def inputScale(self):
gd=GenericDialog("Scale ROI")
gd.addStringField("X Scale:",str(self.xScale))
gd.addStringField("Y Scale:",str(self.yScale))
gd.addStringField("Scale X to pixel:",str(self.impXpixel))
gd.addStringField("Scale Y to pixel:",str(self.impYpixel))
fields=gd.getStringFields()
self.XscaleField,self.YscaleField=fields[0],fields[1]
self.toXfield,self.toYfield=fields[2],fields[3]
self.XscaleField.addTextListener(self)
self.YscaleField.addTextListener(self)
self.toXfield.addTextListener(self)
self.toYfield.addTextListener(self)
gd.showDialog()
if gd.wasOKed():
return(self.xScale,self.yScale)
else:
return(1.0,1.0)
class Colortags:
'''
This class handles persistence of the name of different color channels.
It instantiates a GUI window to capture and show the color names in use.
Other classes such as ColorMerger needs this to determine the color of the channel being processed.
'''
def __init__(self):
self.window = None
self.tags = {}
self.prefkeys = ["{0}.{1}".format(SUBKEY, name) for name in COLORS]
self.userprefs = Prefs()
self.load()
while not self.tags:
self.edit("Please set at least one colortag.\n\n")
def load(self):
'''
Tries to load IBPlib colortags from IJ prefs.
'''
for i in range(7):
storedtags = self.userprefs.getString(".{0}".format(self.prefkeys[i]), "")
if not storedtags:
continue
trimmedtagslist = [t.strip() for t in storedtags.split(",")]
self.tags.update({i:trimmedtagslist})
def edit(self, msg=""):
'''
Opens the color tags dialog to update color tags
'''
self.window = GenericDialog("ColorMerger - Edit color tags")
self.window.addMessage("{0}Separate tags with a comma.\nBlank values will be ignored.".format(msg))
for i in range(7):
try:
self.window.addStringField(COLORS[i], ", ".join(self.tags[i]), 30)
except KeyError:
self.window.addStringField(COLORS[i], "", 30)
self.window.setOKLabel("Save")
self.window.showDialog()
if self.window.wasOKed():
self.__savetags()
self.load()
def __validate(self):
fields = self.window.getStringFields()
newvalues = {}
for i in range(len(fields)):
txt = fields[i].getText()
newvalues.update({i: txt.strip()})
return newvalues
def __savetags(self):
newvalues = self.__validate()
for i, tags in newvalues.items():
key = self.prefkeys[i]
self.userprefs.set(key, tags)
self.userprefs.savePreferences()
def analysis_parameters_gui(rerun_analysis=False, params=None):
"""GUI for setting analysis parameters at the start of a run. TODO: more effectively separate model and view"""
if params is None:
params = Parameters(load_last_params = True);
dialog = GenericDialog("Analysis parameters");
controls = [];
if rerun_analysis:
params.setUseSingleChannel(False);
params.togglePerformUserQC(False);
params.setDoInnerOuterComparison(False);
controls.append(MyControlDefinition(u'Curvature length parameter (\u00b5m): ',
MyControlDefinition.Numeric,
round(params.curvature_length_um, 2),
params.setCurvatureLengthUm));
controls.append(MyControlDefinition(u'Width of region for intensity analysis (\u00b5m): ',
MyControlDefinition.Numeric,
round(params.intensity_profile_width_um, 2),
params.setIntensityProfileWidthUm))
controls.append(MyControlDefinition("Threshold method: ",
MyControlDefinition.Choice,
params.threshold_method,
params.setThresholdMethod,
choices=params.listThresholdMethods(),
enabled=(not rerun_analysis)));
controls.append(MyControlDefinition("Curvature overlay LUT: ",
MyControlDefinition.Choice,
params.curvature_overlay_lut_string,
params.setCurvatureOverlayLUT,
choices=IJ.getLuts()));
controls.append(MyControlDefinition("Curvature kymograph LUT: ",
MyControlDefinition.Choice,
params.curvature_kymograph_lut_string,
params.setCurvatureKymographLUT,
choices=IJ.getLuts()));
controls.append(MyControlDefinition("Labelled species kymograph LUT: ",
MyControlDefinition.Choice,
params.actin_kymograph_lut_string,
params.setActinKymographLUT,
choices=IJ.getLuts()));
controls.append(MyControlDefinition("Labelled species for intensity analysis: ",
MyControlDefinition.String,
params.labeled_species,
params.setLabeledSpecies));
controls.append(MyControlDefinition("Use intensity channel for segmentation too?",
MyControlDefinition.Checkbox,
params.use_single_channel,
params.setUseSingleChannel,
enabled=(not rerun_analysis)));
controls.append(MyControlDefinition("Metadata source: ",
MyControlDefinition.RadioButtonGroup,
params.metadata_source,
params.setMetadataSource,
choices=["Image metadata", "Acquisition metadata"]));
controls.append(MyControlDefinition("Constrain anchors close to manual selections?",
MyControlDefinition.Checkbox,
params.constrain_anchors,
params.toggleConstrainAnchors,
enabled=(not rerun_analysis)));
controls.append(MyControlDefinition("Filter out negative curvatures",
MyControlDefinition.Checkbox,
params.filter_negative_curvatures,
params.setFilterNegativeCurvatures));
controls.append(MyControlDefinition("Account for photobleaching?",
MyControlDefinition.Checkbox,
params.photobleaching_correction,
params.togglePhotobleachingCorrection));
controls.append(MyControlDefinition("Perform quality control of membrane edges?",
MyControlDefinition.Checkbox,
params.perform_user_qc,
params.togglePerformUserQC,
enabled=(not rerun_analysis)));
controls.append(MyControlDefinition("Perform quality control of background regions?",
MyControlDefinition.Checkbox,
params.qc_background_rois,
params.toggleBackgroundQc));
controls.append(MyControlDefinition("Perform spatial cropping?",
MyControlDefinition.Checkbox,
params.perform_spatial_crop,
params.toggleSpatialCrop,
enabled=(not rerun_analysis)));
controls.append(MyControlDefinition("Perform time cropping?",
MyControlDefinition.Checkbox,
params.perform_time_crop,
params.toggleTimeCrop,
enabled=(not rerun_analysis)));
controls.append(MyControlDefinition("Close images on completion?",
MyControlDefinition.Checkbox,
params.close_on_completion,
params.toggleCloseOnCompletion));
controls.append(MyControlDefinition("Compare inner and outer curvature regions?",
MyControlDefinition.Checkbox,
params.inner_outer_comparison,
params.setDoInnerOuterComparison,
enabled=(not rerun_analysis)));
for control in controls:
control.addControl(dialog);
dialog.showDialog();
if dialog.wasCanceled():
raise KeyboardInterrupt("Run canceled");
numeric_controls = [c for c in controls if c.control_type==MyControlDefinition.Numeric];
for nc, nf in zip(numeric_controls, dialog.getNumericFields()):
nc.setter(float(nf.getText()));
string_controls = [c for c in controls if c.control_type==MyControlDefinition.String];
for sc, sf in zip(string_controls, dialog.getStringFields()):
sc.setter(sf.getText());
choice_controls = [c for c in controls if c.control_type==MyControlDefinition.Choice];
for cc, cf in zip(choice_controls, dialog.getChoices()):
cc.setter(cf.getSelectedItem());
checkbox_controls = [c for c in controls if c.control_type==MyControlDefinition.Checkbox];
for cbc, cbf in zip(checkbox_controls, dialog.getCheckboxes()):
cbc.setter(cbf.getState());
radiobuttongroup_controls = [c for c in controls if c.control_type==MyControlDefinition.RadioButtonGroup];
for rbc in radiobuttongroup_controls:
rbc.setter(rbc.checkboxes[[cb.getState() for cb in rbc.checkboxes].index(True)].getLabel());
params.persistParameters();
return params;
def run():
### Default arguments
two_sarcomere_size = 25 # determined by filter used.
rotation_angle = 0.0
### Get the image we'd like to work with.
# Don't need to ask where to save the intermediate image since it'll just be saved in the matchedmyo folder anyway
# may change this though. In case they want to keep that image around.
this_img = WindowManager.getCurrentImage()
if this_img == None:
ud = WaitForUserDialog(
"Please select the image you would like to analyze.")
ud.show()
this_img = WindowManager.getCurrentImage()
img_name = this_img.getTitle()
matchedmyo_path = "/home/AD/dfco222/scratchMarx/matchedmyo/" # this would be grabbed from the prompt
gd = GenericDialog("Preprocessing Options")
gd.addCheckbox("Automatic Resizing/Rotation", True)
gd.addCheckbox("CLAHE", True)
gd.addCheckbox("Normalization to Transverse Tubules", True)
gd.showDialog()
if gd.wasCanceled():
return
auto_resize_rotate = gd.getNextBoolean()
clahe = gd.getNextBoolean()
normalize = gd.getNextBoolean()
if auto_resize_rotate:
# Clear selection so it takes FFT of full image
rotation_angle = auto_resize_angle_measure(this_img,
two_sarcomere_size)
if clahe:
clahe_args = "blocksize={} histogram=256 maximum=3 mask=*None* fast_(less_accurate)".format(
two_sarcomere_size)
IJ.run(this_img, "Enhance Local Contrast (CLAHE)", clahe_args)
if normalize:
# Ask the user to select a subsection of the image that looks healthy-ish.
ud = WaitForUserDialog(
"Please select a subsection exhibiting a measure of healthy TT structure using the Rectangle tool.\n"
+ " Only a single cell or several are needed.\n\n" +
" Press 'OK' when finished.")
ud.show()
IJ.setTool("rectangle")
# Duplicate the selected subsection.
selection = this_img.crop()
IJ.run(selection, "Duplicate...", "title=subsection.tif")
# Grab the subsection image and rotate it.
selection = WindowManager.getImage("subsection.tif")
IJ.run(
selection, "Rotate...",
"angle={} grid=1 interpolation=Bicubic enlarge".format(
rotation_angle))
# Ask the user to select a bounding box that contains only tubules
# NOTE: Need to get rid of initial selection since it's the entire image and it's annoying to click out of
IJ.setTool("rectangle")
IJ.run(selection, "Select None", "")
ud = WaitForUserDialog(
"Select a subsection of the image that contains only tubules and no membrane."
)
ud.show()
# Grab the subsection ImagePlus
selection = WindowManager.getCurrentImage()
this_window = WindowManager.getActiveWindow()
selection_small = selection.crop()
IJ.run(selection, "Close", "")
# NOTE: May not actually display this depending on how the macros work
IJ.run(selection_small, "Duplicate...", "title=subsection_small.tif")
# Smooth the selection using the single TT filter.
# NOTE: It won't read in so we're just going to hard code it in since it's simple
tt_filt_row = "0 0 0 1 1 1 1 1 1 0 0 0 0\n"
tt_filt = ""
for i in range(21):
tt_filt += tt_filt_row
IJ.run("Convolve...", "text1=[" + tt_filt + "] normalize")
# Segment out the TTs from the 'gaps' using Gaussian Adaptive Thresholding.
selection_small = WindowManager.getImage("subsection_small.tif")
IJ.run(selection_small, "Duplicate...", "title=thresholded.tif")
threshed = WindowManager.getImage("thresholded.tif")
IJ.run(threshed, "Auto Local Threshold",
"method=Bernsen radius=7 parameter_1=1 parameter_2=0 white")
# Select the TTs from the thresholded image.
IJ.run(threshed, "Create Selection", "")
tt_selection = WindowManager.getImage("subsection_small.tif")
IJ.selectWindow("thresholded.tif")
IJ.selectWindow("subsection_small.tif")
IJ.run(tt_selection, "Restore Selection", "")
# Get TT intensity statistics.
stat_options = IS.MEAN | IS.MIN_MAX | IS.STD_DEV
stats = IS.getStatistics(tt_selection.getProcessor(), stat_options,
selection_small.getCalibration())
# Calculate pixel ceiling intensity value based on heuristic.
# TODO: Add a catch for data type overflow.
pixel_ceiling = stats.mean + 3 * stats.stdDev
print "px ceil:", pixel_ceiling
# Invert selection to get inter-sarcomeric gap intensity statistics.
IJ.run(tt_selection, "Make Inverse", "")
stat_options = IS.MEAN | IS.MIN_MAX | IS.STD_DEV
stats = IS.getStatistics(tt_selection.getProcessor(), stat_options,
selection_small.getCalibration())
# Calculate pixel floor intensity value based on heuristic.
pixel_floor = stats.mean - stats.stdDev
# TODO: Add a catch for data type underflow.
print "px floor:", pixel_floor
# Threshold original image based on these values.
IJ.selectWindow(this_img.getTitle())
IJ.run(this_img, "Select All", "")
IJ.setMinAndMax(pixel_floor, pixel_ceiling)
IJ.run(this_img, "Apply LUT", "")
## Ask if it is acceptable.
gd = GenericDialog("Acceptable?")
gd.addMessage(
"If the preprocessed image is acceptable for analysis, hit 'OK' to being analysis.\n"
+ " If the image is unacceptable or an error occurred, hit 'Cancel'")
gd.showDialog()
if gd.wasCanceled():
return
## Save the preprocessed image.
imp = IJ.getImage()
fs = FileSaver(imp)
img_save_dir = matchedmyo_path + "myoimages/" # actually get from user at some point
img_file_path = img_save_dir + img_name[:-4] + "_preprocessed.tif"
if os.path.exists(img_save_dir) and os.path.isdir(img_save_dir):
print "Saving image as:", img_file_path
if os.path.exists(img_file_path):
# use dialog box to ask if they want to overwrite
gd = GenericDialog("Overwrite?")
gd.addMessage(
"A file exists with the specified path, \"{}\". Would you like to overwrite it?"
.format(img_file_path))
gd.enableYesNoCancel()
gd.showDialog()
if gd.wasCanceled():
return
elif fs.saveAsTiff(img_file_path):
print "Preprocessed image saved successfully at:", '"' + img_file_path + '"'
else:
print "Folder does not exist or is not a folder!"
### Create the YAML file containing the parameters for classification
## Ask user for YAML input
gd = GenericDialog("YAML Input")
gd.addStringField("imageName", img_file_path, 50)
#gd.addStringField("maskName", "None")
gd.addStringField("outputParams_fileRoot", img_file_path[:-4], 50)
gd.addStringField("outputParams_fileType", "tif")
gd.addNumericField("outputParams_dpi", 300, 0)
gd.addCheckbox("outputParams_saveHitsArray", False)
gd.addStringField("outputParams_csvFile", matchedmyo_path + "results/")
gd.addCheckbox("TT Filtering", True)
gd.addToSameRow()
gd.addCheckbox("LT Filtering", True)
gd.addToSameRow()
gd.addCheckbox("TA Filtering", True)
gd.addNumericField("scopeResolutions_x", 5.0, 3)
gd.addToSameRow()
gd.addNumericField("scopeResolutions_y", 5.0, 3)
gd.addMessage("Enter in filter rotation angles separated by commas.")
gd.addStringField("", "-25, -20, -15, -10, -5, 0, 5, 10, 15, 20, 25", 50)
gd.addCheckbox("returnAngles", False)
gd.addCheckbox("returnPastedFilter", True)
gd.showDialog()
if gd.wasCanceled():
return
strings = [st.text for st in gd.getStringFields()]
#if strings[1] == "None" or "":
# strings[1] = None
nums = [float(num.text) for num in gd.getNumericFields()]
nums[0] = int(nums[0]) # Have to make sure the dpi variable is an integer
checks = [str(bool(boo.state)) for boo in gd.getCheckboxes()]
iter_argument = ','.join(
[str(float(it) - rotation_angle) for it in strings[4].split(',')])
string_block = """imageName: {0[0]}
outputParams:
fileRoot: {0[1]}
fileType: {0[2]}
dpi: {1[0]}
saveHitsArray: {2[0]}
csvFile: {0[3]}
preprocess: False
filterTypes:
TT: {2[1]}
LT: {2[2]}
TA: {2[3]}
scopeResolutions:
x: {1[1]}
y: {1[2]}
iters: [{3}]
returnAngles: {2[4]}
returnPastedFilter: {2[5]}""".format(strings, nums, checks, iter_argument)
im_title = this_img.getTitle()
with cd(matchedmyo_path):
yaml_file_path = "./YAML_files/" + im_title[:-4] + ".yml"
with open("./YAML_files/" + im_title[:-4] + ".yml", "w") as ym:
ym.write(string_block)
print "Wrote YAML file to:", matchedmyo_path + yaml_file_path[2:]
### Run the matchedmyo code on the preprocessed image
with cd(matchedmyo_path):
#os.chdir(matchedmyo_path)
#subprocess.call(["python3", matchedmyo_path+"matchedmyo.py", "fullValidation"])
subprocess.call(
["python3", "matchedmyo.py", "run", "--yamlFile", yaml_file_path])
from java.awt import Color
from java.awt.event import TextListener
from ij import IJ
from ij import Menus
from ij.gui import GenericDialog
#commands = [c for c in ij.Menus.getCommands().keySet()]
# Above, equivalent list as below:
commands = Menus.getCommands().keySet().toArray()
gd = GenericDialog('Command Launcher')
gd.addStringField('Command: ', '');
prompt = gd.getStringFields().get(0)
prompt.setForeground(Color.red)
class TypeListener(TextListener):
def textValueChanged(self, tvc):
if prompt.getText() in commands:
prompt.setForeground(Color.black)
return
prompt.setForeground(Color.red)
# or loop:
#for c in commands:
# if c == text:
# prompt.setForeground(Color.black)
# return
#
#prompt.setForeground(Color.red)
prompt.addTextListener(TypeListener())
gd.showDialog()
if not gd.wasCanceled(): IJ.doCommand(gd.getNextString())
