# this is a python rewrite of the original ijm published at
# https://github.com/Hyojung-Choo/Myosoft/blob/Myosoft-hub/Scripts/central%20nuclei%20counter.ijm
# IJ imports
# TODO: are the imports RoiManager and ResultsTable needed when using the services?
from ij import IJ, WindowManager as wm
from ij.plugin import Duplicator, RoiEnlarger, RoiScaler
from trainableSegmentation import WekaSegmentation
from de.biovoxxel.toolbox import Extended_Particle_Analyzer
# Bio-formats imports
from loci.plugins import BF
from loci.plugins.in import ImporterOptions
# python imports
import time
import os
#@ String (visibility=MESSAGE, value="<html><b> Welcome to Myosoft! </b></html>") msg1
#@ File (label="Select fiber-ROIs zip-file", style="file") roi_zip
#@ File (label="Select image file", description="select your image") path_to_image
#@ File (label="Select directory for output", style="directory") output_dir
#@ Boolean (label="close image after processing", description="tick this box when using batch mode", value=False) close_raw
#@ String (visibility=MESSAGE, value="<html><b> channel positions in the hyperstack </b></html>") msg5
#@ Integer (label="Fiber staining 1 channel number (0=n.a.)", style="slider", min=0, max=5, value=1) fiber_channel_1
#@ Integer (label="Fiber staining 2 channel number (0=n.a.)", style="slider", min=0, max=5, value=2) fiber_channel_2
#@ Integer (label="Fiber staining 3 channel number (0=n.a.)", style="slider", min=0, max=5, value=3) fiber_channel_3
#@ Integer (label="minimum fiber intensity (0=auto)", description="0 = automatic threshold detection", value=0) min_fiber_intensity_1
#@ Integer (label="minimum fiber intensity (0=auto)", description="0 = automatic threshold detection", value=0) min_fiber_intensity_2
#@ Integer (label="minimum fiber intensity (0=auto)", description="0 = automatic threshold detection", value=0) min_fiber_intensity_3
#@ ResultsTable rt
#@ RoiManager rm
def fix_ij_options():
"""put IJ into a defined state
"""
# disable inverting LUT
IJ.run("Appearance...", " menu=0 16-bit=Automatic")
# set foreground color to be white, background black
IJ.run("Colors...", "foreground=white background=black selection=red")
# black BG for binary images and pad edges when eroding
IJ.run("Options...", "black pad")
# set saving format to .txt files
IJ.run("Input/Output...", "file=.txt save_column save_row")
# ============= DON’T MOVE UPWARDS =============
# set "Black Background" in "Binary Options"
IJ.run("Options...", "black")
# scale when converting = checked
IJ.run("Conversions...", "scale")
def fix_ij_dirs(path):
"""use forward slashes in directory paths
Parameters
----------
path : string
a directory path obtained from dialogue or script parameter
Returns
-------
string
a more robust path with forward slashes as separators
"""
fixed_path = str(path).replace("\\", "/")
# fixed_path = fixed_path + "/"
return fixed_path
def open_image_with_BF(path_to_file):
""" use Bio-Formats to opens the first image from an image file path
Parameters
----------
path_to_file : string
path to the image file
Returns
-------
ImagePlus
the first imp stored in a give file
"""
options = ImporterOptions()
options.setColorMode(ImporterOptions.COLOR_MODE_GRAYSCALE)
options.setAutoscale(True)
options.setId(path_to_file)
imps = BF.openImagePlus(options) # is an array of ImagePlus
return imps[0]
def fix_BF_czi_imagetitle(imp):
image_title = os.path.basename( imp.getTitle() )
image_title = image_title.replace(".czi", "")
image_title = image_title.replace(" ", "_")
image_title = image_title.replace("_-_", "")
image_title = image_title.replace("__", "_")
image_title = image_title.replace("#", "Series")
return image_title
def clear_ij_roi_manager(rm):
"""delete all ROIs from the RoiManager
Parameters
----------
rm : RoiManager
a reference of the IJ-RoiManager
"""
rm.runCommand('reset')
def get_threshold_from_method(imp, channel, method):
"""returns the threshold value of chosen IJ AutoThreshold method in desired channel
Parameters
----------
imp : ImagePlus
the imp from which to get the threshold value
channel : integer
the channel in which to get the treshold
method : string
the AutoThreshold method to use
Returns
-------
list
the upper and the lower threshold (integer values)
"""
imp.setC(channel) # starts at 1
ip = imp.getProcessor()
ip.setAutoThreshold(method + " dark")
lower_thr = ip.getMinThreshold()
upper_thr = ip.getMaxThreshold()
ip.resetThreshold()
return lower_thr, upper_thr
def measure_in_all_rois( imp, channel, rm ):
"""measures in all ROIS on a given channel of imp all parameters that are set in IJ "Set Measurements"
Parameters
----------
imp : ImagePlus
the imp to measure on
channel : integer
the channel to measure in. starts at 1.
rm : RoiManager
a reference of the IJ-RoiManager
"""
imp.setC(channel)
rm.runCommand(imp,"Deselect")
rm.runCommand(imp,"Measure")
def change_all_roi_color( rm, color ):
"""change the color of all ROIs in the RoiManager
Parameters
----------
rm : RoiManager
a reference of the IJ-RoiManager
color : string
the desired color. e.g. "green", "red", "yellow", "magenta" ...
"""
number_of_rois = rm.getCount()
for roi in range( number_of_rois ):
rm.select(roi)
rm.runCommand("Set Color", color)
def change_subset_roi_color( rm, selected_rois, color ):
"""change the color of selected ROIs in the RoiManager
Parameters
----------
rm : RoiManager
a reference of the IJ-RoiManager
selected_rois : array
ROIs in the RoiManager to change
color : string
the desired color. e.g. "green", "red", "yellow", "magenta" ...
"""
rm.runCommand("Deselect")
rm.setSelectedIndexes(selected_rois)
rm.runCommand("Set Color", color)
rm.runCommand("Deselect")
def show_all_rois_on_image(rm, imp):
"""shows all ROIs in the ROiManager on imp
Parameters
----------
rm : RoiManager
a reference of the IJ-RoiManager
imp : ImagePlus
the imp on which to show the ROIs
"""
imp.show()
rm.runCommand(imp,"Show All")
def save_all_rois(rm, target):
"""save all ROIs in the RoiManager as zip to target path
Parameters
----------
rm : RoiManager
a reference of the IJ-RoiManager
target : string
the path in to store the ROIs. e.g. /my-images/resulting_rois.zip
"""
rm.runCommand("Save", target)
def save_selected_rois( rm, selected_rois, target ):
"""save selected ROIs in the RoiManager as zip to target path
Parameters
----------
rm : RoiManager
a reference of the IJ-RoiManager
selected_rois : array
ROIs in the RoiManager to save
target : string
the path in to store the ROIs. e.g. /my-images/resulting_rois_subset.zip
"""
rm.runCommand("Deselect")
rm.setSelectedIndexes(selected_rois)
rm.runCommand("save selected", target)
rm.runCommand("Deselect")
def select_positive_fibers( imp, channel, rm, min_intensity ):
"""For all ROIs in the RoiManager, select ROIs based on intensity measurement in given channel of imp.
See https://imagej.nih.gov/ij/developer/api/ij/process/ImageStatistics.html
Parameters
----------
imp : ImagePlus
the imp on which to measure
channel : integer
the channel on which to measure. starts at 1
rm : RoiManager
a reference of the IJ-RoiManager
min_intensity : integer
the selection criterion (here: intensity threshold)
Returns
-------
array
a selection of ROIs which passed the selection criterion (are above the threshold)
"""
imp.setC(channel)
all_rois = rm.getRoisAsArray()
selected_rois = []
for i, roi in enumerate(all_rois):
imp.setRoi(roi)
stats = imp.getStatistics()
if stats.mean > min_intensity:
selected_rois.append(i)
return selected_rois
def open_rois_from_zip( rm, path ):
"""open RoiManager ROIs from zip and adds them to the RoiManager
Parameters
----------
rm : RoiManager
a reference of the IJ-RoiManager
path : string
path to the ROI zip file
"""
rm.runCommand("Open", path)
def preset_results_column( rt, column, value):
"""pre-set all rows in given column of the IJ-ResultsTable with desired value
Parameters
----------
rt : ResultsTable
a reference of the IJ-ResultsTable
column : string
the desired column. will be created if it does not yet exist
value : string or float or integer
the value to be set
"""
for i in range( rt.size() ):
rt.setValue(column, i, value)
rt.show("Results")
def add_results( rt, column, row, value ):
"""adds a value in desired rows of a given column
Parameters
----------
rt : ResultsTable
a reference of the IJ-ResultsTable
column : string
the column in which to add the values
row : array
the row numbers in which too add the values.
value : string or float or integer
the value to be set
"""
for i in range( len( row ) ):
rt.setValue(column, row[i], value)
rt.show("Results")
def enhance_contrast( imp ):
"""use "Auto" Contrast & Brightness settings in each channel of imp
Parameters
----------
imp : ImagePlus
the imp on which to change C&B
"""
for channel in range( imp.getDimensions()[2] ):
imp.setC(channel + 1) # IJ channels start at 1
IJ.run(imp, "Enhance Contrast", "saturated=0.35")
def renumber_rois(rm):
"""rename all ROIs in the RoiManager according to their number
Parameters
----------
rm : RoiManager
a reference of the IJ-RoiManager
"""
number_of_rois = rm.getCount()
for roi in range( number_of_rois ):
rm.rename( roi, str(roi + 1) )
def setup_defined_ij(rm, rt):
"""set up a clean and defined Fiji user environment
Parameters
----------
rm : RoiManager
a reference of the IJ-RoiManager
rt : ResultsTable
a reference of the IJ-ResultsTable
"""
fix_ij_options()
rm.runCommand('reset')
rt.reset()
IJ.log("\\Clear")
execution_start_time = time.time()
setup_defined_ij(rm, rt)
# open image using Bio-Formats
path_to_image = fix_ij_dirs(path_to_image)
raw = open_image_with_BF(path_to_image)
# get image info
raw_image_calibration = raw.getCalibration()
raw_image_title = fix_BF_czi_imagetitle(raw)
# take care of paths and directories
# output_dir = os.path.dirname(str(roi_zip))
# output_dir = fix_ij_dirs(output_dir)
input_rois_path = fix_ij_dirs( roi_zip )
output_dir = fix_ij_dirs(output_dir) + "/2b_fibertyping/"
if not os.path.exists( output_dir ):
os.makedirs( output_dir )
# open ROIS and show on image
open_rois_from_zip( rm, str(input_rois_path) )
change_all_roi_color(rm, "blue")
show_all_rois_on_image( rm, raw )
# update the log for the user
IJ.log( "Now working on " + str(raw_image_title) )
if raw_image_calibration.scaled() == False:
IJ.log("Your image is not spatially calibrated! Size measurements are only possible in [px].")
IJ.log( " -- settings used -- ")
IJ.log( "Selected fiber-ROIs zip-file = " + str(input_rois_path) )
IJ.log( "Fiber staining 1 channel number = " + str(fiber_channel_1) )
IJ.log( "Fiber staining 2 channel number = " + str(fiber_channel_2) )
IJ.log( "Fiber staining 3 channel number = " + str(fiber_channel_3) )
IJ.log( " -- settings used -- ")
# measure size & shape,
IJ.run("Set Measurements...", "area perimeter shape feret's redirect=None decimal=4")
IJ.run("Clear Results", "")
measure_in_all_rois( raw, fiber_channel_1, rm )
# loop through the fiber channels, check if positive, add info to results table
all_fiber_channels = [fiber_channel_1, fiber_channel_2, fiber_channel_3]
all_min_fiber_intensities = [min_fiber_intensity_1, min_fiber_intensity_2, min_fiber_intensity_3]
roi_colors = ["green", "orange", "red"]
all_fiber_subsets =[ [], [], [] ]
for index, fiber_channel in enumerate(all_fiber_channels):
if fiber_channel > 0:
preset_results_column( rt, "channel " + str(fiber_channel) + " positive (" + roi_colors[fiber_channel-1] + ")", "NO" )
if all_min_fiber_intensities[index] == 0:
all_min_fiber_intensities[index] = get_threshold_from_method(raw, fiber_channel, "Mean")[0]
IJ.log( "fiber channel " + str(fiber_channel) + " intensity threshold: " + str(all_min_fiber_intensities[index]) )
positive_fibers = select_positive_fibers( raw, fiber_channel, rm, all_min_fiber_intensities[index] )
all_fiber_subsets[index] = positive_fibers
if len(positive_fibers) > 0:
change_subset_roi_color(rm, positive_fibers, roi_colors[index])
save_selected_rois( rm, positive_fibers, output_dir + "positive_fiber_rois_c" + str( fiber_channel ) + ".zip")
add_results( rt, "channel " + str(fiber_channel) + " positive (" + roi_colors[fiber_channel-1] + ")", positive_fibers, "YES")
# single positive
positive_c1 = all_fiber_subsets[0]
positive_c2 = all_fiber_subsets[1]
positive_c3 = all_fiber_subsets[2]
# double positive
positive_c1_c2 = list( set(all_fiber_subsets[0]).intersection(all_fiber_subsets[1]) )
positive_c1_c3 = list( set(all_fiber_subsets[0]).intersection(all_fiber_subsets[2]) )
positive_c2_c3 = list( set(all_fiber_subsets[1]).intersection(all_fiber_subsets[2]) )
# triple positive
positive_c1_c2_c3 = list( set(positive_c1_c2).intersection(all_fiber_subsets[2]) )
# update ROI color & results table for double and triple positives
if len(positive_c1_c2) > 0:
preset_results_column( rt, "channel 1,2 positive (magenta)", "NO" )
change_subset_roi_color(rm, positive_c1_c2, "magenta")
save_selected_rois( rm, positive_c1_c2, output_dir + "positive_fiber_rois_c1_c2.zip")
add_results( rt, "channel 1,2 positive (magenta)", positive_c1_c2, "YES")
if len(positive_c1_c3) > 0:
preset_results_column( rt, "channel 1,3 positive (yellow)", "NO" )
change_subset_roi_color(rm, positive_c1_c3, "yellow")
save_selected_rois( rm, positive_c1_c3, output_dir + "positive_fiber_rois_c1_c3.zip")
add_results( rt, "channel 1,3 positive (yellow)", positive_c1_c3, "YES")
if len(positive_c2_c3) > 0:
preset_results_column( rt, "channel 2,3 positive (cyan)", "NO" )
change_subset_roi_color(rm, positive_c2_c3, "cyan")
save_selected_rois( rm, positive_c2_c3, output_dir + "positive_fiber_rois_c2_c3.zip")
add_results( rt, "channel 2,3 positive (cyan)", positive_c2_c3, "YES")
if len(positive_c1_c2_c3) > 0:
preset_results_column( rt, "channel 1,2,3 positive (white)", "NO" )
change_subset_roi_color(rm, positive_c1_c2_c3, "white")
save_selected_rois( rm, positive_c1_c2_c3, output_dir + "positive_fiber_rois_c1_c2_c3.zip")
add_results( rt, "channel 1,2,3 positive (white)", positive_c1_c2_c3, "YES")
# save all results together
save_all_rois( rm, output_dir + "all_fiber_type_rois_color-coded.zip" )
rt.save(output_dir + "fibertyping_results.csv")
# dress up the original image, save a overlay-png, present original to the user
raw.show()
show_all_rois_on_image( rm, raw )
raw.setDisplayMode(IJ.COMPOSITE)
enhance_contrast( raw )
IJ.run("From ROI Manager", "") # ROIs -> overlays so they show up in the saved png
qc_duplicate = raw.duplicate()
IJ.saveAs(qc_duplicate, "PNG", output_dir + raw_image_title + "_fibertyping")
qc_duplicate.close()
wm.toFront( raw.getWindow() )
IJ.run("Remove Overlay", "")
raw.setDisplayMode(IJ.GRAYSCALE)
show_all_rois_on_image( rm, raw )
total_execution_time_min = (time.time() - execution_start_time) / 60.0
IJ.log("total time in minutes: " + str(total_execution_time_min))
IJ.log( "~~ all done ~~" )
IJ.selectWindow("Log")
IJ.saveAs("Text", str(output_dir + raw_image_title + "_fibertyping_Log"))
if close_raw == True:
raw.close()