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 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 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]