def run_as_script():
"""
The main entry point of the script, as called by the client via the scripting service, passing the required parameters.
"""
dataTypes = [rstring('Image')]
client = scripts.client(
'Set_Pixel_Size.py',
"""Use this utility if your image has been saved without scaling information (e.g. TIF from Zen Black)""",
scripts.String(
"Data_Type",
optional=False,
grouping="01",
description="Choose source of images (only Image supported)",
values=dataTypes,
default="Image"),
scripts.List(
"IDs",
optional=False,
grouping="02",
description="IDs of images on which to set pixel size").ofType(
rlong(0)),
scripts.Float("X_Pixel_Size",
optional=False,
grouping="03.1",
description="Pixel size in um (micro-metres"),
scripts.Float("Y_Pixel_Size",
optional=False,
grouping="03.2",
description="Pixel size in um (micro-metres"),
scripts.Float("Z_Pixel_Size",
optional=True,
grouping="03.3",
description="Pixel size in um (micro-metres"),
authors=["Daniel Matthews", "QBI"],
institutions=["University of Queensland"],
contact="*****@*****.**",
)
try:
# process the list of args above.
scriptParams = {}
for key in client.getInputKeys():
if client.getInput(key):
scriptParams[key] = client.getInput(key, unwrap=True)
print scriptParams
# wrap client to use the Blitz Gateway
conn = BlitzGateway(client_obj=client)
# process images in Datasets
message = run_processing(conn, scriptParams)
client.setOutput("Message", rstring(message))
finally:
client.closeSession()
def runAsScript():
"""
The main entry point of the script, as called by the client via the
scripting service, passing the required parameters.
"""
dataTypes = [rstring('Image')]
labels = [rstring('Image Name'), rstring('Datasets'), rstring('Tags')]
algorithums = [rstring('Maximum Intensity'), rstring('Mean Intensity')]
roiLabel = """Specify an ROI to pick by specifying it's shape label. \
'FigureROI' by default, (not case sensitive). If matching ROI not found, use \
any ROI."""
formats = [rstring('JPEG'), rstring('PNG'), rstring('TIFF')]
ckeys = COLOURS.keys()
ckeys.sort()
oColours = wrap(OVERLAY_COLOURS.keys())
client = scripts.client(
'ROI_Split_Figure.py',
"""Create a figure of an ROI region as separate zoomed split-channel \
panels.
NB: OMERO.insight client provides a nicer UI for this script under \
'Publishing Options'
See http://help.openmicroscopy.org/scripts.html""",
# provide 'Data_Type' and 'IDs' parameters so that Insight
# auto-populates with currently selected images.
scripts.String(
"Data_Type", optional=False, grouping="01",
description="The data you want to work with.",
values=dataTypes, default="Image"),
scripts.List(
"IDs", optional=False, grouping="02",
description="List of Dataset IDs or Image IDs").ofType(rlong(0)),
scripts.Map(
"Channel_Names", grouping="03",
description="Map of index: channel name for All channels"),
scripts.Bool(
"Merged_Names", grouping="04",
description="If true, label the merged panel with channel names."
" Otherwise label with 'Merged'"),
scripts.List(
"Split_Indexes", grouping="05",
description="List of the channels in the split view panels"),
scripts.Bool(
"Split_Panels_Grey", grouping="06",
description="If true, all split panels are grayscale"),
scripts.Map(
"Merged_Colours", grouping="07",
description="Map of index:int colors for each merged channel."
" Otherwise use existing colour settings"),
scripts.Int(
"Width", grouping="08",
description="Max width of each image panel", min=1),
scripts.Int(
"Height", grouping="09",
description="The max height of each image panel", min=1),
scripts.String(
"Image_Labels", grouping="10",
description="Label images with the Image's Name or it's Datasets"
" or Tags", values=labels),
scripts.String(
"Algorithm", grouping="11",
description="Algorithum for projection.", values=algorithums),
scripts.Int(
"Stepping", grouping="12",
description="The Z-plane increment for projection. Default is 1",
min=1),
scripts.Int(
"Scalebar", grouping="13",
description="Scale bar size in microns. Only shown if image has"
" pixel-size info.", min=1),
scripts.String(
"Format", grouping="14",
description="Format to save image. E.g 'PNG'.",
values=formats, default='JPEG'),
scripts.String(
"Figure_Name", grouping="15",
description="File name of the figure to save."),
scripts.String(
"Overlay_Colour", grouping="16",
description="The color of the scale bar.",
default='White', values=oColours),
scripts.Float(
"ROI_Zoom", grouping="17",
description="How much to zoom the ROI. E.g. x 2. If 0 then zoom"
" roi panel to fit", min=0),
scripts.String("ROI_Label", grouping="18", description=roiLabel),
version="4.3.0",
authors=["William Moore", "OME Team"],
institutions=["University of Dundee"],
contact="*****@*****.**",
)
try:
conn = BlitzGateway(client_obj=client)
commandArgs = client.getInputs(unwrap=True)
print commandArgs
# call the main script, attaching resulting figure to Image. Returns
# the id of the originalFileLink child. (ID object, not value)
fileAnnotation, message = roiFigure(conn, commandArgs)
# Return message and file annotation (if applicable) to the client
client.setOutput("Message", rstring(message))
if fileAnnotation is not None:
client.setOutput("File_Annotation", robject(fileAnnotation._obj))
finally:
client.closeSession()
def run_script():
"""
The main entry point of the script, as called by the client.
Called via the scripting service, passing the required parameters.
"""
data_types = [rstring('Image')]
client = scripts.client(
'Kymograph.py',
"""This script processes Images, which have Line or PolyLine ROIs to \
create kymographs.
Kymographs are created in the form of new OMERO Images, with single Z and T, \
same sizeC as input.""",
scripts.String(
"Data_Type",
optional=False,
grouping="1",
description="Choose source of images (only Image supported)",
values=data_types,
default="Image"),
scripts.List("IDs",
optional=False,
grouping="2",
description="List of Image IDs to process").ofType(
rlong(0)),
scripts.Int("Line_Width",
optional=False,
grouping="3",
default=4,
description="Width in pixels of each time slice",
min=1),
scripts.Bool(
"Use_All_Timepoints",
grouping="4",
default=True,
description="Use every timepoint in the kymograph. If False, only"
" use timepoints with ROI-shapes"),
scripts.Float(
"Time_Increment",
grouping="5",
description="If source movie has no time info, specify increment"
" per time point (seconds)"),
scripts.Float(
"Pixel_Size",
grouping="6",
description="If source movie has no Pixel size info, specify"
" pixel size (microns)"),
version="4.3.3",
authors=["William Moore", "OME Team"],
institutions=["University of Dundee"],
contact="*****@*****.**",
)
try:
script_params = client.getInputs(unwrap=True)
# wrap client to use the Blitz Gateway
conn = BlitzGateway(client_obj=client)
new_images, message = process_images(conn, script_params)
if new_images:
if len(new_images) == 1:
client.setOutput("New_Image", robject(new_images[0]._obj))
elif len(new_images) > 1:
# return the first one
client.setOutput("First_Image", robject(new_images[0]._obj))
client.setOutput("Message", rstring(message))
finally:
client.closeSession()
rlong(0)),
scripts.Int("Line_Width",
optional=False,
grouping="3",
default=4,
description="Width in pixels of each time slice",
min=1),
scripts.Bool(
"Use_All_Timepoints",
grouping="4",
default=True,
description="Use every timepoint in the kymograph. If False, only"
" use timepoints with ROI-shapes"),
scripts.Float(
"Time_Increment",
grouping="5",
description="If source movie has no time info, specify increment"
" per time point (secs)"),
scripts.Float(
"Pixel_Size",
grouping="6",
description="If source movie has no Pixel size info, specify"
" pixel size (microns)"),
version="4.3.3",
authors=["William Moore", "OME Team"],
institutions=["University of Dundee"],
contact="*****@*****.**",
)
try:
# process the list of args above.
description="List of IDs to process.").ofType(rlong(0)),
scripts.Int(
"Channel", optional=False, grouping = "b",
description="Which channel to use for indexing (first channel = 0)",
min=0, default=0),
scripts.Int(
"Z_Plane", optional=False, grouping = "b.2",
description="Which Z plane to use for indexing (first channel = 0)",
min=0, default=0),
scripts.Int(
"Timepoint", optional=False, grouping = "b.3",
description="Which timepoint to use for indexing (first channel = 0)",
min=0, default=0),
scripts.Float("Threshold", optional=False, grouping = "b.4",
description="Threshold value to separate foreground from background "
"(enter negative value in order to use auto thresholding)",
default=-1.0),
scripts.Bool("Save_Images", grouping = "d",
description="Save the thresholded images as new images",
default=False),
scripts.Bool("Delete_previous_Masks", grouping = "e",
description="Delete all previous Masks of the images",
default=True),
scripts.Int("Mask_color_Red", optional=False, grouping = "e.2",
description="Display color of the mask",
min=0, max=255, default=255),
scripts.Int("Mask_color_Green", optional=False, grouping = "e.3",
description="Display color of the mask",
min=0, max=255, default=255),
def run_as_script():
"""
The main entry point of the script, as called by the client via the
scripting service, passing the required parameters.
"""
dataTypes = [rstring("Dataset"), rstring("Image")]
fusion_method = [
rstring('Linear Blending'),
rstring('Average'),
rstring('Median'),
rstring('Max. Intensity'),
rstring('Min. Intensity'),
rstring('Intensity of random input tile'),
rstring('Do not fuse tiles (only write TileConfiguration')
]
client = scripts.client(
'Grid_Stitching.py',
"""Run the "Stitching" FIJI plugin.
MAXIMUM NUMBER OF DATASETS FOR BATCH IS FIVE!""",
scripts.String(
"Data_Type",
optional=False,
grouping="01",
description="Choose source of images (only Image supported)",
values=dataTypes,
default="Image"),
scripts.List("IDs",
optional=False,
grouping="02",
description="IDs of images to be stitched").ofType(
rlong(0)),
scripts.Bool("Single_Channel",
grouping="03",
default=False,
description=("Stitch all channels or a single channel?"
"Uncheck for all channels")),
scripts.Int("Channel",
grouping="03.1",
description="channel to be stitched"),
scripts.Bool("Single_Z",
grouping="04",
default=False,
description=("Stitch all z slices or a single"
"slice? Uncheck for all z slices")),
scripts.Int("Z_slice",
grouping="04.1",
description="z-slice to be stitched"),
scripts.Bool(
"Range_Z",
grouping="05",
default=False,
description="Stitch a range of slices? Uncheck for all z slices"),
scripts.Int("Z_start",
grouping="05.1",
description="start at this z-slice"),
scripts.Int("Z_stop",
grouping="05.2",
description="stop at this z slice"),
scripts.Int("grid_x",
optional=False,
grouping="06",
default=2,
description="how many tiles in the x-direction"),
scripts.Int("grid_y",
optional=False,
grouping="07",
default=2,
description="how many tiles in the y-direction"),
scripts.Int("tile_overlap",
optional=False,
grouping="08",
default=20,
description="percentage overlap between tiles"),
scripts.String("fusion_method",
optional=False,
grouping="09",
default='Linear Blending',
description="method used to fuse the tiles",
values=fusion_method),
scripts.Float("regression_threshold",
optional=False,
grouping="10",
default=0.3,
description="global optimisation parameter"),
scripts.Float("ave_displacement_threshold",
optional=False,
grouping="11",
default=2.5,
description="global optimisation parameter"),
scripts.Float("abs_displacement_threshold",
optional=False,
grouping="12",
default=3.5,
description="global optimisation parameter"),
scripts.Bool("Email_Results",
grouping="13",
default=False,
description="E-mail the results"),
scripts.String("Email_address",
grouping="13.1",
description="Specify e-mail address"),
authors=["Daniel Matthews", "QBI"],
institutions=["University of Queensland"],
contact="*****@*****.**",
)
try:
client.enableKeepAlive(3600)
# process the list of args above.
scriptParams = {}
for key in client.getInputKeys():
if client.getInput(key):
scriptParams[key] = client.getInput(key, unwrap=True)
session = {}
session['ID'] = client.getSessionId()
session['host'] = client.getProperty('omero.host')
print(scriptParams)
# wrap client to use the Blitz Gateway
conn = BlitzGateway(client_obj=client)
if (scriptParams['Email_Results']
and not validate_email(conn, scriptParams)):
client.setOutput("Message", rstring("No valid email address"))
return
# process images in Datasets
robj, message = run_processing(conn, session, scriptParams)
client.setOutput("Message", rstring(message))
if robj is not None:
client.setOutput("Result", robject(robj))
finally:
client.closeSession()
def runAsScript():
"""
The main entry point of the script, as called by the client via the
scripting service, passing the required parameters.
"""
dataTypes = [rstring('Image')]
labels = [rstring('Image Name'), rstring('Datasets'), rstring('Tags')]
algorithums = [rstring('Maximum Intensity'), rstring('Mean Intensity')]
roiLabel = """Specify an ROI to pick by specifying it's shape label. \
'FigureROI' by default, (not case sensitive). If matching ROI not found, use \
any ROI."""
formats = [rstring('JPEG'), rstring('PNG'), rstring('TIFF')]
ckeys = COLOURS.keys()
ckeys.sort()
oColours = wrap(OVERLAY_COLOURS.keys())
client = scripts.client(
'Movie_ROI_Figure.py',
"""Create a figure of movie frames from ROI region of image.
See http://help.openmicroscopy.org/scripts.html""",
scripts.String("Data_Type",
optional=False,
grouping="01",
description="The data you want to work with.",
values=dataTypes,
default="Image"),
scripts.List("IDs",
optional=False,
grouping="02",
description="List of Image IDs").ofType(rlong(0)),
# scripts.List(
# "Merged_Colours", grouping="03",
# description="A list of colours to apply to merged channels.",
# values=cOptions),
scripts.List(
"Merged_Channels",
grouping="03",
description="A list of channel indexes to display, starting at 1."
" E.g. 1, 2, 3").ofType(rint(0)),
scripts.Float(
"Roi_Zoom",
grouping="04",
default=1,
description="How much to zoom the ROI. E.g. x 2. If 0 then ROI"
" panel will zoom to same size as main image"),
scripts.Int(
"Max_Columns",
grouping="04.1",
default=10,
description="The maximum number of columns in the figure, for"
" ROI-movie frames.",
min=1),
scripts.Bool(
"Resize_Images",
grouping="05",
default=True,
description="Images are shown full-size by default, but can be"
" resized below"),
scripts.Int("Width",
grouping="05.1",
description="Max width of each image panel in pixels",
min=1),
scripts.Int("Height",
grouping="05.2",
description="The max height of each image panel in pixels",
min=1),
scripts.String(
"Image_Labels",
grouping="06",
description="Label images with the Image Name or Datasets or"
" Tags",
values=labels),
scripts.Bool("Show_ROI_Duration",
grouping="06.1",
description="If true, times shown as duration from first "
"timepoint of the ROI, otherwise use movie timestamp."),
scripts.Int(
"Scalebar",
grouping="07",
description="Scale bar size in microns. Only shown if image has"
" pixel-size info.",
min=1),
scripts.String(
"Scalebar_Colour",
grouping="07.1",
description="The color of the scale bar and ROI outline.",
default='White',
values=oColours),
scripts.String("Roi_Selection_Label",
grouping="08",
description=roiLabel),
scripts.String(
"Algorithm",
grouping="09",
description="Algorithum for projection, if ROI spans several Z"
" sections.",
values=algorithums),
scripts.String("Figure_Name",
grouping="10",
description="File name of the figure to save.",
default='movieROIFigure'),
scripts.String("Format",
grouping="10.1",
description="Format to save figure.",
values=formats,
default='JPEG'),
version="4.3.0",
authors=["William Moore", "OME Team"],
institutions=["University of Dundee"],
contact="*****@*****.**",
)
try:
conn = BlitzGateway(client_obj=client)
commandArgs = client.getInputs(unwrap=True)
print commandArgs
# call the main script, attaching resulting figure to Image. Returns
# the id of the originalFileLink child. (ID object, not value)
fileAnnotation, message = roiFigure(conn, commandArgs)
# Return message and file annotation (if applicable) to the client
client.setOutput("Message", rstring(message))
if fileAnnotation is not None:
client.setOutput("File_Annotation", robject(fileAnnotation._obj))
finally:
client.closeSession()
def runAsScript():
dataTypes = [rstring('Dataset'), rstring('Image')]
microscopyTypes = [rstring('CONFOCAL'), rstring('TWOPHOTON'), rstring('WIDEFIELD')]
immersionTypes = [rstring('Oil'), rstring('Water'), rstring('Air'), rstring('CLARITY')]
psfTypes = [rstring('Measured PSF'), rstring('Manual theoretical PSF'), rstring('Automatic theoretical PSF')]
client = scripts.client('Omdecon',
"""Omdecon: deconvolution on the OMERO server.
Omdecon uses AIDA, the Adaptative Image Deconvolution Algorithm, to deconvolve images.
More info: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3166524/
Also: https://github.com/erikhom/aida ; https://code.google.com/archive/p/aida-deconvolution/
Omdecon can calculate the theoretical PSF if you don't have one on hand. This feature uses Christoph Gohlke's PSF.py module.
More info: http://www.lfd.uci.edu/~gohlke/code/psf.py.html
READ BEFORE YOU USE OMDECON:
1- Omdecon only works with square images (i.e. 512x512, 600x600).
2- The automatic background subtraction requires a single rectangular ROI.
Make sure this ROI doesn't overlap any important feature on any plane or frame.
3- The automatic theoretical PSF calculation requires the following metadata:
-Objective, Numerical Aperture, Excitation and Emission for all channels and Pixel Calibration.
4- You need to input the right number of channels when setting the PSF manually.
5- Images are saved in the same dataset with "_Deconv" added to the name.
""",
scripts.String("Data_Type", optional=False, grouping="1",
description="Type of data to deconvolve", values=dataTypes, default=rstring('Image')),
scripts.List("IDs", optional=False, grouping="1.1",
description = "List of images to deconvolve.").ofType(rlong(0)),
scripts.String("Background", grouping = "2",
description="Background", values=None, default=None),
scripts.Int("Background_value_to_subtract", optional=False, grouping="2.1",
description = "Manually supply the background to subtract.", default=0),
scripts.Bool("Automatically_measure_background_from_ROI", grouping="2.2",
description = "Trace a rectangular ROI to automatically perform background measurement.", default=False),
scripts.String("PSF Configuration", grouping = "3",
description="PSF settings", values=None, default=None),
scripts.String("Type_of_PSF", grouping = "3.1",
description="Choose the type of PSF you want to use.", values=psfTypes, default=rstring('Measured PSF')),
scripts.Long("Measured_PSF_Image", optional=True, grouping="3.2",
description="Input '0' to calculate the theoretical PSF", default=0),
scripts.String("Manual settings for theoretical PSF (ignored for automatic)", grouping = "4",
description="Supply the parameters here to generate a theoretical PSF with manual settings", values=None, default=None),
scripts.String("Fluorescence_Microscopy_", grouping="4.01",
description="Choose the type of fluorescence microscopy used", values=microscopyTypes, default=rstring('CONFOCAL')),
scripts.String("Imaging_Medium_________", grouping="4.02",
description="Select the media used for imaging", values=immersionTypes, default=rstring('Oil')),
scripts.Float("Objective_NA_____________", optional=False, grouping="4.03",
description="Supply the numerical aperture of the objective", default=1.42),
scripts.Float("Pixel_Size_in_microns", optional=False, grouping="4.04",
description="Enter the size of your pixels", default=0.05),
scripts.Float("Confocal_Pinhole______", optional=False, grouping="4.05",
description="Enter the pinhole radius for confocal microscopy", default=0.55),
scripts.Int("_____Channel_1_-_Excitation",default=488, grouping="5.1",
description="Input '0' if not using this channel"),
scripts.Int("_____Channel_1_-_Emission__",default=510, grouping="5.2",
description="Input '0' if not using this channel"),
scripts.Int("_____Channel_2_-_Excitation",default=0, grouping="5.3",
description="Input '0' if not using this channel"),
scripts.Int("_____Channel_2_-_Emission__",default=0, grouping="5.4",
description="Input '0' if not using this channel"),
scripts.Int("_____Channel_3_-_Excitation",default=0, grouping="5.5",
description="Input '0' if not using this channel"),
scripts.Int("_____Channel_3_-_Emission__",default=0, grouping="5.6",
description="Input '0' if not using this channel"),
version="1.0.0",
authors=["Etienne Labrie-Dion"],
institutions=["Douglas Hospital Research Center, McGill University, Montreal"],
contact="*****@*****.**",
)
try:
#Obtain the user parameters
scriptParams = {}
conn = BlitzGateway(client_obj=client)
client.enableKeepAlive(240)
for key in client.getInputKeys():
if client.getInput(key):
scriptParams[key] = client.getInput(key, unwrap=True)
print scriptParams
robj, message = process(conn, scriptParams)
client.setOutput("Message", rstring(message))
if robj is not None:
client.setOutput("Result", robject(robj))
finally:
#TODO: send an email confirming the end of the deconvolution and success/failure
client.closeSession()