def main():
# Open a .ome.tif image from the Flexoscope.
impath = IJ.getFilePath("Choose .ome.tiff file")
channels = Opener.openUsingBioFormats(impath)
# Show image
# imp.show() # straight to channels object sames memory.
# Split channels.
channels = ChannelSplitter().split(channels)
# Process channel 1.
# subtractzproject(imp, projectionMethod="Median")
channels[1] = ImagePlus()
channels.append(ImagePlus())
channels[1] = subtractzproject(channels[0])
IJ.run(channels[0], "Enhance Contrast...", "saturated=0.3 normalize process_all use")
IJ.run(channels[0], "8-bit", "")
IJ.run(channels[1], "Square", "stack")
IJ.run(channels[1], "Enhance Contrast...", "saturated=0.3 normalize process_all use")
IJ.run(channels[1], "8-bit", "")
# Merge channels.
merge = RGBStackMerge().mergeChannels(channels, True) # boolean keep
merge.show()
def merge_kymographs(kym1_imp, kym2_imp, params):
"""Merge two kymographs"""
mrg_imp = RGBStackMerge().mergeChannels([kym1_imp, kym2_imp], True)
mrg_imp.setTitle("Merged " + params.labeled_species +
" intensity and curvature kymograph")
mrg_imp.show()
return mrg_imp
def main():
# Open a .ome.tif image from the Flexoscope.
impath = IJ.getFilePath("Choose .ome.tiff file")
channels = Opener.openUsingBioFormats(impath)
cal = channels.getCalibration()
# Show image
# imp.show() # straight to channels object sames memory.
# Split channels.
channels = ChannelSplitter().split(channels)
# Process channel 1.
# subtractzproject(imp, projectionMethod="Median")
channels[0] = subtractzproject(channels[0])
IJ.run(channels[0], "8-bit", "")
# Process channel 2.
# glidingprojection(imp, startframe=1, stopframe=None, glidingFlag=True, no_frames_per_integral=3, projectionmethod="Median")
channels[1] = glidingprojection(channels[1])
IJ.run(channels[1], "8-bit", "")
# [Optional] Process channel 3, 4, etc.
# subtractzproject(channels[3], projectionMethod="Median")
# glidingprojection(channels[3], startframe=1, stopframe=None, glidingFlag=True, no_frames_per_integral=3, projectionmethod="Median")
# IJ.run(channels[3], "8-bit", "")
# Merge channels.
merge = RGBStackMerge().mergeChannels(channels, True) # boolean keep
merge.setCalibration(cal)
merge.show()
def do_tubefitting(im_path=im_test_path,
metadata_path=metadata_test_path,
output_path=output_path,
save_output=False):
# todo: fix things so that all operations use a consistent definition of background rather than changing Prefs on the fly...
Prefs.blackBackground = False
info = PrescreenInfo()
info.load_info_from_json(metadata_path)
z_xy_ratio = abs(
info.get_z_plane_spacing_um()) / info.get_xy_pixel_size_um()
#z_xy_ratio = 1.0;
bfimp = bf.openImagePlus(im_path)
imp = bfimp[0]
imp.show()
IJ.run(imp, "Set Scale...", "distance=0 known=0 pixel=1 unit=pixel")
imp = utils.downsample_for_isotropy(imp,
extra_downsample_factor=1.0,
info=info)
rot_seg_imp, rot_proj_imp, egfp_mch_imps = split_and_rotate(imp, info)
depth = rot_seg_imp.getNSlices() if rot_seg_imp.getNSlices(
) > rot_seg_imp.getNFrames() else rot_seg_imp.getNFrames()
width = rot_seg_imp.getWidth()
height = int(round(rot_seg_imp.getHeight() * z_xy_ratio))
# Apply 3d MEDIAN FILTER to denoise and emphasise vessel-associated voxels
fit_basis_imp = threshold_and_binarise(rot_seg_imp, z_xy_ratio)
fit_basis_imp.setTitle("fit_basis_imp")
fit_basis_imp.show()
# plane-wise, use binary-outline
# say the non-zero points then make up basis for fitting to be performed per http://nicky.vanforeest.com/misc/fitEllipse/fitEllipse.html
rois = []
centres = []
major_axes = []
roi_imp = IJ.createImage("rois", width, height, depth, 32)
pts_stack = ImageStack(width, height + 1)
IJ.run(imp, "Line Width...", "line=3")
for zidx in range(fit_basis_imp.getNSlices()):
fit_basis_imp.setZ(zidx + 1)
IJ.run(fit_basis_imp, "Outline", "slice")
IJ.run(fit_basis_imp, "Create Selection", "")
roi = fit_basis_imp.getRoi()
fit_basis_imp.killRoi()
pts = [(pt.x, pt.y) for pt in roi.getContainedPoints()]
clean_pts = convex_hull_pts(pts)
clean_pts = [(x, z_xy_ratio * y) for (x, y) in clean_pts]
# make a stack of clean points...
ip = FloatProcessor(width, height + 1)
pix = ip.getPixels()
for pt in clean_pts:
pix[int(pt[1]) * width + int(pt[0])] = 128
pts_stack.addSlice(ip)
centre, angle, axl = ellipse_fitting.fit_ellipse(clean_pts)
major_axes.append(max(axl))
centres.append(centre)
rot_seg_imp.setZ(zidx + 1)
ellipse_roi = ellipse_fitting.generate_ellipse_roi(centre, angle, axl)
rois.append(ellipse_roi)
IJ.run(imp, "Line Width...", "line=1")
cal = imp.getCalibration()
smooth_centres, tangent_vecs = generate_smoothed_vessel_axis(
centres, pixel_size_um=cal.pixelDepth)
for zidx in range(fit_basis_imp.getNSlices()):
centre = smooth_centres[zidx]
major_axis = major_axes[zidx]
ellipse_roi = EllipseRoi(centre[0] - 2, centre[1], centre[0] + 2,
centre[1], 1.0)
roi_imp.setZ(zidx + 1)
roi_imp.setRoi(ellipse_roi)
IJ.run(roi_imp, "Set...",
"value=" + str(roi_imp.getProcessor().maxValue()) + " slice")
pts_stack_imp = ImagePlus("Cleaned points", pts_stack)
pts_stack_imp.setTitle("pts_stack_imp")
pts_stack_imp.show()
rot_seg_imp.changes = False
rot_seg_imp.close()
egfp_imp = egfp_mch_imps[0]
mch_imp = egfp_mch_imps[1]
imps_to_combine = [egfp_mch_imps[1], egfp_mch_imps[0], roi_imp]
egfp_imp.show()
mch_imp.show()
roi_imp.show()
print("box height um = " +
str(roi_imp.getNSlices() * info.get_xy_pixel_size_um()))
IJ.run(
egfp_imp, "Size...", "width=" + str(width) + " height=" + str(height) +
" depth=" + str(depth) + " average interpolation=Bilinear")
IJ.run(
mch_imp, "Size...", "width=" + str(width) + " height=" + str(height) +
" depth=" + str(depth) + " average interpolation=Bilinear")
#IJ.run("Merge Channels...", "c1=[" + mch_imp.getTitle() +
# "] c2=[" + egfp_imp.getTitle() +
# "] c7=[" + roi_imp.getTitle() + "] create keep");
composite_imp = RGBStackMerge().mergeChannels(imps_to_combine, False)
print(composite_imp)
composite_imp.show()
print("end of vessel centerline id step, image dims = ({}x{}x{})".format(
composite_imp.getWidth(), composite_imp.getHeight(),
composite_imp.getNSlices()))
WaitForUserDialog("pause").show()
# do qc here?
#WM.getImage("Composite").addImageListener(UpdateRoiImageListener(rois));
IJ.run(roi_imp, "8-bit", "")
if save_output:
FileSaver(composite_imp).saveAsTiffStack(
os.path.join(output_path, "segmentation result.tif"))
print(roi_imp)
FileSaver(roi_imp).saveAsTiff(
os.path.join(output_path, "vessel axis.tif"))
egfp_imp.changes = False
mch_imp.changes = False
roi_imp.changes = False
fit_basis_imp.changes = False
pts_stack_imp.changes = False
egfp_imp.close()
mch_imp.close()
#roi_imp.close();
fit_basis_imp.close()
pts_stack_imp.close()
zcoords = [i for i in range(composite_imp.getNSlices())]
xyz_smooth_centres = [(x, y, z)
for ((x, y), z) in zip(smooth_centres, zcoords)]
composite_imp2 = straighten_vessel(composite_imp,
xyz_smooth_centres,
save_output=True)
composite_imp3 = straighten_vessel(composite_imp2,
xyz_smooth_centres,
it=2,
save_output=True)
return composite_imp3
z boolean
c char
b byte
h short
i int
l long
f float
d double
"""
ja = jarray.array([0, 1, 2, 3], 'i')
print(ja)
"""
What if you want to make a java array of a specific class?
You could then name the class as the second argument.
For example
"""
img_dir = git_home + "/tips/ImageJ/"
imp_blobs_1 = IJ.openImage("http://imagej.nih.gov/ij/images/blobs.gif")
IJ.run(imp_blobs_1, "Red", "")
imp_blobs_2 = imp_blobs_1.duplicate()
IJ.run(imp_blobs_2, "Green", "")
imp_blobs_3 = imp_blobs_1.duplicate()
IJ.run(imp_blobs_3, "Blue", "")
img_array = jarray.array([imp_blobs_1, imp_blobs_2, imp_blobs_3], ImagePlus)
gray_stack = RGBStackMerge().mergeHyperstacks(img_array, True)
gray_stack.show()
gray_comp = RGBStackMerge().mergeChannels(img_array, False)
gray_comp.show()