/
hysteresis_segment.py
114 lines (95 loc) · 4.5 KB
/
hysteresis_segment.py
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'''<b>Hysteresis_Segment</b> - perform WMC segmentation, just better GUI and integration
'''
import os
import sys
import cellprofiler.settings as cps
import cellprofiler.cpimage as cpi
import cellprofiler.cpmodule as cpm
import cellprofiler.utilities.jutil as jb
import imagej.imageprocessor as ijiproc
class Hysteresis_Segment(cpm.CPModule):
module_name = "Hysteresis_Segment"
category = "DImage"
variable_revision_number = 1
def create_settings(self):
self.input_image_name = cps.ImageNameSubscriber("Input image name:",doc = "Gray-value image only, no color image")
self.output_image_name = cps.ImageNameProvider("Output image name:","OutputImage", doc = "binary mask output")
self.input_gaussian_filter = cps.Float("Gaussian Filter Size",value=2,doc="Gaussian filter size, recommended around 1~4")
self.input_rolling_ball = cps.Float("Rolling Ball Size",value=256,doc="rolling ball kernel size, recommended around 1/4 of image dimensionality")
self.input_low_seed = cps.Float("Low Seed",value=10,doc="initial value for foreground")
self.input_high_seed = cps.Float("High Seed",value=50,doc="initial value for background")
def settings(self):
return [self.input_image_name,
self.output_image_name,
self.input_gaussian_filter,
self.input_rolling_ball,
self.input_low_seed,
self.input_high_seed]
def visible_settings(self):
result = [self.input_image_name,
self.output_image_name,
self.input_gaussian_filter,
self.input_rolling_ball,
self.input_low_seed,
self.input_high_seed]
return result
def run(self, workspace):
jb.attach()#initialize JVM
input_image_name = self.input_image_name.value
output_image_name = self.output_image_name.value
self.gsize = self.input_gaussian_filter.value
self.rsize = self.input_rolling_ball.value
self.lowseed = self.input_low_seed.value
self.highseed = self.input_high_seed.value
image_set = workspace.image_set
assert isinstance(image_set, cpi.ImageSet)
#prepare input image
input_image = image_set.get_image(input_image_name, must_be_grayscale = True)
input_pixels = input_image.pixel_data
ij_processor = ijiproc.make_image_processor((input_pixels*255.0).astype('float32'))
#JavaScript API
script = """
var img=Packages.ij.ImagePlus(name,ij_processor);
Packages.ij.IJ.run(img, "8-bit", "");
var macro="g_size="+gsize+" r_size="+rsize+" low_edge="+lowseed+" high_edge="+highseed+" noise=-1";
java.lang.System.out.println(macro);
Packages.ij.IJ.run(img, "Hysteresis Segment", macro);
var output_proc=img.getProcessor();
"""
#img.show();
#Packages.ij.WindowManager.setCurrentWindow(img.getWindow());
#"""
in_params={
"name":output_image_name,
"ij_processor": ij_processor,
"gsize":self.gsize,
"rsize":self.rsize,
"lowseed":self.lowseed,
"highseed":self.highseed}
out_params={"output_proc":None}
r = jb.run_script(script, bindings_in = in_params,bindings_out = out_params)
#prepare output image
output_pixels = ijiproc.get_image(out_params["output_proc"], False)
output_image = cpi.Image(output_pixels, parent_image = input_image)
#write output
image_set.add(output_image_name, output_image)
if workspace.frame is not None:
workspace.display_data.input_pixels = input_pixels
workspace.display_data.output_pixels = output_pixels
jb.detach()#close JVM
def is_interactive(self):
return False
def display(self, workspace):
#prepare plot area
figure = workspace.create_or_find_figure(subplots=(2,1))
lead_subplot = figure.subplot(0,0)
#display original image
figure.subplot_imshow_grayscale(
0, 0,
workspace.display_data.input_pixels,
title = self.input_image_name.value)
#display binary mask
figure.subplot_imshow_grayscale(
1, 0,
workspace.display_data.output_pixels,
title = self.output_image_name.value)