction = os.path.join(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="subject_space",
seg_mode=seg_mode), "motion_correction")
dir_output = utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode=seg_mode)
# dir_output = "/home/mebner/tmp"
# # ------- DELETE -----
# dir_output = re.sub("data", "foo+1", dir_output)
# dir_output = re.sub(
# "volumetric_reconstruction/20180126/template_space/seg_auto",
# "", dir_output)
# # -------
# cmd_args.append("--use-fixed-mask 1")
cmd_args.append("--use-moving-mask 1")
# HACK
path_to_initial_transform = os.path.join(
utils.DIR_INPUT_ROOT_DATA, case_id,
"volumetric_reconstruction", "20180126",
"template_space", "seg_manual",
"registration_transform_sitk.txt")
cmd_args.append("--initial-transform %s" %
path_to_initial_transform)
cmd_args.append("--use-flirt 0")
cmd_args.append("--use-regaladin 1")
cmd_args.append("--test-ap-flip 0")
# register remaining recons to registered seg_auto-recon
else:
path_to_reference = utils.get_path_to_recon(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode="seg_auto"),
suffix="ResamplingToTemplateSpace",
)
path_to_initial_transform = os.path.join(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode="seg_auto"),
"registration_transform_sitk.txt")
path_to_recon = utils.get_path_to_recon(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="subject_space",
seg_mode=seg_mode))
dir_input_motion_correction = os.path.join(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="subject_space",
seg_mode=seg_mode), "motion_correction")
dir_output = utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode=seg_mode)
cmd_args.append("--use-fixed-mask 0")
cmd_args.append("--use-moving-mask 0")
cmd_args.append("--initial-transform %s" %
path_to_initial_transform)
cmd_args.append("--use-flirt 0")
cmd_args.append("--use-regaladin 1")
cmd_args.append("--test-ap-flip 0")
cmd_args.append("--moving %s" % path_to_recon)
cmd_args.append("--fixed %s" % path_to_reference)
cmd_args.append("--dir-input %s" % dir_input_motion_correction)
cmd_args.append("--dir-output %s" % dir_output)
cmd_args.append("--write-transform 1")
cmd_args.append("--verbose %d" % verbose)
cmd = "niftymic_register_image %s" % (" ").join(cmd_args)
ph.execute_command(cmd,
flag_print_to_file=flag_batch_script,
path_to_file="%s%d.txt" %
(file_prefix_batch, ph.add_one(batch_ctr)))
if flag_register_to_template_irtk:
dir_input = utils.get_directory_case_recon_seg_mode(
case_id=case_id, recon_space="subject_space", seg_mode="IRTK")
dir_output = utils.get_directory_case_recon_seg_mode(
case_id=case_id, recon_space="template_space", seg_mode="IRTK")
path_to_recon = os.path.join(dir_input, "IRTK_SRR.nii.gz")
path_to_reference = utils.get_path_to_recon(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode="seg_manual"),
suffix="ResamplingToTemplateSpace",
)
path_to_initial_transform = os.path.join(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode="seg_manual"), "registration_transform_sitk.txt")
cmd_args = []
cmd_args.append("--fixed %s" % path_to_reference)
cmd_args.append("--moving %s" % path_to_recon)
cmd_args.append("--initial-transform %s" %
path_to_initial_transform)
cmd_args.append("--use-fixed-mask 0")
cmd_args.append("--use-moving-mask 0")
cmd_args.append("--use-flirt 0")
cmd_args.append("--use-regaladin 1")
cmd_args.append("--test-ap-flip 0")
cmd_args.append("--dir-output %s" % dir_output)
cmd_args.append("--verbose %d" % verbose)
cmd = "niftymic_register_image %s" % (" ").join(cmd_args)
ph.execute_command(cmd)
if flag_show_srr_template_space:
recon_paths = []
show_modes = list(SEG_MODES)
# show_modes.append("IRTK")
for seg_mode in show_modes:
dir_input = utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode=seg_mode)
# # ------- DELETE -----
# dir_input = re.sub("data", "foo+1", dir_input)
# dir_input = re.sub(
# "volumetric_reconstruction/20180126/template_space/seg_auto",
# "", dir_input)
# # -------
path_to_recon_space = utils.get_path_to_recon(
dir_input,
suffix="ResamplingToTemplateSpace",
)
recon_paths.append(path_to_recon_space)
ph.show_niftis(recon_paths)
ph.print_info("Sequence: %s" % (" -- ").join(show_modes))
ph.pause()
ph.killall_itksnap()
# -----------------Reconstruct Volume in Template Space----------------
if flag_reconstruct_volume_template_space:
for seg_mode in SEG_MODES:
path_to_recon_space = utils.get_path_to_recon(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode=seg_mode),
suffix="ResamplingToTemplateSpace",
)
dir_input = os.path.join(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode=seg_mode), "motion_correction")
dir_output = utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode=seg_mode)
# dir_output = os.path.join("/tmp/spina/template_space/%s-%s" % (
# case_id, seg_mode))
cmd_args = []
cmd_args.append("--dir-input %s" % dir_input)
cmd_args.append("--dir-output %s" % dir_output)
cmd_args.append("--reconstruction-space %s" %
path_to_recon_space)
cmd_args.append("--alpha %s" % alpha)
cmd_args.append("--verbose %s" % verbose)
cmd_args.append("--use-masks-srr 0")
# cmd_args.append("--minimizer L-BFGS-B")
# cmd_args.append("--alpha 0.006")
# cmd_args.append("--reconstruction-type HuberL2")
# cmd_args.append("--data-loss arctan")
# cmd_args.append("--iterations 5")
# cmd_args.append("--data-loss-scale 0.7")
cmd = "niftymic_reconstruct_volume_from_slices %s" % \
(" ").join(cmd_args)
ph.execute_command(cmd,
flag_print_to_file=flag_batch_script,
path_to_file="%s%d.txt" %
(file_prefix_batch, ph.add_one(batch_ctr)))
# ----------------Collect SRR results in Template Space----------------
if flag_collect_volumetric_reconstruction_results:
directory = utils.get_directory_case_recon_summary(case_id)
ph.create_directory(directory)
# clear potentially existing files
cmd = "rm -f %s/*.nii.gz" % (directory)
ph.execute_command(cmd)
# Collect SRRs
for seg_mode in SEG_MODES:
path_to_recon_src = utils.get_path_to_recon(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode=seg_mode), )
path_to_recon = os.path.join(
directory, "%s%s.nii.gz" % (prefix_srr, seg_mode))
cmd = "cp -p %s %s" % (path_to_recon_src, path_to_recon)
ph.execute_command(cmd)
# Collect IRTK recon
path_to_recon_src = os.path.join(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode="IRTK"),
"IRTK_SRR_LinearResamplingToTemplateSpace.nii.gz")
path_to_recon = os.path.join(directory,
"%s%s.nii.gz" % (prefix_srr, "irtk"))
cmd = "cp -p %s %s" % (path_to_recon_src, path_to_recon)
ph.execute_command(cmd)
# Collect evaluation mask
path_to_recon = utils.get_path_to_recon(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="subject_space",
seg_mode="seg_auto"))
template_stack_estimator = \
tse.TemplateStackEstimator.from_mask(
ph.append_to_filename(path_to_recon, "_mask"))
path_to_template = \
template_stack_estimator.get_path_to_template()
path_to_template_mask_src = ph.append_to_filename(
path_to_template, "_mask_dil")
path_to_template_mask = "%s/" % directory
cmd = "cp -p %s %s" % (path_to_template_mask_src,
path_to_template_mask)
ph.execute_command(cmd)
if flag_show_volumetric_reconstruction_results:
dir_output = utils.get_directory_case_recon_summary(case_id)
paths_to_recons = []
for seg_mode in RECON_MODES:
path_to_recon = os.path.join(
dir_output, "%s%s.nii.gz" % (prefix_srr, seg_mode))
paths_to_recons.append(path_to_recon)
path_to_mask = "%s/STA*.nii.gz" % dir_output
cmd = ph.show_niftis(paths_to_recons, segmentation=path_to_mask)
sitkh.write_executable_file([cmd], dir_output=dir_output)
ph.pause()
ph.killall_itksnap()
# ---------------------Evaluate Image Similarities---------------------
if flag_evaluate_image_similarities:
dir_input = utils.get_directory_case_recon_summary(case_id)
dir_output = utils.get_directory_case_recon_similarities(case_id)
paths_to_recons = []
for seg_mode in ["seg_auto", "detect", "irtk"]:
path_to_recon = os.path.join(
dir_input, "%s%s.nii.gz" % (prefix_srr, seg_mode))
paths_to_recons.append(path_to_recon)
path_to_reference = os.path.join(
dir_input, "%s%s.nii.gz" % (prefix_srr, "seg_manual"))
path_to_reference_mask = utils.get_path_to_mask(dir_input)
cmd_args = []
cmd_args.append("--filenames %s" % " ".join(paths_to_recons))
cmd_args.append("--reference %s" % path_to_reference)
cmd_args.append("--reference-mask %s" % path_to_reference_mask)
# cmd_args.append("--verbose 1")
cmd_args.append("--dir-output %s" % dir_output)
exe = re.sub("pyc", "py",
os.path.abspath(evaluate_image_similarity.__file__))
cmd_args.insert(0, exe)
# clear potentially existing files
cmd = "rm -f %s/*.txt" % (dir_output)
ph.execute_command(cmd)
cmd = "python %s" % " ".join(cmd_args)
ph.execute_command(cmd)
# -----------------Evaluate Slice Residual Similarities----------------
if flag_evaluate_slice_residual_similarities:
path_to_reference_mask = utils.get_path_to_mask(
utils.get_directory_case_recon_summary(case_id))
dir_output_root = \
utils.get_directory_case_slice_residual_similarities(case_id)
# clear potentially existing files
# cmd = "rm -f %s/*.txt" % (dir_output_root)
# ph.execute_command(cmd)
for seg_mode in SEG_MODES:
dir_input = os.path.join(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode=seg_mode,
), "motion_correction")
path_to_reference = os.path.join(
utils.get_directory_case_recon_summary(case_id),
"%s%s.nii.gz" % (prefix_srr, seg_mode))
dir_output = os.path.join(dir_output_root, seg_mode)
cmd_args = []
cmd_args.append("--dir-input %s" % dir_input)
cmd_args.append("--reference %s" % path_to_reference)
cmd_args.append("--reference-mask %s" % path_to_reference_mask)
cmd_args.append("--use-reference-mask 1")
cmd_args.append("--use-slice-masks 0")
# cmd_args.append("--verbose 1")
cmd_args.append("--dir-output %s" % dir_output)
exe = re.sub("pyc", "py", os.path.abspath(esrs.__file__))
cmd_args.insert(0, exe)
cmd = "python %s" % " ".join(cmd_args)
ph.execute_command(cmd)
# Collect data for blinded analysis
if flag_collect_data_blinded_analysis:
if flag_remove_failed_cases_for_analysis and case_id in RECON_FAILED_CASE_IDS:
continue
dir_input = utils.get_directory_case_recon_summary(case_id)
# pattern = "STA([0-9]+)[_]mask.nii.gz"
pattern = "STA([0-9]+)[_]mask_dil.nii.gz"
p = re.compile(pattern)
gw = [
p.match(f).group(1) for f in os.listdir(dir_input)
if p.match(f)
][0]
dir_output = os.path.join(
utils.get_directory_blinded_analysis(case_id, "open"), case_id)
exe = re.sub("pyc", "py", os.path.abspath(mswm.__file__))
recons = []
for seg_mode in RECON_MODES:
path_to_recon = os.path.join(
dir_input, "%s%s.nii.gz" % (prefix_srr, seg_mode))
cmd_args = []
cmd_args.append("--filename %s" % path_to_recon)
cmd_args.append("--gestational-age %s" % gw)
cmd_args.append("--dir-output %s" % dir_output)
cmd_args.append("--prefix-output %s" % prefix_srr_qa)
cmd_args.append("--verbose 0")
cmd_args.insert(0, exe)
cmd = "python %s" % " ".join(cmd_args)
# ph.execute_command(cmd)
recon = "%s%s" % (prefix_srr_qa,
os.path.basename(path_to_recon))
recons.append(recon)
ph.write_show_niftis_exe(recons, dir_output)
if flag_anonymize_data_blinded_analysis:
dir_input = os.path.join(
utils.get_directory_blinded_analysis(case_id, "open"), case_id)
dir_output_dictionaries = utils.get_directory_anonymized_dictionares(
case_id)
dir_output_anonymized_images = os.path.join(
utils.get_directory_blinded_analysis(case_id, "anonymized"),
case_id)
if not ph.directory_exists(dir_input):
continue
ph.create_directory(dir_output_dictionaries)
ph.create_directory(dir_output_anonymized_images)
data_anonymizer = da.DataAnonymizer()
# Create random dictionary (only required once)
# data_anonymizer.set_prefix_identifiers("%s_" % case_id)
# data_anonymizer.read_nifti_filenames_from_directory(dir_input)
# data_anonymizer.generate_identifiers()
# data_anonymizer.generate_randomized_dictionary()
# data_anonymizer.write_dictionary(
# dir_output_dictionaries, "dictionary_%s" % case_id)
# Read dictionary
data_anonymizer.read_dictionary(dir_output_dictionaries,
"dictionary_%s" % case_id)
# Anonymize files
if 0:
ph.clear_directory(dir_output_anonymized_images)
data_anonymizer.anonymize_files(dir_input,
dir_output_anonymized_images)
# Write executable script
filenames = [
"%s.nii.gz" % f
for f in sorted(data_anonymizer.get_identifiers())
]
ph.write_show_niftis_exe(filenames,
dir_output_anonymized_images)
# Reveal anonymized files
if 1:
filenames = data_anonymizer.reveal_anonymized_files(
dir_output_anonymized_images)
filenames = sorted(["%s" % f for f in filenames])
ph.write_show_niftis_exe(filenames,
dir_output_anonymized_images)
# Reveal additional, original files
# data_anonymizer.reveal_original_files(dir_output)
# relative_directory = re.sub(
# utils.get_directory_blinded_analysis(case_id, "anonymized"),
# ".",
# dir_output_anonymized_images)
# paths_to_filenames = [os.path.join(
# relative_directory, f) for f in filenames]
# ---------------------Analyse Image Similarities---------------------
if flag_analyse_image_similarities or \
flag_analyse_slice_residual_similarities or \
flag_analyse_stacks:
if flag_remove_failed_cases_for_analysis:
if case_id in RECON_FAILED_CASE_IDS:
continue
if cases[case_id]['postrep'] == flag_postop or flag_postop == 2:
cases_similarities.append(case_id)
cases_stacks.append(
utils.get_segmented_image_filenames(
case_id,
# subfolder=utils.SEGMENTATION_INIT,
subfolder=utils.SEGMENTATION_SELECTED,
))
dir_output_analysis = os.path.join(
# "/Users/mebner/UCL/UCL/Publications",
"/home/mebner/Dropbox/UCL/Publications",
"2018_MICCAI/brain_reconstruction_paper")
if flag_analyse_image_similarities:
dir_inputs = []
filename = "image_similarities_postop%d.txt" % flag_postop
for case_id in cases_similarities:
dir_inputs.append(
utils.get_directory_case_recon_similarities(case_id))
cmd_args = []
cmd_args.append("--dir-inputs %s" % " ".join(dir_inputs))
cmd_args.append("--dir-output %s" % dir_output_analysis)
cmd_args.append("--filename %s" % filename)
exe = re.sub("pyc", "py",
os.path.abspath(src.analyse_image_similarities.__file__))
cmd_args.insert(0, exe)
cmd = "python %s" % " ".join(cmd_args)
ph.execute_command(cmd)
if flag_analyse_slice_residual_similarities:
dir_inputs = []
filename = "slice_residuals_postop%d.txt" % flag_postop
for case_id in cases_similarities:
dir_inputs.append(
utils.get_directory_case_slice_residual_similarities(case_id))
cmd_args = []
cmd_args.append("--dir-inputs %s" % " ".join(dir_inputs))
cmd_args.append("--subfolder %s" % " ".join(SEG_MODES))
cmd_args.append("--dir-output %s" % dir_output_analysis)
cmd_args.append("--filename %s" % filename)
exe = re.sub(
"pyc", "py",
os.path.abspath(src.analyse_slice_residual_similarities.__file__))
cmd_args.insert(0, exe)
cmd = "python %s" % " ".join(cmd_args)
# print len(cases_similarities)
# print cases_similarities
ph.execute_command(cmd)
if flag_analyse_stacks:
cases_stacks_N = [len(s) for s in cases_stacks]
ph.print_subtitle("%d cases -- Number of stacks" % len(cases_stacks))
ph.print_info("min: %g" % np.min(cases_stacks_N))
ph.print_info("mean: %g" % np.mean(cases_stacks_N))
ph.print_info("median: %g" % np.median(cases_stacks_N))
ph.print_info("max: %g" % np.max(cases_stacks_N))
elapsed_time = ph.stop_timing(time_start)
ph.print_title("Summary")
print("Computational Time for Pipeline: %s" % (elapsed_time))
return 0