def convert_dir(in_path):
out_path = in_path + "_utf8"
all_paths = []
path_cats, cats = tools.get_paths(in_path)
for path_i, cat_i in zip(path_cats, cats):
text_paths, names = tools.get_paths(path_i)
all_paths += text_paths
tools.make_dir(out_path)
for dir_i in path_cats:
dir_i = dir_i.replace(in_path, out_path)
tools.make_dir(dir_i)
for in_path_i in all_paths:
out_path_i = in_path_i.replace(in_path, out_path)
to_utf8(in_path_i, out_path_i)
print(all_paths)
source_dir = ''
output_dir = './'
infile = sys.argv[1]
else:
# Use hardcoded file
root_dir = '/home/kyager/ppl/AtikurRahman/layered_BCPs_01/'
data_dir = 'L-36/'
#data_dir = 'normal/'
protocol = 'local_avg_realspace'
#protocol = 'thumbnails'
source_dir = os.path.join( root_dir, 'raw', data_dir )
output_dir = os.path.join( root_dir, 'ImAnalysis', data_dir )
tools.make_dir(output_dir)
#infile = 'L-36-3cyc_L-74-3-cyc_O2-at-end_1_q01.tif'
import glob
#infiles = glob.glob(source_dir + '*.tif')
infiles = glob.glob(source_dir + 'L-36-3cyc_L-74-3-cyc_O2-at-end_1_q01.tif')
#infiles = glob.glob(source_dir + '6.tif')
#infiles = glob.glob(source_dir + '2012-09-25 Lam Blend01-2p5KRPM_q02.tif')
infiles.sort()
if True:
environment_init.install_init() # install init_package os.chdir(base['tar_base']) for soft in init_package: tools.extract_file(soft) folder_name = tools.filter(soft) tools.pak_configure(folder_name,folder_name,base['soft_path'],base['tar_base']) tools.pak_make(folder_name,base['tar_base']) # install mysql # extract mysql.tar tools.extract_file(mysql_package) # configure options for dir in ['data','etc'] tools.make_dir(dir,mysql_path) options = """ -DMYSQL_DATADIR=%s \ -DSYSCONFDIR=%s \ -DEXTRA_CHARSETS=all \ -DDEFAULT_CHARSET=utf8 \ -DDEFAULT_COLLATION=utf8_general_ci \ -DENABLED_LOCAL_INFILE=1 \ -DWITH_READLINE=1 \ -DWITH_DEBUG=0 \ -DWITH_EMBEDDED_SERVER=1 \ -DWITH_INNOBASE_STORAGE_ENGINE=1 """ % (os.path.join(mysql_path,'data'),os.path.join(mysql_path,'etc'))
# configure
tools.pak_configure(base['soft_name'],tools.filter(nginx_package),base['soft_path'],base['tar_path'],options)
# change the Makefile to tend to adjust the init
makefile_fix = os.path.join(base['tar_path'],nginx_package,'/objs')
makefile = tools.read_file('makefile_fix' + '/Makefile')
result_1 = re.sub(r'./configure','./configure --prefix=/opt/lnmp/app/init',makefile)
result = re.sub(r'./configure --prefix=/opt/lnmp/app/init --disable-shared','./configure --prefix=/opt/lnmp/app/init',result_1)
tools.write_file(makefile_fix,result)
# make
tools.pak_make(tools.filter(nginx_package),base['tar_path'])
# config
for dir in ['sites-enabled','sites-available']:
tools.make_dir(dir,os.path.join(nginx_path,'conf'))
tools.make_dir('bin',nginx_path)
for file in ['nginx.conf','nginx.sh','herouser_virtualhost']:
temp_content = tools.read_file(cwd + '/' + file)
if 'conf' in file:
temp_file = os.path.join(nginx_path, 'conf') + '/' + file
elif 'sh' in file:
temp_file = os.path.join(nginx_path, 'bin') + '/' + file
elif 'virtualhost' in file:
temp_file = os.path.join(nginx_path, 'conf', 'sites-available') + '/' + file
tools.write_file(temp_file, temp_content)
os.chmod(temp_file,stat.S_IRWXU + stat.S_IRWXG)
tools.make_dir('www/herouser',nginx_path)
def _do_registration(self, modality, fiji_path, registration_script,
registration_params, input_folder,
input_filenames_prefix, output_folder, num_images,
orig_image_size, pixels_per_mm, info_description,
info_parameters):
# modality is 'EM' or 'LM'
# Ensure that folder exists, if not create it and its parent folders.
tools.make_dir(output_folder)
# Tell Fiji to execute a macro that (i) reads the LM/EM images, (ii) merges them into a stack,
# (iii) saves the stack to TIFF, (iv) aligns the slices in this stack
# and (v) saves the aligned stack to TIFF.
# Registration happens either with Fiji's Plugins > Registration > Linear Stack Alignment with SIFT, or with Plugins > StackReg.
busy_string = "Registering {} images...".format(modality)
print(busy_string)
print(
'Starting a headless Fiji and calling the image registration plugin. Please be patient...'
)
script_args = "srcdir='{}',dstdir='{}',prefix='{}',method='{}',numimages='{}',do_invert='{}',do_enhance_contrast='{}',do_crop='{}',roi_x='{}',roi_y='{}',roi_width='{}',roi_height='{}'".format(
input_folder, output_folder, input_filenames_prefix,
registration_params['method'], num_images,
registration_params["invert"],
registration_params["enhance_contrast"],
registration_params["crop"], registration_params["roi"][0],
registration_params["roi"][1], registration_params["roi"][2],
registration_params["roi"][3])
# Note: info about headless ImageJ: https://imagej.net/Headless#Running_macros_in_headless_mode
wait = wx.BusyInfo(busy_string)
retcode, out, err = tools.commandline_exec([
fiji_path, "-Dpython.console.encoding=UTF-8", "--ij2",
"--headless", "--console", "--run", registration_script,
script_args
])
print('Headless Fiji retcode={}\nstdout=\n{}\nstderr={}\n'.format(
retcode, out, err))
del wait
# # # # # # # # # # BEGIN EXPERIMENTAL CODE
update_offsets = True
output_filename = '{}_aligned_stack.tif'.format(
registration_params['method'])
registration_correct = wx.MessageBox(
'Please check the registration result {}.\n'
'Are all images aligned correctly?'.format(
os.path.join(output_folder,
output_filename)), 'Registration successful?',
wx.YES_NO | wx.ICON_QUESTION, self) # "Yes" is selected by default
if registration_correct == wx.NO:
print('User feedback: registration is not correct.')
with RegistrationImportDialog(output_folder, None, wx.ID_ANY,
"Import Registration Output") as dlg:
if dlg.ShowModal() == wx.ID_OK:
print('Using manual registration output')
out = dlg.get_registration_output()
info_description = info_description + ' (Manual)'
else:
print('User feedback: do not use manual registration')
update_offsets = False
else:
print('User feedback: registration is correct.')
# # # # # # # # # # END EXPERIMENTAL CODE
if update_offsets:
# Parse the output of the registration plugin and extract
# the transformation matrices to register each slice onto the next.
print(
'Extracting transformation matrices from registration plugin output'
)
registration_matrices = tools.extract_registration_matrices(
registration_params['method'], out)
print(registration_matrices)
registration_offsets_microns = self.calculate_registration_offsets(
registration_matrices, orig_image_size, pixels_per_mm,
registration_params)
print(
'Registration corrected point-of-interest offsets [micrometer]: '
+ repr(registration_offsets_microns))
# Combine (=sum) existing offsets with this new one
assert self._model.combined_offsets_microns is not None
assert len(self._model.combined_offsets_microns) == len(
registration_offsets_microns)
self._model.combined_offsets_microns = map(
operator.add, self._model.combined_offsets_microns,
registration_offsets_microns)
print('Combined offsets [micrometer]: ' +
repr(self._model.combined_offsets_microns))
# Append offset correction to "history" of existing offsets.
self._model.all_offsets_microns.append({
'name':
info_description,
'parameters':
info_parameters,
'offsets':
registration_offsets_microns
})
else:
print('Registration plugin output NOT used.')
print('Slice offsets NOT updated with corrections.')
#
self._do_save_poi_info(output_folder)
# For debugging / validation: display the offsets table.
tools.show_offsets_table(self._model.all_offsets_microns,
self._model.combined_offsets_microns)