def _process_region(self, it, region, rogroup, region_image,
region_coords):
LOG = getLogger('processor.TesserocrSegmentTable')
# equivalent to GetComponentImages with raw_image=True,
# (which would also give raw coordinates),
# except we are also interested in the iterator's BlockType() here,
index = 0
if rogroup:
for elem in (rogroup.get_RegionRefIndexed() +
rogroup.get_OrderedGroupIndexed() +
rogroup.get_UnorderedGroupIndexed()):
if elem.index >= index:
index = elem.index + 1
while it and not it.Empty(RIL.BLOCK):
bbox = it.BoundingBox(RIL.BLOCK)
polygon = polygon_from_x0y0x1y1(bbox)
polygon = coordinates_for_segment(polygon, region_image,
region_coords)
points = points_from_polygon(polygon)
coords = CoordsType(points=points)
# if xywh['w'] < 30 or xywh['h'] < 30:
# LOG.info('Ignoring too small region: %s', points)
# it.Next(RIL.BLOCK)
# continue
#
# add the region reference in the reading order element
# (but ignore non-text regions entirely)
ID = region.id + "_%04d" % index
subregion = TextRegionType(id=ID,
Coords=coords,
type=TextTypeSimpleType.PARAGRAPH)
block_type = it.BlockType()
if block_type == PT.FLOWING_TEXT:
pass
elif block_type == PT.HEADING_TEXT:
subregion.set_type(TextTypeSimpleType.HEADING)
elif block_type == PT.PULLOUT_TEXT:
subregion.set_type(TextTypeSimpleType.FLOATING)
elif block_type == PT.CAPTION_TEXT:
subregion.set_type(TextTypeSimpleType.CAPTION)
elif block_type == PT.VERTICAL_TEXT:
subregion.set_orientation(90.0)
else:
it.Next(RIL.BLOCK)
continue
LOG.info("Detected cell '%s': %s (%s)", ID, points,
membername(PT, block_type))
region.add_TextRegion(subregion)
if rogroup:
rogroup.add_RegionRefIndexed(
RegionRefIndexedType(regionRef=ID, index=index))
#
# iterator increment
#
index += 1
it.Next(RIL.BLOCK)
def _process_words_in_line(self, result_it, line, line_xywh):
LOG = getLogger('processor.TesserocrRecognize')
if not result_it or result_it.Empty(RIL.WORD):
LOG.warning("No text in line '%s'", line.id)
return
# iterate until IsAtFinalElement(RIL.LINE, RIL.WORD):
word_no = 0
while result_it and not result_it.Empty(RIL.WORD):
word_id = '%s_word%04d' % (line.id, word_no)
LOG.debug("Decoding text in word '%s'", word_id)
bbox = result_it.BoundingBox(RIL.WORD)
# convert to absolute coordinates:
polygon = coordinates_for_segment(polygon_from_x0y0x1y1(bbox),
None, line_xywh) - self.parameter['padding']
polygon2 = polygon_for_parent(polygon, line)
if polygon2 is not None:
polygon = polygon2
points = points_from_polygon(polygon)
word = WordType(id=word_id, Coords=CoordsType(points))
if polygon2 is None:
# could happen due to rotation
LOG.info('Ignoring extant word: %s', points)
else:
line.add_Word(word)
# todo: determine if font attributes available for word level will work with LSTM models
word_attributes = result_it.WordFontAttributes()
if word_attributes:
word_style = TextStyleType(
fontSize=word_attributes['pointsize']
if 'pointsize' in word_attributes else None,
fontFamily=word_attributes['font_name']
if 'font_name' in word_attributes else None,
bold=word_attributes['bold']
if 'bold' in word_attributes else None,
italic=word_attributes['italic']
if 'italic' in word_attributes else None,
underlined=word_attributes['underlined']
if 'underlined' in word_attributes else None,
monospace=word_attributes['monospace']
if 'monospace' in word_attributes else None,
serif=word_attributes['serif']
if 'serif' in word_attributes else None)
word.set_TextStyle(word_style) # (or somewhere in custom attribute?)
# add word annotation unconditionally (i.e. even for glyph level):
word.add_TextEquiv(TextEquivType(
Unicode=result_it.GetUTF8Text(RIL.WORD),
conf=result_it.Confidence(RIL.WORD)/100))
if self.parameter['textequiv_level'] != 'word':
self._process_glyphs_in_word(result_it, word, line_xywh)
if result_it.IsAtFinalElement(RIL.TEXTLINE, RIL.WORD):
break
else:
word_no += 1
result_it.Next(RIL.WORD)
def _process_glyphs_in_word(self, result_it, word, word_xywh):
LOG = getLogger('processor.TesserocrRecognize')
if not result_it or result_it.Empty(RIL.SYMBOL):
LOG.debug("No glyph in word '%s'", word.id)
return
# iterate until IsAtFinalElement(RIL.WORD, RIL.SYMBOL):
glyph_no = 0
while result_it and not result_it.Empty(RIL.SYMBOL):
glyph_id = '%s_glyph%04d' % (word.id, glyph_no)
LOG.debug("Decoding text in glyph '%s'", glyph_id)
# glyph_text = result_it.GetUTF8Text(RIL.SYMBOL) # equals first choice?
glyph_conf = result_it.Confidence(RIL.SYMBOL)/100 # equals first choice?
#LOG.debug('best glyph: "%s" [%f]', glyph_text, glyph_conf)
bbox = result_it.BoundingBox(RIL.SYMBOL)
# convert to absolute coordinates:
polygon = coordinates_for_segment(polygon_from_x0y0x1y1(bbox),
None, word_xywh) - self.parameter['padding']
polygon2 = polygon_for_parent(polygon, word)
if polygon2 is not None:
polygon = polygon2
points = points_from_polygon(polygon)
glyph = GlyphType(id=glyph_id, Coords=CoordsType(points))
if polygon2 is None:
# could happen due to rotation
LOG.info('Ignoring extant glyph: %s', points)
else:
word.add_Glyph(glyph)
choice_it = result_it.GetChoiceIterator()
for (choice_no, choice) in enumerate(choice_it):
alternative_text = choice.GetUTF8Text()
alternative_conf = choice.Confidence()/100
#LOG.debug('alternative glyph: "%s" [%f]', alternative_text, alternative_conf)
if (glyph_conf - alternative_conf > CHOICE_THRESHOLD_CONF or
choice_no > CHOICE_THRESHOLD_NUM):
break
# todo: consider SymbolIsSuperscript (TextStyle), SymbolIsDropcap (RelationType) etc
glyph.add_TextEquiv(TextEquivType(index=choice_no, Unicode=alternative_text, conf=alternative_conf))
if result_it.IsAtFinalElement(RIL.WORD, RIL.SYMBOL):
break
else:
glyph_no += 1
result_it.Next(RIL.SYMBOL)
def process(self):
"""
Performs the recognition.
"""
assert_file_grp_cardinality(self.input_file_grp, 1)
assert_file_grp_cardinality(self.output_file_grp, 1)
self._init_calamari()
for (n, input_file) in enumerate(self.input_files):
page_id = input_file.pageId or input_file.ID
log.info("INPUT FILE %i / %s", n, page_id)
pcgts = page_from_file(self.workspace.download_file(input_file))
page = pcgts.get_Page()
page_image, page_xywh, page_image_info = self.workspace.image_from_page(
page, page_id)
for region in pcgts.get_Page().get_TextRegion():
region_image, region_xywh = self.workspace.image_from_segment(
region, page_image, page_xywh)
textlines = region.get_TextLine()
log.info("About to recognize %i lines of region '%s'",
len(textlines), region.id)
for (line_no, line) in enumerate(textlines):
log.debug("Recognizing line '%s' in region '%s'", line.id,
region.id)
line_image, line_coords = self.workspace.image_from_segment(
line, region_image, region_xywh)
line_image_np = np.array(line_image, dtype=np.uint8)
raw_results = list(
self.predictor.predict_raw([line_image_np],
progress_bar=False))[0]
for i, p in enumerate(raw_results):
p.prediction.id = "fold_{}".format(i)
prediction = self.voter.vote_prediction_result(raw_results)
prediction.id = "voted"
# Build line text on our own
#
# Calamari does whitespace post-processing on prediction.sentence, while it does not do the same
# on prediction.positions. Do it on our own to have consistency.
#
# XXX Check Calamari's built-in post-processing on prediction.sentence
def _sort_chars(p):
"""Filter and sort chars of prediction p"""
chars = p.chars
chars = [
c for c in chars if c.char
] # XXX Note that omission probabilities are not normalized?!
chars = [
c for c in chars if c.probability >=
self.parameter['glyph_conf_cutoff']
]
chars = sorted(chars,
key=lambda k: k.probability,
reverse=True)
return chars
def _drop_leading_spaces(positions):
return list(
itertools.dropwhile(
lambda p: _sort_chars(p)[0].char == " ",
positions))
def _drop_trailing_spaces(positions):
return list(
reversed(_drop_leading_spaces(
reversed(positions))))
def _drop_double_spaces(positions):
def _drop_double_spaces_generator(positions):
last_was_space = False
for p in positions:
if p.chars[0].char == " ":
if not last_was_space:
yield p
last_was_space = True
else:
yield p
last_was_space = False
return list(_drop_double_spaces_generator(positions))
positions = prediction.positions
positions = _drop_leading_spaces(positions)
positions = _drop_trailing_spaces(positions)
positions = _drop_double_spaces(positions)
positions = list(positions)
line_text = ''.join(
_sort_chars(p)[0].char for p in positions)
if line_text != prediction.sentence:
log.warning(
"Our own line text is not the same as Calamari's: '%s' != '%s'",
line_text, prediction.sentence)
# Delete existing results
if line.get_TextEquiv():
log.warning("Line '%s' already contained text results",
line.id)
line.set_TextEquiv([])
if line.get_Word():
log.warning(
"Line '%s' already contained word segmentation",
line.id)
line.set_Word([])
# Save line results
line_conf = prediction.avg_char_probability
line.set_TextEquiv(
[TextEquivType(Unicode=line_text, conf=line_conf)])
# Save word results
#
# Calamari OCR does not provide word positions, so we infer word positions from a. text segmentation
# and b. the glyph positions. This is necessary because the PAGE XML format enforces a strict
# hierarchy of lines > words > glyphs.
def _words(s):
"""Split words based on spaces and include spaces as 'words'"""
spaces = None
word = ''
for c in s:
if c == ' ' and spaces is True:
word += c
elif c != ' ' and spaces is False:
word += c
else:
if word:
yield word
word = c
spaces = (c == ' ')
yield word
if self.parameter['textequiv_level'] in ['word', 'glyph']:
word_no = 0
i = 0
for word_text in _words(line_text):
word_length = len(word_text)
if not all(c == ' ' for c in word_text):
word_positions = positions[i:i + word_length]
word_start = word_positions[0].global_start
word_end = word_positions[-1].global_end
polygon = polygon_from_x0y0x1y1([
word_start, 0, word_end, line_image.height
])
points = points_from_polygon(
coordinates_for_segment(
polygon, None, line_coords))
# XXX Crop to line polygon?
word = WordType(id='%s_word%04d' %
(line.id, word_no),
Coords=CoordsType(points))
word.add_TextEquiv(
TextEquivType(Unicode=word_text))
if self.parameter[
'textequiv_level'] == 'glyph':
for glyph_no, p in enumerate(
word_positions):
glyph_start = p.global_start
glyph_end = p.global_end
polygon = polygon_from_x0y0x1y1([
glyph_start, 0, glyph_end,
line_image.height
])
points = points_from_polygon(
coordinates_for_segment(
polygon, None, line_coords))
glyph = GlyphType(
id='%s_glyph%04d' %
(word.id, glyph_no),
Coords=CoordsType(points))
# Add predictions (= TextEquivs)
char_index_start = 1 # Must start with 1, see https://ocr-d.github.io/page#multiple-textequivs
for char_index, char in enumerate(
_sort_chars(p),
start=char_index_start):
glyph.add_TextEquiv(
TextEquivType(
Unicode=char.char,
index=char_index,
conf=char.probability))
word.add_Glyph(glyph)
line.add_Word(word)
word_no += 1
i += word_length
_page_update_higher_textequiv_levels('line', pcgts)
# Add metadata about this operation and its runtime parameters:
metadata = pcgts.get_Metadata() # ensured by from_file()
metadata.add_MetadataItem(
MetadataItemType(
type_="processingStep",
name=self.ocrd_tool['steps'][0],
value=TOOL,
Labels=[
LabelsType(externalModel="ocrd-tool",
externalId="parameters",
Label=[
LabelType(type_=name,
value=self.parameter[name])
for name in self.parameter.keys()
])
]))
file_id = make_file_id(input_file, self.output_file_grp)
pcgts.set_pcGtsId(file_id)
self.workspace.add_file(ID=file_id,
file_grp=self.output_file_grp,
pageId=input_file.pageId,
mimetype=MIMETYPE_PAGE,
local_filename=os.path.join(
self.output_file_grp,
file_id + '.xml'),
content=to_xml(pcgts))
def process(self):
"""
Perform text recognition with Calamari on the workspace.
If ``texequiv_level`` is ``word`` or ``glyph``, then additionally create word / glyph level segments by
splitting at white space characters / glyph boundaries. In the case of ``glyph``, add all alternative character
hypotheses down to ``glyph_conf_cutoff`` confidence threshold.
"""
log = getLogger('processor.CalamariRecognize')
assert_file_grp_cardinality(self.input_file_grp, 1)
assert_file_grp_cardinality(self.output_file_grp, 1)
for (n, input_file) in enumerate(self.input_files):
page_id = input_file.pageId or input_file.ID
log.info("INPUT FILE %i / %s", n, page_id)
pcgts = page_from_file(self.workspace.download_file(input_file))
page = pcgts.get_Page()
page_image, page_coords, page_image_info = self.workspace.image_from_page(
page, page_id, feature_selector=self.features)
for region in page.get_AllRegions(classes=['Text']):
region_image, region_coords = self.workspace.image_from_segment(
region,
page_image,
page_coords,
feature_selector=self.features)
textlines = region.get_TextLine()
log.info("About to recognize %i lines of region '%s'",
len(textlines), region.id)
line_images_np = []
line_coordss = []
for line in textlines:
log.debug("Recognizing line '%s' in region '%s'", line.id,
region.id)
line_image, line_coords = self.workspace.image_from_segment(
line,
region_image,
region_coords,
feature_selector=self.features)
if ('binarized' not in line_coords['features']
and 'grayscale_normalized'
not in line_coords['features']
and self.network_input_channels == 1):
# We cannot use a feature selector for this since we don't
# know whether the model expects (has been trained on)
# binarized or grayscale images; but raw images are likely
# always inadequate:
log.warning(
"Using raw image for line '%s' in region '%s'",
line.id, region.id)
line_image = line_image if all(line_image.size) else [[0]]
line_image_np = np.array(line_image, dtype=np.uint8)
line_images_np.append(line_image_np)
line_coordss.append(line_coords)
raw_results_all = self.predictor.predict_raw(
line_images_np, progress_bar=False)
for line, line_coords, raw_results in zip(
textlines, line_coordss, raw_results_all):
for i, p in enumerate(raw_results):
p.prediction.id = "fold_{}".format(i)
prediction = self.voter.vote_prediction_result(raw_results)
prediction.id = "voted"
# Build line text on our own
#
# Calamari does whitespace post-processing on prediction.sentence, while it does not do the same
# on prediction.positions. Do it on our own to have consistency.
#
# XXX Check Calamari's built-in post-processing on prediction.sentence
def _sort_chars(p):
"""Filter and sort chars of prediction p"""
chars = p.chars
chars = [
c for c in chars if c.char
] # XXX Note that omission probabilities are not normalized?!
chars = [
c for c in chars if c.probability >=
self.parameter['glyph_conf_cutoff']
]
chars = sorted(chars,
key=lambda k: k.probability,
reverse=True)
return chars
def _drop_leading_spaces(positions):
return list(
itertools.dropwhile(
lambda p: _sort_chars(p)[0].char == " ",
positions))
def _drop_trailing_spaces(positions):
return list(
reversed(_drop_leading_spaces(
reversed(positions))))
def _drop_double_spaces(positions):
def _drop_double_spaces_generator(positions):
last_was_space = False
for p in positions:
if p.chars[0].char == " ":
if not last_was_space:
yield p
last_was_space = True
else:
yield p
last_was_space = False
return list(_drop_double_spaces_generator(positions))
positions = prediction.positions
positions = _drop_leading_spaces(positions)
positions = _drop_trailing_spaces(positions)
positions = _drop_double_spaces(positions)
positions = list(positions)
line_text = ''.join(
_sort_chars(p)[0].char for p in positions)
if line_text != prediction.sentence:
log.warning(
"Our own line text is not the same as Calamari's: '%s' != '%s'",
line_text, prediction.sentence)
# Delete existing results
if line.get_TextEquiv():
log.warning("Line '%s' already contained text results",
line.id)
line.set_TextEquiv([])
if line.get_Word():
log.warning(
"Line '%s' already contained word segmentation",
line.id)
line.set_Word([])
# Save line results
line_conf = prediction.avg_char_probability
line.set_TextEquiv(
[TextEquivType(Unicode=line_text, conf=line_conf)])
# Save word results
#
# Calamari OCR does not provide word positions, so we infer word positions from a. text segmentation
# and b. the glyph positions. This is necessary because the PAGE XML format enforces a strict
# hierarchy of lines > words > glyphs.
def _words(s):
"""Split words based on spaces and include spaces as 'words'"""
spaces = None
word = ''
for c in s:
if c == ' ' and spaces is True:
word += c
elif c != ' ' and spaces is False:
word += c
else:
if word:
yield word
word = c
spaces = (c == ' ')
yield word
if self.parameter['textequiv_level'] in ['word', 'glyph']:
word_no = 0
i = 0
for word_text in _words(line_text):
word_length = len(word_text)
if not all(c == ' ' for c in word_text):
word_positions = positions[i:i + word_length]
word_start = word_positions[0].global_start
word_end = word_positions[-1].global_end
polygon = polygon_from_x0y0x1y1([
word_start, 0, word_end, line_image.height
])
points = points_from_polygon(
coordinates_for_segment(
polygon, None, line_coords))
# XXX Crop to line polygon?
word = WordType(id='%s_word%04d' %
(line.id, word_no),
Coords=CoordsType(points))
word.add_TextEquiv(
TextEquivType(Unicode=word_text))
if self.parameter[
'textequiv_level'] == 'glyph':
for glyph_no, p in enumerate(
word_positions):
glyph_start = p.global_start
glyph_end = p.global_end
polygon = polygon_from_x0y0x1y1([
glyph_start, 0, glyph_end,
line_image.height
])
points = points_from_polygon(
coordinates_for_segment(
polygon, None, line_coords))
glyph = GlyphType(
id='%s_glyph%04d' %
(word.id, glyph_no),
Coords=CoordsType(points))
# Add predictions (= TextEquivs)
char_index_start = 1 # Must start with 1, see https://ocr-d.github.io/page#multiple-textequivs
for char_index, char in enumerate(
_sort_chars(p),
start=char_index_start):
glyph.add_TextEquiv(
TextEquivType(
Unicode=char.char,
index=char_index,
conf=char.probability))
word.add_Glyph(glyph)
line.add_Word(word)
word_no += 1
i += word_length
_page_update_higher_textequiv_levels('line', pcgts)
# Add metadata about this operation and its runtime parameters:
self.add_metadata(pcgts)
file_id = make_file_id(input_file, self.output_file_grp)
pcgts.set_pcGtsId(file_id)
self.workspace.add_file(ID=file_id,
file_grp=self.output_file_grp,
pageId=input_file.pageId,
mimetype=MIMETYPE_PAGE,
local_filename=os.path.join(
self.output_file_grp,
file_id + '.xml'),
content=to_xml(pcgts))
def test_polygon_from_x0y0x1y1(self):
self.assertEqual(polygon_from_x0y0x1y1([100, 100, 200, 200]),
[[100, 100], [200, 100], [200, 200], [100, 200]])
def _process_page(self, it, page, page_image, page_coords, page_id):
LOG = getLogger('processor.TesserocrSegmentRegion')
# equivalent to GetComponentImages with raw_image=True,
# (which would also give raw coordinates),
# except we are also interested in the iterator's BlockType() here,
# and its BlockPolygon()
index = 0
ro = page.get_ReadingOrder()
if not ro:
ro = ReadingOrderType()
page.set_ReadingOrder(ro)
og = ro.get_OrderedGroup()
if og:
# start counting from largest existing index
for elem in (og.get_RegionRefIndexed() +
og.get_OrderedGroupIndexed() +
og.get_UnorderedGroupIndexed()):
if elem.index >= index:
index = elem.index + 1
else:
# new top-level group
og = OrderedGroupType(id="reading-order")
ro.set_OrderedGroup(og)
while it and not it.Empty(RIL.BLOCK):
# (padding will be passed to both BoundingBox and GetImage)
# (actually, Tesseract honours padding only on the left and bottom,
# whereas right and top are increased less!)
bbox = it.BoundingBox(RIL.BLOCK, padding=self.parameter['padding'])
# sometimes these polygons are not planar, which causes
# PIL.ImageDraw.Draw.polygon (and likely others as well)
# to misbehave; however, PAGE coordinate semantics prohibit
# multi-path polygons!
# (probably a bug in Tesseract itself, cf. tesseract#2826):
if self.parameter['crop_polygons']:
polygon = it.BlockPolygon()
else:
polygon = polygon_from_x0y0x1y1(bbox)
polygon = coordinates_for_segment(polygon, page_image, page_coords)
polygon2 = polygon_for_parent(polygon, page)
if polygon2 is not None:
polygon = polygon2
points = points_from_polygon(polygon)
coords = CoordsType(points=points)
if polygon2 is None:
LOG.info('Ignoring extant region: %s', points)
it.Next(RIL.BLOCK)
continue
# if xywh['w'] < 30 or xywh['h'] < 30:
# LOG.info('Ignoring too small region: %s', points)
# it.Next(RIL.BLOCK)
# continue
# region_image_bin = it.GetBinaryImage(RIL.BLOCK)
# if not region_image_bin.getbbox():
# LOG.info('Ignoring binary-empty region: %s', points)
# it.Next(RIL.BLOCK)
# continue
#
# add the region reference in the reading order element
# (will be removed again if Separator/Noise region below)
ID = "region%04d" % index
og.add_RegionRefIndexed(
RegionRefIndexedType(regionRef=ID, index=index))
#
# region type switch
#
block_type = it.BlockType()
if block_type in [
PT.FLOWING_TEXT,
PT.HEADING_TEXT,
PT.PULLOUT_TEXT,
PT.CAPTION_TEXT,
# TABLE is contained in PTIsTextType, but
# it is a bad idea to create a TextRegion
# for it (better set `find_tables` False):
# PT.TABLE,
# will also get a 90° @orientation
# (but that can be overridden by deskew/OSD):
PT.VERTICAL_TEXT
]:
region = TextRegionType(id=ID,
Coords=coords,
type=TextTypeSimpleType.PARAGRAPH)
if block_type == PT.VERTICAL_TEXT:
region.set_orientation(90.0)
elif block_type == PT.HEADING_TEXT:
region.set_type(TextTypeSimpleType.HEADING)
elif block_type == PT.PULLOUT_TEXT:
region.set_type(TextTypeSimpleType.FLOATING)
elif block_type == PT.CAPTION_TEXT:
region.set_type(TextTypeSimpleType.CAPTION)
page.add_TextRegion(region)
if self.parameter['sparse_text']:
region.set_type(TextTypeSimpleType.OTHER)
region.add_TextLine(
TextLineType(id=region.id + '_line', Coords=coords))
elif block_type in [
PT.FLOWING_IMAGE, PT.HEADING_IMAGE, PT.PULLOUT_IMAGE
]:
region = ImageRegionType(id=ID, Coords=coords)
page.add_ImageRegion(region)
elif block_type in [PT.HORZ_LINE, PT.VERT_LINE]:
region = SeparatorRegionType(id=ID, Coords=coords)
page.add_SeparatorRegion(region)
# undo appending in ReadingOrder
og.set_RegionRefIndexed(og.get_RegionRefIndexed()[:-1])
elif block_type in [PT.INLINE_EQUATION, PT.EQUATION]:
region = MathsRegionType(id=ID, Coords=coords)
page.add_MathsRegion(region)
elif block_type == PT.TABLE:
# without API access to StructuredTable we cannot
# do much for a TableRegionType (i.e. nrows, ncols,
# coordinates of cells for recursive regions etc),
# but this can be achieved afterwards by segment-table
region = TableRegionType(id=ID, Coords=coords)
page.add_TableRegion(region)
else:
region = NoiseRegionType(id=ID, Coords=coords)
page.add_NoiseRegion()
# undo appending in ReadingOrder
og.set_RegionRefIndexed(og.get_RegionRefIndexed()[:-1])
LOG.info("Detected region '%s': %s (%s)", ID, points,
membername(PT, block_type))
#
# iterator increment
#
index += 1
it.Next(RIL.BLOCK)
if (not og.get_RegionRefIndexed() and not og.get_OrderedGroupIndexed()
and not og.get_UnorderedGroupIndexed()):
# schema forbids empty OrderedGroup
ro.set_OrderedGroup(None)
