def check_overwrite(self, workspace):
"""Make sure it's ok to overwrite any existing files before starting run
workspace - workspace with all image sets already populated
returns True if ok to proceed, False if user cancels
"""
if self.wants_overwrite_without_warning:
return True
files_to_check = []
metadata_groups = self.get_metadata_groups(workspace)
for metadata_group in metadata_groups:
image_number = metadata_group.image_numbers[0]
files_to_check.append(
self.make_image_file_name(workspace, image_number))
files_to_overwrite = filter(os.path.isfile, files_to_check)
if len(files_to_overwrite) > 0:
if get_headless():
logger.error(
"ExportToACC is configured to refrain from overwriting files and the following file(s) already exist: %s"
% ", ".join(files_to_overwrite))
return False
msg = "Overwrite the following file(s)?\n" +\
"\n".join(files_to_overwrite)
import wx
result = wx.MessageBox(
msg,
caption="ExportToACC: Overwrite existing files",
style=wx.YES_NO | wx.NO_DEFAULT | wx.ICON_QUESTION)
if result != wx.YES:
return False
return True
def show_warning(title, message, get_preference, set_preference):
"""Show a silenceable warning message to the user
title - title for the dialog box
message - message to be displayed
get_preference - function that gets a user preference: do you want to
show this warning?
set_preference - function that sets the user preference if they choose
not to see the warning again.
The message is printed to the console if headless.
"""
from cellprofiler_core.preferences import get_headless
if get_headless():
print(message)
return
if not get_preference():
return
import wx
if wx.GetApp() is None:
print(message)
return
with wx.Dialog(None, title=title) as dlg:
dlg.Sizer = sizer = wx.BoxSizer(wx.VERTICAL)
subsizer = wx.BoxSizer(wx.HORIZONTAL)
sizer.Add(subsizer, 0, wx.EXPAND | wx.ALL, 5)
subsizer.Add(
wx.StaticBitmap(
dlg,
wx.ID_ANY,
wx.ArtProvider.GetBitmap(wx.ART_INFORMATION,
wx.ART_CMN_DIALOG),
),
0,
wx.ALIGN_LEFT | wx.ALIGN_TOP | wx.RIGHT,
5,
)
text = wx.StaticText(dlg, wx.ID_ANY, message)
subsizer.Add(text, 0, wx.ALIGN_LEFT | wx.ALIGN_TOP | wx.ALL, 5)
dont_show = wx.CheckBox(dlg, label="Don't show this message again.")
sizer.Add(dont_show, 0, wx.ALIGN_LEFT | wx.ALL, 5)
buttons_sizer = wx.StdDialogButtonSizer()
buttons_sizer.AddButton(wx.Button(dlg, wx.ID_OK))
buttons_sizer.Realize()
sizer.Add(buttons_sizer, 0, wx.ALIGN_CENTER_HORIZONTAL | wx.ALL, 5)
dlg.Fit()
dlg.ShowModal()
if dont_show.GetValue():
set_preference(False)
def prepare_run(self, workspace):
"""Invoke the image_set_list pickling mechanism and save the pipeline"""
pipeline = workspace.pipeline
image_set_list = workspace.image_set_list
if pipeline.test_mode or self.from_old_matlab:
return True
if self.batch_mode.value:
self.enter_batch_mode(workspace)
return True
else:
path = self.save_pipeline(workspace)
if not cpprefs.get_headless():
import wx
wx.MessageBox(
"CreateBatchFiles saved pipeline to %s" % path,
caption="CreateBatchFiles: Batch file saved",
style=wx.OK | wx.ICON_INFORMATION,
)
return False
def run(self, workspace):
"""Run the module. Add the measurements. """
statistics_dict = {}
ratio_dict = {}
for channel in self.channels:
provider = workspace.image_set.get_image_provider(
channel.cpimage_name.value)
assert isinstance(provider, OmeroImageProvider)
name = provider.get_name()
omero_image_name = provider.get_omero_image_name()
omero_image_id = provider.get_image_id()
pixels_id = provider.get_pixels_id()
z = provider.get_z()
c = provider.get_c()
t = provider.get_t()
header = []
row = []
ratio = []
m = workspace.measurements
measurements = ()
if self.omero_object == MS_DATASET:
measurements += (
(M_DATASET_NAME, self.dataset_name, 3.0),
(M_DATASET_ID, self.omero_object_id.value, 1.0),
)
elif self.omero_object == MS_PLATE:
# CellProfiler starts counting image sets from 1
well = self.wells[workspace.measurements.image_set_number - 1]
well_row = well.getRow().getValue()
well_column = well.getColumn().getValue()
well_id = well.getId().getValue()
measurements += (
(M_PLATE_NAME, self.plate_name, 3.0),
(M_PLATE_ID, self.omero_object_id.value, 1.0),
(M_WELL_ROW, well_row, 1.0),
(M_WELL_COLUMN, well_column, 1.0),
(M_WELL_ID, well_id, 3.0),
)
measurements += (
(M_IMAGE_NAME, omero_image_name, 3.0),
(M_IMAGE_ID, omero_image_id, 1.0),
(M_PIXELS_ID, pixels_id, 1.0),
(M_Z, z, 0.5),
(M_C, c, 0.5),
(M_T, t, 0.5),
)
for tag, value, r in measurements:
m.add_image_measurement("_".join((tag, name)), value)
header.append(tag)
row.append(value)
ratio.append(r)
statistics = [header, row]
ratio = [x / sum(ratio) for x in ratio]
statistics_dict[channel.channel_number.value] = statistics
ratio_dict[channel.channel_number.value] = ratio
workspace.display_data.statistics = statistics_dict
workspace.display_data.ratio = ratio_dict
if cpp.get_headless(): # headless mode
for channel in self.channels:
image_name, channel_number = (
channel.cpimage_name.value,
channel.channel_number.value,
)
print("--- image name: %s\tchannel: %s" %
(image_name, channel_number))
(header,
row) = workspace.display_data.statistics[channel_number]
for i in range(0, len(header)):
print("\t%s: %s" % (header[i], row[i]))
def prepare_run(self, workspace):
"""Set up omero image providers inside the image_set_list"""
pipeline = workspace.pipeline
image_set_list = workspace.image_set_list
if pipeline.in_batch_mode():
# TODO: Rewrite the OmeroImageProvider such that it can be used in batch mode
# e.g., omero session keys could be used to attach to existing sessions to
# keep OmeroImageProviders from creating a new session every time an image should be loaded
return False
if cpp.get_headless():
print(
"OmeroLoadImages running in headless mode: image directory parameter will be used as omero object id"
)
self.omero_object_id.set_value(
int(cpp.get_default_image_directory()))
print("omero object id = %d" % self.omero_object_id.value)
print("omero object type = %s" % self.omero_object.value)
self.create_omero_gateway()
if self.omero_object == MS_IMAGE:
omero_image_list = [
self.omero_gateway.getImage(self.omero_object_id.value)
]
elif self.omero_object == MS_DATASET:
# Get dataset without leaves(=images&pixels)
dataset = self.omero_gateway.getDataset(self.omero_object_id.value,
False)
self.dataset_name = dataset.getName().getValue()
omero_image_list = self.get_images_from_dataset(
self.omero_object_id.value)
elif self.omero_object == MS_PLATE:
self.wells = self.get_wells_from_plate(self.omero_object_id.value)
self.plate_name = self.wells[0].getPlate().getName().getValue()
omero_image_list = []
for well in self.wells:
for wellsample in well.iterateWellSamples():
omero_image_list.append(wellsample.getImage())
# get names and pixels from omero images
pixels_list = []
for omero_image in omero_image_list:
image_id = omero_image.getId().getValue()
pixels_list += self.omero_gateway.getPixelsFromImage(image_id)
# add images to image sets
image_set_count = len(pixels_list)
for i in range(0, image_set_count):
image_set = image_set_list.get_image_set(i)
pixels = pixels_list[i]
pixels_id = pixels.getId().getValue()
sizeZ = pixels.getSizeZ().getValue()
sizeC = pixels.getSizeC().getValue()
sizeT = pixels.getSizeT().getValue()
for channel in self.channels:
for z in range(0, sizeZ):
for t in range(0, sizeT):
c = int(channel.channel_number.value)
self.save_image_set_info(
image_set,
channel.cpimage_name.value,
P_OMERO,
V_OMERO,
self.omero_gateway,
pixels_id,
z,
c,
t,
)
return True
def merge_files(destination, sources, force_headless=False):
is_headless = force_headless or get_headless()
if not is_headless:
import wx
if len(sources) == 0:
return
if not is_headless:
progress = wx.ProgressDialog(
"Writing " + destination,
"Loading " + sources[0],
maximum=len(sources) * 4 + 1,
style=wx.PD_CAN_ABORT
| wx.PD_APP_MODAL
| wx.PD_ELAPSED_TIME
| wx.PD_REMAINING_TIME,
)
count = 0
try:
pipeline = cpp.Pipeline()
has_error = [False]
def callback(caller, event):
if isinstance(event, cpp.event.LoadException):
has_error = True
wx.MessageBox(
message="Could not load %s: %s" %
(sources[0], event.error),
caption="Failed to load %s" % sources[0],
)
has_error[0] = True
pipeline.add_listener(callback)
pipeline.load(sources[0])
if has_error[0]:
return
if destination.lower().endswith(".h5"):
mdest = cpmeas.Measurements(filename=destination,
multithread=False)
h5_dest = True
else:
mdest = cpmeas.Measurements(multithread=False)
h5_dest = False
for source in sources:
if not is_headless:
count += 1
keep_going, skip = progress.Update(count,
"Loading " + source)
if not keep_going:
return
if h5py.is_hdf5(source):
msource = cpmeas.Measurements(filename=source,
mode="r",
multithread=False)
else:
msource = cpmeas.load_measurements(source)
dest_image_numbers = mdest.get_image_numbers()
source_image_numbers = msource.get_image_numbers()
if len(dest_image_numbers) == 0 or len(
source_image_numbers) == 0:
offset_source_image_numbers = source_image_numbers
else:
offset_source_image_numbers = (
np.max(dest_image_numbers) -
np.min(source_image_numbers) + source_image_numbers +
1)
for object_name in msource.get_object_names():
if object_name in mdest.get_object_names():
destfeatures = mdest.get_feature_names(object_name)
else:
destfeatures = []
for feature in msource.get_feature_names(object_name):
if object_name == cpmeas.EXPERIMENT:
if not mdest.has_feature(object_name, feature):
src_value = msource.get_experiment_measurement(
feature)
mdest.add_experiment_measurement(
feature, src_value)
continue
src_values = msource.get_measurement(
object_name,
feature,
image_set_number=source_image_numbers)
mdest[object_name, feature,
offset_source_image_numbers] = src_values
destset = set(destfeatures)
if not is_headless:
keep_going, skip = progress.Update(count + 1,
"Saving to " + destination)
if not keep_going:
return
if not h5_dest:
pipeline.save_measurements(destination, mdest)
finally:
if not is_headless:
progress.Destroy()
def run(self, workspace):
default_output_directory = get_default_output_directory()
tag = "runimagejmacro_" + str(random.randint(100000, 999999))
tempdir = os.path.join(default_output_directory, tag)
os.makedirs(tempdir, exist_ok=True)
try:
for image_group in self.image_groups_in:
image = workspace.image_set.get_image(
image_group.image_name.value)
image_pixels = image.pixel_data
skimage.io.imsave(
os.path.join(tempdir, image_group.output_filename.value),
image_pixels)
if self.executable_file.value[-4:] == ".app":
executable = os.path.join(
default_output_directory,
self.executable_directory.value.split("|")[1],
self.executable_file.value, "Contents/MacOS/ImageJ-macosx")
else:
executable = os.path.join(
default_output_directory,
self.executable_directory.value.split("|")[1],
self.executable_file.value)
cmd = [
executable, "--headless", "console", "--run",
os.path.join(default_output_directory,
self.macro_directory.value.split("|")[1],
self.macro_file.value)
]
cmd += [self.stringify_metadata(tempdir)]
result = subprocess.run(cmd,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
text=True)
for image_group in self.image_groups_out:
if not os.path.exists(
os.path.join(tempdir,
image_group.input_filename.value)):
# Cleanup the error logs for display, we want to remove less-useful lines to keep it succinct.
reject = ('console:', 'Java Hot', 'at org', 'at java',
'[WARNING]', '\t')
# ImageJ tends to report the same few lines over and over, so we'll use a dict as an ordered set.
err = {}
for line in result.stdout.splitlines():
if len(line.strip()) > 0 and not line.startswith(
reject):
err[line] = None
if len(err) > 1:
# Error appears when file loading fails, but can also show up if the macro failed to generate
# an output image. We remove this if it wasn't the only error, as it can be confusing.
err.pop('Unsupported format or not found', None)
err = "\n".join(err.keys())
msg = f"CellProfiler couldn't find the output expected from the ImageJ Macro," \
f"\n File {image_group.input_filename.value} was missing."
if err:
msg += f"\n\nImageJ logs contained the following: \n{err}"
raise FileNotFoundError("Missing file", msg)
image_pixels = skimage.io.imread(
os.path.join(tempdir, image_group.input_filename.value))
workspace.image_set.add(image_group.image_name.value,
Image(image_pixels, convert=False))
finally:
want_delete = True
# Optionally clean up temp directory regardless of macro success
if workspace.pipeline.test_mode and self.debug_mode:
want_delete = False
if not get_headless():
import wx
message = f"Debugging was enabled.\nTemporary folder was not deleted automatically" \
f"\n\nTemporary subfolder is {os.path.split(tempdir)[-1]} in your Default Output Folder\n\nDo you want to delete it now?"
with wx.Dialog(None,
title="RunImageJMacro Debug Mode") as dlg:
text_sizer = dlg.CreateTextSizer(message)
sizer = wx.BoxSizer(wx.VERTICAL)
dlg.SetSizer(sizer)
button_sizer = dlg.CreateStdDialogButtonSizer(
flags=wx.YES | wx.NO)
open_temp_folder_button = wx.Button(
dlg, -1, "Open temporary folder")
button_sizer.Insert(0, open_temp_folder_button)
def on_open_temp_folder(event):
import sys
if sys.platform == "win32":
os.startfile(tempdir)
else:
import subprocess
subprocess.call([
"open",
tempdir,
])
open_temp_folder_button.Bind(wx.EVT_BUTTON,
on_open_temp_folder)
sizer.Add(text_sizer, 0, wx.EXPAND | wx.ALL, 10)
sizer.Add(button_sizer, 0, wx.EXPAND | wx.ALL, 10)
dlg.SetEscapeId(wx.ID_NO)
dlg.SetAffirmativeId(wx.ID_YES)
dlg.Fit()
dlg.CenterOnParent()
if dlg.ShowModal() == wx.ID_YES:
want_delete = True
if want_delete:
try:
for subdir, dirs, files in os.walk(tempdir):
for file in files:
os.remove(os.path.join(tempdir, file))
os.removedirs(tempdir)
except:
logging.error(
"Unable to delete temporary directory, files may be in use by another program."
)
logging.error(
"Temp folder is subfolder {tempdir} in your Default Output Folder.\nYou may need to remove it manually."
)
else:
logging.error(
f"Debugging was enabled.\nDid not remove temporary folder at {tempdir}"
)
pixel_data = []
image_names = []
if self.show_window:
for x in itertools.chain(self.image_groups_in,
self.image_groups_out):
pixel_data.append(
workspace.image_set.get_image(
x.image_name.value).pixel_data)
image_names.append(x.image_name.value)
workspace.display_data.pixel_data = pixel_data
workspace.display_data.display_names = image_names
workspace.display_data.dimensions = workspace.image_set.get_image(
self.image_groups_out[0].image_name.value).dimensions