def roi_figure(conn, command_args):
"""
This processes the script parameters, adding defaults if needed.
Then calls a method to make the figure, and finally uploads and attaches
this to the primary image.
@param: session The OMERO session
@param: command_args Map of String:Object parameters for the script.
Objects are not rtypes, since getValue() was
called when the map was processed below.
But, list and map objects may contain rtypes (need
to call getValue())
@return: the id of the originalFileLink child. (ID object,
not value)
"""
log("ROI figure created by OMERO on %s" % date.today())
log("")
message = "" # message to be returned to the client
pixel_ids = []
image_ids = []
image_labels = []
# function for getting image labels.
def get_image_names(full_name, tags_list, pd_list):
name = full_name.split("/")[-1]
return [name]
# default function for getting labels is getName (or use datasets / tags)
if "Image_Labels" in command_args:
if command_args["Image_Labels"] == "Datasets":
def get_datasets(name, tags_list, pd_list):
return [dataset for project, dataset in pd_list]
get_labels = get_datasets
elif command_args["Image_Labels"] == "Tags":
def get_tags(name, tags_list, pd_list):
return tags_list
get_labels = get_tags
else:
get_labels = get_image_names
else:
get_labels = get_image_names
# Get the images
images, log_message = script_utils.get_objects(conn, command_args)
message += log_message
if not images:
return None, message
# Check for rectangular ROIs and filter images list
images = [image for image in images if image.getROICount("Rectangle") > 0]
if not images:
message += "No rectangle ROI found."
return None, message
# Attach figure to the first image
omero_image = images[0]
# process the list of images. If image_ids is not set, script can't run.
log("Image details:")
for image in images:
image_ids.append(image.getId())
pixel_ids.append(image.getPrimaryPixels().getId())
# a map of imageId : list of (project, dataset) names.
pd_map = figUtil.getDatasetsProjectsFromImages(conn.getQueryService(),
image_ids)
tag_map = figUtil.getTagsFromImages(conn.getMetadataService(), image_ids)
# Build a legend entry for each image
for image in images:
name = image.getName()
image_date = image.getAcquisitionDate()
iid = image.getId()
tags_list = tag_map[iid]
pd_list = pd_map[iid]
tags = ", ".join(tags_list)
pd_string = ", ".join(["%s/%s" % pd for pd in pd_list])
log(" Image: %s ID: %d" % (name, iid))
if image_date:
log(" Date: %s" % image_date)
else:
log(" Date: not set")
log(" Tags: %s" % tags)
log(" Project/Datasets: %s" % pd_string)
image_labels.append(get_labels(name, tags_list, pd_list))
# use the first image to define dimensions, channel colours etc.
size_x = omero_image.getSizeX()
size_y = omero_image.getSizeY()
size_z = omero_image.getSizeZ()
size_c = omero_image.getSizeC()
width = size_x
if "Width" in command_args:
w = command_args["Width"]
try:
width = int(w)
except ValueError:
log("Invalid width: %s Using default value: %d" % (str(w), size_x))
height = size_y
if "Height" in command_args:
h = command_args["Height"]
try:
height = int(h)
except ValueError:
log("Invalid height: %s Using default value" % (str(h), size_y))
log("Image dimensions for all panels (pixels): width: %d height: %d" %
(width, height))
merged_indexes = [] # the channels in the combined image,
merged_colours = {}
if "Merged_Colours" in command_args:
c_colour_map = command_args["Merged_Colours"]
for c in c_colour_map:
rgb = c_colour_map[c]
try:
rgb = int(rgb)
c_index = int(c)
except ValueError:
continue
rgba = image_utils.int_to_rgba(rgb)
merged_colours[c_index] = rgba
merged_indexes.append(c_index)
merged_indexes.sort()
# make sure we have some merged channels
if len(merged_indexes) == 0:
merged_indexes = range(size_c)
merged_indexes.reverse()
merged_names = False
if "Merged_Names" in command_args:
merged_names = command_args["Merged_Names"]
# Make channel-names map. If argument wasn't specified, name by index
channel_names = {}
if "Channel_Names" in command_args:
c_name_map = command_args["Channel_Names"]
for c in range(size_c):
if str(c) in c_name_map:
channel_names[c] = c_name_map[str(c)]
else:
channel_names[c] = str(c)
else:
for c in range(size_c):
channel_names[c] = str(c)
# Make split-indexes list. If no "Split_Indexes", show none:
# http://www.openmicroscopy.org/community/viewtopic.php?f=4&t=940
split_indexes = []
if "Split_Indexes" in command_args:
for index in command_args["Split_Indexes"]:
split_indexes.append(index)
colour_channels = True
key = "Split_Panels_Grey"
if key in command_args and command_args[key]:
colour_channels = False
algorithm = ProjectionType.MAXIMUMINTENSITY
if "Algorithm" in command_args:
a = command_args["Algorithm"]
if (a == "Mean Intensity"):
algorithm = ProjectionType.MEANINTENSITY
stepping = 1
if "Stepping" in command_args:
s = command_args["Stepping"]
if (0 < s < size_z):
stepping = s
scalebar = None
if "Scalebar" in command_args:
sb = command_args["Scalebar"]
try:
scalebar = int(sb)
if scalebar <= 0:
scalebar = None
else:
log("Scalebar is %d microns" % scalebar)
except ValueError:
log("Invalid value for scalebar: %s" % str(sb))
scalebar = None
overlay_colour = (255, 255, 255)
if "Overlay_Colour" in command_args:
r, g, b, a = OVERLAY_COLOURS[command_args["Overlay_Colour"]]
overlay_colour = (r, g, b)
roi_zoom = None
if "ROI_Zoom" in command_args:
roi_zoom = float(command_args["ROI_Zoom"])
if roi_zoom == 0:
roi_zoom = None
roi_label = "FigureROI"
if "ROI_Label" in command_args:
roi_label = command_args["ROI_Label"]
spacer = (width / 50) + 2
fig = get_split_view(conn, image_ids, pixel_ids, split_indexes,
channel_names, merged_names, colour_channels,
merged_indexes, merged_colours, width, height,
image_labels, spacer, algorithm, stepping, scalebar,
overlay_colour, roi_zoom, roi_label)
if fig is None:
log_message = "No figure produced"
log("\n" + log_message)
message += log_message
return None, message
log("")
fig_legend = "\n".join(log_strings)
format = command_args["Format"]
figure_name = "roi_figure"
if "Figure_Name" in command_args:
figure_name = command_args["Figure_Name"]
figure_name = os.path.basename(figure_name)
output = "localfile"
if format == 'PNG':
output = output + ".png"
figure_name = figure_name + ".png"
fig.save(output, "PNG")
mimetype = "image/png"
elif format == 'TIFF':
output = output + ".tiff"
figure_name = figure_name + ".tiff"
fig.save(output, "TIFF")
mimetype = "image/tiff"
else:
output = output + ".jpg"
figure_name = figure_name + ".jpg"
fig.save(output)
mimetype = "image/jpeg"
# Use util method to upload the figure 'output' to the server, attaching
# it to the omeroImage, adding the
# figLegend as the fileAnnotation description.
# Returns the id of the originalFileLink child. (ID object, not value)
namespace = NSCREATED + "/omero/figure_scripts/ROI_Split_Figure"
file_annotation, fa_message = script_utils.create_link_file_annotation(
conn,
output,
omero_image,
output="ROI Split figure",
mimetype=mimetype,
namespace=namespace,
description=fig_legend,
orig_file_path_and_name=figure_name)
message += fa_message
return file_annotation, message
def split_view_figure(conn, script_params):
"""
Processes the arguments, populating defaults if necessary. Prints the
details to log (fig-legend).
Even handles missing arguments that are not optional (from when this ran
from commandline with everything optional)
then calls make_split_view_figure() to make the figure, attaches it to the
Image as an 'originalFile' annotation, with fig-legend as the description.
@return: the id of the originalFileLink child. (ID object, not value)
"""
log("Split-View figure created by OMERO on %s" % date.today())
log("")
message = "" # message to be returned to the client
image_ids = []
pixel_ids = []
image_labels = []
# function for getting image labels.
def get_image_names(full_name, tags_list, pd_list):
name = full_name.split("/")[-1]
return [name.decode('utf8')]
# default function for getting labels is getName (or use datasets / tags)
if script_params["Image_Labels"] == "Datasets":
def get_datasets(name, tags_list, pd_list):
return [dataset.decode('utf8') for project, dataset in pd_list]
get_labels = get_datasets
elif script_params["Image_Labels"] == "Tags":
def get_tags(name, tags_list, pd_list):
return [t.decode('utf8') for t in tags_list]
get_labels = get_tags
else:
get_labels = get_image_names
# Get the images
images, log_message = script_utils.get_objects(conn, script_params)
message += log_message
if not images:
return None, message
# Attach figure to the first image
omero_image = images[0]
# process the list of images
log("Image details:")
for image in images:
image_ids.append(image.getId())
pixel_ids.append(image.getPrimaryPixels().getId())
# a map of imageId : list of (project, dataset) names.
pd_map = figUtil.getDatasetsProjectsFromImages(conn.getQueryService(),
image_ids)
tag_map = figUtil.getTagsFromImages(conn.getMetadataService(), image_ids)
# Build a legend entry for each image
for image in images:
name = image.getName()
image_date = image.getAcquisitionDate()
iid = image.getId()
tags_list = tag_map[iid]
pd_list = pd_map[iid]
tags = ", ".join(tags_list)
pd_string = ", ".join(["%s/%s" % pd for pd in pd_list])
log(" Image: %s ID: %d" % (name, iid))
if image_date:
log(" Date: %s" % image_date)
else:
log(" Date: not set")
log(" Tags: %s" % tags)
log(" Project/Datasets: %s" % pd_string)
image_labels.append(get_labels(name, tags_list, pd_list))
# use the first image to define dimensions, channel colours etc.
size_x = omero_image.getSizeX()
size_y = omero_image.getSizeY()
size_z = omero_image.getSizeZ()
size_c = omero_image.getSizeC()
# set image dimensions
z_start = -1
z_end = -1
if "Z_Start" in script_params:
z_start = script_params["Z_Start"]
if "Z_End" in script_params:
z_end = script_params["Z_End"]
width = "Width" in script_params and script_params["Width"] or size_x
height = "Height" in script_params and script_params["Height"] or size_y
log("Image dimensions for all panels (pixels): width: %d height: %d" %
(width, height))
# Make split-indexes list. If argument wasn't specified, include them all.
split_indexes = []
if "Split_Indexes" in script_params:
split_indexes = script_params["Split_Indexes"]
else:
split_indexes = range(size_c)
# Make channel-names map. If argument wasn't specified, name by index
channel_names = {}
for c in range(size_c):
channel_names[c] = str(c)
if "Channel_Names" in script_params:
c_name_map = script_params["Channel_Names"]
for c in c_name_map:
index = int(c)
channel_names[index] = c_name_map[c].decode('utf8')
merged_indexes = [] # the channels in the combined image,
merged_colours = {}
if "Merged_Colours" in script_params:
c_colour_map = script_params["Merged_Colours"]
for c in c_colour_map:
rgb = c_colour_map[c]
try:
rgb = int(rgb)
c_index = int(c)
except ValueError:
continue
rgba = image_utils.int_to_rgba(rgb)
merged_colours[c_index] = rgba
merged_indexes.append(c_index)
merged_indexes.sort()
else:
merged_indexes = range(size_c)
colour_channels = not script_params["Split_Panels_Grey"]
algorithm = ProjectionType.MAXIMUMINTENSITY
if "Mean Intensity" == script_params["Algorithm"]:
algorithm = ProjectionType.MEANINTENSITY
stepping = min(script_params["Stepping"], size_z)
scalebar = None
if "Scalebar" in script_params:
scalebar = script_params["Scalebar"]
log("Scalebar is %d microns" % scalebar)
r, g, b, a = OVERLAY_COLOURS[script_params["Overlay_Colour"]]
overlay_colour = (r, g, b)
merged_names = script_params["Merged_Names"]
fig = make_split_view_figure(conn, pixel_ids, z_start, z_end,
split_indexes, channel_names, colour_channels,
merged_indexes, merged_colours, merged_names,
width, height, image_labels, algorithm,
stepping, scalebar, overlay_colour)
fig_legend = "\n".join(log_strings)
figure_name = script_params["Figure_Name"]
figure_name = os.path.basename(figure_name)
output = "localfile"
format = script_params["Format"]
if format == 'PNG':
output = output + ".png"
figure_name = figure_name + ".png"
fig.save(output, "PNG")
mimetype = "image/png"
elif format == 'TIFF':
output = output + ".tiff"
figure_name = figure_name + ".tiff"
fig.save(output, "TIFF")
mimetype = "image/tiff"
else:
output = output + ".jpg"
figure_name = figure_name + ".jpg"
fig.save(output)
mimetype = "image/jpeg"
# Upload the figure 'output' to the server, creating a file annotation and
# attaching it to the omero_image, adding the
# fig_legend as the fileAnnotation description.
namespace = NSCREATED + "/omero/figure_scripts/Split_View_Figure"
file_annotation, fa_message = script_utils.create_link_file_annotation(
conn,
output,
omero_image,
output="Split view figure",
mimetype=mimetype,
namespace=namespace,
description=fig_legend,
orig_file_path_and_name=figure_name)
message += fa_message
return file_annotation, message
def test_int_to_rgba(self, color):
v = image_utils.int_to_rgba(color[4])
for x in range(0, 3):
assert color[x] == v[x]