def point():
o = PointI()
populate_shape(o)
o.cx = rdouble(1.0)
o.cy = rdouble(2.0)
o.id = rlong(3L)
return o
def point():
o = PointI()
populate_shape(o)
o.cx = rdouble(1.0)
o.cy = rdouble(2.0)
o.id = rlong(3L)
return o
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 point():
o = PointI()
populate_shape(o)
if SCHEMA_VERSION == "2015-01":
o.cx = rdouble(1.0)
o.cy = rdouble(2.0)
else:
o.x = rdouble(1.0)
o.y = rdouble(2.0)
o.id = rlong(3L)
return o
def point():
o = PointI()
populate_shape(o)
if SCHEMA_VERSION == '2015-01':
o.cx = rdouble(1.0)
o.cy = rdouble(2.0)
else:
o.x = rdouble(1.0)
o.y = rdouble(2.0)
o.id = rlong(3L)
return o
def point(identity_transform):
o = PointI()
populate_shape(o, identity_transform)
if SCHEMA_VERSION == '2015-01':
o.cx = rdouble(1.0)
o.cy = rdouble(2.0)
else:
o.x = rdouble(1.0)
o.y = rdouble(2.0)
o.id = rlong(3)
return o
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_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 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]
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]