diff --git a/1_identify_fibers.py b/1_identify_fibers.py
old mode 100644
new mode 100755
index 15acdcfd82c07bc961bceef36cd0772b794f1425..0c17e2931bca2ce75c1aa3da788fd3c4c33454af
--- a/1_identify_fibers.py
+++ b/1_identify_fibers.py
@@ -1,4 +1,4 @@
-# this is a python rewrite of the original ijm published at
+# 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
@@ -7,6 +7,7 @@ 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
+from ij.measure import ResultsTable
# Bio-formats imports
from loci.plugins import BF
@@ -43,8 +44,9 @@ import os
#@ Integer (label="Fiber staining (MHC) channel number (0=skip)", style="slider", min=0, max=5, value=3) fiber_channel
#@ Integer (label="minimum fiber intensity (0=auto)", description="0 = automatic threshold detection", value=0) min_fiber_intensity
#@ Integer (label="sub-tiling to economize RAM", style="slider", min=1, max=8, value=4) tiling_factor
-#@ ResultsTable rt
+
#@ RoiManager rm
+#@ ResultsTable rt
def fix_ij_options():
@@ -129,7 +131,7 @@ def preprocess_membrane_channel(imp):
IJ.run(imp, "Enhance Contrast", "saturated=0.35")
IJ.run(imp, "Apply LUT", "")
IJ.run(imp, "Enhance Contrast", "saturated=1")
- IJ.run(imp, "8-bit", "")
+ IJ.run(imp, "8-bit", "")
IJ.run(imp, "Invert", "")
IJ.run(imp, "Convolve...", "text1=[-1.0 -1.0 -1.0 -1.0 -1.0\n-1.0 -1.0 -1.0 -1.0 0\n-1.0 -1.0 24.0 -1.0 -1.0\n-1.0 -1.0 -1.0 -1.0 -1.0\n-1.0 -1.0 -1.0 -1.0 0] normalize")
@@ -167,7 +169,7 @@ def apply_weka_model(model_path, imp, tiles_per_dim):
Parameters
----------
model_path : string
- path to the model file
+ path to the model file
imp : ImagePlus
ImagePlus to apply the model to
tiles_per_dim : integer
@@ -181,7 +183,7 @@ def apply_weka_model(model_path, imp, tiles_per_dim):
segmentator = WekaSegmentation()
segmentator.loadClassifier( model_path )
result = segmentator.applyClassifier( imp, [tiles_per_dim, tiles_per_dim], 0, True ) #ImagePlus imp, int[x,y,z] tilesPerDim, int numThreads (0=all), boolean probabilityMaps
-
+
return result
@@ -197,7 +199,7 @@ def process_weka_result(imp):
IJ.run(imp, "8-bit", "")
IJ.run(imp, "Median...", "radius=3")
IJ.run(imp, "Gaussian Blur...", "sigma=2")
- IJ.run(imp, "Auto Threshold", "method=MaxEntropy")
+ IJ.run(imp, "Auto Threshold", "method=MaxEntropy")
IJ.run(imp, "Invert", "")
@@ -216,7 +218,7 @@ def delete_channel(imp, channel_number):
def run_extended_particle_analyzer( imp, eda_parameters ):
- """identifies ROIs in target imp using the extended particle analyzer of the BioVoxxel toolbox
+ """identifies ROIs in target imp using the extended particle analyzer of the BioVoxxel toolbox
with given parameters
Parameters
@@ -229,7 +231,7 @@ def run_extended_particle_analyzer( imp, eda_parameters ):
epa = Extended_Particle_Analyzer()
epa.readInputImageParameters(imp)
epa.setDefaultParameterFields()
-
+
# expose all parameters explicitly
epa.usePixel = False
epa.usePixelForOutput = False
@@ -257,7 +259,7 @@ def run_extended_particle_analyzer( imp, eda_parameters ):
epa.AddToManager = True
epa.ExcludeEdges = False
epa.IncludeHoles = False
-
+
epa.defineParticleAnalyzers()
epa.particleAnalysis( imp.getProcessor(), imp, imp.getTitle() )
@@ -392,7 +394,7 @@ def select_positive_fibers( imp, channel, rm, min_intensity ):
a reference of the IJ-RoiManager
min_intensity : integer
the selection criterion (here: intensity threshold)
-
+
Returns
-------
array
@@ -407,7 +409,7 @@ def select_positive_fibers( imp, channel, rm, min_intensity ):
if stats.mean > min_intensity:
selected_rois.append(i)
- return selected_rois
+ return selected_rois
def preset_results_column( rt, column, value):
@@ -491,8 +493,11 @@ def setup_defined_ij(rm, rt):
execution_start_time = time.time()
+
setup_defined_ij(rm, rt)
+print rt.size()
+
# open image using Bio-Formats
path_to_image = fix_ij_dirs(path_to_image)
raw = open_image_with_BF(path_to_image)
@@ -555,8 +560,8 @@ if fiber_channel > 0:
if min_fiber_intensity == 0:
min_fiber_intensity = get_threshold_from_method(raw, fiber_channel, "Mean")[0]
IJ.log( "automatic intensity threshold detection: True" )
-
- IJ.log( "fiber intensity threshold: " + str(min_fiber_intensity) )
+
+ IJ.log( "fiber intensity threshold: " + str(min_fiber_intensity) )
change_all_roi_color(rm, "blue")
positive_fibers = select_positive_fibers( raw, fiber_channel, rm, min_fiber_intensity )
change_subset_roi_color(rm, positive_fibers, "magenta")
@@ -567,12 +572,19 @@ IJ.run("Set Measurements...", "area perimeter shape feret's redirect=None decima
IJ.run("Clear Results", "")
measure_in_all_rois( raw, membrane_channel, rm )
+rt = ResultsTable.getResultsTable("Results")
+
+print rt.size()
+
if fiber_channel > 0:
+ print rt.size()
preset_results_column( rt, "MHC Positive Fibers (magenta)", "NO" )
+ print rt.size()
add_results( rt, "MHC Positive Fibers (magenta)", positive_fibers, "YES")
+ print rt.size()
rt.save(output_dir + "all_fibers_results.csv")
-
+print "saved the all_fibers_results.csv"
# dress up the original image, save a overlay-png, present original to the user
rm.show()
raw.show()