def process_data(conn,image,rectangles,coords):
"""
Get the coordinates in each roi, write to a file
and append to dataset
"""
message = ""
for i,rect in enumerate(rectangles):
locs_list = []
for c in range(coords.shape[0]):
locs = get_coords_in_roi(coords[c,:,:],rect)
locs_list.append(locs)
file_name = "coords_in_roi_%s.csv" % i
try:
f = open(file_name,'w')
for r in range(locs.shape[0]):
row = locs[r,:]
f.write(','.join([str(c) for c in row])+'\n')
finally:
f.close()
new_file_ann, faMessage = script_util.createLinkFileAnnotation(
conn, file_name, image, output="wrote coords file",
mimetype="text/csv", desc=None)
message += faMessage
return message
def saveImages(conn, scriptParams):
dataType = scriptParams['Data_Type']
formatType = scriptParams['Format']
dirpath='tmp' #Use Root tmp dir so gets cleaned up
unique_folder = 'Exportfiles' + str(time.time()) #random
imagepath = path.join(dirpath, unique_folder)
imagepath = path.sep + imagepath + path.sep
print "Imagepath=", imagepath
# Get the images or datasets
message = ""
objects, logMessage = script_utils.getObjects(conn, scriptParams)
message += logMessage
parent = objects[0] #??
if not objects:
return None, message
if dataType == 'Dataset':
images = []
for ds in objects:
images.extend(list(ds.listChildren()))
if not images:
message += "No image found in dataset(s)"
return None, message
else:
images = objects
imageIds = [i.getId() for i in images]
print "Selected %d images for processing" % len(imageIds)
#Create download folder
if len(imageIds) > 0:
mkdir(imagepath)
#chdir(imagepath)
imagefilenames =[]
for iId in imageIds:
img = conn.getObject("Image", iId)
if img is not None:
z = img.getSizeZ() / 2
t = 0
saveimage = img.renderImage(z,t) # returns PIL Image jpeg
imageFilename = getImageName(img, formatType.lower(), imagepath)
imagefilenames.append(imageFilename)
message += saveAs(saveimage,imageFilename,formatType)
#zip output files
zipfilename = path.join(dirpath, unique_folder)
zipfilename = path.sep + zipfilename + ".zip"
message += createZipFile(zipfilename, imagefilenames)
mimetype = 'application/zip'
outputDisplayName = "Image export zip"
namespace = NSCREATED + "/QBI/Utils/ExportImage"
fileAnnotation, annMessage = script_utils.createLinkFileAnnotation(
conn, zipfilename, parent, output=outputDisplayName, ns=namespace,
mimetype=mimetype)
message += annMessage
return fileAnnotation, message
def process_data(conn, image, file_type, rectangles, localisations, scalex, scaley, cIndex=0):
"""
Get the coordinates in the ROI and form histograms of x and y coordinates
@param conn: the BlitzGateWay connection
@param image: the image containing the ROIs to process. the image were the localisations file is attached.
@param file_type: what type of dataset are we dealing with (see FILE_TYPES global)
@param rectangles: the ROIs scaled to nm
@param coords: the xy coordinates of localisations being histogrammed
@param scalex: multiplier to pixel size to determine bin size in histograms
@param scaley: multiplier to pixel size to determine bin size in histograms
"""
message = ""
pixels = image.getPrimaryPixels()
# note pixel sizes (if available) to set for the new images
physX = pixels.getPhysicalSizeX() * 1000.0
physY = pixels.getPhysicalSizeY() * 1000.0
x = file_type["x_col"]
y = file_type["y_col"]
for i, rect in enumerate(rectangles):
binsx = ceil((rect[2] / physX) / scalex)
binsy = ceil((rect[3] / physY) / scaley)
print "binsx,binsy:", binsx, binsy
rangex = rect[2]
rangey = rect[3]
print "rangex,rangey:", rangex, rangey
locs_df = get_coords_in_roi(localisations[cIndex], rect, file_type)
histx, edgesx = np.histogram(locs_df.loc[:, [x]].values, bins=binsx)
histy, edgesy = np.histogram(locs_df.loc[:, [y]].values, bins=binsy)
hist_dataX = np.zeros((histx.shape[0], 1))
hist_dataX[:, 0] = histx
hist_dataY = np.zeros((histy.shape[0], 1))
hist_dataY[:, 0] = histy
centersx = 0.5 * (edgesx[1:] + edgesx[:-1])
centersy = 0.5 * (edgesy[1:] + edgesy[:-1])
centers_dataX = np.zeros((centersx.shape[0], 1))
centers_dataX[:, 0] = centersx
centers_dataY = np.zeros((centersy.shape[0], 1))
centers_dataY[:, 0] = centersy
file_name = "scatter_hist_in_roi_%s.csv" % i
with file(file_name, "w") as outfile:
outfile.write("# scatter histogram data in x-direction\n")
datax = np.concatenate((centers_dataX, hist_dataX), axis=1)
np.savetxt(outfile, datax, fmt="%-7.2f", delimiter=",", newline="\n")
outfile.write("# scatter histogram data in y-direction\n")
datay = np.concatenate((centers_dataY, hist_dataY), axis=1)
np.savetxt(outfile, datay, fmt="%-7.2f", delimiter=",", newline="\n")
new_file_ann, faMessage = script_util.createLinkFileAnnotation(
conn, file_name, image, output="wrote coords file for ROI %s" % i, mimetype="text/csv", desc=None
)
message += faMessage
return message
def process_data(conn, image, file_type, rectangles, coords):
"""
Get the coordinates in each roi, write to a file
and append to dataset
"""
message = ""
sizeT = image.getSizeT()
frame = FILE_TYPES[file_type]['frame']
if sizeT > 1:
desc = image.getDescription()
if desc:
start = desc.index('Start')
stop = desc.index('Stop')
starts = desc[start + 7:stop - 3]
starts = [int(s) for s in starts.split(',')]
stops = desc[stop + 6:len(desc) - 1]
stops = [int(s) for s in stops.split(',')]
else:
starts = [1]
stops = [coords[0][frame].max]
def coord_gen():
for rect in rectangles:
for c in range(len(coords)):
locs_df = coords[c]
for t in range(sizeT):
coords_in_frames = locs_df[(locs_df[frame] >= starts[t])
& (locs_df[frame] <= stops[t])]
yield get_coords_in_roi(coords_in_frames, rect[:-1],
file_type)
coord_generator = coord_gen()
for rect in rectangles:
rid = rect[-1]
for c in range(len(coords)):
for t in range(sizeT):
file_name = "Coords_ROI%s_Time%s_Channel%s.csv" % (rid, t, c)
with open(file_name, 'w') as f:
locs_df = coord_generator.next()
locs_df.to_csv(f,
sep=',',
float_format='%8.2f',
index=False,
encoding='utf-8')
new_file_ann, faMessage = script_util.createLinkFileAnnotation(
conn,
file_name,
image,
output="wrote coords file for ROI %s" % rid,
mimetype="text/csv",
desc=None)
message += faMessage
return message
def attach_results(conn,ann,image,data,sizeC,sizeR):
file_name = "ripleyl_plot_" + ann.getFile().getName()[:-4] + '.csv'
with file(file_name, 'w') as outfile:
outfile.write('# ripley data for %s channels and %s ROIs: \n' % (sizeC, sizeR ))
data.to_csv(outfile,sep=',',float_format='%8.2f',index=False,encoding='utf-8')
description = "Ripley L function data created from:\n Image ID: %d Annotation_ID: %d"\
% (image.getId(),ann.getId())
new_file_ann, faMessage = script_util.createLinkFileAnnotation(
conn, file_name, image, output="Ripley L Plot csv (Excel) file",
mimetype="text/csv", desc=description)
return new_file_ann
def process_data(conn,image,file_type,rectangles,coords):
"""
Get the coordinates in each roi, write to a file
and append to dataset
"""
message = ""
sizeT = image.getSizeT()
frame = FILE_TYPES[file_type]['frame']
if sizeT > 1:
desc = image.getDescription()
if desc:
start = desc.index('Start')
stop = desc.index('Stop')
starts = desc[start+7:stop-3]
starts = [int(s) for s in starts.split(',')]
stops = desc[stop+6:len(desc)-1]
stops = [int(s) for s in stops.split(',')]
else:
starts = [1]
stops = [coords[0][frame].max]
def coord_gen():
for rect in rectangles:
for c in range(len(coords)):
locs_df = coords[c]
for t in range(sizeT):
coords_in_frames = locs_df[(locs_df[frame]>= starts[t]) & (locs_df[frame]<= stops[t])]
yield get_coords_in_roi(coords_in_frames,rect[:-1],file_type)
coord_generator = coord_gen()
for rect in rectangles:
rid = rect[-1]
for c in range(len(coords)):
for t in range(sizeT):
file_name = "Coords_ROI%s_Time%s_Channel%s.csv" % (rid,t,c)
with open(file_name,'w') as f:
locs_df = coord_generator.next()
locs_df.to_csv(f,sep=',',float_format='%8.2f',index=False,encoding='utf-8')
new_file_ann, faMessage = script_util.createLinkFileAnnotation(
conn, file_name, image, output="wrote coords file for ROI %s" %rid,
mimetype="text/csv", desc=None)
message += faMessage
return message
def attach_results(conn, ann, image, data, sizeC, sizeR):
file_name = "ripleyl_plot_" + ann.getFile().getName()[:-4] + '.csv'
with file(file_name, 'w') as outfile:
outfile.write('# ripley data for %s channels and %s ROIs: \n' %
(sizeC, sizeR))
data.to_csv(outfile,
sep=',',
float_format='%8.2f',
index=False,
encoding='utf-8')
description = "Ripley L function data created from:\n Image ID: %d Annotation_ID: %d"\
% (image.getId(),ann.getId())
new_file_ann, faMessage = script_util.createLinkFileAnnotation(
conn,
file_name,
image,
output="Ripley L Plot csv (Excel) file",
mimetype="text/csv",
desc=description)
return new_file_ann
def processImages(conn, scriptParams):
lineWidth = scriptParams['Line_Width']
fileAnns = []
message = ""
# Get the images
images, logMessage = scriptUtil.getObjects(conn, scriptParams)
message += logMessage
if not images:
return None, message
# Check for line and polyline ROIs and filter images list
images = [image for image in images if
image.getROICount(["Polyline", "Line"]) > 0]
if not images:
message += "No ROI containing line or polyline was found."
return None, message
for image in images:
cNames = []
colors = []
for ch in image.getChannels():
cNames.append(ch.getLabel())
colors.append(ch.getColor().getRGB())
sizeC = image.getSizeC()
if 'Channels' in scriptParams:
scriptParams['Channels'] = [i-1 for i in scriptParams['Channels']]
# Convert user input from 1-based to 0-based
for i in scriptParams['Channels']:
print i, type(i)
else:
scriptParams['Channels'] = range(sizeC)
# channelMinMax = []
# for c in image.getChannels():
# minC = c.getWindowMin()
# maxC = c.getWindowMax()
# channelMinMax.append((minC, maxC))
roiService = conn.getRoiService()
result = roiService.findByImage(image.getId(), None)
lines = []
polylines = []
for roi in result.rois:
roiId = roi.getId().getValue()
for s in roi.copyShapes():
theZ = s.getTheZ() and s.getTheZ().getValue() or 0
theT = s.getTheT() and s.getTheT().getValue() or 0
# TODO: Add some filter of shapes. E.g. text? / 'lines' only
# etc.
if type(s) == omero.model.LineI:
x1 = s.getX1().getValue()
x2 = s.getX2().getValue()
y1 = s.getY1().getValue()
y2 = s.getY2().getValue()
lines.append({'id': roiId, 'theT': theT, 'theZ': theZ,
'x1': x1, 'y1': y1, 'x2': x2, 'y2': y2})
elif type(s) == omero.model.PolylineI:
points = pointsStringToXYlist(s.getPoints().getValue())
polylines.append({'id': roiId, 'theT': theT, 'theZ': theZ,
'points': points})
if len(lines) == 0 and len(polylines) == 0:
print "Image: %s had no lines or polylines" % image.getId()
continue
# prepare column headers, including line-id if we are going to output
# raw data.
lineId = scriptParams['Sum_or_Average'] == 'Average, with raw data' \
and 'Line, ' or ""
colHeader = 'Image_ID, ROI_ID, Z, T, C, %sLine data %s of Line" \
" Width %s\n' % (lineId, scriptParams['Sum_or_Average'],
scriptParams['Line_Width'])
print 'colHeader', colHeader
# prepare a csv file to write our data to...
fileName = "Plot_Profile_%s.csv" % image.getId()
try:
f = open(fileName, 'w')
f.write(colHeader)
if len(lines) > 0:
processLines(conn, scriptParams, image, lines, lineWidth, f)
if len(polylines) > 0:
processPolyLines(
conn, scriptParams, image, polylines, lineWidth, f)
finally:
f.close()
fileAnn, faMessage = scriptUtil.createLinkFileAnnotation(
conn, fileName, image, output="Line Plot csv (Excel) file",
mimetype="text/csv", desc=None)
if fileAnn:
fileAnns.append(fileAnn)
if not fileAnns:
faMessage = "No Analysis files created. See 'Info' or 'Error' for"\
" more details"
elif len(fileAnns) > 1:
faMessage = "Created %s csv (Excel) files" % len(fileAnns)
message += faMessage
return fileAnns, message
def makeThumbnailFigure(conn, scriptParams):
"""
Makes the figure using the parameters in @scriptParams, attaches the
figure to the parent Project/Dataset, and returns the file-annotation ID
@ returns Returns the id of the originalFileLink child. (ID object,
not value)
"""
log("Thumbnail figure created by OMERO")
log("")
message = ""
# Get the objects (images or datasets)
objects, logMessage = scriptUtil.getObjects(conn, scriptParams)
message += logMessage
if not objects:
return None, message
# Get parent
parent = None
if "Parent_ID" in scriptParams and len(scriptParams["IDs"]) > 1:
if scriptParams["Data_Type"] == "Image":
parent = conn.getObject("Dataset", scriptParams["Parent_ID"])
else:
parent = conn.getObject("Project", scriptParams["Parent_ID"])
if parent is None:
parent = objects[0] # Attach figure to the first object
parentClass = parent.OMERO_CLASS
log("Figure will be linked to %s%s: %s"
% (parentClass[0].lower(), parentClass[1:], parent.getName()))
tagIds = []
if "Tag_IDs" in scriptParams:
tagIds = scriptParams['Tag_IDs']
if len(tagIds) == 0:
tagIds = None
showUntagged = False
if (tagIds):
showUntagged = scriptParams["Show_Untagged_Images"]
thumbSize = scriptParams["Thumbnail_Size"]
maxColumns = scriptParams["Max_Columns"]
figHeight = 0
figWidth = 0
dsCanvases = []
if scriptParams["Data_Type"] == "Dataset":
for dataset in objects:
log("Dataset: %s ID: %d"
% (dataset.getName(), dataset.getId()))
images = list(dataset.listChildren())
dsCanvas = paintDatasetCanvas(
conn, images, dataset.getName(), tagIds, showUntagged,
length=thumbSize, colCount=maxColumns)
if dsCanvas is None:
continue
dsCanvases.append(dsCanvas)
figHeight += dsCanvas.size[1]
figWidth = max(figWidth, dsCanvas.size[0])
else:
imageCanvas = paintDatasetCanvas(
conn, objects, "", tagIds,
showUntagged, length=thumbSize, colCount=maxColumns)
dsCanvases.append(imageCanvas)
figHeight += imageCanvas.size[1]
figWidth = max(figWidth, imageCanvas.size[0])
if len(dsCanvases) == 0:
message += "No figure created"
return None, message
figure = Image.new("RGB", (figWidth, figHeight), WHITE)
y = 0
for ds in dsCanvases:
imgUtil.pasteImage(ds, figure, 0, y)
y += ds.size[1]
log("")
figLegend = "\n".join(logLines)
format = scriptParams["Format"]
figureName = scriptParams["Figure_Name"]
figureName = os.path.basename(figureName)
output = "localfile"
if format == 'PNG':
output = output + ".png"
figureName = figureName + ".png"
figure.save(output, "PNG")
mimetype = "image/png"
elif format == 'TIFF':
output = output + ".tiff"
figureName = figureName + ".tiff"
figure.save(output, "TIFF")
mimetype = "image/tiff"
else:
output = output + ".jpg"
figureName = figureName + ".jpg"
figure.save(output)
mimetype = "image/jpeg"
namespace = NSCREATED + "/omero/figure_scripts/Thumbnail_Figure"
fileAnnotation, faMessage = scriptUtil.createLinkFileAnnotation(
conn, output, parent, output="Thumbnail figure", mimetype=mimetype,
ns=namespace, desc=figLegend, origFilePathAndName=figureName)
message += faMessage
return fileAnnotation, message
def run_processing(conn,script_params):
file_anns = []
message = ""
imageIds = []
image_id = script_params['ImageID']
imageIds.append(image_id)
image = conn.getObject("Image",image_id)
if not image:
message = 'Could not find specified image'
return message
file_id = script_params['AnnotationID']
ann = conn.getObject("Annotation",file_id)
if not ann:
message = 'Could not find specified annotation'
return message
radius = script_params['Radius']
#other parameters
if script_params['Convert_coordinates_to_nm']:
cam_pix_size = script_params['Parent_Image_Pixel_Size']
else:
cam_pix_size = 1
file_type = FILE_TYPES[script_params['File_Type']]
path_to_ann = ann.getFile().getPath() + '/' + ann.getFile().getName()
name,ext = os.path.splitext(path_to_ann)
if ('txt' in ext) or ('csv' in ext):
#get the path to the downloaded data
path_to_data = download_data(ann)
#get all the xy coords in that data
locs = parse_sr_data(path_to_data,file_type,cam_pix_size)
sizeC = len(locs)
#get the rois to be processed
rectangles = get_rectangles(conn,image_id)
print 'rectanges:',rectangles
#calculate local density
locs_density = process_data(conn,image,file_type,sizeC,rectangles,locs,radius)
#write the data to a csv
file_name = "localisation_density_" + ann.getFile().getName()[:-4] + '.csv'
with file(file_name, 'a') as outfile:
outfile.write('# localisation density data for %s channels and %s ROIs: \n' % (sizeC, len(locs_density)))
for r in range(len(locs_density)):
outfile.write('# ROI %s\n' % rectangles[r][-1])
outfile.write('Channel,%s,%s,Density within %s [nm]\n' % (file_type['x_col'],file_type['y_col'],str(radius)))
density = locs_density[r][1:,:]
np.savetxt(outfile, density, fmt='%-7.2f', delimiter=',', newline='\n')
new_file_ann, faMessage = script_util.createLinkFileAnnotation(
conn, file_name, image, output="Wrote localisation density csv (Excel) file",
mimetype="text/csv", desc=None)
if new_file_ann:
file_anns.append(new_file_ann)
if not file_anns:
faMessage = "No Analysis files created. See 'Info' or 'Error' for"\
" more details"
elif len(file_anns) > 1:
faMessage = "Created %s csv (Excel) files" % len(file_anns)
else:
message = 'file annotation must be txt or csv'
return message
# clean up
delete_downloaded_data(ann)
message += faMessage
return message
def processImages(conn, scriptParams):
lineWidth = scriptParams['Line_Width']
fileAnns = []
message = ""
# Get the images
images, logMessage = scriptUtil.getObjects(conn, scriptParams)
message += logMessage
if not images:
return None, message
# Check for line and polyline ROIs and filter images list
images = [
image for image in images
if image.getROICount(["Polyline", "Line"]) > 0
]
if not images:
message += "No ROI containing line or polyline was found."
return None, message
for image in images:
cNames = []
colors = []
for ch in image.getChannels():
cNames.append(ch.getLabel())
colors.append(ch.getColor().getRGB())
sizeC = image.getSizeC()
if 'Channels' in scriptParams:
scriptParams['Channels'] = [
i - 1 for i in scriptParams['Channels']
]
# Convert user input from 1-based to 0-based
for i in scriptParams['Channels']:
print i, type(i)
else:
scriptParams['Channels'] = range(sizeC)
# channelMinMax = []
# for c in image.getChannels():
# minC = c.getWindowMin()
# maxC = c.getWindowMax()
# channelMinMax.append((minC, maxC))
roiService = conn.getRoiService()
result = roiService.findByImage(image.getId(), None)
lines = []
polylines = []
for roi in result.rois:
roiId = roi.getId().getValue()
for s in roi.copyShapes():
theZ = s.getTheZ() and s.getTheZ().getValue() or 0
theT = s.getTheT() and s.getTheT().getValue() or 0
# TODO: Add some filter of shapes. E.g. text? / 'lines' only
# etc.
if type(s) == omero.model.LineI:
x1 = s.getX1().getValue()
x2 = s.getX2().getValue()
y1 = s.getY1().getValue()
y2 = s.getY2().getValue()
lines.append({
'id': roiId,
'theT': theT,
'theZ': theZ,
'x1': x1,
'y1': y1,
'x2': x2,
'y2': y2
})
elif type(s) == omero.model.PolylineI:
points = pointsStringToXYlist(s.getPoints().getValue())
polylines.append({
'id': roiId,
'theT': theT,
'theZ': theZ,
'points': points
})
if len(lines) == 0 and len(polylines) == 0:
print "Image: %s had no lines or polylines" % image.getId()
continue
# prepare column headers, including line-id if we are going to output
# raw data.
lineId = scriptParams['Sum_or_Average'] == 'Average, with raw data' \
and 'Line, ' or ""
colHeader = 'Image_ID, ROI_ID, Z, T, C, %sLine data %s of Line" \
" Width %s\n' % (lineId, scriptParams['Sum_or_Average'],
scriptParams['Line_Width'])
print 'colHeader', colHeader
# prepare a csv file to write our data to...
fileName = "Plot_Profile_%s.csv" % image.getId()
try:
f = open(fileName, 'w')
f.write(colHeader)
if len(lines) > 0:
processLines(conn, scriptParams, image, lines, lineWidth, f)
if len(polylines) > 0:
processPolyLines(conn, scriptParams, image, polylines,
lineWidth, f)
finally:
f.close()
fileAnn, faMessage = scriptUtil.createLinkFileAnnotation(
conn,
fileName,
image,
output="Line Plot csv (Excel) file",
mimetype="text/csv",
desc=None)
if fileAnn:
fileAnns.append(fileAnn)
if not fileAnns:
faMessage = "No Analysis files created. See 'Info' or 'Error' for"\
" more details"
elif len(fileAnns) > 1:
faMessage = "Created %s csv (Excel) files" % len(fileAnns)
message += faMessage
return fileAnns, message
def splitViewFigure(conn, scriptParams):
"""
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 makeSplitViewFigure() 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
imageIds = []
pixelIds = []
imageLabels = []
# function for getting image labels.
def getLabels(fullName, tagsList, pdList):
name = fullName.split("/")[-1]
return [name]
# default function for getting labels is getName (or use datasets / tags)
if scriptParams["Image_Labels"] == "Datasets":
def getDatasets(name, tagsList, pdList):
return [dataset for project, dataset in pdList]
getLabels = getDatasets
elif scriptParams["Image_Labels"] == "Tags":
def getTags(name, tagsList, pdList):
return tagsList
getLabels = getTags
# Get the images
images, logMessage = scriptUtil.getObjects(conn, scriptParams)
message += logMessage
if not images:
return None, message
# Attach figure to the first image
omeroImage = images[0]
# process the list of images
log("Image details:")
for image in images:
imageIds.append(image.getId())
pixelIds.append(image.getPrimaryPixels().getId())
pdMap = figUtil.getDatasetsProjectsFromImages(
conn.getQueryService(),
imageIds) # a map of imageId : list of (project, dataset) names.
tagMap = figUtil.getTagsFromImages(conn.getMetadataService(), imageIds)
# Build a legend entry for each image
for image in images:
name = image.getName()
imageDate = image.getAcquisitionDate()
iId = image.getId()
tagsList = tagMap[iId]
pdList = pdMap[iId]
tags = ", ".join(tagsList)
pdString = ", ".join(["%s/%s" % pd for pd in pdList])
log(" Image: %s ID: %d" % (name, iId))
log(" Date: %s" % date.fromtimestamp(imageDate / 1000))
log(" Tags: %s" % tags)
log(" Project/Datasets: %s" % pdString)
imageLabels.append(getLabels(name, tagsList, pdList))
# use the first image to define dimensions, channel colours etc.
sizeX = omeroImage.getSizeX()
sizeY = omeroImage.getSizeY()
sizeZ = omeroImage.getSizeZ()
sizeC = omeroImage.getSizeC()
# set image dimensions
zStart = -1
zEnd = -1
if "Z_Start" in scriptParams:
zStart = scriptParams["Z_Start"]
if "Z_End" in scriptParams:
zEnd = scriptParams["Z_End"]
width = "Width" in scriptParams and scriptParams["Width"] or sizeX
height = "Height" in scriptParams and scriptParams["Height"] or sizeY
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.
splitIndexes = []
if "Split_Indexes" in scriptParams:
splitIndexes = scriptParams["Split_Indexes"]
else:
splitIndexes = range(sizeC)
# Make channel-names map. If argument wasn't specified, name by index
channelNames = {}
for c in range(sizeC):
channelNames[c] = str(c)
if "Channel_Names" in scriptParams:
cNameMap = scriptParams["Channel_Names"]
for c in cNameMap:
index = int(c)
channelNames[index] = cNameMap[c]
mergedIndexes = [] # the channels in the combined image,
mergedColours = {}
if "Merged_Colours" in scriptParams:
cColourMap = scriptParams["Merged_Colours"]
for c in cColourMap:
rgb = cColourMap[c]
rgba = imgUtil.RGBIntToRGBA(rgb)
mergedColours[int(c)] = rgba
mergedIndexes.append(int(c))
mergedIndexes.sort()
else:
mergedIndexes = range(sizeC)
colourChannels = not scriptParams["Split_Panels_Grey"]
algorithm = omero.constants.projection.ProjectionType.MAXIMUMINTENSITY
if "Mean Intensity" == scriptParams["Algorithm"]:
algorithm = omero.constants.projection.ProjectionType.MEANINTENSITY
stepping = min(scriptParams["Stepping"], sizeZ)
scalebar = None
if "Scalebar" in scriptParams:
scalebar = scriptParams["Scalebar"]
log("Scalebar is %d microns" % scalebar)
r, g, b, a = OVERLAY_COLOURS[scriptParams["Overlay_Colour"]]
overlayColour = (r, g, b)
mergedNames = scriptParams["Merged_Names"]
print "splitIndexes", splitIndexes
print "channelNames", channelNames
print "colourChannels", colourChannels
print "mergedIndexes", mergedIndexes
print "mergedColours", mergedColours
print "mergedNames", mergedNames
fig = makeSplitViewFigure(conn, pixelIds, zStart, zEnd, splitIndexes,
channelNames, colourChannels, mergedIndexes,
mergedColours, mergedNames, width, height,
imageLabels, algorithm, stepping, scalebar,
overlayColour)
figLegend = "\n".join(logStrings)
format = JPEG
if scriptParams["Format"] == "PNG":
format = PNG
output = scriptParams["Figure_Name"]
if format == PNG:
output = output + ".png"
fig.save(output, "PNG")
mimetype = "image/png"
else:
output = output + ".jpg"
fig.save(output)
mimetype = "image/jpeg"
# Upload the figure 'output' to the server, creating a file annotation and attaching it to the omeroImage, adding the
# figLegend as the fileAnnotation description.
namespace = omero.constants.namespaces.NSCREATED + "/omero/figure_scripts/Split_View_Figure"
fileAnnotation, faMessage = scriptUtil.createLinkFileAnnotation(
conn,
output,
omeroImage,
output="Split view figure",
mimetype=mimetype,
ns=namespace,
desc=figLegend)
message += faMessage
return fileAnnotation, message
def analyseImage(conn, image, cIndex):
print "\n---------------------"
print "Analysing Image: ", image.getName()
# Get dictionary of tIndex:ellipse
ellipses = getEllipses(conn, image.getId())
# Get dictionary of tIndex:averageIntensity
intensityData = getEllipseData(image, ellipses, cIndex)
# Get dictionary of tIndex:timeStamp (secs)
timeValues = getTimes(conn, image)
# We now have all the Data we need from OMERO
# create lists of times (secs) and intensities...
timeList = []
valueList = []
# ...Ordered by tIndex
for t in range(image.getSizeT()):
if t in intensityData:
timeList.append(timeValues[t])
valueList.append(intensityData[t])
print "Analysing pixel values for %s time points" % len(timeList)
# Find the bleach intensity & time
bleachValue = min(valueList)
bleachTindex = valueList.index(bleachValue)
bleachTime = timeList[bleachTindex]
preBleachValue = valueList[bleachTindex-1]
print "Bleach at tIndex: %s, TimeStamp: %0.2f seconds" \
% (bleachTindex, bleachTime)
print "Before Bleach: %0.2f, After Bleach: %0.2f" \
% (preBleachValue, bleachValue)
# Use last timepoint for max recovery
recoveryValue = valueList[-1]
endTimepoint = timeList[-1]
mobileFraction = (recoveryValue - bleachValue) / \
(preBleachValue - bleachValue)
print "Recovered to: %0.2f, after %0.2f seconds" \
% (recoveryValue, endTimepoint)
print "Mobile Fraction: %0.2f" % mobileFraction
halfRecovery = (recoveryValue + bleachValue)/2
# quick & dirty - pick the first timepoint where we exceed half recovery
recoveryValues = valueList[bleachTindex:]
# just the values & times after bleach time
recoveryTimes = timeList[bleachTindex:]
for t, v in zip(recoveryTimes, recoveryValues):
if v >= halfRecovery:
tHalf = t - bleachTime
break
print "tHalf: %0.2f seconds" % tHalf
csvLines = [
"Time (secs)," + ",".join([str(t) for t in timeList]),
"\n",
"Average pixel value," + ",".join([str(v) for v in valueList]),
"\n",
"tHalf (secs), %0.2f seconds" % tHalf,
"mobileFraction, %0.2f" % mobileFraction
]
f = open("FRAP.csv", "w")
f.writelines(csvLines)
f.close()
namespace = "/omero-user-scripts/example/Simple_FRAP/"
scriptUtil.createLinkFileAnnotation(conn, "FRAP.csv", image, ns=namespace)
return tHalf
def batchImageExport(conn, scriptParams):
# for params with default values, we can get the value directly
splitCs = scriptParams["Export_Individual_Channels"]
mergedCs = scriptParams["Export_Merged_Image"]
greyscale = scriptParams["Individual_Channels_Grey"]
dataType = scriptParams["Data_Type"]
folder_name = scriptParams["Folder_Name"]
folder_name = os.path.basename(folder_name)
format = scriptParams["Format"]
projectZ = "Choose_Z_Section" in scriptParams and \
scriptParams["Choose_Z_Section"] == 'Max projection'
if (not splitCs) and (not mergedCs):
log("Not chosen to save Individual Channels OR Merged Image")
return
# check if we have these params
channelNames = []
if "Channel_Names" in scriptParams:
channelNames = scriptParams["Channel_Names"]
zoomPercent = None
if "Zoom" in scriptParams and scriptParams["Zoom"] != "100%":
zoomPercent = int(scriptParams["Zoom"][:-1])
# functions used below for each imaage.
def getZrange(sizeZ, scriptParams):
zRange = None
if "Choose_Z_Section" in scriptParams:
zChoice = scriptParams["Choose_Z_Section"]
# NB: all Z indices in this script are 1-based
if zChoice == 'ALL Z planes':
zRange = (1, sizeZ+1)
elif "OR_specify_Z_index" in scriptParams:
zIndex = scriptParams["OR_specify_Z_index"]
zIndex = min(zIndex, sizeZ)
zRange = (zIndex,)
elif "OR_specify_Z_start_AND..." in scriptParams and \
"...specify_Z_end" in scriptParams:
start = scriptParams["OR_specify_Z_start_AND..."]
start = min(start, sizeZ)
end = scriptParams["...specify_Z_end"]
end = min(end, sizeZ)
# in case user got zStart and zEnd mixed up
zStart = min(start, end)
zEnd = max(start, end)
if zStart == zEnd:
zRange = (zStart,)
else:
zRange = (zStart, zEnd+1)
return zRange
def getTrange(sizeT, scriptParams):
tRange = None
if "Choose_T_Section" in scriptParams:
tChoice = scriptParams["Choose_T_Section"]
# NB: all T indices in this script are 1-based
if tChoice == 'ALL T planes':
tRange = (1, sizeT+1)
elif "OR_specify_T_index" in scriptParams:
tIndex = scriptParams["OR_specify_T_index"]
tIndex = min(tIndex, sizeT)
tRange = (tIndex,)
elif "OR_specify_T_start_AND..." in scriptParams and \
"...specify_T_end" in scriptParams:
start = scriptParams["OR_specify_T_start_AND..."]
start = min(start, sizeT)
end = scriptParams["...specify_T_end"]
end = min(end, sizeT)
# in case user got zStart and zEnd mixed up
tStart = min(start, end)
tEnd = max(start, end)
if tStart == tEnd:
tRange = (tStart,)
else:
tRange = (tStart, tEnd+1)
return tRange
# Get the images or datasets
message = ""
objects, logMessage = script_utils.getObjects(conn, scriptParams)
message += logMessage
if not objects:
return None, message
# Attach figure to the first image
parent = objects[0]
if dataType == 'Dataset':
images = []
for ds in objects:
images.extend(list(ds.listChildren()))
if not images:
message += "No image found in dataset(s)"
return None, message
else:
images = objects
log("Processing %s images" % len(images))
# somewhere to put images
curr_dir = os.getcwd()
exp_dir = os.path.join(curr_dir, folder_name)
try:
os.mkdir(exp_dir)
except:
pass
# max size (default 12kx12k)
size = conn.getDownloadAsMaxSizeSetting()
size = int(size)
ids = []
# do the saving to disk
for img in images:
pixels = img.getPrimaryPixels()
if (pixels.getId() in ids):
continue
ids.append(pixels.getId())
sizeX = pixels.getSizeX()
sizeY = pixels.getSizeY()
if sizeX*sizeY > size:
log(" ** Can't export a 'Big' image to %s. **" % format)
if len(images) == 1:
return None, "Can't export a 'Big' image to %s." % format
continue
else:
log("Exporting image as %s: %s" % (format, img.getName()))
if format == 'OME-TIFF':
saveAsOmeTiff(conn, img, folder_name)
else:
if img._prepareRE().requiresPixelsPyramid():
log(" ** Can't export a 'Big' image to OME-TIFF. **")
log("\n----------- Saving planes from image: '%s' ------------"
% img.getName())
sizeC = img.getSizeC()
sizeZ = img.getSizeZ()
sizeT = img.getSizeT()
zRange = getZrange(sizeZ, scriptParams)
tRange = getTrange(sizeT, scriptParams)
log("Using:")
if zRange is None:
log(" Z-index: Last-viewed")
elif len(zRange) == 1:
log(" Z-index: %d" % zRange[0])
else:
log(" Z-range: %s-%s" % (zRange[0], zRange[1]-1))
if projectZ:
log(" Z-projection: ON")
if tRange is None:
log(" T-index: Last-viewed")
elif len(tRange) == 1:
log(" T-index: %d" % tRange[0])
else:
log(" T-range: %s-%s" % (tRange[0], tRange[1]-1))
log(" Format: %s" % format)
if zoomPercent is None:
log(" Image Zoom: 100%")
else:
log(" Image Zoom: %s" % zoomPercent)
log(" Greyscale: %s" % greyscale)
log("Channel Rendering Settings:")
for ch in img.getChannels():
log(" %s: %d-%d"
% (ch.getLabel(), ch.getWindowStart(), ch.getWindowEnd()))
try:
savePlanesForImage(
conn, img, sizeC, splitCs, mergedCs, channelNames, zRange,
tRange, greyscale, zoomPercent, projectZ=projectZ,
format=format, folder_name=folder_name)
finally:
# Make sure we close Rendering Engine
img._re.close()
# write log for exported images (not needed for ome-tiff)
logFile = open(os.path.join(exp_dir, 'Batch_Image_Export.txt'), 'w')
try:
for s in logStrings:
logFile.write(s)
logFile.write("\n")
finally:
logFile.close()
if len(os.listdir(exp_dir)) == 0:
return None, "No files exported. See 'info' for more details"
# zip everything up (unless we've only got a single ome-tiff)
if format == 'OME-TIFF' and len(os.listdir(exp_dir)) == 1:
ometiffIds = [t.id for t in parent.listAnnotations(ns=NSOMETIFF)]
print "Deleting OLD ome-tiffs: %s" % ometiffIds
conn.deleteObjects("Annotation", ometiffIds)
export_file = os.path.join(folder_name, os.listdir(exp_dir)[0])
namespace = NSOMETIFF
outputDisplayName = "OME-TIFF"
mimetype = 'image/tiff'
else:
export_file = "%s.zip" % folder_name
compress(export_file, folder_name)
mimetype = 'application/zip'
outputDisplayName = "Batch export zip"
namespace = NSCREATED + "/omero/export_scripts/Batch_Image_Export"
fileAnnotation, annMessage = script_utils.createLinkFileAnnotation(
conn, export_file, parent, output=outputDisplayName, ns=namespace,
mimetype=mimetype)
message += annMessage
return fileAnnotation, message
def movieFigure(conn, commandArgs):
"""
Makes the figure using the parameters in @commandArgs, attaches the figure
to the parent Project/Dataset, and returns the file-annotation ID
@param session The OMERO session
@param commandArgs Map of parameters for the script
@ returns Returns the id of the originalFileLink child. (ID
object, not value)
"""
log("Movie figure created by OMERO on %s" % date.today())
log("")
timeLabels = {
"SECS_MILLIS": "seconds",
"SECS": "seconds",
"MINS": "minutes",
"HOURS": "hours",
"MINS_SECS": "mins:secs",
"HOURS_MINS": "hours:mins"
}
timeUnits = "SECS"
if "Time_Units" in commandArgs:
timeUnits = commandArgs["Time_Units"]
# convert from UI name to timeLabels key
timeUnits = timeUnits.replace(" ", "_")
if timeUnits not in timeLabels.keys():
timeUnits = "SECS"
log("Time units are in %s" % timeLabels[timeUnits])
pixelIds = []
imageIds = []
imageLabels = []
message = "" # message to be returned to the client
# function for getting image labels.
def getImageNames(fullName, tagsList, pdList):
name = fullName.split("/")[-1]
return [name]
# default function for getting labels is getName (or use datasets / tags)
if "Image_Labels" in commandArgs:
if commandArgs["Image_Labels"] == "Datasets":
def getDatasets(name, tagsList, pdList):
return [dataset for project, dataset in pdList]
getLabels = getDatasets
elif commandArgs["Image_Labels"] == "Tags":
def getTags(name, tagsList, pdList):
return tagsList
getLabels = getTags
else:
getLabels = getImageNames
else:
getLabels = getImageNames
# Get the images
images, logMessage = scriptUtil.getObjects(conn, commandArgs)
message += logMessage
if not images:
return None, message
# Attach figure to the first image
omeroImage = images[0]
# process the list of images
log("Image details:")
for image in images:
imageIds.append(image.getId())
pixelIds.append(image.getPrimaryPixels().getId())
# a map of imageId : list of (project, dataset) names.
pdMap = figUtil.getDatasetsProjectsFromImages(conn.getQueryService(),
imageIds)
tagMap = figUtil.getTagsFromImages(conn.getMetadataService(), imageIds)
# Build a legend entry for each image
for image in images:
name = image.getName()
iId = image.getId()
imageDate = image.getAcquisitionDate()
tagsList = tagMap[iId]
pdList = pdMap[iId]
tags = ", ".join(tagsList)
pdString = ", ".join(["%s/%s" % pd for pd in pdList])
log(" Image: %s ID: %d" % (name, iId))
log(" Date: %s" % date.fromtimestamp(imageDate / 1000))
log(" Tags: %s" % tags)
log(" Project/Datasets: %s" % pdString)
imageLabels.append(getLabels(name, tagsList, pdList))
# use the first image to define dimensions, channel colours etc.
sizeX = omeroImage.getSizeX()
sizeY = omeroImage.getSizeY()
sizeZ = omeroImage.getSizeZ()
sizeT = omeroImage.getSizeT()
tIndexes = []
if "T_Indexes" in commandArgs:
for t in commandArgs["T_Indexes"]:
tIndexes.append(t)
print "T_Indexes", tIndexes
if len(tIndexes) == 0: # if no t-indexes given, use all t-indices
tIndexes = range(sizeT)
zStart = -1
zEnd = -1
if "Z_Start" in commandArgs:
zStart = commandArgs["Z_Start"]
if "Z_End" in commandArgs:
zEnd = commandArgs["Z_End"]
width = sizeX
if "Width" in commandArgs:
width = commandArgs["Width"]
height = sizeY
if "Height" in commandArgs:
height = commandArgs["Height"]
spacer = (width / 25) + 2
algorithm = ProjectionType.MAXIMUMINTENSITY
if "Algorithm" in commandArgs:
a = commandArgs["Algorithm"]
if (a == "Mean Intensity"):
algorithm = ProjectionType.MEANINTENSITY
stepping = 1
if "Stepping" in commandArgs:
s = commandArgs["Stepping"]
if (0 < s < sizeZ):
stepping = s
scalebar = None
if "Scalebar_Size" in commandArgs:
sb = commandArgs["Scalebar_Size"]
try:
scalebar = int(sb)
if scalebar <= 0:
scalebar = None
else:
log("Scalebar is %d microns" % scalebar)
except:
log("Invalid value for scalebar: %s" % str(sb))
scalebar = None
overlayColour = (255, 255, 255)
if "Scalebar_Colour" in commandArgs:
r, g, b, a = OVERLAY_COLOURS[commandArgs["Scalebar_Colour"]]
overlayColour = (r, g, b)
maxColCount = 10
if "Max_Columns" in commandArgs:
maxColCount = commandArgs["Max_Columns"]
figure = createMovieFigure(conn, pixelIds, tIndexes, zStart, zEnd, width,
height, spacer, algorithm, stepping, scalebar,
overlayColour, timeUnits, imageLabels,
maxColCount)
log("")
figLegend = "\n".join(logLines)
#print figLegend # bug fixing only
format = commandArgs["Format"]
figureName = "movieFigure"
if "Figure_Name" in commandArgs:
figureName = str(commandArgs["Figure_Name"])
figureName = os.path.basename(figureName)
output = "localfile"
if format == 'PNG':
output = output + ".png"
figureName = figureName + ".png"
figure.save(output, "PNG")
mimetype = "image/png"
elif format == 'TIFF':
output = output + ".tiff"
figureName = figureName + ".tiff"
figure.save(output, "TIFF")
mimetype = "image/tiff"
else:
output = output + ".jpg"
figureName = figureName + ".jpg"
figure.save(output)
mimetype = "image/jpeg"
namespace = NSCREATED + "/omero/figure_scripts/Movie_Figure"
fileAnnotation, faMessage = scriptUtil.createLinkFileAnnotation(
conn,
output,
omeroImage,
output="Movie figure",
mimetype=mimetype,
ns=namespace,
desc=figLegend,
origFilePathAndName=figureName)
message += faMessage
return fileAnnotation, message
def analyseImage(conn, image, cIndex):
print "\n---------------------"
print "Analysing Image: ", image.getName()
# Get dictionary of tIndex:ellipse
ellipses = getEllipses(conn, image.getId())
# Get dictionary of tIndex:averageIntensity
intensityData = getEllipseData(image, ellipses, cIndex)
# Get dictionary of tIndex:timeStamp (secs)
timeValues = getTimes(conn, image)
# We now have all the Data we need from OMERO
# create lists of times (secs) and intensities...
timeList = []
valueList = []
# ...Ordered by tIndex
for t in range(image.getSizeT()):
if t in intensityData:
timeList.append(timeValues[t])
valueList.append(intensityData[t])
print "Analysing pixel values for %s time points" % len(timeList)
# Find the bleach intensity & time
bleachValue = min(valueList)
bleachTindex = valueList.index(bleachValue)
bleachTime = timeList[bleachTindex]
preBleachValue = valueList[bleachTindex - 1]
print "Bleach at tIndex: %s, TimeStamp: %0.2f seconds" \
% (bleachTindex, bleachTime)
print "Before Bleach: %0.2f, After Bleach: %0.2f" \
% (preBleachValue, bleachValue)
# Use last timepoint for max recovery
recoveryValue = valueList[-1]
endTimepoint = timeList[-1]
mobileFraction = (recoveryValue - bleachValue) / \
(preBleachValue - bleachValue)
print "Recovered to: %0.2f, after %0.2f seconds" \
% (recoveryValue, endTimepoint)
print "Mobile Fraction: %0.2f" % mobileFraction
halfRecovery = (recoveryValue + bleachValue) / 2
# quick & dirty - pick the first timepoint where we exceed half recovery
recoveryValues = valueList[bleachTindex:]
# just the values & times after bleach time
recoveryTimes = timeList[bleachTindex:]
for t, v in zip(recoveryTimes, recoveryValues):
if v >= halfRecovery:
tHalf = t - bleachTime
break
print "tHalf: %0.2f seconds" % tHalf
csvLines = [
"Time (secs)," + ",".join([str(t) for t in timeList]), "\n",
"Average pixel value," + ",".join([str(v) for v in valueList]), "\n",
"tHalf (secs), %0.2f seconds" % tHalf,
"mobileFraction, %0.2f" % mobileFraction
]
f = open("FRAP.csv", "w")
f.writelines(csvLines)
f.close()
namespace = "/omero-user-scripts/example/Simple_FRAP/"
scriptUtil.createLinkFileAnnotation(conn, "FRAP.csv", image, ns=namespace)
return tHalf
def writeMovie(commandArgs, conn):
"""
Makes the movie.
@ returns Returns the file annotation
"""
log("Movie created by OMERO")
log("")
message = ""
conn.SERVICE_OPTS.setOmeroGroup('-1')
session = conn.c.sf
gateway = conn
scriptService = session.getScriptService()
queryService = session.getQueryService()
updateService = session.getUpdateService()
rawFileStore = session.createRawFileStore()
# Get the images
images, logMessage = scriptUtil.getObjects(conn, commandArgs)
message += logMessage
if not images:
return None, message
omeroImage = images[
0] # Get the first valid image (should be expanded to process the list)
if commandArgs["RenderingDef_ID"] >= 0:
omeroImage._prepareRenderingEngine(rdid=commandArgs["RenderingDef_ID"])
pixels = omeroImage.getPrimaryPixels()
pixelsId = pixels.getId()
sizeX = pixels.getSizeX()
sizeY = pixels.getSizeY()
sizeZ = pixels.getSizeZ()
sizeC = pixels.getSizeC()
sizeT = pixels.getSizeT()
if (sizeX == None or sizeY == None or sizeZ == None or sizeT == None
or sizeC == None):
return
if (pixels.getPhysicalSizeX() == None):
commandArgs["Scalebar"] = 0
cRange = range(0, sizeC)
if "Channels" in commandArgs and validChannels(commandArgs["Channels"],
sizeC):
cRange = commandArgs["Channels"]
tzList = calculateRanges(sizeZ, sizeT, commandArgs)
timeMap = calculateAquisitionTime(conn, pixelsId, cRange, tzList)
if (timeMap == None):
commandArgs["Show_Time"] = False
if (timeMap != None):
if (len(timeMap) == 0):
commandArgs["Show_Time"] = False
pixelTypeString = pixels.getPixelsType().getValue()
frameNo = 1
omeroImage.setActiveChannels(map(lambda x: x + 1, cRange))
renderingEngine = omeroImage._re
overlayColour = (255, 255, 255)
if "Overlay_Colour" in commandArgs:
r, g, b, a = COLOURS[commandArgs["Overlay_Colour"]]
overlayColour = (r, g, b)
canvasColour = tuple(COLOURS[commandArgs["Canvas_Colour"]][:3])
mw = commandArgs["Min_Width"]
if mw < sizeX:
mw = sizeX
mh = commandArgs["Min_Height"]
if mh < sizeY:
mh = sizeY
ovlpos = None
canvas = None
if sizeX < mw or sizeY < mh:
ovlpos = ((mw - sizeX) / 2, (mh - sizeY) / 2)
canvas = Image.new("RGBA", (mw, mh), canvasColour)
format = commandArgs["Format"]
fileNames = []
# add intro...
if "Intro_Slide" in commandArgs and commandArgs["Intro_Slide"].id:
intro_duration = commandArgs["Intro_Duration"]
intro_fileId = commandArgs["Intro_Slide"].id.val
intro_filenames = write_intro_end_slides(conn, commandArgs,
intro_fileId, intro_duration,
mw, mh)
fileNames.extend(intro_filenames)
# prepare watermark
if "Watermark" in commandArgs and commandArgs["Watermark"].id:
watermark = prepareWatermark(conn, commandArgs, mw, mh)
# add movie frames...
for tz in tzList:
t = tz[0]
z = tz[1]
plane = getPlane(renderingEngine, z, t)
planeImage = numpy.array(plane, dtype='uint32')
planeImage = planeImage.byteswap()
planeImage = planeImage.reshape(sizeX, sizeY)
image = Image.frombuffer('RGBA', (sizeX, sizeY), planeImage.data,
'raw', 'ARGB', 0, 1)
if ovlpos is not None:
image2 = canvas.copy()
image2.paste(image, ovlpos, image)
image = image2
if "Scalebar" in commandArgs and commandArgs["Scalebar"]:
image = addScalebar(commandArgs["Scalebar"], image, pixels,
commandArgs)
planeInfo = "z:" + str(z) + "t:" + str(t)
if "Show_Time" in commandArgs and commandArgs["Show_Time"]:
time = timeMap[planeInfo]
image = addTimePoints(time, pixels, image, overlayColour)
if "Show_Plane_Info" in commandArgs and commandArgs["Show_Plane_Info"]:
image = addPlaneInfo(z, t, pixels, image, overlayColour)
if "Watermark" in commandArgs and commandArgs["Watermark"].id:
image = pasteWatermark(image, watermark)
if format == QT:
filename = str(frameNo) + '.png'
image.save(filename, "PNG")
else:
filename = str(frameNo) + '.jpg'
image.save(filename, "JPEG")
fileNames.append(filename)
frameNo += 1
# add exit frames... "outro"
# add intro...
if "Ending_Slide" in commandArgs and commandArgs["Ending_Slide"].id:
end_duration = commandArgs["Ending_Duration"]
end_fileId = commandArgs["Ending_Slide"].id.val
end_filenames = write_intro_end_slides(conn, commandArgs, end_fileId,
end_duration, mw, mh)
fileNames.extend(end_filenames)
filelist = ",".join(fileNames)
ext = formatMap[format]
movieName = "Movie"
if "Movie_Name" in commandArgs:
movieName = commandArgs["Movie_Name"]
if not movieName.endswith(".%s" % ext):
movieName = "%s.%s" % (movieName, ext)
movieName = re.sub("[$&\;|\(\)<>' ]", "",
movieName) # spaces etc in file name cause problems
framesPerSec = 2
if "FPS" in commandArgs:
framesPerSec = commandArgs["FPS"]
buildAVI(mw, mh, filelist, framesPerSec, movieName, format)
figLegend = "\n".join(logLines)
mimetype = formatMimetypes[format]
if not os.path.exists(movieName):
print "mencoder Failed to create movie file: %s" % movieName
return None, "Failed to create movie file: %s" % movieName
if not commandArgs["Do_Link"]:
originalFile = scriptUtil.createFile(updateService, movieName,
mimetype, movieName)
scriptUtil.uploadFile(rawFileStore, originalFile, movieName)
return originalFile, message
namespace = omero.constants.namespaces.NSCREATED + "/omero/export_scripts/Make_Movie"
fileAnnotation, annMessage = scriptUtil.createLinkFileAnnotation(
conn,
movieName,
omeroImage,
output="Movie",
ns=namespace,
mimetype=mimetype)
message += annMessage
return fileAnnotation._obj, message
def run_processing(conn,script_params):
file_anns = []
message = ""
imageIds = []
image_id = script_params['ImageID']
imageIds.append(image_id)
image = conn.getObject("Image",image_id)
if not image:
message = 'Could not find specified image'
return message
file_id = script_params['AnnotationID']
ann = conn.getObject("Annotation",file_id)
if not ann:
message = 'Could not find specified annotation'
return message
#other parameters
sr_pix_size = script_params['SR_pixel_size']
if script_params['Convert_coordinates_to_nm']:
cam_pix_size = script_params['Parent_Image_Pixel_Size']
else:
cam_pix_size = 1
file_type = script_params['File_Type']
path_to_ann = ann.getFile().getPath() + '/' + ann.getFile().getName()
name,ext = os.path.splitext(path_to_ann)
if ('txt' in ext) or ('csv' in ext):
#get the path to the downloaded data
path_to_data = download_data(ann)
#get all the xy coords in that data
coords = parse_sr_data(path_to_data,file_type,cam_pix_size)
#get the rois to be processed
rectangles,rectIds = get_rectangles(conn,image_id,sr_pix_size)
#calculate near neighbour distances
nn_data,nn_hist,bins = process_data(conn,image,rectangles,coords)
#put the data in an omero table
# put_data_in_table(conn,imageIds,rectIds,nn_data,nn_hist,bins)
#write the data to a csv
file_name = "near_neighbours_" + ann.getFile().getName()[:-4] + '.csv'
print file_name
try:
f = open(file_name,'w')
for r in range(len(nn_data)):
row = nn_data[r]
for chan in range(row.shape[1]):
f.write(','.join([str(c) for c in row[:,chan]])+'\n')
finally:
f.close()
new_file_ann, faMessage = script_util.createLinkFileAnnotation(
conn, file_name, image, output="Wrote near neighbour csv (Excel) file",
mimetype="text/csv", desc=None)
if new_file_ann:
file_anns.append(new_file_ann)
if not file_anns:
faMessage = "No Analysis files created. See 'Info' or 'Error' for"\
" more details"
elif len(file_anns) > 1:
faMessage = "Created %s csv (Excel) files" % len(file_anns)
message += faMessage
else:
message = 'file annotation must be txt or csv'
return message
# clean up
delete_downloaded_data(ann)
message += faMessage
return message
def roiFigure(conn, commandArgs):
"""
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: commandArgs 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
pixelIds = []
imageIds = []
imageLabels = []
# function for getting image labels.
def getImageNames(fullName, tagsList, pdList):
name = fullName.split("/")[-1]
return [name]
# default function for getting labels is getName (or use datasets / tags)
if "Image_Labels" in commandArgs:
if commandArgs["Image_Labels"] == "Datasets":
def getDatasets(name, tagsList, pdList):
return [dataset for project, dataset in pdList]
getLabels = getDatasets
elif commandArgs["Image_Labels"] == "Tags":
def getTags(name, tagsList, pdList):
return tagsList
getLabels = getTags
else:
getLabels = getImageNames
else:
getLabels = getImageNames
# Get the images
images, logMessage = scriptUtil.getObjects(conn, commandArgs)
message += logMessage
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
omeroImage = images[0]
# process the list of images
log("Image details:")
for image in images:
imageIds.append(image.getId())
pixelIds.append(image.getPrimaryPixels().getId())
# a map of imageId : list of (project, dataset) names.
pdMap = figUtil.getDatasetsProjectsFromImages(conn.getQueryService(),
imageIds)
tagMap = figUtil.getTagsFromImages(conn.getMetadataService(), imageIds)
# Build a legend entry for each image
for image in images:
name = image.getName()
iId = image.getId()
imageDate = image.getAcquisitionDate()
tagsList = tagMap[iId]
pdList = pdMap[iId]
tags = ", ".join(tagsList)
pdString = ", ".join(["%s/%s" % pd for pd in pdList])
log(" Image: %s ID: %d" % (name, iId))
if imageDate:
log(" Date: %s" % imageDate)
else:
log(" Date: not set")
log(" Tags: %s" % tags)
log(" Project/Datasets: %s" % pdString)
imageLabels.append(getLabels(name, tagsList, pdList))
# use the first image to define dimensions, channel colours etc.
sizeX = omeroImage.getSizeX()
sizeY = omeroImage.getSizeY()
sizeZ = omeroImage.getSizeZ()
sizeC = omeroImage.getSizeC()
width = sizeX
if "Width" in commandArgs:
w = commandArgs["Width"]
try:
width = int(w)
except:
log("Invalid width: %s Using default value: %d" % (str(w), sizeX))
height = sizeY
if "Height" in commandArgs:
h = commandArgs["Height"]
try:
height = int(h)
except:
log("Invalid height: %s Using default value" % (str(h), sizeY))
log("Image dimensions for all panels (pixels): width: %d height: %d" %
(width, height))
# the channels in the combined image,
if "Merged_Channels" in commandArgs:
# convert to 0-based
mergedIndexes = [c - 1 for c in commandArgs["Merged_Channels"]]
else:
mergedIndexes = range(sizeC) # show all
mergedIndexes.reverse()
# if no colours added, use existing rendering settings.
mergedColours = {}
# Actually, nicer to always use existing rendering settings.
# if "Merged_Colours" in commandArgs:
# for i, c in enumerate(commandArgs["Merged_Colours"]):
# if c in COLOURS:
# mergedColours[i] = COLOURS[c]
algorithm = ProjectionType.MAXIMUMINTENSITY
if "Algorithm" in commandArgs:
a = commandArgs["Algorithm"]
if (a == "Mean Intensity"):
algorithm = ProjectionType.MEANINTENSITY
stepping = 1
if "Stepping" in commandArgs:
s = commandArgs["Stepping"]
if (0 < s < sizeZ):
stepping = s
scalebar = None
if "Scalebar" in commandArgs:
sb = commandArgs["Scalebar"]
try:
scalebar = int(sb)
if scalebar <= 0:
scalebar = None
else:
log("Scalebar is %d microns" % scalebar)
except:
log("Invalid value for scalebar: %s" % str(sb))
scalebar = None
overlayColour = (255, 255, 255)
if "Scalebar_Colour" in commandArgs:
if commandArgs["Scalebar_Colour"] in OVERLAY_COLOURS:
r, g, b, a = OVERLAY_COLOURS[commandArgs["Scalebar_Colour"]]
overlayColour = (r, g, b)
roiZoom = None
if "Roi_Zoom" in commandArgs:
roiZoom = float(commandArgs["Roi_Zoom"])
if roiZoom == 0:
roiZoom = None
maxColumns = None
if "Max_Columns" in commandArgs:
maxColumns = commandArgs["Max_Columns"]
showRoiDuration = False
if "Show_ROI_Duration" in commandArgs:
showRoiDuration = commandArgs["Show_ROI_Duration"]
roiLabel = "FigureROI"
if "Roi_Selection_Label" in commandArgs:
roiLabel = commandArgs["Roi_Selection_Label"]
spacer = (width / 50) + 2
print "showRoiDuration", showRoiDuration
fig = getSplitView(conn, imageIds, pixelIds, mergedIndexes, mergedColours,
width, height, imageLabels, spacer, algorithm, stepping,
scalebar, overlayColour, roiZoom, maxColumns,
showRoiDuration, roiLabel)
# fig.show() # bug-fixing only
if fig is None:
logMessage = "No figure produced"
log("\n" + logMessage)
message += logMessage
return None, message
figLegend = "\n".join(logStrings)
# print figLegend # bug fixing only
format = commandArgs["Format"]
figureName = "movieROIFigure"
if "Figure_Name" in commandArgs:
figureName = commandArgs["Figure_Name"]
figureName = os.path.basename(figureName)
output = "localfile"
if format == 'PNG':
output = output + ".png"
figureName = figureName + ".png"
fig.save(output, "PNG")
mimetype = "image/png"
elif format == 'TIFF':
output = output + ".tiff"
figureName = figureName + ".tiff"
fig.save(output, "TIFF")
mimetype = "image/tiff"
else:
output = output + ".jpg"
figureName = figureName + ".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/Movie_ROI_Figure"
fileAnnotation, faMessage = scriptUtil.createLinkFileAnnotation(
conn,
output,
omeroImage,
output="Movie ROI figure",
mimetype=mimetype,
ns=namespace,
desc=figLegend,
origFilePathAndName=figureName)
message += faMessage
return fileAnnotation, message
def batchImageExport(conn, scriptParams):
# for params with default values, we can get the value directly
splitCs = scriptParams["Export_Individual_Channels"]
mergedCs = scriptParams["Export_Merged_Image"]
greyscale = scriptParams["Individual_Channels_Grey"]
dataType = scriptParams["Data_Type"]
ids = scriptParams["IDs"]
folder_name = scriptParams["Folder_Name"]
format = scriptParams["Format"]
projectZ = "Choose_Z_Section" in scriptParams and scriptParams["Choose_Z_Section"] == 'Max projection'
if (not splitCs) and (not mergedCs):
log("Not chosen to save Individual Channels OR Merged Image")
return
# check if we have these params
channelNames = []
if "Channel_Names" in scriptParams:
channelNames = scriptParams["Channel_Names"]
imgWidth = None
if "Image_Width" in scriptParams:
imgWidth = scriptParams["Image_Width"]
# functions used below for each imaage.
def getZrange(sizeZ, scriptParams):
zRange = None
if "Choose_Z_Section" in scriptParams:
zChoice = scriptParams["Choose_Z_Section"]
# NB: all Z indices in this script are 1-based
if zChoice == 'ALL Z planes':
zRange = (1, sizeZ+1)
elif "OR_specify_Z_index" in scriptParams:
zIndex = scriptParams["OR_specify_Z_index"]
zIndex = min(zIndex, sizeZ)
zRange = (zIndex,)
elif "OR_specify_Z_start_AND..." in scriptParams and "...specify_Z_end" in scriptParams:
start = scriptParams["OR_specify_Z_start_AND..."]
start = min(start, sizeZ)
end = scriptParams["...specify_Z_end"]
end = min(end, sizeZ)
zStart = min(start, end) # in case user got zStart and zEnd mixed up
zEnd = max(start, end)
if zStart == zEnd:
zRange = (zStart,)
else:
zRange = (zStart, zEnd+1)
return zRange
def getTrange(sizeT, scriptParams):
tRange = None
if "Choose_T_Section" in scriptParams:
tChoice = scriptParams["Choose_T_Section"]
# NB: all T indices in this script are 1-based
if tChoice == 'ALL T planes':
tRange = (1, sizeT+1)
elif "OR_specify_T_index" in scriptParams:
tIndex = scriptParams["OR_specify_T_index"]
tIndex = min(tIndex, sizeT)
tRange = (tIndex,)
elif "OR_specify_T_start_AND..." in scriptParams and "...specify_T_end" in scriptParams:
start = scriptParams["OR_specify_T_start_AND..."]
start = min(start, sizeT)
end = scriptParams["...specify_T_end"]
end = min(end, sizeT)
tStart = min(start, end) # in case user got zStart and zEnd mixed up
tEnd = max(start, end)
if tStart == tEnd:
tRange = (tStart,)
else:
tRange = (tStart, tEnd+1)
return tRange
# Get the images or datasets
message = ""
objects, logMessage = script_utils.getObjects(conn, scriptParams)
message += logMessage
if not objects:
return None, message
# Attach figure to the first image
parent = objects[0]
if dataType == 'Dataset':
images = []
for ds in objects:
images.extend( list(ds.listChildren()) )
if not images:
message += "No image found in dataset(s)"
return None, message
else:
images = objects
log("Processing %s images" % len(images))
# somewhere to put images
curr_dir = os.getcwd()
exp_dir = os.path.join(curr_dir, folder_name)
try:
os.mkdir(exp_dir)
except:
pass
# do the saving to disk
if format == 'OME-TIFF':
for img in images:
log("Exporting image as OME-TIFF: %s" % img.getName())
saveAsOmeTiff(conn, img, folder_name)
else:
for img in images:
log("\n----------- Saving planes from image: '%s' ------------" % img.getName())
sizeC = img.getSizeC()
sizeZ = img.getSizeZ()
sizeT = img.getSizeT()
zRange = getZrange(sizeZ, scriptParams)
tRange = getTrange(sizeT, scriptParams)
log("Using:")
if zRange is None: log(" Z-index: Last-viewed")
elif len(zRange) == 1: log(" Z-index: %d" % zRange[0])
else: log(" Z-range: %s-%s" % ( zRange[0],zRange[1]-1) )
if projectZ: log(" Z-projection: ON")
if tRange is None: log(" T-index: Last-viewed")
elif len(tRange) == 1: log(" T-index: %d" % tRange[0])
else: log(" T-range: %s-%s" % ( tRange[0],tRange[1]-1) )
log(" Format: %s" % format)
if imgWidth is None: log(" Image Width: no resize")
else: log(" Image Width: %s" % imgWidth)
log(" Greyscale: %s" % greyscale)
log("Channel Rendering Settings:")
for ch in img.getChannels():
log(" %s: %d-%d" % (ch.getLabel(), ch.getWindowStart(), ch.getWindowEnd()) )
savePlanesForImage(conn, img, sizeC, splitCs, mergedCs, channelNames,
zRange, tRange, greyscale, imgWidth, projectZ=projectZ, format=format, folder_name=folder_name)
# write log for exported images (not needed for ome-tiff)
logFile = open(os.path.join(exp_dir, 'Batch_Image_Export.txt'), 'w')
try:
for s in logStrings:
logFile.write(s)
logFile.write("\n")
finally:
logFile.close()
# zip everything up (unless we've only got a single ome-tiff)
if format == 'OME-TIFF' and len(os.listdir(exp_dir)) == 1:
export_file = os.path.join(folder_name, os.listdir(exp_dir)[0])
mimetype = 'image/tiff'
else:
export_file = "%s.zip" % folder_name
compress(export_file, folder_name)
mimetype='application/zip'
namespace = omero.constants.namespaces.NSCREATED+"/omero/export_scripts/Batch_Image_Export"
fileAnnotation, annMessage = script_utils.createLinkFileAnnotation(conn, export_file, parent,
output="Batch export zip", ns=namespace, mimetype=mimetype)
message += annMessage
return fileAnnotation, message
def batchImageExport(conn, scriptParams):
# for params with default values, we can get the value directly
splitCs = scriptParams["Export_Individual_Channels"]
mergedCs = scriptParams["Export_Merged_Image"]
greyscale = scriptParams["Individual_Channels_Grey"]
dataType = scriptParams["Data_Type"]
folder_name = scriptParams["Folder_Name"]
folder_name = os.path.basename(folder_name)
format = scriptParams["Format"]
projectZ = "Choose_Z_Section" in scriptParams and scriptParams["Choose_Z_Section"] == "Max projection"
if (not splitCs) and (not mergedCs):
log("Not chosen to save Individual Channels OR Merged Image")
return
# check if we have these params
channelNames = []
if "Channel_Names" in scriptParams:
channelNames = scriptParams["Channel_Names"]
zoomPercent = None
if "Zoom" in scriptParams and scriptParams["Zoom"] != "100%":
zoomPercent = int(scriptParams["Zoom"][:-1])
# functions used below for each imaage.
def getZrange(sizeZ, scriptParams):
zRange = None
if "Choose_Z_Section" in scriptParams:
zChoice = scriptParams["Choose_Z_Section"]
# NB: all Z indices in this script are 1-based
if zChoice == "ALL Z planes":
zRange = (1, sizeZ + 1)
elif "OR_specify_Z_index" in scriptParams:
zIndex = scriptParams["OR_specify_Z_index"]
zIndex = min(zIndex, sizeZ)
zRange = (zIndex,)
elif "OR_specify_Z_start_AND..." in scriptParams and "...specify_Z_end" in scriptParams:
start = scriptParams["OR_specify_Z_start_AND..."]
start = min(start, sizeZ)
end = scriptParams["...specify_Z_end"]
end = min(end, sizeZ)
# in case user got zStart and zEnd mixed up
zStart = min(start, end)
zEnd = max(start, end)
if zStart == zEnd:
zRange = (zStart,)
else:
zRange = (zStart, zEnd + 1)
return zRange
def getTrange(sizeT, scriptParams):
tRange = None
if "Choose_T_Section" in scriptParams:
tChoice = scriptParams["Choose_T_Section"]
# NB: all T indices in this script are 1-based
if tChoice == "ALL T planes":
tRange = (1, sizeT + 1)
elif "OR_specify_T_index" in scriptParams:
tIndex = scriptParams["OR_specify_T_index"]
tIndex = min(tIndex, sizeT)
tRange = (tIndex,)
elif "OR_specify_T_start_AND..." in scriptParams and "...specify_T_end" in scriptParams:
start = scriptParams["OR_specify_T_start_AND..."]
start = min(start, sizeT)
end = scriptParams["...specify_T_end"]
end = min(end, sizeT)
# in case user got zStart and zEnd mixed up
tStart = min(start, end)
tEnd = max(start, end)
if tStart == tEnd:
tRange = (tStart,)
else:
tRange = (tStart, tEnd + 1)
return tRange
# Get the images or datasets
message = ""
objects, logMessage = script_utils.getObjects(conn, scriptParams)
message += logMessage
if not objects:
return None, message
# Attach figure to the first image
parent = objects[0]
if dataType == "Dataset":
images = []
for ds in objects:
images.extend(list(ds.listChildren()))
if not images:
message += "No image found in dataset(s)"
return None, message
else:
images = objects
log("Processing %s images" % len(images))
# somewhere to put images
curr_dir = os.getcwd()
exp_dir = os.path.join(curr_dir, folder_name)
try:
os.mkdir(exp_dir)
except:
pass
# max size (default 12kx12k)
size = conn.getDownloadAsMaxSizeSetting()
size = int(size)
ids = []
# do the saving to disk
for img in images:
pixels = img.getPrimaryPixels()
if pixels.getId() in ids:
continue
ids.append(pixels.getId())
sizeX = pixels.getSizeX()
sizeY = pixels.getSizeY()
if sizeX * sizeY > size:
log(" ** Can't export a 'Big' image to %s. **" % format)
if len(images) == 1:
return None, "Can't export a 'Big' image to %s." % format
continue
else:
log("Exporting image as %s: %s" % (format, img.getName()))
if format == "OME-TIFF":
saveAsOmeTiff(conn, img, folder_name)
else:
if img._prepareRE().requiresPixelsPyramid():
log(" ** Can't export a 'Big' image to OME-TIFF. **")
log("\n----------- Saving planes from image: '%s' ------------" % img.getName())
sizeC = img.getSizeC()
sizeZ = img.getSizeZ()
sizeT = img.getSizeT()
zRange = getZrange(sizeZ, scriptParams)
tRange = getTrange(sizeT, scriptParams)
log("Using:")
if zRange is None:
log(" Z-index: Last-viewed")
elif len(zRange) == 1:
log(" Z-index: %d" % zRange[0])
else:
log(" Z-range: %s-%s" % (zRange[0], zRange[1] - 1))
if projectZ:
log(" Z-projection: ON")
if tRange is None:
log(" T-index: Last-viewed")
elif len(tRange) == 1:
log(" T-index: %d" % tRange[0])
else:
log(" T-range: %s-%s" % (tRange[0], tRange[1] - 1))
log(" Format: %s" % format)
if zoomPercent is None:
log(" Image Zoom: 100%")
else:
log(" Image Zoom: %s" % zoomPercent)
log(" Greyscale: %s" % greyscale)
log("Channel Rendering Settings:")
for ch in img.getChannels():
log(" %s: %d-%d" % (ch.getLabel(), ch.getWindowStart(), ch.getWindowEnd()))
try:
savePlanesForImage(
conn,
img,
sizeC,
splitCs,
mergedCs,
channelNames,
zRange,
tRange,
greyscale,
zoomPercent,
projectZ=projectZ,
format=format,
folder_name=folder_name,
)
finally:
# Make sure we close Rendering Engine
img._re.close()
# write log for exported images (not needed for ome-tiff)
logFile = open(os.path.join(exp_dir, "Batch_Image_Export.txt"), "w")
try:
for s in logStrings:
logFile.write(s)
logFile.write("\n")
finally:
logFile.close()
if len(os.listdir(exp_dir)) == 0:
return None, "No files exported. See 'info' for more details"
# zip everything up (unless we've only got a single ome-tiff)
if format == "OME-TIFF" and len(os.listdir(exp_dir)) == 1:
ometiffIds = [t.id for t in parent.listAnnotations(ns=NSOMETIFF)]
print "Deleting OLD ome-tiffs: %s" % ometiffIds
conn.deleteObjects("Annotation", ometiffIds)
export_file = os.path.join(folder_name, os.listdir(exp_dir)[0])
namespace = NSOMETIFF
outputDisplayName = "OME-TIFF"
mimetype = "image/tiff"
else:
export_file = "%s.zip" % folder_name
compress(export_file, folder_name)
mimetype = "application/zip"
outputDisplayName = "Batch export zip"
namespace = NSCREATED + "/omero/export_scripts/Batch_Image_Export"
fileAnnotation, annMessage = script_utils.createLinkFileAnnotation(
conn, export_file, parent, output=outputDisplayName, ns=namespace, mimetype=mimetype
)
message += annMessage
return fileAnnotation, message
def processImages(conn, scriptParams):
fileAnns = []
message =""
# Get the images
images, logMessage = scriptUtil.getObjects(conn, scriptParams)
message += logMessage
if not images:
return None, message
# Check for line and polyline ROIs and filter images list
images = [image for image in images if image.getROICount(["Polyline","Line"])>0]
if not images:
message += "No ROI containing line or polyline was found."
return None, message
for image in images:
print "\nAnalysing Image: %s ID: %s" % (image.getName(), image.getId())
if image.getSizeT() > 1:
message += "%s ID: %s appears to be a time-lapse Image, not a kymograph." % (image.getName(), image.getId())
continue
roiService = conn.getRoiService()
result = roiService.findByImage(image.getId(), None)
secsPerPixelY = image.getPixelSizeY()
micronsPerPixelX = image.getPixelSizeX()
if secsPerPixelY and micronsPerPixelX:
micronsPerSec = micronsPerPixelX / secsPerPixelY
else: micronsPerSec = None
# for each line or polyline, create a row in csv table: y(t), x, dy(dt), dx, x/t (line), x/t (average)
colNames = "\nt_start (pixels), x_start (pixels), t_end (pixels), x_end (pixels), dt (pixels), dx (pixels), x/t, average x/t, speed(um/sec)"
tableData = ""
for roi in result.rois:
for s in roi.copyShapes():
if type(s) == omero.model.LineI:
tableData += "\nLine"
x1 = s.getX1().getValue()
x2 = s.getX2().getValue()
y1 = s.getY1().getValue()
y2 = s.getY2().getValue()
dx = abs(x1-x2)
dy = abs(y1-y2)
dxPerY = float(dx)/dy
tableData += "\n"
tableData += ",".join([str(x) for x in (y1, x1, y2, x2, dy, dx, dxPerY, dxPerY, "")])
if micronsPerSec:
speed = dxPerY * micronsPerSec
tableData += "%s" % speed
elif type(s) == omero.model.PolylineI:
tableData += "\nPolyline"
points = pointsStringToXYlist(s.getPoints().getValue())
xStart, yStart = points[0]
for i in range(1, len(points)):
x1, y1 = points[i-1]
x2, y2 = points[i]
dx = abs(x1-x2)
dy = abs(y1-y2)
dxPerY = float(dx)/dy
avXperY = abs(float(x2-xStart)/(y2-yStart))
tableData += "\n"
tableData += ",".join([str(x) for x in (y1, x1, y2, x2, dy, dx, dxPerY, avXperY, "")])
if micronsPerSec:
speed = dxPerY * micronsPerSec
tableData += "%s" % speed
# write table data to csv...
if len(tableData) > 0:
tableString = "secsPerPixelY: %s" % secsPerPixelY
tableString += '\nmicronsPerPixelX: %s' % micronsPerPixelX
tableString += "\nmicronsPerSec: %s" % micronsPerSec
tableString += "\n"
tableString += colNames
tableString += tableData
print tableString
csvFileName = 'kymograph_velocities_%s.csv' % image.getId()
csvFile = open(csvFileName, 'w')
try:
csvFile.write(tableString)
finally:
csvFile.close()
fileAnn, faMessage = scriptUtil.createLinkFileAnnotation(conn, csvFileName, image,
output="Line Plot csv (Excel) file", mimetype="text/csv", desc=None)
print fileAnn, faMessage
if fileAnn:
fileAnns.append(fileAnn)
else:
print "Found NO lines or polylines to analyse for Image"
if not fileAnns:
faMessage = "No Analysis files created. See 'Info' or 'Error' for more details"
elif len(fileAnns) > 1:
faMessage = "Created %s csv (Excel) files" % len(fileAnns)
message += faMessage
return fileAnns, message
def processImages(conn, scriptParams):
fileAnns = []
message = ""
# Get the images
images, logMessage = scriptUtil.getObjects(conn, scriptParams)
message += logMessage
if not images:
return None, message
# Check for line and polyline ROIs and filter images list
images = [
image for image in images
if image.getROICount(["Polyline", "Line"]) > 0
]
if not images:
message += "No ROI containing line or polyline was found."
return None, message
for image in images:
print "\nAnalysing Image: %s ID: %s" \
% (image.getName(), image.getId())
if image.getSizeT() > 1:
message += "%s ID: %s appears to be a time-lapse Image," \
" not a kymograph." % (image.getName(), image.getId())
continue
roiService = conn.getRoiService()
result = roiService.findByImage(image.getId(), None)
secsPerPixelY = image.getPixelSizeY()
micronsPerPixelX = image.getPixelSizeX()
if secsPerPixelY and micronsPerPixelX:
micronsPerSec = micronsPerPixelX / secsPerPixelY
else:
micronsPerSec = None
# for each line or polyline, create a row in csv table: y(t), x,
# dy(dt), dx, x/t (line), x/t (average)
colNames = "\nt_start (pixels), x_start (pixels), t_end (pixels)," \
" x_end (pixels), dt (pixels), dx (pixels), x/t, average x/t," \
" speed(um/sec)"
tableData = ""
for roi in result.rois:
for s in roi.copyShapes():
if type(s) == omero.model.LineI:
tableData += "\nLine"
x1 = s.getX1().getValue()
x2 = s.getX2().getValue()
y1 = s.getY1().getValue()
y2 = s.getY2().getValue()
dx = abs(x1 - x2)
dy = abs(y1 - y2)
dxPerY = float(dx) / dy
tableData += "\n"
tableData += ",".join([
str(x)
for x in (y1, x1, y2, x2, dy, dx, dxPerY, dxPerY, "")
])
if micronsPerSec:
speed = dxPerY * micronsPerSec
tableData += "%s" % speed
elif type(s) == omero.model.PolylineI:
tableData += "\nPolyline"
points = pointsStringToXYlist(s.getPoints().getValue())
xStart, yStart = points[0]
for i in range(1, len(points)):
x1, y1 = points[i - 1]
x2, y2 = points[i]
dx = abs(x1 - x2)
dy = abs(y1 - y2)
dxPerY = float(dx) / dy
avXperY = abs(float(x2 - xStart) / (y2 - yStart))
tableData += "\n"
tableData += ",".join([
str(x) for x in (y1, x1, y2, x2, dy, dx, dxPerY,
avXperY, "")
])
if micronsPerSec:
speed = dxPerY * micronsPerSec
tableData += "%s" % speed
# write table data to csv...
if len(tableData) > 0:
tableString = "secsPerPixelY: %s" % secsPerPixelY
tableString += '\nmicronsPerPixelX: %s' % micronsPerPixelX
tableString += "\nmicronsPerSec: %s" % micronsPerSec
tableString += "\n"
tableString += colNames
tableString += tableData
print tableString
csvFileName = 'kymograph_velocities_%s.csv' % image.getId()
csvFile = open(csvFileName, 'w')
try:
csvFile.write(tableString)
finally:
csvFile.close()
fileAnn, faMessage = scriptUtil.createLinkFileAnnotation(
conn,
csvFileName,
image,
output="Line Plot csv (Excel) file",
mimetype="text/csv",
desc=None)
print fileAnn, faMessage
if fileAnn:
fileAnns.append(fileAnn)
else:
print "Found NO lines or polylines to analyse for Image"
if not fileAnns:
faMessage = "No Analysis files created. See 'Info' or 'Error'" \
" for more details"
elif len(fileAnns) > 1:
faMessage = "Created %s csv (Excel) files" % len(fileAnns)
message += faMessage
return fileAnns, message
def roiFigure(conn, commandArgs):
"""
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: commandArgs 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
pixelIds = []
imageIds = []
imageLabels = []
# function for getting image labels.
def getImageNames(fullName, tagsList, pdList):
name = fullName.split("/")[-1]
return [name]
# default function for getting labels is getName (or use datasets / tags)
if "Image_Labels" in commandArgs:
if commandArgs["Image_Labels"] == "Datasets":
def getDatasets(name, tagsList, pdList):
return [dataset for project, dataset in pdList]
getLabels = getDatasets
elif commandArgs["Image_Labels"] == "Tags":
def getTags(name, tagsList, pdList):
return tagsList
getLabels = getTags
else:
getLabels = getImageNames
else:
getLabels = getImageNames
# Get the images
images, logMessage = scriptUtil.getObjects(conn, commandArgs)
message += logMessage
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
omeroImage = images[0]
# process the list of images. If imageIds is not set, script can't run.
log("Image details:")
for image in images:
imageIds.append(image.getId())
pixelIds.append(image.getPrimaryPixels().getId())
# a map of imageId : list of (project, dataset) names.
pdMap = figUtil.getDatasetsProjectsFromImages(conn.getQueryService(),
imageIds)
tagMap = figUtil.getTagsFromImages(conn.getMetadataService(), imageIds)
# Build a legend entry for each image
for image in images:
name = image.getName()
imageDate = image.getAcquisitionDate()
iId = image.getId()
tagsList = tagMap[iId]
pdList = pdMap[iId]
tags = ", ".join(tagsList)
pdString = ", ".join(["%s/%s" % pd for pd in pdList])
log(" Image: %s ID: %d" % (name, iId))
if imageDate:
log(" Date: %s" % imageDate)
else:
log(" Date: not set")
log(" Tags: %s" % tags)
log(" Project/Datasets: %s" % pdString)
imageLabels.append(getLabels(name, tagsList, pdList))
# use the first image to define dimensions, channel colours etc.
sizeX = omeroImage.getSizeX()
sizeY = omeroImage.getSizeY()
sizeZ = omeroImage.getSizeZ()
sizeC = omeroImage.getSizeC()
width = sizeX
if "Width" in commandArgs:
w = commandArgs["Width"]
try:
width = int(w)
except:
log("Invalid width: %s Using default value: %d" % (str(w), sizeX))
height = sizeY
if "Height" in commandArgs:
h = commandArgs["Height"]
try:
height = int(h)
except:
log("Invalid height: %s Using default value" % (str(h), sizeY))
log("Image dimensions for all panels (pixels): width: %d height: %d"
% (width, height))
mergedIndexes = [] # the channels in the combined image,
mergedColours = {}
if "Merged_Colours" in commandArgs:
cColourMap = commandArgs["Merged_Colours"]
for c in cColourMap:
rgb = cColourMap[c]
try:
rgb = int(rgb)
cIndex = int(c)
except ValueError:
print "Merged_Colours map should be index:rgbInt. Not %s:%s" \
% (c, rgb)
continue
rgba = imgUtil.RGBIntToRGBA(rgb)
mergedColours[cIndex] = rgba
mergedIndexes.append(cIndex)
mergedIndexes.sort()
# make sure we have some merged channels
if len(mergedIndexes) == 0:
mergedIndexes = range(sizeC)
mergedIndexes.reverse()
mergedNames = False
if "Merged_Names" in commandArgs:
mergedNames = commandArgs["Merged_Names"]
# Make channel-names map. If argument wasn't specified, name by index
channelNames = {}
if "Channel_Names" in commandArgs:
cNameMap = commandArgs["Channel_Names"]
for c in range(sizeC):
if str(c) in cNameMap:
channelNames[c] = cNameMap[str(c)]
else:
channelNames[c] = str(c)
else:
for c in range(sizeC):
channelNames[c] = str(c)
# Make split-indexes list. If no "Split_Indexes", show none:
# http://www.openmicroscopy.org/community/viewtopic.php?f=4&t=940
splitIndexes = []
if "Split_Indexes" in commandArgs:
for index in commandArgs["Split_Indexes"]:
splitIndexes.append(index)
colourChannels = True
if "Split_Panels_Grey" in commandArgs and commandArgs["Split_Panels_Grey"]:
colourChannels = False
algorithm = ProjectionType.MAXIMUMINTENSITY
if "Algorithm" in commandArgs:
a = commandArgs["Algorithm"]
if (a == "Mean Intensity"):
algorithm = ProjectionType.MEANINTENSITY
stepping = 1
if "Stepping" in commandArgs:
s = commandArgs["Stepping"]
if (0 < s < sizeZ):
stepping = s
scalebar = None
if "Scalebar" in commandArgs:
sb = commandArgs["Scalebar"]
try:
scalebar = int(sb)
if scalebar <= 0:
scalebar = None
else:
log("Scalebar is %d microns" % scalebar)
except:
log("Invalid value for scalebar: %s" % str(sb))
scalebar = None
overlayColour = (255, 255, 255)
if "Overlay_Colour" in commandArgs:
r, g, b, a = OVERLAY_COLOURS[commandArgs["Overlay_Colour"]]
overlayColour = (r, g, b)
roiZoom = None
if "ROI_Zoom" in commandArgs:
roiZoom = float(commandArgs["ROI_Zoom"])
if roiZoom == 0:
roiZoom = None
roiLabel = "FigureROI"
if "ROI_Label" in commandArgs:
roiLabel = commandArgs["ROI_Label"]
spacer = (width/50) + 2
fig = getSplitView(
conn, imageIds, pixelIds, splitIndexes, channelNames, mergedNames,
colourChannels, mergedIndexes, mergedColours, width, height,
imageLabels, spacer, algorithm, stepping, scalebar, overlayColour,
roiZoom, roiLabel)
if fig is None:
logMessage = "No figure produced"
log("\n"+logMessage)
message += logMessage
return None, message
# fig.show() # bug-fixing only
log("")
figLegend = "\n".join(logStrings)
# print figLegend # bug fixing only
format = commandArgs["Format"]
figureName = "roiFigure"
if "Figure_Name" in commandArgs:
figureName = commandArgs["Figure_Name"]
figureName = os.path.basename(figureName)
output = "localfile"
if format == 'PNG':
output = output + ".png"
figureName = figureName + ".png"
fig.save(output, "PNG")
mimetype = "image/png"
elif format == 'TIFF':
output = output + ".tiff"
figureName = figureName + ".tiff"
fig.save(output, "TIFF")
mimetype = "image/tiff"
else:
output = output + ".jpg"
figureName = figureName + ".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"
fileAnnotation, faMessage = scriptUtil.createLinkFileAnnotation(
conn, output, omeroImage, output="ROI Split figure",
mimetype=mimetype, ns=namespace, desc=figLegend,
origFilePathAndName=figureName)
message += faMessage
return fileAnnotation, message
def makeThumbnailFigure(conn, scriptParams):
"""
Makes the figure using the parameters in @scriptParams, attaches the
figure to the parent Project/Dataset, and returns the file-annotation ID
@ returns Returns the id of the originalFileLink child. (ID object,
not value)
"""
log("Thumbnail figure created by OMERO")
log("")
message = ""
# Get the objects (images or datasets)
objects, logMessage = scriptUtil.getObjects(conn, scriptParams)
message += logMessage
if not objects:
return None, message
# Get parent
parent = None
if "Parent_ID" in scriptParams and len(scriptParams["IDs"]) > 1:
if scriptParams["Data_Type"] == "Image":
parent = conn.getObject("Dataset", scriptParams["Parent_ID"])
else:
parent = conn.getObject("Project", scriptParams["Parent_ID"])
if parent is None:
parent = objects[0] # Attach figure to the first object
parentClass = parent.OMERO_CLASS
log("Figure will be linked to %s%s: %s" %
(parentClass[0].lower(), parentClass[1:], parent.getName()))
tagIds = []
if "Tag_IDs" in scriptParams:
tagIds = scriptParams['Tag_IDs']
if len(tagIds) == 0:
tagIds = None
showUntagged = False
if (tagIds):
showUntagged = scriptParams["Show_Untagged_Images"]
thumbSize = scriptParams["Thumbnail_Size"]
maxColumns = scriptParams["Max_Columns"]
figHeight = 0
figWidth = 0
dsCanvases = []
if scriptParams["Data_Type"] == "Dataset":
for dataset in objects:
log("Dataset: %s ID: %d" %
(dataset.getName(), dataset.getId()))
images = list(dataset.listChildren())
title = dataset.getName().decode('utf8')
dsCanvas = paintDatasetCanvas(conn,
images,
title,
tagIds,
showUntagged,
length=thumbSize,
colCount=maxColumns)
if dsCanvas is None:
continue
dsCanvases.append(dsCanvas)
figHeight += dsCanvas.size[1]
figWidth = max(figWidth, dsCanvas.size[0])
else:
imageCanvas = paintDatasetCanvas(conn,
objects,
"",
tagIds,
showUntagged,
length=thumbSize,
colCount=maxColumns)
dsCanvases.append(imageCanvas)
figHeight += imageCanvas.size[1]
figWidth = max(figWidth, imageCanvas.size[0])
if len(dsCanvases) == 0:
message += "No figure created"
return None, message
figure = Image.new("RGB", (figWidth, figHeight), WHITE)
y = 0
for ds in dsCanvases:
imgUtil.pasteImage(ds, figure, 0, y)
y += ds.size[1]
log("")
figLegend = "\n".join(logLines)
format = scriptParams["Format"]
figureName = scriptParams["Figure_Name"]
figureName = os.path.basename(figureName)
output = "localfile"
if format == 'PNG':
output = output + ".png"
figureName = figureName + ".png"
figure.save(output, "PNG")
mimetype = "image/png"
elif format == 'TIFF':
output = output + ".tiff"
figureName = figureName + ".tiff"
figure.save(output, "TIFF")
mimetype = "image/tiff"
else:
output = output + ".jpg"
figureName = figureName + ".jpg"
figure.save(output)
mimetype = "image/jpeg"
namespace = NSCREATED + "/omero/figure_scripts/Thumbnail_Figure"
fileAnnotation, faMessage = scriptUtil.createLinkFileAnnotation(
conn,
output,
parent,
output="Thumbnail figure",
mimetype=mimetype,
ns=namespace,
desc=figLegend,
origFilePathAndName=figureName)
message += faMessage
return fileAnnotation, message
def writeMovie(commandArgs, conn):
"""
Makes the movie.
@ returns Returns the file annotation
"""
log("Movie created by OMERO")
log("")
message = ""
session = conn.c.sf
updateService = session.getUpdateService()
rawFileStore = session.createRawFileStore()
# Get the images
images, logMessage = scriptUtil.getObjects(conn, commandArgs)
message += logMessage
if not images:
return None, message
# Get the first valid image (should be expanded to process the list)
omeroImage = images[0]
if commandArgs["RenderingDef_ID"] >= 0:
omeroImage._prepareRenderingEngine(rdid=commandArgs["RenderingDef_ID"])
pixels = omeroImage.getPrimaryPixels()
pixelsId = pixels.getId()
sizeX = pixels.getSizeX()
sizeY = pixels.getSizeY()
sizeZ = pixels.getSizeZ()
sizeC = pixels.getSizeC()
sizeT = pixels.getSizeT()
if (sizeX is None or sizeY is None or sizeZ is None or sizeT is None or
sizeC is None):
return
if (pixels.getPhysicalSizeX() is None):
commandArgs["Scalebar"] = 0
cRange = range(0, sizeC)
cWindows = None
cColours = None
if "ChannelsExtended" in commandArgs and \
validChannels(commandArgs["ChannelsExtended"], sizeC):
cRange = []
cWindows = []
cColours = []
for c in commandArgs["ChannelsExtended"]:
m = re.match('^(?P<i>\d+)(\|(?P<ws>\d+)' +
'\:(?P<we>\d+))?(\$(?P<c>.+))?$', c)
if m is not None:
cRange.append(int(m.group('i'))-1)
cWindows.append([float(m.group('ws')), float(m.group('we'))])
cColours.append(m.group('c'))
elif "Channels" in commandArgs and \
validChannels(commandArgs["Channels"], sizeC):
cRange = commandArgs["Channels"]
tzList = calculateRanges(sizeZ, sizeT, commandArgs)
timeMap = calculateAquisitionTime(conn, pixelsId, cRange, tzList)
if (timeMap is None):
commandArgs["Show_Time"] = False
if (timeMap is not None):
if (len(timeMap) == 0):
commandArgs["Show_Time"] = False
frameNo = 1
omeroImage.setActiveChannels(map(lambda x: x+1, cRange),
cWindows,
cColours)
renderingEngine = omeroImage._re
overlayColour = (255, 255, 255)
if "Overlay_Colour" in commandArgs:
r, g, b, a = COLOURS[commandArgs["Overlay_Colour"]]
overlayColour = (r, g, b)
canvasColour = tuple(COLOURS[commandArgs["Canvas_Colour"]][:3])
mw = commandArgs["Min_Width"]
if mw < sizeX:
mw = sizeX
mh = commandArgs["Min_Height"]
if mh < sizeY:
mh = sizeY
ovlpos = None
canvas = None
if sizeX < mw or sizeY < mh:
ovlpos = ((mw-sizeX) / 2, (mh-sizeY) / 2)
canvas = Image.new("RGBA", (mw, mh), canvasColour)
format = commandArgs["Format"]
fileNames = []
# add intro...
if "Intro_Slide" in commandArgs and commandArgs["Intro_Slide"].id:
intro_duration = commandArgs["Intro_Duration"]
intro_fileId = commandArgs["Intro_Slide"].id.val
intro_filenames = write_intro_end_slides(
conn, commandArgs, intro_fileId, intro_duration, mw, mh)
fileNames.extend(intro_filenames)
# prepare watermark
if "Watermark" in commandArgs and commandArgs["Watermark"].id:
watermark = prepareWatermark(conn, commandArgs, mw, mh)
# add movie frames...
for tz in tzList:
t = tz[0]
z = tz[1]
plane = getPlane(renderingEngine, z, t)
planeImage = numpy.array(plane, dtype='uint32')
planeImage = planeImage.byteswap()
planeImage = planeImage.reshape(sizeX, sizeY)
image = Image.frombuffer('RGBA', (sizeX, sizeY), planeImage.data,
'raw', 'ARGB', 0, 1)
if ovlpos is not None:
image2 = canvas.copy()
image2.paste(image, ovlpos, image)
image = image2
if "Scalebar" in commandArgs and commandArgs["Scalebar"]:
image = addScalebar(
commandArgs["Scalebar"], image, pixels, commandArgs)
planeInfo = "z:"+str(z)+"t:"+str(t)
if "Show_Time" in commandArgs and commandArgs["Show_Time"]:
time = timeMap[planeInfo]
image = addTimePoints(time, pixels, image, overlayColour)
if "Show_Plane_Info" in commandArgs and \
commandArgs["Show_Plane_Info"]:
image = addPlaneInfo(z, t, pixels, image, overlayColour)
if "Watermark" in commandArgs and commandArgs["Watermark"].id:
image = pasteWatermark(image, watermark)
if format == QT:
filename = str(frameNo) + '.png'
image.save(filename, "PNG")
else:
filename = str(frameNo) + '.jpg'
image.save(filename, "JPEG")
fileNames.append(filename)
frameNo += 1
# add exit frames... "outro"
# add intro...
if "Ending_Slide" in commandArgs and commandArgs["Ending_Slide"].id:
end_duration = commandArgs["Ending_Duration"]
end_fileId = commandArgs["Ending_Slide"].id.val
end_filenames = write_intro_end_slides(
conn, commandArgs, end_fileId, end_duration, mw, mh)
fileNames.extend(end_filenames)
filelist = ",".join(fileNames)
ext = formatMap[format]
movieName = "Movie"
if "Movie_Name" in commandArgs:
movieName = commandArgs["Movie_Name"]
movieName = os.path.basename(movieName)
if not movieName.endswith(".%s" % ext):
movieName = "%s.%s" % (movieName, ext)
# spaces etc in file name cause problems
movieName = re.sub("[$&\;|\(\)<>' ]", "", movieName)
framesPerSec = 2
if "FPS" in commandArgs:
framesPerSec = commandArgs["FPS"]
output = "localfile.%s" % ext
buildAVI(mw, mh, filelist, framesPerSec, output, format)
mimetype = formatMimetypes[format]
if not os.path.exists(output):
print "mencoder Failed to create movie file: %s" % output
return None, "Failed to create movie file: %s" % output
if not commandArgs["Do_Link"]:
originalFile = scriptUtil.createFile(
updateService, output, mimetype, movieName)
scriptUtil.uploadFile(rawFileStore, originalFile, movieName)
return originalFile, message
namespace = NSCREATED + "/omero/export_scripts/Make_Movie"
fileAnnotation, annMessage = scriptUtil.createLinkFileAnnotation(
conn, output, omeroImage, ns=namespace,
mimetype=mimetype, origFilePathAndName=movieName)
message += annMessage
return fileAnnotation._obj, message
def splitViewFigure(conn, scriptParams):
"""
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 makeSplitViewFigure() 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
imageIds = []
pixelIds = []
imageLabels = []
# function for getting image labels.
def getLabels(fullName, tagsList, pdList):
name = fullName.split("/")[-1]
return [name]
# default function for getting labels is getName (or use datasets / tags)
if scriptParams["Image_Labels"] == "Datasets":
def getDatasets(name, tagsList, pdList):
return [dataset for project, dataset in pdList]
getLabels = getDatasets
elif scriptParams["Image_Labels"] == "Tags":
def getTags(name, tagsList, pdList):
return tagsList
getLabels = getTags
# Get the images
images, logMessage = scriptUtil.getObjects(conn, scriptParams)
message += logMessage
if not images:
return None, message
# Attach figure to the first image
omeroImage = images[0]
# process the list of images
log("Image details:")
for image in images:
imageIds.append(image.getId())
pixelIds.append(image.getPrimaryPixels().getId())
pdMap = figUtil.getDatasetsProjectsFromImages(conn.getQueryService(), imageIds) # a map of imageId : list of (project, dataset) names.
tagMap = figUtil.getTagsFromImages(conn.getMetadataService(), imageIds)
# Build a legend entry for each image
for image in images:
name = image.getName()
imageDate = image.getAcquisitionDate()
iId = image.getId()
tagsList = tagMap[iId]
pdList = pdMap[iId]
tags = ", ".join(tagsList)
pdString = ", ".join(["%s/%s" % pd for pd in pdList])
log(" Image: %s ID: %d" % (name, iId))
log(" Date: %s" % date.fromtimestamp(imageDate/1000))
log(" Tags: %s" % tags)
log(" Project/Datasets: %s" % pdString)
imageLabels.append(getLabels(name, tagsList, pdList))
# use the first image to define dimensions, channel colours etc.
sizeX = omeroImage.getSizeX()
sizeY = omeroImage.getSizeY()
sizeZ = omeroImage.getSizeZ()
sizeC = omeroImage.getSizeC()
# set image dimensions
zStart = -1
zEnd = -1
if "Z_Start" in scriptParams:
zStart = scriptParams["Z_Start"]
if "Z_End" in scriptParams:
zEnd = scriptParams["Z_End"]
width = "Width" in scriptParams and scriptParams["Width"] or sizeX
height = "Height" in scriptParams and scriptParams["Height"] or sizeY
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.
splitIndexes = []
if "Split_Indexes" in scriptParams:
splitIndexes = scriptParams["Split_Indexes"]
else:
splitIndexes = range(sizeC)
# Make channel-names map. If argument wasn't specified, name by index
channelNames = {}
for c in range(sizeC):
channelNames[c] = str(c)
if "Channel_Names" in scriptParams:
cNameMap = scriptParams["Channel_Names"]
for c in cNameMap:
index = int(c)
channelNames[index] = cNameMap[c]
mergedIndexes = [] # the channels in the combined image,
mergedColours = {}
if "Merged_Colours" in scriptParams:
cColourMap = scriptParams["Merged_Colours"]
for c in cColourMap:
rgb = cColourMap[c]
rgba = imgUtil.RGBIntToRGBA(rgb)
mergedColours[int(c)] = rgba
mergedIndexes.append(int(c))
mergedIndexes.sort()
else:
mergedIndexes = range(sizeC)
colourChannels = not scriptParams["Split_Panels_Grey"]
algorithm = omero.constants.projection.ProjectionType.MAXIMUMINTENSITY
if "Mean Intensity" == scriptParams["Algorithm"]:
algorithm = omero.constants.projection.ProjectionType.MEANINTENSITY
stepping = min(scriptParams["Stepping"], sizeZ)
scalebar = None
if "Scalebar" in scriptParams:
scalebar = scriptParams["Scalebar"]
log("Scalebar is %d microns" % scalebar)
r,g,b,a = OVERLAY_COLOURS[scriptParams["Overlay_Colour"]]
overlayColour = (r,g,b)
mergedNames = scriptParams["Merged_Names"]
print "splitIndexes", splitIndexes
print "channelNames", channelNames
print "colourChannels", colourChannels
print "mergedIndexes", mergedIndexes
print "mergedColours", mergedColours
print "mergedNames", mergedNames
fig = makeSplitViewFigure(conn, pixelIds, zStart, zEnd, splitIndexes, channelNames, colourChannels,
mergedIndexes, mergedColours, mergedNames, width, height, imageLabels, algorithm, stepping, scalebar, overlayColour)
figLegend = "\n".join(logStrings)
format = JPEG
if scriptParams["Format"] == "PNG":
format = PNG
output = scriptParams["Figure_Name"]
if format == PNG:
output = output + ".png"
fig.save(output, "PNG")
mimetype = "image/png"
else:
output = output + ".jpg"
fig.save(output)
mimetype = "image/jpeg"
# Upload the figure 'output' to the server, creating a file annotation and attaching it to the omeroImage, adding the
# figLegend as the fileAnnotation description.
namespace = omero.constants.namespaces.NSCREATED+"/omero/figure_scripts/Split_View_Figure"
fileAnnotation, faMessage = scriptUtil.createLinkFileAnnotation(conn, output, omeroImage,
output="Split view figure", mimetype=mimetype, ns=namespace, desc=figLegend)
message += faMessage
return fileAnnotation, message
def run_processing(conn,script_params):
file_anns = []
message = ""
imageIds = []
image_id = script_params['ImageID']
imageIds.append(image_id)
image = conn.getObject("Image",image_id)
if not image:
message = 'Could not find specified image'
return message
file_id = script_params['AnnotationID']
ann = conn.getObject("Annotation",file_id)
if not ann:
message = 'Could not find specified annotation'
return message
#other parameters
if script_params['Convert_coordinates_to_nm']:
cam_pix_size = script_params['Parent_Image_Pixel_Size']
else:
cam_pix_size = 1
file_type = FILE_TYPES[script_params['File_Type']]
path_to_ann = ann.getFile().getPath() + '/' + ann.getFile().getName()
name,ext = os.path.splitext(path_to_ann)
if ('txt' in ext) or ('csv' in ext):
#get the path to the downloaded data
path_to_data = download_data(ann)
#get all the xy coords in that data
locs = parse_sr_data(path_to_data,file_type,cam_pix_size)
sizeC = len(locs)
#get the rois to be processed
rectangles = get_rectangles(conn,image_id)
#calculate near neighbour distances
nn_hist,bins = process_data(conn,image,file_type,sizeC,rectangles,locs)
print 'nn_hist:',len(nn_hist),nn_hist[0].shape
print 'bins:',bins.shape
#put the data in an omero table
# put_data_in_table(conn,imageIds,rectIds,nn_data,nn_hist,bins)
#write the data to a csv
file_name = "near_neighbours_" + ann.getFile().getName()[:-4] + '.csv'
with file(file_name, 'w') as outfile:
outfile.write('# nearest neighbour data for %s channels and %s ROIs: \n' % (len(nn_hist), nn_hist[0].shape[1] ))
for c, channel in enumerate(nn_hist):
data = np.concatenate((bins,channel),axis=1)
np.savetxt(outfile, data, fmt='%-7.2f', delimiter=',', newline='\n')
if c > 0:
outfile.write('# New channel\n')
new_file_ann, faMessage = script_util.createLinkFileAnnotation(
conn, file_name, image, output="Wrote near neighbour csv (Excel) file",
mimetype="text/csv", desc=None)
if new_file_ann:
file_anns.append(new_file_ann)
if not file_anns:
faMessage = "No Analysis files created. See 'Info' or 'Error' for"\
" more details"
elif len(file_anns) > 1:
faMessage = "Created %s csv (Excel) files" % len(file_anns)
else:
message = 'file annotation must be txt or csv'
return message
# clean up
delete_downloaded_data(ann)
message += faMessage
return message
def batchImageExport(conn, scriptParams):
# for params with default values, we can get the value directly
splitCs = scriptParams["Export_Individual_Channels"]
mergedCs = scriptParams["Export_Merged_Image"]
greyscale = scriptParams["Individual_Channels_Grey"]
dataType = scriptParams["Data_Type"]
ids = scriptParams["IDs"]
folder_name = scriptParams["Folder_Name"]
format = scriptParams["Format"]
projectZ = "Choose_Z_Section" in scriptParams and scriptParams[
"Choose_Z_Section"] == 'Max projection'
if (not splitCs) and (not mergedCs):
log("Not chosen to save Individual Channels OR Merged Image")
return
# check if we have these params
channelNames = []
if "Channel_Names" in scriptParams:
channelNames = scriptParams["Channel_Names"]
imgWidth = None
if "Image_Width" in scriptParams:
imgWidth = scriptParams["Image_Width"]
# functions used below for each imaage.
def getZrange(sizeZ, scriptParams):
zRange = None
if "Choose_Z_Section" in scriptParams:
zChoice = scriptParams["Choose_Z_Section"]
# NB: all Z indices in this script are 1-based
if zChoice == 'ALL Z planes':
zRange = (1, sizeZ + 1)
elif "OR_specify_Z_index" in scriptParams:
zIndex = scriptParams["OR_specify_Z_index"]
zIndex = min(zIndex, sizeZ)
zRange = (zIndex, )
elif "OR_specify_Z_start_AND..." in scriptParams and "...specify_Z_end" in scriptParams:
start = scriptParams["OR_specify_Z_start_AND..."]
start = min(start, sizeZ)
end = scriptParams["...specify_Z_end"]
end = min(end, sizeZ)
zStart = min(start,
end) # in case user got zStart and zEnd mixed up
zEnd = max(start, end)
if zStart == zEnd:
zRange = (zStart, )
else:
zRange = (zStart, zEnd + 1)
return zRange
def getTrange(sizeT, scriptParams):
tRange = None
if "Choose_T_Section" in scriptParams:
tChoice = scriptParams["Choose_T_Section"]
# NB: all T indices in this script are 1-based
if tChoice == 'ALL T planes':
tRange = (1, sizeT + 1)
elif "OR_specify_T_index" in scriptParams:
tIndex = scriptParams["OR_specify_T_index"]
tIndex = min(tIndex, sizeT)
tRange = (tIndex, )
elif "OR_specify_T_start_AND..." in scriptParams and "...specify_T_end" in scriptParams:
start = scriptParams["OR_specify_T_start_AND..."]
start = min(start, sizeT)
end = scriptParams["...specify_T_end"]
end = min(end, sizeT)
tStart = min(start,
end) # in case user got zStart and zEnd mixed up
tEnd = max(start, end)
if tStart == tEnd:
tRange = (tStart, )
else:
tRange = (tStart, tEnd + 1)
return tRange
# Get the images or datasets
message = ""
objects, logMessage = script_utils.getObjects(conn, scriptParams)
message += logMessage
if not objects:
return None, message
# Attach figure to the first image
parent = objects[0]
if dataType == 'Dataset':
images = []
for ds in objects:
images.extend(list(ds.listChildren()))
if not images:
message += "No image found in dataset(s)"
return None, message
else:
images = objects
log("Processing %s images" % len(images))
# somewhere to put images
curr_dir = os.getcwd()
exp_dir = os.path.join(curr_dir, folder_name)
try:
os.mkdir(exp_dir)
except:
pass
# do the saving to disk
if format == 'OME-TIFF':
for img in images:
log("Exporting image as OME-TIFF: %s" % img.getName())
saveAsOmeTiff(conn, img, folder_name)
else:
for img in images:
log("\n----------- Saving planes from image: '%s' ------------" %
img.getName())
sizeC = img.getSizeC()
sizeZ = img.getSizeZ()
sizeT = img.getSizeT()
zRange = getZrange(sizeZ, scriptParams)
tRange = getTrange(sizeT, scriptParams)
log("Using:")
if zRange is None: log(" Z-index: Last-viewed")
elif len(zRange) == 1: log(" Z-index: %d" % zRange[0])
else: log(" Z-range: %s-%s" % (zRange[0], zRange[1] - 1))
if projectZ: log(" Z-projection: ON")
if tRange is None: log(" T-index: Last-viewed")
elif len(tRange) == 1: log(" T-index: %d" % tRange[0])
else: log(" T-range: %s-%s" % (tRange[0], tRange[1] - 1))
log(" Format: %s" % format)
if imgWidth is None: log(" Image Width: no resize")
else: log(" Image Width: %s" % imgWidth)
log(" Greyscale: %s" % greyscale)
log("Channel Rendering Settings:")
for ch in img.getChannels():
log(" %s: %d-%d" %
(ch.getLabel(), ch.getWindowStart(), ch.getWindowEnd()))
savePlanesForImage(conn,
img,
sizeC,
splitCs,
mergedCs,
channelNames,
zRange,
tRange,
greyscale,
imgWidth,
projectZ=projectZ,
format=format,
folder_name=folder_name)
# write log for exported images (not needed for ome-tiff)
logFile = open(os.path.join(exp_dir, 'Batch_Image_Export.txt'), 'w')
try:
for s in logStrings:
logFile.write(s)
logFile.write("\n")
finally:
logFile.close()
# zip everything up (unless we've only got a single ome-tiff)
if format == 'OME-TIFF' and len(os.listdir(exp_dir)) == 1:
export_file = os.path.join(folder_name, os.listdir(exp_dir)[0])
mimetype = 'image/tiff'
else:
export_file = "%s.zip" % folder_name
compress(export_file, folder_name)
mimetype = 'application/zip'
namespace = omero.constants.namespaces.NSCREATED + "/omero/export_scripts/Batch_Image_Export"
fileAnnotation, annMessage = script_utils.createLinkFileAnnotation(
conn,
export_file,
parent,
output="Batch export zip",
ns=namespace,
mimetype=mimetype)
message += annMessage
return fileAnnotation, message
def process_data(conn,script_params,image,file_type,sizeC,rectangles,coords,rmax):
"""
Calculates the ripley l function for coordinates in user-defined
rectangular region of interest
@param conn: the BlitzGateWay connection
@param image: the image being processed
@param sizeC: number of channels in the image
@param rectangles: the regions of interest
@param coords: the localisation coordinates
@param rmax: maximum distance scale for Ripley calculation
"""
updateService = conn.getUpdateService()
parentDataset = image.getParent()
parentProject = parentDataset.getParent()
x = FILE_TYPES[file_type]['x_col']
y = FILE_TYPES[file_type]['y_col']
frame = FILE_TYPES[file_type]['frame']
sizeZ = 1
sizeT = image.getSizeT()
if sizeT > 1:
desc = image.getDescription()
if desc:
start = desc.index('Start')
stop = desc.index('Stop')
starts = desc[start+7:stop-3]
starts = [int(s) for s in starts.split(',')]
stops = desc[stop+6:len(desc)-1]
stops = [int(s) for s in stops.split(',')]
else:
starts = [1]
stops = [coords[0][frame].max]
new_images = []
new_ids = []
for r,rect in enumerate(rectangles):
planes = []
ldf = []
for c in range(sizeC):
locs_df = coords[c]
tt = 0
for t in range(sizeT):
conn.keepAlive()
coords_in_frames = locs_df[(locs_df[frame]>= starts[t]) & (locs_df[frame]<= stops[t])]
locs = get_coords_in_roi(coords_in_frames,rect,file_type)
box = [rect[0],rect[0]+rect[2],rect[1],rect[1]+rect[3]]
pixelsX = math.ceil(float(rect[2] / 50.0))
pixelsY = math.ceil(float(rect[3] / 50.0))
print 'pixelsX,pixelsY:',pixelsX,pixelsY
if len(locs.index)>0:
l = ripleykperpoint(locs.loc[:,[x,y]].values,rmax,box,0)
tx = np.linspace(box[0], box[1], pixelsX)
ty = np.linspace(box[2], box[3], pixelsY)
XI, YI = np.meshgrid(tx, ty)
print 'num x vals,num y vals:',locs.loc[:,[x]].values.shape,locs.loc[:,[y]].values.shape
rbf = Rbf(locs.loc[:,[x]].values, locs.loc[:,[y]].values, l, epsilon=2)
ZI = rbf(XI, YI)
ZI[ZI<0.0] = 0.0
else:
ZI = np.zeros((pixelsY,pixelsX))
l = np.zeros(100)
print 'ZI shape:',ZI.shape
planes.append(ZI)
ldf.append(pd.DataFrame(l))
ripley_df = pd.concat(ldf,join='outer',axis=1)
def plane_gen():
for p in planes:
yield p
imageName = 'Clusters_ROI_ID%s.ome.tif'%rect[-1]
description = "Cluster map for ROI%s of ImageID%s" % (rect[-1],image.getId())
newImg = conn.createImageFromNumpySeq(
plane_gen(), imageName,
sizeZ=sizeZ, sizeC=sizeC, sizeT=sizeT,
description=description)
new_images.append(newImg)
new_ids.append(newImg.getId())
file_name = "RLPP_ROI%s.csv" % (r)
with open(file_name,'w') as f:
f.write('# ripley per point data for %s channels and %s timepoints for ROI%s: \n' % (sizeC, sizeT, rect[-1]))
ripley_df.to_csv(f,sep=',',float_format='%8.2f',index=False,encoding='utf-8')
new_file_ann, faMessage = script_util.createLinkFileAnnotation(
conn, file_name, newImg, output="wrote ripley per point data",
mimetype="text/csv", desc=None)
if script_params['New_Dataset'] and \
len(script_params['Container_Name'].strip()) > 0:
# create a new dataset for new images
datasetName = script_params['Container_Name']
print "\nMaking Dataset '%s' of Images from ROIs of Image: %s" \
% (datasetName, image.getId())
dataset = omero.model.DatasetI()
dataset.name = rstring(datasetName)
desc = "Images in this Dataset are from ROIs of parent Image:\n"\
" Name: %s\n Image ID: %d" % (image.getName(), image.getId())
dataset.description = rstring(desc)
dataset = updateService.saveAndReturnObject(dataset)
parentDataset = dataset
else:
# put new images in existing dataset
dataset = None
if parentDataset is not None and parentDataset.canLink():
parentDataset = parentDataset._obj
else:
parentDataset = None
parentProject = None # don't add Dataset to parent.
if parentDataset is None:
link = None
print "No dataset created or found for new images."\
" Images will be orphans."
else:
link = []
for cid in new_ids:
dsLink = omero.model.DatasetImageLinkI()
dsLink.parent = omero.model.DatasetI(
parentDataset.id.val, False)
dsLink.child = omero.model.ImageI(cid, False)
updateService.saveObject(dsLink)
link.append(dsLink)
if parentProject and parentProject.canLink():
# and put it in the current project
projectLink = omero.model.ProjectDatasetLinkI()
projectLink.parent = omero.model.ProjectI(
parentProject.getId(), False)
projectLink.child = omero.model.DatasetI(
dataset.id.val, False)
updateService.saveAndReturnObject(projectLink)
return new_images, dataset, link, new_ids
def roiFigure(conn, commandArgs):
"""
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: commandArgs 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
pixelIds = []
imageIds = []
imageLabels = []
# function for getting image labels.
def getImageNames(fullName, tagsList, pdList):
name = fullName.split("/")[-1]
return [name]
# default function for getting labels is getName (or use datasets / tags)
if "Image_Labels" in commandArgs:
if commandArgs["Image_Labels"] == "Datasets":
def getDatasets(name, tagsList, pdList):
return [dataset for project, dataset in pdList]
getLabels = getDatasets
elif commandArgs["Image_Labels"] == "Tags":
def getTags(name, tagsList, pdList):
return tagsList
getLabels = getTags
else:
getLabels = getImageNames
else:
getLabels = getImageNames
# Get the images
images, logMessage = scriptUtil.getObjects(conn, commandArgs)
message += logMessage
if not images:
return None, message
# Check for rectangular ROIs and filter images list
images = [image for image in images if image.getROICount("Rect") > 0]
if not images:
message += "No rectangle ROI found."
return None, message
# Attach figure to the first image
omeroImage = images[0]
# process the list of images
log("Image details:")
for image in images:
imageIds.append(image.getId())
pixelIds.append(image.getPrimaryPixels().getId())
# a map of imageId : list of (project, dataset) names.
pdMap = figUtil.getDatasetsProjectsFromImages(conn.getQueryService(),
imageIds)
tagMap = figUtil.getTagsFromImages(conn.getMetadataService(), imageIds)
# Build a legend entry for each image
for image in images:
name = image.getName()
iId = image.getId()
imageDate = image.getAcquisitionDate()
tagsList = tagMap[iId]
pdList = pdMap[iId]
tags = ", ".join(tagsList)
pdString = ", ".join(["%s/%s" % pd for pd in pdList])
log(" Image: %s ID: %d" % (name, iId))
log(" Date: %s" % date.fromtimestamp(imageDate/1000))
log(" Tags: %s" % tags)
log(" Project/Datasets: %s" % pdString)
imageLabels.append(getLabels(name, tagsList, pdList))
# use the first image to define dimensions, channel colours etc.
sizeX = omeroImage.getSizeX()
sizeY = omeroImage.getSizeY()
sizeZ = omeroImage.getSizeZ()
sizeC = omeroImage.getSizeC()
width = sizeX
if "Width" in commandArgs:
w = commandArgs["Width"]
try:
width = int(w)
except:
log("Invalid width: %s Using default value: %d" % (str(w), sizeX))
height = sizeY
if "Height" in commandArgs:
h = commandArgs["Height"]
try:
height = int(h)
except:
log("Invalid height: %s Using default value" % (str(h), sizeY))
log("Image dimensions for all panels (pixels): width: %d height: %d"
% (width, height))
# the channels in the combined image,
if "Merged_Channels" in commandArgs:
# convert to 0-based
mergedIndexes = [c-1 for c in commandArgs["Merged_Channels"]]
else:
mergedIndexes = range(sizeC) # show all
mergedIndexes.reverse()
# if no colours added, use existing rendering settings.
mergedColours = {}
# Actually, nicer to always use existing rendering settings.
# if "Merged_Colours" in commandArgs:
# for i, c in enumerate(commandArgs["Merged_Colours"]):
# if c in COLOURS:
# mergedColours[i] = COLOURS[c]
algorithm = ProjectionType.MAXIMUMINTENSITY
if "Algorithm" in commandArgs:
a = commandArgs["Algorithm"]
if (a == "Mean Intensity"):
algorithm = ProjectionType.MEANINTENSITY
stepping = 1
if "Stepping" in commandArgs:
s = commandArgs["Stepping"]
if (0 < s < sizeZ):
stepping = s
scalebar = None
if "Scalebar" in commandArgs:
sb = commandArgs["Scalebar"]
try:
scalebar = int(sb)
if scalebar <= 0:
scalebar = None
else:
log("Scalebar is %d microns" % scalebar)
except:
log("Invalid value for scalebar: %s" % str(sb))
scalebar = None
overlayColour = (255, 255, 255)
if "Scalebar_Colour" in commandArgs:
if commandArgs["Scalebar_Colour"] in OVERLAY_COLOURS:
r, g, b, a = OVERLAY_COLOURS[commandArgs["Scalebar_Colour"]]
overlayColour = (r, g, b)
roiZoom = None
if "Roi_Zoom" in commandArgs:
roiZoom = float(commandArgs["Roi_Zoom"])
if roiZoom == 0:
roiZoom = None
maxColumns = None
if "Max_Columns" in commandArgs:
maxColumns = commandArgs["Max_Columns"]
showRoiDuration = False
if "Show_ROI_Duration" in commandArgs:
showRoiDuration = commandArgs["Show_ROI_Duration"]
roiLabel = "FigureROI"
if "Roi_Selection_Label" in commandArgs:
roiLabel = commandArgs["Roi_Selection_Label"]
spacer = (width/50) + 2
print "showRoiDuration", showRoiDuration
fig = getSplitView(
conn, imageIds, pixelIds, mergedIndexes, mergedColours, width, height,
imageLabels, spacer, algorithm, stepping, scalebar, overlayColour,
roiZoom, maxColumns, showRoiDuration, roiLabel)
# fig.show() # bug-fixing only
if fig is None:
logMessage = "No figure produced"
log("\n"+logMessage)
message += logMessage
return None, message
figLegend = "\n".join(logStrings)
# print figLegend # bug fixing only
format = commandArgs["Format"]
figureName = "movieROIFigure"
if "Figure_Name" in commandArgs:
figureName = commandArgs["Figure_Name"]
figureName = os.path.basename(figureName)
output = "localfile"
if format == 'PNG':
output = output + ".png"
figureName = figureName + ".png"
fig.save(output, "PNG")
mimetype = "image/png"
elif format == 'TIFF':
output = output + ".tiff"
figureName = figureName + ".tiff"
fig.save(output, "TIFF")
mimetype = "image/tiff"
else:
output = output + ".jpg"
figureName = figureName + ".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/Movie_ROI_Figure"
fileAnnotation, faMessage = scriptUtil.createLinkFileAnnotation(
conn, output, omeroImage, output="Movie ROI figure",
mimetype=mimetype, ns=namespace, desc=figLegend,
origFilePathAndName=figureName)
message += faMessage
return fileAnnotation, message
def run_processing(conn,script_params):
"""
Collects params and starts the processing
@param conn: the BlitzGateWay connection
@param script_params: the parameters collected from the script input
"""
file_anns = []
message = ""
rmax = script_params['Max_radius']
expo_fit = script_params['Fit_exponential_model']
expo_params = [script_params['Exponential_baseline'],\
script_params['Exponential_amplitude'],\
script_params['Exponential_decay']]
expogauss_fit = script_params['Fit_exponential+gaussian_model']
expogauss_params = [script_params['Density'],script_params['PSF'],\
script_params['Amplitude'],script_params['Decay'],\
script_params['Baseline']]
image_ids = script_params['IDs']
for image in conn.getObjects("Image",image_ids):
if not image:
message = 'Could not find specified image'
return message
image_id = image.getId()
sizeC = image.getSizeC()
corr_func = script_params['Pair_correlation']
if ('cross' in corr_func) and (sizeC != 2):
return 'image should have two channels to cross-correlate'
message,output = process_data(conn,image,corr_func,rmax,expo_fit,expo_params,\
expogauss_fit,expogauss_params)
if output:
file_name = "image%s_%s_correlation.csv" % (image_id,corr_func)
with file(file_name, 'w') as outfile:
outfile.write('# auto correlation data for %s ROIs and %s channels: \n' %\
(len(output), output[0]['correlation'].shape[1] ))
for r, pair_corr in enumerate(output):
header = 'radius,'
data = np.concatenate((pair_corr['radius'],pair_corr['correlation']),axis=1)
for i in range(pair_corr['correlation'].shape[1]):
header += 'correlation,'
outfile.write('# Region of interest %s\n' % r)
if pair_corr['exponential_fit'] is not None:
outfile.write('exponential fit params for ROI %s: \n' % r)
outfile.write('Baseline: %s \n' % pair_corr['exponential_params'][:,0])
outfile.write('Amplitude: %s \n' % pair_corr['exponential_params'][:,1])
outfile.write('Decay: %s \n' % pair_corr['exponential_params'][:,2])
for i in range(pair_corr['exponential_fit'].shape[1]):
header += 'fit,'
data = np.concatenate((data,pair_corr['exponential_fit']),axis=1)
if pair_corr['exponential+gaussian_fit'] is not None:
outfile.write('exponential+gaussian fit params for ROI %s: \n' % r)
outfile.write('Density: %s \n' % pair_corr['exponential+gaussiam_params'][:,0])
outfile.write('PSF: %s \n' % pair_corr['exponential+gaussiam_params'][:,1])
outfile.write('Amplitude: %s \n' % pair_corr['exponential+gaussiam_params'][:,2])
outfile.write('Decay: %s \n' % pair_corr['exponential+gaussiam_params'][:,3])
outfile.write('Baseline: %s \n' % pair_corr['exponential+gaussiam_params'][:,4])
for i in range(pair_corr['exponential+gaussian_fit'].shape[1]):
header += 'fit,'
data = np.concatenate((data,pair_corr['exponential+gaussian_fit']),axis=1)
outfile.write(header[:-1] + '\n')
np.savetxt(outfile, data, fmt='%-7.2f', delimiter=',', newline='\n')
new_file_ann, faMessage = script_util.createLinkFileAnnotation(
conn, file_name, image, output="Pair correlation csv file",
mimetype="text/csv", desc=None)
if new_file_ann:
file_anns.append(new_file_ann)
if not file_anns:
faMessage = "No Analysis files created. See 'Info' or 'Error' for"\
" more details"
elif len(file_anns) > 1:
faMessage = "Created %s csv (Excel) files" % len(file_anns)
message += faMessage
if script_params['Email_Results'] and file_anns:
email_results(conn,script_params,image_ids,file_anns)
return message
def movieFigure(conn, commandArgs):
"""
Makes the figure using the parameters in @commandArgs, attaches the figure
to the parent Project/Dataset, and returns the file-annotation ID
@param session The OMERO session
@param commandArgs Map of parameters for the script
@ returns Returns the id of the originalFileLink child. (ID
object, not value)
"""
log("Movie figure created by OMERO on %s" % date.today())
log("")
timeLabels = {"SECS_MILLIS": "seconds",
"SECS": "seconds",
"MINS": "minutes",
"HOURS": "hours",
"MINS_SECS": "mins:secs",
"HOURS_MINS": "hours:mins"}
timeUnits = "SECS"
if "Time_Units" in commandArgs:
timeUnits = commandArgs["Time_Units"]
# convert from UI name to timeLabels key
timeUnits = timeUnits.replace(" ", "_")
if timeUnits not in timeLabels.keys():
timeUnits = "SECS"
log("Time units are in %s" % timeLabels[timeUnits])
pixelIds = []
imageIds = []
imageLabels = []
message = "" # message to be returned to the client
# function for getting image labels.
def getImageNames(fullName, tagsList, pdList):
name = fullName.split("/")[-1]
return [name]
# default function for getting labels is getName (or use datasets / tags)
if "Image_Labels" in commandArgs:
if commandArgs["Image_Labels"] == "Datasets":
def getDatasets(name, tagsList, pdList):
return [dataset for project, dataset in pdList]
getLabels = getDatasets
elif commandArgs["Image_Labels"] == "Tags":
def getTags(name, tagsList, pdList):
return tagsList
getLabels = getTags
else:
getLabels = getImageNames
else:
getLabels = getImageNames
# Get the images
images, logMessage = scriptUtil.getObjects(conn, commandArgs)
message += logMessage
if not images:
return None, message
# Attach figure to the first image
omeroImage = images[0]
# process the list of images
log("Image details:")
for image in images:
imageIds.append(image.getId())
pixelIds.append(image.getPrimaryPixels().getId())
# a map of imageId : list of (project, dataset) names.
pdMap = figUtil.getDatasetsProjectsFromImages(
conn.getQueryService(), imageIds)
tagMap = figUtil.getTagsFromImages(conn.getMetadataService(), imageIds)
# Build a legend entry for each image
for image in images:
name = image.getName()
iId = image.getId()
imageDate = image.getAcquisitionDate()
tagsList = tagMap[iId]
pdList = pdMap[iId]
tags = ", ".join(tagsList)
pdString = ", ".join(["%s/%s" % pd for pd in pdList])
log(" Image: %s ID: %d" % (name, iId))
if imageDate:
log(" Date: %s" % imageDate)
else:
log(" Date: not set")
log(" Tags: %s" % tags)
log(" Project/Datasets: %s" % pdString)
imageLabels.append(getLabels(name, tagsList, pdList))
# use the first image to define dimensions, channel colours etc.
sizeX = omeroImage.getSizeX()
sizeY = omeroImage.getSizeY()
sizeZ = omeroImage.getSizeZ()
sizeT = omeroImage.getSizeT()
tIndexes = []
if "T_Indexes" in commandArgs:
for t in commandArgs["T_Indexes"]:
tIndexes.append(t)
print "T_Indexes", tIndexes
if len(tIndexes) == 0: # if no t-indexes given, use all t-indices
tIndexes = range(sizeT)
zStart = -1
zEnd = -1
if "Z_Start" in commandArgs:
zStart = commandArgs["Z_Start"]
if "Z_End" in commandArgs:
zEnd = commandArgs["Z_End"]
width = sizeX
if "Width" in commandArgs:
width = commandArgs["Width"]
height = sizeY
if "Height" in commandArgs:
height = commandArgs["Height"]
spacer = (width/25) + 2
algorithm = ProjectionType.MAXIMUMINTENSITY
if "Algorithm" in commandArgs:
a = commandArgs["Algorithm"]
if (a == "Mean Intensity"):
algorithm = ProjectionType.MEANINTENSITY
stepping = 1
if "Stepping" in commandArgs:
s = commandArgs["Stepping"]
if (0 < s < sizeZ):
stepping = s
scalebar = None
if "Scalebar" in commandArgs:
sb = commandArgs["Scalebar"]
try:
scalebar = int(sb)
if scalebar <= 0:
scalebar = None
else:
log("Scalebar is %d microns" % scalebar)
except:
log("Invalid value for scalebar: %s" % str(sb))
scalebar = None
overlayColour = (255, 255, 255)
if "Scalebar_Colour" in commandArgs:
r, g, b, a = OVERLAY_COLOURS[commandArgs["Scalebar_Colour"]]
overlayColour = (r, g, b)
maxColCount = 10
if "Max_Columns" in commandArgs:
maxColCount = commandArgs["Max_Columns"]
figure = createMovieFigure(
conn, pixelIds, tIndexes, zStart, zEnd, width, height, spacer,
algorithm, stepping, scalebar, overlayColour, timeUnits, imageLabels,
maxColCount)
log("")
figLegend = "\n".join(logLines)
# print figLegend # bug fixing only
format = commandArgs["Format"]
figureName = "movieFigure"
if "Figure_Name" in commandArgs:
figureName = str(commandArgs["Figure_Name"])
figureName = os.path.basename(figureName)
output = "localfile"
if format == 'PNG':
output = output + ".png"
figureName = figureName + ".png"
figure.save(output, "PNG")
mimetype = "image/png"
elif format == 'TIFF':
output = output + ".tiff"
figureName = figureName + ".tiff"
figure.save(output, "TIFF")
mimetype = "image/tiff"
else:
output = output + ".jpg"
figureName = figureName + ".jpg"
figure.save(output)
mimetype = "image/jpeg"
namespace = NSCREATED + "/omero/figure_scripts/Movie_Figure"
fileAnnotation, faMessage = scriptUtil.createLinkFileAnnotation(
conn, output, omeroImage, output="Movie figure", mimetype=mimetype,
ns=namespace, desc=figLegend, origFilePathAndName=figureName)
message += faMessage
return fileAnnotation, message
def run_processing(conn, script_params):
file_anns = []
message = ""
imageIds = []
image_id = script_params['ImageID']
imageIds.append(image_id)
image = conn.getObject("Image", image_id)
if not image:
message = 'Could not find specified image'
return message
file_id = script_params['AnnotationID']
ann = conn.getObject("Annotation", file_id)
if not ann:
message = 'Could not find specified annotation'
return message
radius = script_params['Radius']
#other parameters
if script_params['Convert_coordinates_to_nm']:
cam_pix_size = script_params['Parent_Image_Pixel_Size']
else:
cam_pix_size = 1
file_type = FILE_TYPES[script_params['File_Type']]
path_to_ann = ann.getFile().getPath() + '/' + ann.getFile().getName()
name, ext = os.path.splitext(path_to_ann)
if ('txt' in ext) or ('csv' in ext):
#get the path to the downloaded data
path_to_data = download_data(ann)
#get all the xy coords in that data
locs = parse_sr_data(path_to_data, file_type, cam_pix_size)
sizeC = len(locs)
#get the rois to be processed
rectangles = get_rectangles(conn, image_id)
print 'rectanges:', rectangles
#calculate local density
locs_density = process_data(conn, image, file_type, sizeC, rectangles,
locs, radius)
#write the data to a csv
file_name = "localisation_density_" + ann.getFile().getName(
)[:-4] + '.csv'
with file(file_name, 'a') as outfile:
outfile.write(
'# localisation density data for %s channels and %s ROIs: \n' %
(sizeC, len(locs_density)))
for r in range(len(locs_density)):
outfile.write('# ROI %s\n' % rectangles[r][-1])
outfile.write(
'Channel,%s,%s,Density within %s [nm]\n' %
(file_type['x_col'], file_type['y_col'], str(radius)))
density = locs_density[r][1:, :]
np.savetxt(outfile,
density,
fmt='%-7.2f',
delimiter=',',
newline='\n')
new_file_ann, faMessage = script_util.createLinkFileAnnotation(
conn,
file_name,
image,
output="Wrote localisation density csv (Excel) file",
mimetype="text/csv",
desc=None)
if new_file_ann:
file_anns.append(new_file_ann)
if not file_anns:
faMessage = "No Analysis files created. See 'Info' or 'Error' for"\
" more details"
elif len(file_anns) > 1:
faMessage = "Created %s csv (Excel) files" % len(file_anns)
else:
message = 'file annotation must be txt or csv'
return message
# clean up
delete_downloaded_data(ann)
message += faMessage
return message
def run_processing(conn, script_params):
"""
Collects params and starts the processing
@param conn: the BlitzGateWay connection
@param script_params: the parameters collected from the script input
"""
file_anns = []
message = ""
rmax = script_params['Max_radius']
expo_fit = script_params['Fit_exponential_model']
expo_params = [script_params['Exponential_baseline'],\
script_params['Exponential_amplitude'],\
script_params['Exponential_decay']]
expogauss_fit = script_params['Fit_exponential+gaussian_model']
expogauss_params = [script_params['Density'],script_params['PSF'],\
script_params['Amplitude'],script_params['Decay'],\
script_params['Baseline']]
image_ids = script_params['IDs']
for image in conn.getObjects("Image", image_ids):
if not image:
message = 'Could not find specified image'
return message
image_id = image.getId()
sizeC = image.getSizeC()
corr_func = script_params['Pair_correlation']
if ('cross' in corr_func) and (sizeC != 2):
return 'image should have two channels to cross-correlate'
message,output = process_data(conn,image,corr_func,rmax,expo_fit,expo_params,\
expogauss_fit,expogauss_params)
if output:
file_name = "image%s_%s_correlation.csv" % (image_id, corr_func)
with file(file_name, 'w') as outfile:
outfile.write('# auto correlation data for %s ROIs and %s channels: \n' %\
(len(output), output[0]['correlation'].shape[1] ))
for r, pair_corr in enumerate(output):
header = 'radius,'
data = np.concatenate(
(pair_corr['radius'], pair_corr['correlation']),
axis=1)
for i in range(pair_corr['correlation'].shape[1]):
header += 'correlation,'
outfile.write('# Region of interest %s\n' % r)
if pair_corr['exponential_fit'] is not None:
outfile.write('exponential fit params for ROI %s: \n' %
r)
outfile.write('Baseline: %s \n' %
pair_corr['exponential_params'][:, 0])
outfile.write('Amplitude: %s \n' %
pair_corr['exponential_params'][:, 1])
outfile.write('Decay: %s \n' %
pair_corr['exponential_params'][:, 2])
for i in range(pair_corr['exponential_fit'].shape[1]):
header += 'fit,'
data = np.concatenate(
(data, pair_corr['exponential_fit']), axis=1)
if pair_corr['exponential+gaussian_fit'] is not None:
outfile.write(
'exponential+gaussian fit params for ROI %s: \n' %
r)
outfile.write(
'Density: %s \n' %
pair_corr['exponential+gaussiam_params'][:, 0])
outfile.write(
'PSF: %s \n' %
pair_corr['exponential+gaussiam_params'][:, 1])
outfile.write(
'Amplitude: %s \n' %
pair_corr['exponential+gaussiam_params'][:, 2])
outfile.write(
'Decay: %s \n' %
pair_corr['exponential+gaussiam_params'][:, 3])
outfile.write(
'Baseline: %s \n' %
pair_corr['exponential+gaussiam_params'][:, 4])
for i in range(pair_corr['exponential+gaussian_fit'].
shape[1]):
header += 'fit,'
data = np.concatenate(
(data, pair_corr['exponential+gaussian_fit']),
axis=1)
outfile.write(header[:-1] + '\n')
np.savetxt(outfile,
data,
fmt='%-7.2f',
delimiter=',',
newline='\n')
new_file_ann, faMessage = script_util.createLinkFileAnnotation(
conn,
file_name,
image,
output="Pair correlation csv file",
mimetype="text/csv",
desc=None)
if new_file_ann:
file_anns.append(new_file_ann)
if not file_anns:
faMessage = "No Analysis files created. See 'Info' or 'Error' for"\
" more details"
elif len(file_anns) > 1:
faMessage = "Created %s csv (Excel) files" % len(file_anns)
message += faMessage
if script_params['Email_Results'] and file_anns:
email_results(conn, script_params, image_ids, file_anns)
return message
def run_processing(conn,script_params):
file_anns = []
message = ""
image_id = script_params['ImageID']
image = conn.getObject("Image",image_id)
if not image:
message = 'Could not find specified image'
return message
file_id = script_params['AnnotationID']
ann = conn.getObject("Annotation",file_id)
if not ann:
message = 'Could not find specified annotation'
return message
#other parameters
sr_pix_size = script_params['SR_pixel_size']
if script_params['Convert_coordinates_to_nm']:
cam_pix_size = script_params['Parent_Image_Pixel_Size']
else:
cam_pix_size = 1
file_type = script_params['File_Type']
rmax = script_params['Max_radius']
path_to_ann = ann.getFile().getPath() + '/' + ann.getFile().getName()
name,ext = os.path.splitext(path_to_ann)
if ('txt' in ext) or ('csv' in ext):
path_to_data = download_data(ann)
coords = parse_sr_data(path_to_data,file_type,cam_pix_size)
rectangles = get_rectangles(conn,image_id,sr_pix_size)
roi_data = process_data(conn,image,rectangles,coords,rmax)
file_name = "ripleyl_plot_" + ann.getFile().getName()[:-4] + '.csv'
print file_name
try:
f = open(file_name,'w')
for chan in range(len(roi_data)):
for r in range(roi_data.shape[0]):
row = roi_data[chan][r,:]
f.write(','.join([str(c) for c in row])+'\n')
finally:
f.close()
new_file_ann, faMessage = script_util.createLinkFileAnnotation(
conn, file_name, image, output="Ripley L Plot csv (Excel) file",
mimetype="text/csv", desc=None)
if new_file_ann:
file_anns.append(new_file_ann)
if not file_anns:
faMessage = "No Analysis files created. See 'Info' or 'Error' for"\
" more details"
elif len(file_anns) > 1:
faMessage = "Created %s csv (Excel) files" % len(file_anns)
message += faMessage
else:
message = 'file annotation must be txt or csv'
return message
# clean up
delete_downloaded_data(ann)
message += faMessage
return message