def image_rois(self, user1, shapes):
"""Return Image with ROIs."""
image = ImageI()
image.name = rstring('Image for ROIs')
image = get_update_service(user1).saveAndReturnObject(image)
# ROI with all but one shapes
rois = []
roi = RoiI()
for shape in shapes[:-1]:
roi.addShape(shape)
roi.setImage(image)
rois.append(roi)
# roi without shapes
roi = RoiI()
roi.setImage(image)
rois.append(roi)
# roi without image
roi = RoiI()
roi.addShape(shapes[-1])
rois.append(roi)
rois = get_update_service(user1).saveAndReturnArray(rois)
rois.sort(key=lambda x: x.id.val)
return image, rois
def _parse_mnu_roi(self, columns):
"""Parses out ROI from OmeroTables columns for 'MNU' datasets."""
log.debug("Parsing %s MNU ROIs..." % (len(columns[0].values)))
image_ids = columns[self.IMAGE_COL].values
rois = list()
# Save our file annotation to the database so we can use an unloaded
# annotation for the saveAndReturnIds that will be triggered below.
self.file_annotation = self.update_service.saveAndReturnObject(self.file_annotation)
unloaded_file_annotation = FileAnnotationI(self.file_annotation.id.val, False)
batch_no = 1
batches = dict()
for i, image_id in enumerate(image_ids):
unloaded_image = ImageI(image_id, False)
roi = RoiI()
shape = PointI()
shape.theZ = rint(0)
shape.theT = rint(0)
values = columns[3].values
shape.cx = rdouble(float(values[i]))
values = columns[2].values
shape.cy = rdouble(float(values[i]))
roi.addShape(shape)
roi.image = unloaded_image
roi.linkAnnotation(unloaded_file_annotation)
rois.append(roi)
if len(rois) == self.ROI_UPDATE_LIMIT:
self.thread_pool.add_task(self.update_rois, rois, batches, batch_no)
rois = list()
batch_no += 1
self.thread_pool.add_task(self.update_rois, rois, batches, batch_no)
self.thread_pool.wait_completion()
batch_keys = batches.keys()
batch_keys.sort()
for k in batch_keys:
columns[self.ROI_COL].values += batches[k]
def create_roi(image, shapes):
updateService = image._conn.getUpdateService()
roi = RoiI()
roi.setImage(image._obj)
for shape in shapes:
roi.addShape(shape)
return updateService.saveAndReturnObject(roi)
def create_roi(conn, img, shapes):
updateService = conn.getUpdateService()
roi = RoiI()
roi.setImage(img._obj)
for shape in shapes:
roi.addShape(shape)
group_id = img.getDetails().getGroup().getId()
print(group_id)
ctx = {'omero.group': str(group_id)}
return updateService.saveAndReturnObject(roi, ctx)
def addRoi(self, request, imageid):
img = self.getImage0(imageid)
if img is None:
return None
data = json.loads(request.body)
shapes = data['shapes']
x = y = l = z = t = -1
for s in shapes:
for k in s:
val = s[k]
if k == "x":
x = int(val)
elif k == "y":
y = int(val)
elif k == "width":
l = int(val)
elif k == "theZ":
z = int(val)
elif k == "theT":
t = int(val)
elif k == "fillColorAsInt":
fill = int(val)
elif k == "strokeColorAsInt":
stroke = int(val)
elif k == "strokeWidth":
strokeWidth = int(val)
if (x < 0 or y < 0 or z < 0 or t < 0 or l <= 0):
return None
updateService = self._connection.getUpdateService()
roi = RoiI()
roi.setImage(img._obj)
rect = RectangleI()
rect.x = rdouble(x)
rect.y = rdouble(y)
rect.width = rdouble(l)
rect.height = rdouble(l)
rect.theZ = rint(z)
rect.theT = rint(t)
rect.setFillColor(rint(fill))
strokeLen = LengthI()
strokeLen.setValue(strokeWidth)
strokeLen.setUnit(UnitsLength.PIXEL)
rect.setStrokeWidth(strokeLen)
rect.setStrokeColor(rint(stroke))
roi.addShape(rect)
if (updateService.saveAndReturnObject(roi) is None):
return None
return self.get_rois(imageid)
def addRoi(self, request, imageid):
img = self.getImage0(imageid)
if img is None:
return None
data = json.loads(request.body)
shapes = data['shapes']
x = y = l = z = t = -1
for s in shapes:
for k in s:
val = s[k]
if k == "x":
x = int(val)
elif k == "y":
y = int(val)
elif k == "width":
l = int(val)
elif k == "theZ":
z = int(val)
elif k == "theT":
t = int(val)
elif k == "fillColorAsInt":
fill = int(val)
elif k == "strokeColorAsInt":
stroke = int(val)
elif k == "strokeWidth":
strokeWidth = int(val)
if (x < 0 or y < 0 or z < 0 or t < 0 or l <= 0):
return None
updateService = self._connection.getUpdateService()
roi = RoiI()
roi.setImage(img._obj)
rect = RectangleI()
rect.x = rdouble(x)
rect.y = rdouble(y)
rect.width = rdouble(l)
rect.height = rdouble(l)
rect.theZ = rint(z)
rect.theT = rint(t)
rect.setFillColor(rint(fill))
strokeLen = LengthI()
strokeLen.setValue(strokeWidth)
strokeLen.setUnit(UnitsLength.PIXEL)
rect.setStrokeWidth(strokeLen)
rect.setStrokeColor(rint(stroke))
roi.addShape(rect)
if (updateService.saveAndReturnObject(roi) is None):
return None
return self.get_rois(imageid)
def make_mask(x, y, h, w):
mask = MaskI()
mask.x = rdouble(x)
mask.y = rdouble(y)
mask.height = rdouble(h)
mask.width = rdouble(w)
mask.bytes = make_circle(h, w)
roi = RoiI()
roi.description = rstring("created by overlapping-masks.py")
roi.addShape(mask)
roi.image = image
roi = update_service.saveAndReturnObject(roi)
print(f"Roi:{roi.id.val}")
def to_rois(cx_column, cy_column, pixels):
unloaded_image = ImageI(pixels.image.id, False)
rois = list()
for index in range(len(cx_column.values)):
cx = rdouble(cx_column.values[index])
cy = rdouble(cy_column.values[index])
roi = RoiI()
shape = PointI()
shape.theZ = rint(0)
shape.theT = rint(0)
shape.cx = cx
shape.cy = cy
roi.addShape(shape)
roi.image = unloaded_image
rois.append(roi)
return rois
def _parse_neo_roi(self, columns):
"""Parses out ROI from OmeroTables columns for 'NEO' datasets."""
log.debug("Parsing %s NEO ROIs..." % (len(columns[0].values)))
image_ids = columns[self.IMAGE_COL].values
rois = list()
# Save our file annotation to the database so we can use an unloaded
# annotation for the saveAndReturnIds that will be triggered below.
self.file_annotation = \
self.update_service.saveAndReturnObject(self.file_annotation)
unloaded_file_annotation = \
FileAnnotationI(self.file_annotation.id.val, False)
batch_no = 1
batches = dict()
for i, image_id in enumerate(image_ids):
unloaded_image = ImageI(image_id, False)
roi = RoiI()
shape = EllipseI()
values = columns[6].values
diameter = rdouble(float(values[i]))
shape.theZ = rint(0)
shape.theT = rint(0)
values = columns[4].values
shape.cx = rdouble(float(values[i]))
values = columns[3].values
shape.cy = rdouble(float(values[i]))
shape.rx = diameter
shape.ry = diameter
roi.addShape(shape)
roi.image = unloaded_image
roi.linkAnnotation(unloaded_file_annotation)
rois.append(roi)
if len(rois) == self.ROI_UPDATE_LIMIT:
self.thread_pool.add_task(
self.update_rois, rois, batches, batch_no)
rois = list()
batch_no += 1
self.thread_pool.add_task(self.update_rois, rois, batches, batch_no)
self.thread_pool.wait_completion()
batch_keys = batches.keys()
batch_keys.sort()
for k in batch_keys:
columns[self.ROI_COL].values += batches[k]
def create_points(conn, df, image):
columns = [
"Sequence", "Tagged Protein", "Sequence is unique", "Location_X",
"Location_Y"
]
df2 = pandas.DataFrame(columns=(['Roi'] + columns))
index = df['Source Name'] == image.getName()
for (s, tp, u, x, y) in zip(*map(lambda x: df[index][x], columns)):
p = PointI()
p.x = rdouble(float(x))
p.y = rdouble(float(y))
p.setTextValue(rstring(tp))
roi = RoiI()
roi.addShape(p)
roi.setName(rstring(tp))
roi.setImage(ImageI(image.getId(), False))
roi = conn.getUpdateService().saveAndReturnObject(roi)
df2.loc[len(df2)] = (roi.getId().getValue(), s, tp, u, x, y)
return df2
def test_roi_thumbnail(self, theT, theZ):
update_service = self.sf.getUpdateService()
img = self.create_test_image(size_x=125,
size_y=125,
size_z=2,
size_t=2,
session=self.sf)
for s in self.shapes():
if theT is not None:
s.theT = rint(theT)
if theZ is not None:
s.theZ = rint(theZ)
roi = RoiI()
roi.addShape(s)
roi.setImage(img)
roi = update_service.saveAndReturnObject(roi)
shape = roi.copyShapes()[0]
# Test ROI thumbnail...
request_url = reverse('webgateway_render_roi_thumbnail',
kwargs={'roiId': roi.id.val})
rsp = get(self.django_client, request_url)
thumb_bytes = BytesIO(rsp.content)
try:
thumb = Image.open(thumb_bytes)
finally:
thumb_bytes.close()
assert thumb.size == (250, 166)
# and Shape thumbnail...
request_url = reverse('webgateway_render_shape_thumbnail',
kwargs={'shapeId': shape.id.val})
rsp = get(self.django_client, request_url)
thumb_bytes = BytesIO(rsp.content)
try:
thumb = Image.open(thumb_bytes)
finally:
thumb_bytes.close()
assert thumb.size == (250, 166)
def parse(self):
provider = self.original_file_provider
data = provider.get_original_file_data(self.original_file)
try:
rows = list(csv.reader(data, delimiter=","))
finally:
data.close()
columns = [
ImageColumn("Image", "", list()),
RoiColumn("ROI", "", list()),
StringColumn("Type", "", 12, list()),
]
for row in rows[1:]:
wellnumber = self.well_name_to_number(row[0])
image = self.analysis_ctx.\
image_from_wellnumber(wellnumber)
# TODO: what to do with the field?!
# field = int(row[1])
# image = images[field]
roi = RoiI()
shape = PointI()
shape.cx = rdouble(float(row[2]))
shape.cy = rdouble(float(row[3]))
shape.textValue = rstring(row[4])
roi.addShape(shape)
roi.image = image.proxy()
rid = self.update_service\
.saveAndReturnIds([roi])[0]
columns[0].values.append(image.id.val)
columns[1].values.append(rid)
columns[2].values.append(row[4])
return MeasurementParsingResult([columns])
def parse(self):
provider = self.original_file_provider
data = provider.get_original_file_data(self.original_file)
try:
rows = list(csv.reader(data, delimiter=","))
finally:
data.close()
columns = [
ImageColumn("Image", "", list()),
RoiColumn("ROI", "", list()),
StringColumn("Type", "", 12, list()),
]
for row in rows[1:]:
wellnumber = self.well_name_to_number(row[0])
image = self.analysis_ctx.\
image_from_wellnumber(wellnumber)
# TODO: what to do with the field?!
# field = int(row[1])
# image = images[field]
roi = RoiI()
shape = PointI()
shape.cx = rdouble(float(row[2]))
shape.cy = rdouble(float(row[3]))
shape.textValue = rstring(row[4])
roi.addShape(shape)
roi.image = image.proxy()
rid = self.update_service\
.saveAndReturnIds([roi])[0]
columns[0].values.append(image.id.val)
columns[1].values.append(rid)
columns[2].values.append(row[4])
return MeasurementParsingResult([columns])
def parse_and_populate_roi(self, columns_as_list):
# First sanity check our provided columns
names = [column.name for column in columns_as_list]
log.debug('Parsing columns: %r' % names)
cells_expected = [name in names for name in self.CELLS_CG_EXPECTED]
nuclei_expected = [name in names for name in self.NUCLEI_CG_EXPECTED]
if (False in cells_expected) and (False in nuclei_expected):
log.warn("Missing CGs for InCell dataset: %r" % names)
log.warn('Removing resultant empty ROI column.')
for column in columns_as_list:
if RoiColumn == column.__class__:
columns_as_list.remove(column)
return
# Reconstruct a column name to column map
columns = dict()
for column in columns_as_list:
columns[column.name] = column
image_ids = columns['Image'].values
rois = list()
# Save our file annotation to the database so we can use an unloaded
# annotation for the saveAndReturnIds that will be triggered below.
self.file_annotation = \
self.update_service.saveAndReturnObject(self.file_annotation)
unloaded_file_annotation = \
FileAnnotationI(self.file_annotation.id.val, False)
# Parse and append ROI
batch_no = 1
batches = dict()
for i, image_id in enumerate(image_ids):
unloaded_image = ImageI(image_id, False)
if False in nuclei_expected:
# Cell centre of gravity
roi = RoiI()
shape = PointI()
shape.theZ = rint(0)
shape.theT = rint(0)
shape.cx = rdouble(float(columns['Cell: cgX'].values[i]))
shape.cy = rdouble(float(columns['Cell: cgY'].values[i]))
roi.addShape(shape)
roi.image = unloaded_image
roi.linkAnnotation(unloaded_file_annotation)
rois.append(roi)
elif False in cells_expected:
# Nucleus centre of gravity
roi = RoiI()
shape = PointI()
shape.theZ = rint(0)
shape.theT = rint(0)
shape.cx = rdouble(float(columns['Nucleus: cgX'].values[i]))
shape.cy = rdouble(float(columns['Nucleus: cgY'].values[i]))
roi.addShape(shape)
roi.image = unloaded_image
roi.linkAnnotation(unloaded_file_annotation)
rois.append(roi)
else:
raise MeasurementError('Not a nucleus or cell ROI')
if len(rois) == self.ROI_UPDATE_LIMIT:
thread_pool.add_task(self.update_rois, rois, batches, batch_no)
rois = list()
batch_no += 1
thread_pool.add_task(self.update_rois, rois, batches, batch_no)
thread_pool.wait_completion()
batch_keys = batches.keys()
batch_keys.sort()
for k in batch_keys:
columns['ROI'].values += batches[k]
params.addId(image_id_src)
count = 0
for mask_src in query_service.findAllByQuery(query, params):
mask_dst = MaskI()
mask_dst.x = mask_src.x
mask_dst.y = mask_src.y
mask_dst.width = mask_src.width
mask_dst.height = mask_src.height
mask_dst.theZ = mask_src.theZ
mask_dst.theC = mask_src.theC
mask_dst.theT = mask_src.theT
mask_dst.bytes = mask_src.bytes
mask_dst.fillColor = mask_src.fillColor
mask_dst.transform = mask_src.transform
roi_dst = RoiI()
roi_dst.description = rstring(
"created by copy-masks script for original mask #{}".format(
mask_src.id.val))
roi_dst.image = ImageI(image_id_dst, False)
roi_dst.addShape(mask_dst)
update_service.saveObject(roi_dst)
count += 1
idr._closeSession()
local._closeSession()
print("from image #{} to #{}, mask count = {}".format(image_id_src,
image_id_dst, count))
def parse_and_populate_roi(self, columns_as_list):
# First sanity check our provided columns
names = [column.name for column in columns_as_list]
log.debug('Parsing columns: %r' % names)
cells_expected = [name in names for name in self.CELLS_CG_EXPECTED]
nuclei_expected = [name in names for name in self.NUCLEI_CG_EXPECTED]
if (False in cells_expected) and (False in nuclei_expected):
log.warn("Missing CGs for InCell dataset: %r" % names)
log.warn('Removing resultant empty ROI column.')
for column in columns_as_list:
if RoiColumn == column.__class__:
columns_as_list.remove(column)
return
# Reconstruct a column name to column map
columns = dict()
for column in columns_as_list:
columns[column.name] = column
image_ids = columns['Image'].values
rois = list()
# Save our file annotation to the database so we can use an unloaded
# annotation for the saveAndReturnIds that will be triggered below.
self.file_annotation = \
self.update_service.saveAndReturnObject(self.file_annotation)
unloaded_file_annotation = \
FileAnnotationI(self.file_annotation.id.val, False)
# Parse and append ROI
batch_no = 1
batches = dict()
for i, image_id in enumerate(image_ids):
unloaded_image = ImageI(image_id, False)
if False in nuclei_expected:
# Cell centre of gravity
roi = RoiI()
shape = PointI()
shape.theZ = rint(0)
shape.theT = rint(0)
shape.cx = rdouble(float(columns['Cell: cgX'].values[i]))
shape.cy = rdouble(float(columns['Cell: cgY'].values[i]))
roi.addShape(shape)
roi.image = unloaded_image
roi.linkAnnotation(unloaded_file_annotation)
rois.append(roi)
elif False in cells_expected:
# Nucleus centre of gravity
roi = RoiI()
shape = PointI()
shape.theZ = rint(0)
shape.theT = rint(0)
shape.cx = rdouble(float(columns['Nucleus: cgX'].values[i]))
shape.cy = rdouble(float(columns['Nucleus: cgY'].values[i]))
roi.addShape(shape)
roi.image = unloaded_image
roi.linkAnnotation(unloaded_file_annotation)
rois.append(roi)
else:
raise MeasurementError('Not a nucleus or cell ROI')
if len(rois) == self.ROI_UPDATE_LIMIT:
self.thread_pool.add_task(
self.update_rois, rois, batches, batch_no)
rois = list()
batch_no += 1
self.thread_pool.add_task(self.update_rois, rois, batches, batch_no)
self.thread_pool.wait_completion()
batch_keys = batches.keys()
batch_keys.sort()
for k in batch_keys:
columns['ROI'].values += batches[k]
